A Study on Garbage Code Redistribution Methods for Heart Failure at City Level by Two Approaches

Background Garbage code (GC) in death surveillance data may affect the statistics on causes of death. The Global Burden of Disease (GBD) studies have proposed several methods to redistribute GCs to the plausible correct underlying cause of death (UCOD). Take heart failure as an example, this study aimed to explore suitable GC redistribution methods at the city level. Methods The cause of death surveillance data was from Weifang city, from 2010 to 2017, and Xuanwei county, from 2010 to 2016, China. Firstly, the death records of heart failure were corrected to UCOD based on the World Health Organization (WHO) guidelines. Secondly, the records with UCOD remaining to be heart failure were proceeded by coarsened exact matching (CEM) and linear regression (LR), respectively. Finally, the change of cause-specific mortalities before and after redistributed by two methods was calculated, respectively. Results The UCOD stated as heart failure was 1556 (0.33%) in Weifang and 226 (0.41%) in Xuanwei, respectively. After redistribution based on the WHO guideline, around 75% of the UCOD records in both cities remained the same. In Weifang, by CEM, heart failure was mainly redistributed to ischemic heart disease (IHD, 45.31%) and hypertensive heart disease (HHD, 21.56%). By LR, 91.20% of heart failure was redistributed to IHD. In Xuanwei, by CEM, heart failure was mainly redistributed to IHD (24.70%), diabetes mellitus and chronic kidney disease (DMCKD 23.25%). By LR, 94.83% of heart failure was redistributed to chronic obstructive pulmonary disease (COPD). Conclusions During conducting GC redistribution, careful consideration of which method to choose is necessary, especially for the city-level data. In this study, the CEM approach might probably be more suitable for the city level, compared to LR.