Birthweight measurement processes and perceived value: qualitative research in one EN-BIRTH study hospital in Tanzania.

BACKGROUND: Globally an estimated 20.5 million liveborn babies are low birthweight (LBW) each year, weighing less than 2500 g. LBW babies have increased risk of mortality even beyond the neonatal period, with an ongoing risk of stunting and non-communicable diseases. LBW is a priority global health indicator. Now almost 80% of births are in facilities, yet birthweight data are lacking in most high-mortality burden countries and are of poor quality, notably with heaping especially on values ending in 00. We aimed to undertake qualitative research in a regional hospital in Dar es Salaam, Tanzania, observing birthweight weighing scales, exploring barriers and enablers to weighing at birth as well as perceived value of birthweight data to health workers, women and stakeholders. METHODS: Observations were undertaken on type of birthweight scale availability in hospital wards. In-depth semi-structured interviews (n = 21) were conducted with three groups: women in postnatal and kangaroo mother care wards, health workers involved in birthweight measurement and recording, and stakeholders involved in data aggregation in Temeke Hospital, Tanzania, a site in the EN-BIRTH study. An inductive thematic analysis was undertaken of translated interview transcripts. RESULTS: Of five wards that were expected to have scales, three had functional scales, and only one of the functional scales was digital. The labour ward weighed the most newborns using an analogue scale that was not consistently zeroed. Hospital birthweight data were aggregated monthly for reporting into the health management information system. Birthweight measurement was highly valued by all respondents, notably families and healthcare workers, and local use of data was considered an enabler. Perceived barriers to high quality birthweight data included: gaps in availability of precise weighing devices, adequate health workers and imprecise measurement practices. CONCLUSION: Birthweight measurement is valued by families and health workers. There are opportunities to close the gap between the percentage of babies born in facilities and the percentage accurately weighed at birth by providing accurate scales, improving skills training and increasing local use of data. More accurate birthweight data are vitally important for all babies and specifically to track progress in preventing and improving immediate and long-term care for low birthweight children

"Every Newborn-BIRTH" protocol: observational study validating indicators for coverage and quality of maternal and newborn health care in Bangladesh, Nepal and Tanzania.

Background: To achieve Sustainable Development Goals and Universal Health Coverage, programmatic data are essential. The Every Newborn Action Plan, agreed by all United Nations member states and >80 development partners, includes an ambitious Measurement Improvement Roadmap. Quality of care at birth is prioritised by both Every Newborn and Ending Preventable Maternal Mortality strategies, hence metrics need to advance from health service contact alone, to content of care. As facility births increase, monitoring using routine facility data in DHIS2 has potential, yet validation research has mainly focussed on maternal recall surveys. The Every Newborn - Birth Indicators Research Tracking in Hospitals (EN-BIRTH) study aims to validate selected newborn and maternal indicators for routine tracking of coverage and quality of facility-based care for use at district, national and global levels. Methods: EN-BIRTH is an observational study including >20 000 facility births in three countries (Tanzania, Bangladesh and Nepal) to validate selected indicators. Direct clinical observation will be compared with facility register data and a pre-discharge maternal recall survey for indicators including: uterotonic administration, immediate newborn care, neonatal resuscitation and Kangaroo mother care. Indicators including neonatal infection management and antenatal corticosteroid administration, which cannot be easily observed, will be validated using inpatient records. Trained clinical observers in Labour/Delivery ward, Operation theatre, and Kangaroo mother care ward/areas will collect data using a tablet-based customised data capturing application. Sensitivity will be calculated for numerators of all indicators and specificity for those numerators with adequate information. Other objectives include comparison of denominator options (ie, true target population or surrogates) and quality of care analyses, especially regarding intervention timing. Barriers and enablers to routine recording and data usage will be assessed by data flow assessments, quantitative and qualitative analyses. Conclusions: To our knowledge, this is the first large, multi-country study validating facility-based routine data compared to direct observation for maternal and newborn care, designed to provide evidence to inform selection of a core list of indicators recommended for inclusion in national DHIS2. Availability and use of such data are fundamental to drive progress towards ending the annual 5.5 million preventable stillbirths, maternal and newborn deaths.Children’s Investment Fund Foundation (CIFF)Swedish Research CouncilUnited States Agency for International DevelopmentSaving Newborn Lives/Save the ChildrenWHOBill & Melinda Gates Foundatio

Importance of patient bed pathways and length of stay differences in predicting COVID-19 hospital bed occupancy in England.

