thesis about vaccine for covid 19

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• v.29(9); 2021 Sep 1

A comprehensive analysis of the efficacy and safety of COVID-19 vaccines

Changjing cai.

1 Department of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China

2 Key Laboratory for Molecular Radiation Oncology of Hunan Province, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China

Yinghui Peng

Edward shen.

3 Department of Life Science, McMaster University, Hamilton, ON L8S 4L8, Canada

Qiaoqiao Huang

Yihong chen, ziyang feng, xiangyang zhang.

5 Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15213, USA

4 National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan 410008, P.R. China

Associated Data

The numbers of cases and deaths from coronavirus disease 2019 (COVID-19) are continuously increasing. Many people are concerned about the efficacy and safety of the COVID-19 vaccines. We performed a comprehensive analysis of the published trials of COVID-19 vaccines and the real-world data from the Vaccine Adverse Event Reporting System. Globally, our research found that the efficacy of all vaccines exceeded 70%, and RNA-based vaccines had the highest efficacy of 94.29%; moreover, Black or African American people, young people, and males may experience greater vaccine efficacy. The spectrum of vaccine-related adverse drug reactions (ADRs) is extremely broad, and the most frequent ADRs are pain, fatigue, and headache. Most ADRs are tolerable and are mainly grade 1 or 2 in severity. Some severe ADRs have been identified (thromboembolic events, 21–75 cases per million doses; myocarditis/pericarditis, 2–3 cases per million doses). In summary, vaccines are a powerful tool that can be used to control the COVID-19 pandemic, with high efficacy and tolerable ADRs. In addition, the spectrum of ADRs associated with the vaccines is broad, and most of the reactions appear within a week, although some may be delayed. Therefore, ADRs after vaccination need to be identified and addressed in a timely manner.

Graphical abstract

The numbers of cases and deaths from COVID-19 are continuously increasing. Cai et al. are the first to comprehensively analyze the efficacy of the existing COVID-19 vaccines and the incidence, spectrum, timing, and clinical features of adverse reactions associated with the COVID-19 vaccines, which can provide reference for general public.

Introduction

As of April 5, 2021, there were more than 131 million confirmed cases and more than 2.8 million deaths due to coronavirus disease 2019 (COVID-19) worldwide. 1 COVID-19 has posed a serious threat to public health worldwide. There is no cure for COVID-19, and only vaccines can stop the spread of the COVID-19 pandemic. According to the World Health Organization (WHO), as of April 5, 2021, 184 vaccines were being evaluated in the preclinical development stage, 85 were in the clinical evaluation stage, and some had partially passed through phase III clinical trials. 2 Vaccination against COVID-19 has now started in 161 locations, covering 91% of the global population. 3 However, the vaccination rates are still low; as of April 5, 2021, the highest rate of full vaccination was 56.2% in Israel, while those in other countries were all lower than 20%, and those in some countries were 0%. 4 A previous study pointed out that 53%–84% of the population needs to be vaccinated against COVID-19 to achieve herd immunity. 5 However, as various mutations of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been reported, herd immunity is becoming more and more unrealistic, unless a vaccine to protect against different variants of SARS-CoV-2 can be developed. Other than protection, vaccination can reduce the severity of COVID-19 infection and be life saving. One of the key reasons for the low vaccination rate is that many people are concerned about the safety and efficacy of the COVID-19 vaccines.

However, no reports have addressed this issue satisfactorily. It is important to perform an analysis of the safety and efficacy of the COVID-19 vaccines. Therefore, we performed a comprehensive analysis to determine the incidence, spectrum, timing, and clinical features of adverse drug reactions (ADRs) and the efficacy of the COVID-19 vaccines.

First, we performed a meta-analysis of the published trials of the COVID-19 vaccines. Furthermore, we retrospectively obtained real-world data from the Vaccine Adverse Event Reporting System (VAERS), which is comanaged by the Centers for Disease Control and Prevention (CDC) and the Food and Drug Administration (FDA) of the United States of America. 6 In our research, we provided a complete overview of the COVID-19 vaccines in terms of the incidence, spectrum, timing, and clinical features of ADRs and efficacy. We do hope this study will provide a guideline for clinicians managing ADRs associated with the COVID-19 vaccines and increase the confidence of the general public in the COVID-19 vaccines.

Efficacy of COVID-19 vaccines

To estimate the efficacy of the COVID-19 vaccines, we evaluated all the COVID-19 vaccine data that have been published from phase III clinical trials; a total of 194,015 cases were included. The overall efficacy was highly heterogeneous (>90%); therefore, we performed subgroup analyses with stratification by vaccine type, sex, and age, which effectively reduced the heterogeneity. The analysis of different types of vaccines showed that the efficacy of inactivated vaccines was 73.11% (95% confidence interval [CI], 34.23; 89.03), the efficacy of protein subunit vaccines was 89.33% (95% CI, 81.44; 93.10), and the efficacy of RNA-based vaccines was 94.29% (95% CI, 93.65; 95.40). The efficacy of the viral vector (non-replicating) vaccines was 79.56% (95% CI, 60.00; 89.92; Table1 ; Figure S2 ; Table S1 ).

The efficacy of COVID-19 vaccines

Since inactivated vaccines and protein subunit vaccines lacked subgroup data, including age and sex, only RNA-based vaccines and viral vector (non-replicating) vaccines were included in the subsequent subgroup analyses. Vaccine efficacy (VE) among male and female participants was 92.70% (95% CI, 81.00; 96.81) and 87.84% (95% CI, 75.78; 93.88), respectively. At the same time, the efficacy of vaccine among 16 to 55 years old recipients was 88.89% (95% CI, 75.45; 94.87) and that among those over 55 years old was 87.62% (95% CI, 76.83; 92.54). Only RNA-based vaccines and viral vector (non-replicating) vaccines provided the data of different races. In the subgroup analysis, VE among Black or African American and White participants was 95.37% (95% CI, 47.92; 100.00) and 89.81% (95% CI, 73.08; 96.15), respectively. We found that all vaccines achieved good efficacy, among which RNA-based vaccines had the highest, whereas inactivated vaccines had the lowest, although they were more than 70% effective. In addition, Black or African American people, males, and the 16- to-55-year-old subgroup experienced greater VE ( Table 1 ; Figure S2 ; Table S1 ).

Incidence of ADRs related to the COVID-19 vaccines

Safety is another important factor when considering vaccines. Therefore, we first performed a meta-analysis of the clinical trial data and then collected real-world data from the VAERS maintained by the CDC in the United States. In the clinical trials analysis, we evaluated a total of 6 phase III clinical trials and 6 phase I/II clinical trials and official reports of phase III results of COVID-19 vaccines, and 56,310 cases were included. Meanwhile, the data of 86,506,742 doses from 5 reports about the thromboembolic events were included, while 603,862,888 doses from 3 reports about the myocarditis/pericarditis events were included. In the real-world analysis, we included 11,936 participants. The results are as follows.

Incidence of ADRs in the meta-analysis of clinical trials

We observed 36 types of ADRs in the clinical trials, among which 8 were observed after vaccination with more than 50% of the vaccines ( Table S2 ), including pain, swelling, fever, fatigue, chills, muscle pain (myalgia), joint pain (arthralgia), and headache. We further conducted a meta-analysis of these 8 ADRs. Similarly, to minimize heterogeneity, we performed subgroup analyses stratified by dose, vaccine type, and age.

Since inactivated vaccines lacked ADRs data of dose 1, only RNA-based vaccines, viral vector (non-replicating) vaccines, and protein subunit vaccines were included in the analyses. The results showed that the most frequently reported ADR was pain (at the injection site) after dose 1 in protein subunit vaccines (38.46%) and RNA-based vaccines (80.97%). Pain was reported more frequently in younger vaccine recipients (16 to 55 years old) than in older vaccine recipients (over 55 years old; 80.00% versus 59.35%). Fatigue was the second most frequent ADR after dose 1 (30.77% of those receiving the protein subunit vaccines and 39.27% of those receiving the RNA-based vaccines). The incidence in the 16- to 55-year-old subgroup was significantly higher than that in the over 55-year-old subgroup (52.72% versus 33.73%). The incidences of other ADRs were below 50%. Headache ranked third, followed by muscle pain (myalgia), joint pain (arthralgia), chills, swelling, and fever. The incidence of ADRs after vaccination with RNA-based vaccines was high, and further analysis of age subgroups indicated that the results were generally consistent with those observed in the overall analysis. Meanwhile, unlike the two vaccines above, in viral vector (non-replicating) vaccines, the most frequently reported ADR was fatigue (56.25%). Headache ranked second, followed by pain, muscle pain (myalgia), joint pain (arthralgia), chills, fever, and swelling ( Table 2 ; Figures S3–S5 ; Table S2 ).

The incidence of ADRs associated with COVID-19 vaccines via meta-analysis

Among participants who received dose 2, the overall incidence of ADRs was higher than that after dose 1. As observed for dose 1, pain was the most frequent ADR. The incidences of pain in patients administered different types of vaccines were as follows: inactivated vaccines (31.75%), protein subunit vaccines (57.69%), RNA-based vaccines (81.76%), and viral vector (non-replicating) vaccines (44.75%). The incidences of pain in different age groups were as follows: 16 to 55 years old (72.40%) and over 55 years old (51.06%). The incidences of ADRs other than pain differed among the various types of vaccines. In particular, the incidence of ADRs was lowest for inactivated vaccines, with incidences of all ADRs less than 10%. In descending order of frequency, the ADRs were headache, swelling, fatigue, chills, joint pain (arthralgia), muscle pain (myalgia), and fever. The ADRs associated with the other three types of vaccines were similar to those after dose 1, with fatigue and headache ranking second and third, respectively. However, more than 50% of recipients experienced headache after dose 2, unlike after dose 1. Moreover, among the other ADRs with incidences less than 50%, chills ranked fifth after dose 2, while it had ranked sixth after dose 1, and the other ADRs in order were joint pain (arthralgia), swelling, and fever. For RNA-based vaccines and viral vector (non-replicating) vaccines, consistent results were obtained among subgroups stratified by age ( Table 2 ; Figures S7–S10 ; Table S2 ).

To assess the severity of vaccine-related ADRs, we calculated the proportions (the ADRs over grade 3/all ADRs) and conducted a meta-analysis according to severity grade. The results showed that the ADRs associated with the RNA-based vaccine (Moderna, BNT162b2) were the most severe, and instances of grade 3 reactions were reported for all 8 ADRs. Fortunately, the proportions were low, and even the largest was less than 20%. Grade 3 ADRs also occurred after vaccination with viral vector (non-replicating) vaccines (AZD1222, Sputnik V); however, the proportions were low (less than 10%). Most ADRs after vaccination with inactivated vaccines (BBIBP) and protein subunit vaccines (NVX-COV2373) were grades 1–2. Importantly, among the participants who received the first dose of an RNA-based vaccine, grade 4 fever was noted, but the proportion was less than 5%. Meanwhile, we also found that younger participants were more likely to report higher-grade ADRs than older participants. For the RNA-based vaccine (Moderna) and protein subunit vaccine (NVX-COV2373), the ADR grades were higher after the second dose than the first dose ( Figure 1 ; Table S2 ).

The severity of vaccine-related ADRs in clinical trials

Stacked bar chart showing the percentage of four ADRs grade after dose 1 or dose 2 of COVID-19 vaccines. (A) pain, (B) swelling, (C) fever, (D) fatigue, (E) chills, (F) muscle pain (myalgia), (G) joint pain (arthralgia), and (H) headache. Grade 1 (dark blue), grade 2 (light blue), grade 3 (red), and grade 4 (brown).

In the analysis of ADRs over grade 3, the incidences were all less than 10%, among which the most frequently reported ADR was fatigue (6.34%) in RNA-based vaccines after dose 2. The ADR grades were higher after the second dose than the first dose in RNA-based vaccines, contrary to the viral vector (non-replicating) vaccines (AZD1222, Sputnik V). What’s more, the incidences of the ADRs over grade 3 in viral vector (non-replicating) vaccines were higher than those in RNA-based vaccines after dose 1 ( Table 2 ; Figures S6 and S11 ; Table S2 ).

The severe and rare ADRs of COVID-19 vaccines

Besides the ones that have been reported in the clinical trials, there are some severe and rare ADRs, such as thromboembolic events and myocarditis/pericarditis events, which may result in death. Our results showed that thromboembolic events were only found in viral vector (non-replicating) vaccines (Ad26.COV2.S and AZD1222), while myocarditis/pericarditis events were reported in both viral vector (non-replicating) vaccines (Ad26.COV2.S and AZD1222) and RNA-based vaccines (BNT162b2 and Moderna). The incidence of thromboembolic events in Ad26.COV2.S (75 cases per million doses) was higher than that in AZD1222 (21 cases per million doses; Figure 2 A; Table S3 ). The incidence of myocarditis/pericarditis events was similar in viral vector (non-replicating) vaccines and RNA-based vaccines (2 versus 3 cases per million doses; Figure 2 B; Table S3 ).

Forest plot of the incidence of thromboembolic events and myocarditis/pericarditis events

Meta-analysis was performed using R statistical software. Event rates and their corresponding 95% confidence intervals were estimated using both a fixed-effects model and a random-effects model. (A) Thromboembolic events and (B) myocarditis/pericarditis events.

Incidence of ADRs associated with RNA-based vaccines in the real world (VAERS)

To evaluate the safety of the COVID-19 vaccines more comprehensively, we retrospectively obtained real-world data pertaining to ADRs associated with RNA-based vaccines from VAERS. A total of 11,936 participants were included in the study, among whom 4,990 were vaccinated with the Moderna vaccine and 6,946 were vaccinated with the Pfizer-BioNTech vaccine ( Table S4 ).

Our research revealed an unexpected phenomenon. The incidence of ADRs in the real world was far lower than that in clinical trials. The ADR with the highest incidence is headache (16.53%), but the spectrum of ADRs is significantly wider than that in clinical trials. We identified more than 700 ADRs, but the incidence of most ADRs (more than 90%) was lower than 1% ( Figure 3 D). To evaluate the tolerance of the vaccine in different populations, we conducted subgroup analyses stratified by age, sex, and vaccine manufacturer. All ADRs with incidences higher than 5% were included. After stratification by the vaccine manufacturer (Moderna and Pfizer-BioNTech), the results showed that there were no significant differences in the incidences of headache, pain, myalgia, and nausea, but the incidences of chills, pyrexia, injection site pain, injection site erythema, pain in the extremities, and injection site swelling were higher among patients vaccinated with the Moderna vaccine than among those vaccinated with the Pfizer-BioNTech vaccine. In contrast, fatigue, dizziness, and dyspnea occurred more frequently in patients vaccinated with the Pfizer-BioNTech vaccine ( Figure 4 ; Table S6 ). The details of the incidences of all ADRs associated with the different vaccines are shown in Table S6 . Headache was still the most frequent ADR after the subgroup analysis was performed with stratification by age. Meanwhile, among those vaccinated with the Moderna vaccine, all ADRs were reported more often in older participants than young participants. The result was the opposite for the Pfizer-BioNTech vaccine ( Figures 3 D and ​ and4; 4 ; Tables S7 and S8 ). The details of the incidences of all ADRs in different age groups are shown in Tables S7 and S8 . In the analysis stratified by sex, we found that regardless of whether the Moderna or Pfizer-BioNTech vaccine was administered, pyrexia ranked first, which was different from the results of the other subgroup analyses. Other than pyrexia and chills, which were more common in males, the incidences of other ADRs were higher in females than in males ( Figures 3 D and ​ and4; 4 ; Tables S9 and S10 ). The details of the incidences of all ADRs stratified by sex are shown in Tables S9 and S10 .

The incidence of ADRs of RNA-based vaccine from real-world data (VAERS)

Log-rank test of ADRs onset time stratified by (A) vaccine type, (B) age, and (C) gender. (D) Heatmap showing the incidence of ADRs. (∗ADRs Spectrum: due to the limitation of figure size, the details are shown in Table S5 .)

The subgroup analyses of ADRs in RNA-based vaccine from real-world data (VAERS)

To evaluate the tolerance of the vaccine in different populations, we conducted subgroup analyses stratified by age, sex, and vaccine manufacturer. All ADRs with incidences higher than 5% were included. (∗No application: the incidences of ADRs under 5% in the subgroups were defined as “no application,” which were not tested by χ 2 .)

We also further explored the timing of the onset of ADRs. Most participants developed symptoms within a week after vaccination, but the longest interval was 60 days. The median symptom onset time for the Moderna and Pfizer-BioNTech vaccines were 2 days and 1 day, respectively, but the difference was not statistically significant ( Figure 3 A, p = 0.07). Symptoms appeared earlier in young participants, and the median interval was 1 day, while in older people, it was 2 days ( Figure 3 B, p < 0.0001). Symptoms appeared earlier in females, with a median interval of 1 day, while in males it was 2 days ( Figure 3 C, p < 0.0001).

COVID-19 remains a global public health threat, although it has been more than a year since the first case was diagnosed. The number of cases and deaths from COVID-19 continues to increase. Undoubtedly, vaccines are the most promising means to control the COVID-19 pandemic. As of April 5, 2021, several vaccines had been approved for public use, including RNA-based vaccines (Moderna and Pfizer-BioNTech), inactivated vaccines (Sinopharm [BBIBP], CoronaVac, Covaxin, Sinopharm [WIBP], and CoviVac), viral vector vaccines (Oxford-AstraZeneca, Sputnik V, Johnson & Johnson, and Convidecia), and protein subunit vaccines (EpiVacCorona, RBD-Dimer). 3 Although vaccinations are continuing to be administered, the vaccinated population only accounts for a small proportion of the entire population, and safety and efficacy are the issues about which many people are concerned.

