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

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.

The study of the mother-child binomium: description and general results.

OBJECTIVE: The present study investigated the characteristics of women during pregnancy/immediate postpartum cycles and the product of their pregnancy. METHOD: Data collection was conducted for a period of three months in 2011, in six maternity hospitals in the State of São Paulo. The data were obtained in an interview with the women after the end of the pregnancy and collected from hospital records. The sample included 7,058 women hospitalized for abortion or childbirth in five hospitals from SUS (Unified Health System) and from only one hospital for private health insurance patients and their 6,602 conceptuses (live births and stillborns). Statistical analysis was based on χ2 tests, with a significance level of α = 5%. RESULTS: It was observed that 6,530 women gave birth and 528 showed interruption of pregnancy. Regarding age, 1,448 were teenagers (20.5%). There were no deaths during hospitalization and 99.8% of women received a medical discharge. Maternal morbidity in the current pregnancy was analyzed, showing urinary tract infection, anemia and excessive vomiting as the main problems. The rate of cesarean sections accounted for 31.1% and complications in childbirth and postpartum were shown, respectively, by 834 (12.8%) and 265 (4.1%) women. The characteristics of the conceptuses were also studied: gestation length (9.3% of preterm among live births, and 68% among the stillborn); birth weight (underweight in 8.2% among live births, and 66% among the stillborn) and morbidity, measured by congenital anomalies and other diseases; these diseases were responsible for ICU stay, transfers to better-equipped hospitals (10 cases) and 37 deaths. Thirteen live births were still hospitalized at the end of the investigation.