Neonatal inpatient dataset for small and sick newborn care in low- and middle-income countries: systematic development and multi-country operationalisation with NEST360.

BACKGROUND: Every Newborn Action Plan (ENAP) coverage target 4 necessitates national scale-up of Level-2 Small and Sick Newborn Care (SSNC) (with Continuous Positive Airway Pressure (CPAP)) in 80% of districts by 2025. Routine neonatal inpatient data is important for improving quality of care, targeting equity gaps, and enabling data-driven decision-making at individual, district, and national-levels. Existing neonatal inpatient datasets vary in purpose, size, definitions, and collection processes. We describe the co-design and operationalisation of a core inpatient dataset for use to track outcomes and improve quality of care for small and sick newborns in high-mortality settings. METHODS: A three-step systematic framework was used to review, co-design, and operationalise this novel neonatal inpatient dataset in four countries (Malawi, Kenya, Tanzania, and Nigeria) implementing with the Newborn Essential Solutions and Technologies (NEST360) Alliance. Existing global and national datasets were identified, and variables were mapped according to categories. A priori considerations for variable inclusion were determined by clinicians and policymakers from the four African governments by facilitated group discussions. These included prioritising clinical care and newborn outcomes data, a parsimonious variable list, and electronic data entry. The tool was designed and refined by > 40 implementers and policymakers during a multi-stakeholder workshop and online interactions. RESULTS: Identified national and international datasets (n = 6) contained a median of 89 (IQR:61-154) variables, with many relating to research-specific initiatives. Maternal antenatal/intrapartum history was the largest variable category (21, 23.3%). The Neonatal Inpatient Dataset (NID) includes 60 core variables organised in six categories: (1) birth details/maternal history; (2) admission details/identifiers; (3) clinical complications/observations; (4) interventions/investigations; (5) discharge outcomes; and (6) diagnosis/cause-of-death. Categories were informed through the mapping process. The NID has been implemented at 69 neonatal units in four African countries and links to a facility-level quality improvement (QI) dashboard used in real-time by facility staff. CONCLUSION: The NEST360 NID is a novel, parsimonious tool for use in routine information systems to inform inpatient SSNC quality. Available on the NEST360/United Nations Children's Fund (UNICEF) Implementation Toolkit for SSNC, this adaptable tool enables facility and country-level comparisons to accelerate progress toward ENAP targets. Additional linked modules could include neonatal at-risk follow-up, retinopathy of prematurity, and Level-3 intensive care.

Towards the definition of a European Digital Building Logbook: A survey.

Both the operational phase and embodied emissions that are introduced during the construction phase through the manufacture, sourcing, and installation of the building's materials and components are significant contributors to carbon emissions from the built environment. It is essential to change the current design and (re)construction processes in order to achieve the energy-saving targets for the EU building stock and move toward a society that is net carbon neutral. This change must be made from both a technical perspective as well as from a methodological perspective. To accomplish this, the EU has suggested several regulations and legislative steps to phase out inefficient structures. The most recent of these initiatives propose the idea of a Digital Building Logbook, which serves as a central repository for all pertinent building data, including information on energy efficiency. In this work, we present a survey of the elements that have been taken into consideration for the creation of the Digital Building Logbook to give an overview of what research has been done so far.

The Utility of Predictive Modeling and a Systems Process Approach to Reduce Emergency Department Crowding: A Position Paper.

Emergency department (ED) crowding and its main causes, exit block and boarding, continue to threaten the quality and safety of ED care. Most interventions to reduce crowding have not been comprehensive or system solutions, only focusing on part of the care procession and not directly affecting boarding reduction. This position paper proposes that the ED crowding problem can be optimally addressed by applying a systems approach using predictive modeling to identify patients at risk of being admitted to the hospital and uses that information to initiate the time-consuming bed management process earlier in the care continuum, shortening the time during which patients wait in the ED for an inpatient bed assignment, thus removing the exit block that causes boarding and subsequently reducing crowding.

Area-specific covid-19 effects on health services utilization in the Democratic Republic of the Congo using routine health information system data.

