Trends in the sequence of initiation of alcohol, tobacco, and marijuana use among adolescents in Argentina and Chile from 2001 to 2017

BACKGROUND

Variation in drug policies, norms, and substance use over time and across countries may affect the normative sequences of adolescent substance use initiation. We estimated relative and absolute time-varying associations between prior alcohol and tobacco use and adolescent marijuana initiation in Argentina and Chile. Relative measures quantify the magnitude of the associations, whereas absolute measures quantify excess risk.

METHODS

We analyzed repeated, cross-sectional survey data from the National Surveys on Drug Use Among Secondary School Students in Argentina (2001-2014) and Chile (2001-2017). Participants included 8th, 10th, and 12th grade students (N = 680,156). Linear regression models described trends over time in the average age of first use of alcohol, tobacco, and marijuana. Logistic regression models were used to estimate time-varying risk ratios and risk differences of the associations between prior alcohol and tobacco use and current-year marijuana initiation.

RESULTS

Average age of marijuana initiation increased and then decreased in Argentina and declined in Chile. In both countries, the relative associations between prior tobacco use and marijuana initiation weakened amid declining rates of tobacco use; e.g., in Argentina, the risk ratio was 19.9 (95% CI: 9.0-30.8) in 2001 and 11.6 (95% CI: 9.0-13.2) in 2014. The relative association between prior alcohol use and marijuana initiation weakened Chile, but not in Argentina. On the contrary, risk differences (RD) increased substantially across both relationships and countries, e.g., in Argentina, the RD for tobacco was 3% (95% CI: 0.02-0.03) in 2001 and 12% (95% CI: 0.11-0.13) in 2014.

CONCLUSION

Diverging trends in risk ratios and risk differences highlight the utility of examining multiple measures of association. Variation in the strength of the associations over time and place suggests the influence of environmental factors. Increasing risk differences indicate alcohol and tobacco use may be important targets for interventions to reduce adolescent marijuana use.

Using Substances to Cope With the COVID-19 Pandemic: U.S. National Data at Age 19 Years

PURPOSE

To examine predictors of using substances to cope with the COVID-19 pandemic, including pandemic-related isolation, stress, economic hardship, demographics, and prepandemic substance use.

METHODS

A U.S. national sample (N = 1,244) was followed from the 12th grade in Spring 2019 to Fall 2020 (M = 19.6 years) when young adults were asked about their use of marijuana, vaping, drinking, and other drugs to cope.

RESULTS

In Fall 2020, 15.7% reported using marijuana, 8.9% increased vaping, and 8.2% increased drinking to cope with social distancing and isolation. In multivariable analyses controlling for demographics and prepandemic substance use, COVID-related isolation was associated with marijuana use (odds ratio = 1.31, 95% confidence interval = 1.06-1.63) and economic hardship with increased drinking (odds ratio = 1.39, 95% confidence interval = 1.01-1.92). There were few demographic differences. Most (>80%) who reported COVID-related substance use coping used that substance before pandemic.

DISCUSSION

Young people reported using substances to cope with the COVID-19 pandemic, especially if they reported prepandemic use.

Age, Period, and Cohort Effects of Internalizing Symptoms Among US Students and the Influence of Self-Reported Frequency of Attaining 7 or More Hours of Sleep: Results From the Monitoring the Future Survey 1991-2019

Adolescent internalizing symptoms have increased since 2010, whereas adequate sleep has declined for several decades. It remains unclear how self-reported sleep attainment has affected internalizing-symptoms trends. Using 1991-2019 data from the Monitoring the Future Study (n ~ 390,000), we estimated age-period-cohort effects in adolescent internalizing symptoms (e.g., loneliness, self-esteem, self-derogation, depressive affect) and the association with yearly prevalence of a survey-assessed, self-reported measure of attaining ≥7 hours of sleep most nights. We focused our main analysis on loneliness and used median odds ratios to measure variance in loneliness associated with period differences. We observed limited signals for cohort effects and modeled only period effects. The feeling of loneliness increased by 0.83% per year; adolescents in 2019 had 0.68 (95% CI: 0.49, 0.87) increased log odds of loneliness compared with the mean, which was consistent by race/ethnicity and parental education. Girls experienced steeper increases in loneliness than boys (P < 0.0001). The period-effect median odds ratio for loneliness was 1.16 (variance = 0.09; 95% CI: 0.06, 0.17) before adjustment for self-reported frequency of getting ≥7 hours sleep versus 1.07 (variance = 0.02; 95% CI: 0.01, 0.03) after adjustment. Adolescents across cohorts are experiencing worsening internalizing symptoms. Self-reported frequency of <7 hours sleep partially explains increases in loneliness, indicating the need for feasibility trials to study the effect of increasing sleep attainment on internalizing symptoms.

