second hand smoke research paper

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• My Account Login
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 09 January 2024

Health effects associated with exposure to secondhand smoke: a Burden of Proof study

• Luisa S. Flor   ORCID: orcid.org/0000-0002-6888-512X 1 , 2 ,
• Jason A. Anderson 1 ,
• Noah Ahmad 1 ,
• Aleksandr Aravkin 1 , 2 ,
• Sinclair Carr 1 ,
• Xiaochen Dai   ORCID: orcid.org/0000-0002-0289-7814 1 ,
• Gabriela F. Gil 1 , 3 ,
• Simon I. Hay   ORCID: orcid.org/0000-0002-0611-7272 1 , 2 ,
• Matthew J. Malloy 1 ,
• Susan A. McLaughlin 1 ,
• Erin C. Mullany 1 ,
• Christopher J. L. Murray   ORCID: orcid.org/0000-0002-4930-9450 1 , 2 ,
• Erin M. O’Connell 1 ,
• Chukwuma Okereke 1 ,
• Reed J. D. Sorensen 1 ,
• Joanna Whisnant 1 ,
• Peng Zheng 1 , 2 &
• Emmanuela Gakidou   ORCID: orcid.org/0000-0002-8992-591X 1 , 2  

Nature Medicine volume  30 ,  pages 149–167 ( 2024 ) Cite this article

7846 Accesses

1 Citations

70 Altmetric

Metrics details

• Risk factors

An Author Correction to this article was published on 30 January 2024

This article has been updated

Despite a gradual decline in smoking rates over time, exposure to secondhand smoke (SHS) continues to cause harm to nonsmokers, who are disproportionately children and women living in low- and middle-income countries. We comprehensively reviewed the literature published by July 2022 concerning the adverse impacts of SHS exposure on nine health outcomes. Following, we quantified each exposure–response association accounting for various sources of uncertainty and evaluated the strength of the evidence supporting our analyses using the Burden of Proof Risk Function methodology. We found all nine health outcomes to be associated with SHS exposure. We conservatively estimated that SHS increases the risk of ischemic heart disease, stroke, type 2 diabetes and lung cancer by at least around 8%, 5%, 1% and 1%, respectively, with the evidence supporting these harmful associations rated as weak (two stars). The evidence supporting the harmful associations between SHS and otitis media, asthma, lower respiratory infections, breast cancer and chronic obstructive pulmonary disease was weaker (one star). Despite the weak underlying evidence for these associations, our results reinforce the harmful effects of SHS on health and the need to prioritize advancing efforts to reduce active and passive smoking through a combination of public health policies and education initiatives.

Similar content being viewed by others

Health effects associated with smoking: a Burden of Proof study

Secondhand smoke increases the risk of developing chronic obstructive pulmonary disease

Burden of diseases attributable to second-hand smoke exposure in Iran adolescents from 2009 to 2020

Tobacco use is one of the leading risk factors for disease burden and mortality worldwide, contributing to 229.8 million (95% uncertainty interval: 213.1–246.4 million) disability-adjusted life years and 8.7 million (8.1–9.3 million) deaths in 2019 (ref. 1 ). Secondhand smoke (SHS) exposure, alternatively referred to as passive or involuntary smoking, is a major tobacco-related public health concern for nonsmokers. Despite a gradual decline in smoking rates over the past half-century 2 , it is estimated that approximately 37% of the global population is still exposed to the smoke emitted from the burning end of tobacco products or exhaled from smokers, with higher rates of exposure among women and children compared to men, and evident racial and economic disparities 3 , 4 . This is concerning as tobacco smoke is composed of thousands of chemicals and compounds, including many carcinogens, which when inhaled damage the human body and lead to disease and death 5 .

The 2019 Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) estimated that 1.3 million (1.0–1.6) deaths were attributable to SHS globally in 2019, with the largest burden concentrated in low- and middle-income countries 6 . These patterns have made SHS a priority for tobacco control efforts, especially after the adoption of the World Health Organization’s Framework Convention on Tobacco Control, a global treaty aimed at implementing evidence-based measures to reduce both active and passive smoking 7 . Therefore, providing an updated summary of the exposure–response relationship between SHS and multiple adverse health outcomes, as well as innovatively quantifying the strength of the evidence supporting these relationships, is essential to continue to inform tobacco control policy, research funders and clinical recommendations and guide individual decisions related to smoking practices.

Over time, advances in understanding the harms of SHS have raised awareness of the importance of protecting nonsmokers from tobacco smoke. Smoke-free initiatives, in particular, have changed attitudes and social norms toward SHS exposure and have been a key contributor to the decline of smoking prevalence 8 . Nevertheless, as world populations grow, the number of smokers continues to rise, increasing the number of nonsmokers at risk of SHS exposure 9 .

Over the past decades, the body of evidence concerning the relationship between SHS and health has greatly evolved with the outline of plausible biological mechanisms and in-depth consideration of the available evidence, moving from the first reported association with lung cancer in the 1986 Surgeon Generals’ report 10 to the inference of causal relationships between SHS and a range of diseases affecting and adverse health outcomes for adults and children, including cardiovascular diseases, some respiratory illnesses, middle ear disease, low birth weight and sudden infant death syndrome 11 , 12 . Additionally, previous research, including meta-analyses, found suggestive evidence of an association between SHS exposure and breast cancer 13 , 14 , 15 . Despite these findings, substantial heterogeneity is detected across and within SHS risk–outcome assessments in terms of quantity and quality of studies and reported strength of associations. Variation across studies in the definitions of risk exposure used is also observed, with some reporting the risk associated with SHS exposure in specific settings 16 or from specific sources (that is, maternal, paternal) 17 . Furthermore, given the limited availability of studies that assess exposure to tobacco smoke on the basis of environmental and biological samples, and the lack of a standard measure of SHS exposure, the units and dose categories reported across studies vary widely. Together, these inconsistencies can limit the comparability and consolidation of evidence concerning the health effects of SHS.

In this context, in this Article, we aimed to quantify the exposure–response associations between SHS and nine health outcomes—lung and breast cancer, ischemic heart disease (IHD), stroke, chronic obstructive pulmonary disease (COPD), lower respiratory infections, asthma, type 2 diabetes and otitis media—as well as the strength of the available evidence, using an objective, comprehensive and comparative framework. The Burden of Proof Risk Function (BPRF) derives a conservative estimate of the smallest harmful effects of SHS exposure on given health outcomes that are consistent with the available evidence and to summarize the strength of risk–outcome associations and their underlying evidence into a star-rating measure, ranging from one star (weak evidence of an association) to five stars (consistent evidence of a strong association), to aid the interpretation and comparability of results 18 . The main findings and policy implications of this work are summarized in Table 1 .

Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines 19 , we systematically searched the literature for studies reporting associations between SHS exposure and each of the nine health outcomes of interest. Definitions of each of the outcomes are reported in Supplementary Table 1 . In total, we reviewed 7,109 unique records published between 1 January 1970 and 31 July 2022 identified in PubMed and Web of Science. Through citation searching, 1,972 additional records were identified for screening. Following our predefined inclusion and exclusion criteria ( Methods ), 410 publications reporting relative risks (RRs) associated with SHS measured as a dichotomous exposure remained for inclusion in our analyses. The data extraction template is presented in Supplementary Table 2 , and the review workflow is detailed for each health outcome in the PRISMA flow diagrams (Supplementary Figs. 1 – 9 ). The majority of the studies used a case–control design ( n  = 235), followed by prospective cohort ( n  = 156), nested case–control ( n  = 10), retrospective cohort ( n  = 5), case–cohort ( n  = 3) and case-crossover ( n  = 1) designs. The BPRF analyses for asthma ( n  = 125) 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 , 117 , 118 , 119 , 120 , 121 , 122 , 123 , 124 , 125 , 126 , 127 , 128 , 129 , 130 , 131 , 132 , 133 , 134 , 135 , 136 , 137 , 138 , 139 , 140 , 141 , 142 , 143 , 144 and lung cancer ( n  = 104) 145 , 146 , 147 , 148 , 149 , 150 , 151 , 152 , 153 , 154 , 155 , 156 , 157 , 158 , 159 , 160 , 161 , 162 , 163 , 164 , 165 , 166 , 167 , 168 , 169 , 170 , 171 , 172 , 173 , 174 , 175 , 176 , 177 , 178 , 179 , 180 , 181 , 182 , 183 , 184 , 185 , 186 , 187 , 188 , 189 , 190 , 191 , 192 , 193 , 194 , 195 , 196 , 197 , 198 , 199 , 200 , 201 , 202 , 203 , 204 , 205 , 206 , 207 , 208 , 209 , 210 , 211 , 212 , 213 , 214 , 215 , 216 , 217 , 218 , 219 , 220 , 221 , 222 , 223 , 224 , 225 , 226 , 227 , 228 , 229 , 230 , 231 , 232 , 233 , 234 , 235 , 236 , 237 , 238 , 239 , 240 , 241 , 242 , 243 , 244 , 245 , 246 , 247 , 248 reported in the present study were based on evidence from the highest number of studies, while COPD ( n  = 21) 48 , 177 , 208 , 225 , 236 , 249 , 250 , 251 , 252 , 253 , 254 , 255 , 256 , 257 , 258 , 259 , 260 , 261 , 262 , 263 , 264 and type 2 diabetes ( n  = 9) 265 , 266 , 267 , 268 , 269 , 270 , 271 , 272 , 273 analyses were based on the lowest number of studies. The included studies represent 623 observations from over 178 locations (Supplementary Table 3 ). Pooled RR estimates for each SHS risk–outcome relationship are provided in Table 2 , along with key analytic parameters and characteristics. Forest plots depicting each risk–outcome association are presented in the Extended Data file (Extended Data Figs. 1 – 9 ), and all included effect sizes by study are reported in Supplementary Tables 4 – 12 .

Cardiovascular diseases

We identified 37 studies (59 observations) 177 , 207 , 208 , 215 , 225 , 236 , 252 , 262 , 274 , 275 , 276 , 277 , 278 , 279 , 280 , 281 , 282 , 283 , 284 , 285 , 286 , 287 , 288 , 289 , 290 , 291 , 292 , 293 , 294 , 295 , 296 , 297 , 298 , 299 , 300 , 301 , 302 quantifying the relationship between SHS exposure and IHD and 20 studies (26 observations) 176 , 207 , 208 , 225 , 236 , 252 , 262 , 278 , 296 , 297 , 303 , 304 , 305 , 306 , 307 , 308 , 309 , 310 , 311 , 312 assessing the relationship between SHS and stroke (Table 2 and Supplementary Tables 4 and 5 ). Our conservative analysis of the effect of SHS on IHD yielded an estimated RR of 1.26 (1.05–1.52) (Table 2 , Fig. 1a and Extended Data Fig. 1 ), inclusive of between-study heterogeneity (gamma). We estimated the BPRF—which corresponds to the fifth quantile of RR closest to null and represents the lowest estimate of harmful SHS risk consistent with available evidence—to be 1.08, suggesting that SHS exposure increases an individual’s risk of IHD by a conservative minimum of 8%. In the BPRF framework, this translates to a risk–outcome score (ROS) of 0.04, which distinguishes the SHS–IHD relationship as a two-star risk–outcome pair, which can be interpreted as weak evidence of an association based on the available data (Table 2 ). Covariates accounting for cases where exposure to SHS was measured at baseline only (rather than multiple times during follow-up) and use of nonprospective cohort design were found to be statistically significant and were adjusted for within our final model (Table 2 ).

a , b , These modified funnel plots show the residuals of the reported mean RR relative to 0, the null value, on the x axis and the residuals of the standard error, as estimated from both the reported standard error and gamma, relative to 0 on the y axis, for IHD ( a ) and stroke ( b ). The light-blue vertical interval corresponds to the 95% uncertainty interval incorporating between-study heterogeneity; the dark-blue vertical interval corresponds to the 95% uncertainty interval (UI) without between-study heterogeneity; the dots are each included observation; the red Xs are outliered observations; the gray dotted line reflects the null log(RR); the blue line is the mean log(RR) for SHS and the outcome of interest; and the red line is the Burden of Proof function at the fifth quantile for these harmful risk–outcome associations.

Similarly, a weak but statistically significant relationship was found between SHS exposure and the risk of stroke. The estimated RR and uncertainty inclusive of between-study heterogeneity was 1.16 (1.03–1.32) (Table 2 , Fig. 1b and Extended Data Fig. 2 ). Based on our conservative interpretation of the data, we estimated a BPRF of 1.05, indicating that exposure to tobacco smoke was associated with at least a 5% higher risk of stroke. This corresponds to a ROS of 0.02 and a two-star rating, consistent with weak evidence. In the final model, we adjusted for potential selection bias (based on percentage follow-up for longitudinal study designs and percentages of cases and controls for which exposure data could be ascertained for case–control designs) and for studies based on self-reported outcomes, as these covariates were found to be statistically significant by our bias covariate algorithm (Table 2 ).

The two-star rating for IHD was consistent with sensitivity analyses in which we restricted the models to studies with a prospective cohort design (Supplementary Table 13 ), subset to observations of never smokers only (Supplementary Table 14 ), and applied both these restrictions at the same time (Supplementary Table 15 ). When restricted to prospective cohort data for never smokers only, the association between SHS and stroke was downgraded to one star (ROS −0.001) (Extended Data Fig. 10 ). We did not detect publication bias, as identified by Egger’s regression test, in the primary analysis or in any of the sensitivity analyses for the cardiovascular outcomes (Table 2 and Supplementary Tables 13 – 15 ).

The conservative BPRF analysis indicated that passive smoking was weakly associated with an increased risk of lung cancer, based on a BPRF of 1.00 and a corresponding ROS of 0.001 (Table 2 ), which translates to a two-star rating at the lower threshold of the two-star range and suggests that SHS exposure was associated with at least around 1% higher risk of lung cancer. When between-study heterogeneity and other sources of uncertainty were accounted for, the estimated RR was 1.37 (0.94–1.99) (Table 2 , Fig. 2a and Extended Data Fig. 3 ). The bias covariate algorithm selected observations that did not originally control for smoking to be adjusted in the final model (Table 2 ). In a sensitivity analysis in which we restricted the data to prospective cohort studies, the strength of the association was even lower (BPRF 0.95, ROS −0.03), downgrading the relationship to a one-star rating (Extended Data Fig. 10 and Supplementary Table 13 ).

a , b , These modified funnel plots show the residuals of the reported mean RR relative to 0, the null value, on the x axis and the residuals of the standard error, as estimated from both the reported standard error and gamma, relative to 0 on the y axis, for lung cancer ( a ) and breast cancer ( b ). The light-blue vertical interval corresponds to the 95% uncertainty interval incorporating between-study heterogeneity; the dark-blue vertical interval corresponds to the 95% uncertainty interval (UI) without between-study heterogeneity; the dots are each included observation; the red Xs are outliered observations; the gray dotted line reflects the null log(RR); the blue line is the mean log(RR) for SHS and the outcome of interest; the red line is the Burden of Proof function at the fifth quantile for these harmful risk–outcome associations.

Our conservative BPRF analysis also found weak evidence of a harmful association between exposure to tobacco smoke and risk of breast cancer (BPRF 0.81, ROS −0.11, one-star rating; Table 2 ). The meta-analysis, which is supported by 51 unique studies 170 , 220 , 313 , 314 , 315 , 316 , 317 , 318 , 319 , 320 , 321 , 322 , 323 , 324 , 325 , 326 , 327 , 328 , 329 , 330 , 331 , 332 , 333 , 334 , 335 , 336 , 337 , 338 , 339 , 340 , 341 , 342 , 343 , 344 , 345 , 346 , 347 , 348 , 349 , 350 , 351 , 352 , 353 , 354 , 355 , 356 , 357 , 358 , 359 , 360 , 361 and 79 observations (Supplementary Table 7 ), yielded an RR of 1.22 (0.75–1.98), inclusive of between-study heterogeneity (Table 2 , Fig. 2b and Extended Data Fig. 4 ). In our model, observations that did not control for smoking and those from study designs other than prospective cohorts were adjusted since these covariates were found to be significant by our algorithm (Table 2 ). In further sensitivity analyses, the one-star relationship was still observed when we restricted to observations from never smokers only (Extended Data Fig. 1 and Supplementary Table 14 ). However, when restricting to prospective cohort studies, we found no statistically significant evidence of an association between exposure to SHS and the risk of breast cancer in our fixed-effect model without between-study heterogeneity; that is, the estimated RR and associated uncertainty without gamma includes the null. These risk–outcome pairs are automatically assigned a zero-star rating, and the BPRF and ROS are not computed (Extended Data Fig. 10 and Supplementary Table 13 ).

Based on Egger’s regression test, no significant evidence of publication bias was found for the main lung cancer and breast cancer models or the exploratory models (Table 2 and Supplementary Tables 13 – 15 ). Visual inspection of the funnel plots supported this finding (Fig. 2 ).

Respiratory conditions

We evaluated the association between exposure to SHS and three respiratory conditions: asthma, lower respiratory infections and COPD. Based on the conservative BPRF framework, the evidence supporting each of these relationships was weak (one-star rating), when between-study heterogeneity and other sources of bias were taken into account. Across these outcomes, no significant publication bias was detected in the primary models (Table 2 ) or in the sensitivity analyses (Supplementary Tables 13 – 16 ). For SHS and asthma, a risk–outcome pair not yet included in the GBD, the estimated RR incorporating between-study heterogeneity into the uncertainty was 1.21 (0.88–1.66) (Table 2 , Fig. 3a and Extended Data Fig. 5 ). Data points associated with a self-reported diagnosis and those restricted to children (age ≤16 years) were adjusted for in our main model, as the corresponding bias covariates were found to be statistically significant (Table 2 ). The BPRF and ROS were 0.93 and −0.04, respectively, which equates to a one-star risk classification. When restricting to prospective cohort studies, a two-star rating for the relationship between SHS and asthma was observed (Extended Data Fig. 10 and Supplementary Tables 13 ).

These modified funnel plots show the residuals of the reported mean RR relative to 0, the null value, on the x axis and the residuals of the standard error, as estimated from both the reported standard error and gamma, relative to 0 on the y axis, for asthma ( a ), lower respiratory infections ( b ) and COPD ( c ). The light-blue vertical interval corresponds to the 95% uncertainty interval incorporating between-study heterogeneity; the dark-blue vertical interval corresponds to the 95% uncertainty interval (UI) without between-study heterogeneity; the dots are each included observation; the red Xs are outliered observations; the gray dotted line reflects the null log(RR); the blue line is the mean log(RR) for SHS and the outcome of interest; the red line is the Burden of Proof function at the fifth quantile for these harmful risk–outcome associations.

The meta-analysis of the risk of lower respiratory infections associated with SHS exposure included 50 studies 53 , 64 , 91 , 134 , 362 , 363 , 364 , 365 , 366 , 367 , 368 , 369 , 370 , 371 , 372 , 373 , 374 , 375 , 376 , 377 , 378 , 379 , 380 , 381 , 382 , 383 , 384 , 385 , 386 , 387 , 388 , 389 , 390 , 391 , 392 , 393 , 394 , 395 , 396 , 397 , 398 , 399 , 400 , 401 , 402 , 403 , 404 , 405 , 406 , 407 and 66 observations (Supplementary Table 9 ) and yielded an RR and uncertainty interval inclusive of between-study heterogeneity of 1.34 (0.81–2.19) (Table 2 , Fig. 3b and Extended Data Fig. 6 ). The BPRF (0.88) and corresponding ROS (−0.06) translated into a one-star rating, consistent with weak evidence of an association between passive smoking and increased risk of lower respiratory infections. The covariate selection algorithm flagged studies performed among populations that were not generalizable and those that used exposure definitions other than current SHS (for example, ever exposure to SHS) to be adjusted in our final model (Table 2 ). The strength of association as measured in the BPRF framework was not sensitive to any additional restrictions we applied to the input data, meaning that the one-star rating was still observed when we subset the data to prospective cohorts, never-smoking samples and a combination of the two (Extended Data Fig. 10 and Supplementary Tables 13 – 15 ).

Similar to the results for asthma and lower respiratory infections, the ROS for COPD was also negative (−0.14), equating to a one-star rating, indicating weak evidence of an association between SHS exposure and the risk of COPD. When accounting for between-study heterogeneity, the RR was 1.44 (0.67–3.12) (Table 2 , Fig. 3c and Extended Data Fig. 7 ). Covariates representing studies that did not control for smoking and those with potential selection bias were found to be significant in our primary model and were adjusted for accordingly (Table 2 ). When including observations from seven prospective cohorts only, we found no statistically significant evidence of an association between SHS exposure and COPD when not including between-study heterogeneity (RR 1.21 (0.93–1.57, without gamma)). This was similar to the result we found when subsetting the data to never-smoking populations (RR 1.15 (0.95–1.40, without gamma)). The one-star association was observed, however, in a sensitivity analysis in which we applied both data restrictions simultaneously (Extended Data Fig. 10 and Supplementary Tables 13 – 15 ).

Other health outcomes

Our conservative Burden of Proof assessment found evidence of weak harmful effects between SHS exposure and risk of type 2 diabetes, with an RR of 1.16 (0.98–1.37) when accounting for between-study heterogeneity (Table 2 , Fig. 4a and Extended Data Fig. 8 ). The BPRF value was 1.01 with a corresponding ROS of 0.005, which suggests that passive smoking is associated with at least a 1% higher risk of type 2 diabetes, translating to a two-star risk. The two-star relationship remained consistent in our sensitivity analysis in which we subset the input data to observations of never smokers only (Extended Data Fig. 10 and Supplementary Table 14 ). Restricting the data to prospective cohort studies resulted in a downgrade in star rating to a one-star risk (Extended Data Fig. 10 and Supplementary Table 13 ). Moreover, the automated covariate selection did not find any significant bias covariates for inclusion in the main or alternative final models (Table 2 and Supplementary Tables 13 – 15 ). No publication bias was found in the type 2 diabetes models.

a , b , These modified funnel plots show the residuals of the reported mean RR relative to 0, the null value, on the x axis and the residuals of the standard error, as estimated from both the reported standard error and gamma, relative to 0 on the y axis, for type 2 diabetes ( a ) and otitis media ( b ). The light-blue vertical interval corresponds to the 95% uncertainty interval incorporating between-study heterogeneity; the dark-blue vertical interval corresponds to the 95% uncertainty interval (UI) without between-study heterogeneity; the dots are each included observation; the red Xs are outliered observations; the gray dotted line reflects the null log(RR); the blue line is the mean log(RR) for SHS and the outcome of interest; the red line is the Burden of Proof function at the fifth quantile for these harmful risk–outcome associations.

For otitis media, our meta-analysis of 24 studies 132 , 385 , 408 , 409 , 410 , 411 , 412 , 413 , 414 , 415 , 416 , 417 , 418 , 419 , 420 , 421 , 422 , 423 , 424 , 425 , 426 , 427 , 428 , 429 and 32 observations (Supplementary Table 12 ) yielded an RR of 1.12 (0.92–1.36) when accounting for between-study heterogeneity (Table 2 , Fig. 4b and Extended Data Fig. 9 ). The corresponding BPRF was 0.95, which equates to a ROS of −0.03 and a one-star rating (weak evidence of association). Bias covariates that captured nonprospective cohort studies and studies in which the outcome of interest was self-reported (rather than diagnosed by a doctor) were detected as significant and adjusted for within our final model (Table 2 ). All studies included in our otitis media model were conducted in never-smoker populations (or classified as such given the age of the studied population ( Methods and Supplementary Information Section 2.2 )); however, when restricting our analysis to prospective cohort studies, the ROS was slightly higher, elevating the risk–outcome relationship to a two-star rating, with no bias covariates found statistically significant (Extended Data Fig. 10 and Supplementary Table 13 ). We found no publication bias in our primary model, but a statistically significant evidence of publication bias was found in our prospective cohort sensitivity analysis.

In this study, we applied the Burden of Proof framework to quantify the relationship between exposure to SHS and nine health outcomes and to assess the strength of the evidence underlying these associations 430 . As suggested by our estimates not accounting for between-study heterogeneity, we found evidence that passive smoking is associated with statistically significant increases in the risk of all nine health outcomes. When taking the BPRF to conservatively interpret the available data by accounting for between-study heterogeneity and other sources of bias, the evidence suggests that being exposed to SHS increased the risk of IHD, stroke and type 2 diabetes by a minimum of 8%, 5% and 1%, respectively, corresponding to two-star associations with SHS. The two-star rating was also found for the relationship with lung cancer, for which SHS was found to increase the risk by a minimum of around 1%. The available evidence of associations between SHS and otitis media, asthma, lower respiratory infections, breast cancer and COPD are weaker and these risk–outcome pairs were classified as one-star associations.

As long known, being exposed to SHS is irrefutably harmful to human health and our findings are broadly in support of tobacco control measures aimed at protecting nonsmokers from tobacco smoke. Overall, we found SHS to have small to moderate quantitative impacts on health—mean effect sizes range from 1.12 for otitis media to 1.44 for COPD—which is in line with previous assessments 13 , 431 , 432 , 433 , 434 , 435 , 436 , 437 , 438 , 439 , 440 , 441 and anticipated on the basis of mechanistic processes leading to diseases 5 . The modest strength of the association coupled with heterogeneity present in the underlying data across all nine risk–outcome pairs analyzed resulted in a body of evidence rated as weak under the proposed BPRF rating system (one and two stars), despite the relatively large number of studies included for some of the outcomes.

Nonetheless, even under our conservative interpretation of the available data using the BPRF approach, a particular area of considerable increased risk is cardiovascular health. This finding is consistent with the conclusions drawn by other studies in regard to both IHD and stroke 431 , 442 , 443 , 444 , 445 . In previous dose–response analyses, the harmful effects of SHS on cardiovascular diseases have been found even at low doses of exposure 446 , 447 , 448 . This is of particular concern as IHD and stroke are the two major causes of premature death and loss of healthy life worldwide 449 . Similarly, our findings also suggest that the risk of lung cancer and type 2 diabetes are also elevated for those exposed to SHS. Lung cancer was the fifth leading cause of death globally in 2019 and type 2 diabetes was the eighth leading cause, highlighting the potential benefit that could be achieved for these causes and overall disease burden by further reducing active and passive smoking 449 .

For otitis media, asthma, lower respiratory infections, breast cancer and COPD, the evidence supporting an association with passive smoking is even weaker, with a one-star rating. In the BPRF framework, one-star associations denote risk–outcome pairs for which it would not be surprising if the inclusion of additional data, when available, modifies our findings. Although we found evidence suggesting an association between SHS exposure and these other investigated health outcomes, the associations did not achieve statistical significance when using the BPRF approach to capture uncertainty that accounts for between-study heterogeneity. These findings highlight that the lack of consistent findings across studies is a major factor underlying the weak ROSs assigned to these exposure–outcome associations. The substantial inconsistency across studies with different designs and degrees of selection and information bias is not unusual for a risk factor with weak strength of associations, such as SHS exposure. In particular, we found insufficient evidence to support an association with SHS when restricting to prospective cohort studies (breast cancer) and never smokers (COPD), even when not incorporating between-study heterogeneity in our estimates of uncertainty. Indeed, authors have drawn markedly different conclusions about the presence and magnitude of association between passive exposure to tobacco smoke and breast cancer, especially when accounting for age group and menopausal status 11 , 12 , 346 , 350 , 450 . Because breast cancer is the most frequent type of cancer in women and accounts for substantial morbidity and mortality, research should continue to examine its association with exposure to SHS 451 .

Our study contributes to previous iterations of the GBD by not only increasing the number of studies informing each of the existing SHS–outcome associations but by assessing the relationship between passive smoking and asthma, a risk–outcome pair not yet incorporated into the GBD but deemed eligible for further consideration. Similar to our findings, population-specific meta-analyses found positive associations between passive exposure to tobacco smoke and both an overall increase in asthma risk within the Asian population 452 and the occurrence of childhood asthma 453 . Expanding the evidence base around SHS and other health outcomes is a means to more accurately capture the full breadth of disease burden attributable to this risk.

Furthermore, the BPRF framework employed in this study addresses many of the limitations of existing meta-analytical approaches 18 . Given the high degree of inconsistency observed across results in the SHS literature, using the BPRF to capture the unexplained sources of variation between studies is particularly relevant for our study. Moreover, the translation of our conservative findings surrounding the health effects of SHS into a star rating simplifies the communication and interpretation of the available evidence. However, viewed in isolation, neither the calculated effect sizes nor the BPRF or star ratings imply causality or lack thereof. These are some of the components to be considered when defining health policy and research funding priorities. The high prevalence of exposure to SHS in a scenario with an increasing number of smokers and the harmful associations with conditions of global relevance warrant policy focus even with weak evidence supporting the analyses when compared to other less prevalent risks associated with rare or less severe outcomes and strong supporting evidence.

In spite of the observed variability in the SHS data, which accounts in part for the ROS and star-rating results we obtained, our study reaffirms that exposure to SHS is a harmful risk factor of great public health importance. As outlined by the World Health Organization, smoke-free policies in combination with strategies promoting active smoking cessation and noninitiation are among the most effective tobacco control interventions to reduce passive smoking and protect health 454 . Studies of the effects of smoke-free laws found that hospital admission and mortality rates for cardiovascular and respiratory conditions decreased after the implementation of smoking bans 455 , 456 , 457 , 458 , 459 . However, comprehensive smoke-free legislation (that is, covering all indoor public places) is in place in only 67 countries, protecting less than 25% of the world’s population 7 . Therefore, faster-paced implementation and adequate enforcement of this type of policy can play an important role in minimizing the burden of smoking-attributable diseases and deaths among nonsmokers. Moreover, private homes remain a major source of SHS exposure, particularly for women and children 3 , 460 , and our findings can help reinforce awareness of the adverse consequences of SHS exposure and promote adoption of voluntary restrictions in homes 461 .

When interpreting this study’s results, a number of limitations need to be taken into consideration, most of which are associated with the limitations of the available data, which in turn may have led to an underestimation of the RRs in our findings. First, we used studies in which exposure to SHS was self-reported, either directly or measured by proxy (that is, living with a smoking parent or spouse), and this can result in misclassification of exposed and nonexposed participants. Second, the information collected by surveys frequently asks about current exposure; this means that we lack information on cumulative exposure to SHS and formerly exposed individuals could have been misclassified as unexposed. Third, to account for the lack of a standardized way of capturing exposure to SHS in existing studies, we classify exposure to SHS as dichotomous (exposed or unexposed); however, this may oversimplify the risk profile associated with SHS by not accounting for differences in intensity or frequency of exposure. Fourth, our results draw upon data that rely on a range of exposure definitions. For example, the underlying studies capture information about exposure to SHS at either home or work and, in the absence of these, at any location more broadly. Previous studies have found different effect sizes for SHS exposure at home and at work 442 , 443 , 462 , a factor that was not investigated in our analysis. However, a covariate was created to assess if data points associated with exposure at any location were significantly different from those associated with exposure at work or home, which is the SHS definition adopted by the GBD. Because we use the GBD exposure definition, we also do not include data for exposure in public settings, which are largely limited. In the included studies, those not exposed at work or home may be exposed to SHS at other settings, and this bias, similar to our first limitation above, will tend to underestimate the true RR. Finally, despite the inclusion of asthma, a new health outcome to be considered for inclusion in the GBD, the outcomes assessed here do not necessarily reflect the harms associated with SHS in full. Future efforts could synthesize the available evidence concerning the relationship between SHS and other health outcomes for which some evidence of an association exist, for example, maternal outcomes and low birth weight 463 .

In conclusion, our study, which examines the relationship between SHS exposure and nine health outcomes using the BPRF framework developed by Zheng and colleagues 430 , reaffirms that SHS should be an area of priority for policymakers, physicians and public health advocates for strengthening tobacco-control measures, especially in locations with high smoking and SHS prevalence. Due to heterogeneity and uncertainty in the data, small effect sizes, small numbers of studies or a combination of these reasons, the existing strength of evidence on the health effects of SHS was considered weak, especially for the relationship with otitis media, asthma, lower respiratory infections, breast cancer and COPD. Even when applying a conservative interpretation of the evidence, our results suggest that exposure to SHS increases the risk to nonsmokers for cardiovascular outcomes, lung cancer and type 2 diabetes. Prospective cohort studies with greater consistency in case definitions, more precise measurement of exposures and larger samples can result in less inconsistent data, and thus more targeted recommendations.

In this study, we employed the BPRF methodology developed by Zheng and colleagues 430 to conservatively estimate the association between SHS exposure and nine health outcomes and assess the strength of the evidence supporting each of these associations. We define SHS as the current exposure, among nonsmokers, to smoke from any combustible tobacco product at home or at work, the same definition used in the GBD studies. BPRF methods have already been employed to assess the health effects associated with smoking 464 , high systolic blood pressure 465 and consumption of unprocessed red meat 466 and vegetables 467 . Specifically, the BPRF framework uses a meta-regression–Bayesian, regularized, trimmed (MR-BRT) tool to estimate pooled RRs, along with uncertainty intervals, accounting for systematic bias, within-study correlation and unexplained between-study heterogeneity. Briefly, we followed the six analytical steps included in the BPRF meta-analytical approach, namely: (1) conducting a systematic review and extracting data from identified studies reporting on the association between SHS exposure and the outcomes of interest; (2) estimating a pooled RR that compares the risk of being exposed to SHS relative to those not exposed to SHS; (3) testing and adjusting for systematic sources of bias within input sources; (4) quantifying unexplained between-study heterogeneity while adjusting for within-study correlation and the number of studies; (5) evaluating publication and reporting bias; and (6) estimating the BPRF to generate a conservative estimate of the risk associated with SHS exposure and to compute a corresponding ROS. The BPRF is defined as the 5th (if harmful) or 95th (if protective) quantile estimate of the risk closest to the null estimate, with the 5th quantile reflecting the smallest harmful effect of a risk exposure on a given health outcome that is consistent with the available evidence. The ROS, which is the signed value of the log RR, reflects the effect size and strength of evidence for each risk–outcome association estimated. ROSs are translated into a star-rating scale from 1 to 5 to aid the interpretation of the results. We describe each of these steps below, and further details are available elsewhere 430 .

Similar to previous studies using BPRF methods 464 , 465 , 466 , 467 , the RRs, BPRFs and ROSs estimated in this study are not specific to or disaggregated by certain populations, meaning that we did not estimate RRs separately by geography, sex or age group. However, the assessment of the association between SHS and breast cancer relied on studies that were conducted in female-only populations. For asthma, we conducted a children-specific sensitivity analysis that is described along other sensitivity analyses below.

The present study complies with the PRISMA guidelines 19 (Supplementary Tables 17 and 18 and Supplementary Figs. 1 – 9 ) and Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER) recommendations (Supplementary Table 19 ) 468 . As a component of the GBD, the present analysis was approved by the University of Washington institutional review board committee (study no. 9060).

Health outcomes of interest

We selected outcomes on the basis of the availability of epidemiological evidence on their potential relationship with SHS. Eight out of the nine outcomes of interest—lung and breast cancer, IHD, stroke, COPD, lower respiratory infections, type 2 diabetes and otitis media—constitute SHS risk–outcome pairs considered in previous iterations of the GBD and were initially selected using the World Cancer Research Fund criteria for convincing or probable evidence as detailed in Murray et al. 1 . Through review of published meta-analyses and systematic reviews and consultations with key external experts, we identified asthma as an additional health outcome of interest to SHS researchers and one for which sufficient literature was available to enable BPRF analytic methods; we therefore included it in our analysis. Reference and alternative definitions of each of the outcomes are listed in Supplementary Table 1 .

Systematic review

We conducted separate systematic reviews to identify peer-reviewed literature reporting relative measures of association quantifying the relationship between SHS exposure and each health outcome of interest. We searched PubMed and Web of Science for studies published between 1 January 1970 and 31 July 2022. Furthermore, we reviewed the citation lists of the systematic reviews and meta-analyses captured in our searches to identify additional pertinent studies.

Briefly, after deduplicating the search results, each study’s title and abstract were manually screened by a single reviewer for inclusion eligibility. Subsequently, the full text was retrieved and screened, and data were extracted from those studies that passed our inclusion criteria of being published in English; being a case–control, cohort, case-cohort or case-crossover study conducted in participant groups likely to be generalizable; using suitable exposure and outcome definitions; and reporting both a relative measure of association (that is, RR, odds ratio or hazard ratio) and some measure of uncertainty (for example, sample size, standard error or confidence intervals). In terms of outcome definitions, studies using either a reference or an alternative health outcome definition met our inclusion criteria (Supplementary Table 1 ). As for SHS exposure, we included studies with varied SHS definitions, including proxies, but restricted to those reporting dichotomous current or ever exposure (that is, yes/no exposure). We excluded studies reporting only former exposure to SHS and those only assessing exposure in specific public settings. To better match our SHS definition, we also excluded studies and observations reporting health risk for current smokers. Finally, for all outcomes but otitis media, lower respiratory infections and asthma, we excluded studies that exclusively assessed childhood exposure to SHS to best account for the exposure temporality reflected in the SHS definition in GBD. In the case that multiple studies provided estimates from the same cohort, we included only the study with the largest sample or follow-up period so as not to duplicate data. The search strings used in each database, detailed inclusion and exclusion criteria, and outcome-specific PRISMA flow diagrams are available in Supplementary Figs. 1 – 9 .

Data from eligible publications were manually extracted into a template designed to capture information about study and sample characteristics, exposure and outcome definitions, ascertainment methods, effect size and corresponding uncertainty reported for each model/population, and covariates included in the statistical analyses. We also assessed each study for risk of potential bias following the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) approach and recorded the information in the extraction template 469 . As part of the exposure definition review, we cataloged multiple aspects of SHS exposure linked to each reported effect size, including the location of exposure (home or/and work combined; home; work; or any/unspecified location), the source of exposure (family; parental; maternal; paternal; spouse; or any/unspecified source), the timing of exposure (current or ever), and the smoking status of the exposed population (nonsmoker; never smoker; former smoker; or any/unspecified). Those studies performed only among children aged 15 years or less with an original ‘unspecified’ smoking status were reassigned to ‘adjusted never smokers’ and treated as ‘never smokers’ and ‘controlled for smoking’ in our analyses. In the GBD, we assume no smoking prevalence for ages under 10 years; given the small prevalence for ages 10–15 and since most of the identified childhood studies included those past age 10, we believe this classification best reflects the smoking status of the studied population in these cases. All extracted data underwent manual quality assurance by the research team to verify accuracy. For a full list of extracted variables, with corresponding definitions, see Supplementary Table 2 .

Estimating pooled RRs for each risk–outcome pair

We selected the effect sizes to be used in our meta-analytic approach within each included study and health outcome based on a prioritization cascade. All included effect sizes are reported in Supplementary Tables 4 – 12 . Starting with the exposure definition, we chose the data points that closest matched the GBD risk definition in terms of the smoking status of the exposed population, followed by the location of exposure, the source of exposure, and the temporality. Thus, data points for nonsmokers currently exposed to SHS at home or work combined were prioritized over the other ones. In the absence of this exact definition, we prioritized the inclusion of effect sizes for each/any of the components of the GBD risk definition (that is, never smoker; former smoker; home; work) over those associated with a broader definition (that is, any/unspecified location or smoking status). Due to data sparsity, ‘ever exposure’ definitions were accepted for inclusion if results for ‘current exposure’ were not available. We did not include observations referring to exposure in specific settings other than home or work (for example, public settings or public transportation) or exposure among current smokers. Bias covariates were created to capture the impact of using alternate exposure definitions.

After this first selection stage, we proceeded with identifying the least granular analyses to be used in our models. For example, within each study and outcome, sex- and age-specific results were dropped in favor of aggregated data points, and results associated with the entire study population were retained over those for subgroup analyses when possible. We also favored observations reporting the risk of incidence and mortality combined over those that estimated each outcome type separately in cases where both were available. Moreover, for stroke, we dropped observations for subtypes (ischemic and hemorrhagic stroke) in favor of those for overall stroke due to data availability restrictions and to allow for best comparability across studies. In our last data selection step, the most-adjusted remainder data points within each study outcome were selected for inclusion in our analyses. This selection process is described in more detail in Supplementary Information .

To reduce the influence on our model of multiple observations coming from the same study, we adjusted the standard errors of effect sizes reported for multiple non-mutually exclusive exposure groups in each study by a factor matching the number of repeated measurements within each age–sex–smoking status group (Supplementary Information Section 2.2 ).

Finally, we used the MR-BRT tool to conduct each risk–outcome meta-regression analysis with the log-space RR of the outcome modeled as the dependent variable and exposure to SHS as the dichotomous independent variable (exposed to SHS versus not exposed to SHS). These analyses generated a single estimate of pooled RR of the given health outcome occurring for those exposed to SHS relative to unexposed counterparts. Following the BPRF methodology, we applied a 10% likelihood-based data-trimming algorithm to detect and remove outliers that may otherwise over-influence the model. This approach is suggested for all analyses with more than ten data points; therefore, it was implemented across all of our primary risk–outcome assessments and most of our sensitivity analyses 470 .

