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Despite calls to incorporate population science into neuroimaging research, most studies recruit small, non-representative samples. Here, we examine whether sample composition influences age-related variation in global measurements of gray matter volume, thickness, and surface area. We apply sample weights to structural brain imaging data from a community-based sample of children aged 3–18 ( N  = 1162) to create a “weighted sample” that approximates the distribution of socioeconomic status, race/ethnicity, and sex in the U.S. Census. We compare associations between age and brain structure in this weighted sample to estimates from the original sample with no sample weights applied (i.e., unweighted). Compared to the unweighted sample, we observe earlier maturation of cortical and sub-cortical structures, and patterns of brain maturation that better reflect known developmental trajectories in the weighted sample. Our empirical demonstration of bias introduced by non-representative sampling in this neuroimaging cohort suggests that sample composition may influence understanding of fundamental neural processes. The influence of sample composition on human neuroimaging results is unknown. Here, the authors weight a large, community-based sample to better reflect the US population and describe how applying these sample weights changes conclusions about age-related variation in brain structure.

Abstract The opioid overdose crisis is driven by an intersecting set of social, structural, and economic forces. Simulation models are a tool to help us understand and address thiscomplex, 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; 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%). Intervention cost-effectiveness was evaluated in 40% of the studies, and 39% focused on services for people with opioid use disorder (OUD). In 61% of the eligible articles, authors discussed calibrating their models to empirical data, and in 31%, validation approaches used in the modeling process were discussed. From the 63 studies that provided model parameters, we extracted the data sources on opioid use, OUD, OUD treatment, cessation or relapse, emergency medical services, and death parameters. From this database, potential model inputs can be identified and models can be compared with prior work. Simulation models should be used to 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.

Although rare, placental abruption is implicated in disproportionately high rates of perinatal morbidity and mortality. Understanding geographic and temporal variations may provide insights into possible amenable factors of abruption. We examined abruption frequencies by maternal age, delivery year, and maternal birth cohorts over three decades across seven countries. Women that delivered in the US (n = 863,879; 1979-10), Canada (4 provinces, n = 5,407,463; 1982-11), Sweden (n = 3,266,742; 1978-10), Denmark (n = 1,773,895; 1978-08), Norway (n = 1,780,271, 1978-09), Finland (n = 1,411,867; 1987-10), and Spain (n = 6,151,508; 1999-12) were analyzed. Abruption diagnosis was based on ICD coding. Rates were modeled using Poisson regression within the framework of an age-period-cohort analysis, and multi-level models to examine the contribution of smoking in four countries. Abruption rates varied across the seven countries (3-10 per 1000), Maternal age showed a consistent J-shaped pattern with increased rates at the extremes of the age distribution. In comparison to births in 2000, births after 2000 in European countries had lower abruption rates; in the US there was an increase in rate up to 2000 and a plateau thereafter. No birth cohort effects were evident. Changes in smoking prevalence partially explained the period effect in the US (P = 0.01) and Sweden (P<0.01). There is a strong maternal age effect on abruption. While the abruption rate has plateaued since 2000 in the US, all other countries show declining rates. These findings suggest considerable variation in abruption frequencies across countries; differences in the distribution of risk factors, especially smoking, may help guide policy to reduce abruption rates.

Firearm violence remains a persistent public health threat. Comparing the impact of targeted high-risk versus population-based approaches to prevention may point to efficient and efficacious interventions. We used agent-based modeling to conduct a hypothetical experiment contrasting the impact of high-risk (disqualification) and population-based (price increase) approaches on firearm homicide in New York City (NYC). We simulated 800,000 agents reflecting a 15% sample of the adult population of NYC. Three groups were considered and disqualified from all firearm ownership for five years, grouped based on prevalence: low prevalence (psychiatric hospitalization, alcohol-related misdemeanor and felony convictions, 0.23%); moderate prevalence (drug misdemeanor convictions, domestic violence restraining orders, 1.03%); and high prevalence (all other felony/misdemeanor convictions, 2.30%). Population-level firearm ownership was impacted by increasing the price of firearms, assuming 1% price elasticity. In this hypothetical scenario, to reduce firearm homicide by 5% in NYC, 25% of the moderate prevalence group, or 12% of the high prevalence group needed to be effectively disqualified; even when all of the low prevalence group was disqualified, homicide did not decrease by 5%. An 18% increase in price similarly reduced firearm homicide by 5.37% (95% CI 4.43-6.31%). Firearm homicide declined monotonically as the proportion of disqualified individuals increased and/or price increased. A combined intervention that both increased price and effectively disqualified \"high-risk\" groups achieved approximately double the reduction in homicide as any one intervention alone. Increasing illegal firearm ownership by 20%, a hypothetical response to price increases, did not meaningfully change results. A key takeaway of our study is that adopting high-risk versus population-based approaches should not be an \"either-or\" question. When individual risk is variable and diffuse in the population, \"high-risk approaches\" to firearm violence need to focus on relatively prevalent groups and be highly efficacious in disarming people at elevated risk to achieve meaningful reductions in firearm homicide, though countering issues of social justice and stigma should be carefully considered. Similar reductions can be achieved with population-based approaches, such as price increases, albeit with fewer such countering issues.

