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COVID-19 Mortality in an Area of Northeast Brazil: Epidemiological Characteristics and Cambridge.Org/Hyg Prospective Spatiotemporal Modelling

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COVID-19 Mortality in an Area of Northeast Brazil: Epidemiological Characteristics and Cambridge.Org/Hyg Prospective Spatiotemporal Modelling Epidemiology and Infection COVID-19 mortality in an area of northeast Brazil: epidemiological characteristics and cambridge.org/hyg prospective spatiotemporal modelling 1,2 2,3 2,4 2,5 Original Paper L. A. Andrade , D. S. Gomes ,S.V.M.A.Lima , A. M. Duque , M. S. Melo1,6 , M. A. O. Góes2,6,7 , C. J. N. Ribeiro2,8 , M. V. S. Peixoto2,9 , Cite this article: Andrade LA et al (2020). 10 1,2 COVID-19 mortality in an area of northeast C. D. F. Souza and A. D. Santos Brazil: epidemiological characteristics and prospective spatiotemporal modelling. 1Graduate Nursing Programme, Federal University of Sergipe, Aracaju, Sergipe, Brazil; 2Collective Health Research – Epidemiology and Infection 148, e288, 1 7. Center, Federal University of Sergipe, Aracaju, Sergipe, Brazil; 3Graduate Programme in Parasitic Biology, Federal https://doi.org/10.1017/S0950268820002915 University of Sergipe, Aracaju, Sergipe, Brazil; 4Department of Nursing, Federal University of Sergipe, Lagarto, Sergipe, Brazil; 5Department of Occupational Therapy, Federal University of Sergipe, Lagarto, Sergipe, Brazil; Received: 20 August 2020 6 7 Revised: 18 November 2020 Sergipe State Department of Health, Aracaju, Sergipe, Brazil; Department of Medicine, Federal University of 8 Accepted: 20 November 2020 Sergipe, Aracaju, Sergipe, Brazil; Federal Institute of Education, Science and Technology of Sergipe, São Cristóvão, Sergipe, Brazil; 9Department of Speech Therapy, Federal University of Sergipe, Aracaju, Sergipe, Brazil Key words: and 10Department of Medicine, Center for the Study of Social and Preventative Medicine, Federal University of COVID-19; mortality; pandemic; space–time Alagoas, Arapiraca, Alagoas, Brazil clusters; spatial analysis Author for correspondence: Abstract L. A. Andrade, This study aimed to analyse the spatial–temporal distribution of COVID-19 mortality in E-mail: [email protected] Sergipe, Northeast, Brazil. It was an ecological study utilising spatiotemporal analysis techni- ques 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 ana- lysis techniques may contribute to surveillance and control of COVID-19 pandemic. Introduction The coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome cor- onavirus 2 (SARS-CoV-2), first emerged in Wuhan city, China, and quickly became a threat to public health worldwide. The infection shows a high potential for spreading and has reached several countries, resulting in being declared as a pandemic by the World Health Organization (WHO) on 11 March 2020 [1]. The pandemic process has caused economic and social consequences in many regions around the world. Additionally, due to the quick dissemination of the virus, as well as the fail- ures in the implementation of strategies to face it in some areas, the number of individuals infected and dead by the disease is increasing and this situation represents a challenge to health systems [1, 2]. According to the WHO, COVID-19 has affected more than 215 countries with approxi- mately 50 million confirmed cases and 1 200 000 deaths worldwide by 10 November 2020, © The Author(s), 2020. Published by which corresponds to a mean case-fatality rate of approximately 2.5% [1]. In November Cambridge University Press. This is an Open 2020, the United States was the country most affected by COVID-19, with more than 10 mil- Access article, distributed under the terms of lion cases and 238 000 deaths. In Europe, one of the most affected continents, United the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), Kingdom, Italy, France and Spain were the countries that registered the most deaths in the which permits unrestricted re-use, region [3]. distribution, and reproduction in any medium, By 23 July 2020, Brazil was the second country in the worldwide ranking of number of cases provided the original work is properly cited. and deaths, and hence, considered the disease epicentre in Latin America [1], accounting for more than 2 million cases and 80 000 deaths. COVID-19 in the Northeast region of Brazil represents a serious public health problem and its impact may be