Articles

<b>Purpose:</b> Although the effect of loss of a loved one on depression is well established, very limited knowledge exists on racial differences in this effect. <b>Aim: </b>In the current study we compared Non-Hispanic White (NHW) and Non-Hispanic Black (NHB) Californians for the effects of loss of a loved one on depression in a representative sample of adults in California. <b>Methods:</b> This cross-sectional study used data from the Survey of California Adults on Serious Illness and End-of-Life 2019. Overall, 1603 people entered our study. We compared 901 (56.2%) NHB and 702 (43.8%) NHW adults (age 18 and older). Race/ethnicity, demographics (age and gender), socio-economic factors (education, income, employment, and marital status), religiosity, and health (self-rated health and number of chronic medical conditions), and depression were measured. To perform data analysis, we used logistic regression models. <b>Results:</b> In the pooled sample, loss of a loved one was not associated with self-reported depression, net of all covariates. Race, however, interacted with loss of a loved one on depression, suggesting a larger association for NHBs compared to NHWs. In race-specific models, loss of a loved one predicted depression for NHBs (<i>OR</i> = 1.54) but not NHWs (<i>OR</i> = 0.99). <b>Conclusion:</b> There are differences between NHBs and NHWs in the effect of loss of a loved one on depression. NHBs show a stronger association between loss of a loved one and depression than NHWs. This result is not in line with the NHB mental health paradox or with NHB resilience but is consistent with the notion that social relations may be more salient for NHBs than for NHWs.

<b>Background</b>: Childhood poverty is a critical determinant of developmental, health, and behavioral outcomes. However, racial and ethnic differences in how families experience and navigate poverty suggest that a one-size-fits-all approach may not be an effective approach for alleviating disparities. Understanding baseline demographic, socioeconomic, health, and behavioral characteristics among families in poverty is crucial to designing equitable interventions. <b>Objective</b><b>:</b><b> </b>To examine racial and ethnic differences in baseline demographic, socioeconomic, health, and behavioral characteristics among families living in poverty, using data from the Baby's First Years (BFY:2018-2019) Study. <b>Methods</b><b>:</b><b> </b>This analysis used baseline data from the BFY study, a randomized controlled trial (RCT) designed to evaluate the effects of monthly cash assistance on children’s developmental outcomes. The sample included 1,050 children and their families, consisting of mothers and, when available, fathers. Descriptive analyses were conducted to compare demographic, socioeconomic, health, and behavioral outcomes across racial and ethnic groups, focusing on Black, Hispanic, and other mothers. <b>Results</b><b>:</b> Significant racial and ethnic differences were observed. Regarding demographic factors, Hispanic mothers were older than White mothers. In terms of socioeconomic factors, Hispanic mothers had fewer years of education compared to White mothers, while Black mothers were more likely to receive food stamps than mothers from other groups. Regarding health and behaviors, Black mothers reported worse self-rated health but were less likely to plan for breastfeeding compared to White mothers. In contrast, Hispanic mothers reported lower levels of depression and were more likely to plan for breastfeeding. <b>Conclusion</b><b>:</b><b> </b>These findings highlight the heterogeneity of experiences among families living in poverty, with significant differences across racial and ethnic groups. Such disparities underscore the importance of tailoring anti-poverty policies to the unique needs of diverse populations. Future research should explore how the effects of interventions, such as cash assistance or guaranteed income, may differ across racial and ethnic groups to inform equitable and effective policy development.

