Mohsen Sadatsafavi

Associate Professor

Relevant Degree Programs

Affiliations to Research Centres, Institutes & Clusters


Great Supervisor Week Mentions

Each year graduate students are encouraged to give kudos to their supervisors through social media and our website as part of #GreatSupervisorWeek. Below are students who mentioned this supervisor since the initiative was started in 2017.


Thank you so much, Mohsen Sadatsafavi, for your supervision and dedication in the last years!

Nelson Gorrin (2019)


Mohsen is a fantastic supervisor. He pushes you to succeed and is supportive at those inevitable times when you don't. He has been instrumental in my development as a scientist and has created a wonderful lab environment for developing as a person. I truly have #GreatSupervisor!

Kate Johnson (2019)


Graduate Student Supervision

Doctoral Student Supervision (Jan 2008 - Nov 2019)
Evaluating strategies for the early detection of chronic obstructive pulmonary disease (2020)

Background: Chronic obstructive pulmonary disease (COPD) is a highly prevalent disease that often goes undiagnosed. Reducing the burden of undiagnosed COPD requires well designed early detection programs that have been formally evaluated. Objectives: The objective of this thesis was to determine whether there are subgroups of COPD patients in which case detection, followed by evidence-based disease management, would be cost-effective compared with the status quo (no case detection). To answer this question, I 1) identified factors that distinguish patients with undiagnosed from those with diagnosed COPD, 2) assessed heterogeneity in the presence of respiratory symptoms, 3) analysed healthcare encounters prior to COPD diagnosis to identify opportunities for case detection, and 4) evaluated the cost-effectiveness of early detection strategies. Methods: I performed a systematic review to generate pooled odds ratios for factors associated with a COPD diagnosis. I used data from a population-based Canadian study to assess heterogeneity between individuals in the occurrence of respiratory symptoms. I characterised healthcare encounters before COPD diagnosis using health administrative data from British Columbia. I combined evidence from Objectives 1-3 in a whole disease model of COPD to assess the cost-effectiveness of case detection strategies implemented during routine primary care visits.Results: Patients with diagnosed COPD were less likely to have mild disease (OR 0.30, 95%CI 0.24–0.37) and more likely to report respiratory symptoms (OR 11.45 95%CI 7.20–18.21) than patients with undiagnosed COPD. However, individual-specific probabilities for the occurrence of symptoms indicated substantial heterogeneity between patients. COPD patients frequently visited primary care physicians before diagnosis (mean 10.29, IQR 4–13 visits/year). In the two years prior to diagnosis, 72.1% of COPD patients had a respiratory-related primary care visit that did not result in a diagnosis. In the preferred case detection strategy, all patients ≥40 years received a screening questionnaire during their routine visits to a primary care physician. This strategy had an Incremental Cost-Effectiveness Ratio of $18,791/QALY compared to no case detection.Conclusions: Patients with undiagnosed COPD have identifiable characteristics, they frequently encounter the healthcare system, and strategies for improving their early detection are cost-effective when combined with guideline-recommended treatment.

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Longitudinal studies of disease progression, health care costs and health-related quality of life in patients with asthma (2016)

This thesis examines the burden of asthma and its determinants though a series of longitudinal observational studies. Objectives: 1) To quantify the natural history of severe asthma and the impact of early risk factors; 2) To examine the influence of socioeconomic status (SES) on excess direct medical costs of moderate-to-severe asthma and guideline-based asthma care; 3) To estimate excess costs of asthma and the economic implications of comorbidities; 4) To assess the joint influences of asthma control and comorbidity on health-related quality of life. Methods: For the first three objectives, administrative health data (for the period of 1997-2013) were obtained from British Columbia (BC) Ministry of Health, and for the last objective data were obtained from the Economic Burden of Asthma (EBA) study in BC. Various models for longitudinal data were applied for each objective. Findings: 1) Most patients (83%) with incident severe asthma transitioned to milder states after 10 years. Low SES and comorbidity at disease onset led to worse long-term prognosis. 2) Across both individual- and neighborhood-levels, there was evidence that low-SES asthma patients and/or their care providers did not follow guideline-based asthma care and subsequently incurred substantially greater excess costs of asthma. 3) Excess costs in patients with asthma were $1187/year (95%CI $1130─$1243) overall, with comorbidity-attributable costs five times higher than asthma-attributable costs, all of which greatly increased with age. 4) Changes in asthma control had a greater effect on disease-specific (AQ5D) than generic (EQ5D) utilities, whereas changes in comorbidity burden had a larger impact on EQ5D than AQ5D utilities. Conclusions: With several novel methodology techniques, this thesis provided evidence for the first-time on the long-term trajectory and burden of asthma. Projection of cost and effectiveness of decisions and policies in asthma care requires a robust understanding of the natural history of asthma, effect of risk factors on this trajectory, and estimates of costs and health-related quality of life associated with asthma. This thesis provides new evidence on all such parameters. These findings have direct relevance to estimating cost-effectiveness of health technologies in asthma and can result in more informed decision-making in health policy and clinical practice.

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Mathematical decision-analytic modelling to evaluate economic and health challenges in asthma and chronic obstructive pulmonary disease (2016)

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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