greater, considering the interiorisation process and its growing expansion to more vulnerable areas [4]. In this region, there were high rates of incidence (1150) and mortality (41.4) per 100 000 inhabitants, which Downloaded from https://www.cambridge.org/core. IP address: 170.106.33.42, on 01 Oct 2021 at 00:58:58, subject to the Cambridge Core terms of use, available at https://www.cambridge.org/core/terms. https://doi.org/10.1017/S0950268820002915 2 L. A. Andrade et al. corresponds to the second most affected region in the country. (d) accumulated mortality rate (per 100 000 inhabitants; consid- The state of Sergipe has followed the exponential increase of ering the number of accumulated deaths by COVID-19 in the rates, with a higher incidence and mortality than the regional state and in each municipality as the numerator and the cor- (2288 cases and 57.2 deaths per 100 000 inhabitants, respectively) responding population as the denominator). [5]. COVID-19 has a heterogeneous geographic dynamic in differ- ent regions. Although SARS-CoV-2 has a high potential for con- Exploratory data analysis tamination and reaches all communities, some areas may be more The epidemiological variables used in the descriptive analysis affected than others, as many regions do not have adequate health were: age, sex, age group, race/colour, comorbidities, educational resources and infrastructure to face the pandemic effectively [6]. level, death site, time between symptom onset and test result, In addition, the elderly and individuals with chronic diseases time between symptom onset and death and time between hospi- are at a higher risk for developing more severe forms and dying talisation and death. Categorical variables were calculated by due to COVID-19 [7]. Consequently, specific characteristics of absolute and relative frequencies and continuous variables were the population and the balance between supply and demand in calculated by median and interquartile range (IQR). the assistance of severe cases can lead to variations in local mor- tality rates [8, 9]. Since the first cases of COVID-19 in Brazil, spatial analysis Time trend analysis techniques using geographic information systems (GIS) have been The temporal trend analysis was performed using the segmented used to map the spread and severity of the pandemic [10, 11]. log-linear regression model. Weekly mortality rates of COVID-19 The use of this epidemiological tool enables the identification of were considered dependent variables and epidemiological weeks the spatial dynamics of the disease, assisting in the planning, were considered independent variables. The Monte Carlo permu- management and evaluation of public health policies [10]. tation test was used to select the best model for inflection points, Therefore, the aim of this study was to analyse the spatio- applying 999 permutations and considering the highest residue temporal distribution of deaths by COVID-19 in the state of determination coefficient (R2)[13, 14]. Sergipe, Northeast, Brazil. The calculation to describe and quantify the trends was devel- oped by percentage increments (APCs, annual percentage Methods changes) and 95% confidence intervals (CIs). If more than one significant inflection was found during the study period, average Study design annual percentage changes (AAPCs) were calculated. The trends We carried out an ecological study using spatial analysis and tem- were considered statistically significant when the APCs exhibited poral trend techniques. All COVID-19 deaths registered in P < 0.05 and corresponding 95% CI did not include zero. Positive Sergipe, as reported by the Sergipe State Department of Health and significant values of APC indicate an increasing trend; nega- (SES/SE), from 2 April to 14 June 2020 were included. The tive and significant as a decreasing trend, and non-significant analysis units were the 75 municipalities of Sergipe and data trends as stable, regardless of APC values [13, 14]. were collected daily, considering the places of residence of the individuals deceased due to COVID-19. Spatial cluster analysis The addresses of the individuals deceased due to COVID-19 in Study area description Sergipe were georeferenced through the capture of latitude and Sergipe is located on the coast of the Northeast region of Brazil and longitude coordinates provided by Google Maps [15] and subse- has 75 municipalities divided
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