An assessment of the distribution of health service benefits in Tanzania

Transcription

1 SHIELD Work Package 3 Report: An assessment of the distribution of health service benefits in Tanzania Suzan Makawia 1, Jane Macha 1, Mariam Ally 3, Josephine Borghi 12 1 Ifakara Health Institute; 2 London School of Hygiene and Tropical Medicine; 3 Ministry of Health and Social Welfare. December

2 Table of Contents Acknowledgements... 4 Executive Summary... 5 Introduction Overview of Health Care Providers in Tanzania Methods Data Sources Utilisation Data Seasonality Adjustment Unit Cost Data Socio economic Status Measurement Factors affecting Benefit Incidence Data Analysis Categorisation of Providers Factors affecting Benefit Incidence Results Benefits from Outpatient Care Benefits from Inpatient Care Total Health Care Benefits Sensitivity Analysis Results Factors Affecting the Distribution of Benefits Discussion References List of Tables Table 1 Distribution of Health Facilities in Tanzania, by Ownership, Table 2 Unit cost estimates from three data sources in Tsh Table 3 Concentration Indices and Dominance Tests for Outpatient Benefits Table 4 Concentration Indices and Dominance Test Results for Inpatient Benefits

3 Table 5 Concentration Indices and Dominance Tests Results for Total Benefits Table 6 Overview of concentration indices for benefit distributions by provider and service type, for each source of unit cost data Table 7 Overview of concentration indices for total benefit distributions by provider ownership, for each source of unit cost data List of Figures Figure 1 Distribution of the Benefits from Public Outpatient Care Figure 2 Concentration curves for Public facilities for Outpatient Benefits Figure 3 Distribution of the Benefits from Private Outpatient Care Figure 4 Inpatient Benefits from Public, Private and Faith based Providers Figure 5 Concentration Curves for Inpatient Benefits Figure 6 Total Benefits from Public, Private and Faith based Providers Figure 7 Total Benefits Vs Need for health care Figure 8 Concentration Curves of Total Benefits and Health Care Need Appendices Appendix 1 Overview of Sample Size Calculation Methods for Household Survey Appendix 2 Details of Sampling Methods for SHIELD Household Survey Appendix 3 Assumptions underlying Unit Cost Calculations Appendix 4 Overview of Variables Included in the Index of Socio Economic Status

4 Acknowledgements This work was support by International Development and Research Centre (Grant number ) and the European Commission (Sixth Framework Programme; Specific Targeted Research Project no: 32289). We thank our SHIELD colleagues in South Africa and Ghana whose work contributed to this paper. We also thank the data collection team for their commitment during the SHIELD household survey in June and July We also thank the district managers and facility in charges who facilitated our undertaking this work, and to all of the households who spared their time to respond to our survey and to participate in our focus group discussions. 4

5 Executive Summary Background Benefit incidence analysis involves the analysis of the distribution of benefits from health care services across the population. It requires the estimation of service utilization along with an estimation of the cost of the service, which values the benefit. This report presents a system wide analysis of the distribution of health care benefits in Tanzania, across the range of providers available in the country, comparing the distribution of benefits among households of different socio economic status. Methods In order to compile detailed information on health service utilization rates, we carried out a household survey in 7 districts/councils: 4 rural districts (Kigoma rural; Singida rural; Mbulu, Kilosa) and 3 urban councils (Morogoro urban; and two councils of Dar es Salaam: Kinondoni and Ilala) covering a total of 12,201 individuals within 2,234 households. The survey took place between June and July The data were weighted to proxy national representation. A one month recall period was adopted for outpatient utilization. Monthly utilization was converted to annual utilization using a seasonal adjustment factor. Inpatient admissions were reported for the previous year. In the absence of reliable unit cost estimates for health services in Tanzania, we derived proxies for unit cost data from three data sources: 1) reports of claims reimbursed for the year 2007/2008 for outpatient and inpatient care by type of provider and level of care divided by total outpatient visits and inpatient admissions, as reported by the largest health insurance company (the National Health Insurance Fund); 2) estimates of total health expenditure derived from the National Health Accounts data of 2006 divided by estimates of total outpatient visits and inpatient admission days from the national Health Management Information Systems of the same year; and 5

6 3) a review of costing studies undertaken in the health sector in Tanzania. We conducted a sensitivity analysis to assess the impact of using different measures of cost on overall benefit incidence results. The analysis of benefit incidence requires a measure of socio economic status. We developed an index of socio economic status based on asset ownership variables, demographic and socio economic variables, and housing particulars and utilities that were found to be highly correlated to consumption expenditure within the Household Budget Survey data of 2000/2001. Households were ranked by the index and grouped into quintiles of equal size from poorest to least poor. In order to ascertain whether a benefit distribution for a given provider was pro rich or pro poor, we constructed bar charts indicating the relative share of total benefits received by each quintile. Further, we analysed the concentration curves and concentration indices. We also compared the distribution of total health care benefits against the distribution of health care need, captured within our household survey through a measure of self assessed health: respondents were asked to rank their health on a scale of 1 5: very good health, good health, average health, poor health and very poor health. Those who ranked their health status as being either poor or very poor were considered to be in need of health care. Results We found that the choice of unit cost estimate had little impact on the distribution of benefits. The results presented are those generated by the unit cost estimates derived from the NHIF data. Benefit Incidence Analysis Benefits from care in public primary facilities were pro poor with the poorest 40% of the population getting more than 40% of the benefits, and the richest 40% getting around 30% 6

