Spatial Disparity and Associated Factors of Cause-Specific Mortality in Small Areas of Brazil

Author(s)

Gayawan, Ezra ^{[1, 2, 3]} ; Lima, Everton E. C. ^{[2, 3, 4]}

Affiliation

^[1] Fed Univ Technol Akure, Dept Stat, Akure - Nigeria

^[2] Univ Estadual Campinas, Coll Philosophy & Human Sci IFCH, Campinas - Brazil

^[3] Univ Estadual Campinas, Populat Studies Ctr NEPO, Campinas - Brazil

^[4] Univ Estadual Campinas, Dept Demog, Campinas, SP - Brazil

• Document type: Journal article
• Source: CANADIAN STUDIES IN POPULATION; v. 48, n. 2-3 JUL 2021.
• Web of Science Citations: 0

Abstract

This study examines the possible clustering in the residual spatial variations of five mortality causes, classified by the tenth ICD, among 644 municipalities of the Brazilian state of Sao Paulo, after accounting for individuals' demographic characteristics and place of death. Mortality data by cause of death for 2014-2016 were sourced from the Brazilian Sistema de Informacoes sobre Mortalidade. We applied a Bayesian model that assumes a multinomial distribution for the polytomous outcome, classifying the causes of death into six categories, namely, infectious diseases, neoplasm, respiratory, cardiovascular, external, and other causes, and simultaneously estimates the spatial patterns, accounting for explanatory variables for the first five causes, taking the other causes as reference. The results show that, relative to the causes in the reference group, there is clustering of high relative risks among the municipalities for all the causes of mortality. The relative risks of death from neoplasms and cardiovascular diseases were lower among men relative to women but much higher for external causes. The identified spatial clustering can be useful in identifying appropriate cost-effective strategies suitable for addressing issues of human well-being in the specific municipalities of the state. (AU)

• FAPESP's process: 18/18649-7 - Small area analysis of levels and dispersion in cause-specific mortality in Brazil and Sub-Saharan Africa with bayesian models for proportion data
• Grantee: Everton Emanuel Campos de Lima
• Support Opportunities: Research Grants - Visiting Researcher Grant - International