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
LinkedIn

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

Kalash Dave
Maîtrise recherche - McGill
Github
Eva Gao
Doctorat - McGill
yachen li Li
Maîtrise recherche - McGill
yachen.li@mail.mcgill.ca
Aidan Licoppe
Maîtrise recherche - McGill
Github
Yannan Shen
Maîtrise recherche - McGill

Publications

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
Yue Li
Motivation: Electronic health record (EHR) data provides a new venue to elucidate disease comorbidities and latent phenotypes for precision … (voir plus)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 (publié)
doi.org
arxiv.org
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… (voir plus)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 (prépublication)
doi.org
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 (publié)
doi.org
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 (publié)
doi.org
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)
doi.org
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… (voir plus)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 (publié)
doi.org
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… (voir plus)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 (publié)
doi.org
[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… (voir plus)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 (publié)
pubmed.ncbi.nlm.nih.gov
Price discounting as a hidden risk factor of energy drink consumption
Hiroshi Mamiya
Erica E. M. Moodie
Alexandra M. Schmidt
Yu Ma
David Buckeridge
Global consumption of caffeinated energy drinks (CED) has been increasing dramatically despite increasing evidence of their adverse health e… (voir plus)ffects. Temporary price discounting is a rarely investigated but potentially powerful food marketing tactic influencing purchasing of CED. Using grocery transaction records generated by food stores in Montreal, we investigated the association between price discounting and purchasing of CED across socio-economic status operationalized by education and income levels in store neighbourhood. The outcome, log-transformed weekly store-level sales of CED, was modelled as a function of store-level percent price discounting, store- and neighbourhood-level confounders, and an interaction term between discounting and each of tertile education and income in store neighbourhood. The model was separately fit to transactions from supermarkets, pharmacies, supercentres, and convenience stores. There were 18,743, 12,437, 3965, and 49,533 weeks of CED sales from supermarkets, pharmacies, supercentres, and convenience stores, respectively. Percent price discounting was positively associated with log sales of CED for all store types, and the interaction between education and discounting was prominent in supercentres: −0.039 [95% confidence interval (CI): −0.051, −0.028] and −0.039 [95% CI: −0.057, −0.021], for middle- and high-education neighbourhoods relative to low-education neighbourhoods, respectively. Relative to low-income areas, the associations of discounting and log CED sales in supercentres for neighbourhoods with middle- and high-income tertile were 0.022 [95% CI: 0.010, 0.033] and 0.015 (95% CI: −0.001, 0.031), respectively. Price discounting is an important driver of CED consumption and has a varying impact across community education and income.
2021-03-16
Canadian Journal of Public Health (publié)
doi.org
Staying Ahead of the Epidemiologic Curve: Evaluation of the British Columbia Asthma Prediction System (BCAPS) During the Unprecedented 2018 Wildfire Season
Sarah B. Henderson
Kathryn T. Morrison
Kathleen E. McLean
Yue Ding
Jiayun Yao
Gavin Shaddick
David Buckeridge
2021-03-12
Frontiers in Public Health (publié)
doi.org
Concurrent prescriptions for opioids and benzodiazepines and risk of opioid overdose: protocol for a retrospective cohort study using linked administrative data
Erin Y Liu
Robyn Tamblyn
Kristian B Filion
David Buckeridge
2021-02-18
BMJ Open (publié)
doi.org
Predicting Infectiousness for Proactive Contact Tracing
Yoshua Bengio
Prateek Gupta
Tegan Maharaj
Nasim Rahaman
Martin Weiss
Tristan Deleu
Eilif Benjamin Muller
Meng Qu
Victor Schmidt
Pierre-Luc St-Charles
Hannah Alsdurf
Olexa Bilaniuk
David Buckeridge
gaetan caron
Pierre Luc Carrier
Joumana Ghosn
satya ortiz gagne
Chris Pal
Irina Rish
Bernhard Schölkopf … (voir 3 de plus)
Abhinav Sharma
Jian Tang
Andrew Robert Williams
The COVID-19 pandemic has spread rapidly worldwide, overwhelming manual contact tracing in many countries and resulting in widespread lockdo… (voir plus)wns for emergency containment. Large-scale digital contact tracing (DCT) has emerged as a potential solution to resume economic and social activity while minimizing spread of the virus. Various DCT methods have been proposed, each making trade-offs between privacy, mobility restrictions, and public health. The most common approach, binary contact tracing (BCT), models infection as a binary event, informed only by an individual's test results, with corresponding binary recommendations that either all or none of the individual's contacts quarantine. BCT ignores the inherent uncertainty in contacts and the infection process, which could be used to tailor messaging to high-risk individuals, and prompt proactive testing or earlier warnings. It also does not make use of observations such as symptoms or pre-existing medical conditions, which could be used to make more accurate infectiousness predictions. In this paper, we use a recently-proposed COVID-19 epidemiological simulator to develop and test methods that can be deployed to a smartphone to locally and proactively predict an individual's infectiousness (risk of infecting others) based on their contact history and other information, while respecting strong privacy constraints. Predictions are used to provide personalized recommendations to the individual via an app, as well as to send anonymized messages to the individual's contacts, who use this information to better predict their own infectiousness, an approach we call proactive contact tracing (PCT). We find a deep-learning based PCT method which improves over BCT for equivalent average mobility, suggesting PCT could help in safe re-opening and second-wave prevention.
2021-01-12
ICLR.cc/2021/Conference (spotlight)
openreview.net
openreview.net