research paper on alcohol addiction

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• Published: 13 November 2019

Evidence-based models of care for the treatment of alcohol use disorder in primary health care settings: protocol for systematic review

• Susan A. Rombouts 1 ,
• James Conigrave 2 ,
• Eva Louie 1 ,
• Paul Haber 1 , 3 &
• Kirsten C. Morley   ORCID: orcid.org/0000-0002-0868-9928 1  

Systematic Reviews volume  8 , Article number:  275 ( 2019 ) Cite this article

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Alcohol use disorder (AUD) is highly prevalent and accounts globally for 1.6% of disability-adjusted life years (DALYs) among females and 6.0% of DALYs among males. Effective treatments for AUDs are available but are not commonly practiced in primary health care. Furthermore, referral to specialized care is often not successful and patients that do seek treatment are likely to have developed more severe dependence. A more cost-efficient health care model is to treat less severe AUD in a primary care setting before the onset of greater dependence severity. Few models of care for the management of AUD in primary health care have been developed and with limited implementation. This proposed systematic review will synthesize and evaluate differential models of care for the management of AUD in primary health care settings.

We will conduct a systematic review to synthesize studies that evaluate the effectiveness of models of care in the treatment of AUD in primary health care. A comprehensive search approach will be conducted using the following databases; MEDLINE (1946 to present), PsycINFO (1806 to present), Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials (CENTRAL) (1991 to present), and Embase (1947 to present).

Reference searches of relevant reviews and articles will be conducted. Similarly, a gray literature search will be done with the help of Google and the gray matter tool which is a checklist of health-related sites organized by topic. Two researchers will independently review all titles and abstracts followed by full-text review for inclusion. The planned method of extracting data from articles and the critical appraisal will also be done in duplicate. For the critical appraisal, the Cochrane risk of bias tool 2.0 will be used.

This systematic review and meta-analysis aims to guide improvement of design and implementation of evidence-based models of care for the treatment of alcohol use disorder in primary health care settings. The evidence will define which models are most promising and will guide further research.

Protocol registration number

PROSPERO CRD42019120293.

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It is well recognized that alcohol use disorders (AUD) have a damaging impact on the health of the population. According to the World Health Organization (WHO), 5.3% of all global deaths were attributable to alcohol consumption in 2016 [ 1 ]. The 2016 Global Burden of Disease Study reported that alcohol use led to 1.6% (95% uncertainty interval [UI] 1.4–2.0) of total DALYs globally among females and 6.0% (5.4–6.7) among males, resulting in alcohol use being the seventh leading risk factor for both premature death and disability-adjusted life years (DALYs) [ 2 ]. Among people aged 15–49 years, alcohol use was the leading risk factor for mortality and disability with 8.9% (95% UI 7.8–9.9) of all attributable DALYs for men and 2.3% (2.0–2.6) for women [ 2 ]. AUD has been linked to many physical and mental health complications, such as coronary heart disease, liver cirrhosis, a variety of cancers, depression, anxiety, and dementia [ 2 , 3 ]. Despite the high morbidity and mortality rate associated with hazardous alcohol use, the global prevalence of alcohol use disorders among persons aged above 15 years in 2016 was stated to be 5.1% (2.5% considered as harmful use and 2.6% as severe AUD), with the highest prevalence in the European and American region (8.8% and 8.2%, respectively) [ 1 ].

Effective and safe treatment for AUD is available through psychosocial and/or pharmacological interventions yet is not often received and is not commonly practiced in primary health care. While a recent European study reported 8.7% prevalence of alcohol dependence in primary health care populations [ 4 ], the vast majority of patients do not receive the professional treatment needed, with only 1 in 5 patients with alcohol dependence receiving any formal treatment [ 4 ]. In Australia, it is estimated that only 3% of individuals with AUD receive approved pharmacotherapy for the disorder [ 5 , 6 ]. Recognition of AUD in general practice uncommonly leads to treatment before severe medical and social disintegration [ 7 ]. Referral to specialized care is often not successful, and those patients that do seek treatment are likely to have more severe dependence with higher levels of alcohol use and concurrent mental and physical comorbidity [ 4 ].

Identifying and treating early stage AUDs in primary care settings can prevent condition worsening. This may reduce the need for more complex and more expensive specialized care. The high prevalence of AUD in primary health care and the chronic relapsing character of AUD make primary care a suitable and important location for implementing evidence-based interventions. Successful implementation of treatment models requires overcoming multiple barriers. Qualitative studies have identified several of those barriers such as limited time, limited organizational capacity, fear of losing patients, and physicians feeling incompetent in treating AUD [ 8 , 9 , 10 ]. Additionally, a recent systematic review revealed that diagnostic sensitivity of primary care physicians in the identification of AUD was 41.7% and that only in 27.3% alcohol problems were recorded correctly in primary care records [ 11 ].

Several models for primary care have been created to increase identification and treatment of patients with AUD. Of those, the model, screening, brief interventions, and referral to specialized treatment for people with severe AUD (SBIRT [ 12 ]) is most well-known. Multiple systematic reviews exist, confirming its effectiveness [ 13 , 14 , 15 ], although implementation in primary care has been inadequate. Moreover, most studies have looked primarily at SBIRT for the treatment of less severe AUD [ 16 ]. In the treatment of severe AUD, efficacy of SBIRT is limited [ 16 ]. Additionally, many patient referred to specialized care often do not attend as they encounter numerous difficulties in health care systems including stigmatization, costs, lack of information about existing treatments, and lack of non-abstinence-treatment goals [ 7 ]. An effective model of care for improved management of AUD that can be efficiently implemented in primary care settings is required.

Review objective

This proposed systematic review will synthesize and evaluate differential models of care for the management of AUD in primary health care settings. We aim to evaluate the effectiveness of the models of care in increasing engagement and reducing alcohol consumption.

By providing this overview, we aim to guide improvement of design and implementation of evidence-based models of care for the treatment of alcohol use disorder in primary health care settings.

The systematic review is registered in PROSPERO international prospective register of systematic reviews (CRD42019120293) and the current protocol has been written according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols (PRISMA-P) recommended for systematic reviews [ 17 ]. A PRISMA-P checklist is included as Additional file  1 .

Eligibility criteria

Criteria for considering studies for this review are classified by the following:

Study design

Both individualized and cluster randomized trials will be included. Masking of patients and/or physicians is not an inclusion criterion as it is often hard to accomplish in these types of studies.

Patients in primary health care who are identified (using screening tools or by primary health care physician) as suffering from AUD (from mild to severe) or hazardous alcohol drinking habits (e.g., comorbidity, concurrent medication use). Eligible patients need to have had formal assessment of AUD with diagnostic tools such as Diagnostic and Statistical Manual of Mental Disorders (DSM-IV/V) or the International Statistical Classification of Diseases and Related Health Problems (ICD-10) and/or formal assessment of hazardous alcohol use assessed by the Comorbidity Alcohol Risk Evaluation Tool (CARET) or the Alcohol Use Disorders Identification test (AUDIT) and/or alcohol use exceeding guideline recommendations to reduce health risks (e.g., US dietary guideline (2015–2020) specifies excessive drinking for women as ≥ 4 standard drinks (SD) on any day and/or ≥ 8 SD per week and for men ≥ 5 SD on any day and/or ≥ 15 SD per week).

Studies evaluating models of care for additional diseases (e.g., other dependencies/mental health) other than AUD are included when they have conducted data analysis on the alcohol use disorder patient data separately or when 80% or more of the included patients have AUD.

Intervention

The intervention should consist of a model of care; therefore, it should include multiple components and cover different stages of the care pathway (e.g., identification of patients, training of staff, modifying access to resources, and treatment). An example is the Chronic Care Model (CCM) which is a primary health care model designed for chronic (relapsing) conditions and involves six elements: linkage to community resources, redesign of health care organization, self-management support, delivery system redesign (e.g., use of non-physician personnel), decision support, and the use of clinical information systems [ 18 , 19 ].

As numerous articles have already assessed the treatment model SBIRT, this model of care will be excluded from our review unless the particular model adds a specific new aspect. Also, the article has to assess the effectiveness of the model rather than assessing the effectiveness of the particular treatment used. Because identification of patients is vital to including them in the trial, a care model that only evaluates either patient identification or treatment without including both will be excluded from this review.

Model effectiveness may be in comparison with the usual care or a different treatment model.

Included studies need to include at least one of the following outcome measures: alcohol consumption, treatment engagement, uptake of pharmacological agents, and/or quality of life.

Solely quantitative research will be included in this systematic review (e.g., randomized controlled trials (RCTs) and cluster RCTs). We will only include peer-reviewed articles.

Restrictions (language/time period)

Studies published in English after 1 January 1998 will be included in this systematic review.

Studies have to be conducted in primary health care settings as such treatment facilities need to be physically in or attached to the primary care clinic. Examples are co-located clinics, veteran health primary care clinic, hospital-based primary care clinic, and community primary health clinics. Specialized primary health care clinics such as human immunodeficiency virus (HIV) clinics are excluded from this systematic review. All studies were included, irrespective of country of origin.

Search strategy and information sources

A comprehensive search will be conducted. The following databases will be consulted: MEDLINE (1946 to present), PsycINFO (1806 to present), Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials (CENTRAL) (1991 to present), and Embase (1947 to present). Initially, the search terms will be kept broad including alcohol use disorder (+synonyms), primary health care, and treatment to minimize the risk of missing any potentially relevant articles. Depending on the number of references attained by this preliminary search, we will add search terms referring to models such as models of care, integrated models, and stepped-care models, to limit the number of articles. Additionally, we will conduct reference searches of relevant reviews and articles. Similarly, a gray literature search will be done with the help of Google and the Gray Matters tool which is a checklist of health-related sites organized by topic. The tool is produced by the Canadian Agency for Drugs and Technologies in Health (CADTH) [ 20 ].

See Additional file  2 for a draft of our search strategy in MEDLINE.

Data collection

The selection of relevant articles is based on several consecutive steps. All references will be managed using EndNote (EndNote version X9 Clarivate Analytics). Initially, duplicates will be removed from the database after which all the titles will be screened with the purpose of discarding clearly irrelevant articles. The remaining records will be included in an abstract and full-text screen. All steps will be done independently by two researchers. Disagreement will lead to consultation of a third researcher.

Data extraction and synthesis

Two researchers will extract data from included records. At the conclusion of data extraction, these two researchers will meet with the lead author to resolve any discrepancies.

In order to follow a structured approach, an extraction form will be used. Key elements of the extraction form are information about design of the study (randomized, blinded, control), type of participants (alcohol use, screening tool used, socio-economic status, severity of alcohol use, age, sex, number of participants), study setting (primary health care setting, VA centers, co-located), type of intervention/model of care (separate elements of the models), type of health care worker (primary, secondary (co-located)), duration of follow-up, outcome measures used in the study, and funding sources. We do not anticipate having sufficient studies for a meta-analysis. As such, we plan to perform a narrative synthesis. We will synthesize the findings from the included articles by cohort characteristics, differential aspects of the intervention, controls, and type of outcome measures.

Sensitivity analyses will be conducted when issues suitable for sensitivity analysis are identified during the review process (e.g., major differences in quality of the included articles).

Potential meta-analysis

In the event that sufficient numbers of effect sizes can be extracted, a meta-analytic synthesis will be performed. We will extract effect sizes from each study accordingly. Two effect sizes will be extracted (and transformed where appropriate). Categorical outcomes will be given in log odds ratios and continuous measures will be converted into standardized mean differences. Variation in effect sizes attributable to real differences (heterogeneity) will be estimated using the inconsistency index ( I 2 ) [ 21 , 22 ]. We anticipate high degrees of variation among effect sizes, as a result moderation and subgroup-analyses will be employed as appropriate. In particular, moderation analysis will focus on the degree of heterogeneity attributable to differences in cohort population (pre-intervention drinking severity, age, etc.), type of model/intervention, and study quality. We anticipate that each model of care will require a sub-group analysis, in which case a separate meta-analysis will be performed for each type of model. Small study effect will be assessed with funnel plots and Egger’s symmetry tests [ 23 ]. When we cannot obtain enough effect sizes for synthesis or when the included studies are too diverse, we will aim to illustrate patterns in the data by graphical display (e.g., bubble plot) [ 24 ].

Critical appraisal of studies

All studies will be critically assessed by two researchers independently using the Revised Cochrane risk-of-bias tool (RoB 2) [ 25 ]. This tool facilitates systematic assessment of the quality of the article per outcome according to the five domains: bias due to (1) the randomization process, (2) deviations from intended interventions, (3) missing outcome data, (4) measurement of the outcome, and (5) selection of the reported results. An additional domain 1b must be used when assessing the randomization process for cluster-randomized studies.

Meta-biases such as outcome reporting bias will be evaluated by determining whether the protocol was published before recruitment of patients. Additionally, trial registries will be checked to determine whether the reported outcome measures and statistical methods are similar to the ones described in the registry. The gray literature search will be of assistance when checking for publication bias; however, completely eliminating the presence of publication bias is impossible.

Similar to article selection, any disagreement between the researchers will lead to discussion and consultation of a third researcher. The strength of the evidence will be graded according to the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach [ 26 ].

The primary outcome measure of this proposed systematic review is the consumption of alcohol at follow-up. Consumption of alcohol is often quantified in drinking quantity (e.g., number of drinks per week), drinking frequency (e.g., percentage of days abstinent), binge frequency (e.g., number of heavy drinking days), and drinking intensity (e.g., number of drinks per drinking day). Additionally, outcomes such as percentage/proportion included patients that are abstinent or considered heavy/risky drinkers at follow-up. We aim to report all these outcomes. The consumption of alcohol is often self-reported by patients. When studies report outcomes at multiple time points, we will consider the longest follow-up of individual studies as a primary outcome measure.

Depending on the included studies, we will also consider secondary outcome measures such as treatment engagement (e.g., number of visits or pharmacotherapy uptake), economic outcome measures, health care utilization, quality of life assessment (physical/mental), alcohol-related problems/harm, and mental health score for depression or anxiety.

This proposed systematic review will synthesize and evaluate differential models of care for the management of AUD in primary health care settings.

Given the complexities of researching models of care in primary care and the paucity of a focus on AUD treatment, there are likely to be only a few studies that sufficiently address the research question. Therefore, we will do a preliminary search without the search terms for model of care. Additionally, the search for online non-academic studies presents a challenge. However, the Gray Matters tool will be of guidance and will limit the possibility of missing useful studies. Further, due to diversity of treatment models, outcome measures, and limitations in research design, it is possible that a meta-analysis for comparative effectiveness may not be appropriate. Moreover, in the absence of large, cluster randomized controlled trials, it will be difficult to distinguish between the effectiveness of the treatment given and that of the model of care and/or implementation procedure. Nonetheless, we will synthesize the literature and provide a critical evaluation of the quality of the evidence.

This review will assist the design and implementation of models of care for the management of AUD in primary care settings. This review will thus improve the management of AUD in primary health care and potentially increase the uptake of evidence-based interventions for AUD.

Availability of data and materials

Not applicable.

