Publications

BACKGROUND: Triangulating findings from MDSR with other sources can better inform maternal health programs. A national Emergency Obstetric and Newborn Care (EmONC) assessment and the Maternal Death Surveillance and Response (MDSR) system provided data to determine the coverage of MDSR implementation in health facilities, the leading causes and contributing factors to death, and the extent to which life-saving interventions were provided to deceased women. METHODS: This paper is based on triangulation of findings from a descriptive analysis of secondary data extracted from the 2016 EmONC assessment and the MDSR system databases. EmONC assessment was conducted in 3804 health facilities. Data from interview of each facility leader on MDSR implementation, review of 1305 registered maternal deaths and 679 chart reviews of maternal deaths that happened form May 16, 2015 to December 15, 2016 were included from the EmONC assessment. Case summary reports of 601 reviewed maternal deaths were included from the MDSR system. RESULTS: A maternal death review committee was established in 64% of health facilities. 5.5% of facilities had submitted at least one maternal death summary report to the national MDSR database. Postpartum hemorrhage (10-27%) and severe preeclampsia/eclampsia (10-24.1%) were the leading primary causes of maternal death. In MDSR, delay-1 factors contributed to 7-33% of maternal deaths. Delay-2, related to reaching a facility, contributed to 32% & 40% of maternal deaths in the EmONC assessment and MDSR, respectively. Similarly, delay-3 factor due to delayed transfer of mothers to appropriate level of care contributed for 29 and 22% of maternal deaths. From the EmONC data, 72% of the women who died due to severe pre-eclampsia or eclampsia were given anticonvulsants while 48% of those dying of postpartum haemorrhage received uterotonics. CONCLUSION: The facility level implementation coverage of MDSR was sub-optimal. Obstetric hemorrhage and severe preeclampsia or eclampsia were the leading causes of maternal death. Delayed arrival to facility (Delay 2) was the predominant contributing factor to facility-based maternal deaths. The limited EmONC provision should be the focus of quality improvement in health facilities.

BACKGROUND: The use of appropriate and relevant nurse-sensitive indicators provides an opportunity to demonstrate the unique contributions of nurses to patient outcomes. The aim of this work was to develop relevant metrics to assess the quality of nursing care in low- and middle-income countries (LMICs) where they are scarce. MAIN BODY: We conducted a scoping review using EMBASE, CINAHL and MEDLINE databases of studies published in English focused on quality nursing care and with identified measurement methods. Indicators identified were reviewed by a diverse panel of nursing stakeholders in Kenya to develop a contextually appropriate set of nurse-sensitive indicators for Kenyan hospitals specific to the five major inpatient disciplines. We extracted data on study characteristics, nursing indicators reported, location and the tools used. A total of 23 articles quantifying the quality of nursing care services met the inclusion criteria. All studies identified were from high-income countries. Pooled together, 159 indicators were reported in the reviewed studies with 25 identified as the most commonly reported. Through the stakeholder consultative process, 52 nurse-sensitive indicators were recommended for Kenyan hospitals. CONCLUSIONS: Although nurse-sensitive indicators are increasingly used in high-income countries to improve quality of care, there is a wide heterogeneity in the way indicators are defined and interpreted. Whilst some indicators were regarded as useful by a Kenyan expert panel, contextual differences prompted them to recommend additional new indicators to improve the evaluations of nursing care provision in Kenyan hospitals and potentially similar LMIC settings. Taken forward through implementation, refinement and adaptation, the proposed indicators could be more standardised and may provide a common base to establish national or regional professional learning networks with the common goal of achieving high-quality care through quality improvement and learning.

BACKGROUND: As more countries progress towards malaria elimination, a better understanding of the most critical health system features for enabling and supporting malaria control and elimination is needed. METHODS: All available health systems data relevant for malaria control were collated from 23 online data repositories. Principal component analysis was used to create domain specific health system performance measures. Multiple regression model selection approaches were used to identify key health systems predictors of progress in malaria control in the 2000-2016 period among 105 countries. Additional analysis was performed within malaria burden groups. RESULTS: There was large heterogeneity in progress in malaria control in the 2000-2016 period. In univariate analysis, several health systems factors displayed a strong positive correlation with reductions in malaria burden between 2000 and 2016. In multivariable models, delivery of routine services and hospital capacity were strongly predictive of reductions in malaria cases, especially in high burden countries. In low-burden countries approaching elimination, primary health center density appeared negatively associated with progress while hospital capacity was positively correlated with eliminating malaria. CONCLUSIONS: The findings presented in this manuscript suggest that strengthening health systems can be an effective strategy for reducing malaria cases, especially in countries with high malaria burden. Potential returns appear particularly high in the area of service delivery.

