Impact of community-based health insurance in rural India on self-medication & financial protection of the insured.

BACKGROUND & OBJECTIVES: The evidence-base of the impact of community-based health insurance (CBHI) on access to healthcare and financial protection in India is weak. We investigated the impact of CBHI in rural Uttar Pradesh and Bihar s0 tates of India on insured households' self-medication and financial position. METHODS: Data originated from (i) household surveys, and (ii) the Management Information System of each CBHI. Study design was "staggered implementation" cluster randomized controlled trial with enrollment of one-third of the treatment group in each of the years 2011, 2012 and 2013. Around 40-50 per cent of the households that were offered to enroll joined. The benefits-packages covered outpatient care in all three locations and in-patient care in two locations. To overcome self-selection enrollment bias, we constructed comparable control and treatment groups using Kernel Propensity Score Matching (K-PSM). To quantify impact, both difference-in-difference (DiD), and conditional-DiD (combined K-PSM with DiD) were used to assess robustness of results. RESULTS: Post-intervention (2013), self-medication was less practiced by insured HHs. Fewer insured households than uninsured households reported borrowing to finance care for non-hospitalization events. Being insured for two years also improved the HH's location along the income distribution, namely insured HHs were more likely to experience income quintile-upgrade in one location, and less likely to experience a quintile-downgrade in two locations. INTERPRETATION & CONCLUSIONS: The realized benefits of insurance included better access to healthcare, reduced financial risks and improved economic mobility, suggesting that in our context health insurance creates welfare gains. These findings have implications for theoretical, ethical, policy and practice considerations.

Mobilizing community-based health insurance to enhance awareness & prevention of airborne, vector-borne & waterborne diseases in rural India.

BACKGROUND & OBJECTIVES: Despite remarkable progress in airborne, vector-borne and waterborne diseases in India, the morbidity associated with these diseases is still high. Many of these diseases are controllable through awareness and preventive practice. This study was an attempt to evaluate the effectiveness of a preventive care awareness campaign in enhancing knowledge related with airborne, vector-borne and waterborne diseases, carried out in 2011 in three rural communities in India (Pratapgarh and Kanpur-Dehat in Uttar Pradesh and Vaishali in Bihar). METHODS: Data for this analysis were collected from two surveys, one done before the campaign and the other after it, each of 300 randomly selected households drawn from a larger sample of Self-Help Groups (SHGs) members invited to join community-based health insurance (CBHI) schemes. RESULTS: The results showed a significant increase both in awareness (34%, p<0.001) and in preventive practices (48%, P=0.001), suggesting that the awareness campaign was effective. However, average practice scores (0.31) were substantially lower than average awareness scores (0.47), even in post-campaign. Awareness and preventive practices were less prevalent in vector-borne diseases than in airborne and waterborne diseases. Education was positively associated with both awareness and practice scores. The awareness scores were positive and significant determinants of the practice scores, both in the pre- and in the post-campaign results. Affiliation to CBHI had significant positive influence on awareness and on practice scores in the post-campaign period. INTERPRETATION & CONCLUSIONS: The results suggest that well-crafted health educational campaigns can be effective in raising awareness and promoting health-enhancing practices in resource-poor settings. It also confirms that CBHI can serve as a platform to enhance awareness to risks of exposure to airborne, vector-borne and waterborne diseases, and encourage preventive practices.

Building awareness to health insurance among the target population of community-based health insurance schemes in rural India.

OBJECTIVE: To evaluate an insurance awareness campaign carried out before the launch of three community-based health insurance (CBHI) schemes in rural India, answering the questions: Has the awareness campaign been successful in enhancing participants' understanding of health insurance? What awareness tools were most useful from the participants' point of view? Has enhanced awareness resulted in higher enrolment? METHODS: Data for this analysis originates from a baseline survey (2010) and a follow-up survey (2011) of more than 800 households in the pre- and post-campaign periods. We used the difference-in-differences method to evaluate the impact of awareness activities on insurance understanding. Assessment of usefulness of various tools was carried out based on respondents' replies regarding the tool(s) they enjoyed and found most useful. An ordinary least square regression analysis was conducted to understand whether insurance knowledge and CBHI understanding are related with enrolment in CBHI. RESULTS: The intervention cohort demonstrated substantially higher understanding of insurance concepts than the control group, and CBHI understanding was a positive determinant for enrolment. Respondents considered the 'Treasure-Pot' tool (an interactive game) as most useful in enhancing awareness to the effects of insurance. CONCLUSIONS: We conclude that awareness-raising is an important prerequisite for voluntary uptake of CBHI schemes and that interactive, contextualised awareness tools are useful in enhancing insurance understanding.

