A scaling investigation of urban form features in Latin America cities.

This paper examines scaling behaviors of urban landscape and street design metrics with respect to city population in Latin America. We used data from the SALURBAL project, which has compiled and harmonized data on health, social, and built environment for 371 Latin American cities above 100,000 inhabitants. These metrics included total urbanized area, effective mesh size, area in km2 and number of streets. We obtained scaling relations by regressing log(metric) on log (city population). The results show an overall sub-linear scaling behavior of most variables, indicating a relatively lower value of each variable in larger cities. We also explored the potential influence of colonization on the current built environment, by analyzing cities colonized by Portuguese (Brazilian cities) or Spaniards (Other cities in Latin America) separately. We found that the scaling behaviors are similar for both sets of cities.

Racial and economic segregation in Brazil: a nationwide analysis of socioeconomic and socio-spatial inequalities / Segregação racial e econômica no Brasil: uma análise nacional das desigualdades socioeconômicas e socioespaciais / Segregación racial y económica en Brasil: un análisis nacional de las desigualdades socioeconómicas y socioespaciales

Abstract This article aims to analyze residential segregation by race (racial segregation) and income (economic segregation) in Brazil and explore its relationship with socioeconomic and socio-spatial factors. Residential segregation was assessed using the dissimilarity index based on the 2010 demographic census and considering urban census tracts since segregation is sociologically considered an urban problem. The results for racial segregation showed that it is more evident in cities in the South and Southeast of Brazil and mainly affects the self-declared black population. The approach used to calculate economic segregation involved examining the income level of different low-income groups. Therefore, we consider families that earned between 0 and 1 minimum wage as the group with the greatest social vulnerability. We did not find significant correlations between racial and income segregation indices with aspects such as urbanization (urban population size). Finally, we present the racial segregation indices stratifying families by income thresholds for the 27 Brazilian capitals and conclude that per capita household income is a preponderant factor for the segregation of the poorest, especially in families whose residents self-identify as black.

Resumo Este artigo tem como objetivo analisar a segregação residencial por raça (segregação racial) e renda (segregação econômica) no Brasil e explorar sua relação com fatores socioeconômicos e socioespaciais. A segregação residencial foi avaliada pelo índice de dissimilaridade baseado no Censo Demográfico de 2010 e considerando setores censitários urbanos, uma vez que a segregação é entendida sociologicamente como um problema urbano. Os resultados mostram que a segregação racial é mais evidente nas cidades do Sul e Sudeste do Brasil, atingindo principalmente a população autodeclarada preta. A abordagem utilizada para calcular a segregação econômica envolveu examinar o nível de renda de diferentes grupos de baixa renda. Portanto, consideramos as famílias que ganham entre 0 e 1 salário mínimo - o grupo de maior vulnerabilidade social. Não encontramos correlações significativas entre os índices de segregação racial e de renda com fatores como a urbanização (tamanho da população urbana). Por fim, apresentamos os índices de segregação racial estratificando as famílias por faixas de renda para as 27 capitais brasileiras e concluímos que a renda domiciliar per capita é fator preponderante para a segregação dos mais pobres, principalmente nas famílias cujos moradores se autodeclaram pretos.

Resumen Este artículo tiene como objetivo analizar la segregación residencial por raza (segregación racial) y renta (segregación económica) en Brasil y explorar su relación con factores socioeconómicos y socioespaciales. La segregación residencial se evaluó utilizando el índice de disimilitud con base en el censo demográfico de 2010 y considerando las secciones censales urbanas ya que la segregación es considerada sociológicamente como un problema urbano. Los resultados para la segregación racial mostraron que esta es más evidente en ciudades del sur y del sudeste de Brasil y que afecta principalmente a la población autodeclarada negra. El enfoque usado para calcular la segregación económica implicó examinar el nivel de ingresos de diferentes grupos de bajos ingresos. Por lo tanto, consideramos que las familias que ganaban entre cero y un salario mínimo son el grupo con mayor vulnerabilidad social. No encontramos correlaciones significativas entre los índices de segregación racial y los de ingresos con factores como la urbanización (tamaño de la población urbana). Finalmente, presentamos los índices de segregación racial estratificando a las familias por umbrales de renta para las 27 capitales brasileñas y concluimos que la renta per cápita de los hogares es un factor preponderante para la segregación de los más pobres, en especial en las familias cuyos habitantes se autodeclaran negros.

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

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.

Mathematical modeling of COVID-19 in 14.8 million individuals in Bahia, Brazil.

COVID-19 is affecting healthcare resources worldwide, with lower and middle-income countries being particularly disadvantaged to mitigate the challenges imposed by the disease, including the availability of a sufficient number of infirmary/ICU hospital beds, ventilators, and medical supplies. Here, we use mathematical modelling to study the dynamics of COVID-19 in Bahia, a state in northeastern Brazil, considering the influences of asymptomatic/non-detected cases, hospitalizations, and mortality. The impacts of policies on the transmission rate were also examined. Our results underscore the difficulties in maintaining a fully operational health infrastructure amidst the pandemic. Lowering the transmission rate is paramount to this objective, but current local efforts, leading to a 36% decrease, remain insufficient to prevent systemic collapse at peak demand, which could be accomplished using periodic interventions. Non-detected cases contribute to a ∽55% increase in R0. Finally, we discuss our results in light of epidemiological data that became available after the initial analyses.