7 of the benefits. Benefits from outpatient care at district hospitals were skewed towards the rich with the richest 20% getting over 40% of benefits. Benefits from outpatient care at regional and referral hospitals were also pro rich. Overall, public sector benefits for outpatients were relatively evenly distributed. Among the non public providers, outpatient care at primary faith based providers was relatively evenly distributed, whereas faith based hospital care was pro rich. Outpatient benefits from formal private for profit providers was heavily pro rich, with the richest 20% getting almost 50% of the benefits, and the poorest 20% getting virtually no benefit. Benefits from pharmacies, which in this study, were largely small drug shops, were relatively evenly distributed. Inpatient benefits from public health centers and district hospitals were pro poor. However, inpatient benefits from regional and referral hospitals were pro rich. Inpatient benefits from private for profit providers were extremely pro rich, the richest 20% are getting more than 80% of total benefits, and the poorest 40% getting almost no benefits. Inpatient benefits from faith based facilities were relatively evenly distributed When combining benefits from outpatient and inpatient care from public and faith based providers, the overall distribution was more or less even. Overall, benefits from privatefor profit providers, including drug shops, were strongly pro rich, with the richest 20% getting more than 45% of the benefits. When comparing the distribution of total health system benefits to need, everyone except the poorest 20% of the population were receiving more health care benefits than their share of need. However, the poorest 20% were receiving a much lower share of benefits relative to their share of need. Factors Affecting the Distribution of Benefits The poor benefit less from health services than the rich because they are not using health services to the same extent as wealthier groups and when they do use services, they are mainly using lower cost, nearby primary public facilities and drug shops. This is due to affordability constraints, especially associated with transport costs in the case of accessing referral care, with health care seeking being sometimes associated with borrowing or 7

8 selling assets. Higher level services are often not accessible to those living in rural areas, and public primary care suffers from a limited availability of drugs and adequately trained personnel, a lack of diagnostic equipment and limited opening hours. Discussion and Conclusions Overall, the distribution of health care benefits in Tanzania is fairly even. All but the poorest group receives a share of benefits that is commensurate with their share of need. Public primary providers are the only providers to substantively benefit the poorest. Higher level public facilities were found to be pro rich. Therefore, public subsidies targeting lower level facilities will benefit the poorest, whereas investments in higher level and referral facilities will be of greater benefit to the rich. Faith based providers are of benefit to the poor for inpatient care, as these providers are often more accessible to households in rural areas and can be flexible in their charging procedures to facilitate access to the poor. The government policy of subsidizing faith based hospitals to act as district hospitals in some areas is therefore of benefit to the poor. More subsidies might be offered to faith based facilities for inpatient care in rural areas, even when district hospitals are present, so as to increase access to hospital care among poorer groups. Private for profit providers essentially benefit the richest portion of the population. The tendency towards a pro rich distribution of many health care providers is a result of wealthier groups using more costly facilities, due to the limited availability and accessibility of quality public health services in rural areas. The rapid growth of the private sector has allowed for much greater health care choice among those with ability to pay. In contrast, the poor make use of lower cost, nearby services, their choice being constrained by factors related to affordability, availability and acceptability. It will, therefore, not be sufficient to simply increase public sector subsidies to increase the share of benefits going to the poor. Rather the complex series of constraints which prevent the poor from taking advantage of these services also needs to be addressed. 8

9 The current design of health insurance in Tanzania also contributes to the observed patterns of benefit distribution. National Health Insurance Fund (NHIF) members, who are generally higher income individuals, have cover in public, private and faith based facilities at all levels of care; whereas Community Health Fund (CHF) members, who tend to be poorer, mainly have cover for primary public care. One way of allowing the poorest to benefit from a broader range of providers is by expanding the benefit package available to CHF members and initiating cross subsidization between health insurance schemes, to finance such a change. However, increasing the share of benefits accruing to the poor through greater access is only one component that public policy should seek to address. Indeed, it is equally important that quality services be made available, to ensure that benefits are health enhancing. 9

