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Ganser I, Buckeridge DL, Heffernan J, Prague M, Thiébaut R. Estimating the population effectiveness of interventions against COVID-19 in France: A modelling study. Epidemics 2024; 46:100744. [PMID: 38324970 DOI: 10.1016/j.epidem.2024.100744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 12/12/2023] [Accepted: 01/22/2024] [Indexed: 02/09/2024] Open
Abstract
BACKGROUND Non-pharmaceutical interventions (NPIs) and vaccines have been widely used to manage the COVID-19 pandemic. However, uncertainty persists regarding the effectiveness of these interventions due to data quality issues, methodological challenges, and differing contextual factors. Accurate estimation of their effects is crucial for future epidemic preparedness. METHODS To address this, we developed a population-based mechanistic model that includes the impact of NPIs and vaccines on SARS-CoV-2 transmission and hospitalization rates. Our statistical approach estimated all parameters in one step, accurately propagating uncertainty. We fitted the model to comprehensive epidemiological data in France from March 2020 to October 2021. With the same model, we simulated scenarios of vaccine rollout. RESULTS The first lockdown was the most effective, reducing transmission by 84 % (95 % confidence interval (CI) 83-85). Subsequent lockdowns had diminished effectiveness (reduction of 74 % (69-77) and 11 % (9-18), respectively). A 6 pm curfew was more effective than one at 8 pm (68 % (66-69) vs. 48 % (45-49) reduction), while school closures reduced transmission by 15 % (12-18). In a scenario without vaccines before November 2021, we predicted 159,000 or 168 % (95 % prediction interval (PI) 70-315) more deaths and 1,488,000 or 300 % (133-492) more hospitalizations. If a vaccine had been available after 100 days, over 71,000 deaths (16,507-204,249) and 384,000 (88,579-1,020,386) hospitalizations could have been averted. CONCLUSION Our results highlight the substantial impact of NPIs, including lockdowns and curfews, in controlling the COVID-19 pandemic. We also demonstrate the value of the 100 days objective of the Coalition for Epidemic Preparedness Innovations (CEPI) initiative for vaccine availability.
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Affiliation(s)
- Iris Ganser
- Univ. Bordeaux, Inserm, BPH Research Center, SISTM Team, UMR 1219 Bordeaux, France; McGill Health Informatics, School of Population and Global Health, McGill University, Montreal, Quebec, Canada
| | - David L Buckeridge
- McGill Health Informatics, School of Population and Global Health, McGill University, Montreal, Quebec, Canada
| | - Jane Heffernan
- Mathematics & Statistics, Centre for Disease Modelling, York University, Toronto, Ontario, Canada
| | - Mélanie Prague
- Univ. Bordeaux, Inserm, BPH Research Center, SISTM Team, UMR 1219 Bordeaux, France; Inria, Inria Bordeaux - Sud-Ouest, Talence, France; Vaccine Research Institute, F-94010 Creteil, France
| | - Rodolphe Thiébaut
- Univ. Bordeaux, Inserm, BPH Research Center, SISTM Team, UMR 1219 Bordeaux, France; Inria, Inria Bordeaux - Sud-Ouest, Talence, France; Vaccine Research Institute, F-94010 Creteil, France; Bordeaux University Hospital, Medical Information Department, Bordeaux, France.
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Mullie L, Afilalo J, Archambault P, Bouchakri R, Brown K, Buckeridge DL, Cavayas YA, Turgeon AF, Martineau D, Lamontagne F, Lebrasseur M, Lemieux R, Li J, Sauthier M, St-Onge P, Tang A, Witteman W, Chassé M. CODA: an open-source platform for federated analysis and machine learning on distributed healthcare data. J Am Med Inform Assoc 2024; 31:651-665. [PMID: 38128123 PMCID: PMC10873779 DOI: 10.1093/jamia/ocad235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 10/28/2023] [Accepted: 12/02/2023] [Indexed: 12/23/2023] Open
Abstract
OBJECTIVES Distributed computations facilitate multi-institutional data analysis while avoiding the costs and complexity of data pooling. Existing approaches lack crucial features, such as built-in medical standards and terminologies, no-code data visualizations, explicit disclosure control mechanisms, and support for basic statistical computations, in addition to gradient-based optimization capabilities. MATERIALS AND METHODS We describe the development of the Collaborative Data Analysis (CODA) platform, and the design choices undertaken to address the key needs identified during our survey of stakeholders. We use a public dataset (MIMIC-IV) to demonstrate end-to-end multi-modal FL using CODA. We assessed the technical feasibility of deploying the CODA platform at 9 hospitals in Canada, describe implementation challenges, and evaluate its scalability on large patient populations. RESULTS The CODA platform was designed, developed, and deployed between January 2020 and January 2023. Software code, documentation, and technical documents were released under an open-source license. Multi-modal federated averaging is illustrated using the MIMIC-IV and MIMIC-CXR datasets. To date, 8 out of the 9 participating sites have successfully deployed the platform, with a total enrolment of >1M patients. Mapping data from legacy systems to FHIR was the biggest barrier to implementation. DISCUSSION AND CONCLUSION The CODA platform was developed and successfully deployed in a public healthcare setting in Canada, with heterogeneous information technology systems and capabilities. Ongoing efforts will use the platform to develop and prospectively validate models for risk assessment, proactive monitoring, and resource usage. Further work will also make tools available to facilitate migration from legacy formats to FHIR and DICOM.
