BACKGROUND: The relationship between the alcohol policy environment (ie, the combined effectiveness and implementation of multiple existing alcohol policies) and youth drinking in the United States has not been assessed. We hypothesized that stronger alcohol policy environments are inversely associated with youth drinking, and this relationship is partly explained by adult drinking.
METHODS: Alcohol Policy Scale (APS) scores that characterized the strength of the state-level alcohol policy environments were assessed with repeated cross-sectional Youth Risk Behavior Survey data of representative samples of high school students in grades 9 to 12, from biennial years between 1999 and 2011.
RESULTS: In fully adjusted models, a 10 percentage point increase in APS scores (representing stronger policy environments) was associated with an 8% reduction in the odds of youth drinking and a 7% reduction in the odds of youth binge drinking. After we accounted for youth-oriented alcohol policies, the subgroup of population-oriented policies was independently associated with lower odds of youth drinking (adjusted odds ratio 0.94; 95% confidence interval 0.92–0.97) and youth binge drinking (adjusted odds ratio 0.96; 95% confidence interval 0.94–0.99). State-level per capita consumption mediated the relationship between population-oriented alcohol policies and binge drinking among youth.
CONCLUSIONS: Stronger alcohol policies, including those that do not target youth specifically, are related to a reduced likelihood of youth alcohol consumption. These findings suggest that efforts to reduce youth drinking should incorporate population-based policies to reduce excessive drinking among adults as part of a comprehensive approach to preventing alcohol-related harms. Future research should examine influence of alcohol policy subgroups and discrete policies.
- AEDS —
- Alcohol Epidemiologic Data System
- AOR —
- adjusted odds ratio
- APS —
- Alcohol Policy Scale
- CI —
- confidence interval
- ER —
- efficacy rating
- IR —
- implementation rating
- YRBS —
- Youth Risk Behavior Surveys
What’s Known on This Subject:
Youth drinking is associated with adult drinking. Alcohol policies can influence youth and adult drinking. However, it is unknown whether alcohol policies influence youth drinking patterns directly or through their effect on adult drinking.
What This Study Adds:
Alcohol policies, including population-oriented policies, are protective for youth drinking. The effect of population-oriented policies may be mediated though effects on adults. These findings suggest that efforts to reduce youth drinking should rely on policies that address all age groups.
Alcohol is the most commonly used drug among adolescents in the United States and is responsible for >4300 deaths annually among those ≤20 year of age.1–3 Alcohol use is also a contributor to the leading causes of death among people aged 10 to 20 years and is a risk factor for a number of acute and long-term health, developmental, and social problems.3–5 Although underage youth (ie, those ≤20 years of age) may drink less often than adults, they typically drink larger quantities than adults when they do drink, and youth aged 12 to 20 years consume approximately two-thirds of their alcohol during binge drinking occasions.6,7
Alcohol policies are a critical means by which to reduce underage drinking and related harms.5,8–10 Although most policies to reduce youth drinking are youth-specific,9 some population-oriented policies (ie, those that are not youth-specific) such as alcohol taxes have been shown to reduce underage drinking and binge drinking.11–13 In addition, the Institute of Medicine has concluded that “it is possible that the most effective way to reduce the extent and adverse consequences of youthful drinking would be to reduce the extent and consequences of adult drinking.”5
However, the relationship between most population-oriented policies and youth drinking has not been assessed, and if there is a relationship it is not clear whether it is due to the effects of those policies on adult consumption. This is plausible because adults model drinking behavior for underage youth,14–17 shape alcohol expectancies in which choices about alcohol consumption are made by youth,18,19 and supply most of the alcoholic beverages consumed by youth.20,21 In addition, youth alcohol consumption and drinking-related behaviors occur in a social context that is related to adult behaviors and other social determinants.17,22,23 Finally, there is evidence that the relationship between alcohol taxes and youth drinking is partly mediated through changes in adult binge drinking.13
Most policy studies have examined the effects of discrete alcohol policies.24–28 However, all states use multiple alcohol policies, which may result in combined or interactive effects,29,30 and this overall alcohol policy environment varies widely in US states.24 Our research team recently developed a scale to measure the policy environment in US states and Washington, DC. Alcohol Policy Scale (APS) scores, with higher scores representing stronger mixes of alcohol policies, demonstrate good construct validity to predict lower odds of binge drinking and alcohol-impaired driving among adults.29,31,32
To date, no study has assessed the relationship between the alcohol policy environment and youth drinking in the United States, nor the independent relationships between population-oriented versus youth-oriented policies and youth drinking. As shown in Fig 1, the objectives of this study were to assess the relationship of the alcohol policy environments with youth drinking and youth binge drinking, determine the independent relationships between population-oriented policies and youth-oriented policies and youth drinking behaviors, and examine whether adult drinking mediates the relationship between population-oriented policies and youth drinking.
