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

Author

Listed:
  • Tao Zhang
  • Meiteng Yu
  • Yang Song
  • Jing Liu

Abstract

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

Suggested Citation

  • Tao Zhang & Meiteng Yu & Yang Song & Jing Liu, 2025. "Heterogeneous Effects of Decreasing the Cost‐Sharing for Outpatient Care on Health Outcomes in China: A Propensity Score Matching and Causal Machine Learning Approach," International Journal of Health Planning and Management, Wiley Blackwell, vol. 40(5), pages 1058-1068, September.
    • Handle: RePEc:bla:ijhplm:v:40:y:2025:i:5:p:1058-1068
    DOI: 10.1002/hpm.3938
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    References listed on IDEAS

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