OBJECTIVES: The objective of this article is to provide estimates of life expectancy for White, Black, and Hispanic populations by socioeconomic factors. Effects of educational, income, employment, and marital status on life expectancy are presented and interpreted. DESIGN: The National Longitudinal Mortality Study, consisting of a number of Current Population Surveys (CPS) linked to mortality information obtained from the National Death Index, provides data to construct life tables for various socioeconomic and demographic groups. Probabilities of death are estimated using a person-year approach to accommodate the aging of the population over 11 years of follow up. RESULTS: Across various ethnicity-race-sex groups, longer life expectancy was observed for individuals with higher levels of education and income, and for those who were married and employed. The differences in life expectancy between levels of the socioeconomic characteristics tended to be larger for men than for women. Also, differences were found to be larger for the non-Hispanic Black population compared to the non-Hispanic White population. Hispanic White men exhibited patterns similar to those of non-Hispanic White and Black men. CONCLUSIONS: For selected ethnicity-race-sex groups, the impact of socioeconomic variables on life expectancy is dramatic. The shorter life expectancy observed among the poor, the less educated, the unmarried, and those not in the labor force, highlights the impact of socioeconomic disadvantage on survival. Further, the substantial 14-year differential favoring the employed over those not in the labor force may be partially explained by unemployment due to poor health. Another reason may be that employed individuals have greater access to health care than do those not in the labor force.
OBJECTIVES: The objective of this article is to provide estimates of life expectancy for White, Black, and Hispanic populations by socioeconomic factors. Effects of educational, income, employment, and marital status on life expectancy are presented and interpreted. DESIGN: The National Longitudinal Mortality Study, consisting of a number of Current Population Surveys (CPS) linked to mortality information obtained from the National Death Index, provides data to construct life tables for various socioeconomic and demographic groups. Probabilities of death are estimated using a person-year approach to accommodate the aging of the population over 11 years of follow up. RESULTS: Across various ethnicity-race-sex groups, longer life expectancy was observed for individuals with higher levels of education and income, and for those who were married and employed. The differences in life expectancy between levels of the socioeconomic characteristics tended to be larger for men than for women. Also, differences were found to be larger for the non-Hispanic Black population compared to the non-Hispanic White population. Hispanic White men exhibited patterns similar to those of non-Hispanic White and Black men. CONCLUSIONS: For selected ethnicity-race-sex groups, the impact of socioeconomic variables on life expectancy is dramatic. The shorter life expectancy observed among the poor, the less educated, the unmarried, and those not in the labor force, highlights the impact of socioeconomic disadvantage on survival. Further, the substantial 14-year differential favoring the employed over those not in the labor force may be partially explained by unemployment due to poor health. Another reason may be that employed individuals have greater access to health care than do those not in the labor force.
Authors: Alexander P Reiner; Elad Ziv; Denise L Lind; Caroline M Nievergelt; Nicholas J Schork; Steven R Cummings; Angie Phong; Esteban González Burchard; Tamara B Harris; Bruce M Psaty; Pui-Yan Kwok Journal: Am J Hum Genet Date: 2005-01-19 Impact factor: 11.025
Authors: Ana F Abraído-Lanza; Adria N Armbrister; Karen R Flórez; Alejandra N Aguirre Journal: Am J Public Health Date: 2006-06-29 Impact factor: 9.308
Authors: Sherry L Baron; Sharon Beard; Letitia K Davis; Linda Delp; Linda Forst; Andrea Kidd-Taylor; Amy K Liebman; Laura Linnan; Laura Punnett; Laura S Welch Journal: Am J Ind Med Date: 2013-03-26 Impact factor: 2.214