This is the first study on the efficacy and safety of COVID-19 vaccines using published clinical trial data and real-world data. We comprehensively analyzed the efficacy of the existing COVID-19 vaccines and their incidence, spectrum, timing, and clinical features of ADRs after vaccination. Our research indicated that the efficacy of all vaccines exceeded 70% and that RNA-based vaccines had the highest efficacy of 94.29%; moreover, young people, Black or African American people, and males may experience greater vaccine efficacy. The spectrum of vaccine-related ADRs is extremely broad, involving multiple systems. The most common ADRs are pain, fatigue, and headache. Most ADRs are tolerable and mainly in grade 1 or 2 in severity; only grade 4 fever has been observed. Some severe ADRs have been identified, though the incidences were low (thromboembolic events, 21–75 cases per million doses; myocarditis/pericarditis, 2–3 cases per million doses). Most symptoms appear soon after vaccination, and many people recover without any medication.

In terms of efficacy, RNA-based vaccines ranked first, reaching greater than 94%, due to their strong immunogenicity and effective presentation of SARS-CoV-2 antigens to the immune system. 7 Currently, mutant virus strains are also attracting attention. RNA-based vaccines may be more effective against these mutant strains owing to their use of the full immunogenicity of SARS-CoV-2. However, the incidence of ADRs is high after vaccination with RNA-based vaccines, reaching over 80% based on the clinical trial data, with the incidences of grade 3 or 4 ADRs accounting for a small proportion. Although the real-world incidence of ADRs was lower than that in the clinical trials, the spectrum was broader, and a large portion of types of ADRs were not observed in clinical trials, suggesting that attention should be given to the identification and treatment of rare ADRs. Meanwhile, myocarditis/pericarditis have been identified in RNA-based vaccines; fortunately, the incidence was low. Protein subunit vaccines had an efficacy of 89%, while the highest incidence of ADRs was only 57%, and highest incidence of the ADRs over grade 3 was 3.85%, significantly lower than that associated with RNA-based vaccines; therefore, it may be a promising candidate. However, because real-world data regarding protein subunit vaccines are lacking and the sample of published data is small, further analysis is needed. Moreover, viral vector (non-replicating) vaccines have an efficacy of 79%, while the highest incidence of ADRs is 40%. In addition, the incidence of ADRs above grade 3 is significantly lower than that associated with RNA-based vaccines. However, some thromboembolic events and myocarditis/pericarditis events have been reported after vaccination with viral vector (non-replicating) vaccines (Ad26.COV2.S and AZD1222), which are very severe. Fortunately, the incidences of thromboembolic events and myocarditis/pericarditis events were low. Inactivated vaccines, in particular, are very safe and easy to preserve and transport, although their efficacy is relatively lower.

In the subgroup analysis, the ADRs after dose 1 of viral vector (non-replicating) vaccine (AZD1222, Sputnik V) occurred more often than dose 2. In contrast, the incidence of ADRs was higher after dose 2 of the RNA-based vaccine produced by Moderna and the protein subunit vaccine called NVX-COV2373. The results suggest that there are differences among the vaccines, and the monitoring of ADRs cannot be taken lightly even if no adverse reaction occurs following dose 1, especially among those receiving RNA-based vaccines (e.g., Moderna) and protein subunit vaccines (e.g., NVX-COV2373). The second dose should not be avoided because of ADRs after dose 1. The process of building tolerance to viral vector (non-replicating) vaccines is gradual in vaccinated recipients. We also found that young people seem to be relatively more prone to higher grade ADRs. We speculate that the relatively stronger immune systems in young people lead to both a higher incidence of ADRs and greater vaccine efficacy. 8 This finding also reduces concerns about vaccinating elderly people. The higher incidence of ADRs among female participants than male participants is puzzling, because it suggests that a stronger immune response was elicited in females, but the efficacy is lower in females than in males. This is inconsistent with the results of previous studies on sex differences. 9 The specific reasons need to be explored further. Furthermore, in the analysis of the timing of the onset of ADRs, we found that young people and females developed symptoms earlier, which may be related to the higher incidence of ADRs and their stronger immune systems. 9 In addition, the interval between vaccination and the development of ADRs in some patients can be up to 60 days, suggesting that the vaccination history should be actively reported when symptoms develop after vaccination and clinicians should pay attention to the lag between vaccination and the development of ADRs.

In the ADR analysis, the real-world data from the VAERS and clinical trial data were compared. We found that there are differences in the spectrum of ADRs, with a wider spectrum of ADRs identified in the real-world data. One plausible explanation is that the data in VAERS are continuously and openly collected. However, only ADRs that occurred within 1 week were counted in most clinical trials, and those that appeared after 1 week were omitted. In addition, the VAERS system lacks a standardized description of symptoms, with multiple different descriptions referring to the same ADR, falsely increasing the spectrum of ADRs. Another surprising finding is that the incidence of ADRs in the real world is far lower than that in clinical trials. Real-world data are only available for RNA-based vaccines, and the sample size is not yet large enough. Additionally, the VAERS is a self-reporting system with reporting bias, 10 and a large number of participants who were vaccinated did not report their ADRs, resulting in a lower incidence rate than in clinical trials.

We also found that few cases of mortality were reported to VAERS, and there was not enough evidence to indicate that the death was related to vaccination after carefully assessing each case. Therefore, a large-scale real-world study is needed for further confirmation.

In addition to the possible bias in VAERS, our study also has other deficiencies. The heterogeneity of several subgroups was large in the meta-analysis. To minimize heterogeneity, we used a total of 5 transformation methods (PFT, PAS, PRAW, PLN, PLOGIT) and chose the method by which the lowest heterogeneity was achieved. 11

In addition, we also conducted sensitivity analyses and multiple subgroup analyses to minimize heterogeneity. Both fixed-effect model and random-effect model were performed. When I 2 was less than 50% and p > 0.1, the fixed-effect model was chosen; otherwise, the random-effect model was chosen. 12 , 13 , 14

The Begg’s and Egger’s tests were not used because there were not more than 10 subjects in each group. 15 Although some subgroups were heterogeneous, we determined that the heterogeneity was derived from the data itself after sufficient statistical correction and analysis, possibly due to factors such as the area in which the study was conducted, the risk of exposure to SARS-CoV-2, and other factors that were beyond our control. Therefore, our research comprehensively demonstrated the efficacy and safety of the COVID-19 vaccines to the greatest extent possible, providing a credible reference for clinical practice and the general public.

In summary, vaccines are a powerful tool against the COVID-19 pandemic, with high efficacy and tolerable adverse reactions. Each vaccine has its own advantages and shortcomings, and every citizen should choose to be vaccinated as soon as possible. In addition, the spectrum of ADRs associated with the vaccines is broad, and most of the reactions appear within a week, although a delay sometimes occurs. Some severe ADRs have been identified, though the incidences were low (thromboembolic events and myocarditis/pericarditis). Therefore, ADRs should be identified and addressed in a timely manner after vaccination. We hope that our research can eliminate fear of the vaccines among the general public and provide guidance regarding the management of vaccine-related side effects in a timely manner.

Materials and methods

Meta-analysis, part 1: the landscape of efficacy and safety of covid-19 vaccines, inclusion criteria.

The study was registered in PROSPERO (CRD42021234481). We identified records by searching PubMed, Medline, EMBASE, and the Cochrane Central Register of Controlled Trials (CENTRAL) for “(COVID-19 OR 2019-nCoV OR SARS-CoV-2) AND vaccine” on March 7, 2021. English-language clinical trials were included.

Exclusion criteria

All 8,215 initially identified studies were screened; those that were clinical trials were included (n = 53), and those in which a vaccine against SARS-CoV-2 was not used were excluded (n = 29). Trials without adverse effect or efficacy data (n = 1) and those with only the clinical trial protocol (n = 1) were excluded.

The remaining trials (n = 22) included 17 phase I/phase II clinical trials of 12 vaccines, and 4 of these vaccines had published phase III clinical trial results (n = 5). We further searched for the remaining 8 vaccines on Google using the following keywords: “(candidate vaccine name or manufacturer) AND (COVID-19 OR 2019-nCoV OR SARS-CoV-2).” Phase I/phase II trials of vaccines that did not have official results from phase III clinical trials were excluded (n = 11).

The remaining trials (n = 11) included 8 different vaccines, phase III clinical trials were updated on June 17, 2021, and a new trial of Ad26.COV2.S vaccine was included (n = 1). Finally, 12 clinical trials were assessed individually, and a total of 194,015 cases were included. 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 The total number of patients treated, the number and type of adverse effects, the VE were compared, and the PRISMA diagram of articles selected for meta-analysis was shown in Figure S1 ( Figure S1 A; Table 3 ).

Clinical trials and the characteristics of COVID-19 vaccines

Part 2: The severe and rare ADRs of COVID-19 vaccines

We identified records by searching PubMed, Medline, EMBASE, Google, and the CENTRAL for “(Thromboembolic OR Myocarditis OR Pericarditis) AND COVID-19 vaccine” on June 17, 2021. English-language clinical trials and official reports were included.

All 2,910,000 results initially identified studies were screened; those that were clinical trials (n = 1), cohort study (n = 3), case reports (n = 12), and official reports (n = 8) were included. Those studies without the data of the exact total number and exact number of patients with thromboembolic or myocarditis or pericarditis were excluded (n = 14). Those official reports that were outdated or without the data of the exact vaccine type (n = 3) were excluded.

The remaining trial (n = 1), 27 cohort study (n = 1), 28 and official reports (n = 5) 29 , 30 , 31 , 32 , 33 were assessed individually, and a total of 86,506,742 doses with thromboembolic events and 603,862,888 doses with myocarditis/pericarditis events were included. The total number of doses, the number and type of adverse effects, and the vaccine types were compared, and the PRISMA diagram of articles selected for meta-analysis is shown in Figure S2 ( Figure S1 B; Table S3 ).

The study is based on data downloaded from the VAERS ( https://vaers.hhs.gov/data.html ). The VAERS is comanaged by the CDC and the FDA and has been used to detect possible safety problems in U.S.-licensed vaccines since 1990. Healthcare providers, vaccine manufacturers, and the public can submit reports to the system. 6

We accessed the VAERS on March 5, 2021 and downloaded data from 2020 and 2021. We included all entries in which the patient had been injected with the Moderna or Pfizer COVID-19 vaccine. Patients injected with COVID-19 vaccines manufactured by unknown developers or vaccines against other pathogens were excluded.

VE was calculated as 1-relative risk (RR): 34 , 35

The incidence of ADRs was extracted by “Engauge Digitizer” from histograms if the raw data were not displayed. 36 The incidences of ADRs were compared with χ2 tests. Other clinical variables of interest were evaluated descriptively. Statistical analyses were performed in GraphPad Prism (version 7, GraphPad Software); the meta-analysis was performed using R statistical software (packages metafor and meta, R Foundation). Event rates and their corresponding 95% confidence intervals were estimated using both a fixed-effects model and a random-effects model. Forest plots were constructed to summarize the data for each analytical group according to the incidence rate and to provide a visual analysis of fatal drug-related events.

Acknowledgments

This study was supported by grants from the National Key R&D Program of China (number 2018YFC1313300), National Natural Science Foundation of China (numbers 81070362, 81172470, 81372629, 81772627, 81874073, and 81974384), key projects from the Nature Science Foundation of Hunan Province (numbers 2015JC3021 and 2016JC2037), the projects from Beijing CSCO Clinical Oncology Research Foundation (numbers Y-HR2019-0182 and Y-2019Genecast-043), and the Fundamental Research Funds for the Central Universities of Central South University University (2020zzts273 and 2019zzts797). We want to show our appreciates to Zirconicusso/Freepik for providing the materials for making the Graphical Abstract.

Author contributions

C.C., Y.P., H.S., S.Z., and Y.H. designed the study. C.C., Y.P., E.S., Q.H., Y.C., P.L., C.G., Z.F., L.G., Y.L., and X.Z. collected the data and performed the major analysis. S.Z. and H.S. supervised the study. C.C. and Y.P. analyzed and interpreted the data. E.S. and Z.F. did the statistical analysis. C.C., Y.P., C.G., and Y.L. drafted the manuscript. All authors read and approved the final manuscript.

Declaration of interests

The authors declare no competing interests.

Supplemental information can be found online at https://doi.org/10.1016/j.ymthe.2021.08.001 .

Supplemental information

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• Published: 24 May 2023

Unraveling attributes of COVID-19 vaccine acceptance and uptake in the U.S.: a large nationwide study

• Sean D. McCabe 1 , 2   na1 ,
• E. Adrianne Hammershaimb 1 , 3 , 4   na1 ,
• David Cheng 1 ,
• Andy Shi 1 , 2 ,
• Derek Shyr 1 , 2 ,
• Shuting Shen 1 , 2 ,
• Lyndsey D. Cole 5 ,
• Jessica R. Cataldi 5 , 6 ,
• William Allen 1 , 7 ,
• Ryan Probasco 1 ,
• Ben Silbermann 1 ,
• Feng Zhang 1 , 8 , 9 , 10 , 11 ,
• Regan Marsh 12 , 13 , 14 ,
• Mark A. Travassos   ORCID: orcid.org/0000-0002-6045-3322 1 , 3 , 4 &
• Xihong Lin   ORCID: orcid.org/0000-0001-7067-7752 1 , 2 , 15 , 16  

Scientific Reports volume  13 , Article number:  8360 ( 2023 ) Cite this article

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• Disease prevention
• Infectious diseases
• Public health

SARS-CoV-2 vaccines are useful tools to combat the Coronavirus Disease 2019 (COVID-19) pandemic, but vaccine reluctance threatens these vaccines’ effectiveness. To address COVID-19 vaccine reluctance and ensure equitable distribution, understanding the extent of and factors associated with vaccine acceptance and uptake is critical. We report the results of a large nationwide study in the US conducted December 2020-May 2021 of 36,711 users from COVID-19-focused smartphone-based app How We Feel on their willingness to receive a COVID-19 vaccine. We identified sociodemographic and behavioral factors that were associated with COVID-19 vaccine acceptance and uptake, and we found several vulnerable groups at increased risk of COVID-19 burden, morbidity, and mortality were more likely to be reluctant to accept a vaccine and had lower rates of vaccination. Our findings highlight specific populations in which targeted efforts to develop education and outreach programs are needed to overcome poor vaccine acceptance and improve equitable access, diversity, and inclusion in the national response to COVID-19.

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Introduction

The emergence in late 2019 of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as a novel human pathogen and causative agent of the global coronavirus disease 2019 (COVID-19) pandemic 1 fueled an unprecedented effort to rapidly develop a vaccine 2 . While the successful development of several effective SARS-CoV-2 vaccines was a major achievement, the defining challenge of the COVID-19 pandemic is ensuring equitable vaccine distribution and high vaccine uptake.

Soon after the identification of the virus, it was estimated that at least 70% of the U.S. population would need to acquire immunity to SARS-CoV-2 to end the COVID-19 pandemic 3 . It was unclear whether natural infection alone would produce sufficient, durable immunity, and vaccination became a major pillar of the public health strategy to control the pandemic. Public opinion polling in early 2020 suggested that as many as 72% of U.S. adults were willing to receive a COVID-19 vaccine once licensed and available. Four months later, the number of U.S. adults willing to receive a SARS-CoV-2 vaccine had sharply declined to as low as 51% 4 . Resistance to vaccination has posed a public health challenge since the smallpox vaccine was first invented, and although the vaccine targets and the cultural context may vary over time and place, common factors associated with reluctance, refusal, and even anti-vaccination activism include mistrust, misinformation, and a belief in the primacy of individual liberty.

In December 2020, two vaccines against COVID-19 received Emergency Use Authorization (EUA) from the U.S. Food and Drug Administration 5 , 6 . The results of phase 3 clinical trials and the subsequent rollout of the Pfizer-BioNTech and Moderna vaccines received significant attention in the media. Opinion polls conducted in December 2020 suggested a subsequent increase in public willingness to receive a COVID-19 vaccine, likely due to the widespread availability of data showing the vaccines to be both safe and effective 7 . Despite Johnson & Johnson’s Janssen COVID-19 vaccine also receiving EUA, national uptake of vaccines declined from mid-April 2021 onward as those reluctant to be vaccinated occupied a greater percentage of the unvaccinated population and information emerged about rare vaccine-related adverse events 8 , 9 , 10 .

How We Feel (HWF) is a web and mobile-phone application developed to facilitate the large-scale collection of data about COVID-19 symptoms, SARS-CoV-2 test results, and transmission-mitigating behaviors and sentiments 11 . Users are assigned a randomly generated number that tracks logins from the same device and are otherwise unidentifiable. Beginning in December 2020, we fielded a question about users’ COVID-19 vaccine intentions. These responses were then related to the user’s subsequent COVID-19 vaccine uptake or refusal.

We hypothesized that responses could provide significant insights into understanding vaccine acceptance across the United States, identifying populations that could be a promising focus of vaccine outreach efforts. We aimed to evaluate associations of the degree of COVID-19 vaccine acceptance in the U.S. and identify characteristics that might influence vaccine acceptance and eventual COVID-19 vaccine uptake. This has the potential to help public health and community leaders develop effective education and outreach programs to overcome vaccine reluctance and ensure equitable vaccine distribution and improved vaccine uptake.

