Racial inequities in homicide rates and homicide methods among Black and White women aged 25-44 years in the USA, 1999-2020: a cross-sectional time series study.

BACKGROUND: In the USA, Black women aged 25-44 years are disproportionately murdered compared with their White counterparts. Despite ongoing efforts to reduce racial and structural inequities, the result of these efforts remains unclear, particularly in light of the COVID-19 pandemic. METHODS: This study examined a cross-sectional time series of homicide death rates, by race, from the Centers for Disease Control and Prevention Wide-ranging Online Data for Epidemiologic Research system. We included data for women aged 25-44 years between 1999 and 2020 among 30 states in the USA. Homicide death was classified using underlying cause and multiple cause of death codes; mortality rates were calculated per 100 000 based on US Census Bureau population sizes. Homicide methods were classified as firearm, cutting or piercing, and other. Firearm homicides were compared with other homicides with logistic regression including covariates of race, time, and their interaction. We report odds ratios and 95% CIs. FINDINGS: In 2020, the homicide rate among Black women was 11·6 per 100 000, compared with 3 per 100 000 among White women. This inequity has persisted over time and is virtually unchanged since 1999. Homicide inequities vary across US states; in 11 states, racial inequities have increased since 1999. The racial inequity was greatest in Wisconsin, where in 2019-20, Black women aged 25-44 years were 20 times more likely to die by homicide than White women. Homicide by firearm is increasing in frequency; women in the USA had 2·44 (95% CI 2·14-2·78) times the odds of homicide involving firearms in 2019-20 compared with 1999-2003. Firearm homicide deaths are disproportionately concentrated among Black women in every region in the USA. INTERPRETATION: Our findings suggest that there is an urgent need to address homicide inequities among Black and White women in the USA. Enacting federal legislation that reduces gun access is a crucial step. Policy makers must address long-standing structural factors that underpin elevated gun violence by implementing sustainable wealth-building opportunities; developing desegregated, mixed income and affordable housing; and increasing green spaces in communities where Black women largely reside. FUNDING: National Institute of Mental Health of the National Institutes of Health.

Bringing home the benefits: Do pro-family employee benefits mitigate the risk of depression from competing workplace and domestic labor roles?

Despite significant historical progress toward gender parity in employment status in the US, women remain more likely to provide domestic labor, creating role competition which may increase depression symptoms. Pro-family employee benefits may minimize the stress of competing roles. We tested whether depressive symptoms were higher among women with vs. without competing roles and whether this effect was greater among women without (vs. with) pro-family benefits. Data included employed women surveyed across 4 waves of the National Longitudinal Survey (2010-2019) (N=9884). Depression symptoms were measured with the Mental Health Inventory (MHI-5). The interaction between competing roles and pro-family employee benefits on depressive symptoms was also compared with non-family-related benefits, using marginal structural models to estimate longitudinal effects in the presence of time-varying confounding. MHI-5 scores were 0.56 points higher (95% CI=0.15, 0.97) among women in competing roles (vs. not). Among women without pro-family benefits, competing roles increased MHI-5 scores by 6.1-points (95% CI=1.14, 11.1). In contrast, there was no association between competing roles and MHI-5 scores among women with access to these benefits (MHI-5 difference=0.44; 95% CI=-0.2, 1.0). Results were similar for non-family-related benefits. Dual workplace and domestic labor role competition increases women's depression symptoms, though broad availability of workplace benefits may attenuate that risk.

Assessing Trends in Internalizing Symptoms among Racialized and Minoritized Adolescents: Results from the Monitoring the Future Survey 2005-2020.

Depressive symptoms have rapidly accelerated among recent US adolescent birth cohorts, yet there remains little understanding of trends among racialized and minoritized groups. These groups may experience depressive symptoms due to the deleterious effects of structural racism. Using 2005-2020 Monitoring the Future survey data, we examine all racialized groups using within-group analyses to observe trends in high depressive symptoms across cohorts. Generally, across racialized groups and ages, the odds of high depressive symptoms increased in recent birth cohorts. For example, among 15-16-year-old students racialized as American Indian or Alaska Native and Black-Hispanic/Latine, the 2003-2006 birth cohort had 3.08 (95% CI: 2.00, 4.76) and 6.95 (95% CI: 2.70, 17.88) times higher odds, respectively, of high depressive symptoms compared to the 1987-1990 birth cohorts. Moreover, in a given year 15-16-year-olds generally experienced the highest depressive symptoms compared to 13-14 and 17-18-year-olds, suggesting that age-effects peaked during mid-adolescence. Depressive symptoms increased among US adolescents by birth cohort, within all racialized and minoritized groups assessed. Public health efforts to reduce disparities may consider barriers such as structural racism that may impact the mental health of racialized/minoritized adolescents while increasing access to culturally competent mental health providers and school-based services.