10 Introduction Benefit incidence analysis involves the analysis of the distribution of benefits from health care services across the population. It indicates which population groups are benefiting from particular health services. It requires the estimation of service utilization along with an estimation of the cost of the service, which values the benefit. Benefit incidence analysis assumes that the more of a service a person uses, the greater the benefit derived. It also assumes that the higher the cost of the service, the greater the benefit. Two key concepts guide the analysis of the distribution of benefits in terms of equity impact. A distribution is said to be pro rich if the rich benefit more than the poor. A distribution is said to be propoor if the poor benefit more than the rich. Traditionally, benefit incidence analyses have been carried out uniquely for public sector facilities, often termed a public benefit incidence analysis. Such an approach allows decision makers to assess who is benefiting from public subsidies or investments in the public sector. Two studies of the distribution of public sector benefits in Tanzania were previously carried out. An study conducted during the early 1990s indicated a pro rich distribution of public health care benefits across all levels of health care provision (Castro Leal et al., 2000a). A more recent study drawing on 2000/2001 data, found public facilities overall to be pro poor (Mbaga and Mackintosh, 2005). However, in both cases, the analysis time frame was prior to the introduction of health insurance, and the data sources did not provide utilisation rates or a disaggregation by level of care. Internationally, there has been very little assessment of the benefit distribution from nonpublic providers, such as private and faith based providers and informal drug shops. Yet, in the Sub Saharan African context, these other providers are often widely used by the population. Further, faith based and private for profit facilities account for a growing share of health care provision in Tanzania (MOHSW, 2006, MOHSW, 2008a, MOHSW, 2008b) and are likely to be of growing importance in the move towards universal coverage, in order to meet the expected increase in the demand for health care. These providers also receive government subsidies. For example, the government contracts faith based 10

11 hospitals to provide health services on its behalf in areas with limited supply of higher level public facilities. Against this background, this study aimed to assess total benefit incidence across the entire health system. This study also attempted to address some of the limitations of earlier benefit incidence analysis studies which have relied on national household surveys of utilization (such as Income and Expenditure Surveys or the Demographic and Health Survey). These surveys typically ask respondents where they sought care, but do not quantify the intensity of use within a given time period. Further, such surveys generally ask respondents about sickness incidence and follow up with health care seeking questions. This means that the utilization data captured are most likely to refer to curative rather than preventive care that is not tied to an illness or injury event. Within the SHIELD project, we designed our own survey to capture detailed information on utilization rates not tied specifically to illness or injury. This report begins by giving an overview of health care providers in Tanzania, and then proceeds to detail the study methods and results. Overview of Health Care Providers in Tanzania The Tanzanian health system is composed of numerous health care providers offering care from the village level (dispensaries) up to the highest level of referral hospitals. The system is comprised of a range of providers of different ownership (public, faith based and private). Health services in Tanzania are provided at three levels: primary, secondary and tertiary. Primary level services are delivered by dispensaries, health centers, and outreach clinics. The secondary level, consisting of district hospitals and faith based (FBO) hospitals, mainly supports the primary level by providing surgical backup services, mostly for obstetric emergencies, and general medical and pediatric inpatient care for common acute conditions. Some of these hospitals also provide some specialized health services. Tertiary 11

12 hospitals, which are regional, or referral/specialized hospitals, provide services similar to those at the secondary level, along with a small range of specialist surgical and medical interventions. Based on most recent statistics, sixty five percent of health facilities are public or parastatal (Table 1), 15% are faith based or voluntary agency run facilities. Private for profit providers represent 19% of the total, and traditional providers 1%. Faith based providers are mainly managed under the Christian Social Services Commission (CSSC) and The Muslim Council of Tanzania. The government assists faith based facilities by providing them with an annual grant that covers local staff salaries. Faith based facilities generally charge user fees for treatment with some exceptions. Table 1 Distribution of Health Facilities in Tanzania, by Ownership, 2006 Provider Ownership Public Parastatal Faithbased Private for Other profit Referral hospitals Regional hospitals District hospitals Other hospitals Health centres Dispensaries 2, Specialised clinics Nursing homes Private laboratories Private X ray Units

13 Methods Data Sources Utilisation Data In order to compile detailed information on health service utilization and utilization rates, we carried out a household survey in 7 districts/councils: 4 rural districts (Kigoma rural; Singida rural; Mbulu, Kilosa) and 3 urban councils (Morogoro urban; and two councils of Dar es Salaam: Kinondoni and Ilala). Rural districts were selected such that they each had at least 10% of the population covered by the voluntary insurance scheme (the Community Health Fund), and to ensure geographical spread and differing topography. Dar es Salaam (DSM) was selected as an urban site due to the diversity of insurance schemes in the area, and the high prevalence of private insurance. DSM was seen to be indicative of higher income urban districts such as Arusha and Mwanza. Morogoro was selected to represent the remaining urban councils in the country, which are of relatively low socio economic status, and did not, at the time of the study, benefit from any insurance for the informal sector. The sample was not designed to be nationally representative but rather to allow for the calculation, with statistical confidence, of the impact of insurance membership on health care utilisation and associated out of pocket expenditure, as this was an important area of investigation within the SHIELD project (Appendix 1). The sampling of households followed a structured process. From the selected districts, we selected wards (or health facilities as a proxy for wards) as the primary sampling unit for our survey. Health facilities were first stratified into three categories in terms of their distance from the district headquarters by vehicle in the dry season 1 : nearby (less than 30 minutes), medium (between 30 minutes and one hour) and far away (more than an hour s drive). Two facilities were then randomly selected from the nearby group, 2 facilities from the medium group and 1 facility from the faraway group. From these facilities a list of 1 This was based on the experience of drivers at the district council. 13

