Evaluation of 'TRY': an algorithm for neonatal continuous positive airways pressure in low-income settings

BACKGROUND: Non-invasive respiratory support using bubble continuous positive airway pressure (bCPAP) is useful in treating babies with respiratory distress syndrome. Despite its proven clinical and cost-effectiveness, implementation is hampered by the inappropriate administration of bCPAP in low-resource settings. A clinical algorithm-'TRY' (based on Tone: good; Respiratory distress; Yes, heart rate above 100 beats/min)-has been developed to correctly identify which newborns would benefit most from bCPAP in a teaching hospital in Malawi. OBJECTIVE: To evaluate the reliability, sensitivity and specificity of TRY when employed by nurses in a Malawian district hospital. METHODS: Nursing staff in a Malawian district hospital baby unit were asked, over a 2-month period, to complete TRY assessments for every newly admitted baby with the following inclusion criteria: clinical evidence of respiratory distress and/or birth weight less than 1.3 kg. A visiting paediatrician, blinded to nurses' assessments, concurrently assessed each baby, providing both a TRY assessment and a clinical decision regarding the need for CPAP administration. Inter-rater reliability was calculated comparing nursing and paediatrician TRY assessment outcomes. Sensitivity and specificity were estimated comparing nurse TRY assessments against the paediatrician's clinical decision. RESULTS: Two hundred and eighty-seven infants were admitted during the study period; 145 (51%) of these met the inclusion criteria, and of these 57 (39%) received joint assessments. The inter-rater reliability was high (kappa 0.822). Sensitivity and specificity were 92% and 96%, respectively. CONCLUSIONS: District hospital nurses, using the TRY-CPAP algorithm, reliably identified babies that might benefit from bCPAP and thus improved its effective implementation

Admissions to a low resource neonatal unit in Malawi using The NeoTree application: A digital perinatal outcome audit

Background: Mobile-health has increasing potential to address health outcomes in under-resourced settings as smart-phone coverage increases. The NeoTree is a mobile-health application co-developed in Malawi to improve the quality of newborn care at the point of admission to neonatal units. While collecting vital demographic and clinical data this interactive platform provides clinical decision-support, and training for the end-users (health care workers (HCW)), according to evidence based national and international guidelines. Objective: Our aims were to examine one month of data collected using the NeoTree in an outcome audit of babies admitted to a district-level neonatal nursery in Malawi and to demonstrate proof of concept of digital audit data in this setting. Methods: Using a phased approach over one month (21 Nov – 19 Dec, 2016), frontline HCWs were trained and supported to use the NeoTree to admit newborns. Discharge data were collected by the research team using a discharge form within the NeoTree ‘NeoDischarge’. Descriptive analysis was conducted on the exported pseudonomysed data and presented to the newborn care department as a digital audit. Results: Of 191 total admissions, 134 (70%) admissions were completed using the NeoTree and 129 (67%) were exported and analysed. Of these 129, 102 (79%) were discharged alive. Overall case fatality rate was 93 per 1000 admitted babies. Prematurity with respiratory distress syndrome, Birth Asphyxia, and Neonatal sepsis contributed to 41.6%, 58.3% and 16.6% of deaths respectively. Deaths may have been under-reported due to phased implementation and some families of babies with imminent deaths self-discharging home. Detailed characterisation of the data enabled departmental discussion of modifiable factors for quality improvement, for example improved thermoregulation of infants. Conclusions: This digital outcome audit demonstrates that data can be captured digitally at the bedside by HCWs in under-resourced newborn facilities and these data can contribute to meaningful review of quality of care/outcomes and potential modifiable factors. Coverage may be improved during future implementation by streamlining the admission process to be solely via digital format. Our results present a new methodology for newborn audit in low-resource settings and are a proof of concept for a novel newborn data system in these settings

Admissions to a Low-Resource Neonatal Unit in Malawi Using a Mobile App and Dashboard: A 1-Year Digital Perinatal Outcome Audit