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Affiliation(s)
- Louis Mullie
- Department of Medicine, Centre Hospitalier de l'Université de Montréal, Montréal, H2X 3E4, Canada
- Faculty of Medicine, Université de Montréal, Montréal, H3C 3J7, Canada
- Mila Quebec Artificial Intelligence Institute, Montréal, H2S 3H1, Canada
| | - Jonathan Afilalo
- Department of Medicine, Jewish General Hospital, Montréal, H3T 1E4, Canada
| | - Patrick Archambault
- Department of Emergency Medicine and Family Medicine, Université Laval, Québec, G1V 0A6, Canada
- Department of Anesthesiology and Critical Care Medicine, Université Laval, Québec, G1V 0A6, Canada
- Centre de Recherche Intégré pour un Système Apprenant en santé et Services Sociaux, Centre intégré de santé et de Services Sociaux de Chaudière-Appalaches, Lévis, G6V 3Z1, Canada
| | - Rima Bouchakri
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Université de Montréal, Montréal, H2X 0A9, Canada
| | - Kip Brown
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Université de Montréal, Montréal, H2X 0A9, Canada
| | - David L Buckeridge
- Mila Quebec Artificial Intelligence Institute, Montréal, H2S 3H1, Canada
- Department of Epidemiology and Biostatistics, School of Population and Global Health, McGill University Health Centre, Montréal, H3A 1G1, Canada
| | | | - Alexis F Turgeon
- Department of Anesthesiology and Critical Care Medicine, Université Laval, Québec, G1V 0A6, Canada
- Centre de recherche du CHU de Québec-Université Laval, Université Laval, Québec, G1V 4G2, Canada
| | - Denis Martineau
- Centre de recherche du CHU de Québec-Université Laval, Université Laval, Québec, G1V 4G2, Canada
| | - François Lamontagne
- Centre de recherche du CHUS, Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, J1G 2E8, Canada
| | - Martine Lebrasseur
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Université de Montréal, Montréal, H2X 0A9, Canada
| | - Renald Lemieux
- Centre de recherche du CHUS, Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, J1G 2E8, Canada
| | - Jeffrey Li
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Université de Montréal, Montréal, H2X 0A9, Canada
| | - Michaël Sauthier
- Faculty of Medicine, Université de Montréal, Montréal, H3C 3J7, Canada
- Department of Pediatrics, Université de Montréal and CHU Sainte-Justine Research Centre, Montréal, H3C 3J7, Canada
| | - Pascal St-Onge
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Université de Montréal, Montréal, H2X 0A9, Canada
| | - An Tang
- Faculty of Medicine, Université de Montréal, Montréal, H3C 3J7, Canada
- Department of Radiology, Centre Hospitalier de l’Université de Montréal, Montréal, H2X 3E4, Canada
| | - William Witteman
- Centre de Recherche Intégré pour un Système Apprenant en santé et Services Sociaux, Centre intégré de santé et de Services Sociaux de Chaudière-Appalaches, Lévis, G6V 3Z1, Canada
| | - Michaël Chassé
- Department of Medicine, Centre Hospitalier de l'Université de Montréal, Montréal, H2X 3E4, Canada
- Faculty of Medicine, Université de Montréal, Montréal, H3C 3J7, Canada
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Murphy TJ, Swail H, Jain J, Anderson M, Awadalla P, Behl L, Brown PE, Charlton CL, Colwill K, Drews SJ, Gingras AC, Hinshaw D, Jha P, Kanji JN, Kirsh VA, Lang ALS, Langlois MA, Lee S, Lewin A, O'Brien SF, Pambrun C, Skead K, Stephens DA, Stein DR, Tipples G, Van Caeseele PG, Evans TG, Oxlade O, Mazer BD, Buckeridge DL. The evolution of SARS-CoV-2 seroprevalence in Canada: a time-series study, 2020-2023. CMAJ 2023; 195:E1030-E1037. [PMID: 37580072 PMCID: PMC10426348 DOI: 10.1503/cmaj.230249] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/04/2023] [Indexed: 08/16/2023] Open
Abstract
BACKGROUND During the first year of the COVID-19 pandemic, the proportion of reported cases of COVID-19 among Canadians was under 6%. Although high vaccine coverage was achieved in Canada by fall 2021, the Omicron variant caused unprecedented numbers of infections, overwhelming testing capacity and making it difficult to quantify the trajectory of population immunity. METHODS Using a time-series approach and data from more than 900 000 samples collected by 7 research studies collaborating with the COVID-19 Immunity Task Force (CITF), we estimated trends in SARS-CoV-2 seroprevalence owing to infection and vaccination for the Canadian population over 3 intervals: prevaccination (March to November 2020), vaccine roll-out (December 2020 to November 2021), and the arrival of the Omicron variant (December 2021 to March 2023). We also estimated seroprevalence by geographical region and age. RESULTS By November 2021, 9.0% (95% credible interval [CrI] 7.3%-11%) of people in Canada had humoral immunity to SARS-CoV-2 from an infection. Seroprevalence increased rapidly after the arrival of the Omicron variant - by Mar. 15, 2023, 76% (95% CrI 74%-79%) of the population had detectable antibodies from infections. The rapid rise in infection-induced antibodies occurred across Canada and was most pronounced in younger age groups and in the Western provinces: Manitoba, Saskatchewan, Alberta and British Columbia. INTERPRETATION Data up to March 2023 indicate that most people in Canada had acquired antibodies against SARS-CoV-2 through natural infection and vaccination. However, given variations in population seropositivity by age and geography, the potential for waning antibody levels, and new variants that may escape immunity, public health policy and clinical decisions should be tailored to local patterns of population immunity.