A total of 36,711 users responded to the vaccine acceptance question. The largest number of respondents came from Connecticut and California with 8697 and 4668, respectively (Supplementary Fig.  1 a). HWF’s user base is approximately 79% female (Supplementary Fig.  1 b) and 83% white (Supplementary Fig.  1 c). Users are 18 years of age or older and are equally distributed by age groups (Supplementary Fig.  1 d). More than 68% of respondents were non-essential workers, and users cover a diverse range of income groups. All descriptive statistics of the study participants are available in Supplementary Table 1 .

In total, 30,618 (83%) were willing (“Likely” or “Very Likely”) to be vaccinated (Fig.  1 a). After applying a census-based post-stratification weight (see Methods), Vermont (92%) and Washington D.C. (88%) had the highest rates of vaccine reluctance while South Dakota (27%) and Louisiana (23%) had the highest rates of undecided users (Fig.  1 b). Weighted bar plots of vaccine reluctance across demographic characteristics revealed that “Undecided” users represented the largest proportion of non-willing users across all demographic groups (Fig.  2 a, Supplementary Table 2 ).

COVID-19 Acceptance rates: ( a ) (Left) Number of responses and (Right) unweighted and weighted percentages. ( b ) Weighted average willingness and undecided rates by state.

Demographic Acceptance Rates: ( a ) Weighted percentages of reluctant responses by race/ethnicity, profession, location, age, income, and use of protective measures. State level weighted reluctance rates by ( b ) cumulative case rates (/100 individuals), ( c ) cumulative death rates (/1000 individuals), ( d ) and average number of users practicing protective behavior.

State level reluctance (“Undecided”, “Unlikely”, or “Very Unlikely”) rates were negatively associated with the average number of users that practiced transmission mitigating behaviors and were positively associated with cumulative COVID-19 case and death rates by January 10, 2021 (Fig.  2 b–d). Unweighted plots are available in Supplementary Fig.  2 .

To assess demographic associations with vaccine acceptance, we fit a univariate logistic regression with socio-demographic, occupation, preexisting medical conditions, geographical and COVID-19 related predictors (Supplementary Table 3 ) and a multivariable logistic regression model to adjust for potential relationships between the predictors (Fig.  3 , Supplementary Table 4 ). We implemented post-stratification weights using census estimates of sex, age, race, and census location (see Methods). People of color reported higher rates of vaccine reluctance compared to white non-Hispanic users (African American OR, 3.94; CI, 3.47, 4.48; p  = 1.26e−96). Vaccine reluctance was more likely among females than males (OR,1.67; CI, 1.51, 1.83; p  = 4.09e−25); younger users than those over 65 years old (18–30 OR, 2.17; CI, 1.86, 2.53; p  = 1.03e−22); those with three or more preexisting conditions than those with zero (OR, 1.19; CI, 1.06, 1.34; p  = 0.0036); and parents than non-parents (OR, 1.26; CI, 1.15, 1.38; p  = 9.61e−7). Individuals that were furloughed or job-seeking were also more vaccine reluctant compared to those working full- or part-time (OR, 1.48; CI, 1.29, 1.70; p  = 4.04e−8). Respondents from the South (OR, 1.25; CI, 1.05, 1.48; p  = 0.0105), from less densely populated areas, or with lower incomes were all more likely to be vaccine reluctant. Users that responded before the Pfizer Emergency Use Authorization (EUA) on December 11, 2020 were more vaccine reluctant than users who responded after the Pfizer EUA (OR, 1.48; CI, 1.37, 1.60; p  = 9.96e−23), users who practiced behavior protective against COVID-19 such as mask-wearing or social distancing were less vaccine reluctant (OR, 0.78; CI, 0.72, 0.85; p  = 6.12e−9), and users that received a COVID test were less vaccine reluctant (OR, 0.79; CI, 0.71, 0.89, p  = 5.88e-5).

Logistic regression-based association analysis results of vaccine acceptance: Forest plots for (Left) unweighted and (Right) weighted multivariable logistic regression analyses for vaccine reluctance with 95% confidence intervals. Non-significant variables at the 0.05 level (white), significant positive associations (red), and significant negative associations (blue).

Nominal logistic regression (see Methods) evaluated whether vaccine reluctance was driven by “Undecided” vs. “Unlikely/Very Unlikely” responses (Supplementary Table 5 ) and was also conducted with a weighted analysis (Supplementary Table 6 ). Reluctance in healthcare workers, those aged 55–64, Asian users, and those in locations with a median income between $70,000 and $100,000 was driven by the “Undecided” group, whereas reluctance in the unemployed, those with 3 + preexisting conditions, and southern users was driven by the “Unlikely” group. Sensitivity analyses were performed for the weighted multivariable and nominal regression analyses with a less restrictive threshold for the trimming weights (Supplementary Table 7 – 8 ) and found similar results. We conducted a sensitivity analysis to assess differences in reluctance in individuals that tested positive for COVID-19 and found no difference in intention based on testing results (see Methods, Supplementary Table 9 – 10 ).

Of the 36,711 users who responded to the vaccine acceptance question, 23,429 also responded to the vaccine uptake question and its distribution is provided in Fig.  4 a. Demographic distributions remained similar to those of respondents of the vaccine acceptance question with a slight increase in the proportion of users ages 65 + (Supplementary Fig.  3 ). Vaccination rates by state are shown in Fig.  4 b for all users that responded to the vaccine uptake question and subset to respondents who were offered a vaccine. Users with lower levels of vaccine acceptance had lower rates of vaccination (Fig.  4 c), and Black and Hispanic/Latinx users reported lower rates of vaccination than White, Non-Hispanic users (Fig.  4 d). Plots of weighted and unweighted vaccination rates across all demographic features are available in Supplementary Figs.  4 – 5 .

Vaccine Uptake Rates: ( a ) Vaccine uptake question responses for all users. ( b ) Weighted vaccination rates by state of (Left) all users that responded to the vaccine uptake question and (right) users that were offered a vaccine. ( c ) Weighted vaccination uptake of users that were offered a vaccine by vaccine acceptance and ( d ) race/ethnicity.

To formally identify demographic features associated with differences in vaccination rates, we conducted an unweighted and weighted multiple logistic regression analysis (see Methods, Fig.  5 , Supplementary Tables 11 – 12 ). All age groups reported lower rates of vaccinations compared to users over 65 (18–30 OR: 0.10; CI, 0.06, 0.18; p  = 1.43e−16); Black users reported lower rates of vaccinations (OR, 0.58; CI, 0.38, 0.91; p  = 0.0165) compared to White non-Hispanic users; essential workers outside of healthcare reported lower rates of vaccinations (OR, 0.64; CI, 0.44, 0.92; p  = 0.0162) compared to non-essential workers; parents reported lower rates of vaccination (OR, 0.63; CI, 0.45, 0.89; p  = 0.0086) compared to users who are not parents; users in areas with a median household income (MHI) of $40–70 K (OR, 0.56; CI, 0.37, 0.85; p  = 0.0066) and $70–100 K (OR, 0.63; CI, 0.42, 0.96; p  = 0.0316) reported lower rates of vaccinations compared to those in areas with a MHI $100 K + ; users logging in from areas with 0–149 people/sq. mi reported lower rates of vaccinations (OR, 0.53; CI, 0.34, 0.82; p  = 0.0049) compared to users in high population density areas; and users that responded “Unlikely/Very Unlikely” (OR, 0.02; CI, 0.01, 0.03; p  = 2.07e−114) and “Undecided” (OR, 0.08; CI, 0.06, 0.12; p  = 1.06e−39) to the vaccine acceptance question reported lower rates of vaccinations compared to willing users.

Logistic regression-based association analysis results of vaccine uptake: Forest plots for (Left) unweighted and (Right) weighted multivariable logistic regression analyses for vaccination uptake with 95% confidence intervals. Non-significant variables at the 0.05 level (white), significant positive associations (red), and significant negative associations (blue).

While vaccination rates were lower in the reluctant group compared to the acceptant group, 86% (2157/2520) of reluctant users were vaccinated. In a formal multiple regression analysis looking at demographic associations with vaccine uptake among reluctant users, similar associations were found (see Methods, Supplementary Table 13 ). Younger age groups, healthcare workers, people from lower income households, and residents of areas with lower population density had lower vaccination rates. Users who responded to the vaccine acceptance question as “Undecided” reported higher rates of vaccination compared to those that responded “Unlikely/Very Unlikely” (OR, 4.57; CI, 3.47, 6.03; p  = 2.26e−26).

In our analysis, increased reluctance was associated with minority race/ethnicity, living in less densely populated regions, and being a healthcare worker. A large proportion of these populations were undecided about COVID-19 vaccination, suggesting that targeted outreach may improve vaccine uptake. In fact, a significant portion of those skeptical or undecided about vaccination were ultimately vaccinated, supporting the idea that perspectives on COVID-19 vaccination are not immutable and may respond to such outreach.

Black respondents had the highest rates of COVID-19 vaccine reluctance and the lowest rates of vaccine uptake relative to other racial and ethnic groups, consistent with other surveys 4 , 9 , 10 , 12 , 13 , 14 , 15 . The history of racist practices within the U.S. healthcare system and research community, such as during the Tuskegee Syphilis Study 16 , and disparities in social determinants of health including poor access to healthcare and limited time off work likely contribute to our findings. Dispelling concerns within the Black community requires extensive, sustained, structured outreach and will be critical to efforts to contain and eliminate COVID-19. The National Institutes of Health’s Community Engagement Alliance (CEAL) provides a model for such outreach, targeting populations that have been hardest hit by the COVID-19 pandemic 17 .

Education and outreach efforts must target several additional populations. This includes rural residents and young adults. Because large proportions of these populations were undecided about COVID-19 vaccination, outreach to these groups must also provide reliable vaccine information tailored to the needs of each community, and different outreach strategies may be needed to address the concerns of those who were undecided and those who were unlikely.

Vaccine acceptance in healthcare workers warrants particular attention. We found that reluctant healthcare workers were less likely than other reluctant workers to change their mind (Supplementary Table 13 ). Others have found that U.S. nurses had the highest degree of COVID-19 vaccine reluctance among healthcare workers 18 . As the profession that enjoys the highest degree of public trust, nurses have an important role to play in promoting vaccine confidence 19 . Furthermore, inadequate vaccine uptake among healthcare workers raises the possibility of sustained COVID-19 transmission in an essential worker population critical to caring for vulnerable members of society, including immunocompromised individuals and children, the majority of whom were not yet eligible for a COVID-19 vaccine by the conclusion of this study 20 , 21 , 22 , 23 .

Addressing regional foci of reluctance to accept a COVID-19 vaccine will be critical in federal resource allocation to combat vaccine reluctance in general. We identified the greatest level of reluctance to accept a COVID-19 vaccine in the South followed by the Midwest. While a survey sponsored by the United States Centers for Disease Control and Prevention (CDC) and conducted in December 2020 found that COVID-19 vaccine hesitancy was most prevalent in the Northeast, followed by the South 15 , other data from the CDC detailing U.S. state and county-level vaccination rates and allocated dose usage have consistently shown that Southern states have lower vaccination rates and lower allocated dose usages compared to other areas of the country 9 . The significance of these phenomena is highlighted by the resurgence of COVID-19 with the spread of the delta variant in the South 24 .

Initial reluctance or indecision regarding COVID-19 vaccination was not fixed and did not necessarily reflect a respondent's eventual vaccination decision. This suggests the need for a multi-pronged approach that includes interventions directed at behavior change. Even if receptivity towards vaccination is low, there may still be significant potential for increasing vaccine uptake, indicating the need for continued implementation of strategies known to be effective, such as health care provider outreach and reminders 25 , 26 .

A study limitation is that our sample may not be generalizable to the broader American public or to populations outside of the U.S., particularly lower- and middle-income countries. How We Feel users are self-selecting, technologically literate, and more likely to have a high baseline level of concern about COVID-19. The user base is inherently skewed by a large proportion of users residing in Connecticut and California and by regional age discrepancies. Given the Connecticut government’s involvement in promoting the application, it’s possible users from Connecticut are more trusting of their state’s government. Census-adjusted, weighted analysis help correct the sampling bias but may not completely remove the potential for bias, and interpretation of our findings should note this. Furthermore, interstate movement of respondents during the pandemic may have affected the geographic distribution of responses. Additionally, we were unable to objectively verify self-reported vaccination; however, in other independent studies, there was a high degree of concordance between self-reported influenza vaccination and respondents’ actual influenza vaccination status 27 , 28 . This provides indirect evidence that self-reported COVID-19 vaccination status is a good proxy of verified vaccination status. Future research needs to be conducted to verify the concordance between the self-reported and registry-based vaccination records.

Further work is needed to better understand how vaccine reluctance relates to novel vaccine uptake in the U.S. and to understand how knowledge, attitudes, and behaviors surrounding COVID-19 vaccines change over time. As COVID-19 vaccines have become widely available to adults and adolescents in the U.S. and COVID-19 restrictions are lifting, our findings affirm the ongoing need to address vaccine reluctance and issues related to access.

Open-source software

We used the following open-source software in the analysis.

R: http://www.r-project.org

Tidyverse: http://www.tidyverse.org

Data.table: https://CRAN.R-project.org/package=data.table

nnet https://CRAN.R-project.org/package=nnet

censusapi https://CRAN.R-project.org/package=censusapi

survey https://CRAN.R-project.org/package=survey

ggplot2 https://CRAN.R-project.org/package=ggplot2

cowplot https://CRAN.R-project.org/package=cowplot

Data collection

Users could freely download the application which was available for both Android and Apple devices. The application was advertised widely on various social media outlets and through a partnership with the Connecticut state government which provided press releases to encourage residents to download the application. Users also heard about the application through word of mouth and through general media coverage. Data on vaccine acceptance was collected between December 4th, 2020 and May 6th 2021 11 . Following guidance from the CDC, users were asked “If a safe, effective coronavirus vaccine were available, how likely would you be to get yourself vaccinated?” Responses were given on a bipolar 5-point Likert scale from “Very Unlikely” to “Very Likely”, with “Undecided” being the middle value. The users first recorded response to the vaccine acceptance question was used in this analysis. On February 12th, 2021, a vaccine uptake question was added. Users were asked “Have you received a COVID-19 vaccine?” and could respond with “Yes”, “No, I haven’t been offered one”, or “No, I have been offered one but declined”. For all uptake models the most recent response was used. A consort diagram is available in Supplementary Fig.  7 to further clarify the number of respondents.

Users also self-reported race/ethnicity, sex, age, occupation, and preexisting conditions. Users who identified as “other” in the gender response were dropped due to small sample size. Neighborhood specific median household income was obtained from the user’s zip code at the time of answering the vaccine acceptance question by using the American Community Survey 5-year average results from 2018. Population density was calculated at the county level for each user based on data from the Yu Group at University of California at Berkeley 29 . State level case and death rates were obtained from USAFACTS 30 . As a proxy for user’s education status, the percentage of residents without a high school degree was included for each user’s county from the Census database.

Race/ethnicity was defined using distinct groups corresponding to “White,” “Black/African-American,” “Hispanic/Latino,” and “Asian” if the user only selected that respective racial group. Users who answered more than one race or ethnicity or selected an option other than the ones listed above were placed in a “multiracial/other” category.

During each login, users reported whether they left their home and for what reason. If they left home, they were then asked what types of protective measurements they used while away (mask, social distancing, cloth mask, and/or avoiding public transportation). We defined “protective behavior” to be if a user either stayed home or wore a mask when outside the home. If the user said that they did not wear a mask outside the home but engaged only in outdoor exercise and maintained physical distance from others, then they were also considered to be practicing protective behavior. We then created a variable that was coded as “1” if they always practiced protective behavior during all logins and a “0” if they failed to be protective during at least one login.

Users were considered to be reluctant to accept a vaccine if they responded as “Very Unlikely,” “Unlikely,” or “Undecided” to the vaccine acceptance question. Using vaccine reluctance as the outcome, a logistic regression was fit using several demographic variables as predictors to identify characteristics of users that were more or less vaccine reluctant. Both a univariate (Supplementary Table 3 ) and a multivariable model (Fig.  3 , Supplementary Table 4 ) were performed to adjust for potential confounding. Only responses from users residing within the United States were used in the modelling. Corresponding odds ratios and 95% confidence intervals are provided, and statistical significance was assessed at the 0.05 level. Analyses were conducted using R (v 3.5.1).

Using the same covariates as in the logistic regression, a nominal logistic regression was fit to assess if results from the logistic regression were driven by individuals being more likely to be in the “Undecided” or “Unlikely” groups. The 5-point Likert scale was reduced to a 3-level bipolar variable for modelling purposes by combining “Very Unlikely” with “Unlikely” and “Very Likely” with “Likely” (Supplementary Table 5 ).

Weighted analysis

To adjust our analyses to a user base that matches the major U.S. census demographics, we implemented a weighted analysis using post-stratification weights. Using the census population estimates of sex, race, age, and census location, a population-based joint distribution was obtained. A user base distribution was also calculated using the same breakdown, and the two proportions were then matched per user. The post-stratification weight was then calculated by dividing the census proportion by the sample proportion plus 1e−4 to avoid issues with smaller user base probabilities. To avoid over or underweighting individuals, the post-stratification weights were trimmed to be between 0.3 and 3 prior to the weighted analysis (Supplementary Table 4 ). For the nominal regression analysis, two separate weighted logistic regressions were conducted. One compared the “Undecided” group vs. the “Likely” group, while the other compared the “Unlikely” group vs. the “Likely” group (Supplementary Table 6 ). To assess the choice of the weight trimming bounds, sensitivity analyses were conducted for both above weighted analyses (Supplementary Table 7 – 8 ) using a threshold of 0.1 and 5. Supplementary Fig.  6 provides the distribution of the post-stratification weights.

