Improving the identification and support for children with asthma at risk of hospital admission.
In this project by the Healthy Children Healthy Families theme of NIHR CLAHRC Yorkshire and Humber we explore the facts that asthma is the most common chronic disease of childhood, affecting over 1 million children in the UK. Treatment with inhaled corticosteriods is effective in reducing exacerbations but prescribing patterns are variable. Bradford has the highest rate of admissions for children in the Yorkshire Region, and children of South Asian origin are almost three times as likely to be admitted as white children. This may relate to disease severity, quality and quantity of primary care, adherence to prophylaxis and differences in health-seeking behaviours.
- Research questions: Can secondary analysis of linked datasets be used to develop risk prediction models to identify populations of children at risk of hospital admission and poor health outcomes? Can patterns of healthcare utilisation be used to inform the local development and/or implementation of allergy care pathways? Can we identify potential interventions which may avoid unwarranted hospital admissions?
- Methods: Using our established SystmOne and BiB datasets we will undertake multivariable, multilevel analysis to determine which factors explain and predict primary care attendance, primary care guideline compliance and hospital admissions. We will investigate patterns of prescribing, uptake of repeat prescriptions and GP attendance in the 6 months prior to acute admission to identify missed opportunities for preventing deterioration. Evidence synthesis will be used to identify potentially cost-effective methods of intervening to improve outcomes.
- Outcomes: This work will link closely with the Avoiding Attendance and Admission in Long Term Conditions theme. Results will be used to inform commissioning of health care and to target quality improvement strategies to high risk children and high use practices, where this is likely to be cost-effective. The results will identify under-utilisation of health care in high risk groups (in real time) and target services to those with the greatest potential to benefit.