Developing efficient and responsive community based micro health insurance in India Reconciling Research and Implementation Needs in Community- Level Cluster Randomised Trials: An EC-FP7 Research Project Pradeep Panda, PhD Micro Insurance Academy, New Delhi Ellen Van de Poel Erasmus University Rotterdam

Developing efficient and responsive community based micro health insurance in India Micro-Insurance Academy is an Indian charity dedicated to training, research, technical assistance and advisory services for micro-insurance units serving the poor With our partners, we are running 3 separate, but similar, RCTs in northern India Each RCT will establish a Community Based Health Insurance (CBHI) scheme – a small-scale, member-operated health insurance scheme, offering limited coverage of defined events Each scheme will be evaluated for its effect on healthcare utilization and healthcare financing Now 30 months into this 5-year project – all CBHI units went live in 2011 Introduction

Developing efficient and responsive community based micro health insurance in India 1. What do we Know about Micro Health Insurance (MHI)?

Developing efficient and responsive community based micro health insurance in India Limited robust evidence on causal impact of MHI schemes: Of the 22 most robustly studied trials, only 2 use a randomised trial methodology… … and only 7 use any form of counterfactual! The remaining evaluations are prone to bias No evaluation has examined a holistic list of outcome indicators And comparability of results across trials may be limited, as all kinds of different schemes have been called “MHI:” national public schemes, private for-profits products, operator run pre-payment programs, and non-profit mutual finds! 1. What do we Know about Micro Health Insurance?

Developing efficient and responsive community based micro health insurance in India 2. Overview of Trials

Developing efficient and responsive community based micro health insurance in India These trials aim to close knowledge gaps on MHI in three ways: 1.Use the “gold standard” randomised trial methodology to boost internal validity 2.Give clear and detailed descriptions of scheme setting and operation to clarify limits of external validity 3.Evaluate a holistic range of outcome indicators to broaden the knowledge base 2.1 Motivation for Trials

Developing efficient and responsive community based micro health insurance in India 1. Kanpur Dehat 2. Pratapgarh 3. Vaishali Uttar Pradesh Bihar 3 separate CRTs at 3 separate sites: 2.2 Scheme Areas & Target Populations

Developing efficient and responsive community based micro health insurance in India At each site, a particular type of MHI scheme is set up: Community Based Health Insurance (CBHI) CBHI schemes are locally based, mutual, not-for-profit programs: All schemes are owned by members All schemes are managed by members All premiums and coverage types are set by members This is why we call it Community Based! 2.2 Scheme Areas & Target Populations

Developing efficient and responsive community based micro health insurance in India Implementation at each site is managed by an NGO implementing partner Each NGO has a network of Self Help Groups (SHGs): village level MFI groups At each site, there are 1400 – 1600 SHG members Members and their families can take part in the trial 2.2 Scheme Areas & Target Populations

Developing efficient and responsive community based micro health insurance in India 4 sequential modules create an eventual impact: 2.3 Description of Treatment Program 2. Awareness Insurance Education (x3) CHAT (x2) Finalization (x1) Enrollment (x1) 1. Design Benefit Options workshop (x1) Awareness Tools workshop (x1) Design workshop (x1) 3. Launching Selection of Officers Training of Officers Installation of MIS Stakeholder Events 4. Live Scheme Submission of Claims Processing of Claims Payout of Claims Key Outcome Indicators

Developing efficient and responsive community based micro health insurance in India 3. Overview of Research Design

Developing efficient and responsive community based micro health insurance in India 1. Effects of CBHI on Healthcare Utilization Levels Key indicators depend on coverage of insurance packages at each trial. Will be drawn from: Maternity Care Usage Rates Outpatient Care Usage Rates Hospitalisation Rates Involuntary Non- Treatment Rates Use of Transport 3.1 Outcome Measures & Tools

Developing efficient and responsive community based micro health insurance in India 2. Effects of CBHI on Healthcare Financing Key Indicators include: Total Healthcare Spending Instance of catastrophic health expenditure (>10% HH Income) Financial Exposure Index (Under Development) 3. Physical Accessibility of Healthcare Village-wise Health Care Index Instance of asset sales, savings liquidation, etc. 3.1 Outcome Measures & Tools

Developing efficient and responsive community based micro health insurance in India Quantitative Household surveys Healthcare Provider surveys Exit interviews Income surveys Insurance Understanding surveys Quantitative Household surveys Healthcare Provider surveys Exit interviews Income surveys Insurance Understanding surveys Qualitative FGDs - SHG members FGDs - Heads of SHG households KIIs - SHG leaders KIIs - Local Healthcare Providers Qualitative FGDs - SHG members FGDs - Heads of SHG households KIIs - SHG leaders KIIs - Local Healthcare Providers 3.1 Outcome Measures & Tools Spatial GPS Mapping Satellite Imaging GIS Imaging Spatial GPS Mapping Satellite Imaging GIS Imaging 3 mutually supportive and integrated research streams: This presentation shows have quantitative and spatial baseline have driven experimental design….

Developing efficient and responsive community based micro health insurance in India 3.2 Important design aspects of the RCT Staggered implementation – Each wave a random third of the target population gets offered CBHI Clustered trial – SHGs are grouped into clusters – Clusters are randomized in 3 treatment groups En-bloc affiliation – All hhs within a SHG need to join the CBHI

Developing efficient and responsive community based micro health insurance in India 3.3 Defining an Implementation Friendly Unit of Randomisation combine Quantitative and Spatial data, and map locations of trial participants “3 Rules” to integrate implementation and research needs when forming clusters: 1.Non-Divisibility: A village cannot be divided over different clusters 2.Equal Size: Clusters must contain (roughly) equal numbers of SHG members 3.Continuity: Each cluster must be geographically continuous

Developing efficient and responsive community based micro health insurance in India 3.3 Defining an Implementation Friendly Unit of Randomisation This transforms villages into clusters….

Developing efficient and responsive community based micro health insurance in India 3.4 Generating Power with a Fixed Population Size Then MDES is calculated: If it is too low, one of the three rules must be broken somewhere to improve power Trial 1 - Pratapgarh Trial 2 - Kanpur Dehat Trial 3 - Vaishali No. SHG Members No HH surveys complete No. Villages No. Clusters (J) Average HHs per cluster Minimum Detectable Effect Size

Developing efficient and responsive community based micro health insurance in India 3.5 Randomization Matching prior to randomization was considered (creating similar triplets of clusters) but did not improve balance on observables Simple randomization was chosen as it does not affect power Less balance on ethnicity/caste & health care supply

Developing efficient and responsive community based micro health insurance in India 3.6 Limitations External validity: – Only effect of CBHI on SHG – Selection of implementation partners Internal validity: – Self-selection of SHGs in CBHI -> intention to treat effect Contamination effects (health related) attrition Multiple treatments (through participatory design of CBHI)

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