Flu (influenza) and flu-like symptoms are among the most common reasons why parents bring their children to see a doctor or nurse, especially during flu outbreaks (such as the 'swine flu pandemic' in 2009-10). For most children, flu is a mild and relatively short illness. However, some children can become more unwell if they get flu, and develop further complications, such as chest, throat and ear infections.
We would like to find out which children are more likely to develop further complications if they get flu or a flu-like illness. To do this, we will study information extracted from the medical records of around 20,000 children who saw a doctor or nurse in their GP surgery during the 'swine flu pandemic' with flu or flu-like symptoms. We will not be able to identify individual children from the information we extract.
Our findings will help doctors and nurses treat children with flu or flu-like symptoms promptly and safely, and provide information to their parents. Our results will also help government policy makers ensure that flu vaccines, medicines (such as Tamiflu and antibiotics) and other health care resources are directed towards the correct 'high risk' groups during future flu outbreaks.
Objective: To identify risk factors for influenza-related complications in children presenting with influenza/influenza-like illness (ILI) in the community.
Study design: Cohort study.
Study population: Children aged 0 to 17 years inclusive who presented with influenza/ILI in CPRD general practices during the 2009/10 influenza pandemic (27th April 2009 to 23rd May 2010).
Outcomes: Our primary outcome will be influenza-related complications within 30 days of presentation with influenza/ILI. Secondary outcomes will be pneumonia, influenza-related complications requiring further intervention (prescription of medication, referral for further investigation, or hospitalisation), hospitalisations or deaths due to influenza-related complications, and all cause hospitalisations and deaths within 30 days of presentation with influenza/ILI.
Data analysis: We will examine the association between potential risk factors and our proposed outcomes using logistic regression techniques to calculate odds ratios with 95% confidence intervals, both unadjusted and adjusted for possible confounders, including age, sex, and vaccination status. We will define potential risk factors as specific underlying conditions where possible. However, for rare conditions, we will combine data to create broader system-based disease categories.
Kay Wang - Chief Investigator - University of Oxford
Antonis Kousoulis - Collaborator - Mental Health Foundation
Christopher Butler - Collaborator - University of Oxford
Clare Bankhead - Collaborator - University of Oxford
Joseph Lee - Collaborator - University of Oxford
Ruth Allanson - Collaborator - South Central Ambulance Service
HES Admitted Patient Care;ONS Death Registration Data;Patient Level Index of Multiple Deprivation;Practice Level Index of Multiple Deprivation