I am an associate professor in the Department of Epidemiology, Biostatistics & Occupational Health at McGill University. I am also a member of the McGill University Centre on Population Dynamics and McGill’s Public Policy and Population Health Observatory. I also hold an endowed chair of Impact of Health and Social Policy on Health Inequalities at Erasmus University Medical Center.
My research focuses on understanding population health and its social distribution, with specific interests in impact evaluation, measuring health inequalities, global health, demography, causal inference, and ethical issues in public health.
More information on specific projects, papers, students, and teaching are in my CV.
PhD in Epidemiologic Science, 2005
University of Michigan
MSPH in Epidemiology, 1999
University of South Carolina
BA in Biology, 1995
Health and air pollution impacts of household energy transitions
Evaluating a reproductive, maternal and newborn health intervention in Tanzania
BACKGROUND: Cannabis use has been linked to impaired driving and fatal accidents. Prior evidence suggests the potential for population-wide effects of the annual cannabis celebration on April 20th (‘4/20’), but evidence to date is limited. METHODS: We used data from the Fatal Analysis Reporting System for the years 1975-2016 to estimate the impact of ‘4/20’ on drivers involved in fatal traffic crashes occurring between 16:20 and 23:59 hours in the USA. We compared the effects of 4/20 with those for other major holidays, and evaluated whether the impact of ‘4/20’ had changed in recent years. RESULTS: Between 1992 and 2016, ‘4/20’ was associated with an increase in the number of drivers involved in fatal crashes (IRR 1.12, 95% CI 0.97 to 1.28) relative to control days 1 week before and after, but not when compared with control days 1 and 2 weeks before and after (IRR 1.05, 95% CI 0.92 to 1.28) or all other days of the year (IRR 0.98, 95% CI 0.88 to 1.10). Across all years we found little evidence to distinguish excess drivers involved in fatal crashes on 4/20 from routine daily variations. CONCLUSIONS: There is little evidence to suggest population-wide effects of the annual cannabis holiday on the number of drivers involved in fatal traffic crashes.
Observational studies are ambiguous, difficult, and necessary for epidemiology. Presently, there are concerns that the evidence produced by most observational studies in epidemiology is not credible and contributes to research waste. I argue that observational epidemiology could be improved by focusing greater attention on 1) defining questions that make clear whether the inferential goal is descriptive or causal; 2) greater utilization of quantitative bias analysis and alternative research designs that aim to decrease the strength of assumptions needed to estimate causal effects; and 3) promoting, experimenting with, and perhaps institutionalizing both reproducible research standards and replication studies to evaluate the fragility of study findings in epidemiology. Greater clarity, credibility, and transparency in observational epidemiology will help to provide reliable evidence that can serve as a basis for making decisions about clinical or population-health interventions.
BACKGROUND: For policy questions where substantial empirical background information exists, conventional frequentist policy analysis is hard to justify. Bayesian analysis quantitatively incorporates prior knowledge, but is not often used in applied policy analysis. METHODS: We combined 2000-2016 data from the Fatal Analysis Reporting System with priors based on past empirical studies and policy documents to study the impact of mandatory seat belt laws on traffic fatalities. We used a Bayesian data augmentation approach to combine information from prior studies with difference-in-differences analyses of recent law changes to provide updated evidence on the impact that upgrading to primary enforcement of seat belt laws has on fatalities. RESULTS: After incorporating the evidence from past studies, we find limited evidence to support the hypothesis that recent policy upgrades affect fatality rates. We estimate that upgrading to primary enforcement reduced fatality rates by 0.37 deaths per billion vehicle miles traveled (95% posterior interval -0.90, 0.16), or a rate ratio of 0.96 (95% posterior interval 0.91, 1.02), and increased the proportion of decedents reported as wearing seat belts by 7 percentage points (95% posterior interval 5, 8), or a risk ratio of 1.18 (95% posterior interval 1.13, 1.24). CONCLUSIONS: Bayesian methods can provide credible estimates of future policy impacts, especially for policy questions that occur in dynamic environments, such as traffic safety.