Doctor: Viet-Thi Tran
Title: Development of tools to measure treatment burden in patients with multiple chronic diseases
Supervisor: Philippe Ravaud
Doctoral school: ED 393 Epidemiology and Biomedical Information Sciences, Université Paris Cité
Date of thesis defense: 28/04/2015
Jury: Véronique Rivain, Marie Zins, France Mentré, Jean-François Bergmann, Serge Gilberg, Philippe Ravaud
As chronic diseases are becoming more prevalent, their management strategies are becoming more complicated. Patients are feeling increasingly burdened by their treatments. This burden of treatment represents the “work of being a patient”. It affects treatment adherence, quality of life and outcomes. In order to take into account this burden, clinicians and researchers needed tools to assess it.
Our first project was the development of the first tool to assess the burden of treatment for patients with chronic conditions: the Treatment Burden Questionnaire (TBQ). Validity and reliability of the tool was tested with 502 in- and outpatients, in France.
Our second project was the adaptation of the TBQ in English by a forward-backward translation method. Measurement properties of the adapted instrument were assessed online and involved 610 patients with chronic conditions from the United States, United Kingdom, Canada, Australia and New Zealand.
The burden of treatment is associated with patients’ context. Patients with similar conditions and treatment regimens may have different burdens depending on their education, culture, beliefs, social support, financial capacities, formal and informal support resources, and healthcare context. Thus, in our third project, we performed a multi-country qualitative study using an online survey to describe and classify the components of the burden of treatment across multiple countries and settings. This study involved 1,053 patients, from 34 different countries. Our results may inform the development of cross cultural instruments to measure the burden of treatment, allowing comparison between countries and contexts.
Our fourth project took the opportunity of this large Web-based qualitative study to assess how different sampling strategies approached data saturation by resampling the data generated. To our knowledge, this was the first study to use resampling techniques for investigating a statistical demonstration of saturation within a dataset. Our results might help researchers predict sample size in future qualitative research.
With our work, we developed the first tools to assess the burden of treatment. Further studies should now focus on interventions intended to mitigate the burden of treatment to develop a Minimally Disruptive Medicine.