BackgroundThe Every Newborn Action Plan (ENAP), launched in 2014, aims to end preventable newborn deaths and stillbirths, with national targets of ≤12 neonatal deaths per 1000 live births and ≤12 stillbirths per 1000 total births by 2030. This requires ambitious improvement of the data on care at birth and of small and sick newborns, particularly to track coverage, quality and equity.MethodsIn a multistage process, a matrix of 70 indicators were assessed by the Every Newborn steering group. Indicators were graded based on their availability and importance to ENAP, resulting in 10 core and 10 additional indicators. A consultation process was undertaken to assess the status of each ENAP core indicator definition, data availability and measurement feasibility. Coverage indicators for the specific ENAP treatment interventions were assigned task teams and given priority as they were identified as requiring the most technical work. Consultations were held throughout.ResultsENAP published 10 core indicators plus 10 additional indicators. Three core impact indicators (neonatal mortality rate, maternal mortality ratio, stillbirth rate) are well defined, with future efforts needed to focus on improving data quantity and quality. Three core indicators on coverage of care for all mothers and newborns (intrapartum/skilled birth attendance, early postnatal care, essential newborn care) have defined contact points, but gaps exist in measuring content and quality of the interventions. Four core (antenatal corticosteroids, neonatal resuscitation, treatment of serious neonatal infections, kangaroo mother care) and one additional coverage indicator for newborns at risk or with complications (chlorhexidine cord cleansing) lack indicator definitions or data, especially for denominators (population in need). To address these gaps, feasible coverage indicator definitions are presented for validity testing. Measurable process indicators to help monitor health service readiness are also presented. A major measurement gap exists to monitor care of small and sick babies, yet signal functions could be tracked similarly to emergency obstetric care.ConclusionsThe ENAP Measurement Improvement Roadmap (2015-2020) outlines tools to be developed (e.g., improved birth and death registration, audit, and minimum perinatal dataset) and actions to test, validate and institutionalise proposed coverage indicators. The roadmap presents a unique opportunity to strengthen routine health information systems, crosslinking these data with civil registration and vital statistics and population-based surveys. Real measurement change requires intentional transfer of leadership to countries with the greatest disease burden and will be achieved by working with centres of excellence and existing networks.

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Background Progress in reducing maternal and neonatal deaths and stillbirths is impeded by data gaps, especially regarding coverage and quality of care in hospitals. We aimed to assess the validity of indicators of maternal and newborn health-care coverage around the time of birth in survey data and routine facility register data.Methods Every Newborn-BIRTH Indicators Research Tracking in Hospitals was an observational study in five hospitals in Bangladesh, Nepal, and Tanzania. We included women and their newborn babies who consented on admission to hospital. Exclusion critiera at admission were no fetal heartbeat heard or imminent birth. For coverage of uterotonics to prevent post-partum haemorrhage, early initiation of breastfeeding (within 1 h), neonatal bag-mask ventilation, kangaroo mother care (KMC), and antibiotics for clinically defined neonatal infection (sepsis, pneumonia, or meningitis), we collected time-stamped, direct observation or case note verification data as gold standard. We compared data reported via hospital exit surveys and via hospital registers to the gold standard, pooled using random effects meta-analysis. We calculated population-level validity ratios (measured coverage to observed coverage) plus individual-level validity metrics. Findings We observed 23 471 births and 840 mother-baby KMC pairs, and verified the case notes of 1015 admitted newborn babies regarding antibiotic treatment. Exit-survey-reported coverage for KMC was 99•9% (95% CI 98•3-100) compared with observed coverage of 100% (99•9-100), but exit surveys underestimated coverage for uterotonics (84•7% [79•1-89•5]) vs 99•4% [98•7-99•8] observed), bag-mask ventilation (0•8% [0•4-1•4]) vs 4•4% [1•9-8•1]), and antibiotics for neonatal infection (74•7% [55•3-90•1] vs 96•4% [94•0-98•6] observed). Early breastfeeding coverage was overestimated in exit surveys (53•2% [39•4-66•8) vs 10•9% [3•8-21•0] observed). "Don't know" responses concerning clinical interventions were more common in the exit survey after caesarean birth. Register data underestimated coverage of uterotonics (77•9% [37•8-99•5] vs 99•2% [98•6-99•7] observed), bag-mask ventilation (4•3% [2•1-7•3] vs 5•1% [2•0-9•6] observed), KMC (92•9% [84•2-98•5] vs 100% [99•9-100] observed), and overestimated early breastfeeding (85•9% (58•1-99•6) vs 12•5% [4•6-23•6] observed). Inter-hospital heterogeneity was higher for register-recorded coverage than for exit survey report. Even with the same register design, accuracy varied between hospitals.Interpretation Coverage indicators for newborn and maternal health care in exit surveys had low accuracy for specific clinical interventions, except for self-report of KMC, which had high sensitivity after admission to a KMC ward or corner and could be considered for further assessment. Hospital register design and completion are less standardised than surveys, resulting in variable data quality, with good validity for the best performing sites. Because approximately 80% of births worldwide take place in facilities, standardising register d...

