PubliclyFinanced Health Insurance Schemes

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Sukumar Vellakkal ([email protected])and Shah Ebrahim ([email protected])are with the South Asia Network for ChronicDiseases, Public Health Foundation of India,New Delhi.

Publicly-Financed HealthInsurance SchemesConcerns about Impact Assessment

Sukumar Vellakkal, Shah Ebrahim

This article stresses that anyimpact assessment of healthinsurance schemes is sensitive tothe methodology as well as thedata used for analysis. It is basedon two recent studies evaluatingthe impact of publicly-financed

health insurance schemes onbeneficiaries.

In the past few years, governmentshave launched many publicly-financedhealth insurance schemes targetingpeople in the informal sector in severallow and middle income countries (LMICs),including India. This has, in turn, ledresearchers to evaluate these schemes interms of their impact on utilisation, out-of-pocket spending, and health outcomes.Two relevant and noteworthy Indianstudies Why Publicly-financed HealthInsurance Schemes Are Ineffective inProviding Financial Risk Protection bySelvaraj and Karan (2012), and StateHealth Insurance and Out-of-pocketHealth Expenditures in Andhra Pradesh,India by V Y Fan, A Karan and A Mahal(2012) are important attempts to con-tribute evidence on the impact of healthinsurance schemes, especially at a time

when governments are infusing massiveamounts of public money into theseschemes but with limited rigorous assess-ment of their impact.

Recently, we concluded a systematic re-view on the impact of publicly-financedhealth insurance schemes for the infor-mal sector in LMICs in which we exam-ined 34 relevant studies from differentcountries (Acharya et al 2012). In general,

we found no clear evidence of protectionfrom financial risk, healthcare utilisation,

or health outcomes for the insured popula-tion. In this context, we would like to com-ment on a few key issues of impact assess-ment of health insurance with special ref-erence to these two new studies on Indiaspublic health insurance schemes and thesubsequent commentary by T R Dilip(2012a, b) on the methodological issues.

Contrasting Results

Selvaraj and Karan found that publicly-

financed health insurance schemes(including the Rashtriya Swasthya BimaYojana (RSBY), Aarogyasri, and the public

health insurance schemes in Tamil Naduand Karnataka) have increased out-of-pocket spending on healthcare. On theother hand, Fan et al found that in thefirst phase of Andhra Pradeshs Aarog-yasri scheme, out-of-pocket inpatient ex-penditure, and to a lesser extent outpa-

tient expenditure, was significantly re-duced. In our systematic review, we alsonoted contrasting findings. For example,

while examining evidence from Viet-nam, Axelson et al (2009) and Wagstaff(2010) found reduction in out-of-pocketspending for the insured, Wagstaff(2007) showed no overall impact on out-of-pocket spending on healthcare. Simi-larly, in China, Lei and Lin (2009) andWagstaff et al (2009) found no evidenceof lower levels of out-of-pocket spendingon healthcare for the insured.

Selvaraj and Karan conclude that sincepublicly-financed health insurance schemesare ineffective in providing financialprotection to the beneficiaries, alterna-tive financial mechanisms need to be ex-plored. However, before making anypolicy decision to continue or discontin-ue the schemes, it is worth examiningthe potential reasons for the low level of

welfare impact on the beneficiaries. We

noted in our systematic review that sev-eral studies give various reasons thatcan undermine the welfare impact ofhealth insurance in LMICs, including thepitfalls in design and implementation ofthe schemes, lack of scientific criteria forselecting private healthcare providers andfixing the package rates/prices for varioushealthcare services under the schemes(provider payment mechanism), and theabsence of proper public awareness ofthe schemes. As Indias healthcare system,

like several other LMICs, is relatively un-regulated, less organised and pluralisticin healthcare delivery, these underlyingfactors may be relevant to the Indianscenario and require further in-depthinvestigation for informed policy decisions.

Methodological Concerns

In a response to Selvaraj and Karansstudy, Dilip (2012a) questions theirmethodology of difference-in-differences

(DID) and argues that the findings arenot robust. We also noted in our reviewthat the results are quite sensitive to


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various comparison methods adopted bystudies. (For example, Wagstaff (2010)outlined different results by using sin-gle, double and triple DID methods withstatistical matching). Since the basicprinciple of the methodology of impactevaluation is counterfactual analysis

(what would have happened to the ben-eficiaries in the absence of the interven-tion (control group)), impact is estimatedby comparing counterfactual outcomesto those observed under the intervention(treatment group.) Therefore, the strengthof any study on impact evaluation dependson how reliably the chosen control groupmirrors the counterfactual situation.

There are several quantitative methodsavailable for impact evaluation conductedeither in experimental or non-experi-mental settings. However, no method isconsidered as the gold standard. Since asimple comparison of outcomes occur-ring pre- and post-insurance periods maybe affected by extraneous factors occur-ring during the time period studied, ran-domised control trials (RCTs) conductedin experimental settings, with a base linesurvey conducted in parallel to launch-ing the programme and then repeatedlater at the time of measuring the impact,

is often considered the preferred method.(King et al (2009) used RCTs for evaluat-ing Mexicos health insurance programmein collaboration with the government.)RCTs are clearly hard to mount and requireconsiderable collaboration with policy-makers and insurance agencies.

