There are 2. 4 million annual neonatal deaths worldwide. Simple, evidence-based interventions such as temperature control could prevent approximately two-thirds of these deaths. However, key problems in implementing these interventions are a lack of newborn-trained healthcare workers and a lack of data collection systems. NeoTree is a digital platform aiming to improve newborn care in low-resource settings through real-time data capture and feedback alongside education and data linkage. This project demonstrates proof of concept of the NeoTree as a real-time data capture tool replacing handwritten clinical paper notes over a 9-month period in a tertiary neonatal unit at Harare Central Hospital, Zimbabwe. We aimed to deliver robust data for monthly mortality and morbidity meetings and to improve turnaround time for blood culture results among other quality improvement indicators. There were 3222 admissions and discharges entered using the NeoTree software with 41 junior doctors and 9 laboratory staff trained over the 9-month period. The NeoTree app was fully integrated into the department for all admission and discharge documentation and the monthly presentations became routine, informing local practice. An essential factor for this success was local buy-in and ownership at each stage of the project development, as was monthly data analysis and presentations allowing us to rapidly troubleshoot emerging issues. However, the laboratory arm of the project was negatively affected by nationwide economic upheaval. Our successes and challenges piloting this digital tool have provided key insights for effective future roll-out in Zimbabwe and other low-income healthcare settings.

Introduction: Improving peri-and postnatal facility-based care in low-resource settings (LRS) could save over 6000 babies' lives per day. Most of the annual 2.4 million neonatal deaths and 2 million stillbirths occur in healthcare facilities in LRS and are preventable through the implementation of cost-effective, simple, evidence-based interventions. However, their implementation is challenging in healthcare systems where one in four babies admitted to neonatal units die. In high-resource settings healthcare systems strengthening is increasingly delivered via learning healthcare systems to optimise care quality, but this approach is rare in LRS.Methods: Since 2014 we have worked in Bangladesh, Malawi, Zimbabwe, and the UK to co-develop and pilot the Neotree system: an android application with accompanying data visualisation, linkage, and export. Its low-cost hardware and state-ofthe-art software are used to support healthcare professionals to improve postnatal care at the bedside and to provide insights into population health trends. Here we summarise the formative conceptualisation, development, and preliminary implementation experience of the Neotree.Results: Data thus far from ~18 000 babies, 400 healthcare professionals in four hospitals (two in Zimbabwe, two in Malawi) show high acceptability, feasibility, usability, and improvements in healthcare professionals' ability to deliver newborn care. The data also highlight gaps in knowledge in newborn care and quality improvement.Implementation has been resilient and informative during external crises, for example, coronavirus disease 2019 (COVID-19) pandemic. We have demonstrated evidence of improvements in clinical care and use of data for Quality Improvement (QI) projects.Msandeni Esther Chiume and Simbarashe Chimhuya are joint last authors.

BackgroundDeaths from COVID-19 have exceeded 1.8 million globally (January 2020). We examined trends in markers of neonatal care before and during the pandemic at two tertiary neonatal units in Zimbabwe and Malawi.MethodsWe analysed data collected prospectively via the NeoTree app at Sally Mugabe Central Hospital (SMCH), Zimbabwe, and Kamuzu Central Hospital (KCH), Malawi. Neonates admitted from 1 June 2019 to 25 September 2020 were included. We modelled the impact of the first cases of COVID-19 (Zimbabwe: 20 March 2020; Malawi: 3 April 2020) on number of admissions, gestational age and birth weight, source of admission referrals, prevalence of neonatal encephalopathy, and overall mortality.FindingsThe study included 3,450 neonates at SMCH and 3,350 neonates at KCH. Admission numbers at SMCH did not initially change after the first case of COVID-19 but fell by 48% during a nurses’ strike (Relative risk (RR) 0·52, 95%CI 0·40-0·68, p < 0·002). At KCH, admissions dropped by 42% (RR 0·58; 95%CI 0·48-0·70; p < 0·001) soon after the first case of COVID-19. At KCH, gestational age and birth weight decreased slightly (1 week, 300 grams), outside referrals dropped by 28%, and there was a slight weekly increase in mortality. No changes in these outcomes were found at SMCH.InterpretationThe indirect impacts of COVID-19 are context-specific. While this study provides vital evidence to inform health providers and policy makers, national data are required to ascertain the true impacts of the pandemic on newborn health.FundingInternational Child Health Group, Wellcome Trust.RESEARCH IN CONTEXTEvidence before this studyWe searched PubMed for evidence of the indirect impact of the COVID-19 pandemic on neonatal care in low-income settings using the search terms neonat*ornewborn, andCOVID-19orSARS-CoV 2orcoronavirus, and the Cochrane low and middle income country (LMIC) filters, with no language limits between 01.10.2019 and 21.11.20. While there has been a decrease in global neonatal mortality rates, the smaller improvements seen in low-income settings are threatened by the direct and indirect impact of the COVID-19 pandemic. A modelling study of this threat predicted between 250000-1.1 million extra neonatal deaths as a result of decreased service provision and access in LMICs. A webinar and survey of frontline maternal/newborn healthcare workers in >60 countries reported a decline in both service attendance and in quality of service across the ante-, peri- and post-natal journey. Reporting fear of attending services, and difficulty in access, and a decrease in service quality due to exacerbation of existing service weaknesses, confusion over guidelines and understaffing. Similar findings were reported in a survey of healthcare workers providing childhood and maternal vaccines in LMICs. One study to date has reported data from Nepal describing an increase in stillbirths and neonatal deaths, with institutional deliveries nearly halved during lockdown.Added value of this studyTo our knowledge, this is the first and only study in Sub-Saharan Africa describing the impact of COVID-19 pandemic on health service access and outcomes for newborns in two countries. We analysed data from the digital quality improvement and data collection tool, the NeoTree, to carry out an interrupted time series analysis of newborn admission rates, gestational age, birth weight, diagnosis of hypoxic ischaemic encephalopathy and mortality from two large hospitals in Malawi and Zimbabwe (n∼7000 babies). We found that the indirect impacts of COVID-19 were context-specific. In Sally Mugabe Central Hospital, Zimbabwe, initial resilience was demonstrated in that there was no evidence of change in mortality, birth weight or gestational age. In comparison, at Kamuzu Central Hospital, Malawi, soon after the first case of COVID-19, the data revealed a fall in admissions (by 42%), gestational age (1 week), birth weight (300 grams), and outside referrals (by 28%), and there was a slight weekly increase in mortality (2%). In the Zimbabwean hospital, admission numbers did not initially change after the first case of COVID-19 but fell by 48% during a nurses’ strike, which in itself was in response to challenges exacerbated by the pandemic.Implications of all the available evidenceOur data confirms the reports from frontline healthcare workers of a perceived decline in neonatal service access and provision in LMICs. Digital routine healthcare data capture enabled rapid profiling of indirect impacts of COVID-19 on newborn care and outcomes in two tertiary referral hospitals, Malawi and Zimbabwe. While a decrease in service access was seen in both countries, the impacts on care provided and outcome differed by national context. Health systems strengthening, for example digital data capture, may assist in planning context-specific mitigation efforts.