Spatial analysis of COVID-19 incidence and the sociodemographic context in Brazil

Abstract

Background Identified in December 2019 in the city of Wuhan, China, the outbreak of COVID-19 spread throughout the world and its impacts affect different populations differently, where countries with high levels of social and economic inequality such as Brazil gain prominence, for understanding of the vulnerability factors associated with the disease. Given this scenario, in the absence of a vaccine or safe and effective antiviral treatment for COVID-19, nonpharmacological measures are essential for prevention and control of the disease. However, many of these measures are not feasible for millions of individuals who live in territories with increased social vulnerability. The study aims to analyze the spatial distribution of COVID-19 incidence in Brazil’s municipalities (counties) and investigate its association with sociodemographic determinants to better understand the social context and the epidemic’s spread in the country. Methods This is an analytical ecological study using data from various sources. The study period was February 25 to September 26, 2020. Data analysis used global regression models: ordinary least squares (OLS), spatial autoregressive model (SAR), and conditional autoregressive model (CAR) and the local regression model called multiscale geographically weighted regression (MGWR). Findings The higher the GINI index, the higher the incidence of the disease at the municipal level. Likewise, the higher the nurse ratio per 1,000 inhabitants in the municipalities, the higher the COVID-19 incidence. Meanwhile, the proportional mortality ratio was inversely associated with incidence of the disease. Discussion Social inequality increased the risk of COVID-19 in the municipalities. Better social development of the municipalities was associated with lower risk of the disease. Greater access to health services improved the diagnosis and notification of the disease and was associated with more cases in the municipalities. Despite universal susceptibility to COVID-19, populations with increased social vulnerability were more exposed to risk of the illness.

SARS-CoV-2 testing disparities across geographical regions from a large metropolitan area in Brazil: Results from a web-based survey among individuals interested in clinical trials for COVID-19 vaccines

“…Individuals living in higher HDI areas may have access to tests through private laboratories and clinics (less prone to shortage in testing). Despite universal susceptibility to COVID-19, populations with increased social vulnerability in Brazil are more and unequally affected by COVID-19, 35 indicating that policies to prioritize low income individuals need to be considered and urgently implemented.…”

Panel Data Modelling of COVID-19 Infected Cases

“…The study period was February 25 to September 26,2020 using the ordinary least squares (OLS), spatial autoregressive model (SAR), and conditional autoregressive model (CAR) and the local regression model called multiscale geographically weighted regression (MGWR). The MGWR model fit improved when compared to the OLS, SAR and CAR 5 .…”

“…Raymundo et al 5 , reported that the MGWR model was the most suited model in comparison of OLS (CCM) model in the study of spatial analysis of COVID-19 incidence and the sociodemographic context in Brazil.…”

Covid-19 pandemic, R&D, vaccines, and the urgent need of UBUNTU practice

“…This pandemic promoted uneven impact on health of populations, among countries and regions [1] , with the severity and mortality being higher in poorer communities. Educational, economic, social, and other negative impacts are more significant in least developed societies, as recently showed in Brazil [ 2 , 3 ]. A global deployment, allocation, followed by local rollout in scale, are dimensions to be tackled aiming at achieving global control of Covid-19.…”