"One for all and all for one": consensus-building within communities in rural India on their health microinsurance package.

INTRODUCTION: This study deals with consensus by poor persons in the informal sector in rural India on the benefit-package of their community-based health insurance (CBHI). In this article we describe the process of involving rural poor in benefit-package design and assess the underlying reasons for choices they made and their ability to reach group consensus. METHODS: The benefit-package selection process entailed four steps: narrowing down the options by community representatives, plus three Choosing Healthplans All Together (CHAT) rounds conducted among female members of self-help groups. We use mixed-methods and four sources of data: baseline study, CHAT exercises, in-depth interviews, and evaluation questionnaires. We define consensus as a community resolution reached by discussion, considering all opinions, and to which everyone agrees. We use the coefficient of unalikeability to express consensus quantitatively (as variability of categorical variables) rather than just categorically (as a binomial Yes/No). FINDINGS: The coefficient of unalikeability decreased consistently over consecutive CHAT rounds, reaching zero (ie, 100% consensus) in two locations, and confirmed gradual adoption of consensus. Evaluation interviews revealed that the wish to be part of a consensus was dominant in all locations. The in-depth interviews indicated that people enjoyed the participatory deliberations, were satisfied with the selection, and that group decisions reflected a consensus rather than majority. Moreover, evidence suggests that pre-selectors and communities aimed to enhance the likelihood that many households would benefit from CBHI. CONCLUSION: The voluntary and contributory CBHI relies on an engaging experience with others to validate perceived priorities of the target group. The strongest motive for choice was the wish to join a consensus (more than price or package-composition) and the intention that many members should benefit. The degree of consensus improved with iterative CHAT rounds. Harnessing group consensus requires catalytic intervention, as the process is not spontaneous.

Enrolment in community-based health insurance schemes in rural Bihar and Uttar Pradesh, India.

This article assesses insurance uptake in three community-based health insurance (CBHI) schemes located in rural parts of two of India's poorest states and offered through women's self-help groups (SHGs). We examine what drives uptake, the degree of inclusive practices of the schemes and the influence of health status on enrolment. The most important finding is that a household's socio-economic status does not appear to substantially inhibit uptake. In some cases scheduled caste/scheduled tribe households are more likely to enrol. Second, households with greater financial liabilities find insurance more attractive. Third, access to the national hospital insurance scheme Rashtriya Swasthya Bima Yojana does not dampen CBHI uptake, suggesting that the potential for greater development of insurance markets and products beyond existing ones would respond to a need. Fourth, recent episodes of illness and self-assessed health status do not influence uptake. Fifth, insurance coverage is prioritized within households, with the household head, the spouse of the household head and both male and female children of the household head, more likely to be insured as compared with other relatives. Sixth, offering insurance through women's SHGs appears to mitigate concerns about the inclusiveness and sustainability of CBHI schemes. Given the pan-Indian spread of SHGs, offering insurance through such groups offers the potential to scale-up CBHI.

Estimating Willingness-to-Pay for health insurance among rural poor in India by reference to Engel's law.

Community-Based Health Insurance (CBHI) (a.k.a. micro health insurance) is a contributory health insurance among rural poor in developing countries. As CBHI schemes typically function with no subsidy income, the schemes' expenditures cannot exceed their premium income. A good estimate of Willingness-To-Pay (WTP) among the target population affiliating on a voluntary basis is therefore essential for package design. Previous estimates of WTP reported materially and significantly different WTP levels across locations (even within one state), making it necessity to base estimates on household surveys. This is time-consuming and expensive. This study seeks to identify a coherent anchor for local estimation of WTP without having to rely on household surveys in each CBHI implementation. Using data collected in 2008-2010 among rural poor households in six locations in India (total 7874 households), we found that in all locations WTP expressed as percentage of income decreases with household income. This reminds of Engel's law on food expenditures. We checked several possible anchors: overall income, discretionary income and food expenditures. We compared WTP expressed as percentage of these anchors, by calculating the Coefficient of Variation (for inter-community variation) and Concentration indices (for intra-community variation). The Coefficient of variation was 0.36, 0.43 and 0.50 for WTP as percent of food expenditures, overall income and discretionary income, respectively. In all locations the concentration index for WTP as percentage of food expenditures was the lowest. Thus, food expenditures had the most consistent relationship with WTP within each location and across the six locations. These findings indicate that like food, health insurance is considered a necessity good even by people with very low income and no prior experience with health insurance. We conclude that the level of WTP could be estimated based on each community's food expenditures, and that this information can be obtained everywhere without having to conduct household surveys.