14 Community Health Fund 2 members was obtained, and a random selection of 35 members made. In districts where CHF membership levels were lower (e.g. Kilosa), facilities were purposively selected to ensure sufficient numbers for the survey. A random selection of National Health Insurance Fund 3 members was also made at neighbouring primary and secondary schools. Members of Social Health Insurance Benefits 4, private insurance (AAR, Strategis) and micro insurance (Vibindo) were also interviewed. More details on the sampling methodology are provided in Appendix 2. Respondents, who were typically the household head, were asked to report all outpatient visits within the previous month and all inpatient admissions within the previous year for everyone in the household. Information was collected on the type of providers visited and the number of visits to each provider. Data were collected from a total of 12,201 individuals in 2,234 households. 1,155 households had a majority of members covered by health insurance. Of these, 523 were CHF member households, 485 were NHIF member households, 56 were households with members of private health insurance, 75 had members of SHIB, and 16 had members of Vibindo. A total of 1,059 non insured households were also interviewed. The field team comprised 24 interviewers and 6 field supervisors. Each attended a 3 day training session in Dar es Salaam. Subsequently, the team travelled to Kilosa district and undertook a 3 day pilot of tools and 119 households were interviewed during this process. For the pilot, households were selected from wards that were not included in the final survey, which were nearby to the district headquarters. The field work took place for 6 weeks between June and July Each field supervisor was intermittently supervised by a senior supervisor who would check on the quality and completeness of the questionnaires and revisit households, if necessary. 2 A voluntary health insurance scheme for the rural informal sector. 3 A mandatory health insurance scheme for the formal sector, covering public servants at the time of the study. 4 A mandatory health insurance scheme for the private formal sector who are members of the National Social Security Fund (NSSF). 14

15 Seasonality Adjustment A one month recall period was adopted for outpatient utilization. A longer recall period was felt to be potentially subject to recall bias. In order to estimate total annual outpatient visits, we therefore had to adjust monthly rates using a seasonal adjustment factor, as the month of observed utilization data may not be representative of the yearly average. In the case of our survey, the recall period for utilization was in most cases May It was not possible to obtain national statistics on the seasonality of service use, and instead we used data from Temeke Hospital compiled during a costing study carried out by Meheus et al. using data from 2006 (unpublished). In order to derive the seasonally adjusted factor for estimating annual outpatient utilization, we divided the average monthly number of outpatient visits for the year 2006, by the number of visits in May We then multiplied this number by 12, obtaining an adjustment factor of The utilization rates for outpatient care obtained from the SHIELD survey were multiplied by this factor to obtain total annual outpatient visits. Unit Cost Data There is no reliable unit cost estimate for health services available in Tanzania at present, although a comprehensive costing study is currently being planned by the Ministry of Health and Social Welfare (MOHSW) and the National Health Insurance Fund (NHIF), and should be completed in In the absence of such data, we derived a proxy of unit cost data from three sources, and conducted a sensitivity analysis to assess the impact of using different measures of cost on overall benefit incidence results. The first, and probably most reliable, source of cost data was that obtained from the National Health Insurance Fund (NHIF). The NHIF provided total claims reimbursed for the year 2007/2008 for outpatient and inpatient care by type of provider and level of care, along with total outpatient visits and inpatient admissions. We divided claims by visits/admissions to obtain unit costs. Details of the assumptions made during the calculation are provided in Appendix 3. 15

16 The second source of data was based on two data sources: total health expenditure for 2006 from the National Health Accounts (MOHSW, 2008a), combined with estimates of total outpatient visits and inpatient admission days from the national Health Management Information Systems of the same year (personal communication, MOHSW). Details of the precise methods of calculation are also indicated in Appendix 3. The final estimates of unit costs were based on a review of costing studies undertaken in the health sector in Tanzania. We identified five studies which compiled estimates of provider costs of care at different facilities/levels of care. We also included the SHIELD household survey data on average out of pocket payments at private for profit facilities, where prices in some sense proxy cost. The details of the studies identified and their results, along with the resulting average estimate of unit cost, are provided in Appendix 3. Table 2 provides an overview of the unit cost estimates derived from the three data sources and used in the BIA. Socio economic Status Measurement The analysis of benefit incidence requires a measure of socio economic status. Due to the challenges of collecting income and consumption data in household surveys, we instead based our income measurement on an index of socio economic status. The index was constructed using a combination of asset ownership variables, demographic and socioeconomic variables, and housing particulars and utilities. Those variables with the highest correlation with consumption expenditure in the Household Budget Survey data (2000/2001) were collected within the SHIELD survey. The population were ranked according to the index and allocated into quintiles of equal size, with quintile 1 representing the poorest 20% of the population, and quintile 5 representing the richest 20%. Details about the variables included in the index are provided in Appendix 4. 16