IPW analysis

To formally assess if there was a difference in vaccine reluctance between those that received a prior positive COVID test and those that received a negative test, we adjust for the demographic biases associated with receiving a COVID test. We first fit a weighted logistic regression to model the probability of receiving a test using all individuals and all demographic features that have been reported in previous analyses while applying the same weighted procedure as above. The coefficients, 95% confidence intervals, and p -values for this analysis are available in Supplementary Table 9 . The fitted probabilities were then used as inverse probability weights (IPWs) in a weighted logistic regression model for vaccine reluctance only including individuals which had received a COVID test. The same predictors for previous weighted models were used and a new variable designating if a user tested positive or negative was included. To avoid extreme high or low weights, the fitted probabilities were trimmed to be between 0.1 and 0.9 or 0.05 and 0.95. The results of both models are available in Supplementary Table 10 .

Vaccine uptake

An unweighted multivariable logistic regression model was fit to identify which demographic features were associated with accepting or rejecting a COVID-19 vaccine. Along with the covariates included in the vaccine intent model, the three-level vaccine acceptance variable (“Very Likely/Likely”, “Undecided”, “Very Unlikely/Unlikely”) was also included in the analysis. Results are available in Supplementary Table 12 (left). To account for the biased sampling, non-response bias, and demographic differences in being offered a vaccine, a weighted multivariable model was fit. First, a weighted multivariable logistic regression model was fit for the probability of an individual responding to the vaccine uptake question with the inclusion of post-stratification weights as was done in the weighted vaccine acceptance model (Supplementary Table 11 A). The fitted probabilities from this model were then used as inverse probability weights to model the probability of a user being offered a vaccine (Supplementary Table 11 B). A user was defined as being offered a vaccine if the user responded to the question “Have you received a COVID-19 vaccine?” with “Yes,” or “No, I have been offered one but declined,” compared to users responding “No, I have not been offered a vaccine.” The fitted probabilities from this model were multiplied by the fitted probabilities from the response model and used as inverse probability weights in a final model which models the probability of accepting or rejecting the vaccine. The coefficients, 95% confidence intervals, and p -values for the final weighted model are available in Supplementary Table 12 . To more formally characterize the attributes associated with vaccine uptake within users that responded as vaccine reluctant, we fit a weighted multivariable logistic regression model subset to only the users who initially responded they were “Very Unlikely” or “Unlikely” to receive a COVID-19 vaccine. Models were fit identically to the above weighted models for all users and results of the final model are available in Supplementary Table 13 .

Ethics statement

Data was obtained from the non-profit organization the How We Feel Project which obtained a commercial IRB approval for the collection of the data. Due to receiving a deidentified dataset, the analysis in this paper was exempt from Institutional Review Board (IRB) approval by Harvard University Longwood Medical Area (HULC) IRB (HULC IRB Protocol No. IRB20-0514) and the Broad Institute of MIT and Harvard IRB (Broad/Harvard IRB Protocol no. EX-1653). When downloading the application, users were informed that their data would be shared securely with scientists, doctors, and public health professionals to stop the spread of COVID-19 and provided informd consent.

Data availability

This work used data from the How We Feel project. The data are not publicly available, but researchers can apply to use the resource. Researchers with an appropriate IRB approval and data security approval to perform research involving human subjects using the How We Feel data can apply to obtain access to data used in the analysis.

Code availability

The analysis code developed for this paper can be found online at https://github.com/mccabes292/HWF_VaccineHes_Paper .

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Acknowledgements

S.D.M. was supported by the United States National Institutes of Health [Grant T32ES007069] and a grant from the Partners in Health during preparation and writing of this manuscript. E.A.H. was supported by the United States National Institutes of Health [Grant T32A1007524] during preparation and writing of this manuscript. X.L. is supported by a grant from the Partners in Health. D.S. is supported by United States National Institutes of Health [Grant T32GM135117]. F.Z. is supported by the Howard Hughes Medical Institute, the McGovern Foundation, and J. and P. Poitras and the Poitras Center. The How We Feel Project is a non-profit corporation. The How We Feel Project thanks many operational volunteers and the HWF participants who took our survey and allowed us to share our analysis. Funding and in-kind donations for the How We Feel Project came from B. and D. Silbermann, F. Zhang and Y. Shi, L. Harp McGovern, D. Cheng, A. Azhir, K.H. Yoon and the Bill & Melinda Gates Foundation.

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These authors contributed equally: Sean D. McCabe and E. Adrianne Hammershaimb.

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The How We Feel Project, San Leandro, CA, USA

Sean D. McCabe, E. Adrianne Hammershaimb, David Cheng, Andy Shi, Derek Shyr, Shuting Shen, William Allen, Ryan Probasco, Ben Silbermann, Feng Zhang, Mark A. Travassos & Xihong Lin

Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA

Sean D. McCabe, Andy Shi, Derek Shyr, Shuting Shen & Xihong Lin

Center for Vaccine Development and Global Health, University of Maryland School of Medicine, Baltimore, MD, USA

E. Adrianne Hammershaimb & Mark A. Travassos

Department of Pediatrics, University of Maryland School of Medicine, Baltimore, MD, USA

Department of Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, USA

Lyndsey D. Cole & Jessica R. Cataldi

Adult and Child Consortium for Health Outcomes Research and Delivery Science, University of Colorado Anschutz Medical Campus and Children’s Hospital Colorado, Aurora, CO, USA

Jessica R. Cataldi

Harvard University, Cambridge, MA, USA

William Allen

Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA

Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA

Howard Hughes Medical Institute, Chevy Chase, MD, USA

Department of Emergency Medicine, Brigham and Women’s Hospital, Boston, MA, USA

Regan Marsh

Department of Emergency Medicine, Harvard Medical School, Boston, MA, USA

Partners in Health, Boston, MA, USA

Broad Institute of MIT and Harvard, Cambridge, MA, USA

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E.A.H. and S.D.M. initiated the project. S.D.M. led data analysis and figure production. S.D.M., D. S. and S. S. cleaned the data. E.A.H., and A.S. contributed to analysis. R. M. provided feedback on analysis. S.D.M. and E.A.H. wrote the manuscript with M.T. and X.L. D. C., W. A., R.P., B.S., F. Z., X. L. designed and implemented the How We Feel application. E.A.H., L.D.C., J.R.C., and R.M. developed the vaccine reluctance and uptake instrument. M.T. and X.L. supervised all aspects of the work.

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McCabe, S.D., Hammershaimb, E.A., Cheng, D. et al. Unraveling attributes of COVID-19 vaccine acceptance and uptake in the U.S.: a large nationwide study. Sci Rep 13 , 8360 (2023). https://doi.org/10.1038/s41598-023-34340-3

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• Published: 18 October 2022

Attitudes and personal beliefs about the COVID-19 vaccine among people with COVID-19: a mixed-methods analysis

• Monica M. Bennett 1 ,
• Megan Douglas 1 ,
• Briget da Graca 1 ,
• Katherine Sanchez 1 , 3 ,
• Mark B. Powers 1 , 2 , 4 &
• Ann Marie Warren 1 , 2 , 4  

BMC Public Health volume  22 , Article number:  1936 ( 2022 ) Cite this article

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Little research is available regarding vaccination attitudes among those recently diagnosed with COVID-19. This is important to investigate, particularly among those experiencing mild-to-moderate illness, given the ongoing need to improve uptake of both initial vaccine series and booster doses, and the divergent ways such an experience could impact attitudes.

From September 3 – November 12, 2021, all patients enrolled in Baylor Scott & White’s “COVID-19 Digital Care Journey for Home Monitoring” were invited to participate in an online survey that included questions about vaccination status and attitudes/opinions regarding COVID-19 and the COVID-19 vaccines. Following an item asking about accordance of COVID-19 vaccination with religious/personal beliefs, participants were asked to describe those beliefs and how they relate to taking/not taking the vaccine.

Of 8,075 patients age ≥ 18 years diagnosed with COVID-19 and invited to join the survey during the study period, 3242 (40.2%) were fully vaccinated. In contrast, among the 149 who completed the questionnaire, 95(63.8%) reported full vaccination. Responses differed significantly between vaccination groups. The vaccinated group strongly agreed that COVID-19 is a major public health problem, the vaccines are safe and effective, and their decision to vaccinate included considering community benefit. The unvaccinated group responded neutrally to most questions addressing safety and public health aspects of the vaccine, while strongly disagreeing with statements regarding vaccine effectiveness and other preventative public health measures. The vaccinated group strongly agreed that taking the vaccine accorded with their religious/personal beliefs, while the unvaccinated group was neutral. In qualitative analysis of the free text responses “risk perception/calculation” and “no impact” of religious/personal beliefs on vaccination decisions were frequent themes/subthemes in both groups, but beliefs related to the “greater good” were a strong driver among the vaccinated, while statements emphasizing “individual choice” were a third frequent theme for the unvaccinated.

Our results show that two of the three factors that drive vaccine hesitancy (complacency, and lack of confidence in the vaccines) are present among unvaccinated adults recently diagnosed with COVID-19. They also show that beliefs emphasizing the importance of the greater good promote public health participation.

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Introduction

Over the course of the COVID-19 pandemic, attitudes towards vaccination in the United States have shifted substantially. In early surveys, conducted before any COVID-19 vaccines were available, 65–78% of respondents reported being at least somewhat likely to accept a COVID-19 vaccine when it became available to them [ 1 , 2 , 3 , 4 , 5 , 6 ]. However, much lower vaccine acceptance rates were reported among racial/ethnic minorities, Republican voters, rural residents, members of lower income households, persons lacking health insurance, and individuals with less education [ 1 , 2 , 3 , 5 , 6 , 7 , 8 , 9 ]. Over time, disparities in vaccine acceptance rates among races and ethnicities decreased [ 10 ] while disparities related to political leanings and education persisted [ 11 , 12 ]. As of August 2022, only 77.1% of U.S. adults have completed the primary vaccine series [ 13 ], with 19% still indicating they will definitely not be vaccinated [ 14 ].

Although the news media are replete with stories of individuals who, after contracting COVID-19 and experiencing serious illness or loss of a loved one, express regret at not having been vaccinated [ 15 , 16 , 17 , 18 ], there is little research evidence regarding vaccine attitudes among people who have contracted COVID-19. Data from the Household Pulse Survey indicate that vaccination receipt was lower and reluctance to be vaccinated was higher among people with a past diagnosis of, or unsure if they previously had, COVID-19 [ 19 , 20 ]. These data did not, however, allow for insight into reasons why that was the case – for example, differentiating between people who feel that having immunity from a previous infection will protect them versus people who experienced a mild case of COVID-19 and, based on that experience, are not concerned about either catching it again or infecting others. It is also not currently known how people who contract COVID-19 after being vaccinated feel about the vaccines – some may, for example, come to doubt the vaccines’ effectiveness based on their own experience, while for others the experience might emphasize the importance of vaccination for avoiding serious illness. Investigating vaccine attitudes among people who have had breakthrough infections can offer important insights for addressing the lag in booster uptake, currently sitting at 48% of those eligible for the first booster [ 13 ].

This manuscript presents survey responses regarding vaccination attitudes from adults surveyed ~ 7 days after receiving a COVID-19 diagnosis. Quantitative results cover Likert scale survey responses about attitudes/beliefs in 6 topic areas: COVID-19 risk, Resources and access to vaccines, Safety of the Covid-19 vaccines, Public health aspects of COVID-19, Influences on vaccination decisions (including religious/personal beliefs), and Confidence in protective measures against COVID-19 (including vaccine effectiveness). Qualitative results provide additional insight regarding the influence of religious/personal beliefs on vaccination decisions.

Study Design, Sample and Data Collection : This was a cross-sectional observational survey-based study. The study protocol was approved by the Institutional Review Board (IRB) at Baylor Scott &White Research Institute (#020–139) and informed consent was obtained from each participant prior to enrolment. No compensation was provided for participation in the study.

Data were obtained from respondents to an online questionnaire comprised of multiple measures; the total survey took about 30 minutes to complete. From September 3, 2021 to November 12, 2021, corresponding to the second half of the delta wave in Texas [ 21 ], all adult (≥ 18 years) ambulatory care patients who tested positive for SarsCoV-2 at a Baylor Scott & White Health (BSWH) facility or were diagnosed with a suspected case of COVID-19 symptoms by a provider at our facilities were invited to participate in the “COVID-19 Digital Care Journey for Home Monitoring”, administered through the MyBSWHealth™ software application and patient portal. The communications sent on Day 7 of the digital care journey (corresponding approximately with the seventh day after diagnosis) included an invitation to participate in this research study. This recruitment strategy has been described in detail elsewhere [ 22 ]. Consent and study data were collected and managed using the Research Electronic Data Capture (REDCap™) platform, hosted at Baylor Scott & White Research Institute. REDCap™ is a secure, web-based platform designed for research data capture [ 23 , 24 ]. The end date for inclusion in this analysis was based on substantial slowing of new enrollees during the first 2 weeks of November 2021 as new cases in the BSWH service areas declined.

Quantitative variables

Demographic variables. Sociodemographic variables collected included age, gender, race, ethnicity, income, education, employment, and marital status. Participants were also presented with a list of medical and mental health conditions and asked to indicate whether or not they had been diagnosed with that condition, and, if they had, whether it was a past or current diagnosis.

COVID-19 Experience : Questions about COVID-19 experience also included whether they had been hospitalized or put on a ventilator due to COVID-19. Participants were also asked whether they had been vaccinated against COVID-19, and, if yes, with which vaccine product and when. Participants who responded no were asked if they would be vaccinated in the future (response options included yes , no , and maybe ).

Fear of COVID-19 Scale . Perceived fear of COVID-19 was assessed with a 7-item validated scale [ 25 ] asking participants to indicate how much they agree or disagree (1 =  strongly disagree and 5 =  strongly agree ) with items assessing fears and worries about Coronavirus-19. The total score is calculated by summing item responses and ranges from 7 to 35 with higher scores suggesting greater fear of COVID-19. The internal consistency, calculated as Chronbach’s alpha, for our sample was 0.89.

COVID-19 Stigma . Stigma related to COVID-19 was assessed using the Stigma Scale for Chronic Illness (SSCI-8), an 8-item questionnaire [ 26 ] initially created to measure perceived stigmatization among people with chronic illnesses. The questionnaire was adapted for COVID-19 by instructing participants to answer items “in reference to your COVID-19 screening status”. Items asked about perceived stigma situations related to ones’ illness with responses indicating how often each item occurred on a 5-point qualitative scale (ranging from 0 =  never to 4 =  always ). All items are summed for a total score ranging from 0 to 32, with higher scores suggesting greater stigmatization. Summed scores are then converted to standardized t-scores [ 27 ]. The internal consistency in our sample was 0.89.

Vaccination beliefs . Participants were asked to evaluate 34 statements, developed through multiple iterations by the study team and based on a combination of evidence available at the time in the literature from broader population surveys regarding factors influencing people’s vaccination decisions (e.g., concerns about safety, effectiveness, and side effects which were raised during the months preceding and immediately following vaccine availability [ 9 ]) and issues being raised locally or in the media related to vaccination decisions (e.g., employer vaccine mandates and religious exemptions [ 28 , 29 , 30 ], and that lack of FDA approval of the COVID-19 vaccines was a reported reason for not yet taking the vaccine [ 31 ] ), indicating their agreement on a 0 to 4 scale ranging from “strongly disagree” to “strongly agree” with a “neutral” option in the middle. Covered topics related to risks associated with COVID-19, their ability to access COVID-19 vaccines, the safety of the COVID-19 vaccines, public health aspects of COVID-19/vaccination against COVID-19, influences on the decision to take/not take the COVID-19 vaccines (including religious/personal beliefs), and confidence in the protection against contracting COVID-19 that vaccination and non-pharmaceutical interventions such as masking and social distancing achieves. Participants were also asked to rate how frequently they had worn a mask when going out in public in the last 2 weeks on a 0 to 3 scale ranging from “none of the time” to “all of the time”. Each item was assessed individually. The internal consistency of the 34 statements was 0.93.

Qualitative variables

The survey included one open-ended question immediately following the quantitative item asking respondents to rate their agreement with the following: “Taking the COVID-19 vaccine is in accordance with my religious/personal beliefs (0- strongly disagree to 4- strongly agree; 5 -prefer not to answer)”. For all participants, including those who selected ‘prefer not to answer’, a free response item then asked, “Please describe those beliefs and how they relate to taking/not taking the vaccine”, thus providing the opportunity to explain the relationship between those beliefs and their decision in their own words.

Quantitative analysis

A participant who received at least one dose of any of the approved COVID-19 vaccines was included in the vaccinated group. Participant demographic and COVID-related characteristics were compared using t-tests for continuous variables, Mann-Whitney U tests for ordinal variables, and chi-square or Fisher’s test for categorical variables. The ranking of vaccine attitudes was compared between groups using Mann-Whitney U tests. All analysis was performed using SAS 9.4.

