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Epidemiology

Alcohol use by adolescents, hazards of alcohol use, factors that contribute to harmful use, genetic, familial, and environmental factors, other factors, adolescent developmental and neurobiological factors, normal adolescent brain development, effect of substances on adolescent brain development, screening and brief interventions, conclusions, lead authors, committee on substance use and prevention, 2018–2019, former committee member, alcohol use by youth.

POTENTIAL CONFLICT OF INTEREST: The authors have indicated they have no potential conflicts of interest to disclose.

FINANCIAL DISCLOSURE: The authors have indicated they have no financial relationships relevant to this article to disclose.

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Sheryl A. Ryan , Patricia Kokotailo , COMMITTEE ON SUBSTANCE USE AND PREVENTION , Deepa R. Camenga , Stephen W. Patrick , Jennifer Plumb , Joanna Quigley , Leslie Walker-Harding; Alcohol Use by Youth. Pediatrics July 2019; 144 (1): e20191357. 10.1542/peds.2019-1357

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Alcohol use continues to be a major concern from preadolescence through young adulthood in the United States. Results of recent neuroscience research have helped to elucidate neurobiological models of addiction, substantiated the deleterious effects of alcohol on adolescent brain development, and added additional evidence to support the call to prevent and reduce underage drinking. This technical report reviews the relevant literature and supports the accompanying policy statement in this issue of Pediatrics .

Alcohol is the substance most widely used by adolescents, often in large volumes, although the minimum legal drinking age across the United States is 21 years. 1 Some people may initiate harmful alcohol consumption in childhood. The prevalence of problematic alcohol use continues to escalate from adolescence into young adulthood. Heavy episodic drinking by students enrolled in college remains a major public health problem. In results of recent research, it has been indicated that brain development continues well into early adulthood 2 and that alcohol consumption can interfere with such development, underscoring concerns that alcohol use by youth is an even greater pediatric health concern than previously thought. 3 , 4 This technical report supports the accompanied policy statement that outlines recommendations from the American Academy of Pediatrics (AAP). 5  

Alcohol, tobacco, and marijuana remain the substances most widely used by youth in the United States. There is both heartening and less heartening news about the use of alcohol by US youth, however. The 2018 Monitoring the Future Study, supported by the National Institute of Drug Abuse and conducted by the University of Michigan, is now in its 44th year of tracking the prevalence of alcohol, tobacco, and other drug use and youth perceptions of such use. A sample of more than 45 000 young people in eighth, 10th, and 12th grade in approximately 380 private and public secondary schools in the United States provides these data. 1 The data include use by youth in all 3 grades in their lifetime, in the past year (annual use), and in the 30 days preceding the survey as well as “binge” drinking, defined as the consumption of 5 or more drinks in a row on at least 1 occasion in the past 2 weeks, and “extreme binge drinking,” defined as the consumption of 10 or more drinks in a row in the previous 2 weeks. The good news is that there has been a long, substantial decline in alcohol use in all of these categories from peaks in the 1990s. For example, in 1997, the highest number of youth reported using alcohol over the previous year (61%); by 2018, 36.1% of youth in the 3 grades surveyed reported use in the 12 months before the survey. Perhaps even more important, the percentage of young people in the 3 grades reporting binge drinking decreased by half or more from peaks in 1997. In 2017, rates of lifetime prevalence, annual prevalence, and 30-day prevalence of alcohol use in all 3 grades showed plateauing, which was interpreted as a sign that the trend of declining rates was at an end. In addition, in 2017, 4% of eighth-graders, 10% of 10th-graders, and 17% of 12th-graders still reported binge drinking in the past 2 weeks, all slightly increased from 2016. 1 However, in 2018, declines in rates of use continued: the 30-day prevalence rates for eighth, 10th, and 12th-graders was 8%, 19%, and 30%, respectively, and the prevalence of binge drinking in the previous 2 weeks in 10th- and 12th-graders declined to 9% and 14%, respectively, although it remained at 4% for eighth-graders. For the 3 grades combined, this survey documented the lowest levels of alcohol use and binge drinking that have been recorded to date. 1 The criterion used for binge drinking as 5 or more drinks in a row has been thought to be too high, especially for younger children and girls, with the literature suggesting that for 9- to 13-year-old children and 14- to 17-year-old girls, binge drinking should be defined as 3 or more drinks. For boys, binge drinking should be defined as 4 or more drinks for those 14 or 15 years old and 5 or more drinks for those 16 or 17 years old. 6  

To examine higher levels of consumption by 12th-graders, the Monitoring the Future study has more recently been tracking 2 levels of extreme binge drinking, defined as having 10 or more or 15 or more drinks in a row on at least 1 occasion in the preceding 2 weeks. These measures have also declined from 11% in 2005 (the first year of this category’s measurement) to 4.6% for the 10 drinks in a row category and from 6% to 2.5% for 15 drinks in a row in 2018. Each of these measures increased slightly from 2016 to 2017 but resumed the decline in 2018. 1 Declines in perceived availability as well as increased peer disapproval of binge drinking may be some of the factors that are contributing to these lower prevalence numbers. 1 These epidemiologic statistics are corroborated by data from 2 other large surveys of youth alcohol use in the United States: the Youth Risk Behavior Survey, conducted biannually by the Centers for Disease Control and Prevention, and the National Survey on Drug Use and Health, conducted annually by the Substance Abuse and Mental Health Services Administration. 7 , 8  

Use of alcohol at an early age is particularly problematic and is associated with future alcohol-related problems. 9 , – 11 Data from the National Longitudinal Alcohol Epidemiologic Study indicate that the prevalence of both lifetime alcohol dependence and alcohol abuse, as defined by the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition criteria, show a striking decrease with increasing age at the onset of alcohol use. 9 According to the National Longitudinal Alcohol Epidemiologic Study, for people 12 years or younger at first use, the prevalence of lifetime alcohol dependence was 40.6%. In contrast, for people who initiated alcohol consumption at 18 years of age, the prevalence was 16.6%, and for those who initiated drinking at 21 years, the prevalence was 10.6%. Similarly, the prevalence of lifetime alcohol abuse was 8.3% for those who initiated use at 12 years or younger, 7.8% for those who initiated at 18 years, and 4.8% for those who initiated at 21 years. The contribution of age at alcohol use initiation to the odds of lifetime dependence and abuse varied little across sex and racial subgroups in the study. 9 In analyses of data from subsequent surveys, researchers have also illustrated this relationship between early initiation of drinking and subsequent alcohol use disorder (AUD). 12 , – 15  

Adolescent alcohol exposure covers a spectrum, from primary abstinence to alcohol dependence. The Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) 16 defines AUD as follows:

A problematic pattern of alcohol use leading to clinically significant impairment or distress as manifested by 2 or more of the following, occurring during a 12-month period: 1. Alcohol is often taken in larger amounts or over a longer period than was intended. 2. There is a persistent desire or unsuccessful efforts to cut down or control alcohol use. 3. A great deal of time is spent in activities necessary to obtain alcohol, use alcohol, or recover from its effects. 4. Craving, or a strong desire or urge to use alcohol. 5. Recurrent alcohol use results in a failure to fulfill major role obligations at work, school, or home. 6. Continued alcohol use despite having persistent or recurrent social or interpersonal problems caused or exacerbated by the effects of alcohol. 7. Important social, occupational, or recreational activities are given up or reduced because of alcohol use. 8. Recurrent alcohol use in situations in which it is physically hazardous. 9. Alcohol use is continued despite knowledge of having a persistent or recurrent physical or psychological problem that is likely to have been caused or exacerbated by alcohol. 10. Tolerance, as defined by either of the following: a. A need for markedly increased amounts of alcohol to achieve intoxication or desired effect. b. A markedly diminished effect with continued use of the same amount of alcohol. 11. Withdrawal, as manifested by either of the following: a. The characteristic withdrawal syndrome for alcohol. b. Alcohol is taken to relieve or avoid withdrawal symptoms. Reprinted with permission from the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (Copyright 2013). American Psychiatric Association. All Rights Reserved.

The disorder is characterized as mild (2–3 symptoms), moderate (4–5 symptoms), or severe (6 or more symptoms). Because these diagnostic criteria were developed largely from research and clinical work with adults, there are limitations to applying these diagnostic criteria to classify alcohol use and associated risks to adolescents. 17 , – 19 As defined by the DSM-5, an adolescent, especially a younger one, may not have had time to develop an AUD, yet the adolescent may be engaging in very risky behavior. Despite being viewed as an improvement in specificity for adolescents, the applicability of these revised criteria may still be limited in that several of the criteria, such as withdrawal, are not typically experienced by adolescents, and other criteria, such as tolerance, have low sensitivity for adolescents. 20 Tolerance can be anticipated as a developmental process that will occur over time in most adolescents who drink. 17 Thus, an adolescent may present with a subsyndromal level of alcohol use that may not meet the formal threshold for addiction or an AUD but that may still be associated with significant impairments in social functioning and well-being. 21 These limitations to applying a diagnostic algorithm designed for adults to children and youth are often cited as a reason for advocating for the development of more age-appropriate criteria.

Alcohol misuse, although not a formal diagnosis, can be defined as “alcohol-related disturbances of behavior, disease, or other consequences that are likely to cause an individual, his/her family, or society harm now or in the future.” 22 Because the term “alcohol misuse” encompasses earlier stages of AUDs that do not meet diagnostic criteria, it may be a more useful concept clinically in pediatrics and when developing alcohol use primary prevention programs for youth.

Underage drinking is associated with wide range of negative consequences for adolescents, including adverse effects on normal brain development and cognitive functioning, risky sexual behavior, physical and sexual assaults, injuries, AUD, blackouts, alcohol overdose, and even death. When compared with use by adults, alcohol use by adolescents is much more likely to be episodic and in larger volumes (binge drinking), which makes alcohol use by those in this age group particularly dangerous. Rapid binge drinking puts the teenager at even higher risk of alcohol overdose or alcohol poisoning, in which suppression of the gag reflex and respiratory drive and hypoglycemia can be fatal. Binge drinking and its sequelae of elevated blood alcohol concentration (BAC) are especially dangerous for young people who, when compared with adults, may be less likely to be sedated and, therefore, more likely to engage in activities such as driving despite impairment in coordination and judgment. 23  

Alcohol use is a major contributor to the leading causes of adolescent death (ie, motor vehicle crashes, homicide, and suicide) in the United States. Motor vehicle crashes rank as the leading cause of death for US teenagers and young adults. Data from the 2017 Youth Risk Behavior Survey found that during the 30 days preceding the survey, 16.5% of high school students nationwide had ridden one or more times in a car or other vehicle driven by someone who had been drinking alcohol. Of the 62.6% of high school students reporting having driven in the 30 days preceding the survey, 5.5% of students had driven a car or other vehicle at least once when they had been drinking alcohol during this time. 7 These data represent a significant linear decline in reports of use while driving after alcohol use or riding with someone who had been drinking since 1991, when rates reported for riding with a drinking driver and driving oneself after drinking were 39.9% and 16.7%, respectively. 7 In further analysis of the Youth Risk Behavior Survey data, it was shown that in 2011, the prevalence of drinking and driving was more than 3 times higher among those youth who binge drank compared with those who reported current alcohol use but not binge drinking (32.1% vs 9.7%). 24  

The important relationship of alcohol use and motor vehicle crashes involving youth is also highlighted by the fact that after the legal drinking age was changed uniformly to 21 years across the United States, the number of motor vehicle fatalities in individuals younger than 21 years decreased significantly. 25 Since 1998, every state has enacted laws establishing a lower BAC for drivers younger than 21 years, referred to as “zero tolerance laws.” These laws are important because young people who drive after consuming any amount of alcohol pose risk to themselves and others. These laws are also estimated to have reduced alcohol-involved fatal crashes among inexperienced drivers by 9% to 24%. 26 Data show that for each 0.02 increase in BAC, the relative risk of a 16- to 20-year-old driver dying in a motor vehicle crash is estimated to be more than double. 27 Graduated driver licensing (GDL) systems have now been adopted in all 50 states and the District of Columbia. 28 These laws indirectly affect drinking and driving by restricting nighttime driving and the transportation of young passengers in the early months after licensure. In a recent national study, it was shown that GDL nighttime driving restrictions were associated with a 13% reduction in fatal drinking driver crashes among drivers 16 to 17 years old compared with drivers 19 to 20 years old who were not under these restrictions. 29 In a Cochrane review, the implementation of GDL was shown to be effective in reducing the crash rates of young drivers and specifically alcohol-related crashes in most studies in the United States and internationally. 30  

Adolescents who report binge drinking violate GDL laws more frequently and engage in more high-risk driving behaviors, such as speeding and using a cell phone while driving. They also received more traffic tickets and reported having more crashes and near crashes. 31 The importance of the additive effect of alcohol with other illicit substances, particularly marijuana, in contributing to motor vehicle crashes should also not be underestimated. Researchers have suggested that the combination of marijuana and alcohol significantly increases the likelihood of a motor vehicle crash, particularly at levels of alcohol that are below legal limits. For example, Dubois et al 32 found that the odds of a motor vehicle crash increased from 66% to 117% with BACs at 0.05 and 0.08, respectively, to 81% and 128% when detectable levels of tetrahydrocannabinol (THC) were present at these same BACs.

Although legislation has greatly improved transportation safety, young people still are involved in a high proportion of fatal motor vehicle accidents involving alcohol. In 2016, the National Highway Traffic Safety Administration reported a 5.6% increase in traffic fatalities from 2015. 33 Although many factors were reported as responsible for this increase, 10 497 people were killed as a direct result of alcohol-impaired driving crashes, accounting for 28% of the total motor vehicle traffic fatalities (37 461 people) in the United States. 33 In fatal crashes in 2016, the second highest percentage of drivers with BACs of 0.08 or higher was for drivers 21 to 24 years old at 26%; the rate for drivers 16 to 20 years old was 15%. 34  

Underage alcohol use and AUD in adolescents are also associated with other mental and physical disorders. AUD is a risk factor for suicide attempts. 35 Miller et al 36 estimated that 9.1% of suicide attempts resulting in hospitalization by people younger than 21 years involved alcohol and that 72% of these cases were attributable to alcohol. Of note, higher minimum legal drinking ages in the United States have been associated with lower youth suicide rates. 37 Psychiatric conditions most likely to co-occur with AUD include mood disorders, particularly depression; anxiety disorders; attention-deficit/hyperactivity disorder; conduct disorders; bulimia; posttraumatic stress disorder; and schizophrenia. 38 Associated physical health problems include trauma sequelae, 39 sleep disturbance, modestly elevated serum liver enzyme concentrations, and dental and other oral abnormalities, 40 despite relatively few abnormalities being evident on physical examination. 40 , 41  

Early alcohol initiation, in particular, has been associated with greater involvement in a number of high-risk behaviors, such as sexual risk-taking (unprotected sexual intercourse, multiple partners, being drunk or high during sexual intercourse, and pregnancy), academic problems, other substance use, and delinquent behavior in mid to later adolescence. 18 , 19 , 38 , 42 , – 45 By young adulthood, early alcohol use is associated with employment problems, other substance abuse, and criminal and violent behavior. 42  

Twin studies in adult populations have consistently demonstrated genetic influences on the use of alcohol, 46 , – 48 but less research has examined genetic influences in the adolescent age range. 49 , – 51 Through a sibling, twin, and adoption study of adolescents, Rhee et al 52 examined the relative contribution of genetics and environment on initiation, use, and problem use of substances. The results of this study demonstrated that for adolescents (compared with adult twin study findings), the magnitude of genetic influences was greater than the effect of shared environmental influences on problem alcohol or drug use. The reverse was true, however, for initiation of use, with shared environmental factors more important than genetic background. In a recent study, Chorlian et al 53 concluded that when alcohol is consumed regularly in the youngest age range, affecting a less-mature brain, the addiction-producing effects in those who have 2 copies of the genetic allele of the cholinergic M2 receptor gene are accelerated, which can lead to rapid transition from regular alcohol use to alcohol dependence. It has been suggested that gene and environmental effects may vary depending on developmental period of the individual and the stage of the problematic use or addiction. 54  

It has been suggested that the progression to heavy or compulsive alcohol or other drug use is strongly influenced by genetics. 54 Specific genetic studies have helped to elucidate the scientific basis for the relationship observed between early initiation of drinking and subsequent AUD. 12 , – 15 A longitudinal study of the genetic and neurophysiologic correlates of AUD in adolescents and young adults has identified neurophysiological endophenotype differences and variants of the cholinergic M2 receptor gene in adolescent brains that have an age-specific influence on the age of onset of such a disorder. 53 The authors reported that among people who became regular users of alcohol before the age of 16 years, a majority of those who became alcohol dependent within 2 years had the risk genotype, whereas the majority of people who became alcohol dependent 4 or more years after the onset of regular drinking did not have the risk genotype. 53 Another study also found an association between a polymorphism of the μ-opioid receptor encoding gene and adolescent alcohol use. 55  

In a number of studies, researchers have demonstrated the importance of family and social factors on the initiation and early use of alcohol and other drugs. Independent of genetic risk, families play an important role in the development of alcohol and other drug problems in youth, and exposure to alcohol or other drug use disorders of parents predicts substance use disorders in children. 56 Generational transmission has been widely hypothesized as a factor shaping the alcohol use patterns of youth. Whether through genetics, social learning, or cultural values and community norms, researchers have repeatedly found a correlation between youth drinking and a number of family factors, such as the drinking practices of parents. 57 , 58 Results of these studies suggest that policies primarily affecting adult drinkers, such as pricing and taxation, hours of sale, and on-premises drink promotions, may also affect underage drinking. Foley et al 59 found in a national sample ( n = 6245) of teenagers 16 to 20 years old whose parents provided alcohol to them and supervised their drinking were less likely to report being regular drinkers or binge drinkers than those who obtained alcohol through friends or nonparent relatives and participated in unsupervised drinking. They also found that teenagers who obtained alcohol from parents for parties that were unsupervised by those parents reported the highest rates of regular and binge drinking. Although the practice of parents buying alcohol for their teenagers and supervising their drinking cannot be recommended, this study highlights the role that parental behaviors toward alcohol can have on an adolescent’s subsequent drinking behaviors. Parental monitoring of children’s use, the convincing conveyance and consistent enforcement of household rules governing use, and perceived consequences of “getting caught” by parents after drinking all protected youth from drinking behaviors. 59 , – 62  

In the United States, approximately 7.5 million children younger than 18 years (10.5% of all children) are reported to live with at least 1 parent who had an AUD in the past year. 63 These children are at increased risk of many behavioral and medical problems, including depression, anxiety disorders, problems with cognitive and verbal skills, and parental abuse or neglect. 64 Children who have a parent with an AUD are also estimated to be 4 times more likely than other children to develop alcohol problems themselves. 65 See the AAP clinical report “ Families Affected by Parental Substance Use ” for further information. 66  

Having friends who use alcohol, tobacco, or other substances is one of the strongest predictors of substance use by youth. 67 Social and physical settings for underage drinking also affect patterns of alcohol consumption. In a special data-analytic study conducted in 2012, the Substance Abuse and Mental Health Services Administration and the Center for Behavioral Health Statistics and Quality, using data from the National Survey on Drug Use and Health, 68 , 69 found that the usual number of drinks consumed by young people is substantially higher when 2 or more other people are present than when drinking by oneself or with 1 other person. Drinking in the presence of others is by far the most common setting for youth, with more than 80% of youth who had consumed alcohol in the past month reporting doing so when at least 2 others were present. 68 , 69 Most young people drink in social contexts that appear to promote heavy consumption. Private residences are the most common setting for youth alcohol consumption, and the majority of underage drinkers report drinking in either someone else’s home or their own. The next most popular drinking locations reported are at a restaurant, bar, or club; at a park, on a beach, or in a parking lot; or in a car or other vehicle. Older youth in the 18- to 20-year-old age group are more likely than younger adolescents to report drinking in restaurants, bars, or clubs, although the absolute rates of such drinking are low compared with drinking in private residences. The data that demonstrate that underage drinking occurs primarily in social settings in groups at a private residence are consistent with previous research findings that underage drinking parties are high-risk settings for binge drinking and associated alcohol problems. 70 Similar findings exist for binge drinking by college students. 71  

Media influences on the use of alcohol by young people are substantial. 72 , 73 Exposure to alcohol marketing increases the likelihood to varying degrees that young people will initiate drinking and drink at higher levels. 74 , 75 Grenard et al 76 have recently demonstrated using prospective data that exposure to alcohol advertising and liking of those ads by adolescents in seventh grade has a significant influence on the severity of alcohol-related problems reported by 10th grade. In 2003, the US alcohol industry voluntarily agreed not to advertise products on television programs for which greater than 30% of the audience is reasonably expected to be younger than 21 years. The National Research Council of the Institute of Medicine (now the National Academy of Medicine) proposed in that same year that the industry standard should move toward a 15% threshold for alcohol advertising on television. A recent evaluation of adherence to these standards conducted in 25 of the largest US television markets revealed that the alcohol industry has not consistently met its self-regulatory standards, indicating the need for continued public health surveillance of youth exposure to alcohol advertising. 77 Young people can be influenced in their alcohol use by other media, including movies, the Internet, and social media. A 2014 study demonstrated that adolescents with exposure to friends’ risky online displays are more likely to use alcohol themselves. 78  

Over the past decade, great strides have been made in understanding the neurobiological basis of addiction. Studies investigating normal brain development have also yielded information that elucidates the effects of alcohol and other drugs on the developing adolescent brain. As summarized by Sowell et al, 79 results of postmortem studies have shown that myelination, a cellular maturational process of the lipid and protein sheath of nerve fibers, begins near the end of the second trimester of fetal development and extends well into the third decade of life and beyond. Autopsy results have revealed both a temporal and spatial systematic sequence of myelination, which progresses from inferior to superior and posterior to anterior regions of the brain. This sequencing results in initial brain myelination occurring in the brainstem and cerebellar regions and myelination of the cerebral hemispheres and frontal lobes occurring last. Converging evidence from electrophysiological and cerebral glucose metabolism studies shows that frontal lobe maturation is a relatively late process, and neuropsychological studies have shown that performance of tasks involving the frontal lobes continues to improve into adolescence and young adulthood.