Geographic Variation, Economic Activity, and Labor Market Characteristics in Trajectories of Suicide in the United States, 2008-2020.

Suicide rates in the United States have increased over the past 15 years, with substantial geographic variation in these increases; yet there have been few attempts to cluster counties by the magnitude of suicide rate changes according to intercept and slope or to identify the economic precursors of increases. We used vital statistics data and growth mixture models to identify clusters of counties by their magnitude of suicide growth from 2008 to 2020 and examined associations with county economic and labor indices. Our models identified 5 clusters, each differentiated by intercept and slope magnitude, with the highest-rate cluster (4% of counties) being observed mainly in sparsely populated areas in the West and Alaska, starting the time series at 25.4 suicides per 100,000 population, and exhibiting the steepest increase in slope (0.69/100,000/year). There was no cluster for which the suicide rate was stable or declining. Counties in the highest-rate cluster were more likely to have agricultural and service economies and less likely to have urban professional economies. Given the increased burden of suicide, with no clusters of counties improving over time, additional policy and prevention efforts are needed, particularly targeted at rural areas in the West.

State-level structural racism and adolescent mental health in the United States.

We explored state-level indicators of structural racism on internalizing symptoms of depressive affect among US adolescents. We merged 16 indicators of state-level structural racism with 2015-19 Monitoring the Future surveys (N=41,258) examining associations with loneliness, self-esteem, self-derogation, and depressive symptoms using regression analyses. Students racialized as Black in states with bans on food stamp eligibility and temporary assistance for drug felony conviction had 1.37 times the odds of high depressive symptoms (95% C.I. 1.01-1.89) compared to students in states without bans. In contrast, students racialized as White living in states with more severe disenfranchisement of people convicted of felonies had lower odds of high self-derogation (OR=0.89, 95% C.I. 0.78-1.02) and high depressive symptoms (OR=0.83, 95% C.I. 0.70-0.99) compared to states with less severe disenfranchisement. These findings demonstrate the need to address the legacy of structural racism at the state level to reduce mental distress for US youth.

Simulating the Simultaneous Impact of Medication for Opioid Use Disorder and Naloxone on Opioid Overdose Death in Eight New York Counties.

BACKGROUND: The United States is in the midst of an opioid overdose epidemic; 28.3 per 100,000 people died of opioid overdose in 2020. Simulation models can help understand and address this complex, dynamic, and nonlinear social phenomenon. Using the HEALing Communities Study, aimed at reducing opioid overdoses, and an agent-based model, Simulation of Community-Level Overdose Prevention Strategy, we simulated increases in buprenorphine initiation and retention and naloxone distribution aimed at reducing overdose deaths by 40% in New York Counties. METHODS: Our simulations covered 2020-2022. The eight counties contrasted urban or rural and high and low baseline rates of opioid use disorder treatment. The model calibrated agent characteristics for opioid use and use disorder, treatments and treatment access, and fatal and nonfatal overdose. Modeled interventions included increased buprenorphine initiation and retention, and naloxone distribution. We predicted a decrease in the rate of fatal opioid overdose 1 year after intervention, given various modeled intervention scenarios. RESULTS: Counties required unique combinations of modeled interventions to achieve a 40% reduction in overdose deaths. Assuming a 200% increase in naloxone from current levels, high baseline treatment counties achieved a 40% reduction in overdose deaths with a simultaneous 150% increase in buprenorphine initiation. In comparison, low baseline treatment counties required 250-300% increases in buprenorphine initiation coupled with 200-1000% increases in naloxone, depending on the county. CONCLUSIONS: Results demonstrate the need for tailored county-level interventions to increase service utilization and reduce overdose deaths, as the modeled impact of interventions depended on the county's experience with past and current interventions.

