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ABSTRACT: Background
Detailed information on how socio-economic characteristics are related to COVID-19 incident cases and maternal deaths is needed. We investigated the spatial distribution of COVID-19 cases and maternal deaths in Brazil and their association with social determinants of health.Methods
This was a population-based ecological study with a spatial analysis of all cases and deaths of COVID-19 in the obstetric population. Data on COVID-19 cases and deaths in the obstetric population, social vulnerability, health inequities, and health system capacity at the municipal level were obtained from several publicly sources in Brazil. A Bayesian empirical local model was used to identify fluctuations of the indicators. Spatial statistic tests were used to identity the spatial clusters and measure the municipalities' risk of COVID-19 in the obstetric population. Beta regression was used to characterise the association between socio-economic indicators and the burden of COVID-19.Findings
A total of 13,858 cases and 1,396 deaths due to COVID-19 were recorded in Brazil from March 2020 to June 2021. There was a variation in the number of cases per municipality, with 105 municipalities with rates from 2,210 to 3,884 cases and 45 municipalities with rates from 3,884 to 7,418 cases per 100,000 live births. The maternal mortality ratio also varied widely across municipalities. There was a spatial dependence on smoothed maternal mortality rates (I Moran 0•10; P = 0•010), and 15 municipalities had higher risk of maternal deaths. Municipalities characterized by lower health resources and higher socioeconomic inequalities presented the highest rates of incidence and maternal mortality by COVID-19.Interpretation
In Brazil, COVID-19 cases and deaths in the obstetric population had a heterogeneous geographical distribution, with well-defined spatial clusters mostly located in the countryside. Municipalities with a high degree of socioeconomic dissimilarities showed higher maternal mortality rates than areas with better social and infrastructure indicators.Funding
None.
SUBMITTER: Siqueira TS
PROVIDER: S-EPMC8432892 | biostudies-literature |
REPOSITORIES: biostudies-literature