Journal
TRANSACTIONS OF THE ROYAL SOCIETY OF TROPICAL MEDICINE AND HYGIENE
Volume 115, Issue 11, Pages 1282-1287Publisher
OXFORD UNIV PRESS
DOI: 10.1093/trstmh/trab144
Keywords
Bayesian spatial analysis; comorbidities; coronavirus; modelling
Funding
- Conselho Nacional de Desenvolvimento Cientifico e Tecnologico [301550/2017-4, 306025/2019-1]
- Sao Paulo Research Foundation [2017/10297-1, 2020/12371-7]
- Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP) [20/12371-7] Funding Source: FAPESP
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The study found higher hospital case fatality rates for men and individuals aged >= 60 years in relation to coronavirus disease 2019 (COVID-19). Per capita income was identified as a significant factor negatively associated with COVID-19 HCFRs. Spatial analysis of these methods and disparities in COVID-19 outcomes may aid in developing policies for at-risk populations in geographically defined areas.
Background: Understanding differences in hospital case fatality rates (HCFRs) of coronavirus disease 2019 (COVID-19) may help evaluate its severity and the capacity of the healthcare system to reduce mortality. Methods: We examined the variability in HCFRs of COVID-19 in relation to spatial inequalities in socio-economic factors, hospital health sector and patient medical condition across the city of Sao Paulo, Brazil. We obtained the standardized hospital case fatality ratio adjusted indirectly by age and sex, which is the ratio between the HCFR of a specific spatial unit and the HCFR for the entire study area. We modelled it using a generalized linear mixed model with spatial random effects in a Bayesian context. Results: We found that HCFRs were higher for men and for individuals >= 60 y of age. Our models identified per capita income as a significant factor that is negatively associated with the HCFRs of COVID-19, even after adjusting for age, sex and presence of risk factors. Conclusions: Spatial analyses of the implementation of these methods and of disparities in COVID-19 outcomes may help in the development of policies for at-risk populations in geographically defined areas.
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