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Please do not cite or quote without permission
1
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:
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–
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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
Haveri
Uttara
Kannada
Bellary
Chitradurga
Shimoga
Davangere
VAS
Non-VAS
0
40
80
160 Kilometers
Sampling Strategy: Choose matching
villages on north side of border
Haveri
Uttara
Kannada
Bellary
Chitradurga
Shimoga
Davangere
VAS
Non-VAS
0
40
80
VAS
Non-VAS
160 Kilometers
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
Self-Reported Health
Pre- and Post-Hospitalization
Pre
Post
Self-Care
Change
Pre
Usual Activities Post
Change
Pre
Walk About Post
Change
Pre
Post
Pain
Change
Pre
Anxiety/Depres
Post
sion
Change
Pre
Overall Health Post
Change
2.99
3.76
0.77
2.96
3.67
0.71
2.99
3.68
0.69
2.82
3.63
0.8
3.14
3.69
0.55
3.05
3.88
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
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•
•
•
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