Policy Data Sources
All policy data sources used uniform ascertainment methods across all states.31 The primary source for 13 of the 29 policies was the Alcohol Policy Information System, which has tracked selected alcohol policies since 1999.33 Eighteen additional data sources were used to collect and code data about policies and their key provisions.31 When multiple data sources were available for some policies, sources were cross-checked and discrepancies were resolved by a public health lawyer using the legal research database WestlawNext.34
Determining APS Scores
A panel of 10 alcohol policy experts was engaged to assist with 3 tasks: nominating and selecting existing alcohol policies, rating the relative efficacy of those policies, and developing implementation ratings for each policy.31
Forty-seven alcohol control policies were initially nominated as effective by panelists.35 We excluded policies that did not exist in the United States, were promulgated at the federal level, did not vary between states, or lacked reliable cross-state data. Examples of excluded policies were blood alcohol content 0.05 laws (do not exist in the United States), restrictions on mass media advertising (promulgated federally), public intoxication laws (all states prohibit public intoxication), and mandatory substance abuse assessment for driving under the influence offenders (no reliable cross-state data). The 8 policies excluded because of inadequate or missing data had low average efficacy ratings.35 Ultimately, 29 policies met inclusion criteria.31
An efficacy rating (ER) was developed for each policy. Based on standardized descriptions, panelists independently rated the efficacy of each policy in 4 domains: reducing binge drinking among adults, reducing impaired driving among adults, reducing drinking among underage youth, and reducing drinking and driving among youth. Because this was a study of youth, ERs for reducing drinking among youth were used for this study.
In consultation with panelists, investigators also developed a legislative implementation rating (IR) for each policy.31 Factors informing policy IRs were typically based on its statutory design (ie, provisions making the policy broadly applicable, effective, or enforceable). IR scales were reviewed by all panelists and revised by investigators after they reviewed the feedback. For each policy, the IR scale score, by state and year, could range from 0.0 (no policy) to 1.0 (full implementation). Although IR scores could vary by state-year, the scoring criteria applied to each policy were uniform across state-years.
Using Policy Data to Calculate APS Scores
Five methods were used to calculate APS scores for each of the 50 US states and Washington, DC from 1999 to 2011. Method 1 involved adding 1 point for each existing policy, method 2 involved summing ERs for each existing policy, method 3 involved summing IRs for each existing policy, method 4 involved summing the products of ERs and IRs for each existing policy, and method 5 involved summing the products between of ERs and IRs for each existing policy by using the inverse of the ER rank relative to other policies. We rescaled all APS scores into standardized ranges by dividing each APS score over the maximum possible APS score of the particular method and multiplying by 100, allowing scores from each method to be compared with the other methods.
Youth- Versus Population-Oriented Policy Subgroups
Alcohol policies were divided into 2 mutually exclusive groups: youth-oriented policies (n = 10) and population-oriented policies (n = 19).29 Population-oriented policies were defined as policies that are not designed primarily for people under the legal drinking age, whereas youth-oriented policies were those that are designed specifically for people ≤20 years of age.
