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

yachen.li@mail.mcgill.ca

Master's Research - McGill University

Master's Research - McGill University

Master's Research - McGill University

Publications

Global SARS-CoV-2 seroprevalence from January 2020 to April 2022: A systematic review and meta-analysis of standardized population-based studies

Isabel Bergeri

Mairead Whelan

Harriet Ware

Lorenzo Subissi

Anthony Nardone

Hannah C. Lewis

Zihan Li

Xiaomeng Ma

Marta Valenciano

Brianna Cheng

Lubna Al Ariqi

Arash Rashidian

Joseph Okeibunor

Tasnim Azim

Pushpa Wijesinghe

Linh-Vi Le

Aisling Vaughan

Richard Pebody

Andrea Vicari

Tingting Yan … (see 9 more)

Mercedes Yanes-Lane

Christian Cao

David A. Clifton

Matthew P. Cheng

Jesse Papenburg

David Buckeridge

Niklas Bobrovitz

Rahul K. Arora

Maria D Van Kerkhove

2022-11-01

PLoS Medicine (published)

Modeling electronic health record data using an end-to-end knowledge-graph-informed topic model

Yuesong Zou

Ahmad Pesaranghader

Ziyang Song

Aman Verma

David Buckeridge

2022-10-25

Scientific Reports (published)

Impact of a vaccine passport on first-dose COVID-19 vaccine coverage by age and area-level social determinants in the Canadian provinces of Quebec and Ontario: an interrupted time series analysis

Jorge Luis Flores Anato

Huiting Ma

M. Hamilton

Yiqing Xia

Sam Harper

David Buckeridge

Marc Brisson

Michael P. Hillmer

Kamil A. Malikov

Aidin Kerem

Reed Beall

S. Baral

Ève Dubé

Sharmistha Mishra

Mathieu Maheu-Giroux

Background: In Canada, all provinces implemented vaccine passports in 2021 to increase vaccine uptake and reduce transmission in non-essenti… (see more)al indoor spaces. We evaluate the impact of vaccine passport policies on first-dose COVID-19 vaccination coverage by age, area-level income and proportion racialized. Methods: We performed interrupted time-series analyses using vaccine registry data linked to census information in Quebec and Ontario (20.5 million people [≥]12 years; unit of analysis: dissemination area). We fit negative binomial regressions to weekly first-dose vaccination, using a natural spline to capture pre-announcement trends, adjusting for baseline vaccination coverage (start: July 3rd; end: October 23rd Quebec, November 13th Ontario). We obtain counterfactual vaccination rates and coverage, and estimated vaccine passports' impact on vaccination coverage (absolute) and new vaccinations (relative). Results: In both provinces, pre-announcement first-dose vaccination coverage was 82% ([≥]12 years). The announcement resulted in estimated increases in vaccination coverage of 0.9 percentage points (p.p.;95% CI: 0.4-1.2) in Quebec and 0.7 p.p. (95% CI: 0.5-0.8) in Ontario. In relative terms, these increases correspond to 23% (95% CI: 10-36%) and 19% (95% CI: 15-22%) more vaccinations. The impact was larger among people aged 12-39 (1-2 p.p.). There was little variability in the absolute impact by area-level income or proportion racialized in either province. Conclusions: In the context of high baseline vaccine coverage across two provinces, the announcement of vaccine passports led to a small impact on first-dose coverage, with little impact on reducing economic and racial inequities in vaccine coverage. Findings suggest the need for other policies to further increase vaccination coverage among lower-income and more racialized neighbourhoods and communities.

2022-10-20

medRxiv (preprint)

Timeliness of reporting of SARS-CoV-2 seroprevalence results and their utility for infectious disease surveillance

Claire Donnici

Natasha Ilincic

Christian Cao

Caseng Zhang

Gabriel Deveaux

David A. Clifton

David Buckeridge

Niklas Bobrovitz

Rahul K. Arora

2022-10-20

Epidemics (published)

Protective effectiveness of prior SARS-CoV-2 infection and hybrid immunity against Omicron infection and severe disease: a systematic review and meta-regression

Niklas Bobrovitz

Harriet Ware

Xiaomeng Ma

Zihan Li

Reza Hosseini

Christian Cao

Anabel Selemon

Mairead Whelan

Zahra Premji

Hanane Issa

Brianna Cheng

L. Abu-Raddad

David Buckeridge

M. D. Kerkhove

Vanessa Piechotta

Melissa M Higdon

Annelies Wilder-Smith

Isabel Bergeri

Daniel R Feikin

Rahul K. Arora … (see 2 more)