Staying Physically Active Is Associated with Better Mental Health and Sleep Health Outcomes during the Initial Period of COVID-19 Induced Nation-Wide Lockdown in Jordan

Jordan, a Middle Eastern country, initially responded to an outbreak of COVID-19 cases within its own borders by imposing a 7-week strict lockdown and closure of international and domestic travel. Such measures drastically influenced lifestyle behaviors of the population. This study aimed to investigate the prevalence of physical activity, and its association with mental and sleep health outcomes among Jordanians during a period of COVID-19 induced lockdown. Validated questionnaires were administered using a web-based platform to evaluate moderate-to-vigorous physical activity (MVPA), anxiety and depressive symptoms, sleep health, and sociodemographic characteristics. A modified Poisson regression model with robust error variance was used to estimate adjusted prevalence ratios (aPRs) and 95% confidence intervals (CIs). Compared to participants who met the guidelines, those who did not had significantly higher prevalence of moderate or severe anxiety symptoms than that of minimal or mild anxiety symptoms and increased depressive symptoms. Insufficient MVPA was associated with higher prevalence of poor sleep quality, short sleep duration (<7 h) and sleep problems. Overall, sufficient MVPA was associated with better mental and sleep health during the COVID-19 induced nation-wide lockdown in Jordan. While further research is necessary, promoting physical activity during the lockdown could potentially improve mental and sleep health outcomes among the population.

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

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

The politics of depression: Diverging trends in internalizing symptoms among US adolescents by political beliefs

Adolescent internalizing symptoms (e.g. depressive affect) have increased over the past decade in the US, particularly among girls. The reasons for these increases are unclear. We hypothesize that increasing exposure to politicized events has contributed to these trends in adolescent internalizing symptoms, and that effects may be differential by political beliefs and sociodemographic characteristics. We analyzed nationally-representative data from 2005 to 2018 Monitoring the Future annual cross-sectional samples of 12th-grade students (N = 86,138). We examined self-reported political beliefs, sex, and parental education as predictors of four internalizing symptom scales over time, including depressive affect. From 2005 to 2018, 19.8% of students identified as liberal and 18.1% identified as conservative, with little change over time. Depressive affect (DA) scores increased for all adolescents after 2010, but increases were most pronounced for female liberal adolescents (b for interaction = 0.17, 95% CI: 0.01, 0.32), and scores were highest overall for female liberal adolescents with low parental education (Mean DA 2010: 2.02, SD 0.81/2018: 2.75, SD 0.92). Findings were consistent across multiple internalizing symptoms outcomes. Trends in adolescent internalizing symptoms diverged by political beliefs, sex, and parental education over time, with female liberal adolescents experiencing the largest increases in depressive symptoms, especially in the context of demographic risk factors including parental education. These findings indicate a growing mental health disparity between adolescents who identify with certain political beliefs. It is therefore possible that the ideological lenses through which adolescents view the political climate differentially affect their mental wellbeing.

Suicidal ideation and attempts following nonmedical use of prescription opioids and related disorder

BACKGROUND

Since 1999, the rate of fatal prescription opioid overdoses and of suicides has dramatically increased in the USA. These increases, which have occurred among similar demographic groups, have led to the hypothesis that the opioid epidemic contributed to increases in suicidal behavior, though the underlying association remains poorly defined. We examine the association between nonmedical use of prescription opioids/opioid use disorder and suicidal ideation/attempts.

METHODS

We used longitudinal data from a national representative sample of the US adult population, the National Epidemiologic Survey on Alcohol and Related Conditions. Participants (n = 34 653) were interviewed in 2001-2002 (wave 1) and re-interviewed approximately 3 years later (wave 2). A propensity score analysis estimated the association between exposure to prescription opioids at wave 1 and prevalent/incident suicidal behavior at wave 2.

