Portrait of David Buckeridge

David Buckeridge

Associate Academic Member

Full Professor, McGill University, Department of Epidemiology, Biostatistics and Occupational Health

Research Topics

Medical Machine Learning

Biography

David Buckeridge is a professor at the School of Population and Global Health at McGill University, as well as chief digital health officer for the McGill University Health Centre and executive scientific director of the Public Health Agency of Canada.

A Tier 1 Canada Research Chair in Health Informatics and Data Science, Buckeridge has projected health system demand for the Canadian province of Quebec, led data management and analytics for the Canadian Immunity Task Force, and supported the World Health Organization in monitoring global immunity to SARS-CoV-2. He has an MD from Queen's University, an MSc in epidemiology from the University of Toronto and a PhD in biomedical informatics from Stanford University. He is a Fellow of the Royal College of Physicians of Canada.

Current Students

Master's Research - McGill University

Master's Research - McGill University

PhD - McGill University

Master's Research - McGill University

Master's Research - McGill University

Master's Research - McGill University

Publications

Transfer functions: learning about a lagged exposure-outcome association in time-series data

Hiroshi Mamiya

Alexandra M. Schmidt

Erica E. M. Moodie

David Buckeridge

Many population exposures in time-series analysis, including food marketing, exhibit a time-lagged association with population health outcom… (see more)es such as food purchasing. A common approach to measuring patterns of associations over different time lags relies on a finite-lag model, which requires correct specification of the maximum duration over which the lagged association extends. However, the maximum lag is frequently unknown due to the lack of substantive knowledge or the geographic variation of lag length. We describe a time-series analytical approach based on an infinite lag specification under a transfer function model that avoids the specification of an arbitrary maximum lag length. We demonstrate its application to estimate the lagged exposure-outcome association in food environmental research: display promotion of sugary beverages with lagged sales.

2021-10-01

medRxiv (preprint)

Estimating the lagged effect of price discounting: a time-series study using transaction data of sugar sweetened beverages.

Hiroshi Mamiya

Alexandra M. Schmidt

Erica E. M. Moodie

David Buckeridge

2021-09-28

medRxiv (preprint)

Monitoring non-pharmaceutical public health interventions during the COVID-19 pandemic

Yannan Shen

Guido Powell

Iris Ganser

Qulu Zheng

Chris Grundy

Anya Okhmatovskaia

David Buckeridge

2021-08-24

Scientific Data (published)

Generating community measures of food purchasing activities using store-level electronic grocery transaction records: an ecological study in Montreal, Canada

Hiroshi Mamiya

Alexandra M. Schmidt

Erica E.M. Moodie

Yu Ma

David Buckeridge

2021-08-23

Public Health Nutrition (published)

Supervised multi-specialist topic model with applications on large-scale electronic health record data

Ziyang Song

Xavier Sumba Toral

Yixin Xu

Aihua Liu

Liming Guo

Guido Powell

Aman Verma

David Buckeridge

Ariane Marelli

Motivation: Electronic health record (EHR) data provides a new venue to elucidate disease comorbidities and latent phenotypes for precision … (see more)medicine. To fully exploit its potential, a realistic data generative process of the EHR data needs to be modelled. Materials and Methods: We present MixEHR-S to jointly infer specialist-disease topics from the EHR data. As the key contribution, we model the specialist assignments and ICD-coded diagnoses as the latent topics based on patient's underlying disease topic mixture in a novel unified supervised hierarchical Bayesian topic model. For efficient inference, we developed a closed-form collapsed variational inference algorithm to learn the model distributions of MixEHR-S. Results: We applied MixEHR-S to two independent large-scale EHR databases in Quebec with three targeted applications: (1) Congenital Heart Disease (CHD) diagnostic prediction among 154,775 patients; (2) Chronic obstructive pulmonary disease (COPD) diagnostic prediction among 73,791 patients; (3) future insulin treatment prediction among 78,712 patients diagnosed with diabetes as a mean to assess the disease exacerbation. In all three applications, MixEHR-S conferred clinically meaningful latent topics among the most predictive latent topics and achieved superior target prediction accuracy compared to the existing methods, providing opportunities for prioritizing high-risk patients for healthcare services. Availability and implementation: MixEHR-S source code and scripts of the experiments are freely available at https://github.com/li-lab-mcgill/mixehrS

