Pharmacy costs groups: a risk-adjuster for capitation payments based on the use of prescribed drugs.
BACKGROUND
Adequate risk-adjustment is critical to the success of market-oriented health care reforms in many countries. A common element of these reforms is that consumers may choose among competing health insurers, which are largely financed through premium-replacing capitation payments mostly based on demographic variables. These very crude health indicators do not reflect expected costs accurately.


OBJECTIVE
This study examines whether the demographic capitation model can be improved by incorporating information on the presence of chronic conditions deduced from the use of prescribed drugs. The revised Chronic Disease Score was used to incorporate this information in the model.


METHODS
A panel data set comprising annual costs and information on prescribed drugs for 3 successive years from Dutch sickness fund members of all ages, is used for the empirical analyses (N = 55,907). The predictive performance of the demographic model is compared with that of a chronic conditions and a Pharmacy Costs Groups (PCG) model, which is a demographic model extended with information on clustered chronic conditions.


RESULTS
The predictive accuracy of the demographic model substantially improved when the model was extended with dummy variables for chronic conditions. The 23 chronic conditions could be clustered into six PCGs without affecting the predictive accuracy. Based on these PCGs 17% of the members were bad risks with a mean predictable loss that exceeds the overall average expenditures.


CONCLUSIONS
The use of information on chronic conditions derived from claims for prescribed drugs is a promising option for improving the system of risk-adjusted capitation payments.
Lamers
L M
LM
Department of Health Policy and Management, Erasmus University Rotterdam, The Netherlands. Lamers@econ.bmg.eur.nl
D002204:Capitation Fee / Q000706:statistics & numerical data; D002908:Chronic Disease; D003361:Cost Allocation / Q000191:economics / Q000706:statistics & numerical data; D011307:Drug Prescriptions / Q000191:economics* / Q000706:statistics & numerical data; D004363:Drug Utilization / Q000191:economics*; D006801:Humans; D018803:Models, Economic; D009426:Netherlands; D010593:Pharmaceutical Services / Q000191:economics* / Q000706:statistics & numerical data; D020379:Risk Adjustment / Q000191:economics* / Q000379:methods / Q000706:statistics & numerical data