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Inequalities in the access to healthy urban structure and housing: an analysis of the Brazilian census data.

Antonio Fernando BoingAlexandra Crispim BoingSankaran Venkata Subramanian
Published in: Cadernos de saude publica (2021)
This study aims (1) to test the association between access to basic sanitation/hygiene services in Brazilian households with their householders' socioeconomic and demographic characteristics; (2) to analyze the distribution of urban health-relevant elements in the census tracts according to their income, education and race/color composition. The information come from the 2010 Brazilian Demographic Census, which collected data regarding both household conditions and urban structure of the census tracts. Prevalence ratios were calculated using crude and adjusted Poisson regression models. The proportional distribution of the census-tract urban structure was performed, according to the deciles of the exploratory variables, and the ratios and the absolute differences between the extreme deciles were calculated. Around 4.8% of the households had no piped water, 34.7% had no sewage collection system, 9.8% had no garbage collection and 39% were considered inadequate. Families whose householders were black, indigenous or brown had lower income and educational level, and lived in the North, Northeast, and Central West regions. They were more likely to be considered inappropriate for not having piped water, sewage collection system, and garbage collection. Moreover, sectors where the majority of the population was black, had lower educational levels and lower income had significantly poor paving, street lighting, afforestation, storm drain, sidewalk and wheelchair ramp. This study analyzed national data from 2010 and provides a baseline for future studies and government planning. The relevant social inequalities reported in this study need to be addressed by effective public policies.
Keyphrases
  • healthcare
  • mental health
  • physical activity
  • electronic health record
  • big data
  • primary care
  • emergency department
  • risk assessment
  • machine learning
  • deep learning