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Introduction Performance-based financing (PBF) has acquired increased prominence as a means of reforming health system purchasing structures in low-income and middle-income countries. A number of impact evaluations have noted that PBF often produces mixed and heterogeneous effects. Still, little systematic effort has been channelled towards understanding what causes such heterogeneity, including looking more closely at implementation processes. Methods Our qualitative study aimed at closing this gap in knowledge by attempting to unpack the mixed and heterogeneous effects detected by the PBF impact evaluation in Cameroon to inform further implementation as the country scales up the PBF approach. We collected data at all levels of the health system (national, district, facility) and at the community level, using a mixture of in-depth interviews and focus group discussions. We combined deductive and inductive analytical techniques and applied analyst triangulation. results Our findings indicate that heterogeneity in effects across facilities could be explained by preexisting infrastructural weaknesses coupled with rigid administrative processes and implementation challenges, while heterogeneity across indicators could be explained by providers’ practices, privileging services where demand-side barriers were less substantive. Conclusion In light of the country’s commitment to scaling up PBF, it follows that substantial efforts (particularly entrusting facilities with more financial autonomy) should be made to overcome infrastructural and demand-side barriers and to smooth implementation processes, thus, enabling healthcare providers to use PBF resources and management models to a fuller potential.
In Cameroon, socioeconomic conditions have deteriorated in recent decades and the relative rank of the country fell from the 97th place in the 1980 Human Development Index to 153rd in 2014.20 Several key millennium development goals were not achieved. Under-five mortality, maternal mortality and general adult mortality (15–60 years) were listed, respectively, at 88/1000, 508/100 000 and 403/1000 in 201521; skilled birth attendance has settled at around 60%–65% since the 1990s,22 and HIV prevalence (4.5% in 2015) continues to be substantially higher than in neighbouring countries.23 With the objective of increasing utilisation and improving the quality of health service delivery, the Ministry of Health (with financial support from the World Bank) in 2011 launched a PBF pilot in the country’s Littoral region, followed by a scale-up in 2012 to North-West, South-West and East regions, covering approximately 500 public, private and faith-based facilities (including primary and secondary facilities) across the four regions.24 25 The introduction of PBF was facilitated by a shift in political discourse favouring the implementation of more accountable health financing purchasing structures.25 Per the design, PBF bonuses were awarded for a list of services with a focus on maternal and childcare (see online Supplementary additional file 1). Bonuses were adjusted for quality of service delivery as determined based on a quality checklist. PBF payments were further adjusted adding an ‘equity bonus’ (a percentage of the basic PBF bonus, which varied between regions) to address contextual differences (such as remoteness, rural location or difficulty in access), which could affect service provision. Furthermore, facilities were to provide free services to the very poor identified in the communities and would receive a higher bonus payment from the PBF programme for services provided to the very poor to account for forgone user fees. PBF facilities filled in a monthly activity report, later verified by the ‘fund-holding agencies’ in collaboration with District Health Management teams, based on which payment was made monthly. PBF bonuses were paid to facilities, with a proportion allocated to health workers (shared using an ‘indice tool’) and the rest, alongside other incomes from user fees and/or budget allocations, used to cover facility expenditures (running costs, small investments, etc) as decided autonomously and outlined in a quarterly business plan prepared jointly by the facility staff.26 bmjgh-2017-000693supp001.pdf An impact evaluation (IE) was carried out between mid-2012 and mid-2015 in 14 of 22 districts across three of the regions (excluding Littoral). Under the IE, all facilities were randomised to receive PBF or one of three alternative control interventions, which provided varied levels of financial support and supervision not conditional on performance.8 We engaged in an explanatory-exploratory cross-sectional qualitative study,27 which sought explanations for patterns observed in the quantitative IE (explanatory), but also allowed an open investigation into the way PBF was experienced across a range of stakeholders (exploratory). During the qualitative study’s preparation, quantitative results as well as emerging interpretations and possible directions for qualitative research were discussed in depth with stakeholders in Yaoundé. Based on these discussions, the team finalised the study design, sampling, data collection strategy and tools and identified key themes to be explored qualitatively. As highlighted earlier, preliminary results from the quantitative IE informed our qualitative work. Similar to other contexts, the IE in Cameroon showed that PBF produced overall positive changes in service coverage for some indicators (eg, maternal and child immunisation, family planning and HIV testing), but not for others (eg, antenatal care, assisted deliveries and child curative consultations) and recognised a limited capacity to reach the very poor. Looking at quality of care, the IE showed that PBF improved the availability of essential inputs and equipment, but not service delivery processes, in spite of an increased presence of qualified health workers and an increased patient satisfaction.8 The programme also induced heterogeneity in impact across and within districts, with some facilities performing better than others. The study team conducted in-depth interviews (IDIs) and focus group discussions (FGDs) in order to capture perspectives on both the supply (providers and managers) and demand side (service users) and to include respondents across health system levels (communities, facilities, districts, regional and central levels). Table 1 illustrates the sample, data collection instrument and themes addressed in each set of interviews. In each region, we interviewed health workers in three purposively selected primary facilities (a high performer, a delayed performer and a low performer) and in one secondary facility to ensure representativeness across districts and across public and non-public facilities. District medical officers (DMOs) were interviewed in the same districts and women (service users) in the catchment areas of the same sampled facilities. Summary of respondents by level, type, number and instrument used FGD, focus group discussions; IDI, in-depth interviews; PBF, performance-based financing. We completed data collection between December 2016 and January 2017 with support from trained research assistants in collaboration with Centre d’Etudes et de Recherches Appliquées en Sciences Sociales (CERASS), using semi-structured interview guides, either in English or French depending on the region. FGDs and IDIs were recorded, verbatim transcribed in the original language (either French or English)and coded without further translation. Coding was performed in the original language of the interview. French quotes included in this paper have been translated, while those in English are reported verbatim with no adjustment. Coding of the transcripts followed a thematic approach, using a mixture of deductive and inductive coding techniques, which is aligned with the explanatory-exploratory study design and allowed us to remain faithful to protocol and research questions, while remaining open to unexpected or unforeseen findings. We applied analyst triangulation (two researchers independently coded the material), data triangulation (multiple sources of data) and method triangulation (different coding techniques). The final interpretation of findings was discussed among all authors and with selected key informants closely related to PBF implementation in Cameroon.
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