Testing and adjusting for biases across study designs and characteristics

Following the GRADE approach, we used the extracted data related to specific study characteristics to create binary covariates that captured potential sources of systematic bias within our input datasets. These covariates reflected the risk of bias associated with study design (prospective cohorts versus others), representativeness of the study population, exposure measurement (measured at baseline only versus multiple times during follow-up), outcome assessment method (self-report versus medical records), degree of control for confounding, and potential for selection bias (based on percentage follow-up for longitudinal study designs and percentages of cases and controls for which exposure data could be ascertained for case–control designs). Additionally, given SHS-specific characteristics, we created covariates to indicate whether a study controlled for smoking, regardless of other confounders, and whether the definition of SHS matched the one in GBD in terms of the location of exposure (home or work exposure versus broader definitions). A covariate reflecting studies performed among females only was also created. For the stroke models, we created two bias covariates to account for possible differences between studies reporting subtype-specific effect size only and those reporting stroke as an aggregated outcome; for asthma we created a specific covariate to indicate if a study was performed among children only (≤16 years old). Detailed information about each of the bias covariates is provided in Supplementary Information Section 5 (Supplementary Table 20 ). We systematically tested for the effect of bias covariates using a selection algorithm, which uses a step-wise Lasso strategy to identify statistically significant covariates at a threshold of 0.05, and adjusted for the selected bias covariates in the final model used to generate the RR estimates. Covariates were eligible for testing if there was a minimum of two data points in the model associated with each covariate value. If multiple covariates had the same distribution of values within a model, we randomly selected one of the covariates to be tested.

Quantifying remaining between-study heterogeneity

After adjusting for study-level bias covariates, we used a linear mixed-effects model to capture the remaining unexplained between-study heterogeneity, in which we included a study-level random slope (gamma) and a study-level random intercept for within-study correlation. We derived the uncertainty of gamma using the inverse Fisher information matrix, which is sensitive to the number of studies, study design and reported uncertainty. The draws of gamma are used to derive the conservative uncertainty interval estimate for our RR (with gamma), estimated from both the uncertainty surrounding the mean effect and the 95th quantile of between-study heterogeneity. The RR without gamma, as reported in Table 2 , is reported with an uncertainty derived without fully accounting for between-study heterogeneity and reflects the RR estimates that are typically reported in traditional meta-analyses, while that with gamma better reflects the degree of consistency across the underlying studies. In this study, the RR metric of primary interest was the pooled RR with 95% uncertainty intervals that are inclusive (using gamma) of the effect of between-study heterogeneity. The estimated gamma for each risk–outcome primary assessment is presented in Supplementary Table 21 .

Evaluating publication and reporting bias

To assess the presence of publication or reporting bias, we visually inspected the funnel plots (Figs. 1 – 4 ) produced for each risk–outcome evaluation, which show the residuals of the reported mean RR against the residuals of the standard error from each individual study. Visual inspection of the plots was accompanied by Egger’s regression tests to test for significant correlation between the standard error and the reported effect size. We did not find evidence of publication or reporting bias across any of the risk–outcome pairs in our primary models. We found publication bias for otitis media in one of our sensitivity analyses. We flagged the potential publication bias but did not correct for it in the model.

Estimating the BPRF

In our final step, we estimated the BPRF, which reflects the most conservative estimate of the association between exposure to SHS and the selected health outcomes that is consistent with the available evidence. For dichotomous harmful risk factors, the BPRF corresponds to the fifth quantile of RR closest to null, derived from the RR model inclusive of between-study heterogeneity. For each risk–outcome pair, the BPRF can be used to compute measures of increased or decreased risk of developing the health outcome due to exposure to the risk factor. BPRF values can be converted into ROSs, defined as the signed value of the average log RR of the BPRF. Large positive ROSs correspond to strong and consistent evidence of an association, while small positive ROSs and negative ROSs reflect weak evidence for an association, based on the available data. To facilitate the interpretation and comparison of the ROS results, the BPRF framework translates the ROS into star rating categories ranging from one to five (one star, ≤0.0 ROS; two stars, >0.0–0.14 ROS; three stars, >0.14–0.41 ROS; four stars, >0.41–0.62 ROS; five stars, >0.62 ROS). A one-star rating indicates weak evidence of association, while a five-star rating indicates very strong evidence. Zero-star risk–outcome pairs are not based on ROSs values but are defined as pairs for which there is no evidence of a statistically significant association between the risk and the health outcome when not accounting for between-study heterogeneity (that is, the 95% uncertainty interval without gamma crosses the null). Risk–outcome pairs receiving a one- through five-star rating are eligible for inclusion in the GBD.

Model validation

The validity of the BPRF approach to meta-analyze data extracted across studies has been extensively and rigorously evaluated by Zheng and colleagues 430 . For the present study, we conducted three main sensitivity analyses to examine the robustness of our primary findings to our data input in which we kept most of the model parameters consistent but (1) restricted our analysis to studies with a prospective cohort design; (2) subset our input data to never-smoking samples only; and (3) applied both these restrictions in conjunction. For asthma, specifically, we ran an additional model in which we restrict the data to those studies performed among children only (≤16 years old). The only modification in our model parameters was related to the implementation of the 10% data trimming, which is dependent on the number of observations available for each outcome model (that is, data are trimmed only if ten observations or more are included). We present the detailed results of these sensitivity analyses in Supplementary Tables 13 – 16 .

Statistical analysis and reproducibility

Analyses were carried out using R version 4.0.5 and Python version 3.10.9.

This investigation relied on existing published data. No statistical method was used to predetermine sample size. For each health outcome, we included all studies that met our inclusion criteria. This study did not engage in primary data collection, randomization or blinding. Therefore, data exclusions were not relevant to the present study, and, as such, no data were excluded from the analyses. We have made our data and code available to foster reproducibility.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

The findings from this study are supported by data extracted from published literature. We cite all studies included in our analyses in our manuscript. Studies’ characteristics are presented in Supplementary Table 3 , and data points included in each analysis are available in Supplementary Tables 4 – 12 . Details on data sources can also be found on the Burden of Proof visualization tool ( https://vizhub.healthdata.org/burden-of-proof/ ).

Code availability

All code used for these analyses is publicly available online ( https://github.com/ihmeuw-msca/burden-of-proof/ ).

Change history

30 january 2024.

A Correction to this paper has been published: https://doi.org/10.1038/s41591-024-02832-y

Murray, C. J. L. et al. Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet 396 , 1223–1249 (2020).

Article   Google Scholar  

Dai, X., Gakidou, E. & Lopez, A. D. Evolution of the global smoking epidemic over the past half century: strengthening the evidence base for policy action. Tob. Control 31 , 129–137 (2022).

Article   PubMed   Google Scholar  

Mbulo, L. et al. Secondhand smoke exposure at home among one billion children in 21 countries: findings from the Global Adult Tobacco Survey (GATS). Tob. Control 25 , e95–e100 (2016).

Gakidou, E. et al. Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet 390 , 1345–1422 (2017).

How Tobacco Smoke Causes Disease: The Biology and Behavioral Basis for Smoking-Attributable Disease: A Report of the Surgeon General (US Department of Health and Human Services, 2010).

Zhai, C. et al. Global, regional, and national deaths, disability-adjusted life years, years lived with disability, and years of life lost for the global disease burden attributable to second-hand smoke, 1990–2019: a systematic analysis for the Global Burden of Disease Study. Sci. Total Environ. 862 , 160677 (2023).

Article   CAS   PubMed   Google Scholar  

WHO Report on the Global Tobacco Epidemic 2021: Addressing New and Emerging Products . (World Health Organization, 2021).

Flor, L. S., Reitsma, M. B., Gupta, V., Ng, M. & Gakidou, E. The effects of tobacco control policies on global smoking prevalence. Nat. Med. 27 , 239–243 (2021).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Reitsma, M. B. et al. Spatial, temporal, and demographic patterns in prevalence of smoking tobacco use and attributable disease burden in 204 countries and territories, 1990–2019: a systematic analysis from the Global Burden of Disease Study 2019. Lancet 397 , 2337–2360 (2021).

The health consequences of involuntary smoking: a report of the Surgeon General, 1986. US Department of Health and Human Services https://stacks.cdc.gov/view/cdc/20799 (1986).

The health consequences of smoking: 50 years of progress. a report of the Surgeon General. US Department of Health and Human Services https://www.cdc.gov/tobacco/ (2014).

Tobacco Smoke and Involuntary Smoking (International Agency for Research on Cancer, 2004).

Kim, A.-S., Ko, H.-J., Kwon, J.-H. & Lee, J.-M. Exposure to secondhand smoke and risk of cancer in never smokers: a meta-analysis of epidemiologic studies. Int. J. Environ. Res. Public Health 15 , 1981 (2018).

Article   PubMed   PubMed Central   Google Scholar  

Macacu, A., Autier, P., Boniol, M. & Boyle, P. Active and passive smoking and risk of breast cancer: a meta-analysis. Breast Cancer Res. Treat. 154 , 213–224 (2015).

Carreras, G. et al. Burden of disease attributable to second-hand smoke exposure: a systematic review. Prev. Med. 129 , 105833 (2019).

Stayner, L. et al. Lung cancer risk and workplace exposure to environmental tobacco smoke. Am. J. Public Health 97 , 545–551 (2007).

Tabuchi, T. et al. Maternal and paternal indoor or outdoor smoking and the risk of asthma in their children: a nationwide prospective birth cohort study. Drug Alcohol Depend. 147 , 103–108 (2015).

Aravkin, A. Y. et al. Reply to: concerns about the Burden of Proof studies. Nat. Med. 29 , 826–827 (2023).

Page, M. J. et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 372 , n71 (2021).

Polk, S. et al. A prospective study of Fel d1 and Der p1 exposure in infancy and childhood wheezing. Am. J. Respir. Crit. Care Med. 170 , 273–278 (2004).

Wang, J. et al. A prospective study on the role of smoking, environmental tobacco smoke, indoor painting and living in old or new buildings on asthma, rhinitis and respiratory symptoms. Environ. Res 192 , 110269 (2021).

Coogan, P. F. et al. Active and passive smoking and the incidence of asthma in the Black Women’s Health Study. Am. J. Respir. Crit. Care Med. 191 , 168–176 (2015).

Thorn, J., Brisman, J. & Torén, K. Adult-onset asthma is associated with self-reported mold or environmental tobacco smoke exposures in the home. Allergy 56 , 287–292 (2001).

Flodin, U., Jönsson, P., Ziegler, J. & Axelson, O. An epidemiologic study of bronchial asthma and smoking. Epidemiology 6 , 503–505 (1995).

van Beijsterveldt, T. C. E. M. & Boomsma, D. I. An exploration of gene–environment interaction and asthma in a large sample of 5-year-old Dutch twins. Twin Res. Hum. Genet. 11 , 143–149 (2008).

Huang, P.-C. et al. Are phthalate exposure related to oxidative stress in children and adolescents with asthma? A cumulative risk assessment approach. Antioxidants 11 , 1315 (2022).

Chan, T.-C., Hu, T.-H., Chu, Y.-H. & Hwang, J.-S. Assessing effects of personal behaviors and environmental exposure on asthma episodes: a diary-based approach. BMC Pulm. Med. 19 , 231 (2019).

Rennie, D. C. et al. Assessment of endotoxin levels in the home and current asthma and wheeze in school-age children. Indoor Air 18 , 447–453 (2008).

Oddy, W. H. et al. Association between breast feeding and asthma in 6 year old children: findings of a prospective birth cohort study. BMJ 319 , 815–819 (1999).

Taveras, E. M. et al. Association of birth weight with asthma-related outcomes at age 2 years. Pediatr. Pulmonol. 41 , 643–648 (2006).

Tadaki, H. et al. Association of cord blood cytokine levels with wheezy infants in the first year of life. Pediatr. Allergy Immunol. 20 , 227–233 (2009).

Aversa, Z. et al. Association of infant antibiotic exposure with childhood health outcomes. Mayo Clin. Proc. 96 , 66–77 (2021).

Vázquez Nava, F. et al. Associations between family history of allergy, exposure to tobacco smoke, active smoking, obesity, and asthma in adolescents. Arch. Bronconeumol. 42 , 621–626 (2006).

Schroer, K. T. et al. Associations between multiple environmental exposures and Glutathione S -Transferase P1 on persistent wheezing in a birth cohort. J. Pediatr. 154 , 401–408, 408.e1 (2009).

Melsom, T. et al. Asthma and indoor environment in Nepal. Thorax 56 , 477–481 (2001).

Norbäck, D. et al. Asthma and rhinitis among Chinese children—indoor and outdoor air pollution and indicators of socioeconomic status (SES). Environ. Int. 115 , 1–8 (2018).

Mumcuoglu, K. Y. et al. Asthma in Gaza refugee camp children and its relationship with house dust mites. Ann. Allergy 72 , 163–166 (1994).

CAS   PubMed   Google Scholar  

Lawson, J. A., Janssen, I., Bruner, M. W., Hossain, A. & Pickett, W. Asthma incidence and risk factors in a national longitudinal sample of adolescent Canadians: a prospective cohort study. BMC Pulm. Med. 14 , 51 (2014).

Pokharel, P. K., Pokharel, P., Bhatta, N. K., Pandey, R. M. & Erkki, K. Asthma symptomatics school children of Sonapur. Kathmandu Univ. Med. J. 5 , 484–487 (2007).

CAS   Google Scholar  

Goksör, E., Amark, M., Alm, B., Gustafsson, P. M. & Wennergren, G. Asthma symptoms in early childhood—what happens then? Acta Paediatr. 95 , 471–478 (2006).

Toizumi, M. et al. Asthma, rhinoconjunctivitis, eczema, and the association with perinatal anthropometric factors in Vietnamese children. Sci. Rep. 9 , 2655 (2019).

Hedman, L., Bjerg, A., Sundberg, S., Forsberg, B. & Rönmark, E. Both environmental tobacco smoke and personal smoking is related to asthma and wheeze in teenagers. Thorax 66 , 20–25 (2011).

Boker, F., Alzahrani, A., Alsaeed, A., Alzhrani, M. & Albar, R. Cesarean section and development of childhood bronchial asthma: is there a risk? Open Access Maced. J. Med. Sci. 7 , 347–351 (2019).

Surdu, S., Montoya, L. D., Tarbell, A. & Carpenter, D. O. Childhood asthma and indoor allergens in native americans in New York. Environ. Health 5 , 22 (2006).

Ehrlich, R. et al. Childhood asthma and passive smoking. Urinary cotinine as a biomarker of exposure. Am. Rev. Respir. Dis. 145 , 594–599 (1992).

Yang, C. Y., Lin, M. C. & Hwang, K. C. Childhood asthma and the indoor environment in a subtropical area. Chest 114 , 393–397 (1998).

Zheng, T. et al. Childhood asthma in Beijing, China: a population-based case–control study. Am. J. Epidemiol. 156 , 977–983 (2002).

David, G. L., Koh, W.-P., Lee, H.-P., Yu, M. C. & London, S. J. Childhood exposure to environmental tobacco smoke and chronic respiratory symptoms in non-smoking adults: the Singapore Chinese Health Study. Thorax 60 , 1052–1058 (2005).

Galobardes, B. et al. Childhood wheezing, asthma, allergy, atopy, and lung function: different socioeconomic patterns for different phenotypes. Am. J. Epidemiol. 182 , 763–774 (2015).

Guo, S.-L., Liu, F., Ren, C.-J., Xing, C.-H. & Wang, Y.-J. Correlations of LTα and NQO1 gene polymorphisms with childhood asthma. Eur. Rev. Med. Pharm. Sci. 23 , 7557–7562 (2019).

Google Scholar  

Carlsten, C., Dimich-Ward, H., DyBuncio, A., Becker, A. B. & Chan-Yeung, M. Cotinine versus questionnaire: early-life environmental tobacco smoke exposure and incident asthma. BMC Pediatr. 12 , 187 (2012).

Patrick, D. M. et al. Decreasing antibiotic use, the gut microbiota, and asthma incidence in children: evidence from population-based and prospective cohort studies. Lancet Respir. Med. 8 , 1094–1105 (2020).

Charoenca, N. et al. Determining the burden of secondhand smoke exposure on the respiratory health of Thai children. Tob. Induc. Dis. 11 , 7 (2013).

Arif, A. A. & Racine, E. F. Does longer duration of breastfeeding prevent childhood asthma in low-income families? J. Asthma 54 , 600–605 (2017).

Usemann, J. et al. Dynamics of respiratory symptoms during infancy and associations with wheezing at school age. ERJ Open Res. 4 , 00037–02018 (2018).

Sherman, C. B., Tosteson, T. D., Tager, I. B., Speizer, F. E. & Weiss, S. T. Early childhood predictors of asthma. Am. J. Epidemiol. 132 , 83–95 (1990).

Milner, J. D., Stein, D. M., McCarter, R. & Moon, R. Y. Early infant multivitamin supplementation is associated with increased risk for food allergy and asthma. Pediatrics 114 , 27–32 (2004).

Midodzi, W. K., Rowe, B. H., Majaesic, C. M., Saunders, L. D. & Senthilselvan, A. Early life factors associated with incidence of physician-diagnosed asthma in preschool children: results from the Canadian Early Childhood Development cohort study. J. Asthma 47 , 7–13 (2010).

Slob, E. M. A. et al. Early-life antibiotic use and risk of asthma and eczema: results of a discordant twin study. Eur. Respir. J. 55 , 1902021 (2020).

Sun, W., Svendsen, E. R., Karmaus, W. J. J., Kuehr, J. & Forster, J. Early-life antibiotic use is associated with wheezing among children with high atopic risk: a prospective European study. J. Asthma 52 , 647–652 (2015).

Grabenhenrich, L. B. et al. Early-life determinants of asthma from birth to age 20 years: a German birth cohort study. J. Allergy Clin. Immunol. 133 , 979–988 (2014).

Chen, Y.-C., Tsai, C.-H. & Lee, Y. L. Early-life indoor environmental exposures increase the risk of childhood asthma. Int. J. Hyg. Environ. Health 215 , 19–25 (2011).

von Kobyletzki, L. B. et al. Eczema in early childhood is strongly associated with the development of asthma and rhinitis in a prospective cohort. BMC Dermatol 12 , 11 (2012).

Håberg, S. E., Stigum, H., Nystad, W. & Nafstad, P. Effects of pre- and postnatal exposure to parental smoking on early childhood respiratory health. Am. J. Epidemiol. 166 , 679–686 (2007).

Boneberger, A. et al. Environmental risk factors in the first year of life and childhood asthma in the Central South of Chile. J. Asthma 48 , 464–469 (2011).

Jaakkola, M. S., Piipari, R., Jaakkola, N. & Jaakkola, J. J. K. Environmental tobacco smoke and adult-onset asthma: a population-based incident case–control study. Am. J. Public Health 93 , 2055–2060 (2003).

Jaakkola, J. J., Nafstad, P. & Magnus, P. Environmental tobacco smoke, parental atopy, and childhood asthma. Environ. Health Perspect. 109 , 579–582 (2001).

Litonjua, A. A., Carey, V. J., Burge, H. A., Weiss, S. T. & Gold, D. R. Exposure to cockroach allergen in the home is associated with incident doctor-diagnosed asthma and recurrent wheezing. J. Allergy Clin. Immunol. 107 , 41–47 (2001).

Ratageri, V. H., Kabra, S. K., Dwivedi, S. N. & Seth, V. Factors associated with severe asthma. Indian Pediatr. 37 , 1072–1082 (2000).

El-Sharif, N., Abdeen, Z., Barghuthy, F. & Nemery, B. Familial and environmental determinants for wheezing and asthma in a case–control study of school children in Palestine. Clin. Exp. Allergy 33 , 176–186 (2003).

Polosa, R., Al-Delaimy, W. K., Russo, C., Piccillo, G. & Sarvà, M. Greater risk of incident asthma cases in adults with allergic rhinitis and effect of allergen immunotherapy: a retrospective cohort study. Respir. Res 6 , 153 (2005).

Pattemore, P. K. et al. Hair nicotine at 15 months old, tobacco exposure and wheeze or asthma from 15 months to 6 years old. Pediatr. Pulmonol. 53 , 443–451 (2018).

Leen, M. G., O’Connor, T., Kelleher, C., Mitchell, E. B. & Loftus, B. G. Home environment and childhood asthma. Ir. Med. J. 87 , 142–144 (1994).

Strachan, D. P., Butland, B. K. & Anderson, H. R. Incidence and prognosis of asthma and wheezing illness from early childhood to age 33 in a national British cohort. BMJ 312 , 1195–1199 (1996).

Morfín-Maciel, B., Barragán-Meijueiro, M., de, L. M. & Nava-Ocampo, A. A. Individual and family household smoking habits as risk factors for wheezing among adolescents. Prev. Med. 43 , 98–100 (2006).

Azizi, B. H., Zulkifli, H. I. & Kasim, S. Indoor air pollution and asthma in hospitalized children in a tropical environment. J. Asthma 32 , 413–418 (1995).

Mommers, M. et al. Indoor environment and respiratory symptoms in children living in the Dutch–German borderland. Int. J. Hyg. Environ. Health 208 , 373–381 (2005).

Yang, C. Y., Tien, Y. C., Hsieh, H. J., Kao, W. Y. & Lin, M. C. Indoor environmental risk factors and childhood asthma: a case–control study in a subtropical area. Pediatr. Pulmonol. 26 , 120–124 (1998).

McConnell, R. et al. Indoor risk factors for asthma in a prospective study of adolescents. Epidemiology 13 , 288–295 (2002).

van der Valk, R. J. P. et al. Interaction of a 17q12 variant with both fetal and infant smoke exposure in the development of childhood asthma-like symptoms. Allergy 67 , 767–774 (2012).

Jedrychowski, W. et al. Length at birth and effect of prenatal and postnatal factors on early wheezing phenotypes. Kraków epidemiologic cohort study. Int. J. Occup. Med. Environ. Health 21 , 111–119 (2008).

PubMed   Google Scholar  

Hunt, A., Crawford, J. A., Rosenbaum, P. F. & Abraham, J. L. Levels of household particulate matter and environmental tobacco smoke exposure in the first year of life for a cohort at risk for asthma in urban Syracuse, NY. Environ. Int. 37 , 1196–1205 (2011).

Milanzi, E. B. et al. Lifetime secondhand smoke exposure and childhood and adolescent asthma: findings from the PIAMA cohort. Environ. Health 16 , 14 (2017).

Sears, M. R. et al. Long-term relation between breastfeeding and development of atopy and asthma in children and young adults: a longitudinal study. Lancet 360 , 901–907 (2002).

Kanoh, M. et al. Longitudinal study of parental smoking habits and development of asthma in early childhood. Prev. Med. 54 , 94–96 (2012).

Lilljeqvist, A.-C., Faleide, A. O. & Watten, R. G. Low birthweight, environmental tobacco smoke, and air pollution: risk factors for childhood asthma? Pediatr. Asthma Allergy Immunol. 11 , 95–102 (1997).

Wada, T. et al. Maternal exposure to smoking and infant’s wheeze and asthma: Japan Environment and Children’s Study. Allergol. Int. 70 , 445–451 (2021).

Tanaka, K. et al. Maternal smoking and environmental tobacco smoke exposure and the risk of allergic diseases in Japanese infants: the Osaka Maternal and Child Health Study. J. Asthma 45 , 833–838 (2008).

Thacher, J. D. et al. Maternal smoking during pregnancy and early childhood and development of asthma and rhinoconjunctivitis—a MeDALL Project. Environ. Health Perspect. 126 , 047005 (2018).

Neuman, Å. et al. Maternal smoking in pregnancy and asthma in preschool children: a pooled analysis of eight birth cohorts. Am. J. Respir. Crit. Care Med. 186 , 1037–1043 (2012).

Frassanito, A. et al. Modifiable environmental factors predispose term infants to bronchiolitis but bronchiolitis itself predisposes to respiratory sequelae. Pediatr. Pulmonol. 57 , 640–647 (2022).

Hwang, B.-F., Liu, I.-P. & Huang, T.-P. Molds, parental atopy and pediatric incident asthma. Indoor Air 21 , 472–478 (2011).

Li, X., Sundquist, J., Calling, S., Zöller, B. & Sundquist, K. Mothers, places and risk of hospitalization for childhood asthma: a nationwide study from Sweden: epidemiology of allergic disease. Clin. Exp. Allergy 43 , 652–658 (2013).

Izuhara, Y. et al. Mouth breathing, another risk factor for asthma: the Nagahama Study. Allergy 71 , 1031–1036 (2016).

Carr, T. F., Stern, D. A., Halonen, M., Wright, A. L. & Martinez, F. D. Non-atopic rhinitis at age 6 is associated with subsequent development of asthma. Clin. Exp. Allergy 49 , 35–43 (2019).

Klinnert, M. D. et al. Onset and persistence of childhood asthma: predictors from infancy. Pediatrics 108 , E69 (2001).

O’Connell, E. J. & Logan, G. B. Parental smoking in childhood asthma. Ann. Allergy 32 , 142–145 (1974).

Clark, S. J., Warner, J. O. & Dean, T. P. Passive smoking amongst asthmatic children. Questionnaire or objective assessment? Clin. Exp. Allergy 24 , 276–280 (1994).

Willers, S., Svenonius, E. & Skarping, G. Passive smoking and childhood asthma. Urinary cotinine levels in children with asthma and in referents. Allergy 46 , 330–334 (1991).

Kim, A. et al. Perinatal factors and the development of childhood asthma. Ann. Allergy Asthma Immunol. 120 , 292–299 (2018).

Hagendorens, M. M. et al. Perinatal risk factors for sensitization, atopic dermatitis and wheezing during the first year of life (PIPO study). Clin. Exp. Allergy 35 , 733–740 (2005).

Carrasco, P. et al. Pre and postnatal exposure to mercury and respiratory health in preschool children from the Spanish INMA Birth Cohort Study. Sci. Total Environ. 782 , 146654 (2021).

Thacher, J. D. et al. Pre- and postnatal exposure to parental smoking and allergic disease through adolescence. Pediatrics 134 , 428–434 (2014).

van der Werff, S. D. et al. Prediction of asthma by common risk factors: a follow-up study in Cuban schoolchildren. J. Investig. Allergol. Clin. Immunol. 23 , 415–420 (2013).

Balemans, W. A. F. et al. Prediction of asthma in young adults using childhood characteristics: development of a prediction rule. J. Clin. Epidemiol. 59 , 1207–1212 (2006).

Rosa, M. J. et al. Prenatal exposure to polycyclic aromatic hydrocarbons, environmental tobacco smoke and asthma. Respir. Med. 105 , 869–876 (2011).

Selby, A. et al. Prevalence estimates and risk factors for early childhood wheeze across Europe: the EuroPrevall birth cohort. Thorax 73 , 1049–1061 (2018).

Elder, D. E., Hagan, R., Evans, S. F., Benninger, H. R. & French, N. P. Recurrent wheezing in very preterm infants. Arch. Dis. Child Fetal Neonatal Ed. 74 , F165–F171 (1996).

Takemura, Y. et al. Relation between breastfeeding and the prevalence of asthma: the Tokorozawa Childhood Asthma and Pollinosis Study. Am. J. Epidemiol. 154 , 115–119 (2001).

Kurukulaaratchy, R. J., Matthews, S. & Arshad, S. H. Relationship between childhood atopy and wheeze: what mediates wheezing in atopic phenotypes? Ann. Allergy Asthma Immunol. 97 , 84–91 (2006).

Al-Qerem, W. A., Ling, J., Pullen, R. & McGarry, K. Reported prevalence of allergy and asthma in children from urban and rural Egypt. Air Qual. Atmos. Health 9 , 613–620 (2016).

Lemanske, R. F. et al. Rhinovirus illnesses during infancy predict subsequent childhood wheezing. J. Allergy Clin. Immunol. 116 , 571–577 (2005).

Pokharel, P. K., Kabra, S. K., Kapoor, S. K. & Pandey, R. M. Risk factors associated with bronchial asthma in school going children of rural Haryana. Indian J. Pediatr. 68 , 103–106 (2001).

Majeed, R., Rajar, U. D. M., Shaikh, N., Majeed, F. & Arain, A. A. Risk factors associated with childhood asthma. J. Coll. Physicians Surg. Pak. 18 , 299–302 (2008).

Al-Kubaisy, W., Ali, S. H. & Al-Thamiri, D. Risk factors for asthma among primary school children in Baghdad, Iraq. Saudi Med. J. 26 , 460–466 (2005).

Mpairwe, H. et al. Risk factors for asthma among schoolchildren who participated in a case–control study in urban Uganda. eLife 8 , e49496 (2019).

Zejda, J. E. & Kowalska, M. Risk factors for asthma in school children—results of a seven-year follow-up. Cent. Eur. J. Public Health 11 , 149–154 (2003).

Ehrlich, R. I. et al. Risk factors for childhood asthma and wheezing. Importance of maternal and household smoking. Am. J. Respir. Crit. Care Med. 154 , 681–688 (1996).

Bozicević, I. & Oresković, S. Risk factors in asthmatic patients in Croatia. Coll. Antropol. 24 , 325–334 (2000).

Kamran, A., Hanif, S. & Murtaza, G. Risk factors of childhood asthma in children attending Lyari General Hospital. J. Pak. Med. Assoc. 65 , 647–650 (2015).

Palvo, F. et al. Risk factors of childhood asthma in Sao Jose do Rio Preto, Sao Paulo, Brazil. J. Trop. Pediatr. 54 , 253–257 (2008).

Karunasekera, K. A., Jayasinghe, J. A. & Alwis, L. W. Risk factors of childhood asthma: a Sri Lankan study. J. Trop. Pediatr. 47 , 142–145 (2001).

Flexeder, C. et al. Second-hand smoke exposure in adulthood and lower respiratory health during 20 year follow up in the European Community Respiratory Health Survey. Respir. Res. 20 , 33 (2019).

Tanaka, K., Miyake, Y., Furukawa, S. & Arakawa, M. Secondhand smoke exposure and risk of wheeze in early childhood: a prospective pregnancy birth cohort study. Tob. Induc. Dis. 15 , 30 (2017).

McKeever, T. M. et al. Siblings, multiple births, and the incidence of allergic disease: a birth cohort study using the West Midlands general practice research database. Thorax 56 , 758–762 (2001).

Hadnadjev, M. & Ilić, M. Smoking and asthma in children. Med. Glas. (Zenica) 8 , 266–272 (2011).

Genuneit, J. et al. Smoking and the incidence of asthma during adolescence: results of a large cohort study in Germany. Thorax 61 , 572–578 (2006).

Horwood, L. J., Fergusson, D. M. & Shannon, F. T. Social and familial factors in the development of early childhood asthma. Pediatrics 75 , 859–868 (1985).

Bergmann, R. L., Edenharter, G., Bergmann, K. E., Lau, S. & Wahn, U. Socioeconomic status is a risk factor for allergy in parents but not in their children. Clin. Exp. Allergy 30 , 1740–1745 (2000).

Fagbule, D. & Ekanem, E. E. Some environmental risk factors for childhood asthma: a case–control study. Ann. Trop. Paediatr. 14 , 15–19 (1994).

Kumar, P. H. & Devgan, A. The association of breastfeeding with childhood asthma: a case–control study from India. Cureus 13 , e19810 (2021).

PubMed   PubMed Central   Google Scholar  

Daigler, G. E., Markello, S. J. & Cummings, K. M. The effect of indoor air pollutants on otitis media and asthma in children. Laryngoscope 101 , 293–296 (1991).

Butland, B. K., Strachan, D. P. & Anderson, H. R. The home environment and asthma symptoms in childhood: two population based case–control studies 13 years apart. Thorax 52 , 618–624 (1997).

Marbury, M. C., Maldonado, G. & Waller, L. The indoor air and children’s health study: methods and incidence rates. Epidemiology 7 , 166–174 (1996).

Nguyen, T. et al. The National Asthma Survey–New York State: association of the home environment with current asthma status. Public Health Rep. 125 , 877–887 (2010).

Bener, A., Ehlayel, M. & Sabbah, A. The pattern and genetics of pediatric extrinsic asthma risk factors in polluted environment. Eur. Ann. Allergy Clin. Immunol. 39 , 58–63 (2007).

Ponsonby, A. L. et al. The relation between infant indoor environment and subsequent asthma. Epidemiology 11 , 128–135 (2000).

Muñoz, B. et al. The relationship among IL-13, GSTP1, and CYP1A1 polymorphisms and environmental tobacco smoke in a population of children with asthma in Northern Mexico. Environ. Toxicol. Pharm. 33 , 226–232 (2012).

Snodgrass, A. M. et al. Tobacco smoke exposure and respiratory morbidity in young children. Tob. Control 25 , e75–e82 (2016).

den Dekker, H. T. et al. Tobacco smoke exposure, airway resistance, and asthma in school-age children: the Generation R Study. Chest 148 , 607–617 (2015).

Murray, C. S. et al. Tobacco smoke exposure, wheeze, and atopy. Pediatr. Pulmonol. 37 , 492–498 (2004).

Khozime, A., Mirsadraee, M. & Borji, H. Toxocara sero-prevalence and its relationship with allergic asthma in asthmatic patients in north-eastern Iran. J. Helminthol. 93 , 677–680 (2019).

Fernando, D. et al. Toxocara seropositivity in Sri Lankan children with asthma. Pediatr. Int. 51 , 241–245 (2009).

Youssef, M. M. et al. Urinary bisphenol A concentrations in relation to asthma in a sample of Egyptian children. Hum. Exp. Toxicol. 37 , 1180–1186 (2018).

Villeneuve, P. J. et al. A case–control study of long-term exposure to ambient volatile organic compounds and lung cancer in Toronto, Ontario, Canada. Am. J. Epidemiol. 179 , 443–451 (2014).

Zhong, L., Goldberg, M. S., Gao, Y. T. & Jin, F. A case–control study of lung cancer and environmental tobacco smoke among nonsmoking women living in Shanghai, China. Cancer Causes Control 10 , 607–616 (1999).

Sasco, A. J. et al. A case–control study of lung cancer in Casablanca, Morocco. Cancer Causes Control 13 , 609–616 (2002).

Wang, S. Y. et al. A comparative study of the risk factors for lung cancer in Guangdong, China. Lung Cancer 14 , S99–S105 (1996).

Robles, A. I. et al. A DRD1 polymorphism predisposes to lung cancer among those exposed to secondhand smoke during childhood. Cancer Prev. Res. 7 , 1210–1218 (2014).

Article   CAS   Google Scholar  

Wang, A. et al. Active and passive smoking in relation to lung cancer incidence in the Women’s Health Initiative Observational Study prospective cohort. Ann. Oncol. 26 , 221–230 (2015).

Zatloukal, P., Kubík, A., Pauk, N., Tomásek, L. & Petruzelka, L. Adenocarcinoma of the lung among women: risk associated with smoking, prior lung disease, diet and menstrual and pregnancy history. Lung Cancer 41 , 283–293 (2003).

Torres-Durán, M. et al. Alpha-1 antitrypsin deficiency and lung cancer risk: a case–control study in never-smokers. J. Thorac. Oncol. 10 , 1279–1284 (2015).

Tubío-Pérez, R. A. et al. Alpha-1 antitrypsin deficiency and risk of lung cancer in never-smokers: a multicentre case–control study. BMC Cancer 22 , 81 (2022).

Ferreccio, C. et al. Arsenic, tobacco smoke, and occupation: associations of multiple agents with lung and bladder cancer. Epidemiology 24 , 898–905 (2013).

Kabat, G. C. Aspects of the epidemiology of lung cancer in smokers and nonsmokers in the United States. Lung Cancer 15 , 1–20 (1996).

Miller, D. P. et al. Association between self-reported environmental tobacco smoke exposure and lung cancer: modification by GSTP1 polymorphism. Int. J. Cancer 104 , 758–763 (2003).

Zhuang, J. et al. Association between smoking and environmental tobacco smoke with lung cancer risk: a case–control study in the Fujian Chinese population. J. Public Health 30 , 2047–2057 (2022).

Sobue, T. Association of indoor air pollution and lifestyle with lung cancer in Osaka, Japan. Int. J. Epidemiol. 19 , S62–S66 (1990).

He, F., Chang, S.-C., Wallar, G. M., Zhang, Z.-F. & Cai, L. Association of XRCC3 and XRCC4 gene polymorphisms, family history of cancer and tobacco smoking with non-small-cell lung cancer in a Chinese population: a case–control study. J. Hum. Genet 58 , 679–685 (2013).

Hirayama, T. Cancer mortality in nonsmoking women with smoking husbands based on a large-scale cohort study in Japan. Prev. Med. 13 , 680–690 (1984).

Wang, F. L., Love, E. J., Liu, N. & Dai, X. D. Childhood and adolescent passive smoking and the risk of female lung cancer. Int. J. Epidemiol. 23 , 223–230 (1994).

Seki, T. et al. Cigarette smoking and lung cancer risk according to histologic type in Japanese men and women. Cancer Sci. 104 , 1515–1522 (2013).

Xu, X. et al. Combined and interaction effect of Chlamydia pneumoniae infection and smoking on lung cancer: a case–control study in Southeast China. BMC Cancer 20 , 903 (2020).

Gallegos-Arreola, M. P. et al. CYP1A1 *2B and *4 polymorphisms are associated with lung cancer susceptibility in Mexican patients. Int. J. Biol. Markers 23 , 24–30 (2008).

Wenzlaff, A. S. et al. CYP1A1 and CYP1B1 polymorphisms and risk of lung cancer among never smokers: a population-based study. Carcinogenesis 26 , 2207–2212 (2005).

Ng, D. P. K., Tan, K.-W., Zhao, B. & Seow, A. CYP1A1 polymorphisms and risk of lung cancer in non-smoking Chinese women: influence of environmental tobacco smoke exposure and GSTM1/T1 genetic variation. Cancer Causes Control 16 , 399–405 (2005).

Yoon, K.-A. et al. CYP1B1, CYP1A1, MPO, and GSTP1 polymorphisms and lung cancer risk in never-smoking Korean women. Lung Cancer 60 , 40–46 (2008).

Gorlova, O. Y. et al. DNA repair capacity and lung cancer risk in never smokers. Cancer Epidemiol. Biomark. Prev. 17 , 1322–1328 (2008).

Li, K. & Yu, S. Economic status, smoking, occupational exposure to rubber, and lung cancer: a case-cohort study. J. Environ. Sci. Health C 20 , 21–28 (2002).

Jee, S. H., Ohrr, H. & Kim, I. S. Effects of husbands’ smoking on the incidence of lung cancer in Korean women. Int. J. Epidemiol. 28 , 824–828 (1999).

Li, J. et al. Environmental tobacco smoke and cancer risk, a prospective cohort study in a Chinese population. Environ. Res. 191 , 110015 (2020).

Jöckel, K. H., Pohlabeln, H., Ahrens, W. & Krauss, M. Environmental tobacco smoke and lung cancer. Epidemiology 9 , 672–675 (1998).

Fontham, E. T. et al. Environmental tobacco smoke and lung cancer in nonsmoking women. A multicenter study. JAMA 271 , 1752–1759 (1994).

Cardenas, V. M. et al. Environmental tobacco smoke and lung cancer mortality in the American Cancer Society’s Cancer Prevention Study. II. Cancer Causes Control 8 , 57–64 (1997).

Stockwell, H. G. et al. Environmental tobacco smoke and lung cancer risk in nonsmoking women. J. Natl Cancer Inst. 84 , 1417–1422 (1992).

Wen, W. et al. Environmental tobacco smoke and mortality in Chinese women who have never smoked: prospective cohort study. BMJ 333 , 376 (2006).

Enstrom, J. E. & Kabat, G. C. Environmental tobacco smoke and tobacco related mortality in a prospective study of Californians, 1960–98. BMJ 326 , 1057 (2003).

Mbeje, N. P., Ginindza, T. & Jafta, N. Epidemiological study of risk factors for lung cancer in KwaZulu-Natal, South Africa. Int. J. Environ. Res. Public Health 19 , 6752 (2022).

Al-Zoughool, M. et al. Exposure to environmental tobacco smoke (ETS) and risk of lung cancer in Montreal: a case–control study. Environ. Health 12 , 112 (2013).

Du, Y. et al. Exposure to environmental tobacco smoke and female lung cancer. Indoor Air 5 , 231–236 (1995).

Boffetta, P. et al. Exposure to environmental tobacco smoke and risk of adenocarcinoma of the lung. Int. J. Cancer 83 , 635–639 (1999).

Zaridze, D., Maximovitch, D., Zemlyanaya, G., Aitakov, Z. N. & Boffetta, P. Exposure to environmental tobacco smoke and risk of lung cancer in non-smoking women from Moscow, Russia. Int. J. Cancer 75 , 335–338 (1998).

Chen, S.-C., Wong, R.-H., Shiu, L.-J., Chiou, M.-C. & Lee, H. Exposure to mosquito coil smoke may be a risk factor for lung cancer in Taiwan. J. Epidemiol. 18 , 19–25 (2008).

Cassidy, A., Myles, J. P., Duffy, S. W., Liloglou, T. & Field, J. K. Family history and risk of lung cancer: age-at-diagnosis in cases and first-degree relatives. Br. J. Cancer 95 , 1288–1290 (2006).

Yang, S.-Y. et al. Fanconi anemia genes in lung adenocarcinoma—a pathway-wide study on cancer susceptibility. J. Biomed. Sci. 23 , 23 (2016).

Yin, Z. et al. Genetic polymorphisms of TERT and CLPTM1L, cooking oil fume exposure, and risk of lung cancer: a case–control study in a Chinese non-smoking female population. Med. Oncol. 31 , 114 (2014).

Han, L. et al. Genetic predisposition to lung adenocarcinoma among never-smoking Chinese with different epidermal growth factor receptor mutation status. Lung Cancer 114 , 79–89 (2017).

Raspanti, G. A. et al. Household air pollution and lung cancer risk among never-smokers in Nepal. Environ. Res. 147 , 141–145 (2016).

Jin, Z.-Y. et al. Household ventilation may reduce effects of indoor air pollutants for prevention of lung cancer: a case–control study in a Chinese population. PLoS ONE 9 , e102685 (2014).