BACKGROUND: Since March 2020, the COVID-19 pandemic has shocked health systems worldwide. This analysis investigated the effects of the pandemic on basic health services utilization in the Democratic Republic of the Congo (DRC) and examined the variability of COVID effects in the capital city Kinshasa, in other urban areas, and in rural areas. METHODS: We estimated time trends models using national health information system data to replicate pre-COVID-19 (i.e., January 2017-February 2020) trajectories of health service utilization, and then used those models to estimate what the levels would have been in the absence of COVID-19 during the pandemic period, starting in March 2020 through March 2021. We classified the difference between the observed and predicted levels as the effect of COVID-19 on health services. We estimated 95% confidence intervals and p-values to examine if the effect of the pandemic, nationally and within specific geographies, was statistically significant. RESULTS: Our results indicate that COVID-19 negatively impacted health services and subsequent recovery varied by service type and by geographical area. COVID-19 had a lasting impact on overall service utilization as well as on malaria and pneumonia-related visits among young children in the DRC. We also found that the effects of COVID-19 were even more immediate and stronger in the capital city of Kinshasa compared with the national effect. Both nationally and in Kinshasa, most affected services had slow and incomplete recovery to expected levels. Therefore, our analysis indicates that COVID-19 continued to affect health services in the DRC throughout the first year of the pandemic. CONCLUSIONS: The methodology used in this article allows for examining the variability in magnitude, timing, and duration of the COVID effects within geographical areas of the DRC and nationally. This analytical procedure based on national health information system data could be applied to surveil health service disruptions and better inform rapid responses from health service managers and policymakers.

Quality of routine health data at the onset of the COVID-19 pandemic in Ethiopia, Haiti, Laos, Nepal, and South Africa.

BACKGROUND: During the COVID-19 pandemic, governments and researchers have used routine health data to estimate potential declines in the delivery and uptake of essential health services. This research relies on the data being high quality and, crucially, on the data quality not changing because of the pandemic. In this paper, we investigated those assumptions and assessed data quality before and during COVID-19. METHODS: We obtained routine health data from the DHIS2 platforms in Ethiopia, Haiti, Lao People's Democratic Republic, Nepal, and South Africa (KwaZulu-Natal province) for a range of 40 indicators on essential health services and institutional deaths. We extracted data over 24 months (January 2019-December 2020) including pre-pandemic data and the first 9 months of the pandemic. We assessed four dimensions of data quality: reporting completeness, presence of outliers, internal consistency, and external consistency. RESULTS: We found high reporting completeness across countries and services and few declines in reporting at the onset of the pandemic. Positive outliers represented fewer than 1% of facility-month observations across services. Assessment of internal consistency across vaccine indicators found similar reporting of vaccines in all countries. Comparing cesarean section rates in the HMIS to those from population-representative surveys, we found high external consistency in all countries analyzed. CONCLUSIONS: While efforts remain to improve the quality of these data, our results show that several indicators in the HMIS can be reliably used to monitor service provision over time in these five countries.

Recent advances in system management, decision support systems, artificial intelligence and computing in anaesthesia.

Advances in artificial intelligence, telemedicine, block-chain technology and electronic medical records are paving the way for a new era in anaesthetic care through automation, non-invasive monitoring, system management and decision support systems. Their utility has been demonstrated in a variety of contexts in the peri-operative setting, including but not limited to, monitoring anaesthesia depth, maintaining drug infusion, predicting hypotension, critical incident evaluation, risk management strategies, antibiotic administration, haemodynamic monitoring, precise ultrasound-guided nerve blocks and a future where possibilities are entirely dependent on how we decide to embrace this progression. The main objective of this article is to provide up-to-date and valuable knowledge about the recent advances in anaesthesia technology during the past few years.

Leveraging electronic logistics management information systems to enhance and optimize supply chain response during public health emergencies: lessons from COVID-19 response in Uganda.

