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Trend and inequality in the completeness of information on race/color in deaths of elderly in the Mortality Information System in Brazil, 2000 to 2015

The quality of information on race/color is a necessary condition for knowing the impact of inequality on mortality. This study aims to analyze the trend and inequality in completeness of race/color in death records of elderly in Brazil’s Mortality Information System (SIM in Portuguese) from 2000 to 2015. The study analyzes the completeness of this variable according to different geographic areas, the race or color most affected by poor completeness of records, and the association between excellent completion of race/color and the municipalities’ territoriality and socioeconomic status. Data on deaths of elderly individuals were obtained from the SIM and information on the population from censuses and Ministry of Health estimates. The study estimates the percent variation in the proportion of incompleteness. The percent variation of black and brown individuals was estimated from 2000 to 2010 from the SIM and censuses. Crude and adjusted logistic regression (95%CI) were used to estimate completeness of race/color as the outcome and territorial and socioeconomic characteristics as independent variables. We found a sharp improvement in quality of completion during the period, especially up to 2006, with an excellent average since 2007. The findings showed territorial inequality at the municipal level. Municipalities with low/medium HDI (with a high proportion of poverty and inequality) showed lower odds of excellent completeness. The adjusted model shows that the region and size of the municipality are characteristics that explain the excellent quality of the race/color variable. Municipalities in Northeast Brazil and small municipalities have the lowest odds of excellent completeness. In conclusion, race/color in the SIM has sufficient quality to be used in studies on inequality of mortality in the elderly, with exceptions at the municipal level.

Keywords:
Aged; Mortality Registries; Data Accuracy; Race Factors; Racism


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