Understanding the Geography of COVID-19 Case Fatality Rates in China: A Spatial Autoregressive Probit-Log Linear Hurdle Analysis

Abstract

This study employs a spatial autoregressive probit-log linear (SAP-Log) hurdle model to investigate the influencing factors on the probability of death and case fatality rate (CFR) of coronavirus disease 2019 (COVID-19) at the city level in China. The results demonstrate that the probability of death from COVID-19 and the CFR level are 2 different processes with different determinants. The number of confirmed cases and the number of doctors are closely associated with the death probability and CFR, and there exist differences in the CFR and its determinants between cities within Hubei Province and outside Hubei Province. The spatial probit model also presents positive spatial autocorrelation in death probabilities. It is worth noting that the medical resource sharing among cities and enjoyment of free medical treatment services of citizens makes China different from other countries. This study contributes to the growing literature on determinants of CFR with COVID-19 and has significant practical implications.

Addressing External Shock in Urban Agglomeration: Implications From the Transmission Pattern of COVID-19 in the Beijing-Tianjin-Hebei Area

“…For example, Zhangjiakou City, one of the most severely affected cities, has not reported any death ( Figure 6 ). The deaths in the BTH area are concentrated in Beijing and Cangzhou because of medical resources shared between cities in the BTH area and the free medical treatment services provided for all infectors ( 44 ). A large number of discharged cases ( Figure 7 ) indicates that the treatment is effective, even though the proportion of the elderly population is relatively high; in fact, people aged 60 and above account for 25.9% of the cumulative confirmed cases in Beijing.…”

Explaining the higher COVID-19 mortality rates among disproportionately Black counties: A decomposition analysis

“…We use the STATA b1x2 package for decomposition analyses ( Gelbach, 2016 ). We have also performed zero-inflated Poisson and spatial analyses used in prior work ( Kathe & Wani, 2021 ; Yu et al, 2022 ) to examine whether the choice of OLS regressions biases our results. Results shown in Appendix Table A3 and Appendix Table A4 confirm that the results from all three approaches are substantively consistent.…”

Clan loyalty and COVID‐19 diffusion: Evidence from China

“…There is also a growing body of literature investigating the links between geographical factors and the spread of COVID‐19 (Ascani et al., 2021; Desmet & Wacziarg, 2022; Giannone et al., 2022; Li & Ma, 2022; Liu et al., 2021; Yu et al., 2022). For example, Sun et al.…”

“…Many studies examine the effect of medical factors on epidemic transmission and mortality rates (Bello-Chavolla et al, 2020;Chen et al, 2020;Kang, 2020;Sorouri et al, 2020). Yu et al (2022) demonstrate that the number of doctors and the number of confirmed cases have a significant influence on COVID-19 mortality rates in China, and medical knowledge and experience are very important for controlling the spread of COVID-19. To limit the spread of COVID-19, the Chinese government adopted innovative and advanced medical systems, such as mobile hospitals (方舱) and online medical services (Sun et al, 2021).…”

“…There is also a growing body of literature investigating the links between geographical factors and the spread of COVID-19 (Ascani et al, 2021;Desmet & Wacziarg, 2022;Giannone et al, 2022;Li & Ma, 2022;Liu et al, 2021;Yu et al, 2022). For example, Sun et al (2020) study the relation between geographical location and cumulative infections in China and find that the spread of COVID-19 is negatively correlated with latitude and altitude, but is not correlated with longitude.…”