Background Accurate birthweight is critical to inform clinical care at the individual level and tracking progress towards national/global targets at the population level. Low birthweight (LBW) < 2500 g affects over 20.5 million newborns annually. However, data are lacking and may be affected by heaping. This paper evaluates birthweight measurement within the Every Newborn Birth Indicators Research Tracking in Hospitals (EN-BIRTH) study. Methods The EN-BIRTH study took place in five hospitals in Bangladesh, Nepal and Tanzania (2017–2018). Clinical observers collected time-stamped data (gold standard) for weighing at birth. We compared accuracy for two data sources: routine hospital registers and women’s report at exit interview survey. We calculated absolute differences and individual-level validation metrics. We analysed birthweight coverage and quality gaps including timing and heaping. Qualitative data explored barriers and enablers for routine register data recording. Results Among 23,471 observed births, 98.8% were weighed. Exit interview survey-reported weighing coverage was 94.3% (90.2–97.3%), sensitivity 95.0% (91.3–97.8%). Register-reported coverage was 96.6% (93.2–98.9%), sensitivity 97.1% (94.3–99%). Routine registers were complete (> 98% for four hospitals) and legible > 99.9%. Weighing of stillbirths varied by hospital, ranging from 12.5–89.0%. Observed LBW rate was 15.6%; survey-reported rate 14.3% (8.9–20.9%), sensitivity 82.9% (75.1–89.4%), specificity 96.1% (93.5–98.5%); register-recorded rate 14.9%, sensitivity 90.8% (85.9–94.8%), specificity 98.5% (98–99.0%). In surveys, “don’t know” responses for birthweight measured were 4.7%, and 2.9% for knowing the actual weight. 95.9% of observed babies were weighed within 1 h of birth, only 14.7% with a digital scale. Weight heaping indices were around two-fold lower using digital scales compared to analogue. Observed heaping was almost 5% higher for births during the night than day. Survey-report further increased observed birthweight heaping, especially for LBW babies. Enablers to register birthweight measurement in qualitative interviews included digital scale availability and adequate staffing. Conclusions Hospital registers captured birthweight and LBW prevalence more accurately than women’s survey report. Even in large hospitals, digital scales were not always available and stillborn babies not always weighed. Birthweight data are being captured in hospitals and investment is required to further improve data quality, researching of data flow in routine systems and use of data at every level.

Background Policymakers need regular high-quality coverage data on care around the time of birth to accelerate progress for ending preventable maternal and newborn deaths and stillbirths. With increasing facility births, routine Health Management Information System (HMIS) data have potential to track coverage. Identifying barriers and enablers faced by frontline health workers recording HMIS source data in registers is important to improve data for use. Methods The EN-BIRTH study was a mixed-methods observational study in five hospitals in Bangladesh, Nepal and Tanzania to assess measurement validity for selected Every Newborn coverage indicators. We described data elements required in labour ward registers to track these indicators. To evaluate barriers and enablers for correct recording of data in registers, we designed three interview tools: a) semi-structured in-depth interview (IDI) guide b) semi-structured focus group discussion (FGD) guide, and c) checklist assessing care-to-documentation. We interviewed two groups of respondents (January 2018–March 2019): hospital nurse-midwives and doctors who fill ward registers after birth (n = 40 IDI and n = 5 FGD); and data collectors (n = 65). Qualitative data were analysed thematically by categorising pre-identified codes. Common emerging themes of barriers or enablers across all five hospitals were identified relating to three conceptual framework categories. Results Similar themes emerged as both barriers and enablers. First, register design was recognised as crucial, yet perceived as complex, and not always standardised for necessary data elements. Second, register filling was performed by over-stretched nurse-midwives with variable training, limited supervision, and availability of logistical resources. Documentation complexity across parallel documents was time-consuming and delayed because of low staff numbers. Complete data were valued more than correct data. Third, use of register data included clinical handover and monthly reporting, but little feedback was given from data users. Conclusion Health workers invest major time recording register data for maternal and newborn core health indicators. Improving data quality requires standardised register designs streamlined to capture only necessary data elements. Consistent implementation processes are also needed. Two-way feedback between HMIS levels is critical to improve performance and accurately track progress towards agreed health goals.