RESULTS

Heavy/frequent (⩾2-3 times a month) prescription opioid use was associated with prevalent suicide attempts [adjusted risk ratio (ARR) = 2.75, 95% CI 1.35-5.60]. Prescription opioid use disorder was associated with prevalent (ARR = 1.98, 95% CI 1.20-3.28) and incident suicidal ideation (ARR = 2.59, 95% CI 1.25-5.37), and prevalent attempts (ARR = 4.19, 95% CI 1.71-10.27). None of the exposures was associated with incident suicide attempts.

CONCLUSIONS

Heavy/frequent opioid use and related disorder were associated with prevalent suicide attempts; opioid use disorder was also associated with the incident and prevalent suicidal ideation. Given population increases in nonmedical use of prescription opioids and disorder, the opioid crisis may have contributed to population increases in suicidal ideation.

G-Computation and Agent-Based Modeling for Social Epidemiology: Can Population Interventions Prevent Posttraumatic Stress Disorder?

Agent-based modeling and g-computation can both be used to estimate impacts of intervening on complex systems. We explored each modeling approach within an applied example: interventions to reduce posttraumatic stress disorder (PTSD). We used data from a cohort of 2,282 adults representative of the adult population of the New York City metropolitan area from 2002-2006, of whom 16.3% developed PTSD over their lifetimes. We built 4 models: g-computation, an agent-based model (ABM) with no between-agent interactions, an ABM with violent-interaction dynamics, and an ABM with neighborhood dynamics. Three interventions were tested: 1) reducing violent victimization by 37.2% (real-world reduction); 2) reducing violent victimization by100%; and 3) supplementing the income of 20% of lower-income participants. The g-computation model estimated population-level PTSD risk reductions of 0.12% (95% confidence interval (CI): -0.16, 0.29), 0.28% (95% CI: -0.30, 0.70), and 1.55% (95% CI: 0.40, 2.12), respectively. The ABM with no interactions replicated the findings from g-computation. Introduction of interaction dynamics modestly decreased estimated intervention effects (income-supplement risk reduction dropped to 1.47%), whereas introduction of neighborhood dynamics modestly increased effectiveness (income-supplement risk reduction increased to 1.58%). Compared with g-computation, agent-based modeling permitted deeper exploration of complex systems dynamics at the cost of further assumptions.

Association of Comorbid Behavioral and Medical Conditions With Cannabis Use Disorder in Pregnancy

IMPORTANCE

Prenatal cannabis use continues to increase, yet studies of the demographic, psychiatric, and medical characteristics associated with cannabis use in pregnancy are limited by size and use of self-report, and often do not consider cannabis use disorder (CUD) or concomitant substance use disorders (SUDs). Understanding the factors associated with CUD in pregnancy is paramount for designing targeted interventions.

OBJECTIVE

To examine the prevalence of co-occurring psychiatric and medical conditions of US pregnant individuals hospitalized with and without CUD by concomitant SUDs.

DESIGN, SETTING, AND PARTICIPANTS

The study analyzed restricted hospital discharge data from the 2010 to 2018 Healthcare Cost and Utilization Project State Inpatient Databases in 35 states. Data were analyzed from January to August 2021. Weighted linear regressions tested whether the prevalence of psychiatric and medical conditions differed between individuals with and without a CUD diagnosis at hospitalization. Inpatient hospitalizations of pregnant patients aged 15 to 44 years with a CUD diagnosis were identified. Pregnant patients aged 15 to 44 years without a CUD diagnosis were identified for comparison. Patients were further stratified based on concomitant SUD patterns: (1) other SUDs, including at least 1 controlled substance; (2) other SUDs, excluding controlled substances; and (3) no other SUDs.

EXPOSURES

CUD in pregnancy.

MAIN OUTCOMES AND MEASURES

Prevalence of demographic characteristics, psychiatric disorders (eg, depression and anxiety), and medical conditions (eg, epilepsy and vomiting).

