Portrait de David Buckeridge

David Buckeridge

Membre académique associé
Professeur titulaire, McGill University, Département d'épidémiologie, biostatistique et santé au travail
Sujets de recherche
Apprentissage automatique médical
Google Scholar
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Biographie

David Buckeridge est professeur titulaire à l'École de santé des populations et de santé mondiale de l'Université McGill, responsable de la santé numérique au Centre universitaire de santé McGill et directeur scientifique exécutif pour l'Agence de la santé publique du Canada. Titulaire d'une chaire de recherche du Canada (niveau 1) en informatique de la santé et en science des données, il a établi les projections concernant la demande dans le système de santé du Québec, dirigé la gestion des données et l'analyse pour le groupe de travail sur l'immunité canadienne et aidé l'Organisation mondiale de la santé à surveiller l'immunité mondiale contre le SRAS-CoV-2. Il est titulaire d'un doctorat en médecine (Université Queen's), d'une maîtrise en épidémiologie (Université de Toronto) et d'un doctorat en informatique biomédicale (Université Stanford), et est membre du Collège royal des médecins du Canada.

Étudiants actuels

Aidan Aidan
Maîtrise recherche - McGill
Github
Kalash Dave
Maîtrise recherche - McGill
Eva Gao
Doctorat - McGill
Gabriel Potvin
Maîtrise recherche - McGill
Rada Rusu
Maîtrise recherche - McGill
Yannan Shen
Maîtrise recherche - McGill

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 … (voir 9 de plus)
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 (publié)
doi.org
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
Yue Li
2022-10-25
Scientific Reports (publié)
doi.org
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… (voir plus)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 (prépublication)
doi.org
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 (publié)
doi.org
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 … (voir 2 de plus)
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… (voir plus)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 (prépublication)
doi.org
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
Yue Li
2022-10-01
Journal of Biomedical Informatics (publié)
doi.org
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 (publié)
doi.org
Automatic Phenotyping by a Seed-guided Topic Model
Ziyang Song
Yuanyi Hu
Aman Verma
David Buckeridge
Yue Li
Electronic health records (EHRs) provide rich clinical information and the opportunities to extract epidemiological patterns to understand a… (voir plus)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 (publié)
doi.org
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 (publié)
doi.org
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 (publié)
doi.org
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 (publié)
doi.org
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… (voir plus)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 (publié)
doi.org