OBJECTIVES

The comorbidities in a patient can complicate treatment planning and affect health outcomes. The Charlson Comorbidity Index (CCI) are widely used to study comorbid conditions but there are no demographics representing inpatients in Japan. In this study, our objective was to validate and recalibrate CCI in a Japanese insurance claims database (IQVIA claims).

METHODS

This observational study used the IQVIA Claims database which integrated the payer claims data of the health insurance union for Japanese workers. The latest hospitalizations for patients aged≥20 years discharged between April 2013 and November 2019 with minimum three months of insurance period from the last admission month were allocated to cohort. Pregnancy or maternity were excluded.

From the cohort, patient background and proportion of each category of comorbidities included in the CCI were analyzed. In the sub-group analyses, characteristics for age, look-back period, and other aspects were also investigated.

RESULTS

From 210,544 of target patients, comorbidities for all populations were shown as follows; Myocardial infarction (1.9%), Congestive heart failure (7.0%), Peripheral vascular disorders (4.4%), Cerebrovascular disease (6.9%), Dementia (0.2%), Chronic pulmonary disorders (12.5%), Rheumatoid arthritis (2.2%), Peptic ulcer disease (13.7%), Mild liver disease (10.5%), Diabetes uncomplicated (3.9%), Diabetes complicated (3.7%), Hemiplegia or paraplegia (0.7%), Renal disease (1.9%), Any malignancy (16.1%), Moderate or severe liver disease (0.5%), Metastatic solid tumor (3.5%), AIDS／HIV (0.1%). Overall these results indicated similar proportions of patient characteristics comparing to previous Japanese study. The detailed result will be presented in the meeting.

CONCLUSIONS

With understanding the patient characteristics, this study is prospected to be applied for general and specific disease can be performed in various disease-related studies. The results of this study can represent the background of Japanese worker inpatient background and both can be used in future studies using health insurance claims databases.