Methodological proposal for the redistribution of deaths due to garbage codes in mortality estimates for Noncommunicable Chronic Diseases

• Autor(a) principal: Teixeira , Renato Azeredo
• Data de Publicação: 2020
• Outros Autores: Ishitani , Lenice Harumi, Marinho , Fátima, Junior, Elzo Pereira Pinto, Katikireddi , Srinivasa Vittal, Malta, Deborah Carvalho
• Tipo de documento: preprint
• Idioma: por
• Título da fonte: SciELO Preprints
• Texto Completo: https://preprints.scielo.org/index.php/scielo/preprint/view/1611
• topic: doenças não transmissíveis; qualidade dos dados de mortalidade; garbage codes; pequenas áreas; noncommunicable diseases; quality of data mortality; garbage codes; small areas

Objective: to propose a method for improving mortality estimates from non-communicable chronic diseases (NCD), including the redistribution of garbage causes in the municipalities of Brazil. Methods: Information Mortality System (SIM) data was used in the three-year periods from 2010 to 2012 and 2015 to 2017, with comparison of age standardized rates before and after correction of NCDs (cardiovascular, chronic respiratory, diabetes and neoplasms). The treatment for data correction included missing data, under-registration and causes of garbage redistribution (CG). The trienniums and Bayesian method were used to estimate mortality rates by improving the fluctuation caused by small numbers at the municipal level. Results: The CG redistribution stage showed greater weight in the corrections, about 40% in 2000 and about 20% from 2007, with stabilization from this year.. Throughout the historical series, the quality of information on causes of death has improved in Brazil, with heterogeneous results being observed among the municipalities. Conclusions: methodological studies that propose the correction and improvement of the SIM are essential for monitoring the mortality rates due to NCDs at regional levels. The methodological proposal applied, for the first time in real data from Brazilian municipalities, is challenging and deserves further improvements. Despite the improvement in the data, the use of rates with raw data is not recommended, as the treatment in the data, the method used in this study for the treatment of raw data showed a great impact on the final estimates.