Abdel-Rahman, O. Incidence and mortality of lung cancer among never smokers in relationship to secondhand smoking: findings from the PLCO trial. Clin. Lung Cancer 21 , 415–420.e2 (2020).

Behera, D. & Balamugesh, T. Indoor air pollution as a risk factor for lung cancer in women. J. Assoc. Physicians India 53 , 190–192 (2005).

Galeone, C. et al. Indoor air pollution from solid fuel use, chronic lung diseases and lung cancer in Harbin, Northeast China. Eur. J. Cancer Prev. 17 , 473–478 (2008).

Sloan, C. D. et al. Indoor and outdoor air pollution and lung cancer in New Hampshire and Vermont. Toxicol. Environ. Chem. 94 , https://doi.org/10.1080/02772248.2012.659930 (2012).

Garfinkel, L., Auerbach, O. & Joubert, L. Involuntary smoking and lung cancer: a case–control study. J. Natl Cancer Inst. 75 , 463–469 (1985).

Lee, C. H. et al. Lifetime environmental exposure to tobacco smoke and primary lung cancer of non-smoking Taiwanese women. Int. J. Epidemiol. 29 , 224–231 (2000).

Johnson, K. C., Hu, J. & Mao, Y., Canadian Cancer Registries Epidemiology Research Group. Lifetime residential and workplace exposure to environmental tobacco smoke and lung cancer in never-smoking women, Canada 1994–97. Int. J. Cancer 93 , 902–906 (2001).

Gao, Y. T. et al. Lung cancer among Chinese women. Int. J. Cancer 40 , 604–609 (1987).

Janerich, D. T. et al. Lung cancer and exposure to tobacco smoke in the household. N. Engl. J. Med. 323 , 632–636 (1990).

Pirie, K. et al. Lung cancer in never smokers in the UK Million Women Study. Int. J. Cancer 139 , 347–354 (2016).

Liang, D. et al. Lung cancer in never-smokers: a multicenter case–control study in north China. Front. Oncol. 9 , 1354 (2019).

Kabat, G. C. & Wynder, E. L. Lung cancer in nonsmokers. Cancer 53 , 1214–1221 (1984).

Wang, T. J., Zhou, B. S. & Shi, J. P. Lung cancer in nonsmoking Chinese women: a case–control study. Lung Cancer 14 , S93–S98 (1996).

Rylander, R. & Axelsson, G. Lung cancer risks in relation to vegetable and fruit consumption and smoking. Int. J. Cancer 118 , 739–743 (2006).

Humble, C. G., Samet, J. M. & Pathak, D. R. Marriage to a smoker and lung cancer risk. Am. J. Public Health 77 , 598–602 (1987).

Koo, L. C., Ho, J. H., Saw, D. & Ho, C. Y. Measurements of passive smoking and estimates of lung cancer risk among non-smoking Chinese females. Int. J. Cancer 39 , 162–169 (1987).

Weiss, J. M. et al. Menstrual and reproductive factors in association with lung cancer in female lifetime nonsmokers. Am. J. Epidemiol. 168 , 1319–1325 (2008).

Hill, S. E., Blakely, T., Kawachi, I. & Woodward, A. Mortality among lifelong nonsmokers exposed to secondhand smoke at home: cohort data and sensitivity analyses. Am. J. Epidemiol. 165 , 530–540 (2007).

McGhee, S. M. et al. Mortality associated with passive smoking in Hong Kong. BMJ 330 , 287–288 (2005).

Boffetta, P. et al. Multicenter case–control study of exposure to environmental tobacco smoke and lung cancer in Europe. J. Natl Cancer Inst. 90 , 1440–1450 (1998).

Davis, A. et al. No association between global DNA methylation in peripheral blood and lung cancer risk in nonsmoking women: results from a multicenter study in Eastern and Central Europe. Eur. J. Cancer Prev. 27 , 1–5 (2018).

Veglia, F. et al. Occupational exposures, environmental tobacco smoke, and lung cancer. Epidemiology 18 , 769–775 (2007).

Masjedi, M. R. et al. Opium could be considered an independent risk factor for lung cancer: a case–control study. Respiration 85 , 112–118 (2013).

Consonni, D. et al. Outdoor particulate matter (PM10) exposure and lung cancer risk in the EAGLE study. PLoS ONE 13 , e0203539 (2018).

Ren, Y.-W. et al. P53 Arg72Pro and MDM2 SNP309 polymorphisms cooperate to increase lung adenocarcinoma risk in Chinese female non-smokers: a case control study. Asian Pac. J. Cancer Prev. 14 , 5415–5420 (2013).

Hole, D. J., Gillis, C. R., Chopra, C. & Hawthorne, V. M. Passive smoking and cardiorespiratory health in a general population in the west of Scotland. BMJ 299 , 423–427 (1989).

Kalandidi, A. et al. Passive smoking and diet in the etiology of lung cancer among non-smokers. Cancer Causes Control 1 , 15–21 (1990).

Rapiti, E., Jindal, S. K., Gupta, D. & Boffetta, P. Passive smoking and lung cancer in Chandigarh, India. Lung Cancer 23 , 183–189 (1999).

Kurahashi, N. et al. Passive smoking and lung cancer in Japanese non-smoking women: a prospective study. Int. J. Cancer 122 , 653–657 (2008).

Brownson, R. C., Alavanja, M. C., Hock, E. T. & Loy, T. S. Passive smoking and lung cancer in nonsmoking women. Am. J. Public Health 82 , 1525–1530 (1992).

Nishino, Y. et al. Passive smoking at home and cancer risk: a population-based prospective study in Japanese nonsmoking women. Cancer Causes Control 12 , 797–802 (2001).

Sun, Y.-Q. et al. Passive smoking in relation to lung cancer incidence and histologic types in Norwegian adults: the HUNT study. Eur. Respir. J. 50 , 1700824 (2017).

Speizer, F. E., Colditz, G. A., Hunter, D. J., Rosner, B. & Hennekens, C. Prospective study of smoking, antioxidant intake, and lung cancer in middle-aged women (USA). Cancer Causes Control 10 , 475–482 (1999).

Kabat, G. C., Stellman, S. D. & Wynder, E. L. Relation between exposure to environmental tobacco smoke and lung cancer in lifetime nonsmokers. Am. J. Epidemiol. 142 , 141–148 (1995).

Shen, X. B., Wang, G. X. & Zhou, B. S. Relation of exposure to environmental tobacco smoke and pulmonary adenocarcinoma in non-smoking women: a case control study in Nanjing. Oncol. Rep. 5 , 1221–1223 (1998).

Lee, P. N., Chamberlain, J. & Alderson, M. R. Relationship of passive smoking to risk of lung cancer and other smoking-associated diseases. Br. J. Cancer 54 , 97–105 (1986).

Schwartz, A. G. et al. Reproductive factors, hormone use, estrogen receptor expression and risk of non small-cell lung cancer in women. J. Clin. Oncol. 25 , 5785–5792 (2007).

Bräuner, E. V. et al. Residential radon and lung cancer incidence in a Danish cohort. Environ. Res. 118 , 130–136 (2012).

Chan-Yeung, M. et al. Risk factors associated with lung cancer in Hong Kong. Lung Cancer 40 , 131–140 (2003).

Minichilli, F. et al. Risk Factors for Lung Cancer in the Province of Lecce: results from the PROTOS case–control study in Salento (Southern Italy). Int. J. Environ. Res Public Health 19 , 8775 (2022).

Ger, L. P., Hsu, W. L., Chen, K. T. & Chen, C. J. Risk factors of lung cancer by histological category in Taiwan. Anticancer Res. 13 , 1491–1500 (1993).

Yang, L. et al. Risk factors shared by COPD and lung cancer and mediation effect of COPD: two center case–control studies. Cancer Causes Control 26 , 11–24 (2015).

Liang, H. et al. Risk of lung cancer following nonmalignant respiratory conditions among nonsmoking women living in Shenyang, Northeast China. J. Women’s Health 18 , 1989–1995 (2009).

Franco-Marina, F., Villalba Caloca, J. & Corcho-Berdugo, A., Grupo Interinstitucional de Cáncer Pulmonar. Role of active and passive smoking on lung cancer etiology in Mexico City. Salud Publica Mex. 48 , S75–S82 (2006).

Phukan, R. K. et al. Role of household exposure, dietary habits and glutathione S-Transferases M1, T1 polymorphisms in susceptibility to lung cancer among women in Mizoram India. Asian Pac. J. Cancer Prev. 15 , 3253–3260 (2014).

Asomaning, K. et al. Second hand smoke, age of exposure and lung cancer risk. Lung Cancer 61 , 13–20 (2008).

He, Y. et al. Secondhand smoke exposure predicted COPD and other tobacco-related mortality in a 17-year cohort study in China. Chest 142 , 909–918 (2012).

Wu, A. H., Henderson, B. E., Pike, M. C. & Yu, M. C. Smoking and other risk factors for lung cancer in women. J. Natl Cancer Inst. 74 , 747–751 (1985).

Liu, Z. Y., He, X. Z. & Chapman, R. S. Smoking and other risk factors for lung cancer in Xuanwei, China. Int. J. Epidemiol. 20 , 26–31 (1991).

Svensson, C., Pershagen, G. & Klominek, J. Smoking and passive smoking in relation to lung cancer in women. Acta Oncol. 28 , 623–629 (1989).

Lam, T. H. et al. Smoking, passive smoking and histological types in lung cancer in Hong Kong Chinese women. Br. J. Cancer 56 , 673–678 (1987).

Cheng, E. S. et al. Solid fuel, secondhand smoke, and lung cancer mortality: a prospective cohort of 323,794 chinese never-smokers. Am. J. Respir. Crit. Care Med. 206 , 1153–1162 (2022).

Hansen, M. S., Licaj, I., Braaten, T., Lund, E. & Gram, I. T. The fraction of lung cancer attributable to smoking in the Norwegian Women and Cancer (NOWAC) Study. Br. J. Cancer 124 , 658–662 (2021).

Dalager, N. A. et al. The relation of passive smoking to lung cancer. Cancer Res. 46 , 4808–4811 (1986).

He, F. et al. The relationship of lung cancer with menstrual and reproductive factors may be influenced by passive smoking, cooking oil fumes, and tea intake: a case–control study in Chinese women. Medicine 96 , e8816 (2017).

Wang, X.-R., Chiu, Y.-L., Qiu, H., Au, J. S. K. & Yu, I. T.-S. The roles of smoking and cooking emissions in lung cancer risk among Chinese women in Hong Kong. Ann. Oncol. 20 , 746–751 (2009).

Spitz, M. R. et al. Variants in inflammation genes are implicated in risk of lung cancer in never smokers exposed to second-hand smoke. Cancer Discov. 1 , 420–429 (2011).

Lan, Q. et al. Variation in lung cancer risk by smoky coal subtype in Xuanwei, China. Int. J. Cancer 123 , 2164–2169 (2008).

Hernández-Garduño, E., Brauer, M., Pérez-Neria, J. & Vedal, S. Wood smoke exposure and lung adenocarcinoma in non-smoking Mexican women. Int. J. Tuberc. Lung Dis. 8 , 377–383 (2004).

Huang, H.-C. et al. Association between chronic obstructive pulmonary disease and PM2.5 in Taiwanese nonsmokers. Int. J. Hyg. Environ. Health 222 , 884–888 (2019).

Johannessen, A., Bakke, P. S., Hardie, J. A. & Eagan, T. M. L. Association of exposure to environmental tobacco smoke in childhood with chronic obstructive pulmonary disease and respiratory symptoms in adults. Respirology 17 , 499–505 (2012).

Xu, F. et al. Better understanding the influence of cigarette smoking and indoor air pollution on chronic obstructive pulmonary disease: a case–control study in Mainland China. Respirology 12 , 891–897 (2007).

Sandler, D. P., Comstock, G. W., Helsing, K. J. & Shore, D. L. Deaths from all causes in non-smokers who lived with smokers. Am. J. Public Health 79 , 163–167 (1989).

Chan-Yeung, M. et al. Determinants of chronic obstructive pulmonary disease in Chinese patients in Hong Kong. Int. J. Tuberc. Lung Dis. 11 , 502–507 (2007).

Vineis, P. et al. Environmental tobacco smoke and risk of respiratory cancer and chronic obstructive pulmonary disease in former smokers and never smokers in the EPIC prospective study. BMJ 330 , 277 (2005).

Salameh, P. et al. Exposure to outdoor air pollution and chronic bronchitis in adults: a case–control study. Int. J. Occup. Environ. Med. 3 , 165–177 (2012).

Pahwa, P. et al. Incidence and longitudinal changes in prevalence of chronic bronchitis in farm and non-farm rural residents of Saskatchewan. J. Occup. Environ. Med. 61 , 347–356 (2019).

Behrendt, C. E. Mild and moderate-to-severe COPD in nonsmokers: distinct demographic profiles. Chest 128 , 1239–1244 (2005).

Gerbase, M. W. et al. Respiratory effects of environmental tobacco exposure are enhanced by bronchial hyperreactivity. Am. J. Respir. Crit. Care Med. 174 , 1125–1131 (2006).

Salama, B. M., Abukanna, A. & Hegazy, A. Risk factors associated with chronic obstructive pulmonary disease in Arar, Saudi Arabia: a case–control study. Med. Sci. 24 , 2487–2493 (2020).

Chen, Y. et al. Risk factors for small airway obstruction among Chinese island residents: a case–control study. PLoS ONE 8 , e68556 (2013).

Wu, C.-F. et al. Second-hand smoke and chronic bronchitis in Taiwanese women: a health-care based study. BMC Public Health 10 , 44 (2010).

Diver, W. R., Jacobs, E. J. & Gapstur, S. M. Secondhand smoke exposure in childhood and adulthood in relation to adult mortality among never smokers. Am. J. Prev. Med. 55 , 345–352 (2018).

Ding, Y. et al. The analyses of risk factors for COPD in the Li ethnic group in Hainan, People’s Republic of China. Int. J. Chron. Obstruct. Pulmon. Dis. 10 , 2593–2600 (2015).

Dennis, R. J., Maldonado, D., Norman, S., Baena, E. & Martinez, G. Woodsmoke exposure and risk for obstructive airways disease among women. Chest 109 , 115–119 (1996).

Hayashino, Y. et al. A prospective study of passive smoking and risk of diabetes in a cohort of workers: the High-Risk and Population Strategy for Occupational Health Promotion (HIPOP-OHP) study. Diabetes Care 31 , 732–734 (2008).

Huang, C. et al. Association between environmental tobacco smoke exposure and risk of type 2 diabetes mellitus in Chinese female never smokers: a population-based cohort study. J. Diabetes 12 , 339–346 (2020).

Zhang, L., Curhan, G. C., Hu, F. B., Rimm, E. B. & Forman, J. P. Association between passive and active smoking and incident type 2 diabetes in women. Diabetes Care 34 , 892–897 (2011).

Kim, B. J., Kim, J. H., Kang, J. G., Kim, B. S. & Kang, J. H. Association between secondhand smoke exposure and diabetes mellitus in 131 724 Korean never smokers using self-reported questionnaires and cotinine levels: gender differences. J. Diabetes 13 , 43–53 (2021).

Kowall, B. et al. Association of passive and active smoking with incident type 2 diabetes mellitus in the elderly population: the KORA S4/F4 cohort study. Eur. J. Epidemiol. 25 , 393–402 (2010).

Zeng, H. et al. Interaction of PTPRD (rs17584499) polymorphism with passive smoking in Chinese women with susceptibility to type 2 diabetes. Int. J. Diabetes Dev. Ctries 43 , 304–308 (2023).

Oba, S. et al. Passive smoking and type 2 diabetes among never-smoking women: the Japan Public Health Center-based Prospective Study. J. Diabetes Investig. 11 , 1352–1358 (2020).

Rias, Y. A. et al. Secondhand smoke correlates with elevated neutrophil–lymphocyte ratio and has a synergistic effect with physical inactivity on increasing susceptibility to type 2 diabetes mellitus: a community-based case control study. Int. J. Environ. Res. Public Health 17 , 5696 (2020).

Jiang, L. et al. Secondhand smoke, obesity, and risk of type II diabetes among California teachers. Ann. Epidemiol. 32 , 35–42 (2019).

Kawachi, I. et al. A prospective study of passive smoking and coronary heart disease. Circulation 95 , 2374–2379 (1997).

Pitsavos, C. et al. Association between exposure to environmental tobacco smoke and the development of acute coronary syndromes: the CARDIO2000 case–control study. Tob. Control 11 , 220–225 (2002).

Awawdi, K., Steiner, H., Green, M. S. & Zelber-Sagi, S. Association between second-hand smoking and acute coronary heart disease among Arab women with multiple risk factors. Eur. J. Public Health 26 , 141–145 (2016).

Ciruzzi, M. et al. Case–control study of passive smoking at home and risk of acute myocardial infarction. Argentine FRICAS Investigators. Factores de Riesgo Coronario en América del Sur. J. Am. Coll. Cardiol. 31 , 797–803 (1998).

Kastorini, C.-M. et al. Comparative analysis of cardiovascular disease risk factors influencing nonfatal acute coronary syndrome and ischemic stroke. Am. J. Cardiol. 112 , 349–354 (2013).

McElduff, P. et al. Coronary events and exposure to environmental tobacco smoke: a case–control study from Australia and New Zealand. Tob. Control 7 , 41–46 (1998).

Clark, M. L., Butler, L. M., Koh, W.-P., Wang, R. & Yuan, J.-M. Dietary fiber intake modifies the association between secondhand smoke exposure and coronary heart disease mortality among Chinese non-smokers in Singapore. Nutrition 29 , 1304–1309 (2013).

Spencer, C. A., Jamrozik, K. & Lambert, L. Do simple prudent health behaviours protect men from myocardial infarction? Int. J. Epidemiol. 28 , 846–852 (1999).

Ding, D. et al. Effect of household passive smoking exposure on the risk of ischaemic heart disease in never-smoke female patients in Hong Kong. Tob. Control 18 , 354–357 (2009).

Svendsen, K. H., Kuller, L. H., Martin, M. J. & Ockene, J. K. Effects of passive smoking in the Multiple Risk Factor Intervention Trial. Am. J. Epidemiol. 126 , 783–795 (1987).

Garland, C., Barrett-Connor, E., Suarez, L., Criqui, M. H. & Wingard, D. L. Effects of passive smoking on ischemic heart disease mortality of nonsmokers. A prospective study. Am. J. Epidemiol. 121 , 645–650 (1985).

Steenland, K., Thun, M., Lally, C. & Heath, C. Environmental tobacco smoke and coronary heart disease in the American Cancer Society CPS-II cohort. Circulation 94 , 622–628 (1996).

Rosenlund, M. et al. Environmental tobacco smoke and myocardial infarction among never-smokers in the Stockholm Heart Epidemiology Program (SHEEP). Epidemiology 12 , 558–564 (2001).

Rashid, N. A., Nawi, A. M. & Khadijah, S. Exploratory analysis of traditional risk factors of ischemic heart disease (IHD) among predominantly Malay Malaysian women. BMC Public Health 19 , 545 (2019).

Muscat, J. E. & Wynder, E. L. Exposure to environmental tobacco smoke and the risk of heart attack. Int. J. Epidemiol. 24 , 715–719 (1995).

Sadeghi, M. et al. Impact of secondhand smoke exposure in former smokers on their subsequent risk of coronary heart disease: evidence from the population-based cohort of the Tehran Lipid and Glucose Study. Epidemiol. Health 42 , e2020009 (2020).

Malinauskiene, V. et al. Outdoor and indoor air pollution and myocardial infarction among women in Kaunas, Lithuania: a case–control study. Pol. J. Environ. Stud. 20 , 969–976 (2011).

Sulo, G., Burazeri, G., Dehghan, A. & Kark, J. D. Partner’s smoking status and acute coronary syndrome: population-based case–control study in Tirana, Albania. Croat. Med. J. 49 , 751–756 (2008).

La Vecchia, C., D’Avanzo, B., Franzosi, M. G. & Tognoni, G. Passive smoking and the risk of acute myocardial infarction GISSI-EFRIM investigations. Lancet 341 , 505–506 (1993).

Janghorbani, M. & Sadeghigolmakani, N. Passive smoking and the risk of coronary heart disease among married non-smoking women. Med. J. Islam. Repub. Iran 11 , 203–208 (1997).

Dobson, A. J., Alexander, H. M., Heller, R. F. & Lloyd, D. M. Passive smoking and the risk of heart attack or coronary death. Med. J. Aust. 154 , 793–797 (1991).

He, Y. et al. Passive smoking at work as a risk factor for coronary heart disease in Chinese women who have never smoked. BMJ 308 , 380–384 (1994).

Gallo, V. et al. Second-hand smoke, cotinine levels, and risk of circulatory mortality in a large cohort study of never-smokers. Epidemiology 21 , 207–214 (2010).

Kobayashi, Y. et al. Secondhand smoke and the risk of incident cardiovascular disease among never-smoking women. Prev. Med. 162 , 107145 (2022).

Notara, V. et al. Smoking determines the 10-year (2004–2014) prognosis in patients with acute coronary syndrome: the GREECS observational study. Tob. Induc. Dis. 13 , 38 (2015).

Rossi, M., Negri, E., La Vecchia, C. & Campos, H. Smoking habits and the risk of non-fatal acute myocardial infarction in Costa Rica. Eur. J. Cardiovasc. Prev. Rehabil. 18 , 467–474 (2011).

Fatmi, Z. et al. Solid fuel use is a major risk factor for acute coronary syndromes among rural women: a matched case control study. Public Health 128 , 77–82 (2014).

Attard, R. et al. The impact of passive and active smoking on inflammation, lipid profile and the risk of myocardial infarction. Open Heart 4 , e000620 (2017).

Nishtar, S. et al. Waist-hip ratio and low HDL predict the risk of coronary artery disease in Pakistanis. Curr. Med. Res. Opin. 20 , 55–62 (2004).

Anderson, C. S. et al. Active and passive smoking and the risk of subarachnoid hemorrhage: an international population-based case–control study. Stroke 35 , 633–637 (2004).

Qureshi, A. I., Suri, M. F. K., Kirmani, J. F. & Divani, A. A. Cigarette smoking among spouses: another risk factor for stroke in women. Stroke 36 , e74–e76 (2005).

You, R. X., Thrift, A. G., McNeil, J. J., Davis, S. M. & Donnan, G. A. Ischemic stroke risk and passive exposure to spouses’ cigarette smoking. Melbourne Stroke Risk Factor Study (MERFS) Group. Am. J. Public Health 89 , 572–575 (1999).

Hou, L. et al. Passive smoking and stroke in men and women: a national population-based case–control study in China. Sci. Rep. 7 , 45542 (2017).

Bonita, R., Duncan, J., Truelsen, T., Jackson, R. T. & Beaglehole, R. Passive smoking as well as active smoking increases the risk of acute stroke. Tob. Control 8 , 156–160 (1999).

Yamada, S. et al. Risk factors for fatal subarachnoid hemorrhage: the Japan Collaborative Cohort Study. Stroke 34 , 2781–2787 (2003).

Malek, A. M., Cushman, M., Lackland, D. T., Howard, G. & McClure, L. A. Secondhand smoke exposure and stroke: The Reasons for Geographic and Racial Differences in Stroke (REGARDS) Study. Am. J. Prev. Med. 49 , e89–e97 (2015).

Glymour, M. M., Defries, T. B., Kawachi, I. & Avendano, M. Spousal smoking and incidence of first stroke: the Health and Retirement Study. Am. J. Prev. Med. 35 , 245–248 (2008).

Nishino, Y. et al. Stroke mortality associated with environmental tobacco smoke among never-smoking Japanese women: a prospective cohort study. Prev. Med. 67 , 41–45 (2014).

Poulsen, A. H. et al. Urinary cadmium and stroke—a case–cohort study in Danish never-smokers. Environ. Res. 200 , 111394 (2021).

Hirose, K. et al. A large-scale, hospital-based case–control study of risk factors of breast cancer according to menopausal status. Jpn J. Cancer Res. 86 , 146–154 (1995).

Lash, T. L. & Aschengrau, A. A null association between active or passive cigarette smoking and breast cancer risk. Breast Cancer Res. Treat. 75 , 181–184 (2002).

Woo, C. et al. A prospective study of passive cigarette smoke exposure and breast cancer. Am. J. Epidemiol. 151 , 11 (2000).

Dossus, L. et al. Active and passive cigarette smoking and breast cancer risk: results from the EPIC cohort. Int. J. Cancer 134 , 1871–1888 (2014).

Lash, T. L. & Aschengrau, A. Active and passive cigarette smoking and the occurrence of breast cancer. Am. J. Epidemiol. 149 , 5–12 (1999).

Hanaoka, T. et al. Active and passive smoking and breast cancer risk in middle-aged Japanese women. Int. J. Cancer 114 , 317–322 (2005).

Roddam, A. W. et al. Active and passive smoking and the risk of breast cancer in women aged 36–45 years: a population based case–control study in the UK. Br. J. Cancer 97 , 434–439 (2007).

Gao, C.-M. et al. Active and passive smoking, and alcohol drinking and breast cancer risk in Chinese women. Asian Pac. J. Cancer Prev. 14 , 993–996 (2013).

Lin, Y. et al. Active smoking, passive smoking, and breast cancer risk: findings from the Japan Collaborative Cohort Study for Evaluation of Cancer Risk. J. Epidemiol. 18 , 77–83 (2008).

Smith, S. J., Deacon, J. M. & Chilvers, C. E. Alcohol, smoking, passive smoking and caffeine in relation to breast cancer risk in young women. UK National Case-Control Study Group. Br. J. Cancer 70 , 112–119 (1994).

El-Sheikh, N. et al. Assessment of human papillomavirus infection and risk factors in Egyptian women with breast cancer. Breast Cancer 15 , 1178223421996279 (2021).

Strumylaite, L. et al. Association between lifetime exposure to passive smoking and risk of breast cancer subtypes defined by hormone receptor status among non-smoking Caucasian women. PLoS ONE 12 , e0171198 (2017).

Luo, J. et al. Association of active and passive smoking with risk of breast cancer among postmenopausal women: a prospective cohort study. BMJ 342 , d1016 (2011).

Hu, M. et al. Bleomycin-induced mutagen sensitivity, passive smoking, and risk of breast cancer in Chinese women: a case–control study. Cancer Causes Control 24 , 629–636 (2013).

Chaveepojnkamjorn, W., Thotong, R., Sativipawee, P. & Pitikultang, S. Body mass index and breast cancer risk among Thai premenopausal women: a case–control study. Asian Pac. J. Cancer Prev. 18 , 3097–3101 (2017).

White, A. J., D’Aloisio, A. A., Nichols, H. B., DeRoo, L. A. & Sandler, D. P. Breast cancer and exposure to tobacco smoke during potential windows of susceptibility. Cancer Causes Control 28 , 667–675 (2017).

Marzouk, D. A. et al. Breast cancer and hormonal intake among Egyptian females. Eur. J. Oncol. 14 , 37–52 (2009).

Rollison, D. E., Brownson, R. C., Hathcock, H. L. & Newschaffer, C. J. Case–control study of tobacco smoke exposure and breast cancer risk in Delaware. BMC Cancer 8 , 157 (2008).

Hsieh, Y.-C. et al. CHRNA9 polymorphisms and smoking exposure synergize to increase the risk of breast cancer in Taiwan. Carcinogenesis 35 , 2520–2525 (2014).

Nishino, Y. et al. Cigarette smoking and breast cancer risk in relation to joint estrogen and progesterone receptor status: a case–control study in Japan. Springerplus 3 , 65 (2014).

Ilic, M., Vlajinac, H. & Marinkovic, J. Cigarette smoking and breast cancer: a case–control study in Serbia. Asian Pac. J. Cancer Prev. 14 , 6643–6647 (2014).

Xue, F., Willett, W. C., Rosner, B. A., Hankinson, S. E. & Michels, K. B. Cigarette smoking and the incidence of breast cancer. Arch. Intern. Med. 171 , 125–133 (2011).

Millikan, R. C. et al. Cigarette smoking, N -acetyltransferases 1 and 2, and breast cancer risk. Cancer Epidemiol. Biomark. Prev. 7 , 371–378 (1998).

Fararouei, M. et al. Dietary habits and physical activity are associated with the risk of breast cancer among young Iranian women: a case–control study on 1010 premenopausal women. Clin. Breast Cancer 19 , e127–e134 (2019).

Tang, L.-Y. et al. Effects of passive smoking on breast cancer risk in pre/post-menopausal women as modified by polymorphisms of PARP1 and ESR1. Gene 524 , 84–89 (2013).

Fu, X. J. et al. Environmental and DNA repair risk factors for breast cancer in South China. Int. J. Hyg. Environ. Health 218 , 313–318 (2015).

Yassin, S., Younis, M., Abuzerr, S., Darwish, M. & Mustafa, A. A. Extrinsic risk factors for women breast cancer in Gaza Strip, Palestine: associations and interactions in a case–control study. Adv. Breast Cancer Res. 8 , 11–30 (2019).

Dianatinasab, M., Fararouei, M., Mohammadianpanah, M., Zare-Bandamiri, M. & Rezaianzadeh, A. Hair coloring, stress, and smoking increase the risk of breast cancer: a case–control study. Clin. Breast Cancer 17 , 650–659 (2017).

Ahern, T. P., Lash, T. L., Egan, K. M. & Baron, J. A. Lifetime tobacco smoke exposure and breast cancer incidence. Cancer Causes Control 20 , 1837–1844 (2009).

Zhao, Y., Shi, Z. & Liu, L. Matched case–control study for detecting risk factors of breast cancer in women living in Chengdu. Zhonghua Liu Xing Bing Xue Za Zhi 20 , 91–94 (1999).

Alberg, A. J. et al. N -acetyltransferase 2 (NAT2) genotypes, cigarette smoking, and the risk of breast cancer. Cancer Detect. Prev. 28 , 187–193 (2004).

Johnson, K. C., Hu, J. & Mao, Y., Canadian Cancer Registries Epidemiology Research Group. Passive and active smoking and breast cancer risk in Canada, 1994–97. Cancer Causes Control 11 , 211–221 (2000).

Anderson, L. N., Cotterchio, M., Mirea, L., Ozcelik, H. & Kreiger, N. Passive cigarette smoke exposure during various periods of life, genetic variants, and breast cancer risk among never smokers. Am. J. Epidemiol. 175 , 289–301 (2012).

Pirie, K. et al. Passive smoking and breast cancer in never smokers: prospective study and meta-analysis. Int. J. Epidemiol. 37 , 1069–1079 (2008).

Shrubsole, M. J. et al. Passive smoking and breast cancer risk among non-smoking Chinese women. Int. J. Cancer 110 , 605–609 (2004).

Li, B. et al. Passive smoking and breast cancer risk among non-smoking women: a case–control study in China. PLoS ONE 10 , e0125894 (2015).

Liu, L. et al. Passive smoking and other factors at different periods of life and breast cancer risk in chinese women who have never smoked—a case–control study in Chongqing, People’s Republic of China. Asian Pac. J. Cancer Prev. 1 , 131–137 (2000).

Reynolds, P. et al. Passive smoking and risk of breast cancer in the California teachers study. Cancer Epidemiol. Biomark. Prev. 18 , 3389–3398 (2009).

Wartenberg, D. et al. Passive smoking exposure and female breast cancer mortality. J. Natl Cancer Inst. 92 , 1666–1673 (2000).

Tong, J. et al. Passive smoking exposure from partners as a risk factor for ER+/PR+ double positive breast cancer in never-smoking Chinese urban women: a hospital-based matched case control study. PLoS ONE 9 , e97498 (2014).

Niehoff, N. et al. Polycyclic aromatic hydrocarbons and postmenopausal breast cancer: an evaluation of effect measure modification by body mass index and weight change. Environ. Res. 152 , 17–25 (2017).

De Silva, M., Senarath, U., Gunatilake, M. & Lokuhetty, D. Prolonged breastfeeding reduces risk of breast cancer in Sri Lankan women: a case–control study. Cancer Epidemiol. 34 , 267–273 (2010).

Morabia, A., Bernstein, M., Héritier, S. & Khatchatrian, N. Relation of breast cancer with passive and active exposure to tobacco smoke. Am. J. Epidemiol. 143 , 918–928 (1996).

Kariri, M. et al. Risk factors for breast cancer in Gaza Strip, Palestine: a case–control study. Clin. Nutr. Res 6 , 161–171 (2017).

Hosseinzadeh, M. et al. Risk factors for breast cancer in Iranian women: a hospital-based case–control study in Tabriz, Iran. J. Breast Cancer 17 , 236–243 (2014).

Bonner, M. R. et al. Secondhand smoke exposure in early life and the risk of breast cancer among never smokers (United States). Cancer Causes Control 16 , 683–689 (2005).

Zahali, Z., Mitra, A., Abd Rashid, A. A. & Jan Mohamed, H. J. Serum cotinine and passive smoking status associated with non-smoking newly diagnosed women with breast cancer in Malaysia. Middle East J. Cancer 12 , 87–96 (2021).

Pimhanam, C., Sangrajrang, S. & Ekpanyaskul, C. Tobacco smoke exposure and breast cancer risk in Thai urban females. Asian Pac. J. Cancer Prev. 15 , 7407–7411 (2014).

Metsola, K. et al. XRCC1 and XPD genetic polymorphisms, smoking and breast cancer risk in a Finnish case–control study. Breast Cancer Res. 7 , R987–R997 (2005).

Dwedar, I. A. & El Sayed, M. A. Association between passive smoking and community-acquired pneumonia among the adult population. Egypt. J. Chest Dis. Tuberc. 67 , 457 (2018).

Ramesh Bhat, Y., Manjunath, N., Sanjay, D. & Dhanya, Y. Association of indoor air pollution with acute lower respiratory tract infections in children under 5 years of age. Paediatr. Int. Child Health 32 , 132–135 (2012).

Schulte-Hobein, B., Schwartz-Bickenbach, D., Abt, S., Plum, C. & Nau, H. Cigarette smoke exposure and development of infants throughout the first year of life: influence of passive smoking and nursing on cotinine levels in breast milk and infant’s urine. Acta Paediatr. 81 , 550–557 (1992).

Baker, R. J. et al. Coal home heating and environmental tobacco smoke in relation to lower respiratory illness in Czech children, from birth to 3 years of age. Environ. Health Perspect. 114 , 1126–1132 (2006).

Nuesslein, T. G., Beckers, D. & Rieger, C. H. Cotinine in meconium indicates risk for early respiratory tract infections. Hum. Exp. Toxicol. 18 , 283–290 (1999).

Arlington, L. et al. Duration of solid fuel cookstove use is associated with increased risk of acute lower respiratory infection among children under six months in rural central India. PLoS ONE 14 , e0224374 (2019).

Islam, M., Sultana, Z. Z., Iqbal, A., Ali, M. & Hossain, A. Effect of in-house crowding on childhood hospital admissions for acute respiratory infection: a matched case–control study in Bangladesh. Int. J. Infect. Dis. 105 , 639–645 (2021).

Bermúdez Barrezueta, L. et al. Effect of prenatal and postnatal exposure to tobacco in the development of acute bronchiolitis in the first two years of life. An. Pediatr. (Engl. Ed.) 94 , 385–395 (2021).

Loeb, M. et al. Environmental risk factors for community-acquired pneumonia hospitalization in older adults. J. Am. Geriatr. Soc. 57 , 1036–1040 (2009).

Miyahara, R. et al. Exposure to paternal tobacco smoking increased child hospitalization for lower respiratory infections but not for other diseases in Vietnam. Sci. Rep. 7 , 45481 (2017).

Barsam, F. J. B. G. et al. Factors associated with community-acquired pneumonia in hospitalised children and adolescents aged 6 months to 13 years old. Eur. J. Pediatr. 172 , 493–499 (2013).

Goetghebuer, T., Kwiatkowski, D., Thomson, A. & Hull, J. Familial susceptibility to severe respiratory infection in early life. Pediatr. Pulmonol. 38 , 321–328 (2004).

Roda, C. et al. Formaldehyde exposure and lower respiratory infections in infants: findings from the PARIS cohort study. Environ. Health Perspect. 119 , 1653–1658 (2011).

le Roux, D. M., Myer, L., Nicol, M. P. & Zar, H. J. Incidence and severity of childhood pneumonia in the first year of life in a South African birth cohort: the Drakenstein Child Health Study. Lancet . Glob. Health 3 , e95–e103 (2015).

Colley, J. R., Holland, W. W. & Corkhill, R. T. Influence of passive smoking and parental phlegm on pneumonia and bronchitis in early childhood. Lancet 2 , 1031–1034 (1974).

Taylor, B. & Wadsworth, J. Maternal smoking during pregnancy and lower respiratory tract illness in early life. Arch. Dis. Child 62 , 786–791 (1987).

Duijts, L. et al. Maternal smoking in prenatal and early postnatal life and the risk of respiratory tract infections in infancy. The Generation R study. Eur. J. Epidemiol. 23 , 547–555 (2008).

Nenna, R. et al. Modifiable risk factors associated with bronchiolitis. Ther. Adv. Respir. Dis. 11 , 393–401 (2017).

Fergusson, D. M. & Horwood, L. J. Parental smoking and respiratory illness during early childhood: a six-year longitudinal study. Pediatr. Pulmonol. 1 , 99–106 (1985).

McConnochie, K. M. & Roghmann, K. J. Parental smoking, presence of older siblings, and family history of asthma increase risk of bronchiolitis. Am. J. Dis. Child 140 , 806–812 (1986).

Rylander, E., Pershagen, G., Eriksson, M. & Bermann, G. Parental smoking, urinary cotinine, and wheezing bronchitis in children. Epidemiology 6 , 289–293 (1995).

Almirall, J. et al. Passive smoking at home is a risk factor for community-acquired pneumonia in older adults: a population-based case–control study. BMJ Open 4 , e005133 (2014).

Tupasi, T. E. et al. Patterns of acute respiratory tract infection in children: a longitudinal study in a depressed community in Metro Manila. Rev. Infect. Dis. 12 , S940–S949 (1990).

Fuentes-Leonarte, V. et al. Pre- and postnatal exposure to tobacco smoke and respiratory outcomes during the first year. Indoor Air 25 , 4–12 (2015).

Behrooz, L. et al. Prenatal and postnatal tobacco smoke exposure and risk of severe bronchiolitis during infancy. Respir. Med. 140 , 21–26 (2018).

Lanari, M. et al. Prenatal tobacco smoke exposure increases hospitalizations for bronchiolitis in infants. Respir. Res. 16 , 152 (2015).

Broughton, S. et al. Prospective study of healthcare utilisation and respiratory morbidity due to RSV infection in prematurely born infants. Thorax 60 , 1039–1044 (2005).

Wright, A. L., Holberg, C., Martinez, F. D. & Taussig, L. M. Relationship of parental smoking to wheezing and nonwheezing lower respiratory tract illnesses in infancy. Group Health Medical Associates. J. Pediatr. 118 , 207–214 (1991).

Koch, A. et al. Risk factors for acute respiratory tract infections in young Greenlandic children. Am. J. Epidemiol. 158 , 374–384 (2003).

Törmänen, S. et al. Risk factors for asthma after infant bronchiolitis. Allergy 73 , 916–922 (2018).

Farr, B. M. et al. Risk factors for community-acquired pneumonia diagnosed by general practitioners in the community. Respir. Med. 94 , 422–427 (2000).

Farr, B. M., Bartlett, C. L., Wadsworth, J. & Miller, D. L. Risk factors for community-acquired pneumonia diagnosed upon hospital admission. British Thoracic Society Pneumonia Study Group. Respir. Med. 94 , 954–963 (2000).

Grant, C. C. et al. Risk factors for community-acquired pneumonia in pre-school-aged children. J. Paediatr. Child Health 48 , 402–412 (2012).

Victora, C. G., Fuchs, S. C., Flores, J. A., Fonseca, W. & Kirkwood, B. Risk factors for pneumonia among children in a Brazilian metropolitan area. Pediatrics 93 , 977–985 (1994).

Verani, J. R. et al. Risk factors for presumed bacterial pneumonia among HIV-uninfected children hospitalized in Soweto, South Africa. Pediatr. Infect. Dis. J. 35 , 1169–1174 (2016).

Broor, S. et al. Risk factors for severe acute lower respiratory tract infection in under-five children. Indian Pediatr. 38 , 1361–1369 (2001).

Robledo-Aceves, M. et al. Risk factors for severe bronchiolitis caused by respiratory virus infections among Mexican children in an emergency department. Medicine 97 , e0057 (2018).

Hassan, M. K. & Al-Sadoon, I. Risk factors for severe pneumonia in children in Basrah. Trop. Doct. 31 , 139–141 (2001).

Farzana, R., Hoque, M., Kamal, M. S. & Choudhury, M. M. U. Role of parental smoking in severe bronchiolitis: a hospital based case–control study. Int. J. Pediatr. 2017 , 9476367 (2017).

Liyanage, G., Kaneshapillai, A. & Kanthasamy, S. Serum vitamin D level and risk of community-acquired pneumonia: a case–control study. Interdiscip. Perspect. Infect. Dis. 2021 , 2157337 (2021).

Johnson, A. W. & Aderele, W. I. The association of household pollutants and socio-economic risk factors with the short-term outcome of acute lower respiratory infections in hospitalized pre-school Nigerian children. Ann. Trop. Paediatr. 12 , 421–432 (1992).

Liu, Y., Lu, C., Deng, M., Norbäck, D. & Sun, S. The effect of prenatal and early-postnatal exposure to classical air pollution on childhood pneumonia in China. Indoor Built Environ. 31 , 170–185 (2022).