Associations Between Structural Stigma and Allostatic Load Among Sexual Minorities: Results From a Population-Based Study.

OBJECTIVE: Structural forms of stigma and discrimination are associated with adverse health outcomes across numerous stigmatized groups, including lesbian, gay, and bisexual (LGB) individuals. However, the biological consequences of structural stigma among LGB populations are understudied. To begin to address this gap, we assessed associations between indicators of structural stigma (i.e., state-level policies) targeting LGB individuals and allostatic load (AL) indices representing physiological dysregulations. METHODS: Pooled data from the continuous 2001-2014 National Health and Nutritional Examination Survey were analyzed (LGB: n = 864; heterosexual: n = 20,310). Ten state-level LGB-related policies (e.g., employment nondiscrimination protections, same-sex marriage) were used to operationalize structural stigma. A sex-specific AL index representing 11 immune, metabolic, and cardiovascular biomarkers was estimated. Multilevel models were used to examine associations between structural stigma and AL, net of nine individual-level characteristics (e.g., education, race/ethnicity, age, and health behaviors). RESULTS: Sexual minority men living in states with low levels of structural stigma experienced significantly lower AL ( ß = -0.45, p = .02) compared with sexual minority men living in states with high structural stigma (i.e., fewer protective policies). There was no significant association between structural stigma and AL among sexual minority women. CONCLUSIONS: By demonstrating direct associations between structural stigma and indices of physiological dysregulation, our findings provide a mechanistic understanding of how the social environment can "get under the skin and skull" for sexual minority men in the United States. Future research should explore whether these mechanisms generalize to other marginalized groups exposed to structural stigma.

Changing adolescent activity patterns and the correlation of self-esteem and externalizing mental health symptoms across time: results from the USA from 1991 through 2020.

BACKGROUND: Common adolescent psychiatric symptoms cluster into two dominant domains: internalizing and externalizing. Both domains are linked to self-esteem, which serves as a protective factor against a wide range of internalizing and externalizing problems. This study examined trends in US adolescents' self-esteem and externalizing symptoms, and their correlation, by sex and patterns of time use. METHODS: Using Monitoring the Future data (N = 338 896 adolescents, grades:8/10/12, years:1991-2020), we generated six patterns of time use using latent profile analysis with 17 behavior items (e.g. sports participation, parties, paid work). Groups were differentiated by high/low engagement in sports and either paid work or high/low peer socialization. Within each group, we mapped annual, sex-stratified means of (and correlation between) self-esteem and externalizing factors. We also examined past-decade rates of change for factor means using linear regression and mapped proportions with top-quartile levels of poor self-esteem, externalizing symptoms, or both. RESULTS: We found consistent increases in poor self-esteem, decreases in externalizing symptoms, and a positive correlation between the two across nearly all activity groups. We also identified a relatively constant proportion of those with high levels of both in every group. Increases in poor self-esteem were most pronounced for female adolescents with low levels of socializing, among whom externalizing symptoms also increased. CONCLUSIONS: Rising trends in poor self-esteem are consistent across time use groups, as is the existence of a group facing poor self-esteem and externalizing symptoms. Effective interventions for adolescents' poor self-esteem/co-occurring symptoms are needed broadly, but especially among female adolescents with low peer socialization.

Annual Research Review: Sex, gender, and internalizing conditions among adolescents in the 21st century - trends, causes, consequences.

Internalizing conditions of psychopathology include depressive and anxiety disorders; they most often onset in adolescence, are relatively common, and contribute to significant population morbidity and mortality. In this research review, we present the evidence that internalizing conditions, including depression and anxiety, as well as psychological distress, suicidal thoughts and self-harm, and fatal suicide, are considerably increasing in adolescent populations across many countries. Evidence indicates that increases are currently greatest in female adolescents. We present an epidemiological framework for evaluating the causes of these increases, and synthesize research on whether several established risk factors (e.g., age of pubertal transition and stressful life events) and novel risk factors (e.g., digital technology and social media) meet conditions necessary to be plausible causes of increases in adolescent internalizing conditions. We conclude that there are a multitude of potential causes of increases in adolescent internalizing conditions, outline evidence gaps including the lack of research on nonbinary and gender nonconforming populations, and recommend necessary prevention and intervention foci from a clinical and public health perspective.

