Journal of Health and Social Behavior最新文献

Sexual and gender minorities (SGMs) have experienced progressive change over the last 50 years. However, this group still reports worse health and health care experiences. An innovative survey instrument that applies stereotype threat to the health care setting, health care stereotype threat (HCST), offers a new avenue to examine these disparities. We harmonized two national probability data sets of SGMs-Generations and TransPop-capturing 503 gay men, 297 lesbians, 467 bisexuals, and 221 trans people. Using these data, we, first, explored how HCST's association with self-rated health and psychological distress changed while considering more established constructs: discrimination and stigma. Second, we examined how HCST's association varied across SGM groups. Results suggest that HCST is a unique predictor net of the associations with discrimination and stigma. Furthermore, results highlight the more consequential associations for trans people on well-being compared to gay men. We discuss implications of these findings for future research and potential interventions.

Preventive health care use can reduce the risk of disease, disability, and death. Thus, it is critical to understand factors that shape preventive care use. A growing body of research identifies structural sexism as a driver of population health, but it remains unknown if structural sexism is linked to preventive care use and, if so, whether the relationship differs for women and men. Gender performance and gendered power and resource allocation perspectives lead to competing hypotheses regarding these questions. This study explores the relationship between structural sexism and preventive care in gender-stratified, multilevel models that combine data from the Behavioral Risk Factor Surveillance System with state-level data (N = 425,454). We find that in states with more structural sexism, both men and women were less likely to seek preventive care. These findings support the gender performance hypothesis for men and the gendered power and resource allocation hypothesis for men and women.

Less than 1% of studies on racialized health inequities have empirically examined their root cause: structural racism. Moreover, there has been a disconnect between the conceptualization and measurement of structural racism. This study advances the field by (1) distilling central tenets of theories of structural racism to inform measurement approaches, (2) conceptualizing U.S. states as racializing institutional actors shaping health, (3) developing a novel latent measure of structural racism in states, (4) using multilevel models to quantify the association between structural racism and five individual-level health outcomes among respondents from the Health and Retirement Study (N = 9,020) and the Behavioral Risk Factor Surveillance System (N = 308,029), and (5) making our measure of structural racism publicly available to catalyze research. Results show that structural racism is consistently associated with worse health for Black people but not White people. We conclude by highlighting this study's contributions (theoretical, methodological, and substantive) and important avenues for future research on the topic.

In early 2020, when COVID-19 began to spread in the United States, many Twitter users called it the "Chinese virus," blaming racial outgroups for the pandemic. I collected tweets containing the "Chinese virus" derivatives posted from March to August 2020 by users within the United States and created a data set with 141,290 tweets published by 50,695 users. I calculated the ratio of users who supported the racist naming of COVID-19 per county and merged Twitter data with the county-level census. Multilevel regression models show that counties with higher COVID-19 mortality or infection rates have more support for the racist naming. Second, the mortality and infection rates effects are stronger in counties with faster minority growth. Moreover, it is mainly in poor counties that minority growth enlarges the effects of infection and mortality rates. These findings relate to the theories on disease-induced xenophobia and the debate between conflict and contact theories.

Republicans and conservatives report better self-rated health and well-being compared to Democrats and liberals, yet they are more likely to reside in geographic areas with heavy COVID-19 morbidity and mortality. This harmed health on "both sides" of political divides, occurring in a time of rapid sociopolitical upheaval, warrants the revisiting of psychosocial mechanisms linked to political health differences. Drawing on national Gallup data (early 2021), we find that predicted differences in health or well-being vary substantially by ideology, party, voting behavior, and policy beliefs, with model fit depending on how politics are measured. Differences in self-rated health, psychological distress, happiness, trouble sleeping, and delayed health care tend to reveal worse outcomes for Democrats or liberals. Such differences often are reduced to insignificance by some combination of mastery, meritocratic beliefs, perceived social support, and COVID-19-related exposures and attitudes. Policy beliefs predict health differences most robustly across outcomes and mechanism adjustments.

An expansive and methodologically varied literature designed to investigate racial disparities in health now exists. Empirical evidence points to an overlapping, complex web of social conditions that accelerate the pace of aging and erodes long-term health outcomes among people of color, especially Black Americans. However, a social exposure-or lack thereof-that is rarely mentioned is time use. The current paper was specifically designed to address this shortcoming. First, we draw on extant research to illustrate how and why time is a critical source of racial disparities in health. Second, we employ fundamental causes theory to explain the specific mechanisms through which the differential distribution of time across race is likely to give rise to unequal health outcomes. Finally, we introduce a novel conceptual framework that identifies and distinguishes between four distinct forms of time use likely to play an outsized role in contributing to racial disparities in health.

Health care research has long overlooked the intersection of multiple social inequalities. This study examines influenza vaccination inequities at the intersection of sexuality, gender, and race-ethnicity. Using data from the 2013 to 2018 National Health Interview Survey (N = 166,908), the study shows that sexual, gender, and racial-ethnic identities jointly shaped flu vaccination. Specifically, White gay men had the highest vaccination rate (56%), while Black bisexual women had the lowest rate (23%). Across Black, Hispanic, and White individuals, sexual minority women had lower vaccination rates than heterosexual women, but sexual minority men had higher or similar vaccination rates than heterosexual men. Economic enabling, noneconomic enabling, and need-based factors together explained a substantial portion of these gaps. However, they cannot explain all the disadvantages faced by Black lesbian, bisexual, and heterosexual women and Black heterosexual men. Findings offer new evidence of hidden health care inequities and inform health policies from an intersectional perspective.

Recent scientific and policy initiatives frame clinical settings as sites for intervening upon inequality. Electronic health records and data analytic technologies offer opportunity to record standard data on education, employment, social support, and race-ethnicity, and numerous audiences expect biomedicine to redress social determinants based on newly available data. However, little is known on how health practitioners and institutional actors view data standardization in relation to inequity. This article examines a public safety-net health system's expansion of race, ethnicity, and language data collection, drawing on 10 months of ethnographic fieldwork and 32 qualitative interviews with providers, clinic staff, data scientists, and administrators. Findings suggest that electronic data capture institutes a decontextualized racialization within biomedicine as health practitioners and data workers rely on biological, cultural, and social justifications for collecting racial data. This demonstrates a critical paradox of stratified biomedicalization: The same data-centered interventions expected to redress injustice may ultimately reinscribe it.

During the COVID-19 pandemic, parents experienced difficulties around employment and children's schooling, likely with detrimental mental health implications. We analyze National Longitudinal Survey of Youth 1997 data (N = 2,829) to estimate depressive symptom changes from 2019 to 2021 by paid work status and children's schooling modality, considering partnership status, gender, and race-ethnicity differences. We draw on cumulative disadvantage theory alongside strained advantage theory to test whether mental health declines were steeper for parents with more disadvantaged statuses or for parents with more advantaged statuses. Parents with work disruptions, without paid work, or with children in remote school experienced the greatest increases in depressive symptoms, with steepest increases among single parents without paid work and single parents with children in remote school (cumulative disadvantage), fathers without paid work (strained advantage), and White parents with remote school (strained advantage). We discuss the uneven impacts of the pandemic on mental health and implications for long-term health disparities.