RESULTS

The sample included 20 914 591 hospitalizations of individuals who were pregnant. The mean (SD) age was 28.24 (5.85) years. Of the total number of hospitalizations, 249 084 (1.19%) involved CUD and 20 665 507 (98.81%) did not. The proportion of prenatal hospitalizations involving CUD increased from 0.008 in 2010 to 0.02 in 2018. Analyses showed significant differences in the prevalence of almost every medical and psychiatric outcome examined between hospitalizations with and without CUD diagnoses, regardless of concomitant SUDs. Elevations were seen in depression (0.089; 95% CI, 0.083-0.095), anxiety (0.072; 95% CI, 0.066-0.076), and nausea (0.036; 95% CI, 0.033-0.040]) among individuals with CUD only at hospitalization compared with individuals with no SUDs at hospitalization.

CONCLUSIONS AND RELEVANCE

Considerable growth was observed in the prevalence of CUD diagnoses among individuals hospitalized prenatally and in the prevalence of depression, anxiety, nausea, and other conditions in individuals with CUD at hospitalization. This study highlights the need for more screening, prevention, and treatment, particularly in populations with co-occurring CUD and psychiatric disorders. Research on the determinants and outcomes associated with CUD during pregnancy is needed to guide clinicians, policy makers, and patients in making informed decisions.

Concerts, bars, parties, and raves: Differential risk for drug use among high school seniors according to venue attendance

Background: Nightlife attendance is an established risk factor for drug use, but studies have not focused on adolescent general population samples or compared risk according to type(s) of venues attended. We examined whether attendance of various types of venues was associated with drug use. Methods: Data were examined from an annual nationally representative survey of high school seniors in the US (2014-2019, N = 11,565). We determined whether past-year attendance of parties, concerts, bars or nightclubs, and raves or dance music events was associated with past-year drug use using mixed-effects logistic regression. Mall and movie attendance were examined as negative controls. Results: Compared to those who reported not attending specific venues, attending parties was associated with higher odds of using alcohol in particular (aOR = 5.03, 95% CI: 3.92-6.44). Attending concerts was associated with higher odds for use of alcohol, cannabis, ecstasy, and nonmedical use of prescription stimulants and opioids. All drugs examined were concentrated among those who attend bars, nightclubs, raves, and dance parties. Rave or dance party attendance was associated with higher odds for use of ecstasy (aOR = 3.71, 95% CI: 2.50-5.50) and methamphetamine (aOR = 4.92, 95% CI: 2.43-9.96) in particular, and bar or nightclub attendance was associated with higher odds of use of cocaine (aOR = 6.49, 95% CI: 4.37-9.63), ecstasy (aOR = 6.49, 95% CI: 4.54-9.27), and methamphetamine (aOR = 5.49, 95% CI: 2.57-11.72) in particular. Attending movies was associated with lower odds for use of cocaine and nonmedical prescription stimulant use. Conclusion: We determined differential risk of drug use depending on types of venues attended by adolescents.

Associations between COVID-19 mobility restrictions and economic, mental health, and suicide-related concerns in the US using cellular phone GPS and Google search volume data

During the COVID-19 pandemic, US populations have experienced elevated rates of financial and psychological distress that could lead to increases in suicide rates. Rapid ongoing mental health monitoring is critical for early intervention, especially in regions most affected by the pandemic, yet traditional surveillance data are available only after long lags. Novel information on real-time population isolation and concerns stemming from the pandemic's social and economic impacts, via cellular mobility tracking and online search data, are potentially important interim surveillance resources. Using these measures, we employed transfer function model time-series analyses to estimate associations between daily mobility indicators (proportion of cellular devices completely at home and time spent at home) and Google Health Trends search volumes for terms pertaining to economic stress, mental health, and suicide during 2020 and 2021 both nationally and in New York City. During the first pandemic wave in early-spring 2020, over 50% of devices remained completely at home and searches for economic stressors exceeded 60,000 per 10 million. We found large concurrent associations across analyses between declining mobility and increasing searches for economic stressor terms (national proportion of devices at home: cross-correlation coefficient (CC) = 0.6 (p-value <0.001)). Nationally, we also found strong associations between declining mobility and increasing mental health and suicide-related searches (time at home: mood/anxiety CC = 0.53 (<0.001), social stressor CC = 0.51 (<0.001), suicide seeking CC = 0.37 (0.006)). Our findings suggest that pandemic-related isolation coincided with acute economic distress and may be a risk factor for poor mental health and suicidal behavior. These emergent relationships warrant ongoing attention and causal assessment given the potential for long-term psychological impact and suicide death. As US populations continue to face stress, Google search data can be used to identify possible warning signs from real-time changes in distributions of population thought patterns.