17 Table 2 Unit cost estimates from three data sources in Tsh 2008 Provider types NHIF (1) NHA/HMIS (2) Review of costing studies (3) OPD IPD OPD IPD OPD IPD Public Dispensary 1,717 2,378 2,672 Health centre 2,012 17,215 3,472 17,358 2,910 8,569 District Hospital 3,126 37,516 4,345 21,726 2,020 8,569 Regional 3,410 51,026 5,214 26,071 1,820 8,569 Hospital Referral hospital 13,059 75,819 28, ,050 2,692 9,218 Faith based Dispensary 3,468 2,621 2,447 Health centre 3,394 27,827 3,827 17,393 2,447 12,233 Hospital 5,817 84,285 4,345 21,726 5,236 15,677 Private for profit Dispensary 9,243 2,383 3,674 Health centre 6,651 33,528 3,479 19,133 7,060 5,810 Hospital 5, ,316 18,759 93,794 7,060 20,942 Pharmacy/drug shop 1,134 5 NA 1,134 Factors affecting Benefit Incidence Focus group discussions (FGDs) and in depth interviews (IDIs) were carried out to explore access barriers to care, specifically the affordability, availability and acceptability of health services, and how this impacts on the observed distribution of health care benefits. A total 5 Although the estimate of unit cost for pharmacy from the NHIF was 10,243 Tsh, this was felt to be extremely high and driven by the fact that NHIF only accredits private pharmacies which are largely in urban areas. To get representation of a broader range of pharmacies, including drug shops, we instead used the data from SHIELD on the average out of pocket expenditures incurred in pharmacies/drug shops. 17

18 of 22 focus group discussions were held in Kigoma, Mbulu, Ilala and Kinondoni, with members of different health insurance schemes, the non insured, and health service managers. Two in depth interviews were held with the in charges of rural public primary facilities in the two rural sites. Data Analysis In order to ascertain whether a benefit distribution for a given provider was pro rich or pro poor, we constructed bar charts indicating the relative share of total benefits received by each quintile. Further, we analysed the concentration curves and concentration indices. A concentration curve plots the cumulative proportion of benefits (Y axis) against the cumulative proportion of the population ranked by socioeconomic status (X axis). comparing the concentration curve to the 45 degree line of perfect equality (when the poorest 20% get 20% of the benefits and the richest 20% get 20% of the benefits) it is possible to ascertain if a distribution is pro rich or pro poor. If the concentration curve lies above the 45 degree line, the distribution is said to be pro poor and if the concentration curve lies below the 45 degree line, the distribution is said to be pro rich. Dominance tests can also be carried out to ascertain whether the concentration curve is significantly higher or lower than the 45 degree line, i.e. whether the difference is statistically significant. The concentration index is calculated as twice the area between the 45 degree line (of perfect equality) and the concentration curve. distribution, in terms of being pro rich or pro poor. By The index can also be used to summarise the A negative concentration index indicates a pro poor distribution of benefits. A positive concentration index indicates a pro rich distribution of benefits. The data were analysed using Stata 11. The Stata do file for estimating the distributions of benefits by quintile, the concentration curves, dominance tests and concentration indices was adapted from O Donnell (O'Donnell, 2007). In health care, fairness implies that the benefits of health care should be distributed according to the population s need for health care. We know that the poorest groups in 18

19 society have the greatest need for health care due to their economic vulnerability which makes them more prone to illness. We therefore compared the distribution of benefits to that of need. To measure need in our survey, people were asked to rank their health on a scale of 1 5: very good health, good health, average health, poor health and very poor health. Those who ranked their health status as being either poor or very poor were considered to be in need. Categorisation of Providers In some cases the sample size for analysing individual providers was too small and we had to combine providers within our analysis. For example, for the analysis of outpatient care benefits, we combined dispensaries and health centres as primary care facilities. We also combined public regional and referral hospitals, as the sample size for referral hospitals was very small. In the case of private for profit providers, we combined dispensaries, health centres and hospitals, due to limited sample sizes for these individual providers. Pharmacies and drug shops were considered as private for profit providers and grouped together as a single provider. In the case of inpatient care, we considered that inpatient admissions only took place at health centres and higher level facilities, although in some cases dispensaries do offer inpatient care. Further, for public providers, we combined health centres and district hospitals due to the limited sample size for health centres. For both faith based and private for profit providers we also combined health centres and hospitals. Some faith based providers have been designated to act as public district hospitals and receive grants from the government. These providers were considered as public providers in the current study. Factors affecting Benefit Incidence Thematic content analysis was used to analyze the qualitative data. Themes were initially identified and short summaries of the data compiled. The themes were then used for detailed data coding. The codes were repeatedly reviewed by different researchers for 19