Hindcasts and forecasts of suicide mortality in US: A modeling study.

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.

Adverse childhood experiences among black sexually minoritized men and Black transgender women in Chicago.

BACKGROUND: Adverse childhood experiences (ACE) are important predictors of mental health outcomes in adulthood. However, commonly used ACE measures such as the Behavioural Risk Factor Surveillance System (BRFSS) have not been validated among Black sexually minoritized men (SMM) nor transgender women (TW), whom are known to have higher rates of ACE and poorer mental health outcomes. Assessing the psychometric properties of the measure is important for health equity research, as measurements that are not valid for some populations will render uninterpretable results. METHODS: Data are drawn from the Neighborhoods and Networks (N2) study, a longitudinal cohort of Black SMM and TW living in Southern Chicago. We conducted confirmatory factor analysis, correlation analysis and a two-parameter Item Response Theory (IRT) on the BRFSS ACE measure, an 11-item measure with 8 domains of ACE. RESULTS: One hundred forty seven participants (85% cisgender male) completed the BRFSS ACE measurement in the N2 study with age ranges from 16-34. The cohort were from a low socioeconomic background: about 40% of the cohort were housing insecure and made than $10,000 or less annually. They also have a high number of ACEs; 34% had endorsed 4 or more ACE domains. The three-factor structure fit the BRFSS ACE measure best; the measurement consisted of three subscales: of "Household Dysfunction", "Emotional / Physical", and "Sexual Abuse" (CFI = 0.975, TLI = 0.967, and RMSEA = 0.051). When the 8 domains of ACE were summed to one score, the total score was is correlated with depressive symptoms and anxiety scores, establishing concurrent validity. Item Response Theory model indicated that the "parental separation" domain had a low discrimination (slope) parameter, suggesting that this domain does not distinguish well between those with and without high ACE. CONCLUSIONS: The BRFFS ACE measure had adequate reliability, a well-replicated structure and some moderate evidence of concurrent validity among Black SMM and TW. The parental separation domain does not discriminate between those with high and low ACE experiences in this population. With changing population demographics and trends in marriage, further examination of this item beyond the current study is warranted to improve health equity research for all.

Examining racial and ethnic heat exposure disparities in New York City (NYC) across different spatial and political scales through geographic effect measure modification.

Structural racism in the United States has resulted in neighborhoods with higher proportions of non-Hispanic Black (Black) or Hispanic/Latine residents having more features that intensify, and less that cool, the local-heat environment. This study identifies areas of New York City (NYC) where racial/ethnic heat exposure disparities are concentrated. We analyzed data from the 2013-2017 American Community Survey, U.S Landsat-8 Analysis Ready Data on summer surface temperatures, and NYC Land Cover Dataset at the census tract-level (n = 2098). Four cross-sectional regression modeling strategies were used to estimate the overall City-wide association, and associations across smaller intra-city areas, between tract-level percent of Black and percent Hispanic/Latine residents and summer day surface temperature, adjusting for altitude, shoreline, and nature-cover: overall NYC linear, borough-specific linear, Community District-specific linear, and geographically weighted regression models. All three linear regressions identified associations between neighborhood racial and ethnic composition and summer day surface temperatures. The geographically weighted regression models, which address the issue of spatial autocorrelation, identified specific locations (such as northwest Bronx, central Brooklyn, and uptown Manhattan) within which racial and ethnic disparities for heat exposures are concentrated. Through examining the overall effects and geographic effect measure modification across spatial scales, the results of this study identify specific geographic areas for intervention to mitigate heat exposure disparities experienced by Black and Hispanic/Latine NYC residents.

Childhood internalizing, externalizing and attention symptoms predict changes in social and nonsocial screen time.

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.

Social and economic determinants of drug overdose deaths: a systematic review of spatial relationships.