BackgroundSubstance-related interactions with the criminal justice system are a potential touchpoint to identify people at risk for firearm violence. We used an agent-based model to simulate the change in firearm violence after disqualifying people from owning a firearm given prior alcohol- and drug-related misdemeanors. MethodsWe created a population of 800,000 agents reflecting a 15% sample of the adult New York City population.ResultsDisqualification from purchasing firearms for 5 years after an alcohol-related misdemeanor conviction reduced population-level rates of firearm homicide by 1.0% [95% CI 0.4–1.6%] and suicide by 3.0% [95% CI 1.9–4.0%]. Disqualification based on a drug-related misdemeanor conviction reduced homicide by 1.6% [95% CI 1.1–2.2%] and suicide by 4.6% [95% CI 3.4–5.8%]. Reductions were generally 2 to 8 times larger for agents meeting the disqualification criteria.ConclusionsDenying firearm access based on a history of drug and alcohol misdemeanors may reduce firearm violence among the high-risk group. Enactment of substance use-related firearms denial criteria needs to be balanced against concerns about introducing new sources of disenfranchisement among already vulnerable populations.

Purpose Mental health disparities between sexual minorities and heterosexuals remain inadequately understood, especially across levels of educational attainment. The purpose of the present study was to test whether education modifies the association between sexual orientation and mental disorder. Methods We compared the odds of past 12-month and lifetime psychiatric disorder prevalence (any Axis-I, any mood, any anxiety, any substance use, and comorbidity) between lesbian, gay, and bisexual (LGB) and heterosexual individuals by educational attainment (those with and without a bachelor’s degree), adjusting for covariates, and tested for interaction between sexual orientation and educational attainment. Data are drawn from the National Epidemiologic Survey on Alcohol and Related Conditions, a nationally representative survey of non-institutionalized US adults ( N  = 34,653; 577 LGB). Results Sexual orientation disparities in mental health are smaller among those with a college education. Specifically, the disparity in those with versus those without a bachelor’s degree was attenuated by 100 % for any current mood disorder, 82 % for any current Axis-I disorder, 76 % for any current anxiety disorder, and 67 % for both any current substance use disorder and any current comorbidity. Further, the interaction between sexual orientation and education was statistically significant for any current Axis-I disorder, any current mood disorder, and any current anxiety disorder. Our findings for lifetime outcomes were similar. Conclusions The attenuated mental health disparity at higher education levels underscores the particular risk for disorder among LGBs with less education. Future studies should consider selection versus causal factors to explain the attenuated disparity we found at higher education levels.

