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How do performance-based financing programmes measure quality of care? A descriptive analysis of 68 quality checklists from 28 low- and middle-income countries.

ABSTRACT: This paper seeks to systematically describe the length and content of quality checklists used in performance-based financing programmes, their similarities and differences, and how checklists have evolved over time. We compiled a list of supply-side, health facility-based performance-based financing (PBF) programmes in low- and lower middle-income countries based on a document review. We then solicited PBF manuals and quality checklists from implementers and donors of these PBF mechanisms. We entered each indicator from each quality checklist into a database verbatim in English, and translated into English from French where appropriate, and categorized each indicator according to the Donabedian framework and an author-derived categorization. We extracted 8,490 quality indicators from 68 quality checklists across 32 PBF implementations in 28 countries. On average, checklists contained 125 indicators; within the same program, checklists tend to grow as they are updated. Using the Donabedian framework, 80% of indicators were structure-type, 19% process-type, and less than 1% outcome-type. The author-derived categorization showed that 57% of indicators relate to availability of resources, 24% to managing the facility and 17% assess knowledge and effort. There is a high degree of similarity in a narrow set of indicators used in checklists for common service types such as maternal, neonatal and child health. We conclude that performance-based financing offers an appealing approach to targeting specific quality shortfalls and advancing toward the Sustainable Development Goals of high quality coverage. Currently most indicators focus on structural issues and resource availability. There is scope to rationalize and evolve the quality checklists of these programs to help achieve national and global goals to improve quality of care.

SUBMITTER: Josephson E

PROVIDER: S-EPMC5886109 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature

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How do performance-based financing programmes measure quality of care? A descriptive analysis of 68 quality checklists from 28 low- and middle-income countries.

Josephson Erik E Gergen Jessica J Coe Martha M Ski Samantha S Madhavan Supriya S Bauhoff Sebastian S

Health policy and planning 20171001 8

This paper seeks to systematically describe the length and content of quality checklists used in performance-based financing programmes, their similarities and differences, and how checklists have evolved over time. We compiled a list of supply-side, health facility-based performance-based financing (PBF) programmes in low- and lower middle-income countries based on a document review. We then solicited PBF manuals and quality checklists from implementers and donors of these PBF mechanisms. We en ...[more]

PMID: 28549142

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Project description:ObjectiveTo describe how quality of care is incorporated into performance-based financing (PBF) programs, what quality indicators are being used, and how these indicators are measured and verified.MethodsAn exploratory scoping methodology was used to characterize the full range of quality components in 32 PBF programs, initiated between 2008 and 2015 in 28 low- and middle-income countries, totaling 68 quality tools and 8,490 quality indicators. The programs were identified through a review of the peer-reviewed and gray literature as well as through expert consultation with key donor representatives.FindingsMost of the PBF programs were implemented in sub-Saharan Africa and most were funded primarily by the World Bank. On average, PBF quality tools contained 125 indicators predominately assessing maternal, newborn, and child health and facility management and infrastructure. Indicators were primarily measured via checklists (78%, or 6,656 of 8,490 indicators), which largely (over 90%) measured structural aspects of quality, such as equipment, beds, and infrastructure. Of the most common indicators across checklists, 74% measured structural aspects and 24% measured processes of clinical care. The quality portion of the payment formulas were in the form of bonuses (59%), penalties (27%), or both (hybrid) (14%). The median percentage (of a performance payment) allocated to health facilities was 60%, ranging from 10% to 100%, while the median percentage allocated to health care providers was 55%, ranging from 20% to 80%. Nearly all of the programs included in the analysis (91%, n=29) verified quality scores quarterly (every 3 months), typically by regional government teams.ConclusionPBF is a potentially appealing instrument to address shortfalls in quality of care by linking verified performance measurement with strategic incentives and could ultimately help meet policy priorities at the country and global levels, including the ambitious Sustainable Development Goals. The substantial variation and complexity in how PBF programs incorporate quality of care considerations suggests a need to further examine whether differences in design are associated with differential program impacts.