BACKGROUND: Health supply chain is crucial for proper functioning of a health system and advancing national and international health security goals. The Coronavirus 2019 pandemic caused major challenges for health supply chain systems in Uganda and globally. OBJECTIVES: This study involved literature review to examine how the electronic logistics management information system and related digital systems were harnessed be best support public health emergencies. METHODS: We describe how the health supply chain system leveraged the emergency Electronic Logistic Management Information System developed during the Ebola epidemic in 2019 to support the COVID-19 response in Uganda. The findings are based on the analysis of reports, guidelines, and discussions with stakeholders involved in implementing the electronic Management Information System during the COVID-19 response. Lessons and experiences are shared on how the system supported data visibility, use and health commodity management. RESULTS: A web-based emergency Electronic Management Information System was developed to support the supply chain system during preparedness and response to the Ebola Virus. The system facilitated coordination, information management and provided real-time data for planning, decision making, and distribution of commodities during the COVID-19 response. To address any human resource challenges, 863 staff were trained and mentored in the use of the system. The system enabled the Ministry of Health to track the distribution of Medical Counter Measures through the warehouses, eight regional pre-positioning centers, and over 2000 user units in 136 district vaccine stores. In addition, the system provided quality data for the quantification and monitoring of commodities at all levels of care. Over 1800 bulk orders were processed through the system. Real time stock status reports were transmitted from all national, regional, district and health facility levels. Procurement tracking reports, stock gap analysis and partner contribution were all accessible and visible in the system. This supported the Ministry of Health's resource mobilization and coordination efforts. CONCLUSIONS: Availability of reliable, quality real-time data are essential for effective decision making during public health emergencies. The emergency Electronic Logistic Management Information Systems supported health authorities to mount coordinated and effective responses to ensure timely availability of commodities and supplies to support the COVID-19 pandemic response. Lessons learnt from the Ebola epidemic response were translated into actions that enabled effective preparedness and response to the COVID-19 pandemic.

Understanding reasons for and strategic responses to administrative health data misreporting in an Indian state.

The misreporting of administrative health data creates an inequitable distribution of scarce health resources and weakens transparency and accountability within health systems. In the mid-2010s, an Indian state introduced a district ranking system to monitor the monthly performance of health programmes alongside a set of data quality initiatives. However, questions remain about the role of data manipulation in compromising the accuracy of data available for decision-making. We used qualitative approaches to examine the opportunities, pressures and rationalization of potential data manipulation. Using purposive sampling, we interviewed 48 district-level respondents from high-, middle- and low-ranked districts and 35 division- and state-level officials, all of whom had data-related or programme monitoring responsibilities. Additionally, we observed 14 district-level meetings where administrative data were reviewed. District respondents reported that the quality of administrative data was sometimes compromised to achieve top district rankings. The pressure to exaggerate progress was a symptom of the broader system for assessing health performance that was often viewed as punitive and where district- and state-level superiors were viewed as having limited ability to ensure accountability for data quality. However, district respondents described being held accountable for results despite lacking the adequate capacity to deliver on them. Many rationalized data manipulation to cope with high pressures, to safeguard their jobs and, in some cases, for personal financial gain. Moreover, because data manipulation was viewed as a socially acceptable practice, ethical arguments against it were less effective. Potential entry points to mitigate data manipulation include (1) changing the incentive structures to place equal emphasis on the quality of data informing the performance data (e.g. district rankings), (2) strengthening checks and balances to reinforce the integrity of data-related processes within districts and (3) implementing policies to make data manipulation an unacceptable anomaly rather than a norm.

Roles and Research Trends of Neuroscience on Major Information Systems Journal: A Bibliometric and Content Analysis.

