A demand side study. Independent study report submitted in partial fulfillment for the degree of Master of Public Health. Lakshmi Durga Chava

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1 Enrollment and renewal decisions of subscribers and non-subscribers of Ultra Poor and Non-Ultra Poor for BRAC, Micro Health Insurance scheme for rural women in Bangladesh: A demand side study Independent study report submitted in partial fulfillment for the degree of Master of Public Health Lakshmi Durga Chava Address: H-84, Madhuranagar, Hyderabad Andhra Pradesh, India Technical Supervisor: Dr. Md. Khairul Islam Country Director, ORBIS International, Dhaka James P Grant School of Public Health BRAC University 66 Mohakali, Dhaka 1212 January 2006

2 Table of contents 1 INTRODUCTION BACKGROUND LITERATURE REVIEW Micro Health Insurance Schemes in the developing world Enrolment Issues in Micro Health Insurance Schemes Micro Health Insurance Schemes in Bangladesh BRAC Micro Health Insurance Scheme RATIONALE OBJECTIVES GENERAL OBJECTIVES SPECIFIC OBJECTIVES METHODOLOGY RESEARCH DESIGN STUDY SITE STUDY POPULATION SAMPLE SIZE SAMPLING DATA ANALYSIS Quantitative Qualitative ETHICAL CONSENT RESULTS QUANTITATIVE AND QUALITATIVE FINDINGS Enrollment in Phulbari Socioeconomic and demographic Characteristics of the respondents Determinants of knowledge about MHIB Determinants of enrollment under MHIB Socio demographic Characteristics NGO membership Knowledge about MHIB Enrollment process Services at Shushastho Other facilitating factors Programme implementation factors Determinants of renewal for MHIB Renewal process Service delivery at Shushastho Experiences in availing services Feedback from the subscribers Perceptions of the Programme staff Orientation

3 Advocacy Supportive supervision and guidance MIS DISCUSSION CONCLUSION AND RECOMMENDATIONS BIBLIOGRAPHY ACKNOWLEDGEMENTS APPENDICES QUESTIONNAIRE AND CHECKLISTS VILLAGES PROFILE FGD PARTICIPANTS SUBSCRIPTION PROFILE SUPPORTING TABLES FOR RESULTS CASE STUDIES BRAC MICRO HEALTH INSURANCE SCHEME

4 List of tables TABLE 4.1:METHODS AND TOOLS FOR QUALITATIVE DATA COLLECTION TABLE 5.1: CUMULATIVE COVERAGE TABLE 5.2: ENROLMENT PERIOD AND PACKAGE DETAILS OF THE STUDY SAMPLE TABLE 5.3: SOCIO- ECONOMIC AND DEMOGRAPHIC PROFILE TABLE 5.4: STATUS OF NGO MEMBERSHIP, AWARENESS ABOUT MHIB, INTEREST TO JOIN MHIB TABLE 5.5:DETERMINANTS FOR AWARENESS ABOUT MHIB TABLE 5.6: NGO MEMBERSHIP TABLE 5.7: KNOWLEDGE ABOUT MHIB TABLE 5.8: AFFORDABLE PREMIUM TABLE 5.9:EXPECTED FACILITIES AT SHUSHASTHO TABLE 5.10: TREATMENT QUALITY TABLE 5.11: DISCRIMINATION TABLE 5.12: MINIMUM OUT-OF-POCKET EXPENDITURE TABLE 5.13: DISTANCE CLOSE TO SHUSHASTHO TABLE 5.14: TRANSPORTATION COST TABLE 5.15: SIGNIFICANT DETERMINANTS FOR ENROLLMENT TABLE 5.16:SIGNIFICANT DETERMINANTS FOR RENEWAL TABLE 10.1:QUESTIONNAIRE TABLE 10.2:STUDY VILLAGES PROFILE TABLE 10.3 FGD PARTICIPANTS PROFILE TABLE 10.4:PHULBARI AT GLANCE TABLE 10.5: EDUCATION TABLE 10.6:POVERTY STATUS TABLE 10.7:DISTANCE FROM SHUSHASTHO TABLE 10.8:NGO MEMBERSHIP TABLE 10.9:EDUCATION TABLE 10.10: OCCUPATION OF SPOUSE TABLE 10.11: DISTANCE FROM SHUSHASTHO TABLE 10.12: MEMBERSHIP IN BRAC NGO TABLE 10.13MEMBERSHIP IN GRAMEEN NGO TABLE 10.14:RESPONSIVENESS OF STAFF TABLE 10.15: USE OF CARD AND RENEWAL STATUS TABLE 10.16: VISIT TO OTHER HOSPITALS TABLE 10.17: ENTIRE FAMILY COVERAGE TABLE 10.18:QUALITY CARE TABLE 10.19LIST OF DOCTORS IN AND AROUND PHULBARI List of figures FIGURE 5-1: INTEREST TO JOIN FIGURE 5-2: RENEWAL STATUS FIGURE 5-3.INTEREST TO RENEW FIGURE 10-1PROCESS OF FOCUS GROUP DISCUSSIONS FIGURE 10-2: PROCESS OF STRUCTURED INTERVIEWS

