Original Articles
Vol. 15 No. 2 (2020)
Spatial association between the incidence rate of COVID-19 and poverty in the São Paulo municipality, Brazil
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All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Received: 9 July 2020
Accepted: 10 September 2020
Accepted: 10 September 2020
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In this article, we investigated the spatial dependence of the incidence rate by COVID-19 in the São Paulo municipality, Brazil, including the association between the spatially smoothed incidence rate (INC_EBS) and the social determinants of poverty, the average Salary (SAL), the percentage of households located in slums (SLUMS) and the percentage of the population above 60 years of age (POP>60Y). We used data on the number notified cases accumulated per district by May 18, 2020. The spatial dependence of the spatially smoothed incidence rate was investigated through the analysis of univariate local spatial autocorrelation using Moran's I. To evaluate the spatial association between the INC_EBS and the determinants SAL, POP>60Y and SLUMS, we used the local bivariate Moran's I. The results showed that the spatially smoothed incidence rate for COVID-19 presented significant spatial autocorrelation (I = 0.333; P<0.05), indicating that the cases were concentrated in clusters of neighbouring districts. The INC_EBS showed a negative spatial association with SAL (I = -0.253, P<0.05) and POP>60Y (I = -0.398, P<0.05). We also found that the INC_EBS showed a positive spatial association with households located in the slums (I = 0.237, P<0.05). Our study concluded that the households where the population most vulnerable to COVID-19 resides were spatially distributed in the districts with lower salaries, higher percentages of slums and lower percentages of the population above 60 years of age.
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Abrams EM, Szefler SJ, 2020. COVID-19 and the impacts of social determinants of health. Lancet Respir Med. Epub 2020 May 18 DOI: https://doi.org/10.1016/S2213-2600(20)30234-4
Adhikari, SP, Meng S, Wu YJ, Mao YP, Ye, RX, 2020. Epidemiology, causes, clinical manifestation and diagnosis, prevention and control of coronavirus disease (COVID-19) during the early outbreak period: a scoping review. Infect Dis Poverty 9:29. DOI: https://doi.org/10.1186/s40249-020-00646-x
Ahmed F, Ahmed, N., Pissarides C, Stiglitz J, 2020. Why inequality could spread COVID-19. Lancet Public Health. Epub 2020 Apr 2. DOI: https://doi.org/10.1016/S2468-2667(20)30085-2
Anselin L, 1995. Local indicators of spatial association – LISA. Geogr Anal 27:93-115. DOI: https://doi.org/10.1111/j.1538-4632.1995.tb00338.x
Anselin L, Lozano-Gracia N, Koschinky J, 2006. Rate Transformations and Smoothing. Technical Report. Urbana, IL: Spatial Analysis Laboratory, Department of Geography, University of Illinois, USA.
Anselin L, Syabri I, Smirnov O, 2010. Visualizing multivariate spatial correlation with dynamically linked windows. In: New Tools for Spatial Data Analysis: Proceedings of the Specialist Meeting, edited by Luc Anselin and Sergio Rey. University of California, Santa Barbara: Center for Spatially Integrated Social Science (CSISS).
Anselin L, 2019. GeoDa v. 1.14.07, August 2019. University of Chicago, Center for Spatial Data Science.
Bambra C, Riordan R, Ford J, Matthews F, 2020. The COVID-19 pandemic and health inequalities. J Epidemiol Community Health 2020 Jun 12. DOI: https://doi.org/10.1136/jech-2020-214401
Borjas GJ, 2020. Demographic determinants of testing incidence and COVID-19 infections in New York City Neighborhoods. Institute of Labor Economics, April 2020, IZA Discussion Papers No. 13115. DOI: https://doi.org/10.3386/w26952
CEM, 2020. Centro de Estudos da Metrópole. ReSolution. https://centrodametropole.fflch.usp.br/pt-br. Accessed on 10 June 2020.
Chen N, Zhou M, Dong X, 2020. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study, Lancet 2020, January 30. DOI: https://doi.org/10.1016/S0140-6736(20)30211-7
Dahab M, van Zandvoort K, Flasche S, Warsame A, Spiegel PB, Waldman RJ, 2020. Checchi F, 2020. COVID-19 control in low-income settings and displaced populations: what can realistically be done? London School of Hygiene & Tropical Medicine, 20 March 2020. DOI: https://doi.org/10.1186/s13031-020-00296-8
Corburn J, Vlahov, D, Mberu B, Riley L, Caiaffa W, Rashid S et al., 2020. Slum health: arresting COVID-19 and improving well-being in urban informal settlements. J Urban Health. https://doi.org/10.1007/s11524-020-00438-6 DOI: https://doi.org/10.1007/s11524-020-00438-6
Dorn van A, Cooney RE, Sabin ML, 2020. COVID-19 exacerbating inequalities in the US. Lancet Epub April 16. https://doi.org/10.1016/S0140-6736(20)30893-X DOI: https://doi.org/10.1016/S0140-6736(20)30893-X
Frúgoli Jr, H, 1998. O Centro, a Avenida Paulista e a Avenida Luiz Carlos Berrini na Perspectiva de Suas Associações: Centralidade Urbana e Exclusão social. PhD thesis, University of São Paulo, São Paulo, 305pp.
