Search for: All records

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. 

Causal decomposition analysis is among the rapidly growing number of tools for identifying factors (“mediators”) that contribute to disparities in outcomes between social groups. An example of such mediators is college completion, which explains later health disparities between Black women and White men. The goal is to quantify how much a disparity would be reduced (or remain) if we hypothetically intervened to set the mediator distribution equal across social groups. Despite increasing interest in estimating disparity reduction and the disparity that remains, various estimation procedures are not straightforward, and researchers have scant guidance for choosing an optimal method. In this article, the authors evaluate the performance in terms of bias, variance, and coverage of three approaches that use different modeling strategies: (1) regression-based methods that impose restrictive modeling assumptions (e.g., linearity) and (2) weighting-based and (3) imputation-based methods that rely on the observed distribution of variables. The authors find a trade-off between the modeling assumptions required in the method and its performance. In terms of performance, regression-based methods operate best as long as the restrictive assumption of linearity is met. Methods relying on mediator models without imposing any modeling assumptions are sensitive to the ratio of the group-mediator association to the mediator-outcome association. These results highlight the importance of selecting an appropriate estimation procedure considering the data at hand. 

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.