20 validation and reliability and compared to the initial data summaries. QSR Nvivo 8 was used for coding and analysis. The analysis of access was considered in relation to three concepts: affordability, availability and acceptability. Results Overall we found that the choice of unit cost estimate had little impact on the distribution of benefits. Hence, in the subsequent presentation of results, we have focused on the results generated using the NHIF unit cost estimates, and we subsequently highlight where results differ when using alternative estimates of unit cost. Benefits from Outpatient Care Benefits from care in public primary facilities are pro poor (Figure 1) with the poorest 40% of the population getting more than 40% of the benefits, and the richest 40% getting around 30% of the benefits. This is also highlighted by the concentration curve which dominates the 45 degree line of equality (Figure2; Table 3). The pro poor nature of the distribution is also indicated by the significant negative concentration index ( 0.10) (Table 3). Benefits from outpatient care at district hospitals were skewed towards the rich with the richest 20% getting over 40% of benefits. Although the concentration index was not significant, the 45 degree line dominated the concentration curve (Figure 2; Table 3). Benefits from outpatient care at regional and referral hospitals were also pro rich, illustrated by the significant positive concentration index. The poorest 20% get very little benefit from these providers. Overall, public sector benefits for outpatients are relatively evenly distributed, the concentration index is not significant, but the 45 degree line dominates the concentrated curve. 20

21 Figure 1 Distribution of the Benefits from Public Outpatient Care 45% 40% 35% 30% 25% 20% 15% 10% 5% 0% Primary District hospitals Reg. & Ref. hospitals All public sector Poorest 20% 2nd poorest Middle 2nd richest Richest 20% Figure 2 Concentration curves for Public facilities for Outpatient Benefits cumulative share of benefits cumul shares of benefits OP(poorest fi rst) Primary public Op District Op Reg. & Reff. OP line of equality Among the non public providers, outpatient care at primary faith based providers is relatively evenly distributed, the concentration curve is not dominating or dominated by the 45 degree line, and the concentration index is not significant (Figure 3 and Table 3) whereas faith based hospital care is pro rich (the richest 40% are getting 50% of the benefits, whereas the poorest 40% are gettingg 20%). Outpatient benefits from formal 21

22 private for profit providers is heavily pro rich, with the richest 20% getting almost 50% of the benefits, and the poorest 20% getting virtually no benefit. Benefits from pharmacies, which in this study, were largely smalll drug shops, are relatively evenly distributed, the 45 degree line dominates the concentration curve but the concentration index is not significant. Figure 3 Distribution of the Benefits from Private Outpatient Care 50% 40% 30% 20% 10% 0% Primary FBOs Hospital FBOs Private Pharmacy Poorest 20% 2nd poorest Middle 2nd richest Richest 20% Benefits from Inpatient Care Inpatient benefits from public health centers and district hospitals are significantly pro poor as indicatedd by the negative concentrationn index, however, the concentration curve crossess the 45 degree line and hence the dominance test cannot be estimated (Table 4). However, inpatient benefits from regional and referral hospitals are pro rich, as 40% of the poorest groups are getting very little benefit, compared to the richest 40% (Figure 4). Inpatient benefits from private for profit providers are extremely pro rich, 40% almost no benefits. Inpatient benefits from faith based facilities are relatively evenly distributed, the concentration curve dominates the 45 degree line suggesting the distribution is pro poor, but the concentration index is negativee but not significant (Figure 5, Table the richest 20% are getting more than 80% of total benefits, and the poorest 4). 222

23 Table 3 Concentration Indices and Dominance Tests for Outpatient Benefits Providers Concentration Index Public Primary 0.10** Districtt Hospital 0.15 Regional & Referral 0.14* ** Hospitals All Public 0.01 Primary FBO 0.07 Dominance Test of Concentration Curve relative to 45 degree line CCC Dominates 45 Dominates 45 Dominates 45 Dominates Non Dominance FBO Hospital Private formal Pharmacy 0.19* 0.37* *** Dominates 45 Dominates 45 Dominates NOTE TO TABLE: * Significant at p<0.1; **Significant at p<0..05; ***Significant at p<0.01 Figure 4 Inpatient Benefits from Public, Private and Faith based Providers 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Health centres & Dist. Hosp Reg. & Ref. hospitals Poorest 20% 2nd poorest Middle Private 2nd richest FBO Richest 20% 23

24 Figure 5 Concentration Curves for Inpatient Benefits cumulative share of benefits cumul shares of benefits for Public & Private IP(poorest fi rst) Public Health cntrs & District hosp. IP Public Reg. & Reff. Hosp. IP Private Health cntrs & Hosp. IP Faith based Health cntrs & Hosp. IP line of equality Table 4 Concentration Indices and Dominance Test Results for Inpatient Benefits Provider Concentration Dominance Test of Index Concentration Curve relative to 45 degree line Health centre and district hospital 0.17** Curve crosses 45 degree line Regional & Referral 0.29*** 45 Dominates Hospital FBO 0.12 CC Dominates Private 0.68*** 45 Dominates Total Health Care Benefits When combining benefits from outpatient and inpatient care from public providers, the overall distribution is more or less even, although the poorest 20% are getting less than 10% of all benefits (Figure 6). Indeed, the concentration index is positive but not significant, although the 45 degree line dominates the concentration curve indicating a prorich distribution (Table 5). Overall benefits from private for profit providers, including drug shops, are strongly pro rich, with the richest 20% getting more than 45% of the benefits. Overall benefits from faith based providers are evenly distributed, the 24