BACKGROUND: Ensuring quality of care during pregnancy and childbirth is crucial to improving health outcomes and reducing preventable mortality and morbidity among women and their newborns. In this pursuit, WHO developed a framework and standards, defining 31 quality statements and 352 quality measures to assess and improve quality of maternal and newborn care in health-care facilities. We aimed to assess the capacity of globally used, large-scale facility assessment tools to measure quality of maternal and newborn care as per the WHO framework. METHODS: We identified assessment tools through a purposive sample that met the following inclusion criteria: multicountry, facility-level, major focus on maternal and newborn health, data on input and process indicators, used between 2007 and 2017, and currently in use. We matched questions in the tools with 274 quality measures associated with inputs and processes within the WHO standards. We excluded quality measures relating to outcomes because these are not routinely measured by many assessment tools. We used descriptive statistics to calculate how many quality measures could be assessed using each of the tools under review. Each tool was assigned a 1 for fulfilling a quality measure based on the presence of any or all components as indicated in the standards. FINDINGS: Five surveys met our inclusion criteria: the Service Provision Assessment (SPA), developed for the Demographic and Health Surveys programme; the Service Availability and Readiness Assessment, developed by WHO; the Needs Assessment of Emergency Obstetric and Newborn Care developed by the Averting Maternal Death and Disability programme at Columbia University; and the World Bank's Service Delivery Indicator (SDI) and Impact Evaluation Toolkit for Results Based Financing in Health. The proportion of quality measures covered ranged from 62% for the SPA to 12% for the SDI. Although the broadest tool addressed parts of each of the 31 quality statements, 68 (25%) of 274 input and process quality measures were not measured at all. Measures of health information systems and patient experience of care were least likely to be included. INTERPRETATION: Existing facility assessment tools provide a valuable way to assess quality of maternal and newborn care as one element within the national measurement toolkit. Guidance is clearly needed on priority measures and for better harmonisation across tools to reduce measurement burden and increase data use for quality improvement. Targeted development of measurement modules to address important gaps is a key priority for research. FUNDING: None.

OBJECTIVES: Recent studies have identified large and systematic deficits in clinical care in low-income countries that are likely to limit health gains. This has focused attention on effectiveness of pre-service education. One approach to assessing this is observation of clinical performance among recent graduates providing care. However, no studies have assessed performance in a standard manner across countries. We analysed clinical performance among recently graduated providers in nine low- or middle-income countries. METHODS: Service Provision Assessments from Haiti, Kenya, Malawi, Namibia, Nepal, Rwanda, Senegal, Tanzania, and Uganda were used. We constructed a Good Medical Practice Index that assesses completion of essential clinical actions using direct observations of care (range 0-1), calculated index scores by country and clinical cadre, and assessed the role of facility and clinical characteristics using regression analysis. RESULTS: Our sample consisted of 2223 clinicians with at least one observation of care. The Good Medical Practice score for the sample was 0.50 (SD = 0.20). Nurses and midwives had the highest score at 0.57 (SD = 0.20), followed by associate clinicians at 0.43 (SD = 0.18), and physicians at 0.42 (SD = 0.16). The average national performance varied from 0.63 (SD = 0.18) in Uganda to 0.39 (SD = 0.17) in Nepal, persisting after adjustment for facility and clinician characteristics. CONCLUSIONS: These results show substantial gaps in clinical performance among recently graduated clinicians, raising concerns about models of clinical education. Competency-based education should be considered to improve quality of care in LMICs. Observations of care offer important insight into the quality of clinical education.

OBJECTIVES: The nature of patient-provider interactions and communication is widely documented to significantly impact on patient experiences, treatment adherence and health outcomes. Yet little is known about the broader contextual factors and dynamics that shape patient-provider interactions in high HIV prevalence and limited-resource settings. Drawing on qualitative research from five sub-Saharan African countries, we seek to unpack local dynamics that serve to hinder or facilitate productive patient-provider interactions. METHODS: This qualitative study, conducted in Kisumu (Kenya), Kisesa (Tanzania), Manicaland (Zimbabwe), Karonga (Malawi) and uMkhanyakude (South Africa), draws upon 278 in-depth interviews with purposively sampled people living with HIV with different diagnosis and treatment histories, 29 family members of people who died due to HIV and 38 HIV healthcare workers. Data were collected using topic guides that explored patient testing and antiretroviral therapy treatment journeys. Thematic analysis was conducted, aided by NVivo V.8.0 software. RESULTS: Our analysis revealed an array of inter-related contextual factors and power dynamics shaping patient-provider interactions. These included (1) participants' perceptions of roles and identities of 'self' and 'other'; (2) conformity or resistance to the 'rules of HIV service engagement' and a 'patient-persona'; (3) the influence of significant others' views on service provision; and (4) resources in health services. We observed that these four factors/dynamics were located in the wider context of conceptualisations of power, autonomy and structure. CONCLUSION: Patient-provider interaction is complex, multidimensional and deeply embedded in wider social dynamics. Multiple contextual domains shape patient-provider interactions in the context of HIV in sub-Saharan Africa. Interventions to improve patient experiences and treatment adherence through enhanced interactions need to go beyond the existing focus on patient-provider communication strategies.

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.