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UBC Theses and Dissertations

Mathematical decision-analytic modelling to evaluate economic and health challenges in asthma and chronic obstructive pulmonary disease Zafari, Zafar


Background: Reducing the burden associated with asthma and chronic obstructive pulmonary disease (COPD) requires addressing challenging care gaps. Mathematical decision-analytic models are among the best tools to address such challenges. Objectives: My overall aim in this thesis was to identify cost-effective treatments in asthma, and to quantify the value of personalizing treatments in COPD. These goals led to four specific objectives: 1) To inform the economic and health impact of improving adherence to the standard controller medications in asthma; 2) To assess the cost-effectiveness step-up treatment options for severe asthma patients; 3) To build a framework for individualized prediction of lung function in COPD; and 4) To quantify the value of personalizing COPD treatments. Methods: Cohort-based models were used to quantify the benefit of improving adherence to controller medications and evaluating the cost-effectiveness of treatments for severe asthma. Mixed-effects regression with external validation was undertaken to project lung function decline up to 11 years for COPD. Microsimulation was used to fully incorporate disease heterogeneity to evaluate the return on investment from individualizing treatments in COPD. All modeling studies were based on careful evidence synthesis and original data analyses whenever required. Results: Improving adherence to controller medications in asthma results in a gain of 0.13 quality-adjusted life years (QALYs) at the incremental cost of $3,187 per patient over 10 years. Even with full adherence, 23% of patients would remain uncontrolled. For this group, the addition of bronchial thermoplasty was associated with an incremental cost-effectiveness ratio of $78,700/QALY. Clinical variables explain 88% of variability in lung function decline. The efforts towards individualizing treatments based on patients’ clinical traits would be associated with an additional $1,265 net benefit per person. Conclusion: The analyses in this thesis demonstrate the value of mathematical simulation models in evaluating the outcomes of policies and scenarios. It is unlikely that any empirical research per se would be able to provide the insight generated in this thesis regarding the identified care gaps. Mathematical models can not only be used to evaluate the outcomes associated with specific interventions, but also to objectively document the return on investment in personalized medicine.

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