Keskinoglu, P., Cimrin, D. & Aksakoglu, G. The impact of passive smoking on the development of lower respiratory tract infections in children. J. Trop. Pediatr. 53 , 319–324 (2007).

Dina, A. D. & Djuwita, R. The role of exclusive breastfeeding in reducing pneumonia prevalence in children under five. J. Gizi dan Pangan 16 , 89–98 (2021).

Wenten, M. et al. TNF-308 modifies the effect of second-hand smoke on respiratory illness-related school absences. Am. J. Respir. Crit. Care Med. 172 , 1563–1568 (2005).

Pullan, C. R. & Hey, E. N. Wheezing, asthma, and pulmonary dysfunction 10 years after infection with respiratory syncytial virus in infancy. Br. Med. J. 284 , 1665–1669 (1982).

Prins-van Ginkel, A. C. et al. Acute otitis media during infancy: parent-reported incidence and modifiable risk factors. Pediatr. Infect. Dis. J. 36 , 245–249 (2017).

Niclasen, J., Obel, C., Homøe, P., Kørvel-Hanquist, A. & Dammeyer, J. Associations between otitis media and child behavioural and learning difficulties: results from a Danish cohort. Int. J. Pediatr. Otorhinolaryngol. 84 , 12–20 (2016).

Koch, A., Homøe, P., Pipper, C., Hjuler, T. & Melbye, M. Chronic suppurative otitis media in a birth cohort of children in Greenland: population-based study of incidence and risk factors. Pediatr. Infect. Dis. J. 30 , 25–29 (2011).

Alho, O. P., Kilkku, O., Oja, H., Koivu, M. & Sorri, M. Control of the temporal aspect when considering risk factors for acute otitis media. Arch. Otolaryngol. Head. Neck Surg. 119 , 444–449 (1993).

Bentdal, Y. E., Karevold, G., Nafstad, P. & Kvaerner, K. J. Early acute otitis media: predictor for AOM and respiratory infections in schoolchildren? Int. J. Pediatr. Otorhinolaryngol. 71 , 1251–1259 (2007).

Daly, K. A., Pirie, P. L., Rhodes, K. L., Hunter, L. L. & Davey, C. S. Early otitis media among Minnesota American Indians: the Little Ears Study. Am. J. Public Health 97 , 317–322 (2007).

Yang, C. Y. et al. Effects of indoor environmental factors on risk for acute otitis media in a subtropical area. J. Toxicol. Environ. Health A 56 , 111–119 (1999).

Adair-Bischoff, C. E. & Sauve, R. S. Environmental tobacco smoke and middle ear disease in preschool-age children. Arch. Pediatr. Adolesc. Med. 152 , 127–133 (1998).

Teele, D. W., Klein, J. O. & Rosner, B. Epidemiology of otitis media during the first seven years of life in children in greater Boston: a prospective, cohort study. J. Infect. Dis. 160 , 83–94 (1989).

Daly, K. A. et al. Epidemiology of otitis media onset by six months of age. Pediatrics 103 , 1158–1166 (1999).

Stenstrom, R., Bernard, P. A. & Ben-Simhon, H. Exposure to environmental tobacco smoke as a risk factor for recurrent acute otitis media in children under the age of five years. Int. J. Pediatr. Otorhinolaryngol. 27 , 127–136 (1993).

Clamp, P. J. et al. Factors associated with the development of paediatric chronic otitis media by age nine: a prospective longitudinal cohort study of 6560 children. J. Laryngol. Otol. https://doi.org/10.1017/S0022215120002182 (2020).

Samson, D. et al. Follow up of a birth cohort to identify prevalence and risk factors for otitis media among Indian children in the eighth year of life. Int. J. Pediatr. Otorhinolaryngol. 137 , 110201 (2020).

da Costa, J. L., Navarro, A., Neves, J. B. & Martin, M. Household wood and charcoal smoke increases risk of otitis media in childhood in Maputo. Int. J. Epidemiol. 33 , 573–578 (2004).

Collet, J. P., Larson, C. P., Boivin, J. F., Suissa, S. & Pless, I. B. Parental smoking and risk of otitis media in pre-school children. Can. J. Public Health 86 , 269–273 (1995).

Ey, J. L. et al. Passive smoke exposure and otitis media in the first year of life. Group Health Medical Associates. Pediatrics 95 , 670–677 (1995).

Håberg, S. E. et al. Prenatal and postnatal parental smoking and acute otitis media in early childhood. Acta Paediatr. 99 , 99–105 (2010).

Pukander, J., Luotonen, J., Timonen, M. & Karma, P. Risk factors affecting the occurrence of acute otitis media among 2–3-year-old urban children. Acta Otolaryngol. 100 , 260–265 (1985).

Wijayanti, S. P. M. et al. Risk factors for acute otitis media in primary school children: a case–control study in Central Java, Indonesia. J. Public Health Res. 10 , 1909 (2021).

Tainio, V. M. et al. Risk factors for infantile recurrent otitis media: atopy but not type of feeding. Pediatr. Res. 23 , 509–512 (1988).

Ståhlberg, M. R., Ruuskanen, O. & Virolainen, E. Risk factors for recurrent otitis media. Pediatr. Infect. Dis. 5 , 30–32 (1986).

Jensen, R. G., Koch, A., Homøe, P. & Bjerregaard, P. Tobacco smoke increases the risk of otitis media among Greenlandic Inuit children while exposure to organochlorines remain insignificant. Environ. Int. 54 , 112–118 (2013).

Zheng, P. et al. The Burden of Proof studies: assessing the evidence of risk. Nat. Med. 28 , 2038–2044 (2022).

Khoramdad, M. et al. Association between passive smoking and cardiovascular disease: a systematic review and meta-analysis. IUBMB Life 72 , 677–686 (2020).

Zhang, D. et al. Dose-related effect of secondhand smoke on cardiovascular disease in nonsmokers: systematic review and meta-analysis. Int. J. Hyg. Environ. Health 228 , 113546 (2020).

Lee, P. N. & Hamling, J. S. Environmental tobacco smoke exposure and risk of breast cancer in nonsmoking women. An updated review and meta-analysis. Inhal. Toxicol. 28 , 431–454 (2016).

Lee, P. N., Thornton, A. J., Forey, B. A. & Hamling, J. S. Environmental tobacco smoke exposure and risk of stroke in never smokers: an updated review with meta-analysis. J. Stroke Cerebrovasc. Dis. 26 , 204–216 (2017).

Lee, P. N., Forey, B. A., Coombs, K. J., Hamling, J. S. & Thornton, A. J. Epidemiological evidence relating environmental smoke to COPD in lifelong non-smokers: a systematic review. F1000Res 7 , 146 (2018).

Huang, J. et al. Influencing factors of lung cancer in nonsmoking women: systematic review and meta-analysis. J. Public Health 44 , 259–268 (2022).

DiFranza, J. R. & Lew, R. A. Morbidity and mortality in children associated with the use of tobacco products by other people. Pediatrics 97 , 560–568 (1996).

Pan, A., Wang, Y., Talaei, M., Hu, F. B. & Wu, T. Relation of active, passive, and quitting smoking with incident type 2 diabetes: a systematic review and meta-analysis. Lancet Diabetes Endocrinol. 3 , 958–967 (2015).

Zhang, Y. et al. Risk factors for chronic and recurrent otitis media—a meta-analysis. PLoS ONE 9 , e86397 (2014).

Liu, W., Wang, B., Xiao, Y., Wang, D. & Chen, W. Secondhand smoking and neurological disease: a meta-analysis of cohort studies. Rev. Environ. Health 36 , 271–277 (2021).

Zhu, B., Wu, X., Wang, X., Zheng, Q. & Sun, G. The association between passive smoking and type 2 diabetes: a meta-analysis. Asia Pac. J. Public Health 26 , 226–237 (2014).

He, J. et al. Passive smoking and the risk of coronary heart disease—a meta-analysis of epidemiologic studies. N. Engl. J. Med. 340 , 920–926 (1999).

Lee, P. N., Forey, B. A., Hamling, J. S. & Thornton, A. J. Environmental tobacco smoke exposure and heart disease: a systematic review. World J. Meta-Anal. 5 , 14–40 (2017).

Fischer, F. & Kraemer, A. Meta-analysis of the association between second-hand smoke exposure and ischaemic heart diseases, COPD and stroke. BMC Public Health 15 , 1202 (2015).

Lv, X. et al. Risk of all-cause mortality and cardiovascular disease associated with secondhand smoke exposure: a systematic review and meta-analysis. Int. J. Cardiol. 199 , 106–115 (2015).

Law, M. R., Morris, J. K. & Wald, N. J. Environmental tobacco smoke exposure and ischaemic heart disease: an evaluation of the evidence. BMJ 315 , 973–980 (1997).

Whincup, P. H. et al. Passive smoking and risk of coronary heart disease and stroke: prospective study with cotinine measurement. BMJ 329 , 200–205 (2004).

Oono, I. P., Mackay, D. F. & Pell, J. P. Meta-analysis of the association between secondhand smoke exposure and stroke. J. Public Health 33 , 496–502 (2011).

Vos, T. et al. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet 396 , 1204–1222 (2020).

Egan, K. M. et al. Active and passive smoking in breast cancer: prospective results from the Nurses’ Health Study. Epidemiology 13 , 138–145 (2002).

Kocarnik, J. M. et al. Cancer incidence, mortality, years of life lost, years lived with disability, and disability-adjusted life years for 29 cancer groups from 2010 to 2019. JAMA Oncol. 8 , 420–444 (2022).

Sio, Y. Y. & Chew, F. T. Risk factors of asthma in the Asian population: a systematic review and meta-analysis. J. Physiol. Anthropol. 40 , 22 (2021).

He, Z. et al. The association between secondhand smoke and childhood asthma: a systematic review and meta-analysis. Pediatr. Pulmonol. 55 , 2518–2531 (2020).

Protection from Exposure to Second-Hand Tobacco Smoke: Policy Recommendations (World Health Organization, 2007).

Stallings-Smith, S., Zeka, A., Goodman, P., Kabir, Z. & Clancy, L. Reductions in cardiovascular, cerebrovascular, and respiratory mortality following the National Irish Smoking Ban: interrupted time-series analysis. PLoS ONE 8 , e62063 (2013).

Akter, S. et al. Evaluation of population-level tobacco control interventions and health outcomes: a systematic review and meta-analysis. JAMA Netw. Open 6 , e2322341 (2023).

Chu, M. et al. Effects of a smoke-free policy in Xi’an, China: impact on hospital admissions for acute ischemic heart disease and stroke. Front. Public Health 10 , 898461 (2022).

Rando-Matos, Y. et al. Smokefree legislation effects on respiratory and sensory disorders: a systematic review and meta-analysis. PLoS ONE 12 , e0181035 (2017).

Gao, M. et al. The effect of smoke-free legislation on the mortality rate of acute myocardial infarction: a meta-analysis. BMC Public Health 19 , 1269 (2019).

Huque, R. & Siddiqi, K. Smoke-free homes: the final frontier. Tob. Prev. Cessat. 7 , 1–3 (2021).

Gallus, S. et al. Voluntary home smoking ban: prevalence, trend and determinants in Italy. Eur. J. Public Health 26 , 841–844 (2016).

Du, Y. et al. Lung cancer occurrence attributable to passive smoking among never smokers in China: a systematic review and meta-analysis. Transl. Lung Cancer Res. 9 , 204–217 (2020).

The Health Consequences of Involuntary Exposure to Tobacco Smoke: A Report of the Surgeon General (US Department of Health and Human Services, 2006).

Dai, X. et al. Health effects associated with smoking: a Burden of Proof study. Nat. Med. 28 , 2045–2055 (2022).

Razo, C. et al. Effects of elevated systolic blood pressure on ischemic heart disease: a Burden of Proof study. Nat. Med. 28 , 2056–2065 (2022).

Lescinsky, H. et al. Health effects associated with consumption of unprocessed red meat: a Burden of Proof study. Nat. Med. 28 , 2075–2082 (2022).

Stanaway, J. D. et al. Health effects associated with vegetable consumption: a Burden of Proof study. Nat. Med. 28 , 2066–2074 (2022).

Stevens, G. A. et al. Guidelines for accurate and transparent health estimates reporting: the GATHER statement. Lancet 388 , e19–e23 (2016).

Guyatt, G. H. et al. GRADE guidelines: 4. Rating the quality of evidence—study limitations (risk of bias). J. Clin. Epidemiol. 64 , 407–415 (2011).

Zheng, P., Barber, R., Sorensen, R. J. D., Murray, C. J. L. & Aravkin, A. Y. Trimmed constrained mixed effects models: formulations and algorithms. J. Comput. Graph. Stat. 30 , 544–556 (2021).

Download references

Acknowledgements

Research reported in this publication was supported by the Bill & Melinda Gates Foundation (award OPP1152504, E.G.) and Bloomberg Philanthropies (award 47386, E.G.). The funders of the study had no role in study design, data collection, data analysis, data interpretation, writing of the final report or the decision to publish.

Author information

Authors and affiliations.

Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA

Luisa S. Flor, Jason A. Anderson, Noah Ahmad, Aleksandr Aravkin, Sinclair Carr, Xiaochen Dai, Gabriela F. Gil, Simon I. Hay, Matthew J. Malloy, Susan A. McLaughlin, Erin C. Mullany, Christopher J. L. Murray, Erin M. O’Connell, Chukwuma Okereke, Reed J. D. Sorensen, Joanna Whisnant, Peng Zheng & Emmanuela Gakidou

Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, USA

Luisa S. Flor, Aleksandr Aravkin, Simon I. Hay, Christopher J. L. Murray, Peng Zheng & Emmanuela Gakidou

Department of Global Health, University of Washington, Seattle, WA, USA

Gabriela F. Gil

You can also search for this author in PubMed   Google Scholar

Contributions

J.W., J.A.A., N.A., C.O., G.F.G. and L.S.F. were primarily responsible for seeking, cataloging, extracting or cleaning data. S.C., C.O., G.F.G., J.A.A., J.W., L.S.F., N.A. and X.D. designed or coded figures and tables. M.J.M., R.J.D.S., L.S.F. and E.G. provided data or critical feedback on data sources. C.J.L.M., E.G., G.F.G., L.S.F., R.J.D.S., S.I.H., X.D. and S.C. provided critical feedback on methods or results. A.A., C.J.L.M., E.G., L.S.F., S.I.H. and S.A.M. drafted the work or revised it critically for important intellectual content. E.C.M., E.M.O., E.G., L.S.F. and S.I.H. managed the overall research enterprise. A.A., C.J.L.M., P.Z., R.J.D.S. and S.C. developed methods or computational machinery. L.S.F. was primarily responsible for applying analytical methods to produce estimates. L.S.F. wrote the first draft of the manuscript. S.I.H., E.M.O., E.G. and L.S.F. managed the estimation or publication process.

Corresponding author

Correspondence to Luisa S. Flor .

Ethics declarations

Competing interests.

The authors of this manuscript declare no competing interests.

Peer review

Peer review information.

Nature Medicine thanks Bo Xi and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editors: Jennifer Sargent and Ming Yang, in collaboration with the Nature Medicine team.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended data fig. 1 forest plot of the association between secondhand smoke exposure and ischemic heart disease..

This forest plot presents the estimated mean relative risk, its 95% uncertainty intervals (UI), and the data points underlying the estimates for ischemic heart disease in association with secondhand smoke exposure (two-star rating of the risk-outcome relationship). The color of the point indicates whether the point was detected and trimmed as an outlier. The light blue interval corresponds to the 95% UI incorporating between-study heterogeneity; the dark blue interval corresponds to the 95% UI without between-study heterogeneity. The black vertical dotted line reflects the null relative risk value (one) and the red vertical line is the burden of proof function at the 5th quantile for this harmful risk-outcome association. The black data points and horizontal lines each correspond to a mean effect size and 95% UI from the included study identified on the y-axis. We included multiple observations from a single study when effects were reported by location or source of exposure and/or separately by sex or other subgroups. See Supplementary Table 4 for more details on included observations from each study (n = 37 studies).

Extended Data Fig. 2 Forest plot of the association between secondhand smoke exposure and stroke.

This forest plot presents the estimated mean relative risk, its 95% uncertainty intervals (UI), and the data points underlying the estimates for ischemic heart disease in association with secondhand smoke exposure (two-star rating of the risk-outcome relationship). The color of the point indicates whether the point was detected and trimmed as an outlier. The light blue interval corresponds to the 95% UI incorporating between-study heterogeneity; the dark blue interval corresponds to the 95% UI without between-study heterogeneity. The black vertical dotted line reflects the null relative risk value (one) and the red vertical line is the burden of proof function at the 5th quantile for this harmful risk-outcome association. The black data points and horizontal lines each correspond to a mean effect size and 95% UI from the included study identified on the y-axis. We included multiple observations from a single study when effects were reported by location or source of exposure and/or separately by sex or other subgroups. See Supplementary Table 5 for more details on included observations from each study (n = 20 studies).

Extended Data Fig. 3 Forest plot of the association between secondhand smoke exposure and lung cancer.

This forest plot presents the estimated mean relative risk, its 95% uncertainty intervals (UI), and the data points underlying the estimates for ischemic heart disease in association with secondhand smoke exposure (two-star rating of the risk-outcome relationship). The color of the point indicates whether the point was detected and trimmed as an outlier. The light blue interval corresponds to the 95% UI incorporating between-study heterogeneity; the dark blue interval corresponds to the 95% UI without between-study heterogeneity. The black vertical dotted line reflects the null relative risk value (one) and the red vertical line is the burden of proof function at the 5th quantile for this harmful risk-outcome association. The black data points and horizontal lines each correspond to a mean effect size and 95% UI from the included study identified on the y-axis. We included multiple observations from a single study when effects were reported by location or source of exposure and/or separately by sex or other subgroups. See Supplementary Table 6 for more details on included observations from each study (n = 104 studies).

Extended Data Fig. 4 Forest plot of the association between secondhand smoke exposure and breast cancer.

This forest plot presents the estimated mean relative risk, its 95% uncertainty intervals (UI), and the data points underlying the estimates for ischemic heart disease in association with secondhand smoke exposure (one-star rating of the risk-outcome relationship). The color of the point indicates whether the point was detected and trimmed as an outlier. The light blue interval corresponds to the 95% UI incorporating between-study heterogeneity; the dark blue interval corresponds to the 95% UI without between-study heterogeneity. The black vertical dotted line reflects the null relative risk value (one) and the red vertical line is the burden of proof function at the 5th quantile for this harmful risk-outcome association. The black data points and horizontal lines each correspond to a mean effect size and 95% UI from the included study identified on the y-axis. We included multiple observations from a single study when effects were reported by location or source of exposure and/or separately by sex or other subgroups. See Supplementary Table 7 for more details on included observations from each study (n = 51 studies).

Extended Data Fig. 5 Forest plot of the association between secondhand smoke exposure and asthma.

This forest plot presents the estimated mean relative risk, its 95% uncertainty intervals (UI), and the data points underlying the estimates for ischemic heart disease in association with secondhand smoke exposure (one-star rating of the risk-outcome relationship). The color of the point indicates whether the point was detected and trimmed as an outlier. The light blue interval corresponds to the 95% UI incorporating between-study heterogeneity; the dark blue interval corresponds to the 95% UI without between-study heterogeneity. The black vertical dotted line reflects the null relative risk value (one) and the red vertical line is the burden of proof function at the 5th quantile for this harmful risk-outcome association. The black data points and horizontal lines each correspond to a mean effect size and 95% UI from the included study identified on the y-axis. We included multiple observations from a single study when effects were reported by location or source of exposure and/or separately by sex or other subgroups. See Supplementary Table 8 for more details on included observations from each study (n = 125 studies).

Extended Data Fig. 6 Forest plot of the association between secondhand smoke exposure and lower respiratory infections.

This forest plot presents the estimated mean relative risk, its 95% uncertainty intervals (UI), and the data points underlying the estimates for ischemic heart disease in association with secondhand smoke exposure (one-star rating of the risk-outcome relationship). The color of the point indicates whether the point was detected and trimmed as an outlier. The light blue interval corresponds to the 95% UI incorporating between-study heterogeneity; the dark blue interval corresponds to the 95% UI without between-study heterogeneity. The black vertical dotted line reflects the null relative risk value (one) and the red vertical line is the burden of proof function at the 5th quantile for this harmful risk-outcome association. The black data points and horizontal lines each correspond to a mean effect size and 95% UI from the included study identified on the y-axis. We included multiple observations from a single study when effects were reported by location or source of exposure and/or separately by sex or other subgroups. See Supplementary Table 9 for more details on included observations from each study (n = 50 studies).

Extended Data Fig. 7 Forest plot of the association between secondhand smoke exposure and chronic obstructive pulmonary disease.

This forest plot presents the estimated mean relative risk, its 95% uncertainty intervals (UI), and the data points underlying the estimates for ischemic heart disease in association with secondhand smoke exposure (one-star rating of the risk-outcome relationship). The color of the point indicates whether the point was detected and trimmed as an outlier. The light blue interval corresponds to the 95% UI incorporating between-study heterogeneity; the dark blue interval corresponds to the 95% UI without between-study heterogeneity. The black vertical dotted line reflects the null relative risk value (one) and the red vertical line is the burden of proof function at the 5th quantile for this harmful risk-outcome association. The black data points and horizontal lines each correspond to a mean effect size and 95% UI from the included study identified on the y-axis. We included multiple observations from a single study when effects were reported by location or source of exposure and/or separately by sex or other subgroups. See Supplementary Table 10 for more details on included observations from each study (n = 21 studies).

Extended Data Fig. 8 plot of the association between secondhand smoke exposure and type 2 diabetes mellitus.

This forest plot presents the estimated mean relative risk, its 95% uncertainty intervals (UI), and the data points underlying the estimates for ischemic heart disease in association with secondhand smoke exposure (two-star rating of the risk-outcome relationship). The color of the point indicates whether the point was detected and trimmed as an outlier. The light blue interval corresponds to the 95% UI incorporating between-study heterogeneity; the dark blue interval corresponds to the 95% UI without between-study heterogeneity. The black vertical dotted line reflects the null relative risk value (one) and the red vertical line is the burden of proof function at the 5th quantile for this harmful risk-outcome association. The black data points and horizontal lines each correspond to a mean effect size and 95% UI from the included study identified on the y-axis. We included multiple observations from a single study when effects were reported by location or source of exposure and/or separately by sex or other subgroups. See Supplementary Table 11 for more details on included observations from each study (n = 9 studies).

Extended Data Fig. 9 plot of the association between secondhand smoke exposure and otitis media.

This forest plot presents the estimated mean relative risk, its 95% uncertainty intervals (UI), and the data points underlying the estimates for ischemic heart disease in association with secondhand smoke exposure (one-star rating of the risk-outcome relationship). The color of the point indicates whether the point was detected and trimmed as an outlier. The light blue interval corresponds to the 95% UI incorporating between-study heterogeneity; the dark blue interval corresponds to the 95% UI without between-study heterogeneity. The black vertical dotted line reflects the null relative risk value (one) and the red vertical line is the burden of proof function at the 5th quantile for this harmful risk-outcome association. The black data points and horizontal lines each correspond to a mean effect size and 95% UI from the included study identified on the y-axis. We included multiple observations from a single study when effects were reported by location or source of exposure and/or separately by sex or other subgroups. See Supplementary Table 12 for more details on included observations from each study (n = 24 studies).

Extended Data Fig. 10 Summarized results of the primary model and sensitivity analyses conducted across all nine health outcomes.

This heatmap reports the summarized results of the main model and the sensitivity analyses (columns) conducted for each of the nine health outcomes (rows) reported in this study. Detailed results for each of the sensitivity models are presented in the Supplementary Information (Supplementary Tables 13 – 16 ). Sensitivity analyses reflect the impact of restricting the input data to 1) prospective cohort studies, 2) observations associated with never-smokers, and 3) both prospective cohort studies and never-smoking samples. For asthma, we additionally restrict the data to children population aged 16 or less. General model parameters remained constant across models; we trimmed 10% of the data if more than 10 observations were available for the specific model. The color of the blue boxes and the number depicted in each box corresponds to the resulting risk-outcome score (ROS) calculated for models in which the estimates of association without incorporating between-study heterogeneity were statistically significant. Grey boxes depict models that did not pass this threshold and, thus, ROS did not apply (NA). For models that did pass this threshold, the ROS reflects a conservative interpretation of the data that aligns with the Burden of Proof approach incorporating between-study heterogeneity and other sources of uncertainty. The ROS is translated into a star rating from 1 to 5 stars based on thresholds outlined in Zheng et al. The star rating for each model result is reported as the yellow stars in each box. A one-star association suggests that there is weak evidence supporting estimates of an association between the risk and the outcome. A two-star association reflects that there is weak-to-moderate evidence suggesting an association between the risk and outcome, and additional stars illustrate increasing strength of evidence.

Supplementary information

Supplementary information.

Supplementary information.

Reporting Summary

Rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Flor, L.S., Anderson, J.A., Ahmad, N. et al. Health effects associated with exposure to secondhand smoke: a Burden of Proof study. Nat Med 30 , 149–167 (2024). https://doi.org/10.1038/s41591-023-02743-4

Download citation

Received : 08 June 2023

Accepted : 28 November 2023

Published : 09 January 2024

Issue Date : January 2024

DOI : https://doi.org/10.1038/s41591-023-02743-4

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

This article is cited by

• Huai-Lei Juan
• Jiun-Hung Geng

Scientific Reports (2024)

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

A systematic review of secondhand tobacco smoke exposure and smoking behaviors: Smoking status, susceptibility, initiation, dependence, and cessation

Affiliations.

• 1 College of Nursing, University of Kentucky, Lexington, KY, USA. Electronic address: [email protected].
• 2 Department of Counselling Psychology, University of Kentucky, Lexington, KY USA.
• PMID: 25863004
• DOI: 10.1016/j.addbeh.2015.03.018

Objectives: To examine the association between secondhand tobacco smoke exposure (SHSe) and smoking behaviors (smoking status, susceptibility, initiation, dependence, and cessation).

Methods: Terms and keywords relevant to smoking behaviors and secondhand tobacco smoke exposure were used in a search of the PubMed database. Searches were limited to English language peer-reviewed studies up till December 2013. Included papers: a) had clearly defined measures of SHSe and b) had clearly defined measures of outcome variables of interest. A total of 119 studies were initially retrieved and reviewed. After further review of references from the retrieved studies, 35 studies were finally selected that met all eligibility criteria.

Results: The reviewed studies consisted of thirty-five (89.7%) studies with differing measures of SHSe (including questionnaire and biological measures) and varying definitions of main outcome variables of interest between studies. The majority of the studies (77%) were cross-sectional in nature. The majority of studies found that SHSe was associated with greater likelihood of being a smoker, increased susceptibility and initiation of smoking, greater nicotine dependence among nonsmokers, and poorer smoking cessation.

Conclusions: The review found positive associations between SHSe and smoking status, susceptibility, initiation and nicotine dependence and a negative association with smoking cessation. In light of design limitations, future prospective and clinical studies are needed to better understand the mechanisms whereby SHSe influences smoking behaviors.

Keywords: Nicotine dependence; Secondhand smoke; Smoking cessation; Smoking initiation; Smoking status; Smoking susceptibility.

Copyright © 2015 Elsevier Ltd. All rights reserved.

Publication types

• Systematic Review
• Smoking / epidemiology*
• Smoking Cessation / statistics & numerical data*
• Tobacco Smoke Pollution / statistics & numerical data*
• Tobacco Use Disorder / epidemiology*
• Tobacco Smoke Pollution

• Open access
• Published: 14 February 2019

Second-hand smoke exposure in adulthood and lower respiratory health during 20 year follow up in the European Community Respiratory Health Survey

• Claudia Flexeder   ORCID: orcid.org/0000-0003-3974-1482 1 ,
• Jan-Paul Zock 2 , 3 , 4 ,
• Deborah Jarvis 5 , 6 ,
• Giuseppe Verlato 7 ,
• Mario Olivieri 8 ,
• Geza Benke 9 ,
• Michael J. Abramson 9 ,
• Torben Sigsgaard 10 ,
• Cecilie Svanes 11 , 12 ,
• Kjell Torén 13 ,
• Dennis Nowak 14 , 15 ,
• Rain Jõgi 16 ,
• Jesús Martinez-Moratalla 17 , 18 ,
• Pascal Demoly 19 , 20 ,
• Christer Janson 21 ,
• Thorarinn Gislason 22 , 23 ,
• Roberto Bono 24 ,
• Mathias Holm 25 ,
• Karl A. Franklin 26 ,
• Judith Garcia-Aymerich 2 , 3 , 4 ,
• Valérie Siroux 27 ,
• Bénédicte Leynaert 28 ,
• Sandra Dorado Arenas 29 ,
• Angelo Guido Corsico 30 , 31 ,
• Antonio Pereira-Vega 32 ,
• Nicole Probst-Hensch 33 , 34 ,
• Isabel Urrutia Landa 35 ,
• Holger Schulz 1 , 15 &
• Joachim Heinrich 1 , 14 , 36  

Respiratory Research volume  20 , Article number:  33 ( 2019 ) Cite this article

6994 Accesses

26 Citations

9 Altmetric

Metrics details

Early life exposure to tobacco smoke has been extensively studied but the role of second-hand smoke (SHS) for new-onset respiratory symptoms and lung function decline in adulthood has not been widely investigated in longitudinal studies. Our aim is to investigate the associations of exposure to SHS in adults with respiratory symptoms, respiratory conditions and lung function over 20 years.

We used information from 3011 adults from 26 centres in 12 countries who participated in the European Community Respiratory Health Surveys I-III and were never or former smokers at all three surveys. Associations of SHS exposure with respiratory health (asthma symptom score, asthma, chronic bronchitis, COPD) were analysed using generalised linear mixed-effects models adjusted for confounding factors (including sex, age, smoking status, socioeconomic status and allergic sensitisation). Linear mixed-effects models with additional adjustment for height were used to assess the relationships between SHS exposure and lung function levels and decline.

Reported exposure to SHS decreased in all 26 study centres over time. The prevalence of SHS exposure was 38.7% at baseline (1990–1994) and 7.1% after the 20-year follow-up (2008–2011). On average 2.4% of the study participants were not exposed at the first, but were exposed at the third examination. An increase in SHS exposure over time was associated with doctor-diagnosed asthma (odds ratio (OR): 2.7; 95% confidence interval (95%-CI): 1.2–5.9), chronic bronchitis (OR: 4.8; 95%-CI: 1.6–15.0), asthma symptom score (count ratio (CR): 1.9; 95%-CI: 1.2–2.9) and dyspnoea (OR: 2.7; 95%-CI: 1.1–6.7) compared to never exposed to SHS. Associations between increase in SHS exposure and incidence of COPD (OR: 2.0; 95%-CI: 0.6–6.0) or lung function (β: − 49 ml; 95%-CI: -132, 35 for FEV 1 and β: − 62 ml; 95%-CI: -165, 40 for FVC) were not apparent.

Exposure to second-hand smoke may lead to respiratory symptoms, but this is not accompanied by lung function changes.

Introduction

Exposure to second-hand smoke remains one of the most common indoor pollutants worldwide. In an overview paper from 2011 as many as 40% of children, 35% of women, and 33% of men were regularly exposed to second-hand smoke indoors worldwide [ 1 ]. Children exposed to passive smoke have deficits in lung growth [ 2 , 3 , 4 , 5 ]. However, the effect of environmental tobacco smoke on respiratory disorders and lung function has not been widely investigated and the associations are less clear in adults [ 6 , 7 , 8 ].

Emerging evidence indicates that exposure to second-hand smoke is related to the development of chronic obstructive pulmonary disease (COPD). Based on three studies [ 8 , 9 , 10 ], a meta-analysis [ 11 ] found an increased relative risk (RR = 1.7, 95% CI: 1.4–2.0) of COPD defined by spirometry in people exposed to passive smoking. A link between exposure to second-hand smoke and an accelerated loss of lung function [ 6 , 8 ] was suggested, but the evidence is not strong. Results from cross-sectional analyses of data from middle aged adults participating in the European Community Respiratory Health Survey (ECRHS) showed adverse effects of passive smoking on respiratory symptoms including increased bronchial responsiveness, but the negative association with lung function was not statistically significant [ 12 ]. In addition, a variety of early life factors including maternal smoking during pregnancy showed an association with asthma and poor lung function in adulthood [ 13 , 14 ]. A recent report on life-long exposure to tobacco smoke and lung function trajectories to middle age reported accelerated lung function decline in the exposed subjects [ 15 ]. However, some research [ 16 , 17 ] indicates that current or former smokers often suffer from respiratory symptoms, although lung function is still within normal range and the criteria for COPD assessed by spirometry are not met. While there is mounting evidence that second-hand smoke exposure causes respiratory symptoms and lung function deficits at younger ages including young adulthood, the impact in older age groups is less clear.

We aimed to analyse the association of exposure to second-hand smoke with respiratory diseases such as asthma, bronchitis and COPD, asthma-related symptoms and spirometric pulmonary function in long-term follow ups of young and middle aged adults within a large European multicentre study (ECRHS).

Study population

The European Community Respiratory Health Survey (ECRHS) is a multicentre population-based cohort study that began in 1990–1994. Fifty-six centres across Europe and other parts of the world from 25 countries took part. Young adults aged between 20 and 44 years were selected at random from available population-based registers to take part in the survey. It was a two-stage study, with around 200,000 participants in the questionnaire stage 1, and 26,000 in the clinical stage 2. In the follow-up survey (ECRHS II) of the clinical stage 2 more than 10,000 adults from 29 centres in 14 countries participated (1998–2001). Detailed descriptions of the methods for ECRHS I and ECRHS II have previously been published [ 18 , 19 ]. ECRHS III was the third wave of data collection on the cohort, beginning in 2008. Those who took part in the clinical stages of ECRHS I and II were again contacted, with responders invited to a local fieldwork centre, situated in an outpatient clinic or lung function laboratory. Information was gathered from standardised interviews by well-trained fieldworkers.

The current analyses were restricted to 3011 never and former smoking adults from the random sample who participated in all three surveys and had information on second-hand smoke exposure at all three examinations.

Definition of smoking and second-hand smoke

At each survey participants were asked “Have you ever smoked for as long as a year?”, and if yes, “Do you smoke now as of one month ago?” Current smokers answered both questions in the affirmative and were excluded. Those who answered the lead question in the negative were classified as never smokers, and ex-smokers were those who answered they had smoked but did not in the last month. Smokers and former smokers were asked about duration of smoking and number of cigarettes smoked per day and pack years were calculated. For analytical purposes smoking status was considered as categorical variables never smoker, ex-smoker with less than 15 pack years and ex-smoker with at least 15 pack years.

Exposure to second-hand smoke was assessed by the question “Have you been regularly exposed to tobacco smoke in the last 12 months?”. Study participants answering in the affirmative were classified as being exposed to second-hand smoke.

Definition of respiratory health parameters

Information on the following respiratory symptoms and diseases were collected: physician-diagnosed asthma, chronic bronchitis and COPD, as well as on respiratory symptoms such as wheeze, dyspnoea, cough and sputum. The asthma related symptoms were combined in an asthma score [ 20 ]. The following criteria for outcome assessment were used:

Physician-diagnosed asthma: “Have you ever had asthma?” and “Was this confirmed by a doctor?” were answered in the affirmative.

Asthma symptom score: The sum of positive answers to the following five questions, i.e. the asthma score ranges from 0 to 5, according to Sunyer et al. [ 20 ]: 1) “Have you been breathless while wheezing in the last 12 months?”; 2) “Have you been woken up with a feeling of chest tightness in the last 12 months?”; 3) “Have you had an attack of shortness of breath whilst at rest in the last 12 months?”; 4) “Have you had an attack of shortness of breath after activity in the last 12 months?” and 5) “Have you been woken by an attack of shortness of breath in the last 12 months?”

Nocturnal dyspnoea: “Have you been woken by an attack of shortness of breath at any time during the last twelve months?” was answered in the affirmative.

Cough: positive answer to at least one of “Have you been woken by an attack of coughing at any time in the last twelve months?”, “Do you usually cough first thing in the morning in the winter?” and “Do you usually cough during the day or night in the winter?”

Sputum: positive answer to at least one of the following questions: “Do you usually bring up phlegm from your chest first thing in the morning in the winter?” and “Do you usually bring up any phlegm from your chest during the day or at night in the winter?”

Chronic bronchitis: “Do you usually cough during the day or night on most days for as much as three months per year?” and “Do you usually bring up any phlegm from your chest on most days for as much as three months per year?” were answered in the affirmative.

Lung function testing

Lung function testing was performed by spirometry during the clinical examination according to the ATS/ERS recommendations [ 21 ]. Lung function measures were performed in a sitting position while the subjects were wearing nose clips. At least five, but not more than nine, forced expiratory manoeuvres were performed. The maximum forced expiratory volume in 1 s (FEV 1 ) and maximum forced vital capacity (FVC) of the technically acceptable manoeuvres were determined. Spirometric lung function measurements pre-bronchodilation were used in the current analyses. Different spirometers were used across the study centres and follow-up time points within study centres.

Standardised z-scores were calculated based on the reference equations for spirometry from the Global Lung function Initiative (GLI - https://www.ers-education.org/guidelines/global-lung-function-initiative.aspx ) [ 22 ].

The presence of COPD was based on lung function testing. Study participants with a ratio of FEV 1 and FVC (measured pre-bronchodilation) below the lower limit of normal (LLN) according to the reference equations for spirometry from the Global Lung function Initiative [ 22 ] were classified as COPD patients. It was also defined as the ratio of FEV 1 and FVC (measured pre-bronchodilation) below 0.7.

Definition of confounders

Potential confounding variables were assessed by questionnaire or measured at the physical examination. These included sex, age, maternal smoking during pregnancy and/or childhood, paternal smoking during childhood and occupational exposure to dust and fumes. Smoking status was defined as never smoker, ex-smoker with less than 15 pack years and ex-smoker with at least 15 pack years. Socioeconomic status was defined based on the age when fulltime education was completed (less than 17 years, 17 to 20 years and more than 20 years).

Allergen specific IgE was measured at baseline against D. pteronyssinus , cat, timothy grass and Cladosporium using the Pharmacia CAP System and allergic sensitisation was defined as being sensitised to any of these allergens using a cut-off of 0.35 kUA/L.

Height and weight were measured without shoes and in light clothes at the physical examination.

Statistical analyses

We modelled the longitudinal impact of changes of second-hand smoke exposure on respiratory health outcomes. The effect of change in second-hand smoke exposure over two examinations on lung function parameters as well as respiratory symptoms and diseases at follow-up was analysed separately for ECRHS I-II, ECRHS II-III and ECRHS I-III. Therefore, study participants were categorised into four groups: those not exposed to second-hand smoke at both examinations (reference category); those not exposed to second-hand smoke at the first examination, but at the second examination (SHS increase); those exposed to second-hand smoke at the first examination, but not at the second examination (SHS decrease) and those exposed to second-hand smoke at both examinations (SHS both). Mixed effects logistic regression models and negative binomial mixed effects models with random intercept for study centre were used for respiratory symptoms/diseases and asthma symptom score, respectively. Linear mixed effects models with random intercept for study centre were used to assess the association of change in second-hand smoke exposure and lung function parameters. All models were adjusted for sex, age, weight, maternal and paternal smoking, exposure to dust/fumes, allergic sensitisation, smoking status and socioeconomic status assessed at baseline and additionally for baseline respiratory symptom/disease and lung function, respectively. The models for the association between change in second-hand smoke exposure and lung function were additionally adjusted for height, weight squared and age squared (to model the non-linear relationship of weight and age with lung function). All continuous covariates were standardised (with mean 0 and variance 1).

The associations between exposure to second-hand smoke at baseline and lung function parameters at the three surveys were analysed to evaluate the effect of second-hand smoke exposure on lung function over time. Therefore, linear mixed effects models were fitted with random intercept for study participants nested in the study centre, and an interaction term between second-hand smoke exposure and time of follow-up, i.e. the time between the particular examinations, was included to model the impact of second-hand smoke exposure on lung function decline [ 23 ].

Interaction terms with sex, maternal smoking and paternal smoking were tested. Results from stratified analyses are therefore reported. In addition, sensitivity analyses restricted to never smokers at all three surveys ( n  = 1974 lifetime never smokers) were performed. For the association between change in second-hand smoke exposure over time and lung function at follow-up, additional analyses using percent predicted values according to the Global Lung function Initiative [ 22 ] were conducted.

The results for the association between second-hand smoke exposure with respiratory symptoms and diseases are presented as odds ratio (OR) with corresponding 95% confidence interval (CI), whereas the results for the association of second-hand smoke exposure with lung function parameters are presented as regression coefficients (β) with corresponding 95% CI. For the asthma symptom score, the results are presented as count ratio (CR) with corresponding 95% CI.

All analyses were performed using the statistical software R, version 3.4.3 [ 24 ], and the R packages “lme4” and “lmerTest”.

Description of study population and temporal changes of second-hand smoke exposure and lung function

The analyses were based on 3011 non-smoking adults from 26 study centres who participated in all three surveys and had information on second-hand smoke exposure at all three examinations (Fig.  1 ). The prevalence of reported exposure to second-hand smoke decreased in all participating study centres from ECRHS I to III (Table  1 ). Overall, at the first examination, 38.7% were exposed to second-hand smoke, 23.0% at the second examination and 7.1% at the third examination. The prevalences were highest in Spain.

Flow chart of study population

The prevalence of respiratory symptoms and diseases and the distribution of lung function parameters and confounding variables in each survey are summarised in Table  2 .

