Heterogeneous Effects of Decreasing the Cost-Sharing for Outpatient Care on Health Outcomes in China: A Propensity Score Matching and Causal Machine Learning Approach.

Background: To improve accessibility and financial support for outpatient services, China introduced a scheme to decrease cost-sharing for outpatient care under the Urban Employee Basic Medical Insurance. This study evaluates the health impacts of this policy and examines its heterogeneous effects.

Methods: Utilising data from the 2018 China Health and Retirement Longitudinal Study, we analysed 2896 individual-level observations across 105 prefectures. Propensity score matching and a causal forest model were applied to evaluate the effects on chronic disease status, body pain, self-rated health, and hospitalisation, while accounting for various demographic, socioeconomic, residential, health-related behaviours, and prefecture-specific factors.

Results: The reduction in cost-sharing was significantly linked to decreased probabilities of chronic disease (Average Treatment Effect (ATE) = -0.0619, p < 0.01), body pain (ATE = -0.0715, p < 0.05), and hospitalisation (ATE = -0.0592, p < 0.001), as well as improved self-rated health (ATE = 0.1557, p < 0.001). These benefits may be attributed to reduced out-of-pocket payments for outpatient care (ATE = -287.6112, p < 0.01) and increased outpatient visits (ATE = 0.0414 visits, p < 0.05). Causal forest analyses revealed that older individuals, those with higher educational attainment, higher household income, urban residents, and those engaging in healthier behaviours exhibited larger treatment effects.

Conclusions: Decreasing outpatient cost-sharing in China has beneficial health outcomes, with variations in its impact based on socio-economic status and health behaviours. It is advisable to further increase reimbursement rates and broaden benefit packages for outpatient care, while addressing the unequal distribution of benefits.