Can Combining Performance-Based Financing With Equity Measures Result in Greater Equity in Utilization of Maternal Care Services? Evidence From Burkina Faso

Background: As countries reform health financing systems towards universal health coverage, increasing concerns emerge on the need to ensure inclusion of the most vulnerable segments of society, working to counteract existing inequities in service coverage. To this end, selected countries in sub-Saharan Africa have decided to couple performance-based financing (PBF) with demand-side equity measures. Still, evidence on the equity impacts of these more complex PBF models is largely lacking. We aimed at filling this gap in knowledge by assessing the equity impact of PBF combined with equity measures on utilization of maternal health services in Burkina Faso. Methods: Our study took place in 24 districts in rural Burkina Faso. We implemented an experimental design (clusterrandomized trial) nested within a quasi-experimental one (pre-and post-test design with independent controls). Our analysis relied on self-reported data on pregnancy history from 9999 (baseline) and 11 010 (endline) women of reproductive age (15-49 years) on use of maternal healthcare and reproductive health services, and estimated effects using a difference-in-differences (DID) approach, purposely focused on identifying program effects among the poorest wealth quintile. Results: PBF improved the utilization of few selected maternal health services compared to status quo service provision. These benefits, however, were not accrued by the poorest 20%, but rather by the other quintiles. PBF combined with equity measures did not produce better or more equitable results than standard PBF, with specific differences only on selected outcomes. Conclusion: Our findings challenge the notion that implementing equity measures alongside PBF is sufficient to produce an equitable distribution in program benefits and point at the need to identify more innovative and contextsensitive measures to ensure adequate access to care for the poorest. Our findings also highlight the importance of considering changing policy environments and the need to assess interferences across policies. Keywords: Performance-Based Financing, Equity, Equity Measures, Maternal Health Services, Burkina Faso Copyright: © 2022 The Author(s); Published by Kerman University of Medical Sciences. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/ licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Burkina Faso is a landlocked West African country with a population of 18.6 million and a life expectancy of 60 years. Infant and under-five mortality rates stand respectively at 61 and 89 deaths per 1000 live births. An estimated 41.1 percent of the population live below the national poverty line of US$1.90 a day. Maternal mortality remains high at an estimated 371 per 100 000 live births. Multiple challenges related to maternal care persist, including serious inequities in access linked to sub-standard quality, low geographical accessibility, and financial barriers. 15 Prior to PBF, Burkina Faso undertook several health financing reforms to increase coverage and reduce inequities in access to and utilization of maternal health services, such as removal of user fees for antenatal care (ANC) services in 2002, and an 80% removal of user fees for delivery care in 2007, with a provision for full exemption of the ultra-poor. 16 Later in 2016, with the introduction of national free healthcare policy, known as the gratuité, the government removed all user fees for services delivered to children under the age of 5 years and to pregnant and lactating women. 17 As described below, the introduction of the national free healthcare policy induced the Ministry of Health to modify PBF prices, specifically to remove the equity measure additional payments (implemented in PBF2 and PBF3) for selected services targeted by both the national free healthcare policy and the PBF program. 18 Following an initial pilot in the districts of Titao, Leo, and Boulsa, starting in January 2014, Burkina Faso piloted PBF combined with different equity interventions in 12 districts distributed across 6 regions (Boucle du Mouhoun, Centre-Nord, Centre-Ouest, Nord, Sud-Ouest, Centre-Est) in which health facilities were rewarded by the Ministry of Health for achievement of defined health service indicators using a case-based payment system, adjusted for quality of care after verification. More details on the intervention design have been described elsewhere. 18 In brief, PBF was implemented according to 4 different models, 3 of which included an equity intervention targeting specifically the ultra-poor, as summarized in Table 1. The details of the ultra-poor selection process have been described elsewhere. 19,20 Abbreviations: PBF, performance-based financing; CBHI, Community-based health insurance. Policy-makers expected the equity measures to induce increased utilization among the ultra-poor through 4 different pathways. First, they assumed that the targeting process would sensitize communities and particularly the targeted ultra-poor to the importance of health service utilization in case of need. Second, they assumed that the equity component would sensitize health workers to the importance of making specific efforts to facilitate health service utilization among the ultra-poor. Third, the removal of user fees for the targeted ultra-poor was assumed to reduce barriers to healthcare utilization among the ultra-poor. And finally, the elevated price levels for treating the targeted ultra-poor patients were assumed to enable and motivate health facilities to provide services to the ultra-poor free of charge. To address our study primary objective of measuring the equity impact of PBF combined with equity measures compared to standard PBF alone, we inevitably needed to investigate the effect of PBF compared to status-quo service provision in the first place. To do so, we adopted a design that combined experimental