Sowell et al 79 documented reduction in gray matter in the regions of the frontal cortex between adolescence and adulthood, which probably reflects increased myelination in the peripheral regions of the cortex. Gray matter loss, with pruning and elimination of neural connections during normative adolescent development, reflects a sculpting process that progresses in a caudal-to-rostral direction. The prefrontal cortex is the last area to reach adult maturation, and this may not be completed until young adulthood. 80 These changes are thought to improve cognitive processing in adulthood, such as cognitive control (ie, the ability to discount rewards) and executive functioning in risk-reward decision-making. 80 Results of neuropsychological studies have shown that the prefrontal cortex areas are essential for functions such as response inhibition, emotional regulation, planning, and organization, all of which may continue to develop between adolescence and young adulthood. Conversely, parietal, temporal, and occipital lobes show little change in maturation between adolescence and adulthood. Parietal association cortices are involved in spatial relationships and sensory functions, and the lateral temporal lobes are associated with auditory and language processing; these functions are largely mature by adolescence. Hence, the observed patterns of brain maturational changes are consistent with cognitive development. 79 Connections are being fine-tuned in adolescence with the pruning of overabundant synapses and the strengthening of relevant connections with development and experience. It is likely that the further development of the prefrontal cortex aids in the filtering of information and suppression of inappropriate actions. 80  

Our current understanding of the biology of brain development in the adolescent has lent support to several models that explain the vulnerability of the adolescent to AUDs. One of these models posits that because the subcortical systems that are important for incentive and reward mature earlier than the areas responsible for cognitive control, this results in an “imbalance.” Thus, activation and reinforcement of those incentive and reward pathways in response to the substance used may occur. This leaves youth uniquely vulnerable to the motivational aspects of alcohol and other drugs and the development of problematic substance use. 21 Without the modulating effect of cognitive control, an adolescent may be less able to resist the short-term result of using substances, compared with long-term, goal-oriented behaviors, such as abstaining. Given that these maturation imbalances in the development of different brain systems is greatest during adolescence, it is not surprising that teenagers may not be able to regulate the emotional or motivational states experienced with the use of substances as adults. 3 , 21 Researchers studying the role of several neurotransmitters in the development and maintenance of substance use and dependence have elucidated the underlying effects of these neurotransmitters in key areas of the brain involved in substance dependence and addiction.

Alcohol interacts with a number of neurotransmitter systems throughout the brain, including the inhibitory neurotransmitters γ-aminobutyric acid and glutamate, that are responsible for the euphoric as well as sedating effects of alcohol intoxication. In addition, neurons that release the neurotransmitter dopamine are activated by all addictive substances, including alcohol. The activation of dopamine release in the nucleus accumbens subregion of the basal ganglia, the area involved in both reward experiences and motivation, results in the “rewarding effect” experienced by users of alcohol and other drugs. In addition, the brain’s endogenous opioid system and the 3 opioid receptors (μ, κ, and δ) interact with the dopamine system and play a key role in the effect that substances such as alcohol have on “rewards” and incentives to continue use of a substance. Brain imaging studies have demonstrated that both the opioid and the dopamine neurotransmitter systems are activated during alcohol and other substance use. The reader is referred to the comprehensive discussion of this in the Surgeon General’s 2016 report: “Facing Addiction in America: The Surgeon General’s Report on Alcohol, Drugs, and Health.” 81  

Determining the specific effect of alcohol exposure or dependence on brain function and structure is challenging given potential biological differences that are normative versus those reflective of recent or past use of substances other than alcohol or of comorbid psychiatric disorders. In several studies, researchers using animal models have demonstrated the inhibition of the growth of adolescent neural progenitor cells with acute alcohol ingestions; similar results were observed with binge alcohol ingestion. 82 , 83 Chronic alcohol ingestion in animal models also disrupts neurogenesis primarily in the hippocampus, an area of the brain especially important for memory. 84  

In adolescents, varying levels of alcohol ingestion ranging from binge-pattern drinking to AUDs have been correlated with both structural and functional brain changes. 21 For example, hippocampal asymmetry was increased and hippocampal volumes were decreased in adolescents with alcohol abuse or dependence patterns compared with both controls who did not use substances and those reporting both alcohol and cannabis use. 85 In another study, adolescents with AUDs had smaller overall and white matter prefrontal cortex volumes compared with nondrinking controls, with girls with AUDs having larger decreases than boys with AUDs. 86 In studies in which researchers used diffusion tensor imaging techniques, which are used to assess white matter architecture, adolescent binge drinking or alcohol use was correlated with reduced factional anisotropy, which is an index that measures neural fiber tract integrity and organization. 87 , – 90 These changes in white matter tract integrity were seen in multiple brain pathways, including those in the corpus callosum as well as limbic, brainstem, and cortical projection fibers. 87 , – 89 It is important to note, however, that all of these studies are correlational and that a true causal relationship between alcohol use in youth and subsequent brain changes has not been demonstrated with this research.

Deficits in neurocognitive function have also been found in adolescents using both alcohol and marijuana compared with controls using no substances. These include deficits in attention, visuospatial processing in teenagers experiencing alcohol withdrawal, poorer performance with verbal and nonverbal retention tasks in adolescents reporting protracted alcohol use, and reduced speed of information processing and overall memory and executive functioning in those reporting alcohol dependence. 4 , 91 , – 93 These abnormalities are postulated to result, in part, from the morphologic and functional changes seen in specific brain areas involved in memory (hippocampus) and executive function and decision-making (prefrontal cortex). In addition, genetic predisposition, such as family history of alcoholism, may enhance the vulnerability of specific brain areas, such as the hippocampus, to the effects of alcohol use in adolescents. 94 These potential genetic factors and epigenetic contributors (the impact of environmental and social factors on gene expression) are areas of active study. 21 The Adolescent Brain and Cognitive Development study, supported by the National Institutes of Health and the National Institute on Drug Abuse, is a 10-year longitudinal study that started in 2015 designed to assess the environmental, social, genetic, and biological factors involved in adolescent brain and cognitive development. The initial year of recruitment and baseline assessment of 11 875 10-year-olds has been completed, and this study holds great promise in terms of informing scientists and clinicians of the effect of licit and illicit substances, among many factors being studied, on the trajectory of brain development and cognitive functioning over the course of adolescent and young adulthood. 95  

Several recent Cochrane reviews have examined the prevention of substance abuse in young people through family-based prevention programs, 96 universal school-based prevention programs, 97 brief school-based interventions, 98 universal multicomponent prevention programs, 99 and mentoring programs. 100 Although there were variations in programs in all of these reviews and generally few high-quality studies, all of these prevention strategies showed some success. Family-based prevention programs typically take the form of supporting the development of parenting skills, including parental support, nurturing behaviors, establishing clear boundaries or rules, and parental monitoring. The development of social and peer resistance skills and the development of positive peer affiliations can also be addressed in these programs. The Cochrane systematic review found that “the effects of family-based prevention are small but generally consistent and persistent into the medium- to longer-term” 96 and are consistent with an earlier systematic review supporting the effectiveness of family-focused prevention programs. 101  

Recognition of the pervasive use of alcohol among young people, the hazards that may be encountered with even low-level use, and the association between early initiation of alcohol use and future alcohol problems underscores the need to integrate our approaches to alcohol and other drug use by youth into pediatric primary care. The AAP recommends that pediatricians screen and discuss substance use as part of anticipatory guidance and preventive care. 102 , – 104 Screening, brief intervention, and referral to treatment (SBIRT) for youth is such an integrated approach that has grown in recent years to bridge the gap between universal prevention programs and specialty substance abuse treatment by pediatric primary care providers. 105 , 106 The reader is referred to the AAP clinical report on SBIRT for pediatricians. 104 The effectiveness of SBIRT is well supported for addressing hazardous use of alcohol by adults in medical settings, but there is less evidence for its effectiveness in adolescents. 107 , – 115  

Several screening strategies have been validated and used to identify youth at risk for or involved in the use of alcohol and other substances that can be incorporated into general psychosocial screening efforts, such as interviewing strategies like HEADSS (home, education, activities, drugs and alcohol, sex, suicidality) 116 and SSHADESS (strengths, school, home, activities, drugs and alcohol, substance use, emotions and depression, sexuality, safety). 117 The CRAFFT is a tool developed for screening adolescents for alcohol and other substance use with 3 introductory questions followed by 6 questions using the CRAFFT mnemonic. 118 It has been well validated and is brief enough for use in busy clinical settings. 119 In 2011, the National Institute on Alcohol Abuse and Alcoholism (NIAAA) collaborated with the AAP to develop a brief screening tool to assist health care providers in identifying alcohol use, AUD, and risk for use in children and adolescents ages 9 to 18 years. 120 This tool includes brief 2-question screeners and support materials about brief intervention and referral to treatment and is designed to help surmount common obstacles to youth alcohol screening in primary care. The screen administration varies by age and grade and focuses on drinking frequency over the previous 12 months to determine level of risk. 121 This tool has been expanded to include tobacco and other substances and is sensitive and specific for identifying substance use disorders in a pediatric clinic population. 122 Although developed for use primarily in the primary care setting, Spirito et al 123 have demonstrated its usefulness in screening for AUDs in pediatric emergency settings.

In several studies, researchers have confirmed the validity of using a single question about the frequency of use of alcohol and other drugs over the previous 12 months to determine level of risk. 124 Studying a population of adolescents and young adults in rural Pennsylvania, Clark et al 124 compared a single question of past-year frequency of alcohol use versus comprehensive diagnostic interviews on the basis of DSM-5 criteria for AUD to determine the validity of this question in identifying problematic alcohol use. They found both high sensitivity and specificity for adolescents ages 12 to 17 years using 3 or more days with 1 or more drinks as a cutoff to identify AUDs. For young adults 18 to 20 years of age, using 12 or more days or 12 or more drinks over the previous year also had excellent ability to identify AUDs. 124 Levy et al 125 have also validated a single-question screen, referred to as the “S2BI”: “In the past year, how many times have you used alcohol?” They have found that responses that include never, once or twice, monthly, weekly, almost daily, or daily can differentiate between those with mild, moderate, and severe AUDs, per DSM-5 criteria, and can indicate those individuals who would benefit from education versus brief intervention or more-specific substance abuse treatment. 125 This screening question has also been shown to identify problematic use of illicit drugs, over-the-counter medications, and tobacco. These screening tools, as well as the NIAAA screening tool, continue to be validated, and the results reported here are promising.

Questions often remain about how to incorporate parents into this screening process and how and when to provide confidentiality for a youth’s report of underage alcohol use. The NIAAA 2-question screening tool recommends that screening begin as early as 9 to 11 years of age, and given that most preteens will be questioned in the presence of a parent or guardian, this offers an opportunity to discuss the parent’s philosophy regarding alcohol use by minors, situations in which they might deem it appropriate (such as at holidays), and their own practices regarding their own drinking and consequences for their child’s drinking. This screening can also be performed routinely for all adolescents during preventive care visits. For the older adolescent, whenever possible, it is preferable to include parents in any discussion with a youth who reports drinking; however, when this is seen by the youth as a major deterrent to his or her alliance with the provider and there are no “red flag” behaviors that are believed to be unsafe, such as the youth riding or driving after drinking, heavy binge drinking, or when an AUD is suspected, maintaining confidentiality and counseling the adolescent is often preferable because this maintains the alliance between the provider and the adolescent. There are no hard and fast rules as to when parents should be included in discussions about their adolescent’s alcohol use; this can be a delicate matter and is generally a judgment call by the primary medical provider, unless the safety of the youth is put in jeopardy by drinking behaviors. Studies have shown that parents tend to underestimate the extent of their teenagers’ drinking behaviors, and including parents in the discussions with their teenagers often serves to highlight a greater amount of use than what is anticipated by parents. Discussions about minimizing risk, such as contracting with the youth to call parents if they are concerned about friends drinking while driving, may also be helpful. Students Against Destructive Decisions is a youth-focused organization promoting healthy and safe decision-making, especially around driving behaviors. The Students Against Destructive Decisions Web site ( https://www.sadd.org/what-we-care-about/ ) provides educational information as well as the “Contract for Life,” which is a contract that teenagers sign along with their parents, promising to avoid alcohol and other substances when driving.

Once screening has been conducted and the level of risk has been determined, the provider can provide anticipatory guidance supporting abstinence, perform brief intervention strategies, or refer the adolescent for further evaluation or to a higher level of treatment. Brief intervention strategies are short, efficient, office-based techniques that health care providers who work with adolescents can use to detect alcohol use and intervene. On the basis of the principles of motivational interviewing, these procedures can be readily performed in the office setting, build on the individual’s readiness to change drinking behaviors, and support the adolescent’s need for involvement in one’s own health care choices and decisions. Harris et al 105 have provided an excellent review of counseling strategies at different levels of risk behaviors of young people, and the NIAAA Alcohol Screening Practitioner Guide provides strategies for brief intervention at different ages. 120 D’Onofrio and colleagues 126 have developed a brief (5- to 7-minute) scripted intervention approach, the Brief Negotiation Interview (BNI), for use with adults reporting harmful and hazardous alcohol use in the emergency setting, and Ryan et al 127 have adapted this BNI for use in a pediatric residency training setting for use with adolescents in a primary care clinic. Pediatrics residents trained in the BNI reported that this intervention was easily learned and highly applicable in clinical settings with teens reporting alcohol and other illicit substance use. 127  

The National Institute on Drug Abuse publication “Principles of Adolescent Substance Use Disorders Treatment: A Research Guide” is a comprehensive guide of evidence-based approaches to treating adolescent substance use disorders and emphasizes that treatment is not “one size fits all” but requires taking into consideration the needs of the individual, including his or her developmental stage; cognitive abilities; the influence of friends, family, and others; and mental and physical health conditions. 128 The AAP clinical report on SBIRT also includes a list of optimal standards for a substance use disorder treatment program. 66 Behavioral therapies are effective in treating alcohol and other substance use disorders as well as multiple substances and include individual therapy, such as cognitive-behavioral therapy and motivational enhancement therapy. Family-based approaches, including multidimensional family therapy and multisystemic therapy, have been proven to be effective. 129 Addiction medications for AUD include acamprosate, disulfiram, and naltrexone. Medication-assisted therapies are not commonly used to treat adolescent AUDs but may be used in specific circumstances. These medications are approved by the US Food and Drug Administration for treatment of people 18 years and older.

In most cases, the primary care pediatrician’s initial role is to identify, through screening, teenagers in need of intervention and referral for further treatment. However, continued involvement by the primary pediatric provider with the teenager and the family, through regular follow-up and care coordination, is essential in any treatment plan after referral.

Although it is heartening that alcohol use among adolescents and youth has decreased over the last several years, researchers have even more clearly elucidated links between alcohol use and deleterious effects on adolescents’ developing brains as well as other aspects of their physical and mental health. Pediatricians are in an excellent position to recognize risk factors for use and screen for hazardous use among youth. Pediatricians can also assess youth whose screening results are positive for alcohol use to determine the level of intervention needed. Brief intervention techniques used by pediatricians have been shown to be effective in a limited number of studies and may be especially helpful in aiding youth and their families to obtain appropriate treatment of AUDs. Pediatricians also have an important advocacy role in health systems’ changes as well as legislative efforts, such as increasing alcohol taxes, resisting efforts to weaken minimum drinking age laws, and supporting GDL programs. 130 , 131  

Drs Ryan and Kokotailo were directly involved in the planning, researching, and writing of this report; and both authors approved the final manuscript as submitted.

This document is copyrighted and is property of the American Academy of Pediatrics and its Board of Directors. All authors have filed conflict of interest statements with the American Academy of Pediatrics. Any conflicts have been resolved through a process approved by the Board of Directors. The American Academy of Pediatrics has neither solicited nor accepted any commercial involvement in the development of the content of this publication.

Technical reports from the American Academy of Pediatrics benefit from expertise and resources of liaisons and internal (AAP) and external reviewers. However, technical reports from the American Academy of Pediatrics may not reflect the views of the liaisons or the organizations or government agencies that they represent.

The guidance in this report does not indicate an exclusive course of treatment or serve as a standard of medical care. Variations, taking into account individual circumstances, may be appropriate.

All technical reports from the American Academy of Pediatrics automatically expire 5 years after publication unless reaffirmed, revised, or retired at or before that time.

FUNDING: No external funding.

American Academy of Pediatrics

alcohol use disorder

blood alcohol concentration

Brief Negotiation Interview

Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition

graduated driver licensing

National Institute on Alcohol Abuse and Alcoholism

screening, brief intervention, and referral to treatment

Sheryl A. Ryan, MD, FAAP

Patricia Kokotailo, MD, MPH, FAAP

Sheryl A. Ryan, MD, FAAP, Chairperson

Deepa R. Camenga, MD, MHS, FAAP

Stephen W. Patrick, MD, MPH, MS, FAAP

Jennifer Plumb, MD, MPH, FAAP

Joanna Quigley, MD, FAAP

Leslie Walker-Harding, MD, FAAP

Gregory Tau, MD, PhD – American Academy of Child and Adolescent Psychiatry

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Open Access

Peer-reviewed

Research Article

Alcohol Use among Adolescent Youth: The Role of Friendship Networks and Family Factors in Multiple School Studies

* E-mail: [email protected]

Affiliation Department of Sociology, Cornell University, Ithaca, New York, United States of America

Affiliation Departments of Criminology, Law and Society and Sociology, University of California Irvine, Irvine, California, United States of America

Affiliation Departments of Sociology and Statistics, University of California Irvine, Irvine, California, United States of America

Affiliation Department of Psychology and Social Behavior, University of California Irvine, Irvine, California, United States of America

Affiliation Program in Public Health, University of California Irvine, Irvine, California, United States of America

• Cheng Wang, 
• John R. Hipp, 
• Carter T. Butts, 
• Rupa Jose, 
• Cynthia M. Lakon

• Published: March 10, 2015
• https://doi.org/10.1371/journal.pone.0119965
• Reader Comments

To explore the co-evolution of friendship tie choice and alcohol use behavior among 1,284 adolescents from 12 small schools and 976 adolescents from one big school sampled in the National Longitudinal Study of Adolescent to Adult Health (AddHealth), we apply a Stochastic Actor-Based (SAB) approach implemented in the R-based Simulation Investigation for Empirical Network Analysis (RSiena) package. Our results indicate the salience of both peer selection and peer influence effects for friendship tie choice and adolescent drinking behavior. Concurrently, the main effect models indicate that parental monitoring and the parental home drinking environment affected adolescent alcohol use in the small school sample, and that parental home drinking environment affected adolescent drinking in the large school sample. In the small school sample, we detect an interaction between the parental home drinking environment and choosing friends that drink as they multiplicatively affect friendship tie choice. Our findings suggest that future research should investigate the synergistic effects of both peer and parental influences for adolescent friendship tie choices and drinking behavior. And given the tendency of adolescents to form ties with their friends' friends, and the evidence of local hierarchy in these networks, popular youth who do not drink may be uniquely positioned and uniquely salient as the highest rank of the hierarchy to cause anti-drinking peer influences to diffuse down the social hierarchy to less popular youth. As such, future interventions should harness prosocial peer influences simultaneously with strategies to increase parental support and monitoring among parents to promote affiliation with prosocial peers.

Citation: Wang C, Hipp JR, Butts CT, Jose R, Lakon CM (2015) Alcohol Use among Adolescent Youth: The Role of Friendship Networks and Family Factors in Multiple School Studies. PLoS ONE 10(3): e0119965. https://doi.org/10.1371/journal.pone.0119965

Academic Editor: Jesse Lawton Clark, David Geffen School of Medicine at UCLA, UNITED STATES

Received: August 14, 2014; Accepted: January 18, 2015; Published: March 10, 2015

Copyright: © 2015 Wang 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: This research uses data from AddHealth, a program project directed by Kathleen Mullan Harris and designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris at the University of North Carolina at Chapel Hill. Information on how to obtain the AddHealth data files is available on the AddHealth website ( http://www.cpc.unc.edu/addhealth ).

Funding: This research is funded by National Institutes of Health Grant (5R21DA031152-02) administered through the Program in Public Health at the University of California Irvine. The funders had no role in study design, data collection and analysis, decision to publish,or preparation of the manuscript.

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

Introduction

In the United States, the prevalence of adolescent drinking has declined since the late 1990s—however the problem is far from being solved. Recent reports find that 71% of 9th- to 12th-grade students indicate having had at least one drink of alcohol (other than a few sips) during their lifetime, with about one fifth of them starting to drink by age 13 [ 1 ]. The high prevalence of underage drinkers, and early initiation into drinking practices, has led to decades of research on the consequences of adolescent alcohol use. Studies have linked adolescent drinking to other adolescent problem behaviors, including automobile accidents [ 2 , 3 , 4 , 6 ], drug abuse [ 3 , 4 , 8 ], engagement in risky sexual activities [ 2 , 3 , 4 ], school absenteeism [ 3 , 9 ], and poor or failing grades [ 3 , 4 , 5 , 7 ]. Thus, despite the declining rates of alcohol use among youth, adolescent drinking remains a key agenda item for the public health community to address.

Ecological models [ 10 , 11 ] suggest that multiple contextual influences shape adolescent development and health behavior. Adolescence is a critical time period during which various contextual influences—including those exerted by youths' friends and parents prominently as the primary socialization forces—affecting adolescent development and their health behavior. Theoretical intuition from ecological models suggests that both peer and parental influences might act both independently and in synergy as they impact the simultaneous processes of adolescent friendship tie choice and adolescent drinking.

Peer networks are a salient reference group for youth, with peers playing a particularly important and influential role in helping shape adolescents' evolving social worlds [ 12 ]. Various characteristics of adolescent peer networks are salient for youths' friendship tie choices. Reciprocity among adolescent friendship pairs is a strong force affecting friendship tie formation among adolescent youth [ 13 ]. Indicators of degree, including in-degree centrality which is the popularity of an actor in a network [ 14 ], and in-in degree assortativity, which is the tendency to choose network actors who are similarly popular, are important for friendship tie selection, as popular youth may have many options for forming friendships, and likewise may be more likely to choose similarly popular friends. Another degree based network indicator, out-degree, is an indicator of the number of ties a network actor sends [ 14 ], and reflects an actor's expansiveness in the network. Adolescents who send out more friendship tie nominations are salient in a network. In addition, the number of transitive triplets and three-cycles, which reflect local hierarchy in a network, are likely important for friendship tie choice as youth occupying positions in these triadic structures have an opportunity to influence one another, likely on multiple dimensions including on friendship tie choices.