Association of Childhood Adversity With Morbidity and Mortality in US Adults: A Systematic Review

IMPORTANCE

Childhood adversity (CA) is a powerful determinant of long-term physical and mental health that is associated with elevated risk for chronic disease and psychopathology. However, the degree to which CA contributes to mortality as a preventable driver of ill-health and death is unknown.

OBJECTIVE

To estimate the contribution of CA to health behaviors, including smoking and sedentary behavior, as well as the annual mortality attributable to CA in the US through influences on leading causes of death (eg, cardiovascular disease).

EVIDENCE REVIEW

For this systematic review, the PsycINFO and MEDLINE databases were searched on November 15, 2019. The databases were searched for publications from inception (1806 for PsycINFO, 1946 for MEDLINE) to November 15, 2019. Meta-analyses of the associations between CA and morbidity outcomes were included. The population attributable fraction (PAF) was calculated from these associations along with the estimated US prevalence of CA. The PAF was then applied to the number of annual deaths associated with each cause of death to estimate the number of deaths that are attributable to CA. Additionally, the PAF was applied to the incidence of health behaviors to derive the number of cases attributable to CA. Exposure to 1 or more experiences of adversity before the age of 18 years was analyzed, including abuse, neglect, family violence, and economic adversity.

FINDINGS

A total of 19 meta-analyses with 20 654 832 participants were reviewed. Childhood adversity accounted for approximately 439 072 deaths annually in the US, or 15% of the total US mortality in 2019 (2 854 838 deaths), through associations with leading causes of death (including heart disease, cancer, and suicide). In addition, CA was associated with millions of cases of unhealthy behaviors and disease markers, including more than 22 million cases of sexually transmitted infections, 21 million cases of illicit drug use, 19 million cases of elevated inflammation, and more than 10 million cases each of smoking and physical inactivity. The greatest proportion of outcomes attributable to CA were for suicide attempts and sexually transmitted infections, for which adversity accounted for up to 38% and 33%, respectively.

CONCLUSIONS AND RELEVANCE

The results of this systematic review suggest that CA is a leading contributor to morbidity and mortality in the US and may be considered a preventable determinant of mortality. The prevention of CA and the intervention on pathways that link these experiences to elevated disease risk should be considered a critical public health priority.

Cannabis use disorder treatment use and perceived treatment need in the United States: Time trends and age differences between 2002 and 2019

BACKGROUND

As trends in CUD treatment are understudied, we examined time trends in CUD treatment and perceived treatment need among people with CUD overall and by age group.

METHODS

Data from the 2002-2019 National Survey on Drug Use and Health included 43,307 individuals who met past-year DSM-5-proxy CUD criteria. Last/current treatment for cannabis use (i.e., any or specialty CUD treatment) and perceived treatment need were regressed on survey year and age (12-17, 18-25, ≥26) using generalized linear spline models. Time-varying effect modification assessed the magnitude of age-treatment associations over time.

RESULTS

Between 2002 and 2019, 6.1% of people with CUD used any CUD treatment, 2.8% used specialty treatment, and 2.2% perceived a treatment need. CUD treatment use decreased by 54.23% between 2002 and 2019 (9.11%-4.17%). Compared with adolescents, adults ages 18-25 were less likely to use specialty CUD treatment [aRR: 0.70: 95% CI: 0.52, 0.93] and ages ≥26 were more likely to perceive treatment need [aRR: 1.84: 95% CI: 1.19, 2.83]. Age-specific differences in the time-varying magnitude of associations were observed (e.g., in 2010 perceived treatment need was higher in ages ≥26 versus ages 12-17 [aOR: 2.34, 95% CI: 1.47, 3.71]).

CONCLUSIONS

CUD treatment is decreasing and young adults have lower treatment use compared with adolescents. Attitudes towards cannabis use harms are shifting, potentially contributing to decreasing CUD treatment utilization and perceived treatment need. Future research should identify treatment barriers, especially among young adults with the lowest CUD treatment use.

