Spatiotemporal risk assessment and COVID-19 trend estimation in a federative unit in northeastern Brazil

Author:

da Silva Janiel Conceição1,de Sousa Giana Gislanne da Silva2,de Oliveira Rayanne Alves1,Santos Lívia Fernanda Siqueira1,Pascoal Livia Maia12,Santos Floriacy Stabnow1,Rolim Isaura Leticia Tavares Palmeira2,Costa Ana Cristina Pereira de Jesus1,Serra Maria Aparecida Alves de Oliveira1,Fontoura Iolanda Graepp3,Bezerra Janaina Miranda3,Aragão Francisca Bruna Arruda4,Ramos Antônio Carlos Vieira5,Lima Cynthia Cardoso Dias1,Fontoura Volmar Morais6,dos Santos Leonardo Hunaldo1,Neto Marcelino Santos12ORCID

Affiliation:

1. Graduate Program in Health and Technology, Federal University of Maranhão , Imperatriz , Maranhão, Brazil

2. Graduate Program in Nursing, Federal University of Maranhão , São Luís, Brazil

3. Center for Social Sciences, Health and Technology, Federal University of Maranhão , Imperatriz , Maranhão, Brazil

4. Interunit Doctoral Program in Nursing, University of São Paulo , Ribeirão Preto, Brazil

5. University of São Paulo at Ribeirão Preto College of Nursing , Ribeirão Preto, São Paulo Brazil

6. Nursing Department, State University of Tocantins , Augustinópolis, Tocantins , Brazil

Abstract

ABSTRACT Background Coronavirus disease 2019 (COVID-19) has spread worldwide, causing a high burden of morbidity and mortality, and has affected the various health service systems in the world, demanding disease monitoring and control strategies. The objective of this study was to identify risk areas using spatiotemporal models and determine the COVID-19 time trend in a federative unit of northeastern Brazil. Methods An ecological study using spatial analysis techniques and time series was carried out in the state of Maranhão, Brazil. All new cases of COVID-19 registered in the state from March 2020 to August 2021 were included. Incidence rates were calculated and spatially distributed by area, while the spatiotemporal risk territories were identified using scan statistics. The COVID-19 time trend was determined using Prais–Winsten regressions. Results Four spatiotemporal clusters with high relative risks for the disease were identified in seven health regions located in the southwest/northwest, north and east of Maranhão. The COVID-19 time trend was stable during the analysed period, with higher rates in the regions of Santa Inês in the first and second waves and Balsas in the second wave. Conclusions The heterogeneously distributed spatiotemporal risk areas and the stable COVID-19 time trend can assist in the management of health systems and services, facilitating the planning and implementation of actions toward the mitigation, surveillance and control of the disease.

Funder

Foundation for Research and Scientific and Technological Development of Maranhão

CAPES

Publisher

Oxford University Press (OUP)

Subject

Infectious Diseases,Public Health, Environmental and Occupational Health,General Medicine,Parasitology

Reference45 articles.

1. Aging in COVID-19: vulnerability, immunity and intervention;Chen;Ageing Res Rev,2021

2. The heterogeneous severity of COVID-19 in African countries: a modeling approach;Musa,2022

3. WHO coronavirus (COVID-19) dashboard;World Health Organization

4. Covid-19 no Nordeste do Brasil: primeiro ano de pandemia e incertezas que estão por vir;Kerr;Rev Saúde Púb,2021

5. Boletim Epidemiológico COVID-19;Secretaria de Estado da Saúde do Maranhão

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