with quasi-experimental elements. More specifically, we conducted a cluster-randomized trial nested within a pre-and post-test study with independent controls. Hereafter, we describe the different elements of our study in detail, referring to the quasi-experiment as study component 1 and to the cluster-randomized trial as study component 2. Figure provides a summary of the details of both study components including final sample sizes used in the analysis. Summary of Study Designs and Sampling Procedures. Abbreviation: PBF, performance-based financing. For the study component 1, six regions (Boucle du Mouhoun, Centre-Nord, Centre-Ouest, Nord, Sud-Ouest, Centre-Est) were identified non-randomly by the government and its development partners as intervention regions. Within each region, 2 districts were selected as intervention districts, ie, destined to receive PBF, and 2 districts (when not possible in a neighbouring region) as control, ie, continue with status quo service provision with no PBF. The intervention districts were purposely selected based on poor performance on selected maternal health indicators. 18 Control districts were selected to be as similar as possible (also in terms of performance on maternal health indicators) to intervention districts. This study component was set to allow a comparison between PBF districts (12) and districts (12) with status quo service provision without PBF. Since the intervention was assigned at district levels, districts effectively functioned as clusters for this study component. For the study component 2, ten out of 12 districts (2 with community-based health insurance (CBHI) where CBHI was pre-existing to allow implementation) were targeted by the Ministry of Health and development partners for randomization due to financial constraints (not enough funds to allow targeting across all 12 selected districts once calculations for targeting costs were made). In 8 out of 10 targeted districts in 4 regions (Centre-East, Centre-Nord, Sud-Ouest, Nord), clusters (primary healthcare facilities) were randomized to receive either PBF1 or PBF combined with either one of 2 equity measures, PBF2, and PBF3 so as to test the additional effect of combining standard PBF with equity measures as one way of reducing inequities in access to and utilization of health services. Randomization took place within the framework of public randomization ceremonies in which concerned district and regions took turns in drawing primary healthcare facility names from a box containing all primary healthcare facility names in the 4 regions (Centre-East, Centre-Nord, Sud-Ouest, Nord) starting with the selection of pre-defined PBF model, and followed by the assignment of primary healthcare facilities in the order in which they were drawn from the box. 18 For example, in the 8 districts of the 4 regions (Centre-East, Centre-Nord, Sud-Ouest, Nord), this was done as follows: first facility: PBF1, second facility: PBF2; third facility: PBF3, fourth facility: PBF1, fifth facility: PBF2; and sixth facility: PBF3; etc. In these 8 districts of the 4 regions (Centre-East, Centre-Nord, Sud-Ouest, Nord) concerned by the three-arm randomization, this resulted into samples of 90 PBF1 facilities, 83 PBF2 facilities, and 84 PBF3 facilities. In the Boucle du Mouhoun region where 2 districts already implementing CBHI were targeted, 59 facilities were randomized to receive either PBF1 or PBF4 (following the same procedure outlined above), generating samples of 29 PBF1 and 30 PBF4 facilities. The 18 facilities which had been implementing CBHI prior to the launch of the study were excluded from randomization and hence from our study. Nevertheless, for ethical reasons, these facilities all implemented PBF in addition to CBHI. This study component was set to allow us to measure the benefit of combining PBF with an equity measure compared to implementing standard PBF on its own. In particular, we used this experimental component to measure the additional equity effects of PBF2, PBF3, and PBF4 compared to PBF1. For both study components, we used repeated cross-sectional household survey data collected at baseline from November 2013-March 2014 and at endline from April-June 2017. Sampling followed a three-stage cluster sampling procedure. First, for each primary healthcare facility included in the study (416 in intervention districts and 117 in the control districts — the number of facilities included in the study is larger for intervention compared to control districts since in intervention districts, we took a census of all facilities while in control districts we randomly selected one third of all facilities), we randomly selected one village. Second, within each village, we randomly selected 15 out of all households identified in each village where at least one woman was pregnant or had completed a pregnancy in the prior 24 months (inclusion criteria). Third, within a household, we interviewed all women of reproductive age (15-49 years), irrespective of whether they had a recent history of pregnancy. The survey collected information on use of reproductive and maternal health services from women of reproductive age (15-49 years). Data on use of family planning were collected from all women of reproductive age regardless of marital status while data on use of maternal health services were collected only from women with a recent pregnancy. At baseline in study component 1, our sample comprised 9999 (7766 in intervention group, 2233 in control group) women of reproductive age (15-49 years) of whom 6568 (5074 in intervention group, 1494 in control group) had completed pregnancy 24 months prior to the survey. For the study component 2, at baseline, our sample comprised 6292 (1730 in PBF1, 1602 in PBF2, 1606 in PBF3 in the 4 regions [Centre-East, Centre-Nord, Sud-Ouest, Nord] and 521in PBF1 and 833 in PBF4 in Boucle du Mouhoun) women of reproductive age (15-49 years) of whom 4052 (1112 in PBF1, 970 in PBF2, 1026 in PBF3 in the 4 regions [Centre-East, Centre-Nord, Sud-Ouest, Nord] and 