Illness Mapping: a time and cost effective method to estimate healthcare data needed to establish community-based health insurance.

BACKGROUND: Most healthcare spending in developing countries is private out-of-pocket. One explanation for low penetration of health insurance is that poorer individuals doubt their ability to enforce insurance contracts. Community-based health insurance schemes (CBHI) are a solution, but launching CBHI requires obtaining accurate local data on morbidity, healthcare utilization and other details to inform package design and pricing. We developed the "Illness Mapping" method (IM) for data collection (faster and cheaper than household surveys). METHODS: IM is a modification of two non-interactive consensus group methods (Delphi and Nominal Group Technique) to operate as interactive methods. We elicited estimates from "Experts" in the target community on morbidity and healthcare utilization. Interaction between facilitator and experts became essential to bridge literacy constraints and to reach consensus.The study was conducted in Gaya District, Bihar (India) during April-June 2010. The intervention included the IM and a household survey (HHS). IM included 18 women's and 17 men's groups. The HHS was conducted in 50 villages with1,000 randomly selected households (6,656 individuals). RESULTS: We found good agreement between the two methods on overall prevalence of illness (IM: 25.9% ±3.6; HHS: 31.4%) and on prevalence of acute (IM: 76.9%; HHS: 69.2%) and chronic illnesses (IM: 20.1%; HHS: 16.6%). We also found good agreement on incidence of deliveries (IM: 3.9% ±0.4; HHS: 3.9%), and on hospital deliveries (IM: 61.0%. ± 5.4; HHS: 51.4%). For hospitalizations, we obtained a lower estimate from the IM (1.1%) than from the HHS (2.6%). The IM required less time and less person-power than a household survey, which translate into reduced costs. CONCLUSIONS: We have shown that our Illness Mapping method can be carried out at lower financial and human cost for sourcing essential local data, at acceptably accurate levels. In view of the good fit of results obtained, we assume that the method could work elsewhere as well.

Can the rural poor in India afford to treat non-communicable diseases.

OBJECTIVE: Non-communicable diseases (NCD) are on the increase in low-income countries, where healthcare costs are paid mostly out-of-pocket. We investigate the financial burden of NCD vs. communicable diseases (CD) among rural poor in India and assess whether they can afford to treat NCD. METHODS: We used data from two household surveys undertaken in 2009-2010 among 7389 rural poor households (39 205 individuals) in Odisha and Bihar. All persons from the sampled households, irrespective of age and gender, were included in the analysis. We classify self-reported illnesses as NCD, CD or 'other morbidities' following the WHO classification. RESULTS: Non-communicable diseases accounted for around 20% of the diseases in the month preceding the survey in Odisha and 30% in Bihar. The most prevalent NCD, representing the highest share in outpatient costs, were musculoskeletal, digestive and cardiovascular diseases. Cardiovascular and digestive problems also generated the highest inpatient costs. Women, older persons and less-poor households reported higher prevalence of NCD. Outpatient costs (consultations, medicines, laboratory tests and imaging) represented a bigger share of income for NCD than for CD. Patients with NCD were more likely to report a hospitalisation. CONCLUSION: Patients with NCD in rural poor settings in India pay considerably more than patients with CD. For NCD cases that are chronic, with recurring costs, this would be aggravated. The cost of NCD care consumes a big part of the per person share of household income, obliging patients with NCD to rely on informal intra-family cross-subsidisation. An alternative solution to finance NCD care for rural poor patients is needed.

Is RSBY India's platform to implementing universal hospital insurance?