Over the past decade, neuroscience has been integrated into information systems as a new methodology and perspective to study and solve related problems. Therefore, NeuroIS has emerged as a new cutting-edge research field. This review aimed to identify, summarize, and classify existing NeuroIS publications through knowledge mapping and bibliometric analysis. To effectively understand the development trend of NeuroIS, this study referred to the journal selection index of the Association of Business Schools in 2021 and journals above three stars in the field of information management as the main selection basis. A total of 99 neuroscience papers and their citation data were included from 19 major information systems journals of SCI/SSCI. This study analyzed bibliometric data from 2010 to 2021 to identify the most productive countries, universities, authors, journals, and prolific publications in NeuroIS. To this end, VOSviewer was used to visualize mapping based on co-citation, bibliographic coupling, and co-occurrence. Keywords with strong citation bursts were also identified in this study. This signifies the evolution of this research field and may reveal potential research directions in the near future. In selecting research methods and analysis tools for NeuroIS, content analysis was used to further conclude and summarize the relevant trends. Moreover, a co-citation network analysis was conducted to help understand how the papers, journals, and authors in the field were connected and related, and to identify the seminal or pioneering major literature. For researchers, network maps visualized mainstream research and provided a structural understanding of NeuroIS. The review concludes by discussing potential research topics in this field.

Assessing the Use of Hospital Information Systems (HIS) to Support Decision-Making: A Cross-Sectional Study in Public Hospitals in the Huíla Health Region of Southern Angola.

Hospital information systems could be relevant tools to inform hospital managers, support better management decisions in healthcare, and increase efficiency. Nonetheless, hospital managers' effective use of these systems to support decision-making in Angola is unknown. Our study aimed to analyse the use of hospital information systems as a tool to support decision-making by hospital managers in Huíla, Angola. It was a descriptive, cross-sectional study inducted between July and September 2017 in seven hospitals in Huíla Province, Angola, specifically in the cities of Lubango and Matala. Thirty-six members of the hospital boards filled out a self-questionnaire that consisted of twenty questions based on the following issues: Characterisation of the interviewee's profile; availability of information in the institution; and quality and usefulness of the available operational information. At least two thirds of the participants reported being unsatisfied or relatively satisfied with each assessed hospital information systems-specific feature. More than 50% have rarely or never used the health information system to support decision-making. Most managers do not use hospital information systems to support management-related decision-making in Angola. Improving the ability of hospital information systems to compute adequate indicators and training for hospital managers could be targets for future interventions to support better management-related decision-making in Angolan healthcare.

Higher education managers' perspectives on quality management and technology acceptance: A tale of elders, mediators, and working bees in times of Covid-19.

The Covid-19 pandemic has emphasized the role of educational management information systems (EMIS) for quality management (QM) in higher education, and set new directions for post-pandemic studies. Successful implementation of QM processes depends largely on managers' perceptions about quality and educational technology. However, higher education managers' profiles regarding these quality perceptions and their EMIS acceptance have been insufficiently investigated so far. In response to this research gap, we identified such profiles based on a quantitative survey of N = 70 managers from Chilean higher education institutions during the Covid-19 pandemic. A cluster analysis revealed three distinct manager types: "Elders" (oldest participants, almost equally distributed across positions, with least frequent EMIS access, moderate EMIS acceptance, and highest QM perceptions), "Mediators" (in operational and middle-management positions, with moderately frequent access to EMIS, and lowest EMIS acceptance and QM perceptions), and "Working Bees" (younger females in operational positions, with most frequent EMIS access, highest EMIS acceptance, and moderate QM perceptions). Knowledge of these profiles may enable customized training in the recovery after the Covid-19 pandemic.

Codeveloping an effective EMPA to maturity in an acute NHS Trust: an implementer report.

INTRODUCTION: University Hospitals Leicester has codeveloped, with Nervecentre, an Electronic Prescribing and Medicines Administration System that meets specific clinical and interoperability demands of the National Health Service (NHS). METHODS: The system was developed through a frontline-led and agile approach with a project team consisting of clinicians, Information Technology (IT) specialists and the vendor's representatives over an 18-month period. RESULTS: The system was deployed successfully with more than a thousand transcriptions during roll-out. Despite the high caseload and novelty of the system, there was no increase in error rates within the first 3 months of roll-out. Healthcare professionals perceived the new system as efficient with improved clinical workflow, and safe through an integrated medication alert system. DISCUSSION: This case study demonstrates how NHS trusts can successfully co-develop, with vendors, new IT systems which meet interoperability standards such as Fast Healthcare Interoperability Resources, while improving front line clinical experience. CONCLUSION: Alternative methods to the 'big bang' deployment of IT projects, such as 'gradual implementation', must be demonstrated and evaluated for their ability to deliver digital transformation projects in the NHS successfully.