Gibson L, Rush D, 2020. Novel coronavirus in Cape Town informal settlements: feasibility of using informal dwelling outlines to identify high risk areas for COVID-19 transmission from a social distancing perspective. JMIR Public Health Surveill. Epub 2020 April 6. https://doi.org/10.2196/18844 DOI: https://doi.org/10.2196/18844
Khalatbari-Soltani S, Cumming RG, Delpierre C, Kelly-Irving, M, 2020. Importance of collecting data on socioeconomic determinants from the early stage of the COVID-19 outbreak onwards. J Epidemiol Community Health. Epub 2020 May 8. https://doi.org/doi:10.1136/jech-2020-214297. DOI: https://doi.org/10.1136/jech-2020-214297
Liu T, Hu J, Kang M, Lin L, Zhong H, Xiao J, et al. Transmission dynamics of 2019 novel coronavirus (2019-nCoV). bioRxiv 2020 January 26. https://doi.org/10.1101/2020.01.25.919787. DOI: https://doi.org/10.1101/2020.01.25.919787
Pereira RJ, Nascimento GNL, Gratão LHA, Pimenta RS, 2020. The risk of COVID-19 transmission in favelas and slums in Brazil. Public Health, 2020 Jun 183:42-43. https://doi.org/10.1016/j.puhe.2020.04.042. DOI: https://doi.org/10.1016/j.puhe.2020.04.042
Rede Nossa São Paulo, 2017. Mapa da Desigualdade 2017. Available from: https://nossasaopaulo.org.br/portal/mapa_2017_completo.pdf. Accessed: 15 April 2020.
SEADE, 2020. Sistema Estadual de Análise de Dados. Available from: https://www.seade.gov.br/ Accessed:10 April 2020.
Secretaria de Desenvolvimento Urbano. Prefeitura Municipal de São Paulo, 2020. http://www.prefeitura.sp.gov.br/cidade/secretarias/urbanismo. Accessed:10 April 2020.
Secretaria Municipal da Saúde, 2020a. Boletim diário COVID-19 - 64. Released on May 29, 2020. http://www.prefeitura.sp.gov.br/cidade/secretarias/saude/vigilancia_em_saude. Prefeitura Municipal de São Paulo. Accessed: 04 June 2020.
Secretaria Municipal da Saúde, 2020b. COVID-19 Relatório Situacional. Released on May 29, 2020. http://www.prefeitura.sp.gov.br/cidade/secretarias/saude/vigilancia_em_saude. Prefeitura Municipal de São Paulo. Accessed: 04 June 2020.
Shen M, Peng Z, Xiao Y, Zhang L, 2020. Modelling the epidemic trend of the 2019 novel coronavirus outbreak in China, 2020. bioRxiv 2020 January 25. DOI: https://doi.org/10.1101/2020.01.23.916726
Sloan C, Chandrasekhar R, Mitchel EF, Schaffner W, Lindegren ML, 2015. Socioeconomic disparities and influenza hospitalizations, Tennessee, USA. Emerg Infect Dis 21(9). DOI: https://doi.org/10.3201/eid2109.141861
Smirnov NV, 1939. On the estimation of the discrepancy between empirical curves of distribution for two independent samples. Bull Math de l’Univ de Moscou, 2, 3-14.
Sohrabi C, Alsafib Z, O'Neill N, Khanb, M, Kerwanc A, Al-Jabirc A, Iosifidis C, Aghad, R, 2020. World Health Organization declares global emergency: A review of the 2019 novel coronavirus (COVID-19). Int J Surg. Epub 2020 Feb 26. DOI: https://doi.org/10.1016/j.ijsu.2020.03.036
Souza MD, 2020. Em SP, bairros com maior incidência por covid-19 estão no centro "pobre". Brasil de Fato. Released on May 28, 2020. Available from https://www.brasildefato.com.br/2020/05/28/em-sp-bairros-com-maior-incidência-por-covid-19-estao-no-centro-pobre. Accessed:10 June 2020.
Wasdani KP, Prasad A, 2020. The impossibility of social distancing among the urban poor: the case of an Indian slum in the times of COVID-19. Local Environ 25:5. DOI: https://doi.org/10.1080/13549839.2020.1754375
Zhu N, Zhang D, Wang W, Li X, Yang B, Song J, 2020. A novel coronavirus from patients with pneumonia in China, 2019. N Engl J Med 2020 382:727-733. DOI: https://doi.org/10.1056/NEJMoa2001017
How to Cite
Spatial association between the incidence rate of COVID-19 and poverty in the São Paulo municipality, Brazil. (2020). Geospatial Health, 15(2). https://doi.org/10.4081/gh.2020.921
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