Exploring Geographic Variation in US Mortality Rates Using a Spatial Durbin Approach

Abstract

Previous studies focused on identifying the determinants of mortality in US counties have examined the relationships between mortality and explanatory covariates within a county only, and have ignored the well-documented spatial dependence of mortality. We challenge earlier literature by arguing that the mortality rate of a certain county may also be associated with the features of its neighboring counties beyond its own features. Drawing from both the spillover (i.e., same direction effect) and social relativity (i.e., opposite direction effect) perspectives, our spatial Durbin modeling results indicate that both theoretical perspectives provide valuable frameworks to guide the modeling of mortality variation in US counties. Our empirical findings support that mortality rate of a certain county is associated with the features of its neighbors beyond its own features. Specifically, we found support for the spillover perspective in which the percentage of the Hispanic population, concentrated disadvantage, and the social capital of a specific county are negatively associated with the mortality rate in the specific county and also in neighboring counties. On the other hand, the following covariates fit the social relativity process: health insurance coverage, percentage of non-Hispanic other races, and income inequality. Their direction of the associations with mortality in the specific county is opposite to that of the relationships with mortality in neighboring counties. Methodologically, spatial Durbin modeling addresses the shortcomings of traditional analytic approaches used in ecological mortality research such as ordinary least squares, spatial error, and spatial lag regression. Our results produce new insights drawn from unbiased estimates.

Modeling the Importance of Within- and Between-County Effects in an Ecological Study of the Association Between Social Capital and Mental Distress

“…The importance of SES has been discussed (2,26,27), and our results confirm this relationship. Income ratio is also important and our finding echoes the literature, suggesting income inequality follows the social relativity theory as neighbors with high income inequality reduce the sense of relative deprivation, which in turn improves population health (28). Moreover, prevalence of mental distress increases with the percentage of female population within (and beyond) a county.…”

Looking through a different lens: Examining the inequality-mortality association in U.S. counties using spatial panel models

“…Wilkinson, 2006; Zheng, 2012). However, as noted, there are many reasons why county is a desirable unit of analysis and several cross-sectional studies using county-level data reported a significant inequality-mortality relationship, even after accounting for the bias introduced by spatial structure (McLaughlin & Stokes, 2002; Sparks & Sparks, 2010; T.-C. Yang & Jensen, 2014; Tse-Chuan Yang, Noah, & Shoff, 2015). Putting our finding (i.e., non-significant association) into perspective and assuming that use of county as the analytic unit is appropriate, we find that controlling for the temporal trend eliminates the relationship of inequality with mortality or that the variation in inequality in the past few decades is too little to show a significant relationship with mortality.…”

Population Well-Being Measures Help Explain Geographic Disparities In Life Expectancy At The County Level

“…9 We did not adjust for characteristics of neighboring counties; we also did not account for the heterogeneity in life expectancy within counties.…”

“…1,3–9 County attributes such as rurality and social capital have previously been linked with longevity but were not included in this study. 33,34 …”