In addition to shaping adolescents' friendship tie choices, peer networks also shape adolescent drinking behavior. Two important socialization processes operating in peer networks are peer influence and peer selection. Peer influence is the process wherein youth alter their behavior such that it aligns with that of their peers. Peer selection is the process by which youth select friends who are similar to themselves, on various dimensions. Adolescent alcohol use can be conceptualized as a result of both peer influence and selection processes, with friends influencing one another's drinking behavior and adolescents selecting friends who engage in similar levels of drinking [ 15 – 20 ]. And in-degree popularity has also been positively related to adolescent drinking behavior across studies [ 21 – 24 ].

Stochastic Actor-Based (SAB) models [ 25 , 26 ] have offered keen insights into adolescent alcohol use, allowing researchers to disentangle peer selection and peer influence effects. These dynamic models estimate friendship tie choice and drinking behavior changes simultaneously. One study found both selection (i.e., "birds of a feather" or homophily effects) and influence (i.e., social contagion) effects for drinking behavior when using longitudinal data of youth in the West of Scotland [ 27 ]. Similar studies based on longitudinal data collected in Netherlands [ 28 ], Italy [ 29 ], Finland [ 30 ], and Sweden [ 31 ] also found both selection and influence processes underlying adolescent drinking behavior similarity. The first study to utilize the SAB modeling strategy to examine the drinking behavior among adolescents in the United States was Mundt, Mercken, and Zakletskaia [ 32 ], using two waves of data from the National Longitudinal Study of Adolescent to Adult Health (AddHealth), a nationally representative survey of US adolescents from 7th to 12th grades between 1995 and 1996. Contrary to the findings from international studies, Mundt, Mercken, and Zakletskaia [ 32 ] found only a significant selection effect but no influence effect for alcohol use. A study utilizing data from 450 15-to-17 year-old students attending a public high school in the Northeastern United States between 2004 and 2006 showed the opposite effect, with only a significant peer influence effect on alcohol use [ 33 ]. Moreover, a third study, using the PROSPER dataset—which followed 13,214 6th-to-9th grade adolescents from 50 classrooms in Iowa and Pennsylvania during the fall of 2002 and 2003—reported both significant influence and selection effects for drinking behavior similarity [ 34 ].

Parents are also influential referents in adolescents' social worlds, affecting both adolescent friendship tie choices and their drinking behavior. Parents exert influences on the socialization of adolescent youth through multiple processes, including the provision of social support and parental monitoring. Indeed, parental influences have a critical impact on adolescent development, particularly in the area of building youths' social competence regarding friendship formation. One study indicated that familial factors are associated with social competence, peer acceptance, and the ability to form and maintain close friendships among youth [ 35 ]. Moreover, theoretical guidance from Social Control Theory [ 36 , 37 ] casts parental monitoring as a key process deterring youth from affiliating with delinquent peers, which may then act to lessen the probability that youth begin using substances such as alcohol. As such, parents may affect the likelihood that their adolescent children will select friends who drink. A recent cross-sectional study examining adolescent substance use suggested that youth reared in families characterized by a lack of familial obligations, emotional closeness, and support, were more likely to affiliate with substance using peers, and that moreover, having these peer relationships was associated with more substance use [ 38 ]. To the extent that parents are successful at monitoring their children, the adolescent child may have less opportunity to associate with friends who drink, as parents may prohibit friendships with other youth who drink or prohibit their adolescent children from being in situations which present opportunities to drink [ 39 ]. It is therefore possible that parents' characteristics might interact with youths' friendship selection as both factors may impact one another in relation to adolescent friendship networks.

Past studies also demonstrate that parental influences affect adolescent drinking behavior directly. Previous research indicates that parental support prevents the early initiation of alcohol use and reduces the frequency of alcohol use among adolescents [ 40 , 41 , 42 ]. Moreover, parental monitoring and supervision have been negatively associated with adolescent alcohol use [ 19 , 43 ]. Not all parental influences, however, are protective for alcohol use, as youth in homes in which parents drink or parents display high levels of permissiveness for adolescent drinking are more likely to increase their alcohol use levels over time [ 44 , 45 , 46 ].

It is possible that peer and parental influences may function together in impacting adolescent friendship tie choices and drinking behavior, given insights from ecological models of development suggesting that influences from these two contexts may act synergistically [ 10 ]. In a study of 4,230 7th to 12th graders, parental drug (including binge drinking) attitudes had an indirect effect on the risk of adolescent drug use, which was mediated through peer drug use [ 47 ]. In another study, Marshal and Chassin [ 48 ] found that parental support and discipline buffered the effects of peer group affiliation on alcohol use of female adolescents. It may be that parental attitudes and behavior relevant to monitoring, support, and parental drinking operate synergistically with youths' affiliations with peers who use alcohol in affecting youths' friendship tie choices. Having said that, extant studies have not systematically examined these parental influences as they affect the co-evolving processes of friendship tie choice behavior and alcohol use among adolescents, nor have they examined the interactions under study. As such, whereas past studies have found a positive peer influence effect on adolescent alcohol use [ 15 – 20 ], the current study goes a step further to examine whether youth whose parents engage in close monitoring or provide strong emotional support, or have a home environment condoning drinking behavior, are more or less likely to adopt the drinking behavior of their friends through a peer influence effect on their own drinking behavior. Secondly, the current study also assesses whether parental support, parental monitoring, and the parental home drinking environment interact with whether youth choose friends who drink as both factors might multiplicatively affect youths' friendship tie choice.

Finally, we also examine the effect of depressive symptoms among adolescents on their alcohol use. Adolescent drinking has been positively related to depression in previous studies [ 23 , 49 – 53 ]. Depressive symptoms in adolescent youth have also been related to youths' friendship tie choice behavior [ 54 ]. As such, we examine the role of depressive symptoms as they relate to adolescent drinking behavior.

The current study builds upon extant literature examining the importance of peer influence and selection processes related to adolescent friendship tie choice and alcohol use. In addition, we also focus on the role of key parental influences on adolescent friendship tie choice and alcohol use behavior. With data from the AddHealth study, we explore the co-evolution of adolescents' friendship ties and their drinking behavior over two waves. We hypothesize that we will observe both peer influence and peer selection effects, as adolescents will select friends with similar alcohol use levels, and adjust their drinking behavior based on the drinking behavior of their friends. We also expect that parental monitoring, parental support, and the parental home drinking environment, will influence the friendship tie choice and adolescent drinking, both directly and synergistically.

Data and Methods

The data utilized in this study come from early waves of AddHealth. The respondent record/information was anonymized and de-identified prior to analysis. This study has been approved by the Institutional Review Board of the University of California, Irvine (2013). This study does not employ human subjects directly, as our analyses utilize secondary data which are de-identified. AddHealth participants provided written informed consent for participation in all aspects of AddHealth in accordance with the University of North Carolina School of Public Health Institutional Review Board guidelines that are based on the Code of Federal Regulations on the Protection of Human Subjects 45CFR46: http://www.hhs.gov/ohrp/humansubjects/guidance/45cfr46.html . Written informed consent was given by participants (or next of kin/caregiver) for their answers to be used in this study.

AddHealth is a longitudinal study of a stratified sample of US schools from 7th to 12th grades [ 55 ]. The AddHealth data is one of the richest adolescent network data sources collected to date in the United States. The network boundary is defined by a meaningful social and policy-relevant unit, the school, with information on basic demographics (i.e., gender, race, age), attitudes and behaviors, and ecological structures (i.e., family, school, neighborhood), as well as on all friendship relations. Thus these data are ideal for examining the co-evolution of friendship networks and drinking behavior in school based contexts. Although the AddHealth data were collected nearly 20 years ago, these data continue to be relevant to the mechanisms of in-school friendship formation, and continue to be widely used in current public health studies [ 32 , 56 – 62 ].

AddHealth contains a saturated sample of 16 schools out of the total 132 participant schools [ 55 ]. Among the 16 schools, there is a special education school with constant student turnover, and another school with an administrative error in which the students' IDs at the earlier wave could not be matched with those at later waves. Thus these two schools are not suitable for longitudinal network analysis. Of the remaining 14 schools, two are large schools (over 1,000 students enrolled) often called "Jefferson High" and "Sunshine High" [ 56 , 63 ] whose macro settings were quite different from the other 12 small schools with fewer than 200 students enrolled [ 64 , 65 ]. (Among the twelve small schools, network size, or number of respondents, is between 30 and 197. The mean is 107 and SD is 53.46.) Given computational power issues, we could not estimate the model for the biggest school, "Sunshine High." Since our analysis requires longitudinal measures of friendship networks, we focus on two samples: 1) a saturated sampling of 1,284 students from 12 small schools, and 2) that of 976 students from the second biggest school, "Jefferson High" [ 63 ].

During the administration of the AddHealth survey, all students in 14 schools were invited to take the survey over three waves. Information on the social and demographic characteristics of the respondents was collected, as well as their risk behaviors including alcohol use. Adolescent sociometric networks were constructed from a network elicitation item asking respondents to name up to five male and five female best friends from a name list of students in his/her school, and thus researchers are able to attain longitudinal complete sociometric networks in these schools. The measures of youths' parental contexts came from a parent survey between April and December of 1995, the same time when the respondents also took a wave 1 In-Home Survey. However, due to another administrative error, about 37% respondents in the 12 small schools and about 5% respondents in "Jefferson High" were recorded to nominate only one (instead of five) female and male best friends. To overcome this limited nomination difficulty, in this study we utilize the information retrieved from the first (In-School Survey during 1994 and 1995) and third (wave 2 In-Home Survey between April and December of 1996) time points for our dynamic network analysis, skipping that from the second time point (wave 1 In-Home Survey).

The dependent (behavior) variable measures drinking frequency in the past 12 months. At wave 1, the survey question was "During the past twelve months, how often did you drink beer, wine, or liquor?" with response categories of "0—never", "1–1 once or twice", "2–2 once a month or less", "3–2 or 3 days a month", "4—once or twice a week", "5–3 to 5 days a week", and "6—nearly every day." In waves 2 and 3, the survey question was "During the past 12 months, on how many days did you drink alcohol?" with response categories of "1—every day or almost every day", "2–3 to 5 days a week", "3–1 or 2 days a week", "4–2 or 3 days a month", "5—once a month or less (3–12 times in the past 12 months)", "6–1 or 2 days in the past 12 months", and "7—never". We recoded these such that response categories specify non-drinkers (0 = never), casual-drinkers (1 = 1 or 2 days), light-drinkers (2 = once a month or less or 3–12 times in the past 12 months), medium-drinkers (3 = 2 or 3 days a month), and heavy-drinkers (4 = more than 1 or 2 days a week). (We do not have a measure of drinking intensity. The only related question asked over all three time points was "During the past twelve months, how often did you get drunk?" However, students may interpret the number of drinks required to be "drunk" very differently, and we therefore do not include this measure.)

Predictors of drinking behavior include gender (0 = male, 1 = female), grade (7~12), depressive symptoms, parental support, parental monitoring, and the parental home drinking environment. Depressive symptom status is generated as a factor score of 19 ordinal items modified from the Center for Epidemiologic Studies Depression Scale (CES-D; Cronbach's α = 0.87) [ 66 ]. Parental support and parental monitoring are computed as standardized factor scores (means = 0, standard deviations = 1) through confirmatory factor analysis, with Root Mean Squared Error of Approximation (RMSEA) about. 05 and Comparative Fit Index (CFI) greater than. 95, which both suggest a good fit. Items indicating parental support include whether the student had talked about a personal problem with their parents (0 = no, 1 = yes), whether the parents and the student communicated well, whether the parents were warm and loving, whether the student reported a "good relationship" with parents (same response categories for three items above: 1 = strongly disagree, 2 = disagree, 3 = neither agree nor disagree, 4 = agree, 5 = strongly agree), the student's closeness to their parents, and how much the student felt his or her parents cared about him or her (same response categories for two items above: 1 = not at all, 2 = very little, 3 = somewhat, 4 = quite a bit, 5 = very much). Items indicating parental monitoring include whether parents let the student make decisions about a weekend curfew, the people the student hung around with, how much television the student watched, which television program the student watched, and what the week night bedtime was (0 = yes, 1 = no), and the presence of parents when the student was back from school (0 = never, 1 = almost never, 2 = some of the time, 3 = most of the time, 4 = always, 5 = they brought the student home from school), eating dinner (0~7 days per week), and going to bed (0 = never, 1 = almost never, 2 = some of the time, 3 = most of the time, 4 = always). Home drinking environment is measured by summing up two binary measures (0 = no, 1 = yes): parental drinking was coded as "yes" if the parent reported that they drank at least once a month, and alcohol availability was coded as "yes" if the adolescent respondents reported positively that "alcohol is easily available at home".

Plan of Analysis

To explore the co-evolution of friendship networks and drinking behavior in continuous Markov time, we utilize the SAB model with the R-based Simulation Investigation for Empirical Network Analysis (RSiena) package [ 67 ]. The SAB model assumes that a respondent will make decisions that optimize his or her network and behavior status in the next time step based on his or her current state of network-behavioral configuration, which is referred to as the objective function. The objective function is defined as f(β,x) = Σ k β k S ik (x), where β k is the k th estimated parameter for the actor-specific effect s ik (x) and x is the joint network-behavioral state. Positive values of the objective function indicate the preferred direction of changes, while negative values suggest the avoidance of such changes. In RSiena, the objective function of network changes and behavior changes are estimated simultaneously to generate both a network and a behavioral equation. Together, these constitute a set of interdependent equations with the rate functions λ i (α,x), which indicate the expected frequency of changes in the networks or behavior the actors make between observation points. The model is then estimated by simulating the networks and behavior forward in time. Thus, there are many micro-steps in the model in which actors update their objective functions regarding alcohol use behavior and network tie choice. A Method of moments (MoM) estimation is used to estimate the network and behavior parameters such that the main characteristics of the networks and behaviors are most closely approximated.

As shown in Table 1 , in the network equation predicting friendship tie choice, we include several structural network effects, i.e., out-degree and reciprocity capturing tie preference, transitive triplets and three cycles measuring triadic closure, and in-degree popularity and in-in degree assortativity (square root) differentiating the tendency towards preferential attachment vs. degree assortativity [ 68 , 69 ]. The network equation (friendship tie choice) also controls for similarity measures, including similarity on gender, grade, and parent education level (as a proxy of family socio-economic status). The function of parental influences on friendship tie choice is tested by the inclusion of respondents' parental support, parental monitoring, and their parental home drinking environment. For the behavior variable alcohol use ( z ), we specify it as a main effect on alter attractiveness (drinking alter), as a main effect on network activity of ego (drinking ego), and as a similarity (homophily) effect.

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https://doi.org/10.1371/journal.pone.0119965.t001

As shown in Table 2 , in the drinking behavior equation, the linear and quadratic shape parameters model the long-term trend in alcohol use. The in-degree item measures whether adolescents receiving more in-coming ties (more popular) drank more over time. We measure the peer influence effect as the sum of negative absolute difference between ego's and alters' behavior averaged by ego's out-degree. Additional covariates such as gender, grade, and depressive symptoms are controlled to test how the parental factors—i.e., parental home drinking environment, parental support, and parental monitoring—affected adolescent drinking in Model 1.

https://doi.org/10.1371/journal.pone.0119965.t002

Whereas Model 1 includes all the measures described (the "main effects" model), Models 2 through 4 assess the moderating role of an adolescent's parental factors. Thus, we included interaction variables of selecting a friend who drank (drinking alter) with parental support, parental monitoring, and the parental home drinking environment in Models 2, 3, and 4, respectively. Likewise, in these same models we included interaction terms of friends' influence effect (peer influence) with parental support, parental monitoring, and the parental home drinking environment in Models 2, 3, and 4, respectively.

It should be noted that when the interaction between parental support and friends' influence was added to the behavior equation of Model 2 for the 12 small schools, many of the effects were dramatically inconsistent with that in Model 1, 3, and 4: both their parameters and standard errors appear to be magnified (as shown in the shaded area of S1 Table ). A further examination of the correlations among the parameters suggests collinearity issues due to this specific interaction effect. We therefore removed this interaction from the behavior equation of Model 2 for the 12 small schools. We have also tried estimating a Model 5 for each sample, with all potential moderators included in the same SAB model. However, due to the high collinearity among these interaction effects, the model cannot reach convergence.

There are various approaches to combining multiple networks, including a meta-analysis approach and combining different sub-projects into one multi-group project with rate parameters allowed to differ across sub-projects. In this study we followed Cheadle and Goosby [ 64 ] and Cheadle and Schwadel [ 65 ] to explore the general tendency of the network and behavior dynamics in the 12 small schools by combining their friendship networks into one large network and using structural zeroes to indicate that ties between the schools are not permitted (see [ 67 ], page 81).

Although we aimed to explore the general tendency of network and behavior dynamics in the 12 small schools, we also considered possible variations across schools along several key dimensions: (1) urban, suburban vs. rural, (2) public vs. private, (3) single race vs. multiple race, and (4) different response rates. For the first three dimensions, we estimated ancillary models including interactions between dummy variables for these contextual variables and key drinking effects. They were insignificant, suggesting that co-evolution of friendship tie choice and drinking behavior was similar across different types of schools (e.g., see S2 Table along with S1 File ). Among the 12 small schools, 10 had response rates above 70% and 2 had response rates between 55%~70%. To account for the influence from response rate, we ran ancillary models with friendship networks of the 10 small schools combined into one large network and compared the results with those from the 12 small schools. The parameters were quite similar in these separate models. These results are available from the authors upon request.

In this way we can list the results of the 12 small schools and "Jefferson High" side by side to observe their similarities and differences. Respondents showing up at either an early or later observation point are included in the analysis. They were also allowed to join or leave their networks (e.g., graduates, movers, dropouts), with structural zeroes indicating they were no longer there at this time point (see [ 67 , 70 ]). Missing data are handled by RSiena software and imputed within the models as Huisman and Steglich [ 71 ], and Ripley, Snijders, Boda, Vörös, and Preciado [ 67 ] suggested. We assessed the goodness-of-fit of the models by comparing network statistics and drinking distribution of 1,000 simulations based on our model to the observed network and the fit was quite good (e.g., see S1 and S2 Figs. along with S2 File ).

Descriptive Results

The alcohol use and network descriptive statistics of the 12 small schools and "Jefferson High" are summarized in Table 3 . (The distribution of drinking behavior in each of 12 small schools is shown in S3 Table .) Among the small school sample, 52% students reported they were non-drinkers during the In-School Survey, and this proportion increased to 61.1% during the wave 2 In-Home Survey. Although the number of non-drinkers also increased in "Jefferson High", as suggested by the proportion of light- (2 = once a month or less or 3–12 times in the past 12 months), medium- (3 = 2 or 3 days a month), and heavy-drinkers (4 = more than 1 or 2 days a week), drinking was a far more prevalent behavior in this school than in small schools. The group size of casual-drinkers (1 = 1 or 2 days) decreased in both samples, and their members either became non-drinkers, or chose to increase their drinking frequency to other levels.

https://doi.org/10.1371/journal.pone.0119965.t003

Among the 1,284 respondents in the 12 small schools, the number of out-going ties decreased from 6,671 during the In-School Survey to 2,704 during the wave 2 In-Home Survey due to graduation, moving, dropping out, and attrition/non-response/missing network data. A similar pattern was also observed in "Jefferson High". The proportional change of reciprocal ties was more variable in the small schools (from 0.45 to 0.33). The transitivity index, which captures the tendency for individuals to experience triadic closure, was found to be relatively stable over time, although stronger in small schools (34%) compared to "Jefferson High" (18~20%). As indicated by the Jaccard index, there was a high turnover of friendship ties in both samples, with only 21~22% of ties persisting over the two waves.

The descriptive statistics of covariates are reported in Table 4 .

https://doi.org/10.1371/journal.pone.0119965.t004

Network Evolution: Friendship Tie Choice

As shown in the network equation of Model 1 in Table 5 and Table 6 , we observe significantly positive parameters for drinking similarity in both samples, although the estimated parameter is about 135% larger in the 12 small schools ( b = .33, p <. 01) than in "Jefferson High" ( b = .14, p <. 05). These provide evidence of a peer selection effect, as students were more likely to select as friends others with similar levels of alcohol use.

https://doi.org/10.1371/journal.pone.0119965.t005

https://doi.org/10.1371/journal.pone.0119965.t006

In both samples we also find that those drinking more frequently were more popular than those drinking less frequently, although they seemed to be even appealing in small schools ( b = .28, p <. 05) compared to "Jefferson High" ( b = .11, p <. 01). Adolescents having higher drinking levels were inclined to nominate fewer best friends that those with lower drinking levels, but this effect is only significant in "Jefferson High" ( b = -.08, p <. 05).

In terms of structural network effects, the significantly negative out-degree ( b = -2.09, p <. 001 in small schools and b = -2.51, p <. 001 in "Jefferson High") and the significantly positive reciprocity parameters ( b = 1.79, p <. 001 in small schools and b = 2.73, p <. 001 in "Jefferson High") suggest that adolescents in both samples tended to form fewer ties, but when they did such ties were more likely to be reciprocal ones. The adolescents also preferred to be friends with their friends' friends (triadic closure), as indicated by the positive transitive triplets effect ( b = .23, p <. 001 in small schools and b = .51, p <. 001 in "Jefferson High"). The significantly negative three-cycle effect implies a tendency toward local hierarchy ( b = -.14, p <. 01 in small schools and b = -.43, p <. 001 in "Jefferson High"). Adolescents were more likely to be named as a tie if they already had many in-coming ties (high in-degree popularity) ( b = .07, p <. 001 in small schools and b = .04, p <. 05 in "Jefferson High"). And the negative parameter for in-in degree assortativity (square root) again suggests the presence of preferential attachment as adolescents with high in-degree were more likely to be nominated as best friends by those with low in-degree, but this effect is only significant in the small school sample ( b = -.07, p <. 01).

We find that homophily preferences in gender and grade drove friendship tie formation: adolescents were more likely to send friendship nominations to other adolescents with the same gender ( b = .20, p <. 001 in small schools and b = .27, p <. 001 in "Jefferson High") and in the same grade ( b = .45, p <. 001 in small schools and b = .41, p <. 001 in "Jefferson High"). The homophily effects of parental educational levels are insignificant in both samples, after controlling for the other measures in the model.