We noted in our review that in theabsence ofRCTs, quasi-experimental studydesigns are widely used. These include:(1) Regression discontinuity design, orRDD (comparing the healthcare-related

outcome of those who are eligible forinsurance at the margin to those who are

just above the eligibility); (2) statisticalmatching methods, which include exactmatching, propensity score matching, andcoarsened matching, for generating a sub-sample of the control group that match

with the treatment group (case) basedon observable characteristics; (3) DIDby comparing the healthcare outcomebetween the treatment and control groups

of the post-insurance period to thepre-insurance period; and (4) instru-mental variable methods that require the

selection of suitable instruments. Basedon the availability of suitable data sets,researchers sometimes combine the DIDmethods with statistical matching ones togenerate treatment and control group inboth periods.

Selvaraj and Karan have used the DID

method, comparing the health expendi-ture of 2004-05 (pre-insurance period)and 2009-10 (post-insurance period).Fan et al (2012) also used the DID method,but used two baseline periods: 1999-2000and 2004-05 (double DID rather than asingle DID). In our opinion, Selvaraj andKaran could have provided a more robustanalysis had they considered 1999-2000and 2004-05 as baselines for measuringthe trends in health expenditure. It israther more likely that further applica-tion of a statistical matching methodand combining it with the DID with morebaseline period data (as used by Wagstaffet al 2009 and Wagstaff 2010) may haveyielded more robust results. However,the main issue here is that the availablesurvey data, i e, the consumer expendi-ture survey (CES) ofthe National SampleSurvey Office (NSSO) does not provideinformation on whether the householdis covered by health insurance. In this

regard, the classification of the sampleas treatment and non-treatment groupin both periods by distinguishing thedistricts to with and without enrolmentin health insurance by Selvaraj and Karanis a good attempt to define groups withthe potential to benefit from insurancefrom those who could not.

But to what extent can the interven-tion districts and control districts serveas the treatment and control group ineach period? This question is related to

the health insurance enrolment rate also,an issue raised by Dilip (2012b). Selvarajand Karan have studied not only RSBYbut also three state-level schemes. How-ever, RSBY is a national level scheme

with enrolment in several states andhas relatively more coverage than stategovernment schemes. A recent study(Dror and Vellakkal 2012) found that RSBYcovered only 10% of Indias population(as on 31 March 2011), which is only 28%

of the below the poverty line (BPL)households (as per the Tendulkar Com-mittee estimate). Furthermore, this study

found that although RSBY operatesin 24of the 30 states/territories, it covers allthe districts in only 10 states, and withfocused coverage in few states, e g, over55% of the total in three large states(Bihar, Uttar Pradesh and West Bengal).Moreover, not all those districts withRSBY intervention have significantly cov-ered a majority of their BPL target bene-ficiaries. This may lead to several biases

with the impact evaluation, v iz, (1) theinterstate variation of health expendi-ture in general would affect the compar-ison of out-of-pocket spending betweenthe intervention districts and controldistricts because the intervention dis-tricts will be over-weighted by a fewstates, and (2) the treatment group in theintervention district also includes peo-ple without health insurance (dilutingany possible effect of insurance) andthus challenges the validity of the com-parison between treatment and controlgroup at the district level.

Another issue raised by Dilip (2012b)is that Selvaraj and Karans study com-pares the out-of-pocket spending onhealthcare of 1.35% of households in the2009-10 population to the remaining98.65%. In our opinion, this is not true;

in fact, they compared the healthcareexpenditure between the treatment andcontrol group. More importantly, theanalysis was made by classifying thepopulation into income quintiles. As themain target beneficiaries of the schemesare BPL households, the policy implica-tions of the results from the analysisrelevant to the two bottom income quin-tiles are worth considering. Dilip alsoraises the related issue that some struc-tural and policy changes that happened

between both time periods due to aplethora of changes in the health systemmight lead to changes in health expend-iture. However, Selvaraj and Karansstudy uses DID, and one of the reasonsfor using the DID method instead of a

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simple pre- and post-comparative analysisis to control for these extraneous factors.

Data Issues

As we know, RSBY and several state-levelschemes have been implemented throughmultiple insurance companies, and the

Aarogyasri scheme in Andhra Pradesh wasalso run by an insurance company duringthe study reference period of Fan et al.

As none of the publicly available data-bases have the characteristics to facilitatesound impact assessment, the authors ofboth studies made good attempts to usethe CES round undertaken by the NSSO.The general understanding about the CESround is that the information collectedreflects the expenditure incurred by therespondents and, therefore, these areessentially out-of-pocket spending.

The CES round collected informationon health expenditure under two maincategories: institutional (inpatient) andnon-institutional (outpatient), with vari-ous subcategories. However, the healthexpenditure was recorded without anydistinction in out-of-pocket spending byhouseholds, and the reimbursement fromhealth insurance companies (or from thegovernments) to the household directly

or through the hospitals. The utility ofthe CES round data for impact assess-ment would have been more if such adistinction of the health expenditure ismade at least for the data of the post-insurance period. However, if it is as-sumed that the health insurance schemesare fully cashless, as both studies did, sucha distinction is not necessary becausethe health expenditure data reported inthe NSSO are essentially out-of-pocketspending alone as cashless reimburse-

ments from the health insurance schemesdo not reflect in the CES round.