Project description:BackgroundPrimary care has the potential to address a large proportion of people's health needs, promote equity, and contain costs, but only if it provides high-quality health services that people want to use. 40 years after the Declaration of Alma-Ata, little is known about the quality of primary care in low-income and middle-income countries. We assessed whether existing facility surveys capture relevant aspects of primary care performance and summarised the quality of primary care in ten low-income and middle-income countries.MethodsWe used Service Provision Assessment surveys, the most comprehensive nationally representative surveys of health systems, to select indicators corresponding to three of the process quality domains (competent systems, evidence-based care, and user experience) identified by the Lancet Global Health Commission on High Quality Health Systems in the Sustainable Development Goals Era. We calculated composite and domain quality scores for first-level primary care facilities across and within ten countries with available facility assessment data (Ethiopia, Haiti, Kenya, Malawi, Namibia, Nepal, Rwanda, Senegal, Tanzania, and Uganda).FindingsData were available for 7049 facilities and 63 869 care visits. There were gaps in measurement of important outcomes such as user experience, health outcomes, and confidence, and processes such as timely action, choice of provider, affordability, ease of use, dignity, privacy, non-discrimination, autonomy, and confidentiality. No information about care competence was available outside maternal and child health. Overall, scores for primary care quality were low (mean 0·41 on a scale of 0 to 1). At a domain level, scores were lowest for user experience, followed by evidence-based care, and then competent systems. At the subdomain level, scores for patient focus, prevention and detection, technical quality of sick-child care, and population-health management were lower than those for other subdomains.InterpretationFacility surveys do not capture key elements of primary care quality. The available measures suggest major gaps in primary care quality. If not addressed, these gaps will limit the contribution of primary care to reaching the ambitious Sustainable Development Goals.FundingBill & Melinda Gates Foundation.

Project description:PurposeTo develop a set of actionable quality indicators for critical care suitable for use in low- or middle-income countries (LMICs).MethodsA list of 84 candidate indicators compiled from a previous literature review and stakeholder recommendations were categorised into three domains (foundation, process, and quality impact). An expert panel (EP) representing stakeholders from critical care and allied specialties in multiple low-, middle-, and high-income countries was convened. In rounds one and two of the Delphi exercise, the EP appraised (Likert scale 1-5) each indicator for validity, feasibility; in round three sensitivity to change, and reliability were additionally appraised. Potential barriers and facilitators to implementation of the quality indicators were also reported in this round. Median score and interquartile range (IQR) were used to determine consensus; indicators with consensus disagreement (median < 4, IQR ≤ 1) were removed, and indicators with consensus agreement (median ≥ 4, IQR ≤ 1) or no consensus were retained. In round four, indicators were prioritised based on their ability to impact cost of care to the provider and recipient, staff well-being, patient safety, and patient-centred outcomes.ResultsSeventy-one experts from 30 countries (n = 45, 63%, representing critical care) selected 57 indicators to assess quality of care in intensive care unit (ICU) in LMICs: 16 foundation, 27 process, and 14 quality impact indicators after round three. Round 4 resulted in 14 prioritised indicators. Fifty-seven respondents reported barriers and facilitators, of which electronic registry-embedded data collection was the biggest perceived facilitator to implementation (n = 54/57, 95%) Concerns over burden of data collection (n = 53/57, 93%) and variations in definition (n = 45/57, 79%) were perceived as the greatest barrier to implementation.ConclusionThis consensus exercise provides a common set of indicators to support benchmarking and quality improvement programs for critical care populations in LMICs.

Project description:IntroductionHealth financing reforms in low- and middle- income countries (LMICs) over the past decades have focused on achieving equity in financing of health care delivery through universal health coverage. Benefit and financing incidence analyses are two analytical methods for comprehensively evaluating how well health systems perform on these objectives. This systematic review assesses progress towards equity in health care financing in LMICs through the use of BIA and FIA.Methods and findingsKey electronic databases including Medline, Embase, Scopus, Global Health, CinAHL, EconLit and Business Source Premier were searched. We also searched the grey literature, specifically websites of leading organizations supporting health care in LMICs. Only studies using benefit incidence analysis (BIA) and/or financing incidence analysis (FIA) as explicit methodology were included. A total of 512 records were obtained from the various sources. The full texts of 87 references were assessed against the selection criteria and 24 were judged appropriate for inclusion. Twelve of the 24 studies originated from sub-Saharan Africa, nine from the Asia-Pacific region, two from Latin America and one from the Middle East. The evidence points to a pro-rich distribution of total health care benefits and progressive financing in both sub-Saharan Africa and Asia-Pacific. In the majority of cases, the distribution of benefits at the primary health care level favoured the poor while hospital level services benefit the better-off. A few Asian countries, namely Thailand, Malaysia and Sri Lanka, maintained a pro-poor distribution of health care benefits and progressive financing.ConclusionStudies evaluated in this systematic review indicate that health care financing in LMICs benefits the rich more than the poor but the burden of financing also falls more on the rich. There is some evidence that primary health care is pro-poor suggesting a greater investment in such services and removal of barriers to care can enhance equity. The results overall suggest that there are impediments to making health care more accessible to the poor and this must be addressed if universal health coverage is to be a reality.