Abbreviations

Alcohol use disorder

Alcohol Use Disorders Identification test

Canadian Agency for Drugs and Technologies in Health

The Comorbidity Alcohol Risk Evaluation

Cochrane Central Register of Controlled Trials

Diagnostic and Statistical Manual of Mental Disorders

Human immunodeficiency virus

10 - International Statistical Classification of Diseases and Related Health Problems

Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols

Screening, brief intervention, referral to specialized treatment

Standard drinks

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Discipline of Addiction Medicine, Central Clinical School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia

Susan A. Rombouts, Eva Louie, Paul Haber & Kirsten C. Morley

NHMRC Centre of Research Excellence in Indigenous Health and Alcohol, Central Clinical School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia

James Conigrave

Drug Health Services, Royal Prince Alfred Hospital, Camperdown, NSW, Australia

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Contributions

KM and PH conceived the presented idea of a systematic review and meta-analysis and helped with the scope of the literature. KM is the senior researcher providing overall guidance and the guarantor of this review. SR developed the background, search strategy, and data extraction form. SR and EL will both be working on the data extraction and risk of bias assessment. SR and JC will conduct the data analysis and synthesize the results. All authors read and approved the final manuscript.

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Correspondence to Kirsten C. Morley .

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Supplementary information

Additional file 1..

PRISMA-P 2015 Checklist.

Additional file 2.

Draft search strategy MEDLINE. Search strategy.

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Rombouts, S.A., Conigrave, J., Louie, E. et al. Evidence-based models of care for the treatment of alcohol use disorder in primary health care settings: protocol for systematic review. Syst Rev 8 , 275 (2019). https://doi.org/10.1186/s13643-019-1157-7

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DOI : https://doi.org/10.1186/s13643-019-1157-7

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BMI indicates body mass index; SES, socioeconomic status.

a Variables smoking status, SES, drinking pattern, former drinker bias only, occasional drinker bias, median age, and gender were removed.

b Variables race, diet, exercise, BMI, country, follow-up year, publication year, and unhealthy people exclusion were removed.

eAppendix. Methodology of Meta-analysis on All-Cause Mortality and Alcohol Consumption

eReferences

eFigure 1. Flowchart of Systematic Search Process for Studies of Alcohol Consumption and Risk of All-Cause Mortality

eTable 1. Newly Included 20 Studies (194 Risk Estimates) of All-Cause Mortality and Consumption in 2015 to 2022

eFigure 2. Funnel Plot of Log-Relative Risk (In(RR)) of All-Cause Mortality Due to Alcohol Consumption Against Inverse of Standard Error of In(RR)

eFigure 3. Relative Risk (95% CI) of All-Cause Mortality Due to Any Alcohol Consumption Without Any Adjustment for Characteristics of New Studies Published between 2015 and 2022

eFigure 4. Unadjusted, Partially Adjusted, and Fully Adjusted Relative Risk (RR) of All-Cause Mortality for Drinkers (vs Nondrinkers), 1980 to 2022

eTable 2. Statistical Analysis of Unadjusted Mean Relative Risk (RR) of All-Cause Mortality for Different Categories of Drinkers for Testing Publication Bias and Heterogeneity of RR Estimates From Included Studies

eTable 3. Mean Relative Risk (RR) Estimates of All-Cause Mortality Due to Alcohol Consumption up to 2022 for Subgroups (Cohorts Recruited 50 Years of Age or Younger and Followed up to 60 Years of Age)

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• Errors in Figure and Supplement JAMA Network Open Correction May 9, 2023

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Zhao J , Stockwell T , Naimi T , Churchill S , Clay J , Sherk A. Association Between Daily Alcohol Intake and Risk of All-Cause Mortality : A Systematic Review and Meta-analyses . JAMA Netw Open. 2023;6(3):e236185. doi:10.1001/jamanetworkopen.2023.6185

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Association Between Daily Alcohol Intake and Risk of All-Cause Mortality : A Systematic Review and Meta-analyses

• 1 Canadian Institute for Substance Use Research, University of Victoria, Victoria, British Columbia, Canada
• 2 Department of Psychology, University of Portsmouth, Portsmouth, Hampshire, United Kingdom
• Correction Errors in Figure and Supplement JAMA Network Open

Question   What is the association between mean daily alcohol intake and all-cause mortality?

Findings   This systematic review and meta-analysis of 107 cohort studies involving more than 4.8 million participants found no significant reductions in risk of all-cause mortality for drinkers who drank less than 25 g of ethanol per day (about 2 Canadian standard drinks compared with lifetime nondrinkers) after adjustment for key study characteristics such as median age and sex of study cohorts. There was a significantly increased risk of all-cause mortality among female drinkers who drank 25 or more grams per day and among male drinkers who drank 45 or more grams per day.

Meaning   Low-volume alcohol drinking was not associated with protection against death from all causes.

Importance   A previous meta-analysis of the association between alcohol use and all-cause mortality found no statistically significant reductions in mortality risk at low levels of consumption compared with lifetime nondrinkers. However, the risk estimates may have been affected by the number and quality of studies then available, especially those for women and younger cohorts.

Objective   To investigate the association between alcohol use and all-cause mortality, and how sources of bias may change results.

Data Sources   A systematic search of PubMed and Web of Science was performed to identify studies published between January 1980 and July 2021.

Study Selection   Cohort studies were identified by systematic review to facilitate comparisons of studies with and without some degree of controls for biases affecting distinctions between abstainers and drinkers. The review identified 107 studies of alcohol use and all-cause mortality published from 1980 to July 2021.

Data Extraction and Synthesis   Mixed linear regression models were used to model relative risks, first pooled for all studies and then stratified by cohort median age (<56 vs ≥56 years) and sex (male vs female). Data were analyzed from September 2021 to August 2022.

Main Outcomes and Measures   Relative risk estimates for the association between mean daily alcohol intake and all-cause mortality.

Results   There were 724 risk estimates of all-cause mortality due to alcohol intake from the 107 cohort studies (4 838 825 participants and 425 564 deaths available) for the analysis. In models adjusting for potential confounding effects of sampling variation, former drinker bias, and other prespecified study-level quality criteria, the meta-analysis of all 107 included studies found no significantly reduced risk of all-cause mortality among occasional (>0 to <1.3 g of ethanol per day; relative risk [RR], 0.96; 95% CI, 0.86-1.06; P  = .41) or low-volume drinkers (1.3-24.0 g per day; RR, 0.93; P  = .07) compared with lifetime nondrinkers. In the fully adjusted model, there was a nonsignificantly increased risk of all-cause mortality among drinkers who drank 25 to 44 g per day (RR, 1.05; P  = .28) and significantly increased risk for drinkers who drank 45 to 64 and 65 or more grams per day (RR, 1.19 and 1.35; P  < .001). There were significantly larger risks of mortality among female drinkers compared with female lifetime nondrinkers (RR, 1.22; P  = .03).

Conclusions and Relevance   In this updated systematic review and meta-analysis, daily low or moderate alcohol intake was not significantly associated with all-cause mortality risk, while increased risk was evident at higher consumption levels, starting at lower levels for women than men.

The proposition that low-dose alcohol use protects against all-cause mortality in general populations continues to be controversial. 1 Observational studies tend to show that people classified as “moderate drinkers” have longer life expectancy and are less likely to die from heart disease than those classified as abstainers. 2 Systematic reviews and meta-analyses of this literature 3 confirm J-shaped risk curves (protective associations at low doses with increasing risk at higher doses). However, mounting evidence suggests these associations might be due to systematic biases that affect many studies. For example, light and moderate drinkers are systematically healthier than current abstainers on a range of health indicators unlikely to be associated with alcohol use eg, dental hygiene, exercise routines, diet, weight, income 4 ; lifetime abstainers may be systematically biased toward poorer health 5 ; studies fail to control for biases in the abstainer reference group, in particular failing to remove “sick quitters” or former drinkers, many of whom cut down or stop for health reasons 2 ; and most studies have nonrepresentative samples leading to an overrepresentation of older White men. Adjustment of cohort samples to make them more representative has been shown to eliminate apparent protective associations. 6 Mendelian randomization studies that control for the confounding effects of sociodemographic and environmental factors find no evidence of cardioprotection. 7

We published 2 previous systematic reviews and meta-analyses that investigated these hypotheses. The first of these focused on all-cause mortality, 8 finding negligible reductions in mortality risk with low-volume alcohol use when study-level controls were introduced for potential bias and confounding, such as the widespread practice of misclassifying former drinkers and/or current occasional drinkers as abstainers (ie, not restricting reference groups to lifetime abstainers). 8 Our alcohol and coronary heart disease (CHD) mortality meta-analysis of 45 cohort studies 9 found that CHD mortality risk differed widely by age ranges and sex of study populations. In particular, young cohorts followed up to old age did not show significant cardio-protection for low-volume use. Cardio-protection was only apparent among older cohorts that are more exposed to lifetime selection biases (ie, increasing numbers of “sick-quitters” in the abstainer reference groups and the disproportionate elimination of drinkers from the study sample who had died or were unwell).

The present study updates our earlier systematic review and meta-analysis for all-cause mortality and alcohol use, 8 including studies published up to July 2021 (ie, 6.5 years of additional publications). The study also investigated the risk of all-cause mortality for alcohol consumption according to (1) median ages of the study populations (younger than 56 years or 56 years and older), replicating the methods of Zhao et al 9 ; (2) the sex distribution of the study populations, and (3) studies of cohorts recruited before a median age of 51 years of age and followed up in health records until a median age of at least 60 years (ie, with stricter rules to further minimize lifetime selection biases). Because younger cohorts followed up to an age at which they may experience heart disease are less likely to be affected by lifetime selection biases, 9 we hypothesized that such studies would be less likely to show reduced mortality risks for low-volume drinkers. Finally, we reran the analyses using occasional drinkers (<1 drink per week) as the reference, for whom physiological health benefits are unlikely. Occasional drinkers are a more appropriate reference group, given evidence demonstrating that lifetime abstainers may be biased toward ill health. 10

The present study updates the systematic reviews and meta-analyses described above 8 by including studies published up to July 2021 to investigate whether the risk differed for subgroups. The study protocol was preregistered on the Open Science Framework. 11 Inclusion criteria, search strategy, study selection, data extraction, and statistical analytical methods of the study are summarized in later sections (see eAppendix in Supplement 1 for more details).

The systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses ( PRISMA ) reporting guideline. 12 The review sought cohort studies of all-cause mortality and alcohol consumption. We identified all potentially relevant articles published up to July 31, 2021, regardless of language, by searching PubMed and Web of Science, through reference list cross-checking of previous meta-analyses (eFigure 1 in Supplement 1 ). There were 87 studies identified by Stockwell et al. 8 After inclusion of 20 new studies meeting inclusion criteria, there were a total of 107 cohort studies (eTable 1 in Supplement 1 ). 13 - 32

Three coders (J. Z., F. A., and J. C.) reviewed all eligible studies to extract and code data independently from all studies fulfilling the inclusion criteria. Data extracted included (1) outcome, all-cause mortality; (2) measures of alcohol consumption; (3) study characteristics, including cohort ages at recruitment and follow-up; (4) types of misclassification error of alcohol consumers and abstainers; (5) controlled variables in individual studies. Alcoholic drinks were converted into grams per day according to country-specific definitions if not otherwise defined. 33 , 34

We also assessed publication bias, heterogeneity, and confounding of covariates that might potentially affect the association of interest using several statistical approaches. 35 - 41 Relative risk (RR), including hazard ratios or rate ratios, were converted to natural log-transformed formats to deal with skewness. Publication bias was assessed through visual inspection of the funnel plot of log-RR of all-cause mortality due to alcohol consumption against the inverse standard error of log-RR 42 and Egger’s linear regression method. 36 We also plotted forest graphs of log-RR of all-cause mortality for any level of drinking to assess heterogeneity among studies. 42 The between-study heterogeneity of RRs were assessed using Cochran Q 37 and the I 2 statistic. 38 If heterogeneity was detected, mixed-effects models were used to obtain the summarized RR estimates. Mixed-effects regression analyses were performed in which drinking groups and control variables were treated as fixed-effects with a random study effect because of significant heterogeneity. 43

All analyses were weighted by the inverse of the estimated variance of the natural log relative risk. Variance was estimated from reported standard errors, confidence intervals, or number of deaths. The weights for each individual study were created using the inverse variance weight scheme and used in mixed regression analysis to get maximum precision for the main results of the meta-analysis. 42 In comparison with lifetime abstainers, the study estimated the mean RR of all-cause mortality for former drinkers (ie, now completely abstaining), current occasional (<9.1 g per week), low-volume (1.3-24.0 g per day), medium-volume (25.0-44.0 g per day), high-volume (45.0-64.0 g) and highest-volume drinkers (≥65.0 grams per day). The analyses adjusted for the potential confounding effects of study characteristics including the median age and sex distribution of study samples, drinker biases, country where a study was conducted, follow-up years and presence or absence of confounders. Analyses were also repeated using occasional drinkers as the reference group. We used t tests to calculate P values, and significance was set at .05. All statistical analyses were performed using SAS version 9.4 (SAS Institute) and the SAS MIXED procedure was used to model the log-transformed RR. 44 Data were analyzed from September 2021 to August 2022.

There were 724 estimates of the risk relationship between level of alcohol consumption and all-cause mortality from 107 unique studies 13 - 32 , 45 - 131 , including 4 838 825 participants and 425 564 deaths available for the analysis. Table 1 describes the sample characteristics of the metadata. Of 39 studies 13 , 15 , 18 , 21 , 23 - 26 , 29 , 31 , 45 - 47 , 49 , 50 , 52 - 54 , 57 - 59 , 62 , 64 , 70 , 80 , 81 , 85 , 87 , 91 , 94 , 96 , 100 , 104 , 107 , 118 , 124 , 125 , 127 , 130 reporting RR estimates for men and women separately, 33 14 , 17 , 48 , 51 , 61 , 63 , 66 , 68 , 69 , 72 , 76 , 79 , 83 , 84 , 86 , 88 , 90 , 92 , 93 , 97 , 98 , 101 , 103 , 105 , 109 - 111 , 113 - 115 , 119 , 120 , 128 were for males only, 8 16 , 65 , 73 , 99 , 102 , 108 , 112 , 123 for females only, and 30 13 , 19 - 22 , 26 - 30 , 32 , 55 , 56 , 67 , 71 , 74 , 75 , 77 , 78 , 82 , 84 , 89 , 95 , 106 , 116 , 117 , 121 , 122 , 126 , 129 for both sexes. Twenty-one studies 13 , 17 , 19 , 21 , 22 , 26 , 27 , 45 - 58 (220 risk estimates) were free from abstainer bias (ie, had a reference group of strictly defined lifetime abstainers). There were 50 studies 14 - 16 , 18 , 20 , 23 - 25 , 29 , 59 - 99 (265 risk estimates) with both former and occasional drinker bias; 28 studies 28 , 30 - 32 , 100 - 122 , 130 (177 risk estimates) with only former drinker bias; and 8 studies 123 - 129 , 131 (62 risk estimates) with only occasional drinker bias.

Unadjusted mean RR estimates for most study subgroups categorized by methods/sample characteristics showed markedly or significantly higher RRs for alcohol consumers as a group vs abstainers. Exceptions were for studies with less than 10 years of follow-up and those with some form of abstainer bias ( Table 1 ). Bivariable analyses showed that mortality risks for alcohol consumers varied considerably according to other study characteristics, such as quality of the alcohol consumption measure, whether unhealthy individuals were excluded at baseline, and whether socioeconomic status was controlled for ( Table 1 ).

No evidence of publication bias was detected either by inspection of symmetry in the funnel plot of log-RR estimates and their inverse standard errors (eFigure 2 in Supplement 1 ) or by Egger linear regression analysis (eTable 2 in Supplement 1 , all P > .05 for each study group). Significant heterogeneity was observed across studies for all drinking categories confirmed by both the Q statistic ( Q 723  = 5314.80; P  < .001) and I 2 estimates (all >85.87%). (See eFigure 3 in Supplement 1 for forest plot of unadjusted risk estimates of mortality risks for the 20 newly identified studies).