2021-08-01

Proceedings of the 12th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (published)

Geographical concentration of COVID-19 cases by social determinants of health in 16 large metropolitan areas in Canada - a cross-sectional study

Yiqing Xia

Huiting Ma

Gary Moloney

Héctor A. Velásquez García

Monica Sirski

Naveed Janjua

David Vickers

Tyler Williamson

Alan Katz

Kristy Yu

Rafal Kustra

David Buckeridge

Marc Brisson

Stefan Baral

Sharmistha Mishra

Mathieu Maheu-Giroux

Background: There is a growing recognition that strategies to reduce SARS-CoV-2 transmission should be responsive to local transmission dyna… (see more)mics. Studies have revealed inequalities along social determinants of health, but little investigation was conducted surrounding geographic concentration within cities. We quantified social determinants of geographic concentration of COVID-19 cases across sixteen census metropolitan areas (CMA) in four Canadian provinces. Methods: We used surveillance data on confirmed COVID-19 cases at the level of dissemination area. Gini (co-Gini) coefficients were calculated by CMA based on the proportion of the population in ranks of diagnosed cases and each social determinant using census data (income, education, visible minority, recent immigration, suitable housing, and essential workers) and the corresponding share of cases. Heterogeneity was visualized using Lorenz (concentration) curves. Results: Geographic concentration was observed in all CMAs (half of the cumulative cases were concentrated among 21-35% of each city's population): with the greatest geographic heterogeneity in Ontario CMAs (Gini coefficients, 0.32-0.47), followed by British Columbia (0.23-0.36), Manitoba (0.32), and Quebec (0.28-0.37). Cases were disproportionately concentrated in areas with lower income, education attainment, and suitable housing; and higher proportion of visible minorities, recent immigrants, and essential workers. Although a consistent feature across CMAs was concentration by proportion visible minorities, the magnitude of concentration by social determinants varied across CMAs. Interpretation: The feature of geographical concentration of COVID-19 cases was consistent across CMAs, but the pattern by social determinants varied. Geographically-prioritized allocation of resources and services should be tailored to the local drivers of inequalities in transmission in response to SARS-CoV-2's resurgence.

2021-07-26

medRxiv (preprint)

Smart About Meds (SAM): a pilot randomized controlled trial of a mobile application to improve medication adherence following hospital discharge

Bettina Habib

David Buckeridge

Melissa Bustillo

Santiago Nicolas Marquez

Manish Thakur

Thai Tran

Daniala L Weir

Robyn Tamblyn

2021-07-01

JAMIA Open (published)

Smart about medications (SAM): a digital solution to enhance medication management following hospital discharge

Santiago Márquez Fosser

Nadar Mahmoud

Bettina Habib

Daniala L Weir

Fiona Chan

Rola El Halabieh

Jeanne Vachon

Manish Thakur

Thai Tran

Melissa Bustillo

Caroline Beauchamp

André Bonnici

David Buckeridge

Robyn Tamblyn

2021-06-18

JAMIA Open (published)

Inferring global-scale temporal latent topics from news reports to predict public health interventions for COVID-19

Zhi Wen

Guido Powell

Imane Chafi

David Buckeridge

Y. K. Li

2021-05-24

Patterns (published)

Recurrent Traumatic Brain Injury Surveillance Using Administrative Health Data: A Bayesian Latent Class Analysis