Table  3 shows the change in second-hand smoke exposure from ECRHS I-II, ECRHS II-III as well as ECRHS I-III. Almost 7% (ECRHS I-II) and 2.5% (ECRHS II-III) of the study participants were not exposed at the first, but were exposed at the second examination.

The distribution of the lung function parameters as well as the annual decline are summarised in Table  4 . All lung function parameters (FEV 1 , FVC and FEV 1 /FVC) decreased over time, with greater decline in the second 10 year follow-up period (ECRHS II-III; 42 ml/year decline in FEV 1 ) compared to the first 10 year period (ECRHS I-II; 24 ml/year decline in FEV 1 ) as expected with ageing of the population.

Adjusted associations between change in second-hand smoke exposure over time and respiratory symptoms and diseases at follow-up [ECRHS I-II, ECRHS II-III and ECRHS I-III]

Adjusted time variant analysis of second-hand smoke exposure showed that those reporting increased second-hand smoke exposure had increased risks for the development of doctor-diagnosed asthma, chronic bronchitis and increased asthma symptom score, reaching conventional levels of significance from ECRHS II-III as well as from ECRHS I-III overall (Fig.  2 ). However, compared to those not exposed on both occasions there was no evidence that those reporting exposure on both occasions had an increased risk of asthma or chronic bronchitis. However, asthma score did increase in this group compared to the non-exposed. An increased risk of nocturnal dyspnoea was observed only for those reporting increased second-hand smoke exposure between the first and the third survey but not for those exposed at both surveys.

Associations between change in second-hand smoke (SHS) exposure over time and respiratory symptoms/diseases at follow-up. SHS never: no SHS exposure at both examinations (reference category); SHS increase: no SHS exposure at first examination but at second examination; SHS decrease: SHS exposure at first examination but not at second examination; SHS both: SHS exposure at both examinations. All models are adjusted for sex, age, weight, maternal smoking, paternal smoking, combination of smoking status and pack years, education, exposure to dust/fumes, allergic sensitisation (at baseline) and baseline respiratory symptom/disease

Spirometrically defined COPD was not statistically significantly associated with changes in second-hand smoke exposure at any of the examined periods over the 20 years after adjustment for several selected confounders (Fig. 2 ).

Adjusted associations between change in second-hand smoke exposure over time and lung function parameters at follow-up

There was no association of second-hand smoke exposure with FEV 1 .

Study participants exposed to second-hand smoke at the first as well as at the second survey (ECRHS I-II) had a reduced forced vital capacity at the second survey (approximately 50 ml) compared to those not exposed to second-hand smoke at these two surveys (Fig.  3 ) – but there was no clear or consistent pattern of association over the entire study period.

Associations between change in second-hand smoke (SHS) exposure over time and lung function at follow-up. SHS never: no SHS exposure at both examinations (reference category); SHS increase: no SHS exposure at first examination but at second examination; SHS decrease: SHS exposure at first examination but not at second examination; SHS both: SHS exposure at both examinations. All models are adjusted for sex, age, age squared, weight, weight squared, height, maternal smoking, paternal smoking, combination of smoking status and pack years, education, exposure to dust/fumes, allergic sensitisation (at baseline) and baseline lung function

Similarly the ratio of FEV 1 /FVC showed associations with increased second-hand smoke exposure from ECRHS I-II but no consistent pattern when the data were examined over the entire period.

Sensitivity analyses using percent predicted values according to the Global Lung function Initiative showed comparable results (Additional file 1 : Table S1).

Adjusted associations between second-hand smoke exposure at the first examination and lung function as well as lung function decline

The associations of exposure to second-hand smoke at the first examination (ECRHS I) with lung function as well as lung function decline from ECRHS I-III are summarised in Table  5 . Exposure to second-hand smoke at the first examination resulted in reduced FEV 1 and FVC over time, with stronger effects for males compared to females. It also shows that those exposed to second-hand smoke at the first examination had a slightly slower decline in lung function compared to those not exposed.

Sensitivity analyses restricted to lifetime never smokers, i.e. participants who were never smokers at all three surveys showed comparable results (data not shown).

This study investigated the association of exposure to second-hand smoke with respiratory symptoms and diagnoses, as well as lung function and lung function decline in never and former smoking participants in ECRHS I-III. We show that the proportion of the studied population exposed to second-hand smoke fell markedly over the follow-up period of 20 years. Individuals who became exposed to second-hand smoke over time were at increased risk of doctor-diagnosed asthma, chronic bronchitis as well as a higher asthma symptom score. Associations between increase in second-hand smoke exposure and incidence of COPD or lung function were not apparent.

Comparison with results from other epidemiology studies

Only a few studies have examined the association of exposure to second-hand smoke with onset of asthma in adulthood. In the prospective U.S. Black Women’s Health Study, Coogan et al. observed a positive association of passive smoke exposure with the incidence of adult-onset asthma over 15 years of follow-up in 46,182 women aged 21 to 69 years at baseline [ 25 ]. Non-smoking study participants who were exposed to second-hand smoke had a 21% increase (adjusted HR: 1.2; 95%-CI: 1.0–1.5) in asthma incidence compared to those not exposed. Similar findings were observed in two Finnish population-based case-control studies [ 26 , 27 ]. Exposure to second-hand smoke at the workplace or at home increased the risk for the development of asthma during a period of 2.5 years [ 27 ]. In our study, an increase in second-hand smoke exposure over time was associated with an increased risk of doctor-diagnosed asthma. An increased asthma symptom score was observed for those reporting increased second-hand smoke exposure as well as for those exposed on both occasions. A decrease in second-hand smoke exposure was also associated with an increased asthma symptom score. However, this association was not consistent, being only recorded from ECRHS I to ECRHS II, and the strength of the association was rather low. Of note, the ECRHS questionnaire had not been specifically devised to assess changes in respiratory symptoms after smoking cessation or decrease in second-hand smoke exposure. Overall our study findings are consistent with results of the few other studies on second-hand smoke and asthma development in adults.

Previous studies have investigated the association of exposure to second-hand smoke with respiratory symptoms and diseases, especially COPD. For instance, Eisner et al. [ 6 ] analysed the effect of lifetime exposure to second-hand smoke on the risk for the development of COPD in 2112 adults (including current, former and never smokers) aged 55 to 75 years in the U.S. It showed a positive significant association between cumulative exposure to second-hand smoke at home (adjusted OR: 1.6; 95%-CI: 1.1–2.2) as well as at work (adjusted OR: 1.4; 95%-CI: 1.0–1.8) with self-reported doctor-diagnosed COPD. A Chinese study [ 8 ] also investigated the relationship of self-reported density and duration of exposure to passive smoking with respiratory symptoms (cough, phlegm and shortness of breath) and COPD (FEV 1 /FVC < 0.7 measured pre-bronchodilation) based on data from 15,379 never smoking adults in the Guangzhou Biobank Cohort Study. Exposure to second-hand smoke at home and at work was significantly associated with an increased risk of COPD (adjusted OR: 1.5; 95%-CI: 1.2–1.9) and any respiratory symptoms (adjusted OR: 1.2; 95%-CI: 1.1–1.3).

An increased risk of COPD, defined using the fixed ratio of FEV 1 /FVC < 0.7 measured post-bronchodilation, was seen in those with second-hand smoke exposure in a study [ 28 ] of 2182 lifelong never smokers taking part in the Obstructive Lung Disease in Northern Sweden (OLIN) studies. Exposure to second-hand smoke was categorised into several groups based on previous and current exposure to second-hand smoke at home and at work. The strongest associations were seen in those ever exposed at home and at both previous and current work (adjusted OR: 3.8; 95%-CI: 1.3–11.2) as well as for those currently exposed at home and at both previous and current work (adjusted OR: 5.7; 95%-CI: 1.5–22.5). A significant dose dependent relationship of exposure to second-hand smoke with mortality from different diseases, including COPD amongst other causes of death, could be shown in another study [ 10 ]. In contrast, a study conducted by Chan-Yeung et al. [ 9 ] found no association between exposure to second-hand smoke and an increased risk for COPD in a small sex- and age-matched case-control study comprising 289 patients and controls, respectively, in Hong Kong, China.

The different associations between exposure to second-hand smoke and COPD in the above studies might be due to the different definition of COPD as some studies used questionnaire-based information whereas others used spirometric measurements. Furthermore, some studies were restricted to lifetime never smokers compared to studies also including active smokers. We have shown no significant association between increase in second-hand smoke exposure and incidence of COPD based on lung function testing in our study, which was restricted to never and former smokers. The different observed associations might also be due to residual confounding in some studies or potential misclassification of self-reported exposure to second-hand smoke in our study.

Another study, based on Taiwan’s National Health Insurance Bureau claims data, investigated the association of exposure to second-hand smoke and chronic bronchitis in women [ 29 ] and showed that women who were exposed to second-hand smoke had a 3.7 (95%-CI: 1.2–11.3) higher risk of chronic bronchitis compared to those not exposed to second-hand smoke. Furthermore, exposure to second-hand smoke was also associated with mild (adjusted OR: 1.8; 95%-CI: 1.1–2.9) and moderate (adjusted OR: 3.8; 95%-CI: 1.7–8.6) COPD as defined by GOLD.

We have shown a significant positive association of new exposure to second-hand smoke between two surveys with chronic bronchitis at follow-up, defined as having cough and sputum. However there was no evidence of a decrease in chronic bronchitis if exposure to second-hand smoke stopped over the same time frame. In addition, exposure to second-hand smoke was not associated with COPD defined by a ratio of FEV 1 /FVC below 0.7 [ 30 , 31 ]. According to the original classification of COPD from the Global Initiative for Chronic Obstructive Lung Disease (GOLD) in 2001 [ 32 ], stage 0 “at risk” is characterised by chronic symptoms (sputum production and cough) with still normal spirometry, i.e. the ratio between FEV 1 and FVC of at least 0.7. This GOLD stage 0 would be similar to the definition of chronic bronchitis used in this analysis which requires a positive answer to both the question on cough and the question on sputum, independent of lung function. Moreover, the overlap between chronic bronchitis and COPD defined by spirometry was quite small in this study. As an effect of exposure to second-hand smoke was found in this study only for chronic bronchitis, but not for COPD, one might speculate that these results indicate a transient effect, but not structural changes in the airways as would be common in COPD patients.

Although COPD is generally considered a disease characterised by a progressive, gradually accelerating decline in FEV 1 Macklem has pointed out that increase in residual volume (RV) is the first functional abnormality in chronic bronchitis [ 33 ]. Thus, gas trapping with reduction of FVC is an early abnormality because RV increases more than the total lung capacity (TLC). The observed decrease in FEV 1 occurs because of a reduction in FVC. The FEV 1 /FVC ratio will decrease because of loss of lung elastic recoil, a sine qua non of COPD [ 33 , 34 ] but early in the disease the decrease in FVC may exceed that in FEV 1 with a paradoxical effect on the FEV 1 /FVC ratio. This may explain why we did not see an association with COPD defined by spirometric lung function parameters.

A reduced FEV 1 and FVC over time was observed for those reporting exposure to second-hand smoke at the first examination, with stronger effects for males compared to females. Several studies have investigated the association between second-hand smoke exposure and lung function, suggesting sex differences in vulnerability [ 35 , 36 , 37 , 38 ]. However, the findings are inconsistent. Some studies found adverse effects of passive smoking on spirometric lung function parameters for both sexes [ 35 , 38 ], whereas another study found stronger effects for women compared to men [ 36 ]. Our results are consistent with findings of the study conducted by Masjedi et al. [ 37 ] showing a negative association between second-hand smoke exposure and lung function among men, but not among women.

Janson et al. [ 39 ] investigated changes and determinants for changes in active as well as passive smoking in the first and second survey of the European Community Respiratory Health Survey showing that exposure to second-hand smoke was higher among subjects with lower socio-economic status and educational level. Furthermore, subjects exposed to second-hand smoke were less likely to quit smoking suggesting that a decrease in second-hand smoke exposure might be effective in decreasing active smoking. In our study, exposure to second-hand smoke decreased during the 20 years of follow-up where for many of the study centres the decrease between the second 10 year follow-up period was stronger compared to the first 10 year period. However, second-hand smoke exposure was still present in all participating centres.

Strengths and limitations

The European Community Respiratory Health Survey has a longitudinal study design with two follow-ups approximately 10 and 20 years, respectively, after the first survey and therefore we can model the association between changes in second-hand smoke exposure over time with respiratory health outcomes. We are also able to investigate the effect of exposure to second-hand smoke at baseline on lung function decline using spirometric measurements that were performed and quality controlled according to well established guidelines. Also the large study population of around 2000 never-smoking study participants and the high number of participating centres and countries are further strengths.

However this study has some limitations. The information on second-hand smoke exposure as well as respiratory symptoms and diseases was obtained using self-administered questionnaires completed at follow-up and no biomarkers for exposure to second-hand smoke were available. Moreover, the information on second-hand some exposure was only requested for the last 12 months at each survey and not for the total study period. Furthermore, information on the number of cigarettes smoked by other people was not available. No dose-related association between second-hand smoke exposure and respiratory health has been investigated which has to be taken into account when drawing conclusions.

In addition, against a backdrop of falling smoking rates and smoke free legislation across Europe only a small proportion of the study group became newly exposed to second-hand smoke over the period of the study. Questionnaire-based information on second-hand smoke exposure might be prone to reporting bias as subjects having respiratory symptoms or diseases might tend to be more affected. Siroux et al. [ 40 ] has found no indication that asthma status influences reporting of exposure to second-hand smoke in childhood or adulthood but we cannot exclude that our results are related to reporting biases. The use of different spirometers across study centres and surveys could have resulted in temporal differences in lung function measurements. Sensitivity analyses using lung function values corrected for this change showed comparable results. Furthermore, it was difficult to disentangle the survey and age effects due to three time points comprising two follow-ups each after approximately 10 years as different findings were observed for the association of new exposure to second-hand smoke between two surveys with respiratory symptoms and diseases at follow-up.

The questions on cough and sputum were only requested for winter and not for summer, as these symptoms are often more worse during the winter months. Furthermore, no information on the change of the ventilation equipment used for air cleaning during the follow-up periods was available and thus could not be considered as potential confounding variable.

In a longitudinal analysis of adults, following a multi-centre cohort over twenty years, exposure to second-hand smoke decreased substantially during the study period. Second-hand smoke exposure in adults was associated with an increased risk for asthma and chronic bronchitis. Our results support further restrictions on smoking in public places.

Oberg M, Jaakkola MS, Woodward A, Peruga A, Prüss-Ustün A. Worldwide burden of disease from exposure to second-hand smoke: a retrospective analysis of data from 192 countries. Lancet. 2011;377(9760):139–46.

Article   Google Scholar  

Strachan DP, Cook DG. Health effects of passive smoking. 5. Parental smoking and allergic sensitisation in children. Thorax. 1998;53:117–23.

Article   CAS   Google Scholar  

Strachan DP, Cook DG. Health effects of passive smoking. 6. Parental smoking and childhood asthma: longitudinal and case-control studies. Thorax. 1998 Mar;53:204–12.

Cook DG, Strachan DP, Carey IM. Health effects of passive smoking. 9. Parental smoking and spirometric indices in children. Thorax. 1998;53:884–93.

Cook DG, Strachan DP. Health effects of passive smoking. 10. Summary of effects of parental smoking on the respiratory health of children and implications for research. Thorax. 1999;54:357–66.

Eisner MD, Balmes J, Katz PP, Trupin L, Yelin EH, Blanc PD. Lifetime environmental tobacco smoke exposure and the risk of chronic obstructive pulmonary disease. Environ Health. 2005;4(1):7.

Sobonya R, Burrows B. The epidemiology of emphysema. Clin Chest Med. 1983;4(3):351–8.

CAS   PubMed   Google Scholar  

Yin P, Jiang CQ, Cheng KK, Lam TH, Lam KH, Miller MR, et al. Passive smoking exposure and risk of COPD among adults in China: the Guangzhou biobank cohort study. Lancet. 2007;370(9589):751–7.

Chan-Yeung M, Ho ASS, Cheung AHK, Liu RWT, Yee WKS, Sin KM, et al. Determinants of chronic obstructive pulmonary disease in Chinese patients in Hong Kong. Int J Tuberc Lung Dis. 2007;11(5):502–7.

McGhee SM, Ho SY, Schooling M, Ho LM, Thomas GN, Hedley AJ, et al. Mortality associated with passive smoking in Hong Kong. BMJ. 2005;330(7486):287–8.

Fischer F, Kraemer A. Meta-analysis of the association between second-hand smoke exposure and ischaemic heart diseases. COPD and stroke BMC Public Health. 2015;15:1202.

Janson C, Chinn S, Jarvis D, Zock JP, Torén K, Burney P, et al. Effect of passive smoking on respiratory symptoms, bronchial responsiveness, lung function, and total serum IgE in the European Community respiratory health survey: a cross-sectional study. Lancet. 2001;358(9299):2103–9.

Accordini S, Calciano L, Johannessen A, Portas L, Benediktsdottir B, Bertelsen RJ, et al. A three-generation study on the association of tobacco smoking with asthma. Int J Epidemiol. 2018;47:1106–17.

Svanes C, Sunyer J, Plana E, Dharmage S, Heinrich J, Jarvis D, et al. Early life origins of chronic obstructive pulmonary disease. Thorax. 2010;65:14–20.

Allinson JP, Hardy R, Donaldson GC, Shaheen SO, Kuh D, Wedzicha JA. Combined impact of smoking and early-life exposures on adult lung function trajectories. Am J Respir Crit Care Med. 2017;196:1021–30.

Regan EA, Lynch DA, Curran-Everett D, Curtis JL, Austin JHM, Grenier PA, et al. Clinical and radiologic disease in smokers with normal spirometry. JAMA Intern Med. 2015;175(9):1539–49.

Woodruff PG, Barr RG, Bleecker E, Christenson SA, Couper D, Curtis JL, et al. Clinical significance of symptoms in smokers with preserved pulmonary function. N Engl J Med. 2016;374(19):1811–21.

European Community Respiratory Health Survey II Steering Committee. The European Community respiratory health survey II. Eur Respir J. 2002;20(5):1071–9.

Burney PG, Luczynska C, Chinn S, Jarvis D. The European Community respiratory health survey. Eur Respir J. 1994;7(5):954–60.

Sunyer J, Pekkanen J, Garcia-Esteban R, Svanes C, Künzli N, Janson C, et al. Asthma score: predictive ability and risk factors. Allergy. 2007;62:142–8.

Miller MR, Hankinson J, Brusasco V, Burgos F, Casaburi R, Coates A, et al. Standardisation of spirometry. Eur Respir J. 2005;26:319–38.

Quanjer PH, Stanojevic S, Cole TJ, Baur X, Hall GL, Culver BH, et al. Multi-ethnic reference values for spirometry for the 3-95-yr age range: the global lung function 2012 equations. Eur Respir J. 2012;40(6):1324–43.

Fuertes E, Carsin AE, Antó JM, Bono R, Corsico AG, Demoly P, et al. Leisure-time vigorous physical activity is associated with better lung function: the prospective ECRHS study. Thorax. 2018;73:376–84.

R Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria; 2017. Available from: https://www.R-project.org/ .

Coogan PF, Castro-Webb N, Yu J, O’Connor GT, Palmer JR, Rosenberg L. Active and passive smoking and the incidence of asthma in the black Women’s health study. Am J Respir Crit Care Med. 2015;191:168–76.

Lajunen TK, Jaakkola JJK, Jaakkola MS. The synergistic effect of heredity and exposure to second-hand smoke on adult-onset asthma. Am J Respir Crit Care Med. 2013;188:776–82.

Jaakkola MS, Piipari R, Jaakkola N, Jaakkola JJK. Environmental tobacco smoke and adult-onset asthma: a population-based incident case-control study. Am J Public Health. 2003;93:2055–60.

Hagstad S, Bjerg A, Ekerljung L, Backman H, Lindberg A, Rönmark E, et al. Passive smoking exposure is associated with increased risk of COPD in never smokers. Chest. 2014;145(6):1298–304.

Wu CF. Feng NH, Chong IW, Wu KY, Lee CH, Hwang JJ, et al. Second-hand smoke and chronic bronchitis in Taiwanese women: a health-care based study BMC Public Health. 2010;10:44.

Global Initiative for Chronic Obstructive Lung Disease. Pocket guide to COPD diagnosis, management, and prevention. A guide for health care professionals; 2017. Available from: https://goldcopd.org/ .

Global Initiative for Chronic Obstructive Lung Disease. Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease; 2006. Available from: https://goldcopd.org/ .

Pauwels RA, Buist AS, Calverley PM, Jenkins CR, Hurd SS, Committee GS. Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease. NHLBI/WHO global initiative for chronic obstructive lung disease (GOLD) workshop summary. Am J Respir Crit Care Med. 2001;163:1256–76.

Macklem PT. Therapeutic implications of the pathophysiology of COPD. Eur Respir J. 2010;35:676–80.

Hogg JC, Paré PD, Hackett TL. The contribution of small airway obstruction to the pathogenesis of chronic obstructive pulmonary disease. Physiol Rev. 2017;97:529–52.

Langhammer A, Johnsen R, Gulsvik A, Holmen TL, Bjermer L. Sex differences in lung vulnerability to tobacco smoking. Eur Respir J. 2003;21:1017–23.

Eisner MD. Environmental tobacco smoke exposure and pulmonary function among adults in NHANES III: impact on the general population and adults with current asthma. Environ Health Perspect. 2002;110:765–70.

Masjedi MR, Kazemi H, Johnson DC. Effects of passive smoking on the pulmonary function of adults. Thorax. 1990;45:27–31.

Janzen B, Karunanayake C, Rennie D, Pickett W, Lawson J, Kirychuk S, et al. Gender differences in the association of individual and contextual exposures with lung function in a rural Canadian population. Lung. 2017;195:43–52.

Janson C, Künzli N, de Marco R, Chinn S, Jarvis D, Svanes C, et al. Changes in active and passive smoking in the European Community respiratory health survey. Eur Respir J. 2006;27(3):517–24.

Siroux V, Guilbert P, Le Moual N, Oryszczyn MP, Kauffmann F. Influence of asthma on the validity of reported lifelong environmental tobacco smoke in the EGEA study. Eur J Epidemiol. 2004;19:841–9.

Download references

Acknowledgements

ECRHS I coordinating centre and project management group

Coordinating Centre (London): P Burney, S Chinn, C Luczynska, D Jarvis, E Lai.

Project Management Group: P Burney (Project leader UK), S Chinn (UK), C Luczynska (UK), D Jarvis (UK), P Vermeire (Antwerp), H Kesteloot (Leuven), J Bousquet (Montpellier), D Nowak (Hamburg), J Prichard (Dublin), R de Marco (Verona), B Rijcken (Groningen), JM Anto (Barcelona), J Alves (Oporto), G Boman (Uppsala), N Nielsen (Copenhagen), P Paoletti (Pisa).

ECRHS II principal investigators and senior scientific teams

Australia (M. Abramson, E.H Walters, J. Raven); Belgium: South Antwerp and Antwerp City (P. Vermeire, J. Weyler, M. van Sprundel, V. Nelen); Estonia: Tartu (R. Jõgi, A. Soon); France: Paris (F. Neukirch, B. Leynaert, R. Liard, M. Zureik), Grenoble (I. Pin, J. Ferran-Quentin); Germany: Erfurt (J. Heinrich, M. Wjst, C. Frye, I. Meyer); Iceland: Reykjavik (T. Gislason, E. Bjornsson, D. Gislason, K.B Jörundsdóttir); Italy: Turin (R. Bono, M. Bugiani, P.Piccioni, E. Caria, A. Carosso, E. Migliore, G. Castiglioni), Verona (R. de Marco, G. Verlato, E. Zanolin, S. Accordini, A. Poli, V. Lo Cascio, M. Ferrari, I. Cazzoletti), Pavia (A. Marinoni, S. Villani, M. Ponzio, F. Frigerio, M. Comelli, M. Grassi, I. Cerveri, A. Corsico); Norway: Bergen (A. Gulsvik, E. Omenaas, C. Svanes, B. Laerum); Spain: Albacete (J. Martinez-Moratalla Rovira, E. Almar, M. Arévalo, C. Boix, G González, J.M. Ignacio García, J. Solera, J Damián), Galdakao (N. Muñozguren, J. Ramos, I. Urrutia, U. Aguirre), Barcelona (J. M. Antó, J. Sunyer, M. Kogevinas, J. P. Zock, X. Basagaña, A. Jaen, F. Burgos, C. Acosta), Huelva (J. Maldonado, A. Pereira, J.L. Sanchez), Oviedo (F. Payo, I. Huerta, A. de la Vega, L Palenciano, J Azofra, A Cañada); Sweden: Göteborg (K. Toren,L. Lillienberg, A. C. Olin, B. Balder, A. Pfeifer-Nilsson, R. Sundberg), Umea (E. Norrman, M. Soderberg, K. Franklin, B. Lundback, B. Forsberg, L. Nystrom), Uppsala (C. Janson, G. Boman, D. Norback, G. Wieslander, M. Gunnbjornsdottir); Switzerland: Basel (N. Kuenzli, B. Dibbert, M. Hazenkamp, M. Brutsche, U. Ackermann-Liebrich); United Kingdom: Ipswich (D. Jarvis, R. Hall, D. Seaton), Norwich (D. Jarvis, B. Harrison).

ECRHS III principal investigators and senior scientific teams

Australia: Melbourne (M. Abramson, G. Benke, S. Dharmage, B. Thompson, S. Kaushik); Belgium: South Antwerp & Antwerp City (J. Weyler, M. van Sprundel, V. Nelen, E. Van de Mieroop); France: Bordeaux (C. Raherison, P.O Girodet), Grenoble (I. Pin, V. Siroux, J.Ferran, J.L Cracowski), Montpellier (P. Demoly, A.Bourdin, I. Vachier), Paris (B. Leynaert, D. Soussan, D. Courbon, C. Neukirch, L. Alavoine, X. Duval, I. Poirier); Germany: Erfurt (J. Heinrich, E. Becker, G. Woelke, O. Manuwald), Hamburg (H. Magnussen, D. Nowak, A-M Kirsten); Iceland: Reykjavik (T. Gislason, B. Benediktsdottir, D. Gislason, E.S Arnardottir, M. Clausen, G. Gudmundsson, L. Gudmundsdottir, H. Palsdottir, K. Olafsdottir, S. Sigmundsdottir, K. Bara-Jörundsdottir); Italy: Pavia (I. Cerveri, A.Corsico, A. Grosso, F. Albicini, E. Gini, E.M Di Vincenzo, V. Ronzoni, S. Villani, F. Campanella, F. Manzoni, L. Rossi, O. Ferraro), Turin (M. Bugiani, R. Bono, P. Piccioni, R. Tassinari, V. Bellisario), Verona (R de Marco, S. Accordini, L. Calciano, L. Cazzoletti, M. Ferrari, A.M Fratta Pasini, F. Locatelli, P. Marchetti, A. Marcon, E. Montoli, G. Nguyen, M. Olivieri, C. Papadopoulou, C.Posenato, G. Pesce, P. Vallerio, G. Verlato, E. Zanolin); Norway: (C. Svanes, E. Omenaas, A. Johannessen, T. Skorge, F. Gomez Real); Spain: Albacete (J. Martinez-Moratalla Rovira, E. Almar, A. Mateos, S. García, A. Núñez, P.López, R. Sánchez, E Mancebo), Barcelona (J M. Antó, J.P Zock, J. Garcia-Aymerich, X. Basagaña, F. Burgos, C. Sanjuas, S Guerra), Galdakao (N. Muñozguren, I. Urrutia, U. Aguirre, S. Pascual), Huelva (J Antonio Maldonado, A. Pereira, J. Luis Sánchez, L. Palacios), Oviedo (F. Payo, I. Huerta, N. Sánchez, M. Fernández, B. Robles); Sweden: Göteborg (K. Torén, M. Holm, J-L Kim, A-C Olin, A. Dahlman-Höglund), Umea (B. Forsberg, L. Braback, E. Norrman, L. Modig, B. Järvholm, H. Bertilsson, K. Franklin, C. Wahlgreen, M. Soderberg) Uppsala (B. Andersson, D. Norback, U. Spetz Nystrom, G. Wieslander, G.M Bodinaa Lund, K. Nisser); Switzerland: Basel (N.M. Probst-Hensch, N. Künzli, D. Stolz, C. Schindler, T. Rochat, J.M. Gaspoz, E. Zemp Stutz, M. Adam, C. Autenrieth, I. Curjuric, J. Dratva, A. Di Pasquale, R. Ducret-Stich, E. Fischer, L. Grize, A. Hensel, D. Keidel, A. Kumar, M. Imboden, N. Maire, A. Mehta, H. Phuleria, M. Ragettli, M. Ritter, E. Schaffner, G.A Thun, A. Ineichen, T. Schikowski, M. Tarantino, M. Tsai); UK: Ipswich (N. Innes), Norwich (A. Wilson).

ECRHS I financial support

The following grants helped to fund the local studies.

Australia: Asthma Foundation of Victoria, Allen and Hanbury’s; Belgium: Belgian Science Policy Office, National Fund for Scientific Research; Estonia: Estonian Science Foundation, grant no 1088; France: Ministère de la Santé, Glaxo France, Insitut Pneumologique d’Aquitaine, Contrat de Plan Etat-Région Languedoc-Rousillon, CNMATS, CNMRT (90MR/10, 91AF/6), Ministre delegué de la santé, RNSP, France; GSF; Germany: Bundesminister für Forschung und Technologie; Italy: Ministero dell’Università e della Ricerca Scientifica e Tecnologica, CNR, Regione Veneto grant RSF n. 381/05.93; Norway: Norwegian Research Council project no. 101422/310; Spain: Fondo de Investigación Sanitaria (#91/0016–060-05/E, 92/0319 and #93/0393), Hospital General de Albacete, Hospital General Juan Ramón Jiménez, Dirección Regional de Salud Pública (Consejería de Sanidad del Principado de Asturias), CIRIT (1997 SGR 00079) and Servicio Andaluz de Salud; Sweden: The Swedish Medical Research Council, the Swedish Heart Lung Foundation, the Swedish Association against Asthma and Allergy; Switzerland: Swiss national Science Foundation grant 4026–28099; UK: National Asthma Campaign, British Lung Foundation, Department of Health, South Thames Regional Health Authority.

The co-ordination of this work was supported by the European Commission.

ECRHS II financial support

The following grants helped to fund the local studies

Australia: National Health and Medical Research Council; Belgium: Antwerp: Fund for Scientific Research (grant code, G.0402.00), University of Antwerp, Flemish Health Ministry; Estonia: Tartu Estonian Science Foundation grant no 4350; France: Bordeaux: Institut Pneumologique d’Aquitaine; Grenoble: Programme Hospitalier de Recherche Clinique – Direction de la Recherche Clinique (DRC) de Grenoble 2000 number 2610, Ministry of Health, Ministere de l’Emploi et de la Solidarite, Direction Generale de la Sante, Centre Hospitalier Universitaire (CHU) de Grenoble, Comite des Maladies Respiratoires de l’Isere; Montpellier: Programme Hospitalier de Recherche Clinique – DRC de Grenoble 2000 number 2610, Ministry of Health, Direction de la Recherche Clinique, CHU de Grenoble, Ministere de l’Emploiet de la Solidarite, Direction Generale de la Sante, Aventis (France), Direction Regionale des Affaires Sanitaires et Sociales Languedoc-Roussillon; Paris: Ministere de l’Emploi et de la Solidarite, Direction Generale de la Sante,Union Chimique Belge- Pharma (France), Aventis (France), Glaxo France, Programme Hospitalier de Recherche Clinique – DRC de Grenoble 2000 number 2610, Ministry of Health, Direction de la Recherche Clinique, CHU de Grenoble; Germany: Erfurt: GSF – National Research Centre for Environment and Health, Deutsche Forschungsgemeinschaft (grant code, FR1526/1–1); Hamburg: GSF – National Research Centre for Environment and Health, Deutsche Forschungsgemeinschaft (grant code, MA 711/4–1); Iceland: Reykjavik: Icelandic Research Council, Icelandic University Hospital Fund; Italy: Pavia: GlaxoSmithKline Italy, Italian Ministry of University and Scientific and Technological Research (MURST), Local University Funding for Research 1998 and 1999; Turin: Azienda Sanitaria Locale 4 Regione Piemonte (Italy), Azienda Ospedaliera Centro Traumatologico Ospedaliero/Centro Traumatologico Ortopedico—Istituto Clinico Ortopedico Regina Maria Adelaide Regione Piemonte; Verona: Ministero dell’Universita´ e della Ricerca Scientifica (MURST), Glaxo Wellcome spa: Norway: Bergen: Norwegian Research Council, Norwegian Asthma and Allergy Association, Glaxo Wellcome AS, Norway Research Fund; Spain: Fondo de Investigacion Santarias (grant codes, 97/0035–01,99/0034–01 and 99/0034 02), HospitalUniversitario de Albacete, Consejeria deSanidad; Barcelona: Sociedad Espanola de Neumologı’a y Cirugı’a Toracica, Public Health Service (grant code, R01 HL62633–01), Fondo de Investigaciones Santarias (grant codes, 97/0035–01, 99/0034–01, and 99/0034–02), Consell Interdepartamentalde Recerca i Innovacio´ Tecnolo’gica (grant code, 1999SGR 00241) Instituto de Salud Carlos III; Red deCentros de Epidemiologı’a y Salud Pu′blica, C03/09,Redde Basesmoleculares y fisiolo’gicas de lasEnfermedadesRespiratorias,C03/011and Red de Grupos Infancia y Medio Ambiente G03/176; Huelva: Fondo de Investigaciones Santarias (grant codes, 97/0035–01, 99/0034–01, and 99/0034–02); Galdakao: Basque Health Department; Oviedo: Fondo de Investigaciones Sanitaria (97/0035–02, 97/0035, 99/0034–01, 99/0034–02, 99/0034–04, 99/0034–06, 99/350, 99/0034--07), European Commission (EU-PEAL PL01237), Generalitat de Catalunya (CIRIT 1999 SGR 00214), Hospital Universitario de Albacete, Sociedad Española de Neumología y Cirugía Torácica (SEPAR R01 HL62633–01) Red de Centros de Epidemiología y Salud Pública (C03/09), Red de Bases moleculares y fisiológicas de las Enfermedades Respiratorias (C03/011) and Red de Grupos Infancia y Medio Ambiente (G03/176; 97/0035–01, 99/0034–01, and 99/0034–02); Sweden: Göteborg, Umea, Uppsala: Swedish Heart Lung Foundation, Swedish Foundation for Health Care Sciences and Allergy Research, Swedish Asthma and Allergy Foundation, Swedish Cancer and Allergy Foundation, Swedish Council for Working Life and Social Research (FAS); Switzerland: Basel: Swiss National Science Foundation, Swiss Federal Office for Education and Science, Swiss National Accident Insurance Fund; UK: Ipswich and Norwich: Asthma UK (formerly known as National Asthma Campaign).

ECRHS III financial support

Australia: National Health & Medical Research Council; Belgium: Antwerp South, Antwerp City: Research Foundation Flanders (FWO), grant code G.0.410.08.N.10 (both sites); Estonia: Tartu: SF0180060s09 from the Estonian Ministry of Education; France (All): Ministère de la Santé. Programme Hospitalier de Recherche Clinique (PHRC) national 2010; Bordeaux: INSERM U897 Université Bordeaux segalen; Grenoble: Comitee Scientifique AGIRadom 2011; Paris: Agence Nationale de la Santé, Région Ile de France, domaine d’intérêt majeur (DIM); Germany: Erfurt: German Research Foundation HE 3294/10–1; Hamburg: German Research Foundation MA 711/6–1, NO 262/7–1; Iceland: Reykjavik: The Landspitali University Hospital Research Fund, University of Iceland Research Fund, ResMed Foundation, California, USA, Orkuveita Reykjavikur (Geothermal plant), Vegagerðin (The Icelandic Road Administration (ICERA). Italy: All Italian centres were funded by the Italian Ministry of Health, Chiesi Farmaceutici SpA, in addition Verona was funded by Cariverona foundation, Education Ministry (MIUR); Norway: Norwegian Research council grant no 214123, Western Norway Regional Health Authorities grant no 911631, Bergen Medical Research Foundation; Spain: Fondo de Investigación Sanitaria (PS09/02457, PS09/00716 09/01511, PS09/02185 PS09/03190), Servicio Andaluz de Salud, Sociedad Española de Neumología y Cirurgía Torácica (SEPAR 1001/2010); Barcelona: Fondo de Investigación Sanitaria (FIS PS09/00716); Galdakao: Fondo de Investigación Sanitaria (FIS 09/01511); Huelva: Fondo de Investigación Sanitaria (FIS PS09/02185) and Servicio Andaluz de Salud; Oviedo: Fondo de Investigación Sanitaria (FIS PS09/03190); Sweden: All centres were funded by The Swedish Heart and Lung Foundation, The Swedish Asthma and Allergy Association, The Swedish Association against Lung and Heart Disease. Swedish Research Council for health, working life and welfare (FORTE); Göteborg: also received further funding from the Swedish Council for Working life and Social Research; Umea: also received funding from Vasterbotten Country Council ALF grant; Switzerland: The Swiss National Science Foundation (grants no 33CSCO-134276/1, 33CSCO-108796, 3247BO-104283, 3247BO-104288, 3247BO-104284, 3247–065896, 3100–059302, 3200–052720, 3200–042532, 4026–028099) The Federal office for forest, environment and landscape, The Federal Office of Public Health, The Federal Office of Roads and Transport, the canton’s government of Aargan, Basel-Stadt, Basel-Land, Geneva, Luzern, Ticino, Valais and Zürich, the Swiss Lung League, the canton’s Lung League of Basel Stadt/Basel, Landschaft, Geneva, Ticino, Valais and Zurich, SUVA, Freiwillige Akademische Gesellschaft, UBS Wealth Foundation, Talecris Biotherapeutics GmbH, Abbott Diagnostics, European Commission 018996 (GABRIEL), Wellcome Trust WT 084703MA; UK: Medical Research Council (MRC), support of the National Institute for Health Research through the Primary Care Research Network.

Availability of data and materials

The datasets used and analysed during the current study are available from the authors upon reasonable request.

Author information

Authors and affiliations.

Institute of Epidemiology, Helmholtz Zentrum München – German Research Center for Environmental Health, Ingolstädter Landstraße 1, 85764, Neuherberg, Germany

Claudia Flexeder, Holger Schulz & Joachim Heinrich

Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain

Jan-Paul Zock & Judith Garcia-Aymerich

Universitat Pompeu Fabra (UPF), Barcelona, Spain

CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain

MRC-PHE Centre for Environment and Health, Imperial College London, London, UK

Deborah Jarvis

National Heart and Lung Institute, Imperial College London, London, UK

Unit of Epidemiology and Medical Statistics, Department of Diagnostics and Public Health, University of Verona, Verona, Italy

Giuseppe Verlato

University Hospital of Verona, Verona, Italy

Mario Olivieri

School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia

Geza Benke & Michael J. Abramson

Department of Public Health, Aarhus University, Aarhus, Denmark

Torben Sigsgaard

Centre for International Health, University of Bergen, Bergen, Norway

Cecilie Svanes

Department of Occupational Medicine, Haukeland University Hospital, Bergen, Norway

Department of Occupational and Environmental Medicine, Sahlgrenska University Hospital, Gothenburg, Sweden

Kjell Torén

Institute and Outpatient Clinic for Occupational, Social and Environmental Medicine, University Hospital Munich (LMU), Munich, Germany

Dennis Nowak & Joachim Heinrich

Comprehensive Pneumology Center Munich (CPC-M), Member of the German Center for Lung Research (DZL), Munich, Germany

Dennis Nowak & Holger Schulz

Lung Clinic, Tartu University Clinics, Tartu, Estonia

Servicio de Neumología del Complejo, Servicio de Salud de Castilla – La Mancha (SESCAM), Hospitalario Universitario de Albacete, Albacete, Spain

Jesús Martinez-Moratalla

Facultad de Medicina de Albacete, Universidad de Castilla – La Mancha, Albacete, Spain

Department of Pulmonology, Division of Allergy, Hôpital Arnaud de Villeneuve, University Hospital of Montpellier, Montpellier, France

Pascal Demoly

Inserm, Sorbonne Université, Equipe EPAR – IPLESP, Paris, France

Department of Medical Sciences, Respiratory, Allergy and Sleep Research, Uppsala University, Uppsala, Sweden

Christer Janson

Department of Sleep, Landspitali National University Hospital of Iceland, Reykjavik, Iceland

Thorarinn Gislason

Faculty of Medicine, University of Iceland, Reykjavik, Iceland

Department of Public Health and Pediatrics, University of Turin, Turin, Italy

Roberto Bono

Mathias Holm

Department of Surgical and Perioperative Sciences, Surgery, Umea University, Umea, Sweden

Karl A. Franklin

Institute for Advanced Biosciences, UGA-Inserm U1209-CNRS UMR 5309, Joint Research Center, Team of Environmental Epidemiology Applied to Reproduction and Respiratory Health, Site Santé – Allée des Alpes, 38700 La Tronche, Grenoble, France

Valérie Siroux

Inserm, UMR 1152, Pathophysiology and Epidemiology of Respiratory Diseases, Paris, France, UMR 1152, University Paris Diderot Paris, Paris, France

Bénédicte Leynaert

Pulmonology Department, Galdakao-Usansolo Hospital, Galdakao, Biscay, Spain

Sandra Dorado Arenas

Division of Respiratory Diseases, IRCCS Policlinico San Matteo Foundation, Pavia, Italy

Angelo Guido Corsico

Department of Internal Medicine and Therapeutics, University of Pavia, Pavia, Italy

Respiratory and Allergy Clinical Unit, Universitary Hospitalary Complex, Huelva, Spain

Antonio Pereira-Vega

Swiss Tropical and Public Health Institute, Basel, Switzerland

Nicole Probst-Hensch

Department of Public Health, University of Basel, Basel, Switzerland

Pulmonary Department, Hospital Galdakao, Galdakao, Biscay, Spain

Isabel Urrutia Landa

Allergy and Lung Health Unit, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia

Joachim Heinrich

You can also search for this author in PubMed   Google Scholar

Contributions

JH and CF designed the study. CF conducted the statistical analyses and wrote the initial draft. All authors provided substantial contributions to the conception or design of the work, the acquisition, analysis or interpretation of data, revised the manuscript and approved the final version.