Youth Drinking Data Source, Measures
Youth drinking data were obtained from biennial state-based Youth Risk Behavior Surveys (YRBS) from 1999 to 2011 (7 survey years). YRBS is a school-based survey developed by the Centers for Disease Control and Prevention that has assessed health risk behaviors among a representative sample of schools and students in 9th through 12th grades since 1991. Over the life of the survey YRBS has used nearly identical methods and questions, and the survey has been validated, including with respect to alcohol questions.36,37 Data from states with response rates ≥60% are cleaned and weighted to be representative of that state’s population of students in grades 9 through 12. For the study period from 1999 through 2011, the total number of states with weighted data (which were used for analyses) ranged from 20 to 44 states per survey year and included a total of 637 106 respondents from 238 state-year strata. Alcohol consumption was defined as consumption of ≥1 drinks of alcohol during the past 30 days; binge drinking was defined as reporting ≥1 days of consuming ≥5 drinks “in a row” within the past 30 days. The reliability kappas for these 2 items (70.9 for youth drinking; 67.6 for youth binge drinking) were calculated previously with the 1999 data and were considered substantial.36,37
State- and Individual-Level Covariates, State per Capita Alcohol Consumption
State-level covariates included proportions of the population aged ≥21 years, gender, race or ethnicity (non-Hispanic white, non-Hispanic black, Hispanic, and other race), level of urbanization, median household income, religiosity (proportion Catholic), number of police officers per capita, and geographic region (Northeast, Midwest, South, and West); year was modeled as a categorical variable. Individual-level covariates included age, gender, race or ethnicity, and past-month tobacco use.
State adult per capita alcohol consumption was obtained from the Alcohol Epidemiologic Data System (AEDS), sponsored by the federal government.38 AEDS extracts the volume of each beverage type (beer, spirits, wine) annually in each state, from complete sales and tax receipts reports or alcohol shipment data, and converts these data into gallons of ethanol. Per capita consumption was determined based on the population aged ≥21 years of age for each state and year based on US Census data. Per capita consumption from AEDS is highly correlated with adult alcohol consumption, based on population-based surveys.39
APS scores from each of the 5 calculation methods were used to predict the same-year prevalence of youth drinking and binge drinking in states; in subsequent analyses APS scores from the method with the highest goodness of fit (method 3, P < .0001, R2 = 0.29 for both youth drinking and binge drinking; see the Appendix) were used to predict the odds of individual-level youth drinking and youth binge drinking in the same state-year. Individual-level odds ratios were based on an absolute 10 percentage point increase of APS scores using generalized estimating equations models adjusting for YRBS weights with multiple-year adjustment and the clustering of individuals within sampling units. Because Utah may be considered an outlier with respect to alcohol consumption, we conducted a sensitivity analysis excluding Utah. Additional analyses were stratified on the basis of gender, grade, and race or ethnicity and on the basis of policy subgroup (youth-oriented vs population-oriented policies). Finally, mediation analyses were conducted to determine whether per capita alcohol consumption explained the association between population-oriented policies and youth drinking measures.40 Sobel’s SE approximation was used to test the significance of the mediating variable effect.41
Relationship Between APS Scores and Youth Drinking
In bivariate analyses, a 10 percentage point increase in the APS score was associated with >10% lower odds of drinking and binge drinking (Table 1). In fully adjusted models accounting for state- and individual-level covariates and year, a 10 percentage point increase in the APS score was associated with reduced odds of youth drinking (adjusted odds ratio [AOR] 0.92; 95% confidence interval [CI], 0.90–0.95) and youth binge drinking (AOR 0.93; 95% CI, 0.91–0.96). In a sensitivity analysis that excluded Utah from the fully adjusted model, results were similar and remained significant.
Among demographic subgroups, reduced odds of drinking and binge drinking were similar and significant on the basis of gender, grade level, and among non-Hispanic white and Hispanic youth (Table 2). For non-Hispanic black youth and non-Hispanic other races, however, there was no significant relationship between APS scores and either drinking or binge drinking.
Independent Effects of Population-Oriented Versus Youth-Oriented Policy Subgroups
Across the 238 state-year strata, the correlation between population-oriented APS scores and youth-oriented APS scores was r = 0.51 (P < .01). In adjusted models for individual-level drinking measures, there were significant inverse associations between the population-oriented policy subgroup and the odds of youth drinking and binge drinking, and also between the youth-oriented policy subgroup and youth drinking and binge drinking (Table 3). In a model that also controlled for the youth-oriented policy subgroup, the population-oriented policies subgroup was independently inversely associated with youth drinking (AOR 0.94; 95% CI, 0.92–0.97) and youth binge drinking (AOR 0.96; 95% CI, 0.94–0.99). The youth-oriented policy subgroup was also independently inversely associated with youth drinking (AOR 0.98; 95% CI, 0.96–0.99) and binge drinking (AOR 0.97; 95% CI, 0.95–0.99) after we controlled for the population-oriented policy subgroup.
