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India - Karnataka: An Experimental Evaluation of Government Health Insurance and Health Outcomes

A presentation by Somil Nagpal, delivered during "Transforming Health Systems Through Results-Based Financing," an event held during the Third Global Symposium on Health Systems Research in Cape Town on September 30, 2014. This event was hosted by the Health Results Innovation Trust Fund at The World Bank, in partnership with the PBF Community of Practice in Africa.

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India - Karnataka: An Experimental Evaluation of Government Health Insurance and Health Outcomes

  1. 1. 1 Impact of Health Insurance on Catastrophic Illness for The Poor An Impact Evaluation from Karnataka, India (Funded by the HRITF) September 30, 2014 Please do not cite or quote without permission
  2. 2. Lets Start With a Brief Video.. • http://www.youtube.com/watch?feature=play er_profilepage&v=XW8jTHvOBRI
  3. 3. Treatment of Catastrophic Illness is Efficacious but Expensive • Catastrophic illness such as heart disease or cancer can have devastating consequences for the poor • The poor with catastrophic illness face a tough trade-off: – If left untreated  premature mortality – If treated  improved health but catastrophic hospital bills
  4. 4. But Does Health Insurance for the Poor Really Save Lives? • We use the staggered rollout of a health insurance program for catastrophic illness for the poor in Karnataka to empirically evaluate whether health insurance saves lives • Why real life might be different than theory: – Poor are already getting care without insurance – Insurance subsidy is not enough to increase utilization of care – Insured poor are getting poor quality care – The wrong patients are getting care – Covered treatments are not efficacious • Therefore, we also evaluate impacts on financial outcomes, utilization of care, etc to understand the mechanisms through which insurance affects health
  5. 5. Evidence on the Health Effects of Health Insurance for the Poor • Mixed evidence on how health insurance for the poor affects health – No impact on child mortality in Costa Rica (Dow et al. 2003) – No impact on overall health in Mexico (King et al. 2009) – Mixed results in China (Wagstaff et al. 2009) – No impact on child health in Ghana (Ansah et al. 2009) – No impact/increase in mortality in Burkina Faso (Fink et al. 2013) – Improved childhood mortality in Thailand (Gruber et al. 2013)
  6. 6. VAS: Bundled prospective payment • Provides free hospital services for those Below the Poverty Line- no separate enrolment needed • Results based purchasing of predefined bundle of services (packages) from public and private hospitals – 402 tertiary care service packages (increased to 447 now) focusing on serious illnesses with high cost implications • Pre-authorization required before surgery and post operative investigation to avoid fraud
  7. 7. Experimental Design • In 2010 VAS was first rolled out in only half the state of Karnataka (northern part) • Survey households close to the north-south or eligibility border – Households on north side are eligible for VAS and households on south side are ineligible – Eligible and ineligible areas are close in proximity • Used matching strategy to further ensure similarity between eligible and ineligible areas • Compare outcomes across eligible and ineligible areas – geographic regression discontinuity
  8. 8. Sampling Strategy: Define eligibility border Eligible for VAS Ineligible for VAS 0 50 100 200 Kilometers
  9. 9. Sampling Strategy: Choose districts on the eligibility border Eligible for VAS Ineligible for VAS VAS Non-VAS 0 50 100 200 Kilometers
  10. 10. Sampling Strategy: Choose taluks on south side of eligibility border Eligible for VAS Ineligible for VAS VAS Non-VAS 0 50 100 200 Kilometers
  11. 11. Sampling Strategy: Choose villages in south side of border within chosen taluks VAS Non-VAS 0 40 80 160 Kilometers Bellary Uttara Haveri Kannada Shimoga Davangere Chitradurga
  12. 12. Sampling Strategy: Choose matching villages on north side of border VAS Non-VAS 0 40 80 160 Kilometers Bellary Uttara Haveri Kannada Shimoga Davangere Chitradurga VAS Non-VAS 0 75 150 300 Kilometers
  13. 13. Summary of Sampling Strategy • Used matching strategy to further ensure similarity between eligible and ineligible areas 1. Selected only districts that were directly north and directly south of the eligibility border 2. Randomly selected VAS ineligible villages in Taluks nested against eligibility border 3. Matched ineligible villages to eligible villages in selected districts on demographic and socioeconomic characteristics using 2001 Census
  14. 14. Data Collection: Enumeration Survey • All households in selected villages – 44,562 VAS-eligible Household – 38,186 VAS-ineligible Households • Information on: – BPL Status – Hospitalizations in past year and for which conditions – Mortality in past year and for which conditions
  15. 15. Data Collection: Detailed Household Survey • Completed by: – All BPL households with a hospitalization for a covered condition – ~10% random sample of households with an uncovered condition • Information on details of hospitalization – Out-of-pocket costs – Name and location of hospital – Length of stay
  16. 16. Study Sample
  17. 17. VAS Reduced Mortality for Covered Conditions for BPL Households
  18. 18. But No Difference in Mortality for APL households
  19. 19. Why Do We See a Mortality Effect? Lower Out of Pocket Costs Less Forgone Care or Higher Utilization of Care Better Health
  20. 20. VAS Resulted in Lower Out-of-Pocket Costs for VAS Covered Conditions Out-of-Pocket Expenditures for VAS Covered Conditions
  21. 21. VAS Beneficiaries Improved After Surgery and Are Now Relatively Healthy Pre- and Post-Hospitalization Self-Care Self-Reported Health Pre 2.99 Post 3.76 Change 0.77 Usual Activities Pre 2.96 Post 3.67 Change 0.71 Walk About Pre 2.99 Post 3.68 Change 0.69 Pain Pre 2.82 Post 3.63 Change 0.8 Anxiety/Depres sion Pre 3.14 Post 3.69 Change 0.55 Overall Health Pre 3.05 Post 3.88 Change 0.82
  22. 22. Limitations • Observational or quasi-experimental design, however: – Good ex-post matching – Null results for APL households • Migration: – Likely bias against finding – Difficult in practice to change address on BPL card • Measurement error in cause of death: – Null results for APL – Over-reporting of deaths due to greater awareness of VAS conditions might bias against our findings – Results driven by cancer and cardiac care – Distribution of cause of death is similar to verbal autopsy study
  23. 23. Why VAS but Not Others? • VAS is better targeted – Covers only the poor • No premiums and enrollment – Covers expensive care that is otherwise unaffordable – Covers treatments that are efficacious • Outreach and Health Camps • Has a pre-authorization process • Pent up demand so long term effects might be smaller • Need a large sample size to detect mortality effects
  24. 24. Next Steps Analysis underway to look at: • Insurance or financial risk protection value – What is the value of face less uncertain medical costs? • Changes in treatment seeking behavior – Do you see a doctor for chest pain? • Appropriateness of care – Was the bypass surgery really required? • Cost-Benefit analysis

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