Quality

Introduction: Commission on Global Surgery proposed the perioperative mortality rate (POMR) as one of the six key indicators of the strength of a country's surgical system. Despite its widespread use in high-income settings, few studies have described procedure-specific POMR across low-income and middle-income countries (LMICs). We aimed to estimate POMR across a wide range of surgical procedures in LMICs. We also describe how POMR is defined and reported in the LMIC literature to provide recommendations for future monitoring in resource-constrained settings. Methods: We did a systematic review of studies from LMICs published from 2009 to 2014 reporting POMR for any surgical procedure. We extracted select variables in duplicate from each included study and pooled estimates of POMR by type of procedure using random-effects meta-analysis of proportions and the Freeman-Tukey double arcsine transformation to stabilise variances. Results: We included 985 studies conducted across 83 LMICs, covering 191 types of surgical procedures performed on 1 020 869 patients. Pooled POMR ranged from less than 0.1% for appendectomy, cholecystectomy and caesarean delivery to 20%-27% for typhoid intestinal perforation, intracranial haemorrhage and operative head injury. We found no consistent associations between procedure-specific POMR and Human Development Index (HDI) or income-group apart from emergency peripartum hysterectomy POMR, which appeared higher in low-income countries. Inpatient mortality was the most commonly used definition, though only 46.2% of studies explicitly defined the time frame during which deaths accrued. Conclusions: Efforts to improve access to surgical care in LMICs should be accompanied by investment in improving the quality and safety of care. To improve the usefulness of POMR as a safety benchmark, standard reporting items should be included with any POMR estimate. Choosing a basket of procedures for which POMR is tracked may offer institutions and countries the standardisation required to meaningfully compare surgical outcomes across contexts and improve population health outcomes.

Quality improvement approaches can strengthen action on a range of global health priorities. Quality improvement efforts are uniquely placed to reorient care delivery systems towards integrated people-centred health services and strengthen health systems to achieve Universal Health Coverage (UHC). This article makes the case for addressing shortfalls of previous agendas by articulating the critical role of quality improvement in the Sustainable Development Goal era. Quality improvement can stimulate convergence between health security and health systems; address global health security priorities through participatory quality improvement approaches; and improve health outcomes at all levels of the health system. Entry points for action include the linkage with antimicrobial resistance and the contentious issue of the health of migrants. The work required includes focussed attention on the continuum of national quality policy formulation, implementation and learning; alongside strengthening the measurement-improvement linkage. Quality improvement plays a key role in strengthening health systems to achieve UHC.

The gap between implementers and researchers of quality improvement (QI) has hampered the degree and speed of change needed to reduce avoidable suffering and harm in health care. Underlying causes of this gap include differences in goals and incentives, preferred methodologies, level and types of evidence prioritized and targeted audiences. The Salzburg Global Seminar on 'Better Health Care: How do we learn about improvement?' brought together researchers, policy makers, funders, implementers, evaluators from low-, middle- and high-income countries to explore how to increase the impact of QI. In this paper, we describe some of the reasons for this gap and offer suggestions to better bridge the chasm between researchers and implementers. Effectively bridging this gap can increase the generalizability of QI interventions, accelerate the spread of effective approaches while also strengthening the local work of implementers. Increasing the effectiveness of research and work in the field will support the knowledge translation needed to achieve quality Universal Health Coverage and the Sustainable Development Goals.

During the Salzburg Global Seminar Session 565-'Better Health Care: How do we learn about improvement?', participants discussed the need to unpack the 'black box' of improvement. The 'black box' refers to the fact that when quality improvement interventions are described or evaluated, there is a tendency to assume a simple, linear path between the intervention and the outcomes it yields. It is also assumed that it is enough to evaluate the results without understanding the process of by which the improvement took place. However, quality improvement interventions are complex, nonlinear and evolve in response to local settings. To accurately assess the effectiveness of quality improvement and disseminate the learning, there must be a greater understanding of the complexity of quality improvement work. To remain consistent with the language used in Salzburg, we refer to this as 'unpacking the black box' of improvement. To illustrate the complexity of improvement, this article introduces four quality improvement case studies. In unpacking the black box, we present and demonstrate how Cynefin framework from complexity theory can be used to categorize and evaluate quality improvement interventions. Many quality improvement projects are implemented in complex contexts, necessitating an approach defined as 'probe-sense-respond'. In this approach, teams experiment, learn and adapt their changes to their local setting. Quality improvement professionals intuitively use the probe-sense-respond approach in their work but document and evaluate their projects using language for 'simple' or 'complicated' contexts, rather than the 'complex' contexts in which they work. As a result, evaluations tend to ask 'How can we attribute outcomes to the intervention?', rather than 'What were the adaptations that took place?'. By unpacking the black box of improvement, improvers can more accurately document and describe their interventions, allowing evaluators to ask the right questions and more adequately evaluate quality improvement interventions.

