Covid-19 hospital mortality using spatial hierarchical models: cohort design with 74,994 registers

OBJECTIVE To investigate the relationship between covid-19 hospital mortality and risk factors, innovating by considering contextual and individual factors and spatial dependency and using data from the city of São Paulo, Brazil. METHODS The study was performed with a spatial hierarchical retrospective cohort design using secondary data (individuals and contextual data) from hospitalized patients and their geographic unit residences. The study period corresponded to the first year of the pandemic, from February 25, 2020 to February 24, 2021. Mortality was modeled with the Bayesian context, Bernoulli probability distribution, and the integrated nested Laplace approximations. The demographic, distal, medial, and proximal covariates were considered. RESULTS We found that per capita income, a contextual covariate, was a protective factor (odds ratio: 0.76 [95% credible interval: 0.74–0.78]). After adjusting for income, the other adjustments revealed no differences in spatial dependence. Without income inequality in São Paulo, the spatial risk of death would be close to one in the city. Other factors associated with high covid-19 hospital mortality were male sex, advanced age, comorbidities, ventilation, treatment in public healthcare settings, and experiencing the first covid-19 symptoms between January 24 and February 24, 2021. CONCLUSIONS Other than sex and age differences, geographic income inequality was the main factor responsible for the spatial differences in the risk of covid-19 hospital mortality. Investing in public policies to reduce socioeconomic inequities, infection prevention, and other intersectoral measures should focus on lower per capita income, to control covid-19 hospital mortality.

Examining socio-economic factors to understand the hospital case fatality rates of COVID-19 in the city of São Paulo, Brazil.

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 São 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.