Background:</b> Early life socioeconomic conditions and race/ethnicity are critical determinants of long-term health and behavioral outcomes. Epigenetic changes, particularly those measured by the GrimAge biomarker, may mediate the impact of these early adversities on later life outcomes. This study investigates the relationships between race/ethnicity, poverty at birth, epigenetic aging at age 15, and subsequent self-rated health, school discipline, depression, and school dropout at age 22. We explored sex differences in these paths. <b>Methods:</b> Data were drawn from the Fragile Families and Child Wellbeing Study (FFCWS), which included 733 youth with comprehensive follow-up data up to age 22. Structural Equation Modeling (SEM) was employed to assess the pathways from race/ethnicity and poverty at birth to epigenetic aging (GrimAge) at age 15, and subsequently to self-rated health and school discipline at age 22. The model controlled for potential confounders including sex, family structure, and parental education. <b>Results:</b> Race/ethnicity and poverty at birth were significantly associated with higher GrimAge scores at age 15 (p < 0.05). Higher GrimAge scores were predictive of poorer self-rated health (β = -0.08, p < 0.05) and increased instances of school discipline (β = 0.13, p < 0.01) at age 22. The indirect effects of race/ethnicity and poverty at birth on self-rated health and school discipline through GrimAge were also significant (p < 0.05), suggesting that epigenetic aging partially mediates these relationships. Sex differences were also observed. Poverty at birth predicted faster epigenetic aging at age 15 for males not females. We also observed that faster epigenetic aging at age 15 was predictive of school discipline of male not female participants at age 22. In contrast, faster epigenetic aging at age 15 was predictive of self-rated health (SRH) of female not male participants at age 22. <b>Conclusions:</b> This study provides evidence that with some sex differences, race/ethnicity and poverty at birth contribute to accelerated epigenetic aging (GrimAge) by age 15, which in turn predicts poorer self-rated health and increased school discipline issues by age 22. These findings emphasize the importance of early interventions targeting social determinants to mitigate long-term health and behavioral disparities. Addressing these early life conditions is crucial for improving health equity and outcomes in young adulthood.

Intersectionality has significantly enhanced our understanding of how overlapping social identities—such as race, ethnicity, gender, sex, class, and sexual orientation—interact to shape individual experiences. However, despite its theoretical importance, much of the existing literature has relied on qualitative approaches to define and study intersectionality, limiting its application in predictive modeling, comparative analysis, and policy development. This paper introduces the concept of Quantitative Intersectionality Scoring System (QISS), a novel approach that assigns numerical scores to intersecting identities, thereby enabling a more systematic and data-driven analysis of intersectional effects. We argue that QISS can substantially enhance the utility and predictive validity of quantitative models by capturing the complexities of multiple, overlapping social determinants. By presenting concrete examples, such as the varying impacts of socioeconomic mobility on life expectancy among different intersectional groups, we demonstrate how QISS can yield more precise and reliable forecasts. Such a shift would allow policymakers and service providers to dynamically assess economic and health needs, as well as the uncertainties around them, as individuals move through different social and economic contexts. QISS-based models could be more responsive to the complexities of intersecting identities, allowing for a more quantified and nuanced evaluation of policy interventions. We conclude by discussing the challenges of implementing QISS and emphasizing the need for further research to validate these quantifications using robust quantitative methods. Ultimately, adopting QISS has the potential to improve the accuracy of predictive models and the effectiveness of policies aimed at promoting social justice and health equity.

We used 15 years of follow up of<b> </b>854 racially and ethnically diverse birth cohort who were followed from birth to age 15. Structural equation modeling (SEM) was used to examine the effects of race/ethnicity, maternal education, and family structure on poverty at birth, as well as the effects of poverty at birth on epigenetic changes at age 15. We also explored variations by sex. <b>Results: </b>Our findings indicate that Black and Latino families had lower maternal education and married family structure which in turn predicted poverty at birth. Poverty at birth then was predictive of epigenetic changes 15 years later when the index child was 15. This suggested that poverty at birth partially mediates the effects of race/ethnicity, maternal education, and family structure on epigenetic changes of youth at age 15. There was an effect of poverty status at birth on DNA methylation of male but not female youth at age 15. Thus, poverty at birth may have a more salient effect on long term epigenetic changes of male than female youth. <b>Conclusions: </b>Further studies are needed to understand the mechanisms underlying the observed sex differences in the effects of poverty as a mechanism that connects race/ethnicity, maternal education, and family structure to epigenetic changes later in life.

This study aims to consider how science fiction television series <i>Doctor Who</i> (Sydney Newman, 1963-1989, 2005) has undergone changes in representations of ethnicity since 2005. The Doctor, who is a representation of immigrants from the very beginning, was embodied in white, heterosexual men until the 11th season. Last season is the first season since 1963 when a woman is the Doctor. In addition, this season, the Doctor's three companions are Ryan who is a black man, Yaz who is a Pakistani-British woman and Graham who is a middle-aged white man mourning his wife Grace who was black. In this study, it is aimed to analyze the features that make this season different from the others in terms of representations of ethnicity by using visual data analysis techniques and Smith's national identity theory. In this study, it has been proposed that the previous episodes of the <i>Doctor Who</i> television series were problematic in case of representations of ethnicity, and the ongoing representations of ethnicity are changing under the leadership of the female Doctor and his ethnically diverse companions during the era of Chris Chibnall and the episodes are examined using this framework.