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Association of urban inequality and income segregation with COVID-19 mortality in Brazil

Author

Listed:
  • J Firmino de Sousa Filho
  • Uriel M Silva
  • Larissa L Lima
  • Aureliano S S Paiva
  • Gervásio F Santos
  • Roberto F S Andrade
  • Nelson Gouveia
  • Ismael H Silveira
  • Amélia A de Lima Friche
  • Maurício L Barreto
  • Waleska Teixeira Caiaffa

Abstract

Socioeconomic factors have exacerbated the impact of COVID–19 worldwide. Brazil, already marked by significant economic inequalities, is one of the most affected countries, with one of the highest mortality rates. Understanding how inequality and income segregation contribute to excess mortality by COVID–19 in Brazilian cities is essential for designing public health policies to mitigate the impact of the disease. This paper aims to fill in this gap by analyzing the effect of income inequality and income segregation on COVID–19 mortality in large urban centers in Brazil. We compiled weekly COVID–19 mortality rates from March 2020 to February 2021 in a longitudinal ecological design, aggregating data at the city level for 152 Brazilian cities. Mortality rates from COVID-19 were compared across weeks, cities and states using mixed linear models. We estimated the associations between COVID-19 mortality rates with income inequality and income segregation using mixed negative binomial models including city and week-level random intercepts. We measured income inequality using the Gini index and income segregation using the dissimilarity index using data from the 2010 Brazilian demographic census. We found that 88.2% of COVID–19 mortality rates variability was between weeks, 8.5% between cities, and 3.3% between states. Higher-income inequality and higher-income segregation values were associated with higher COVID–19 mortality rates before and after accounting for all adjustment factors. In our main adjusted model, rate ratios (RR) per 1 SD increases in income inequality and income segregation were associated with 17% (95% CI 9% to 26%) and 11% (95% CI 4% to 19%) higher mortality. Income inequality and income segregation are long-standing hallmarks of large Brazilian cities. Risk factors related to the socioeconomic context affected the course of the pandemic in the country and contributed to high mortality rates. Pre-existing social vulnerabilities were critical factors in the aggravation of COVID–19, as supported by the observed associations in this study.

Suggested Citation

  • J Firmino de Sousa Filho & Uriel M Silva & Larissa L Lima & Aureliano S S Paiva & Gervásio F Santos & Roberto F S Andrade & Nelson Gouveia & Ismael H Silveira & Amélia A de Lima Friche & Maurício L Ba, 2022. "Association of urban inequality and income segregation with COVID-19 mortality in Brazil," PLOS ONE, Public Library of Science, vol. 17(11), pages 1-12, November.
    • Handle: RePEc:plo:pone00:0277441
    DOI: 10.1371/journal.pone.0277441
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    References listed on IDEAS

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    1. Garcia-López, Miquel-Àngel & Moreno-Monroy, Ana I., 2018. "Income segregation in monocentric and polycentric cities: Does urban form really matter?," Regional Science and Urban Economics, Elsevier, vol. 71(C), pages 62-79.
    2. Barber, Sharrelle & Diez Roux, Ana V. & Cardoso, Letícia & Santos, Simone & Toste, Veronica & James, Sherman & Barreto, Sandhi & Schmidt, Maria & Giatti, Luana & Chor, Dora, 2018. "At the intersection of place, race, and health in Brazil: Residential segregation and cardio-metabolic risk factors in the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil)," Social Science & Medicine, Elsevier, vol. 199(C), pages 67-76.
    3. Tavares, Fernando Flores & Betti, Gianni, 2021. "The pandemic of poverty, vulnerability, and COVID-19: Evidence from a fuzzy multidimensional analysis of deprivations in Brazil," World Development, Elsevier, vol. 139(C).
    4. Jonathan Haughton & Shahidur R. Khandker, 2009. "Handbook on Poverty and Inequality," World Bank Publications - Books, The World Bank Group, number 11985, April.
    5. Marcello Barbosa Otoni Gonçalves Guedes & Sanderson José Costa de Assis & Geronimo José Bouzas Sanchis & Diego Neves Araujo & Angelo Giuseppe Roncalli Da Costa Oliveira & Johnnatas Mikael Lopes, 2021. "COVID-19 in Brazilian cities: Impact of social determinants, coverage and quality of primary health care," PLOS ONE, Public Library of Science, vol. 16(9), pages 1-12, September.
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