5 Summary Empirical findings from international evidence showed that the main factors hampering people s enrolment in MHI 1 in the developing world are the problems with the affordability of premiums, the trust in the integrity and competence of the managers, the attractiveness of the benefit package and the quality of care that is offered by the providers. (Carrin, G. et al, 2005). Bangladesh provides context to study MHI schemes where the NGOs involved in implementation of the schemes. It would be appropriate to understand the perceptions of the community for improved coverage and provide recommendations for redesigning of Micro Health Insurance scheme for rural women in Bangladesh (MHIB) and strengthening of networks for better implementation of community based health insurance schemes in the country. The general objective of the study was to understand and analyze the underlying factors responsible for enrolment and renewal decisions from the perspectives of subscribers and nonsubscribers of Ultra Poor and Non-Ultra Poor 2 for BRAC MHIB. The specific objectives of the study were to: Construct the socio-economic and demographic profiles; analyze the underlying factors for enrollment and renewal decisions of subscribers; understand the perceptions of nonsubscribers for non enrollment; study the perspectives of health care providers and programme staff at field level in reaching the community and make recommendations for improvements of the scheme and future research. Using the Cross sectional study design the quantitative data was collected from 426 respondents through structured interviews in 20 selected villages of Phulbari, Dinajpur district. The qualitative data was collected from Focus Group Discussions (FGDs), In-depth-interviews, 1 Micro Health Insurance (MHI) is defined as a type of health insurance where accessibility to essential health services is made available to individuals and families, who are unable to afford formal health insurance schemes, through affordable premiums and low prices for health services. 2 The operational definition for Non-Ultra Poor in the study included the three quintiles of the population (the poor, the middle and the rich) who were the members or non members of village organisation (VO/NVO) of an NGO. 5

6 case studies and the observations of on going activities at Shushastho to know the perspectives of programme implementers, subscribers and non-subscribers of MHIB in support of the data obtained from structured individual interviews. Bivariate analysis and logistic regression were applied for quantitative data analysis. The quantitative results revealed that there were no substantial differences between the subscribers and the non-subscribers of Ultra Poor as well as the Non-Ultra Poor in the status of socio economic and demographic profiles. The differences were significant in terms of their NGO membership that influenced them to be aware of MHIB schemes in turn influenced their decisions to subscribe under MHIB. It also explicitly came up that if the health facilities established do not meet their health priorities and the discounts or exemptions included in the package without considering the indirect transportation cost that would add up to their out-of-pocket expenditure, no other factors would influence their decisions to become the subscribers under MHIB. The qualitative results also showed that the underlying factors influencing the enrollment and renewal decisions were mainly related to the upgraded facilities at Shushastho, quality of treatment and equity in delivery of services for the poor and the rich. Added to this, the system did not have space for the subscriber s feedback and action to improve the situation since inception of the project. It also revealed that the none of the significant determinants for enrollment were significant for renewal under MHIB. The perceptions were different in utilisation of services at Shushastho. While the out-of pocket expenditure even to pay 20% of their contribution and transportation mattered for the Ultra Poor, the quality treatment and feasibilities were mattered for Non-ultra Poor. The nonsubscribers suffered with inadequate knowledge about MHIB to subscribe and not having NGO 6

7 membership, where as the subscribers had the negative experiences in utilization of services and the burden with additional expenditure towards transportation and medicines. Trust with BRAC TB treatment and satisfaction with the treatment provided for general health problems were masked by the dissatisfaction with the treatment available for women health problems and childhood illnesses. Stringent administrative procedures of Shushastho putting the members especially the poor opt for others such as government facilities or Mission hospitals where they get either free or get time to pay after the treatment. The experiences on the issue of discrimination came out explicitly among the subscribers of Non-Ultra Poor, the category that included the poor, the middle class and the rich. The subscribers from tribal villages too had the experience of discrimination due to the problems in communicating in their local dialect. However, it was not expressed explicitly as a concern among the Ultra Poor who were mostly satisfied with the medicines given for their common general health problems and the ability of the staff to counsel who would come with general health problems than with chronic or other women health problems. Lack of technical expertise at all levels; no space for community participation and no planned strategy for advocacy with the support of NGO network were found to be the major operational missing factors under MHIB. The study findings recommend for the establishment of NGO Network to bring awareness about MHIB by encouraging women to have the memberships in NGOs as well as enrollment under MHIB and strengthen the community based health insurance schemes in the country. It also recommends to conduct feasibility studies and actuarial analysis to plan and implement the scheme with the community ownership. Further research is needed to know the best practices that are working well in other MHIBs implemented by other NGOs in the country. 7

8 1 Introduction 1.1 Background Health financing systems through general taxation or through the development of social health insurance are generally recognized to be powerful methods to achieve universal coverage with adequate financial protection for all against healthcare costs. Many low-income countries recognize the impediments to universal financial protection. (Van Ginneken, 1999a). Other financing methods, which would circumvent political and organizational difficulties at the national level, are therefore explored, including the direct involvement of communities in health financing. In the past, cost recovery for health care via user fees was established in many developing countries as a response to severe constraints on government finance. User fee policies were also seen as a possible expression of community financing. However, most studies alert decision makers to the negative effects of user fees on the demand for care, especially that of the poorest households (McPake, 1993). The involvement of the community in health financing was spurred, among others, by the Declaration of Alma Ata in 1978 (Bose & Desai, 1983), urging maximum community participation in organization of primary health care. Community based Health Insurance (CHI) is a common denominator for voluntary health insurance schemes, organized at the level of the community that are labeled alternatively as micro-insurance schemes (Dror & Jacquier, 1999). Micro Health Insurance (MHI) is defined as a type of health insurance where accessibility to essential health services is made available to individuals and families, who are unable to afford formal health insurance schemes, through affordable premiums and low prices for health services. The empirical findings of international evidence showed that the main factors hampering people s enrolment in MHI in the developing world are the problems with the 8

