Abstract
Background
U.S. racial/ethnic mortality disparities are well-documented and central to debates on social inequalities in health. Standard measures, like life expectancy or years of life lost, are based on synthetic populations and do not account for the real underlying populations experiencing the inequalities.
Methods
We analyze U.S. mortality disparities comparing Asian Americans, Blacks, Hispanics, and Native Americans/Alaska Natives to Whites using 2019 CDC and NCHS data. We develop a novel approach that estimates the mortality Gap, Adjusted for Population structure (GAP) by accounting for real-population exposures. GAP is tailored for analyses where age structures are a fundamental component, not merely a confounder. We highlight the magnitude of inequalities by comparing GAP against standard metrics’ estimates of loss of life due to leading causes of death.
Results
Based on GAP, Black and Native American mortality disadvantage is as deadly or deadlier than circulatory diseases (U.S. top cause of death); and is overall 72% (Men: 47%, Women: 98% women) and 65% (Men: 45%, Women: 92%) larger than life-expectancy measured disadvantage. Asian Americans and Hispanics have, according to GAP, a mortality advantage over Whites that is over three (Men: 176% , Women: 283%) and two times (Men: 123%, Women: 190%) larger than that based on life expectancy, respectively.
Conclusions
Mortality inequalities based on standard metrics’ synthetic populations can differ markedly from GAP estimates. We demonstrate that standard metrics underestimate racial/ethnic disparities through disregarding actual population age structures. For health policy, exposure-corrected inequalities such as GAP may provide a more reasonable signal on where to allocate scarce resources.
U.S. racial/ethnic mortality disparities are well-documented and central to debates on social inequalities in health. Standard measures, like life expectancy or years of life lost, are based on synthetic populations and do not account for the real underlying populations experiencing the inequalities.
Methods
We analyze U.S. mortality disparities comparing Asian Americans, Blacks, Hispanics, and Native Americans/Alaska Natives to Whites using 2019 CDC and NCHS data. We develop a novel approach that estimates the mortality Gap, Adjusted for Population structure (GAP) by accounting for real-population exposures. GAP is tailored for analyses where age structures are a fundamental component, not merely a confounder. We highlight the magnitude of inequalities by comparing GAP against standard metrics’ estimates of loss of life due to leading causes of death.
Results
Based on GAP, Black and Native American mortality disadvantage is as deadly or deadlier than circulatory diseases (U.S. top cause of death); and is overall 72% (Men: 47%, Women: 98% women) and 65% (Men: 45%, Women: 92%) larger than life-expectancy measured disadvantage. Asian Americans and Hispanics have, according to GAP, a mortality advantage over Whites that is over three (Men: 176% , Women: 283%) and two times (Men: 123%, Women: 190%) larger than that based on life expectancy, respectively.
Conclusions
Mortality inequalities based on standard metrics’ synthetic populations can differ markedly from GAP estimates. We demonstrate that standard metrics underestimate racial/ethnic disparities through disregarding actual population age structures. For health policy, exposure-corrected inequalities such as GAP may provide a more reasonable signal on where to allocate scarce resources.
| Original language | English |
|---|---|
| Number of pages | 9 |
| Journal | Epidemiology |
| Volume | 34 |
| Issue number | 3 |
| Early online date | 2 Mar 2023 |
| DOIs | |
| Publication status | Published - 1 May 2023 |
Keywords
- Exposure
- Racial disparities
- Mortality
- Age structure
Access to Document
- Pifarre-i-Arolas_2023_Epidemiology_US-Racial-Ethnic_CC
Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc. This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.