Background: Predicting bed occupancy for hospitalised patients with COVID-19 requires understanding of length of stay (LoS) in particular bed types. LoS can vary depending on the patient’s “bed pathway” - the sequence of transfers of individual patients between bed types during a hospital stay. In this study, we characterise these pathways, and their impact on predicted hospital bed occupancy. Methods: We obtained data from University College Hospital (UCH) and the ISARIC4C COVID-19 Clinical Information Network (CO-CIN) on hospitalised patients with COVID-19 who required care in general ward or critical care (CC) beds to determine possible bed pathways and LoS. We developed a discrete-time model to examine the implications of using either bed pathways or only average LoS by bed type to forecast bed occupancy. We compared model-predicted bed occupancy to publicly available bed occupancy data on COVID-19 in England between March and August 2020. Results: In both the UCH and CO-CIN datasets, 82% of hospitalised patients with COVID-19 only received care in general ward beds. We identified four other bed pathways, present in both datasets: “Ward, CC, Ward”, “Ward, CC”, “CC” and “CC, Ward”. Mean LoS varied by bed type, pathway, and dataset, between 1.78 and 13.53 days. For UCH, we found that using bed pathways improved the accuracy of bed occupancy predictions, while only using an average LoS for each bed type underestimated true bed occupancy. However, using the CO-CIN LoS dataset we were not able to replicate past data on bed occupancy in England, suggesting regional LoS heterogeneities. Conclusions: We identified five bed pathways, with substantial variation in LoS by bed type, pathway, and geography. This might be caused by local differences in patient characteristics, clinical care strategies, or resource availability, and suggests that national LoS averages may not be appropriate for local forecasts of bed occupancy for COVID-19. Trial registration: The ISARIC WHO CCP-UK study ISRCTN66726260 was retrospectively registered on 21/04/2020 and designated an Urgent Public Health Research Study by NIHR.</p

Electronic data collection for multi-country, hospital-based, clinical observation of maternal and newborn care: EN-BIRTH study experiences.

BACKGROUND: Observation of care at birth is challenging with multiple, rapid and potentially concurrent events occurring for mother, newborn and placenta. Design of electronic data (E-data) collection needs to account for these challenges. The Every Newborn Birth Indicators Research Tracking in Hospitals (EN-BIRTH) was an observational study to assess measurement of indicators for priority maternal and newborn interventions and took place in five hospitals in Bangladesh, Nepal and Tanzania (July 2017-July 2018). E-data tools were required to capture individually-linked, timed observation of care, data extraction from hospital register-records or case-notes, and exit-survey data from women. METHODS: To evaluate this process for EN-BIRTH, we employed a framework organised around five steps for E-data design, data collection and implementation. Using this framework, a mixed methods evaluation synthesised evidence from study documentation, standard operating procedures, stakeholder meetings and design workshops. We undertook focus group discussions with EN-BIRTH researchers to explore experiences from the three different country teams (November-December 2019). Results were organised according to the five a priori steps. RESULTS: In accordance with the five-step framework, we found: 1) Selection of data collection approach and software: user-centred design principles were applied to meet the challenges for observation of rapid, concurrent events around the time of birth with time-stamping. 2) Design of data collection tools and programming: required extensive pilot testing of tools to be user-focused and to include in-built error messages and data quality alerts. 3) Recruitment and training of data collectors: standardised with an interactive training package including pre/post-course assessment. 4) Data collection, quality assurance, and management: real-time quality assessments with a tracking dashboard and double observation/data extraction for a 5% case subset, were incorporated as part of quality assurance. Internet-based synchronisation during data collection posed intermittent challenges. 5) Data management, cleaning and analysis: E-data collection was perceived to improve data quality and reduce time cleaning. CONCLUSIONS: The E-Data system, custom-built for EN-BIRTH, was valued by the site teams, particularly for time-stamped clinical observation of complex multiple simultaneous events at birth, without which the study objectives could not have been met. However before selection of a custom-built E-data tool, the development time, higher training and IT support needs, and connectivity challenges need to be considered against the proposed study or programme's purpose, and currently available E-data tool options

Labour and delivery ward register data availability, quality, and utility-Every Newborn-birth indicators research tracking in hospitals (EN-BIRTH) study baseline analysis in three countries

BACKGROUND: Countries with the highest burden of maternal and newborn deaths and stillbirths often have little information on these deaths. Since over 81% of births worldwide now occur in facilities, using routine facility data could reduce this data gap. We assessed the availability, quality, and utility of routine labour and delivery ward register data in five hospitals in Bangladesh, Nepal, and Tanzania. This paper forms the baseline register assessment for the Every Newborn-Birth Indicators Research Tracking in Hospitals (EN-BIRTH) study. METHODS: We extracted 21 data elements from routine hospital labour ward registers, useful to calculate selected maternal and newborn health (MNH) indicators. The study sites were five public hospitals during a one-year period (2016-17). We measured 1) availability: completeness of data elements by register design, 2) data quality: implausibility, internal consistency, and heaping of birthweight and explored 3) utility by calculating selected MNH indicators using the available data. RESULTS: Data were extracted for 20,075 births. Register design was different between the five hospitals with 10-17 of the 21 selected MNH data elements available. More data were available for health outcomes than interventions. Nearly all available data elements were > 95% complete in four of the five hospitals and implausible values were rare. Data elements captured in specific columns were 85.2% highly complete compared to 25.0% captured in non-specific columns. Birthweight data were less complete for stillbirths than live births at two hospitals, and significant heaping was found in all sites, especially at 2500g and 3000g. All five hospitals recorded count data required to calculate impact indicators including; stillbirth rate, low birthweight rate, Caesarean section rate, and mortality rates. CONCLUSIONS: Data needed to calculate MNH indicators are mostly available and highly complete in EN-BIRTH study hospital routine labour ward registers in Bangladesh, Nepal and Tanzania. Register designs need to include interventions for coverage measurement. There is potential to improve data quality if Health Management Information Systems utilization with feedback loops can be strengthened. Routine health facility data could contribute to reduce the coverage and impact data gap around the time of birth