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Affiliation(s)
- Tanya J Murphy
- COVID-19 Immunity Task Force (Murphy, Swail, Jain, Evans, Oxlade, Mazer, Buckeridge), School of Population and Global Health, McGill University, Montréal, Que.; Department of Community Health and Epidemiology (Anderson, Behl), University of Saskatchewan; Saskatchewan Health Authority (Anderson), Population Health, Saskatoon, Sask.; Department of Molecular Genetics (Awadalla), University of Toronto; Department of Computational Biology (Awadalla), Ontario Institute for Cancer Research; Centre for Global Health Research (Brown), Unity Health Toronto and University of Toronto, Toronto, Ont.; Public Health Laboratory (Charlton, Hinshaw, Tipples), Alberta Precision Laboratories, University of Alberta Hospital; Department of Laboratory Medicine and Pathology (Charlton, Tipples), and Li Ka Shing Institute of Virology, University of Alberta, Edmonton, Alta.; Lunenfeld-Tanenbaum Research Institute at Mount Sinai Hospital (Colwill, Gingras), Sinai Health System, Toronto, Ont.; Canadian Blood Services (Drews); Department of Laboratory Medicine and Pathology (O'Brien, Pambrun, Drews), University of Alberta, Edmonton, Alta.; Department of Molecular Genetics (Gingras, Skead), University of Toronto; Centre for Global Health Research (Jha), Unity Health Toronto and University of Toronto, Toronto, Ont.; Public Health Laboratory (Kanji), Alberta Precision Laboratories, Foothills Medical Centre, and Section of Medical Microbiology (Kanji), Department of Pathology and Laboratory Medicine, and Division of Infectious Diseases, Department of Medicine, University of Calgary, Calgary, Alta.; Ontario Health Study (Kirsh, Skead), Ontario Institute for Cancer Research; Department of Molecular Genetics (Kirsh, Skead), and Dalla Lana School of Public Health (Kirsh), University of Toronto, Toronto, Ont.; Roy Romanow Provincial Lab (Lang), Saskatchewan Health Authority; College of Medicine (Lang), University of Saskatchewan, Saskatoon, Sask.; Department of Biochemistry, Microbiology and Immunology (Langlois), and Centre for Infection, Immunity and Inflammation (Langlois), University of Ottawa, Ottawa, Ont.; Division of Infectious Diseases-Regina (Lee), University of Saskatchewan; Saskatchewan Health Authority (Lee), Saskatoon, Sask.; Medical Affair and Innovation (Lewin), Héma-Québec, Montréal, Que.; Departments of Epidemiology and Community Medicine (O'Brien), and Pathology and Laboratory Medicine (Pambrun), Faculty of Medicine, University of Ottawa, Ottawa, Ont.; Department of Mathematics & Statistics (Stephens), McGill University, Montréal, Que.; Department of Medical Microbiology (Stein, Van Caeseele), University of Manitoba, and Cadham Provincial Laboratory, Winnipeg, Man.; School of Population and Global Health (Evans), McGill University; The Research Institute of the McGill University Health Centre (Mazer, Buckeridge), Montréal, Que
| | - Hanna Swail
- COVID-19 Immunity Task Force (Murphy, Swail, Jain, Evans, Oxlade, Mazer, Buckeridge), School of Population and Global Health, McGill University, Montréal, Que.; Department of Community Health and Epidemiology (Anderson, Behl), University of Saskatchewan; Saskatchewan Health Authority (Anderson), Population Health, Saskatoon, Sask.; Department of Molecular Genetics (Awadalla), University of Toronto; Department of Computational Biology (Awadalla), Ontario Institute for Cancer Research; Centre for Global Health Research (Brown), Unity Health Toronto and University of Toronto, Toronto, Ont.; Public Health Laboratory (Charlton, Hinshaw, Tipples), Alberta Precision Laboratories, University of Alberta Hospital; Department of Laboratory Medicine and Pathology (Charlton, Tipples), and Li Ka Shing Institute of Virology, University of Alberta, Edmonton, Alta.; Lunenfeld-Tanenbaum Research Institute at Mount Sinai Hospital (Colwill, Gingras), Sinai Health System, Toronto, Ont.; Canadian Blood Services (Drews); Department of Laboratory Medicine and Pathology (O'Brien, Pambrun, Drews), University of Alberta, Edmonton, Alta.; Department of Molecular Genetics (Gingras, Skead), University of Toronto; Centre for Global Health Research (Jha), Unity Health Toronto and University of Toronto, Toronto, Ont.; Public Health Laboratory (Kanji), Alberta Precision Laboratories, Foothills Medical Centre, and Section of Medical Microbiology (Kanji), Department of Pathology and Laboratory Medicine, and Division of Infectious