Oliver Lasry

Nandini Dendukuri

Judith Marcoux

David Buckeridge

Background: The initial injury burden from incident TBI is significantly amplified by recurrent TBI (rTBI). Unfortunately, research assessin… (see more)g the accuracy to conduct rTBI surveillance is not available. Accurate surveillance information on recurrent injuries is needed to justify the allocation of resources to rTBI prevention and to conduct high quality epidemiological research on interventions that mitigate this injury burden. This study evaluates the accuracy of administrative health data (AHD) surveillance case definitions for rTBI and estimates the 1-year rTBI incidence adjusted for measurement error. Methods: A 25% random sample of AHD for Montreal residents from 2000 to 2014 was used in this study. Four widely used TBI surveillance case definitions, based on the International Classification of Disease and on radiological exams of the head, were applied to ascertain suspected rTBI cases. Bayesian latent class models were used to estimate the accuracy of each case definition and the 1-year rTBI measurement-error-adjusted incidence without relying on a gold standard rTBI definition that does not exist, across children (18 years), adults (18-64 years), and elderly (> =65 years). Results: The adjusted 1-year rTBI incidence was 4.48 (95% CrI 3.42, 6.20) per 100 person-years across all age groups, as opposed to a crude estimate of 8.03 (95% CrI 7.86, 8.21) per 100 person-years. Patients with higher severity index TBI had a significantly higher incidence of rTBI compared to patients with lower severity index TBI. The case definition that identified patients undergoing a radiological examination of the head in the context of any traumatic injury was the most sensitive across children [0.46 (95% CrI 0.33, 0.61)], adults [0.79 (95% CrI 0.64, 0.94)], and elderly [0.87 (95% CrI 0.78, 0.95)]. The most specific case definition was the discharge abstract database in children [0.99 (95% CrI 0.99, 1.00)], and emergency room visits claims in adults/elderly [0.99 (95% CrI 0.99, 0.99)]. Median time to rTBI was the shortest in adults (75 days) and the longest in children (120 days). Conclusion: Conducting accurate surveillance and valid epidemiological research for rTBI using AHD is feasible when measurement error is accounted for.

2021-05-14

Frontiers in Neurology (published)

Evaluating the Integration of One Health in Surveillance Systems for Antimicrobial Use and Resistance: A Conceptual Framework

Cécile Aenishaenslin

Barbara Häsler

André Ravel

E. Jane Parmley

Sarah Mediouni

Houda Bennani

Katharina D. C. Stärk

David Buckeridge

It is now widely acknowledged that surveillance of antimicrobial resistance (AMR) must adopt a “One Health” (OH) approach to successfull… (see more)y address the significant threats this global public health issue poses to humans, animals, and the environment. While many protocols exist for the evaluation of surveillance, the specific aspect of the integration of a OH approach into surveillance systems for AMR and antimicrobial Use (AMU), suffers from a lack of common and accepted guidelines and metrics for its monitoring and evaluation functions. This article presents a conceptual framework to evaluate the integration of OH in surveillance systems for AMR and AMU, named the Integrated Surveillance System Evaluation framework (ISSE framework). The ISSE framework aims to assist stakeholders and researchers who design an overall evaluation plan to select the relevant evaluation questions and tools. The framework was developed in partnership with the Canadian Integrated Program for Antimicrobial Resistance Surveillance (CIPARS). It consists of five evaluation components, which consider the capacity of the system to: [1] integrate a OH approach, [2] produce OH information and expertise, [3] generate actionable knowledge, [4] influence decision-making, and [5] positively impact outcomes. For each component, a set of evaluation questions is defined, and links to other available evaluation tools are shown. The ISSE framework helps evaluators to systematically assess the different OH aspects of a surveillance system, to gain comprehensive information on the performance and value of these integrated efforts, and to use the evaluation results to refine and improve the surveillance of AMR and AMU globally.

2021-03-24

Frontiers in Veterinary Science (published)

[Strengthening the culture of public health surveillance and population health monitoring].

Arnaud Chiolero

David Buckeridge

St'ephane Cullati

Public health surveillance is the systematic and ongoing collection, analysis and interpretation of data to produce information useful for d… (see more)ecision-making. With the development of data science, surveillance methods are evolving through access to big data. More data does not automatically mean more information. For example, the massive amounts of data on Covid-19 was not easily transformed in useful information for decision-making. Further, data scientists have often difficulties to make their analyses useful for decision-making. For the implementation of evidence-based and data-driven public health practice, the culture of public health surveillance and population health monitoring needs to be strengthened.

2021-03-17

Revue medicale suisse (published)

pubmed.ncbi.nlm.nih.gov