PURPOSE: To synthesize the available evidence on the extent to which area-level socioeconomic conditions are associated with drug overdose deaths in the United States. METHODS: We performed a systematic review (in MEDLINE, EMBASE, PsychINFO, Web of Science, EconLit) for papers published prior to July 2022. Eligible studies quantitatively estimated the association between an area-level measure of socioeconomic conditions and drug overdose deaths in the US, and were published in English. We assessed study quality using the Effective Public Health Practice Project Quality Assessment Tool. The protocol was preregistered at Prospero (CRD42019121317). RESULTS: We identified 28 studies that estimated area-level effects of socioeconomic conditions on drug overdose deaths in the US. Studies were scored as having moderate to serious risk of bias attributed to both confounding and in analysis. Socioeconomic conditions and drug overdose death rates were moderately associated, and this was a consistent finding across a large number of measures and differences in study designs (e.g., cross-sectional versus longitudinal), years of data analyzed, and primary unit of analysis (e.g., ZIP code, county, state). CONCLUSIONS: This review highlights the evidence for area-level socioeconomic conditions are an important factor underlying the geospatial distribution of drug overdose deaths in the US and the need to understand the mechanisms underlying these associations to inform future policy recommendations. The current evidence base suggests that, at least in the United States, employment, income, and poverty interventions may be effective targets for preventing drug overdose mortality rates.

Poverty and birth cohort effects of experiencing the 2007-2009 Great Recession during adolescence on major depressive episodes and mental health treatment of young adults in the United States.

PURPOSE: Household economic adversity during adolescence is hypothesized to be a risk factor for poor mental health later in life. To test this hypothesis, we conducted a quasi-experimental analysis of an economic shock, the Great Recession of 2007-2009. We tested if going through adolescence during the Great Recession was associated with increased risk of major depressive episodes (MDE) and mental health treatment in young adulthood with potential moderation by household poverty to explore differences by economic adversity. METHODS: We analyzed data on young adults age 18-29 years from the 2005-2019 National Survey on Drug Use and Health (N = 145,394). We compared participants who were adolescents during the recession to those followed-up prior to the recession. Regression analysis tested effect modification by household poverty status. RESULTS: Adolescent exposure to the Great Recession was associated with higher likelihood of MDE during young adulthood (aOR = 1.30, 95% CI = 1.23, 1.37); there was no relationship with mental health treatment. Effects on MDE were stronger among those in households with higher incomes compared to those living in poverty. CONCLUSION: Findings support the hypothesis that exposure to the Great Recession during adolescence may have increased risk for MDE, but raise questions about whether the mechanism of this association is economic distress.

Causal Knowledge as a Prerequisite for Interrogating Bias: Reflections on Hernán et al. 20 Years Later.

In their seminal 2002 paper, "Causal Knowledge as a Prerequisite for Confounding Evaluation: An Application to Birth Defects Epidemiology," Hernán et al. (Am J Epidemiol. 2002;155(2):176-184) emphasized the importance of using theory rather than data to guide confounding control, focusing on colliders as variables that share characteristics with confounders but whose control may actually introduce bias into analyses. In this commentary, we propose that the importance of this paper stems from the connection the authors made between nonexchangeability as the ultimate source of bias and structural representations of bias using directed acyclic graphs. This provided both a unified approach to conceptualizing bias and a means of distinguishing between different sources of bias, particularly confounding and selection bias. Drawing on examples from the paper, we also highlight unresolved questions about the relationship between collider bias, selection bias, and generalizability and argue that causal knowledge is a prerequisite not only for identifying confounders but also for developing any hypothesis about potential sources of bias.

Invited Commentary: Modern Epidemiology Confronts COVID-19-Reflections From Psychiatric Epidemiology.

Dimitris et al. (Am J Epidemiol. 2022;191(6):980-986) outline how the coronavirus disease 2019 (COVID-19) pandemic has, with mixed results, put epidemiology under the spotlight. While epidemiologic theory and methods have been critical in many successes, the ongoing global death toll from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the sometimes chaotic public messaging underscore that epidemiology as a field has room for improvement. Here, we use examples from psychiatric epidemiologic studies conducted during the COVID-19 era to reflect on errors driven by overlooking specific major methodological advances of modern epidemiology. We focus on: 1) use of nonrepresentative sampling in online surveys, which limits the potential knowledge to be gained from descriptive studies and amplifies collider stratification bias in causal studies; and 2) failure to acknowledge multiple versions of exposures (e.g., lockdown, school closure) and differences in prevalence of effect measure modifiers across contexts, which causes violations of the consistency assumption and lack of effect transportability. We finish by highlighting: 1) the heterogeneity of psychiatric epidemiologic results during the pandemic across place and sociodemographic groups and over time; 2) the importance of following the foundational advancements of modern epidemiology even in emergency settings; and 3) the need to limit the role of political agendas in cherry-picking and reporting epidemiologic evidence.

