Dataset Information

Conversations about race in Black and White US families: Before and after George Floyd's death.

ABSTRACT: Research has shown that Black parents are more likely than White parents to have conversations about race with their children, but few studies have directly compared the frequency and content of these conversations and how they change in response to national events. Here we examine such conversations in the United States before and after the killing of George Floyd. Black parents had conversations more often than White parents, and they had more frequent conversations post-Floyd. White parents remained mostly unchanged and, if anything, were less likely to talk about being White and more likely to send colorblind messages. Black parents were also more worried than White parents-both that their children would experience racial bias and that their children would perpetrate racial bias, a finding that held both pre- and post-Floyd. Thus, even in the midst of a national moment on race, White parents remained relatively silent and unconcerned about the topic.

SUBMITTER: Sullivan JN

PROVIDER: S-EPMC8463880 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

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Publications

Conversations about race in Black and White US families: Before and after George Floyd's death.

Sullivan J Nicky JN Eberhardt Jennifer L JL Roberts Steven O SO

Proceedings of the National Academy of Sciences of the United States of America 20210901 38

Research has shown that Black parents are more likely than White parents to have conversations about race with their children, but few studies have directly compared the frequency and content of these conversations and how they change in response to national events. Here we examine such conversations in the United States before and after the killing of George Floyd. Black parents had conversations more often than White parents, and they had more frequent conversations post-Floyd. White parents r ...[more]

PMID: 34518224

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Project description:Background and objectivesLoneliness is consistently linked to worse depression/depressive symptoms; however, there are few studies that have examined whether the relationship between loneliness and depressive symptoms varies by race. The purpose of this study was to determine whether race moderated the relationship between loneliness and depressive symptoms.Research design and methodsData come from the 2014 wave of the Health and Retirement Study (HRS) Core survey and Psychosocial Leave-Behind Questionnaire; only black and white older adults were included in the analysis (N = 6,469). Depressive symptoms were operationalized by the eight-item Center for Epidemiological Studies-Depression scale; however, the "felt lonely" item was removed given concerns with collinearity. Loneliness was operationalized using the Hughes 3-Item Loneliness Scale. Sociodemographic variables included gender, age, education, household income, employment status, marital status, and living alone or with others. Furthermore, social support and negative interactions from family members and friends, and religious service attendance were included in the analysis. Lastly, we created an interaction term between race and loneliness. All analyses used survey weights to account for the complex multistage sampling design of the HRS. Missing data were multiply imputed.ResultsIn multivariable analysis, we found race significantly moderated the relationship between loneliness and depressive symptoms while controlling for sociodemographic covariates, social support and negative interaction variables, and religious service attendance.Discussion and implicationsOur findings demonstrate a differential racial effect for loneliness and depressive symptoms. For both blacks and whites, greater loneliness affected depressive symptoms; however, the effect was stronger among whites than it was for blacks. Given this is one of the first studies to examine the differential effects of race on loneliness and depressive symptoms, more research is necessary to determine the consistency of these results.

Project description:ImportanceStroke is the fifth-highest cause of death in the US and a leading cause of serious long-term disability with particularly high risk in Black individuals. Quality risk prediction algorithms, free of bias, are key for comprehensive prevention strategies.ObjectiveTo compare the performance of stroke-specific algorithms with pooled cohort equations developed for atherosclerotic cardiovascular disease for the prediction of new-onset stroke across different subgroups (race, sex, and age) and to determine the added value of novel machine learning techniques.Design, setting, and participantsRetrospective cohort study on combined and harmonized data from Black and White participants of the Framingham Offspring, Atherosclerosis Risk in Communities (ARIC), Multi-Ethnic Study for Atherosclerosis (MESA), and Reasons for Geographical and Racial Differences in Stroke (REGARDS) studies (1983-2019) conducted in the US. The 62 482 participants included at baseline were at least 45 years of age and free of stroke or transient ischemic attack.ExposuresPublished stroke-specific algorithms from Framingham and REGARDS (based on self-reported risk factors) as well as pooled cohort equations for atherosclerotic cardiovascular disease plus 2 newly developed machine learning algorithms.Main outcomes and measuresModels were designed to estimate the 10-year risk of new-onset stroke (ischemic or hemorrhagic). Discrimination concordance index (C index) and calibration ratios of expected vs observed event rates were assessed at 10 years. Analyses were conducted by race, sex, and age groups.ResultsThe combined study sample included 62 482 participants (median age, 61 years, 54% women, and 29% Black individuals). Discrimination C indexes were not significantly different for the 2 stroke-specific models (Framingham stroke, 0.72; 95% CI, 0.72-073; REGARDS self-report, 0.73; 95% CI, 0.72-0.74) vs the pooled cohort equations (0.72; 95% CI, 0.71-0.73): differences 0.01 or less (P values >.05) in the combined sample. Significant differences in discrimination were observed by race: the C indexes were 0.76 for all 3 models in White vs 0.69 in Black women (all P values <.001) and between 0.71 and 0.72 in White men and between 0.64 and 0.66 in Black men (all P values ≤.001). When stratified by age, model discrimination was better for younger (<60 years) vs older (≥60 years) adults for both Black and White individuals. The ratios of observed to expected 10-year stroke rates were closest to 1 for the REGARDS self-report model (1.05; 95% CI, 1.00-1.09) and indicated risk overestimation for Framingham stroke (0.86; 95% CI, 0.82-0.89) and pooled cohort equations (0.74; 95% CI, 0.71-0.77). Performance did not significantly improve when novel machine learning algorithms were applied.Conclusions and relevanceIn this analysis of Black and White individuals without stroke or transient ischemic attack among 4 US cohorts, existing stroke-specific risk prediction models and novel machine learning techniques did not significantly improve discriminative accuracy for new-onset stroke compared with the pooled cohort equations, and the REGARDS self-report model had the best calibration. All algorithms exhibited worse discrimination in Black individuals than in White individuals, indicating the need to expand the pool of risk factors and improve modeling techniques to address observed racial disparities and improve model performance.

Dataset Information

Conversations about race in Black and White US families: Before and after George Floyd's death.

Publications

Conversations about race in Black and White US families: Before and after George Floyd's death.

Similar Datasets

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