Role of Firearm Ownership on 2001-2016 Trends in U.S. Firearm Suicide Rates

INTRODUCTION

In the U.S., state-level household firearm ownership is strongly associated with firearm suicide mortality rates. Whether the recent increases in firearm suicide are explained by state-level household firearm ownership rates and trends remains unknown.

METHODS

Mortality data from the U.S. National Vital Statistics System and an estimate of state-level household firearm ownership rate were used to conduct hierarchical age-period-cohort (random-effects) modeling of firearm suicide mortality between 2001 and 2016. Models were adjusted for individual-level race and sex and for state-level poverty rate, unemployment rate, median household income in U.S. dollars, population density, and elevation.

RESULTS

Between 2001 and 2016, the crude national firearm suicide mortality rate increased from 6.8 to 8.0 per 100,000, and household firearm ownership rate remained relatively stable, at around 40%. Both variables were markedly heterogeneous and correlated at the state level. Age-period-cohort models revealed period effects (affecting people across ages) and cohort effects (affecting specific birth cohorts) underlying the recent increases in firearm suicide. Individuals born after 2000 had higher firearm suicide rates than most cohorts born before. A 2001-2006 decreasing period effect was followed, after 2009, by an increasing period effect that peaked in 2015. State-level household firearm ownership rates and trends did not explain cohort effects and only minimally explained period effects.

CONCLUSIONS

State-level firearm ownership rates largely explain the state-level differences in firearm suicide but only marginally explain recent increases in firearm suicide. Although firearms in the home increase firearm suicide risk, the recent national rise in firearm suicide might be the result of broader, more distal causes of suicide risk.

HiTOP Assessment of Externalizing Antagonism and Disinhibition

This article outlines the Phase 1 efforts of the HiTOP Measure Development group for externalizing constructs, which include disinhibited externalizing, antagonistic externalizing, attention deficit hyperactivity disorder, substance use, and externalizing/maladaptive behaviors. We provide background on the constructs included and the process and issues involved in developing a measure for this diverse range of psychopathology symptoms, traits, and behaviors.

A Systematic Review of Simulation Models to Track and Address the Opioid Crisis

The opioid overdose crisis is driven by an intersecting set of social, structural, and economic forces. Simulation models offer a tool to help us understand and address this complex, dynamic, and nonlinear social phenomenon. We conducted a systematic review of the literature on simulation models of opioid use and overdose up to September 2019. We extracted modeling types, target populations, interventions, and findings. Further, we created a database of model parameters used for model calibration, and evaluated study transparency and reproducibility. Of the 1,398 articles screened, we identified 88 eligible articles. The most frequent types of models were compartmental (36%), Markov (20%), system dynamics (16%), and Agent-Based models (16%). Over a third evaluated intervention cost-effectiveness (40%), and another third (39%) focused on treatment and harm reduction services for people with opioid use disorder (OUD). More than half (61%) discussed calibrating their models to empirical data, and 31% discussed validation approaches used in their modeling process. From the 63 studies that provided model parameters, we extracted the data sources on opioid use, OUD, OUD treatment, cessation/relapse, emergency medical services, and mortality parameters. This database offers a tool that future modelers can use to identify potential model inputs and evaluate comparability of their models to prior work. Future applications of simulation models to this field should actively tackle key methodological challenges, including the potential for bias in the choice of parameter inputs, investment in model calibration and validation, and transparency in the assumptions and mechanics of simulation models to facilitate reproducibility.

Forecasting future prevalence and gender differences in binge drinking among young adults through 2040

BACKGROUND

Binge drinking among adolescents and young adults has changed over time, but patterns differ by age and gender. Identifying high-risk groups to target future efforts at reducing drinking in this population remains a public health priority. Forecasting methods can provide a better understanding of variation and determinants of future binge drinking prevalence.

METHODS

We implemented regression-based forecasting models to estimate the prevalence and gender differences in binge drinking among cohort groups of U.S. young adults, ages 18, 23-24, and 29-30 through 2040. Forecasting models were adjusted for covariates accounting for changes in demographic, Big-5 social roles (e.g., residential independence), and drinking norms and related substance use, to understand the drivers of forecasted binge drinking estimates.