In utero exposure to tobacco smoke is associated with adverse neonatal outcomes; the association with later childhood mental health outcomes remains controversial. We used a strategy involving comparison of maternal and paternal smoking reports in a sample pooling data from six diverse European countries. Data were drawn from mother ( N  = 4,517) and teacher ( N  = 4,611) reported attention deficit and hyperactivity disorder (ADHD) symptoms in school children aged 6–11 in Turkey, Romania, Bulgaria, Lithuania, Germany, and the Netherlands, surveyed in 2010. Mothers report on self and husband’s smoking patterns during the pregnancy period. Logistic regression used with control covariates including demographics, maternal distress, live births, region, and post-pregnancy smoking. In unadjusted models, maternal prenatal smoking was associated with probable ADHD based on mother [Odds Ratio (OR) = 1.82, 95 % Confidence Interval (CI) 1.45–2.29], teacher (OR = 1.69, 95 % CI 1.33–2.14) and mother plus teacher (OR = 1.49, 95 % CI 1.03–2.17) report. Paternal prenatal smoking was similarly associated with probable ADHD in unadjusted models. When controlled for relevant confounders, maternal prenatal smoking remained a risk factor for offspring probable ADHD based on mother report (OR = 1.44, 95 % CI 1.06–1.96), whereas the effect of paternal prenatal smoking diminished (e.g., mother report: OR = 1.17, 95 % CI 0.92–1.49). Drawing on data from a diverse set of countries across Europe, we document that the association between maternal smoking and offspring ADHD is stronger than that of paternal smoking during the pregnancy period and offspring ADHD. To the extent that confounding is shared between parents, these results reflect a potential intrauterine influence of smoking on ADHD in children.

Background Mental health problems and mental health related mortality have increased among adolescents, particularly girls. These trends have implications for etiology and prevention and suggest new and emerging risk factors in need of attention. The present study estimated age, period, and cohort effects in depressive symptoms among US nationally representative samples of school attending adolescents from 1991 to 2018. Methods Data are drawn from 1991 to 2018 Monitoring the Future yearly cross-sectional surveys of 8th, 10th, and 12th grade students ( N  = 1,260,159). Depressive symptoms measured with four questions that had consistent wording and data collection procedures across all 28 years. Age–period–cohort effects estimated using the hierarchical age–period–cohort models. Results Among girls, depressive symptoms decreased from 1991 to 2011, then reversed course, peaking in 2018; these increases reflected primarily period effects, which compared to the mean of all periods showed a gradual increase starting in 2012 and peaked in 2018 (estimate = 1.15, p  < 0.01). Cohort effects were minimal, indicating that increases are observed across all age groups. Among boys, trends were similar although the extent of the increase is less marked compared to girls; there was a declining cohort effect among recently born cohorts, suggesting that increases in depressive symptoms among boys are slower for younger boys compared to older boys in recent years. Trends were generally similar by race/ethnicity and parental education, with a positive cohort effect for Hispanic girls born 1999–2004. Conclusions Depressive symptoms are increasing among teens, especially among girls, consistent with increases in depression and suicide. Population variation in psychiatric disorder symptoms highlight the importance of current environmental determinants of psychiatric disorder risk, and provide evidence of emerging risk factors that may be shaping a new and concerning trend in adolescent mental health.

Background While accumulating research has tested the hypothesis that screen time causes psychiatric symptoms in children, less attention has been paid to the hypothesis that children with psychiatric symptoms change their patterns of screen time and digital media use. We aimed to test whether children with psychiatric symptoms subsequently change their patterns of screen time and digital media use. Methods N = 9,066 children primarily aged 9–10 in the Adolescent Brain Cognitive Development Study at baseline and 1-year later. Psychiatric symptoms included internalizing, attention, and externalizing symptoms. Screen time was measured as ordinally defined weekday and weekend time on social and nonsocial [e.g., YouTube] digital media). Models assessed psychiatric symptoms as predictors of screen time, and screen time as predictors of psychiatric symptoms, controlled for baseline measures of each, sex, age, race/ethnicity, and income. Results Children with psychiatric symptoms spent more time on non-social media one year later compared with peers. Considering total psychiatric problems, clinical levels of problems predicted higher levels of weekday (OR = 1.22, 95% CI 1.22–1.23) and weekend (OR = 1.10, 95% CI 1.09–1.11) nonsocial screen time. For nearly all analyses of psychiatric symptoms predicting screen time, associations were highest for a non-social screen time outcome rather than a social screen time outcome (Highest OR = 1.65, 95% CI 1.63–1.67, clinical rule breaking predicting weekday nonsocial screen time). Comparable magnitude associations were observed for social and nonsocial media use predicting future psychiatric symptoms, suggesting bidirectionality. Conclusion Children with psychiatric symptoms have different subsequent media use patterns, including higher rates of subsequent nonsocial engagement. Ensuring that ongoing data collection and analysis efforts attend to temporality and transitions in the relation between media use and psychiatric symptoms will accelerate progress in the field.