Predicting mortality from 57 economic, behavioral, social, and psychological factors

Eli Puterman (), Jordan Weiss, Benjamin A. Hives, Alison Gemmill, Deborah Karasek, Wendy Berry Mendes and David H. Rehkopf ()
Additional contact information
Eli Puterman: School of Kinesiology, The University of British Columbia, Vancouver, BC V6T1Z1, Canada
Jordan Weiss: Population Studies Center and the Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA 19104
Benjamin A. Hives: School of Kinesiology, The University of British Columbia, Vancouver, BC V6T1Z1, Canada
Alison Gemmill: Department of Population, Family and Reproductive Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205
Deborah Karasek: Preterm Birth Initiative, Department of Obstetrics, Genecology & Reproductive Sciences, University of California, San Francisco, CA 94158
Wendy Berry Mendes: Department of Psychiatry, University of California, San Francisco, CA 94143
David H. Rehkopf: School of Medicine, Stanford University, Palo Alto, CA 94304

Proceedings of the National Academy of Sciences, 2020, vol. 117, issue 28, 16273-16282

Abstract: Behavioral and social scientists have identified many nonbiological predictors of mortality. An important limitation of much of this research, however, is that risk factors are not studied in comparison with one another or from across different fields of research. It therefore remains unclear which factors should be prioritized for interventions and policy to reduce mortality risk. In the current investigation, we compare 57 factors within a multidisciplinary framework. These include ( i ) adverse socioeconomic and psychosocial experiences during childhood and ( ii ) socioeconomic conditions, ( iii ) health behaviors, ( iv ) social connections, ( v ) psychological characteristics, and ( vi ) adverse experiences during adulthood. The current prospective cohort investigation with 13,611 adults from 52 to 104 y of age (mean age 69.3 y) from the nationally representative Health and Retirement Study used weighted traditional (i.e., multivariate Cox regressions) and machine-learning (i.e., lasso, random forest analysis) statistical approaches to identify the leading predictors of mortality over 6 y of follow-up time. We demonstrate that, in addition to the well-established behavioral risk factors of smoking, alcohol abuse, and lack of physical activity, economic (e.g., recent financial difficulties, unemployment history), social (e.g., childhood adversity, divorce history), and psychological (e.g., negative affectivity) factors were also among the strongest predictors of mortality among older American adults. The strength of these predictors should be used to guide future transdisciplinary investigations and intervention studies across the fields of epidemiology, psychology, sociology, economics, and medicine to understand how changes in these factors alter individual mortality risk.

Keywords: mortality; transdisciplinary; social; behavioral; data-driven (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:nas:journl:v:117:y:2020:p:16273-16282

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