Small-sample inference for cluster-based outcome-dependent sampling schemes in resource-limited settings: Investigating low birthweight in Rwanda.

The neonatal mortality rate in Rwanda remains above the United Nations Sustainable Development Goal 3 target of 12 deaths per 1000 live births. As part of a larger effort to reduce preventable neonatal deaths in the country, we conducted a study to examine risk factors for low birthweight. The data were collected via a cost-efficient cluster-based outcome-dependent sampling (ODS) scheme wherein clusters of individuals (health centers) were selected on the basis of, in part, the outcome rate of the individuals. For a given data set collected via a cluster-based ODS scheme, estimation for a marginal model may proceed via inverse-probability-weighted generalized estimating equations, where the cluster-specific weights are the inverse probability of the health center's inclusion in the sample. In this paper, we provide a detailed treatment of the asymptotic properties of this estimator, together with an explicit expression for the asymptotic variance and a corresponding estimator. Furthermore, motivated by the study we conducted in Rwanda, we propose a number of small-sample bias corrections to both the point estimates and the standard error estimates. Through simulation, we show that applying these corrections when the number of clusters is small generally reduces the bias in the point estimates, and results in closer to nominal coverage. The proposed methods are applied to data from 18 health centers and 1 district hospital in Rwanda.

Reducing Crowding in Emergency Departments With Early Prediction of Hospital Admission of Adult Patients Using Biomarkers Collected at Triage: Retrospective Cohort Study.

BACKGROUND: Emergency department crowding continues to threaten patient safety and cause poor patient outcomes. Prior models designed to predict hospital admission have had biases. Predictive models that successfully estimate the probability of patient hospital admission would be useful in reducing or preventing emergency department "boarding" and hospital "exit block" and would reduce emergency department crowding by initiating earlier hospital admission and avoiding protracted bed procurement processes. OBJECTIVE: To develop a model to predict imminent adult patient hospital admission from the emergency department early in the patient visit by utilizing existing clinical descriptors (ie, patient biomarkers) that are routinely collected at triage and captured in the hospital's electronic medical records. Biomarkers are advantageous for modeling due to their early and routine collection at triage; instantaneous availability; standardized definition, measurement, and interpretation; and their freedom from the confines of patient histories (ie, they are not affected by inaccurate patient reports on medical history, unavailable reports, or delayed report retrieval). METHODS: This retrospective cohort study evaluated 1 year of consecutive data events among adult patients admitted to the emergency department and developed an algorithm that predicted which patients would require imminent hospital admission. Eight predictor variables were evaluated for their roles in the outcome of the patient emergency department visit. Logistic regression was used to model the study data. RESULTS: The 8-predictor model included the following biomarkers: age, systolic blood pressure, diastolic blood pressure, heart rate, respiration rate, temperature, gender, and acuity level. The model used these biomarkers to identify emergency department patients who required hospital admission. Our model performed well, with good agreement between observed and predicted admissions, indicating a well-fitting and well-calibrated model that showed good ability to discriminate between patients who would and would not be admitted. CONCLUSIONS: This prediction model based on primary data identified emergency department patients with an increased risk of hospital admission. This actionable information can be used to improve patient care and hospital operations, especially by reducing emergency department crowding by looking ahead to predict which patients are likely to be admitted following triage, thereby providing needed information to initiate the complex admission and bed assignment processes much earlier in the care continuum.

Examining policy intentions and actual implementation practices: How organizational factors influence health management information systems in Uttar Pradesh, India.