Adult Consumption as Mediator of Population-Oriented Policies and Youth Drinking Relationship
Across the 238 state-year strata, general population-oriented policies were significantly inversely associated with state adult per capita alcohol consumption (r = −0.46, P < .01), and adult per capita consumption was positively correlated with youth drinking prevalence (r = 0.46, P < .01) and binge drinking prevalence (r = 0.49, P < .01). For example, Fig 2 shows the correlations of the overall policy scale (n = 29 policies) and the 2 policy subgroups with youth drinking prevalence in 2005 (the study midpoint). Because population-oriented policies were significantly inversely associated with youth drinking and binge drinking for individual-level analysis (Table 3), we assessed adult per capita consumption as a possible mediator of the relationship between population-oriented policies and youth drinking measures. After we controlled for adult per capita consumption, the adjusted association between the population-oriented policies and youth drinking was partially mediated (AOR 0.96; 95% CI, 0.93–0.98; Sobel test statistic = −5.93, P < .001), and the association between population-oriented policies and youth binge drinking was fully mediated (AOR 0.97; 95% CI, 0.95–1.00; Sobel test statistic = −4.80, P < .001).
This is the first study to assess the relationship between the alcohol policy environment (based on 29 expert-nominated policies) and alcohol consumption among high school students in the United States. We found strong inverse relationships between state alcohol policies and the odds of individual-level youth drinking and binge drinking. Specifically, a 10 percentage point increase in APS score (representing stronger policy environments) was associated with an 8% reduction in the odds of youth drinking and a 7% reduction in the odds of binge drinking in fully adjusted models. This finding adds to the literature by demonstrating the effect size associated with the multiple alcohol policies that exist in all states.
In an international context, the Alcohol Policy Index developed by Brand et al,42 which weighted policies based on their efficacy and implementation, was inversely related to per capita alcohol consumption in 30 countries. Paschall et al11 found an inverse relationship between Brand’s policy index and national youth drinking prevalence. Based on Brand’s index, Gilligan et al43 found a significant inverse association between higher alcohol prices and other policies with weekly drinking among youth but no significant relationship with drinking to the point of subjective intoxication.
We also found an inverse relationship between population-oriented policies (ie, those not specifically targeting youth) and youth drinking measures, even after we controlled for youth-oriented policies. This finding supports the literature that some population-oriented policies (eg, increased alcohol taxation, reduced alcohol outlet density) are associated with a reduced likelihood of youth drinking.17,22,44 Accounting for youth-oriented policies when examining the effect of population-oriented policies also adds to previous research by minimizing confounding stemming from the fact that states with stronger population-oriented alcohol policies also tend to have stronger youth-oriented policies.
We also found evidence that adult drinking mediates the relationship between population-oriented policies and youth drinking. This finding is consistent with our previous work demonstrating that the relationship between state alcohol taxes (which do not target youth) and youth drinking is partly mediated through reduced adult binge drinking.13 Therefore, it is likely that there are both direct and indirect pathways by which population-oriented alcohol policies may influence youth consumption. Overall, these findings lend empirical support to the Institute of Medicine’s judgment that parents and adults must be a key target of strategies to reduce and prevent underage drinking.5
The effect magnitude of the APS for reducing the odds of youth binge drinking was similar to that for reducing the odds of adult binge drinking.29 For youth drinking measures, however, APS score calculation methods that incorporated policy efficacy ratings (in addition to implementation ratings) did not improve goodness of fit, whereas incorporating both efficacy and implementation ratings improved goodness of fit to predict adult binge drinking.31 One possible explanation is that panelists’ policy efficacy ratings for adults had greater construct validity than their efficacy ratings for youth. Another possibility is that effects of youth-oriented policies in some studies may be confounded by the presence or subsequent adoption of population-oriented policies that might overestimate the effect of youth-oriented policies.
Relationships between APS scores and youth drinking measures were similar between gender and between grade categories but inconsistent between race and ethnicity categories, a finding that is similar to our previous work exploring relationships between APS scores and adult binge drinking.29 In both instances there were no statistically significant relationships between respondents who were non-Hispanic black and of non-Hispanic other races. Of note, black adults and youth both have a lower prevalence of drinking and binge drinking compared with other racial and ethnic groups,29,31 and they may be subject to other state- or individual-level influences for which we did not account.
