The Sociospatial Factors of Death: Analyzing Effects of Geospatially-distributed Variables in a Bayesian Mortality Model for Hong Kong

Alshaabi, T.1,2,4, Dewhurst, D.1,2,3, Bagrow, J.1,5, Dodds, P.1,2,4, Danforth, C.1,2,5

PLoS ONE 16(3): e0247795 (March 2021)

Human mortality is in part a function of multiple socioeconomic factors that differ both spatially and temporally. Adjusting for other covariates, the human lifespan is positively associated with household wealth. However, the extent to which mortality in a geographical region is a function of socioeconomic factors in both that region and its neighbors is unclear. There is also little information on the temporal components of this relationship. Using the districts of Hong Kong over multiple census years as a case study, we demonstrate that there are differences in how wealth indicator variables are associated with longevity in (a) areas that are affluent but neighbored by socially deprived districts versus (b) wealthy areas surrounded by similarly wealthy districts. We also show that the inclusion of spatially-distributed variables reduces uncertainty in mortality rate predictions in each census year when compared with a baseline model. Our results suggest that geographic mortality models should incorporate nonlocal information (e.g., spatial neighbors) to lower the variance of their mortality estimates, and point to a more in-depth analysis of sociospatial spillover effects on mortality rates.

¹ Vermont Complex Systems Center, University of Vermont
² Computational Story Lab, University of Vermont
³ Charles River Analytics
⁴ Department of Computer Science, University of Vermont
⁵ Department of Mathematics & Statistics, University of Vermont

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