As for how the parental influences affected friendship tie choice, we find that adolescents who received more parental support nominate more friends in the 12 small schools ( b = .35, p <. 001). This effect for parental support was insignificant in "Jefferson High". And we find that more parental monitoring and a home drinking environment did not affect tie choice.

When focusing on the moderating effect of family contexts on friendship tie choice in Models 2 to 4 of Tables 5 and 6 , although the signs of interaction parameters are as expected, the only statistically significant effect is that between the parental home drinking environment and drinking alter in the small school sample ( b = .18, p <. 05): whereas higher-level drinkers were more popular in general, those with high levels of drinking in their home environment were particularly likely to form ties with those who drink more, see Fig. 1 .

https://doi.org/10.1371/journal.pone.0119965.g001

Behavior Evolution: Alcohol use

As shown in the behavior equation of Model 1 in Table 5 and Table 6 , we find a significant peer influence effect ( b = .22, p <. 05 in the small schools and b = .36, p <. 05 in "Jefferson High"), implying that adolescents tended to match the drinking behavior of their friends (i.e., as the drinking frequencies of their friends increased, so did their drinking propensity over time).

The significantly negative linear shape effect ( b = -1.78, p <. 001 in small schools and b = -1.36, p <. 001 in "Jefferson High") and the significantly positive quadratic shape effect ( b = .30, p <. 001 in small schools and b = .23, p <. 001 in "Jefferson High") suggest that adolescents tended to drink less over time, but there was also a self-reinforcement effect of alcohol use: there appeared to be a tendency towards polarization on both ends of drinking behavior, as adolescents were inclined to either become or remain a non-drinker or escalate to heavy use. Popular adolescents (with higher values of in-degree) were not found to drink more frequently in either case. Additional significant effects were found for grade in small schools ( b = .03, p <. 05) and gender in "Jefferson High" ( b = -.09, p <. 01): higher-grade adolescents in the 12 small schools and males in "Jefferson High" were generally found to increase their drinking frequency over time more than their counterparts.

Regarding the parental influences, we find that adolescents who experienced more drinking at home were more likely to increase their own drinking frequency ( b = .10, p <. 001 in small schools and b = .06, p <. 05 in "Jefferson High"). Adolescents with higher levels of parental monitoring engaged in less drinking behavior over time, but this effect is only significant in small school sample ( b = -.39, p <. 05). The parental support respondents received had a negative effect on the frequency of adolescent drinking, although not significant in either case.

Turning to the interaction effects included in Model 2 to Model 4 of Table 5 and Table 6 , there was no evidence that parental support, parental monitoring, or the parental home drinking environment moderated the peer influence effect in either sample.

This study aimed to disentangle peer selection and peer influence processes and other key network effects simultaneously with salient parental influences by examining the co-evolution of friendship tie choices and drinking behavior among adolescents in two AddHealth samples. Because the network size and drinking prevalence were relatively different in the two samples—e.g., Jefferson High was a much larger school with intensive adolescent alcohol use—one might presume that the co-evolution mechanisms of friendship tie choices and drinking behavior would differ. Our models demonstrate that to the contrary, the evidence across the two samples was typically quite similar. In the friendship tie choice equation, most measures had effects similar in direction and statistical significance. The differences were mostly in magnitude of the effects: in the friendship tie choice equation, whereas the network structural effects such as reciprocity, transitive triplets, and three-cycles were larger in "Jefferson High", the popularity of heavier drinkers and the selection effect of choosing ties based on similarity of drinking behavior were smaller in "Jefferson High". The effect of peer influence was stronger in "Jefferson High" with more drinking behavior, implying that such a context may be particularly important for transmitting norms about drinking behavior among adolescents. The most notable differences were that in small schools with lower levels of alcohol use, parental support had a much stronger effect on friendship tie choice, the parental home drinking environment had a much stronger effect on forming ties with those who drink more, and parental monitoring appeared more effective in reducing drinking behavior.

Our findings demonstrate that peer selection played a significant role in facilitating drinking behavior similarity in the adolescents' friendship networks. Adolescents preferred to form friendships with those who displayed similar levels of alcohol use. These results support previous findings regarding the importance of peer selection in accounting for behavioral similarities across dyads in friendship networks [ 32 , 34 ]. At the same time, our findings also indicate the important role of peer influence in friendship networks, as do previous studies [ 33 , 34 ], demonstrating the importance of peer influence in adolescent networks. Overall, our findings suggest that both peer and parental factors were instrumental in shaping both friendship tie choice and drinking behavior.

Regarding our findings pertaining to friendship tie choice, we observe that heavy-drinkers received more in-coming ties and hence were more popular. Also using data from the AddHealth In-School survey, Balsa, Homer, French, and Norton [ 21 ] found that popularity (also measured as ego's in-degree) was positively associated with drinking frequency. However, they acknowledged that their cross-sectional design prevented them from assessing the causal ordering or the extent that a reciprocated relationship existed between these two measures [ 21 ]. Our findings suggest a unilateral relationship between alcohol use and network popularity: whereas alcohol use increased popularity, more popular adolescents did not drink more over time.

Our findings also suggest that while adolescents' peer relationships were central to their lives, parents still had influence on both adolescent alcohol use and friendship tie choice. First, in line with previous studies [ 44 , 45 , 46 ], adolescents were more likely to engage in underage drinking if the parents provided a favorable environment for adolescent alcohol use. In our study, these respondents in such family environments were not only particularly likely to form friendship ties with adolescents with high drinking levels, but our model implies that through peer influence they would over time engage in higher levels of alcohol use.

We also observed a negative relationship between parental monitoring and drinking in the small school sample. Our finding is consistent with prior studies indicating a negative relationship between parental monitoring and adolescent drinking behavior [ 19 , 43 ]. That parental monitoring is risk protective for substance use has been shown in these studies, which highlight the continuing role of parents in enforcing rules and discipline during the high school years helped reduce the alcohol use level among adolescents, at least in the small school sample.

Our findings also indicated a negative relationship between parental support and alcohol use. This finding is consistent with past studies indicating a negative relationship between parental support and drinking behavior [ 40 , 41 , 42 ]. It is likely that the provision of parental support renders adolescents more able to develop social competencies necessary to form friendship ties.

Our findings differ from a previous study using Add Health [ 32 ] that did not find a significant influence effect. There are several reasons for these different results. First, there is different sample selection, as our study utilizes data from the In-School Survey and the wave 2 In-Home survey, but not data from the wave 1 In-Home Survey to avoid difficulty due to limited nominations, whereas Mundt and colleagues [ 32 ] selected respondents from the wave 1 In-Home survey and the wave 2 In-Home Survey. Second, our study includes respondents present in either wave, whereas Mundt and colleagues [ 32 ] only included those who completed the survey at the first time point (assuming those not completing the survey are not part of the network). Third, our study integrates parental factors into the friendship tie choice and drinking equations, which had important effects in the models. Fourth, we utilized a different strategy for handling multiple networks, as we combined the friendship networks of the 12 small schools into one large network, whereas Mundt and colleagues [ 32 ] employed a meta-analysis. Finally, we utilized a different peer influence specification. Each of these factors likely contributes to the differences in results across these two studies.

Our study has several limitations. First, all the analyses are based on self-reported data from AddHealth. One consequence of using self-reports of illicit substance use is underreporting. Some studies find that adolescent reporting is better than parent, peer, or other reporting [ 72 ]. Still, future studies may benefit by using multiple measures of alcohol use (e.g. employing physiological or biological indicators of alcohol use) to ensure high internal validity [ 73 ]. Second, the friendship networks were retrieved through a name generator limited to a maximum of five male and five female best friends and thus were not a fully accurate portrayal of a respondent's peer network. It is unclear how our findings would have differed had the adolescents sampled been allowed to nominate all of their friends. Third, the AddHealth data did not include information on alcohol use behavior of other family members, i.e., respondents' brothers and sisters. It would be much better for future studies to account for various levels of familial influences.

Despite these limitations, our findings have implications for future studies. Our findings suggest merit in further examination of the role of the parental influences under study as they affect co-evolution of friendship networks and drinking behavior among US adolescents, as well as the mechanisms underlying the relationship between alcohol use and network popularity. Given that those in a home environment which favored drinking were particularly likely to form friendship ties with higher-drinking-level adolescents suggests a need to study this possibility more closely.

Our findings also have practical implications for health behavior change interventions targeting adolescent alcohol use. Motivated by intuition from intervention studies applying concepts from the opinion leader literature [ 74 , 75 ] employing the general strategy of identifying popular youth as means to transmit prosocial peer influences through a network system, we suggest that one way to promote positive peer influences against drinking and to likewise dampen the influence of drinkers in peer networks is to target popular youth who do not drink and are connected through transitive triplets. Given the hierarchical structuring of our data (as indicated by the significantly positive transitive triplet and significantly negative three cycle effect), popular youth are uniquely positioned, and as well uniquely salient in the highest rank of the hierarchy. Peer influences exerted by popular youth will likely diffuse down the social hierarchy to less popular youth. In addition, given the salience of reciprocated ties among adolescent youth, another intervention strategy would target mutually reciprocated friendship pairs of youth (i.e., drinking pairs, non-drinking pairs, and mixed drinking status pairs) to promote anti-drinking peer influences, social support, and resistance training skills to influence one another to stop drinking or not begin drinking. Lastly, parents should be targeted to both increase their capacity to provide support to and monitoring of their adolescent children, in order to help their children foster friendships with prosocial peers who are not substance users.

In sum, this study examined the co-evolution of friendship networks and drinking behavior among two representative samples of US adolescents. Adolescents with similar alcohol use levels were more likely to form friendships than their peers with more dissimilar alcohol use levels, and adolescents also adjusted their drinking behavior to match that of their best friends. Moreover, we found that those who drank more were more popular, but popular adolescents did not drink more over time. Our findings also indicate that the parental home drinking environment had a positive effect on adolescent drinking over time. Overall, our findings suggest in the importance of disentangling the effects of friendship networks and family contexts when trying to understand the co-evolution of adolescent friendship tie choice and alcohol use. Future studies should further explore the risk and protective aspects of these peer and parental environments for adolescent alcohol use.

Supporting Information

S1 fig. gof testing of sab model 1 for jefferson high..

https://doi.org/10.1371/journal.pone.0119965.s001

S2 Fig. GOF Testing of SAB Model 1 for 12 small schools.

https://doi.org/10.1371/journal.pone.0119965.s002

S1 File. School contexts with key drinking effects.

https://doi.org/10.1371/journal.pone.0119965.s003

S2 File. Goodness-of-Fit (GOF) Testing.

https://doi.org/10.1371/journal.pone.0119965.s004

S1 Table. Stochastic Actor-Based model of friendship tie choice and adolescent drinking behavior, for 12 small schools ( n = 1,284).

https://doi.org/10.1371/journal.pone.0119965.s005

S2 Table. Ancillary models including interaction terms between school contexts and key drinking effects for 12 small schools ( n = 1,284).

https://doi.org/10.1371/journal.pone.0119965.s006

S3 Table. Distribution of drinking behavior in the twelve small schools.

https://doi.org/10.1371/journal.pone.0119965.s007

Author Contributions

Analyzed the data: CW RJ. Contributed reagents/materials/analysis tools: CW CTB. Wrote the paper: CW JRH RJ CML.

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• Population Study
• Published: 27 September 2017

Alcohol use in early adolescence: findings from a survey among middle school students in Italy

• Rossella Zucco 1 ,
• Franco Montesano 2 ,
• Stefania Esposito 2 ,
• Aida Bianco 1 &
• Carmelo G A Nobile 3   na1  

Pediatric Research volume  82 ,  pages 915–919 ( 2017 ) Cite this article

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• Paediatrics

The aims of this study were to measure the extent of alcohol use among a sample of early adolescents and to provide information on the factors influencing the consumption.

Data were collected via self-administered anonymous questionnaires from 1,520 middle school students (mean age of 13.1 years (range 12–15 years)), who were recruited from a random sample of public schools in Calabria Region, Italy.

A total of 1,032 participants completed the survey for a response rate of 68%. Nearly 70% of the respondents had drunk at least once during their lifetime, and 16.7% reported consuming alcohol during 30 days before the survey. Multivariate analysis showed that the factors associated with the consumption of alcohol were being male (odds ratio (OR) 0.58, 95% confidence interval (CI) 0.41–0.80), being older (OR 1.88, 95% CI 1.37–2.56), living in an urban area (OR 0.29, 95% CI 0.21–0.40), reporting a sad self-perceived mood (OR 2.76, 95% CI 1.87–4.48), reporting parental drinking habits (OR 7.11, 95% CI 5.02–10.08), and not considering alcohol use as an unhealthy behavior (OR 2.43, 95% CI 1.11–5.31).

Alcohol use among early adolescents is widespread. Multicomponent interventions are required in order to reduce the average levels of alcohol drinking among early adolescents.

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Early adolescence (occurring between the ages of 10 and 15 years), is characterized by rapid physical, cognitive, and social transformations ( 1 ), during which youth often get engage in risky behaviors, such as alcohol use ( 2 , 3 ).

In the United States, results from the 2013 National Youth Risk Behavior Survey (YRBS) indicated that 66.2% of 13–16-year-old adolescents consumed at least one drink of alcohol on at least 1 day during their lifetime, and 18.6% had drunk alcohol for the first time before 13 years of age ( 4 ). Moreover, data reported from the European School Survey Project on Alcohol and Other Drugs (ESPAD) showed that at least 70% of the interviewed students have drunk alcohol at least once during their lifetime, with an average of 87% in the 2011 survey ( 5 ). Alcohol use in 12 months and 30 days preceding the survey was 79% and 57%, respectively, and the rates of “binge drinking”, defined as five or more drinks on the same occasion ( 5 , 6 ), were 43% for boys and 38% for girls. Alcohol use has also grown among Italian adolescents. According to the Italian National Institute of Statistics (ISTAT), in 2014, about 10% of 11–15-year-old Italian adolescents were current drinkers ( 7 ). These data are extremely worrying, because early adolescent brain, especially the hippocampus, may be particularly vulnerable to the effects of alcohol ( 8 ), thus predisposing the young drinker to alcohol, mental health, and neuro-cognitive problems, that can persist into adulthood ( 8 , 9 ). Young people who start drinking before the age of 15 years are reported to be four times more likely to meet the criteria for alcohol-dependence at some point in their lives, and early alcohol use is associated not only with more regular and higher levels of alcohol use and dependence in adulthood, but also with an increased risk of other substance use in later adolescence ( 10 , 11 ).

However, if drinking habits are now well documented in high school students ( 6 , 12 ), few studies have focused on alcohol use in children and middle school students ( 13 , 14 ). Therefore, the primary aim of this study was to measure the extent of alcohol use among a sample of middle school students in Calabria region, Italy, whereas the secondary aims were to evaluate knowledge, attitudes, and behavior regarding alcohol consumption, and to evaluate the potential factors influencing alcohol use.

The study was conducted from October 2014 to May 2015 on a sample of 1,520 eligible students. Data were collected as part of a multimodal intervention for the primary prevention of risky health behaviors among middle school students called “Luoghi di Prevenzione” (LdP), that is a part of the National Prevention Plan of the Italian Ministry of Health ( 15 ).

A total of 24 public middle schools, randomly selected from a total of 50 middle schools in the geographic area of Catanzaro, in South Italy, was involved in the study. Before starting data collection, a meeting with the head of each selected school was arranged to present the project and to obtain permission and collaboration. Then, all students attending the third year (eighth grade) were invited to participate. During the hours of school attendance, a letter summarizing the purpose of the study and pointing out the voluntary and confidential nature of participation, an informed consent form to be delivered to the parents, and a questionnaire, was given to each selected student.

The questionnaire was developed based on the previous studies ( 4 , 6 ), and was pretested for length and content on a sample of 30 potential respondents. The final survey was two pages in length, which was designed to be completed within 10 min and formulated into five sections: (1) sociodemographic characteristics (gender, age, residence, attended school, and class); (2) self-perceived mood (happy/normal, sad/depressed); (3) knowledge regarding alcohol policies (alcohol sales to minors, blood alcohol concentration limits for drivers, and so on); (4) attitudes toward alcohol drinking; and (5) behaviors regarding alcohol use (ever drunk alcohol in a lifetime, frequency of alcohol consumption, types of alcoholic beverages consumed, and parental drinking habits).

Alcohol consumption was also measured by asking how often (if ever) the student had drunk alcohol and the quantity per drinking occasion by asking the amount generally consumed each time. Respondents who reported drinking any beverage at least once (not for a special event) in the 30 days preceding the survey were classified as ‘current drinkers’ ( 12 ). A ‘drink’ was defined as a glass of wine, a can of beer, a shot of liquor, or a mixed drink. “Drunkenness” was defined as a transient condition following the administration of alcohol or other psychoactive substances, resulting in disturbances in the level of consciousness, cognition, perception, effect or behavior, or other psychophysiological functions and responses ( 16 ). “Binge drinking” was defined as five or more alcoholic drinks on the same occasion ( 5 , 6 ).

Each section elicited responses in two formats: closed-ended questions with multiple answers possible and yes or no questions. The questionnaire culminated with the option of providing additional comments. As a part of LdP program, the study protocol was approved by the Ethics Committee of the Local Health Authority of Reggio Emilia, Italy.

Statistical analysis

Descriptive analyses were used to describe demographic characteristics and risk behaviors of participants, and to determine the prevalence of alcohol consumption. Data were summarized into frequencies and percentages. A multivariable backward stepwise logistic regression model was constructed to determine the explanatory variables independently related to a dichotomous measure of whether or not alcohol was used at least once in the lifetime. A model was developed according to the Hosmer and Lemeshow strategy ( 17 ), with the following steps: (1) univariate analysis of each variable considered, using the appropriate test statistic (chi-square test or t -test); (2) inclusion of any variable whose univariate test has a P -value <0.25; and (3) the results of the logistic regression analysis are presented as odds ratios (ORs) and 95% confidence intervals (CIs ). A two-sided P -value for all tests of <0.05 was considered to be a statistically significant difference. The significance level for a variable’s entry to the model was set at 0.2 and at 0.4 for removal.

The following explanatory variables were potentially included in the model: age (continuous), sex (male=0, female=1), place of residence (urban area=0, rural area=1), self-perceived daily mood (happy/normal=0, sad/depressed=1), parental alcohol use (no=0, yes=1), and attitudes toward alcohol use (agree with a belief that alcohol use is an unhealthy behavior=0, disagree with a belief that alcohol use is an unhealthy behavior=1), and correct knowledge about blood alcohol concentration limit for drivers (no=0, yes=1).

Stata version 14 statistical software package was used in conducting all data analyses ( 18 ).

A total of 1,032 participants with a mean age of 13.1 years (range 12–15 years), completed the survey for a response rate of 68%. The main sociodemographic characteristics of the study population are shown in Table 1 . Almost all students (98%) said that alcohol can cause addiction, and 69.7% knew the minimum legal age to purchase alcohol. Moreover, 88.5% of samples agreed that drinking alcohol is an unhealthy and risky behavior.

The respondents’ alcohol use pattern is reported in Table 2 . Almost 70% of the adolescents reported drinking alcohol at least once in their lifetime. Among alcohol users, the prevalence of current drinkers, that is, adolescents that had drunk on at least 1 day during the previous 30 days, was 16.7%. About 10% of respondents reported drunkenness experience at least once in their lifetime and 2% reported binge drinking. The average number of drinks on a single occasion is 1.5.

Beer is the most consumed alcoholic beverage (74%). Furthermore, 20.7% reported hard liquor consumption. More than half of the eligible adolescents (59.4%) reported alcohol use at home or at friends parties (46.8%).

About 30% of students reported drinking as a positive experience. The most frequently reported reasons for consuming alcohol were for celebrating or partying (33.5%) and to get away from problems (23.3%). Almost three-quarters (70.8%) of the overall sample said that their parents drink alcohol daily; among the adolescents who used alcohol, this percentage rises to 82.1%.

At univariate analysis, alcohol use was significantly higher among older students ( t 1,030 =−5.83, P <0.001), boys ( χ 2 =18.70, P <0.001), in those who lived in an urban area ( χ 2 =72.64, P <0.001), in those who did not know the correct alcohol concentration limit for drivers ( χ 2 =4.22, P =0.04), in those who reported a sad/depressed self-perceived mood ( χ 2 =38.74, P <0.001), who did not agree that drinking is an unhealthy behavior ( χ 2 =34.65, P <0.001), and in those who reported parental drinking habits ( χ 2 =190.75, P <0.001) ( Table 3 ).

The results of the multiple logistic regression analysis substantially confirmed the findings of the univariate analysis. Indeed, factors independently associated with the consumption of alcohol were being male (OR 0.58, 95% CI 0.41–0.80), being older (OR 1.88, 95% CI 1.37–2.56), living in an urban area (OR 0.29, 95% CI 0.21–0.40), reporting a sad self-perceived mood (OR 2.76, 95% CI 1.87–4.48), reporting parental drinking habits (OR 7.11, 95% CI 5.02–10.08), and not considering alcohol use as an unhealthy behavior (OR 2.43, 95% CI 1.11–5.31) ( Table 3 ).

As far as we know, the present investigation is one of the few Italian studies aiming at evaluating alcohol consumption patterns among middle school students ( 19 , 20 ), whereas most of the other studies have been focusing on young adults ( 6 , 12 ). Moreover, the study provides useful information on factors influencing alcohol consumption in early adolescents.

Our results indicate high alcohol-drinking frequencies among younger adolescents, and confirm an extremely early contact with alcoholic beverages in Italy. These figures are similar to those reported in the literature. Indeed, our findings are comparable with the data for Italy emerging from the Health Behaviour in school-aged children (HBSC) 2010 survey, which indicated that 20.7% of 13-year olds drink alcohol at least monthly ( 21 ). Similar data were also drawn from a recent European study, which reported that 73.4% of adolescents aged 10–13 years consumed alcoholic beverages at least once in their lifetime. However, this study shows a lower percentage of drunkenness experiences compared with our data (3.7% vs. 9.2%) ( 14 ). The observations emerging from our study are extremely alarming, because of potential hazardous alcohol consumption patterns in adult life ( 10 ).

