title: Can we capture the intersections? Older Black women, education, and health.
abstract: BACKGROUND
  Race/ethnicity, gender, and socioeconomic status are the three most prominent factors to predict health outcomes. Despite the fact that persistent health inequalities are found between groups, we know little about how the interrelatedness of these social positions influences the health of older adults.
  
  
  OBJECTIVE
  In this study, we apply a feminist intersectional approach to the study of health inequalities, treating social variables as multiplicative rather than additive to capture the mutually constitutive dimensions of race/ethnicity, gender, and education.
  
  
  METHODS
  This paper makes use of data from the National Social Life, Health and Aging Project, a nationally representative sample of 3,005 community-dwelling U.S. adults aged 57 to 85 years old, to explore intersections of race, gender, and education. We use a combination of stratified analysis with an interaction term to test multiplicative effects.
  
  
  RESULTS
  First, our findings confirm that Black women with less than a high school education have the poorest self-rated health. Second, at the bivariate level, we find highly educated White men are not the converse of lower educated Black women. Third, at the multivariate level, we find being Black and female has an effect on health beyond those already accounted for by race and gender.
  
  
  CONCLUSIONS
  This research demonstrates the explanatory power of an intersectionality approach to deepen understanding of the overlapping, simultaneous production of health inequalities by race, class, and gender.
authors: Department of Sociology, Case Western Reserve University, Cleveland, OH 44106-7124, USA. susan.hinze@case.edu
authors: Susan W
authors: SW
authors: Hinze
authors: Jielu
authors: J
authors: Lin
authors: Tanetta E
authors: TE
authors: Andersson
mesh_terms: D001741:Black or African American / Q000706:statistics & numerical data*; D000368:Aged; D000369:Aged, 80 and over; D003627:Data Interpretation, Statistical; D004522:Educational Status; D005260:Female; D054624:Health Status Disparities; D006801:Humans; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D044469:Racial Groups; D012737:Sex Factors; D012923:Social Class; D012959:Socioeconomic Factors; D014481:United States