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Decomposition of the US black/white inequality in premature mortality, 2010–2015: an observational study
  1. Mathew V Kiang1,2,
  2. Nancy Krieger2,
  3. Caroline O Buckee3,4,
  4. Jukka Pekka Onnela5,
  5. Jarvis T Chen2
  1. 1 Center for Population Health Sciences, Stanford University, Palo Alto, California, USA
  2. 2 Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
  3. 3 Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
  4. 4 Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
  5. 5 Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
  1. Correspondence to Dr Mathew V Kiang; mkiang{at}stanford.edu

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.

  • Bayesian joint model
  • racial/ethnic inequality
  • risk decomposition
  • spatial epidemiology

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

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Footnotes

  • Twitter @mathewkiang, @Caroline_OF_B, @jponnela

  • Correction notice This article has been corrected since it was published. Equation on page 3 is updated.

  • Contributors MK and JC designed the study, acquired the data and developed the statistical plan. MK did the data analysis. MK, JPO, NK, CB and JC interpreted the results and suggested critical additional analyses. MK prepared the initial draft of the manuscript, tables and figures. MK, JPO, NK, CB and JC provided critical revisions in successive drafts. MK, JPO, NK, CB and JC reviewed the manuscript and approved of the version to be published.

  • Funding JC and MK were supported through a Nodal Award from Dana Farber/Harvard Cancer Center (P30CA006516). JC was funded in part by the National Institute on Child Health and Human Development of the National Institutes of Health (R01HD092580). MK received support from the National Institute on Minority Health and Health Disparities of the National Institutes of Health (DP2MD010478). The content is solely the responsibility of the authors and does not necessarily reflect the official views of the funders.

  • Map disclaimer The depiction of boundaries on the map(s) in this article do not imply the expression of any opinion whatsoever on the part of BMJ (or any member of its group) concerning the legal status of any country, territory, jurisdiction or area or of its authorities. The map(s) are provided without any warranty of any kind, either express or implied.

  • Competing interests None declared.

  • Patient consent for publication Not required.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data availability statement Data may be obtained from a third party and are not publicly available.

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