Relative Age Within School Grade, Including Delayed and Accelerated School Start: Associations With Midlife Psychiatric Disorders, Suicide, and Alcohol- and Drug-Related Mortality.

Within a school grade, children who are young for grade are at increased risk of psychiatric diagnoses, but the long-term implications remain understudied, and associations with students who delay or accelerate entry underexplored. We used Norwegian birth cohort records (birth years: 1967-1976, n = 626,928) linked to records in midlife. On-time school entry was socially patterned; among those born in December, 23.0% of children in the lowest socioeconomic position (SEP) delayed school entry, compared with 12.2% among the highest SEP. Among those who started school on time, there was no evidence for long-term associations between birth month and psychiatric/behavioral disorders or mortality. Controlling for SEP and other confounders, delayed school entry was associated with increased risk of psychiatric disorders and mortality. Children with delayed school entry were 1.31 times more likely to die by suicide (95% confidence interval: 1.07, 1.61) by midlife, and 1.96 times more likely to die from drug-related death (95% confidence interval: 1.59, 2.40) by midlife than those born late in the year who started school on time. Associations with delayed school entry are likely due to selection, and results thus underscore that long-term health risks can be tracked early in life, including through school entry timing, and are highly socially patterned.

Investigating a Paradox: Toward a Better Understanding of the Relationships Between Racial Group Membership, Stress, and Major Depressive Disorder.

Epidemiologic studies in the United States routinely report a lower or equal prevalence of major depressive disorder (MDD) for Black people relative to White people. Within racial groups, individuals with greater life stressor exposure experience greater prevalence of MDD; however, between racial groups this pattern does not hold. Informed by theoretical and empirical literature seeking to explain this "Black-White depression paradox," we outline 2 proposed models for the relationships between racial group membership, life stressor exposure, and MDD: an effect modification model and an inconsistent mediator model. Either model could explain the paradoxical within- and between-racial group patterns of life stressor exposure and MDD. We empirically estimated associations under each of the proposed models using data from 26,960 self-identified Black and White participants in the National Epidemiologic Survey on Alcohol and Related Conditions III (United States, 2012-2013). Under the effect modification model, we estimated relative risk effect modification using parametric regression with a cross-product term, and under the inconsistent mediation model, we estimated interventional direct and indirect effects using targeted minimum loss-based estimation. We found evidence of inconsistent mediation (i.e., direct and indirect effects operating in opposite directions), suggesting a need for greater consideration of explanations for racial patterns in MDD that operate independent of life stressor exposure. This article is part of a Special Collection on Mental Health.

Prediction of Suicide Attempts Among Persons with Depression: A Population-Based Case Cohort Study.

Studies have highlighted the potential importance of modeling interactions for suicide attempt prediction. This case-cohort study identified risk factors for suicide attempts among persons with depression in Denmark using statistical approaches that do (random forests) or do not model interactions (least absolute shrinkage and selection operator regression [LASSO]). Cases made a non-fatal suicide attempt (n = 6,032) between 1995 and 2015. The comparison subcohort was a 5% random sample of all persons in Denmark on January 1, 1995 (n = 11,963). We used random forests and LASSO for sex-stratified prediction of suicide attempts from demographic variables, psychiatric and somatic diagnoses, and treatments. Poisonings, psychiatric disorders, and medications were important predictors for both sexes. Area under the receiver operating characteristic curve (AUC) values were higher in LASSO models (0.85 [95% CI = 0.84, 0.86] in men; 0.89 [95% CI = 0.88, 0.90] in women) than random forests (0.76 [95% CI = 0.74, 0.78] in men; 0.79 [95% CI = 0.78, 0.81] in women). Automatic detection of interactions via random forests did not result in better model performance than LASSO models that did not model interactions. Due to the complex nature of psychiatric comorbidity and suicide, modeling interactions may not always be the optimal statistical approach to enhancing suicide attempt prediction in high-risk samples.