Alcohol use was associated with multiple factors that warrant careful attention. Male gender and an older age at the time of assessment were associated with alcohol use. These findings are consistent with the literature, suggesting that alcohol use increases with age throughout adolescence, and reaches its peak in males in late adolescence and early adulthood ( 22 , 23 ). Moreover, as reported in previous studies ( 24 ), young people who live in an urban area are more likely to use alcohol. The place of residence may influence the drinking behaviors according to alcohol availability, norms for acceptable drinking behaviors, demographic characteristics, and economic factors. Obviously, adolescents with an urban residence may have more chance to purchase alcohol and to participate in social events or parties where alcohol is more likely to be available.

Another important finding from the present study is that parental drinking and lenient parental alcohols specific attitudes are positively associated with an adolescent’s alcohol use. As reported in previous studies ( 25 , 26 , 27 ), parental alcohol use is directly associated with an adolescent’s alcohol use as well. It is well known that family is the favored context for learning beliefs, patterns, and values that affect the broader regulatory social environment, and for this reason, it is considered a privileged context on which to intervene to reduce the adolescents’ behavioral problems. Indeed, researchers have underlined the importance of parent-training interventions for adolescents with alcohol use ( 27 ).

Consistent with prior research, our data analysis also showed that alcohol users felt significantly more depressed and were lacking self-assurance ( 28 , 29 ). These results suggest that alcohol is often used as a self-medication or a coping strategy with stress or anxiety. Coping motives thus not only identify a reason or a potential trigger for drinking, but may also suggest a more uncontrolled style of drinking that is less responsive to social controls for drinking.

Furthermore, as expected, alcohol users in our study had a positive attitude toward alcohol use than other students and they did not agree that drinking is an unhealthy behavior ( 14 ). This finding suggests a low awareness or acceptance of the risks related to alcohol abuse, and highlights the need of correct prevention campaigns, especially addressed for young people. In this context, some interventions appear to be more effective, particularly those that are interactive, those based on the social influences approach, and those adopting a multimodal approach ( 30 ).

The results of our study should be interpreted in light of a few potential limitations. First of all, it should be noted that, since this study has a cross-sectional design, the relationship between the predictor variables and the dependent variables should not be taken as a cause-and-effect relationship; the study is able to only describe general associations. This study has a possible limitation regarding the method of collecting the information on alcohol consumption, as the data were obtained from a self-administered questionnaire. However, when the recall is restricted to a short period of time, and respondents are provided with anonymity and privacy and believe that the assessment is conducted for important reasons, the use of a self-reported method is valid for avoiding an unreliable recall of behavior ( 31 ).

Furthermore, our study involved only one Italian region. Although childhood development is generally consistent regardless of geography, there might be some differences in the substance use of our sample that may be due to cultural factors ( 32 ). However, our data are generally consistent with those generated by previous international lifestyle surveillance surveys conducted in our area on the same topics, and we are confident that the findings of the study may be representative of the Southern regions.

Our results suggest that alcohol use among early adolescence is widespread. Multicomponent interventions are required in order to minimize and reduce the average levels of alcohol drinking among early and mid-adolescents. Prevention programs should therefore focus particularly on the years of transition from primary to secondary school in an effort to delay the onset of alcohol use.

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Carmelo G A Nobile: At the time of this study he was with the Department of Health Sciences, University of Catanzaro "Magna Græcia", Catanzaro (Italy)

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Department of Health Sciences, University of Catanzaro "Magna Græcia", Catanzaro, Italy

Rossella Zucco & Aida Bianco

Addiction Department, Drug Addiction Service, U.O.C. SerT Soverato, Azienda Sanitaria Provinciale, Catanzaro, Italy

Franco Montesano & Stefania Esposito

Department of Pharmacy, Health and Nutritional Sciences, University of Calabria, Cosenza, Italy

Carmelo G A Nobile

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Correspondence to Aida Bianco .

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Zucco, R., Montesano, F., Esposito, S. et al. Alcohol use in early adolescence: findings from a survey among middle school students in Italy. Pediatr Res 82 , 915–919 (2017). https://doi.org/10.1038/pr.2017.206

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DOI : https://doi.org/10.1038/pr.2017.206

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Drinking Over the Lifespan: Focus on Early Adolescents and Youth

Affiliation.

• 1 Department of Behavioral Sciences and Health Education, Emory University, Atlanta, Georgia.
• PMID: 27159816
• PMCID: PMC4872619

Historical trends in alcohol use among U.S. adolescents, as well as data regarding alcohol-related traffic fatalities among youth, indicate decreases in alcohol use. Nevertheless, alcohol use patterns still indicate high rates of binge drinking and drunkenness and the co-occurrence of alcohol use among youth with risky sexual activity, illicit substance use, and poor school performance. This article discusses unique elements of alcohol use among adolescents relative to adults that pose risks for alcohol misuse and alcohol-related problems. These differences range from patterns of drinking to differential sensitivity to alcohol. Developmental differences between adolescents and adults also are discussed with regard to age-normative developmental tasks and distinctions in brain development that may affect differences in drinking patterns. Epidemiologic findings on sexual-minority youth are provided, as are global trends in alcohol use among early adolescents and youth. It is proposed that using information about differences between youth and adults will be helpful in directing future etiologic and intervention research by capitalizing on unique biological, psychological, and social factors that may affect the success of efforts to reduce alcohol use among early adolescents and youth.

Publication types

• Research Support, N.I.H., Extramural
• Adolescent Behavior
• Adolescent Development*
• Alcoholism / epidemiology*
• Alcoholism / ethnology
• Alcoholism / psychology
• Binge Drinking / epidemiology*
• Binge Drinking / ethnology
• Binge Drinking / psychology
• Global Health
• Minority Groups
• Risk-Taking
• Sex Factors
• Underage Drinking / ethnology
• Underage Drinking / psychology
• Underage Drinking / statistics & numerical data*
• United States / epidemiology

Grants and funding

• K05 AA021143/AA/NIAAA NIH HHS/United States
• P30 DA027827/DA/NIDA NIH HHS/United States
• K05-AA-021143/AA/NIAAA NIH HHS/United States

• Underage Drinking

Underage alcohol consumption is common in the United States and can have harmful outcomes. A comprehensive approach that includes effective policy strategies can prevent underage drinking and related harms.

Underage drinking is a significant public health problem in the U.S. Excessive drinking is responsible for about 4,000 deaths and more than 240,000 years of potential life lost among people under age 21 each year. 1 Underage drinking cost the U.S. $24 billion in 2010. 2

Underage Drinking is Common

Learn about the Minimum Legal Drinking Age laws

Alcohol is the most commonly used substance among young people in the U.S. 3

The 2021 Youth Risk Behavior Survey 3  found that among high school students, during the past 30 days

• 23% drank alcohol.
• 11% binge drank .
• 5% of drivers drove after drinking alcohol.
• 14% rode with a driver who had been drinking alcohol.

Rates of current and binge drinking among high school students have generally been declining in recent decades. Although males historically had higher rates, in 2019 and 2021, female high school students were more likely to drink alcohol and binge drink than male high school students. 3,4

Underage Drinking is Dangerous

Youth who drink alcohol are more likely to experience 4-7

• School problems, such as higher rates of absences or lower grades.
• Social problems, such as fighting or lack of participation in youth activities.
• Legal problems, such as arrest for driving or physically hurting someone while drunk.
• Physical problems, such as hangovers or illnesses.
• Unwanted, unplanned, and unprotected sexual activity.
• Disruption of normal growth or sexual development.
• Physical and sexual violence .
• Increased risk of suicide  and homicide.
• Alcohol-related motor vehicle crashes  and other unintentional injuries, such as burns, falls, or drowning.
• Memory problems.
• Misuse of other substances.
• Changes in brain development that may have life-long effects.
• Alcohol poisoning.

In general, the risk of youth experiencing these problems is greater for those who binge drink than for those who do not binge drink. 6,7

Early initiation of drinking is associated with development of an alcohol use disorder  later in life. 8

Underage Drinking is Associated with Adult Drinking

Studies show a relationship between underage drinking behaviors and the drinking behaviors of adult relatives, adults in the same household, and adults in the same community and state.

• There is a relationship between youth and adult drinking, including binge drinking, in states and communities. 9-11  A 5% increase in binge drinking among adults in a community is associated with a 12% increase in the chance of underage drinking. 10
• Among adolescents whose peers drink alcohol, those whose parents binge drink are more likely to drink alcohol than those whose parents do not. 12

Underage Drinking is Preventable

State alcohol policy environments influence underage drinking, as well as excessive drinking among adults. Comprehensive approaches that include effective population-level policy strategies can reduce underage drinking. 10,13,14  The Community Preventive Services Task Force recommends several effective strategies for preventing excessive drinking, 15  including:

• Increasing alcohol taxes.
• Having commercial host (“dram shop”) liability laws.
• Regulating the number and concentration of alcohol outlets.
• Enforcing laws prohibiting alcohol sales to minors.

The Surgeon General’s Report on Alcohol, Drugs, and Health describes other strategies that can complement effective alcohol policies, such as national media campaigns targeting youth and adults, reducing youth exposure to alcohol advertising, and the development of comprehensive community-based programs. 5 Read more about the prevention of excessive alcohol use , including underage drinking.

• Centers for Disease Control and Prevention. Alcohol-Related Disease Impact Application website . Accessed April 16, 2024.
• Sacks JJ, Gonzales KR, Bouchery EE, Tomedi LE, Brewer RD. 2010 national and state costs of excessive alcohol consumption. Am J Prev Med 2015; 49:e73–e79.
• Centers for Disease Control and Prevention. 2021 Youth Risk Behavior Survey Data. Available at: https://www.cdc.gov/healthyyouth/data/yrbs/index.htm . Accessed on September 13, 2023.
• Jones CM, Clayton HB, Deputy NP, Roehler, DR, Ko JY, Esser MB, Brookmeyer KA, Hertz MF. Prescription opioid misuse and use of alcohol and other substances among high school students — Youth Risk Behavior Survey, United States, 2019 . MMWR Suppl 2020;69(Suppl-1):38–46.
• U.S. Department of Health and Human Services (HHS), Office of the Surgeon General. Facing addiction in America: The Surgeon General’s report on alcohol, drugs, and health . Washington, DC: HHS, 2016.
• Miller JW, Naimi TS, Brewer RD, Jones SE. Binge drinking and associated health risk behaviors among high school students. Pediatrics 2007;119:76–85.
• Esser MB, Guy GP, Zhang K, Brewer RD. Binge drinking and prescription opioid misuse in the U.S., 2012-2014 . Am J Prev Med 2019;57,197-208.
• Buchmann AF, Schmid B, Blomeyer D, et al. Impact of age at first drink on vulnerability to alcohol-related problems: Testing the marker hypothesis in a prospective study of young adults . J Psychiatr Res 2009;43:1205–1212.
• Nelson DE, Naimi TS, Brewer RD, Nelson HA. State alcohol-use estimates among youth and adults, 1993–2005 . Am J Prev Med 2009;36:218–224
• Xuan Z, Nelson TF, Heeren T, et al. Tax policy, adult binge drinking, and youth alcohol consumption in the United States . Alcohol Clin Exp Res 2013;37:1713–1719.
• Paschall MJ, Lipperman-Kreda S, Grube JW. Effects of the local alcohol environment on adolescents’ drinking behaviors and beliefs . Addiction 2014;109:407–416.
• Olson JS, Crosnoe R. The interplay of peer, parent, and adolescent drinking . Soc Sci Q 2018;99:1349–1362.
• Xuan Z, Blanchette JG, Nelson TF, et al. Youth drinking in the United States: Relationships with alcohol policies and adult drinking . Pediatrics 2015;136:18–27.
• Blanchette JG, Lira MC, Heeren TC, Naimi TS. Alcohol policies in U.S. states, 1999–2018 . J Stud Alcohol Drugs . 2020;81:58–67.
• Excessive Alcohol Consumption. The Guide to Community Preventive Services website . Accessed September 16, 2022.
• The Community Guide: Enhanced Enforcement of Laws Prohibiting Sales to Minors
• Youth Risk Behavior Surveillance System Findings
• Monitoring the Future Findings
• National Survey on Drug Use and Health Findings
• Age 21 Minimum Legal Drinking Age

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Alcohol's Effects on Health

Research-based information on drinking and its impact.

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Get the facts about underage drinking.

Underage drinking is a serious public health problem in the United States. Alcohol is the most widely used substance among America’s youth and can cause them enormous health and safety risks.

The consequences of underage drinking can affect everyone—regardless of age or drinking status.

Either directly or indirectly, we all feel the effects of the aggressive behavior, property damage, injuries, violence, and deaths that can result from underage drinking. This is not simply a problem for some families—it is a nationwide concern.

Underage Drinking Statistics

Many Youth Drink Alcohol

In 2022, according to the National Survey on Drug Use and Health (NSDUH), about 19.7% of youth ages 14 to 15 reported having at least 1 drink in their lifetime. 1

In 2022, 5.8 million youth ages 12 to 20 reported drinking alcohol beyond “just a few sips” in the past month. 2

Adolescent alcohol use differs by race and ethnicity. For example, at age 14, White, Black, and Hispanic youth are equally likely to drink. By age 18, White and Hispanic youth are twice as likely to drink than Black youth. 3

How Much Is a Drink?

In the United States, a standard drink is defined as any beverage containing 0.6 fluid ounces or 14 grams of pure alcohol (also known as an alcoholic drink equivalent), which is found in:

• 12 ounces of beer with about 5% alcohol content
• 5 ounces of wine with about 12% alcohol content
• 1.5 ounces of distilled spirits with about 40% alcohol content

The percentage of pure alcohol, expressed here as alcohol by volume (alc/vol), varies within and across beverage types. Although the standard drink amounts are helpful for following health guidelines, they may not reflect customary serving sizes. A large cup of beer, an overpoured glass of wine, or a single mixed drink could contain much more alcohol than a standard drink.

Youth Often Binge Drink

People ages 12 to 20 drink 3.2% of all alcohol consumed in the United States. Although youth drink less often than adults, when they do drink, they drink more. Approximately 90% of all beverages containing alcohol consumed by youth are consumed by youth who engage in binge drinking (see the "What Is Binge Drinking?" box). 4  

In 2022, 3.2 million youth ages 12 to 20 reported binge drinking at least once in the past month. 2

In 2022, approximately 646,000 youth ages 12 to 20 reported binge drinking on 5 or more days over the past month. 2

Drinking Patterns Vary by Age and Gender

Alcohol use often begins during adolescence and becomes more likely as adolescents age. In 2022, fewer than 2 in 100 adolescents ages 12 to 13 reported drinking alcohol in the past month, and fewer than 1 in 100 engaged in binge drinking. 5 Among respondents ages 16 to 17, fewer than 1 in 5 reported drinking, and fewer than 1 in 10 reported binge drinking. 5 Implementing prevention strategies during early adolescence is needed to prevent this escalation, particularly because earlier alcohol use is associated with a higher likelihood of a variety of alcohol-related consequences. 6

Historically, adolescent boys were more likely to drink and binge drink than girls. Now, that relationship has reversed. Past-month alcohol use among adolescents ages 12 to 17 has declined more in recent years for  boys than girls, with more girls reporting more alcohol use (8.5% vs. 5.5%) and binge drinking (4.0% vs. 2.6%) than boys. 7,8

Underage Drinking Is Dangerous

Underage drinking poses a range of risks and negative consequences. It is dangerous because it:

• Causes many deaths . Alcohol is a significant factor in the deaths of people younger than age 21 in the United States each year. This includes deaths from motor vehicle crashes, homicides, alcohol overdoses, falls, burns, drowning, and suicides.
• Causes many injuries . Drinking alcohol can cause youth to have accidents and get hurt. In 2011 alone, about 188,000 people younger than age 21 visited an emergency room for alcohol-related injuries. 9
• Impairs judgment . Drinking can lead to poor decisions about taking risks, including unsafe sexual behavior, drinking and driving, and aggressive or violent behavior.
• Increases the risk of physical and sexual assault.  Underage binge drinking is associated with an increased likelihood of being the victim or perpetrator of interpersonal violence. 10
• Can lead to other problems . Drinking may cause youth to have trouble in school or with the law. Drinking alcohol is also associated with the use of other substances.
• Increases the risk of alcohol problems later in life . Research shows that people who start drinking before the age of 15 are at a higher risk for developing alcohol use disorder (AUD) later in life. For example, adults ages 26 and older who began drinking before age 15 are 3.5 times more likely to report having AUD in the past year than those who waited until age 21 or later to begin drinking. 11
• Interferes with brain development . Research shows that people’s brains keep developing well into their 20s. Alcohol can alter this development, potentially affecting both brain structure and function. This may cause cognitive or learning problems as well as may increase vulnerability for AUD, especially when people start drinking at a young age and drink heavily. 12,13

Why Do So Many Youth Drink?

As children mature, it is natural for them to assert their independence, seek new challenges, and engage in risky behavior. Underage drinking is one such behavior that attracts many adolescents. They may want to try alcohol but often do not fully recognize its effects on their health and behavior. Other reasons youth drink alcohol include:

Peer pressure

Increased independence or the desire for it

In addition, many youth have easy access to alcohol. In 2022, among adolescents ages 12 to 14 who reported drinking alcohol in the past month, 97.7% reported getting it for free the last time they drank. 14  In many cases, adolescents have access to alcohol through family members or find it at home.

What Is Binge Drinking?

The National Institute on Alcohol Abuse and Alcoholism (NIAAA) defines binge drinking as a pattern of drinking that brings blood alcohol concentration (BAC) to 0.08%—or 0.08 grams of alcohol per deciliter—or more.* This typically happens if a woman has 4 or more drinks, or a man has 5 or more drinks, within about 2 hours. 15  Research shows that fewer drinks in the same timeframe result in the same BAC in youth: only 3 drinks for girls, and 3 to 5 drinks for boys, depending on their age and size. 16

*A BAC of 0.08% corresponds to 0.08 grams per deciliter, or 0.08 grams per 100 milliliters.

Preventing Underage Drinking

Preventing underage drinking is a complex challenge. Any successful approach must consider many factors, including:

Personality

Rate of maturation and development

Level of risk

Social factors

Environmental factors

Several key approaches have been found to be successful. They are:

• Individual-level interventions . This approach seeks to change the way youth think about alcohol so they are better able to resist pressures to drink.
• School-based interventions . These are programs that provide students with the knowledge, skills, motivation, and opportunities they need to remain alcohol-free.
• Family-based interventions . These are efforts to empower parents to set and enforce clear rules against drinking, as well as improve communication between children and parents about alcohol.
• Community-based interventions . Community-based interventions are often coordinated by local coalitions working to mitigate risk factors for alcohol misuse.
• Policy-level interventions . This approach makes alcohol harder to get—for example, by raising the price of alcohol and keeping the U.S. Minimum Legal Drinking Age at 21. Enacting zero-tolerance laws that outlaw driving after any amount of drinking for people younger than 21 can also help prevent problems.

The Role Parents Play

Parents and teachers can play a meaningful role in shaping youth’s attitudes toward drinking. Parents, in particular, can have either a positive or negative influence.

Parents can help their children avoid alcohol problems by:

Talking about the dangers of drinking

Drinking responsibly if they choose to drink

Serving as positive role models in general

Not making alcohol available

Getting to know their children’s friends

Having regular conversations about life in general

Connecting with other parents about sending clear messages about the importance of youth not drinking alcohol

Supervising all parties to make sure there is no alcohol

Encouraging kids to participate in healthy and fun activities that do not involve alcohol 

Research shows that children of actively involved parents are less likely to drink alcohol. 17  However, if parents provide alcohol to their kids (even small amounts), have positive attitudes about drinking, and engage in alcohol misuse, adolescents have an increased risk of misusing alcohol. Moreover, if the adolescent has a parent with AUD, they are less likely to be protected from alcohol misuse through parental engagement and other factors. 18

Warning Signs of Underage Drinking

Adolescence is a time of change and growth, including behavior changes. These changes usually are a normal part of growing up but sometimes can point to an alcohol problem. Parents, families, and teachers should pay close attention to the following warning signs that may indicate underage drinking: 19,20

Changes in mood, including anger and irritability

Academic or behavioral problems in school

Rebelliousness

Changing groups of friends

Low energy level

Less interest in activities or care in appearance

Finding alcohol among an adolescent's belongings

Smelling alcohol on an adolescent's breath

Problems concentrating or remembering

Slurred speech

Coordination problems

Treating Underage Drinking Problems

Screening youth for alcohol use and AUD is very important and may prevent problems down the road. Screening by a primary care provider or other health practitioner (e.g., pediatrician) provides an opportunity to identify problems early and address them before they escalate. It also allows adolescents to ask questions of a knowledgeable adult. NIAAA and the American Academy of Pediatrics both recommend that all youth be regularly screened for alcohol use.

