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: 2021
• Outros Autores: Ishitani,Lenice Harumi, Marinho,Fátima, Pinto Junior,Elzo Pereira, Katikireddi,Srinivasa Vittal, Malta,Deborah Carvalho
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Revista brasileira de epidemiologia (Online)
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1415-790X2021000200419
• topic: Noncommunicable diseases; Data accuracy; Cause of death; Small-area analysis

ABSTRACT: Objective: To propose a method for improving mortality estimates from noncommunicable diseases (NCD), including the redistribution of garbage codes in Brazilian municipalities. Methods: Brazilian Mortality Information System (MIS) was used as a data source to estimate age standardized mortality rates, before and after correction, for NCD (cardiovascular, chronic respiratory, diabetes, and neoplasms). The treatment for the correction of data addressed missing data, under-registration, and redistribution of garbage codes (GCs). Three-year periods 2010–2012 and 2015–2017, and the Bayesian method were used to estimate mortality rates, reducing the effect of fluctuation caused by small numbers at the municipal level. Results: GCs redistribution step showed greater weight in corrections, about 40% in 2000 and roughly 20% as from 2007, with stabilization starting in this year. Throughout the historical series, the quality of information on causes of death has improved in Brazil, with heterogeneous results being observed among municipalities. Conclusion: Methodological studies that propose correction and improvement of the MIS are essential for monitoring mortality rates due to NCD at regional levels. The methodological proposal applied, for the first time in real data from Brazilian municipalities, is challenging and deserves further improvements. Improving the quality of the data is essential in order to build more accurate estimates based on the raw data from the SIM.