Decomposition of the US black/white inequality in premature mortality, 2010–2015: an observational study. Issue 11 (19th November 2019)

• Record Type: Journal Article
• Authors: Kiang, Mathew V; Krieger, Nancy; Buckee, Caroline O; Onnela, Jukka Pekka; Chen, Jarvis T
• Is Part Of: BMJ open. Volume 9:Issue 11(2019)
• Publication Date: 2019-11-19
• Subjects: Bayesian joint model -- racial/ethnic inequality -- risk decomposition -- spatial epidemiology; Medicine -- Research -- Periodicals; 610.72
• DOI: 10.1136/bmjopen-2019-029373
• Languages: English
• ISSNs: 2044-6055

Abstract

Abstract : Objective: Decompose the US black/white inequality in premature mortality into shared and group-specific risks to better inform health policy. Setting: All 50 US states and the District of Columbia, 2010 to 2015. Participants: A total of 2.85 million non-Hispanic white and 762 639 non-Hispanic black US-resident decedents. Primary and secondary outcome measures: The race-specific county-level relative risks for US blacks and whites, separately, and the risk ratio between groups. Results: There is substantial geographic variation in premature mortality for both groups and the risk ratio between groups. After adjusting for median household income, county-level relative risks ranged from 0.46 to 2.04 (median: 1.03) for whites and from 0.31 to 3.28 (median: 1.15) for blacks. County-level risk ratios (black/white) ranged from 0.33 to 4.56 (median: 1.09). Half of the geographic variation in white premature mortality was shared with blacks, while only 15% of the geographic variation in black premature mortality was shared with whites. Non-Hispanic blacks experience substantial geographic variation in premature mortality that is not shared with whites. Moreover, black-specific geographic variation was not accounted for by median household income. Conclusion: Understanding geographic variation in mortality is crucial to informing health policy; however, estimating mortality is difficult at small spatial scales or for small subpopulations. Bayesian joint spatial models ameliorate many of these issues and can provide a nuanced decomposition of risk. Using premature mortality as an example application, we show that Bayesian joint spatial models are a powerful tool as researchers grapple with disentangling neighbourhood contextual effects and sociodemographic compositional effects of an area when evaluating health outcomes. Further research is necessary in fully understanding when and how these models can be applied in an epidemiological setting.