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The Effects of Allostatic Load on Racial/Ethnic Mortality Differences in the United States

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  • Jeffrey T. Howard

    (US Army Institute of Surgical Research)

  • P. Johnelle Sparks

    (University of Texas at San Antonio)

Abstract

This study expands on previous findings of racial/ethnic and allostatic load (AL) associations with mortality by addressing whether differential AL levels by race/ethnicity may explain all-cause mortality differences. This study used data from the third National Health and Nutrition Survey public-use file, gathered between 1988 and 1994, with up to 18 years of mortality follow-up (n = 11,733). AL scores were calculated using a 10-biomarker algorithm based on clinically determined thresholds. Results of discrete-time hazard models suggest that AL is associated with increased mortality risks, independent of other factors, including race/ethnicity and SES. The results also suggest that the AL–mortality association is stronger for non-Hispanic blacks than for non-Hispanic whites, and that at low levels of AL observed mortality differences between non-Hispanic blacks and non-Hispanic whites are non-significant. These findings suggest that mortality differences between non-Hispanic blacks and non-Hispanic whites may be the result of how early life exposure causes premature aging and increased mortality risks. More attention to resource allocation and local environments is needed to understand why non-Hispanic blacks experience premature aging that leads to differential mortality risks compared to non-Hispanic whites.

Suggested Citation

  • Jeffrey T. Howard & P. Johnelle Sparks, 2016. "The Effects of Allostatic Load on Racial/Ethnic Mortality Differences in the United States," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 35(4), pages 421-443, August.
    • Handle: RePEc:kap:poprpr:v:35:y:2016:i:4:d:10.1007_s11113-016-9382-4
    DOI: 10.1007/s11113-016-9382-4
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    2. Vincenzo Carrieri & Apostolos Davillas & Andrew M. Jones, 2020. "A latent class approach to inequity in health using biomarker data," Health Economics, John Wiley & Sons, Ltd., vol. 29(7), pages 808-826, July.

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