RESULTS

From the last observed cohort group (years varied by age) through 2040, unadjusted binge drinking prevalence was forecasted to decrease from 26% (95% CI: 20, 33%) (2011-15) to 11% (95% CI: 4, 27%) at age 18, decrease from 38% (95% CI: 30, 45%) (2006-2010) to 34% (95% CI: 18, 55%) at ages 23/24, and increase from 32% (95% CI: 25, 40%) (2001-2005) to 35% (95% CI: 16, 59%) at ages 29/30. Gender-stratified forecasts show a continuation in the narrowing of binge drinking prevalence between young men and women, though the magnitude of narrowing differs by age. Estimated trends were partially explained by changing norms regarding drinking and other substance use, though these indirect effects explained less of the total trend as age increased.

CONCLUSIONS

Understanding how covariates influence binge drinking trends can guide public health policies to leverage the most important determinants of future binge drinking to reduce the harm caused by binge drinking from adolescence to adulthood.

Trends in Ketamine Use, Exposures, and Seizures in the United States up to 2019

Objectives. To determine whether there have been shifts in nonmedical ketamine use, poisonings ("exposures"), and seizures. Methods. We used generalized additive models to detect trends in past-year use (2006-2019), exposures (1991-2019), and seizures (2000-2019) involving ketamine in the United States. Results. There was a quarterly increase in self-reported past-year nonmedical ketamine use in 2006 to 2014 (Β = 0.21; P = .030) and an increase in 2015 to 2019 (Β = 0.29; P = .036), reaching a peak of 0.9% in late 2019. The rate of exposures increased from 1991 through 2019 (Β = 0.87; P = .006), and there was an increase to 1.1 exposures per 1 000 000 population in 2014, with rates remaining stable through 2019. The rate of ketamine seizures increased from 2000 through 2019 (Β = 2.27; P < .001), with seizures reaching a peak in 2019 at 3.2 per 1000 seizures. Conclusions. Indicators suggest that ketamine use and availability has increased, including before increased medical indications, but nonmedical use is still currently uncommon despite increased acceptance and media coverage. (Am J Public Health. 2021;111(11):2046-2049. https://doi.org/10.2105/AJPH.2021.306486).

Davis C, Rudolph KE, Keyes KM, Hasin DS, Cerdá M. Prescription Opioid Laws and Opioid Dispensing in US Counties: Identifying Salient Law Provisions With Machine Learning

BACKGROUND

Hundreds of laws aimed at reducing inappropriate prescription opioid dispensing have been implemented in the United States, yet heterogeneity in provisions and their simultaneous implementation have complicated evaluation of impacts. We apply a hypothesis-generating, multistage, machine-learning approach to identify salient law provisions and combinations associated with dispensing rates to test in future research.

METHODS

Using 162 prescription opioid law provisions capturing prescription drug monitoring program (PDMP) access, reporting and administration features, pain management clinic provisions, and prescription opioid limits, we used regularization approaches and random forest models to identify laws most predictive of county-level and high-dose dispensing. We stratified analyses by overdose epidemic phases-the prescription opioid phase (2006-2009), heroin phase (2010-2012), and fentanyl phase (2013-2016)-to further explore pattern shifts over time.

RESULTS

PDMP patient data access provisions most consistently predicted high-dispensing and high-dose dispensing counties. Pain management clinic-related provisions did not generally predict dispensing measures in the prescription opioid phase but became more discriminant of high dispensing and high-dose dispensing counties over time, especially in the fentanyl period. Predictive performance across models was poor, suggesting prescription opioid laws alone do not strongly predict dispensing.

CONCLUSIONS

Our systematic analysis of 162 law provisions identified patient data access and several pain management clinic provisions as predictive of county prescription opioid dispensing patterns. Future research employing other types of study designs is needed to test these provisions' causal relationships with inappropriate dispensing and to examine potential interactions between PDMP access and pain management clinic provisions. See video abstract at, http://links.lww.com/EDE/B861.

Suicide prediction among men and women with depression: A population-based study

BACKGROUND

Accurate identification of persons at risk of suicide is challenging because suicide is a rare outcome with a multifactorial origin. The purpose of this study was to predict suicide among persons with depression using machine learning methods.

METHODS

A case-cohort study was conducted in Denmark between January 1, 1995 and December 31, 2015. Cases were all persons who died by suicide and had an incident depression diagnosis in Denmark (n = 2,774). The comparison subcohort was a 5% random sample of all individuals in Denmark at baseline, restricted to persons with an incident depression diagnosis during the study period (n = 11,963). Classification trees and random forests were used to predict suicide.

