A pilot study on the drug price revision strategy in Japan: A comparison among fiscal years 2018, 2020, and 2022
Cite this dataset
Nakagawa, Naoto et al. (2024). A pilot study on the drug price revision strategy in Japan: A comparison among fiscal years 2018, 2020, and 2022 [Dataset]. Dryad. https://doi.org/10.5061/dryad.q573n5tnf
Abstract
Objective: Japan has resumed its health technology assessment to decide how to reduce high-cost drug prices. While drug price rules in Japan are comprehensive, they do not necessarily capture differences in product characteristics. This study examined the drug price revision strategy in Japan using migraine treatment with triptans as an example. Cost data from fiscal years (FY) 2018, 2020, and 2022 were utilized.
Methods: A cost-utility analysis was conducted from the perspective of healthcare payers, focusing on Japanese patients aged over 18 years experiencing migraines. The study employed a base-case model with probabilities derived from a network meta-analysis. Direct costs included medical and drug costs. Effectiveness was assessed using the European Quality of Life five-dimensions—3-level questionnaire. Deterministic and probabilistic sensitivity analyses were conducted to examine the level of uncertainty.
Results: In FY2018, sumatriptan and eletriptan were cost-effective; however, the other triptans were dominated by sumatriptan. In FY2020, sumatriptan and eletriptan were cost-effective, and rizatriptan was extended-dominated; nevertheless, the other triptans were dominated by sumatriptan. In FY2022, naratriptan and eletriptan were cost-effective; however, the other triptans were dominated by naratriptan. The hierarchy of triptan strategies varied in each fiscal year.
Conclusions: This study provides valuable insights into the drug price revision strategy in Japan. The variations could be problematic because in Japan, formulary management of triptans, for example, those for migraine, may face revaluation every other year. Discussions regarding this issue will be further explored in the future.
README: A pilot study on the drug price revision strategy in Japan: A comparison among fiscal years 2018, 2020, and 2022
Author(s)
Naoto Nakagawa, Phar.D., Ph.D.
School of Pharmaceutical Sciences, Ohu University, Koriyama, Fukushima, Japan
31-1 Misumido, Tomita-machi, Koriyama, Fukushima 963-8611, Japan
Email: n-nakagawa@pha.ohu-u.ac.jp
Mizuha Konno, BS, RPh
School of Pharmaceutical Sciences, Ohu University, Koriyama, Fukushima, Japan
31-1 Misumido, Tomita-machi, Koriyama, Fukushima 963-8611, Japan
Email: 715036@ohu-u.jp
Masami Kashiwabara, B.S., R. Ph,
QOL pharmacy
Email: m-kashiwabara@qol-net.co.jp
Shinya Shimoji, PhD
Shimoji Neurology Clinic
Email: shimojishinya@icloud.com
Jun Mochimaru, BS, RPh
QOL pharmacy
Email: j-mochimaru@qol-net.co.jp
Tadao Inoue, Ph.D.
Yamagata University Faculty of Medicine
Email: tadaino7151@gmail.com
Leanne Lai, Ph.D.
College of Pharmacy, Kaohsiung Medical University, Kaohsiung City, Taiwan
Email: LL33317@gmail.com
File list
Input data for FY2018
Input data for FY2020
Input data for FY2022
Input data for probabilistic sensitivity analysis for FY2018
Input data for probabilistic sensitivity analysis for FY2020
Input data for probabilistic sensitivity analysis for FY2022
File descriptions
Details for: Input data for FY2018
The file is a table to analyze for input with TreeAge Pro.\
Format(s): .csv
Dimensions: 24 rows x 5 columns
Variables:
- Name: c_XXX means cost variables (unit is 'yen'), p_XXX means probability variables, and u_XXX means utility variables (unit is 'QALY').
- Description: definition of each variable.
- Root Definition: individual variables for a base-case scenario.
- Low and High columns: the low and the high numbers for one-way sensitivity analysis.
Details for: Input data for FY2020
The file is a table to analyze for input with TreeAge Pro.
Format(s): .csv
Dimensions: 24 rows x 5 columns
Variables:
- Name: c_XXX means cost variables (unit is 'yen'), p_XXX means probability variables, and u_XXX means utility variables (unit is 'QALY').
- Description: definition of each variable.
- Root Definition: individual variables for a base-case scenario.
- Low and High columns: the low and the high numbers for one-way sensitivity analysis.
Details for: Input data for FY2022
The file is a table to analyze for input with TreeAge Pro.
Format(s): .csv
Dimensions: 24 rows x 5 columns
Variables:
- Name: c_XXX means cost variables (unit is 'yen'), p_XXX means probability variables, and u_XXX means utility variables (unit is 'QALY').
- Description: definition of each variable.
- Root Definition: individual variables for a base-case scenario.
- Low and High columns: the low and the high numbers for one-way sensitivity analysis.
Details for: Input data for probabilistic sensitivity analysis for FY2018
This file is a table to analyze to input for probabilistic sensitivity analysis with TreeAge Pro.
Format(s): .csv
Dimensions: 19 rows x 4 columns
Variables:
- Type_Distribution: data distribution. Beta shows that a value is from 0 to 1.
- Name: d_XXX means distribution variables.
- Param 1: alpha for beta distribution parameter
- Param 2: beta for beta distribution parameter
Details for: Input data for probabilistic sensitivity analysis for FY2020
This file is a table to analyze to input for probabilistic sensitivity analysis with TreeAge Pro.
Format(s): .csv
Dimensions: 19 rows x 4 columns
Variables:
- Type_Distribution: data distribution. Beta shows that a value is from 0 to 1.
- Name: d_XXX means distribution variables.
- Param 1: alpha for beta distribution parameter
- Param 2: beta for beta distribution parameter
Details for: Input data for probabilistic sensitivity analysis for FY2022
This file is a table to analyze to input for probabilistic sensitivity analysis with TreeAge Pro.
Format(s): .csv
Dimensions: 19 rows x 4 columns
Variables:
- Type_Distribution: data distribution. Beta shows that a value is from 0 to 1.
- Name: d_XXX means distribution variables.
- Param 1: alpha for beta distribution parameter
- Param 2: beta for beta distribution parameter
Version changes
August 2024: The author descriptions in the Descrbe database were added because the author descriptions in the Descrbe database and README files have been unified.
Feb 2024: These files have been updated because our previous CEA manuscript had not been accepted yet. Therefore, we have reanalysed in terms of different viewpoints.