However, there is the possibility that theCES round ofNSSO data 2009-10 did notcomprise out-of-pocket spending alonebut combined both out-of-pocket spend-ing and payments received by householdsfrom the schemes (insurance companies)directly or through the hospitals. Thesupporting documents distributed along

with the CES round data lead us to believe

that the health expenditure reported inthe survey is not just out-of-pocketspending alone but a combined total of

both out-of-pocket spending and thecontribution received from insurancecompanies.

Of the eight documents in the foldertitled Instructions to Field Staff withthe CES round ofNSSO data 2009-10, thedocument named ins66chap3.doc (or

the document titled ins.66_1.0.pdf inthe NSSO website (NSSO 2010)), pagenumber C-2, states:

The following are part of Consumer Expend-iture and should not be missed... Paymentsfor medical care reimbursed or directly paidby insurance company.

More elaborately, in the same docu-ment (page number C-33, paragraphnumber 3.9.14):

...On the other hand, when an insurancecompany makes a payment to the samplehousehold (or directly to a hospital under thecashless system) in settlement of a claimmade by the household for medical reimburse-ment, the amount is to be shown as medicalexpenditure of the household against items410 to 414. In other words, the value of medi-cal goods and services on which expenditureis incurred will be recorded in Block 9 orBlock 10, EITHER if incurred by the house-hold itself, whether or not reimbursed byemployer or insurance company, ORif paidby the employer or by the insurance com-pany directly to the hospital.

(In the instructions, items 410 to 414refer to the institutional (in-patient)health expenditure.)

The above statements clearly demon-strate that although the reported healthexpenditure in the NSSO survey was notout-of-pocket spending alone but a mixof this and the contribution from insur-ance schemes and other sources, the re-searchers have treated it as out-of-pocketspending only. Therefore, the CES roundofNSSO data 2009-10 is invalid for mak-

ing any impact assessment of healthinsurance schemes.

Furthermore, in similar context, asseveral studies have used the previousrounds of the CES round data (when thehealth insurance schemes were not inplace) to measure the financial burdenof households due to health expenditurebecause the reported health expendi-ture was just out-of-pocket spending onhealthcare at that time, there can be a

possibility of doing similar analysis bymisreading the CES round ofNSSO data2009-10 as it is out-of-pocket spending

on healthcare, so we invite the attentionof other researchers also to consider theabove issues before making any analysis

with this data set.

Concluding Remarks

In recent years, several publicly-financed

health insurance schemes have beenlaunched in the country and attemptsare ongoing to assess their impact. Ourmain concern is that the impact assess-ment of health insurance schemes is sen-sitive to the methodology as well as thedata used for analysis. Although the twostudies have taken a practical approach,for planning purposes and to enhancemore impact value of interventions, it isbetter to consider the limitations of theanalysis and the generalisability of thefindings prudently.

Furthermore, the absence of a dedicateddata set is one of the main constraintsfor robust impact assessment. As hugefunds have been invested in severalhealth insurance schemes, it is impera-tive that the data sets on the schemesshould be designed with inputs from theresearch community and made availableto them by the concerned authorities foran independent and unbiased assess-

ment of the schemes. Further, we alsoappeal to the NSSO to collect more infor-mation relevant to the health insuranceschemes and other similar social securityschemes in the forthcoming surveys sothat rigorous evaluation of these schemescan be made to inform policies.

References

Acharya, A, S Vellakkal, F Taylor, E Masset, A Satija,M Burke and S Ebrahim (2012): Impact of

National Health Insurance for the Poor and theInformal Sector in Low and Middle-income

Countries: A Systematic Review (London: EPPI-Centre, Social Science Research Unit, Instituteof Education, University of London).

Axelson, H, S Bales, P Minh, B Ekman and U Gerdtham(2009): Health Financing for the Poor ProducesPromising Short-term Effects on Utilisationand Out-of-pocket Expenditure: Evidence from

Vietnam, International Journal for Equity inHealth, 8(1): p 20.

Dilip, T R (2012a): On Publicly-Financed Health Insur-ance Schemes: Is the Analysis Premature?,Econo-mic & Political Weekly, 5 May, XLVII(18): 79-80.

(2012b): Why Use Consumer Expenditure Sur-veys for Analysis of the RSBY?, Economic &Political Weekly, 1 September, Vol XLVII, No 35.

Dror, D and S Vellakkal (2012): Is RSBY IndiasPlatform to Implementing Universal HospitalInsurance?, The Indian Journal of Medical

Research, 135(1): 56.


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Fan, V Y, A Karan and A Mahal (2012): StateHealth Insurance and Out-of-pocket HealthExpenditures in Andhra Pradesh, India,Inter-national Journal of Health Care Finance and

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PubliclyFinanced Health Insurance Schemes