BACKGROUND: It is increasingly apparent that access to healthcare without adequate quality of care is insufficient to improve population health outcomes. We assess whether the most commonly measured attribute of health facilities in low- and middle-income countries (LMICs)-the structural inputs to care-predicts the clinical quality of care provided to patients. METHODS AND FINDINGS: Service Provision Assessments are nationally representative health facility surveys conducted by the Demographic and Health Survey Program with support from the US Agency for International Development. These surveys assess health system capacity in LMICs. We drew data from assessments conducted in 8 countries between 2007 and 2015: Haiti, Kenya, Malawi, Namibia, Rwanda, Senegal, Tanzania, and Uganda. The surveys included an audit of facility infrastructure and direct observation of family planning, antenatal care (ANC), sick-child care, and (in 2 countries) labor and delivery. To measure structural inputs, we constructed indices that measured World Health Organization-recommended amenities, equipment, and medications in each service. For clinical quality, we used data from direct observations of care to calculate providers' adherence to evidence-based care guidelines. We assessed the correlation between these metrics and used spline models to test for the presence of a minimum input threshold associated with good clinical quality. Inclusion criteria were met by 32,531 observations of care in 4,354 facilities. Facilities demonstrated moderate levels of infrastructure, ranging from 0.63 of 1 in sick-child care to 0.75 of 1 for family planning on average. Adherence to evidence-based guidelines was low, with an average of 37% adherence in sick-child care, 46% in family planning, 60% in labor and delivery, and 61% in ANC. Correlation between infrastructure and evidence-based care was low (median 0.20, range from -0.03 for family planning in Senegal to 0.40 for ANC in Tanzania). Facilities with similar infrastructure scores delivered care of widely varying quality in each service. We did not detect a minimum level of infrastructure that was reliably associated with higher quality of care delivered in any service. These findings rely on cross-sectional data, preventing assessment of relationships between structural inputs and clinical quality over time; measurement error may attenuate the estimated associations. CONCLUSION: Inputs to care are poorly correlated with provision of evidence-based care in these 4 clinical services. Healthcare workers in well-equipped facilities often provided poor care and vice versa. While it is important to have strong infrastructure, it should not be used as a measure of quality. Insight into health system quality requires measurement of processes and outcomes of care.

Introduction: Measurement of effective coverage (quality-corrected coverage) of essential health services is critical to monitoring progress towards the Sustainable Development Goal for health. We combine facility and household surveys from eight low-income and middle-income countries to examine effective coverage of maternal and child health services. Methods: We developed indices of essential clinical actions for antenatal care, family planning and care for sick children from existing guidelines and used data from direct observations of clinical visits conducted in Haiti, Kenya, Malawi, Namibia, Rwanda, Senegal, Tanzania and Uganda between 2007 and 2015 to measure quality of care delivered. We calculated healthcare coverage for each service from nationally representative household surveys and combined quality with utilisation estimates at the subnational level to quantify effective coverage. Results: Health facility and household surveys yielded over 40 000 direct clinical observations and over 100 000 individual reports of healthcare utilisation. Coverage varied between services, with much greater use of any antenatal care than family planning or sick-child care, as well as within countries. Quality of care was poor, with few regions demonstrating more than 60% average performance of basic clinical practices in any service. Effective coverage across all eight countries averaged 28% for antenatal care, 26% for family planning and 21% for sick-child care. Coverage and quality were not strongly correlated at the subnational level; effective coverage varied by as much as 20% between regions within a country. Conclusion: Effective coverage of three primary care services for women and children in eight countries was substantially lower than crude service coverage due to major deficiencies in care quality. Better performing regions can serve as examples for improvement. Systematic increases in the quality of care delivered-not just utilisation gains-will be necessary to progress towards truly beneficial universal health coverage.

