Assessing the Quality of Mortality Data in Zunyi, China: A Comparative Study of Garbage Coding Before and After Intervention

Abstract

BACKGROUND: Accurate mortality data are crucial for understanding mortality patterns, informing public health strategies, and evaluating national health programs. In 2022 and 2023, the Centers for Disease Control and Prevention in Zunyi, China, provided specialized training to staff responsible for cause-of-death surveillance. METHODS: This study evaluated the quality of cause-of-death data reported by healthcare organizations in Zunyi city before and after the intervention, with a focus on the classification and extent of garbage codes. By comparing the distributions of various causes of death and their changes over the two years, we analyzed the differences and distribution patterns of garbage codes. The study participants were grouped by age and sex. RESULTS: The cause-of-death data from Zunyi demonstrated good completeness over the two-year period. The proportion of definite causes of death increased significantly from 87.5% to 94.8%, whereas the proportion of unusable causes decreased notably, from 7.32% to 2.87%. Similarly, the proportion of garbage codes relative to total deaths decreased from 12.60% to 5.20%, with significant reductions in categories 3 and 5. The major garbage codes in both years exhibited a positively skewed distribution, which was primarily associated with aging and cardiovascular diseases. The proportion of garbage codes decreased across both the male and the female groups over the age of 65. CONCLUSION: This study offers a cost-effective approach to improve the quality of cause-of-death data through a junk code-based assessment method. By implementing these measures, the accuracy and utility of cause-of-death data can be greatly enhanced.