Qualitative analysis

To analyze free text responses, a consensual qualitative research (CQR) approach[ 32 ] was used to derive meaning about the impact of personal or religious beliefs for participants based on whether they were vaccinated or not vaccinated. Coding was conducted by three study researchers (BdG, KS, MB; see reflexivity statements in the supplement for brief background) and overseen by a qualitative research trainer (MD). Additionally, an auditor was used for review and provided input when coders could not reach consensus (AMW). Data were divided into ‘vaccinated’ and ‘unvaccinated’ groups. All blank or non-text responses were removed prior to coding (n = 4). Coders and trainer met to form preliminary domains based on visual inspection of all responses (N = 145). Each response, or case, representing a single participant was inspected for fit into domains and could apply to multiple coding categories (e.g., 2, 3, or 4 codes). The coders individually reviewed each case looking for patterns and fit within initial domains. All 3 coders and the trainer then met to discuss rationale and reached consensus on all but 7 codes. Inter-rater agreement was calculated using Krippendorff’s alpha reliability coefficient [ 33 ] (alpha = 0.885), which exceeds the 0.823 cutpoint considered to indicate good agreement [ 34 ]. Responses were then sorted into each core idea and a final review was conducted to ensure fit within themes; consensus was reached on all domain and themes with the exception of 4 cases which were then reviewed by an auditor. The coders met one final time to discuss auditor’s input and were able to reach 100% consensus.

Between September 3, 2021 and November 12, 2021, 8,075 unique patients age ≥ 18 years tested positive for COVID-19 at a BSWH facility and thus received the invitation to participate in the COVID-19 Digital Health Journey through which participants for this research study were recruited [ 22 ]. Of those 8,075 patients, 4,122 (51.1%) were not vaccinated for COVID-19, 711 (8.8%) were partially vaccinated (i.e., had received 1 dose of a 2-dose regimen), and 3,242 (40.2%) were fully vaccinated.

There were 151 individuals who enrolled in the study between September 3, 2021 and November 12, 2021 and completed the vaccine questions. Two participants who indicated they had not been diagnosed with COVID-19 were removed. The proportion who had completed a primary vaccination series was substantially higher among study participants (69.1%) than in the eligible population of patients who received a COVID-19 diagnosis during this period (40.2%). Survey items with incomplete data are indicated in Table  1 , and any missing data was excluded from analysis of that item.

Quantitative results

Demographic characteristics and responses to COVID-19 questions stratified by vaccination status are summarized in Table  1 .

The vaccinated and unvaccinated groups differed significantly on age (p < 0.001; with the vaccinated group being older), education (p < 0.001; with the vaccinated group having completed higher levels of education), income (p = 0.003; with the vaccinated group earning higher), and COVID-19 experience (with the vaccinated group being less likely to have been hospitalized, p < 0.001; and to have experienced less stigma related to COVID-19, p = 0.042). Pfizer (52%) and Moderna (49%) were the most common vaccines taken with a median time since the last dose of 192 days. The majority of the unvaccinated group indicated they would or maybe would get the vaccine in the future (70%), while 30% maintained they will not get the COVID-19 vaccine.

Table  2 compares the vaccine beliefs between groups. All beliefs were statistically different between the two groups, with the exception of worry about COVID-19 variants.

The vaccinated group more strongly agreed with statements about vaccines reducing risk of getting or suffering severe illness with COVID-19 (all p < 0.001). The vaccinated group also indicated greater knowledge of where to get a vaccine, better access to it, and greater agreement that the vaccines are safe (all p < 0.01).

When considering the evidence of the safety and effectiveness of the vaccine, the vaccinated group disagreed more than the unvaccinated group that evidence of the safety and effectiveness of the vaccine was lacking, and that FDA approval was important to their vaccination decision (all p < 0.01). The vaccinated group strongly agreed that COVID-19 was a major public health problem, and that their decision to vaccinate should also include the benefit to the community (all p < 0.001). In contrast, the median response for the unvaccinated group to most questions addressing safety and public health aspects of the vaccine, including considering community in their vaccination decision, was neutral. The unvaccinated group also indicated stronger disagreement regarding the effectiveness of vaccines in preventing COVID-19, likelihood that they will get recommended booster doses, and confidence in public health measures such as wearing masks or social distancing to prevent individual infection with, or community spread of COVID-19. Further, approximately 85% of the vaccinated group wore a mask most or all of the time around others or in public in the past two weeks compared to 68% of the unvaccinated group (p = 0.009).

Regarding factors influencing vaccination decisions, both groups agreed that their decision would not be different if a vaccine was required or incentivized by their employer, school, or state, with the vaccinated group having higher levels of agreement. The vaccinated group had more encouragement to get vaccinated from family and friends (p < 0.001) and health care providers (p < 0.001), and strongly agreed that taking the vaccine was in accordance with their religious/personal beliefs compared to over half (57%) of the unvaccinated providing a neutral response (p < 0.001).

Qualitative results

Final coding themes, subthemes, descriptions, frequencies, and examples to the open-ended item inquiring about how religious/personal beliefs relate to taking or not taking the vaccine are presented in Table  3 .

Responses and counts (%) are grouped by vaccination status. Primary themes for both groups included: (1) Religious/Personal Beliefs, (2) Community versus Self, (3) Medical, and (4) Miscellaneous. A complete list of items under each theme is provided in Supplementary Tables 1 and 2 for the unvaccinated and vaccinated groups, respectively.

The unvaccinated sample included 29 total cases, and 47 codes were applied based on observed themes and subthemes. The most frequent subthemes in this group were ‘references religion’ (19%; e.g., My body is my temple. ), ‘risk perception/ calculation’ (17%; e.g., I do not think the vaccine is against my beliefs. I just don’t see that they work when vaccinated people are getting just as sick as unvaccinated people, in my opinion ), indicating that their religious or personal beliefs had ‘no impact’ on their vaccination decision (15%; e.g., My being a born again Christian has nothing to do with getting vaccinated ), ‘needs more information/research’ (13%; e.g., I don’t believe that they have tested it long enough to prove it works ), and ‘emphasizes individual choice’ (11%; e.g., It is my choice as what I do with my body ). Many of the responses in the unvaccinated group, although mentioning religion, stated that their religion did not influence their decision and were double coded as both ‘religion having no impact’ or ‘referencing religion’ and ‘individual choice’ (e.g., Our worldwide church has urged all members to get the vaccine and to wear mask. I do not believe it is a right of the church or government to enforce or mandate forms of medical care. ).

The vaccinated sample included 99 cases, and 163 codes were applied. The most frequent subthemes in this group were ‘greater good,’ meaning a reference to the betterment of a group larger than the self (such as family or community, e.g., I wanted it, to protect my family and to show them it is okay [to] get vaccinated ), with 25% of responses indicating this influenced their choice. The next top three frequently cited themes were similar to the unvaccinated group, although endorsements were made in the opposite direction: ‘references religion’ (15%; e.g., Love of Neighbor; Clothe the poor and feed the hungry, support the widow ), ‘risk perception/calculation’ (15%; e.g., COVID-19 is clearly a disease that will be reduced/eradicated only through herd immunity supported by vaccination. While there is a risk in the vaccine, for most people, this is less than the disease itself ), or ‘no impact’ of religious or personal beliefs on the decision to get vaccinated (13%; e.g., My faith is open to all medical procedures and treatments ). The next most frequently referenced subtheme was ‘belief in science/vaccines’ which suggests that their decision was impacted by a belief or trust in the scientific process (9%; e.g., While I am a Christian, I believe the science and research that has gone into the development and testing of the vaccines. They are safe and effective .). Similar to the unvaccinated group, many referenced their religious beliefs, but stated these did not have an impact on their decision (e.g., My religion has nothing to do with this ). About half of the responses coded as ‘risk perception/calculation’ (12/25) also referenced the ‘greater good’ theme suggesting that this group also factored in other individuals’ perceived risk into their own risk calculation (e.g., We should protect those that can’t protect themselves, I chose the vaccine so I didn’t infect 10 year old, baby granddaughters and other children/high risk people. I’m also high risk. ”). The ‘greater good’ theme was also dually referenced in some of the ‘religious’ themed responses (9/40; e.g., As a Christian I try to help others. I try not to think of myself first .) Frequencies of all overlapping codes are provided in Supplementary Tables 3 and 4 for the unvaccinated and vaccinated group, respectively.

In this mixed methods survey of adults who tested positive for COVID during the delta wave surge, we found distinctly divergent views on a range of beliefs and attitudes toward the COVID-19 vaccine. The vaccinated group strongly agreed that COVID-19 is a major public health problem, the vaccines are safe and effective, and their decision to vaccinate included considering community benefit. The unvaccinated group responded neutrally to most questions addressing safety and public health aspects of the vaccine, while strongly disagreeing with statements regarding vaccine effectiveness and other preventative public health measures. The vaccinated group strongly agreed that taking the vaccine accorded with their religious/personal beliefs, while the unvaccinated group was neutral. In qualitative analysis of the free text responses to the question asking participants to describe how their religious/personal beliefs relate to their decision to take/not take a COVID-19 vaccine, “risk perception/calculation” and “no impact” of religious/personal beliefs on vaccination decisions were frequent themes/subthemes in both groups, but beliefs related to the “greater good” were a strong driver among the vaccinated, while statements emphasizing “individual choice” were a third frequent theme for the unvaccinated.

The vaccinated group in our study sample was less likely to be hospitalized than the unvaccinated group, which is consistent with the evidence regarding the vaccines’ effectiveness against serious illness and related public health messaging [ 35 ] and may have contributed to the vaccinated participants’ beliefs about reduced risk of severe illness, greater agreement that the vaccines are safe, and that FDA approval was not necessary for them to take the vaccine. The recognition we found among the vaccinated that COVID-19 is a major public health problem and indication that their decision to vaccinate would benefit the community is similar to previous findings from the UK that one of the significant differences between vaccine-hesitant compared to vaccine-accepting individuals was a lower level of altruism in the former [ 36 ].

Our findings related to lower education and income in the unvaccinated patients with COVID-19 are consistent with known hesitations in these groups [ 12 ] and suggest a need for tailored messaging campaigns. Disagreement that vaccines are effective in preventing spread of COVID-19 and low confidence in public health measures such as wearing masks or social distancing was greatest among the unvaccinated group, despite their own COVID-19 infection, similar to findings in the UK and Ireland [ 37 ]. Though the majority of unvaccinated respondents did indicate they would be getting a vaccine in the future, the 30% who indicated they would not is substantially higher than the 19% of US adults overall who continue to report they definitely will not be vaccinated against COVID-19 [ 14 ]. As our sample predominantly captured individuals who experienced mild to moderate illness with COVID-19 able to be managed at home, it may be that the experience of mild illness reinforced perceptions that vaccination against COVID-19 is not necessary in this group. Also worth considering, however, is that opportunities may have been missed during these patients’ COVID-related healthcare encounters (including digital communications) for their providers to educate them about the safety and effectiveness of the COVID-19 vaccines available. Certainly, room for improvement in communication about vaccination is seen in the significantly lower agreement of the unvaccinated group towards the statement that “My healthcare provider encourages vaccination against COVID-19 for everyone eligible for the vaccines.” Further research would be needed to explore the mechanisms underlying this difference between vaccinated and unvaccinated groups. More specifically, future research could tease apart whether unvaccinated patients are seeing providers who are less likely to recommend vaccination for everyone eligible, whether providers are not discussing vaccinations with those they know to be opposed, or whether providers are giving the same information and recommendations to all their patients but the patients are perceiving it differently. Such work could provide valuable insight into effective strategies for addressing vaccine hesitancy, but unfortunately lies beyond the scope of the data collected for our study.

Numerous themes emerged from the qualitative responses which provide insight into how religious and personal beliefs relate to vaccination decisions. Importantly, in this sample of unvaccinated adults in the largest not-for-profit health system in Texas, we found mixed reporting of religious views influencing decisions, but with the vaccinated group more strongly agreeing that their religious/personal beliefs were in accordance with taking the COVID-19 vaccine. Further, many of those vaccinated mentioned their religious beliefs in conjunction with the concept of “greater good”, whereas the unvaccinated group was more likely to report that their religious beliefs had no impact in their vaccination decision – or, where it did, supporting the decision not to vaccinate as aligned with individual choice. Previous studies using US samples have reported that beliefs in an engaged God are associated with greater mistrust in the COVID-19 vaccine, an effect amplified among those with lower educational achievement [ 38 ]. Leaders and followers of various world religions, including Judaism, Protestant Christianity, and Catholicism, have been known to decline other vaccines due to the belief that it interferes with God’s will or faith in divine protection and healing [ 39 , 40 ]. More specific religious objections have also been identified, such as objections among Muslims related to porcine or non-halal ingredients [ 41 , 42 , 43 ], as well as Ramadan fasting (and the risk that adverse vaccine reactions could lead to breaking the fast) [ 44 ], and among Catholics related to the use of cell lines derived from aborted fetuses in vaccine production [ 45 , 46 ]. Similar religious taboos have been found among the reasons for non-vaccination among followers of Hinduism and Sikhism [ 43 ].

Other beliefs expressed by the unvaccinated respondents suggested themes and subthemes beyond the question’s focus on religious/personal beliefs. These reflected themes found in recent reviews of COVID-19 vaccine hesitancy, including beliefs that the vaccines are not safe or effective and concerns about their rapid development [ 47 ]. Also common among unvaccinated respondents in this sample were themes related to personal choice and freedom, issues that have generated protests and unrest around the globe related to work and travel requirements for vaccination. These themes echo similar objections raised in the past against requirements for vaccination against other diseases, such as those seen in reaction to the smallpox vaccine [ 48 , 49 ].

The vaccinated participants in the current sample appeared highly motivated by personal sense of duty to protect others. Even as it related to a risk calculation, their concerns encompassed their loved ones and people at high risk for bad outcomes from COVID infection. These findings align with qualitative research on collective problem solving which found civic duty to be a strong predictor of compliance and makes people almost immune to other people’s attitudes in a crisis [ 50 ]. Similar to research on voting practices, people with a strong sense of civic duty may view compliance with public health recommendations as a moral obligation that they must abide by to be a good citizen [ 51 ].

Limitations

As with all surveys, this study is not without limitations. The qualitative responses were captured via an online survey free text response item, which might have reduced social desirability bias, but did not allow for clarification or follow-up questioning as done with interview style qualitative research. Additionally, bias may have impacted the qualitative coding process. We attempted to reduce this by (1) having one member of the team (trainer) with experience in qualitative research oversee the coding process without actually coding any items; (2) by involving different perspectives from three different coders and providing a brief summary of coder’s personal background and beliefs in an effort to be transparent about potential biases; and (3) by providing all raw responses in supplementary tables as well as descriptions and examples of each classification.

This study was also limited by selection bias. Although recruitment through the COVID Digital Health Care Journey has many advantages related to time, cost, and reach, as with most digital recruitment strategies, it has a tendency to result in overrepresentation of white, educated, and female participants, limitations which we have detailed elsewhere [ 22 ]. While the ~ 2% completion rate we saw among eligible patients is not dissimilar from the 4% reported by previous disease-specific research studies using patient portal messaging for recruitment [ 52 ], specific points at which potential participants may have been lost include: not having/not registering for or not interacting with the patient portal or application (all being more likely among groups with less access to high speed internet and lower technological literacy); experiencing more severe symptoms, which might have required hospitalization prior to receiving the research invitation on Day 7 of the digital care journey or have left the potential participant feeling too ill to interact with the survey (both more likely in unvaccinated individuals); and experiencing survey fatigue before reaching the vaccine attitudes and demographics questions needed for this analysis.

The substantial difference observed in the proportion of study participants who were fully vaccinated (69.2%), compared to the proportion of those eligible to participate who were fully vaccinated (40.2%) indicates selection bias away from the unvaccinated. Furthermore, unvaccinated individuals who did participate may have been more likely to be “pro-research” than the unvaccinated individuals who did not participate. If so, our results regarding differences in attitudes to the COVID-19 vaccines are likely underestimates compared to the population.

Finally, all the survey responses analyzed were collected during the Delta wave in late 2021. It is possible that different attitudes towards vaccination would have been found in earlier or later waves – although in which direction attitudes may have varied is open to speculation. During the Omicron surge in the winter of 2021/2022, for example, the highly contagious nature of this variant might have made some people view vaccines more favorably, while the lesser severity of many of the cases might have made others more inclined to view the vaccines as unnecessary.

Implications for practice/policy/further research

Even prior to the current pandemic, the World Health Organization identified vaccine hesitancy as one of the greatest threats to global health [ 53 ]. The reasons people choose not to vaccinate are complex, with identified barriers including complacency, inconvenience, and lack of confidence. Our results showed indications of greater complacency and lack of confidence in vaccines among unvaccinated individuals diagnosed with COVID-19. We also found marked differences in the way vaccinated and unvaccinated individuals viewed the relationship between their religious/personal beliefs and their vaccination decision, with the former emphasizing beliefs related to contributing to the “greater good” while the latter reported no impact or emphasized beliefs related to individual choice. These differences existed even between respondents who identified themselves as belonging to the same religion, providing valuable insight into how emphasis of different aspects or priorities in a religion can influence healthcare and public health decisions.

Data availability

The quantitative data analyzed during the current study are not publicly available due to the inclusion of private health information, but are available from the corresponding author on reasonable request. All qualitative responses are included in the supplementary information.

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We gratefully acknowledge support for this work from the Baylor Scott & White Dallas Foundation and the W.W. Caruth, Jr. Fund at Communities Foundation of Texas.

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All authors contributed to the study design and development of survey questions. MB, BdG, and KS were coders for qualitative analysis. MD was the qualitative trainer. MB and MD carried out analyses. MB, MD, BdG, and KS drafted the manuscript, with review from AMW and MP. All authors read and approved the final manuscript.