RESULTS

In men with depression, there was a high risk of suicide among those who were prescribed other analgesics and antipyretics (i.e., non-opioid analgesics such as acetaminophen), prescribed hypnotics and sedatives, and diagnosed with a poisoning (n = 96; risk = 81%). In women with depression, there was an elevated risk of suicide among those who were prescribed other analgesics and antipyretics, anxiolytics, and hypnotics and sedatives, but were not diagnosed with poisoning nor cerebrovascular diseases (n = 338; risk = 58%).

DISCUSSION

Psychiatric disorders and their associated medications were strongly indicative of suicide risk. Notably, anti-inflammatory medications (e.g., acetaminophen) prescriptions, which are used to treat chronic pain and illnesses, were associated with suicide risk in persons with depression. Machine learning may advance our ability to predict suicide deaths.

Sex-Specific Risk Profiles for Suicide Among Persons with Substance Use Disorders in Denmark

BACKGROUND AND AIMS

Persons with substance use disorders (SUDs) are at elevated risk of suicide death. We identified novel risk factors and interactions that predict suicide among men and women with SUD using machine learning.

DESIGN

Case-cohort study.

SETTING

Denmark.

PARTICIPANTS

The sample was restricted to persons with their first SUD diagnosis during 1995 to 2015. Cases were persons who died by suicide in Denmark during 1995 to 2015 (n = 2774) and the comparison subcohort was a 5% random sample of individuals in Denmark on 1 January 1995 (n = 13 179).

MEASUREMENTS

Suicide death was recorded in the Danish Cause of Death Registry. Predictors included social and demographic information, mental and physical health diagnoses, surgeries, medications, and poisonings.

FINDINGS

Persons among the highest risk for suicide, as identified by the classification trees, were men prescribed antidepressants in the 4 years before suicide and had a poisoning diagnosis in the 4 years before suicide; and women who were 30+ years old and had a poisoning diagnosis 4 years before and 12 months before suicide. Among men with SUD, the random forest identified five variables that were most important in predicting suicide; reaction to severe stress and adjustment disorders, drugs used to treat addictive disorders, age 30+ years, antidepressant use, and poisoning in the 4 prior years. Among women with SUD, the random forest found that the most important predictors of suicide were prior poisonings and reaction to severe stress and adjustment disorders. Individuals in the top 5% of predicted risk accounted for 15% of all suicide deaths among men and 24% of all suicides among women.

CONCLUSIONS

In Denmark, prior poisoning and comorbid psychiatric disorders may be among the most important indicators of suicide risk among persons with substance use disorders, particularly among women.

Cohort and age trends in age 35-45 prevalence of alcohol use disorder symptomology, by severity, sex, race, and education

AIMS

To present national trends by age and cohort among middle-aged adults in the prevalence of AUD symptomology, by severity, sex, race, and education.

DESIGN

National, multi-cohort longitudinal probability samples of US adults, with data collected at ages 35, 40, and 45 among 14 cohorts who reached age 45 between 2003 and 2016.

SETTING

Data were collected via self-administered questionnaires to adults in the United States.

PARTICIPANTS

The sample consisted of 20,634 individuals.

MEASUREMENTS

5-year prevalence of symptoms consistent with a DSM-5 AUD.

FINDINGS

Between ages 35-45 prevalence of any AUD symptoms decreased 19 %; decreases were most evident between ages 35-40. From 2003 to 2016, AUD symptoms were steady across cohort. However, because the pace of decrease across ages 35-45 slowed across cohort, cohort differences emerged at specific ages: age 35 prevalence decreased 18 % across cohort, but age 45 prevalence was equivalent across cohort. Age and cohort effects, and their interaction, did not vary by AUD severity level. Declines in AUD symptoms across age were 17 % slower for women, and declines in AUD symptoms across age and cohort were 11 % and 29 % slower, respectively, for those with a college degree. The protection afforded by a college degree was reversed among mild AUD and most pronounced for severe AUD.

CONCLUSIONS

AUDs may be more plastic and responsive to intervention during early mid-life than later. Despite progress in reducing the burden of AUD in the US population among younger middle-aged adults, an increased focus remains necessary as they continue to age.