Diseases, Department of Medicine, University of Calgary, Calgary, Alta.; Ontario Health Study (Kirsh, Skead), Ontario Institute for Cancer Research; Department of Molecular Genetics (Kirsh, Skead), and Dalla Lana School of Public Health (Kirsh), University of Toronto, Toronto, Ont.; Roy Romanow Provincial Lab (Lang), Saskatchewan Health Authority; College of Medicine (Lang), University of Saskatchewan, Saskatoon, Sask.; Department of Biochemistry, Microbiology and Immunology (Langlois), and Centre for Infection, Immunity and Inflammation (Langlois), University of Ottawa, Ottawa, Ont.; Division of Infectious Diseases-Regina (Lee), University of Saskatchewan; Saskatchewan Health Authority (Lee), Saskatoon, Sask.; Medical Affair and Innovation (Lewin), Héma-Québec, Montréal, Que.; Departments of Epidemiology and Community Medicine (O'Brien), and Pathology and Laboratory Medicine (Pambrun), Faculty of Medicine, University of Ottawa, Ottawa, Ont.; Department of Mathematics & Statistics (Stephens), McGill University, Montréal, Que.; Department of Medical Microbiology (Stein, Van Caeseele), University of Manitoba, and Cadham Provincial Laboratory, Winnipeg, Man.; School of Population and Global Health (Evans), McGill University; The Research Institute of the McGill University Health Centre (Mazer, Buckeridge), Montréal, Que
| | - Jaspreet Jain
- COVID-19 Immunity Task Force (Murphy, Swail, Jain, Evans, Oxlade, Mazer, Buckeridge), School of Population and Global Health, McGill University, Montréal, Que.; Department of Community Health and Epidemiology (Anderson, Behl), University of Saskatchewan; Saskatchewan Health Authority (Anderson), Population Health, Saskatoon, Sask.; Department of Molecular Genetics (Awadalla), University of Toronto; Department of Computational Biology (Awadalla), Ontario Institute for Cancer Research; Centre for Global Health Research (Brown), Unity Health Toronto and University of Toronto, Toronto, Ont.; Public Health Laboratory (Charlton, Hinshaw, Tipples), Alberta Precision Laboratories, University of Alberta Hospital; Department of Laboratory Medicine and Pathology (Charlton, Tipples), and Li Ka Shing Institute of Virology, University of Alberta, Edmonton, Alta.; Lunenfeld-Tanenbaum Research Institute at Mount Sinai Hospital (Colwill, Gingras), Sinai Health System, Toronto, Ont.; Canadian Blood Services (Drews); Department of Laboratory Medicine and Pathology (O'Brien, Pambrun, Drews), University of Alberta, Edmonton, Alta.; Department of Molecular Genetics (Gingras, Skead), University of Toronto; Centre for Global Health Research (Jha), Unity Health Toronto and University of Toronto, Toronto, Ont.; Public Health Laboratory (Kanji), Alberta Precision Laboratories, Foothills Medical Centre, and Section of Medical Microbiology (Kanji), Department of Pathology and Laboratory Medicine, and Division of Infectious Diseases, Department of Medicine, University of Calgary, Calgary, Alta.; Ontario Health Study (Kirsh, Skead), Ontario Institute for Cancer Research; Department of Molecular Genetics (Kirsh, Skead), and Dalla Lana School of Public Health (Kirsh), University of Toronto, Toronto, Ont.; Roy Romanow Provincial Lab (Lang), Saskatchewan Health Authority; College of Medicine (Lang), University of Saskatchewan, Saskatoon, Sask.; Department of Biochemistry, Microbiology and Immunology (Langlois), and Centre for Infection, Immunity and Inflammation (Langlois), University of Ottawa, Ottawa, Ont.; Division of Infectious Diseases-Regina (Lee), University of Saskatchewan; Saskatchewan Health Authority (Lee), Saskatoon, Sask.; Medical Affair and Innovation (Lewin), Héma-Québec, Montréal, Que.; Departments of Epidemiology and Community Medicine (O'Brien), and Pathology and Laboratory Medicine (Pambrun), Faculty of Medicine, University of Ottawa, Ottawa, Ont.; Department of Mathematics & Statistics (Stephens), McGill University, Montréal, Que.; Department of Medical Microbiology (Stein, Van Caeseele), University of Manitoba, and Cadham Provincial Laboratory, Winnipeg, Man.; School of Population and Global Health (Evans), McGill University; The Research Institute of the McGill University Health Centre (Mazer, Buckeridge), Montréal, Que
| | - Maureen Anderson