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Bennett, M.M., Douglas, M., da Graca, B. et al. Attitudes and personal beliefs about the COVID-19 vaccine among people with COVID-19: a mixed-methods analysis. BMC Public Health 22 , 1936 (2022). https://doi.org/10.1186/s12889-022-14335-x

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Research Article

COVID-19 and vaccine hesitancy: A longitudinal study

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Rady School of Management, University of California San Diego, La Jolla, California, United States of America

Roles Conceptualization, Data curation, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

Roles Conceptualization, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Visualization, Writing – original draft, Writing – review & editing

• Ariel Fridman, 
• Rachel Gershon, 
• Ayelet Gneezy

• Published: April 16, 2021
• https://doi.org/10.1371/journal.pone.0250123
• Peer Review
• Reader Comments

How do attitudes toward vaccination change over the course of a public health crisis? We report results from a longitudinal survey of United States residents during six months (March 16 –August 16, 2020) of the COVID-19 pandemic. Contrary to past research suggesting that the increased salience of a disease threat should improve attitudes toward vaccines, we observed a decrease in intentions of getting a COVID-19 vaccine when one becomes available. We further found a decline in general vaccine attitudes and intentions of getting the influenza vaccine. Analyses of heterogeneity indicated that this decline is driven by participants who identify as Republicans, who showed a negative trend in vaccine attitudes and intentions, whereas Democrats remained largely stable. Consistent with research on risk perception and behavior, those with less favorable attitudes toward a COVID-19 vaccination also perceived the virus to be less threatening. We provide suggestive evidence that differential exposure to media channels and social networks could explain the observed asymmetric polarization between self-identified Democrats and Republicans.

Citation: Fridman A, Gershon R, Gneezy A (2021) COVID-19 and vaccine hesitancy: A longitudinal study. PLoS ONE 16(4): e0250123. https://doi.org/10.1371/journal.pone.0250123

Editor: Valerio Capraro, Middlesex University, UNITED KINGDOM

Received: November 12, 2020; Accepted: February 14, 2021; Published: April 16, 2021

Copyright: © 2021 Fridman et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All data and code are publicly available on the Open Science Framework at https://osf.io/kgvdy/ .

Funding: UC San Diego Global Health Initiative (GHI): awarded to all authors; Project number: 1001288. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. https://medschool.ucsd.edu/som/medicine/divisions/idgph/research/Global-Health/grant-recipients/2019-2020/Pages/Faculty-Postdoc-Travel-and-Research.aspx .

Competing interests: The authors have declared that no competing interests exist.

Introduction

Vaccinations are among the most important public health tools for reducing the spread and harm caused by dangerous diseases [ 1 ]. The World Health Organization estimates that vaccines prevented at least 10 million deaths between 2010–2015 worldwide [ 2 ]. Despite considerable evidence showing vaccines are safe [ 3 , 4 ], there is increasing skepticism toward vaccination [ 5 , 6 ]. Vaccine hesitancy has led to a decline in vaccine uptake and to an increase in the prevalence of vaccine-preventable diseases (VPDs) [ 7 , 8 ]. Ironically, the objection to vaccines is commonly a consequence of their effectiveness—because individuals have lower exposure to VPDs, they are less concerned about contracting them [ 9 ], which consequently leads to greater vaccine hesitancy [ 10 ]. The COVID-19 pandemic has created a new reality where individuals are faced with a previously unknown disease and its effects, providing a unique opportunity to investigate vaccine attitudes during a period of heightened disease salience. The present research reports findings from a longitudinal study conducted during the COVID-19 health crisis, in which we measured changes in attitudes toward a prospective vaccine, as well as shifts in vaccine attitudes in general.

Factors influencing vaccine attitudes and behaviors

Past research has identified a variety of situational and individual-level factors that influence vaccine attitudes and behavior, the most prominent of which are risk perceptions and demographic characteristics.

Assessments of risk are influenced by both cognitive evaluations (i.e., objective features of the situation such as probabilities of outcomes) and affective reactions [ 11 ], as well as by contextual factors (e.g., the information that is most available or salient at the time [ 12 ]). For example, research shows that media coverage plays a significant role in determining the extent to which we take threats seriously [ 13 ]. When individuals perceive heightened risk of a threat, they become more favorable toward interventions that mitigate that threat, including vaccination (for a meta-analysis on the effect of perceived risk on intentions and behaviors, see [ 14 ]). In the case of COVID-19, this would suggest more positive attitudes toward a vaccine and greater likelihood to get vaccinated. Indeed, research suggests that individuals should exhibit a greater interest in vaccinations during a pandemic because disease threat is more salient [ 15 ].

Past efforts to improve vaccine attitudes have had limited success or even backfired; for example, messages refuting claims about the link between vaccines and autism, as well as messages featuring images of children who were sick with VPDs, had negative effects on vaccine attitudes among those who were already hesitant to vaccinate [ 16 ]. In contrast, messaging that increases disease threat salience has shown promise in reducing vaccine hesitancy [ 5 ], and there is evidence suggesting that increased threat salience for a particular disease may also increase intentions to vaccinate for other diseases [ 17 ]. Building on these findings, we expected to find an increase in pro-vaccine attitudes and in individuals’ interest in a COVID-19 vaccine when the perceived threat of the COVID-19 virus increased.

Vaccine attitudes are also influenced by a variety of demographic and ideological factors (for a review, see [ 18 ]). For example, perceptions of vaccine risk differ among individuals of different ethnic backgrounds [ 19 ], and there is extant work demonstrating a positive correlation between socioeconomic status (SES) and vaccine hesitancy [ 20 , 21 ]. Socio-demographic factors are also linked to vaccine-related behaviors: among college students, those whose parents have attained a higher level of education are more likely to get immunized [ 22 ], and researchers have identified age as a predictor for receiving the influenza vaccine [ 23 ].

Political ideology is another well-documented determinant of vaccine-related attitudes and behaviors. Despite a common belief that liberals tend toward anti-vaccination attitudes in the United States, there is strong evidence that this trend is more present among conservatives [ 24 , 25 ]. According to a recent Gallup Poll, Republicans are twice as likely to believe the widely debunked myth that vaccines cause autism [ 26 ]. Recent work has shown that exposure to anti-vaccination tweets by President Trump—the first known U.S. president to publicly express anti-vaccination attitudes—has led to increased concern about vaccines among his supporters [ 27 ]. Based on these findings, and in conjunction with the sentiments expressed by the White House that diminished the significance of the pandemic [ 28 ], we expected to find diverging trends between Democrats and Republicans.

The current research

We collected vaccine-related attitudes of individuals living in the U.S. over a six-month period. Beginning in March 2020, we elicited attitudes from a cohort of the same individuals every month. We began data collection before any COVID-19 lockdown measures were in place (i.e., prior to the nation’s first shelter-in-place order [ 29 ]). Hence, our data spans the early phase of the pandemic, when there were fewer than 2,000 total confirmed cases in the U.S., through the following six months, at which point cumulative cases reached over 5.3 million [ 30 ].

Despite our prediction—that a public health crisis would increase disease threat, consequently increasing pro-vaccine attitudes and interest in vaccination—our data show an overall decrease in favorable attitudes toward vaccines. A closer look at the data revealed that political orientation explains more variance than any other socio-demographic variable. Specifically, participants who identify as Republican showed a decrease in their intention to get the COVID-19 vaccine and the influenza vaccine as well as a general decrease in pro-vaccine attitudes, whereas Democrats’ responses to these measures did not show a significant change during this period.

Our work is the first, to our knowledge, to longitudinally measure individuals’ attitudes toward vaccines. In doing so, our findings advance the understanding of how vaccine attitudes might change during an unprecedented public health crisis, revealing a strong association between political party affiliation and vaccine attitudes.

Participants

We recruited a panel of U.S. residents on Amazon’s Mechanical Turk platform to respond to multiple survey waves. To incentivize completion of all waves, we informed participants their payment would increase for subsequent surveys. Participants were paid 30 cents for wave 1, 40 cents for wave 2, and 60 cents for waves 3 and 4, $1.00 for wave 5, and $1.20 for wave 6. In addition, participants were informed that those who completed the first three waves would enter a $100 raffle. The median survey completion time was 5.5 minutes. The sample size for the first wave was 1,018, and the number of participants ranged from 608–762 on subsequent waves (see S1 Table for attrition details). This project was certified as exempt from IRB review by the University of California, San Diego Human Research Protections Program (Project #191273XX).

Our panel represents the broad and diverse population of the United States. The first wave sample included participants from all 50 states (except Wyoming) and Washington D.C., with an age range of 18 to 82 years old (mean = 38.48, median = 35). Approximately half (53%) identified as male, 46% as female, and.6% as other. The racial makeup in our sample was: 80% White, 9% Asian, 6% Black or African American, 4% multiple racial or ethnic identities, and 1% other. Relative to the U.S. Census (2019) [ 31 ] estimates, our sample over-represents White and Asian individuals, and under-represents Black or African American individuals and other racial groups.

We elicited political affiliation using a 6-point Likert scale, ranging from Strongly Republican to Strongly Democratic. In wave 1, 62% identified as Democrats and 38% identified as Republican, which is consistent with results from the most recent General Social Survey (GSS) [ 32 ]. There was no significant change in mean political identity from wave 1 to waves 2–6 (see S2 Table ). We classified participants as Democrats or Republicans using wave 1 political party affiliation. See S2 Appendix for additional details about the correlation of political party affiliation with age, gender, and SES.

Questions and measures

Our primary measure of interest was participants’ stated intention to get the COVID-19 vaccine when it becomes available. We were also interested in their perceptions of COVID-19 threat, general vaccination attitudes, and intention to get the flu shot. For all measures, except flu shot intentions, we combined multiple items to create a composite measure (see S2 Table for specific questions and construct compositions). Questions designed to measure general vaccination attitudes were adapted from prior work [ 33 ].

Additional measures of interest were participants’ trust in broad institutions (media, local government, and federal government). These trust measures followed different trends from each other, and therefore were not combined. At the end of the survey, participants responded to demographic questions. We retained all questions used in wave 1 throughout all six waves (our survey included additional items not reported in this paper; see S2 and S3 Tables for a complete list of measured items).

Data and analysis plan

Only participants with non-missing and non-duplicated responses were included in the analyses (see S1 Appendix for additional details). For all outcomes of interest, we tested for linear trends over time using a fixed effects regression specification [ 34 ]. All regression results include individual-level fixed effects, and standard errors are clustered at the individual level, to adjust for within-person correlation. We used this approach to control for the impact of omitted or unobserved time-invariant variables. P-values are not adjusted for multiple testing (see [ 35 ]). All analyses were conducted using R (version 4.0.2), and regressions were run using the package “fixest” (version 0.6.0). All materials, data, and additional analyses including robustness checks can be found here: https://osf.io/kgvdy/ .

We report results for three different vaccination-related measures: attitudes toward a COVID-19 vaccine, general vaccination attitudes, and flu shot intentions. All measures showed a decreasing trend (Ps < .001, except flu shot intentions where p = .05) for the 6-month duration of the study, indicating a reduction in pro-vaccination attitudes and intention to get vaccinated (COVID-19 and influenza vaccines). See S4 Table for full results of all regressions.

Heterogeneity in trend by political party

To better understand whether the decline in vaccine attitudes over time was driven by a particular factor, we used a data-driven approach, regressing all demographic characteristics on vaccine attitudes, in separate regressions. These demographics included education, income, SES, race, gender, an item measuring whether participants considered themselves to be a minority, whether the participant has children, and political party. Education, income, and SES were median split; race and gender were dummy coded; and political party affiliation was dichotomized into Democrat or Republican. Among all demographic characteristics, separating time trends by political affiliation (by adding an interaction term) attained the greatest adjusted within-R 2 in explaining vaccination attitude measures. In other words, political party affiliation explains the greatest within-individual variation in vaccine attitudes over time.

An analysis of responses by political affiliation revealed that the observed decreasing trend in all three vaccine measures was mostly driven by participants who identified as Republican (all Ps < .05), whereas Democrats’ responses showed either no significant trend (for COVID-19 vaccination and flu shot intentions: Ps >.67) or a significantly less negative time trend (general vaccination: p < .001). For these regressions, and moving forward, all results included interactions between wave and political party as well as interactions for wave and age, and wave and SES, to control for potentially different time trends associated with these variables. In each regression we also tested whether the strength of political affiliation moderates the observed results, and we reported the result when it did. We also conducted ANOVAs to compare mean responses for the outcomes of interest between Democrats and Republicans, separately for each wave (see S5 Table ).

COVID-19 vaccination attitudes ( Fig 1 , Panel A).

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

Points represent means, and error bars represent 95% confidence intervals. All scale responses range from 1 to 7.

https://doi.org/10.1371/journal.pone.0250123.g001

A two-item construct ( r = .78) was created, with greater values corresponding to more favorable responses.

In wave 1, Democrats ( M = 5.39, SD = 1.55) had more favorable attitudes toward a COVID-19 vaccine than Republicans ( M = 4.57, SD = 1.76; t = -7.38, p < .001, d = -.48, 95% CI = [-.61, -.35]). Among Democrats, there was no significant time trend ( β = .02, SE = .04, p >.67) whereas Republicans’ responses followed a decreasing time trend ( β = -.09, SE = .05, p = .046). These trends were significantly different from each other ( β = -.11, SE = .02, p < .001).

General vaccination attitudes ( Fig 1 , Panel B).

A ten-item construct ( α = .95) was created, with greater values corresponding to a more positive attitude toward vaccination in general.

In wave 1, Democrats ( M = 5.83, SD = 1.15) expressed more favorable general vaccination attitudes than Republicans ( M = 5.17, SD = 1.31; t = -7.91, p < .001, d = -.52, 95% CI = [-.66, -.39]). Although both Democrats and Republicans had a decreasing time trend (Democrats: β = -.04, SE = .02, p = .029; Republicans: β = -.09, SE = .02, p < .001), the trend for Republicans was significantly more negative ( β = -.04, SE = .01, p < .001).

Flu shot intentions ( Fig 1 , Panel C).

We asked participants whether they plan to get the flu shot next year, with greater values indicating greater intentions.

In wave 1, Democrats ( M = 4.84, SD = 2.34) indicated greater intentions to vaccinate against the flu than Republicans ( M = 4.35, SD = 2.39; t = -3.15, p = .002, d = -.21, 95% CI = [-.34, -.08]). Among Democrats, there was no significant time trend ( β = .01, SE = .04, p = .86), suggesting their vaccination intentions remained largely stable. Republicans’ responses, however, revealed a decreasing time trend ( β = -.12, SE = .04, p = .005), and the two trends were significantly different from each other ( β = -.12, SE = .02, p < .001).

Our analyses revealed an interaction with political affiliation strength among Republicans, whereby participants who identified as more strongly Republican had a more negative time trend ( β = -.05, SE = .02, p = .027). This interaction was not significant for Democrats ( β = -.02, SE = .01, p = .19).

Perceived threat of COVID-19 ( Fig 2 ).

https://doi.org/10.1371/journal.pone.0250123.g002

A three-item construct ( α = .82) was created, with greater perceived threat about COVID-19.

In wave 1, Democrats ( M = 4.26, SD = 1.25) expressed greater perceived threat of COVID-19 than Republicans ( M = 3.90, SD = 1.39; t = -4.14, p < .001, d = -.40, 95% CI = [-.27, -.14]). Democrats’ responses showed an increasing time trend ( β = .08, SE = .04, p = .033), indicating they became increasingly concerned about the threat posed by the virus over time. Among Republicans, there was no significant time trend ( β = -.01, SE = .04, p = .83). These trends were significantly different from each other ( β = -.09, SE = .02, p < .001). While our data does not render causal claims, it is possible that the divergence in COVID-19 threat perceptions over time among Republicans and Democrats contributes to the divergence in vaccine attitudes between these groups over time. We revisit this proposition in the General Discussion.

Our analyses revealed an interaction with political affiliation strength among Democrats—participants who identified as more strongly Democrat had a more positive time trend ( β = .03, SE = .01, p = .019), suggesting an increasing threat perception over time. This interaction was not significant for Republicans ( β = .01, SE = .02, p = .61).

Trust in broad institutions.

The measures of trust in media, local government, and federal government were not highly correlated ( α = .66), and were therefore analyzed separately.

Trust in media ( Fig 3 , Panel A) . In wave 1, Democrats ( M = 3.61, SD = 1.66) reported greater trust in the media than Republicans ( M = 2.73, SD = 1.65; t = -8.12, p < .001, d = -.53, 95% CI = [-.66, -.39]). There was no significant time trend for either Democrats ( β = .02, SE = .04, p = .57) or Republicans ( β = -.05, SE = .04, p = .20). However, the trend for Republicans was significantly more negative ( β = -.07, SE = .02, p < .001). The different trends we observe for Democrats and Republicans with respect to trust in the media may explain the divergence in perceived threat and vaccine attitudes between these groups over time (see General discussion ).

https://doi.org/10.1371/journal.pone.0250123.g003

Trust in local government ( Fig 3 , Panel B) . In wave 1, Democrats ( M = 4.07, SD = 1.60) indicated lower trust in local government than Republicans ( M = 4.28, SD = 1.60; t = 2.01, p = .045, d = .13, 95% CI = [.003,.26]). Among Democrats, there was no significant time trend ( β = -.06, SE = .04, p = .18), though among Republicans, there was a decreasing time trend ( β = -.11, SE = .05, p = .015). These trends were significantly different from each other ( β = -.06, SE = .02, p = .004).

Trust in federal government ( Fig 3 , Panel C) . In wave 1, Democrats ( M = 2.96, SD = 1.67) expressed lower trust in the federal government than Republicans ( M = 4.08, SD = 1.60; t = 10.52, p < .001, d = .68, 95% CI = [.55,.82]). Both Democrats and Republicans had decreasing time trends (Democrats: β = -.08, SE = .04, p = .036; Republicans: β = -.10, SE = .04, p = .025). These trends were not significantly different from each other ( β = -.02, SE = .02, p = .37).