Performance-based financing (PBF) programs are increasingly implemented in low and middle-income countries to improve health service quality and utilization. In April 2011, a PBF pilot program was launched in Boulsa, Leo and Titao districts in Burkina Faso with the objective of increasing the provision and quality of maternal health services. We evaluate the impact of this program using facility-level administrative data from the national health management information system (HMIS). Primary outcomes were the number of antenatal care visits, the proportion of antenatal care visits that occurred during the first trimester of pregnancy, the number of institutional deliveries and the number of postnatal care visits. To assess program impact we use a difference-in-differences approach, comparing changes in health service provision post-introduction with changes in matched comparison areas. All models were estimated using ordinary least squares (OLS) regression models with standard errors clustered at the facility level. On average, PBF facilities had 2.3 more antenatal care visits (95% CI [0.446-4.225]), 2.1 more deliveries (95% CI [0.034-4.069]) and 9.5 more postnatal care visits (95% CI [6.099, 12.903]) each month after the introduction of PBF. Compared to the service provision levels prior to the interventions, this implies a relative increase of 27.7 percent for ANC, of 9.2 percent for deliveries, and of 118.7 percent for postnatal care. Given the positive results observed during the pre-pilot period and the limited resources available in the health sector, the PBF program in Burkina Faso may be a low-cost, high impact intervention to improve maternal and child health.

BACKGROUND: Achievement of diabetes care goals is suboptimal globally. Diabetes-focused quality improvement (QI) is effective but remains untested in South Asia. OBJECTIVE: To compare the effect of a multicomponent QI strategy versus usual care on cardiometabolic profiles in patients with poorly controlled diabetes. DESIGN: Parallel, open-label, pragmatic randomized, controlled trial. (ClinicalTrials.gov: NCT01212328). SETTING: Diabetes clinics in India and Pakistan. PATIENTS: 1146 patients (575 in the intervention group and 571 in the usual care group) with type 2 diabetes and poor cardiometabolic profiles (glycated hemoglobin [HbA1c] level ≥8% plus systolic blood pressure [BP] ≥140 mm Hg and/or low-density lipoprotein cholesterol [LDLc] level ≥130 mg/dL). INTERVENTION: Multicomponent QI strategy comprising nonphysician care coordinators and decision-support electronic health records. MEASUREMENTS: Proportions achieving HbA1c level less than 7% plus BP less than 130/80 mm Hg and/or LDLc level less than 100 mg/dL (primary outcome); mean risk factor reductions, health-related quality of life (HRQL), and treatment satisfaction (secondary outcomes). RESULTS: Baseline characteristics were similar between groups. Median diabetes duration was 7.0 years; 6.8% and 39.4% of participants had preexisting cardiovascular and microvascular disease, respectively; mean HbA1c level was 9.9%; mean BP was 143.3/81.7 mm Hg; and mean LDLc level was 122.4 mg/dL. Over a median of 28 months, a greater percentage of intervention participants achieved the primary outcome (18.2% vs. 8.1%; relative risk, 2.24 [95% CI, 1.71 to 2.92]). Compared with usual care, intervention participants achieved larger reductions in HbA1c level (-0.50% [CI, -0.69% to -0.32%]), systolic BP (-4.04 mm Hg [CI, -5.85 to -2.22 mm Hg]), diastolic BP (-2.03 mm Hg [CI, -3.00 to -1.05 mm Hg]), and LDLc level (-7.86 mg/dL [CI, -10.90 to -4.81 mg/dL]) and reported higher HRQL and treatment satisfaction. LIMITATION: Findings were confined to urban specialist diabetes clinics. CONCLUSION: Multicomponent QI improves achievement of diabetes care goals, even in resource-challenged clinics. PRIMARY FUNDING SOURCE: National Heart, Lung, and Blood Institute and UnitedHealth Group.