Pharmacoepidemiology and drug safety · 2021
Effect of ICD-9-CM to ICD-10-CM coding system transition on identification of common conditions: An interrupted time series analysis.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Hsu Meng-Chen, Wang Chi-Chuan, Huang Ling-Ya, Lin Chih-Ying, Lin Fang-Ju, Toh Sengwee
Аннотация
PURPOSE: To evaluate the effect of diagnostic coding system transition on the identification of common conditions recorded in Taiwan's national claims database.
METHODS: Using the National Health Insurance Research Database, we estimated the 3-month prevalence of recorded diagnosis of 32 conditions based on the ICD-9-CM codes in 2014-2015 and the ICD-10-CM codes in 2016-2017. Two algorithms were assessed for ICD-10-CM: validated ICD-10 codes in the literature and codes translated from ICD-9-CM using an established mapping algorithm. We used segmented regression analysis on time-series data to examine changes in the 3-month prevalence (both level and trend) before and after the ICD-10-CM implementation.
RESULTS: Significant changes in the level were found in 19 and 11 conditions when using the ICD-10 codes from the literature and mapping algorithm, respectively. The conditions with inconsistent levels by both of the algorithms were valvular heart disease, peripheral vascular disease, mild liver disease, moderate to severe liver disease, metastatic cancer, rheumatoid arthritis and collagen vascular diseases, coagulopathy, blood loss anemia, deficiency anemia, alcohol abuse, and psychosis. Nine conditions had significant changes in the trend when using the ICD-10 codes from the literature or mapping algorithm.
CONCLUSIONS: Less than half of the 32 conditions studied had a smooth transition between the ICD-9-CM and ICD-10-CM coding systems. Researchers should pay attention to the conditions where the coding definitions result in inconsistent time series estimates.
METHODS: Using the National Health Insurance Research Database, we estimated the 3-month prevalence of recorded diagnosis of 32 conditions based on the ICD-9-CM codes in 2014-2015 and the ICD-10-CM codes in 2016-2017. Two algorithms were assessed for ICD-10-CM: validated ICD-10 codes in the literature and codes translated from ICD-9-CM using an established mapping algorithm. We used segmented regression analysis on time-series data to examine changes in the 3-month prevalence (both level and trend) before and after the ICD-10-CM implementation.
RESULTS: Significant changes in the level were found in 19 and 11 conditions when using the ICD-10 codes from the literature and mapping algorithm, respectively. The conditions with inconsistent levels by both of the algorithms were valvular heart disease, peripheral vascular disease, mild liver disease, moderate to severe liver disease, metastatic cancer, rheumatoid arthritis and collagen vascular diseases, coagulopathy, blood loss anemia, deficiency anemia, alcohol abuse, and psychosis. Nine conditions had significant changes in the trend when using the ICD-10 codes from the literature or mapping algorithm.
CONCLUSIONS: Less than half of the 32 conditions studied had a smooth transition between the ICD-9-CM and ICD-10-CM coding systems. Researchers should pay attention to the conditions where the coding definitions result in inconsistent time series estimates.