Real Picture of Patients with Charcot-Marie-Tooth Disease in Japan: IQVIA Claims Database

Author(s)

Kado Y1, Tamai Y2, Kim SW3, Demiya S2
1IQVIA Solutions Japan, Shinagawa, 13, Japan, 2IQVIA Solutions Japan, Minato-ku, Tokyo, Japan, 3IQVIA Solutions Japan, Minato, 13, Japan

Objectives: The objective of the study is to clarify the real picture of patients with Charcot-Marie-Tooth Disease (CMT disease) using IQVIA Claims database.

Methods: This study analyzed IQVIA Claims database in October 2021. IQVIA Claims database contains the payer claims data of the health insurance union for Japanese workers and their families and their data of annual health checkup. It includes 4.88 million unique subjects in September 2021. This study examined sex, age, comorbidities, treatments, and health checkup data among patients with CMT disease.

Results: 149 patients were identified to have diagnosis of CMT disease (ICD10: G600, disease code: 8834519). Out of 149 patients, 75 patients (50.3 %) were men, and 74 patients (49.7%) were women. 38 patients (25.5%) were 0 - 19 years old, 46 patients (30.9%) were 20 - 39 years old, 46 patients (30.9%) were 40 - 59 years old, and 9 patients (6.0%) were 60 years old or older. 52 patients (34.9%) had peripheral nervous disorders as comorbidities, 9 patients (6.0%) had a gait disturbance, 5 patients (3.4%) had lower back pain, and 3 patients (2.0%) had constipation. The mean height (m), weight (kg), and BMI were 1.67, 65.2, and 23.3 in men, and 1.59, 52.8, and 20.9 in women, respectively. Only 8.2% of patients had a habit of physical exercise for more than two times per week. Detailed results will be shown in the meeting in the ISPOR 2022 Conference.

Conclusion: This study using IQVIA Claims database could provide baseline characteristics to understand patients with rare diseases including CMT disease.

Conference/Value in Health Info

2022-05, ISPOR 2022, Washington, DC, USA

Value in Health, Volume 25, Issue 6, S1 (June 2022)

Code

EPH173

Topic

Epidemiology & Public Health, Real World Data & Information Systems, Study Approaches

Topic Subcategory

Disease Classification & Coding, Health & Insurance Records Systems

Disease

Musculoskeletal Disorders

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