Introduction: Understanding the extent and cause of high neonatal deaths rates in Sub-Saharan Africa is a challenge, especially in the presence of poor-quality and inaccurate data. The NeoTree digital data capture and quality improvement system has been live at Kamuzu Central Hospital, Neonatal Unit, Malawi, since April 2019. Objective: To describe patterns of admissions and outcomes in babies admitted to a Malawian neonatal unit over a 1-year period via a prototype data dashboard. Methods: Data were collected prospectively at the point of care, using the NeoTree app, which includes digital admission and outcome forms containing embedded clinical decision and management support and education in newborn care according to evidence-based guidelines. Data were exported and visualised using Microsoft Power BI. Descriptive and inferential analysis statistics were executed using R. Results: Data collected via NeoTree were 100% for all mandatory fields and, on average, 96% complete across all fields. Coverage of admissions, discharges, and deaths was 97, 99, and 91%, respectively, when compared with the ward logbook. A total of 2,732 neonates were admitted and 2,413 (88.3%) had an electronic outcome recorded: 1,899 (78.7%) were discharged alive, 12 (0.5%) were referred to another hospital, 10 (0.4%) absconded, and 492 (20%) babies died. The overall case fatality rate (CFR) was 204/1,000 admissions. Babies who were premature, low birth weight, out born, or hypothermic on admission, and had significantly higher CFR. Lead causes of death were prematurity with respiratory distress (n = 252, 51%), neonatal sepsis (n = 116, 23%), and neonatal encephalopathy (n = 80, 16%). The most common perceived modifiable factors in death were inadequate monitoring of vital signs and suboptimal management of sepsis. Two hundred and two (8.1%) neonates were HIV exposed, of whom a third [59 (29.2%)] did not receive prophylactic nevirapine, hence vulnerable to vertical infection. Conclusion: A digital data capture and quality improvement system was successfully deployed in a low resource neonatal unit with high (1 in 5) mortality rates providing and visualising reliable, timely, and complete data describing patterns, risk factors, and modifiable causes of newborn mortality. Key targets for quality improvement were identified. Future research will explore the impact of the NeoTree on quality of care and newborn survival

Semiclassical transmission across transition states

It is shown that the probability of quantum-mechanical transmission across a phase space bottleneck can be compactly approximated using an operator derived from a complex Poincar\'e return map. This result uniformly incorporates tunnelling effects with classically-allowed transmission and generalises a result previously derived for a classically small region of phase space.Comment: To appear in Nonlinearit

Indirect impacts of the COVID-19 pandemic at two tertiary neonatal units in Zimbabwe and Malawi: an interrupted time series analysis.

OBJECTIVES: To examine indirect impacts of the COVID-19 pandemic on neonatal care in low-income and middle-income countries. DESIGN: Interrupted time series analysis. SETTING: Two tertiary neonatal units in Harare, Zimbabwe and Lilongwe, Malawi. PARTICIPANTS: We included a total of 6800 neonates who were admitted to either neonatal unit from 1 June 2019 to 25 September 2020 (Zimbabwe: 3450; Malawi: 3350). We applied no specific exclusion criteria. INTERVENTIONS: The first cases of COVID-19 in each country (Zimbabwe: 20 March 2020; Malawi: 3 April 2020). PRIMARY OUTCOME MEASURES: Changes in the number of admissions, gestational age and birth weight, source of admission referrals, prevalence of neonatal encephalopathy, and overall mortality before and after the first cases of COVID-19. RESULTS: Admission numbers in Zimbabwe 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 0.41 to 0.66, p0.05). CONCLUSIONS: The indirect impacts of COVID-19 are context-specific. While our 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

The NeoTree application: developing an integrated mHealth solution to improve quality of newborn care and survival in a district hospital in Malawi

More than two-thirds of newborn lives could be saved worldwide if evidence-based interventions were successfully implemented. We developed the NeoTree application to improve quality of newborn care in resource-poor countries. The NeoTree is a fully integrated digital health intervention that combines immediate data capture, entered by healthcare workers (HCW) on admission, while simultaneously providing them with evidence-based clinical decision support and newborn care education. We conducted a mixed-methods intervention development study, codeveloping and testing the NeoTree prototype with HCWs in a district hospital in Malawi. Focus groups explored the acceptability and feasibility of digital health solutions before and after implementation of the NeoTree in the clinical setting. One-to-one theoretical usability workshops and a 1-month clinical usability study informed iterative changes, gathered process and clinical data, System Usability Scale (SUS) and perceived improvements in quality of care. HCWs perceived the NeoTree to be acceptable and feasible. Mean SUS before and after the clinical usability study were high at 80.4 and 86.1, respectively (above average is >68). HCWs reported high-perceived improvements in quality of newborn care after using the NeoTree on the ward. They described improved confidence in clinical decision-making, clinical skills, critical thinking and standardisation of care. Identified factors for successful implementation included a technical support worker. Coproduction, mixed-methods approaches and user-focused iterative development were key to the development of the NeoTree prototype, which was shown to be an agile, acceptable, feasible and highly usable tool with the potential to improve the quality of newborn care in resource-poor settings

Protocol for an intervention development and pilot implementation evaluation study of an e-health solution to improve newborn care quality and survival in two low-resource settings, Malawi and Zimbabwe: Neotree.

