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Abstract ObjectivesChildhood abuse has been extensively studied in relation to later-life health, yet relatively little attention has been given to understanding the nuanced dynamics across victim–perpetrator relationships. This study addresses this gap by identifying typologies of familial perpetrators of childhood abuse in a national sample and examining their associations with various health outcomes, including physical and mental health as well as substance abuse. MethodsWe used 2 waves of data from the Midlife in the US Study (n = 6,295, mean age = 46.9 at baseline). The analysis was completed in 3 stages. Using Latent Class Analysis (LCA), we identified subpopulations of victims with distinct familial perpetrator histories. With assigned LCA memberships and propensity score weighting, we investigated the extent to which specific victim–perpetrator relationships are associated with health outcomes measured at baseline and a 10-year follow-up adjusting for other early-life risks. We evaluated whether the observed associations differ across the waves. ResultsParental and sibling abuse commonly co-occur, surpassing the occurrence of single perpetrators. Although minimal health disparities are evident between sibling-only abuse and no/little abuse groups at baseline, parent-only abuse is associated with compromised health outcomes. Severe abuse from both siblings and parents is linked to the most adverse health outcomes. At the follow-up survey, the associations between familiar abuse and health outcomes weakened, particularly for substance abuse. DiscussionThis study, delving into family relationships, family violence, and health disparities, provides new evidence to augment our comprehension of the enduring link between childhood abuse and health within the family context. 

Abstract ObjectivesLow-cost debt can potentially enhance wealth and indirectly benefit health, yet Black Americans disproportionately lack this type of debt, which may constrain their ability to accumulate wealth throughout their lives and across generations. Our objectives are to develop a novel debt–asset measure, use it to quantify the Black–White differential in debt–asset profiles, and estimate its contribution to the racial gap in cognition. MethodsUsing the Health and Retirement Study (1998–2020), we grouped individuals based on debt and asset information during the preretirement period of ages 55–61, including the absence of debt and the relative amount of debt compared to assets. Linear mixed models were used to examine the extent to which cognition in later life (ages 62–80) differs across these debt–asset profiles and its role in explaining the racial disparity in cognition. ResultsCompared with Whites, Blacks were more likely to fall into categories characterized by high debt-to-asset ratio (DAR) or limited asset ownership. Low-asset nonborrowers displayed the poorest cognition, followed closely by high-DAR borrowers. The Black–White differential in debt–asset profiles contributed to the racial gap in cognition. DiscussionThere were 2 unfavorable debt–asset profiles: high debt relative to assets and little or no debt due to a lack of assets, which was more prevalent among Blacks than Whites. We discuss how institutional and structural racism shapes Black–White disparities in debt–asset profiles, such as limited access to borrowing opportunities, thereby contributing to health inequalities, including cognition. 

The underrepresentation of women in science, technology, engineering and mathematics (STEM) fields has been a subject of extensive research and policy debate. However, there is limited clarity regarding the specific mechanisms that generate these disparities, and which interventions are most effective in reducing the gap. In this study, we use causal decomposition analysis to estimate how the gender gap in STEM participation would change if we were to intervene on women’s self-efficacy beliefs in mathematics. Women tend to underestimate their abilities in math-related fields, which can affect their educational and career choices. The question we ask is to what extent the gender gap in individuals’ enrollment in STEM majors and identification with mathematics would be reduced if self-efficacy in mathematics were set to be equal across gender categories. The results suggest that equalizing this target factor will reduce the observed disparities in math identity by 53%, and in the enrollment of STEM majors by 2.5%. The modest influence of self-efficacy on enrollment disparities suggests that it is not the predominant factor. We discuss the implications of our empirical findings, as well as how causal decomposition analysis can benefit social and behavioral disparities research. 

Causal decomposition analysis aims to identify risk factors (referred to as “mediators”) that contribute to social disparities in an outcome. Despite promising developments in causal decomposition analysis, current methods are limited to addressing a time-fixed mediator and outcome only, which has restricted our understanding of the causal mechanisms underlying social disparities. In particular, existing approaches largely overlook individual characteristics when designing (hypothetical) interventions to reduce disparities. To address this issue, we extend current longitudinal mediation approaches to the context of disparities research. Specifically, we develop a novel decomposition analysis method that addresses individual characteristics by (a) using optimal dynamic treatment regimes (DTRs) and (b) conditioning on a selective set of individual characteristics. Incorporating optimal DTRs into the design of interventions can be used to strike a balance between equity (reducing disparities) and excellence (improving individuals’ outcomes). We illustrate the proposed method using the High School Longitudinal Study data. 

Mathematics literacy is crucial in many STEM fields, yet Black and Hispanic students are less likely to achieve high math proficiency. While previous literature investigated potential factors to mitigate racial or gendered disparities in mathematics literacy, few studies attended to the conditions under which the causal interpretation of the results obtained can be established. Guided by intersectionality theory and causal decomposition analysis, we examined the degree to which disparities in mathematics literacy (a) exist at the intersection of race and gender and (b) can be reduced by hypothetical interventions that equalize school socioeconomic status (SES) or opportunity to learn (OTL) across groups. We found large racial/ethnic differences in math literacy favoring Asians and whites and much smaller gender differences. We also found that equalizing school SES may reduce disparities for Black and Hispanic males and females; equalizing OTL may reduce disparities for Black and Hispanic males as well as Asian males and females; compared to white males. Our findings suggest that interventions that target specific race–gender groups are required to reduce disparities in math literacy.