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COVID-19 mortality in an area of northeast Brazil: epidemiological characteristics and prospective spatiotemporal modelling
This study aimed to analyse the spatial–temporal distribution of COVID-19 mortality in Sergipe, Northeast, Brazil. It was an ecological study utilising spatiotemporal analysis techniques that included all deaths confirmed by COVID-19 in Sergipe, from 2 April to 14 June 2020. Mortality rates were cal...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cambridge University Press
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7729172/ https://www.ncbi.nlm.nih.gov/pubmed/33256878 http://dx.doi.org/10.1017/S0950268820002915 |
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author | Andrade, L. A. Gomes, D. S. Lima, S. V. M. A. Duque, A. M. Melo, M. S. Góes, M. A. O. Ribeiro, C. J. N. Peixoto, M. V. S. Souza, C. D. F. Santos, A. D. |
author_facet | Andrade, L. A. Gomes, D. S. Lima, S. V. M. A. Duque, A. M. Melo, M. S. Góes, M. A. O. Ribeiro, C. J. N. Peixoto, M. V. S. Souza, C. D. F. Santos, A. D. |
author_sort | Andrade, L. A. |
collection | PubMed |
description | This study aimed to analyse the spatial–temporal distribution of COVID-19 mortality in Sergipe, Northeast, Brazil. It was an ecological study utilising spatiotemporal analysis techniques that included all deaths confirmed by COVID-19 in Sergipe, from 2 April to 14 June 2020. Mortality rates were calculated per 100 000 inhabitants and the temporal trends were analysed using a segmented log-linear model. For spatial analysis, the Kernel estimator was used and the crude mortality rates were smoothed by the empirical Bayesian method. The space–time prospective scan statistics applied the Poisson's probability distribution model. There were 391 COVID-19 registered deaths, with the majority among ⩾60 years old (62%) and males (53%). The most prevalent comorbidities were hypertension (40%), diabetes (31%) and cardiovascular disease (15%). An increasing mortality trend across the state was observed, with a higher increase in the countryside. An active spatiotemporal cluster of mortality comprising the metropolitan area and neighbouring cities was identified. The trend of COVID-19 mortality in Sergipe was increasing and the spatial distribution of deaths was heterogeneous with progression towards the countryside. Therefore, the use of spatial analysis techniques may contribute to surveillance and control of COVID-19 pandemic. |
format | Online Article Text |
id | pubmed-7729172 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Cambridge University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-77291722020-12-11 COVID-19 mortality in an area of northeast Brazil: epidemiological characteristics and prospective spatiotemporal modelling Andrade, L. A. Gomes, D. S. Lima, S. V. M. A. Duque, A. M. Melo, M. S. Góes, M. A. O. Ribeiro, C. J. N. Peixoto, M. V. S. Souza, C. D. F. Santos, A. D. Epidemiol Infect Original Paper This study aimed to analyse the spatial–temporal distribution of COVID-19 mortality in Sergipe, Northeast, Brazil. It was an ecological study utilising spatiotemporal analysis techniques that included all deaths confirmed by COVID-19 in Sergipe, from 2 April to 14 June 2020. Mortality rates were calculated per 100 000 inhabitants and the temporal trends were analysed using a segmented log-linear model. For spatial analysis, the Kernel estimator was used and the crude mortality rates were smoothed by the empirical Bayesian method. The space–time prospective scan statistics applied the Poisson's probability distribution model. There were 391 COVID-19 registered deaths, with the majority among ⩾60 years old (62%) and males (53%). The most prevalent comorbidities were hypertension (40%), diabetes (31%) and cardiovascular disease (15%). An increasing mortality trend across the state was observed, with a higher increase in the countryside. An active spatiotemporal cluster of mortality comprising the metropolitan area and neighbouring cities was identified. The trend of COVID-19 mortality in Sergipe was increasing and the spatial distribution of deaths was heterogeneous with progression towards the countryside. Therefore, the use of spatial analysis techniques may contribute to surveillance and control of COVID-19 pandemic. Cambridge University Press 2020-12-01 /pmc/articles/PMC7729172/ /pubmed/33256878 http://dx.doi.org/10.1017/S0950268820002915 Text en © The Author(s) 2020 http://creativecommons.org/licenses/by/4.0/ http://creativecommons.org/licenses/by/4.0/This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Paper Andrade, L. A. Gomes, D. S. Lima, S. V. M. A. Duque, A. M. Melo, M. S. Góes, M. A. O. Ribeiro, C. J. N. Peixoto, M. V. S. Souza, C. D. F. Santos, A. D. COVID-19 mortality in an area of northeast Brazil: epidemiological characteristics and prospective spatiotemporal modelling |
title | COVID-19 mortality in an area of northeast Brazil: epidemiological characteristics and prospective spatiotemporal modelling |
title_full | COVID-19 mortality in an area of northeast Brazil: epidemiological characteristics and prospective spatiotemporal modelling |
title_fullStr | COVID-19 mortality in an area of northeast Brazil: epidemiological characteristics and prospective spatiotemporal modelling |
title_full_unstemmed | COVID-19 mortality in an area of northeast Brazil: epidemiological characteristics and prospective spatiotemporal modelling |
title_short | COVID-19 mortality in an area of northeast Brazil: epidemiological characteristics and prospective spatiotemporal modelling |
title_sort | covid-19 mortality in an area of northeast brazil: epidemiological characteristics and prospective spatiotemporal modelling |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7729172/ https://www.ncbi.nlm.nih.gov/pubmed/33256878 http://dx.doi.org/10.1017/S0950268820002915 |
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