9 affordability of premiums, the trust in the integrity and competence of the managers, the attractiveness of the benefit package and the quality of care that is offered by the providers. (Carrin G et al, 2005). More research is still needed on understanding the institutional strengths and weaknesses of community involvement in health care financing, in monitoring and evaluating their impacts on financial protection, increasing access to needed health care, and combating social exclusion of the poor. (Preker AS et al., 2004). In this regard, BRAC MHIB in Bangladesh is not an exception. This study will focus on understanding the perceptions of subscribers and non-subscribers of Ultra poor and Non-Ultra Poor in their enrollment and renewal decisions for BRAC Micro Health Insurance scheme. 1.2 Literature review Micro Health Insurance Schemes in the developing world A growing literature on voluntary, non-profit health insurance schemes in recent years from low and middle-income countries of Asia and Africa is now available with varying experiences and lessons learned. An evaluation of Vimo SEWA, the Self Employed Women s Association Medical Insurance Fund in Gujarat, India, found such schemes to be successful in protecting poor households against the uncertain risk of medical expenses by covering people below the poverty line (Ranson, 2002). Another study looked at the performance of a health card insurance scheme implemented by the govt. of Burundi and found it to be a useful tool for women to take healthcare decisions independently, but at the same time adverse household selection (selecting only the poor households) made risk-sharing sub-optimal (Arhin, 1994). Schemes that cover hospital inpatient care have increased the use of health care in settings as diverse as the People s Republic of China (Bogg L et al., 1996), the Democratic Republic of the Congo, Ghana (Atim C, 1999), and Kenya. (Musau SN,1999). Improved financial protection was 9

10 achieved through reducing scheme members out-of-pocket spending, while increasing their use of health care services (Desmet A, 1999, Supakankunti S, 1997, Criel B et al., 1999). However, some research has suggested that the poorest of the poor and socially excluded groups were often not included in community-based initiatives for the financing of health care (Jutting J, 2001, Arhin DC, 1994) Enrolment Issues in Micro Health Insurance Schemes In the Maliando Mutual Health Organization in Guinea-Conakry, subscription dropped from 8% to 6% of the target population mainly because the participants grew disappointed with the quality of care offered at the health centre level (Criel & Waelkens, 2003). However, memberships rates generally are low in the beginning, but increase as the performance of the CHI convinces the population that subscribing may be profitable. A variety of factors influence people s decision to join the schemes given the voluntary character of CHI. Affordability of premiums or contributions is often mentioned as one of the main determinants of membership. For instance in the Nkoranza Scheme in Ghana, the estimated cost of contributions varied from 5% to 10% of annual household budgets (Atim, 1998). It was recognized that such contributions could be a financial obstacle to membership. The technical arrangements made by the scheme management may influence people s perception of personal benefits. An example is the unit of enrolment. In the Rwandan Project Study, large households with more than five members had a greater probability to enroll in the CHIs than households with fewer people (Schneider & Diop, 2001). The timing of collecting the contributions may also matter for membership. Annual contributions, collected at the time of harvest of cash crops, seem to be prevalent among schemes in rural areas (Bennett et al., 1998). 10

11 Trust in the integrity and competence of the managers of the CHI may also have an effect on enrolment. The existence of entry points in the community, such as a micro-credit scheme, a development co-operative or other social groups, may facilitate the establishment of CHI. If such existing initiatives have won the population s trust it may become easier to start up a CHI. (van Ginneken, 1999a). Trust can be enhanced when people see that their preferences matter. When the scheme administrators tend to be responsive to the community s preference, people s overall satisfaction with the community scheme s services is likely to increase. The quality of care offered through the CHI is another factor to be considered. The latter was highlighted in an evaluation of the Maliando scheme in Guinea-Conakry. People referred to rapid recovery, good health personnel, good drugs and a welcoming environment at the health facility as the most important features of quality. When membership was discussed specifically, lack of quality of care was cited as the most important cause of non-enrolment. (Criel& Waelkens, 2003). It is also important to see whether community health insurance is accessible across different population groups. One conclusion from the WHO Study was that very few schemes reached the vulnerable population groups, unless government or others facilitated their membership through subsidies (Bennett et al., 1998). In the Thie`s Study, income appeared to be a significant factor in explaining enrolment. Belonging to lower and upper income terziles decreased and increased enrolment respectively. When households classified themselves into poor and non-poor, it also appeared that the self-reported poor had a lower probability to join CHI than higher income households (Jutting, 2001; Jakab et al., 2001). One way to increase insurance membership for poor households is to introduce exemptions. 11