To rule out differential attrition, we tested whether the composition of our sample (i.e., age, gender, and political party) changed over time (see S1 Table ). Specifically, we tested whether participants who responded to waves 2–6 were significantly different at baseline (wave 1) from the full sample at baseline. The only significant change detected (Ps < .05) was with respect to participants’ age, though the differences were small—the average age was 38.5 at baseline, and remained between 39.9 and 40.8 at baseline among participants who responded to subsequent waves. We found no other systematic pattern of attrition among our participants.

General discussion

Over the course of six months of the COVID-19 pandemic, beginning with a relatively early phase prior to any U.S. directives to stay home (March 2020) and continuing through a cumulation of over 5 million cases (August 2020), we found a decrease in pro-vaccine attitudes and COVID-19 vaccination intentions, as well as reduced intentions to get the influenza vaccine. These findings are contrary to our prediction that increased salience of COVID-19 would improve attitudes toward vaccines.

Our analyses identify political ideology as the best predictor of the decreasing time trend across our three vaccine-related attitudes and intentions measures. In particular, we found that while Democrats’ responses remained fairly stable over time, Republicans shifted away from their lower initial responses and from Democrats’ responses, leading to increased polarization throughout the six-month period.

Contrary to the polarization observed in our data, social and behavioral scientists have long argued that groups facing threats often come together, demonstrating stronger social cohesion [ 36 ], and more cooperative behaviors [ 37 , 38 ]. Researchers have also found that individuals’ sense of shared identity plays a role in promoting cooperative behavior in response to threat [ 39 – 41 ]. Considering our results in the context of these findings might suggest that our respondents’ sense of shared identity was dominated by their political ideology, as opposed to a broader (e.g. American) identity.

What might be going on?

Although the nature of our data does not render causal claims, it highlights potential explanations. First, we note that participants’ ratings of perceived COVID-19 threat followed a similar diverging pattern by party affiliation to our three vaccine-related measures during the study period. Democrats perceived COVID-19 threat to be greater at the start of the study than Republicans did, and this gap widened significantly as the study progressed. This trend is consistent with previous research showing that vaccine hesitancy is related to perceived risk of a threat; when a VPD threat level is low, individuals are less motivated to take preventative action (i.e., immunize; for a review, see [ 42 ]).

Our data offers one potential explanation for the polarization of threat perception: Republican and Democratic participants in our study reported consuming different sources of information. The most commonly checked news source for Republicans was Fox News (Republicans: 50%, Democrats: 8%; χ 2 = 164.55, p < .001) and for Democrats was CNN (Democrats: 47%, Republicans: 23%, χ 2 = 43.08, p < .001, see S6 Table ). Corroborating this proposition, a Pew Research Center poll conducted in March 2020 found that 56% of respondents whose main news source is Fox News believed that “the news media have greatly exaggerated the risks about the Coronavirus outbreak,” whereas this was only true for 25% of those whose main news source is CNN [ 43 ]. Of note, Facebook and Instagram, were also in the top four most consumed news sources for participants affiliated with either party. Extant work describes these platforms as echo chambers [ 44 , 45 ], which may exacerbate partisan exposure to news and information.

Another trend highlighted by our data shows that similar to vaccine attitudes, Republicans’ trust in the media decreased significantly more during our study than Democrats’, suggesting these patterns might be related. According to Dr. Heidi Larson, an expert on vaccine hesitancy and founder of the Vaccine Confidence Project, misinformation regarding vaccinations is more likely to take root when individuals do not trust the information source [ 46 ]. Future research might further examine the role of trust in the media on vaccine attitudes.

While trust in media or media exposure may be driving COVID-19 threat perceptions and vaccine attitudes, there are many other possible explanatory factors that are not captured by our data or analyses. For example, it is possible that threat perceptions were influenced by how a respondents’ county or state was affected by COVID-19; up until June 2020, COVID-19 cases were more common in Democrat-leaning states [ 47 ], which might have amplified its salience early on and influenced attitudes and behavior. Further, although we included individual-level fixed-effects which control for all time invariant participant characteristics, and controlled for different trends by age and SES, we cannot rule out the possibility that other factors (e.g., educational attainment or population density) may have influenced the observed trends. Finally, as our data collection began after the onset of COVID-19, it is possible that the trend we observe for Republicans represents a return to a pre-pandemic baseline of vaccine-related attitudes.

Contributions

This work advances our understanding of how health-related attitudes evolve over time. Our focus on vaccine-related attitudes and intentions is important because experts agree that having enough people vaccinate against COVID-19 is key to stemming the pandemic [ 48 ]. More broadly, negative attitudes toward vaccination in general, and reduced vaccine uptake, is increasingly a public health concern [ 49 ]. This research provides insight into the trends of such vaccine hesitancy, underlining the importance of risk salience and its roots in ideology and media exposure.

This work also contributes to our understanding of political parties and polarization. Numerous anecdotes and reports have demonstrated a partisan divide in Americans’ response to the COVID-19 pandemic. For example, research found greater negative affective responses to wearing a face covering among politically right (vs. left) leaning individuals [ 50 ]. Here, we show that although these observations are valid, the reality is more nuanced. For example, our analyses reveal that polarization on vaccine measures—both attitudes and intentions—is driven primarily by self-identified Republicans’ gradual movement away from their initial responses whereas Democrats’ responses remained largely stable. This insight has important practical implications: It informs us about the dynamics of individuals’ attitudes, bringing us closer to understanding the underlying factors that influence attitudes and behaviors. Equipped with this knowledge, one could design more effective communications and interventions.

Note on methodology and data availability

The present study contributes to a small but growing literature in the social sciences using longitudinal data [ 51 ]. Using a longitudinal methodology allowed us to track individual-level changes over time. Merely observing a single point in time would allow us to observe across-group differences, but would lack the bigger picture of how polarization between these groups evolved. Another key advantage of panel data is that it allows us to include individual-level fixed effects, which control for the impact of omitted or unobserved time-invariant variables. Finally, panel data allows for more accurate inference of model parameters [ 52 ].

While the focus of this paper is vaccine attitudes, our broad dataset offers a unique opportunity to understand attitudes and behavior over time. Due to the richness of our data, its unique nature, and its timeliness, we believe it is important to make it available to other researchers interested in exploring it and publishing additional findings. The complete dataset is available at https://osf.io/kgvdy/ (see S2 and S3 Tables for all items collected).

Supporting information

S1 appendix. additional information about sample exclusions..

https://doi.org/10.1371/journal.pone.0250123.s001

S2 Appendix. Additional information about political party affiliation.

https://doi.org/10.1371/journal.pone.0250123.s002

S1 Table. Attrition table.

https://doi.org/10.1371/journal.pone.0250123.s003

S2 Table. Summary table of measures and constructs included in the text.

https://doi.org/10.1371/journal.pone.0250123.s004

S3 Table. Summary table of measures excluded from the text.

https://doi.org/10.1371/journal.pone.0250123.s005

S4 Table. Regression results.

https://doi.org/10.1371/journal.pone.0250123.s006

S5 Table. Outcome measures by political party affiliation.

https://doi.org/10.1371/journal.pone.0250123.s007

S6 Table. Summary of news sources.

https://doi.org/10.1371/journal.pone.0250123.s008

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Economic Evaluation of COVID-19 Immunization Strategies: A Systematic Review and Narrative Synthesis

• Systematic Review
• Published: 10 April 2024

Cite this article

• Enxue Chang 1   na1 ,
• Haofei Li 1   na1 ,
• Wanji Zheng 1   na1 ,
• Lan Zhou 1 ,
• Yanni Jia 1 ,
• Yiyin Cao 1 ,
• Xiaoying Zhu 2 , 3 ,
• Juan Xu 4 ,
• Mao You 6 ,
• Kejun Liu 6 ,
• Mingsi Wang 1 &
• Weidong Huang   ORCID: orcid.org/0009-0008-9580-6862 1  

Explore all metrics

This study aimed to systematically assess global economic evaluation studies on COVID-19 vaccination, offer valuable insights for future economic evaluations, and assist policymakers in making evidence-based decisions regarding the implementation of COVID-19 vaccination.

Searches were performed from January 2020 to September 2023 across seven English databases (PubMed, Web of Science, MEDLINE, EBSCO, KCL-Korean Journal Dataset, SciELO Citation Index, and Derwent Innovations Index) and three Chinese databases (Wanfang Data, China Science and Technology Journal, and CNKI). Rigorous inclusion and exclusion criteria were applied. Data were extracted from eligible studies using a standardized data collection form, with the reporting quality of these studies assessed using the Consolidated Health Economic Evaluation Reporting Standards 2022 (CHEERS 2022).

Of the 40 studies included in the final review, the overall reporting quality was good, evidenced by a mean score of 22.6 (ranging from 10.5 to 28). Given the significant heterogeneity in fundamental aspects among the studies reviewed, a narrative synthesis was conducted. Most of these studies adopted a health system or societal perspective. They predominantly utilized a composite model, merging dynamic and static methods, within short to medium-term time horizons to simulate various vaccination strategies. The research strategies varied among studies, investigating different doses, dosages, brands, mechanisms, efficacies, vaccination coverage rates, deployment speeds, and priority target groups. Three pivotal parameters notably influenced the evaluation results: the vaccine's effectiveness, its cost, and the basic reproductive number ( R 0 ). Despite variations in model structures, baseline parameters, and assumptions utilized, all studies identified a general trend that COVID-19 vaccination is cost-effective compared to no vaccination or intervention.

Conclusions

The current review confirmed that COVID-19 vaccination is a cost-effective alternative in preventing and controlling COVID-19. In addition, it highlights the profound impact of variables such as dose size, target population, vaccine efficacy, speed of vaccination, and diversity of vaccine brands and mechanisms on cost effectiveness, and also proposes practical and effective strategies for improving COVID-19 vaccination campaigns from the perspective of economic evaluation.

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Enxue Chang, Haofei Li, Wanji Zheng, Lan Zhou, Yanni Jia, Wen Gu, Yiyin Cao, Mingsi Wang & Weidong Huang

School of Elderly Care Services and Management, Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China

Xiaoying Zhu

Nossal Institute for Global Health, School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia

Cancer Hospital Chinese Academy of Medical Sciences, Shenzhen Center, Shenzhen, China

Shenzhen Health Capacity Building and Continuing Education Center, Shenzhen, China

National Health Development Research Center, Beijing, 100191, China

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Chang, E., Li, H., Zheng, W. et al. Economic Evaluation of COVID-19 Immunization Strategies: A Systematic Review and Narrative Synthesis. Appl Health Econ Health Policy (2024). https://doi.org/10.1007/s40258-024-00880-6

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Study: COVID-19 Vaccine Not Linked to Sudden Cardiac Death Among Young People

Cases of myocarditis and pericarditis have rarely been observed after COVID-19 vaccination. The CDC says the benefits of the COVID-19 vaccine outweigh any known risks.

No Link: COVID Vax, Sudden Cardiac Death

Lynne Sladky | AP-File

A healthcare worker fills a syringe with the Pfizer COVID-19 vaccine at Jackson Memorial Hospital, Oct. 5, 2021, in Miami.

The COVID-19 vaccine is not linked to sudden cardiac death among previously healthy young people, according to a new study published by the Centers for Disease Control and Prevention.

Investigators at the Oregon Health Authority looked at death certificate data for people ages 16-30 from June 2021 to December 2022 with cardiac or undetermined causes of death listed. They identified 40 deaths among people who received either the Pfizer or Moderna COVID-19 vaccine.

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Of those 40, three deaths occurred within 100 days of vaccination, with two in people who had underlying illness and one whose cause of death was undetermined. No death certificate attributed death to vaccination, according to the study .

“The data do not support an association of COVID-19 vaccination with sudden cardiac death among previously healthy young persons,” the researchers wrote.

The CDC has stated that cases of myocarditis and pericarditis – inflammation of the heart muscle and the lining outside the heart, respectively – have rarely been observed after COVID-19 vaccination. It’s more common among young males within a week of receiving the second shot. Most patients respond well to medicine and recover quickly.

The CDC maintains that the benefits of the COVID-19 vaccine outweigh any known risks.

The incorrect idea that COVID-19 vaccines are linked to death in young people gained immense attention on social media from conspiracy theorists with the hashtag #diedsuddenly. Certain events in real life, including Buffalo Bills player Damar Hamlin's cardiac arrest, offered fuel for speculation without evidence.

The CDC and other public health officials faced an uphill battle during the pandemic to convince people the COVID-19 vaccine was safe as public figures like Elon Musk, Joe Rogan and Robert F. Kennedy Jr. spread skepticism .

Still, more than 81% of all adults got at least one COVID-19 vaccine, according to CDC data .

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Tags: Coronavirus , CDC , vaccines , pandemic , public health , death

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Assessment of Risk for Sudden Cardiac Death Among Adolescents and Young Adults After Receipt of COVID-19 Vaccine — Oregon, June 2021–December 2022

Weekly / April 11, 2024 / 73(14);317–320

Juventila Liko, MD 1 ; Paul R. Cieslak, MD 1 ( View author affiliations )

What is already known about this topic?

In April 2021, cases of myocarditis after COVID-19 vaccination, particularly among young male vaccine recipients, were reported to the Vaccine Adverse Event Reporting System.

What is added by this report?

To determine risk for sudden cardiac death among adolescents and young adults after COVID-19 vaccination, investigators examined June 2021–December 2022 Oregon death certificate data for decedents aged 16–30 years. Of 40 deaths that occurred among persons who had received an mRNA COVID-19 vaccine dose, three occurred ≤100 days after vaccination. Among these, two occurred in persons with underlying illness, and one decedent had an undetermined cause of death.

What are the implications for public health practice?

The data do not support an association of COVID-19 vaccination with sudden cardiac death among previously healthy young persons. COVID-19 vaccination is recommended for all persons aged ≥6 months to prevent COVID-19 and complications, including death.

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COVID-19 vaccination has been associated with myocarditis in adolescents and young adults, and concerns have been raised about possible vaccine-related cardiac fatalities in this age group. In April 2021, cases of myocarditis after COVID-19 vaccination, particularly among young male vaccine recipients, were reported to the Vaccine Adverse Event Reporting System. To assess this possibility, investigators searched death certificates for Oregon residents aged 16–30 years who died during June 2021–December 2022 for cardiac or undetermined causes of death. For identified decedents, records in Oregon’s immunization information system were reviewed for documentation of mRNA COVID-19 vaccination received ≤100 days before death. Among 1,292 identified deaths, COVID-19 was cited as the cause for 30. For 101 others, a cardiac cause of death could not be excluded; among these decedents, immunization information system records were available for 88, three of whom had received an mRNA COVID-19 vaccination within 100 days of death. Of 40 deaths that occurred among persons who had received an mRNA COVID-19 vaccine dose, three occurred ≤100 days after vaccination. Two of these deaths were attributed to chronic underlying conditions; the cause was undetermined for one. No death certificate attributed death to vaccination. These data do not support an association between receipt of mRNA COVID-19 vaccine and sudden cardiac death among previously healthy young persons. COVID-19 vaccination is recommended for all persons aged ≥6 months to prevent COVID-19 and complications, including death.

Introduction

In December 2020, the Food and Drug Administration authorized two COVID-19 mRNA vaccines for use in the United States. Early vaccine supplies were prioritized for health care personnel and long-term care facility residents, with phased vaccination of other persons, beginning with those who were older or had high-risk medical conditions, and concluding with healthy younger persons ( 1 ). In Oregon, healthy persons aged ≥16 years became eligible for COVID-19 vaccination on April 19, 2021. In April 2021, reports of myocarditis after COVID-19 vaccination, particularly among young male vaccine recipients, began to appear.* , † Investigators in Israel estimated that the risk for myocarditis associated with receipt of mRNA COVID-19 vaccine was 2.13 per 100,000 among vaccine recipients, and was highest among adolescents and young adult males (10.69 per 100,000) ( 2 ). Published accounts suggest that postvaccination myocarditis is typically mild and associated with good outcomes after brief hospitalization ( 3 , 4 ). As of July 17, 2023, no fatal cases of myocarditis in Oregon had been reported to the federal Vaccine Adverse Event Reporting System (VAERS); however, because VAERS is a passive reporting system, adverse events after vaccination are likely underestimated. In late 2022, reports of sudden deaths among previously healthy young athletes, with suggested attribution to COVID-19 vaccination, appeared in the lay press § and then in the medical literature ( 5 , 6 ). To ascertain whether young persons in Oregon might be dying from cardiac causes shortly after having received a COVID-19 vaccine dose, Oregon death certificate data were reviewed.

Data Sources

Oregon law requires that a certificate of death be completed for each death in Oregon. Oregon’s vital records system abides by CDC’s National Center for Health Statistics’ data-quality standards ¶ , including extensive quality-assurance review. An independent source of data for assessing the completeness of death certificate reporting is not available. Data on Oregon resident deaths occurring outside the state are also collected through interstate exchange agreements. The ALERT Immunization Information System (IIS) is Oregon’s statewide and lifespan immunization registry. During the COVID-19 pandemic, reporting of all COVID-19 vaccinations to ALERT IIS was mandated in Oregon.

