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Mental health disorders have become a global problem, garnering considerable attention. However, the root causes of deteriorating mental health remain poorly understood, with existing literature predominantly concentrating on socioeconomic conditions and psychological factors. This study uses multi‐linear and geographically weighted regressions (GWR) to examine the associations between built and natural environmental attributes and the prevalence of depression in US counties. The findings reveal that job sprawl and land mixed use are highly correlated with a lower risk of depression. Additionally, the presence of green spaces, especially in urban area, is associated with improved mental health. Conversely, higher concentrations of air pollutants, such as PM2.5 and CO, along with increased precipitation, are linked to elevated depression rates. When considering spatial correlation through GWR, the impact of population density and social capital on mental health displays substantial spatial heterogeneity. Further analysis, focused on two high depression risk clustering regions (northwestern and southeastern counties), reveals nuanced determinants. In northwestern counties, depression rates are more influenced by factors like precipitation and socioeconomic conditions, including unemployment and income segregation. In southeastern counties, population demographic characteristics, particularly racial composition, are associated with high depression prevalence, followed by built environment factors. Interestingly, job growth and crime rates only emerge as significant factors in the context of high depression risks in southeastern counties. This study underscores the robust linkages and spatial variations between built and natural environments and mental health, emphasizing the need for effective depression treatment to incorporate these multifaceted factors. Plain Language Summary: This study examines the associations between built and natural environmental attributes and the prevalence of depression in US counties. The findings reveal that greater job sprawl and land mixed use are associated with lower depression prevalence. Additionally, the presence of green spaces in urban area is associated with reduced depression prevalence. Conversely, higher concentrations of air pollutants, such as PM2.5 and CO, along with increased precipitation, are linked to elevated depression rates. Associations between population density and social capital with depression prevalence display substantial spatial heterogeneity. Further analysis, focused on two high depression risk clustering regions (northwestern and southeastern counties), reveals region‐specific determinants. Key Points: Job sprawl, land mixed use, and green space associate with lower depression risk; the roles of air pollution and precipitation are oppositeAssociation of population density and social capital with depression prevalence exhibit significant spatial diversityPrecipitation and socioeconomic status mainly affect northwestern counties; population demographics plays key role in southeastern counties [ABSTRACT FROM AUTHOR]

Policy PointsPolicies that increase county income levels, particularly for middle‐income households, may reduce low birth weight rates and shrink disparities between Black and White infants.Given the role of aggregate maternal characteristics in predicting low birth weight rates, policies that increase human capital investments (e.g., funding for higher education, job training) could lead to higher income levels while improving population birth outcomes.The association between county income levels and racial disparities in low birth weight is independent of disparities in maternal risks, and thus a broad set of policies aimed at increasing income levels (e.g., income supplements, labor protections) may be warranted. Context: Low birth weight (LBW; <2,500 grams) and infant mortality rates vary among place and racial group in the United States, with economic resources being a likely fundamental contributor to these disparities. The goals of this study were to examine time‐varying county median income as a predictor of LBW rates and Black‐White LBW disparities and to test county prevalence and racial disparities in maternal sociodemographic and health risk factors as mediators. Methods: Using national birth records for 1992–2014 from the National Center for Health Statistics, a total of approximately 27.4 million singleton births to non‐Hispanic Black and White mothers were included. Data were aggregated in three‐year county‐period observations for 868 US counties meeting eligibility requirements (n = 3,723 observations). Sociodemographic factors included rates of low maternal education, nonmarital childbearing, teenage pregnancy, and advanced‐age pregnancy; and health factors included rates of smoking during pregnancy and inadequate prenatal care. Among other covariates, linear models included county and period fixed effects and unemployment, poverty, and income inequality. Findings: An increase of $10,000 in county median income was associated with 0.34 fewer LBW cases per 100 live births and smaller Black‐White LBW disparities of 0.58 per 100 births. Time‐varying county rates of maternal sociodemographic and health risks mediated the association between median income and LBW, accounting for 65% and 25% of this estimate, respectively, but racial disparities in risk factors did not mediate the income association with Black‐White LBW disparities. Similarly, county median income was associated with very low birth weight rates and related Black‐White disparities. Conclusions: Efforts to increase income levels—for example, through investing in human capital, enacting labor union protections, or attracting well‐paying employment—have broad potential to influence population reproductive health. Higher income levels may reduce LBW rates and lead to more equitable outcomes between Black and White mothers. [ABSTRACT FROM AUTHOR]

Late adolescents ( N = 256; Mage = 17.5) who spent a larger proportion of their early life in poverty exhibited less persistence when confronted by a challenging task. Greater chaos during early adolescence also predicted less task persistence at age 17. However, the effects of poverty were moderated by chaos such that if chaos levels were high during early adolescence, task persistence was uniformly lower among late adolescents, irrespective of childhood poverty. Only when chaos levels were relatively low did poverty matter for future task persistence. Furthermore, the interactive effects of chaos and early childhood poverty were independent of child ability, and of concurrent chaos and poverty. [ABSTRACT FROM AUTHOR]