INTRODUCTION: Every year 2.4 million deaths occur worldwide in babies younger than 28 days. Approximately 70% of these deaths occur in low-resource settings because of failure to implement evidence-based interventions. Digital health technologies may offer an implementation solution. Since 2014, we have worked in Bangladesh, Malawi, Zimbabwe and the UK to develop and pilot Neotree: an android app with accompanying data visualisation, linkage and export. Its low-cost hardware and state-of-the-art software are used to improve bedside postnatal care and to provide insights into population health trends, to impact wider policy and practice. METHODS AND ANALYSIS: This is a mixed methods (1) intervention codevelopment and optimisation and (2) pilot implementation evaluation (including economic evaluation) study. Neotree will be implemented in two hospitals in Zimbabwe, and one in Malawi. Over the 2-year study period clinical and demographic newborn data will be collected via Neotree, in addition to behavioural science informed qualitative and quantitative implementation evaluation and measures of cost, newborn care quality and usability. Neotree clinical decision support algorithms will be optimised according to best available evidence and clinical validation studies. ETHICS AND DISSEMINATION: This is a Wellcome Trust funded project (215742_Z_19_Z). Research ethics approvals have been obtained: Malawi College of Medicine Research and Ethics Committee (P.01/20/2909; P.02/19/2613); UCL (17123/001, 6681/001, 5019/004); Medical Research Council Zimbabwe (MRCZ/A/2570), BRTI and JREC institutional review boards (AP155/2020; JREC/327/19), Sally Mugabe Hospital Ethics Committee (071119/64; 250418/48). Results will be disseminated via academic publications and public and policy engagement activities. In this study, the care for an estimated 15 000 babies across three sites will be impacted. TRIAL REGISTRATION NUMBER: NCT0512707; Pre-results

Wigner's Dynamical Transition State Theory in Phase Space: Classical and Quantum

A quantum version of transition state theory based on a quantum normal form (QNF) expansion about a saddle-centre-...-centre equilibrium point is presented. A general algorithm is provided which allows one to explictly compute QNF to any desired order. This leads to an efficient procedure to compute quantum reaction rates and the associated Gamov-Siegert resonances. In the classical limit the QNF reduces to the classical normal form which leads to the recently developed phase space realisation of Wigner's transition state theory. It is shown that the phase space structures that govern the classical reaction d ynamicsform a skeleton for the quantum scattering and resonance wavefunctions which can also be computed from the QNF. Several examples are worked out explicitly to illustrate the efficiency of the procedure presented.Comment: 132 pages, 31 figures, corrected version, Nonlinearity, 21 (2008) R1-R11

Auditing use of antibiotics in Zimbabwean neonates.

Background: Neonatal sepsis is a major cause of morbidity and mortality in low-income settings. As signs of sepsis are non-specific and deterioration precipitous, antibiotics are often used profusely in these settings where diagnostics may not be readily available. Harare Central Hospital, Zimbabwe, delivers 12000 babies per annum admitting ∼4800 to the neonatal unit. Overcrowding, understaffing and rapid staff turnover are consistent problems. Suspected sepsis is highly prevalent, and antibiotics widely used. We audited the impact of training and benchmarking intervention on rationalizing antibiotic prescription using local, World Health Organization-derived, guidelines as the standard. Methods: An initial audit of admission diagnosis and antibiotic use was performed between 8th May - 6th June 2018 as per the audit cycle. An intern training programme, focusing on antimicrobial stewardship and differentiating between babies 'at risk of' versus 'with' clinically-suspected sepsis was instituted post-primary audit. Re-audit was conducted after 5 months. Results: Sepsis was the most common admitting diagnosis by interns at both time points but reduced at repeat audit (81% versus 59%, P<0.0001). Re-audit after 5 months demonstrated a decrease in antibiotic prescribing at admission and discharge. Babies prescribed antibiotics at admission decreased from 449 (98%) to 96 (51%), P<0.0001. Inpatient days of therapy (DOT) reduced from 1243 to 1110/1000 patient-days. Oral amoxicillin prescription at discharge reduced from 349/354 (99%) to 1% 1/161 (P<0.0001). Conclusion: A substantial decrease in antibiotic use was achieved by performance feedback, training and leadership, although ongoing performance review will be key to ensuring safety and sustainability