• J Urban Health · Oct 2014

    Racial disparities in diabetes mortality in the 50 most populous US cities.

    • Summer Rosenstock, Steve Whitman, Joseph F West, and Michael Balkin.
    • Sinai Urban Health Institute, Sinai Health System, Chicago, IL, USA, summer.rosenstock@sinai.org.
    • J Urban Health. 2014 Oct 1; 91 (5): 873885873-85.

    AbstractWhile studies have consistently shown that in the USA, non-Hispanic Blacks (Blacks) have higher diabetes prevalence, complication and death rates than non-Hispanic Whites (Whites), there are no studies that compare disparities in diabetes mortality across the largest US cities. This study presents and compares Black/White age-adjusted diabetes mortality rate ratios (RRs), calculated using national death files and census data, for the 50 most populous US cities. Relationships between city-level diabetes mortality RRs and 12 ecological variables were explored using bivariate correlation analyses. Multivariate analyses were conducted using negative binomial regression to examine how much of the disparity could be explained by these variables. Blacks had statistically significantly higher mortality rates compared to Whites in 39 of the 41 cities included in analyses, with statistically significant rate ratios ranging from 1.57 (95 % CI: 1.33-1.86) in Baltimore to 3.78 (95 % CI: 2.84-5.02) in Washington, DC. Analyses showed that economic inequality was strongly correlated with the diabetes mortality disparity, driven by differences in White poverty levels. This was followed by segregation. Multivariate analyses showed that adjusting for Black/White poverty alone explained 58.5 % of the disparity. Adjusting for Black/White poverty and segregation explained 72.6 % of the disparity. This study emphasizes the role that inequalities in social and economic determinants, rather than for example poverty on its own, play in Black/White diabetes mortality disparities. It also highlights how the magnitude of the disparity and the factors that influence it can vary greatly across cities, underscoring the importance of using local data to identify context specific barriers and develop effective interventions to eliminate health disparities.

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