Data from the 2010 New York City Community Health Survey and 2010 Census were utilized. Survey respondents included 7,777 adults (60% female) from 34 neighborhoods. Phone-interviews stratified by neighborhood asked respondents about health behaviors and neighborhood perceptions. Census data includes the proportion of residents within each race category, which was used to create the neighborhood racial typology. Self-rated health was a one-item measure with responses ranging from poor to excellent. Neighborhood segregation values were assigned to each neighborhood, which included the groups of white (reference group), black, Hispanic, and mixed. Analysis included multilevel ordered logistic regression models. Models were stratified by race (white, black, and Hispanic) and included demographic controls including age, income, marital, employment, and education status.
Univariate, bivariate and within group differences will be discussed. We found that White women living in Hispanic (Coeff=-0.73, p=0.09) or mixed (Coef=-0.40, p=0.07) racially segregated neighborhoods is marginally significantly associated with poorer health. As expected, black women living in racially segregated Black (Coef=-0.92, p<0.01) or Hispanic (Coef=-1.01, p<0.00) neighborhoods is associated with poorer health. Yet, for Hispanic women there is no significant association between neighborhood type and health. Overall, women belonging to racial/ethnic minorities compared to men in their racial group report poorer health than white women.
We found that neighborhood racial segregation impacts women’s health differently than men for white and black residents. Social work practitioners should consider utilizing a system’s approach to address social determinants of health, to improve health across the lifespan, with the individual, within the family and community by increasing social services and resources in neighborhoods. Policy makers for health equality should include gender specific initiatives that would improve neighborhood structures. Future research should attempt to identify underlying mechanisms that contribute to gender differences in health status.