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Racial/ethnic inequities in the associations of allostatic load with all-cause and cardiovascular-specific mortality risk in U.S. adults.

Luisa N BorrellElena Rodríguez-ÁlvarezFlorence J Dallo
Published in: PloS one (2020)
Non-Hispanic blacks have higher mortality rates than non-Hispanic whites whereas Hispanics have similar or lower mortality rates than non-Hispanic blacks and whites despite Hispanics' lower education and access to health insurance coverage. This study examines whether allostatic load, a proxy for cumulative biological risk, is associated with all-cause and cardiovascular (CVD)-specific mortality risks in US adults; and whether these associations vary with race/ethnicity and further with age, sex and education across racial/ethnic groups. Data from the third National Health and Nutritional Examination Survey (NHANES III, 1988-1994) and the 2015 Linked Mortality File were used for adults 25 years or older (n = 13,673 with 6,026 deaths). Cox proportional hazards regression was used to estimate the associations of allostatic load scores (2 and ≥3 relative to ≤1) with a) all-cause and b) CVD-specific mortality risk among NHANES III participants before and after controlling for selected characteristics. Allostatic load scores are associated with higher all-cause and CVD-specific mortality rates among U.S. adults aged 25 years or older, with stronger rates observed for CVD-specific mortality. All-cause mortality rates for each racial/ethnic group differed with age and education whereas for CVD-specific mortality rates, this difference was observed for sex. Our findings of high allostatic load scores associated with all-cause and CVD-specific mortality among US adults call attention to monitor conditions associated with the allostatic load's biomarkers to identify high-risk groups to help monitor social inequities in mortality risk, especially premature mortality.
Keyphrases
  • cardiovascular events
  • healthcare
  • risk factors
  • health insurance
  • physical activity
  • coronary artery disease
  • quality improvement
  • mental health
  • machine learning
  • climate change
  • big data
  • deep learning
  • data analysis