12 A study conducted in Tanzania from the demand side also showed that the income, village of residence, seasonality of income, joint family and quality of care at health centers are the main determinants of membership. But, Religion, sex, ethnicity and education did not affect the decision of households to join the scheme (Jutting JP et al., 2004) Micro Health Insurance Schemes in Bangladesh The pro-poor strategy of risk-pooling and pre-payment measures for healthcare financing has not drawn the attention of the policy makers in Bangladesh until recently. A few studies in the last few years examined some small-scale experimental community based schemes on health insurance initiated by voluntary non-profit organizations. One such study evaluated the two largest health insurance schemes in rural Bangladesh implemented by the Ganoshasthya Kendro (GK) and Grameen Health programme as a factor of social mobilization for participation and managing healthcare delivery and financing (Desmet et al., 1999). Beside technical performance based on the usual indicators for assessing health insurance schemes, the study also noted the potential of the schemes to get communities involved in healthcare management in general, and scheme management in particular, was noted. They concluded that the natural link that exists between health insurance and micro-credit lending to decrease economic burden of illnesses on households, could further promote this process. A multi-country analysis of household income-expenditure survey data for 59 countries, including Bangladesh, identified three key preconditions for catastrophic health expenditure: availability of health services requiring payment, low capacity to pay, and the lack of prepayment or health insurance. (Xu et al., 2003). Also, unofficial payments for supposedly free public health services and out-of-pocket spending on health services is the most common form of healthcare financing in Bangladesh (about 60%) (NHA 1999) and represent a significant 12

13 financial burden for households. The income erosion effect of ill health for the poor households in Bangladesh, especially the extreme poor 3 is also well documented (Sen, 1997, Sen, 2003, Hulme, 2003). In the absence of any risk pooling or pre-payment/co-payment schemes, this aggravates the vulnerable situation of the disadvantaged population groups. Ultimately, this leads to untreated morbidity, reduced access to care, irrational use of drugs and long time impoverishment (Whitehead, et al., 2001) Related to policies to increase access of the poor to MHI, most schemes can be qualified as deficient. Gonosasthya Kendra (GK) Scheme introduced a pro-poor policy to differentiating contributions according to one of four socio-economic groups (the destitute, poor, middle class and rich ). An important finding is that the membership rates between the two lowest socio-economic groups are substantially higher than in the other groups. However, after 15 years of operation of the GK scheme 20% of the destitute group and more than half of the poor group had still not been reached. The contribution levels and other payments are still said to be too excessive especially for the poor as well as the lower middle-income group of the middle class (Desmet et al. 1999). The household s geographical location is a determinant of inequality in access. For instance, in the GK scheme, membership among the two lowest socio-economic groups appeared to be related to distance: up to 90% of that target population from nearby villages subscribed, whereas only 35% did so for the target population in the distant villages (Desmet et al., 1999). The MHI projects for poor rural women in Bangladesh funded by ILO, supports community MHI schemes by eight voluntary non-profit NGOs, aiming at ensuring women s access to quality health care services (WEEH 2003). Generally, the benefits are open to all but with some variety in the service packaging and with differently applicable pricing programme 3 36% of its 130-million+ population living on <US$ 1 per day. 13

14 for the members and the non-members. With some variability of components and pricing, the copayment components are a set of common features of these projects. The micro-credit programmes work as the main institutional support structure for the MHI schemes. An early evaluation of the schemes found these successful in providing quality health services at low price and affordable premiums to a large disadvantaged group of people, especially women and children BRAC 4 Micro Health Insurance Scheme BRAC launched health insurance scheme in Shulla in May The salient features were that the group should have at least 75% of the population or 175 people to enter the scheme with an advance payment of premium of 4seers (3.75Kilos) per person. After running for 4 years this was abandoned as it did not reach those most in need and administration was cumbersome. Later, BRAC formed the programme of social protection as it was observed that the awareness of the difficulties faced by poor women and their families in accessing quality health services through lack of information and financial resources, placing them at a high risk of spending substantial proportion of their limited income on health care. The programme was launched in July 2001 in Narshingdi Upzilla and then in November 2001 in Phulbari Upzilla. Despite the efforts to increase membership, renewal rates for the BRAC Micro Health Insurance scheme remain low. It must also be remembered that BRAC MHIB is still in its early stage and has only been operating for a short period. As the scheme matures, popularity and membership are expected to increase. BRAC premiums are not based on any actuarial calculations or feasibility study. They are based on field staff members informal discussions with community members in the Narshingdi area. All the premiums and fees charged to VO members are at a subsidized rate, 4 Bangladesh Rural Advancement Committee, the national level NGO. 14

15 with the intention that premiums paid by non-vo members would cross-subsidize the VO members. To be sustainable there is a need to reassess the premium rates to be feasible, increase renewal rates and encourage timely renewal. Further, the rural poor do not normally understand the concept of risk pooling and are skeptical of the scheme. Therefore, MHI programmes require not just proper management, but more importantly, behaviour change for the user group through client education. This behaviour change is one of the major hurdles that the scheme has not yet overcome (Ahmed MU, et al., 2005). 2 Rationale The four years of experience in implementing MHIB, the previous study reports and progress reports from the field revealed three striking observations. Firstly, the enrolment was quite low among ultra poor and high among Non-Ultra poor 5. Secondly, the renewal rate was very low among Ultra Poor as well as Non-Ultra poor. Thirdly, the renewal rate was very low among non-vo members compared to VO members. For sustainability and scaling up of any health insurance scheme, the membership and level of prepayment have to be complemented with risk pooling across members of the scheme. Risk pooling is in principle beneficial because it will allow financial resources to be shared between the healthy and the sick. Further, BRAC s desire to introduce Micro Health Insurance scheme grew out of the need to include the neglected segment and address the issues of equity, affordability and accessibility to health care. But BRAC MHIB could not show its expected impact in an appreciative way. 5 The operational definition for Non-Ultra Poor in the study included the three quintiles of the population (poor, middle and rich) that are the members of Village Organisation (VO) or non members of village organisation (NVO) of an NGO. 15

