使用政策学习来告知医疗保险目标:印度尼西亚的案例研究
Vishalie Shah1, Andrew M Jones2, Ivana Malenica3
1Centre for Health Economics, University of York, York, UK.
最佳的政策学习可以改善印度尼西亚的医疗保险分配. 机器学习为区域 (APBD) 和国家 (APBN) 计划确定了更好的福利分配,减少了家庭灾难性的医疗支出.
科学领域:
- 卫生经济学 卫生经济学
- 公共政策 公共政策
- 机器学习 机器学习
背景情况:
- 印度尼西亚运营着两个补贴医疗保险计划:APBD (区域) 和APBN (国家).
- 有针对性的拨款对于最大限度地减少招生人群的灾难性医疗支出至关重要.
- 现有的分配策略可能不适合各种家庭需求.
研究的目的:
- 制定最佳的政策规则,用于分配注册人员到印度尼西亚的APBD和APBN医疗保险计划.
- 通过使用数据驱动的方法,尽量减少灾难性的医疗支出.
- 探索预算限制对最佳政策拨款的影响.
主要方法:
- 利用国家调查数据进行分析.
- 采用超级学习者整体方法,将回归和机器学习结合起来.
- 估计的条件平均治疗效应,以告知政策规则.
主要成果:
- 根据家庭特征 (人口统计,社会经济状况,地理位置) APBD与APBN入学的财务影响有很大差异.
- 最佳的政策规则将城市家庭分配给APBD,农村家庭分配给APBN,这种模式在预算限制下得到了放大.
- 两种最佳分配策略 (受约束和不受约束) 与当前方法相比降低了预期的灾难性支出风险.
结论:
- 最佳政策学习为公共医疗保险中公平资源分配提供了数据驱动的解决方案.
- 机器学习可以有效地识别针对性卫生政策的异质治疗效应.
- 调查结果为发展中国家更有效的医疗融资策略提供了信息.
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