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Introduction Burkina Faso is one among many countries in sub-Saharan Africa having invested in Universal Health Coverage (UHC) policies, with a number of studies have evaluated their impacts and equity impacts. Still, no evidence exists on how the distributional incidence of health spending has changed in relation to their implementation. Our study assesses changes in the distributional incidence of public and overall health spending in Burkina Faso in relation to the implementation of UHC policies. Methods We combined National Health Accounts data and household survey data to conduct a series of Benefit Incidence Analyses. We captured the distribution of public and overall health spending at three time points. We conducted separate analyses for maternal and curative services and estimated the distribution of health spending separately for different care levels. Results Inequalities in the distribution of both public and overall spending decreased significantly over time, following the implementation of UHC policies. Pooling data on curative services across all care levels, the concentration index (CI) for public spending decreased from 0.119 (SE 0.013) in 2009 to -0.024 (SE 0.014) in 2017, while the CI for overall spending decreased from 0.222 (SE 0.032) in 2009 to 0.105 (SE 0.025) in 2017. Pooling data on institutional deliveries across all care levels, the CI for public spending decreased from 0.199 (SE 0.029) in 2003 to 0.013 (SE 0.002) in 2017, while the CI for overall spending decreased from 0.242 (SE 0.032) in 2003 to 0.062 (SE 0.016) in 2017. Persistent inequalities were greater at higher care levels for both curative and institutional delivery services. Conclusion Our findings suggest that the implementation of UHC in Burkina Faso has favoured a more equitable distribution of health spending. Nonetheless, additional action is urgently needed to overcome remaining barriers to access, especially among the very poor, further enhancing equality.
Burkina Faso is a landlocked country located in West Africa, with a population of 21 million. In 2019, the country’s Gross Domestic Product (GDP, i.e. the total monetary value of all finished goods and services in a country over one year) per capita stood at US$787, placing it among the world’s poorest countries.20 The 2019 Human Development Index ranked Burkina Faso 182 out of 188 countries.21 Despite substantial improvements over the course of the last few years, health indicators still largely lag behind regional averages. Life expectancy is at 62 years.21 Maternal and under-five mortality are estimated at 320/100 000 and 88/1000 live births, respectively.22 Malaria, acute respiratory infections and diarrhoea still account for the largest proportion of child mortality, often coupled with an underlying situation of malnutrition, with nearly 25% of all children being classified as stunted.22 Health service delivery is organised in a three-tier system, with primary facilities (Centre de Santé et Promotion Sociale) located in rural areas; district hospitals located in each district capital; and regional and national referral hospitals located in the regional capitals and the national capital Ouagadougou.23 Public facilities provide the vast majority of health services.24 The health sector suffers from a generalised lack of resources. Total health expenditure is estimated at 5% of GDP, equivalent to Purchasing Power Parity US$109. Government expenditure amounts to 58% of total health expenditure, including contributions by development partners being estimated at 23% of this total. Private health expenditure is substantial as user charges continue to be applied, with more than 80% of all private expenditure on health not being channelled through prepaid and pooled mechanisms.25 The poor health outcomes described above are largely the result of low access to services, with people largely under-using the care they need. The literature has consistently reported that geographical barriers, due to the scarcity of health facilities, and financial barriers, due to user charges, continue to hamper access to healthcare services.26–28 Over the years, the country has put in place several health financing reforms aimed at fostering progress towards UHC, with a particular focus on maternal and child care. Specifically, in 2002, the Ministry of Health abolished user fees for antenatal care services and then in 2007 introduced a policy, generally referred to as SONU (soins obstétricaux and néonataux d’urgence), aimed at strengthening the provision of obstetric and newborn services. An essential element of SONU was introducing an 80% subsidy for all population groups and a 100% subsidy for the poorest for delivery services. Although the policy was not as effective in reducing out-of-pocket payments as initially expected,29–32 evidence indicates that it resulted in substantial increases in health service utilisation.33 In 2014, the Ministry of Health, with financial and technical support from the World Bank, expanded an existing performance-based financing (PBF) pilot intervention from 3 to 15 out of 63 districts, combining traditional PBF with three different equity measures. Results from the impact evaluation point at modest and not homogenous effects, well below the expectations, which had been placed on the programme.27 34 In June 2016, the Ministry of Health launched the so-called gratuité, that is, a free healthcare programme targeting specifically pregnant and lactating women and children under 5 years old.35 In addition, starting in 2009, the government prescribed that the worst-off (les indigents) should be fully exempted from paying user fees for all preventive and curative services provided by public facilities, but a study indicated that healthcare