Pooled unadjusted estimates (724 observations) showed significantly higher risk for former drinkers (RR, 1.22; 95% CI, 1.11-1.33; P  = .001) and significantly lower risk for low-volume drinkers (RR, 0.85; 95% CI, 0.81-0.88; P  = .001) compared with abstainers as defined in the included studies ( Table 2 ; eFigure 4 in Supplement 1 ). In the fully adjusted model, mortality RR estimates increased for all drinking categories, becoming nonsignificant for low-volume drinkers (RR, 0.93; 95% CI, 0.85-1.01; P  = .07), occasional drinkers (>0 to <1.3 g of ethanol per day; RR, 0.96; 95% CI, 0.86-1.06; P  = .41), and drinkers who drank 25 to 44 g per day (RR, 1.05; 95% CI, 0.96-1.14; P  = .28). There was a significantly increased risk among drinkers who drank 45 to 64 g per day (RR, 1.19; 95% CI, 1.07-1.32; P  < .001) and 65 or more grams (RR, 1.35; 95% CI, 1.23-1.47; P  < .001). The Figure shows the changes in RR estimates for low-volume drinkers when removing each covariate from the fully adjusted model. In most cases, removing study-level covariates tended to yield lower risk estimates from alcohol use.

Table 2 presents the RR estimates when occasional drinkers were the reference group. In fully adjusted models, higher though nonsignificant mortality risks were observed for both abstainers and medium-volume drinkers (RR, 1.04; 95% CI, 0.94-1.16; P  = .44 and RR, 1.09; 95% CI, 0.96-1.25; P  = .19, respectively). There were significantly elevated risks for both high and higher volume drinkers (RR, 1.24; 95% CI, 1.07-1.44; P  = .004 and RR, 1.41; 95% CI, 1.23-1.61; . P  = 001, respectively).

As hypothesized, there was a significant interaction between cohort age and mortality risk ( P  = .02; F 601  = 2.93) and so RR estimates for drinkers were estimated in analyses stratified by median age of the study populations at enrollment ( Table 3 ). In unadjusted and partially adjusted analyses, older cohorts displayed larger reductions in mortality risk associated with low-volume consumption than younger cohorts. However, in fully adjusted analyses with multiple covariates included for study characteristics, these differences disappeared. Younger cohorts also displayed greater mortality risks than older cohorts at higher consumption levels. Among studies in which participants were recruited at age 50 years or younger and followed up to age 60 years (ie, there was likely reduced risk of lifetime selection bias) higher RR estimates were observed for all drinking groups vs lifetime abstainers. These differences were significant in all drinking groups except low-volume drinkers (eTable 3 in Supplement 1 ).

Across all levels of alcohol consumption, female drinkers had a higher RR of all-cause mortality than males ( P for interaction  = .001). As can be seen in Table 4 , all female drinkers had a significantly increased mortality risk compared with female lifetime nondrinkers (RR, 1.22; 95% CI, 1.02-1.46; P  = .03). Compared with lifetime abstainers, there was significantly increased risk of all-cause mortality among male drinkers who drank 45 to 64 g per day (RR, 1.15; 95% CI, 1.03-1.28; P  = .01) and drank 65 or more (RR, 1.34; 95% CI, 1.23-1.47; P  < .001), and among female drinkers who drank 25 to 44 g per day (RR, 1.21; 95% CI, 1.08-1.36; P  < .01), 45 to 64 g (RR, 1.34; 95% CI, 1.11-1.63; P  < .01) and 65 or more grams (RR, 1.61; 95% CI, 1.44-1.80; P  = .001).

In fully adjusted, prespecified models that accounted for effects of sampling, between-study variation, and potential confounding from former drinker bias and other study-level covariates, our meta-analysis of 107 studies found (1) no significant protective associations of occasional or low-volume drinking (moderate drinking) with all-cause mortality; and (2) an increased risk of all-cause mortality for drinkers who drank 25 g or more and a significantly increased risk when drinking 45 g or more per day.

Several meta-analytic strategies were used to explore the role of abstainer reference group biases caused by drinker misclassification errors and also the potential confounding effects of other study-level quality covariates in studies. 2 Drinker misclassification errors were common. Of 107 studies identified, 86 included former drinkers and/or occasional drinkers in the abstainer reference group, and only 21 were free of both these abstainer biases. The importance of controlling for former drinker bias/misclassification is highlighted once more in our results which are consistent with prior studies showing that former drinkers have significantly elevated mortality risks compared with lifetime abstainers.

In addition to presenting our fully adjusted models, a strength of the study was the examination of the differences in relative risks according to unadjusted and partially adjusted models, including the effect of removing individual covariates from the fully adjusted model. We found evidence that abstainer biases and other study characteristics changed the shape of the risk relationship between mortality and rising alcohol consumption, and that most study-level controls increased the observed risks from alcohol, or attenuated protective associations at low levels of consumption such that they were no longer significant. The reduced RR estimates for occasional or moderate drinkers observed without adjustment may be due to the misclassification of former and occasional drinkers into the reference group, a possibility which is more likely to have occurred in studies of older cohorts which use current abstainers as the reference group. This study also demonstrates the degree to which observed associations between consumption and mortality are highly dependent on the modeling strategy used and the degree to which efforts are made to minimize confounding and other threats to validity.

It also examined risk estimates when using occasional drinkers rather than lifetime abstainers as the reference group. The occasional drinker reference group avoids the issue of former drinker misclassification that can affect the abstainer reference group, and may reduce confounding to the extent that occasional drinkers are more like low-volume drinkers than are lifetime abstainers. 2 , 8 , 132 In the unadjusted and partially adjusted analyses, using occasional drinkers as the reference group resulted in nonsignificant protective associations and lower point estimates for low-volume drinkers compared with significant protective associations and higher point estimates when using lifetime nondrinkers as the reference group. In the fully adjusted models, there were nonsignificant protective associations for low-volume drinkers whether using lifetime abstainers or occasional drinkers as the reference group, though this was only a RR of 0.97 for the latter.

Across all studies, there were few differences in risk for studies when stratified by median age of enrollment above or below age 56 years in the fully adjusted analyses. However, in the subset of studies who enrolled participants aged 50 years or younger who were followed for at least 10 years, occasional drinkers and medium-volume drinkers had significantly increased risk of mortality and substantially higher risk estimates for high- and higher-volume consumption compared with results from all studies. This is consistent with our previous meta-analysis for CHD, 9 in which younger cohorts followed up to older age did not show a significantly beneficial association of low-volume consumption, while older cohorts, with more opportunity for lifetime selection bias, showed marked, significant protective associations.

Our study also found sex differences in the risk of all-cause mortality. A larger risk of all-cause mortality for women than men was observed when drinking 25 or more grams per day, including a significant increase in risk for medium-level consumption for women that was not observed for men. However, mortality risk for mean consumption up to 25 g per day were very similar for both sexes.

A number of limitations need to be acknowledged. A major limitation involves imperfect measurement of alcohol consumption in most included studies, and the fact that consumption in many studies was assessed at only 1 point in time. Self-reported alcohol consumption is underreported in most epidemiological studies 133 , 134 and even the classification of drinkers as lifetime abstainers can be unreliable, with several studies in developed countries finding that the majority of self-reported lifetime abstainers are in fact former drinkers. 135 , 136 If this is the case, the risks of various levels of alcohol consumption relative to presumed lifetime abstainers are underestimates. Merely removing former drinkers from analyses may bias studies in favor of drinkers, since former drinkers may be unhealthy, and should rightly be reallocated to drinking groups according to their history. However, this has only been explored in very few studies. Our study found that mortality risk differed significantly by cohort age and sex. It might be that the risk is also higher for other subgroups, such as people living with HIV, 137 a possibility future research should investigate.

The number of available studies in some stratified analyses was small, so there may be limited power to control for potential study level confounders. However, the required number of estimates per variable for linear regression can be much smaller than in logistic regression, and a minimum of at least 2 estimates per variable is recommended for linear regression analysis, 138 suggesting the sample sizes were adequate in all models presented. It has been demonstrated that a pattern of binge (ie, heavy episodic) drinking removes the appearance of reduced health risks even when mean daily volume is low. 139 Too few studies adequately controlled for this variable to investigate its association with different outcomes across studies. Additionally, our findings only apply to the net effect of alcohol at different doses on all-cause mortality, and different risk associations likely apply for specific disease categories. The biases identified here likely apply to estimates of risk for alcohol and all diseases. It is likely that correcting for these biases will raise risk estimates for many types of outcome compared with most existing estimates.

This updated meta-analysis did not find significantly reduced risk of all-cause mortality associated with low-volume alcohol consumption after adjusting for potential confounding effects of influential study characteristics. Future longitudinal studies in this field should attempt to minimize lifetime selection biases by not including former and occasional drinkers in the reference group, and by using younger cohorts (ie, age distributions that are more representative of drinkers in the general population) at baseline.

Accepted for Publication: February 17, 2023.

Published: March 31, 2023. doi:10.1001/jamanetworkopen.2023.6185

Correction: This article was corrected on May 9, 2023, to fix errors in the Figure and Supplement.

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2023 Zhao J et al. JAMA Network Open .

Corresponding Author: Jinhui Zhao, PhD, Canadian Institute for Substance Use Research, University of Victoria, PO Box 1700 STN CSC, Victoria, BC V8Y 2E4, Canada ( [email protected] ).

Author Contributions: Drs Zhao and Stockwell had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Zhao, Stockwell, Naimi, Churchill, Sherk.

Acquisition, analysis, or interpretation of data: Zhao, Stockwell, Naimi, Clay.

Drafting of the manuscript: Zhao, Stockwell, Clay.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Zhao, Churchill.

Obtained funding: Zhao, Stockwell, Sherk.

Administrative, technical, or material support: Zhao, Stockwell, Naimi.

Supervision: Zhao, Stockwell, Naimi.

Conflict of Interest Disclosures: Dr Stockwell reported receiving personal fees from Ontario Public Servants Employees Union for expert witness testimony and personal fees from Alko outside the submitted work. Dr Sherk reported receiving grants from Canadian Centre on Substance Use and Addiction (CCSA) during the conduct of the study. No other disclosures were reported.

Funding/Support: This study was partly funded by the CCSA as a subcontract for a Health Canada grant to develop guidance for Canadians on alcohol and health.

Role of the Funder/Sponsor: Health Canada had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. CCSA staff conducted a preliminary search to identify potentially relevant articles but did not participate in decisions about inclusion/exclusion of studies, coding, analysis, interpretation of results or approving the final manuscript.

Data Sharing Statement: See Supplement 2 .

Additional Contributions: We gratefully acknowledge contributions by Christine Levesque, PhD (CCSA), and Nitika Sanger, PhD (CCSA), who conducted a preliminary literature search for potentially relevant articles. We also acknowledge the leadership of Drs Catherine Paradis, PhD (CCSA), and Peter Butt, MD (University of Saskatchewan), who cochaired the process of developing Canada’s new guidance on alcohol and health, a larger project which contributed some funds for the work undertaken for this study. We are grateful to Fariha Alam, MPH (Canadian Institute for Substance Use and Research), for her help coding the studies used in this study. None of them received any compensation beyond their normal salaries for this work.

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EDITORIAL article

Editorial: changing addiction problems and care responses during and after a major crisis: emergence of a ‘new normal'.

• 1 National Addiction Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
• 2 South London and Maudsley (SLaM) NHS Foundation Trust, London, United Kingdom
• 3 Department of Health Service and Population Research Department, King's College London, London, United Kingdom
• 4 Department of Psychological Medicine, King's College London, London, United Kingdom
• 5 AIDS Program, Yale University, New Haven, CT, United States

Editorial on the Research Topic Changing addiction problems and care responses during and after a major crisis: emergence of a ‘new normal'

A major crisis can be understood as a significant societal event that disrupts the status quo in many spheres of life. Whether man-made (like revolutions, wars, or economic catastrophes) or natural disasters (like earthquakes, forest fires, or pandemics), crises are often polyphonic, with several disruptions happening simultaneously. Crises have occurred throughout the history of humanity, and the contemporary world continues to witness many, ranging from COVID-19 to wars to revolutions to natural disasters. Furthermore, crises put an enormous strain on societal resources and preparedness to mitigate their effects needs to be significantly improved and better understood. High-income countries may have relatively more resources prior to a crisis, but in the absence of crisis mitigation systems and processes, they may prove ill-prepared and still be left helpless. Lessons from Low- and Middle-Income Countries (LMIC) that have mitigated various risks over extended periods of time could thus be invaluable. This Research Topic brings together diverse research contributions on addiction in the context of crises, with a focus on both damage during and after a crisis, and the opportunities for innovation and improvement post-crisis. Collectively, these contributions begin to unpack key lessons about how addiction treatment systems at all levels—individual patients and providers, facilities, and institutions of national and international caliber—cope with and adapt to crises. They also examine the extent to which emergency solutions for providing addiction treatment services during a crisis are sustainable over time and the changes they can set in motion. Most importantly, this Research Topic shines light on empirical evidence that lifts the “fog of crisis”, to highlight the experiences and implications of crisis from the perspectives of different stakeholders and to build an understanding of the reality of what has happened.

This Research Topic features 13 articles and, while they all focus on substance use disorders including opioids ( Kabembo ) and alcohol ( Nikitin et al. ), they also capture the diversity of approaches to studying addiction problems and care responses during and after major crises in terms of methodologies, level of analysis, regional focus, and specific crises explored. Five articles report qualitative studies, three report findings from randomized controlled trials, four are based on survey research, and one reviews mixed-methods data from several sources. Of the 13 articles published in this Research Topic, six focus on HICs and seven on LMICs. However, all but one of the latter are written by HIC authors in collaboration with LMIC co-authors. This collaboration is undeniably positive but in our experience as Co-Editors who have encouraged and mentored many other LMIC authors (who sent in their ideas for a contribution), it was challenging for many potential contributors to set aside the demands of the crisis merely to complete and document academic analyses, largely because the crisis context required potential contributors in LMICs, like Mexico or Ukraine, to take on extra teaching, clinical care, and/or consultancy work to meet urgent response needs and to mitigate the escalating cost of living. Perhaps an immediate lesson to be learned is that research and timely analysis of crises need protected time through grant funding. Geographically, five articles focus on Ukraine, four on England, one on the United States, one on Zambia, one on Uganda, and one on the Western Europe region. Eleven articles investigate contemporary crises while one focuses on the aftermath of the Ugandan war a few years ago, and one article describes the status quo before COVID-19 and the war in Ukraine. From an editorial perspective, we would like to highlight three key themes in this corpus of articles.

The first key theme evident in all 13 articles is the value of empirical research during and after the crisis , to provide lessons for the future that are genuinely international ( Sekeris et al. ). Presenting evidence on how addiction treatment systems adapt to crises helps to overcome a mono-directional and inadvertently neo-colonial perspective, where practices from the West/HICs are often naively considered as universal therapeutic templates to be better rebuilt everywhere after a crisis. For example, articles by Nikitin et al. and Ponticello et al. , which examine addiction treatment in Ukraine during the Russian invasion emphasize the importance of considering the impact of crises on the implementation process of OAT scale-up efforts and point to local Ukrainian-grown solutions for flexibility and responsiveness to patient needs in complex and rapidly changing environments. Similarly, Dellamura et al. take a step further and suggest that imposing on Ukrainian addiction treatment providers a system of accelerated performance indicators based on the recommendations of the Global Fund and other international authorities several years before the COVID-19 and war crises, without expanding the resources and addressing clinicians' concerns, increased the structural deficits in the healthcare system that the crisis exposed, while simultaneously fostering the ingenuity of healthcare workers to function in the absence of support, a crucial skill for survival under even more adverse circumstances. Describing the opioid epidemic as a man-made and avoidable crisis, McDonald et al. point to the role of transparency, accountability and the need for robust scientific research during crises.

