A comparison of disease-specific medical expenditures in Japan using the principal diagnosis method and the

Shinichi Tanihara1, Etsuji Okamoto, Hiroshi Une

  • 1Department of Hygiene and Preventive Medicine, School of Medicine, Fukuoka University, Jonan-ku, Fukuoka, Japan. taniyan@cis.fukuoka-u.ac.jp

Insights

Conventional health insurance claims analysis overestimates medical spending for principal diagnoses. A new proportional distribution method (PDM) using all diagnoses provides more accurate disease-specific medical expenditure estimates.

Area of Science:

  • Health economics
  • Medical informatics
  • Public health

Background:

  • Technical limitations in Japanese health insurance claims (HICs) hinder comprehensive data analysis.
  • Current methods for disease-specific medical expenditure estimation often rely on a single principal diagnosis per HIC, even with multiple diagnoses present.

Purpose of the Study:

  • To develop and evaluate a method for estimating disease-specific medical expenditures that accounts for all diagnoses within a health insurance claim.

Main Methods:

  • Utilized 169,622 outpatient HICs from employer-provided health insurance in May 2006.
  • Compared the conventional single-principal-diagnosis method with a proportional distribution method (PDM) that incorporates all diagnoses.

Main Results:

  • The PDM revealed that principal diagnoses accounted for 52.4% of diabetes mellitus and 19.6% of digestive system diseases.
  • Estimated medical expenditures for diabetes mellitus were 1.49 times higher with the conventional method compared to PDM.
  • Estimated expenditures for other digestive system diseases were 0.64 times with the conventional method compared to PDM, indicating potential over- and underestimation by the conventional approach.

Conclusions:

  • The conventional method for estimating disease-specific medical expenditures requires improvement.
  • Incorporating all diagnostic information from HICs using methods like PDM is crucial for accurate expenditure analysis.
Abstract

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