Your browser doesn't support javascript.
loading
Predicting mortality from 57 economic, behavioral, social, and psychological factors.
Puterman, Eli; Weiss, Jordan; Hives, Benjamin A; Gemmill, Alison; Karasek, Deborah; Mendes, Wendy Berry; Rehkopf, David H.
Afiliação
  • Puterman E; School of Kinesiology, The University of British Columbia, Vancouver, BC V6T1Z1, Canada; eli.puterman@ubc.ca drehkopf@stanford.edu.
  • Weiss J; Population Studies Center and the Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA 19104.
  • Hives BA; School of Kinesiology, The University of British Columbia, Vancouver, BC V6T1Z1, Canada.
  • Gemmill A; Department of Population, Family and Reproductive Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205.
  • Karasek D; Preterm Birth Initiative, Department of Obstetrics, Genecology & Reproductive Sciences, University of California, San Francisco, CA 94158.
  • Mendes WB; Department of Psychiatry, University of California, San Francisco, CA 94143.
  • Rehkopf DH; School of Medicine, Stanford University, Palo Alto, CA 94304 eli.puterman@ubc.ca drehkopf@stanford.edu.
Proc Natl Acad Sci U S A ; 117(28): 16273-16282, 2020 07 14.
Article em En | MEDLINE | ID: mdl-32571904
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.
Assuntos
Palavras-chave

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Mortalidade / Previsões Tipo de estudo: Etiology_studies / Health_economic_evaluation / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Aged / Aged80 / Female / Humans / Male / Middle aged País/Região como assunto: America do norte Idioma: En Revista: Proc Natl Acad Sci U S A Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Mortalidade / Previsões Tipo de estudo: Etiology_studies / Health_economic_evaluation / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Aged / Aged80 / Female / Humans / Male / Middle aged País/Região como assunto: America do norte Idioma: En Revista: Proc Natl Acad Sci U S A Ano de publicação: 2020 Tipo de documento: Article