The second thread running through all the articles is the magnitude of the resilience of addiction care in LMICs and HICs in the face of crisis, while also highlighting the limitations and costs of this resilience in terms of the burdens shouldered by individuals (patients and healthcare workers), families and caregivers, and facilities and care systems. As described in this Research Topic, across all contexts studied (from rural England to war-affected communities in Ukraine to post-conflict refugee camps in Uganda to an urban Californian community during the COVID-19 pandemic), addiction care was already, operating under conditions of overstretched resources pre-crisis ( Makoha and Denov ; Fstkchian et al. ; Kabembo ; Scott et al. ). However, while the need for care intensified during the crisis and each context provided evidence of individual and collective ingenuity and adaptability that allowed, by and large, addiction care to be maintained for vulnerable patients, the uncomfortable question raised by the articles is the risk of working beyond capacity becoming solidified as the new normal. When the new normal is the crisis itself, then the context is permanently dangerous and unpredictable, and with the risk that resources will be permanently inadequate.

The final key theme to emerge from the Research Topic is the value of local knowledge (and the local experts who embody and practice it) as the principal ingredient in crisis mitigation in addiction care . No practice (e.g., allowing take-home doses of methadone for stable patients, or the use of telehealth) could prove universally sufficient without being promptly adapted and tailored to the local context, and articles by Galvez et al. and Ponticello et al. detail how this is being done during the war in Ukraine in prisons and in the community ( Mazhnaya et al. ). However, evidence from rural English communities during the COVID-19 crisis suggests that these practices need to be applied with caution ( Scott et al. ), both due to the relative digital illiteracy of some of the most vulnerable patients ( Gilchrist et al. ), and because the elimination of regular contact with the provider deprived such patients of the essential touch-base care points that gave them social support and medical advice ( Campbell et al. ).

In sum, we present this Research Topic as a valuable start to examining how addiction problems and care responses change during a major crisis and we call for continued work on this topic. First, we see extensive opportunities to integrate diverse evidence from different regions and across historical periods. More empirical and longitudinal research is needed to understand, for example, the responses to COVID-19 or the war in Ukraine (to take just two crises) over time, and to assess the long-term effects of any mitigation measures on individuals, facilities, and the national addiction care system. Systematic reviews of adaptations to addiction care during crises, as well as analyses of large international datasets, would be a crucial addition to the extant evidence base. Second, several article proposals that did not make it into this Research Topic have raised our awareness of gaps in knowledge that still need to be bridged. These concern geographic regions (e.g., Central Asia and Latin America) that remain unexplored in the current Research Topic, as well as substantive topics (e.g., drug trade/supply responses to crises; or the digital technologies in addiction care during and after crises) that still require attention. From conversations with potential authors, we realized the potential importance of articles that analyze the decision-making of local or (inter)national addiction care authorities during a crisis about how to re-organize and change patient care, in the absence of readily available guidelines for such decisions: these result in great uncertainty, multiple risks, and a heavy burden of responsibility if anything goes wrong, and this needs to be better understood, described and investigated. It is our hope that such research will be undertaken and featured in future Research Topics. Finally, we see this Research Topic as one of the first steps toward the genuine de-colonization of international guidelines for the provision of addiction care during crises. Evidence from different contexts needs to be examined and brought together to allow both HICs and LMICs to benefit from mutual learning.

It has been a privilege to guest edit this Research Topic and we thank all the contributors for their insightful research. We believe that this Research Topic can be useful to readers in academia, clinical care, public policy, and wherever there are work-related interests and/or lived and living experiences that involve addiction care, and we hope that this will provoke further reflection and discussion on what crises can teach us if we are willing to learn.

Author contributions

JS: Conceptualization, Investigation, Methodology, Resources, Supervision, Writing – original draft, Writing – review & editing. AD: Conceptualization, Methodology, Project administration, Writing – original draft, Writing – review & editing. HS: Conceptualization, Investigation, Methodology, Project administration, Writing – original draft, Writing – review & editing. JR: Conceptualization, Methodology, Resources, Supervision, Writing – original draft, Writing – review & editing.

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. JS was supported by the NIHR Biomedical Research Centre for Mental Health at South London and Maudsley NHS Foundation Trust and King's College London. JR was supported by NIMH (34MH130260).

Acknowledgments

The editorial team would like to thank every author and reviewer for their contribution to this Research Topic.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's note

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Keywords: crisis, addiction, substance use disorder, new normal, lessons

Citation: Strang J, Deac AA, Skipper H and RozanovaI J (2024) Editorial: Changing addiction problems and care responses during and after a major crisis: emergence of a ‘new normal'. Front. Public Health 12:1451141. doi: 10.3389/fpubh.2024.1451141

Received: 18 June 2024; Accepted: 19 June 2024; Published: 11 July 2024.

Edited and reviewed by: Wulf Rössler , Charité University Medicine Berlin, Germany

Copyright © 2024 Strang, Deac, Skipper and RozanovaI. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Julia RozanovaI, julia.rozanova@kcl.ac.uk

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• Published: 08 November 2021

Acute effects of alcohol on social and personal decision making

• Hanna Karlsson 1   na1 ,
• Emil Persson   ORCID: orcid.org/0000-0003-2994-0541 2   na1 ,
• Irene Perini   ORCID: orcid.org/0000-0002-5972-0913 1 ,
• Adam Yngve   ORCID: orcid.org/0000-0003-1012-7286 1 ,
• Markus Heilig 1   na1 &
• Gustav Tinghög   ORCID: orcid.org/0000-0002-8159-1249 2 , 3   na1  

Neuropsychopharmacology volume  47 ,  pages 824–831 ( 2022 ) Cite this article

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Social drinking is common, but it is unclear how moderate levels of alcohol influence decision making. Most prior studies have focused on adverse long-term effects on cognitive and executive function in people with alcohol use disorders (AUD). Some studies have investigated the acute effects of alcohol on decision making in healthy people, but have predominantly used small samples and focused on a narrow selection of tasks related to personal decision making, e.g., delay or probability discounting. Here, we conducted a large ( n  = 264), preregistered randomized placebo-controlled study (RCT) using a parallel group design, to systematically assess the acute effects of alcohol on measures of decision making in both personal and social domains. We found a robust effect of a 0.6 g/kg dose of alcohol on both moral judgment and altruistic behavior, but no effects on several measures of risk taking or waiting impulsivity. These findings suggest that alcohol at low to moderate doses selectively moderates decision making in the social domain, and promotes utilitarian decisions over those dictated by rule-based ethical principles (deontological). This is consistent with existing theory that emphasizes the dual roles of shortsighted information processing and salient social cues in shaping decisions made under the influence of alcohol. A better understanding of these effects is important to understand altered social functioning during alcohol intoxication.

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Introduction.

There is a lack of systematic research on the effects of moderate alcohol intake on decision making in non-clinical populations. This may be related to the difficulties that go into designing these types of studies, and the fact that prior research has been primarily focused on the adverse consequences of alcohol use disorders (AUD) on physiology and behavior. Numerous studies have investigated impairments in interpersonal behavior and decision-making processes in patients with AUD, but these studies cannot disaggregate the direct effects of alcohol from functional consequences of alcohol-induced organ damage, such as e.g., well documented alcohol-induced regional gray matter loss in AUD [ 1 ].

In healthy volunteers, alcohol intake can influence incentive motivation through activation of canonical dopaminergic brain reward system, but these effects vary by gender and genetics [ 2 , 3 , 4 , 5 ]. Enhanced emotional reactivity and increased positive mood have also been linked to alcohol intake in non-threatening environments [ 6 , 7 ]. It is furthermore widely held that alcohol results in broad and non-selective impairments of cognitive function, but this notion has recently been questioned. A meta-analysis of studies that examined the effects of alcohol on event-related potentials suggests that alcohol intake results in relatively selective impairments of attention, automatic auditory processing, and performance monitoring [ 8 ]. Similarly, alcohol is commonly held to increase impulsivity, but available studies make it difficult to disentangle to what extent impulsivity is a cause vs. a consequence of alcohol use, and also point to the moderating influence of emotional states [ 9 ].

Few studies have examined acute effects of alcohol on motivated behavior and decision making under a level of experimental control that allows causal inferences. For instance, many of the existing studies have used survey data to compare the behavior of people who abuse alcohol to those who do not. Although there are also placebo-controlled laboratory studies, most of these have used small samples and focused on a narrow selection of tasks related to personal decision making, primarily risk taking and impulsivity [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 ]. Even for these tasks, there is a lack of converging evidence. Some studies found increased risk taking due to alcohol [ 11 , 13 ], while others found no effect [ 10 , 12 , 14 , 15 , 19 , 20 ]. Similarly, waiting impulsivity has been found to increase [ 19 ] or decrease [ 16 ] following alcohol intake, but the majority of studies have found mixed or no effects [ 10 , 11 , 14 , 15 , 17 ]. Prototypical tasks for altruism and moral judgment have only been included in a minority of studies, with mixed results for both types of tasks [ 19 , 20 , 21 , 22 ]. In addition, some studies have used an observational field paradigm, typically approaching people in a bar with a structured questionnaire [ 22 , 23 , 24 ]. Whereas important insights can be obtained from these observational studies, they cannot provide answers about the causal relationship between alcohol intake and behavior, as they are inherently correlational, and also prone to selection bias.

Here, we therefore investigated how moderate acute alcohol intoxication influences basic social and personal decision making central to a wide variety of everyday behaviors: altruistic behavior and distributional preference, moral judgment, waiting impulsivity, and choice under risk. To this end, we conducted a preregistered (see https://osf.io/sf5em ) randomized placebo-controlled study, using a general task paradigm and a substantially larger sample ( n  = 264) than previous studies. We randomized participants to alcohol (0.6 and 0.51 g/kg for males and females, resp.) or placebo, and assessed moral judgment using standard sacrificial dilemmas (trolley problems) thought to probe the interaction between emotional intuitions and controlled cognitive processes in moral cognition [ 25 , 26 , 27 , 28 ]. Prosocial behavior was assessed using modified versions of the dictator game [ 29 , 30 ]. For risk taking, we used two different tasks, covering both intuitive-cognitive aspects of decision making, via standard prospect theory gambles [ 31 ], and more affect-laden decisions from experience, using the Balloon Analog Risk Task (BART; [ 32 ]). Finally, waiting impulsivity was assessed using a prototypical task that captures participants’ preferences for real monetary rewards delivered at different points in time [ 33 , 34 ]. We assessed both general discounting (over relatively short delays) and temporal inconsistency in discounting, known as present bias, which is a characteristic property of discounting models that feature a sharp rise in the discounting rate for rewards delivered closer to today, such as quasi-hyperbolic discounting [ 34 , 35 ].

Materials and methods

Ethics statement.

The study was approved by the Regional Ethical Review Board of Linköping (ref 2016/496-31) and all participants provided written informed consent.

Open science

The preregistration together with data, analysis codes (main analyses), and experimental materials are available via the project’s OSF repository ( https://osf.io/sf5em ). Individual level data for the main analyses are shown in Supplementary materials Fig. S 1 –S 4 . We preregistered six main questions of interest for this data collection; this paper is focused on the first four of them.

Participants

Healthy volunteers were recruited using advertisements in social media, flyers, and the Online Recruitment System for Economic Experiments ORSEE [ 36 ] at Linköping University, Sweden. Eligible participants were randomized to alcohol ( n  = 128) or placebo ( n  = 136). The groups were similar in terms of baseline characteristics, including age, sex, education, alcohol consumption as measured with AUDIT, and personality traits measured with NEO-FFI (Table  1 ). The distribution of AUDIT scores was also very similar in both groups, and shown in Supplementary Materials (Fig. S 1 ). Our final sample size is smaller than the pre-specified target of n = 300 because we had to stop enrolling participants due to the onset of the COVID-19 pandemic.

Study timeline

The study visit consisted of five phases (Fig.  1B ): screening, questionnaires for baseline assessments, treatment phase (intake of drink), decision-making tasks performed at a computer, and a finishing phase with end of session questionnaires. The study was conducted in a computer lab in sessions of up to 15 participants, who were seated in separate cubicles and did not interact with each other.

A CONSORT diagram of study participant. B study timeline. C time course of BrAC (mean ± SD).

Screening and eligibility

During the screening phase, prospective participants were evaluated for eligibility by a research nurse or a physician. Detailed eligibility criteria are provided in Supplementary Materials. In brief, subjects were excluded if they had any psychiatric disorder, were pregnant, had any previous neurological condition or if they were at risk of alcohol or other substance use disorders except nicotine. Alcohol Use Disorder Identification Test [AUDIT; [ 37 ]] was used to assess the presence of AUD or hazardous drinking. Weight and sex were noted. Breath alcohol concentration (BrAC) baseline was measured using a breathalizer. A total of 316 individuals were evaluated, and 265 were included. Of these, 129 were allocated to placebo and 136 were assigned to alcohol (Fig.  1A ).

Baseline assessments

Baseline personality traits were obtained using the NEO Five Factor Inventory [NEO-FFI; [ 38 ]]. The Symptom checklist-90 [SCL-90; [ 39 ]] was used to measure symptoms of anxiety and depression. The Family Tree Questionnaire [FTQ; [ 40 ]] was used to assess family history of alcohol problems. The Biphasic Alcohol Effect Scale [BAES; [ 41 ]] was used to measure stimulant and sedative effects of alcohol.

Alcohol administration

Participants were informed that they would receive alcohol, corresponding to a BrAC of 0.6‰ or placebo, and were randomized to one of these in a parallel group design (see Fig.  1A ). In the alcohol group, male participants received a 0.6 g/kg dose of alcohol using a 12% solution. The solution was made using 95% ethanol mixed with cranberry juice. To adjust for known differences in body water, women received 85% of the alcohol administered to men. In the placebo group participants received a 1% alcohol solution. In both groups, the drink was divided into three glasses. Participants in both the alcohol and placebo group were required to finish each glass within five minutes. After the last glass, participants had a break for 15 min before proceeding with the decision-making tasks. Breath alcohol concentration (BrAC) was measured at baseline, 25 min later, just before the decision-making tasks and after additional appr. 45 min, as soon as the participant finished the session. The Biphasic Alcohol Effect Scale [BAES; [ 41 ]] was performed every time BrAC was measured and the Drug Effect Questionnaire [DEQ; [ 42 ]] was measured the second and third time BrAC was measured.

Decision-making tasks

For detailed task description and instructions, see Supplementary Materials. In brief, tasks focused on four domains of decision making: waiting impulsivity, choice under risk, moral judgment, and prosocial behavior. Tasks were presented on a computer screen using Qualtrics and Inquisit software. Divider screens prevented participants from seeing each other’s responses. Tasks were presented in a block-randomized order. At the end of the experiment, one decision for each subject was randomly selected and paid out for real (using the cell phone payment system Swish) together with the show-up fee of 150 SEK (appr. $15) that participants received for participating in the study.

