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    • br Methods br Results Table provides descriptive

    2018-10-30


    Methods
    Results Table 1 provides descriptive statistics for the final data set. Table 2 provides the correlations between the state-level variables. Unsurprisingly, a large number of gender inequity variables were significantly correlated. These correlations were all positive, with the exception of labour force and relative poverty (r=−0.31). The strongest correlation was between the provider access and reproductive rights measures (r=0.60). The income inequality measure was significantly positively correlated with three of the gender inequity measures. The strongest of these p97 correlations was with the labour force measure (r=0.65). Notably, the income inequality measure was also negatively correlated with the relative poverty measure (r=−0.32) suggesting increasing family income inequality has a weak negative correlation with the extent of inequity in men\'s and women\'s poverty. The GDP measure was negatively correlated with the reproductive rights (r=−0.41), provider access (r=–0.64), elected office (r=–0.43) and earnings (r=−0.40) measures. The Gini and GDP were not significantly correlated. Table 3 provides the odds ratios for the gender inequity variables in each of the logistic multilevel models (see supplementary results for full regression tables). The results were mixed. The majority of the measures showed no statistically significant association. However, four measures did show a statistically significant association. In all cases greater gender inequity was associated with increased mortality risk. In the 18–90+ age group 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 were all positively associated with mortality risk (see Fig. 1). Sensitivity testing comparing PQL2 and MCMC estimation for the elected office measure in the 18–90+ age group showed almost identical results. Sensitivity testing with state-level median household income substituted for GDP in the 18–90+ age group, in general, led to only small changes in the model (see Supplementary results).
    Discussion The finding that gender inequity in earnings was a predictor of higher mortality in men deserves particular consideration given the extensive debates regarding the possible health effects of income inequality (Kondo et al., 2009; Kondo et al., 2012; Lynch et al., 2004; Pickett & Wilkinson, 2015; Wilkinson & Pickett, 2006). One obvious explanation is that gender inequity in earnings is a marker of the same effect. However, in the 18–64 years model the earnings measure and the family Gini measure both showed a statistically significant effect (see Supplementary table 2). The finding that gender inequity in earnings and income inequality independently exert an effect on men\'s health should be explored further. It is unlikely that the associations for the gender inequity measures were confounded by the effects of socioeconomic position, as the models controlled extensively for socioeconomic factors at both the individual and state levels. However, the possibility of the observed associations being confounded by broader socioeconomic processes cannot be discounted. In particular, gender inequity may indicate processes such as the extent of welfare state formation (Bolzehndahl & Brooks, 2007) or the extent of social integration and support. Examining these pathways was outside of the scope of the analysis. A further consideration is the effect of religious factors on health. In particular, there are large denominational differences between states in the US (Pew Research Center, 2008). These denominational differences have the potential to confound the relationship between gender inequity and men\'s health. This could occur because states with high concentrations of conservative religious populations may be less likely to be gender equitable, such as in the case of reproductive rights, and concurrently contain more individuals who follow religious edicts that influence health.