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Does gender inequity increase men's mortality risk in the United States? A multilevel analysis of data from the National Longitudinal Mortality Study

A number of theoretical approaches suggest that gender inequity may give rise to health risks for men. This study undertook a multilevel analysis to ascertain if state-level measures of gender inequity are predictors of men's mortality in the United States. Data for the analysis were taken prim...

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Detalles Bibliográficos
Autores principales: Kavanagh, Shane A., Shelley, Julia M., Stevenson, Christopher
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5769061/
https://www.ncbi.nlm.nih.gov/pubmed/29349229
http://dx.doi.org/10.1016/j.ssmph.2017.03.003
Descripción
Sumario:A number of theoretical approaches suggest that gender inequity may give rise to health risks for men. This study undertook a multilevel analysis to ascertain if state-level measures of gender inequity are predictors of men's mortality in the United States. Data for the analysis were taken primarily from the National Longitudinal Mortality Study, which is based on a random sample of the non-institutionalised population. The full data set included 174,703 individuals nested within 50 states and had a six-year follow-up for mortality. Gender inequity was measured by nine variables: higher education, reproductive rights, abortion provider access, elected office, management, business ownership, labour force participation, earnings and relative poverty. Covariates at the individual level were age, income, education, race/ethnicity, marital status and employment status. Covariates at the state level were income inequality and per capita gross domestic product. The results of logistic multilevel modelling showed a number of measures of state-level gender inequity were significantly associated with men's mortality. In all of these cases greater gender inequity was associated with an increased mortality risk. In fully adjusted models for all-age adult men the elected office (OR 1.05 95% CI 1.01–1.09), business ownership (OR 1.04 95% CI 1.01–1.08), earnings (OR 1.04 95% CI 1.01–1.08) and relative poverty (OR 1.07 95% CI 1.03–1.10) measures all showed statistically significant effects for each 1 standard deviation increase in the gender inequity z-score. Similar effects were seen for working-age men. In older men (65+ years) only the earnings and relative poverty measures were statistically significant. This study provides evidence that gender inequity may increase men's health risks. The effect sizes while small are large enough across the range of gender inequity identified to have important population health implications.