Corresponding author

Correspondence to Claudia Flexeder .

Ethics declarations

Ethics approval and consent to participate.

Local ethics committees at each centre approved the study protocols, and all the participants gave their written informed consent.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Additional file

Additional file 1:.

Table S1. Associations between change in second-hand smoke (SHS) exposure over time and lung function at follow-up [percent predicted values according to the Global Lung function Initiative – GLI]. (DOCX 18 kb)

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/ ), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated.

Reprints and permissions

About this article

Cite this article.

Flexeder, C., Zock, JP., Jarvis, D. et al. Second-hand smoke exposure in adulthood and lower respiratory health during 20 year follow up in the European Community Respiratory Health Survey. Respir Res 20 , 33 (2019). https://doi.org/10.1186/s12931-019-0996-z

Download citation

Received : 26 October 2018

Accepted : 04 February 2019

Published : 14 February 2019

DOI : https://doi.org/10.1186/s12931-019-0996-z

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Lung function
• Respiratory symptoms

Respiratory Research

ISSN: 1465-993X

• General enquiries: [email protected]

• Open access
• Published: 18 December 2021

Exposure to second-hand smoke during early life and subsequent sleep problems in children: a population-based cross-sectional study

• Li-Zi Lin 1   na1 ,
• Shu-Li Xu 1   na1 ,
• Qi-Zhen Wu 1   na1 ,
• Yang Zhou 2 ,
• Hui-Min Ma 3 ,
• Duo-Hong Chen 4 ,
• Peng-Xin Dong 5 ,
• Shi-Min Xiong 6 ,
• Xu-Bo Shen 6 ,
• Pei-En Zhou 1 ,
• Ru-Qing Liu 1 ,
• Gongbo Chen 1 ,
• Hong-Yao Yu 1 ,
• Bo-Yi Yang 1 ,
• Xiao-Wen Zeng 1 ,
• Li-Wen Hu 1 ,
• Yuan-Zhong Zhou 6 &
• Guang-Hui Dong   ORCID: orcid.org/0000-0002-2578-3369 1  

Environmental Health volume  20 , Article number:  127 ( 2021 ) Cite this article

9150 Accesses

12 Citations

Metrics details

Previous studies have revealed that current secondhand smoke exposure showed highly suggestive evidence for increased risk of simultaneous sleep problems in children. Data on the associations between early-life exposure to SHS with subsequent sleep problems in children were scarce. We aimed to evaluate the associations of early-life SHS exposure with sleep problems in children.

In this cross-sectional study, children were recruited from elementary and middle schools in Liaoning Province, China between April 2012 and January 2013. We assessed early-life SHS exposure (pregnancy and the first 2 years of life) via questionnaires. Sleep problems and different types of sleep-related symptoms were measured based on the validated tool of the Sleep Disturbance Scale for Children (SDSC). Generalized linear mixed models were applied to estimate the associations of early-life SHS exposure with sleep problems.

We included a total of 45,562 children (22,657 [49.7%] males; mean [SD] age, 11.0 [2.6] years) and 6167 of them (13.5%) were exposed to early-life SHS during both pregnancy and the first 2 years of life. Compared with unexposed counterparts, children exposed to early-life SHS had higher total T-scores of SDSC (β = 4.32; 95%CI: 4.06, 4.58) and higher odds of increased sleep problems (OR = 2.14; 95%CI: 1.89, 2.42). When considering different sleep-related symptoms, the associations between early-life SHS exposure and symptom of sleep-wake transition disorders (i.e., bruxism) were the strongest in all analyses.

Conclusions

Early-life SHS exposure was associated with higher odds of global sleep problems and different sleep-related symptoms in children aged 6–18 years. Our findings highlight the importance to strengthen efforts to support the critical importance of maintaining a smoke-free environment especially in early life.

Peer Review reports

In 2019, China still accounted for more than one-third of the global tobacco use according to the latest Global Burden of Disease Study [ 1 ]. There are over 341 million smokers in China and the prevalence of smoking in Chinese men has reached 49.7% [ 1 ], indicating one important issue of exposure to second-hand smoke (SHS) in Chinese children [ 2 ]. Many studies, including ours, have already identified that SHS exposure during early-life caused numerous health consequences in children, including severe asthma attacks, impaired lung function and respiratory symptoms, neurodevelopmental disorders and so on [ 3 , 4 , 5 ]. Recent studies have linked SHS exposure to sleep problems in children, which has emerged as another public health issue due to the increasing prevalence worldwide [ 6 ]. It has been estimated that the pooled prevalence of sleep problems among children in mainland China was 37.6% and the prevalence of specific sleep-related symptoms varied greatly [ 7 ]. Since good sleep quality is a well-recognized predictor of physical and mental health during childhood and adolescence [ 8 ], it’s of importance to understand the associations between SHS exposure and sleep problems in children especially in China.

Most of the previous observational studies across different countries have evaluated current SHS exposure and simultaneous sleep problems in children (eTable.1–2 in the Supplement ). Specifically, these studies focused on the associations of current SHS exposure with several sleep-related symptoms including initiating and maintaining sleep, sleep-breathing, day time sleepiness and night awakenings. However, the associations might be different when considering other types of sleep-related symptoms (parasomnias, sleep hyperhidrosis, etc.). Since they were seldom discussed in previous studies, detailed investigations of different types of sleep symptoms are still needed. Moreover, there has been minimal attention to the potential impact of SHS exposure in the early life (i.e., during pregnancy and the first 2 years of life), and we only found two cohort studies (the UK [ 9 ] and the USA [ 10 ]) addressing prenatal SHS exposure and symptom of sleep-breathing in children throughout early childhood. Mechanically, sleep behaviors are regulated by the central nervous system, therefore being sensitive to alterations in brain neurochemistry [ 11 ]. Chemicals in SHS can cross placental barrier during pregnancy and disrupt neurochemistry or influence the development of infants’ brain structure by interfering with the breathing process [ 11 ]. However, to our knowledge, no studies include measures of early-life SHS exposure during both pregnancy and the first 2 years of life.

Therefore, the objective of the present study was to investigate the associations between early-life exposure to SHS and sleep problems in Chinese children. We improved on previous studies by using a more comprehensive and validated measurement of sleep problems, and parent-reported SHS exposure during early life. We hypothesized that the associations between SHS exposure and sleep problems might be different when considering different types of sleep-related symptoms and the exposure timing of early-life.

Study population and overall design

This cross-sectional study was embedded in the second wave of the Seven Northeastern Cities study between April 2012 and January 2013. The sampling strategy was developed as follows: a representative sample of Liaoning province located in Northeastern China was generated by randomly selecting half of the 14 cities in this province. One elementary school and one middle school was randomly chosen in 24 urban districts from the selected seven cities. From each grade level of the included schools, we invited students of one or two classrooms to participate in this study, who lived in the study area for at least 2 years before the start of this study. Finally, a total of 48,612 eligible children and adolescents aged 6 to 18 years have participated in this survey, and 45,562 of them have completed the assessments of sleep behaviors. This study was approved by the Ethical Review Committee for Biomedical Research, Sun Yat-sen University. We declared that we followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline for cross-sectional studies.

In each school, we organized face-to-face appointments for the teachers and the principals to introduce the aims, proposed methods and the procedures of our study. We aimed to incentivize permission for parents by using active communication techniques with the help of the teachers and the principals. We provided standard procedures for the school teachers. School teachers were required to explain the study aim, obtain the informed consent, and distribute the questionnaires in regular parent-teacher conferences. Parents could fill out the questionnaires during the conference or take it home and return it in a sealed envelope. Parents had the right to decline to consent and refuse to join the study.

Parent-reported sleep disturbance and related problems of their children

We asked parents to fill out the Sleep Disturbance Scale for Children (SDSC) to measure sleep quality in all children. The SDSC was a 26-item parent-rated scale developed by Oliviero Bruni in 1996 [ 12 ]. Each item was rated on a 5-point Likert scale: 1 = never; 2 = occasionally (once or twice a month); 3 = sometimes (once or twice a week); 4 = often (three to five times a week) and 5 = always (six or seven times a week). The SDSC provides a total score of sleep disturbance and six domain scores including: (1) disorders of initiating and maintaining sleep (DIMS), such as sleep duration, sleep latency, night awakenings, and anxiety falling asleep; (2) sleep breathing disorders (SBD), such as snoring and breathing problems; (3) disorders of arousal (DA), such as sleepwalking, sleep terrors, and nightmares; (4) sleep–wake transition disorders (SWTD), such as rhythmic movements, hypnic jerks, sleep talking, and bruxism; (5) disorders of excessive somnolence (DOES), such as difficulty waking up, morning tiredness, and inappropriate napping; and (6) sleep hyperhidrosis (SHY), such as nocturnal sweating. The total score and the subscale scores can then be converted to a T-score so that we can compare them among children in different age groups. We also used validated cut-offs to yield proxies for sleep disturbance and increased problems in the six domains within the clinical range (i.e., T-scores ≥70). In addition, both sleep duration and sleep latency could be derived based on two of the items in the SDSC. We defined inappropriate sleep duration (i.e., < 7 h) and sleep latency (i.e., > 45 min) according to the international consensus recommendations [ 13 , 14 ].

We have already conducted a validation study to revise the Chinese version of SDSC in the first wave of the Seven Northeastern Cities study, and it is reliable in screening parent-reported sleep problems in Chinese children (Cronbach’s α = 0.81). The detail of the study is described elsewhere [ 15 ].

Assessment of SHS exposure

We collected information on SHS exposure via questionnaires. We defined having exposure to early-life SHS based on an affirmative answer to the two questions: (1) Did anyone who lived with the mother during her pregnancy smoke anywhere inside the house? and (2) Did anyone who lived with the child during his or her first 2 years smoke anywhere inside the house? Therefore, exposure to early-life SHS was a category variable encoded as (1) unexposed; (2) ever exposed during pregnancy or the first 2 years of life; and (3) ever exposed during both pregnancy and the first 2 years of life.

We also collected information on the current number of cigarettes smoked inside the house per day during weekdays and weekends by all family members who lived with the child, and therefore we defined having current SHS exposure if any family member who lived with the child smoked cigarettes.

Statistical analyses

We conducted data analyses from April 32,021 to May 3, 2021. We calculated means and standard deviations for continuous variables and percentages for categorical variables. The differences across different SHS exposure groups were determined using ANOVA test for continuous variables and chi-square tests for categorical variables.

We analyzed the associations of exposure to early-life SHS with sleep problems in children by fitting generalized linear mixed models with an identity link (continuous outcomes with gaussian distribution) or logit link (binary outcomes with binomial distribution) function. We fitted crude models with school as random intercept. We fitted adjusted models for each outcome, adjusting for covariates collected from questionnaires (child age, sex, only child, preterm birth and low birth weight, parental educational levels, yearly household income, maternal age during pregnancy, maternal smoking and alcohol consumption during pregnancy). We conducted stratified analyses by sex and by child age, and whether the associations varied with sex or child age were assessed from the heterogeneity of effect across strata and the significance of interaction terms.

To verify the robustness of the results, sensitivity analyses were performed: (1) we examined the associations by excluding children with parent-reported asthma ( n  = 2257, 4.95%) since previous studies have found strong associations between SHS exposure and sleep problems in asthmatic children [ 16 ]; (2) we grouped SHS exposure into more detailed categories of exposure by considering pregnancy and the first 2 years of life separately and re-analyzed the data; (3) we used current SHS exposure derived from the questionnaires to confirm the associations between current SHS exposure and simultaneous sleep problems since most of previous studies have confirmed the above association.

Statistical analyses were conducted with the statistical software R 4.0.3 (R Core Team 2020). We presented the results as estimates (β) and odds ratios (OR) with the 95% confidence interval (CI). A P value < 0.05 for two-sided test was considered statistically significant.

Characteristics of the study population

As shown in Table  1 , 34,642 of the 45,562 children (76.0%) were not exposed to SHS during early life; 4753 of them (10.4%) were ever exposed during pregnancy or the first 2 years of life; and 6167 of them (13.5%) were ever exposed during both pregnancy and the first 2 years of life. There were significant differences among children in different SHS groups in all characteristics. The total score of sleep problems measured by SDSC was 40.0 ± 8.6 with no sex difference, and the prevalence of short sleep duration and long sleep latency were 7.0 and 1.2%, respectively. The detail for the six domain scores was shown in eTable. 3 .

Associations between early-life SHS exposure and sleep problems

In the adjusted model (Table  2 ), compared with unexposed counterparts, children ever exposed during pregnancy or the first 2 years of life had higher total T-scores of SDSC (β = 2.69; 95%CI: 2.40, 2.98), and children ever exposed during both pregnancy and the first 2 years of life had the highest total T-scores of SDSC (β = 4.32; 95%CI: 4.06, 4.58). Similarly, children with early-life SHS exposure had higher T-scores in the six domains of the SDSC with the estimates ranging from 1.24 to 4.09, and we observed the strongest associations in the analyses of SWTD T-score (β = 4.09; 95%CI: 3.82, 4.35 for children ever exposed during both pregnancy and the first 2 years of life).

When using the cut-offs to analyze the associations between early-life SHS exposure and the proxies for increased problems in the total scores of SDSC and six domains within the clinical range, we found similar results compared with those using the continuous T-scores (Table  3 ). For example, compared with unexposed counterparts, children ever exposed during both pregnancy and the first 2 years of life had higher odds of increased sleep problems (OR = 2.14; 95%CI: 1.89, 2.42). We also observed the strongest associations in the analyses of increased SWTD (OR = 1.97; 95%CI: 1.76, 2.20 for children ever exposed during both pregnancy and the first 2 years of life). Meanwhile, when extracting the information of short sleep duration and long sleep latency, we only found that early-life SHS exposure was associated with higher odds of long sleep latency (e.g., OR = 1.54; 95%CI: 1.24, 1.92 for children ever exposed during both pregnancy and the first 2 years of life).

The results were similar in stratified analyses by sex or by child age, and we only found significant modification of sex or child age on the associations of early-life SHS exposure with the T-score of DOES and/or SHY (Table  4 ). The association between SHS exposure and T-score of DOES was stronger in females ( P interaction  = 0.0298), while the association between SHS exposure and T-score of SHY was stronger in males ( P interaction  = 0.0253). When considering child age, the association between SHS and T-score of DOES was stronger in older children ( P interaction  < 0.001).

Sensitivity analyses

When grouping the early-life SHS exposure into two detailed categories (pregnancy and the first 2 years of life), the results remained similar (eTables. 4 and 5 ). We repeated the analyzes by excluding asthmatic children and the effect estimates were similar to those in the main analysis (eTable. 6 ). We also repeated the analyses using current SHS exposure, and the associations remained similar with smaller effect estimates effect (eTable. 7 ).

In the present study, we found that early-life SHS exposure was associated with global sleep problems in children aged 6–18 years. Specifically, we found that the associations were stronger in children with symptoms related to SWTD. In addition, we found significant sex and age differences in the associations of early-life SHS exposure with the DOES and SHY T-score.

Most of the previous studies focused on studying the associations between current SHS exposure and one specific sleep-related symptom without considering the global sleep problems. We only found one cross-sectional study was conducted in Taiwan, China using a comprehensive measure of Pittsburg Sleep Quality Index (PSQI), and indicated a positive but null association between current SHS exposure and poor sleep quality in 213 adolescents (10–15 years) [ 17 ]. In our study, we have validated the Chinese version of SDSC to measure global sleep problems, and we confirmed that the associations were stronger in early life compared with those in current period, highlighting vulnerable exposure windows that can occur as early as the prenatal and early postnatal periods. Mechanistically, nicotine or cotinine (a major metabolite of nicotine) in tobacco smoke can easily cross the placental barrier when exposed prenatally [ 18 ]. Animal studies of neonatal rats provide experimental evidence that prenatal nicotine exposure impaired the central chemoreception of the central nervous system that control respiratory activity [ 19 ]. Postnatally, as a stimulant, nicotine can indirectly inhibit sleep-promoting neurons in the ventrolateral preoptic area of young rats via nicotinic presynaptic enhancement of noradrenaline release [ 20 ]. Meanwhile, the critical exposure window for the harmful effects of nicotine on fetal lung development is hypothesized to occur in the second and third trimester [ 21 ], and both prenatal and postnatal nicotine can impair upper airway neuromuscular protective reflexes in animal models [ 22 ]. Consistent with these mechanisms, our findings indicated that children’s sleep quality might benefit from effective and continuous interventions for smoking cessation targeting general population, which are still needed to protect children from SHS exposure especially during their early life.

Previous observational studies focused on studying sleep-related symptoms of DIMS, SBD, DA and DOES with inconsistent results. We have extended the findings by considering two more symptoms of SWTD and SHY, which were prevalent but seldom discussed in children. One randomized controlled study of 498 Italian children aged 8–11 years suggested that interventions on parental smoking reduced the prevalence of sleep bruxism (one symptom of SWTD) [ 23 ]. One community-based two-generation survey in Northern Europe, Spain and Australia provided first evidence that adult offspring population whose parents were ex-smokers had higher odds of nocturnal sweating (one symptom of SHY) [ 24 ]. Since relevant studies were scare, more studies are needed to understand the underlying mechanisms of above associations in children. Surprisingly, we were the first to observe the strongest associations between SHS exposure and children’s SWTD symptoms in all analyses when compared with others. Sleep bruxism is a rhythmic or non-rhythmic SWTD symptom that involves masticatory muscle activity during sleep, which is regulated by the central nervous system related to tooth contact [ 25 ]. The prevalence of sleep bruxism varied and decreased with age in children (3.5–40.6%), which were either ignored or unnoticed by the parents [ 26 ]. Therefore, we proposed that SWTD symptom especially sleep bruxism should be well considered in future studies.

In this study, we have identified significant sex differences in the associations between early-life SHS exposure and the T-score of DOES and SHY, but the potential mechanism remained unclear. Our results might be supported by previous animal data that lung function alterations in mice were sex-specific with males being more susceptible to early-life SHS exposure [ 27 ]. Moreover, steroid hormones might modulate sleep behaviors, and females typically report poorer sleep quality and more sleep disturbance across different stages of life [ 28 ]. For example, a cross-sectional study of 9261 school-aged Japanese children suggested that a higher proportion of females reported daytime sleepiness (a symptom of DOES) in comparison to males [ 29 ]. Specifically, when considering child age, we also found stronger SHS-DOES association in children with older age, especially for children in middle school. The pubertal change might delayed sleep phase and disrupted sleep patterns in middle school students [ 30 ], and stress of school performance might also contribute to poorer sleep for older adolescents [ 31 ]. Therefore, symptom of DOES might be more sensitive to early-life SHS in females and in older adolescents. Although few studies have investigated symptom of SHY, previous study also indicated that androgen deprivation therapy for prostate cancer is highly associated with an increased occurrence of night sweating [ 28 ]. However, interpretation should be cautious because the sex-specific associations might be resulted from the sex-specific response to nicotine exposure or the sex differences of sleep behaviors or both. Besides, we did not find sex-specific association between early-life exposure and the binary outcomes. Therefore, more studies are needed to understand the role of sex and child age on the associations between early-life SHS exposure and sleep problems.

Strength and limitations

Several limitations should be well noted. First, due to the cross-sectional nature of the current data, we were unable to assess the causality of our findings, and longitudinal studies are needed to demonstrate the causal inference. Second, we used self-reported questionnaires to obtain information of SHS exposure, resulting in potential recall bias and exposure misclassification. This bias would likely to have been non-differential by the outcomes, and assuming the sensitivity plus the specificity for the exposure measurement was > 1, the results would have been biased towards the null. However, we observed mostly consistent dose-dependent associations across all SDSC domains, and the method we used remained to be the most cost-effective to assess SHS exposure in observational studies with large sample of children [ 32 ]. Third, measurement errors might occur for older children because the SDSC questionnaires were filled by their parents, who might be less likely to be aware of their sleeping status. However, the ‘accuracy-practicality’ trade-off exists in large population-based study, and it is more feasible for us to ensure identical and standard procedures across different primary and middle schools. Fourth, the prevalence of sleep problems and sleep-related symptoms in this study were smaller than the pooled prevalence reported by the results of the meta-analysis of Chinese studies [ 7 ]. However, our data was similar to those reported from the studies using the same measure of the SDSC [ 15 , 33 ]. It should be noted that most previous studies used one question to extract information of sleep problems, and therefore the prevalence might be overestimated. Despite these limitations, our study had notable strengths, including a large sample size of Asian children with a wide range of age groups, comprehensive information on the exposure, outcomes and covariates, all which helped to improve the sufficient power of this study and strengthen the robustness of our findings. Most importantly, this is the first study to add evidence to examine the associations between early-life SHS exposure and various sleep outcomes in a vulnerable population of children.

In conclusion, we found that children aged 6–18 years exposed to higher early-life SHS had higher odds of global sleep problems, and the associations were the strongest in children with symptom related to SWTD (i.e., sleep bruxism). Our findings highlight the importance to strengthen public health efforts and support the critical importance of maintaining a smoke-free environment especially in early life.

Availability of data and materials

The datasets generated and/or analyzed during the current study are not publicly available but are available from the corresponding author on reasonable request.

Abbreviations

Second-hand smoke

Sleep Disturbance Scale for Children

Disorders of initiating and maintaining sleep

Sleep breathing disorders

Disorders of arousal

Sleep-wake transition disorders

Disorders of excessive somnolence

Sleep hyperhidrosis

GBD 2019 Tobacco Collaborators. Spatial, temporal, and demographic patterns in prevalence of smoking tobacco use and attributable disease burden in 204 countries and territories, 1990-2019: a systematic analysis from the Global Burden of Disease Study 2019. Lancet. 2021;397(10292):2337–60.

Xi B, Liang Y, Liu Y, et al. Tobacco use and second-hand smoke exposure in young adolescents aged 12-15 years: data from 68 low-income and middle-income countries. Lancet Glob Health. 2016;4(11):e795–805.

Article   Google Scholar  

Hu LW, Yang M, Chen S, et al. Effects of in utero and postnatal exposure to secondhand smoke on lung function by gender and asthma status: the Seven Northeastern Cities (SNEC) study. Respiration. 2017;93(3):189–97.

Lin LZ, Xu SL, Wu QZ, et al. Association of prenatal, early postnatal, or current exposure to secondhand smoke with attention-deficit/hyperactivity disorder symptoms in children. JAMA Netw Open. 2021;4(5):e2110931.

Thacher JD, Schultz ES, Hallberg J, et al. Tobacco smoke exposure in early life and adolescence in relation to lung function. Eur Respir J. 2018;51(6):1702111.

Safa F, Chaiton M, Mahmud I, Ahmed S, Chu A. The association between exposure to second-hand smoke and sleep disturbances: a systematic review and meta-analysis. Sleep Health. 2020;6(5):702–14.

Chen X, Ke ZL, Chen Y, Lin X. The prevalence of sleep problems among children in mainland China: a meta-analysis and systemic-analysis. Sleep Med. 2021;83:248–55.

Paiva T. Epidemiology of sleep disorders in children and adolescents. In: Nevšímalová S, Bruni O, editors. Sleep disorders in children. Cham: Springer International Publishing; 2017. p. 53–67.

Chapter   Google Scholar  

Ramirez FD, Groner JA, Ramirez JL, et al. Prenatal and childhood tobacco smoke exposure are associated with sleep-disordered breathing throughout early childhood. Acad Pediatr. 2021;21(4):654–62.

Beebe DW, Rausch J, Byars KC, Lanphear B, Yolton K. Persistent snoring in preschool children: predictors and behavioral and developmental correlates. Pediatrics. 2012;130(3):382–9.

Liu JH, Ghastine L, Um P, Rovit E, Wu TN. Environmental exposures and sleep outcomes: a review of evidence, potential mechanisms, and implications. Environ Res. 2021;196:110406.

Article   CAS   Google Scholar  

Bruni O, Ottaviano S, Guidetti V, et al. The Sleep Disturbance Scale for Children (SDSC) construct ion and validation of an instrument to evaluate sleep disturbances in childhood and adolescence. J Sleep Res. 1996;5(4):251–61.

Hirshkowitz M, Whiton K, Albert SM, et al. National Sleep Foundation’s sleep time duration recommendations: methodology and results summary. Sleep Health. 2015;1(1):40–3.

Ohayon M, Wickwire EM, Hirshkowitz M, et al. National Sleep Foundation’s sleep quality recommendations: first report. Sleep Health. 2017;3(1):6–19.

Huang MM, Qian Z, Wang J, Vaughn MG, Lee YL, Dong GH. Validation of the sleep disturbance scale for children and prevalence of parent-reported sleep disorder symptoms in Chinese children. Sleep Med. 2014;15(8):923–8.

Yolton K, Xu YY, Khoury J, et al. Associations between secondhand smoke exposure and sleep patterns in children. Pediatrics. 2010;125(2):E261–8.

Lin YT, Lin MH, Wu KY, Lee YL. Secondhand smoke effects on rhinoconjunctivitis and sleep quality in an adolescent asthma study. Ann Allergy Asthma Immunol. 2020;125(6):717–9.

McEvoy CT, Spindel ER. Pulmonary effects of maternal smoking on the fetus and child: effects on lung development, respiratory morbidities, and life long lung health. Paediatr Respir Rev. 2017;21:27–33.

Google Scholar  

Lei F, Yan X, Zhao F, et al. Impairment of central chemoreception in neonatal rats induced by maternal cigarette smoke exposure during pregnancy. PLoS One. 2015;10(9):e0137362.

Saint-Mleux B, Eggermann E, Bisetti A, et al. Nicotinic enhancement of the noradrenergic inhibition of sleep-promoting neurons in the ventrolateral preoptic area. J Neurosci. 2004;24(1):63–7.

Kuniyoshi KM, Rehan VK. The impact of perinatal nicotine exposure on fetal lung development and subsequent respiratory morbidity. Birth Defects Res. 2019;111(17):1270–83.

Krishnan V, Dixon-Williams S, Thornton JD. Where there is smokethere is sleep apnea: exploring the relationship between smoking and sleep apnea. Chest. 2014;146(6):1673–80.

Montaldo L, Montaldo P, Caredda E, D'Arco A. Association between exposure to secondhand smoke and sleep bruxism in children: a randomised control study. Tob Control. 2012;21(4):392–5.

Lindberg E, Janson C, Johannessen A, et al. Sleep time and sleep-related symptoms across two generations - results of the community-based RHINE and RHINESSA studies. Sleep Med. 2020;69:8–13.

Soares JP, Moro J, Massignan C, et al. Prevalence of clinical signs and symptoms of the masticatory system and their associations in children with sleep bruxism: a systematic review and meta-analysis. Sleep Med Rev. 2021;57:101468.

Manfredini D, Restrepo C, Diaz-Serrano K, Winocur E, Lobbezoo F. Prevalence of sleep bruxism in children: a systematic review of the literature. J Oral Rehabil. 2013;40(8):631–42.

Noel A, Xiao R, Perveen Z, Zaman H, Le Donne V, Penn A. Sex-specific lung functional changes in adult mice exposed only to second-hand smoke in utero. Respir Res. 2017;18(1):104.

Mong JA, Cusmano DM. Sex differences in sleep: impact of biological sex and sex steroids. Philos Trans R Soc Lond Ser B Biol Sci. 2016;371(1688):20150110.

Gaina A, Sekine M, Hamanishi S, et al. Daytime sleepiness and associated factors in Japanese school children. J Pediatr. 2007;151(5):518–22 522.e511–514.

Sadeh A, Dahl RE, Shahar G, Rosenblat-Stein S. Sleep and the transition to adolescence: a longitudinal study. Sleep. 2009;32(12):1602–9.

Bauducco SV, Flink IK, Jansson-Frojmark M, Linton SJ. Sleep duration and patterns in adolescents: correlates and the role of daily stressors. Sleep Health. 2016;2(3):211–8.

Avila-Tang E, Elf JL, Cummings KM, et al. Assessing secondhand smoke exposure with reported measures. Tob Control. 2013;22(3):156–63.

Agca S, Gorker I, Turan FN, Ozturk L. Validity and reliability of the Turkish version of sleep disturbance scale for children. Sleep Med. 2021;84:56–62.

Download references

Acknowledgements

We thank all the participants and their parents.

The research was funded by the National Key Research and Development Program of China (No. 2018YFC1004300; No. 2018YFC1004302; No. 2018YFE0106900), the National Natural Science Foundation of China (No. 82073502; No. M-0420; No. 81872583; No. 81872582), Guangdong Provincial Natural Science Foundation Team Project (2018B030312005), Fundamental Research Funds for the Central Universities (19ykjc01; 20ykzd10), Natural Science Foundation of Guangdong Province (No. 2021A1515012212; No. 2021A151011754; No. 2021B15150020015; No. 2020A1515011131; No. 2019A050510017; No. 2018B05052007; No. 2017A090905042), the Science and Technology Program of Guangzhou (No. 201807010032; No. 201803010054; No. 201903010023). The funder/sponsor did not participate in the work.

Author information

Li-Zi Lin, Shu-Li Xu and Qi-Zhen Wu contributed equally as co-first authors.

Authors and Affiliations

Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, 74 Zhongshan 2nd Road, Yuexiu District, Guangzhou, 510080, China

Li-Zi Lin, Shu-Li Xu, Qi-Zhen Wu, Pei-En Zhou, Ru-Qing Liu, Gongbo Chen, Hong-Yao Yu, Bo-Yi Yang, Xiao-Wen Zeng, Li-Wen Hu & Guang-Hui Dong

State Environmental Protection Key Laboratory of Environmental Pollution Health Risk Assessment, South China Institute of Environmental Sciences, Ministry of Environmental Protection, Guangzhou, 510655, China

State Key Laboratory of Organic Geochemistry and Guangdong Key Laboratory of Environmental Protection and Resources Utilization, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou, 510640, China

Department of Air Quality Forecasting and Early Warning, Guangdong Environmental Monitoring Center, State Environmental Protection Key Laboratory of Regional Air Quality Monitoring, Guangdong Environmental Protection Key Laboratory of Atmospheric Secondary Pollution, Guangzhou, 510308, China

Duo-Hong Chen

Nursing College, Guangxi Medical University, Nanning, 530021, China

Peng-Xin Dong

School of Public Health, Zunyi Medical University, 6 Xuefu Road, Xinpuxin District, Zunyi, 563060, China

Shi-Min Xiong, Xu-Bo Shen & Yuan-Zhong Zhou

You can also search for this author in PubMed   Google Scholar

Contributions

Dr. Lin conceptualized and designed the study, carried out the initial analyses, drafted the initial manuscript, and reviewed and revised the manuscript. Ms. Xu designed the data collection instruments, collected data, and reviewed and revised the manuscript. Ms. Wu designed the data collection instruments, collected data, and reviewed and revised the manuscript. Dr. Zhou, Dr. Ma, Dr. Chen, Dr. Dong, Dr. Xiong, Dr. Shen, Mr. Zhou collected data, and critically reviewed the manuscript for important intellectual content. Dr. Liu, Dr. Chen, Dr. Yu Prof. Zeng and Dr. Yang coordinated data collection, and critically reviewed the manuscript for important intellectual content. Prof. Hu, Prof. Dong and Prof. Zhou conceptualized and designed the study, coordinated and supervised data collection, and critically reviewed the manuscript for important intellectual content. All authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work.

Corresponding author

Correspondence to Li-Wen Hu .

Ethics declarations

Ethics approval and consent to participate.

This study was approved by the Ethical Review Committee for Biomedical Research, Sun Yat-sen University.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1: etable.1..

Basic information of previous observational studies regarding the associations of exposure to SHS with sleep-related problems in general children population α . eTable.2. Summaries of the results of previous observational studies regarding the associations of exposure to SHS with sleep-related problems in general children population. eTable.3. Distribution of the SDSC score in the participants. eTable.4. Associations of SHS exposure during early life using prenatal and early postnatal periods with the scores of the SDSC in children aged 6–18 years a . eTable.5. Associations of SHS exposure during early life using prenatal and early postnatal periods with the increased problems within the clinical range using the SDSC in children aged 6–18 years a . eTable.6. Associations of SHS exposure during early life with sleep problems in asthmatic children a . eTable.7. Associations of current SHS exposure with simultaneous sleep problems in children aged 6–18 years a .

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Lin, LZ., Xu, SL., Wu, QZ. et al. Exposure to second-hand smoke during early life and subsequent sleep problems in children: a population-based cross-sectional study. Environ Health 20 , 127 (2021). https://doi.org/10.1186/s12940-021-00793-0

Download citation

Received : 16 August 2021

Accepted : 04 October 2021

Published : 18 December 2021

DOI : https://doi.org/10.1186/s12940-021-00793-0

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Secondhand smoke
• Public health

Environmental Health

ISSN: 1476-069X

• General enquiries: [email protected]

Tobacco, Nicotine, and E-Cigarettes Research Report What are the effects of secondhand and thirdhand tobacco smoke?

Secondhand smoke is a significant public health concern and driver of smoke-free policies. Also called passive or secondary smoke, secondhand smoke increases the risk for many diseases. 55 Exposure to environmental tobacco smoke among nonsmokers increases lung cancer risk by about 20 percent. 48 Secondhand smoke is estimated to cause approximately 53,800 deaths annually in the United States. 55 Exposure to tobacco smoke in the home is also a risk factor for asthma in children. 56

Smoking also leaves chemical residue on surfaces where smoking has occurred, which can persist long after the smoke itself has been cleared from the environment. This phenomenon, known as "thirdhand smoke," is increasingly recognized as a potential danger, especially to children, who not only inhale fumes released by these residues but also ingest residues that get on their hands after crawling on floors or touching walls and furniture. More research is needed on the risks posed to humans by thirdhand smoke, but a study in mice showed that thirdhand smoke exposure has several behavioral and physical health impacts, including hyperactivity and adverse effects on the liver and lungs. 57

• Search Menu
• Advance Articles
• Editor's Choice
• Supplements
• E-Collections
• Virtual Roundtables
• Author Videos
• Author Guidelines
• Submission Site
• Open Access Options
• About The European Journal of Public Health
• About the European Public Health Association
• Editorial Board
• Advertising and Corporate Services
• Journals Career Network
• Self-Archiving Policy
• Terms and Conditions
• Explore Publishing with EJPH
• Journals on Oxford Academic
• Books on Oxford Academic

Article Contents

Introduction, acknowledgments.

• < Previous

Questionnaire-based second-hand smoke assessment in adults

• Article contents
• Figures & tables
• Supplementary Data

Mónica Pérez-Ríos, Anna Schiaffino, María José López, Manel Nebot, Iñaki Galán, Marcela Fu, José María Martínez-Sánchez, Albert Moncada, Agustín Montes, Carles Ariza, Esteve Fernández, Questionnaire-based second-hand smoke assessment in adults, European Journal of Public Health , Volume 23, Issue 5, October 2013, Pages 763–767, https://doi.org/10.1093/eurpub/cks069

• Permissions Icon Permissions

Background: Numerous studies have assessed second-hand smoke (SHS) exposure but a gold standard remains to be established. This study aimed to review how SHS exposure has been assessed in adults in questionnaire-based epidemiological studies. Methods: A literature search of original papers in English, French, Italian or Spanish published from January 2000 to May 2011 was performed using PubMed. The variables recorded for each study included target population, sample size, validation of the SHS questions, study design and phrasing of every question used to assess SHS exposure. For each item, information such as the setting where exposure was assessed or the indicator used to ascertain SHS exposure was extracted. Results: We retrieved 977 articles, of which 335 matched the inclusion criteria. The main objective of 75.8% of the studies was to assess SHS exposure.The proportion of validated questions aiming to ascertain SHS exposure was 17.9%. Most studies collected data only for one (40.3%) or two settings (33.4%), most frequently the home (83.9%) and workplace (57%). The most commonly used indicator to ascertain exposure was the presence of smokers and 68.9% of the studies included an item to assess the intensity of SHS exposure. Conclusions: The variability in the indicators and items used to ascertain SHS exposure is very high, whereas the use of items derived from validated studies remains low. Identifying the diverse settings where SHS exposure may occur is essential to accurately assess exposure over time. A standard set of items to identify SHS exposure in distinct settings is needed.

Exposure to second-hand smoke (SHS) is a well-known risk factor for a range of diseases including lung cancer and coronary heart disease in adult non-smokers. SHS carries high morbidity and mortality, causing more than 600 000 deaths worldwide in 2004. 1 Consequently, accurate assessment of SHS exposure is crucial 2 to quantify the associated risks and monitor the prevalence in the population.

Although studies aiming to assess SHS exposure have accumulated over the last few decades, there is no consensus on how this exposure should be quantified. Several approaches have been employed to measure exposure, including the use of biomarkers, environmental markers and questionnaires. 3 Although in recent years there has been a move towards objective exposure assessment, questionnaires are the most commonly used tool to ascertain retrospective and current SHS exposure among the population. In addition to their simplicity and low cost, questionnaires are able to capture variability in the duration and perceived intensity of exposure. Furthermore, these instruments allow exposure to be distinguished according to settings, although they can lead to misclassification of exposure. 4

As early as 1986, the Report of the Surgeon General ‘The Health Consequences of Involuntary Smoking’ 5 stressed the need to develop validated questionnaires to assess SHS exposure in distinct micro-environments. Although some studies have assessed the validity of such questionnaires, 6 – 11 a gold standard has not yet been established, and the variability of the indicators and questions used to ascertain SHS exposure is still very high. Consequently, comparable and sensitive indicators of SHS exposure are urgently needed. A first step in designing an optimal questionnaire is to identify the distinct measurement approaches currently in use. A review of questionnaires assessing SHS exposure in children has already been published. 12 However, no review of questionnaires assessing SHS exposure in adults has been published to date. Thus, the objective of this study was to review how SHS exposure in adults has been assessed in questionnaire-based epidemiological studies.

A literature search was carried out using the search engine PubMed (US National Library of Medicine, Bethesda, MD, USA). The terms (MeSH/keywords) used in the search were (tobacco smoke pollution OR environmental tobacco smoke OR passive smok* OR secondhand smoke OR second hand smoke OR involuntary smoke) AND (case–control OR cohort OR prospective OR cross-sectional OR before–after). The search was limited to original articles in English, French, Italian or Spanish published from January 2000 to May 2011. Qualitative and non-original papers, papers assessing SHS without using questionnaires and those focusing on SHS exposure in children or adolescents (population aged <19 years) were excluded.

The review process consisted of the following stages:

design of the search strategy;

review of abstracts and selection of those meeting the inclusion criteria;

checking of excluded abstracts by another researcher and their inclusion in the next step if recommended after the review;

acquisition of the full text of the selected abstracts and extraction of the pre-defined variables in those meeting the inclusion criteria. Reasons for excluding studies were noted; and

contacting the corresponding author by e-mail when the required information was not available in the article (a second e-mail was sent after waiting 8 days for a response).

Even after contacting the authors, we excluded studies with insufficient information (no information on questions) to ascertain how SHS exposure was assessed.

The variables recorded for each study were the following: main objective of the study (SHS as the main dependent or independent variable vs. SHS as adjustment or non-principal independent variable), study design, target population, sample size, country and year of the study, type of questionnaire, validation of the SHS item, data source of the population, name of the study or project (if available) and phrasing of the items used to assess SHS exposure.

Characteristics of 335 studies assessing SHS exposure by questionnaires (PubMed search, 2000–11)

a: Not available for all studies

b: Multiple response

SHS: Second-hand smoke

The variables recorded for each item on SHS exposure were: setting where exposure was reported (home, work, leisure time, transportation, other places and unspecified), type of indication of SHS exposure (smell, presence of smokers, self-perception of being exposed, other and any type of combination of these) and intensity of exposure (duration, number of cigarettes smoked or number of smokers in the presence of the exposed person, other and any type of combination of these).

The initial search identified 977 articles. We excluded 496 publications after reviewing the abstracts, and a further 112 articles after reviewing the full text. Hence, 369 articles were reviewed. Of these, 186 (50.4%) did not include items on SHS exposure but we were able to include 79 of these papers after contacting the authors by e-mail. A further 73 papers with partial information were included. Finally, 335 papers were included in the review ( figure 1 ). The complete list of references and studies' characteristics is available in Supplementary Appendix A1 .

Selection process flow chart of the papers included in the study (PubMed search, 2000–11)

Table 1 shows the main characteristics of the 335 studies. In 75.8% of the studies, the main objective was to assess SHS exposure descriptively or to evaluate its health consequences. Half of the studies focused on the general population and most (51.3%) had a cross-sectional design. Three out of four studies were conducted in Europe or America and 46.6% were administered face-to-face. The proportion of validated items aiming to ascertain SHS exposure was low (17.9%). Most questionnaires collected data only for one setting (40.3%) or two settings (33.4%). The most frequent settings studied were homes (83.9%) and workplaces (57%).

