Organisation for Economic Co-operation and Development, 2, Rue André Pascal, 75775 Paris Cedex 16, France.
Health Policy. 2012 Sep;107(1):1-10. doi: 10.1016/j.healthpol.2012.05.001. Epub 2012 Jun 8.
Concerns about health care expenditure growth and its long-term sustainability have risen to the top of the policy agenda in many OECD countries. As continued growth in spending places pressure on government budgets, health services provision and patients' personal finances, policy makers have launched forecasting projects to support policy planning. This comparative analysis reviewed 25 models that were developed for policy analysis in OECD countries by governments, research agencies, academics and international organisations.
We observed that the policy questions that need to be addressed drive the choice of forecasting model and the model's specification. By considering both the level of aggregation of the units analysed and the level of detail of health expenditure to be projected, we identified three classes of models: micro, component-based, and macro. Virtually all models account for demographic shifts in the population, while two important influences on health expenditure growth that are the least understood include technological innovation and health-seeking behaviour.
The landscape for health forecasting models is dynamic and evolving. Advances in computing technology and increases in data granularity are opening up new possibilities for the generation of system of models which become an on-going decision support tool capable of adapting to new questions as they arise.
在许多经合组织国家,对医疗保健支出增长及其长期可持续性的担忧已成为政策议程的首要议题。随着支出的持续增长给政府预算、医疗服务提供和患者个人财务带来压力,政策制定者已经启动了预测项目来支持政策规划。本项比较分析审查了 25 个模型,这些模型是由政府、研究机构、学术界和国际组织为经合组织国家的政策分析而开发的。
我们观察到,需要解决的政策问题推动了预测模型的选择和模型的具体说明。通过同时考虑分析单位的聚合水平和要预测的医疗支出的详细程度,我们确定了三类模型:微观、基于组件和宏观。几乎所有模型都考虑了人口的人口结构变化,而对医疗支出增长影响最小的两个重要因素,包括技术创新和寻求医疗服务的行为。
卫生预测模型的格局是动态和不断发展的。计算技术的进步和数据粒度的增加为生成系统模型开辟了新的可能性,这些模型成为一个持续的决策支持工具,能够适应新出现的问题。