Waiting impulsivity

This was assessed using a prototypical task that measures participants’ preferences for rewards delivered at different points in time [ 33 , 34 ]. Participants chose repeatedly between smaller rewards delivered sooner (SS) and larger rewards delivered later (LL). We tested for two distinct types of discounting; a general form of impatience, based on the proportion of smaller-sooner choices each person made in the first block of items ( pr. smaller-sooner ), and a specific form of impatience known as present bias, which is based on the difference (for each participant) between choices made in the first and second blocks of items ( diff. pr. smaller-sooner ). Present bias is a characteristic property of discounting models that feature a sharp rise in the discounting rate for rewards delivered closer to today, such as quasi-hyperbolic discounting [ 34 , 35 ].

Risk taking

One of the tasks to examine risk taking used standard prospect-theory gambles [ 31 ]. We used incentivized binary choices between a lottery and a certain amount of money in three different domains: gain, loss, mixed. We used the proportion of choices where the gamble was our main dependent variable for each domain ( pr. risky choices ). Using this task enabled us to characterize choices after the expected patterns of prospect theory [ 31 ], which emphasizes greater risk aversion for gains than losses and disproportionate weighting of the loss component in mixed prospects.

The second task in this domain was the Balloon Analog Risk Task [BART; [ 32 ]], in which participants were presented with a picture of a balloon and could earn money by pumping up the balloon by clicking a button. Each click earned them 0.1 SEK and caused the balloon to incrementally inflate. If the balloon was overinflated, it exploded, and all money earned for that trial was lost. If instead participants had chosen to cash-out prior to the balloon exploding, the money earned for that trial was added to their sum for this task. Our main dependent variable was the average number of pumps per trial, excluding trials where the balloon exploded ( avg. pumps per balloon ).

Moral judgment

This was assessed using four sacrificial moral dilemmas (trolley problems) that involved a conflict between utilitarian and deontological moral foundations [ 25 , 43 , 44 ]. In each dilemma, participants were faced with the possibility of saving a certain number of people by sacrificing one individual. Killing the single person while saving the others is consistent with utilitarian judgment, while not pulling the switch is consistent with deontological judgment, whereby actively causing harm to another person is morally unacceptable regardless of overall consequences. The main dependent variable for moral judgment was based on participants’ responses to four moral dilemmas (switch, footbridge, fumes, and shark; see Supplementary materials for details), presented in random order, and calculated as the proportion of utilitarian choices made by each participant ( pr. utilitarian choices ).

Prosocial behavior

This was assessed using two different tasks, designed to measure both altruistic behavior and preference for equality versus efficiency in distributions. Both were modified versions of the dictator game [ 29 , 30 ].

In the first task, participants were endowed with 50 SEK (appr. $5) and decided how much of it to keep for themselves and how much to donate to a well-known charity organization (Swedish Heart-Lung Foundation). The main dependent variable was the amount donated ( donation to charity ).

In the second task, subjects chose repeatedly between binary allocations of money (for themselves and another anonymous participant). Each item featured a choice between an equal distribution and an unequal but more efficient distribution, for example 40 SEK (appr. $4) each vs 40 SEK for me and 50 SEK for the other participant. We used the proportion (for each person) of choices where the equal allocation was chosen over the more efficient allocation ( pr. equality ).

Statistical analysis

The main analysis plan was specified before data collection begun, see the preregistration for details. STATISTICA 13.0 (Dell Inc, Tulsa, OK) was used for all analyses. One-way ANOVA, with group (alcohol or placebo) as a between-subject factor, and a pre-set alpha=0.05, were the preregistered main tests. Subject-level data for main tests are provided in Supplementary Materials, Fig. S 1 –S 4 . Secondary analyses (not preregistered) additionally assessed the potential influence of baseline subject characteristics (age, sex, personality measures, and alcohol use as measured by the AUDIT). Covariates were retained in analysis models if they were a significant predictor, or if they reduced the residual variance by more than 10%; otherwise, they were excluded. In additional analyses (also not preregistered) we compared self-reported effects of alcohol (stimulant, sedative, strength of drug effect, desirability) across the two conditions, based on subjects’ responses to the Biphasic Alcohol Effect Scale (BAES) and the Drug Effect Questionnaire (DEQ).

No BrAC alcohol was detected in the placebo group at any timepoint, or in the alcohol group at baseline. In the alcohol group, a BrAC of appr. 0.5‰ was reached by the time behavioral testing started, and remained stable at that level until completion of testing (Fig.  1C ). Using the Biphasic Alcohol Effects Scale [ 41 ], the alcohol group showed the expected stimulant as well as sedative effects of alcohol compared to the placebo group. On the Drug Effects Questionnaire [ 42 ], there was a clear effect of alcohol on the “Feel drug” and “High” items (Fig.  2 ). Neither “Like” nor “Want more” items were affected. The proportion of participants who correctly guessed their allocation was 95.5% in the alcohol group, and 69% in the placebo group. No unexpected adverse events were noted.

A – D Mean responses on the Drug Effect Questionnaire (DEQ) before and after the decision-making tasks. Error bars indicate 95% Confidence Intervals. E , F Mean responses to the Biphasic Alcohol Effects Scale (BAES). Error bars indicate 95% Confidence Intervals. Significant alcohol effects for all items are indicated in the Results section.

Moral judgment in sacrificial dilemmas

Preference for utilitarian responding was increased in the alcohol group (one way ANOVA: F 1, 262  = 5.71, p  = 0.02; Cohen’s d = 0.29; Fig.  3A ). This remained unchanged when controlling for potential confounds. In the final ANCOVA, agreeableness ( p  < 0.01), gender ( p  = 0.06) and hazardous alcohol use, as measured with the AUDIT ([ 37 ]; p  = 0.02) contributed to the model, and all correlated negatively with utilitarian choices. Exploratory analyses indicated that the effect of alcohol on moral judgment was driven by the switch and fumes dilemmas, and to some extent the shark dilemma, while no corresponding effect was seen in the footbridge dilemma.

A Moral judgment. Main panel: overall proportion of utilitarian choices. Inset: proportion of participants in each group who chose the utilitarian option, for the respective scenario. B Donation to charity. Main panel: Total amount of money donated. Inset: distribution of amounts donated to the charity, by group. Ten Swedish kronor (SEK) was approximately equal to one USD at the time of the experiment. Tick marks on the x-axis show the midpoints of equally-sized bins (10 SEK wide), except at the endpoints, where bin size is smaller. Error bars indicate 95% Confidence Intervals. Sample size is n  = 128 for placebo and n  = 136 for alcohol.

Participants in the alcohol group donated more money to a charity ( F 1, 262  = 4.83, p  = 0.03; Cohen’s d = 0.27; Fig.  3B ). This remained unchanged when controlling for potential confound of baseline subject characteristics. In the final model, agreeableness ( p  < 0.01) and hazardous alcohol use as measured with the AUDIT ( p  = 0.02) significantly contributed to the model. Agreeableness was positively correlated with donations and AUDIT was negatively correlated.

Equality/efficiency tradeoffs did not differ between groups (0.27 ± 0.38 vs. 0.27 ± 0.39; F 1, 262  < 0.01, p  = 0.98); thus, participants in both groups were reluctant to pursue equality of resources if redistribution had a cost. This result remained unchanged when controlling for potential confounds. In the final model, age ( p  < 0.01), neuroticism ( p  < 0.01), extraversion ( p  < 0.01), openness ( p  = 0.02), conscientiousness ( p  = 0.01) and gender ( p  < 0.01) significantly contributed to the model. Openness correlated negatively with equality. Female gender, age, neuroticism, extraversion and conscientiousness correlated positively with equality.

Risk taking – prospect theory gambles & BART

Behavior in the prospect gambles was similar in the two groups (Fig.  4 ). There was a tendency for decreased risk taking in the alcohol group for gains (0.59 ± 0.29 vs. 0.65 ± 0.22; F 1, 262  = 3.58, p  = 0.06), but no effect, or trend in the loss (0.49 ± 0.22 vs. 0.45 ± 0.22; F 1, 262  = 1.72, p  = 0.19), or in the mixed domain (0.49 ± 0.21 vs. 0.47 ± 0.22; F 1, 262  = 0.64, p  = 0.42). When all three domains were combined, the alcohol and placebo groups were virtually indistinguishable (0.52 ± 0.18 vs. 0.52 ± 0.15; F 1,262  < 0.01, p  = 0.96; Cohen’s d = −0.01). This remained unchanged when controlling for potential confounds. In the final model, age ( p  < 0.01), extraversion ( p  = 0.01), conscientiousness ( p  = 0.03) and agreeableness ( p  = 0.06) significantly contributed to the model or showed a tendency to do so. Age and extraversion were positively correlated with risk taking, while agreeableness and conscientiousness were negatively correlated with risk taking.

A Mean proportion of trials where individuals chose the gamble over the certain option, separated by domain (gain, loss, mixed). Error bars indicate 95% Confidence Intervals calculated from t tests. B Distribution of the average number of pumps per balloon on the Balloon Analog Risk Task (BART). Sample size is n  = 128 for placebo and n  = 136 for alcohol, except for BART where two individuals in placebo and three in alcohol could not participate in the task due to software issues.

Similarly, there was no difference in risk taking on the Balloon Analog Risk Task (BART) between alcohol and placebo (Fig.  4 ; 43.4 ± 14.1 vs. 43.5 ± 14.2; F 1,257  < 0.01, p  = 0.99; Cohen’s d = −0.002). This remained unchanged when controlling for potential confounds. In the final model, neuroticism ( p  = 0.01) and conscientiousness ( p  = 0.05) were significant covariates. Both were negatively correlated with adjusted average number of pumps.

There was no statistically significant difference between groups for waiting impulsivity (0.24 ± 0.31 vs. 0.29 ± 0.31; F 1,262  = 2.21, p  = 0.14), or present bias (0.0007 ± 0.15 vs. 0.03 ± 0.18; F 1,262  = 2.59, p  = 0.11). Results were similar when all individual decisions were combined (0.24 ± 0.30 vs. 0.28 ± 0.29; F 1,262  = 1.25, p  = 0.26; Cohen’s d = −0.14). Thus, any possible effect of alcohol on waiting impulsivity was small and insignificant, and the bound on the 95% confidence interval in the hypothesized direction, i.e., increased waiting impulsivity following alcohol intake, was close to zero. These results remained unchanged when controlling for potential confounds.

We conducted a large, preregistered RCT to assess acute effects of alcohol on measures of decision making in personal and social domains. A 0.6 g/kg dose of alcohol did not influence personal decisions, but robustly moderated social decision making. In particular, subjects in the alcohol group showed an increased utilitarian preference in sacrificial moral dilemmas, and donated more money to charity in a modified dictator-game task. As an internal validation of these findings, we detected the expected effects of personality traits, independently of the alcohol effects. Although participants’ level of alcohol use, as measured by the AUDIT scale, correlated negatively both with their utilitarian decisions and charitable donations, the effects of alcohol on these outcomes did not interact with the level of alcohol use, and thus did not differ across the spectrum of use included in the study. For personal decision making, we did not find an effect of alcohol at the dose given on any of several risk-taking measures or waiting impulsivity. As an internal validation, we reliably replicated known patterns of results with all our tasks, e.g., increased risk seeking for losses and selective sensitivity to harmful actions across different moral dilemmas. Thus, our null findings are unlikely a result of compromised task calibration or unusual sample composition. Our findings are also unlikely to be explained by effects on elements of decision making that are related to impulse control, since, at the moderate level of alcohol intoxication used, we found no effects in tasks specifically designed to capture this dimension of behavior.

Our results for moral judgment, that subjects became increasingly utilitarian, differ from the few previous studies. Francis and colleagues [ 21 ] recently conducted a placebo-controlled study on moral judgment, using both traditional moral dilemmas and an adapted virtual-reality moral behavior task. They found no effects of alcohol on any of these tasks. In contrast, Duke and Bègue [ 22 ] found that alcohol intake correlated with increased utilitarian responding, but only on the footbridge dilemma and not on the switch dilemma, in a study conducted at two bars in France. However, the results from these two studies should be interpreted with caution, given the small sample sizes and the correlational nature of the data in the latter study. Our findings are contrary to what would be expected based on the widely held dual-process theory of moral cognition [ 25 , 28 ]. According to this theory, the effects of alcohol to increase emotional reactivity and weaken cognitive control should give increased preference for deontological rather than utilitarian actions. In fact, we find the opposite, i.e. increased utilitarian responding due to alcohol. A possible account of this finding is that acute alcohol intoxication primarily affects moral judgment through effects on its cognitive elements, and does so by subtly shifting the balance between perceived costs and benefits in the utilitarian calculation. This is broadly consistent with findings indicating an important role of frontocortical brain areas in social decision making [ 45 ], and a higher sensitivity of these neocortical structures to alcohol effects compared to subcortical brain structures that generate incentive salience and affective signals [ 1 ].

Acute effects of alcohol on altruistic behavior using real monetary rewards have hardly been assessed at all previously. Two previous studies found no effect or a tendency for a negative effect on altruism following alcohol intake [ 19 , 20 ]. In contrast, we found that alcohol made people more altruistic, donating a larger proportion of their money (around ten percentage points more than the placebo group) to charity. This is a modest effect size, but appears to be highly specific, as it was found at a modest dose of alcohol at which there were no discernible effects on impulsivity or risk taking. We had no a priori expectation about the direction of the effect on altruism. In principle, these results can also be rationalized using alcohol myopia theory [ 46 , 47 , 48 ], which emphasizes impaired attention and thus increased reliance on salient stimuli following acute alcohol intoxication. The need of the charity recipients is arguably a salient cue in the task that we used, and it is possible that this is what caused increased donations in the alcohol group.

Previous studies on personal decision making for risk and impulsivity have found mixed results [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 49 ], but most studies have been limited by a small sample size. Prior to our study, Bernhardt et al. [ 10 ] was probably the most well-powered study to date ( n  = 54 adolescent males in a within-subject design), and their results are similar to what we found, with no effects on waiting impulsivity or on risk taking in gain, loss, or mixed domains. Taken together, this strongly suggests that alcohol taken at moderated doses by healthy social drinkers has small or no effects on risk taking or waiting impulsivity. For the Balloon Analog Risk Task (BART), we are aware of only one previous study that was adequately powered, Rose et al. [ 50 ] with n  = 142 in a between-subjects design; e.g., all other studies reviewed by Harmon et al. [ 51 ] had <33 subjects per treatment cell. Interestingly, whereas Rose et al. found increased risk taking (more pumps) due to alcohol intake (Cohen’s d = 0.40 at a 0.6 g/kg dose of alcohol), our results clearly favored a no-effects interpretation, with the 95% confidence interval bounded at an effect size or appr. Cohen’s d = 0.25 in either direction. Thus, more studies are needed to determine the acute effects alcohol on the BART. Of note, while the BART is commonly viewed as a generic “risk taking task”, its original evaluation suggested that it may in fact be more related to sensation seeking and impaired behavioral inhibition [ 32 ], i.e. facets of the impulsivity distinct from those involved in trading off the magnitude of gains or losses vs. their probability.

Our study has several strengths as well as limitations. Among the former, it had a large sample size and a preregistered analysis plan. This is important given that prior studies are for the most part small and without transparent control of analytical flexibility. The combination of small sample sizes, high analytical flexibility and publication bias has been a perfect storm for generating irreproducible findings [ 52 , 53 , 54 , 55 ]. However, despite a larger sample than previous studies, we had insufficient power to conduct otherwise relevant subgroup analyses, for example based on gender or quantitative traits, beyond using them as covariates in the analysis. For the same reason, we did not attempt to capture biphasic effects of alcohol. Finally, we were not able to control for expectation effects by adding more conditions, while blinding was not successful. These limitations may affect the generalizability of our findings.

