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    • br Materials and method br

    2018-10-30


    Materials and method
    Results Fig. 1 shows the age-standardized annual probability of death among U.S-born women aged 45–89 years by U.S. state, where the NLMS data is centered on approximately 1990. It displays the well-documented geographic clustering of especially-high mortality states in the Appalachian and southeastern areas, and especially-low mortality states in the upper plains. The patterns are similar to maps of life expectancy at age 50 using vital statistics data (e.g., Wilmoth et al., 2011); slight discrepancies will result from the NLMS sampling frame, which excludes the institutionalized population, and our focus on U.S.-born individuals. A summary of the individual and contextual variables used in our analysis is provided in Table 1. The individual variables are age-standardized. The contextual latent factors are Z-scores. For parsimony the table shows the 10 states with the lowest mortality and the 10 with the highest mortality among women aged 45–89 years (all states are shown in fgf receptor inhibitor Table A.2). The age-standardized annual probability of death during the study period ranges from a low of 1.27% in Hawaii (followed by 1.37% in South Dakota and 1.48% in North Dakota) to a high of 2.16% in Nevada (right after 2.10% in West Virginia and 2.06% in Tennessee). The disparity is substantial; the probability of death in Nevada is 1.7 times greater than Hawaii. To illustrate the associations between the variables and women\'s mortality, we underline low-mortality states if they were among the 10 “best” scores for each characteristic associated with a healthy life (e.g., low proportion of women without a high school credential), and we bold high-mortality states if they were among the 10 “worst” scores for each characteristic (e.g., high proportion without a credential). An intriguing finding, also suggested by Macintyre et al. (2002), is that if a characteristic is associated with low mortality in a state, the lack of that characteristic does not necessarily correspond with high mortality, and vice versa. This is most pronounced for social cohesion. A high degree of cohesion strongly corresponds to low mortality (among the 10 states with the “best” scores for cohesion, 7 were among the lowest-mortality states), but low cohesion does not strongly correspond to high mortality (among the 10 states with the “worst” scores for cohesion, just 3 were among the highest mortality states).
    Discussion Four findings are noteworthy. First, inequalities in women\'s mortality across states appear to reflect the differential distribution of both individual and contextual characteristics. The individual characteristics we examined accounted for 30%, and the contextual characteristics accounted for 53%, of the variation in women\'s mortality among states. Accounting for both characteristics explained 62% of the variation and no significant differences between any two states remained. To be clear, this does not imply that the remaining variance in state mortality is zero. As in effectively all empirical analyses, there remains some residual variance in the outcome, which potentially reflects measurement errors and unmeasured factors. When interpreting these percentages, it is important to remember that individual and contextual factors are interrelated in complex ways: individuals and contexts can shape each other. Nonetheless, the results provide compelling evidence that inequalities in women\'s mortality cannot be reduced to women\'s personal choices and characteristics; instead, the influence of socioeconomic and political contexts must be considered. Another important finding is that two of the five contextual features that we examined—social cohesion and economic environment—are particularly important predictors of women\'s mortality and the between-state variation in women\'s mortality. Similarly, a multilevel analysis of neighborhoods in England and Scotland (Stafford et al., 2005) found that indicators of social cohesion were especially important for women\'s self-rated health, followed by economic indicators, with a much smaller role for sociopolitical and physical environments.