目標：全民健保次級資料已成為醫療服務研究重要之資料來源，如何適當測量病人共病(合併症；comorbidity)情形，為一重要議題，然國內尚未有研究針對不同學者發展之共病測量方法及其適用情況進行實證探討。方法：採回溯性世代研究，選取5種共病方法進行比較，包括Deyo等、Romano等(D-M’s)、D'Hoore等三種版本之Charlson Comorbidity Index (CCI)、以次級資料發展之Elixhauser、及以藥物處方情形發展之Chronic Disease Score。以2002年因慢性腎臟疾病、肺炎住院之病人為對象，比較不同方法預測院內及住院一年內死亡情形之差異。共病
採用「類別」及「權重」二種分析方式。判斷共病之資料期間或來源，包括「當次住院」、「當次併前一年住院」、「當次併前一年住院及門診」三種。以邏輯斯迴歸之c統計量比較各方法之相對表現。結果：類別模式下，以D-M’s CCI及Elixhauser方法表現較佳。權重模式時，以D-M’s CCI表現較佳。對CCI方法，以當次併前一年住院資料表現最佳。以類別模式之Elixhauser預測院內死亡情形，僅以當次住院資料表現最佳。增加住院前門診就醫資料則無法改進預測力。結論：三種版本之CCI方法中，以D-M’s CCI表現較佳，建議未來研究者可選用此方法，並使用「當次併前一年住院」資料。當研究樣本數夠大，可採類別變項之D-M’s CCI或Elixhauser方法。使用Elixhauser預測院內死亡情形時，建議可僅使用「當次住院」資料。當研究樣本數小採計算權重分數時，建議使用D-M’s CCI。

Objectives: National Health Insurance databases have become an important resource for studies in health services research. It is important to measure comorbidity; however, there has been no study to investigate the performance of the various available claims-based measures of comorbidity in Taiwan. Methods: Five different measures of comorbidity including Deyo’s Charlson Comorbidity Index (CCI), D-M’s CCI, D’Hoore’s CCI, Elixhauser and Chronic Disease Score (CDS), were chosen for investigation in this retrospective cohort study. We compared the performance of the five measures of comorbidity in predicting in-hospital and one-year mortality among patients with chronic renal disease and pneumonia. The measures of comorbidity were implemented as individual components (the presence or absence of the comorbid condition), and also as an index (weighted sum of comorbidity indicators). The measures of comorbidity were
created based on 3 sources of data: the index hospitalization, the index and prior hospitalizations, and the index and prior hospitalizations as well as outpatient visits. The c-statistics of logistic regression were used to compare performance. Results: Better discrimination was achieved with the D-M’s CCI or the Elixhauser method when using individual components. When measures of comorbidity were used as indices, better discrimination was achieved with the D-M’s CCI. For CCI methods, patient information available from both the index and prior hospitalizations
yielded the best results. For the Elixhauser method, patient information available from the index hospitalization yielded the best results when predicting in-hospital death. Adding prior outpatient data did not improve the performance of the measures. Conclusions: D-M’s CCI performed
best, and future investigators might consider this method. When the sample size is large enough, D-M’s CCI or the Elixhauser method could be implemented as the individual components. If the sample size is small, then D-M’s CCI used as an index and information from the index and prior
hospitalizations would be more appropriate.