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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Please do not cite or quote without permission
Lets Start With a Brief Video.. 
• http://www.youtube.com/watch?feature=play 
er_profilepage&v=XW8jTHvOBRI
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
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
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)
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
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
Sampling Strategy: Define eligibility 
border 
Eligible for VAS 
Ineligible for VAS 
0 50 100 200 Kilometers
Sampling Strategy: Choose districts on 
the eligibility border 
Eligible for VAS 
Ineligible for VAS 
VAS 
Non-VAS 
0 50 100 200 Kilometers
Sampling Strategy: Choose taluks on 
south side of eligibility border 
Eligible for VAS 
Ineligible for VAS 
VAS 
Non-VAS 
0 50 100 200 Kilometers
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
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
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
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
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
Study Sample
VAS Reduced Mortality for Covered 
Conditions for BPL Households
But No Difference in Mortality for 
APL households
Why Do We See a Mortality Effect? 
Lower Out of Pocket Costs 
Less Forgone Care or Higher Utilization of Care 
Better Health
VAS Resulted in Lower Out-of-Pocket 
Costs for VAS Covered Conditions 
Out-of-Pocket Expenditures 
for VAS Covered Conditions
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
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
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
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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India - Karnataka: An Experimental Evaluation of Government Health Insurance and Health Outcomes

  • 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. Lets Start With a Brief Video.. • http://www.youtube.com/watch?feature=play er_profilepage&v=XW8jTHvOBRI
  • 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. 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. 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. 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. 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. Sampling Strategy: Define eligibility border Eligible for VAS Ineligible for VAS 0 50 100 200 Kilometers
  • 9. Sampling Strategy: Choose districts on the eligibility border Eligible for VAS Ineligible for VAS VAS Non-VAS 0 50 100 200 Kilometers
  • 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. 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. 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. 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. 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. 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
  • 17. VAS Reduced Mortality for Covered Conditions for BPL Households
  • 18. But No Difference in Mortality for APL households
  • 19. Why Do We See a Mortality Effect? Lower Out of Pocket Costs Less Forgone Care or Higher Utilization of Care Better Health
  • 20. VAS Resulted in Lower Out-of-Pocket Costs for VAS Covered Conditions Out-of-Pocket Expenditures for VAS Covered Conditions
  • 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. 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. 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. 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

Notes de l'éditeur

  1. Draw border VAS north non-VAS south
  2. Districts, just blue and red
  3. This fiugure
  4. Households that reported BPL but were unable to provide a card had the following mortality rates: VAS Side: .0076 Non-VAS Side: .3350 Difference: .327***