Race, Socioeconomic Status, Health Locus of Control, and Body Mass Index

Assari, Shervin; Najand, Babak

doi:10.29245/2578-2959/2023/2.1279

Cited by 2 publications

(2 citation statements)

References 55 publications

(63 reference statements)

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“…This suggests that parental marital status plays a different role in cognitive development trajectories across race subgroups. Our models effectively quantified the weaker positive effects of high SES on cognitive development in systemically disadvantaged subgroups, a phenomenon known as marginalized-related diminished returns (MDRs) 51,52,53 . Therefore, making the comparison, for example, that children in two-parent households are more likely to be cognitively enriched, than in single-parent households, is not a ground truth that is universally applicable, and may propagate a White-centric narrative onto all race subgroups.…”

Section: Discussionmentioning

confidence: 99%

Diversity-aware Population Models: Quantifying Associations between Socio-Spatial Factors and Cognitive Development in the ABCD Cohort

Osayande,

Marotta,

Aggarwal

et al. 2024

Preprint

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Population-level analyses are inherently complex due to a myriad of latent confounding effects that underlie the interdisciplinary topics of research interest. Despite the mounting demand for generative population models, the limited generalizability to underrepresented groups hinders their widespread adoption in downstream applications. Interpretability and reliability are essential for clinicians and policymakers, while accuracy and precision are prioritized from an engineering standpoint. Thus, in domains such as population neuroscience, the challenge lies in determining a suitable approach to model population data effectively. Notably, the traditional strata-agnostic nature of existing methods in this field reveals a pertinent gap in quantitative techniques that directly capture major sources of population stratification. The emergence of population-scale cohorts, like the Adolescent Brain Cognitive Development℠ (ABCD) Study, provides unparalleled opportunities to explore and characterize neurobehavioral and sociodemographic relationships comprehensively. We propose diversity-aware population modeling, a framework poised to standardize systematic incorporation of diverse attributes, structured with respect to intrinsic population stratification to obtain holistic insights. Here, we leverage Bayesian multilevel regression and poststratification, to elucidate inter-individual differences in the relationships between socioeconomic status (SES) and cognitive development. We constructed 14 varying-intercepts and varying-slopes models to investigate 3 cognitive phenotypes and 5 sociodemographic variables (SDV), across 17 US states and 5 race subgroups. SDVs exhibited systemic socio-spatial effects that served as fundamental drivers of variation in cognitive outcomes. Low SES was disproportionately associated with cognitive development among Black and Hispanic children, while high SES was a robust predictor of cognitive development only among White and Asian children, consistent with the minorities’ diminished returns (MDRs) theory. Notably, adversity-susceptible subgroups demonstrated an expressive association with fluid cognition compared to crystallized cognition. Poststratification proved effective in correcting group attribution biases, particularly in Pennsylvania, highlighting sampling discrepancies in US states with the highest percentage of marginalized participants in the ABCD Study©. Our collective analyses underscore the inextricable link between race and geographic location within the US. We emphasize the importance of diversity-aware population models that consider the intersectional composition of society to derive precise and interpretable insights across applicable domains.

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Section: Discussionmentioning

confidence: 99%

Diversity-aware Population Models: Quantifying Associations between Socio-Spatial Factors and Cognitive Development in the ABCD Cohort

Osayande,

Marotta,

Aggarwal

et al. 2024

Preprint

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“…There is strong evidence that neighbourhoods with more favourable environments are associated with better child mental health outcomes. For instance, a study from the US using data from the Adolescent Brain Cognitive Development (ABCD) study found that a higher average score of neighbourhood safety reported by caregivers was associated with a lower score of internalising symptoms in children (β = -0.91) (Assari, 2021). Furthermore, neighbourhoods with favourable built environments (e.g., more green space) (Franklin et al, 2020;McCormick, 2017), higher cohesion or trust (Butler et al, 2012;Dahal et al, 2018;Eijgermans et al, 2022), and lower social disorder (Sim & Georgiades, 2022) are also associated with better child mental health and well-being.…”

Section: Introductionmentioning

confidence: 99%

Neighbourhood characteristics and socioeconomic inequalities in child mental health: cross-sectional and longitudinal findings from the Growing Up in Ireland Study

Putra,

McInerney,

Robinson

et al. 2023

Preprint

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Background: We examined the role of neighbourhood characteristics in explaining socioeconomic inequalities in child mental health in Ireland.Methods: Data from Cohort ’08 of Growing Up in Ireland with Wave 3 (age 5) as the baseline and Wave 5 (age 9) as the follow-up (n=8,373 for cross-sectional and n=6,349 for longitudinal analyses) were used. Socioeconomic status (SES) indicators were caregiver education, occupation, and equivalised household income. Caregiver and teacher reports of total difficulties score (TDS) from the Strength and Difficulties Questionnaire were combined with a higher score indicating more mental health difficulties. Twenty neighbourhood items rated by caregivers were grouped into five domains (neighbourhood safety, built environments, cohesion, interaction, and disorder) using exploratory factor analysis. We used linear regression to assess the associations between SES indicators, neighbourhood characteristics, and TDS, controlling for sociodemographic covariates. Mediation analysis informed the extent to which (%) neighbourhood domains explained the SES-TDS associations. Network psychometric analysis was used to identify neighbourhood items that were most strongly linked to TDS.Results: Lower SES was consistently associated with a higher baseline, follow-up TDS, and longitudinal changes in TDS. Favourable neighbourhood domains (higher safety, cohesion, interaction, lower disorder) were associated with lower baseline and follow-up TDS. Neighbourhood safety, cohesion, and disorder were also associated with longitudinal changes in TDS across waves. Four neighbourhood domains (except built environments) explained the associations between SES and baseline and follow-up TDS by 3% to 18% in separate single mediation models, and these four domains in concert explained 12% to 23% in multiple mediation models. Built environments domain predicted TDS and mediated the associations between SES and baseline TDS (5% to 11%) in urban children only. No mediation of the associations between SES and longitudinal changes in TDS by neighbourhood domains was observed. Findings from network analysis indicated that specific concerns over “people being drunk or taking drugs in public” and whether “this is a safe neighbourhood” may act as key conceptual bridges between neighbourhood characteristics and TDS.Conclusions: Neighbourhood characteristics may explain cross-sectional socioeconomic inequalities in child mental health. Improving neighbourhood characteristics where lower-SES children live may be important to reduce SES-based inequalities in child mental health.

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Race, Socioeconomic Status, Health Locus of Control, and Body Mass Index

Cited by 2 publications

References 55 publications

Diversity-aware Population Models: Quantifying Associations between Socio-Spatial Factors and Cognitive Development in the ABCD Cohort

Diversity-aware Population Models: Quantifying Associations between Socio-Spatial Factors and Cognitive Development in the ABCD Cohort

Neighbourhood characteristics and socioeconomic inequalities in child mental health: cross-sectional and longitudinal findings from the Growing Up in Ireland Study

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