25 concentration index is not significant, and however, the concentrationn curve dominates the 45 degree line indicating the distribution is pro poor (Table 5). Figure 6 Total Benefits from Public, Private and Faith based Providers 50% 45% 40% 35% 30% 25% 20% 15% 10% 5% 0% Public Private for profit FBO Total benefits Poorest 20% 2nd poorest Middle 2nd richest Richest 20% Table 5 Concentrationn Indices and Dominance Tests Results for Total Benefits Concentration Index Dominance Test of Concentration Curve relative to 45 degree line Total Public Benefits Total FBO Benefits Total Private Benefits Total Health System Benefits *** * Dominates CC Dominates 45 Dominates 45 Dominates When comparing the distribution of total healthh system benefits to need, we can see thatt everyone except the poorest 20% of the population is receiving more health care benefits than they need. However, the poorest 20% are only receiving half of the benefits they need (Figure 7). 25

26 Figure 7 Total Benefits Vs Need for health care 25% 20% 15% 10% 5% 0% Poorest 20% 2nd poorest Middle % Shares of benefits 2nd richest Richest 20 Need When comparing the distribution of need to the 45 degree line of equality, the distribution lies slightly above the line of equality, indicating that the poorest groups have a higher levell of need (Figure 8). The distribution of total benefits clearly lies below both the 45 degreee line and the line of need. Figure 8 Concentration Curves of Total Benefits and Health Care Need cumulative share of benefits cumul shares of total benefits for Public & Private(poorest fi rst) Total benefits Public & Private Need line of equality Sensitivity Analysis Results When comparing the overall results from using the three approaches to unit cost measurement, we found that there was no difference in the direction of the concentrationn 26

27 indices or the conclusion regarding the distribution of benefits being pro rich or pro poor (Table 6). The only providers where differences were observed were total public outpatient benefits, and total public and total faith based benefits. However, the concentration indices for these providers were not statistically significant in any of the three cases. In terms of the distribution of total benefits, the concentration index was positive for the three methods but was only significant for the NHA and Literature review methods (Table 7). Table 6 Overview of concentration indices for benefit distributions by provider and service type, for each source of unit cost data Providers NHIF NHA Review NHIF NHA Review Outpatient Inpatient Public 0.10** 0.09** 0.10** Primary District ** 0.17** 0.17** Hospital Reg & Ref 0.14** 0.12** 0.17*** 0.29*** 0.39*** 0.27*** Hospitals Total Public FBO Primary FBO 0.19* 0.19** 0.19* Hospital Total FBO 0.15** 0.11* 0.13** Private 0.37*** 0.48*** 0.43*** 0.68*** 0.69*** 0.56*** Pharmacy

28 Table 7 Overview of concentration indices for total benefit distributions by provider ownership, for each source of unit cost data Benefits NHIF NHA Review Total Public Total FBO Total Private 0.36*** 0.37*** 0.33*** Total Benefits ** 0.06* Factors Affecting the Distribution of Benefits The reason for the lower level of benefits among the poor, is that health benefits are determined by use of services and those who do not use do not benefit and those who use higher level care receive greater benefits. Therefore, the poor benefit less because they are not using health services to the same extent as wealthier groups and when they do, they are mainly using lower cost, nearby, lower cost primary public facilities and drug shops. One of the major barriers to access is the limited affordability of health care services for poor people, especially in relation to drugs, laboratory tests and transport. Transport costs can escalate rapidly for those living in rural areas, particularly when travelling to referral facilities, or seeking to purchase drugs that are not available at lower level facilities. Transport costs were especially burdensome to patients who were seriously ill or had chronic conditions and needed to visit services regularly. I have diabetes and I have to travel to Haidom (mission hospital) every month at my own cost despite me being a member of the fund, am travelling to get drugs which are not available at the dispensary, which I can t manage that regularly (FGD, Insured, Mbulu, Tanzania) In instances where illness was perceived as an emergency, families who were unable to pay would resort to borrowing from friends, families and money lenders. Alternatively they would sell assets or delay care seeking in order to finance health care. Borrowing was 28

29 reported to have devastating effects on already poor families by instigating a vicious cycle of debt and further repayments. You go and borrow from your neighbor if you don t have, but normally this requires interest to be paid. If you don t want to borrow, then you sell crops or other assets you have like furniture s (FGD, Uninsured, Mbulu, Tanzania) The limited availability of quality public primary health care facilities was a deterrent to their use by those who could afford alternative treatment options. In public facilities, drugs were more likely to be out of stock and diagnostic equipment was often lacking. Many people mentioned being referred onwards as the services they needed were not available at their first port of call. However, there was reportedly low adherence to referral requirements among the poor due to distance and cost, with sometimes serious health consequences. Unpredictable opening hours in rural lower level public facilities also affected service availability. Participants expressed frustration over their inability to use the service when they need it. Staff attitudes were one of the main factors affecting service acceptability along with health service quality. Negative attitudes deterred better off patients from using lower level public services, while positive attitudes by health providers in private facilities, attracted wealthier people to access care at these facilities. It is much better to access care at the private facilities because they have a positive attitude towards us, they consult patients politely. In the public facilities due to high workload they are so harsh (FGD, Insured, Kigoma, Tanzania). People also reported concern about the skills of staff at lower level facilities to deal with certain health problems, further encouraging those with the ability to pay to seek care elsewhere. 29

