Alshaabi Thayer, Dewhurst David R, Bagrow James P, Dodds Peter S, Danforth Christopher M
Vermont Complex Systems Center, University of Vermont, Burlington, VT, United States of America.
Computational Story Lab, University of Vermont, Burlington, VT, United States of America.
PLoS One. 2021 Mar 24;16(3):e0247795. doi: 10.1371/journal.pone.0247795. eCollection 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.
人类死亡率部分取决于多种社会经济因素，这些因素在空间和时间上都存在差异。在调整了其他协变量后，人类寿命与家庭财富呈正相关。然而，一个地理区域的死亡率在多大程度上是该区域及其邻域社会经济因素的函数尚不清楚。关于这种关系的时间成分的信息也很少。以香港多个普查年份的地区为案例研究，我们证明，在（a）富裕但与社会贫困地区相邻的地区与（b）被同样富裕地区包围的富裕地区中，财富指标变量与长寿的关联方式存在差异。我们还表明，与基线模型相比，纳入空间分布变量可降低每个普查年份死亡率预测的不确定性。我们的结果表明，地理死亡率模型应纳入非本地信息（例如空间邻域）以降低其死亡率估计的方差，并指出需要对死亡率的社会空间溢出效应进行更深入的分析。
