How can we improve the safe provision and efficiency of healthcare for children with acute lower respiratory tract infections?
Acute lower respiratory tract infections (ALRTIs) remain one of the leading causes of childhood morbidity and mortality, causing around 921,000 deaths in children ≤5 years globally in 2015 (15% of under-5 deaths).1 Between 2000 and 2015, global hospital admissions for childhood pneumonia increased 3-fold.2 Respiratory syncytial virus (RSV) is the most prevalent viral cause of ALRTIs, which resulted in 33 million new cases and 3.2 million hospital admission globally in children ≤5 years in 2015.3 In the past decade, whilst deaths remain uncommon, there has been a rapid rise in very short hospital admissions associated with ALRTIs. This has substantially influenced health and incurred economic and social costs (e.g., nosocomial infection risks, out of pocket expenses and declined employment among parents). The ongoing COVID-19 pandemic has amply demonstrated the limited resilience of healthcare systems to novel respiratory viruses with greater clinical impact. Changes in pathogenicity/transmissibility of circulating (e.g., RSV) or emerging (e.g., SARS-CoV-2) viruses could exceed critical demand for healthcare. Understanding healthcare use and capacity for ALRTIs is crucial for evaluating seasonal and pandemic preparedness. The findings will support (a) an understanding of risk/benefit for community based care of ALRTIs with anticipated rapid recovery and (b) prepare modelling to facilitate the rapid incorporation of the early pandemic data to inform public health and healthcare resource adaptation and upscaling to create capacity and reduce bottlenecks in care.
The aim of this project is to develop an algorithm that identifies children ≤5 years with ALRTIs who could be safely cared for at home and to assess the impact of the emerging pathogen strains against standard heath care resource use by children with ALRTIs. This is to support clinical decision-making at the level of individual patients and improve efficiency of hospital care to reduce the impact of ALRTIs on hospitals and families, as well as to inform health service planning.
The key objectives are:
Synthesise the evidence for risk factors and tools used to risk stratify for childhood ALRTIs associated length of hospital stay and safe discharge.
Identify and characterise children who were safely discharged and had very short hospital admissions without readmission within next 14 days.
Generate and validate internally and externally an algorithm to identify children (at triage) who could be safely sent home with parental counselling on safety netting and red flags marking clinical deterioration.
Explore the epidemiological characteristics of previously and currently encountered pathogen strains of ALRTIs.
Assess the impact of a virus strain with high transmissibility and high case fatality ratio against standard health care resource use by using mathematical models to capture the dynamics of infection.
Completion of this PhD will provide the student with an in-depth understanding of population health methodologies and the ability to communicate and collaborate with scientists across disciplines (epidemiology, personalised medicine, biostatistics, and mathematics). Core technical areas of learning will include respiratory epidemiology, personalised medicine in primary care, predictive modelling, mathematical modelling, and scientific programming and databases. The student will develop or extend their programming expertise in languages, such as R or Python. Emphasis will be placed on developing and sharing code for the wider scientific community through platforms such as GitHub.
The student will have the opportunity to attend training courses in statistical methods for risk prediction and prognostic models e.g., at Keele University and dynamic modelling e.g., at London School of Hygiene & Tropical Medicine, as well as undertake exchange visits to modelling colleagues in the UK and collaborators in Europe and internationally. Soft skills in scientific communication and collaboration will be fostered via the interdisciplinary supervisory team and participation in different forums and conferences with specialist and lay audiences and through the opportunity to submit journal articles.
- PhD position
- University of Edinburgh
- Closing date
- January 20th, 2022
- Posted on
- December 2nd, 2021 11:32
- Last updated
- December 2nd, 2021 11:32