Patterns of Social Determinants of Health and Child Mental Health, Cognition, and Physical Health

Abstract

ImportanceSocial determinants of health (SDOH) influence child health. However, most previous studies have used individual, small-set, or cherry-picked SDOH variables without examining unbiased computed SDOH patterns from high-dimensional SDOH factors to investigate associations with child mental health, cognition, and physical health.ObjectiveTo identify SDOH patterns and estimate their associations with children’s mental, cognitive, and physical developmental outcomes.Design, Setting, and ParticipantsThis population-based cohort study included children aged 9 to 10 years at baseline and their caregivers enrolled in the Adolescent Brain Cognitive Development (ABCD) Study between 2016 and 2021. The ABCD Study includes 21 sites across 17 states.ExposuresEighty-four neighborhood-level, geocoded variables spanning 7 domains of SDOH, including bias, education, physical and health infrastructure, natural environment, socioeconomic status, social context, and crime and drugs, were studied. Hierarchical agglomerative clustering was used to identify SDOH patterns.Main Outcomes and MeasuresAssociations of SDOH and child mental health (internalizing and externalizing behaviors) and suicidal behaviors, cognitive function (performance, reading skills), and physical health (body mass index, exercise, sleep disorder) were estimated using mixed-effects linear and logistic regression models.ResultsAmong 10 504 children (baseline median [SD] age, 9.9 [0.6] years; 5510 boys [52.5%] and 4994 girls [47.5%]; 229 Asian [2.2%], 1468 Black [14.0%], 2128 Hispanic [20.3%], 5565 White [53.0%], and 1108 multiracial [10.5%]), 4 SDOH patterns were identified: pattern 1, affluence (4078 children [38.8%]); pattern 2, high-stigma environment (2661 children [25.3%]); pattern 3, high socioeconomic deprivation (2653 children [25.3%]); and pattern 4, high crime and drug sales, low education, and high population density (1112 children [10.6%]). The SDOH patterns were distinctly associated with child health outcomes. Children exposed to socioeconomic deprivation (SDOH pattern 3) showed the worst health profiles, manifesting more internalizing (β = 0.75; 95% CI, 0.14-1.37) and externalizing (β = 1.43; 95% CI, 0.83-2.02) mental health problems, lower cognitive performance, and adverse physical health.ConclusionsThis study shows that an unbiased quantitative analysis of multidimensional SDOH can permit the determination of how SDOH patterns are associated with child developmental outcomes. Children exposed to socioeconomic deprivation showed the worst outcomes relative to other SDOH categories. These findings suggest the need to determine whether improvement in socioeconomic conditions can enhance child developmental outcomes.

Impact of sex, race, and social determinants of health on neonatal outcomes

“…Recognizing this, there has been an increase in programs addressing SDOH as part of the healthcare spending in the U.S., with about $ 2.5 billion spent between 2017 and 2019 ( 232 ). There have been attempts to synthesize the interplay of different SDOH items and formulate SDOH patterns, which can then be analyzed within a defined group—SDOH pattern one from affluent communities and lowest social vulnerabilities; SDOH pattern 2—from high stigma environment and high level of implicit bias; SDOH pattern three from highly deprived socio-economic environments characterized by low income and poverty; and lastly SDOH pattern four from high crime and disruptive environments with lowest levels of support and highest levels of disruption ( 233 ). In a study using these patterns, physical and mental outcomes in later childhood were highly correlated with the SDOH patterns ( 233 ).…”

“…There have been attempts to synthesize the interplay of different SDOH items and formulate SDOH patterns, which can then be analyzed within a defined group—SDOH pattern one from affluent communities and lowest social vulnerabilities; SDOH pattern 2—from high stigma environment and high level of implicit bias; SDOH pattern three from highly deprived socio-economic environments characterized by low income and poverty; and lastly SDOH pattern four from high crime and disruptive environments with lowest levels of support and highest levels of disruption ( 233 ). In a study using these patterns, physical and mental outcomes in later childhood were highly correlated with the SDOH patterns ( 233 ). Furthermore, new data collection and analysis tools using data mining from EMRs could incorporate SDOH to help anticipate and manage population health ( 234 ).…”

Interventions for Weight Management in Children and Adolescents

“…These environmental influences are shaped by larger structural forces that have systematically disadvantaged communities that have experienced discrimination, such as Black, Hispanic/Latino, and Native American communities, with corresponding higher prevalence of high BMI in these communities (eFigure 8 in the Supplement). For example, neighborhood environment has an impact on physical activity; crime rates and access to nearby parks are known to affect physical activity in young people . Further, financial insecurity is an important driver of dietary behaviors; families with limited financial resources must spend a higher proportion of their household income on food, and insufficient food budgets can drive families to prioritize cost-effectiveness over healthfulness to help reduce financial strain .…”

“…[90][91][92] For example, neighborhood environment has an impact on physical activity; crime rates and access to nearby parks are known to affect physical activity in young people. 90,93 Further, financial insecurity is an important driver of dietary behaviors; families with limited financial resources must spend a higher proportion of their household income on food, and insufficient food budgets can drive families to prioritize cost-effectiveness over healthfulness to help reduce financial strain. 91 Additionally, stress, uncertainty, and long work hours for parents constrain their ability to prepare and serve healthy foods and are associated with haphazard meal planning, emotional eating, and snacking on sweets among adults with higher BMI.…”

Classification of Suicide Attempt Risk Using Environmental and Lifestyle Factors in 3 Large Youth Cohorts

“…Exposomic scores consistently explain substantial variance in suicide attempt over and above demographic variables and an established clinical indicator of suicide risk—family history. We suggest that exposomic scores could be implemented alongside existing clinical risk factors and potentially alongside clinical, genetic, or geocoded derived data to optimize individual youth suicide attempt risk classification. Future clinical research should test the relevance of exposomic scores in low- or middle-income countries, address implementation of exposomic scores in clinical settings, and test their contribution to youth suicide prevention.…”