2012年-世界发展银行全球_The_Impact_of_RSBY_on_Hospital_Utilization_and_Out-of-Pocket_Health_Expenditure_33页_1mb

Summary of "The Impact of RSBY on Hospital Utilization and Out-of-Pocket Health Expenditure"

Core Content

This study examines the impact of the Rashtriya Swasthya Bima Yojana (RSBY), a government subsidized health insurance scheme in India, on hospital utilization and out-of-pocket (OOP) health expenditure among Below Poverty Line (BPL) households. The research uses a difference-in-differences (DiD) approach with matching to estimate the effects of RSBY in its early implementation phase (2008–2010) using data from the National Sample Survey Organization (NSSO) rounds 61 (2004–05) and 66 (2009–10).

Main Findings

• RSBY Overview: Launched in 2008, RSBY provides cashless coverage of up to Rs. 30,000 ($600) per year for hospitalization, but does not cover outpatient procedures, diagnostic tests, or pre/post-operative care. It is available to BPL households, who must pay Rs. 30 annually for coverage. The scheme is implemented through public-private partnerships at the district level.

• Impact on Healthcare Expenditure: The study finds a small decrease in OOP outpatient and total medical expenditure for target households. This suggests that RSBY may have reduced financial barriers to accessing healthcare services, particularly inpatient care.

• Impact on Hospital Utilization: There is limited evidence of an increase in hospitalization rates among BPL households, with regional variations in the nature of the impact. The results are not conclusive on whether the reduction in outpatient spending is due to decreased need for outpatient care or due to hospitals converting outpatient treatments to inpatient to benefit from the scheme.

• Methodological Approach: The study uses difference-in-differences with coarsened exact matching to estimate the causal effect of RSBY. It also explores treatment intensity, i.e., the length of time a household has been covered by RSBY before the survey, and its effect on outcomes.

• Control Group Selection: Control districts are divided into two categories:

  • Control 1: Districts where RSBY was planned but not launched during the survey.
  • Control 2: Districts where RSBY was not planned at all.
  • The study matches treatment and control districts based on district-level covariates to ensure comparability.

• Sample Size: The study includes 297 control districts and 204 treatment districts, with a total of 186,065 households. Of these, 102,810 are from the pre-intervention round and 83,255 from the post-intervention round.

• Results After Matching: After matching, the sample is reduced to 376 districts. The matching process aligns the means of household covariates across treatment and control groups, improving the reliability of the estimates.

Key Information

• RSBY Coverage: As of February 2012, 27 million households in 24 states were enrolled, with 3.1 million hospitalization cases covered.
• OOP Spending in India: India has one of the highest OOP health spending rates globally, with 78% of total health spending and 94% of private health spending being out-of-pocket.
• Catastrophic Spending: Hospitalization expenses can push 24% of Indians below the poverty line, highlighting the financial burden of inpatient care.
• Matching Method: The study uses coarsened exact matching (Iacus, King, and Porro, 2011), which groups continuous variables into categories and matches based on these coarsened values. This method is considered more reliable than propensity score matching in certain applications.
• Limitations: The study notes that the early phase of RSBY may not have fully revealed its long-term effects. Additionally, the results may be influenced by collusion between patients and hospitals to convert outpatient care into inpatient care.
• Need for Further Research: The authors suggest that more recent data would be needed to better understand the long-term impact of RSBY.

Conclusion

The RSBY scheme has shown a small but positive impact on reducing OOP health expenditure among BPL households, though the effect on hospitalization rates is limited and regionally variable. The study highlights the importance of methodological rigor in evaluating health insurance schemes, especially in the context of non-random implementation. The use of matching and triple differences helps to mitigate some of the biases associated with the scheme's rollout. However, the results suggest that more time and data are needed to fully assess RSBY's effectiveness.