Asthma is a chronic lung condition affecting over 5 million people in the UK. It is caused by inflammation of the airways which leads to symptoms such as wheezing, breathlessness and coughing. Asthma can vary in severity from mild to life-threatening and different treatments can be prescribed depending upon the symptoms a patient has. It is often associated with high levels of allergy markers in the blood and the airways. The aim of this study is to use data from the Clinical Practice Research Datalink to identify patients with moderate or severe asthma. We will look at the types of medications patients are receiving and compare the number and cost of their health contacts with patients who have mild asthma and patients without asthma. In addition, we will assess how many people develop related conditions such as eczema, and to measure how the severity of a patient’s asthma and their eosinophil levels (a type of blood cell which is part of the body’s immune system and often rises in patients with allergy) affects healthcare usage.
We aim to study clinical and activity outcomes of a population with moderate to severe asthma. The primary objectives are to determine the baseline distribution of blood eosinophil levels and lung function (FEV1) in patients diagnosed with moderate-to-severe asthma; to determine how these impact asthma outcomes and health service utilization and to determine the impact of changing levels of eosinophils and FEV1. Data will be selected from the Clinical Practice Research Datalink including linked Hospital Episode Statistics inpatient and outpatient data. Subjects will be selected from between 2010-2012 by clinical code (Read/ICD-10); disease severity will be based upon first asthma prescription received during the study period. Identified patients will be matched to two sets of controls by age, gender and primary care practice. The first set will have mild asthma defined by first prescription and the second will have no history of asthma. Baseline characteristics of all study cohorts will be presented. Rates of health service utilisation will be compared using Poisson regression models and time to binary co-morbidity outcomes will be compared using Cox proportional hazard models.
Christopher Morgan - Chief Investigator - Pharmatelligence Limited t/a Human Data Sciences
Bethan Jones - Corresponding Applicant - Pharmatelligence Limited t/a Human Data Sciences
Ellen Hubbuck - Collaborator - Pharmatelligence Limited t/a Human Data Sciences
Sara Jenkins-Jones - Collaborator - Pharmatelligence Limited t/a Human Data Sciences
HES Admitted Patient Care;HES Outpatient;Patient Level Index of Multiple Deprivation