The sociospatial factors of death: Analyzing effects of geospatially-distributed variables in a Bayesian mortality model for Hong Kong

TL;DR

This study investigates how socioeconomic and spatial factors influence mortality rates in Hong Kong, revealing that neighboring regions' wealth impacts local longevity and that incorporating spatial data improves mortality predictions.

Contribution

The paper introduces a Bayesian mortality model that accounts for sociospatial spillover effects, highlighting the importance of nonlocal socioeconomic variables in mortality analysis.

Findings

Spatially-distributed variables reduce mortality prediction uncertainty.

Wealth effects on longevity vary based on neighboring regions' socioeconomic status.

Inclusion of spatial data enhances model accuracy across census years.

Abstract

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.