BACKGROUND & OBJECTIVES: In 2008, India's Labour Ministry launched a hospital insurance scheme called Rashtriya Swasthya Bima Yojana (RSBY) covering 'Below Poverty Line' (BPL) households. RSBY is implemented through insurance companies; premiums are subsidized by Union and States governments (75 : 25%). We examined RSBY's enrolment of BPL, costs vs. budgets and policy ramifications. METHODS: Numbers of BPL are obtained by following criteria of two committees appointed for this task. District-specific premiums are weighted to obtain national average premiums. Using the BPL estimates and national premiums, we calculated overall expected costs of full roll-out of the RSBY per annum, and compared it to Union government budget allocations. RESULTS: By March 31, 2011, RSBY enrolled about 27.8 per cent of the number of BPL households following the Tendulkar Committee estimates (37.6% following the Lakdawala Committee criteria). The average national weighted premium was Rs. 530 per household per year in 2011. The expected cost of premium to the union government of enrolling the entire BPL population in financial year (FY) 2010-11 would be Rs. 33.5 billion using Tendulkar count of BPL (or Rs. 24.6 billion following Lakdawala count), representing about 0.3 per cent (or 0.2%, respectively) of the total union budget. The RSBY budget allocation for FY 2010-11 was only about 0.037 per cent of the total union budget, sufficient to pay premiums of only 34 per cent of the BPL households enrolled by March 31, 2011. INTERPRETATION & CONCLUSIONS: RSBY could be the platform for universal health insurance when (i) the budget allocation will match the required funds for maintenance and expansion of the scheme; (ii) the scheme would ensure that beneficiaries' rights are legally anchored; and (iii) RSBY would attract large numbers of premium-paying (non-BPL) households.

Hardship financing of healthcare among rural poor in Orissa, India.

BACKGROUND: This study examines health-related "hardship financing" in order to get better insights on how poor households finance their out-of-pocket healthcare costs. We define hardship financing as having to borrow money with interest or to sell assets to pay out-of-pocket healthcare costs. METHODS: Using survey data of 5,383 low-income households in Orissa, one of the poorest states of India, we investigate factors influencing the risk of hardship financing with the use of a logistic regression. RESULTS: Overall, about 25% of the households (that had any healthcare cost) reported hardship financing during the year preceding the survey. Among households that experienced a hospitalization, this percentage was nearly 40%, but even among households with outpatient or maternity-related care around 25% experienced hardship financing.Hardship financing is explained not merely by the wealth of the household (measured by assets) or how much is spent out-of-pocket on healthcare costs, but also by when the payment occurs, its frequency and its duration (e.g. more severe in cases of chronic illnesses). The location where a household resides remains a major predictor of the likelihood to have hardship financing despite all other household features included in the model. CONCLUSIONS: Rural poor households are subjected to considerable and protracted financial hardship due to the indirect and longer-term deleterious effects of how they cope with out-of-pocket healthcare costs. The social network that households can access influences exposure to hardship financing. Our findings point to the need to develop a policy solution that would limit that exposure both in quantum and in time. We therefore conclude that policy interventions aiming to ensure health-related financial protection would have to demonstrate that they have reduced the frequency and the volume of hardship financing.

'First we go to the small doctor': first contact for curative health care sought by rural communities in Andhra Pradesh & Orissa, India.

BACKGROUND & OBJECTIVES: Against the backdrop of insufficient public supply of primary care and reports of informal providers, the present study sought to collect descriptive evidence on 1 st contact curative health care seeking choices among rural communities in two States of India - Andhra Pradesh (AP) and Orissa. METHODS: The cross-sectional study design combined a Household Survey (1,810 households in AP; 5,342 in Orissa), 48 Focus Group Discussions (19 in AP; 29 in Orissa), and 61 Key Informant Interviews with healthcare providers (22 in AP; 39 in Orissa). RESULTS: In AP, 69.5 per cent of respondents accessed non-degree allopathic practitioners (NDAPs) practicing in or near their village; in Orissa, 40.2 per cent chose first curative contact with NDAPs and 36.2 per cent with traditional healers. In AP, all NDAPs were private practitioners, in Orissa some pharmacists and nurses employed in health facilities, also practiced privately. Respondents explained their choice by proximity and providers' readiness to make house-calls when needed. Less than a quarter of respondents chose qualified doctors as their first point of call: mostly private practitioners in AP, and public practitioners in Orissa. Amongst those who chose a qualified practitioner, the most frequent reason was doctors' quality rather than proximity. INTERPRETATION & CONCLUSIONS: The results of this study show that most rural persons seek first level of curative healthcare close to home, and pay for a composite convenient service of consulting-cum-dispensing of medicines. NDAPs fill a huge demand for primary curative care which the public system does not satisfy, and are the de facto first level access in most cases.

Reconciling research and implementation in micro health insurance experiments in India: study protocol for a randomized controlled trial.