This study investigates the implementation of a recent health management information systems (HMIS) policy reform in Uttar Pradesh, India, which aims to improve the quality and use of HMIS data in decision-making. Through in-depth interviews, meeting observations and a policy document review, this study sought to capture the experiences of district-level staff (street-level bureaucrats) who were responsible for HMIS policy implementation. Findings revealed that issues of weak HMIS implementation were partly due to human resources shortages both in number and technical skill. Delays in recruitment and the presence of inactive staff overburdened existing staff and weakened the implementation of HMIS activities at the block- and district-levels. District staff also explained how inadequate computer literacy and limited technical understanding further contributed to low HMIS data quality. The organizational culture was even more constraining: working within a very rigid and hierarchical organization was challenging for district data staff, who were expected to manage day-to-day HMIS activities, but lacked the discretion and authority to do so effectively. Consequently, they had to escalate minor issues to district leadership for action and were expected to follow their supervisors' directives- even if they contradicted HMIS policy guidelines. High performance pressures associated with achieving top district rankings deviated focus away from HMIS data quality issues. Many district-level respondents described their superiors' "fixation" with becoming a top-ranking district often resulted in disregard for the quality of data informing district rankings. Furthermore, the review of district rankings only partially encouraged district-level leadership to investigate reasons for low-performing indicators. Instead, low district rankings often resulted in punitive action. The study recommends the importance of incorporating the perspectives of district staff, and recognizing their discretion, and authority when designing policy implementation processes, and finally concludes with potential strategies for strengthening the current HMIS policy reform.

Disruptions in maternal and child health service utilization during COVID-19: analysis from eight sub-Saharan African countries.

The coronavirus-19 pandemic and its secondary effects threaten the continuity of essential health services delivery, which may lead to worsened population health and a protracted public health crisis. We quantify such disruptions, focusing on maternal and child health, in eight sub-Saharan countries. Service volumes are extracted from administrative systems for 63 954 facilities in eight countries: Cameroon, Democratic Republic of Congo, Liberia, Malawi, Mali, Nigeria, Sierra Leone and Somalia. Using an interrupted time series design and an ordinary least squares regression model with facility-level fixed effects, we analyze data from January 2018 to February 2020 to predict what service utilization levels would have been in March-July 2020 in the absence of the pandemic, accounting for both secular trends and seasonality. Estimates of disruption are derived by comparing the predicted and observed service utilization levels during the pandemic period. All countries experienced service disruptions for at least 1 month, but the magnitude and duration of the disruptions vary. Outpatient consultations and child vaccinations were the most commonly affected services and fell by the largest margins. We estimate a cumulative shortfall of 5 149 491 outpatient consultations and 328 961 third-dose pentavalent vaccinations during the 5 months in these eight countries. Decreases in maternal health service utilization are less generalized, although significant declines in institutional deliveries, antenatal care and postnatal care were detected in some countries. There is a need to better understand the factors determining the magnitude and duration of such disruptions in order to design interventions that would respond to the shortfall in care. Service delivery modifications need to be both highly contextualized and integrated as a core component of future epidemic response and planning.

[Analysis of potentially inappropriate prescriptions according to the START criteria in nursing homes]. / Análisis de las prescripciones potencialmente inadecuadas en centros sociosanitarios según los criterios START.

BACKGROUND AND OBJECTIVE: The potentially inappropriate prescription by omission of a drug is defined as the failure to prescribe drugs that are clinically indicated. The objective of this article is to describe and analyse the evolution of inappropriate prescriptions by omission in nursing homes of a health department. MATERIAL AND METHODS: Retrospective observational descriptive study carried out in nursing homes of the Valencia-Clínico-Malvarrosa health department during the period 2016-2018. All institutionalized patients during this period were included. The prevalence of potentially inappropriate prescriptions by omission was assessed based on version 2 of the START criteria. The variables came from the electronic medical records of ambulatory care of the Conselleria de Sanitat (Abucasis). RESULTS: 2251 different patients were selected, mean age of 79,53years, 69% women, and an average of 4,60 chronic drugs/resident. A total of 2647 inappropriate prescriptions by omission were identified during the study period, and the results were similar during these 3years. The most prevalent START criteria were those related to the musculoskeletal system and the cardiovascular system, and those related to analgesic consumption. The mean value of inappropriate prescriptions by omission prevalence for the period studied were 39.54%. CONCLUSION: The results of our study confirm a high prevalence of potentially inappropriate prescriptions by omission in residents of nursing homes, and the maintenance of this prevalence during the 3years of the study.