Some features of the study are both strengths and limitations. For instance, we ensured a high degree of experimental control, at the expense of assessing the effects of alcohol in a standardized, sterile laboratory environment. As expected under these conditions, while self-ratings of intoxication (“feeling effect” and “high”) were robustly influenced by alcohol, neither “liking” nor “wanting” ratings were affected. On one hand, this suggests that our findings are unlikely to be primarily driven by expectations, since expectations of alcohol effects are linked to experiencing alcohol in a naturalistic context. At the same time, alcohol effects on decision making under laboratory conditions may differ from those “in the wild”. Similarly, although we make a distinction between personal and social decision making in terms of outcomes, all decisions in our study were taken in private in front of a computer. Thus, future studies could extend our findings by investigating the effects of alcohol on social decisions made in a public setting (e.g., observed by an audience), where social signaling and reputational concerns also come into play.

Designing the experiment, we emphasized task comprehension, and all decisions that involved money were incentivized (participants were paid for one randomly drawn decision at the end). Payments were implemented via a standard cell phone transfer system in order to circumvent concerns about differential transactions costs in the waiting-impulsivity task [ 56 ]. However, as a potential side effect, this made the larger-later option in this task more attractive than we had anticipated, resulting in a more than usual amount of upper censoring (people who chose the larger-later option for all trials) for this task. Our results for waiting impulsivity should be interpreted with this limitation in mind. Similarly, our finding that alcohol did not influence impulsivity, may not generalize to higher doses, or other populations. Also, even at the dose used, effects on impulsivity might be present in people with substance use disorders, externalizing psychopathology, or both.

The pattern of our results suggests that alcohol selectively moderates decision making in the social domain, at least for low-moderate doses of alcohol. This is consistent with existing theory that emphasizes the dual roles of shortsighted information processing and salient social cues in shaping decisions under the influence of alcohol [ 46 ]. Our findings are obtained in social drinkers without any AUD, but have potentially important implications for attempts to understand the emergence of AUD. Most prior alcohol challenge studies have focused exclusively on personal decision making, but changes in social cognition, ultimately resulting in social marginalization and exclusion, are at the core of the addictive process [ 57 , 58 ]. It has recently been shown that communicating deontologically rather than utilitarian-motivated decisions may be more advantageous to signal trustworthiness as group member [ 59 , 60 ]. Impairments in the ability to signal trustworthiness caused by alcohol use could contribute to social marginalization. These alcohol-induced effects on social cognition are likely to interact with pre-existing vulnerabilities to influence social functioning. Our findings highlight the importance of taking the social dimension of decision making into account to better understand the process of developing AUD.

Taking a broader perspective, to policymakers and everyday decision-makers alike, it is useful to know that the influence of alcohol on decision making is sensitive to social cues. Whether alcohol is ultimately good or bad for people’s decisions will likely depend on context. Perhaps surprisingly, from the narrow perspective of our sample and the specific tasks that we used, social outcomes were more advantageous among people who were given alcohol compared to people who were not.

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Acknowledgements

We are grateful to Åsa Axén, Sandra Boda, Sarah Gustavson, Lisbet Severin, Lina Koppel, Theodor Arlestig and David Andersson for assisting with data collection.

This work was supported by the Swedish Research Council (MH: 2013-07434; GT: 2018-01755) and the Swedish Research Council for Health, Working Life and Welfare (EP: 2020-00864). Funders had no role in study design, data collection, analysis, decision to publish, or preparation of the manuscript.

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These authors contributed equally: Hanna Karlsson, Emil Persson, Markus Heilig, Gustav Tinghög.

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Center for Social and Affective Neuroscience, Department of Biomedical and Clinical Sciences, Linköping University, 581 83, Linköping, Sweden

Hanna Karlsson, Irene Perini, Adam Yngve & Markus Heilig

Department of Management and Engineering, Division of Economics, Linköping University, 581 83, Linköping, Sweden

Emil Persson & Gustav Tinghög

The National Center for Priority Setting in Health Care, Department of Medical and Health Sciences, Linköping University, 581 83, Linköping, Sweden

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MH and GT provided funding for the study. HK, EP, IP, MH, and GT designed the study. HK, AY and GT collected the data. HK and EP analyzed the data and drafted the manuscript. All authors revised the manuscript and approved the final manuscript for submission.

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Karlsson, H., Persson, E., Perini, I. et al. Acute effects of alcohol on social and personal decision making. Neuropsychopharmacol. 47 , 824–831 (2022). https://doi.org/10.1038/s41386-021-01218-9

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The effects of alcohol use on academic achievement in high school

Ana i. balsa.

a Research Professor, Center for Applied Research on Poverty, Family, and Education, Department of Economics, Universidad de Montevideo; Prudencio de Pena 2440, Montevideo, 11600, Uruguay; Phone: (+598 2) 707 4461 ext 300; Fax: (+598 2) 707 4461 ext 325; yu.ude.mu@aslaba

Laura M. Giuliano

b Assistant Professor, Department of Economics, University of Miami, Coral Gables, FL 33124, United States; [email protected]

Michael T. French

c Professor of Health Economics, Health Economics Research Group, Department of Sociology, Department of Economics, and Department of Epidemiology and Public Health, University of Miami, Coral Gables, FL 33124, United States; ude.imaim@hcnerfm

This paper examines the effects of alcohol use on high school students’ quality of learning. We estimate fixed-effects models using data from the National Longitudinal Study of Adolescent Health. Our primary measure of academic achievement is the student’s GPA abstracted from official school transcripts. We find that increases in alcohol consumption result in small yet statistically significant reductions in GPA for male students and in statistically non-significant changes for females. For females, however, higher levels of drinking result in self-reported academic difficulty. The fixed-effects results are substantially smaller than OLS estimates, underscoring the importance of addressing unobserved individual heterogeneity.

1. Introduction

In the United States, one in four individuals between the ages of 12 and 20 drinks alcohol on a monthly basis, and a similar proportion of 12 th graders consumes five or more drinks in a row at least once every two weeks ( Newes-Adeyi, Chen, Williams, & Faden, 2007 ). Several studies have reported that alcohol use during adolescence affects educational attainment by decreasing the number of years of schooling and the likelihood of completing school ( Chatterji & De Simone, 2005 ; Cook & Moore, 1993 ; Gil-Lacruz & Molina, 2007 ; Koch & McGeary, 2005 ; McCluskey, Krohn, Lizotte, & Rodriguez, 2002 ; NIDA, 1998 ; Renna, 2007 ; Yamada, Kendrix, & Yamada, 1996 ) Other research using alternative estimation techniques suggests that the effects of teen drinking on years of education and schooling completion are very small and/or non-significant ( Chatterji, 2006 ; Dee & Evans, 2003 ; Koch & Ribar, 2001 ).

Despite a growing literature in this area, no study has convincingly answered the question of whether alcohol consumption inhibits high school students’ learning. Alcohol consumption could be an important determinant of how much a high school student learns without having a strong impact on his or her decision to stay in school or attend college. This question is fundamental and timely, given recent research showing that underage drinkers are susceptible to the immediate consequences of alcohol use, including blackouts, hangovers, and alcohol poisoning, and are at elevated risk of neurodegeneration (particularly in regions of the brain responsible for learning and memory), impairments in functional brain activity, and neurocognitive defects ( Zeigler et al., 2004 ).

A common and comprehensive measure of high school students’ learning is Grade Point Average (GPA). GPA is an important outcome because it is a key determinant of college admissions decisions and of job quality for those who do not attend college. Only a few studies have explored the association between alcohol use and GPA. Wolaver (2002) and Williams, Powell, and Wechsler (2003) have studied this association among college students, while DeSimone and Wolaver (2005) have investigated the effects of underage drinking on GPA during high school. The latter study found a negative association between high school drinking and grades, although it is not clear whether the effects are causal or the result of unobserved heterogeneity.

Understanding the relationship between teenage drinking and high school grades is pertinent given the high prevalence of alcohol use among this age cohort and recent research on adolescent brain development suggesting that early heavy alcohol use may have negative effects on the physical development of brain structure ( Brown, Tapert, Granholm, & Delis, 2000 ; Tapert & Brown, 1999 ). By affecting the quality of learning, underage drinking could have an impact on both college admissions and job quality independent of its effects on years of schooling or school completion.

In this paper, we estimate the effects of drinking in high school on the quality of learning as captured by high school GPA. The analysis employs data from Waves 1 and 2 of the National Longitudinal Study of Adolescent Health (Add Health), a nationally representative study that captures health-related behaviors of adolescents in grades 7 through 12 and their outcomes in young adulthood. Our analysis contributes to the literature in several ways. First, we focus on the effect of drinking on academic achievement during high school. To date, and to the best of our knowledge, only one other study in the literature has analyzed the consequences of underage drinking on high school GPA. Second, rather than rely on self-reported GPA, we use objective GPA data from academic transcripts, reducing the potential for systematic biases in the estimation results. Third, we take advantage of the longitudinal nature of the Add Health data and use fixed-effects models to purge the analysis of time invariant unobserved heterogeneity. Fixed-effects techniques are superior to instrumental variables (IV) estimation when the strength and reliability of the instruments are suspect ( French & Popovici, 2009 ). Finally, we explore a variety of mechanisms that could underlie a detrimental effect of alcohol use on grades. In addition to analyzing mediators related to exposure to education (days of school skipped), we investigate the effect of drinking on students’ ability to focus on and adhere to academic objectives.

2. Background and significance

Behavioral research has found that educational performance is highly correlated with substance abuse (e.g., Bukstein, Cornelius, Trunzo, Kelly, & Wood, 2005 ; Hawkins, Catalano, & Miller, 1992 ). Economic studies that look at the link between alcohol use and educational outcomes have customarily focused on measures of educational attainment such as graduation (from high school or college), college matriculation, and years of school completed (e.g., Bray, Zarkin, Ringwalt, & Qi, 2000 ; Chatterji, 2006 ; Cook & Moore, 1993 ; Dee & Evans 2003 ; Koch & Ribar, 2001 ; Mullahy & Sindelar, 1994 ; Renna, 2008 ; Yamada et al., 1996 ). Consistent with the behavioral research, early economic studies found that drinking reduced educational attainment. But the most rigorous behavioral studies and the early economic studies of attainment both faced the same limitation: they were cross-sectional and subject to potential omitted variables bias. Some of these cross-sectional economic studies attempted to improve estimation by using instrumental variables (IV). Cook and Moore (1993) and Yamada et al. (1996) found that heavy or frequent drinking in high school adversely affects high school and college completion. Nevertheless, the validity and reliability of the instruments in these studies are open to debate ( Chatterji, 2006 ; Dee & Evans, 2003 ; French & Popovici, 2009 ).

By contrast, more recent economic studies that arguably use better estimation methods have found that drinking has modest or negligible effects on educational attainment. Dee and Evans (2003) studied the effects of teen drinking on high school completion, college entrance, and college persistence. Employing changes in the legal drinking age across states over time as an instrument, they found no significant effect of teen drinking on educational attainment. Koch and Ribar (2001) reached a similar conclusion applying family fixed effects and instrumental variables to NLSY data. Though they found that drinking had a significant negative effect on the amount of schooling completed among men, the effect was small. Finally, Chatterji (2006) used a bivariate probit model of alcohol use and educational attainment to gauge the sensitivity of the estimates to various assumptions about the correlation of unobservable determinants of these variables. She concluded that there is no evidence of a causal relationship between alcohol use and educational attainment when the correlation coefficient is fixed at plausible levels.

Alcohol use could conceivably affect a student’s quality of learning and academic performance regardless of its impact on school completion. This possibility is suggested by Renna (2008) , who uses a research design similar to that used by Dee and Evans (2003) and finds that although binge drinking does not affect high school completion rates, it does significantly increase the probability that a student graduates with a GED rather than a high school diploma. Drinking could affect learning through a variety of mechanisms. Recent neurological research suggests that underage drinking can impair learning directly by causing alterations in the structure and function of the developing brain with consequences reaching far beyond adolescence ( Brown et al., 2000 ; White & Swartzwelder, 2004 ). Negative effects of alcohol use can emerge in areas such as planning and executive functioning, memory, spatial operations, and attention ( Brown et al., 2000 ; Giancola & Mezzich, 2000 ; Tapert & Brown, 1999 ). Alcohol use could also affect performance by reducing the number of hours committed to studying, completing homework assignments, and attending school.

We are aware of five economic studies that have examined whether drinking affects learning per se. Bray (2005) analyzed this issue indirectly by studying the effect of high school students’ drinking on subsequent wages, as mediated through human capital accumulation. He found that moderate high school drinking had a positive effect on returns to education and therefore on human capital accumulation. Heavier drinking reduced this gain slightly, but net effects were still positive. The other four studies approached the question directly by focusing on the association between drinking and GPA. Three of the GPA studies used data from the Harvard College Alcohol Study. Analyzing data from the study’s 1993 wave, both Wolaver (2002) and Williams et al. (2003) estimated the impact of college drinking on the quality of human capital acquisition as captured by study hours and GPA. Both studies found that drinking had a direct negative effect on GPA and an indirect negative effect through reduced study hours. Wolaver (2007) used data from the 1993 and 1997 waves and found that both high school and college binge drinking were associated with lower college GPA for males and females. For females, however, study time in college was negatively correlated with high school drinking but positively associated with college drinking.

To our knowledge, only one study has looked specifically at adolescent drinking and high school GPA. Analyzing data from the Youth Risk Behavior Survey, DeSimone and Wolaver (2005) used standard regression analysis to estimate whether drinking affected high school GPA. Even after controlling for many covariates, they found that drinking had a significant negative effect. Their results showed that the GPAs of binge drinkers were 0.4 points lower on average for both males and females. They also found that the effect of drinking on GPA peaked for ninth graders and declined thereafter and that drinking affected GPA more by reducing the likelihood of high grades than by increasing the likelihood of low grades.

All four GPA studies found that drinking has negative effects on GPA, but they each faced two limitations. First, they relied on self-reported GPA, which can produce biased results due to recall mistakes and intentional misreporting ( Zimmerman, Caldwell, & Bernat, 2006 ). Second, they used cross-sectional data. Despite these studies’ serious efforts to address unobserved individual heterogeneity, it remains questionable whether they identified a causal link between drinking and GPA.

In sum, early cross-sectional studies of educational attainment and GPA suggest that drinking can have a sizeable negative effect on both outcomes. By contrast, more recent studies of educational attainment that use improved estimation methods to address the endogeneity of alcohol use have found that drinking has negligible effects. The present paper is the first study of GPA that controls for individual heterogeneity in a fixed-effects framework, and our findings are consistent with the more recent studies of attainment that find small or negligible effects of alcohol consumption.

Add Health is a nationally representative study that catalogues health-related behaviors of adolescents in grades 7 through 12 and associated outcomes in young adulthood. An initial in-school survey was administered to 90,118 students attending 175 schools during the 1994/1995 school year. From the initial in-school sample, 20,745 students (and their parents) were administered an additional in-home interview in 1994–1995 and were re-interviewed one year later. In 2001–2002, Add Health respondents (aged 18 to 26) were re-interviewed in a third wave to investigate the influence of health-related behaviors during adolescence on individuals when they are young adults. During the Wave 3 data collection, Add Health respondents were asked to sign a Transcript Release Form (TRF) that authorized Add Health to identify schools last attended by study participants and request official transcripts from the schools. TRFs were signed by approximately 92% of Wave 3 respondents (about 70% of Wave 1 respondents).