- COVID-19 Immunity Task Force (Murphy, Swail, Jain, Evans, Oxlade, Mazer, Buckeridge), School of Population and Global Health, McGill University, Montréal, Que.; Department of Community Health and Epidemiology (Anderson, Behl), University of Saskatchewan; Saskatchewan Health Authority (Anderson), Population Health, Saskatoon, Sask.; Department of Molecular Genetics (Awadalla), University of Toronto; Department of Computational Biology (Awadalla), Ontario Institute for Cancer Research; Centre for Global Health Research (Brown), Unity Health Toronto and University of Toronto, Toronto, Ont.; Public Health Laboratory (Charlton, Hinshaw, Tipples), Alberta Precision Laboratories, University of Alberta Hospital; Department of Laboratory Medicine and Pathology (Charlton, Tipples), and Li Ka Shing Institute of Virology, University of Alberta, Edmonton, Alta.; Lunenfeld-Tanenbaum Research Institute at Mount Sinai Hospital (Colwill, Gingras), Sinai Health System, Toronto, Ont.; Canadian Blood Services (Drews); Department of Laboratory Medicine and Pathology (O'Brien, Pambrun, Drews), University of Alberta, Edmonton, Alta.; Department of Molecular Genetics (Gingras, Skead), University of Toronto; Centre for Global Health Research (Jha), Unity Health Toronto and University of Toronto, Toronto, Ont.; Public Health Laboratory (Kanji), Alberta Precision Laboratories, Foothills Medical Centre, and Section of Medical Microbiology (Kanji), Department of Pathology and Laboratory Medicine, and Division of Infectious Diseases, Department of Medicine, University of Calgary, Calgary, Alta.; Ontario Health Study (Kirsh, Skead), Ontario Institute for Cancer Research; Department of Molecular Genetics (Kirsh, Skead), and Dalla Lana School of Public Health (Kirsh), University of Toronto, Toronto, Ont.; Roy Romanow Provincial Lab (Lang), Saskatchewan Health Authority; College of Medicine (Lang), University of Saskatchewan, Saskatoon, Sask.; Department of Biochemistry, Microbiology and Immunology (Langlois), and Centre for Infection, Immunity and Inflammation (Langlois), University of Ottawa, Ottawa, Ont.; Division of Infectious Diseases-Regina (Lee), University of Saskatchewan; Saskatchewan Health Authority (Lee), Saskatoon, Sask.; Medical Affair and Innovation (Lewin), Héma-Québec, Montréal, Que.; Departments of Epidemiology and Community Medicine (O'Brien), and Pathology and Laboratory Medicine (Pambrun), Faculty of Medicine, University of Ottawa, Ottawa, Ont.; Department of Mathematics & Statistics (Stephens), McGill University, Montréal, Que.; Department of Medical Microbiology (Stein, Van Caeseele), University of Manitoba, and Cadham Provincial Laboratory, Winnipeg, Man.; School of Population and Global Health (Evans), McGill University; The Research Institute of the McGill University Health Centre (Mazer, Buckeridge), Montréal, Que
| | - Philip Awadalla
- COVID-19 Immunity Task Force (Murphy, Swail, Jain, Evans, Oxlade, Mazer, Buckeridge), School of Population and Global Health, McGill University, Montréal, Que.; Department of Community Health and Epidemiology (Anderson, Behl), University of Saskatchewan; Saskatchewan Health Authority (Anderson), Population Health, Saskatoon, Sask.; Department of Molecular Genetics (Awadalla), University of Toronto; Department of Computational Biology (Awadalla), Ontario Institute for Cancer Research; Centre for Global Health Research (Brown), Unity Health Toronto and University of Toronto, Toronto, Ont.; Public Health Laboratory (Charlton, Hinshaw, Tipples), Alberta Precision Laboratories, University of Alberta Hospital; Department of Laboratory Medicine and Pathology (Charlton, Tipples), and Li Ka Shing Institute of Virology, University of Alberta, Edmonton, Alta.; Lunenfeld-Tanenbaum Research Institute at Mount Sinai Hospital (Colwill, Gingras), Sinai Health System, Toronto, Ont.; Canadian Blood Services (Drews); Department of Laboratory Medicine and Pathology (O'Brien, Pambrun, Drews), University of Alberta, Edmonton, Alta.; Department of Molecular Genetics (Gingras, Skead), University of Toronto; Centre for Global Health Research (Jha), Unity Health Toronto and University of Toronto, Toronto, Ont.; Public Health Laboratory (Kanji), Alberta Precision Laboratories, Foothills Medical Centre, and Section of Medical Microbiology (Kanji), Department of Pathology and Laboratory Medicine, and Division of Infectious Diseases, Department of Medicine, University of Calgary, Calgary, Alta.; Ontario Health Study (Kirsh, Skead), Ontario Institute for Cancer Research; Department of Molecular Genetics (Kirsh, Skead), and Dalla Lana School of Public Health (Kirsh), University of Toronto, Toronto, Ont.; Roy Romanow Provincial Lab (Lang), Saskatchewan