Project description:IntroductionChild malnutrition continues to be a significant global public health concern. Nutrition-related interventions have changed and diversified over the last two decades, with increasing emphasis on nutrition-sensitive programmes that address underlying determinants of child malnutrition. Cash transfer programmes (CTPs) are used with increasing popularity in lower-income and middle-income countries to improve both food/nutrition insecurity and resilience. Available studies, however, provide mixed findings on the outcomes of CTPs for child nutritional status. This review is the first stage of a research project to develop evidence-informed theories of how CTPs affect child malnutrition. These will be empirically tested in the field and contribute to a better understanding of how, why, for whom and in what circumstances CTPs can be implemented to optimise impacts on child nutritional status.Methods and analysisThis realist review is informed by available standards for realist reviews and follows a five-step process. In step 1, an initial scoping of literature identified potential contextual factors and underlying mechanisms that influence nutritional outcomes, and potential theories developed to address our research question. In step 2, a systematic literature search using multiple databases will be undertaken with papers screened using defined inclusion/exclusion criteria. In step 3, included studies will be appraised, data extracted into a bespoke data extraction tool and used to test and further refine our explanatory framework. The fourth step will synthesise, using a mix of inductive and deductive analytical processes to identify patterns, link chains of inference and tracking and linking of articles. The final step involves dissemination of a preliminary theory for feedback prior to empirically testing it in Kenya and Ethiopia where large-scale CTPs are being implemented.Ethics and disseminationThis review will not involve primary data collection. Findings will be presented in accordance with Realist and Meta-Narrative Evidence Synthesis: Evolving Standards guidelines and published in a peer-reviewed journal.Trial registration numberCRD42018110735.

Project description:BackgroundHealth systems are often fragmented in low- and middle-income countries (LMICs). This can increase inefficiencies and restrict progress towards universal health coverage. The objective of the systematic review described in this protocol will be to evaluate and synthesize the evidence concerning the impacts of health systems financing fragmentation in LMICs.MethodsLiterature searches will be conducted in multiple electronic databases, from their inception onwards, including MEDLINE, EMBASE, LILACS, CINAHL, Scopus, ScienceDirect, Scielo, Cochrane Library, EconLit, and JSTOR. Gray literature will be also targeted through searching OpenSIGLE, Google Scholar, and institutional websites (e.g., HMIC, The World Bank, WHO, PAHO, OECD). The search strings will include keywords related to LMICs, health system financing fragmentation, and health system goals. Experimental, quasi-experimental, and observational studies conducted in LMICs and examining health financing fragmentation across any relevant metric (e.g., the presence of different health funders/insurers, risk pooling mechanisms, eligibility categories, benefits packages, premiums) will be included. Studies will be eligible if they compare financing fragmentation in alternative settings or at least two-time points. The primary outcomes will be health system-related goals such as health outcomes (e.g., mortality, morbidity, patient-reported outcome measures) and indicators of access, services utilization, equity, and financial risk protection. Additional outcomes will include intermediate health system objectives (e.g., indicators of efficiency and quality). Two reviewers will independently screen all citations, abstract data, and full-text articles. Potential conflicts will be resolved through discussion and, when necessary, resolved by a third reviewer. The methodological quality (or risk of bias) of selected studies will be appraised using established checklists. Data extraction categories will include the studies' objective and design, the fragmentation measurement and domains, and health outcomes linked to the fragmentation. A narrative synthesis will be used to describe the results and characteristics of all included studies and to explore relationships and findings both within and between the studies.DiscussionEvidence on the impacts of health system fragmentation in LMICs is key for identifying evidence gaps and priority areas for intervention. This knowledge will be valuable to health system policymakers aiming to strengthen health systems in LMICs.Systematic review registrationPROSPERO CRD42020201467.

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How do performance-based financing programmes measure quality of care? A descriptive analysis of 68 quality checklists from 28 low- and middle-income countries.

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How do performance-based financing programmes measure quality of care? A descriptive analysis of 68 quality checklists from 28 low- and middle-income countries.

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