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Factors associated with the quality of death certification in Brazilian municipalities: A data-driven non-linear model

Author

Listed:
  • Guilherme Augusto Zimeo Morais
  • João Luiz Miraglia
  • Bruno Zoca de Oliveira
  • Sóstenes Mistro
  • Wilian Hiroshi Hisatugu
  • Djeniffer Greffin
  • Clément Bernardo Marques
  • Eduardo Pontes Reis
  • Hugo Martins de Lima
  • Claudia Szlejf

Abstract

Studies evaluating the local quality of death certification in Brazil focused on completeness of death reporting or inappropriate coding of causes of death, with few investigating missing data. We aimed to use missing and unexpected values in core topics to assess the quality of death certification in Brazilian municipalities, to evaluate its correlation with the percentage of garbage codes, and to employ a data-driven approach with non-linear models to investigate the association of the socioeconomic and health infrastructure context with quality of death statistics among municipalities. This retrospective study used data from the Mortality Information System (2010–2017), and municipal data regarding healthcare infrastructure, socioeconomic characteristics, and death rates. Quality of death certification was assessed by missing or unexpected values in the following core topics: dates of occurrence, registration, and birth, place of occurrence, certifier, sex, and marital status. Models were fit to classify municipalities according to the quality of death certification (poor quality defined as death records with missing or unexpected values in core topics ≥ 80%). Municipalities with poor quality of death certification (43.9%) presented larger populations, lower death rates, lower socioeconomic index, healthcare infrastructure with fewer beds and physicians, and higher proportion of public healthcare facilities. The correlation coefficients between quality of death certification assessed by missing or unexpected values and the proportion of garbage codes were weak (0.11–0.49), but stronger for municipalities with lower socioeconomic scores. The model that best fitted the data was the random forest classifier (ROC AUC = 0.76; precision-recall AUC = 0.78). This innovative way of assessing the quality of death certification could help quality improvement initiatives to include the correctness of essential fields, in addition to garbage coding or completeness of records, especially in municipalities with lower socioeconomic status where garbage coding and the correctness of core topics appear to be related issues.

Suggested Citation

  • Guilherme Augusto Zimeo Morais & João Luiz Miraglia & Bruno Zoca de Oliveira & Sóstenes Mistro & Wilian Hiroshi Hisatugu & Djeniffer Greffin & Clément Bernardo Marques & Eduardo Pontes Reis & Hugo Mar, 2023. "Factors associated with the quality of death certification in Brazilian municipalities: A data-driven non-linear model," PLOS ONE, Public Library of Science, vol. 18(8), pages 1-16, August.
    • Handle: RePEc:plo:pone00:0290814
    DOI: 10.1371/journal.pone.0290814
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    References listed on IDEAS

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    1. Frederick Solt, 2020. "Measuring Income Inequality Across Countries and Over Time: The Standardized World Income Inequality Database," Social Science Quarterly, Southwestern Social Science Association, vol. 101(3), pages 1183-1199, May.
    2. Ligia Vizeu Barrozo & Michel Fornaciali & Carmen Diva Saldiva de André & Guilherme Augusto Zimeo Morais & Giselle Mansur & William Cabral-Miranda & Marina Jorge de Miranda & João Ricardo Sato & Edson , 2020. "GeoSES: A socioeconomic index for health and social research in Brazil," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-17, April.
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