Lamers L M
Department of Health Policy and Management, Erasmus University Rotterdam, The Netherlands.
Med Care. 1999 Aug;37(8):824-30. doi: 10.1097/00005650-199908000-00012.
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.
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.
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.
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.
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.
充分的风险调整对于许多国家以市场为导向的医疗改革的成功至关重要。这些改革的一个共同要素是消费者可以在相互竞争的健康保险公司之间进行选择，这些公司主要通过保费替代人头费来融资，而人头费大多基于人口统计学变量。这些非常粗略的健康指标并不能准确反映预期成本。
本研究探讨是否可以通过纳入从处方药使用情况推断出的慢性病信息来改进人口统计学人头费模型。使用修订后的慢性病评分将此信息纳入模型。
使用一个面板数据集进行实证分析，该数据集包含来自荷兰所有年龄段疾病基金成员连续三年的年度成本和处方药信息（N = 55,907）。将人口统计学模型的预测性能与慢性病模型和药房成本组（PCG）模型进行比较，PCG模型是一个扩展了聚类慢性病信息的人口统计学模型。
当模型通过慢性病虚拟变量进行扩展时，人口统计学模型的预测准确性有了显著提高。23种慢性病可以聚类为六个PCG，而不会影响预测准确性。基于这些PCG，17%的成员属于高风险，其平均可预测损失超过总体平均支出。
利用从处方药索赔中得出的慢性病信息是改进风险调整人头费支付系统的一个有前景的选择。