Some youth can experience serious problems as a result of drinking, including AUD, which require intervention by trained professionals. Professional treatment options include:

• Attending individual or group counseling sessions one or more times per week
• Receiving a prescription from a primary care provider or psychiatrist to help reduce alcohol cravings
• Participating in family therapy to build a supportive foundation for recovery

For more information, please visit: niaaa.nih.gov

1.  SAMHSA, Center for Behavioral Health Statistics and Quality (CBHSQ) [Internet]. 2022 National Survey on Drug Use and Health. Table 2.8B—Alcohol use in lifetime, past year, and past month: among people aged 12 or older; by detailed age category, percentages, 2021 and 2022. [cited 2023 Dec 13]. Available from: https://www.samhsa.gov/data/sites/default/files/reports/rpt42728/NSDUHDetailedTabs2022/NSDUHDetailedTabs2022/NSDUHDetTabsSect2pe2022.htm#tab2.8b 2. SAMHSA, CBHSQ [Internet]. 2022 National Survey on Drug Use and Health. Table 2.44A—Alcohol use in lifetime, past year, and past month and binge alcohol and heavy alcohol use in past month: among people aged 12 to 20; by demographic characteristics, numbers in thousands, 2021 and 2022 [cited 2023 Dec 12]. Available from: https://www.samhsa.gov/data/sites/default/files/reports/rpt42728/NSDUHDetailedTabs2022/NSDUHDetailedTabs2022/NSDUHDetTabs2 -44and2-45pe2022.pdf   3. Chen CM, Yoon YH [Internet]. NIAAA Surveillance Report #116: Trends in underage drinking in the United States, 1991–2019. Figure 1-5. NSDUH: Prevalence of drinking in the past 30 days among 12- to 20-year-olds, by age, sex, and race/Hispanic origin. Sterling, VA: CSR, Inc.; 2021 March [cited 2023 Feb 20]. Available from: https://pubs.niaaa.nih.gov/publications/surveillance116/figures19.htm#f… 4. Calculated using past 30-day quantity and frequency of alcohol use and past 30-day frequency of binge drinking (4+ drinks for females and 5+ drinks for males on the same occasion) from the 2022 NSDUH public-use data file. SAMHSA, CBHSQ [Internet]. 2022 National Survey on Drug Use and Health (NSDUH-2022-DS0001). Public-use file dataset. [cited 2024 Jan 12]. Available from: https://www.datafiles.samhsa.gov/dataset/national-survey-drug-use-and-health-2022-nsduh-2022-ds0001   5.  SAMHSA. CBHSQ [Internet]. 2022 National Survey on Drug Use and Health. Table 2.9B—Alcohol, binge alcohol, and heavy alcohol use in past month: among people aged 12 or older; by detailed age category, percentages, 2021 and 2022 [cited 2023 Dec 13]. Available from: https://www.samhsa.gov/data/sites/default/files/reports/rpt42728/NSDUHDetailedTabs2022/NSDUHDetailedTabs2022/NSDUHDetTabsSect2pe2022.htm#tab2.9b 6.  Hingson RW, Zha W. Age of drinking onset, alcohol use disorders, frequent heavy drinking, and unintentionally injuring oneself and others after drinking. Pediatrics. 2009 Jun;123(6):1477–484. PubMed PMID: 19482757 7.  Chen CM, Yoon YH, Faden VB [Internet]. Surveillance Report #107: Trends in underage drinking in the United States, 1991–2015. Bethesda, MD: NIAAA; 2017 March [cited 2023 Feb 20]. Available from: https://pubs.niaaa.nih.gov/publications/surveillance107/Underage15.htm

8.  Past-month alcohol use: consuming a drink of a beverage containing alcohol (a can or bottle of beer, a glass of wine or a wine cooler, a shot of distilled spirits, or a mixed drink with distilled spirits in it), not counting a sip or two from a drink in the past 30 days. Past-month binge drinking: consuming five or more drinks on the same occasion for males or four or more drinks on the same occasion for females on at least 1 day in the past 30 days. Population prevalence estimates (%) are weighted by the person-level analysis weight and derived from the CBHSQ 2022 National Survey on Drug Use and Health (NSDUH-2022-DS0001) public-use file. [cited 2024 Jan 12]. Available from: https://www.datafiles.samhsa.gov/dataset/national-survey-drug-use-and-health-2022-nsduh-2022-ds0001 9. SAMHSA, CBHSQ. The DAWN Report: Alcohol and drug combinations are more likely to have a serious outcome than alcohol alone in emergency department visits involving underage drinking. Rockville, MD: SAMHSA; 2014 [cited 2023 Feb 20]. Available from: https://www.samhsa.gov/data/sites/default/files/spot143-underage-drinki… 10. Waterman EA, Lee KDM, Edwards KM. Longitudinal associations of binge drinking with interpersonal violence among adolescents. J Youth Adolesc. 2019 Jul;48:1342–52, 2019. PubMed PMID: 31079263 11. Age at drinking onset: age when first drank a beverage containing alcohol (a can or bottle of beer, a glass of wine or a wine cooler, a shot of liquor, or a mixed drink with liquor in it), not counting a sip or two from a drink. AUD: having met two or more of the 11 AUD diagnostic criteria according to the American Psychiatric Association Diagnostic and Statistical Manual of Mental Disorders, 5th edition (DSM-5). AUD risk across different ages at drinking onset is compared using the prevalence ratio weighted by the person-level analysis weight. Derived from the CBHSQ 2022 National Survey on Drug Use and Health (NSDUH-2021-DS0001) public-use file. [cited 2024 Jan 12]. Available from: https://www.datafiles.samhsa.gov/dataset/national-survey-drug-use-and-health-2022-nsduh-2022-ds0001 12. Squeglia LM, Tapert SF, Sullivan EV, Jacobus J, Meloy MJ, Rohlfing T, Pfefferbaum A. Brain development in heavy-drinking adolescents. Am J Psychiatry. 2015 Jun;172(6):531–42, 2015. PubMed PMID: 25982660 13. Pfefferbaum A, Kwon D, Brumback T, Thompson WK, Cummins K, Tapert SF, Brown SA, Colrain IM, Baker FC, Prouty D, De Bellis MD, Clark DB, Nagel BJ, Chu W, Park SH, Pohl KM, Sullivan EV. Altered brain developmental trajectories in adolescents after initiating drinking. Am J Psychiatry. 2018 Apr;175(4):370–80. PubMed PMID: 29084454 14. SAMHSA, CBHSQ [Internet]. 2022 National Survey on Drug Use and Health. Table 8.20B—Source where alcohol was obtained for most recent use in past month: among past month alcohol users aged 12 to 20, by age group and gender: Percentages, 2021 and 2022. [cited 2023 Dec 13]. Available from: https://www.samhsa.gov/data/sites/default/files/reports/rpt42728/NSDUHDetailedTabs2022/NSDUHDetailedTabs2022/NSDUHDetTabsSect8pe2022.htm#tab8.20b 15. NIAAA. Defining binge drinking. What colleges need to know now. Bethesda (MD): National Institutes of Health; 2007 Nov [cited 2023 Feb 20]. Available from: https://www.collegedrinkingprevention.gov/media/1College_Bulletin-508_3… 16. Chung T, Creswell KG, Bachrach R, Clark DB, Martin CS. Adolescent binge drinking: developmental context and opportunities for prevention. Alcohol Res. 2018;39(1):5–15. PubMed PMID: 30557142

17. van der Vorst H, Engels RC, Meeus W, Deković M. The impact of alcohol-specific rules, parental norms about early drinking and parental alcohol use on adolescents’ drinking behavior. J Child Psychol Psychiatry. 2006 Dec;47(12):1299–306. PubMed PMID: 17176385 18. Yap M, Cheong T, Zaravinos-Tsakos F, Lubman DI, Jorm, A. Modifiable parenting factors associated with adolescent alcohol misuse: a systematic review and meta-analysis of longitudinal studies. Addiction. 2017 Jul;112(7):1142–62, 2017. PubMed PMID: 28178373 19. Rusby JC, Light JM, Crowley R, Westling E. Influence of parent-youth relationship, parental monitoring, and parent substance use on adolescent substance use onset. J Fam Psychol. 2018 Apr;32(3):310–20. PubMed PMID: 29300096 20. SAMHSA [Internet]. How to tell if your child is drinking alcohol. Rockville (MD): SAMHSA; [updated 2022 Apr 14; cited 2023 Feb 20]. Available from: https://www.samhsa.gov/underage-drinking/parent-resources/how-tell-if-y…

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Volume 42 Issue 1 January 13, 2022

Age, Period, and Cohort Effects in Alcohol Use in the United States in the 20th and 21st Centuries: Implications for the Coming Decades

Part of the Topic Series: NIAAA 50th Anniversary Festschrift

Katherine M. Keyes

Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York

This article is part of a Festschrift commemorating the 50th anniversary of the National Institute on Alcohol Abuse and Alcoholism (NIAAA). Established in 1970, first as part of the National Institute of Mental Health and later as an independent institute of the National Institutes of Health, NIAAA today is the world’s largest funding agency for alcohol research. In addition to its own intramural research program, NIAAA supports the entire spectrum of innovative basic, translational, and clinical research to advance the diagnosis, prevention, and treatment of alcohol use disorder and alcohol-related problems. To celebrate the anniversary, NIAAA hosted a 2-day symposium, “Alcohol Across the Lifespan: 50 Years of Evidence-Based Diagnosis, Prevention, and Treatment Research,” devoted to key topics within the field of alcohol research. This article is based on Dr. Keyes’ presentation at the event. NIAAA Director George F. Koob, Ph.D., serves as editor of the Festschrift.

Introduction

Alcohol consumption, including any alcohol use; patterns of high-risk use, including binge drinking; and alcohol use disorder (AUD) incidence and prevalence, differs substantially over time and by life stage. Variation also occurs across demographic groups, and such differences themselves vary across time and place. In the first quarter of the 21st century, changes in incidence and prevalence of alcohol use and alcohol-related health consequences have been accelerating. Understanding the magnitude and direction of these changes informs hypotheses regarding the reasons underlying alcohol consumption changes across time and development, including both long-term historical changes as well as abrupt shifts. It also permits determining the optimal focus of research and targets of services. Such surveillance is informed by science and statistical considerations of variation by age, period, and cohort effects.

Age-, period-, and cohort-effect estimation has proved to be an extraordinarily useful framework for organizing and interpreting data, uncovering patterns, and identifying causes of trends in incidence and prevalence of many health conditions and mortality over time. This article provides an overview of the conceptual basis of such effects as related to alcohol consumption, and reviews recent studies of age-period-cohort variation, especially regarding gender, social class, and specific beverage and drinking patterns.

Age, Period, and Cohort Effects and Their Importance

Age effects.

Age effects refer to the effects of a person’s age on their health. They may be caused by the accumulation of exposure or social experiences; critical and sensitive developmental windows; or immunological periods of vulnerability, such as infancy and end of life. Extensive evidence documents that alcohol use is most likely to begin during adolescence or young adulthood, peak during the transition to adulthood, and generally decrease thereafter. 1,2 However, these age patterns are not static; in the United States, for example, the onset and peak of alcohol use has been shifting in recent decades to a later point in development. 3 Because onset and persistence of alcohol use are in part social phenomena and are amenable to policy interventions (e.g., changes in minimum legal drinking age laws), 4 the specific structure and magnitude of age effects are historically variable. However, the general patterns of onset early in adult maturation, and desistence during adulthood, have been largely stable over historical time.

Period Effects

Period effects refer to changes in outcome that affect all individuals alive in a particular period—that is, a year or set of years. Reasons for period effects include changing environmental or social factors that affect incidence and persistence of certain behaviors or disorders, policy or law changes, or other environmental conditions that affect health. For alcohol use, numerous factors have been associated with substantial changes in consumption patterns, including major policy initiatives to restrict access to alcohol, such as U.S. Prohibition from 1920 to 1933, and broad economic factors, such as booms and recessions that affect spending on nonessential goods. The general social climate for heavy drinking has also changed over time as advocacy movements placed the dangers of heavy consumption into stark focus, followed by policies to increase criminal sanctions on impaired driving. 5 However, as detailed below, such policy changes are not simply period effects because they often impact age groups differently; therefore, their effects may manifest as cohort effects.

Cohort Effects

Against the backdrop of age and period effects, cohort effects have also proven to be powerfully predictive of a range of health behavior, including alcohol use. Cohort effects can perhaps be most efficiently conceptualized as age-by-period interactions. 6 For example, a cohort effect would be apparent if historical change across time in a health behavior such as alcohol consumption resulted in increasing overall prevalence (i.e., a period effect), but the increase in prevalence is faster or slower for people in different age groups (i.e., an age by period interaction). Cohort effects can also be conceptualized as a unique rate of an outcome for individuals depending on birth year. 7

Before reviewing the current literature on cohort effects in alcohol use, it is important to understand that cohort effects are powerfully predictive of many health outcomes, and critical to consider when evaluating trends. There are numerous historical examples of particular birth cohorts with increased rates of disease outcomes and mortality in the United States, including all-cause mortality, 8,9 tuberculosis, 10 peptic ulcer, 11 lung cancer, 12 and other diseases. More recently, the strong influence of generational cohort effects is apparent in the leading U.S. contributors to premature mortality, including obesity, hepatitis C, drug overdose, and suicide. 13–16 Similarly, numerous studies in recent decades have found that alcohol use and health outcomes related to heavy consumption cluster by birth cohort, as well as have exhibited age and period effects at various points in history. Cohort effects have long been documented in substance use, 17,18 including alcohol use and alcohol-related harms, 19 as described in more detail below.

Recent Alcohol Use Time Trends in the United States

Time trends in alcohol use and alcohol-related harms have been dynamic in the United States, especially over the last 2 decades. Among adolescents, the prevalence of alcohol use has declined. Data from two major nationally representative surveys—Monitoring the Future and the National Survey on Drug Use and Health—converge in demonstrating these reductions. Although the specific prevalence of any alcohol use and binge drinking differs between the two surveys, both document substantial, sustained reductions in adolescent drinking over the last 20 years. 20,21 The most recently published data from the Monitoring the Future Study, depicted in Figure 1, show the trend in past 2-week binge drinking among 12th grade adolescents through 2019; as the figure shows, binge drinking declined from a peak in approximately 1982 to less than 20% for both boys and girls in 2019. 22

In contrast, adult alcohol use and binge drinking has been increasing. A meta-analysis of six national surveys of alcohol use found (Figure 2) that from 2000 to 2016, the overall prevalence of binge drinking increased approximately 7.5% per decade across the 2 decades analyzed. 23 Importantly, however, these increases were primarily concentrated among women, as discussed further below.

The observation that changes over time in alcohol consumption differed by age immediately raises the possibility of cohort effects. Indeed, many studies using different data sources and analytical approaches have documented cohort effects for numerous alcohol-related outcomes. Generally, post-World War II U.S. birth cohorts had higher rates of consumption than earlier cohorts, 19,24 ,25 driving much of the increase in consumption in the 1970s and 1980s. For many of these studies, however, reliance on retrospective recall is a common limitation. Avoiding this limitation, Kerr et al. 24 , 26 used the National Alcohol Surveys, which reports current consumption patterns that are less subject to recall issues. These analyses documented that several birth cohorts had higher risks of alcohol consumption and binge drinking throughout the life course, especially men born in the late 1970s and women born in the early 1980s. In contrast, among cohorts born in the 1990s and later, alcohol use has consistently been declining during adolescence and early adulthood. However, those same cohorts have exhibited accelerating drinking after transition to adulthood. 27

In sum, the cohorts of today’s adults who are now in their 30s and 40s were part of the historical shift toward declining alcohol consumption in adolescence. This decline is explained in part by shifts in the minimum legal drinking age across states, especially in the 1980s, 27 yet declines continued thereafter, potentially aided by focused prevention efforts on reducing underage drinking. However, because drinking then accelerated during the transition to adulthood, adult rates of drinking did not benefit from these prevention efforts. Indeed, Patrick et al. (2019) have documented an overarching historical shift in the age effect on binge drinking among recently born cohorts; thus, the peak age of binge drinking in 1996 to 2004 was 2 years later than it was in 1976 to 1985. 3

In addition to these overall age, period, and cohort effects, additional variation across other levels of dynamic change have implications for prevention, policy, and causal etiology assessments. Three areas of variation that have received substantial attention are gender, socioeconomic status, and beverage type.

Effects of Gender

Men consume more alcohol and are more likely to have AUD compared with women, 1 but the gender gap has been closing for decades in the United States and elsewhere. 19,25 However, the manner in which the gender gap is closing differs by birth cohort. Among today’s birth cohorts of adolescents (i.e., those born in and around the same year), the gender gap is closing because for more than 30 years, alcohol consumption and binge drinking have declined among both boys and girls, but the decline is faster for boys than girls (see Figure 1). 28 Conversely, in adults, alcohol consumption and binge drinking have increased, especially in the past 10 years, and those increases have been greater for women than for men (see Figure 2). 23 The recent increases in drinking among women reflect the high-risk cohorts identified by Kerr et al. 26 as they age into middle-adulthood. Interestingly, compared to earlier generations, these cohorts of women progressed through adolescence with lower alcohol use and binge drinking, yet had a faster acceleration of their drinking during the transition to adulthood, resulting in high levels of alcohol use and strong cohort effects in adulthood. 27

Additional analyses have indicated that the increases in alcohol consumption and binge drinking among women in midlife are concentrated among those with high levels of education, 29 occupational prestige, 30 and income, 29 suggesting that traditional gender norms sanctioning alcohol consumption are shifting among women now occupying traditionally male statuses and spaces. The human costs of these increases in consumption are reflected in alcohol-related mortality rates. These rates have doubled between 1999 and 2016, 31 with the largest increases observed among women and adults emerging into midlife, consistent with alcohol consumption trends.

Effects of Socioeconomic Status

Historically, the role of socioeconomic status has been a critical axis for examining trends over time in alcohol consumption, as exemplified by the higher consumption rates in adult women, who are increasingly occupying high socioeconomic positions. Overall, individuals with a higher socioeconomic status are less likely to fully abstain from alcohol compared to those with a lower status. 32 The relationship between socioeconomic status and binge drinking or AUD, however, is more mixed and depends on the socioeconomic indicator, population, and time period analyzed. 33–35 Further, population distributions of socioeconomic status are an outcome of economic conditions (i.e., income and wealth are functions of times of economic expansions and recessions); therefore, trends in socioeconomic status, and who achieves and maintains high status positions, are important potential drivers of population trends.

Renewed attention to theories of the relationship between social class and health has been prompted by evidence that recent increases in U.S. mortality, including alcohol-related and other substance-related mortality, are concentrated among men with less than a high school education. 36 However, these findings run counter to available data on heavy drinking birth cohorts. The birth cohorts identified by Case and Deaton 36 are different than the birth cohorts emerging into adulthood in the 1970s and 1980s or those of college age in 2002 to 2012, suggesting that the dynamics of alcohol-related harm are likely to substantially change in the decades to come. Indeed, National Alcohol Survey data show that cohort trends in U.S. alcohol consumption are primarily driven by changes in education. 37 As more recent cohorts have entered college at higher rates, drinking and binge drinking have become concentrated in these college-attending young adults. The alcohol consumption cohort effect of those born in the late 1970s and early 1980s is attributable largely to their high rates of college attendance. Conversely, however, there may be signs of emerging socioeconomic differences when considered across gender (more on gendered trends in alcohol consumption below). For example, from 2002 to 2012, binge drinking was largely stable among college-attending young adults, but slightly increased among non-college enrolled women (from 29% to 33%) while decreasing among non-college-enrolled men. 38 Continued surveillance of the role of socioeconomic status within trends in alcohol consumption, and beyond education into other indicators, is warranted.

Effects of Beverage Type

Another important area for research is variation in alcohol consumption dynamics by type of alcoholic beverage. Although all alcoholic beverages are carcinogenic, beverage types vary in ethanol concentration and potential for harm, as well as in their prevalence and popularity across demographic groups. A growing literature indicates that the types of alcoholic beverages that individuals in the United States are consuming are dynamic and may depend on cohort. Kerr et al. (2004) 39 found that pre-1940s cohorts preferred spirits throughout the life course compared with later cohorts. In contrast, cohorts born in the 1940s through 1970s, especially men, tended to prefer beer, and wine has been gaining dominance in beverage preferences among younger cohorts. These changes may be related at least in part to marketing and sales efforts by the alcohol industry to increase profits. For example, the increase in wine consumption, which has been observed in alcohol sales surveillance, 40 is commensurate with the increases in income and education in the United States, as wine is marketed as a prestige product and is often sold at high price points. Additional analyses have found that the alcohol content of beverages is increasing in the United States, 41 ,42 portending potential further harm and greater rates of AUD.

The dynamics of cohort effects on beverage preferences are particularly salient for the role of alcohol policy and reduction of alcohol-related harms. Sales restrictions and alcohol taxes have a substantial, demonstrable overall impact on population-level consumption and alcohol-related harms, 43 although this varies to some extent by age of consumer, level of consumption, and beverage type. 44 For example, tax variations by beverage type can influence trends in the consumption of particular beverages. Spirit and wine consumption is typically most sensitive to price and tax policy changes, 45 and although consumption of spirits has been increasing in the United States in recent years, there has been little change in tax and price regulations. This suggests that one driver of the increase in spirits consumption is that they are becoming effectively less expensive over time. Beer and wine are also regulated differently in many states; thus, changing dynamics in the popularity of each beverage have implications for how effective beverage-specific alcohol taxes are in reducing sales and, consequently, harm. Regulations related to alcohol sales and consumption that can respond to market changes in beverage preferences (e.g., increased taxes on wine and spirits that reflect their growing share of the alcohol market) may be an important lever for promoting public health in the coming decades.

Differences in Drinking Patterns Among Cohorts

Taken together, the literature on age, period, and cohort effects in alcohol research indicates that different cohorts have different drinking patterns and that socioeconomic and demographic factors are critical to contextualizing the observed trends. Although it is possible to document time and cohort trends with the available data, understanding why alcohol consumption patterns are changing is more challenging.

Certainly, alcohol policies play a fundamental role in determining population-level patterns of consumption, and the way that policies target particular demographic groups (intentionally or unintentionally) creates opportunities for cohort effects to emerge. For example, the adoption of a minimum legal drinking age of 21 across states throughout the 1980s mediates a portion of the decline in alcohol consumption among U.S. adolescents since then. 27 However, consumption has continued to decline for decades after the increase in drinking age, suggesting that additional factors, such as the public health investment in underage drinking prevention, provided further benefits. Numerous other policies have shifted and impacted population-level alcohol consumption since the U.S. Prohibition, including restrictions on where and when alcohol can be sold, state monopolies on sales, criminal penalties for hazardous use, and others. 46,47 These policies likely have affected different age groups in different ways, depending on their developmental stage when exposed to newly restrictive or permissive alcohol policies.

Of course, alcohol policies are not the only determinant of alcohol consumption and, consequently, of age, period, and cohort effects. Substantial research has evaluated the impact of social norms and social roles, as well as community and societal norms and values on changes in alcohol use over time. 48,49 Social values have an inherent role in the use of alcohol, and the acceptability of drinking and drunkenness within and across social groups at different times and different life stages is potentially a powerful factor influencing population-level consumption. For example, heavy consumption on college campuses, especially within social institutions such as Greek life, 50 is often normative and expected, but norms and values around alcohol use swiftly change as young adults encounter the social norms of early adulthood. 51 Moreover, these normative trajectories and patterns become variable as societal roles and values themselves change. For example, religious attendance and the importance of religion have long been a robust predictor of decreased alcohol consumption. 52 However, the centrality of religion to U.S. adolescents and adults has been declining for more than a decade, 53 and this decline explains a portion of the cohort effects in binge drinking among today’s adults. 54 Monitoring these and other broader societal changes is critical to determining the influences that mediate shifts in alcohol consumption over time.

