Cargando…
Predicting later life health status and mortality using state-level socioeconomic characteristics in early life
Studies extending across multiple life stages promote an understanding of factors influencing health across the life span. Existing work has largely focused on individual-level rather than area-level early life determinants of health. In this study, we linked multiple data sets to examine whether ea...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2016
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5047283/ https://www.ncbi.nlm.nih.gov/pubmed/27713921 http://dx.doi.org/10.1016/j.ssmph.2016.04.005 |
_version_ | 1782457392980557824 |
---|---|
author | Hamad, Rita Rehkopf, David H. Kuan, Kai Y. Cullen, Mark R. |
author_facet | Hamad, Rita Rehkopf, David H. Kuan, Kai Y. Cullen, Mark R. |
author_sort | Hamad, Rita |
collection | PubMed |
description | Studies extending across multiple life stages promote an understanding of factors influencing health across the life span. Existing work has largely focused on individual-level rather than area-level early life determinants of health. In this study, we linked multiple data sets to examine whether early life state-level characteristics were predictive of health and mortality decades later. The sample included 143,755 U.S. employees, for whom work life claims and administrative data were linked with early life state-of-residence and mortality. We first created a “state health risk score” (SHRS) and “state mortality risk score” (SMRS) by modeling state-level contextual characteristics with health status and mortality in a randomly selected 30% of the sample (the “training set”). We then examined the association of these scores with objective health status and mortality in later life in the remaining 70% of the sample (the “test set”) using multivariate linear and Cox regressions, respectively. The association between the SHRS and adult health status was β=0.14 (95%CI: 0.084, 0.20), while the hazard ratio for the SMRS was 0.96 (95%CI: 0.93, 1.00). The association between the SHRS and health was not statistically significant in older age groups at a p-level of 0.05, and there was a statistically significantly different association for health status among movers compared to stayers. This study uses a life course perspective and supports the idea of “sensitive periods” in early life that have enduring impacts on health. It adds to the literature examining populations in the U.S. where large linked data sets are infrequently available. |
format | Online Article Text |
id | pubmed-5047283 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-50472832017-12-01 Predicting later life health status and mortality using state-level socioeconomic characteristics in early life Hamad, Rita Rehkopf, David H. Kuan, Kai Y. Cullen, Mark R. SSM Popul Health Article Studies extending across multiple life stages promote an understanding of factors influencing health across the life span. Existing work has largely focused on individual-level rather than area-level early life determinants of health. In this study, we linked multiple data sets to examine whether early life state-level characteristics were predictive of health and mortality decades later. The sample included 143,755 U.S. employees, for whom work life claims and administrative data were linked with early life state-of-residence and mortality. We first created a “state health risk score” (SHRS) and “state mortality risk score” (SMRS) by modeling state-level contextual characteristics with health status and mortality in a randomly selected 30% of the sample (the “training set”). We then examined the association of these scores with objective health status and mortality in later life in the remaining 70% of the sample (the “test set”) using multivariate linear and Cox regressions, respectively. The association between the SHRS and adult health status was β=0.14 (95%CI: 0.084, 0.20), while the hazard ratio for the SMRS was 0.96 (95%CI: 0.93, 1.00). The association between the SHRS and health was not statistically significant in older age groups at a p-level of 0.05, and there was a statistically significantly different association for health status among movers compared to stayers. This study uses a life course perspective and supports the idea of “sensitive periods” in early life that have enduring impacts on health. It adds to the literature examining populations in the U.S. where large linked data sets are infrequently available. Elsevier 2016-04-30 /pmc/articles/PMC5047283/ /pubmed/27713921 http://dx.doi.org/10.1016/j.ssmph.2016.04.005 Text en © 2016 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Article Hamad, Rita Rehkopf, David H. Kuan, Kai Y. Cullen, Mark R. Predicting later life health status and mortality using state-level socioeconomic characteristics in early life |
title | Predicting later life health status and mortality using state-level socioeconomic characteristics in early life |
title_full | Predicting later life health status and mortality using state-level socioeconomic characteristics in early life |
title_fullStr | Predicting later life health status and mortality using state-level socioeconomic characteristics in early life |
title_full_unstemmed | Predicting later life health status and mortality using state-level socioeconomic characteristics in early life |
title_short | Predicting later life health status and mortality using state-level socioeconomic characteristics in early life |
title_sort | predicting later life health status and mortality using state-level socioeconomic characteristics in early life |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5047283/ https://www.ncbi.nlm.nih.gov/pubmed/27713921 http://dx.doi.org/10.1016/j.ssmph.2016.04.005 |
work_keys_str_mv | AT hamadrita predictinglaterlifehealthstatusandmortalityusingstatelevelsocioeconomiccharacteristicsinearlylife AT rehkopfdavidh predictinglaterlifehealthstatusandmortalityusingstatelevelsocioeconomiccharacteristicsinearlylife AT kuankaiy predictinglaterlifehealthstatusandmortalityusingstatelevelsocioeconomiccharacteristicsinearlylife AT cullenmarkr predictinglaterlifehealthstatusandmortalityusingstatelevelsocioeconomiccharacteristicsinearlylife |