Tables 2 and 3 show the distribution characteristics of the papers’ items. We identified 665 items ( Supplementary Appendix A2 ) that mainly assessed home (42.3%) and workplace (28.7%) exposure ( table 2 ). The most common way of assessing exposure was ascertaining the presence of smokers (e.g. sharing of physical space with smokers, such as being in front of a smoker or in the presence of smokers in the same room or office). Nevertheless, most studies (68.9%) also included items with the objective of assessing the risks associated with the exposure. These items tried to assess the intensity of exposure, depending on the setting studied, the most frequent being leisure time (89.8%). The most frequent way of assessing SHS exposure was ascertaining the presence of smokers, independently of the study design. Information on the intensity of exposure was less frequently included in cross-sectional or longitudinal studies than in other designs ( table 3 ). Questions extracted from all studies are available as Supplementary Appendices 1 and Supplementary Data , and more information is available from the authors upon request.

Characteristics of SHS exposure questions ( n  = 665) by setting of exposure (PubMed search, 2000–11)

Characteristics of SHS exposure questions ( n  = 665) by study design (PubMed search, 2000–11)

This review shows wide variability in the indicators used to obtain information on self-reported exposure to SHS in adults. All the different approaches used to assess SHS were limited in its accuracy due to failure to consider all the components involved such as: setting, perceived intensity or duration. Furthermore, it also precludes comparisons among studies, which shows the importance of standardizing the way this information is collected. There are some initiatives to standarize data collection, such as Global Youth Tobacco Survey, which apply the same questionnaire across the world are very useful.

Only a small proportion of the studies (17.9%) used validated items to assess SHS exposure. In those studies that included some kind of validation, it was mainly done by means of biomarkers such as serum nicotine or salivary cotinine. These biomarkers give us information about the individual exposure. However, this ‘aggregate’ measure of exposure at the individual level does not allow us to distinguish between the sources of exposure, that is, the setting where SHS exposure takes place. For example, a non-smoker bartender working in a smoke-free pub may be exposed at home if he/she lives with a smoker, thus showing a determined concentration of serum nicotine. In order to validate questions that ascertain individual exposure, environmental markers are less used, due to the difficulty of extrapolating these results to an individual level. However, they can be useful in some specific cases, for example, when only one setting of exposure is analysed. So, in our opinion, a gold standard approach based only in biological or environmental markers of SHS is not recommended and personal questions related to perceived exposure are necessary.

Most studies focus only on the settings where exposure takes place most frequently over time (home and workplace), ignoring other settings that may be less important in terms of duration of exposure (leisure and transport), but not necessarily in terms of intensity. On the other hand, comprehensive legislation aimed at protecting the non-smoking population from SHS has been implemented in several countries. 13 This legislation could change exposure profiles, reducing exposure at work and in some leisure settings (hospitality venues) but increasing it in other places such as outdoors settings (trains or bus stops). Identifying the diverse settings where SHS exposure may occur, is essential for appropriate assessment of exposure over time. 4

The great advantage of questionnaires is that they allow for a detailed ascertainment of exposure and this should be specially valued. But the first step, in order to identify and describe in depth the settings where exposure takes place, should be to distinguish between exposed or non-exposed population to SHS. After this, the identification of all the settings is essential in order to accurately assess exposure over time. Once the settings are identified, it is necessary to obtain information regarding the intensity and duration of the exposure in each setting. Time of exposure is the proxy mostly used in order to assess exposure intensity, especially in studies with questions validated with biomarkers. However, time itself does not cover all the dimensions of the intensity, since SHS concentration is also an important issue. When the evocated period of time included is long or not specified, recall bias could appear. It is vital to state clearly the time period in which the exposure of interests takes place and, especially if a biomarker or an environmental marker has been used to validate exposure ascertainment questions. In this case, questions related to time of exposure should be set in the same time frame as environmental or biological markers. Number of smokers or tobacco smell intensity are less frequently used as proxys of intensity. Their use may be of interest, but it would be recommended to obtain additional information, such as proximity to smokers, ventilation sources, etc.

A limitation of our study is that the search was limited to papers indexed in PubMed and published between 2000 and mid-2011 and thus articles published in journals not covered by this database were excluded. However, PubMed includes most of the journals in epidemiology, public health, respiratory diseases and environmental sciences where studies on SHS are usually published. The revision only includes manuscripts published from 2000 because this responded to our aim of analyses, how exposure to SHS is currently assessed. Moreover, almost no research on SHS had been published before the landmark articles showing an association between exposure to SHS and lung cancer in non-smoking women published in the 1980s. 14 , 15 Interestingly, one out of four identified papers cited studies conducted before the 1990s, which may point to a delay in the publication of some reports. To widen the scope of our search, we included papers written in English, Spanish, French or Italian, which could be fully understood by the researchers. Another strength of our review lies in the exhaustive search for relevant studies independently of the study design or the setting(s) of exposure.

Some indicators might be over-represented because the same questionnaire is sometimes used in multiple publications. This is the case, for example, of the Nurses Health Study, the US National Health and Nutrition Examination Survey (NHANES), the European Community Respiratory Health Survey (ERCHS) and the Californian Teachers Study. Furthermore, whereas in some studies the use of questionnaires from other studies is clearly indicated (in the methods or acknowledgement sections) other studies might have used them without mentioning the source of the questionnaire in the manuscript.

This review offers an outline of how SHS exposure is currently assessed around the world in questionnaire-based epidemiological studies and, despite its limitations, constitutes a first step towards a standardization of SHS exposure assessment. The study indicates that future analyses should take into account the setting of exposure and the population studied. Further studies, focussing on papers that included validated questionnaires, would allow us to obtain a questionnaire or a set of questionnaires to assess the prevalence of SHS exposure in the population in a valid and reliable way. 4 , 16 , 17 These questionnaires should include items that are able to ascertain whether the interviewees are exposed or not in a dichotomous way and also to measure the intensity of the exposure. This could be done using the number of smokers around and the duration of the exposure. Importantly, these questionnaires would allow for comparison of SHS levels across different studies and populations.

An analysis of the published questions is the first step before attempting to reach a consensus on the optimal way to assess SHS exposure using questionnaires. Such analyses have been rarely performed, whereas the ‘new study – new questionnaire’ approach seems to be very frequent. Any consensus should include a generic measure of exposure, the main settings where exposure to SHS must be studied, as well as a detailed description on how SHS exposure and its intensity should be assessed.

In conclusion, exposure assessment based solely on biological or environmental indicators is unable to estimate prevalence of exposure to SHS because it does not include relevant information about personal characteristics. Also, the variability in the indicators and items used to ascertain SHS exposure is very high, whereas the use of items derived from validated studies remains low. Thus, identifying the diverse settings where SHS exposure may occur is essential to accurately assess exposure over time. A standard set of items to identify SHS exposure in distinct settings is needed.

This work was partly supported by the Spanish Society of Epidemiology. EF, MF, AS, and JMMS are partly supported by the Instituto de Salud Carlos III, Government of Spain (grant RD06/0020/0089 for Thematic Network of Cooperative Research on Cancer, and grant PI081436) and the Ministry of Finance and Knowledge, Government of Catalonia (grant 2009SGR192).

Conflicts of interest : None declared.

We want to thank to the Spanish Society of Epidemiology (SEE) for its support to the Working Group on Smoking (Grupo de Trabajo de Tabaquismo GTt).

To our knowledge, this is the first review of how exposure to SHS is currently assessed by questionnaires in epidemiological studies. It could be useful for professionals working in tobacco control in order to identify the most frequent items used to ascertain SHS exposure in each setting.

Further studies, focused on papers including validated questionnaires, would allow the construction of an instrument to estimate the prevalence of SHS exposure in the population in a valid and reliable way.

Google Scholar

• passive smoking
• epidemiologic studies
• gold standard

Supplementary data

Email alerts, citing articles via.

• Contact EUPHA
• Recommend to your Library

Affiliations

• Online ISSN 1464-360X
• Print ISSN 1101-1262
• Copyright © 2024 European Public Health Association
• About Oxford Academic
• Publish journals with us
• University press partners
• What we publish
• New features  
• Open access
• Institutional account management
• Rights and permissions
• Get help with access
• Accessibility
• Advertising
• Media enquiries
• Oxford University Press
• Oxford Languages
• University of Oxford

Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide

• Copyright © 2024 Oxford University Press
• Cookie settings
• Cookie policy
• Privacy policy
• Legal notice

This Feature Is Available To Subscribers Only

Sign In or Create an Account

This PDF is available to Subscribers Only

For full access to this pdf, sign in to an existing account, or purchase an annual subscription.

Advertisement

Secondhand and thirdhand smoke: a review on chemical contents, exposure routes, and protective strategies

• Review Article
• Published: 12 June 2023
• Volume 30 , pages 78017–78029, ( 2023 )

Cite this article

• Hossein Arfaeinia 1 , 2 ,
• Maryam Ghaemi 3 ,
• Anis Jahantigh 4 ,
• Farshid Soleimani 1 &
• Hassan Hashemi 5  

3013 Accesses

6 Citations

3 Altmetric

Explore all metrics

Secondhand smoke (SHS: a mixture of sidestream and mainstream smoke) and thirdhand smoke (THS: made up of the pollutants that settle indoors after smoking in closed environments) are a significant public health concern. SHS and THS contain various chemicals which can be released into the air or settle on surfaces. At present, the hazards of SHS and THS are not as well documented. In this review, we describe the chemical contents of THS and SHS, exposure routes, vulnerable groups, health effects, and protective strategies. The literature search was conducted for published papers on September 2022 in Scopus, Web of Science, PubMed, and Google Scholar databases. This review could provide a comprehensive understanding of the chemical contents of THS and SHS, exposure routes, vulnerable groups, health effects, protective strategies, and future researches on environmental tobacco smoke.

Similar content being viewed by others

Subnational exposure to secondhand smoke in Iran from 1990 to 2013: a systematic review

Spatiotemporal variability of exposure to secondhand smoke in Iran during 2009–2020: a systematic review

The challenges of limiting exposure to ths in vulnerable populations.

Avoid common mistakes on your manuscript.

Introduction

Nearly 20% of the world smokes tobacco (Gowing et al. 2015 ; Roser 2019 ; Ritchie and Roser 2013 ), and global statistics predicted that the number of smokers will rise up to 1.6 billion by 2025 (James et al. 2022 ). Tobacco was associated with 8.71 million deaths globally in 2019 (WHO 2021 ) that contained 13.6% of all human deaths and 7.89% of all disability-adjusted life-years (DALYs) (GBD 2021 ; Reitsma et al. 2021 ). Tobacco smoke contains about 7000 chemicals (at least 69 carcinogenic chemicals) (Rodgman and Perfetti 2013 ; Services 2014 ), and smoking is a cause of about 30 percent of all cancer deaths (National Cancer Policy Forum 2013 ). During each smoking session, the accumulated chemicals are divided among different components, including mainstream smoke (MS: the smoke which the smoker breathes out during the puffs), sidestream smoke (SS: the smoke that is released from the end of a burning cigarette), secondhand smoke (SHS: made up of SS (~85%) and exhaled MS (~15%)) (Hang et al. 2020 ), and thirdhand smoke (THS: made up of the pollutants that settle indoors after smoking in closed environments) (Counts et al. 2005 ; Ding et al. 2006 ; Ajab et al. 2014 ; Jacob III et al. 2017 ; Hang et al. 2020 ; Vu et al. 2021 ; Soleimani et al. 2022a ).

The health effects of firsthand smoke (MS) are well known, and research precedence has recently changed to identify the importance of SHS and THS (Bird and Staines-Orozco 2016 ; Lee et al. 2019 ). SHS is an important public health concern (Alzahrani 2020 ; Farrell et al. 2022 ; Schiavone et al. 2022 ). Exposure to tobacco smoke among passive/nonsmokers increases lung cancer risk (Weiss et al. 1983 ; Schwartz and Cote 2016 ; Mantzoros et al. 2017 ; Esdras et al. 2021 ; Mariano et al. 2022 ), and approximately one million people are estimated to die worldwide from passive smoking annually (Drope and Schluger 2018 ). SHS is estimated to cause more than 53,000 deaths in the United States annually (Jacobs et al. 2013 ). Also, exposure to SHS and THS in the home and/or closed environments is a risk factor for asthma in children (Al-Sayed and Ibrahim 2014 ; Xanthopoulou and Kousoulis 2014 ; den Dekker et al. 2015 ; Rajani et al. 2017 ; Butz et al. 2019 ).

Furthermore, THS has recently been recognized as a new threat. SHS refers to passive smoking in which nonsmokers are exposed to MS and SS due to smoking by another person (Sikorska-Jaroszynska et al. 2012 ; Mourino et al. 2022 ), and THS is mentioned to the exposure to smoke-related pollutants by entering an environment in a place in which someone has smoked and/or in contact with a smoker (Park and Sim 2022 ). The contaminants of tobacco smoke settle on the smoker’s body, as well as on different surfaces, including walls, carpets, curtains, and furniture, and can re-contaminate smokers and nonsmokers (Winickoff et al. 2009 ; Ferrante et al. 2013 ; Jacob III et al. 2017 ; Aquilina et al. 2022 ). THS exposure may happen long after SHS appears (Becquemin et al. 2010 ; Protano and Vitali 2011 ; Hang et al. 2018 ). Toxic substances remain on different surfaces even weeks and/or months after smoking (Matt et al. 2011a ; Hang et al. 2018 ).

These toxic substances may be released into indoor air, react with atmospheric species (i.e., ozone and nitrous acid (HNO 2 )), and subsequently produce toxins that were not at first present in firsthand smoke (Sleiman et al. 2014 ; Collins et al. 2018 ). SHS and THS are considered as important sources of indoor exposure to tobacco-related chemicals (Sleiman et al. 2014 ; Tsai et al. 2018 ). However, the chemical contents and their concentration levels in environmental tobacco smoke (THS and SHS) are still poorly understood. To increase understanding of indoor environmental smoke as a source of health risks to humans, we conducted a narrative scientific review to investigate the chemical contents of THS and SHS, exposure routes, vulnerable groups, health effects, and protective strategies.

Materials and methods

The literature search was conducted for this narrative review in September 2022. Articles were recorded through a literature search in Scopus, Web of Science, PubMed, and Google Scholar databases. Articles (English language only) were only included if they investigated the chemical contents of SHS and THS and or discussed exposure routes, vulnerable groups, health effects, and control strategies of these environmental tobacco smokes. Search terms included (“Thirdhand smoke” OR “Third-hand smoke” OR “Secondhand smoke” OR “Second-hand smoke” OR “Side stream smoke” OR “Sidestream smoke” OR “Involuntary smoking” OR “Passive smoking”) AND (Chemical* OR “Chemical composition” OR “Chemical compound*” OR “Chemical constituents” OR “Environmental tobacco smoke”) AND (“Exposure routes”) AND (“Vulnerable groups”) AND (“Health effects”) AND (“Protective strategies”). These terms were used separately and combined to discover search results.

The initial search recorded 518 articles. Other language papers, duplicate papers, insufficient detail and not peer-reviewed papers, and papers that did not sufficiently discover our specific objectives were removed. After the application of these criteria, one hundred seventy-four scientific articles were nominated for full-text review. Also, we reviewed all the reference lists of the residual articles to find more related articles.

All remaining articles were revised to assure the claim of the last inclusion criteria and were additionally sieved by selecting only those meeting the following criteria: (1) research articles that measured at least one chemical constituent of SHS and/or THS and (2) articles that discussed at least one of the following issues: exposure routes, vulnerable groups, health effects, and protective strategies against SHS and/or THS chemical compositions. Lastly, after utilization of all criteria, 98 papers were involved in the current narrative review.

Results and discussion

The mean and/or range concentration levels of the chemical content of SHS are provided in Table 1 . As shown, polycyclic aromatic hydrocarbons (PAHs) and nicotine were the most common chemicals measured in reviewed articles. Although heavy metals are known as one of the critical constituents of chemical components of MS and SS and cigarette butts (Soleimani et al. 2021 ; Soleimani et al. 2022 ), these chemicals have been less noticed in SHS. Despite limited studies on the chemical content of SHS, these reported that SHS might contain different chemicals that pose significant health and environmental worries. While numerous chemicals of concern have been recognized in SHS, there is still work to be done. Additional studies are essential to survey the chemical contents of SHS and parameters involved in the concentration levels of these chemicals. More researches are required to assess more strictly the range of chemicals found in SHS, mainly those largely ignored.

The mean and/or range concentration levels of the chemical content of THS are provided in Table 2 . As shown, nicotine and tobacco-specific nitrosamines (TSNAs: NNN and NNK) were the most frequent chemicals measured in included studies. Nicotine may persevere in indoor environments similar to some pesticides that persist outdoors. DDT, nitrosamines, nicotine, PAHs, phthalates, bisphenol A, and flame retardants in cigarette smoke are semi-volatile organic compounds. Once released indoors, they stuck to surfaces and desorb slowly back into the air or react to form other chemical mixtures (Weschler and Nazaroff 2008 ). Exposure of passive smokers to SHS causes a significant increase in urinary levels of metabolites of the tobacco-specific biomarkers (i.e., NNK). The presence of these metabolites links exposure to SHS with an increased risk for lung cancer (Services 2006 ).

Nicotine and other semi-volatile organic compounds are also found in human biological fluids (Control and Prevention 2009 ). Although THS is well known as a main source of indoor exposure to tobacco-related chemicals (Sleiman et al. 2014 ), the studies examining its chemical contents are limited. Therefore, the existing information is still unable to demonstrate a complete and dependable sight of the chemicals related to THS. Further studies are required to assess the possible health effects of THS. Although diverse chemicals have been known in THS, many toxic constituents have not been studied in recent investigations. Future experimental studies are necessary to assess the chemical contents focusing on those that are ignored in the previous researches. Also, there is a lack of scientific information on the reaction of the chemical content of THS with oxidants and the development of byproducts.

Hitherto, more than 50 toxic constituents have been recognized in THS (Sleiman et al. 2010a ; Jacob III et al. 2017 ; Matt et al. 2021 ). In a study, the concentration levels of nicotine in the air of nonsmokers’ homes were 10 times lower than in those of smokers’ homes (Figueiró et al. 2016 ). In other studies, the acrolein concentrations in the air of smoker’s home were three times higher than outdoor air (Nazaroff and Singer 2004 ; Sleiman et al. 2014 ). Also, the level of nicotine in air samples ranged from 0.021 μg/m 3 (nonsmoker local cars) to 0.047 μg/m 3 (smoker local cars) (Matt et al. 2013 ). Also, the nicotine values in surface samples ranged from 0.7 μg/m 2 (nonsmoker cars) to 1.9 μg/m 2 in smoker cars (Matt et al. 2013 ). THS ingredients may settle on surfaces and air dust particles (Jacob III et al. 2017 ). Surface-attached THS constituents can be found in indoor air for days to months, and adequate time is provided for chemical changes in household air and surfaces (Jacob III et al. 2017 ). For instance, nicotine may react with nitrous acid and form carcinogenic TSNAs, subsequently (Sleiman et al. 2010b ; Ramírez et al. 2014 ). The results of Tang et al.’s (Tang et al. 2022 ) study showed potential long-term health risks for passive/nonsmokers in homes contaminated with THS (Tang et al. 2022 ). Inhalation, direct dermal contact, gas-to-skin deposition, and epidermal nitrosation of nicotine are the main exposure routes of TSNAs (Tang et al. 2022 ). In addition, nicotine in indoor environment can be affected by the presence of ozone (Petrick et al. 2010 ). The reaction of ozone and absorbed nicotine causes the alarming THS and oxidation products including cotinine, myosmine, N-methyl formamide, and nicotine-1-oxide that these secondary byproducts may be back to the gas phase and affect indoor air quality over longer periods after smoking (Petrick et al. 2011a ).

Exposure routes to SHS and TSH

Exposure to tobacco smoke, well known as passive smoking, can accrue through direct exposure to tobacco smoke (SHS and THS) and is estimated to be the cause of nearly 1% of worldwide mortality (Torres et al. 2018 ). SHS may persist for hours indoors and become more toxic over time (Schick and Glantz 2007 ). Different parameters such as airflow patterns, ventilation, the distance between smokers and passive smokers, and smoking occurrence can affect human exposure (Services 2006 ). SHS exposure occurs when nonsmokers breathe the smoke exhaled by people who smoke and/or burn tobacco products. Homes are the main areas where residents are exposed to secondhand smoke (Walton et al. 2020 ). People are exposed to SHS in different places where they spend varying extents of time (Services 2006 ).

The burning of tobacco products as a source of SHS releases the resulting concentrations of secondhand smoke, contacting hazardous pollutants into the indoor air where people live. This concentration depends on diverse features, including the intensity of smoking, ventilation, design, and operation of a building and different methods that remove smoke from the air. Total exposure can be estimated by measuring SHS concentrations in prominent places and assessing the time spent in these environments (Council 1986 ; Services 2006 ). In Walton et al. ( 2020 ), nearly 25% of US students reported breathing SHS in their homes, and 23% stated breathing SHS in cars (Walton et al. 2020 ).

The existence and levels of THS can be measured through environmental matrix sampling (i.e., air, dust, and on different surfaces) (Jacob III et al. 2017 ). Nondietary ingestion and dermal absorption are the significant exposure routes to THS chemicals, which make children particularly vulnerable owing to their hand-to-mouth behavior and premature metabolism, among other reasons (Jacob III et al. 2017 ). Matt et al. reported high levels of nicotine on household surfaces and on the hands of smoking mothers (Matt et al. 2011a ). They also reported THS levels in nonsmokers’ homes that had been lately where smokers dwelled (Matt et al. 2011b ) and in used cars (Matt et al. 2008 ; Fortmann et al. 2010 ). Nicotine and other THS components have been observed in indoor environments in which tobacco has been smoked frequently and in nonsmoking indoor environments near commuted places by smokers (Jacob III et al. 2017 ). THS can be found even in areas where smoking bans are severely applied (i.e., neonatal intensive care units in hospitals). In a study, Northrup et al. reported that furniture had measurable surface nicotine, and both cotinine and 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone were identified in children urine samples (Northrup et al. 2016a ). Therefore, it can be concluded that THS is ubiquitous, even in highly controlled and smoke-free environments, and humans are continuously exposed to its chemical content. In general, there are three usual exposure pathways for SHS and THS, including (1) inhalation of chemicals in the house dust, (2) dermal exposure by touching the polluted surfaces and direct absorption of toxins in the indoor air, and (3) oral exposure via hand-to-mouth transfer of surface remains after touching polluted items, mouthing of object dishes (Li et al. 2021 ; Yeh et al. 2022 ).

Health effects

SHS and THS have been shown to increase the risks of different health effects in nonsmokers exposed to normal environmental levels. Previous scientific reports have confirmed that exposure to SHS is a public health hazard (Singer et al. 2003 ; Petrick et al. 2011b ; Jung et al. 2012 ; Northrup et al. 2016b ). Repeated exposure to SHS may lead to several diseases, including asthma and pneumonia, sudden infant death syndrome, lung cancer, increased left ventricular mass, and heart attacks (Services 2006 ; Skipina et al. 2021 ). Also, it has been proved that chemicals in SHS can pass the placental barrier during pregnancy and affect the growth of infants’ brain structure by interfering with the breathing system (Liu et al. 2021 ). Lin et al. ( 2021 ) study also showed that early-life SHS exposure was associated with different sleep-related symptoms in 6–18-year-old children (Lin et al. 2021 ). Exposure to SHS during early life and following sleep problems in children have direct adverse effects on the heart and blood vessels and increase the risk for coronary heart disease and stroke (Services 2006 ; Services 2014 ).

Inhaling SHS causes lung cancer in nonsmokers (Wald et al. 1986 ; Melloni 2014 ). Some research also suggests that SHS may increase the risk of breast cancer (Lee and Hamling 2016 ; Kim et al. 2018 ), nasal sinus cavity cancer (Benninger 1999 ), and nasopharyngeal cancer (Rodenstein and Stănescu 1985 ) in adults; leukemia, lymphoma, and brain tumors in children (Boffetta et al. 2000 ; Hofhuis et al. 2003 ); and obesity in boys in early adolescence (Miyamura et al. 2023 ). Previous studies have confirmed that nonsmokers are at increased risk of death from ischemic heart disease (Helsing et al. 1988 ; Kawachi et al. 1997 ), lung cancer (Garfinkel 1981 ; Cardenas et al. 1997 ), and all causes (Svendsen et al. 1987 ; Sandler et al. 1989 ). In a study, Du et al. ( 2020 ) reported that nearly 16 percent of lung cancer cases among never smokers in China were probably attributable to passive smoking (Du et al. 2020 ). In another study, Kim et al. ( 2018 ) reported that SHS may increase the risk of lung and breast cancer for nonsmokers (Kim et al. 2018 ). Results of another meta-analysis revealed that a nonsmoking spouse has a higher risk for lung cancer when their spouse is a smoker (Taylor et al. 2007 ). In addition, passive smoking is harmful to mental health and a risk factor for dementia, as well as have a negative effect on depressive symptoms (Ling and Heffernan 2016 ; Lange et al. 2020 ; Park et al. 2021 ). Moreover, an animal study has shown that mice exposed to incredible levels of SHS have an increased level of carcinogen-DNA adducts, formed by covalent binding for carcinogen molecules to chemical moieties in DNA (Hackshaw et al. 1997 ). A study by Pirie et al. ( 2008 ) showed that the risk of breast cancer does not increase for passive smoker women who are regularly exposed to SHS at their home (Pirie et al. 2008 ). However, Gram et al. ( 2021 ) study results showed that 1 in 14 breast cancer cases is preventable in the lack of SHS exposure from parents within childhood in never-smoking women (Hori et al. 2016 ; Gram et al. 2021 ). Nitrosamines, as identified compounds in THS, can lead to cancer (Ramírez et al. 2014 ). In general, different effects such as damage in the liver and lungs, poor wound healing, oxidative stress and inflammation, insulin resistance, or hyperactivity were the primary health effects of THS exposure in mice (Jacob III et al. 2017 ). Also, SHS and THS exposure might have a significant role in the incidence, transmission, and development of COVID-19 in susceptible groups (Mahabee-Gittens et al. 2020 ). In a study, both active smoking and passive smoking were reported as risk factors for poor sleep quality (Zhou et al. 2018 ).

In addition, this finding that SHS causes different diseases such as lower respiratory illnesses in infancy and early childhood (Fergusson et al. 1980 ; Al-Delaimy et al. 2002 ; Services 2006 ), middle ear disease and adenotonsillectomy (Jones et al. 2012 ), cervical, breast, and lung cancer in nonsmokers (Winkelstein Jr 1990 ; Zhong et al. 2000 ; Terry et al. 2011 ) is vital not only from a public health perspective but also in the social and economic issues and active efforts by medical professionals and politicians to reduce exposure of the nonsmoking community to SHS. More actions are required to control the dangerous effects of passive smoking, particularly in women and infants, and public health actions should prioritize reducing levels of passive smoking at home. It seems that the most significant achievements can be attained with this action about the prevention of different diseases associated with passive smoking.

Vulnerable populations

Based on World Health Organization (WHO) statistics, approximately 40% of children are exposed to SHS (Öberg et al. 2011 ). In most countries, it has been estimated that 15–70 percent of the population is exposed to SHS (Wong et al. 2012 ). In a study, results showed that 37.8% of children were exposed to SHS at home in Chongqing (Huang et al. 2023 ). In another study, the prevalence of SHS exposure at home was 46.8% among the middle school students in Northern Thailand (Phetphum and Noosorn 2020 ). SHS and THS may be especially dangerous to vulnerable peoples. For instance, individuals with asthma might be more vulnerable to SHS and THS exposure (Barnoya and Navas-Acien 2012 ). Children, particularly infants, are probable to be among the most vulnerable populations regarding both exposure and health effects of THS (Matt et al. 2004 ; Sleiman et al. 2010b ; Matt et al. 2011a ). Infants and children may be highly exposed to THS via different dermally, orally, and inhalation routes in house dust and surface (WHO 2017 ). These groups are at high risk for exposure to THS than adults due to their behaviors, such as crawling and putting nonfood objects in their mouths, as well as tending to spend more time on floors (Jacob III et al. 2017 ). Tobacco smoke is also a known hazard for pregnant women, and SHS and THS pose a health risk for them (Sun et al. 2021 ). Exposure to THS may also be a risk factor for postpartum depression among pregnant women (Wang et al. 2018 ). There is also evidence that THS may reduce a mother’s breast milk (Northrup et al. 2021 ). Due to these adverse effects, it is vital that pregnant women must not be exposed to firsthand SHS or THS smoke. THS can also have a severe impact on lung development in infants (Rehan et al. 2011 ). Overall, children and pregnant women are predominantly susceptible to THS exposure because they could touch and/or breathe in the toxic substances from contaminated surfaces (Rehan et al. 2011 ).

• Protective strategies

Global efforts and regulations have been strengthened to prevent passive smoking (Park 2023 ). Since there is no risk-free level of exposure to SHS (Services 2006 ), the most essential way/method to protect against SHS and THS is to quit smoking and to support others to stop. Applying smoking bans in indoor environments is another practical approach to protect public health (Services 2006 ). Environmental conditions, such as opening windows, sitting in a separate area, ventilation, air conditioning, or a fan, cannot be safe against SHS and THS exposure. The only way to completely protect from the dangers of environmental tobacco smoke is through 100% smoke-free environments. Government and local authorities can keep children and/or other passive smokers from SHS and THS in the places they live, visit, and work by using confirmed methods to remove smoking in indoor environments of public places. Smoking in other places, using fans, or smoking in front of an open window does not prevent THS exposure. Disinfecting homes and/or cars that are used by a smoker may be costly because the smoke residue can stain surfaces. The smell of smoke also can remain in surfaces and building materials. Repairing services for homes and other buildings affected by tobacco smoke also may be effective for avoiding exposure and remediation (Jacob III et al. 2017 ). Also, removing severely affected materials (i.e., carpets and furniture) and/or using cleaners for washing also can be effective. Ammonia-based cleaners and ozone generators are suggested to remove tobacco odors (Jacob III et al. 2017 ). In a study, the nicotine and PAHs that adsorbed onto surfaces were effectively removed by the ozonation and reduced their concentration levels to initial levels (Tang et al. 2021 ). The results of the same study have shown that ozonation of THS-contaminated environment led to formation of secondary organic aerosol, as well as increased the concentration levels of VOCs, carbonyls, and particles (Tang et al. 2021 ). There is a lack of scientific reports on the efficiency and byproducts of these treatments that may be created. Several asthmagens have been found among the byproducts from the ozonation of nicotine (Sleiman et al. 2010a ). Additional studies of the risks associated with the use of ozone in the remediation of THS, potential ozonation byproducts formed in these process, and possible advantages of ozonation are recommended.

Exposure to SHS and THS is a general health problem globally. Although public awareness and information of the dangers of THS exposure increase annually, it is still generally ignored in health and environmental strategies. To overcome this, research should pay attention to filling the gaps in our present understanding of SHS and THS chemicals content, long-term exposure effects, byproducts, toxicology, and particularly the mechanism of health effects of these exposures on susceptible populations. A comprehensive conception of human exposure to SHS and THS contaminants in different environments can supply intuitions for health policymakers to assess the throughout health effects of smoking at the population level, as well as for plans and apply appropriate policies for the protection of the health of vulnerable populations, and efficient methods for the clean-up these pollutants to decrease exposure to SHS and THS.

Data availability

Not applicable.

Adesina OA, Nwogu AS, Sonibare JA (2021) Indoor levels of polycyclic aromatic hydrocarbons (PAHs) from environment tobacco smoke of public bars. Ecotoxicol Environ Saf 208:111604

Article   CAS   Google Scholar  

Adlkofer F, Scherer G, Conze C, Angerer J, Lehnert G (1990) Significance of exposure to benzene and other toxic compounds through environmental tobacco smoke. J Cancer Res Clin Oncol 116(6):591–598

Ajab H, Yaqub A, Malik SA, Junaid M, Yasmeen S, Abdullah MA (2014) Characterization of toxic metals in tobacco, tobacco smoke, and cigarette ash from selected imported and local brands in Pakistan. Sci World J:2014

Al-Delaimy W, Crane J, Woodward A (2002) Passive smoking, as measured by hair nicotine, and severity of acute lower respiratory illnesses among children. Tobacco Ind Dis 1(1):1–8

Google Scholar  

Al-Sayed EM, Ibrahim KS (2014) Second-hand tobacco smoke and children. Toxicol Ind Health 30(7):635–644

Alzahrani SH (2020) Levels and factors of knowledge about the related health risks of exposure to secondhand smoke among medical students: A cross-sectional study in Jeddah, Saudi Arabia. Tob Induc Dis 18:88

Aquilina NJ, Havel CM, Benowitz NL, Jacob P 3rd (2022) Tobacco-specific and combustion pollutants in settled house dust in Malta. J Environ Expo Assess 1:7

Barnoya J, Navas-Acien A (2012) Protecting the world from secondhand tobacco smoke exposure: where do we stand and where do we go from here? Nicotine Tobacco Res 15(4):789–804

Article   Google Scholar  

Becquemin M, Bertholon J, Bentayeb M, Attoui M, Ledur D, Roy F, Roy M, Annesi-Maesano I, Dautzenberg B (2010) Third-hand smoking: indoor measurements of concentration and sizes of cigarette smoke particles after resuspension. Tobacco Control 19(4):347–348

Benninger MS (1999) The impact of cigarette smoking and environmental tobacco smoke on nasal and sinus disease: a review of the literature. Am J Rhinol 13(6):435–438

Bird Y, Staines-Orozco H (2016) Pulmonary effects of active smoking and secondhand smoke exposure among adolescent students in Juárez, Mexico. Int J Chron Obstruct Pulmon Dis 11:1459

Boffetta P, Trédaniel J, Greco A (2000) Risk of childhood cancer and adult lung cancer after childhood exposure to passive smoke: a meta-analysis. Environ Health Perspect 108(1):73–82

Bolte G, Heitmann D, Kiranoglu M, Schierl R, Diemer J, Koerner W, Fromme H (2008) Exposure to environmental tobacco smoke in German restaurants, pubs and discotheques. J Expo Sci Environ Epidemiol 18(3):262–271

Braun M, Klingelhöfer D, Müller R, Groneberg DA (2021) The impact of second-hand smoke on nitrogen oxides concentrations in a small interior. Sci Rep 11(1):1–8

Brunnemann KD, Cox JE, Hoffmann D (1992) Analysis of tobacco-specific N-nitrosamines in indoor air. Carcinogenesis 13(12):2415–2418

Butz AM, Bollinger ME, Ogborn J, Morphew T, Mudd SS, Kub JE, Bellin MH, Lewis-Land C, DePriest K, Tsoukleris M (2019) Children with poorly controlled asthma: randomized controlled trial of a home-based environmental control intervention. Pediatr Pulmonol 54(3):245–256

Cardenas VM, Thun MJ, Austin H, Lally CA, Clark WS, Greenberg RS, Heath CW (1997) Environmental tobacco smoke and lung cancer mortality in the American Cancer Society’s Cancer Prevention Study II. Cancer Causes Control 8(1):57–64

Chuang JC, Mack GA, Kuhlman MR, Wilson NK (1991) Polycyclic aromatic hydrocarbons and their derivatives in indoor and outdoor air in an eight-home study. Atmos Environ Part B. Urban Atmos 25(3):369–380

Collins DB, Wang C, Abbatt JP (2018) Selective uptake of third-hand tobacco smoke components to inorganic and organic aerosol particles. Environ Sci Technol 52(22):13195–13201

Control CFD and Prevention (2009) Fourth national report on human exposure to environmental chemicals, Atlanta, GA

National Research Council (1986) Committee on passive smoking. Environmental tobacco smoke: measuring exposures and assessing health effects. National Academies Press (US), Washington (DC)

Counts M, Morton M, Laffoon S, Cox R, Lipowicz P (2005) Smoke composition and predicting relationships for international commercial cigarettes smoked with three machine-smoking conditions. Regul Toxicol Pharmacol 41(3):185–227

den Dekker HT, Sonnenschein-van der Voort AM, de Jongste JC, Reiss IK, Hofman A, Jaddoe VW, Duijts L (2015) Tobacco smoke exposure, airway resistance, and asthma in school-age children: the generation R study. Chest 148(3):607–617

Destaillats H, Singer BC, Lee SK, Gundel LA (2006) Effect of ozone on nicotine desorption from model surfaces: evidence for heterogeneous chemistry. Environ Sci Technol 40(6):1799–1805

Ding YS, Yan XZJ, Jain RB, Lopp E, Tavakoli A, Polzin GM, Stanfill SB, Ashley DL, Watson CH (2006) Determination of 14 polycyclic aromatic hydrocarbons in mainstream smoke from US brand and non-US brand cigarettes. Environ Sci Technol 40(4):1133–1138

Drope J, Schluger NW (2018) The tobacco atlas. American Cancer Society

Du Y, Cui X, Sidorenkov G, Groen HJ, Vliegenthart R, Heuvelmans MA, Liu S, Oudkerk M, de Bock GH (2020) Lung cancer occurrence attributable to passive smoking among never smokers in China: a systematic review and meta-analysis. Trans Lung Cancer Res 9(2):204

Emmons KM, Hammond SK, Fava JL, Velicer WF, Evans JL, Monroe AD (2001) A randomized trial to reduce passive smoke exposure in low-income households with young children. Pediatrics 108(1):18–24

Esdras Z, Ayoub K, Sanaa S, Hajar K, Hatim T, Tarek C, Mouna B, Zineb B, Nadia B, Hassan J (2021) Squamous cell lung cancer in a non-smoking woman: a case report with review of the literature. World J Med Case Rep 2(3):51

Farrell KR, Weitzman M, Karey E, Lai TK, Gordon T, Xu S (2022) Passive exposure to e-cigarette emissions is associated with worsened mental health. BMC Public Health 22(1):1–12

Fergusson D, Horwood L, Shannon F (1980) Parental smoking and respiratory illness in infancy. Arch Dis Childhood 55(5):358–361

Ferrante G, Simoni M, Cibella F, Ferrara F, Liotta G, Malizia V, Corsello G, Viegi G, La Grutta S (2013) Third-hand smoke exposure and health hazards in children. Monaldi Arch Chest Dis 79(1)

Figueiró LR, Ziulkoski AL, Dantas DCM (2016) Thirdhand smoke: cuando el peligro va más allá de lo que se ve o se siente. Cadernos de Saúde Pública 32(11)

Fortmann AL, Romero RA, Sklar M, Pham V, Zakarian J, Quintana PJ, Chatfield D, Matt GE (2010) Residual tobacco smoke in used cars: futile efforts and persistent pollutants. Nicotine Tobacco Res 12(10):1029–1036

Garfinkel L (1981) Time trends in lung cancer mortality among nonsmokers and a note on passive smoking. JNCI: J Natl Cancer Inst 66(6):1061–1066

GBD TC (2021) Spatial, temporal, and demographic patterns in prevalence of smoking tobacco use and attributable disease burden in 204 countries and territories, 1990–2019: a systematic analysis from the Global Burden of Disease Study 2019. Lancet (London, England) 397(10292):2337

Goniewicz M, Czogała J, Kośmider L, Koszowski B, Zielińska-Danch W, Sobczak A (2009) Exposure to carbon monoxide from second-hand tobacco smoke in Polish pubs. Cent Eur J Public Health 17(4):220–222

Gowing LR, Ali RL, Allsop S, Marsden J, Turf EE, West R, Witton J (2015) Global statistics on addictive behaviours: 2014 status report. Addiction 110(6):904–919

Gram IT, Wiik AB, Lund E, Licaj I, Braaten T (2021) Never-smokers and the fraction of breast cancer attributable to second-hand smoke from parents during childhood: the Norwegian Women and Cancer Study 1991–2018. Int J Epidemiol 50(6):1927–1935

Grimmer G, Brune H, Dettbarn G, Naujack K-W, Mohr U, Wenzel-Hartung R (1988) Contribution of polycyclic aromatic compounds to the carcinogenicity of sidestream smoke of cigarettes evaluated by implantation into the lungs of rats. Cancer Lett 43(3):173–177

Hackshaw AK, Law MR, Wald NJ (1997) The accumulated evidence on lung cancer and environmental tobacco smoke. Bmj 315(7114):980–988

Hammond SK (1999) Exposure of US workers to environmental tobacco smoke. Environ Health Perspect 107(suppl 2):329–340

Hang B, Wang P, Zhao Y, Chang H, Mao J-H, Snijders AM (2020) Thirdhand smoke: genotoxicity and carcinogenic potential. Chron Dis Trans Med 6(1):27–34

Hang B, Wang Y, Huang Y, Wang P, Langley SA, Bi L, Sarker AH, Schick SF, Havel C, Jacob P 3rd, Benowitz N, Destaillats H, Tang X, Xia Y, Jen KY, Gundel LA, Mao JH, Snijders AM (2018) Short-term early exposure to thirdhand cigarette smoke increases lung cancer incidence in mice. Clin Sci (Lond) 132(4):475–488

Helsing K, Sandler D, Comstock G, Chee E (1988) Heart disease mortality in nonsmokers living with smokers. Am J Epidemiol 127(5):915–922

Henderson E, Rodriguez Guerrero LA, Continente X, Fernández E, Tigova O, Cortés-Francisco N, Semple S, Dobson R, Tzortzi A, Vyzikidou VK, Gorini G, Geshanova G, Mons U, Przewozniak K, Precioso J, Brad R, López MJ, Fernández E, Castellano Y et al (2023) Measurement of airborne nicotine, as a marker of secondhand smoke exposure, in homes with residents who smoke in 9 European countries. Environ Res 219:115118