Minal K Patel

Lorenzo Subissi

Background We aimed to systematically review the magnitude and duration of the protective effectiveness of prior infection (PE) and hybrid i… (see more)mmunity (HE) against Omicron infection and severe disease. Methods We searched pre-print and peer-reviewed electronic databases for controlled studies from January 1, 2020, to June 1, 2022. Risk of bias (RoB) was assessed using the Risk of Bias In Non-Randomized Studies of Interventions (ROBINS-I)-Tool. We used random-effects meta-regression to estimate the magnitude of protection at 1-month intervals and the average change in protection since the last vaccine dose or infection from 3 months to 6 or 12 months. We compared our estimates of PE and HE to previously published estimates of the magnitude and durability of vaccine effectiveness (VE) against Omicron. Findings Eleven studies of prior infection and 15 studies of hybrid immunity were included. For prior infection, there were 97 estimates (27 at moderate RoB and 70 at serious RoB), with the longest follow up at 15 months. PE against hospitalization or severe disease was 82.5% [71.8-89.7%] at 3 months, and 74.6% [63.1-83.5%] at 12 months. PE against reinfection was 65.2% [52.9-75.9%] at 3 months, and 24.7% [16.4-35.5%] at 12 months. For HE, there were 153 estimates (78 at moderate RoB and 75 at serious RoB), with the longest follow up at 11 months for primary series vaccination and 4 months for first booster vaccination. Against hospitalization or severe disease, HE involving either primary series vaccination or first booster vaccination was consistently >95% for the available follow up. Against reinfection, HE involving primary series vaccination was 69.0% [58.9-77.5%] at 3 months after the most recent infection or vaccination, and 41.8% [31.5-52.8%] at 12 months, while HE involving first booster vaccination was 68.6% [58.8-76.9%] at 3 months, and 46.5% [36.0-57.3%] at 6 months. Against hospitalization or severe disease at 6 months, hybrid immunity with first booster vaccination (effectiveness 95.3% [81.9-98.9%]) or with primary series alone (96.5% [90.2-98.8%]) provided significantly greater protection than prior infection alone (80.1% [70.3-87.2%]), first booster vaccination alone (76.7% [72.5-80.4%]), or primary series alone (64.6% [54.5-73.6%]). Results for protection against reinfection were similar. Interpretation Prior infection and hybrid immunity both provided greater and more sustained protection against Omicron than vaccination alone. All protection estimates waned quickly against infection but remained high for hospitalisation or severe disease. Individuals with hybrid immunity had the highest magnitude and durability of protection against all outcomes, reinforcing the global imperative for vaccination.

2022-10-04

medRxiv (preprint)

MixEHR-Guided: A guided multi-modal topic modeling approach for large-scale automatic phenotyping using the electronic health record

Yuri Ahuja

Yuesong Zou

Aman Verma

David Buckeridge

2022-10-01

Journal of Biomedical Informatics (published)

Synthetic data as an enabler for machine learning applications in medicine

Jean-Francois Rajotte

Robert Bergen

David Buckeridge

Khaled El Emam

Raymond Ng

Elissa Strome

2022-10-01

iScience (published)

Automatic Phenotyping by a Seed-guided Topic Model

Ziyang Song

Yuanyi Hu

Aman Verma

David Buckeridge

Electronic health records (EHRs) provide rich clinical information and the opportunities to extract epidemiological patterns to understand a… (see more)nd predict patient disease risks with suitable machine learning methods such as topic models. However, existing topic models do not generate identifiable topics each predicting a unique phenotype. One promising direction is to use known phenotype concepts to guide topic inference. We present a seed-guided Bayesian topic model called MixEHR-Seed with 3 contributions: (1) for each phenotype, we infer a dual-form of topic distribution: a seed-topic distribution over a small set of key EHR codes and a regular topic distribution over the entire EHR vocabulary; (2) we model age-dependent disease progression as Markovian dynamic topic priors; (3) we infer seed-guided multi-modal topics over distinct EHR data types. For inference, we developed a variational inference algorithm. Using MixEHR-Seed, we inferred 1569 PheCode-guided phenotype topics from an EHR database in Quebec, Canada covering 1.3 million patients for up to 20-year follow-up with 122 million records for 8539 and 1126 unique diagnostic and drug codes, respectively. We observed (1) accurate phenotype prediction by the guided topics, (2) clinically relevant PheCode-guided disease topics, (3) meaningful age-dependent disease prevalence. Source code is available at GitHub: https://github.com/li-lab-mcgill/MixEHR-Seed.

2022-08-14

Knowledge Discovery and Data Mining (published)

Estimating the lagged effect of price discounting: a time-series study on sugar sweetened beverage purchasing in a supermarket

Hiroshi Mamiya

Alexandra M. Schmidt

Erica E. M. Moodie

David Buckeridge

2022-08-06

BMC Public Health (published)

BioCaster in 2021: automatic disease outbreaks detection from global news media

Zaiqiao Meng

Anya Okhmatovskaia

Maxime Polleri

Yannan Shen

Guido Powell

Zihao Fu

Iris Ganser

Meiru Zhang

Nicholas B King

David Buckeridge

Nigel Collier

2022-07-28

Bioinformatics (published)

Revisiting Transfer Functions: Learning About a Lagged Exposure-Outcome Association in Time-Series Data

Hiroshi Mamiya

Alexandra M. Schmidt

Erica E. M. Moodie

David Buckeridge

2022-07-11

International Journal of Public Health (published)

Survival Modelling for Data From Combined Cohorts: Opening the Door to Meta Survival Analyses and Survival Analysis Using Electronic Health Records

James H. McVittie

Ana F. Best

David B. Wolfson

David A. Stephens

Julian Wolfson

David Buckeridge

Shahinaz M. Gadalla

Non‐parametric estimation of the survival function using observed failure time data depends on the underlying data generating mechanism, i… (see more)ncluding the ways in which the data may be censored and/or truncated. For data arising from a single source or collected from a single cohort, a wide range of estimators have been proposed and compared in the literature. Often, however, it may be possible, and indeed advantageous, to combine and then analyse survival data that have been collected under different study designs. We review non‐parametric survival analysis for data obtained by combining the most common types of cohort. We have two main goals: (i) to clarify the differences in the model assumptions and (ii) to provide a single lens through which some of the proposed estimators may be viewed. Our discussion is relevant to the meta‐analysis of survival data obtained from different types of study, and to the modern era of electronic health records.

2022-06-16

International Statistical Review (published)