TY - JOUR
T1 - Predicting mortality from 57 economic, behavioral, social, and psychological factors
AU - Puterman, Eli
AU - Weiss, Jordan
AU - Hives, Benjamin A.
AU - Gemmill, Alison
AU - Karasek, Deborah
AU - Mendes, Wendy Berry
AU - Rehkopf, David H.
N1 - Funding Information:
ACKNOWLEDGMENTS. The HRS is supported by the National Institute on Aging (NIA; U01 AG009740) and the Social Security Administration. The Midlife in the United States (MIDUS) investigation was supported by NIA Grants P01-AG020166 and R01-AG019239. The original study was supported by the John D. and Catherine T. MacArthur Foundation Research Network on Successful Midlife Development. The funding sources had no involvement in the study design; data collection, analysis, or interpretation; nor the writing and submission of this article. This research was supported by the Canada Research Chairs program (E.P.) and a Population Research Training Grant (NIH T32 HD007242) awarded to the Population Studies Center at the University of Pennsylvania by the National Institutes of Health’s (NIH)’s Eunice Kennedy Shriver National Institute of Child Health and Human Development (J.W.). D.H.R. was supported by the National Institute on Aging at the National Institutes of Health (K01AG047280). This work was supported by the National Institute on Aging at the National Institutes of Health with a grant to the Stress Measurement Network (R24AG048024).
Publisher Copyright:
© 2020 National Academy of Sciences. All rights reserved.
PY - 2020/7/14
Y1 - 2020/7/14
N2 - 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.
AB - 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.
KW - Behavioral
KW - Data-driven
KW - Mortality
KW - Social
KW - Transdisciplinary
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U2 - 10.1073/pnas.1918455117
DO - 10.1073/pnas.1918455117
M3 - Article
C2 - 32571904
AN - SCOPUS:85088179864
SN - 0027-8424
VL - 117
SP - 16273
EP - 16282
JO - Proceedings of the National Academy of Sciences of the United States of America
JF - Proceedings of the National Academy of Sciences of the United States of America
IS - 28
ER -