For example, the coming years will be critical to determining the effects of health knowledge regarding alcohol-related risks on population consumption. For decades, low levels of alcohol consumption were considered protective, especially for cardiovascular health. 55 The evidence supporting this hypothesis, however, was subject to substantial confounding, 56 and dissemination of the message of alcohol’s protective effects was well-funded by the alcohol industry, which had a clear financial incentive. 55 Recently, studies using large administrative databases and quasi-experimental designs, such as Mendelian randomization, have called into question and refuted the idea that a moderate level of alcohol consumption benefits health. 57,58 The extent to which public health messages shift to reflect this change in scientific consensus may be important in reducing population-level alcohol-related harms. These changes may further manifest as cohort effects, as the dissemination and implementation of health information and guidelines are likely to affect age groups differently as they progress through the life course.

Conclusions

Alcohol consumption continues to be a leading contributor to morbidity and mortality, both in the United States and worldwide. Although significant progress in reducing adolescent and young adult alcohol use has been achieved and sustained for decades, it is offset by increases in drinking during the transition to adulthood. The cohorts currently at midlife, especially women, are increasing alcohol consumption and binge drinking at greater levels than other cohorts, portending health consequences that may persist for decades. Understanding the motivations for consumption, destigmatizing the use of services to reduce consumption, and increasing the availability and accessibility of such services are necessary to improve population health. Moreover, age, period, and cohort effect estimations are critical surveillance tools for epidemiology and population health research. Such assessments have already answered critical questions and uncovered patterns in the data that specifically identify high-risk groups requiring prevention and intervention efforts.

Acknowledgments

Dr. Keyes would like to thank Dr. Deborah Hasin for insightful feedback and edits on this paper. This article was supported by National Institutes of Health grant R01AA026861.

Correspondence

Address correspondence concerning this article to Katherine M. Keyes, Ph.D., Columbia University, Mailman School of Public Health, 722 West 168th Street, Room 724, New York, NY 10032. Email: [email protected]

Disclosures

The author declares no competing financial or nonfinancial interests.

Publisher's note

This article was based on a presentation by Dr. Keyes at the NIAAA 50th Anniversary Science Symposium, “Alcohol Across the Lifespan: 50 Years of Evidence-Based Diagnosis, Prevention, and Treatment Research,” held on November 30–December 1, 2020. Links to the videocast are available on the NIAAA 50th Anniversary Science Symposium agenda webpage. Opinions expressed in contributed articles do not necessarily reflect the views of the NIAAA, National Institutes of Health. The U.S. government does not endorse or favor any specific commercial product or commodity. Any trade or proprietary names appearing in Alcohol Research: Current Reviews are used only because they are considered essential in the context of the studies reported herein.

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In Chance for Trump, Youth at Rally See Him as Answer to Economic Woes

A young Donald Trump supporter holds up a sign at a rally in Green Bay, Wisconsin, April 2, 2024. REUTERS/Nathan Layne

By Nathan Layne and Tim Reid

GREEN BAY, Wisconsin (Reuters) - Thin with a boyish face and earrings in both ears, 23-year-old Isayah Turner does not look like a stereotypical Trump supporter, who tend to be middle aged or older.

Nevertheless, Turner drove two hours from his home outside Milwaukee on a recent Tuesday to see Republican presidential candidate Donald Trump at a rally in Green Bay, Wisconsin, one of a contingent of young voters there that some opinion polls suggest could be a growing and important demographic for Trump.

For Democratic incumbent Joe Biden, who overwhelmingly won the youth vote in 2020, an erosion of his support among young voters could potentially dampen his hopes of a second term.

Turner, who runs a dog breeding business with his mother, voted for Trump in 2020. He supports Trump's pro-oil drilling stance, his opposition to gun control - Turner owns several firearms - and his pledge to crack down on illegal immigration.

"I cannot think of one thing that Trump did that upset me while he was in office. And now with Biden in office there are countless things I disagree with," Turner told Reuters. "A lot of my friends are on the same page as me."

A Reuters/Ipsos poll in March showed Americans age 18-29 favoring Biden over Trump by just 3 percentage points - 29% to 26% - with the rest favoring another candidate or unsure of who if anyone would get their vote.

If Trump, 77, stays close to Biden, 81, in this demographic all the way to Election Day on Nov. 5 it would be a major gain compared to 2020, when Biden won the youth vote by 24 points.

Concerns about Biden's age and his support of Israel in its war against Hamas in Gaza have fueled the erosion of his support among young voters at a time he is also losing Hispanic voters.

There are also signs young people are slowly warming to the Republican Party, despite Biden's efforts to keep them on side by trying to cancel student debt, expand affordable housing and reverse curbs on abortion rights.

The share of Americans between 18-29 who identify as Republicans has ticked higher, from 24% in 2016 to 26% in 2020 and 28% so far this year, Reuters/Ipsos polling shows.

Despite a mixture of cold winds, sleet and rain, some 3,000 Trump supporters lined up outside a Green Bay convention center on April 2 to see Trump. The crowd skewed older, as usual, but there were hundreds of young people as well.

Reuters interviewed 20 people under the age of 30 to understand their support. The most common reason given for backing the former president was inflation and the perception the economy was not working for them, underscoring how the rise in prices for daily staples is more salient for some than high stock prices and low unemployment during the Biden years.

"I make decent money and I can't afford a home on the salary I make now," said Steve Wendt, 26, a security guard at a nearby hospital. "It's time to get a man back into office that is going to lower our prices."

At the same time, a majority said they agreed with Trump's reticence about aiding Ukraine in its war with Russia, an isolationist stance at odds with Biden's foreign policy agenda.

Collin Crego, 19, a history student, said funds spent overseas would be better used to tackle domestic issues like drug addiction.

"I don't really like what we are doing with Ukraine," Crego said. "When I hear him (Trump) talk, he's very patriotic, very 'America First' and I like that."

Of the 20 people Reuters interviewed, 15 cited inflation or other economic concerns for why they support Trump, while a dozen said his plan to restrict immigration was important to them.

All said they were unbothered by the four criminal cases Trump is facing, or the idea that his efforts to overturn the 2020 election made him a threat to democracy. One was Black, the other 19 were white. Eight will be casting their first presidential ballot this year.

Caitlyn Huenink, 20, said being a young Trump supporter can be hard because left-leaning young people tend to frown on her views. She said, however, that she has recently seen changes among her peer group at University of Wisconsin–Green Bay.

"They're more open to the way I think and more of my friends are becoming Republican," she said.

'MAKING ENDS MEET'

To be sure, a group of young people willing to brave inclement weather to see Trump are not a representative sample of the broader electorate, and polling this early in the cycle could prove off. Younger people vote less frequently than older Americans, making them especially difficult to predict.

Moreover, some opinion surveys indicate that Biden is holding on to his significant advantage with the youth.

An Economist/YouGov poll conducted last week showed 51% of voters under 30 picking Biden, versus 32% for Trump, while the Harvard Youth Poll, released Thursday, put Biden's lead over Trump among likely young voters at 19 points.

"Donald Trump is not winning the youth vote," John Della Volpe, director in charge of the Harvard poll, told Reuters.

The Biden campaign is not sitting still. In March it launched a $30 million ad buy across digital platforms and announced a project to reach students and recruit volunteers in high schools and on college campuses. It is working to inform younger people of the administration's investments in green energy and efforts to protect abortion access.

"That's why the campaign is working tirelessly to earn the votes of young voters — investing earlier than ever and leveraging every opportunity to connect with young voters," said Eve Levenson, the campaign's youth engagement director.

The latest Marist College poll was nevertheless a red flag for Biden. Conducted in March, it showed Trump 2 points ahead among Millennial and Gen-Z voters, with 61% of 18-29 year olds saying they disapprove of the job Biden is doing as president.

The Trump campaign sees young people as a demographic for potential gains in 2024, a campaign adviser told reporters last month. He said the economy and overseas conflicts -- Trump often claims Russia's attack on Ukraine would not have happened on his watch -- were key topics to message about to this group.

"Like many Americans, young people can't afford rent, gas, or groceries, and they're struggling to buy a home because real wages have plummeted," said Anna Kelly, a spokesperson for the Republican National Committee.

Kelly also pointed to a finding in the Harvard poll - that only 9% percent of young Americans think the U.S. is on the right track - as proof that some were turning to Trump.

Among young voters, Trump appears to be doing better with males. The Harvard poll put Biden's lead among young men at just 6 percentage points, down 20 points from 4 years ago. Trump's deficit with women was 33 points, largely unchanged.

Della Volpe says that gender gap likely reflects several factors. One is that young men feel they are losing the right to speak frankly due to progressive views they believe are imposed on them about political correctness and toxic masculinity. These concerns are reinforced by Trump and podcasters like Jordan Peterson, popular with young men.

Trump has attended several Ultimate Fighting Championship events this election cycle, which are favored by young men. He also showed up at a Philadelphia sneaker convention where he put his golden "Never Surrender High-Tops" up for sale.

It was the kind of campaign stop meant to resonate with voters like Turner, a sneaker aficionado who was wearing a $400 pair of Nikes when Reuters spent an afternoon with him at his dog business two days after the rally.

Turner talked about the challenges of operating a business. He said gasoline was a major expense as he frequently drives to breeders hours away.

Turner said it was his Trump-loving mother, a former backer of President Barack Obama, who got him interested in politics.

Like other young people Reuters met at the rally, Turner said it was Trump's way of speaking without care for the political consequences that made him attractive. He said some of Trump's dehumanizing rhetoric bothers him, but he believes - as Trump has claimed - that Biden is the true threat to America.

"Some of it is extreme," Turner said of Trump's speech. "But at the same time if it means the country is going to do phenomenally better... and it's still going to be a free country I can take my feelings getting hurt in exchange for that."

(Reporting by Nathan Layne, Tim Reid and Jason Lange, editing by Ross Colvin and Alistair Bell)

Copyright 2024 Thomson Reuters .

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Alcohol use among adolescents in India: a systematic review

Abhijit nadkarni.

1 Centre for Global Mental Health, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK

2 Addictions Research Group, Sangath, Porvorim, Goa, India

3 Department of Psychology, Harvard University, 33 Kirkland Street, Cambridge, USA

Devika Gupta

Sonal gupta, urvita bhatia.

4 Oxford Brookes University, Oxford, UK

Niharika Tiwari

5 Tees, Esk and Wear Valleys NHS Foundation Trust, Darlington, UK

6 Social Finance, Tintagel House, 92 Albert Embankment, London, UK

Godwin Fernandes

Richard velleman.

7 Department of Psychology, University of Bath, Bath, UK

Associated Data

For supplementary material accompanying this paper visit https://doi.org/10.1017/gmh.2021.48.

Alcohol use is typically established during adolescence and initiation of use at a young age poses risks for short- and long-term health and social outcomes. However, there is limited understanding of the onset, progression and impact of alcohol use among adolescents in India. The aim of this review is to synthesise the evidence about prevalence, patterns and correlates of alcohol use and alcohol use disorders in adolescents from India.

Systematic review was conducted using relevant online databases, grey literature and unpublished data/outcomes from subject experts. Inclusion and exclusion criteria were developed and applied to screening rounds. Titles and abstracts were screened by two independent reviewers for eligibility, and then full texts were assessed for inclusion. Narrative synthesis of the eligible studies was conducted.

Fifty-five peer-reviewed papers and one report were eligible for inclusion in this review. Prevalence of ever or lifetime alcohol consumption ranged from 3.9% to 69.8%; and prevalence of alcohol consumption at least once in the past year ranged from 10.6% to 32.9%. The mean age for initiation of drinking ranged from 14.4 to 18.3 years. Some correlates associated with alcohol consumption included being male, older age, academic difficulties, parental use of alcohol or tobacco, non-contact sexual abuse and perpetuation of violence.

The evidence base for alcohol use among adolescents in India needs a deeper exploration. Despite gaps in the evidence base, this synthesis provides a reasonable understanding of alcohol use among adolescents in India and can provide direction to policymakers.

Introduction

According to the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019, among adolescents and young adults (aged 10–24 years), alcohol-attributable burden is second highest among all risk factors contributing to disability-adjusted life years in this age group (GBD 2019 Risk Factors Collaborators, 2020 ). The exposure of the adolescent brain to alcohol is shown to result in various cognitive and functional deficits related to verbal learning, attention, and visuospatial and memory tasks, and behavioural inefficiencies such as disinhibition and elevated risk-taking (Spear, 2018 ). Alcohol consumption in adolescents results in a range of adverse outcomes across several domains and includes road traffic accidents and other non-intentional injuries, violence, mental health problems, intentional self-harm and suicide, HIV and other infectious diseases, poor school performance and drop-out, and poor employment opportunities (Hall et al ., 2016 ).

Adolescence is a critical period in which exposure to adversities such as poverty, family conflict and negative life experiences (e.g. violence) can have long-term emotional and socio-economic consequences for adolescents, their families and communities (Knapp et al ., 1999 ; Knapp et al ., 2002 ). Substance use, including alcohol, is typically established during adolescence and this period is peak risk for onset and intensification of substance use behaviours that pose risks for short- and long-term health (Anthony and Petronis, 1995 ; DeWit et al ., 2000 ; Hallfors et al ., 2005 ; Schmid et al ., 2007 ; Hadland and Harris, 2014 ). As such, early initiation of alcohol use among adolescents can provide a useful indication of the potential future burden among adults including increased risk for academic failure, mental health problems, antisocial behaviour, physical illness, risky sexual behaviours, sexually transmitted diseases, early-onset dementia and the development of alcohol use disorders (AUDs) (Hingson et al ., 2006 ; King and Chassin, 2007 ; Dawson et al ., 2008 ; Nordström et al ., 2013 ).

India continues to develop rapidly, and accounts for most of the increase in alcohol consumption per capita for WHO's South-East Asia region (World Health Organization, 2018 ). Although India has a relatively high abstinence rate, many people who do drink are either risky drinkers or have AUDs (Benegal, 2005 ; Rehm et al ., 2009 ). Finally, the existing policies in India have failed to reduce the harm from alcohol because the implementation of alcohol control efforts is fragmented, lacks consensus, is influenced by political considerations, and is driven by narrow economic and not health concerns (Gururaj et al ., 2021 ).

India has the largest population of adolescents globally (253 million people aged 10–19 years), constituting 21% of the population (Government of India, 2011 ; Boumphrey, 2012 ). Additionally, adolescents as young as 13–15 years of age have started consuming alcohol in India (Gururaj et al ., 2016 ). Despite this growing public health problem, the official policy response in India remains primarily focused on AUDs, particularly alcohol dependence in adults, with an absolute disregard for the potential of prevention programmes. One potential reason for this is the limited understanding of the onset and progression of alcohol use and AUDs amongst adolescents in India. The aim of this paper is to bridge that knowledge gap by synthesising the evidence about the prevalence and correlates of alcohol use and AUDs in adolescents from India.

The specific objectives are to examine the following in adolescents from India: (a) prevalence of current and lifetime use of alcohol, (b) prevalence of current AUDs, (c) patterns (e.g. frequency, quantity) of alcohol use, (d) sociodemographic, social and clinical correlates of alcohol use and AUDs, and (e) explanatory models of and attitudes towards alcohol use and AUDs, e.g. perceptions of the problem and its causes. This paper synthesises the evidence about alcohol and AUDs using data from a comprehensive review that we conducted of any substance use and substance use disorders amongst adolescents in India.

Systematic review . The review protocol was registered prospectively on Prospero (registration ID CRD 42017080344).

Inclusion and exclusion criteria

There were no limits placed on the year of publication of the paper, gender of the participants and study settings in India. We only included English language publications as academic literature from India is predominantly published in such publications. Adolescents were defined as anyone between 10 and 24 years of age (Sawyer et al ., 2018 ). Studies reporting alcohol use and/or AUDs in a wider age range (including 10–24 years) were included only if data were separately presented for the 10–24-year age group. We included observational studies (surveys, case-control studies, cohort studies), qualitative studies and intervention studies (only if baseline prevalence data were presented). We included studies which examined alcohol use and AUDs defined as per the International Classification of Diseases (ICD)/Diagnostic and Statistical Manual of Mental Disorders (DSM)/clinical criteria or using a standardised screening or diagnostic tool.

We searched the following databases: PsycARTICLES, PsycInfo, Embase, Global Health, CINAHL, Medline and Indmed. The search strategy was organised under the following concepts: substance (e.g. alcohol, drug), misuse/use disorder (e.g. addiction, intoxication), young people (e.g. adolescent, child) and India (e.g. India, names of individual Indian states). The detailed search strategy is listed in Appendix A .

Two reviewers (DG and KW) independently inspected the titles and abstracts of studies identified through the database search. Any conflicts about eligibility between the two reviewers were resolved by AN. If the title and abstract did not offer enough information, the full paper was retrieved to ascertain whether it was eligible for inclusion. Screening of full texts was done by AN, AG and DG; and any conflicts about eligibility were resolved by UB. Screening of the results of the search was done using Covidence ( https://www.covidence.org/ ), an online screening and data extraction tool.

AN searched the following resources to identify relevant grey literature: Open Grey, OAlster, Google, ProQuest, official English language websites of the World Health Organization and World Bank, English language websites of ministries of each state and union territory within India responsible for substance misuse as well as the official websites of the Indian Narcotics Control Bureau and Ministry of Social Justice and Empowerment.

Any grey literature with relevant data published by a recognised non-governmental organisation, state, national or international organisation was included. Studies were included based on the robustness of study design and quality of data. If there were multiple editions of any published piece of grey literature, only the latest published edition of that report was included. Once retrieved, their titles, content pages and summaries were read by AN and if deemed eligible they were added to a list of potentially eligible reports. If the grey literature's summary, content and title did not include enough information, then the full text was examined by AN to determine eligibility for inclusion.

Finally, experts in the field of substance use disorders in India were contacted to explore if they could identify any further useful sources of information and were invited to submit unpublished data and unreported outcomes for possible inclusion into the review. Reference lists of selected studies, grey literature and relevant reviews were inspected for additional potential studies.

A formal data extraction worksheet was designed to extract data relevant to the study aims. The following data were extracted: centre (e.g. name of city), sampling technique, sample (e.g. general population), sample size, age(s), tool used to measure alcohol use and/or AUD, definitions of alcohol use and AUD, prevalence of alcohol use and/or AUD, age of initiation, type of alcohol, quantity and frequency of alcohol use, attitudes towards alcohol use, effect of alcohol on health, social, educational and other domains, and risk factors/correlates of alcohol use and or AUD. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (Moher et al ., 2015 ), a record was made of the number of papers retrieved, the number of papers excluded and the reasons for their exclusion. AT independently performed data extraction, AG checked the data extraction, and AN arbitrated any unresolved issues. The quality of reporting of included studies was examined using the STROBE Statement – checklist of items that should be included in reports of observational studies (Von Elm et al ., 2007 ).

A descriptive analysis of the data was conducted, and the results are mainly reported in a narrative format focusing on each of the objectives described above (Popay et al ., 2006 ).

In total, 6464 references were identified through the search strategies described above. Overall, 251 records were eligible for the wider review, of which 55 were about alcohol use and have been reported in this paper ( Fig. 1 ). Additionally, one report of magnitude of substance use in India which was recommended by an expert was also included (Ambekar et al ., 2019 ).

PRISMA flow diagram.

Study descriptions

One study was conducted online (Gupta et al ., 2018 ) and one in a national treatment centre in North India (Mandal et al ., 2019 ), both of which potentially had access to participants from across the country ( Table 1 ). All the rest were conducted at a single or multiple settings in a city, town, district, village or state. The sample size of the studies ranged from 23 (Bhad et al ., 2017 ) to 7350 (Jaisoorya et al ., 2016 ). In studies that reported mean age of the samples, it ranged from 13.10 years (Pillai et al ., 2008 ) to 20.56 years (Garg et al ., 2009 ).

Description of studies included in the review

Prevalence of alcohol use and AUD

The prevalence of ever use or lifetime use, broadly defined as consumption of alcohol at least once in their lifetime, ranged from 3.9% in school students aged 12–18 years (Rani and Sathiyaskaran, 2013 ) to 69.8% in 22–23-year-old medical students (Kundapur and Kodyalamoole, 2016 ) ( Table 2 ). Ever use in females ranged from 6.5% in students from class 8 to class 12 (age 12–19 years) (Jaisoorya et al ., 2016 ) to 52% in an online survey of adolescents aged 13–17 years (Gupta et al ., 2018 ), and in males it ranged from 9.79% in students from classes 9 and 11 (age up to 17 years) (Kotwal et al ., 2005 ) to 47% in an online survey of adolescents aged 13–17 years (Gupta et al ., 2018 ). The prevalence of ever use in rural areas ranged from 7.37% in high school students (Tsering et al ., 2010 ) to 20% in students aged 15–19 years (Kumar et al ., 2016 ), and in urban areas it ranged from 5.23% in high school students (Tsering et al ., 2010 ) to 23.08% in students aged 15–19 years (Kumar et al ., 2016 ).

Prevalence of alcohol use and alcohol use disorders

Current use

The definition of current use of alcohol varied across studies. The more commonly used definitions were alcohol consumption at least once in the past year for which the prevalence ranged from 10.6% in senior high school students aged 12–18 years (Mohan et al ., 1981 ) to 32.9% in 15–19-year-old individuals from rural settings (Mohan et al ., 1978b ); and at least once in the past 30 days (month) for which the prevalence ranged from 2.1% (Sharma et al ., 2015 ) in 15–19-year olds from disadvantaged urban settings and 35.6% in injectable drug users attending needle and syringe programme centres (Armstrong et al ., 2013 ). Some studies did not define current use and others used non-standard definition of current use such as ‘who had not used drugs either daily or weekly in the past month’ (27.6%) (Gupta et al ., 1987 ), and ‘habit of using alcohol, 3 days or more a week’ (0.8%) (Jayakrishnan et al ., 2016 ). The biggest countrywide survey of substance use in India reported a prevalence of current alcohol use to be 1.3% amongst those aged 10–17 years (Ambekar et al ., 2019 ).