Hofhuis W, de Jongste JC, Merkus P (2003) Adverse health effects of prenatal and postnatal tobacco smoke exposure on children. Arch Dis Childhood 88(12):1086–1090

Hoh E, Hunt RN, Quintana PJE, Zakarian JM, Chatfield DA, Wittry BC, Rodriguez E, Matt GE (2012) Environmental tobacco smoke as a source of polycyclic aromatic hydrocarbons in settled household dust. Environ Sci Technol 46(7):4174–4183

Hori M, Tanaka H, Wakai K, Sasazuki S, Katanoda K (2016) Secondhand smoke exposure and risk of lung cancer in Japan: a systematic review and meta-analysis of epidemiologic studies. Japanese J Clin Oncol 46(10):942–951

Huang L, Cao Y, Zhang Z, Zhang Y, Kuang M, Luo Y, Zhang L (2023) Status and correlates of children’s exposure to secondhand smoke at home: a survey in Chongqing, China. Tobacco Ind Dis 21

Jacob P III, Benowitz NL, Destaillats H, Gundel L, Hang B, Martins-Green M, Matt GE, Quintana PJ, Samet JM, Schick SF (2017) Thirdhand smoke: new evidence, challenges, and future directions. Chem Res Toxicol 30(1):270–294

Jacobs M, Alonso AM, Sherin KM, Koh Y, Dhamija A, Lowe AL, Committee APP (2013) Policies to restrict secondhand smoke exposure: American College of Preventive Medicine position statement. Am J Prev Med 45(3):360–367

James JM, George G, Cherian MR, Rasheed N (2022) Thirdhand smoke composition and consequences: a narrative review. Public Health Toxicol 2(3):1–6

Jenkins RA, Tomkins B, Guerin MR (2000) The chemistry of environmental tobacco smoke: composition and measurement. CRC Press

Book   Google Scholar  

Jones LL, Hassanien A, Cook DG, Britton J, Leonardi-Bee J (2012) Parental smoking and the risk of middle ear disease in children: a systematic review and meta-analysis. Arch Pediatr Adolesc Med 166(1):18–27

Jung JW, Ju YS, Kang HR (2012) Association between parental smoking behavior and children’s respiratory morbidity: 5-year study in an urban city of South Korea. Pediatr Pulmonol 47(4):338–345

Kassem NO, Daffa RM, Liles S, Jackson SR, Kassem NO, Younis MA, Mehta S, Chen M, Jacob P 3rd, Carmella SG, Chatfield DA, Benowitz NL, Matt GE, Hecht SS, Hovell MF (2014) Children’s exposure to secondhand and thirdhand smoke carcinogens and toxicants in homes of hookah smokers. Nicotine Tob Res 16(7):961–975

Kawachi I, Colditz GA, Speizer FE, Manson JE, Stampfer MJ, Willett WC, Hennekens CH (1997) A prospective study of passive smoking and coronary heart disease. Circulation 95(10):2374–2379

Kim A-S, Ko H-J, Kwon J-H, Lee J-M (2018) Exposure to secondhand smoke and risk of cancer in never smokers: a meta-analysis of epidemiologic studies. Int J Environ Res Public Health 15(9):1981

Kim YM, Harrad S, Harrison RM (2001) Concentrations and sources of VOCs in urban domestic and public microenvironments. Environ Sci Technol 35(6):997–1004

Klus H, Begutter H, Scherer G, Tricker AR, Adlkofer F (1992) Tobacco-specific and volatile N-nitrosamines in environmental tobacco smoke of offices. Indoor Environ 1(6):348–350

CAS   Google Scholar  

Kraev TA, Adamkiewicz G, Hammond SK, Spengler JD (2009) Indoor concentrations of nicotine in low-income, multi-unit housing: associations with smoking behaviours and housing characteristics. Tob Control 18(6):438–444

Lange S, Koyanagi A, Rehm J, Roerecke M, Carvalho AF (2020) Association of tobacco use and exposure to secondhand smoke with suicide attempts among adolescents: findings from 33 countries. Nicotine Tob Res 22(8):1322–1329

Leaderer BP, Hammond SK (1991) Evaluation of vapor-phase nicotine and respirable suspended particle mass as markers for environmental tobacco smoke. Environ Sci Techno 25(4):770–777

Lee M, Ha M, Hong Y-C, Park H, Kim Y, Kim E-J, Kim Y, Ha E (2019) Exposure to prenatal secondhand smoke and early neurodevelopment: Mothers and Children’s Environmental Health (MOCEH) study. Environ Health 18(1):1–11

Lee PN, Hamling JS (2016) Environmental tobacco smoke exposure and risk of breast cancer in nonsmoking women. An updated review and meta-analysis. Inhal Toxicol 28(10):431–454

Li L, Hughes L, Arnot JA (2021) Addressing uncertainty in mouthing-mediated ingestion of chemicals on indoor surfaces, objects, and dust. Environ Int 146:106266

Lin LZ, Xu SL, Wu QZ, Zhou Y, Ma HM, Chen DH, Dong GH (2021) Exposure to second-hand smoke during early life and subsequent sleep problems in children: a population-based cross-sectional study. Environ Health 20(1):1–10

Ling J, Heffernan T (2016) The cognitive deficits associated with second-hand smoking. Front Psychiatry 46

Liu J, Ghastine L, Um P, Rovit E, Wu T (2021) Environmental exposures and sleep outcomes: a review of evidence, potential mechanisms, and implications. Environ Res 196:110406

Mahabee-Gittens EM, Merianos AL, Matt GE (2020) Letter to the editor regarding:“an imperative need for research on the role of environmental factors in transmission of novel coronavirus (COVID-19)”—secondhand and thirdhand smoke as potential sources of COVID-19. Environ Sci Technol 54(9):5309–5310

Mantzoros A, Teloniatis SI, Lymperi M, Tzortzi A, Behrakis P (2017) Cardiorespiratory response to exercise of nonsmokers occupationally exposed to second hand smoke (SHS). Tob Prev Cessat 3:1

Mariano LC, Warnakulasuriya S, Straif K, Monteiro L (2022) Secondhand smoke exposure and oral cancer risk: a systematic review and meta-analysis. Tob Control 31(5):597–607

Martin P, Heavner DL, Nelson PR, Maiolo KC, Risner CH, Simmons PS, Morgan WT, Ogden MW (1997) Environmental tobacco smoke (ETS): a market cigarette study. Environ Int 23(1):75–90

Matt G, Quintana P, Hovell M, Bernert J, Song S, Novianti N, Juarez T, Floro J, Gehrman C, Garcia M (2004) Households contaminated by environmental tobacco smoke: sources of infant exposures. Tob Control 13(1):29–37

Matt GE, Fortmann AL, Quintana PJ, Zakarian JM, Romero RA, Chatfield DA, Hoh E, Hovell MF (2013) Towards smoke-free rental cars: an evaluation of voluntary smoking restrictions in California. Tob Control 22(3):201–207

Matt GE, Quintana PJ, Destaillats H, Gundel LA, Sleiman M, Singer BC, Jacob P III, Benowitz N, Winickoff JP, Rehan V (2011a) Thirdhand tobacco smoke: emerging evidence and arguments for a multidisciplinary research agenda. Environ Health Perspect 119(9):1218–1226

Matt GE, Quintana PJ, Zakarian JM, Fortmann AL, Chatfield DA, Hoh E, Uribe AM, Hovell MF (2011b) When smokers move out and non-smokers move in: residential thirdhand smoke pollution and exposure. Tob Control 20(1):e1–e1

Matt GE, Quintana PJ, Hoh E, Dodder NG, Mahabee-Gittens EM, Padilla S, Markman L, Watanabe K (2021) Tobacco smoke is a likely source of lead and cadmium in settled house dust. J Trace Elem Med Biol 63:126656

Matt GE, Quintana PJ, Hovell MF, Chatfield D, Ma DS, Romero R, Uribe A (2008) Residual tobacco smoke pollution in used cars for sale: air, dust, and surfaces. Nicotine Tob Res 10(9):1467–1475

Melloni BB (2014) Lung cancer in never-smokers: radon exposure and environmental tobacco smoke. Eur Respiratory Soc 44:850–852

Miyamura K, Nawa N, Isumi A, Doi S, Ochi M, Fujiwara T (2023) Impact of exposure to secondhand smoke on the risk of obesity in early adolescence. Pediatr Res 93(1):260–266

Mourino N, Ruano-Raviña A, Varela Lema L, Fernández E, López MJ, Santiago-Pérez MI, Rey-Brandariz J, Giraldo-Osorio A, Pérez-Ríos M (2022) Serum cotinine cut-points for secondhand smoke exposure assessment in children under 5 years: a systemic review. Plos One 17(5):e0267319

National Cancer Policy Forum, B. O. H. C. S., Institute of Medicine (2013) Reducing tobacco-related cancer incidence and mortality: workshop summary. National Academies Press (US), Washington (DC)

Nazaroff WW, Singer BC (2004) Inhalation of hazardous air pollutants from environmental tobacco smoke in US residences. J Expos Sci Environ Epidemiol 14(1):S71–S77

Northrup TF, Jacob P III, Benowitz NL, Hoh E, Quintana PJ, Hovell MF, Matt GE, Stotts AL (2016a) Thirdhand smoke: state of the science and a call for policy expansion. Public Health Rep 131(2):233–238

Northrup TF, Khan AM, Jacob P, Benowitz NL, Hoh E, Hovell MF, Matt GE, Stotts AL (2016b) Thirdhand smoke contamination in hospital settings: assessing exposure risk for vulnerable paediatric patients. Tob Control 25(6):619–623

Northrup TF, Stotts AL, Suchting R, Matt GE, Quintana PJ, Khan AM, Green C, Klawans MR, Johnson M, Benowitz N (2021) Thirdhand smoke associations with the gut microbiomes of infants admitted to a neonatal intensive care unit: an observational study. Environ Res 197:111180

Öberg M, Jaakkola MS, Woodward A, Peruga A, Prüss-Ustün A (2011) Worldwide burden of disease from exposure to second-hand smoke: a retrospective analysis of data from 192 countries. Lancet 377(9760):139–146

Palmiotto G, Pieraccini G, Moneti G, Dolara P (2001) Determination of the levels of aromatic amines in indoor and outdoor air in Italy. Chemosphere 43(3):355–361

Park M-B (2023) Concordance assessment through comparison with urine cotinine: does self-report adequately reflect passive smoking? Tob Ind Dis 21

Park M-B, Kwan Y, Sim B, Lee J (2021) Association between urine cotinine and depressive symptoms in non-smokers: national representative sample in Korea. J Affect Disord 294:527–532

Park M-B, Sim B (2022) Evaluation of thirdhand smoke exposure after short visits to public facilities (noraebang and internet cafés): a prospective cohort study. Toxics 10(6):307

Petrick L, Destaillats H, Zouev I, Sabach S, Dubowski Y (2010) Sorption, desorption, and surface oxidative fate of nicotine. Phys Chem Chem Phys 12(35):10356–10364

Petrick LM, Sleiman M, Dubowski Y, Gundel LA, Destaillats H (2011a) Tobacco smoke aging in the presence of ozone: a room-sized chamber study. Atmos Environ 45(28):4959–4965

Petrick LM, Svidovsky A, Dubowski Y (2011b) Thirdhand smoke: heterogeneous oxidation of nicotine and secondary aerosol formation in the indoor environment. Environ Sci Technol 45(1):328–333

Phetphum C, Noosorn N (2020) Prevalence of secondhand smoke exposure at home and associated factors among middle school students in Northern Thailand. Tob Ind Dis 18

Pirie K, Beral V, Peto R, Roddam A, Reeves G, Green J, Collaborators MWS (2008) Passive smoking and breast cancer in never smokers: prospective study and meta-analysis. Int J epidemiol 37(5):1069–1079

Protano C, Vitali M (2011) The new danger of thirdhand smoke: why passive smoking does not stop at secondhand smoke. Environ Health Perspect 119(10):a422–a422

Rajani NB, Vlachantoni IT, Vardavas CI, Filippidis FT (2017) The association between occupational secondhand smoke exposure and life satisfaction among adults in the European Union. Tob Ind Dis 15(1):1–5

Ramírez N, Özel MZ, Lewis AC, Marcé RM, Borrull F, Hamilton JF (2012) Determination of nicotine and N-nitrosamines in house dust by pressurized liquid extraction and comprehensive gas chromatography—nitrogen chemiluminiscence detection. J Chromatogr A 1219:180–187

Ramírez N, Özel MZ, Lewis AC, Marcé RM, Borrull F, Hamilton JF (2014) Exposure to nitrosamines in thirdhand tobacco smoke increases cancer risk in non-smokers. Environ Int 71:139–147

Rehan VK, Sakurai R, Torday JS (2011) Thirdhand smoke: a new dimension to the effects of cigarette smoke on the developing lung. Am J Physiol-Lung Cell Mol Physiol

Reitsma MB, Kendrick PJ, Ababneh E, Abbafati C, Abbasi-Kangevari M, Abdoli A, Abedi A, Abhilash E, Abila DB, Aboyans V (2021) Spatial, temporal, and demographic patterns in prevalence of smoking tobacco use and attributable disease burden in 204 countries and territories, 1990–2019: a systematic analysis from the Global Burden of Disease Study 2019. Lancet 397(10292):2337–2360

Ritchie H, Roser M (2013) Smoking. Published online at OurWorldInData.org

Rodenstein DO, Stănescu DC (1985) Pattern of inhalation of tobacco smoke in pipe, cigarette, and never smokers. Am Rev Respir Dis 132(3):628–632

Rodgman A, Perfetti TA (2013) The chemical components of tobacco and tobacco smoke. CRC Press

Roser HRAM (2019) Smoking. Published online at OurWorldInData.org . Retrieved from: https://ourworldindata.org/smoking  [Online Resource]. Accessed Jan 2022

Sakuma H, Kusama M, Munakata S, Ohsumi T, Sugawara S (1983) The distribution of cigarette smoke components between mainstream and sidestream smoke: I. Acidic components. Contribut Tob Nicotine Res 12(2):63–71

Sandler DP, Comstock GW, Helsing KJ, Shore DL (1989) Deaths from all causes in non-smokers who lived with smokers. Am J Public Health 79(2):163–167

Scherer G, Ruppert T, Daube H, Kossien I, Riedel K, Tricker AR, Adlkofer F (1995) Contribution of tobacco smoke to environmental benzene exposure in Germany. Environ Int 21(6):779–789

Schiavone S, Anderson C, Mons U, Winkler V (2022) Prevalence of second-hand tobacco smoke in relation to smoke-free legislation in the European Union. Preventive Med 154:106868

Schick SF, Glantz S (2007) Concentrations of the carcinogen 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone in sidestream cigarette smoke increase after release into indoor air: results from unpublished tobacco industry research. Cancer Epidemiol Prev Biomark 16(8):1547–1553

Schwartz AG, Cote ML (2016) Epidemiology of lung cancer. Lung Cancer Pers Med:21–41

Services (2006) The health consequences of involuntary exposure to tobacco smoke: a report of the Surgeon General, Atlanta, GA: US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health, 2006

Services (2014) The health consequences of smoking—50 years of progress: a report of the Surgeon General, Atlanta, GA: US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health, 2014

Sikorska-Jaroszynska MH, Mielnik-Blaszczak M, Krawczyk D, Nasilowska-Barud A, Blaszczak J (2012) Passive smoking as an environmental health risk factor. Ann Agric Environ Med 19(3)

Singer BC, Hodgson AT, Nazaroff WW (2003) Gas-phase organics in environmental tobacco smoke: 2. Exposure-relevant emission factors and indirect exposures from habitual smoking. Atmos Environ 37(39-40):5551–5561

Skipina TM, Upadhya B, Soliman EZ (2021) Exposure to secondhand smoke is associated with increased left ventricular mass. Tob Ind Dis 19

Sleiman M, Destaillats H, Smith JD, Liu CL, Ahmed M, Wilson KR, Gundel LA (2010a) Secondary organic aerosol formation from ozone-initiated reactions with nicotine and secondhand tobacco smoke. Atmos Environ 44(34):4191–4198

Sleiman M, Gundel LA, Pankow JF, Jacob P, Singer BC, Destaillats H (2010b) Formation of carcinogens indoors by surface-mediated reactions of nicotine with nitrous acid, leading to potential thirdhand smoke hazards. Proc Natl Acad Sci 107(15):6576–6581

Sleiman M, Logue JM, Luo W, Pankow JF, Gundel LA, Destaillats H (2014) Inhalable constituents of thirdhand tobacco smoke: chemical characterization and health impact considerations. Environ Sci Technol 48(22):13093–13101

Sleiman M, Maddalena RL, Gundel LA, Destaillats H (2009) Rapid and sensitive gas chromatography–ion-trap tandem mass spectrometry method for the determination of tobacco-specific N-nitrosamines in secondhand smoke. J Chromatogr A 1216(45):7899–7905

Soleimani F, Dobaradaran S, De-la-Torre GE, Schmidt TC, Saeedi R (2021) Content of toxic components of cigarette, cigarette smoke vs cigarette butts: a comprehensive systematic review. Sci Total Environ 813:152667

Soleimani F, Dobaradaran S, Vazirizadeh A, Mohebbi G, Ramavandi B, De-la-Torre GE, Nabipour I, Schmidt TC, Novotny TE, Maryamabadi A, Kordrostami Z (2022) Chemical contents and toxicity of cigarette butts leachates in aquatic environment: a case study from the Persian Gulf Region. Chemosphere 311:137049

Sun W, Huang X, Wu H, Zhang CJ, Yin Z, Fan Q, Wang H, Jayavanth P, Akinwunmi B, Wu Y (2021) Maternal tobacco exposure and health-related quality of life during pregnancy: a national-based study of pregnant women in China. Health Qual Life Outcomes 19(1):1–9

Svendsen KH, Kuller LH, Martin MJ, Ockene JK (1987) Effects of passive smoking in the multiple risk factor intervention trial. Ame J Epidemiol 126(5):783–795

Tang X, Benowitz N, Gundel L, Hang B, Havel CM, Hoh E, Jacob Iii P, Mao J-H, Martins-Green M, Matt GE (2022) Thirdhand exposures to tobacco-specific nitrosamines through inhalation, dust ingestion, dermal uptake, and epidermal chemistry. Environ Sci Technol 56(17):12506–12516

Tang X, González NR, Russell ML, Maddalena RL, Gundel LA, Destaillats H (2021) Chemical changes in thirdhand smoke associated with remediation using an ozone generator. Environ Res 198:110462

Taylor R, Najafi F, Dobson A (2007) Meta-analysis of studies of passive smoking and lung cancer: effects of study type and continent. Int J Epidemiol 36(5):1048–1059

Terry PD, Thun MJ, Rohan TE (2011) Does tobacco smoke cause breast cancer?, SAGE Publications Sage UK: London. Women’s Health 7:405–408

Thomas JL, Hecht SS, Luo X, Ming X, Ahluwalia JS, Carmella SG (2014) Thirdhand tobacco smoke: a tobacco-specific lung carcinogen on surfaces in smokers’ homes. Nicotine Tob Res 16(1):26–32

Torres S, Merino C, Paton B, Correig X, Ramírez N (2018) Biomarkers of exposure to secondhand and thirdhand tobacco smoke: recent advances and future perspectives. Int J Environ Res Public Health 15(12)

Tsai J, Homa DM, Gentzke AS, Mahoney M, Sharapova SR, Sosnoff CS, Caron KT, Wang L, Melstrom PC, Trivers KF (2018) Exposure to secondhand smoke among nonsmokers—United States, 1988–2014. Morb Mortality Wkly Rep 67(48):1342

Van Deusen A, Hyland A, Travers MJ, Wang C, Higbee C, King BA, Alford T, Cummings KM (2009) Secondhand smoke and particulate matter exposure in the home. Nicotine Tob Res 11(6):635–641

Vu AT, Hassink MD, Taylor KM, McGuigan M, Blasiole A, Valentin-Blasini L, Williams K, Watson CH (2021) Volatile organic compounds in mainstream smoke of sixty domestic little cigar products. Chem Res Toxicol 34(3):704–712

Vu-Duc T, Huynh C-K (1989) Sidestream tobacco smoke constituents in indoor air modelled in an experimental chamber—polycyclic aromatic hydrocarbons. Environ Int 15(1-6):57–64

Wald NJ, Nanchahal K, Thompson SG, Cuckle HS (1986) Does breathing other people’s tobacco smoke cause lung cancer? Br Med J (Clin Res Ed) 293(6556):1217–1222

Walton K, Gentzke AS, Murphy-Hoefer R, Kenemer B, Neff LJ (2020) Peer reviewed: exposure to secondhand smoke in homes and vehicles among US youths, United States, 2011–2019. Prev Chron Dis 17

Wamboldt FS, Balkissoon RC, Rankin AE, Szefler SJ, Hammond SK, Glasgow RE, Dickinson WP (2008) Correlates of household smoking bans in low-income families of children with and without asthma. Fam Process 47(1):81–94

Wang L, Fu K, Li X, Kong B, Zhang B (2018) Exposure to third-hand smoke during pregnancy may increase the risk of postpartum depression in China. Tob Ind Dis 16

Weiss ST, Tager IB, Schenker M, Speizer FE (1983) The health effects of involuntary smoking. Am Rev Resp Dis 128(5):933–942

Weschler CJ, Nazaroff WW (2008) Semivolatile organic compounds in indoor environments. Atmos Environ 42(40):9018–9040

WHO (2017) Inheriting a sustainable world? World Health Organization, Atlas on children’s health and the environment

WHO (2021) WHO report on the global tobacco epidemic, 2021: addressing new and emerging products. World Health Organization

Winickoff JP, Friebely J, Tanski SE, Sherrod C, Matt GE, Hovell MF, McMillen RC (2009) Beliefs about the health effects of “thirdhand” smoke and home smoking bans. Pediatrics 123(1):e74–e79

Winkelstein W Jr (1990) Smoking and cervical cancer—current status: a review. Am J Epidemiol 131(6):945–957

Wong SL, Malaison E, Hammond D, Leatherdale ST (2012) Secondhand smoke exposure among Canadians: cotinine and self-report measures from the Canadian Health Measures Survey 2007–2009. Nicotine Tob Res 15(3):693–700

Wu D, Landsberger S, Larson SM (1995) Evaluation of elemental cadmium as a marker for environmental tobacco smoke. Environ Sci Technol 29(9):2311–2316

Xanthopoulou E, Kousoulis AA (2014) Exploring the development of asthma in children with parental and in utero exposure to smoking. Tobacco Induced Diseases 12(1):1

Yeh K, Li L, Wania F, Abbatt JP (2022) Thirdhand smoke from tobacco, e-cigarettes, cannabis, methamphetamine and cocaine: partitioning, reactive fate, and human exposure in indoor environments. Environ Int 160:107063

Zhang S, Qiao S, Chen M, Xia Y, Hang B, Cheng S (2015) A investigation of thirdhand smoke pollution in 3 types of places of Nanjing, 2014. Zhonghua yu fang yi xue za zhi Chinese J Prev Med 49(1):31–35

Zhong L, Goldberg MS, Parent M-É, Hanley JA (2000) Exposure to environmental tobacco smoke and the risk of lung cancer: a meta-analysis. Lung Cancer 27(1):3–18

Zhou B, Ma Y, Wei F, Zhang LE, Chen X, Peng S, Xiong F, Peng X, NiZam B, Zou Y, Huang K (2018) Association of active/passive smoking and urinary 1-hydroxypyrene with poor sleep quality: a cross-sectional survey among Chinese male enterprise workers. Tob Ind Dis 16

Download references

The authors would like to thank the financial support of this work by Bushehr University of Medical Sciences (Project No. 2377) and Iranian National Institute for Oceanography and Atmospheric Science (INIOAS) for their cooperation.

Author information

Authors and affiliations.

Systems Environmental Health and Energy Research Center, The Persian Gulf Biomedical Sciences Research Institute, Bushehr University of Medical Sciences, Bushehr, Iran

Hossein Arfaeinia & Farshid Soleimani

Department of Environmental Health Engineering, Faculty of Health and Nutrition, Bushehr University of Medical Sciences, Bushehr, Iran

Hossein Arfaeinia

Iranian National Institute for Oceanography and Atmospheric Science, No. 3, Etemadzadeh St., Fatemi Ave, Tehran, 1411813389, Iran

Maryam Ghaemi

Health Promotion Research Center, Zahedan University of Medical Sciences, Zahedan, Iran

Anis Jahantigh

Research Center for Health Sciences, Institute of Health, Department of Environmental Health Engineering, School of Health, Shiraz University of Medical Sciences, Shiraz, Iran

Hassan Hashemi

You can also search for this author in PubMed   Google Scholar

Contributions

Hossein Arfaeinia: analysis, software, writing the first draft; Maryam Ghaemi: methodology, writing—review and editing; Anis Jahantigh: methodology, initial search; Farshid Soleimani: conceptualization, supervision, writing—review; Hassan Hashemi: writing—review and editing.

Corresponding author

Correspondence to Farshid Soleimani .

Ethics declarations

Ethics approval and consent to participate, consent for publication.

All authors have read the manuscript and have agreed to submit it in its current form for consideration for publication in the Journal.

Competing interests

The authors declare no competing interests.

Additional information

Responsible Editor: Philippe Garrigues

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Arfaeinia, H., Ghaemi, M., Jahantigh, A. et al. Secondhand and thirdhand smoke: a review on chemical contents, exposure routes, and protective strategies. Environ Sci Pollut Res 30 , 78017–78029 (2023). https://doi.org/10.1007/s11356-023-28128-1

Download citation

Received : 16 March 2023

Accepted : 01 June 2023

Published : 12 June 2023

Issue Date : July 2023

DOI : https://doi.org/10.1007/s11356-023-28128-1

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Secondhand smoke
• Thirdhand smoke
• Exposure routes
• Vulnerable groups
• Find a journal
• Publish with us
• Track your research

Disclaimer » Advertising

• HealthyChildren.org

• Previous Article
• Next Article

The Role of Secondhand Smoke Research in Protecting Nonsmokers

POTENTIAL CONFLICT OF INTEREST: The authors have indicated they have no potential conflicts of interest to disclose.

FINANCIAL DISCLOSURE: The authors have indicated they have no financial relationships relevant to this article to disclose.

• Split-Screen
• Article contents
• Figures & tables
• Supplementary Data
• Peer Review
• CME Quiz Close Quiz
• Open the PDF for in another window
• Get Permissions
• Cite Icon Cite
• Search Site

Julie A. Gorzkowski , Jonathan D. Klein; The Role of Secondhand Smoke Research in Protecting Nonsmokers. Pediatrics January 2018; 141 (Supplement_1): S6–S9. 10.1542/peds.2017-1026D

Download citation file:

• Ris (Zotero)
• Reference Manager

In the 53 years since the release of the landmark US Surgeon General’s Report on Smoking and Health, our understanding of the burden of tobacco use and secondhand smoke (SHS) exposure has grown exponentially. Research has proven that tobacco use and SHS exposure causes a significant proportion of 6 of the world’s 8 leading causes of death, including heart disease, cardiovascular disease, chronic obstructive pulmonary disease, and many cancers. 1 Scientists and clinicians in medicine and public health have also built a strong evidence base for tobacco control, and have implemented educational and policy initiatives and clinical support services to prevent tobacco use, protect nonsmokers from SHS, and encourage cessation. Although these efforts have been successful in reducing rates of tobacco use and smoke exposure in some countries, there remains significant work to be done.

Each year, tobacco use and SHS exposure continue to kill. By World Health Organization estimates, 6 000 000 people die of tobacco annually, with 10% of these deaths caused by SHS. 1 , 2 Much of the burden of this fully preventable disease has shifted to vulnerable populations, including children and low-income families. Children are exposed to SHS at higher rates than any other age group, resulting in a disproportionate disease burden; globally, nearly 1 in 4 of the deaths caused by SHS occur in children, most often from respiratory infection in the early years of life. 3  

Children are most frequently exposed to tobacco smoke by parents or caregivers in homes or in vehicles, but they are also exposed in public places, and in multiunit housing settings. This leaves children with “no voice and no choice” in the face of dangerous toxins that can shape their future; children exposed to tobacco are at higher risk for asthma, ear infections, sudden infant death syndrome, and behavioral and cognitive difficulties. 4 , 5 Clinicians, families, and child health advocates need continued evidence to best protect children. Indeed, child survival goals are unlikely to be met unless the contributions of tobacco smoke exposure to low birth weight, prematurity, hunger, and food insecurity, in both high- and low-income countries, are recognized and prevented.

Scientific research is the foundation of effective tobacco control practice and policy, and scientists from many disciplines have contributed to our current understanding of this epidemic. The Flight Attendant Medical Research Institute (FAMRI) has uniquely focused its grant-making on medical and scientific research on tobacco smoke exposure. FAMRI was founded out of a class-action lawsuit brought on behalf of nonsmoking flight attendants against the tobacco industry. In 2006, with FAMRI support, the American Academy of Pediatrics established the Julius B. Richmond Center of Excellence, a research center dedicated to preventing child and family exposure to tobacco and SHS. Since FAMRI began operations, and during the first decade of FAMRI’s support of the American Academy of Pediatrics Richmond Center’s SHS research, Richmond Center investigators and others in the scientific community have detailed the trajectory of tobacco and tobacco-related diseases and developed and tested treatments to reduce morbidity and mortality. Researchers have identified psychosocial and pharmacological methods to support effective cessation and have designed screening and counseling tools to help clinicians ask the right questions about tobacco use and SHS exposure in clinical settings. Child health tobacco control investigators have monitored trends in tobacco use and related public opinion and have translated this evidence into targeted interventions and policy solutions that have changed social norms around smoking and SHS exposure. Through careful and systematic work, tobacco control leaders have mobilized the evidence and helped save countless lives.

In the articles in this issue of Pediatrics , authors explore topics in child health and tobacco control and in implementation of the evidence base for eliminating SHS exposure. These findings reveal that the evidence for protecting children and youth is strong and continues to grow and that a committed group of scientists are addressing needed questions in pediatric tobacco control and SHS research.

If we contemplate a future tobacco endgame in which every child is free from tobacco and SHS, several priorities are clear. First, the scientific community must continue to improve detection and measurement of tobacco exposure in the environment and in the human body and must work to incorporate these measures into interventions to protect children and other nonsmokers from the dangers of SHS and tobacco. Second, we must continue to improve the quality and quantity of tobacco-control policies in our communities, and the delivery of effective interventions in clinical settings, by ensuring that every child is screened for tobacco use and exposure at every visit and that parents and families receive effective support to eliminate use and exposure. Third, we must continue to add to our understanding of ways that tobacco impacts the human body to strengthen future efforts to protect individuals from the harms of smoke exposure. Fourth, we must continue to monitor the prevalence of SHS exposure of children, and of tobacco use among youth, including the recent rapid increase in the use of electronic cigarettes and other novel products with which manufacturers are seeking to addict the next generation. Finally, we need to increase efforts to translate science into policy and practice and to increase the adoption of effective policy solutions, such as smoke-free air laws and Tobacco 21 policies.

The tobacco control climate has changed rapidly in recent decades, and scientific advances have increasingly resulted in the identification of effective solutions to ending the tobacco epidemic. Although research into tobacco and SHS is critical to protecting child health and increasing quality of life for millions of people worldwide, strategic implementation of what is already known is a continuing challenge that also requires dedicated efforts from the scientific community. The evidence provided in the release of tobacco industry documents reveals the industry’s history of efforts to generate false evidence and mislead the public about the harms caused by tobacco. Additionally, as evidence reveals, the industry continues to use similar tactics in opposition to efforts to protect the health of children and other nonsmokers in the United States and in other countries. 1 , 2 , 6 Dr Julius B. Richmond, for whom our center is named, often spoke of the need for scientific evidence, social strategies, and political will as essential ingredients needed to bring about change. To truly achieve a world free of tobacco and SHS, renewed efforts are needed to bring these 3 ingredients together for the health of all children.

Flight Attendant Medical Research Institute

secondhand smoke

Ms Gorzkowski drafted the initial manuscript and reviewed and revised the manuscript; Dr Klein conceptualized the commentary and reviewed and revised the manuscript; and all authors approved the final manuscript as submitted.

FUNDING: Supported by a Center of Excellence grant from the Flight Attendant Medical Research Institute to the American Academy of Pediatrics.

Competing Interests

Advertising Disclaimer »

Citing articles via

Email alerts.

Affiliations

• Editorial Board
• Editorial Policies
• Journal Blogs
• Pediatrics On Call
• Online ISSN 1098-4275
• Print ISSN 0031-4005
• Pediatrics Open Science
• Hospital Pediatrics
• Pediatrics in Review
• AAP Grand Rounds
• Latest News
• Pediatric Care Online
• Red Book Online
• Pediatric Patient Education
• AAP Toolkits
• AAP Pediatric Coding Newsletter

First 1,000 Days Knowledge Center

Institutions/librarians, group practices, licensing/permissions, integrations, advertising.

• Privacy Statement | Accessibility Statement | Terms of Use | Support Center | Contact Us
• © Copyright American Academy of Pediatrics

This Feature Is Available To Subscribers Only

Sign In or Create an Account

Secondhand Smoke and Cancer

What is secondhand smoke.

Secondhand smoke (sometimes called passive smoke, environmental tobacco smoke, or involuntary smoke) is a mixture of sidestream smoke (the smoke from the burning tip of a cigarette or other smoked tobacco product) and mainstream smoke (smoke exhaled by a smoker that is diluted by the surrounding air) ( 1 – 3 ).

Major settings of exposure to secondhand smoke include workplaces, public places such as bars, restaurants and recreational settings, and homes ( 4 ). Workplaces and homes are especially important sources of exposure because of the length of time people spend in these settings. The home is a particularly important source of exposure for infants and young children. Children and nonsmoking adults can also be exposed to secondhand smoke in vehicles, where levels of exposure can be high. Exposure levels can also be high in enclosed public places where smoking is allowed, such as restaurants, bars, and casinos, resulting in substantial exposures for both workers and patrons ( 3 ).

In the United States, most secondhand smoke comes from cigarettes, followed by pipes, cigars, and other smoked tobacco products.

How is secondhand smoke exposure measured?

Secondhand smoke exposure can be measured by testing indoor air for respirable (breathable) suspended particles (particles small enough to reach the lower airways of the human lung) or individual chemicals such as nicotine or other harmful and potentially harmful constituents of tobacco smoke ( 3 , 5 ).

Exposure to secondhand smoke can also be evaluated by measuring the level of biomarkers such as cotinine (a byproduct of nicotine metabolism ) in a nonsmoker’s blood, saliva, or urine ( 1 ). Nicotine, cotinine, and other chemicals present in secondhand smoke have been found in the body fluids of nonsmokers exposed to secondhand smoke.

Does secondhand smoke contain harmful chemicals?

Yes. Many of the harmful chemicals that are in the smoke inhaled by smokers are also found in secondhand smoke ( 1 , 3 , 6 , 7 ), including some that cause cancer ( 1 , 3 , 7 , 8 ).

These include:

• Tobacco-specific nitrosamines
• Benzo[ α ]pyrene
• 1,3–butadiene (a hazardous gas)
• Cadmium (a toxic metal)
• Formaldehyde
• Acetaldehyde

Many factors affect which chemicals and how much of them are found in secondhand smoke. These factors include the type of tobacco used in manufacturing a specific product, the chemicals (including flavorings such as menthol ) added to the tobacco, the way the tobacco product is smoked, and—for cigarettes, cigars, little cigars, and cigarillos—the material in which the tobacco is wrapped ( 1 – 3 , 7 ).

Does secondhand smoke cause cancer?

Yes. The U.S. Environmental Protection Agency, the U.S. National Toxicology Program, the U.S. Surgeon General , and the International Agency for Research on Cancer have all classified secondhand smoke as a known human carcinogen (a cancer-causing agent) ( 1 , 3 , 7 , 9 ). In addition, the National Institute for Occupational Safety and Health (NIOSH) has concluded that secondhand smoke is an occupational carcinogen ( 3 ).

The Surgeon General estimates that, during 2005-2009, secondhand smoke exposure caused more than 7,300 lung cancer deaths among adult nonsmokers each year ( 10 ). 

Some research also suggests that secondhand smoke may increase the risk of breast cancer, nasal sinus cavity cancer, and nasopharyngeal cancer in adults ( 10 ) and the risk of leukemia , lymphoma , and brain tumors in children ( 3 ). Additional research is needed to determine whether a link exists between secondhand smoke exposure and these cancers.

What are the other health effects of exposure to secondhand smoke?

Secondhand smoke is associated with disease and premature death in nonsmoking adults and children ( 3 , 7 ). Exposure to secondhand smoke irritates the airways and has immediate harmful effects on a person’s heart and blood vessels . It increases the risk of heart disease by about 25 to 30% ( 3 ). In the United States, secondhand smoke is estimated to cause nearly 34,000 heart disease deaths each year ( 10 ). Exposure to secondhand smoke also increases the risk of stroke by 20 to 30% ( 10 ).

Secondhand smoke exposure during pregnancy has been found to cause reduced fertility , pregnancy complications, and poor birth outcomes, including impaired lung development, low birth weight , and preterm delivery ( 11 ).

Children exposed to secondhand smoke are at increased risk of sudden infant death syndrome , ear infections , colds, pneumonia , bronchitis , and more severe asthma . Being exposed to secondhand smoke slows the growth of children’s lungs and can cause them to cough, wheeze, and feel breathless ( 3 , 7 , 10 ).

There is no safe level of exposure to secondhand smoke. Even low levels of secondhand smoke can be harmful.

How can you protect yourself and your family from secondhand smoke?

The only way to fully protect nonsmokers from secondhand smoke is to eliminate smoking in indoor workplaces and public places and by creating smokefree policies for personal spaces, including multiunit residential housing. Opening windows, using fans and ventilation systems, and restricting smoking to certain rooms in the home or to certain times of the day does not eliminate exposure to secondhand smoke ( 3 , 4 ).

Steps you can take to protect yourself and your family include:

• not allowing smoking in your home
• not allowing anyone to smoke in your car, even with the windows down
• making sure the places where your children are cared for are tobacco free
• teaching children to avoid secondhand smoke
• seeking out restaurants, bars, and other places that are smokefree (if your state still allows smoking in public areas)
• protecting your family from secondhand smoke and being a good role model by not smoking or using any other type of tobacco product. For help to quit see smokefree.gov or call 1-877-44U-QUIT.

Do electronic cigarettes emit secondhand smoke?

Electronic cigarettes (also called e-cigarettes, vape pens, vapes, and pod mods) are battery-powered devices designed to heat a liquid, which typically contains nicotine , into an aerosol for inhalation by a user. Following inhalation, the user exhales the aerosol ( 12 ).

The use of electronic cigarettes results in exposure to secondhand aerosols (rather than secondhand smoke). Secondhand aerosols contain harmful and potentially harmful substances, including nicotine, heavy metals like lead, volatile organic compounds, and cancer-causing agents. More information about these devices is available on CDC’s Electronic Cigarettes page.

What is being done to reduce nonsmokers’ exposure to secondhand smoke?

On the federal level, several policies restricting smoking in public places have been implemented. Federal law prohibits smoking on airline flights, interstate buses, and most trains. Smoking is also prohibited in most federal buildings by Executive Order 13058 of 1997 . The Pro-Children Act of 1994 prohibits smoking in facilities that routinely provide federally funded services to children. The Department of Housing and Urban Development published a final rule in December 2016, which was fully implemented in July 2018, that prohibits the use of cigarettes , cigars , pipes , and hookah (waterpipes) in public housing authorities, including all living units, indoor common areas, and administrative offices, as well as outdoor areas within 25 feet of buildings.

Many state and local governments have enacted laws that prohibit smoking in workplaces and public places, including restaurants, bars, schools, hospitals, airports, bus terminals, parks, and beaches. These smokefree policies have substantially decreased exposure to secondhand smoke in many U.S. workplaces ( 13 ). More than half of all states have implemented comprehensive smokefree laws that prohibit smoking in indoor areas of workplaces, restaurants, and bars, and some states and communities also have enacted laws regulating smoking in multi-unit housing and cars ( 14 ). The American Nonsmokers' Rights Foundation provides a list of state and local smokefree air policies .

To highlight the health risks from secondhand smoke, the National Cancer Institute requires that meetings and conferences organized or primarily sponsored by NCI be held in a state, county, city, or town that has adopted a comprehensive smokefree policy, unless specific circumstances justify an exception to this policy. 

Healthy People 2020 , a comprehensive nationwide health promotion and disease prevention framework established by the U.S. Department of Health and Human Services (HHS), includes several objectives addressing the goal of reducing illness, disability, and death caused by tobacco use and secondhand smoke exposure. For 2020, the Healthy People goal is to reduce the proportion of nonsmokers exposed to secondhand smoke by 10%. To assist with achieving this goal, Healthy People 2020 includes ideas for community interventions, such as encouraging the introduction of smokefree policies in all workplaces and other public gathering places, such as public parks, sporting arenas, and beaches.

Because of these policies and other actions, the percentage of nonsmokers who are exposed to secondhand smoke declined from 52.5% during 1999–2000 to 25.3% during 2011–2014 ( 15 ). Exposure to secondhand smoke declined among all population subgroups, but disparities still exist. During 2011–2014, 38% of children ages 3–11 years, 50% of non-Hispanic blacks, 48% of people living below the poverty level, and 39% of people living in rental housing were exposed to secondhand smoke ( 15 ).