COVID-19 in Brazil: spatial risk, social vulnerability, human development, clinical manifestations and predictors of mortality – a retrospective study with data from 59 695 individuals

Abstract

Brazil ranks second in the number of confirmed cases of COVID-19 worldwide. In spite of this, coping measures differ throughout the national territory, as does the disease's impact on the population. This cross-sectional observational study, with 59 695 cases of COVID-19 registered in the state of Alagoas between March and August 2020, analysed clinical-epidemiological variables, incidence rate, mortality rate, case fatality rate (CFR) and the social indicators municipal human development index (MHDI) and social vulnerability index (SVI). Moran statistics and regression models were applied. Logistic regression analysis was applied to determine the predictors of death. The incidence rate was 1788.7/100 000 inhabitants; mortality rate was 48.0/100 000 and CFR was 2.7%. The highest incidence rates were observed in municipalities with better human development (overall MHDI (I = 0.1668; p = 0.002), education MHDI (I = 0.1649; p = 0.002) and income MHDI (I = 0.1880; p = 0.005)) and higher social vulnerability (overall SVI (I = 0.0599; p = 0.033)). CFR was associated with higher social vulnerability (SVI human capital (I = 0.0858; p = 0.004) and SVI urban infrastructure (I = 0.0985; p = 0.040)). Of the analysed cases, 55.4% were female; 2/3 were Black or Brown and the median age was 41 years. Among deaths, most were male (919; 57.4%) and elderly (1171; 73.1%). The predictors of death were male sex, advanced age and the presence of comorbidities. In Alagoas, Brazil, the disease has undergone a process of interiorisation and caused more deaths in poorer municipalities. The presence of comorbidities and advanced age were predictors of death.

COVID-19 risk areas associated with social vulnerability in northeastern Brazil: an ecological study in 2020

“…The need for income in the informal sector of the economy, linked to socio-spatial vulnerability, makes it difficult to establish appropriate prophylactic measures in these areas [10]. In this sense, spatial analyses are essential to understand community risk and provide strategic decision making, ranging from social distance measures to resource allocation [11,12]. The first confirmed case of COVID-19 in Maceió occurred 11 days after the first official report of COVID-19 in Brazilian territory, on February 26, 2020.…”

What Makes Urban Communities More Resilient to COVID-19? A Systematic Review of Current Evidence

“…Spatial heterogeneity is the main factor that makes communities differ in their resilience. From a spatial and temporal perspective, analysis of areas with concentrated COVID-19 infection rates showed that areas with poor social resilience also had high infection rates, but areas with strong social resilience could also have high infection rates if they were not able to fulfill policies well or allocate resources appropriately [ 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 ].…”

Heterogeneity and Risk of Bias in Studies Examining Risk Factors for Severe Illness and Death in COVID-19: A Systematic Review and Meta-Analysis

“…Of the non-duplicate 9850 articles, 8274 were ineligible after screening the titles and abstracts, and 1017 articles were excluded after full-text reviews. This resulted in 559 articles that were appropriate for inclusion in the systematic review, and 542 of them were also included in the meta-analysis [ 3 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , …”