Social vulnerability and COVID-19 in Maringá, Brazil
Tóm tắt
This research explores the relationship between COVID-19 and social vulnerability on an intra-urban scale. For this, two composite indicators of social vulnerability have been constructed. The composite indicator constructed by the Benefit-of-the-Doubt considers spatial heterogeneity. It weakly captures the conceptually most significant individual indicator of social vulnerability (R=-0.39), as it overestimates the above-average performance sub-indicators. The composite indicator constructed by the Principal Component Analysis considers that the sub-indicators have the same weights in different census tracts, resulting in a highly consistent composite indicator as a multidimensional phenomenon concept (R=-0.93). These findings allow reaching four conclusions. First, the direction and strength of correlations associated with COVID-19 are sensitive to the method employed to construct the composite indicator and not just the geographic scale and space. Second, Medium and High social vulnerability census tracts concentrate 97% of the population but only 93% of COVID-19 cases and deaths. Third, people living in census tracts of None and Low social vulnerability are 3.87 and 2.13 times more likely to be infected or die from COVID-19. Fourth, policies to combat COVID-19 in the study area should prioritize older populations regardless of their social conditions.
Tài liệu tham khảo
Fatima, M., O’Keefe, K. J., Wei, W., Arshad, S., & Gruebner, O. (2021). Geospatial analysis of COVID-19: A scoping review. International Journal of Environmental Research and Public Health, 18(5), 2336
Azarpazhooh, M. R., Morovatdar, N., Avan, A., Phan, T. G., Divani, A. A., Yassi, N., & Di Napoli, M. (2020). COVID-19 pandemic and burden of non-communicable diseases: an ecological study on data of 185 countries. Journal of Stroke and Cerebrovascular Diseases, 29(9), 105089
Valev, D. (2020). Relationships of total COVID-19 cases and deaths with ten demographic, economic and social indicators. medRxiv
Libório, M. P., Ekel, P. Y., de Abreu, J. F., & Laudares, S. (2021). Factors that most expose countries to COVID-19: a composite indicators-based approach. GeoJournal, 1–15
Li, W. X. (2021). Worldwide inverse correlation between Bacille Calmette–Guérin (BCG) immunization and COVID-19 mortality. Infection, 49(3), 463–473
Benita, F., & asca-Sanchez, F. (2021). The main factors influencing COVID-19 spread and deaths in Mexico: A comparison between Phases I and II.Applied Geography,102523
Khazanchi, R., Beiter, E. R., Gondi, S., Beckman, A. L., Bilinski, A., & Ganguli, I. (2020). County-level association of social vulnerability with COVID-19 cases and deaths in the USA. Journal of general internal medicine, 35(9), 2784–2787
Karaye, I. M., & Horney, J. A. (2020). The impact of social vulnerability on COVID-19 in the US: an analysis of spatially varying relationships. American journal of preventive medicine, 59(3), 317–325
Sung, B. (2021). A spatial analysis of the association between social vulnerability and the cumulative number of confirmed deaths from COVID-19 in United States counties through November 14, 2020. Osong Public Health and Research Perspectives, 12(3), 149–157. doi: https://doi.org/10.24171/j.phrp.2020.0372
Paez, A., Lopez, F. A., Menezes, T., Cavalcanti, R., & Pitta, M. G. D. R. (2020). A spatio-temporal analysis of the environmental correlates of COVID‐19 incidence in Spain. Geographical analysis, 53(3), 397–421. Doi: https://doi.org/10.1111/gean.12241
Kalla, M. I., Lahmar, B., Geullouh, S., & Kalla, M. (2021). Health geo-governance to assess the vulnerability of Batna, Algeria to COVID-19: the role of GIS in the fight against a pandemic. GeoJournal, 1–14. Doi: https://doi.org/10.1007/s10708-021-10449-8
Henao-Cespedes, V., Garcés-Gómez, Y. A., Ruggeri, S., & Henao-Cespedes, T. M. (2021). Relationship analysis between the spread of COVID-19 and the multidimensional poverty index in the city of Manizales, Colombia. The Egyptian Journal of Remote Sensing and Space Science. doi: https://doi.org/10.1016/j.ejrs.2021.04.002
Biggs, E. N., Maloney, P. M., Rung, A. L., Peters, E. S., & Robinson, W. T. (2021). The relationship between social vulnerability and COVID-19 incidence among louisiana census tracts. Frontiers in Public Health, 8, 1048
Tavares, F. F., & Betti, G. (2021). The pandemic of poverty, vulnerability, and COVID-19: evidence from a fuzzy multidimensional analysis of deprivations in Brazil. World Development, 139, 105307
The Lancet Global Health, 9(6), E782-E792
Souza, C. D. F., Machado, M. F., & do Carmo, R. F. (2020). Human development, social vulnerability and COVID-19 in Brazil: a study of the social determinants of health. Infectious Diseases of Poverty, 9(1), 1–10
Baggio, J. A. O., Machado, M. F., Carmo, D., Armstrong, R. F. C., Dos Santos, A., A. D., & De Souza, C. D. F. (2021). COVID-19 in Brazil: spatial risk, social vulnerability, human development, clinical manifestations and predictors of mortality–a retrospective study with data from 59 695 individuals. Epidemiology & Infection, 149, doi:https://doi.org/10.1017/S0950268821000935
Souza, C. D. F., Carmo, R. F., & Machado, M. F. (2020). The burden of COVID-19 in Brazil is greater in areas with high social deprivation. Journal of Travel Medicine, 27(7), 1–3
Viezzer, J., & Biondi, D. (2021). The influence of urban, socio-economic, and eco-environmental aspects on COVID-19 cases, deaths and mortality: A multi-city case in the Atlantic Forest, Brazil. Sustainable Cities and Society, 69, 102859
Kong, J. D., Tekwa, E. W., & Gignoux-Wolfsohn, S. A. (2021). Social, economic, and environmental factors influencing the basic reproduction number of COVID-19 across countries.PloS one, 16(6), e0252373
Castro, R. R., Santos, R. S. C., Sousa, G. J. B., Pinheiro, Y. T., Martins, R. R. I. M., Pereira, M. L. D., & Silva, R. A. R. (2021). Spatial dynamics of the COVID-19 pandemic in Brazil. Epidemiology & Infection, 149
Souza, C. M. M., Mello, B. J., Florit, L. F., Ramalho, Â. M. C., de Moraes Souza, Y. M., Jeremias, J. T. F., & de Aguiar, P. D. (2021). Social environmental vulnerability approach on the COVID-19 epoch: a case study in Blumenau (SC), Brazil. Research Society and Development, 10(10), e161101018739–e161101018739
Souza, A. P. G. D., Mota, C. M. D. M., Rosa, A. G. F., Figueiredo, C. J. J. D., & Candeias, A. L. B. (2022). A spatial-temporal analysis at the early stages of the COVID-19 pandemic and its determinants: The case of Recife neighborhoods, Brazil. PloS one, 17(5), e0268538
Nardo, M., Saisana, M., Saltelli, A., & Tarantola, S. (2005). Tools for composite indicators building. European Comission Ispra, 15(1), 19–20
Mazziotta, M., & Pareto, A. (2017). Synthesis of indicators: The composite indicators approach. Complexity in society: From indicators construction to their synthesis (pp. 159–191). Cham: Springer
Mazziotta, M., & Pareto, A. (2019). Use and misuse of PCA for measuring well-being. Social Indicators Research, 142(2), 451–476
El Gibari, S., Gómez, T., & Ruiz, F. (2019). Building composite indicators using multicriteria methods: a review. Journal of Business Economics, 89(1), 1–24
Greco, S., Ishizaka, A., Tasiou, M., & Torrisi, G. (2019). On the methodological framework of composite indices: A review of the issues of weighting, aggregation, and robustness. Social indicators research, 141(1), 61–94
Saisana, M., Saltelli, A., & Tarantola, S. (2005). Uncertainty and sensitivity analysis techniques as tools for the quality assessment of composite indicators. Journal of the Royal Statistical Society: Series A (Statistics in Society), 168(2), 307–323
Libório, M. P., Martinuci, O. D. S., Machado, A. M. C., Hadad, R. M., Bernardes, P., & Camacho, V. A. L. (2021). Adequacy and Consistency of an Intraurban Inequality Indicator Constructed through Principal Component Analysis. The Professional Geographer, 73(2), 282–296
Dialga, I., & Giang, L. T. H. (2017). Highlighting methodological limitations in the steps of composite indicators construction. Social Indicators Research, 131(2), 441–465
IBGE (2021). Cidades. Brasil Paraná Maringá. Extracted on December 14, 2021, from: https://cidades.ibge.gov.br/brasil/pr/maringa/panorama
IBGE (2010). Censo Demográfico. Extracted on December 14, 2021, from: https://censo2010.ibge.gov.br/resultados.html
Eurostat (2019). European Statistical: Recovery Dashboard. Extracted on December 14, 2021, from: https://ec.europa.eu/eurostat
Guerriero, I. C. Z. (2016). Resolução nº 510 de 7 de abril de 2016 que trata das especificidades éticas das pesquisas nas ciências humanas e sociais e de outras que utilizam metodologias próprias dessas áreas. Ciência & Saúde Coletiva, 21, 2619–2629
Jolliffe, I. T., & Cadima, J. (2016). Principal component analysis: a review and recent developments. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2065), 20150202
Cherchye, L., Moesen, W., Rogge, N., & Van Puyenbroeck, T. (2007). An introduction to ‘benefit of the doubt’composite indicators. Social Indicators Research, 82(1), 111–145
Zanella, A., Camanho, A. S., & Dias, T. G. (2015). Undesirable outputs and weighting schemes in composite indicators based on data envelopment analysis. European Journal of Operational Research, 245(2), 517–530
Arretche, M. (2018). Paths of inequality in Brazil: a half-century of changes. Springer
Libório, M. P., Ekel, P. Y., Martinuci, O. D. S., Figueiredo, L. R., Hadad, R. M., Lyrio, R. D. M., & Bernardes, P. (2022). Fuzzy set based intra-urban inequality indicator. Quality & Quantity, 56(2), 667–687