Understanding the Geography of COVID-19 Case Fatality Rates in China: A Spatial Autoregressive Probit-Log Linear Hurdle Analysis.
Front Public Health
; 10: 751768, 2022.
Article
en En
| MEDLINE
| ID: mdl-35242729
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.
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Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
COVID-19
Tipo de estudio:
Prognostic_studies
Límite:
Humans
País como asunto:
Asia
Idioma:
En
Año:
2022
Tipo del documento:
Article