providers rarely apply this disposition also due to lack of knowledge.36 Our study uses BIA to examine how equality in health spending has evolved in Burkina Faso. Given the impossibility of applying BIA in a strictly speaking longitudinal manner due to the nature of the methodology and of the available data, we describe our study as quasi longitudinal. More specifically, as displayed in figure 1, we repeated the BIA at three different time points to explore changes in the distributional impact of health spending in relation to the different health financing reforms implemented in the country. Policy reforms and data sources mapped in relation to one another. DHS, demographic health survey; LCMS, living conditions monitoring survey; PBF, performance based financing survey; NHA, national health accounts. Furthermore, in line with the proposition postulated by McIntyre and Ataguba,37 our work considered both public and overall spending on health, leading to the estimation of two different sets of measures. The former one hereafter referred to as Public Spending BIA, captures the distributional impact of government spending, including aid received as budget support, on healthcare to measure to what extent different socioeconomic groups have benefited from government subsidies in the health sector over time. The latter one, hereafter referred to as Overall Spending BIA, builds directly on the methodological guidance provided by McIntyre and Ataguba to capture the distributional impact of overall spending on health, including contributions made by donors, including bilateral, multilateral and private aid earmarked for specific health interventions and by households (in the form of out-of-pocket spending). The decision to carry out these two parallel analyses stemmed from the recognition that while it is important to monitor the equity implications of government spending captured by the former analysis, it is equally important to assess the equity implications of overall health system performance captured by the latter analysis.37 For completeness, we also examined the distributional impact of donor spending alone. Moreover, conscious that the reforms implemented over time targeted different users and different services, we addressed the distributional impact of health spending separately for curative health services and institutional deliveries. In addition, for each set of services, we computed both stratified estimates to account for levels of care, differentiating benefit incidence measures for public primary healthcare centres versus public hospitals and pooled ones, aggregating information across levels of care. Given the data at our disposal, we could not include care availed and spending incurred at private facilities. However, one needs to consider that private health service provision remains very limited in Burkina Faso and concentrates almost exclusively in urban centres.25 The computation of BIA relies on two sets of data: data on health service utilisation stratified by socioeconomic status and data on the cost of health services. We derived information on health service utilisation stratified by socioeconomic status from three different sources: Enquete Multisectorielle Continue (EMC equivalent to Living Standard Measurement Study); Demographic and Health Surveys (DHS) and the population-based survey conducted within the framework of the impact evaluation of the PBF programme implemented in the country between 2014 and 2018, hereafter referred to as the PBF survey. In line with the literature,38 we derived information on health services cost from the recurrent health expenditure data reported in the National Health Accounts (NHA). Details of both the EMC and the DHS sampling and data collection procedures have been described elsewhere.39 40 In brief, both the EMC and the DHS are nationally representative repeated cross-sectional surveys conducted by the National Statistical Office with assistance from either the World Bank (EMC) or the US Agency for International Development (DHS). The EMC focuses on assessing households’ living conditions, including socioeconomic status and health service utilisation. The DHS focuses more specifically on maternal care, including an institutional delivery indicator. Due to the lack of a nationally representative sample capturing the utilisation of curative health service and/or institutional delivery, we relied on the 2017 round of the PBF survey for the computation of the most recent health service utilisation estimates. Sampling and data collection procedures have been described in detail elsewhere.41 In brief, the PBF survey collected information on a wide range of health outcomes, including utilisation of curative and maternal care services, in 8 out of 13 regions. Since regions were not randomly selected for inclusion in the study, PBF survey data cannot be considered fully representative at country level. Nonetheless, the PBF survey represents the only recent large-scale survey reporting individual-level information on health service utilisation and allowing for stratification by socioeconomic status after 2015. As such, it was the best data source at disposal for a BIA in Burkina Faso. Table 1 illustrates which datasets were used for which year and for which service and briefly describes the corresponding sampling strategy. Summary information on population survey data employed in the study NHA provide detailed information on the financial flow related to healthcare in a country, using a standardised framework called System of Health Accounts. To derive unit costs for curative health services and institutional deliveries, we extracted data on three sources of health spending in NHA: recurrent public health spending, donor health spending and household out-of-pocket expenditures (OOPE). Information could be differentiated by the typology of service (ie, curative services and institutional delivery) and by the level of care (ie, hospital vs primary care centre). For our analysis, to ensure accurately matching unit costs and utilisation data, we used NHA from each of the years for which we also had utilisation data, derived from either the EMC, the DHS or the PBF survey. This means that our analysis relies on year-specific unit cost estimates. Following general methodological guidance on BIA,37 first, we estimated healthcare utilisation at different levels of care (and for institutional deliveries and curative services separately) for each wealth quintile, with quintile 1 being the poorest and quintile 5 being the least poor; and then annualsed healthcare utilisation by multiplying the estimate obtained from our data by 1 for institutional delivery (given a recall period of 12 months in both DHS and PBF survey) and by either 26 or 13 for curative services (given a recall period of 14 days in the EMC and of 28 days in the PBF survey, respectively). We relied on different wealth measures to stratify utilisation rates by socioeconomic status. EMC data allowed for wealth to be assessed using consumption expenditure; DHS and PBF data allowed for wealth to be assessed using the same set of variables to derive an asset-based measure. We applied the constant unit subsidy assumption to estimate public and donor unit costs.42 We relied on the constant unit cost assumption to estimate OOPE unit costs. This estimate was adjusted to reflect differences by quintile, using data derived from the study on OOPE conducted by Nakovics et al.43 The study had used the baseline round of the PBF survey to estimate OOPE for curative services and their distribution across socioeconomic strata in a sample of 7844 households distributed across eight regions. Given that the study was conducted under the leadership of the lead author, we had access to the raw dataset and could verify the data needed for this BIA analysis. This adjustment was motivated by the awareness that OOPE differs by quintiles; it follows that ignoring the distribution of OOPE across quintiles would have resulted in an overestimation of OOPE among the lower income groups. Following the constant unit subsidy/cost assumption, the unit subsidy/cost for healthcare level ί is equal to total subsidies/expenditure for healthcare level ί divided by total healthcare utilisation for healthcare level ί. where Tj is the value of the total health subsidy/cost imputed to the socioeconomic group j. Uij represents the number of health visits (utilisation of care) of socioeconomic group j at healthcare level or health facility type ί, and Ui is the total healthcare visits at that healthcare level or health facility type by the different socioeconomic groups and SiUi is the unit subsidy/cost of healthcare provision at level ί, which is assumed to be constant at that level of care. Si is the government, donor and household OOPE health spending.44 First, across all analysis sets, we estimated the distribution of financial benefits accrued by different socioeconomic groups as follows: where Bij is a benefit incidence for socioeconomic group i at the level of care j, Pij is the number of people in socioeconomic i using health services at the level of care j, Pj is the total of people using health services at the level of care j and Sj is the share of health expenditure at the level of care j. Our estimates are presented as concentration indexes (CIs), which quantify the degree of wealth-related inequality and is defined as two times the area between the concentration curve and the line of equality.42 The standardised CI (Ch) is estimated as follows: where hi is the health variable (eg, healthcare utilisation) for individual ί, μ is the mean of health variable, Ri is individual i’s fraction socioeconomic rank and Cov(hi,Ri) is the covariance. We used convenient regression to allow the calculation of the SEs of the CI. The formula is: where 2σ2R is the variance of the fractional rank variable. β is the estimator of the CI. The CI takes a negative (positive) value when the concentration curve lies above (below) the line of equality, indicating a pro-poor (pro-least poor) distribution of the health variable. If there is no wealth-related inequality, the CI is zero. Given that the CI handles the wealth quintiles as being ordered along a continuous scale, when using the term pro-poor or pro-least-poor hereafter, we do not refer to a specific quintile, but to the overall direction of the distribution being more in favour of lowest (Q1-Q2) or highest (Q4-Q5) quintiles. We adjusted the CI by the sampling weights of the EMC and DHS household surveys to scale up our estimates to the national population. Second, to test whether the concentration curve dominates (lies above) or is dominated (lies below) by the line of the equality at all its ordinates, we computed the test of the dominance of the concentration curve against the 45 degree line of equality at a 5% significant level.42 Last, to ensure that our BIA results would be consistent and not biased by the fact that for 2017, we used data from an own survey conducted only in a subportion of all regions (in the absence of a nationally representative dataset), we conducted a sensitivity analysis, comparing national estimates to only estimates from these eight regions also for the prior years. Since the study is based exclusively on secondary data, patient and public involvement were not applicable. Before being published, results were disseminated in-country using a digital webinar platform, with opportunities for policymakers and civil society representatives to participate and comment on emerging findings.
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