The main outcome of interest, GPA, was abstracted from school transcripts and linked to respondents at each wave. Because most of the in-home interviews during Waves 1 and 2 were conducted during the Spring or Summer (at the end of the school year) and alcohol use questions referred to the past 12 months, we linked the in-home questionnaires with GPA data corresponding to the school year in which the respondent was enrolled or had just completed at the time of the interview.

The in-home questionnaires in Waves 1 and 2 offer extensive information on the student’s background, risk-taking behaviors, and other personal and family characteristics. These instruments were administered by computer assisted personal interview (CAPI) and computer assisted self-interview (CASI) techniques for more sensitive questions such as those on alcohol, drug, and tobacco use. Studies show that the mode of data collection can affect the level of reporting of sensitive behaviors. Both traditional self-administration and computer assisted self-administered interviews have been shown to increase reports of substance use or other risky behaviors relative to interviewer-administered approaches ( Azevedo, Bastos, Moreira, Lynch, & Metzger, 2006 ; Tourangeau & Smith, 1996 ; Wright, Aquilino, & Supple, 1998 ). Several measures of alcohol use were constructed on the basis of the CAPI/CASI questions: (1) whether the student drank alcohol at least once per week in the past 12 months, (2) whether the student binged (drank five or more drinks in a row) at least once per month in the past 12 months, (3) the average number of days per month on which the student drank in the past 12 months, (4) the average number of drinks consumed on any drinking day in the past 12 months, and (5) the total number of drinks per month consumed by the student in the past year.

Individual characteristics obtained from the in-home interviews included age, race, gender, grade in school, interview date, body mass index, religious beliefs and practices, employment status, health status, tobacco use, and illegal drug use. To capture environmental changes for respondents who changed schools, we constructed indicators for whether the respondent attended an Add Health sample school or sister school (e.g. the high school’s main feeder school) in each wave. We also considered family characteristics such as family structure, whether English was spoken at home, the number of children in the household, whether the resident mother and resident father worked, whether parents worked in blue- or white-collar jobs, and whether the family was on welfare. Finally, we took into account a number of variables describing interview and household characteristics as assessed by the interviewer: whether a parent(s) or other adults were present during the interview; whether the home was poorly kept; whether the home was in a rural, suburban, or commercial area; whether the home environment raised any safety concerns; and whether there was evidence of alcohol use in the household.

Respondents to the in-home surveys were also asked several questions about how they were doing in school. We constructed measures of how often the respondents skipped school, whether they had been suspended, and whether they were having difficulties paying attention in school, getting along with teachers, or doing their homework. We analyzed these secondary outcomes as possible mediators of an effect of alcohol use on GPA.

Our fixed-effects methodology required high school GPA data for Waves 1 and 2. For this reason, we restricted the sample to students in grades 9, 10, or 11 in Wave 1 (N=22,792) who were re-interviewed in Waves 2 and 3 (N=14,390), not mentally disabled (N=13,632), and for whom transcript data were available at Wave 3 (N=10,430). In addition, we excluded 1,846 observations that had missing values on at least one of the explanatory or control variables. 1 The final sample had 8,584 observations, which corresponded to Wave 1 and Wave 2 responses for 4,292 students with no missing information on high school GPA or other covariates across both waves. Male respondents accounted for 48% of the sample.

Table 1 shows summary statistics for the analysis sample by wave and gender. Abstracted GPA averages 2.5 for male students and 2.8 for female students, 2 with similar values in Waves 1 and 2. Approximately 9% of males and 6% of females reported drinking alcohol at least one time per week in Wave 1. The prevalence of binge drinking (consuming five or more drinks in a single episode) at least once a month is slightly higher: 11% among males and 7% among females. On average, the frequency of drinking in Wave 1 is 1.34 days per month for male respondents and 0.94 days per month for female respondents, while drinking intensity averages 2.8 drinks per episode for males and 2.2 drinks per episode for females. By Wave 2, alcohol consumption increases in all areas for both males and females. The increases for males are larger, ranging from an 18% increase in the average number of drinks per episode to a 55% increase in the fraction who binge monthly.

Summary Statistics

Note : Based on responses to survey questions regarding most recently completed school year.

Of the Wave 1 respondents, 87% of males and 90% of females had skipped school at least once in the past year, with males averaging 1.47 days skipped and females averaging 1.37 days. Further, 11% of males and 7% of females had been suspended at least once. Regarding the school difficulty measures, 50% of male respondents in Wave 1 reported at least one type of regular difficulty with school: 32% had difficulty paying attention, 15% did not get along with their teachers, and 35% had problems doing their homework. Among females, 40% had at least one difficulty: 25% with paying attention, 11% with teachers, and 26% with homework.

Table 2 tabulates changes in dichotomous measures of problem drinking by gender. Among males, 82.6% did not drink weekly in either wave; 8.1% became weekly drinkers in Wave 2; 4.8% stopped drinking weekly in Wave 2; and the remaining 4.5% drank weekly in both waves. Among females, 88.5% did not drink weekly in either wave; 5.3% became weekly drinkers in Wave 2; 3.7% stopped drinking weekly in Wave 2; and 2.5% drank weekly in both waves. The trends in monthly binging were similar, with the number of students who became monthly bingers exceeding that of students who stopped bingeing monthly in Wave 2. The proportion of respondents reporting binge-drinking monthly in both waves (6.6% and 3.4% for men and women, respectively) was higher than the fraction of students who reported drinking weekly in both waves.

Tabulation of Changes in Dichotomous Measures of Alcohol Use By Gender

4. Empirical methods and estimation issues

We examined the impact of adolescent drinking on GPA using fixed-effects estimation techniques. The following equation captures the relationship of interest:

where GPA it is grade point average of individual i during the Wave t school year, A it is a measure of alcohol consumption, X it is a set of other explanatory variables, c i are unobserved individual effects that are constant over time, ε it is an error term uncorrelated with A it and X it , and α, β a , and β x are parameters to estimate.

The coefficient of interest is β a , the effect of alcohol consumption on GPA. The key statistical problem in the estimation of β a is that alcohol consumption is likely to be correlated with individual-specific unobservable characteristics that also affect GPA. For instance, an adolescent with a difficult family background may react by shirking responsibilities at school and may, at the same time, be more likely to participate in risky activities. For this reason, OLS estimation of Equation (1) used with cross-sectional or pooled longitudinal data is likely to produce biased estimates of β a . In this paper, we took advantage of the two high school-administered waves in Add Health and estimated β a using fixed-effects techniques. Because Waves 1 and 2 were only one year apart, it is likely that most unobserved individual characteristics that are correlated with both GPA and alcohol use are constant over this short period. Subtracting the mean values of each variable over time, Equation (1) can be rewritten as:

Equation (2) eliminates time invariant individual heterogeneity ( c i ) and the corresponding bias associated with OLS estimation of Equation (1) .

We estimated Equation (2) using different sets of time-varying controls ( X it ). 3 We began by controlling only for unambiguously exogenous variables and progressively added variables that were increasingly likely to be affected by alcohol consumption. The first set of controls included only the respondent’s grade level, indicators for attending the sample school or sister school, and the date of the interview. In a second specification, we added household characteristics and interviewer remarks about the household and the interview. This specification includes indicators for the presence of parents and others during the interview and thus controls for a potentially important source of measurement error in the alcohol consumption variables. 4 The third specification added to the second specification those variables more likely to be endogenous such as BMI, religious beliefs/practices, employment, and health status. A fourth specification included tobacco and illegal drug use. By adding these behavioral controls, which could either be mediators or independent correlates of the drinking-GPA association, we examined whether the fixed-effects estimates were influenced by unmeasured time variant individual characteristics.

The fifth and sixth specifications were aimed at assessing possible mechanisms flowing from changes in alcohol use to changes in GPA. Previous research has found that part of the association between alcohol consumption and grades can be explained by a reduction in study hours. Add Health did not directly ask respondents about study effort. It did, however, ask about suspensions and days skipped from school. These school attendance variables were added to the set of controls to test whether an effect of alcohol use on human capital accumulation worked extensively through the quantity of, or exposure to, schooling. Alternatively, an effect of alcohol use on grades could be explained by temporary or permanent alterations in the structure and functioning of an adolescent’s developing brain with resulting changes in levels of concentration and understanding (an intensive mechanism). To test for the mediating role of this pathway, we added a set of dichotomous variables measuring whether the student reported having trouble at least once a week with each of the following: (i) paying attention in school, (ii) getting along with teachers, and (iii) doing homework.

Finally, we considered the number of days the student skipped school and the likelihood of having difficulties with school as two alternative outcomes and estimated the association between these variables and alcohol use, applying the same fixed-effects methodology as in Equation (2) . To analyze difficulties with school as an outcome, we constructed a dichotomous variable that is equal to one if the student faced at least one of the three difficulties listed above. We estimated the effect of alcohol use on this variable using a fixed-effects logit technique.

Separate regressions were run for male and female respondents. The literature shows that males and females behave differently both in terms of alcohol use ( Ham & Hope, 2003 ; Johnston, O’Malley, Bachman, & Schulenberg, 2007 ; Schulenberg, O’Malley, Bachman, Wadsworth, & Johnston, 1996 ; Wechsler, Davenport, Dowdall, Moeykens, & Castillo, 1994 ) and school achievement ( Dwyer & Johnson, 1997 ; Jacob, 2002 ; Kleinfeld, 1998 ). These gender differences are clearly evident in the summary statistics presented in Table 1 . Furthermore, the medical literature suggests that there may be gender differences in the impact of alcohol consumption on cognitive abilities (e.g. Hommer, 2003 ).

In addition to examining differential effects by gender, we tested for differential effects of alcohol use along three other dimensions: age, the direction of change in alcohol use (increases vs. decreases), and initial GPA. These tests, as well as other extensions and robustness checks, are described in Section 6.

Table 3 shows the fixed-effects estimates for β a from Equation (2) . Each cell depicts a different model specification defined by a particular measure of alcohol use and a distinctive set of control variables. Rows (a)-(d) denote the alcohol use variable(s) in each specification, and Columns (1)-(6) correspond to the different sets of covariates. Control variables are added hierarchically from (1) to (3). We first adjusted only by grade level, sample school and sister school indicators, and interview date (Column (1)). We then added time-varying household characteristics and interviewer assessments (Column (2)), followed by other individual time-varying controls (Column (3)). Column (4) adds controls for the use of other substances, which could either be correlates or consequences of alcohol use. Columns (5) and (6) consider other potential mediators of the effects found in (1)-(3) such as days skipped, suspensions from school, and academic difficulties.

Fixed effects Estimates; Dependent Variable = GPA

Notes : See Table 1 for list of control variables in each model specification. Robust standard errors in parentheses;

The results for males provide evidence of a negative yet small effect of alcohol use on GPA. No major changes were observed in the estimates across the different specifications that incrementally added more controls, suggesting that the results are probably robust to unmeasured time-varying characteristics. In what follows, therefore, we describe the results in Column (3), which controls for the greatest number of individual time-varying factors (with the exception of tobacco and illicit drug use). Weekly drinking and monthly binge drinking are both negatively associated with GPA, but neither of these coefficients is statistically significant (Rows (a) and (b)). The continuous measure of alcohol consumption has a statistically significant coefficient (Row (c)), suggesting that increasing one’s alcohol intake by 100 drinks per month reduces GPA by 0.07 points, or 2.8% relative to the mean. The results in Row (d) suggest that variation in both the frequency and the intensity of alcohol use contributes to the estimated effect on grades. An increase of one day per month in drinking frequency reduces GPA by 0.005 points, and consumption of one additional drink per episode reduces GPA by 0.004 points.

Columns (4)-(6) report the estimates of interest after controlling for use of other substances, days skipped or suspended from school, and difficulties with school. Relative to the effects identified in Column (3), controlling for tobacco and illegal drug use reduces the negative effect of total number of drinks on GPA by 9% or 0.006 GPA points (see row (c), Column (4)). Adding the school attendance variables to the set of controls in Column (3) results in a point estimate of −0.06 or 0.01 GPA points below the coefficient in Column (3) (see Column (5)). Adding the school difficulty variables results in a reduction in GPA of 0.007 GPA points or a 10% decrease relative to the estimate in Column (3). While not shown in the table, the inclusion of both school difficulty and attendance variables as controls explains approximately 20% of the effect of alcohol use on grades, with the alcohol use estimates remaining statistically significant at the 10% level.

For females, the estimated coefficients are much smaller than those for males, and for two measures (binge-drinking and drinking frequency), the estimates are actually positive. However, none of the coefficients are statistically significant at conventional levels. 5 Interestingly, after controlling for substance use, difficulties with school, and school attendance, the estimates become less negative or more positive. But they remain statistically non significant.

Table 4 shows the effect of alcohol use on the number of school days skipped during the past year. These results are qualitatively similar to the findings for GPA, suggesting some small and statistically significant effects for males but no significant effects for females. For males, increasing the number of drinks per month by 100 leads to an additional 0.72 days skipped (p<0.10) when controlling for household features, interviewer comments, and individual characteristics such as body mass index, religiosity, employment, and health status (see Column (3), Row (c)). Controlling for tobacco and illegal drug use reduces the coefficient slightly to 0.69 days. The results in Row (d) suggest that this effect is driven mainly by variation in drinking intensity, with an additional drink per episode resulting in an increase of 0.06 days skipped.

Fixed-effects Estimates; Dependent Variable = School Days Skipped

Notes : Robust standard errors in parentheses;

Table 5 contains estimates of the relationship between alcohol use and our dichotomous measure of having difficulty in school. For males, we found one small but statistically significant effect: consumption of an additional 100 drinks per month is associated with a 4% increase in the probability of having trouble in school. For females, the estimated coefficients are all positive and larger than those found for males, and four out of five are statistically significant. The probability of having trouble in school is roughly 11% higher for females who drink weekly relative to those who do not, and there is a similar effect for monthly binge drinking (Rows (a) and (b)). Furthermore, the likelihood of difficulties increases by 7% with an additional 100 drinks per month (Row (c)). These findings suggest that female students suffer adverse consequences from alcohol consumption, even if these effects do not translate into lower grades. Finally, in Row (d), we see that these adverse effects are driven by increases in drinking frequency rather than drinking intensity.

Fixed-effects Logit Estimates; Dependent Variable = Difficulty with School

Notes : Dependent variable is a dummy variable equal to one if respondent had trouble at least once a week with one or more of the following: (1) paying attention in school, (2) getting along with teachers, or (3) doing homework. Robust standard errors in parentheses;

Our main results thus far point to two basic conclusions. After controlling for individual fixed effects, alcohol use in high school has a relatively minor influence on GPA. But there are also some interesting gender differences in these effects. For males, we find small negative effects on GPA that are partially mediated by increased school absences and difficulties with school-related tasks. For females, on the other hand, we find that alcohol use does not significantly affect GPA, but female drinkers encounter a higher probability of having difficulties at school.

Our basic estimates of the effects of drinking on GPA complement those of Koch and Ribar (2001) , who find small effects of drinking on school completion for males and non-significant effects for females. However, our analysis of school-related difficulties suggests that females are not immune to the consequences of drinking. Namely, females are able to compensate for the negative effects of drinking (e.g., by working harder or studying more) so that their grades are unaffected. This interpretation is consistent with Wolaver’s (2007) finding that binge drinking in college is associated with increased study hours for women but with reduced study hours for men. It is also reminiscent of findings in the educational psychology and sociology literatures that girls get better grades than boys, and some of this difference can be explained by gender differences in classroom behavior ( Downey & Vogt Yuan, 2005 ) or by greater levels of self-discipline among girls ( Duckworth & Seligman, 2006 ).