Despite controlling for unrestricted time effects in generalized estimating equations models, these analyses are largely cross-sectional. However, many of the policies included in the APS scales have evidence of effectiveness based on longitudinal studies,28,44–47 which reduces concern about reverse causation. Although we accounted for a wide range of relevant state- and individual-level covariates, confounding remains a potential threat to validity. The efficacy and implementation ratings developed by the investigators and panelists were informed by a limited evidence base and are therefore partly subjective.48 Furthermore, policies that are promulgated at the federal or local levels, those that did not vary between states, and those with insufficient data were not incorporated in the APS scores. Therefore, it is possible that our APS–youth drinking effect sizes underestimate the impact of all alcohol policies within states.
Enforcement efforts contribute to the effectiveness of some policies,49 but there are no publicly available cross-state data about enforcement for most policies. Overall, limitations related to the imprecision of the policy exposure variables may have biased the results toward the null hypothesis. Finally, the YRBS samples include high-school aged youth who attend school and are therefore not representative of all underage youth, especially those 19 to 20 years of age.
State alcohol policies, including those that do not target youth specifically, are inversely associated with youth drinking and binge drinking. These findings are relevant to policy debates about the best way to prevent and reduce underage drinking, and about the importance of comprehensive strategies that also include policies to reduce excessive drinking among adults. To the extent that alcohol use and intoxication at young ages are strong predictors of alcohol-related problems in adulthood,50–52 the findings are also relevant to optimizing strategies to reduce alcohol-related health and social costs among the general population. Future work should better evaluate the independent effect of additional policy subgroups (eg, those affecting price, physical availability of alcohol) and discrete policies on youth drinking measures. Among adults, we found that 2 policies (state taxes and alcohol outlet density) accounted for almost half of the effects size based on all assessed policies.29
We acknowledge the contributions of the following people, who served as alcohol policy consultants for this project: Thomas Babor, PhD, Robert Brewer, MD, MSPH, Frank Chaloupka, PhD, Paul Gruenewald, PhD, Harold Holder, PhD, Michael Klitzner, PhD, James Mosher, JD, Rebecca Ramirez, MPH, Robert Reynolds, MA, and Traci Toomey, PhD.
We thank Chetna Mehrotra Naimi for her love, support and contributions to the development and completion of this project.
- Accepted April 1, 2015.
- Address correspondence to Timothy S. Naimi, MD, MPH, Section of General Internal Medicine, Boston Medical Center, 801 Massachusetts Ave, Boston, MA 02118. E-mail:
Dr Xuan helped design the study, supervised statistical analysis, interpreted data and results, and drafted and revised the manuscript; Mr Blanchette coordinated data collection on alcohol policies, contributed to the development of the Alcohol Policy Scale, conducted statistical analyses, interpreted data and results, and made critical revisions of the manuscript for important intellectual content; Dr Nelson contributed to conception and design of the study, contributed to interpretation of data results, and made critical revisions of the manuscript for important intellectual content; Ms Nguyen collected data, conducted statistical analyses, and made critical revisions of the manuscript for important intellectual content; Dr Hadland contributed to the analytic design and interpretation of data and revised the manuscript for important intellectual content; Ms Oussayef contributed to the acquisition and interpretation of alcohol policy data and made critical revisions of the manuscript for important intellectual content; Dr Heeren contributed to study conceptualization and design, interpreted data and results, and made critical revisions of the manuscript for important intellectual content; Dr Naimi conceptualized and designed the study, supervised the study, interpreted data and results, and drafted and revised the manuscript; and all authors approved the final manuscript as submitted.
FINANCIAL DISCLOSURE: The authors have indicated they have no financial relationships relevant to this article to disclose.
FUNDING: Supported by National Institutes of Health (NIH) grant AA018377. The content and views expressed in this manuscript are those of the coauthors and do not necessarily represent those of the National Institute on Alcohol Abuse and Alcoholism or the NIH. Dr Hadland is supported by NIH grants T71 MC00009 (MCH/Health Resources and Services Administration) and 1T32 HD075727 (NIH/National Institute of Child Health and Human Development). Funded by the National Institutes of Health (NIH).
POTENTIAL CONFLICT OF INTEREST: The authors have indicated they have no potential conflicts of interest to disclose.
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