Carlos Eduardo Raymundo1, Marcella Cini Oliveira2, Tatiana de Araujo Eleuterio1,3, Suzana Rosa André4, Marcele Gonçalves da Silva2, Eny Regina da Silva Queiroz1, Roberto de Andrade Medronho1,2. 1. Instituto de Estudos em Saúde Coletiva, Universidade Federal do Rio de Janeiro, Rio de Janeiro, State of Rio de Janeiro, Brazil. 2. Faculdade de Medicina, Universidade Federal do Rio de Janeiro, Rio de Janeiro, State of Rio de Janeiro, Brazil. 3. Departamento de Enfermagem em Saúde Pública, Universidade do Estado do Rio de Janeiro, Rio de Janeiro, State of Rio de Janeiro, Brazil. 4. Escola de Enfermagem Anna Nery, Universidade Federal do Rio de Janeiro, Rio de Janeiro, State of Rio de Janeiro, 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.
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.
Authors: Thiago S Torres; Paula M Luz; Lara E Coelho; Cristina Jalil; Gisely G Falco; Leonardo P Sousa; Emilia Jalil; Daniel R B Bezerra; Sandra W Cardoso; Brenda Hoagland; Claudio J Struchiner; Valdilea G Veloso; Beatriz Grinsztejn Journal: Braz J Infect Dis Date: 2021-08-02 Impact factor: 3.257
Authors: Lucas Almeida Andrade; Wandklebson Silva da Paz; Alanna G C Fontes Lima; Damião da Conceição Araújo; Andrezza M Duque; Marcus Valerius S Peixoto; Marco Aurélio O Góes; Carlos Dornels Freire de Souza; Caíque J Nunes Ribeiro; Shirley V M Almeida Lima; Márcio Bezerra-Santos; Allan Dantas Dos Santos Journal: Am J Trop Med Hyg Date: 2021-11-10 Impact factor: 3.707
Authors: Kurubaran Ganasegeran; Mohd Fadzly Amar Jamil; Maheshwara Rao Appannan; Alan Swee Hock Ch'ng; Irene Looi; Kalaiarasu M Peariasamy Journal: Int J Environ Res Public Health Date: 2022-02-13 Impact factor: 3.390
Authors: Nushrat Nazia; Zahid Ahmad Butt; Melanie Lyn Bedard; Wang-Choi Tang; Hibah Sehar; Jane Law Journal: Int J Environ Res Public Health Date: 2022-07-06 Impact factor: 4.614
Authors: Kamil Faisal; Sultanah Alshammari; Reem Alotaibi; Areej Alhothali; Omaimah Bamasag; Nusaybah Alghanmi; Manal Bin Yamin Journal: Int J Environ Res Public Health Date: 2022-03-16 Impact factor: 3.390