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Binge and heavy drinking are preventable causes of mortality and morbidity. Alcohol consumption by women who parent is damaging to child health, and it is concerning that women in the United States in their reproductive years have experienced increased drinking over the past decade. Although media attention has focused on the drinking status of women who are child-rearing, it remains unclear whether binge and heavy drinking vary by parenting status and sex. We examined national trends in binge drinking, defined as 5 or more drinks in a single day for men and 4 or more drinks for women, and heavy drinking, defined as 60 or more days with binge episodes in a year. We used survey-weighted logistic regression from the 2006-2018 waves of the cross-sectional National Health Interview Survey (NHIS, N = 239,944 eligible respondents) to study time trends in drinking outcomes by sex, age, and parenting status. Binge drinking increased for both sexes in nearly all age groups, with the largest increase among women ages 30-44 without children (from 21% reporting binge drinking in 2006 to 42% in 2018); the exception was young men (ages 18-29) with children, among whom binge drinking declined. By 2012, the prevalence of binge drinking among young men with children (38.5%) declined to below that of young women without children (39.2%) and stayed lower thereafter. Despite widespread increases in binge drinking, heavy drinking declined or remained stable for all groups except older women (ages 45-55) without children (odds ratio [OR] for heavy drinking each year = 1.06, 95% CI 1.02-1.10) and women ages 30-44, regardless of parenting status. For binge drinking outcomes only, we saw evidence of interaction in drinking trends by parenting status, but this was shown to be confounded by sex and age. Men and women with children reported consistently lower levels of drinking than those without children. Rates of abstention mirrored trends in binge outcomes for both sexes, limiting concerns about invariance. Study limitations include self-reported data and measurement invariance in binge drinking cutoffs across study years. This study demonstrated that trends in binge and heavy drinking over time were not differential by parenting status for women; rather, declines and increases over time were mainly attributable to sex and age. Women both with and without children are increasing binge and heavy drinking; men, regardless of parenting status, and women without children consumed more alcohol than women with children. Regardless of impact on child health, increased drinking rates in the past decade are concerning for adult morbidity and mortality: binge drinking has increased among both sexes, and heavy drinking has increased among older women. Men and women of all ages and parenting status should be screened for heavy alcohol use and referred to specialty care as appropriate.

Investigating suicides following the death of Robin Williams, a beloved actor and comedian, on August 11th, 2014, we used time-series analysis to estimate the expected number of suicides during the months following Williams' death. Monthly suicide count data in the US (1999-2015) were from the Centers for Disease Control and Prevention Wide-ranging ONline Data for Epidemiologic Research (CDC WONDER). Expected suicides were calculated using a seasonal autoregressive integrated moving averages model to account for both the seasonal patterns and autoregression. Time-series models indicated that we would expect 16,849 suicides from August to December 2014; however, we observed 18,690 suicides in that period, suggesting an excess of 1,841 cases (9.85% increase). Although excess suicides were observed across gender and age groups, males and persons aged 30-44 had the greatest increase in excess suicide events. This study documents associations between Robin Williams' death and suicide deaths in the population thereafter.

Background Exposure to stress is potentially important in the pathway to alcohol use and alcohol use disorders. Stressors occur at multiple time points across the life course, with varying degrees of chronicity and severity. Method We review evidence from epidemiologic studies on the relationship between four different stressors (fateful/catastrophic events, child maltreatment, common adult stressful life events in interpersonal, occupational, financial, and legal domains, and minority stress) and alcohol consumption and alcohol use disorders. Results Studies generally demonstrate an increase in alcohol consumption in response to exposure to terrorism or other disasters. Research has demonstrated little increase in incident alcohol use disorders, but individuals with a history of alcohol use disorders are more likely to report drinking to cope with the traumatic event. Childhood maltreatment is a consistent risk factor for early onset of drinking in adolescence and adult alcohol use disorders, and accumulating evidence suggests that specific polymorphisms may interact with child maltreatment to increase risk for alcohol consumption and disorder. Stressful life events such as divorce and job loss increase the risk of alcohol disorders, but epidemiologic consensus on the specificity of these associations across gender has not been reached. Finally, both perceptions of discrimination and objective indicators of discrimination are associated with alcohol use and alcohol use disorders among racial/ethnic and sexual minorities. Conclusion Taken together, these literatures demonstrate that exposure to stress is an important component in individual differences in risk for alcohol consumption and alcohol use disorders. However, many areas of this research remain to be studied, including greater attention to the role of various stressors in the course of alcohol use disorders and potential risk moderators when individuals are exposed to stressors.

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.

Although current classification systems have greatly contributed to the reliability of psychiatric diagnoses, they ignore the unique role of individual symptoms and, consequently, potentially important information is lost. The network approach, in contrast, assumes that psychopathology results from the causal interplay between psychiatric symptoms and focuses specifically on these symptoms and their complex associations. By using a sophisticated network analysis technique, this study constructed an empirically based network structure of 120 psychiatric symptoms of twelve major DSM-IV diagnoses using cross-sectional data of the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC, second wave; N = 34,653). The resulting network demonstrated that symptoms within the same diagnosis showed differential associations and indicated that the strategy of summing symptoms, as in current classification systems, leads to loss of information. In addition, some symptoms showed strong connections with symptoms of other diagnoses, and these specific symptom pairs, which both concerned overlapping and non-overlapping symptoms, may help to explain the comorbidity across diagnoses. Taken together, our findings indicated that psychopathology is very complex and can be more adequately captured by sophisticated network models than current classification systems. The network approach is, therefore, promising in improving our understanding of psychopathology and moving our field forward.