16 Many studies were conducted in Bangladesh to assess the performance of the schemes looking at operational and administrative issues: supply side. An internal study was conducted by BRAC in two villages of Madhobdi with focus on various factors related to operation of the MHIB pilot project. With the given scenario of the health insurance schemes in Bangladesh, it would be appropriate to understand the perceptions of the community for improved coverage and provide recommendations for redesigning of BRAC, MHIB and to develop networks for better implementation of community based health insurance schemes in Bangladesh. 3 Objectives 3.1 General Objectives To study and analyze the underlying factors responsible for enrolment and renewal decisions from the perspectives of subscribers and non-subscribers of Ultra Poor and Non-Ultra Poor for BRAC Micro Health Insurance scheme. 3.2 Specific Objectives To construct socio-demographic profiles of subscribers and non-subscribers of Ultra Poor and Non Ultra Poor in the study area. To analyze the underlying factors for enrollment and renewal decisions of Ultra Poor and Non Ultra Poor subscribers. To understand the perceptions of non-subscribers of Ultra Poor and Non-Ultra Poor for not being enrolled so far and their interest to join. To study the perspectives of health care providers and programme staff at field level in reaching the target poor. To give recommendations for improvements of the scheme and future research. 16

17 4 Methodology 4.1 Research design The Cross sectional study design was used to collect the quantitative data through structured Interviews. Use of exploratory study techniques like In-depth Interviews, Focus Group Discussions, case studies and observation of ongoing activities were used for collection of the qualitative data. 4.2 Study site BRAC has been implementing MHIB in Madhobdi Upzilla in Narashingdi district and Phulbari Upzilla in Dinajpur district. The current study was conducted in Phulbari located in Northern Bangladesh, the poverty stricken area in the country. The details of block profile were shown in the appendices table Study Population The source population for this study was from all quintiles of population in Phulbari Upzilla. The reference population was from the following categories: Subscribers 6 and Non-subscribers 7 of Ultra Poor and Non-Ultra Poor for BRAC MHIB Health care providers of Shushastho 8 and Diagnostic centers. MHIB program implementers ( TCO 9,PO 10, Shastho Shebika 11 ) at field level and at BRAC office, Dhaka. 6 Enrolled under BRAC MHIB 7 Not enrolled under BRAC MHIB 8 BRAC Health Center 9 Training and communication co-ordinator for MHIB 10 Programme organiser for MHIB 11 BRAC s Community Health Volunteer 17

18 4.4 Sample size For quantitative data, the sample size was decided by taking the criteria of prevalence of not knowing about MHIB among non-subscribers. No study was conducted on the prevalence of factors influencing the decisions of enrollment or renewal of Micro Health Insurance schemes except in a short study conducted by BRAC that showed not knowing about MHIB among nonsubscribers accounted for 40%. Using Epi info, for a cross sectional study with 95% confidence interval; 80% power, odds ratio of 2.0 for 40% prevalence of Not knowing about MHI among non subscribers, a sample size of 288 with 142 subscribers and 142 non-subscribers was proposed. To take care the design effects, 0.4 to 0.5 times more of the sample was proposed. So, a total sample of 426 was covered for collection of quantitative data. Subscribers (213) Non-Subscribers (213) Ultra Poor (46) Non- Ultra Poor (167) Ultra Poor (46) Non- Ultra Poor (167) The quantitative data was collected with the help of four research Assistants under the constant supervision and guidance of the researcher from 5 th December to 22 nd December at Phulbari. All the Research Assistants were oriented for 2 days exclusively on the use of the questionnaire to elicit information from the respondents. Subsequently a day was spent in the field for the practice of Research Assistants before the actual data was collected. The Qualitative data was collected personally by the researcher with the help of a translator who was familiar with the local dialect. 18

19 Table 4.1:Methods and tools for qualitative data collection Method Sample size Respondents Focus Group Discussions (FGDs) 4 FGDs with members each. Programme staff of MHIB, Phulbari Subscribers & Non-subscribers Only subscribers BRAC school teachers, Shastho Shebikas In-depth Interviews 10 Medical Officer, Shushastho, Phulbari Training and communication Organiser Shastho Kurmi, Shastho Shebika Program Manger, Area Office, Phulbari Manager, Unnathi Managers of 3 diagnostic centers Senior sector in-charge, BRAC, Dhaka Case Studies 4 Ultra Poor Non Ultra Poor Subscriber Non subscriber 4.5 Sampling The villages were selected using purposive sampling method. The selection of villages was done considering the distance from Shushastho that were close and far away. The distance close to Shushastho is defined as the distance within 1.5 Km to 5 Km and has good access to local transportation such as Van or can be reached by walking. The distance far way from Shushastho is defined as the distance >5Km and can reach only by using public/private transport. Informal discussions with the programme staff were also considered to include approximately 50% of the villages with high enrollment and renewal rates and 50% with low enrollment and renewal rates among the 20 villages selected that are close and far way from Shushastho. Due to the availability of less number of Ultra Poor card holders compared to Non- Ultra Poor card holders in all the villages selected, the ratio of Ultra Poor and Non Ultra Poor 19