When interpreting our results, there are some important caveats to keep in mind. First, we must emphasize that they reflect the contemporaneous effects of alcohol use. As such, they say nothing about the possible cumulative effects that several years of drinking might have on academic performance. Second, we can only examine the effect of alcohol use on GPA for those students who remain in school. Unfortunately, we cannot address potential selection bias due to high school dropouts because of the high rate of missing GPA data for those students who dropped out after Wave 1. 6 Third, we acknowledge that our fixed-effects results could still be biased if we failed to account for important time-varying individual characteristics that are associated with GPA differentials across waves. It is reassuring, however, that our results are generally insensitive to the subsequent inclusion of additional time-varying (and likely endogenous) characteristics, such as health status, employment, religiosity, tobacco use, and illicit drug use. Finally, we cannot rule out possible reverse causality whereby academic achievement affects alcohol use. Future research using new waves of the data may provide further insight on this issue. In the next section, we discuss some additional issues that we are able to explore via robustness checks and extensions.

6. Robustness checks and extensions

6.1. ols versus fixed effects.

In addition to running fixed-effects models, we estimated β a using OLS. Separate regressions were run by gender and by wave. We first regressed GPA on measures of alcohol use and the full set of time-varying controls used in the fixed-effects estimation (see Column (3), Table 3 ). Next, we added other time-invariant measures such as demographics, household characteristics, and school characteristics. Finally, we controlled for tobacco and illegal drug use. The comparison between fixed-effects and OLS estimates (Appendix Table A1 ) sheds light on the extent of the bias in β ^ a OLS . For males, OLS estimates for Wave 1 were 3 to 6 times larger (more negative) than fixed-effects estimates (depending on the measure of alcohol use), and OLS estimates in Wave 2 were 3 to 4 times larger than those from the fixed-effects estimation. The bias was even more pronounced for females. Contrary to the results in Table 3 , OLS estimates for females were statistically significant, quantitatively large, and usually more negative than the estimates for males.

OLS Cross-sectional Estimates; Dependent Variable = GPA

6.2. Outlier analysis

Concerns about misreporting at the extreme tails of the alcohol use distributions led us to re-estimate the fixed-effects model after addressing these outliers. A common method for addressing extreme outliers without deleting observations is to “winsorize” ( Dixon, 1960 ). This technique reassigns all outlier values to the closest value at the beginning of the user-defined tail (e.g., 1%, 5%, or 10% tails). For the present analysis, we used both 1% and 5% tails. As a more conventional outlier approach, we also re-estimated the models after dropping those observations in the 1% tails. In both cases we winsorized or dropped the tails using the full Wave 1 and Wave 2 distribution (in levels) and then estimated differential effects.

After making these outlier corrections, the estimates for males became larger in absolute value and more significant, but the estimates for females remained statistically non-significant with no consistent pattern of change. 7 For males, dropping the 1% tails increased the effect of 100 drinks per month on GPA to −0.15 points (from −0.07 points when analyzing the full sample). Winsorizing the 5% tails further increased the estimated effect size to −0.31 points.

We offer two possible interpretations of these results for males. First, measurement error is probably more substantial among heavier drinkers and among respondents with the biggest changes in alcohol consumption across waves, which could cause attenuation bias at the top end. 8 Second, the effect of drinks per month on GPA could be smaller among male heavier drinkers, suggesting non-linear effects. Interestingly, neither of these concerns appears to be important for the analysis of females.

6.3. Differential effects

Thus far we have reported the differential effects of alcohol use on GPA for males and females. Here, we consider differential effects along three other dimensions: age, direction of change in alcohol use (increases vs. decreases), and initial GPA. To examine the first two of these effects, we added to Equation (2) interactions of the alcohol use measure with dichotomous variables indicating (i) that the student was 16 or older, and (ii) that alcohol use had decreased between Waves 1 and 2. 9 For males, the negative effects of drinking on GPA were consistently larger among respondents who were younger than 16 years old. None of the interaction terms, however, were statistically significant. We found no consistent or significant differences in the effect of alcohol consumption between respondents whose consumption increased and those whose consumption decreased between Waves 1 and 2. All results were non-significant and smaller in magnitude for females. It should be noted, however, that the lack of significant effects could be attributed, at least in part, to low statistical power as some of the disaggregated groups had less than 450 observations per wave.

To examine whether drinking is more likely to affect low achievers (those with initial low GPA) than high achievers (higher initial GPA), we estimated two fixed-effects linear probability regressions. The first regression estimated the impact of alcohol use on the likelihood of having an average GPA of C or less, and the second regression explored the effect of drinking on the likelihood of having a GPA of B- or better. For males, we found that monthly binging was negatively associated with the probability of obtaining a B- or higher average and that increases in number of drinks per month led to a higher likelihood of having a GPA of C or worse. Frequency of drinking, rather than intensity, was the trigger for having a GPA of C or worse. For females, most coefficient estimates were not significant, although the frequency of drinking was negatively associated with the probability of having a GPA of C or worse.

6.4 Self-reported versus abstracted GPA

One of the key advantages of using Add Health data is the availability of abstracted high school grades. Because most educational studies do not have such objective data, we repeated the fixed-effects estimation of Equation (2) using self-reported GPA rather than transcript-abstracted GPA. To facilitate comparison, the estimation sample was restricted to observations with both abstracted and self-reported GPA (N=2,164 for males and 2,418 for females).

The results reveal another interesting contrast between males and females. For males, the results based on self-reported grades were fairly consistent with the results based on abstracted grades, although the estimated effects of binging and drinking intensity were somewhat larger (i.e., more negative) when based on self-reported grades. But for females, the results based on self-reported grades showed positive effects of alcohol consumption that were statistically significant at the 10% level for three out of five consumption measures (monthly binging, total drinks per month, and drinks per episode). Furthermore, with the exception of the frequency measure (drinking days per month), the estimated effects were all substantially larger (i.e., more positive) when based on self-reported GPA. This suggests that females who drink more intensively tend to inflate their academic performance in school, even though their actual performance is not significantly different from that of those who drink less. Males who drink more intensely, on the other hand, may tend to deflate their academic accomplishments.

6.5. Analysis of dropouts

In Table 3 , we estimated the effects of alcohol consumption on GPA conditional on being enrolled in school during the two observation years. While increased drinking could lead an adolescent to drop out of school, reduced drinking could lead a dropout to re-enroll. Our GPA results do not address either of these possible effects. Of those who were in 9 th grade in Wave 1, roughly 2.3% dropped out before Wave 2. Of those who were in 10 th and 11 th grades in Wave 1, the dropout rates were 3.7% and 5.0%, respectively. Our core estimates would be biased if the effect of alcohol use on GPA for non-dropouts differed systematically from the unobserved effect of alcohol use on GPA for dropouts and re-enrollers in the event that these students had stayed in school continuously.

To determine whether dropouts differed significantly from non-dropouts, we compared GPA and drinking patterns across the two groups. Unfortunately, dropouts were much more likely to have missing GPA data for the years they were in school, 10 so the comparison itself has some inherent bias. Nevertheless, for those who were not missing Wave 1 GPA data, we found that mean GPA was significantly lower for dropouts (1.11) than for those students who stayed in school at least another year (2.66). Dropouts were also older in Wave 1 (16.9 vs. 15.9 years old) and more likely to be male (54% vs. 48%). They also consumed alcohol more often and with greater intensity in the first wave. While there is evidence of differences across the two groups in Wave 1, it is unclear whether dropouts would have differed systematically with respect to changes in GPA and in drinking behavior over time if they had stayed in school. Due to the small number of dropout observations with Wave 1 GPA data, we could not reliably estimate a selection correction model.

6.6. Attrition and missing data

As described in the data section, a large fraction of the Add Health respondents who were in 9th, 10th, or 11th grade in Wave 1 were excluded from our analysis either because they did not participate in Waves 2 or 3, did not have transcript data, or had missing data for one or more variables used in the analysis. (The excluded sample consisted of 7,104 individuals out of a total of 11,396 potentially eligible.) Mean characteristics were compared for individuals in the sample under analysis (N=4,292) and excluded respondents (N=7,104) in Wave 1. Those in the analysis sample had higher GPAs (both self-reported and abstracted, when available) and were less likely to have difficulties at school, to have been suspended from school, or to have skipped school. They were less likely to drink or to drink intensively if they drank. They were more likely to be female and White, speak English at home, have highly educated parents, have a resident mother or father at home, and be in good health. They were less likely to have parents on welfare, live in commercial areas or poorly kept buildings, and smoke and use drugs.

The above comparisons suggest that our estimates are representative of the sample of adolescents who participated in Waves 2 and 3 but not necessarily of the full 9 th , 10 th , and 11 th grade sample interviewed at baseline. To assess the magnitude and sign of the potential attrition bias in our estimates, we considered comparing fixed-effects estimates for these two samples using self-reported GPA as the dependent variable. But self-reported GPA also presented a considerable number of missing values, especially for those in the excluded sample at Wave 2. Complete measures of self-reported GPA in Waves 1 and 2 were available for 60% of the individuals in the analysis sample and for less than 30% of individuals in the excluded sample.

As an alternative check, we used OLS to estimate the effects of alcohol use on self-reported GPA in Wave 1 for the excluded sample, and compared these to OLS coefficients for our analysis sample in Wave 1. The effects of alcohol use on self-reported grades were smaller for individuals excluded from our core analysis. Because the excluded individuals tend to consume more alcohol, the finding of smaller effects for these individuals is consistent with either of the two explanations discussed in Section 6.2 above. First, the effect of consuming alcohol on GPA could be smaller for those who drink more. And second, measurement error is probably more serious among heavier drinkers, potentially causing more attenuation bias in this sample.

To summarize, the analysis described above suggests that some caution should be exercised when extrapolating the results in this paper to other populations. Due to missing data, our analysis excludes many of the more extreme cases (in terms of grades, substance use, and socioeconomic status). However, our analysis suggests that the effects of alcohol use on grades are, if anything, smaller for these excluded individuals. It therefore supports our main conclusions that the effects of alcohol use on GPA tend to be small and that failure to account for unobserved individual heterogeneity is responsible for some of the large negative estimates identified in previous research.

7. Conclusion

Though a number of investigations have studied the associations between alcohol use and years of schooling, less is known about the impact of adolescent drinking on the process and quality of learning for those who remain in school. Moreover, studies that have examined the impact of drinking on learning have faced two important limitations. First, they have relied on self-reported grades as the key measure of learning and are therefore subject to potential biases that result from self-reporting. Second, they have relied on cross-sectional data and suffer from potential biases due either to unobserved individual heterogeneity or to weak or questionable instrumental variables.

In the present study, we contribute to the existing literature by exploiting several unique features of the nationally representative Add Health survey. First, we measure learning with grade point averages obtained from the respondents’ official school transcripts. Second, we exploit Add Health’s longitudinal design to estimate models with individual fixed effects. This technique eliminates the bias that results from time-invariant unobserved individual heterogeneity in the determinants of alcohol use and GPA. Finally, we explore a variety of pathways that could explain the association between alcohol use and grades. In particular, we examine the effects of alcohol consumption on both the quantity of schooling—as measured by days of school skipped—and the quality—as measured by difficulties with concentrating in school, getting along with teachers, or completing homework.

The main results show that, in general, increases in alcohol consumption result in statistically significant but quantitatively small reductions in GPA for male students and in statistically non-significant changes for females. For both males and females, comparisons of the fixed-effects models with standard cross-sectional models suggest that large biases can result from the failure to adequately control for unobserved individual heterogeneity. Our findings are thus closely aligned with those of Koch and Ribar (2001) and Dee and Evans (2003) , who reach a similar conclusion regarding the effects of drinking on school completion.

Our analysis also reveals some interesting gender differences in how alcohol consumption affects learning in high school. Our results suggest that for males, alcohol consumption has a small negative effect on GPA and this effect is partially mediated by increased school absences and by difficulties with school-related tasks. For females, however, we find that alcohol use does not significantly affect GPA, even though it significantly increases the probability of encountering difficulties at school. Gender differences in high school performance are well documented in the educational psychology and sociology literatures, yet no previous studies have estimated gender differences in high school learning that are directly associated with alcohol use. Our study is therefore unique in that regard.

Finally, our study also highlights the potential pitfalls of using self-reported grades to measure academic performance. Not only do we find evidence that use of self-reports leads to bias; we also find that the bias differs by gender, as drinking is associated with grade inflation among females and grade deflation among males. Hence, the conceptual discoveries uncovered in this research may be as important for future investigations as the empirical results are for current educational programs and policies.

Acknowledgements

Financial assistance for this study was provided by research grants from the National Institute on Alcohol Abuse and Alcoholism (R01 AA15695, R01 AA13167, and R03 AA016371) and the National Institute on Drug Abuse (RO1 DA018645). This research uses data from Add Health, 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, and funded by grant P01-HD31921 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, with cooperative funding from 23 other federal agencies and foundations. Special acknowledgment is due Ronald R. Rindfuss and Barbara Entwisle for assistance in the original design. Information on how to obtain the Add Health data files is available on the Add Health website ( http://www.cpc.unc.edu/addhealth ). No direct support was received from grant P01-HD31921 for this analysis. We gratefully acknowledge the input of several colleagues at the University of Miami. We are also indebted to Allison Johnson, William Russell, and Carmen Martinez for editorial and administrative assistance. The authors are entirely responsible for the research and results reported in this paper, and their position or opinions do not necessarily represent those of the University of Miami, the National Institute on Alcohol Abuse and Alcoholism, or the National Institute on Drug Abuse.

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1 Due to a significant fraction of missing responses, we imputed household income and household welfare status using both predicted values on the basis of other covariates and the sample mean for households that were also missing some of the predicting covariates. We added dummy variables to indicate when an observation was imputed.

2 Grades and numerical grade-point equivalents have been established for varying levels of a student’s academic performance. These grade-point equivalents are used to determine a student’s grade-point average. Grades of A, A-, and B+ with respective grade-point equivalents of 4.00, 3.67, and 3.33 represent an “excellent” quality of performance. Grades of B, B−, and C+ with grade-point equivalents of 3.00, 2.67, and 2.33 represent a “good” quality of performance. A grade of C with grade-point equivalent of 2.00 represents a “satisfactory” level of performance, a grade of D with grade-point equivalent of 1.00 represents a “poor” quality of performance, and a grade of F with grade-point equivalent of 0.00 represents failure.

3 Note that some demographics (e.g., race, ethnicity) and other variables that are constant over time do not appear in Equation (2) because they present no variation across waves.

4 Of particular concern is the possibility that measurement error due to misreporting varies across waves—either because of random recall errors or because of changes in the interview conditions. (For example, the proportion of interviews in which others were present declined from roughly 42% to 25% between Wave 1 and Wave 2.) Such measurement error could lead to attenuation bias in our fixed-effects model. On the other hand, reporting biases that are similar and stable over time are eliminated by the fixed-effects specification.

5 We tested the significance of these differences by pooling males and females and including an interaction of a gender dummy with the alcohol consumption measure in each model. We found statistically significant differences in the effects of monthly bingeing, drinks per month, and drinking days per month.

6 If alcohol use has small or negligible effects on school completion - as found by Chatterji (2006) , Dee and Evans (2003) , and Koch and Ribar (2001) - then such selection bias will also be small.

7 These results are not presented in the tables but are available from the authors upon request.

8 Examination of the outliers showed that only 15% of those who reported a total number of drinks above the 95th percentile of the distribution did so in both waves.

9 These fixed-effects regressions were adjusted by the same set of controls as in Table (3) , Column (3).

10 More than two-thirds of those who dropped out between Waves 1 and 2 were missing Wave 1 GPA data

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