Effect of integrated urban and rural residents medical insurance on the utilisation of medical services by residents in China: a propensity score matching with difference-in-differences regression approach

BMJ Open. 2019 Feb 19;9(2):e026408. doi: 10.1136/bmjopen-2018-026408.

Abstract

Objectives: In this study, we aim to evaluate the effect of urban and rural resident medical insurance scheme (URRMI) on the utilisation of medical services by urban and rural residents in the four pilot provinces.

Setting and participants: The sample used in this study is 13 305 individuals, including 2620 in the treatment group and 10 685 in the control group, from the 2011 and 2015 surveys of China Health and Retirement Longitudinal Study.

Outcome measures: Propensity score matching and difference-in-differences regression approach (PSM-DID) is used in the study. First, we match the baseline data by using kernel matching. Then, the average treatment effect of the four outcome variables are analysed by using the DID model. Finally, the robustness of the PSM-DID estimation is tested by simple model and radius matching.

Results: Kernel matching have improved the overall balance after matching. The URRMI policy has significantly reduced the need-but-not outpatient care and significantly increased outpatient care cost and inpatient care cost for rural residents, with DID value of -0.271, 0.090 and 0.256, respectively. After robustness test, the DID competing results of four outcome variables are consistent.

Conclusions: URRMI has a limited effect on the utilisation of medical and health services by all residents, but the effect on rural residents is obvious. The government should establish a unified or income-matching payment standard to prevent, control the use of medical insurance funds and increase its efforts to implement URRMI integration in more regions to improve overall fundraising levels.

Keywords: China; difference-in-differences regression; propensity score matching; urban and rural residents medical insurance; utilisation of medical services.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Aged
  • China
  • Female
  • Health Services / statistics & numerical data*
  • Healthcare Disparities / statistics & numerical data
  • Hospitalization
  • Humans
  • Inpatients
  • Insurance, Health / economics
  • Insurance, Health / statistics & numerical data*
  • Logistic Models
  • Longitudinal Studies
  • Male
  • Middle Aged
  • Propensity Score
  • Rural Population / statistics & numerical data*
  • Urban Population / statistics & numerical data*