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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Please do not cite or quote without permission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=player_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 PoorReally 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

Sampling Strategy: Choose districts on the eligibility border

Sampling Strategy: Choose taluks on south side of eligibility border

Sampling Strategy: Choose villages in south side of border within chosen taluks

Sampling Strategy: Choose matching villages on north side of border

Summary of Sampling Strategy

• Used matching strategy to further ensure similarity between eligible and ineligible areas1. Selected only districts that were directly north

and directly south of the eligibility border2. Randomly selected VAS ineligible villages in

Taluks nested against eligibility border3. 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?

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-CarePre 2.99Post 3.76Change 0.77

Usual ActivitiesPre 2.96Post 3.67Change 0.71

Walk AboutPre 2.99Post 3.68Change 0.69

PainPre 2.82Post 3.63Change 0.8

Anxiety/Depression

Pre 3.14Post 3.69Change 0.55

Overall HealthPre 3.05Post 3.88Change 0.82

Self-Reported HealthPre- and Post-Hospitalization

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