Some studies reported the prevalence of AUDs and defined them using standardised tools (Alcohol Use Disorder Identification Test [AUDIT], CAGE questionnaire, Alcohol, Smoking and Substance Involvement Screening Test [ASSIST]), ICD 10 criteria or bespoke definitions. Among medical students (18–23 years) who were drinkers, the prevalence of hazardous drinking was 19.29% (Anandi et al ., 2018 ), alcohol dependence was 3.7–10% (Kundapur and Kodyalamoole, 2016 ; Haorongbam et al ., 2018 ), binge drinking 14–30% (Kundapur and Kodyalamoole, 2016 ; Anandi et al ., 2018 ) and ‘problem drinking’ (not defined) was 41.46% (Garg et al ., 2009 ). Among students of classes 8, 10 and 12 (12–19 years), 1.6% (2% males, 0% females) of lifetime users had alcohol dependence (Jaisoorya et al ., 2016 ). In adolescent street children (11–19 years), 37% had AUD defined as recurrent substance use resulting in one or more of the following occurring in 12 months: failure to fulfil major role obligations at work or home leads to a physically hazardous situation, or causes legal problems (Gaidhane et al ., 2008 ).

Patterns of drinking

Among drinkers, 0.6–10.4% consumed every day (Armstrong et al ., 2013 ; Jaisoorya et al ., 2016 ; Kundapur and Kodyalamoole, 2016 ), 19.1–40% consumed at least once a week (Armstrong et al ., 2013 ; Kundapur and Kodyalamoole, 2016 ), 3.8% consumed weekly (Jaisoorya et al ., 2016 ), 9.5% consumed less than once a week (Armstrong et al ., 2013 ) and 10.6% consumed monthly (Jaisoorya et al ., 2016 ) ( Table 3 ). Usual median number of drinks consumed among those between 13 and 17 years was 3.5 for both males and females (Gupta et al ., 2018 ). Among 10–19-year-old males from an urban slum over the past month, 54.2% consumed up to 50 ‘pegs’ of alcohol (Kokiwar and Jogdand, 2011 ). Among males from a low-income community, in those between 18 and 20 years, 88.2% were ‘low drinking’ (low amount/low frequency, low amount/moderate frequency or substantial amount/low frequency), 9.3% were moderate drinking (low amount/high frequency or substantial amount/moderate frequency) and 2.5% were high drinking (substantial amount/high frequency); and in those between 20 and 24 years, 82.6% were low drinking, 13.5% were moderate drinking and 3.8% were high drinking (Singh et al ., 2010 ).

Initiation of, attitudes towards, patterns of and correlates of drinking

Initiation age

The mean age for initiation of drinking ranged from 14.4 to 18.3 years ( Table 3 ). The mean age of initiation was significantly lower in rural areas compared to urban areas [10.66 ( s.d. 4.02) v . 12.5 ( s.d. 3.57); p  < 0.0001] (Nagendra and Koppad, 2017 ); and locally brewed alcohol [mean ( s.d. ) 11.09 (2.775)] was initiated at a younger age compared to commercially available alcohol in an industrial town [mean ( s.d. ) 13.90 (2.194)] (Mahanta et al ., 2016 ).

Among male substance use disorder patients at drug deaddiction centres, 41.3% had initiated alcohol use between 10 and 19 years (Bashir et al ., 2015 ). Among 22–23-year-old medical students, 25.6% had started consuming alcohol between 15 and 17 years, and 10.4% had started consuming alcohol before they were 15 years (Kundapur and Kodyalamoole, 2016 ).

In students between 18 and 22 years, 18.0% had initiated drinking between 10 and 14 years, 55.1% had initiated between 15 and 19 years, and 26.9% after 19 years (Mohanty et al ., 2013 ). Among medical and dental students, 4.26% initiated before 12 years, 19.15% initiated between 12 and 18 years, and 76.60% initiated after 18 years (Rathore et al ., 2015 ). Comparing males and females, 5.88% males ( v . 0% females) initiated before 12 years, 16.18% ( v . 26.92%) initiated between 12 and 18 years, and 77.94% ( v . 73.08%) initiated after 18 years (Rathore et al ., 2015 ). Finally, comparing urban and rural drinkers, 6.50% urban drinkers ( v . 6.10% rural) initiated before 8 years, 8.94% ( v . 10.98%) initiated between 9 and 10 years, 27.65% ( v . 39.02%) initiated between 11 and 12 years, 26.83% ( v . 30.49%) initiated between 12 and 14 years, 24.39% ( v . 10.98%) initiated between 15 and 16 years, and 5.69% ( v . 2.44%) initiated after 17 years (Kumar et al ., 2016 ).

Knowledge and attitudes

Overall, 55.3% of college-going students (17–21 years) believed that there was no risk of harmful effects of alcohol; with more females than males who believed that there was no risk (69.4% v . 43.4%); and a higher proportion from villages (64.4%) thought there was no risk as compared to those from towns (60.7%) or cities (50.0%) (Kalpana and Kavya, 2012 ) ( Table 3 ). Among medical students (22–23 years), 44% considered it safe to consume alcohol, and 88% believe drinking patterns are mood-dependent (Kundapur and Kodyalamoole, 2016 ).

In medical students (17–23 years), reasons for initiation of drinking included curiosity (19.6%), attending a party (17.5%), friends' influence (15.2%) and social gatherings (9.8%); and reasons for continued use included enjoyment (31.5%), as a coping mechanism for depressive symptoms (17.8%), socialisation (14.8%) and to take mind off other issues (9.6%) (Haorongbam et al ., 2018 ). Among college-going students (mean age 16.7 years; s.d. 0.5) there was a stronger endorsement of negative reinforcements (e.g. cognitive impairment, risk taking) than of possible positive reinforcements (e.g. sociability, tension reduction); and compared to males, significantly more females felt alcohol consumption could not reduce tension and endorsed increased sociability and cognitive impairment (Sandhya et al ., 2013 ). Knowledge of harm of alcohol among substance users was greater in adolescents from urban than rural areas (61.5% v . 30.8%) (Tsering et al ., 2010 ).

Risk factors/correlates

The cross-sectional nature of the studies only allowed the examination of correlates of alcohol use ( Table 3 ). Alcohol consumption was associated with being male (Medhi et al ., 2006 ; Mohanan et al ., 2014 ; Jaisoorya et al ., 2016 ; Kundapur and Kodyalamoole, 2016 ; Anandi et al ., 2018 ; Mandal et al ., 2019 ), older age (Medhi et al ., 2006 ; Rathore et al ., 2015 ; Jaisoorya et al ., 2016 ; Gupta et al ., 2018 ; Mandal et al ., 2019 ) and going to private rather than public schools (Jain et al ., 2012 ; Rani and Sathiyaskaran, 2013 ). Specifically for locally brewed alcohol, it was associated with younger age and rural residence (Mandal et al ., 2019 ). Alcohol consumption was associated with having a part-time job, and failing a subject or a year in school (Jaisoorya et al ., 2016 ).

Alcohol use in adolescents was associated with parental/guardian's use of alcohol or tobacco, lack of parental supervision, and not having ‘understanding’ parents (Rani and Sathiyaskaran, 2013 ; Mohanan et al ., 2014 ; Jayakrishnan et al ., 2016 ; Mandal et al ., 2019 ). Alcohol use decreased with a decrease in the frequency of friends sharing alcohol-related information on Facebook and YouTube; and increased frequency of sharing personal alcohol-related content on Twitter was associated with an increase in alcohol use (Gupta et al ., 2018 ). Alcohol consumption was also associated with close friends using substances (any type) or peer pressure to drink alcohol (Mandal et al ., 2019 ).

Alcohol consumption was associated with tobacco use, illicit drug use, attention deficit hyperactivity disorder (ADHD) symptoms, suicidal thinking, planning and attempts, and non-contact sexual abuse and perpetuation of violence (Nadkarni et al ., 2015 ; Jaisoorya et al ., 2016 ). Finally, higher acceptance of alcohol is associated with lower spirituality, less religiosity, less ‘God Consciousness’ and less formal religious practices (Sukhwal and Suman, 2013 ).

Quality of reporting studies

In 42 of the 57 studies, there was appropriate reporting of more than 70% of the 22 STROBE criteria ( Appendix B ). Only one study reported on all the 22 criteria (Nadkarni et al ., 2015 ). For 15 of the 22 criteria, there was appropriate reporting in more than 70% of the studies. The poorest reporting was about study biases, generalisability of the findings, and role of the funder.

The existing evidence base has several limitations which preclude a robust synthesis and any conclusions we draw are, at best, exploratory in nature. Although the information about AUDs is relatively limited, the prevalence among drinkers appears to be high, and the patterns of drinking in a reasonably high proportion were suggestive of risky drinking (heavy drinking that puts the drinker at risk of developing problems), especially considering that this is a young population with a relatively short drinking history.

This is consistent with the steady rise in recorded alcohol consumption in most developing countries, albeit from relatively low base prevalence rates. It also parallels the increases in adult per capita consumption of alcohol and heavy episodic drinking that have been observed in India and other developing economies in east Asia, south Asia and southeast Asia (Shield et al ., 2020 ). Amongst adolescents, the prevalence of current alcohol use in Sri Lanka was 3.4% (95% CI 2.6–4.3) (Senanayake et al ., 2018 ), lifetime alcohol use in males was 45% (26% risky drinking) in Pakistan (Shahzad et al ., 2020 ), alcohol use was reported by 19% from traditional non-alcohol using ethnic groups and 40% from traditional alcohol using ethnic groups in Nepal (Parajuli et al ., 2015 ), and 13% in Bhutan (Norbu and Perngparn, 2014 ).

The data about patterns of drinking observed among adolescents in India are inconclusive but there appears to be some tendency towards heavy drinking. Among adolescents across several countries, there are consistent reports of binge drinking as a social norm among peer groups (Russell-Bennett et al ., 2010 ). The prevalence of binge drinking increases from age 15–19 years to the age of 20–24 years, and among drinkers, binge drinking is higher among the 15–19 years age group compared with the total population of drinkers (World Health Organization, 2018 ). This means that 15–24-year-old current drinkers often drink in heavy drinking sessions, and hence, except for the Eastern Mediterranean Region, the prevalence of such drinking among drinkers is high in adolescents (around 45–55%) (World Health Organization, 2018 ).

In India, the age of initiation commonly was mid- to late-teens; and male gender, rural residence and locally brewed alcohol were associated with earlier initiation of drinking. Across most of the world, initiation of alcohol use among adolescents takes place at an early age, usually before the age of 15 years. Among 15-year-olds, there is a high prevalence of alcohol use (50–70%) during the past 30 days in many countries of the Americas, Europe and Western Pacific; and the prevalence is relatively lower in African countries (10–30%) (World Health Organization, 2018 ). However, across the world, there is a huge variation in alcohol use among boys and girls of 15 years of age and vary from 1.2% to 74.0% in boys and 0% to 73.0% in girls (World Health Organization, 2018 ). Finally, with the strategic targeting of adolescents as alcohol consumers by the industry, increasing overall population prevalence and normalisation of drinking alcohol, and the increasing normalisation by virtue of learning more about how adolescents in other countries drink, one could speculate that the age of initiation would reduce and prevalence of alcohol consumption in adolescents in India would rise, in the coming years.

In India, knowledge about alcohol and its potential harms was limited in rural areas. The reasons for starting and continuing drinking were a mix of expected enhancement of positive experiences and dampening of negative affect. This is consistent with findings in Indian adults where alcohol consumption was seen to be mainly associated with expectations about reduction in psychosocial stress and providing pleasure (Nadkarni et al ., 2013 ). Across the world, adolescents primarily report drinking for social motives or enjoyment – enjoyment (Argentina) (Jerez and Coviello, 1998 ), to make nights out more pleasurable (UK) (Plant et al ., 1990 ) and being social (Canada) (Kairouz et al ., 2002 ). Coping motives, on the other hand, are less common, but are associated with AUDs later in adulthood (Carpenter and Hasin, 1999 ). The difference in drinking motives between adolescents from India (a mix of pleasure and coping) and other countries (primarily pleasure), and the similarity between reasons given by Indian adolescents and Indian adults, possibly reflect contextual/cultural differences and will have implications on transferability of interventions from other contexts and wider age-applicability of interventions developed for adults in India.

We can broadly organise our findings about correlates for drinking into socio-demographic characteristics (e.g. age, gender), immediate environment (e.g. parents, friends, digital space) and clinical correlates (e.g. other substance use, suicidal thoughts). Risk and protective factors influencing the use of alcohol in adolescents are both proximal and distal factors and include individual cognitions and peer-influence risk factors (e.g. attitudes favourable to alcohol use and peer drinking), family environment (e.g. parental discipline and family bonding) and school context (e.g. academic commitment and achievement) (Bryant et al ., 2003 ; Fisher et al ., 2007 ; Patock-Peckham and Morgan-Lopez, 2010 ). Most commonly adolescent males drink more often than adolescent females, but there has been some blurring of the distinction between the genders in developed countries (Currie et al ., 2004 ; Hibell et al ., 2009 ). This convergence of drinking patterns is particularly seen in the Nordic countries, Ireland, the UK and the USA, and manifests as almost equal prevalence rates for consumption of spirits and similar frequency of intoxication for both genders (Hibell et al ., 2009 ). Evidence from South Asian countries indicates that male gender, age greater than 14 years, depression, religious beliefs, parental/family members' drinking, reduced parental supervision, peer-drinking/pressure/approval and urban neighbourhood are associated with adolescent drinking (Athauda et al ., 2020 ).

The most important study finding is that despite several studies over the years, the evidence base has several gaps, notably the limited geographical span, small sample sizes and heterogeneous definitions of alcohol use and AUDs. Of particular importance are the various sample selection strategies, especially for the smaller studies, which limit the generalisability of findings. Another gap is the lack of consistency in the measurement of alcohol use, which is especially critical in a context where ‘standard drink’ does not translate semantically or literally into the vernacular, and there is an immense variability in the types of alcoholic beverages (commercial, licit non-commercial, illicit home-brewed, adulterated alcoholic beverages) and in the type and size of vessels from which alcohol is poured or consumed in. Additionally, there were several gaps in the reporting of many studies which raise questions about their internal validity. In the absence of critical information such as data sources, measurement and statistical methods, it is difficult to draw an inference about the robustness of the studies which had inadequate reporting ( Appendix B ). Finally, although the cross-sectional design of the studies allows us to examine the prevalence of alcohol use and AUDs, it limits the conclusions that we can draw about causal relationships between the various potential risk factors and alcohol use/AUDs.

Although the included studies are not without limitations that are important to consider before drawing conclusions, this synthesis allows us to get a reasonable understanding of alcohol use among adolescents in India and derive preliminary conclusions that the prevalence is high and rising, which brings with it the attendant burden of the associated adverse impacts. Furthermore, despite the gaps in the available data, it carries several implications for policy makers. Because alcohol is an important cause of motor vehicle accidents and suicide, which are the leading causes of death among adolescents in India (Joshi et al ., 2017 ), interventions that seek to help adolescents avoid or better manage alcohol consumption are a priority. Examples of such evidence-based interventions include public health engagement campaigns to increase awareness of alcohol-related harms, advocacy through community engagement/mobilisation to promote better enforcement of laws related to drinking, engagement with alcohol outlets to promote responsible beverage service, and engaging adolescents and families including through peer-led classroom curriculum to enhance the resilience of adolescents, improve family socialisation and increase awareness of alcohol-related harms (McLeroy et al ., 2003 ; Hawkins et al ., 2008 ; Wakefield, Loken, and Hornik, 2010 ; Hallgren and Andréasson, 2013 ). The most important implication of our review, however, is the need to develop the very nascent literature base through robust studies, especially longitudinal research that can support evidence-based prevention interventions and policy change. Future studies should focus on increasing their geographical span and sample sizes, ensure the use of standard definitions of alcohol use and AUDs which are consistent with global literature, and acknowledge and examine contextual variations in types of alcoholic beverages and type and size of vessels from which alcohol is poured or consumed in. Introducing such measures will enhance the robustness, validity and generalisability of the findings; and allow for better comparisons over time and geography. This would require greater support from the Government through ensuring availability of in-country research funding, prioritisation of the issue and utilisation of the evidence generated to inform its policy on alcohol.

Our review is limited by our inclusion criterion related to language. However, this might not be a major limitation considering that peer-reviewed journals in India are only in English as far as we are aware, and researchers generally disseminate their outputs in English language journals. Our review's major strength lies in its originality (the first such review to comprehensively map the landscape of substance use among adolescents in India), use of robust processes (e.g. double screening) and examination of grey literature to identify any relevant evidence.

To conclude, the evidence base for alcohol use amongst adolescents in India needs further and deeper exploration, but in the meanwhile, the available evidence allows us to get a preliminary understanding of the issue and to make a case for policy action to tackle alcohol consumption in this age group.

Acknowledgements

We would like to acknowledge Professor Pratima Murthy, Professor Vivek Benegal and Professor Atul Ambekar for helping us identify relevant grey literature.

Appendix A: Search strategy

• disorders.tw
• withdraw*.tw
• withdrawal syndrome.tw
• withdrawal syndrome/
• screening.tw
• overdose.tw
• megadose.tw
• dependen*.tw
• intoxication.tw
• intoxication/
• behavior.tw
• alcoholi*.tw
• delirium.tw
• binge drink*.tw
• binge drink*/
• consumption.tw
• consumption/
• cessation.tw
• OR (1–50)
• Substance.tw
• ‘purple drank’.tw
• ‘purple drank’/
• unclassified drug.tw
• unclassified drug/
• chlorobenzoic acid derivative.tw
• chlorobenzoic acid derivative/
• methadone.tw
• morphine.tw
• buprenorphine.tw
• buprenorphine/
• diamorphine.tw
• diamorphine/
• amphetamine.tw
• amphetamine/
• amphetamine derivative.tw
• amphetamine derivative/
• cigarette.tw
• electronic cigarette.tw
• electronic cigarette/
• benzodiazepine derivative.tw
• benzodiazepine derivative/
• benzodiazepine.tw
• benzodiazepine/
• cannabi*.tw
• ‘brown sugar’.tw
• ‘brown sugar’/
• medical cannabi*.tw
• medical cannabi*/
• tetrahydrocannabinol.tw
• tetrahydrocannabinol/
• cocaine derivative.tw
• cocaine derivative/
• chlorpheniramine.tw
• chlorpheniramine/
• ‘cough syrup’.tw
• ‘cough syrup’/
• dexamphetamine.tw
• dexamphetamine/
• dextromethorphan.tw
• dextromethorphan/
• 3,4 methylenedioxyamphetamine.tw
• 3,4 methylenedioxyamphetamine/
• psychedelic agent.tw
• psychedelic agent/
• 4 aminobutyric acid.tw
• 4 aminobutyric acid/
• 4 hydroxybutyric acid.tw
• 4 hydroxybutyric acid/
• Ketamine.tw
• nicotine.tw
• inhalant.tw
• kava extract.tw
• kava extract/
• smokeless tobacco.tw
• smokeless tobacco/
• laughing gas.tw
• laughing gas/
• nitrous oxide.tw
• nitrous oxide/
• Lysergic acid diethylamide.tw
• Lysergic acid diethylamide/
• magic mushroom.tw
• magic mushroom/
• hallucinogenic fungus.tw
• hallucinogenic fungus/
• mari#uana.tw
• Midomafetamine.tw
• Midomafetamine/
• methamphetamine.tw
• Methamphetamine/
• Crystal meth.tw
• Crystal meth/
• Amobarbital.tw
• Amobarbital/
• Methylphenidate.tw
• Methylphenidate/
• Modafinil.tw
• Morphine.tw
• ‘paint thinner’.tw
• ‘paint thinner’/
• promethazine.tw
• promethazine/
• psilocybin#.tw
• psilocybin#/
• Quaalude.tw
• Methaqualone.tw
• Methaqualone/
• Salvia divinorum.tw
• Salvia divinorum/
• Psychotropic agent.tw
• Psychotropic agent/
• Chewing tobacco.tw
• Chewing tobacco/
• Tramadol.tw
• Sildenafil.tw
• Sildenafil/
• Eszopiclone.tw
• Eszopiclone/
• Zaleplon.tw
• Zoipidem.tw
• Zopiclone.tw
• Hypnotic agent.tw
• Hypnotic agent/
• Prescription durg.tw
• Prescription drug/
• Prescription medicine.tw
• Prescription medicine/
• Prescription medication.tw
• Prescription medication/
• OR (52-255)
• adolescen*.tw
• adolescen*/
• student*.tw
• young adult*.tw
• young adult*/
• OR (257-274)
• ‘Indian union’.tw
• ‘Indian union’/
• Andaman and Nicobar Island*.tw
• Andaman and Nicobar Island/
• Andhra Pradesh.tw
• Andhra Pradesh/
• Arunachal Pradesh.tw
• Arunachal Pradesh/
• Dadra and Nagar Haveli.tw
• Dadra and Nagar Haveli/
• Chhattisgarh.tw
• Chhattisgarh/
• Daman and Diu.tw
• Daman and Diu/
• National Capital Territory of New Delhi.tw
• National Capital Territory of New Delhi/
• Himachal Pradesh.tw
• Himachal Pradesh/
• Jammu and Kashmir.tw
• Janmu and Kashmir/
• Jharkhand.tw
• Karnataka.tw
• Travancore-Cochin.tw
• Travancore-Cochin/
• Madhya Pradesh.tw
• Madhya Pradesh/
• Madhya Bharat.tw
• Madhya Bharat/
• Maharashtra.tw
• Maharashtra/
• Meghalaya.tw
• Nagaland.tw
• Chandigarh.tw
• Chandigarh/
• Rajasthan.tw
• Tamil Nadu.tw
• Tamil Nadu/
• Madras State.tw
• Madras State/
• Telangana.tw
• Uttarakhand.tw
• Uttarakhand/
• Uttaranchal.tw
• Uttaranchal/
• Uttar Pradesh.tw
• Uttar Pradesh/
• United Provinces.tw
• United Provinces/
• West Bengal.tw
• West Bengal/
• Lakshadweep.tw
• Lakshadweep/
• P#d#cherry.tw
• P#d#cherry/
• OR (276–375)
• 51 AND 256 AND

Appendix B: Quality of reporting of peer-reviewed studies included in the review (excluding the report)

Supplementary material, financial support.

This research did not receive any specific grant from funding agencies in the public, commercial or not-for-profit sectors.

Conflict of interest

There are no real or perceived conflicts of interest in undertaking or publishing this research.

Ethical standards

As this is a systematic review, it did not involve any direct data collection from human subjects.

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