Increasing newborn survival in low resource settings: harnessing real-time data, digital innovation and community engagement to improve newborn healthcare.

Improving peri- and postnatal facility-based care in low-resource settings could save over 6000 babies' lives per day globally. Most of the annual 2.4 million neonatal deaths and 2 million stillbirths occur in healthcare facilities in low-resource settings and are preventable through the implementation of cost-effective interventions. However, significant gaps exist in the evidence-base to inform protocols for the treatment of common neonatal disorders for low-resource settings and in how these should best be implemented to ensure maximal and equitable impact. With this Professorship I will build on work I have led over the last 7 years to co-develop, implement, and evaluate an app-based solution to improve postnatal newborn care: the Neotree. Also I will build on work from the last 6 months to strengthen community engagement in newborn care and research. Finally I will continue to support research and leadership through the doctoral post, by supervising and mentoring Malawian and Zimbabwean clinicians and researchers, and by promoting open data. My AIM is to reduce preventable neonatal mortality by empowering locally-driven widespread improvement in the perinatal and postnatal clinical care of newborns. My OBJECTIVES are to: 1. Extend Neotree to new outcomes (quality of perinatal care) and populations (rural health clinics) to help with the care of over a million babies and mothers annually in Zimbabwe and Malawi. 2. Substantially improve neonatal outcomes in communities already using Neotree by optimising clinical care pathways for thermoregulation, convulsions, low birth weight, prematurity, hypoglycaemia, HIV, respiratory distress, neonatal encephalopathy, sepsis, syphilis, jaundice and congenital abnormalities. 3. Actively and explicitly involve families in newborn care research and also in the co-creation of family centred clinical pathways. METHODS We will develop user-centric iterative development of the software, data pipeline, and user-interface will be used to extend Neotree functionality to: (i) perinatal care; (ii) rural health clinics and (iii) optimised decision support. Qualitative methods, informed by behavioural sciences frameworks, will explore (i) sustainability of Neotree implementation, and acceptability, feasibility and usability of (ii) optimised clinical decision support, (iii) perinatal care functionality and (iv) rural health clinic implementation. We will conduct 12 focus group discussions, 9 in Zimbabwe and 3 in Malawi, and 49 semi-structured interviews, 43 in Zimbabwe and 6 in Malawi with healthcare providers, parents and stakeholders. Total participants for new qualitative data collection ~170. Interrogation of the Neotree dataset (minimum sample size 28,000) using statistical and machine learning methods will be used to refine current diagnostic algorithms and validate them prospectively under a strict non-inferiority approach. Clinical and data pipeline processes will be developed to enable continuous, evidence-based optimisation of the clinical decision support. We will deliver a 2-year programme of community involvement and engagement including: (i) research participation and training in public engagement; (ii) arts-based dialogues with parents in rural Zimbabwe; and (iii) co-production of family-centred models of care. My OUTPUTS will include peer-reviewed publications, presentations in international conferences, surveillance dashboard reports, curated databases, learning materials and open-source code. I will ensure these outputs have impact through key team members, collaborators and their networks.