BACKGROUND: Microinsurance or Community-Based Health Insurance is a promising healthcare financing mechanism, which is increasingly applied to aid rural poor persons in low-income countries. Robust empirical evidence on the causal relations between Community-Based Health Insurance and healthcare utilisation, financial protection and other areas is scarce and necessary. This paper contains a discussion of the research design of three Cluster Randomised Controlled Trials in India to measure the impact of Community-Based Health Insurance on several outcomes. METHODS/DESIGN: Each trial sets up a Community-Based Health Insurance scheme among a group of micro-finance affiliate families. Villages are grouped into clusters which are congruous with pre-existing social groupings. These clusters are randomly assigned to one of three waves of implementation, ensuring the entire population is offered Community-Based Health Insurance by the end of the experiment. Each wave of treatment is preceded by a round of mixed methods evaluation, with quantitative, qualitative and spatial evidence on impact collected. Improving upon practices in published Cluster Randomised Controlled Trial literature, we detail how research design decisions have ensured that both the households offered insurance and the implementers of the Community-Based Health Insurance scheme operate in an environment replicating a non-experimental implementation. DISCUSSION: When a Cluster Randomised Controlled Trial involves randomizing within a community, generating adequate and valid conclusions requires that the research design must be made congruous with social structures within the target population, to ensure that such trials are conducted in an implementing environment which is a suitable analogue to that of a non-experimental implementing environment.

Microinsurance: innovations in low-cost health insurance.

Microinsurance--low-cost health insurance based on a community, cooperative, or mutual and self-help arrangements-can provide financial protection for poor households and improve access to health care. However, low benefit caps and a low share of premiums paid as benefits--both designed to keep these arrangements in business--perversely limited these schemes' ability to extend coverage, offer financial protection, and retain members. We studied three schemes in India, two of which are member-operated and one a commercial scheme, using household surveys of insured and uninsured households and interviews with managers. All three enrolled poor households and raised their use of hospital services, as intended. Financial exposure was greatest, and protection was least, in the commercial scheme, which imposed the lowest caps on benefits and where income was the lowest.

Cost of illness: evidence from a study in five resource-poor locations in India.

BACKGROUND & OBJECTIVE: In India, health services are funded largely through out-of-pocket spendings (OOPS). We carried out this study to collect data on the cost of an illness episode and parameters affecting cost in five locations in India. METHODS: The data were obtained through a household survey carried out in 2005 in five locations among resource-poor persons in rural India. The analysis was based on self-reported illness episodes and their costs. The study was based on 3,531 households (representing 17,323 persons) and 4,316 illness episodes. RESULTS: The median cost of one illness episode was INR 340. When costs were calculated as per cent of monthly income per person, the median value was 73 per cent of that monthly income, and could reach as much as 780 per cent among the 10 per cent most exposed households. The estimated median per-capita cost of illness was 6 per cent of annual per-capita income. The ratio of direct costs to indirect costs was 67:30. The cost of illness was lower among females in all age groups, due to lower indirect costs. 61 per cent of total illnesses, costing 37.4 per cent of total OOPS, were due to acute illnesses; chronic diseases represented 17.7 per cent of illnesses but 32 per cent of costs. Our study showed that hospitalizations were the single most costly component on average, yet accounted for only 11 per cent of total on an aggregated basis, compared to drugs that accounted for 49 per cent of total aggregated costs. Locations differed from each other in the absolute cost of care, in distribution of items composing the total cost of care, and in supply. INTERPRETATION & CONCLUSION: Interventions to reduce the cost of illness should be context-specific, as there is no "one-size-fits-all" model to establish the cost of healthcare for the entire sub-continent. Aggregated expenses, rather than only hospitalizations, can cause catastrophic consequences of illness.

Field based evidence of enhanced healthcare utilization among persons insured by micro health insurance units in Philippines.

Underutilization of healthcare is common among rural and low-income population segments in countries with lower income or inequitable income distribution. Micro health insurance units (MIUs) are created by informal sector groups because people cannot access health insurance or are dissatisfied with the programmes they can access. The policy choice to support MIUs relies on evidence that affiliation with these schemes increases healthcare utilization. This article examines new evidence of the association between affiliation with MIUs and healthcare utilization. We analyzed field data collected in 6 MIUs in the Philippines in 2002 (through a household survey encompassing 890 insured- and 1063 uninsured households). The two cohorts did not differ in demographic parameters, and differed only marginally in income and education levels, both higher amongst the insured. Insured persons reported higher hospitalization rates, higher rates of professionally-attended deliveries, lower rates of delivery at home, a higher frequency of primary-care physician encounters, a higher rate of diagnosed chronic diseases, and better drug compliance among chronically ill. Increased utilization by the insured is not due to adverse selection, judging by two facts: morbidity of the two cohorts, as assessed by a proxy indicator (the reported number of episodes of illness) did not differ; and rates of deliveries were even slightly higher among the uninsured. We conclude that MIUs in the Philippines can alleviate underutilization of heath care.