Back

Social Determinants of Health and Cumulative Incidence of Mortality among U.S. Adults without Major Chronic Diseases

Claudel, S. E.; Verma, A.

2024-03-18 public and global health
10.1101/2024.03.16.24304395 medRxiv
Show abstract

BackgroundSocial determinants of health (SDOH) are widely known to contribute to poor health outcomes and premature mortality among individuals with prevalent diseases. Understanding the impact of SDOH on mortality among adults without major chronic diseases may inform public policy in the United States (U.S.). MethodsWe conducted a prospective observational study using the National Health and Nutrition Examination Survey data (1999-2018) among 11,413 adults without hypertension, diabetes, hyperlipidemia, severe obesity, chronic kidney disease, cardiovascular disease, chronic respiratory disease, cancer, liver disease, arthritis, hepatitis B or C, human immunodeficiency virus, or pregnancy. We calculated 15-year adjusted cumulative incidences of all-cause mortality by baseline SDOH and described the trends in the prevalence of adverse SDOH. ResultsThe mean {+/-}SD age was 34.9{+/-}11.2 years. Over a median follow-up of 10.3 years, 275 participants died. The prevalence of low educational attainment decreased over the study period from 19.8% to 12.1%, while the prevalence of food insecurity increased from 11.5% to 23.1%. The 15-year adjusted cumulative incidences of all-cause mortality were 5.6% (95% CI, 2.8-8.5), 5.2% (95%CI, 3.2-7.3), 4.9% (95%CI, 2.7-7.2), 4.0% (95%CI, 2.8-5.2) for participants who had < 100 % of the federal poverty level, below high school education, food insecurity, and were born in the U.S., respectively. In the final adjusted model, a 1-point increase in cumulative SDOH score was associated with 33% increased risk of all-cause mortality. ConclusionsIn this study of community-dwelling U.S. adults without major chronic diseases, we demonstrate a pronounced increase in all-cause mortality associated with adverse SDOH.

Matching journals

The top 9 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.