[Estimation of disease-specific costs in a dataset of health insurance claims and its validation using simulation data].
OBJECTIVE
To estimate disease-specific costs in a dataset of health insurance claims with multiple diagnoses with known aggregate cost per claim and unknown disease-specific cost of each diagnosis using PDM (Proportional Disease Magnitude) method, validate its accuracy using simulation data with Monte Carlo method and improve its accuracy by developing an adjustment formula.


METHODS
Developed simulation data with pre-assigned disease-specific costs, applied PDM method using arithmetic means of per-diem-per-disease cost as magnitude, validated its accuracy by observing the correlation between estimates by PDM method and known disease-specific costs and formulated an adjustment formula to improve accuracy. The reproducibility of the findings was assessed using Monte Carlo method by repeating the same procedures.


RESULTS
The observed arithmetic means of per-diem-per-disease cost did not match well with actual values resulting in unsatisfactory accuracy. However, when the observed means were adjusted with a formula in which the observed mean is multiplied by (observed mean/overall mean) in the power of 2, PDM method yielded an accurate estimate of disease-specific cost. The accuracy was reproduced by Monte Carlo method with 0.9 or above R square value and slope of regression line in 76, 56 out of 100 iterations respectively.


CONCLUSIONS
PDM method proved to be an objective, reproducible and accurate method for estimation of disease-specific costs of health insurance claims.
Okamoto
Etsuji
E
Hata
Eiichi
E
D007344:Insurance Claim Reporting / Q000706:statistics & numerical data*; D007564:Japan; D009010:Monte Carlo Method; D009313:National Health Programs / Q000191:economics*