The World Health Organization recommends that treatment of depression in low and middle-income countries with a scarcity of psychiatrists could be done in primary care and should include prescription of antidepressant medications for moderate and severe depression. Little is known, however, about the actual practices of antidepressant prescription by primary care physicians in low and middle-income countries, nor about adherence by people receiving such prescriptions. In a large study of primary care clinics in Goa, India, we examined the relationship of actual to recommended prescribing practices for depression, among all patients who screened positive for common mental disorder. We also examined other patient and clinic characteristics associated with antidepressant prescription, and self-reported adherence over a one-month period. Patients attending 24 primary care clinics were screened for common mental disorders. Those who screened positive were eligible to enroll in a trial to assess the effectiveness of a collaborative stepped care (CSC) intervention for mental disorders. Physicians in the 12 intervention and 12 control clinics (usual care) were free to prescribe antidepressants and follow-up interviews were conducted at 2, 6 and 12 months. Screening results were shared with the physician, but they were blinded to the diagnosis generated later using a standardized diagnostic interview administered by a health counsellor. We categorized these later diagnoses as \"moderate/severe depression\", \"mild depression or non-depression diagnosis\", and \"no diagnosis\". We used a two-level hierarchical logistic regression model to examine diagnostic and other factors associated with antidepressant prescription and one-month adherence. Overall, about 47% of screened positive patients (n = 1320) received an antidepressant prescription: 60% of those with moderate/severe depression, 48% of those with mild depression or non-depression diagnosis, and 31% with no diagnosis. Women (OR 1.29; 95%CI 1.04-1.60) and older adults (OR 1.80; 95%CI 1.32-2.47) were more likely to receive an antidepressant prescription. While the overall rate of antidepressant prescription was similar in clinics with and without CSC, patients without any diagnosis were more likely to receive a prescription (OR 2.20 95% CI 1.03-4.70) in the usual care clinics. About 47% of patients adhered to antidepressant treatment for one month or more and adherence was significantly better among older adults (OR 3.92; 95% CI 1.70-8.93) and those who received antidepressant as part of the CSC treatment model (OR 6.10 95% CI 3.67-10.14) compared with those attending the usual care clinic. Antidepressants were widely prescribed following screening in primary care, but prescription patterns were in poor accord with WHO recommendations. The data suggest under-prescription for people with moderate/severe depression; over-prescription for people with mild depression or non-depression diagnoses; and over-prescription for people with no disorders. For all diagnoses adherence was low, especially in usual care clinics. To address these concerns, there is an urgent need to study and develop strategies in primary care practices to limit unnecessary antidepressant prescriptions, target prescription for those patients who clearly benefit, and to improve adherence to antidepressant treatment. ClinicalTrials.gov Identifier: NCT00446407.

Objectives. To assess cohort effects in prescription opioid and heroin overdose mortality in the United States. Methods. Using the National Center for Health Statistics’ multiple-cause-of-death file for 1999 to 2014, we performed an age–period–cohort analysis of drug overdose mortality in the United States. Results. Compared with those born in 1977 and 1978, individuals born between 1947 and 1964 experienced excess risks of prescription opioid overdose death (e.g., for the 1955–1956 birth cohort, rate ratio [RR] = 1.27; 95% confidence interval [CI] = 1.09, 1.48) and of heroin overdose death (e.g., for the 1953–1954 birth cohort, RR = 1.32; 95% CI = 1.11, 1.57). Those born between 1979 and 1992 also experienced an increased risk of heroin overdose death (e.g., for the 1989–1990 birth cohort, RR = 1.23; 95% CI = 1.01, 1.50). The cohort effects were consistent between sexes. Conclusions. Individuals born between 1947 and 1964 and between 1979 and 1992 are particularly afflicted by the opioid epidemic. Intervention programs are needed to reduce the excess overdose mortality in these specific demographic groups.

Deaths by suicide, as well as suicidal ideations, plans and attempts, have been increasing in the US for the past two decades. Deployment of effective interventions would require timely, geographically well-resolved estimates of suicide activity. In this study, we evaluated the feasibility of a two-step process for predicting suicide mortality: a) generation of hindcasts , mortality estimates for past months for which observational data would not have been available if forecasts were generated in real-time; and b) generation of forecasts with observational data augmented with hindcasts. Calls to crisis hotline services and online queries to the Google search engine for suicide-related terms were used as proxy data sources to generate hindcasts. The primary hindcast model ( auto ) is an Autoregressive Integrated Moving average model (ARIMA), trained on suicide mortality rates alone. Three regression models augment hindcast estimates from auto with call rates ( calls ), GHT search rates ( ght ) and both datasets together ( calls_ght ). The 4 forecast models used are ARIMA models trained with corresponding hindcast estimates. All models were evaluated against a baseline random walk with drift model. Rolling monthly 6-month ahead forecasts for all 50 states between 2012 and 2020 were generated. Quantile score (QS) was used to assess the quality of the forecast distributions. Median QS for auto was better than baseline (0.114 vs. 0.21. Median QS of augmented models were lower than auto , but not significantly different from each other (Wilcoxon signed-rank test, p > .05). Forecasts from augmented models were also better calibrated. Together, these results provide evidence that proxy data can address delays in release of suicide mortality data and improve forecast quality. An operational forecast system of state-level suicide risk may be feasible with sustained engagement between modelers and public health departments to appraise data sources and methods as well as to continuously evaluate forecast accuracy.

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.