Health Authority; College of Medicine (Lang), University of Saskatchewan, Saskatoon, Sask.; Department of Biochemistry, Microbiology and Immunology (Langlois), and Centre for Infection, Immunity and Inflammation (Langlois), University of Ottawa, Ottawa, Ont.; Division of Infectious Diseases-Regina (Lee), University of Saskatchewan; Saskatchewan Health Authority (Lee), Saskatoon, Sask.; Medical Affair and Innovation (Lewin), Héma-Québec, Montréal, Que.; Departments of Epidemiology and Community Medicine (O'Brien), and Pathology and Laboratory Medicine (Pambrun), Faculty of Medicine, University of Ottawa, Ottawa, Ont.; Department of Mathematics & Statistics (Stephens), McGill University, Montréal, Que.; Department of Medical Microbiology (Stein, Van Caeseele), University of Manitoba, and Cadham Provincial Laboratory, Winnipeg, Man.; School of Population and Global Health (Evans), McGill University; The Research Institute of the McGill University Health Centre (Mazer, Buckeridge), Montréal, Que
| | - Lesley Behl
- COVID-19 Immunity Task Force (Murphy, Swail, Jain, Evans, Oxlade, Mazer, Buckeridge), School of Population and Global Health, McGill University, Montréal, Que.; Department of Community Health and Epidemiology (Anderson, Behl), University of Saskatchewan; Saskatchewan Health Authority (Anderson), Population Health, Saskatoon, Sask.; Department of Molecular Genetics (Awadalla), University of Toronto; Department of Computational Biology (Awadalla), Ontario Institute for Cancer Research; Centre for Global Health Research (Brown), Unity Health Toronto and University of Toronto, Toronto, Ont.; Public Health Laboratory (Charlton, Hinshaw, Tipples), Alberta Precision Laboratories, University of Alberta Hospital; Department of Laboratory Medicine and Pathology (Charlton, Tipples), and Li Ka Shing Institute of Virology, University of Alberta, Edmonton, Alta.; Lunenfeld-Tanenbaum Research Institute at Mount Sinai Hospital (Colwill, Gingras), Sinai Health System, Toronto, Ont.; Canadian Blood Services (Drews); Department of Laboratory Medicine and Pathology (O'Brien, Pambrun, Drews), University of Alberta, Edmonton, Alta.; Department of Molecular Genetics (Gingras, Skead), University of Toronto; Centre for Global Health Research (Jha), Unity Health Toronto and University of Toronto, Toronto, Ont.; Public Health Laboratory (Kanji), Alberta Precision Laboratories, Foothills Medical Centre, and Section of Medical Microbiology (Kanji), Department of Pathology and Laboratory Medicine, and Division of Infectious Diseases, Department of Medicine, University of Calgary, Calgary, Alta.; Ontario Health Study (Kirsh, Skead), Ontario Institute for Cancer Research; Department of Molecular Genetics (Kirsh, Skead), and Dalla Lana School of Public Health (Kirsh), University of Toronto, Toronto, Ont.; Roy Romanow Provincial Lab (Lang), Saskatchewan Health Authority; College of Medicine (Lang), University of Saskatchewan, Saskatoon, Sask.; Department of Biochemistry, Microbiology and Immunology (Langlois), and Centre for Infection, Immunity and Inflammation (Langlois), University of Ottawa, Ottawa, Ont.; Division of Infectious Diseases-Regina (Lee), University of Saskatchewan; Saskatchewan Health Authority (Lee), Saskatoon, Sask.; Medical Affair and Innovation (Lewin), Héma-Québec, Montréal, Que.; Departments of Epidemiology and Community Medicine (O'Brien), and Pathology and Laboratory Medicine (Pambrun), Faculty of Medicine, University of Ottawa, Ottawa, Ont.; Department of Mathematics & Statistics (Stephens), McGill University, Montréal, Que.; Department of Medical Microbiology (Stein, Van Caeseele), University of Manitoba, and Cadham Provincial Laboratory, Winnipeg, Man.; School of Population and Global Health (Evans), McGill University; The Research Institute of the McGill University Health Centre (Mazer, Buckeridge), Montréal, Que
| | - Patrick E Brown