Optimal allocation in stratified cluster-based outcome-dependent sampling designs.

In public health research, finite resources often require that decisions be made at the study design stage regarding which individuals to sample for detailed data collection. At the same time, when study units are naturally clustered, as patients are in clinics, it may be preferable to sample clusters rather than the study units, especially when the costs associated with travel between clusters are high. In this setting, aggregated data on the outcome and select covariates are sometimes routinely available through, for example, a country's Health Management Information System. If used wisely, this information can be used to guide decisions regarding which clusters to sample, and potentially obtain gains in efficiency over simple random sampling. In this article, we derive a series of formulas for optimal allocation of resources when a single-stage stratified cluster-based outcome-dependent sampling design is to be used and a marginal mean model is specified to answer the question of interest. Specifically, we consider two settings: (i) when a particular parameter in the mean model is of primary interest; and, (ii) when multiple parameters are of interest. We investigate the finite population performance of the optimal allocation framework through a comprehensive simulation study. Our results show that there are trade-offs that must be considered at the design stage: optimizing for one parameter yields efficiency gains over balanced and simple random sampling, while resulting in losses for the other parameters in the model. Optimizing for all parameters simultaneously yields smaller gains in efficiency, but mitigates the losses for the other parameters in the model.

Birthweight: EN-BIRTH multi-country validation study.

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.

Stillbirths including intrapartum timing: EN-BIRTH multi-country validation study.

BACKGROUND: An estimated >2 million babies stillborn around the world each year lack visibility. Low- and middle-income countries carry 84% of the burden yet have the least data. Most births are now in facilities, hence routine register-recording presents an opportunity to improve counting of stillbirths, but research is limited, particularly regarding accuracy. This paper evaluates register-recorded measurement of hospital stillbirths, classification accuracy, and barriers and enablers to routine recording. METHODS: The EN-BIRTH mixed-methods, observational study took place in five hospitals in Bangladesh, Nepal and Tanzania (2017-2018). Clinical observers collected time-stamped data on perinatal care and birth outcomes as gold standard. To assess accuracy of routine register-recorded stillbirth rates, we compared birth outcomes recorded in labour ward registers to observation data. We calculated absolute rate differences and individual-level validation metrics (sensitivity, specificity, percent agreement). We assessed misclassification of stillbirths with neonatal deaths. To examine stillbirth appearance (fresh/macerated) as a proxy for timing of death, we compared appearance to observed timing of intrauterine death based on heart rate at admission. RESULTS: 23,072 births were observed including 550 stillbirths. Register-recorded completeness of birth outcomes was > 90%. The observed study stillbirth rate ranged from 3.8 (95%CI = 2.0,7.0) to 50.3 (95%CI = 43.6,58.0)/1000 total births and was under-estimated in routine registers by 1.1 to 7.3 /1000 total births (register: observed ratio 0.9-0.7). Specificity of register-recorded birth outcomes was > 99% and sensitivity varied between hospitals, ranging from 77.7-86.1%. Percent agreement between observer-assessed birth outcome and register-recorded birth outcome was very high across all hospitals and all modes of birth (> 98%). Fresh or macerated stillbirth appearance was a poor proxy for timing of stillbirth. While there were similar numbers of stillbirths misclassified as neonatal deaths (17/430) and neonatal deaths misclassified as stillbirths (21/36), neonatal deaths were proportionately more likely to be misclassified as stillbirths (58.3% vs 4.0%). Enablers to more accurate register-recording of birth outcome included supervision and data use. CONCLUSIONS: Our results show these routine registers accurately recorded stillbirths. Fresh/macerated appearance was a poor proxy for intrapartum stillbirths, hence more focus on measuring fetal heart rate is crucial to classification and importantly reduction in these preventable deaths.