20 subscribers was done in 1:4. The same ratio was continued for selection of non-subscribers of Ultra Poor and Non-Ultra Poor in every village. The selection of subscribers was done using the list provided by Phulbari Area Office and the help of the Shastho Shebika. In every village, all the available Ultra Poor cardholders were interviewed. The Ultra Poor and Non-Ultra Poor cardholders were interviewed in the ratio of 1:4 in every village. The non-subscribers were selected using the systematic random sampling by counting every third house from a Shashstho Shebika s house that was mostly located in the center of the village. If the third house happened to be a subscriber, then proceeded to the next number till the required number of non-subscribers obtained in the village in proportion to the subscribers. For qualitative data collection, the case studies and FGDs were conducted in the same villages selected for quantitative data collection. Observation of ongoing activities at Shushastho was done. In-depth interviews were held with programme staff and service providers as mentioned in table Data analysis Quantitative The quantitative data was collected from 426 respondents in 20 villages using structured questionnaire that was coded and field-tested. (Shown in the appendices Table 10.1). The data collection was done with the help of 4 research assistants. The data cleaning was done before preparing the data set in SPSS. The data was entered in SPSS and 10% of the data was checked for the correctness of the data entered. Frequencies and descriptive statistics were calculated using SPSS. Further bi-variate analysis and logistic regression were done to determine the significance of factors responsible for awareness of MHIB, enrollment and renewal decisions among subscribers and non-subscribers of Ultra Poor and Non-Ultra Poor. 20

21 4.6.2 Qualitative The qualitative data was processed and analyzed using Atlas-ti soft ware. Data processing was done by sorting, categorizing, coding of the data collected for subsequent computer analysis, compiling data under different variables, comparison of the facts and identifying relation between variables. 4.7 Ethical consent James.P.Grant School of Public Health, BRAC University obtained formal consent from BRAC organization to conduct the field study at Phulbari. Subsequently, before collection of the data, informant written consent was obtained from each of the respondent of the study and ensured their confidentiality. In case of the respondents who could not do signature, the investigator read the contents of the consent form and explained and she/ he certified the same. 5 Results 5.1 Quantitative and Qualitative findings The quantitative data was collected from 426 respondents in 20 selected villages. 55.2% of the respondents were from the villages located at < 5Km(1.5-5Km) from Shushastho and 44.8% were from the villages located at >5km (6-28Km). The village profiles were shown in the appendices table The data was collected from 213 subscribers and 213 non-subscribers and each of the categories included with 46 Ultra poor and 167 Non-Ultra Poor respondents. The qualitative data was collected to know the perspectives of programme implementers, subscribers and non-subscribers of MHIB in support of the data obtained from structured individual interviews. The data was collected from 4 Focus Group Discussions (FGDs), 10 In- 21

22 depth-interviews, 4 case studies and also the observations of on going activities at Shushastho. The profile of the respondents of the FGDs was shown in the appendices Enrollment in Phulbari The cumulative enrollment status of Ultra Poor and Non-Ultra Poor under different packages of MHIB, from November 2001 to December 2005 was: Table 5.1: Cumulative coverage Category Family Health Package Pre-paid Pregnancy Package Ultra Poor Non-Ultra Poor VO NVO Total From the above cumulative coverage, the current study covered, 21.5% of Ultra Poor subscribers (46) under Equity package and 3.8% of Non-Ultra Poor subscribers (140) under General package and only 1% of Non-Ultra Poor subscribers (27) under PPP package. The details of the packages and the enrollment period of the 213 subscribers in the study were: Table 5.2: Enrolment period and package details of the study sample Enrollment period Number % Enrolled package Number % 1 year General years PPA years Equity years Total Total

23 Table 5.3: Socio- economic and demographic profile S.No Characteristic Subscriber (N=213) Non-Subscriber ( N=213) Ultra Poor Non- Ultra Poor Ultra Poor Non- Ultra Poor (N=46) (N=167) (N=46)(N=167) 1 Age %of respondents with <45years % of respondents between years Education %of respondents with no schooling % of respondents with Primary Education % of respondents with Secondary Education and above Family type and size % of HHs in joint families % of women headed HHs %of HHs with family size < %of HHs with family size > Occupation % of respondents involved in other occupations (Beggar, maid servant, day labour etc) % of respondents with spouse employed % of respondents involved in Income generating activities Income % of families with monthly income <500Tk % of families with monthly income % of families with monthly income % of families with monthly income > meal days in a month % of families with 3meal days in the entire month %of families with <7 days in a month % of families with no 3 meal days in a month Housing %of respondents with own housing Clothing % of respondents with <2pairs of clothes %of respondents with 3 to 5 pairs of clothes % of respondents with 6 to 10 pairs of clothes Cash availability during health emergencies % of respondents with regular cash availabilities % of respondents with seasonal cash availability % of respondents have to borrow cash