- COVID-19 Immunity Task Force (Murphy, Swail, Jain, Evans, Oxlade, Mazer, Buckeridge), School of Population and Global Health, McGill University, Montréal, Que.; Department of Community Health and Epidemiology (Anderson, Behl), University of Saskatchewan; Saskatchewan Health Authority (Anderson), Population Health, Saskatoon, Sask.; Department of Molecular Genetics (Awadalla), University of Toronto; Department of Computational Biology (Awadalla), Ontario Institute for Cancer Research; Centre for Global Health Research (Brown), Unity Health Toronto and University of Toronto, Toronto, Ont.; Public Health Laboratory (Charlton, Hinshaw, Tipples), Alberta Precision Laboratories, University of Alberta Hospital; Department of Laboratory Medicine and Pathology (Charlton, Tipples), and Li Ka Shing Institute of Virology, University of Alberta, Edmonton, Alta.; Lunenfeld-Tanenbaum Research Institute at Mount Sinai Hospital (Colwill, Gingras), Sinai Health System, Toronto, Ont.; Canadian Blood Services (Drews); Department of Laboratory Medicine and Pathology (O'Brien, Pambrun, Drews), University of Alberta, Edmonton, Alta.; Department of Molecular Genetics (Gingras, Skead), University of Toronto; Centre for Global Health Research (Jha), Unity Health Toronto and University of Toronto, Toronto, Ont.; Public Health Laboratory (Kanji), Alberta Precision Laboratories, Foothills Medical Centre, and Section of Medical Microbiology (Kanji), Department of Pathology and Laboratory Medicine, and Division of Infectious Diseases, Department of Medicine, University of Calgary, Calgary, Alta.; Ontario Health Study (Kirsh, Skead), Ontario Institute for Cancer Research; Department of Molecular Genetics (Kirsh, Skead), and Dalla Lana School of Public Health (Kirsh), University of Toronto, Toronto, Ont.; Roy Romanow Provincial Lab (Lang), Saskatchewan Health Authority; College of Medicine (Lang), University of Saskatchewan, Saskatoon, Sask.; Department of Biochemistry, Microbiology and Immunology (Langlois), and Centre for Infection, Immunity and Inflammation (Langlois), University of Ottawa, Ottawa, Ont.; Division of Infectious Diseases-Regina (Lee), University of Saskatchewan; Saskatchewan Health Authority (Lee), Saskatoon, Sask.; Medical Affair and Innovation (Lewin), Héma-Québec, Montréal, Que.; Departments of Epidemiology and Community Medicine (O'Brien), and Pathology and Laboratory Medicine (Pambrun), Faculty of Medicine, University of Ottawa, Ottawa, Ont.; Department of Mathematics & Statistics (Stephens), McGill University, Montréal, Que.; Department of Medical Microbiology (Stein, Van Caeseele), University of Manitoba, and Cadham Provincial Laboratory, Winnipeg, Man.; School of Population and Global Health (Evans), McGill University; The Research Institute of the McGill University Health Centre (Mazer, Buckeridge), Montréal, Que
| | - Carmen L Charlton
- COVID-19 Immunity Task Force (Murphy, Swail, Jain, Evans, Oxlade, Mazer, Buckeridge), School of Population and Global Health, McGill University, Montréal, Que.; Department of Community Health and Epidemiology (Anderson, Behl), University of Saskatchewan; Saskatchewan Health Authority (Anderson), Population Health, Saskatoon, Sask.; Department of Molecular Genetics (Awadalla), University of Toronto; Department of Computational Biology (Awadalla), Ontario Institute for Cancer Research; Centre for Global Health Research (Brown), Unity Health Toronto and University of Toronto, Toronto, Ont.; Public Health Laboratory (Charlton, Hinshaw, Tipples), Alberta Precision Laboratories, University of Alberta Hospital; Department of Laboratory Medicine and Pathology (Charlton, Tipples), and Li Ka Shing Institute of Virology, University of Alberta, Edmonton, Alta.; Lunenfeld-Tanenbaum Research Institute at Mount Sinai Hospital (Colwill, Gingras), Sinai Health System, Toronto, Ont.; Canadian Blood Services (Drews); Department of Laboratory Medicine and Pathology (O'Brien, Pambrun, Drews), University of Alberta, Edmonton, Alta.; Department of Molecular Genetics (Gingras, Skead), University of Toronto; Centre for Global Health Research (Jha), Unity Health Toronto and University of Toronto, Toronto, Ont.; Public Health Laboratory (Kanji), Alberta Precision Laboratories, Foothills Medical Centre, and Section of Medical Microbiology (Kanji), Department of Pathology and Laboratory Medicine, and Division of Infectious Diseases, Department of Medicine, University of Calgary, Calgary, Alta.; Ontario Health Study (Kirsh, Skead), Ontario Institute for Cancer Research; Department of Molecular Genetics (Kirsh, Skead), and Dalla Lana School of Public Health (Kirsh), University of Toronto, Toronto, Ont.; Roy Romanow Provincial Lab (Lang), Saskatchewan Health Authority; College of Medicine (Lang), University of Saskatchewan, Saskatoon, Sask.; Department of Biochemistry, Microbiology and Immunology (Langlois), and Centre for Infection, Immunity and Inflammation (Langlois), University of Ottawa, Ottawa, Ont.; Division of Infectious Diseases-Regina (Lee), University of