Data Analysis

To ascertain the occurrence of sudden cardiac deaths among adolescents or young adults that might plausibly be attributed to recent COVID-19 vaccination, investigators searched the Oregon death certificate database to identify persons aged 16–30 years who died during June 1, 2021–December 31, 2022 with “sudden death,” “arrhythmia,” “dysrhythmia,” “asystole,” “cardiac arrest,” “myocarditis,” “congestive heart failure,” “unknown,” “undetermined,” or “pending” cited among the immediate or four possible entries for underlying causes of death and other significant conditions contributing to death. Among the subset of decedents for whom death from a cardiac cause could not be ruled out by accompanying information in the death certificate database, records of mRNA COVID-19 vaccination within 100 days ( 7 ) before the date of death were retrieved from ALERT(IIS. Findings were stratified by sex. This activity was reviewed by the Oregon Health Authority, deemed not research, and was conducted consistent with applicable federal law and Oregon Health Authority policy.**

In Oregon, during June 2021–December 2022, a total of 1,292 deaths among persons aged 16–30 years were identified. These decedents included 925 (72%) males and 367 (28%) females ( Figure ).

Male Decedents

Among the 925 male decedents, no death certificate listed vaccination either as the immediate or as a contributing cause of death. Overall, 17 (2%) deaths among males were attributed to COVID-19. Death certificates cited noncardiac causes of death or other conditions contributing to death for 842 (91%) of the male decedents. Among the remaining 66 (7%) male decedents, excluding a cardiac cause of death based on the death certificate was not possible. Among these 66 decedents, IIS vaccination records were available for 58 (88%); receipt of at least one mRNA COVID-19 vaccination was recorded for 24 (41%).

Among the 24 male decedents with an mRNA COVID-19 vaccination record in IIS, two (8%) died within 100 days of having received the vaccine. The first death was recorded as having occurred in a natural manner 21 days after COVID-19 vaccination. The immediate cause of death noted on the death certificate was congestive heart failure attributed to hypertension; other significant conditions included morbid obesity, type 2 diabetes, and obstructive sleep apnea. The second decedent had received a COVID-19 vaccine dose 45 days before the date of death; the cause of death was recorded as “undetermined natural cause.” Toxicology results were negative for alcohol, cannabinoids, methamphetamine, and opiates; aripiprazole, ritalinic acid, and trazodone were detected. Follow-up with the medical examiner could neither confirm nor exclude a vaccine-associated adverse event as a cause of death for this decedent.

Female Decedents

Among the 367 female decedents, no death certificate listed vaccination as either the immediate or a contributing cause of death. Thirteen (4%) deaths were attributed to COVID-19. Noncardiac causes were recorded on the death certificates for 319 (87%) decedents. Among the remaining 35 (10%) female decedents, IIS records for 30 (86%) were identified, 16 (53%) of whom had documentation of receipt of at least 1 mRNA COVID-19 vaccine dose. Only one of these deaths occurred within 100 days of having received an mRNA COVID-19 vaccine dose; the decedent died 4 days after COVID-19 vaccination. The manner of death was recorded as natural, and the immediate cause was listed as undetermined but as a consequence of chronic respiratory failure with hypoxia attributed to mitral stenosis.

Electronic health record data from 40 U.S. health care systems during January 2021–January 2022, showed that the risk for cardiac complications was significantly higher after COVID-19 infection than after mRNA COVID-19 vaccination among persons aged ≥5 years ( 8 ). Data from CDC’s National Center for Health Statistics show a background mortality rate from diseases of the heart among Oregonians aged 15–34 years of 2.9 and 4.1 deaths per 100,000, during 2019 and 2021, respectively. Although the rate was higher during the pandemic year of 2021, myocarditis remained an infrequent cause of death among persons in this age group. †† Detection of a small difference in mortality rate from myocarditis would require a larger sample size.

In this study of 1,292 deaths among Oregon residents aged 16–30 years during June 2021–December 2022, none could definitively be attributed to cardiac causes within 100 days of receipt of an mRNA COVID-19 vaccine dose; one male died from undetermined causes 45 days after receipt of a COVID-19 vaccine. During May 1, 2021–December 31, 2022, a total of 979,289 doses of COVID-19 vaccines were administered to Oregonians aged 16–30 years (unpublished data, ALERT IIS, 2024.)

During the same period, COVID-19 was cited as the cause of death for 30 Oregon residents in this age group. Among these 30 decedents, ALERT IIS had records for 22 (73%), only three of whom had received any COVID-19 vaccination. Studies have shown significant reductions in COVID-19–related mortality among vaccinated persons; during the first 2 years of COVID-19 vaccine availability in the United States, vaccination prevented an estimated 18.5 million hospitalizations and 3.2 million deaths ( 9 ).

Limitations

The findings in this report are subject to at least two limitations. First, this report cannot exclude the possibility of vaccine-associated cardiac deaths >100 days after COVID-19 vaccine administration. However, published data indicate that potential adverse events associated with vaccinations tend to occur within 42 days of vaccine receipt ( 10 ). Second, small population size made it less likely that Oregon would see a rare event such as sudden cardiac death among adolescents and young adults.

Implications for Public Health Practice

These data do not support an association between receipt of mRNA COVID-19 vaccine and sudden cardiac death among previously healthy young persons. COVID-19 vaccination is recommended for all persons aged ≥6 months to prevent COVID-19 and complications, including death.

Acknowledgments

Michael Day, Tasha Martin, Anne Vancuren, Center for Health Statistics, Oregon Public Health Division; Rebecca Millius, Office of the Chief Medical Examiner, Medical Examiner Division, Oregon State Police.

Corresponding author: Juventila Liko, [email protected] .

1 Public Health Division, Oregon Health Authority, Portland, Oregon

All authors have completed and submitted the International Committee of Medical Journal Editors form for disclosure of potential conflicts of interest. No potential conflicts of interest were disclosed.

* www.cdc.gov/vaccines/acip/work-groups-vast/report-2021-05-17.html

† https://www.cdc.gov/vaccines/acip/meetings/downloads/slides-2021-06/04-COVID-Lee-508.pdf

§ https://www.nytimes.com/2022/01/28/technology/covid-vaccines-misinformation.html

¶ https://www.oregon.gov/oha/PH/BirthDeathCertificates/VitalStatistics/death/Pages/index.aspx

** 45 C.F.R. part 46.102(l)(2), 21 C.F.R. part 56; 42 U.S.C. Sect. 241(d); 5 U.S.C. Sect. 552a; 44 U.S.C. Sect. 3501 et seq.

†† https://wonder.cdc.gov/ucd-icd10-expanded.html (Accessed February 12, 2024).

• Dooling K, Marin M, Wallace M, et al. The Advisory Committee on Immunization Practices’ updated interim recommendation for allocation of COVID-19 vaccine—United States, December 2020. MMWR Morb Mortal Wkly Rep 2021;69:1657–60. https://doi.org/10.15585/mmwr.mm695152e2 PMID:33382671
• Witberg G, Barda N, Hoss S, et al. Myocarditis after COVID-19 vaccination in a large health care organization. N Engl J Med 2021;385:2132–9. https://doi.org/10.1056/NEJMoa2110737 PMID:34614329
• Power JR, Keyt LK, Adler ED. Myocarditis following COVID-19 vaccination: incidence, mechanisms, and clinical considerations. Expert Rev Cardiovasc Ther 2022;20:241–51. https://doi.org/10.1080/14779072.2022.2066522 PMID:35414326
• Behers BJ, Patrick GA, Jones JM, et al. Myocarditis following COVID-19 vaccination: a systematic review of case reports. Yale J Biol Med 2022;95:237–47. PMID:35782472
• Polykretis P, McCullough PA. Rational harm-benefit assessments by age group are required for continued COVID-19 vaccination. Scand J Immunol 2022;98:e13242. https://doi.org/10.1111/sji.13242 PMID:38441161
• Sun CLF, Jaffe E, Levi R. Increased emergency cardiovascular events among under-40 population in Israel during vaccine rollout and third COVID-19 wave. Sci Rep 2022;12:6978. https://doi.org/10.1038/s41598-022-10928-z PMID:35484304
• Sexson Tejtel SK, Munoz FM, Al-Ammouri I, et al. Myocarditis and pericarditis: case definition and guidelines for data collection, analysis, and presentation of immunization safety data. Vaccine 2022;40:1499–511. https://doi.org/10.1016/j.vaccine.2021.11.074 PMID:35105494
• Block JP, Boehmer TK, Forrest CB, et al. Cardiac complications after SARS-CoV-2 infection and mRNA COVID-19 vaccination—PCORnet, United States, January 2021–January 2022. MMWR Morb Mortal Wkly Rep 2022;71:517–23. https://doi.org/10.15585/mmwr.mm7114e1 PMID:35389977
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FIGURE . Deaths* among persons aged 16–30 years, by sex, cause of death, † and mRNA COVID-19 vaccination status §,¶, ** (N = 1,292) — Oregon, June 2021–December 2022

* Coded on the death certificate as sudden death, arrhythmia, dysrhythmia, asystole, cardiac arrest, myocarditis, congestive heart failure, unknown, undetermined, or pending.

† Cardiac versus noncardiac.

§ Six of the 34 males who did not receive mRNA COVID-19 vaccine received Janssen (Johnson & Johnson) vaccine.

¶ An alternative plausible cause of death was identified for one of the males who had been vaccinated ≤100 days before death. After review of death certificate and medical examiner findings, an adverse event from COVID‐19 vaccination could neither be confirmed nor excluded as the cause for the other decedent.

** The only female decedent vaccinated ≤100 days before death was vaccinated 4 days before death. The manner of death was recorded as natural, and the immediate cause was “undetermined” as a consequence of chronic respiratory failure with hypoxia due to mitral stenosis.

Suggested citation for this article: Liko J, Cieslak PR. Assessment of Risk for Sudden Cardiac Death Among Adolescents and Young Adults After Receipt of COVID-19 Vaccine — Oregon, June 2021–December 2022. MMWR Morb Mortal Wkly Rep 2024;73:317–320. DOI: http://dx.doi.org/10.15585/mmwr.mm7314a5 .

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Covid vaccines not linked to fatal heart problems in young people, CDC finds

There is no evidence that mRNA Covid vaccines cause fatal cardiac arrest or other deadly heart problems in teens and young adults, a Centers for Disease Control and Prevention report published Thursday shows.

Ever since the vaccines from Pfizer and Moderna were authorized in late 2020, anti-vaccination groups in the U.S. have blamed the shots for fatal heart problems in young athletes.

One of the most notorious examples of vaccine misinformation involves Buffalo Bills safety Damar Hamlin , 26, who in 2023 collapsed on “Monday Night Football” as a result of cardiac arrest. Hamlin was resuscitated on the field and eventually recovered. He returned to play for the Bills last season. 

“When Damar Hamlin went down, immediately comments were getting made that it was possibly vaccine-related,” said study co-author Dr. Paul Cieslak, the medical director of communicable diseases and immunizations at Oregon Health Authority’s public health division. “This is kind of what we were trying to address with this analysis.”

The findings in the new report come from the  analysis of nearly 1,300 death certificates of Oregon residents ages 16 to 30 who died from any heart condition or unknown reasons between June 1, 2021, and Dec. 31, 2022.

During this time period, nearly 1 million teens and young adults in the state had gotten a Covid vaccine, the authors wrote.

The authors refined their focus to people who got an mRNA Covid vaccine from Pfizer or Moderna and died within 100 days of being vaccinated.

Out of 40 deaths that occurred among people who got an mRNA Covid vaccine, three occurred within that time frame.

Two of the deaths were attributed to chronic underlying health conditions. 

The third death was recorded as an “undetermined natural cause,” with toxicology tests returning negative for alcohol, cannabis, methamphetamine or other illicit substances. 

The medical examiner could neither confirm nor exclude Covid vaccination as the cause of death; however, none of the death certificates attributed the fatalities to the vaccines.

While it remains unclear whether the vaccine caused the third death, Cieslak noted that the analysis showed that 30 people died from Covid during the time frame, the majority of whom were not vaccinated.

“When you’re balancing risks and benefits, you have to look at that and go, ‘You got to bet on the vaccine,’” he said. 

Dr. Leslie Cooper, chair of the cardiology department at the Mayo Clinic, who was not involved in the study, said the researchers were actually “quite generous” in their analysis, adding that the 100-day time frame following vaccination was a large one.

“They went above and beyond to try and capture any possible cardiac death from vaccinations,” he said.

Cardiac arrest occurs when the heart stops beating and pumping blood to the rest of the body. It’s not the same as a heart attack, which happens when blood flow to the heart’s muscle becomes limited or blocked, or myocarditis, which is an inflammation of the heart muscle. 

For people under 35, the causes of cardiac arrest are often unclear. It could be the result of genetic defects or heart malfunctions, such as problems with the valves of the heart. 

Even with the lengthy time frame, Cooper added, the analysis shows that the risk of sudden death in young adults after being vaccinated is significantly lower than the risk of sudden cardiac death from all causes — about 1 in 500,000 per year, compared to 1 in 100,000 per year, according to his estimates.

The data shows “no signal for any elevation in cardiac deaths associated with the Covid mRNA vaccines,” he said. “Their conclusions are quite reasonable.”

No vaccine has ever been conclusively linked to sudden cardiac death, said Dr. Ofer Levy, the director of the Precision Vaccines Program at Boston Children’s Hospital.

Although the mRNA vaccines have been linked to a small risk of myocarditis , the heart condition tends to be much milder than what is typically seen with traditional myocarditis from Covid infection, he added, and most people fully recover within a few days . 

“This adds to evidence that people don’t drop dead from getting their mRNA Covid vaccines,” Levy said of the study.

Berkeley Lovelace Jr. is a health and medical reporter for NBC News. He covers the Food and Drug Administration, with a special focus on Covid vaccines, prescription drug pricing and health care. He previously covered the biotech and pharmaceutical industry with CNBC.

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Will biopharma companies shun vaccine r&d in a new pandemic.

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In the spring of 2020, Covid-19 essentially shut down the U.S. as well as most of the rest of the world. It was terrifying to hear the news reports of hospitals being overwhelmed with those suffering from the virus and seeing tragic pictures of refrigerator trucks filled with those who succumbed. It was hard to envision how the world was going to survive the pandemic.

Millions died across the globe. Yet, we ultimately survived thanks to the efforts of the biopharmaceutical industry. Vaccines from Moderna and BioNTech/Pfizer PFE were miraculously discovered and developed in record time with vaccines being administered to healthcare personnel by December 2020. But other companies such as J&J, Sanofi/GSK GSK , AstraZeneca, and Novavax NVAX also were eventually successful with their own Covid-19 vaccine programs giving people around the world options for protection against this virus. Not every company’s efforts were successful. For example, Merck nobly developed two vaccines but unfortunately neither proved viable.

It must also be noted that many other companies advanced research programs to treat those already infected with Covid-19, such as Regeneron whose Covid-19 antibody saved many lives including, in all likelihood, that of President Donald Trump. Other companies produced important antivirals such as Gilead (Veklury, remdesivir), Merck (Lagevrio/molnupiravir) and Pfizer (Paxlovid, nirmatrelvir). When Covid-19 hit, the biopharmaceutical industry devoted tremendous resources trying to come up with treatments that would save lives. Thankfully, many of these efforts worked.

While Covid-19 hasn’t gone completely away, it is in our rearview mirror. Thus, it was of interest to read Matt Herper’s terrific analysis of how those companies that developed the Covid-19 vaccines have since fared. Titled “During the pandemic, were great vaccines bad business? A company-by-company review” , Herper shows that, while the revenues that were generated from the vaccines were “staggering”, long-term success wasn’t guaranteed. In fact, the data suggest that by shifting a company’s prime focus to discovering, developing and manufacturing a Covid-19 vaccine, other parts of the company understandably suffered.

The poster child for this is Pfizer. Despite having generated sales of its Covid-19 vaccine (Comirnaty) of over $75 billion in its first two years on the market, Pfizer’s stock price has declined 32% since the start of the pandemic. As a result, Pfizer is in the midst of a $4 billion cost cutting program. As Herper states: “Directing an entire 83,000-person company to take on a global catastrophe may not have been without consequences to the rest of the business.” Contrast this with Merck’s experience. Despite having failed in its vaccine efforts, Merck’s stock price has risen 56% over this same period – it was able to keep focus on its existing pipeline. Herper believes that this experience will impact biopharma going forward.

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“But the lesson for large drugmakers is likely to be that when many big pharmaceutical manufacturers decided to prioritize a vaccine effort, they weren’t necessarily making a sound business decision. That doesn’t bode well for the world if there is another pandemic.”

When Pfizer CEO, Dr. Albert Bourla, made the decision to collaborate with BioNTech to find a Covid-19 vaccine, he wasn’t exactly doing it from a position of strength. He had only been CEO for 14 months. He was investing in mRNA technology which was totally unproven. Yet, when told the amount of money needed to do this would likely be over $2 billion, Bourla responded that “this won’t break us” . Pretty brave talk from a new CEO whose 2019 sales were only $41 billion.

Yet, despite Pfizer’s financial performance in the last few years, it’s hard to believe Bourla won’t respond in exactly the same way should another pandemic hit. And despite Merck’s lack of success against the Covid-19 vaccine, it too will ramp up its vaccine R&D machine to combat another pandemic, as will GSK, Astra/Zeneca, Sanofi and the rest of the biopharma industry. How can they not? Only biopharma could have combated Covid-19; no other industry has the capacity to do so. When the world was in crisis, this industry responded. If need be, it will again, despite the financial consequences.

Corrected, April 11 : An earlier version of this article reported incorrectly which company makes the antiviral drug Veklury or remdesivir. It is made by Gilead, not Amgen.

John L. LaMattina is the former president of Pfizer Global R&D and is the author o three books including: Pharma & Profits: Balancing Innovation, Medicine, and Drug Prices.

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