Asthma is a public health challenge as a highly prevalent respiratory disease affecting people of all ages. For effective asthma management it is important to treat patients with the right inhaler device to ensure optimal medication dose delivery. Studies indicated that the inhaler device is important in the asthma management and impacts patient outcomes. Asthma is managed by different preventer treatments including inhaled corticosteroids. One of them, Qvar (beclometasone dipropionate), indicated as treatment of mild, moderate and severe asthma, is available in three different device types (Qvar Easi-Breathe, Qvar Autohaler and Qvar Aerosol) in the United Kingdom. The first two types are breath-actuated inhalers which release the medication dose during inhalation through the device. The third type is a metered-dose inhaler that is a 'press and breathe' device: for the adequate dose to be delivered, patients need to have good coordination between pressing (device actuation) and breathing (inhaling) the medication. The need to coordinate device actuation and inhalation can lead to improper inhaler use, especially without proper training. This study aims to compare breath-actuated Qvar Easi-Breathe and Qvar Autohaler, with metered-dose inhaler Qvar Aerosol. We will also compare breath-actuated Qvar to other metered-dose inhalers (beclometasone treatment not restricted to Qvar).
We will conduct a hypothesis testing study to determine if Qvar via breath-actuated inhaler (BAI) is associated with different real world outcomes compared to Qvar via metered-dose inhaler (MDI) in the United Kingdom (UK). The secondary objective is to compare Qvar BAI to all beclometasone MDI other than Qvar MDI. A sub-analysis will be conducted focusing only on the most recent BAI, Qvar Easi-Breathe. Clinical Practice Research Datalink (CPRD) data will be used. The primary outcome will be asthma control and secondary outcomes will include severe exacerbations, asthma severity, use of rescue medicines and oral corticosteroids, resource utilisation and costs, and treatment patterns. A retrospective, matched cohort analysis will be performed by constructing a sample of patients balanced on confounding variables using the closest propensity score. Propensity scores will be constructed using a logistic regression of the type of prescribed inhaler on confounding factors identified from literature searches and clinical opinion. Logistic regressions will be conducted for binary endpoints, Poisson or negative binomial regressions for count data and Normal distribution for continuous endpoints using the propensity score matched sample.
Maud Pacou - Chief Investigator - Amaris
Drifa Belhadi - Corresponding Applicant - Amaris
Aline Gauthier - Collaborator - Amaris
Bernard Sfeir - Collaborator - Teva Pharmaceuticals Ltd
Calvin Small - Collaborator - Teva Pharmaceuticals Ltd
Clemence Fradet - Collaborator - Amaris
Drifa Belhadi - Collaborator - Amaris
John Holmes - Collaborator - Teva Pharmaceuticals Ltd
Karthik Ramakrishnan - Collaborator - Teva Pharmaceuticals Ltd
Sophie Marguet - Collaborator - Amaris