24 5.1.2 Socioeconomic and demographic Characteristics of the respondents The results in the table 5.3 showed that there were no substantial variations between the subscribers and the non-subscribers in terms of age, education, and family size, availability of food, clothing and shelter. But, the interesting contrast was observed between the Ultra Poor and the Non-Ultra Poor in all aspects of socio-economic and demographic characteristics. Age, education and Family Type While most of the Ultra Poor subscribers were with >45 years of age, the Non-Ultra Poor subscribers were with <45 years. When none of the Ultra Poor could reach secondary education, 40% of the Non-Ultra Poor had secondary education and above. While 94% of the Ultra Poor were single and from women headed households, only 6% of Non-Ultra Poor were from women headed households and one fourth of Non-Ultra Poor were in joint families. Occupation Most of the Ultra Poor were either beggars or maidservants. But, one third of the Non- Ultra Poor were involved in income generating activities such as poultry, dairy, tailoring etc., While 30-45% of Ultra Poor had an average monthly income of <500TK, none of the Non-Ultra Poor were in this category and 85% of the them had income between TK. 3meal days, Clothing and Housing and cash availability during emergencies When 95% of Non-Ultra Poor had three meals all the days, it was only 20-30% for Ultra poor. While 70-80% of ultra poor had <2 pairs, majority of Non-Ultra Poor had between 2 to 5 pairs of clothes. While most of the Non-ultra Poor were in living in their own houses, 40 to 50% of ultra Poor were having own housing. While majority of the Ultra Poor need to borrow money during health emergencies, only 25% of Non-Ultra Poor mentioned that they need to borrow. 24

25 Table 5.4: Status of NGO membership, Awareness about MHIB, Interest to join MHIB S.No Characteristic 1 NGO membership Subscriber (N=213) Ultra Poor (N=46) Non- Ultra Poor (N=167) Non-Subscriber (N=213) Ultra Poor (N=46) Non- Ultra Poor (N=167) % of respondents having membership in NGOs %of respondents having membership in BRAC %of respondents having membership in Grameen %of respondents having membership in other NGOs % of respondents taken recent loan amount TK % of respondents taken recent loan amount - >5001TK Knowledge about MHI scheme a b % of respondents having awareness about MHI Source of information BRAC staff Vo members Other NGO staff Other sources Knowledge about details of packages General package Pre-paid Pregnancy package Equity package Renewal /Enrollment Interested Need time to think Not interested

26 NGO membership A majority of the population had memberships in NGOs such as such as BRAC, Grameen, ASA, Karitas, CCDB, ADRA and many other locally based MF institutions. Most of them had multiple memberships and had facility to access credit. Among the subscribers, both Ultra Poor (39%) and Non-Ultra poor (77%) had memberships with NGOs. When the subscribers of Ultra Poor (11%) and Non-Ultra poor (35%) had memberships with BRAC NGO, the non-subscribers of Ultra Poor (9%) had mostly with Grameen and the Non-Ultra Poor (27%) had mostly with other NGOs like CCDB, Karitas etc. Knowledge about MHIB Majority of the subscribers of ultra poor (85%) and Non-Ultra Poor (90%) were aware of MHIB. But, in case of non-subscribers, only 4% of Ultra Poor and 51% of Non-Ultra Poor were known about MHIB. Among the subscribers, the Ultra Poor (83%) and Non-Ultra Poor (87%) had the source of information mainly from BRAC staff (Programme staff, Shushastho staff or Shastho Shebika) and the rest had from the VO members and other sources. Regarding the details of packages, majority the subscribers were known only about the package for which they were enrolled and not about all the three packages of MHIB and the percentages were less among the non-subscribers. The Ultra Poor subscribers knew about the Equity package (83%), the subscribers (70% and 45%) and non-subscribers (42% and 33%) of Non-Ultra Poor were aware of General/ Family health package and Pre-paid Pregnancy packages respectively. But, the Ultra Poor non-subscribers were completely unaware of the details of even the Equity package. 26

27 5.1.3 Determinants of knowledge about MHIB Education The results showed that the odds of the members attending school being aware of MHIB were 2.33 times more than those not attending school among the subscribers. For nonsubscribers, it was 2.37 times more than those not attending school. It was found to be significant among the subscribers and the non-subscribers at 5% level. It was shown in the appendices table 10.5 Poverty status Regarding the poverty status, the results showed that the odds of members of Non-Ultra poor being aware of MHIB were 0.63 times more than Ultra Poor among the subscribers. It was not found to be significant at 5% level. For non-subscribers, it was 22.3 times more than the Ultra Poor and it was significant at 5% level. It was shown in the appendices table Distance from Shushastho Regarding geographical location of the village from Shushastho, the results showed that the odds of being at the distance of <5Km to be aware of MHIB were 0.61 times more than at the distance of >5Km among the subscribers. It was not found to be significant at 5% level. But, in case of non-subscribers, it was 0.52 times more than the distance from >5Km and was found to be significant at 5% level. It was shown in the appendices table10.7. NGO membership Among the subscribers, the results showed that the odds of having NGO membership to be aware of MHIB were 3.03 times more than not having membership and was found to be significant at 5%level. In case non-subscribers, it was 2.95 times more than not having membership. It was found to be significant at 5% level. It was shown in appendices table