30 Majority of the staff in our dispensaries are unskilled and they are expected to provide services even if they don t have enough skills, leading to wrong prescriptions due to lack of skills (FGD, HFC members, Mbulu, Tanzania,) A lack of diagnostic equipment at public primary facilities was also a concern, due to potential misdiagnosis and paying for unnecessary drugs. The doctor here uses his experience to treat us because we don t have a laboratory and diagnostic equipment. ( ) He will just look at you and guess. He does not have any equipment to check what problem you have, he will just treat you the way he thinks, but he might also be wrong in the process (FGD, Uninsured person, Kigoma district) Discussion Overall, the distribution of health care benefits in Tanzania is fairly even. All but the poorest group receives a share of benefits that is commensurate with their share of need. Public primary providers are the only providers to substantively benefit the poorest, a finding which is consistent with previous studies in Tanzania and other developing countries (Mbaga and Mackintosh, 2005, Sahn and Younger, 2000, Wagstaff, 2010, Mangham, 2006, Davoodi et al., 2010). However, in an earlier study of Tanzania, Castro Leal (2000) found that the poorest 20% received a lower share of primary public benefits than the rich (18% versus 21% respectively) (Castro Leal et al., 2000). The shift to a propoor distribution is likely a result of increasing allocations of government spending to lower level facilities. Indeed, the share of total government expenditure allocated to health centres increased from 24.7% in year 2002/2003 to 38% in year 2005/2006 (MOHSW, 2008a). This trend is likely to be further enhanced through the primary health services development programme (PHSDP), a government programme to support the construction of primary level public facilities. 30

31 Previous studies were limited to the analysis of public benefit incidence. Our analysis indicates that the inclusion of benefits from non public providers also impacts the benefit distribution. Faith based providers are of benefit to the poor for inpatient care. In rural areas, even in districts which have a public district hospital, households that live at distance often opt for inpatient care at faith based providers to save on transport costs, as reported elsewhere (McIntyre et al., 2006). Indeed, distance to secondary level public facilities has been commonly reported to limit the use of such services among poorer groups (Pannarunothai and Mills, 1997, Hausmann Muela et al., 2003, Olujimi, 2007, Gulliford, 2009, Nonvignon et al., 2010). Further, faith based providers sometimes are more flexible in their charging procedures to facilitate access to poorer households. Given the pro poor nature of higher level faith based facilities, the government policy of subsidizing faith based hospitals to act as district hospitals in some areas, will therefore be of benefit to the poor. Given this situation, more subsidies might be offered to faith based facilities for inpatient care in rural areas, even when district hospitals are present, so as to increase access to health care to poorer groups. Private for profit providers essentially benefit the richest portion of the population. Efforts to enhance or expand such facilities in the current context will only benefit the rich. No previous analyses of benefit incidence have considered how the distribution of benefits compares with that of need. In our case, we were keen to compare the relative benefit and need distributions to ensure that benefits were commensurate with need, this being the ultimate test for horizontal equity. However, we faced challenges in measuring need. We found that self reported health was a poor proxy for need. In our data, the poor were much less likely to report illness or injury, although we know that the burden of ill health is much higher among this group. Similarly counter intuitive findings have been reported in other lower income countries when using self reported illness (Baker and Van der Gaag, 1993, McIntyre and Gilson, 1998). This is likely due to the lower recognition of illness /injury among poorer groups (Sen, 1993, McIntyre and Gilson, 1998). In recognition of these 31

32 challenges, we used self assessed health which provided a more intuitive distribution of need across wealth groups. Undertaking benefit incidence analyses in low income countries such as Tanzania is generally constrained by limitations in the availability of data. Existing household budget surveys do not collect detailed information on health care utilization rates. We, therefore, relied heavily on our own household survey which, although not nationally representative, gave a reliable picture of the national burden of the distribution of benefits, and allowed us to examine the benefit incidence of health services in Tanzania. In addition, obtaining reliable unit cost was a major challenge in the analysis of benefit incidence. There has been no comprehensive national study of health sector costs, although one is planned for However, we were reassured to see the high level of consistency of the results emerging from the sensitivity analysis, which indicated that the unit cost figure had relatively limited effect on overall findings. Obtaining reliable unit cost data is a challenge that has been faced by analysts in other countries. For example, in one study using Tanzanian data, costs were based on budgeted rather than actual expenditures for health facilities, and there was limited disaggregation by type of facility (Castro Leal et al., 2000). A further limitation is that we were unable to explore the benefit incidence arising from traditional care. Indeed, very few respondents acknowledged using such services, although such services are widespread across the country, and evidence suggests that traditional healers represent the first contact with the health system for over 70 percent of the population in Tanzania (Homsy et al., 2004 and McMillen, 2004). Further research should seek to elicit data on the use of these services to enhance our understanding of this part of the system and how it interplays with the formal sector. Less direct questioning techniques may be required to encourage respondents to describe contact with these providers, possibly complemented with qualitative research techniques such as focus group discussions. 32