363 in PBF1 and 581 in PBF4 in Boucle du Mouhoun) had completed pregnancy 24 months prior to the survey. At endline, in study component 1, our sample comprised 11 010 (8432 in intervention group, 2578 in control group) women of reproductive age (15-49 years) of whom 6371 (4932 in intervention group, 1439 in control group) had completed pregnancy 24 months prior to the survey. For the study component 2, at endline, our sample comprised 6856 (1884 in PBF1, 1722 in PBF2, 1752 in PBF3 in the 4 regions [Centre-East, Centre-Nord, Sud-Ouest, Nord], and 570 in PBF1 and 928 in PBF4 in Boucle du Mouhoun) women of reproductive age (15-49 years) of whom 4065 (1099 in PBF1, 983 in PBF2, 1010 in PBF3 in the 4 regions [Centre-East, Centre-Nord, Sud-Ouest, Nord], and 368 in PBF1 and 605 in PBF4 in Boucle du Mouhoun) had completed pregnancy 24 months prior to the survey. Table 2 summarizes all outcomes and control variables. Our outcomes were selected to capture service coverage (defined as utilization given need, ie, pregnancy status) along the reproductive and maternal health service continuum and to reflect services which were incentivized by the PBF program, namely: ANC in the first trimester, at least 4 antenatal care (ANC4+) visits, at least 2 doses of tetanus toxoid vaccine (TTV2+), iron supplementation, HIV testing in pregnancy, facility-based delivery, at least 1 postnatal care (PNC1+) visit, at least 3 postnatal care (PNC3+) visits, and modern family planning methods (female sterilization, male sterilization, intrauterine device [IUD]/spiral, injectables/depoprovera, implants/norplant, male condom, female condom, diaphragm, foam/jelly). To improve the estimation precision, we included a number of control variables, which have the potential to explain the variation in outcome indicators from our previous work. 21 We relied on multiple correspondence analysis — run separately on baseline and endline samples —to generate a wealth index based on asset ownership and dwelling characteristics. 22 Given our specific research focus and the intervention’s intention to treat the ultra-poor 23 and in line with prior literature, 8 we divided households in 2 wealth brackets corresponding to the Lowest 20% (ie, ultra-poor) and the rest — Upper 80%. Abbreviations: ANC, antenatal care; TTV, tetanus toxoid vaccine; PNC, postnatal care. aIncludes female sterilization, male sterilization, intrauterine device (IUD)/spiral, injectables/depoprovera, implants/norplant, male condom, female condom, diaphragm, foam/jelly. First, we used t tests to assess systematic differences in the distribution of Outcome variables and Control variables across study arms for both study components. Second, to assess the overall impact of PBF compared to status quo, we relied on study component 1 and used a difference-in-differences (DID) estimation approach, 24 comparing intervention districts (irrespective of specific study arm) with control districts. We estimated a linear probability model, where we clustered standard errors at district level. In addition, for each outcome, we included village fixed effects and several individual-level covariates (equation 1): where Ydvit outcome for individual i from villagev in district d at time t with t as (baseline, endline ); Y17t is dummy variable representing endline; PBFd is dummy variable denoting PBF exposure (1 = PBF, 0 = control); αv is village fixed effects capturing time-invariant unobserved differences across villages. Xit is vector of individual-level covariates; and εdvit is error term. λ is the variable of interest (interaction term between PBF and endline) that gives the DID estimate for the effect of being located in a PBF district. To determine overall PBF effects compared to status quo by socio-economic status group, we estimated regression model 1 by wealth bracket, following Lannes et al. 8 Third, to answer our key question on the equity impact of the PBF models integrating equity interventions, we relied exclusively on study component 2 of our study (10 districts) and also used DID to estimate a linear probability model as in equation 1, but with standard errors clustered at village level where randomization occurred. In this study component, equation 2 and equation 3 pertain, respectively, to the 8 districts in 4 regions (Centre-East, Centre-Nord, Sud-Ouest, Nord) where PBF1, PBF2, and PBF3 were randomized and to the 2 districts in Boucle du Mouhoun region where PBF1 and PBF4 were randomized: where Yvit is outcome for individual i from village v at time t with t as (baseline, endline ) in the intervention districts. λ2 and λ3 are variables of interest that give the DID estimates for the effects of being resident in PBF2 and PBF3 compared to PBF1, respectively, and λ4 is the variable of interest that give the DID estimate for the effect of being resident in PBF4 compared to PBF1 in the Boucle du Mouhoun region. Similarly to what we described earlier, to estimate specific effects by socio-economic status, we performed separate analyses by wealth bracket. 8 Furthermore, for study component 1, we performed several robustness checks to account for the small number of clusters (24 districts). We did so in light of the existing literature suggesting that: (1) a small number of clusters results in a higher likelihood of estimating downwards-biased standard errors, potentially leading to over rejection of the null hypothesis, ie, suggesting significant program impact while in reality there is none or very little impact 25 ; and (2) bias arising from a small number of clusters is more acute in situations characterized by an imbalance in cluster sample sizes. 26 Hence, to account for these 2 problems pertaining to our study component 1, we relied on the ‘wild bootstrap’ method for the related analyses. This method relies on a bootstrap t- procedure instead of bootstrapping the standard errors. 25 We performed all analyses using Stata14 (Stata Corporation, Texas, USA).

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