28 Table 5.5:Determinants for awareness about MHIB B S.E. Wald df Sig. Exp(B) Step 1(a) Ctg(1) Distance(1) Schooling2(1) NGOmember14(1) BRACNGO14a(1) Constant a Variable(s) entered on step 1: Ctg, Distance, Schooling2, NGOmember14, BRACNGO14a. The results of logistic regression in the table 5.5 showed that while the poverty status was not found to be significant, the distance from Shushastho, attending to school; having NGO membership and membership with BRAC NGO and from Shushastho were found to be the be the determinants to be knowledgeable about MHIB and were significant at 5% level Determinants of enrollment under MHIB Socio demographic Characteristics Education For the Ultra Poor, the results showed that the odds of attending school to be the subscriber was 1.99 times more than not attending school. But the difference was not significant at 5% level. For the Non-Ultra Poor, the odds of attending school to be subscriber was 1.75 times more than not attending school and was found to be significant at 5% level as shown in the appendices table 10.9 Occupation Among the Ultra Poor, the odds of having occupation to be MHIB subscriber were 2.95 times more than without any occupation. In case of Non-Ultra Poor, it was 1.37 times more than 28

29 not attending school. But, the difference was not found to be significant at 5% level among the Ultra Poor as well as Non-Ultra Poor. Spouse occupation The results showed that the odds of spouse employment to be the subscriber were 0.67 times more than without the spouse employment among the ultra poor, and was not significant. But, among the Non-ultra Poor, it was 0.27 times more and was significant at 5% level. It was shown in the appendices table Distance from Shushastho Among the Ultra poor, the results showed that the odds of the villages located at <5Km from Shushastho to be the MHIB subscriber was 0.64 times more than the distance >5Km.In case of Non-ultra Poor, it was 0.79 times more than the distance from >5Km. And it was not found to be significant at 5% level among the Ultra Poor as well as Non-Ultra Poor. It was shown in the appendices table NGO membership Table 5.6: NGO Membership Category UP NGO membership Odds ratio CI P-value MHIB Subscriber Yes No Total Yes 18 (78.3%) 5 (21.7%) 23 (100%) No 28 (40.6%) 41 (59.4%) 69(100%) Yes 129 (57.8%) 94 (42.2%) 223(100%) NUP No 38 (34.2%) 73 (65.8%) 111(100%) Among the Ultra Poor, the results showed that odds of having NGO membership to be the MHIB subscriber were 5.27 times more than without having NGO membership. It was

30 times more than without having NGO membership among the Non-Ultra Poor. It was found to be significant at 5% level among the Ultra Poor and the Non-Ultra Poor as shown in the table 5.6. Membership in BRAC NGO The results also showed that the odds of having BRAC NGO membership to become the subscriber were 5.49 times more than without having BRAC NGO membership among the Ultra Poor. It was found to be significant (P value:0. 091). In case of Non-ultra Poor, it was 3.17 times more than without having BRAC NGO membership. And was found to be significant at 5% level. The table was shown in appendices table Membership in Grameen NGO It was also showed that having membership in Grameen NGO did not influence to become MHIB subscriber than without membership and were not significant at 5%level among the Ultra Poor as well as the Non-Ultra Poor. The table was shown in appendices table Knowledge about MHIB Category UP NUP Awareness about MHIB Table 5.7: Knowledge about MHIB Subscription to MHIB Odds ratio CI P-value Yes No Total Yes 39 (95.1%) 2 (4.9%) 41(100%) No 7 (13.7%) 44 (86.3%) 51(100%) Yes 150 (63.8%) 85 (36.2%) 235(100%) No 17 (17.2%) 82 (82.8% 99 (100%) The results showed that the odds of having awareness about MHIB to become the MHIB subscriber was times more than not having awareness about MHIB among the Ultra Poor. In case of Non-Ultra Poor, it was 8.51 times more than without having awareness about MHIB. It was found to be significant at 5% level as shown in the table

31 Enrollment process Affordable premium Table 5.8: Affordable Premium Status Affordable premium Odds ratio CI P-value Yes No Total NUP Subscriber 78 (64.5%) 43 (35.5%) 121(100%) Non-sub 91 (90.1%) 10 (9.9%) 101(100%) We do not mind to pay more premium if all the facilities such as caesarian operations, Ultra-sonogram, X-rays and ambulance are provided at Shushastho and treated our cases without referring to Dinajpur or other hospitals. Non-Ultra Poor Subscribers, from villages located>5km, participated in FGD mentioned As the ultra poor were enrolled under MHIB without any payment of premium, it was not the concern of them. The issue of premium came up among the Non-Ultra Poor who were either VO or NVO members. The results showed that odds of the subscriber mentioning about the affordable premium as the reason for enrollment was 0.20 times more than the non-subscriber and was found to be very significant at 5% level shown in the table Services at Shushastho Expected facilities at Shushastho Table 5.9:Expected facilities at Shushastho Status Availabilities of all facilities Odds ratio CI P-value Yes No Total UP Subscriber 5 (10.9%) 41 (89.1%) 46 (100%) Undefined Non-subscriber 0 (0%) 46 (100%) 46(100%) NUP Subscriber 66 (39.5%) 101 (60.5%) 167(100%) Non-subscriber 22 (13.2%) 145 (86.8%) 167(100%) The results showed that the availability of facilities such as equipment for Caesarian operations, X-ray, Ultra-Sonogram and all types of diagnostic tests at Shushastho were the primary concerns for enrolment under MHIB. In response to the question asked about the 31

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