Saskatchewan; Saskatchewan Health Authority (Lee), Saskatoon, Sask.; Medical Affair and Innovation (Lewin), Héma-Québec, Montréal, Que.; Departments of Epidemiology and Community Medicine (O'Brien), and Pathology and Laboratory Medicine (Pambrun), Faculty of Medicine, University of Ottawa, Ottawa, Ont.; Department of Mathematics & Statistics (Stephens), McGill University, Montréal, Que.; Department of Medical Microbiology (Stein, Van Caeseele), University of Manitoba, and Cadham Provincial Laboratory, Winnipeg, Man.; School of Population and Global Health (Evans), McGill University; The Research Institute of the McGill University Health Centre (Mazer, Buckeridge), Montréal, Que
| | - Karen Colwill
- COVID-19 Immunity Task Force (Murphy, Swail, Jain, Evans, Oxlade, Mazer, Buckeridge), School of Population and Global Health, McGill University, Montréal, Que.; Department of Community Health and Epidemiology (Anderson, Behl), University of Saskatchewan; Saskatchewan Health Authority (Anderson), Population Health, Saskatoon, Sask.; Department of Molecular Genetics (Awadalla), University of Toronto; Department of Computational Biology (Awadalla), Ontario Institute for Cancer Research; Centre for Global Health Research (Brown), Unity Health Toronto and University of Toronto, Toronto, Ont.; Public Health Laboratory (Charlton, Hinshaw, Tipples), Alberta Precision Laboratories, University of Alberta Hospital; Department of Laboratory Medicine and Pathology (Charlton, Tipples), and Li Ka Shing Institute of Virology, University of Alberta, Edmonton, Alta.; Lunenfeld-Tanenbaum Research Institute at Mount Sinai Hospital (Colwill, Gingras), Sinai Health System, Toronto, Ont.; Canadian Blood Services (Drews); Department of Laboratory Medicine and Pathology (O'Brien, Pambrun, Drews), University of Alberta, Edmonton, Alta.; Department of Molecular Genetics (Gingras, Skead), University of Toronto; Centre for Global Health Research (Jha), Unity Health Toronto and University of Toronto, Toronto, Ont.; Public Health Laboratory (Kanji), Alberta Precision Laboratories, Foothills Medical Centre, and Section of Medical Microbiology (Kanji), Department of Pathology and Laboratory Medicine, and Division of Infectious Diseases, Department of Medicine, University of Calgary, Calgary, Alta.; Ontario Health Study (Kirsh, Skead), Ontario Institute for Cancer Research; Department of Molecular Genetics (Kirsh, Skead), and Dalla Lana School of Public Health (Kirsh), University of Toronto, Toronto, Ont.; Roy Romanow Provincial Lab (Lang), Saskatchewan Health Authority; College of Medicine (Lang), University of Saskatchewan, Saskatoon, Sask.; Department of Biochemistry, Microbiology and Immunology (Langlois), and Centre for Infection, Immunity and Inflammation (Langlois), University of Ottawa, Ottawa, Ont.; Division of Infectious Diseases-Regina (Lee), University of Saskatchewan; Saskatchewan Health Authority (Lee), Saskatoon, Sask.; Medical Affair and Innovation (Lewin), Héma-Québec, Montréal, Que.; Departments of Epidemiology and Community Medicine (O'Brien), and Pathology and Laboratory Medicine (Pambrun), Faculty of Medicine, University of Ottawa, Ottawa, Ont.; Department of Mathematics & Statistics (Stephens), McGill University, Montréal, Que.; Department of Medical Microbiology (Stein, Van Caeseele), University of Manitoba, and Cadham Provincial Laboratory, Winnipeg, Man.; School of Population and Global Health (Evans), McGill University; The Research Institute of the McGill University Health Centre (Mazer, Buckeridge), Montréal, Que
| | - Steven J Drews
- COVID-19 Immunity Task Force (Murphy, Swail, Jain, Evans, Oxlade, Mazer, Buckeridge), School of Population and Global Health, McGill University, Montréal, Que.; Department of Community Health and Epidemiology (Anderson, Behl), University of Saskatchewan; Saskatchewan Health Authority (Anderson), Population Health, Saskatoon, Sask.; Department of Molecular Genetics (Awadalla), University of Toronto; Department of Computational Biology (Awadalla), Ontario Institute for Cancer Research; Centre for Global Health Research (Brown), Unity Health Toronto and University of Toronto, Toronto, Ont.; Public Health Laboratory (Charlton, Hinshaw, Tipples), Alberta Precision Laboratories, University of Alberta Hospital; Department of Laboratory Medicine and Pathology (Charlton, Tipples), and Li Ka Shing Institute of Virology, University of Alberta, Edmonton, Alta.; Lunenfeld-Tanenbaum Research Institute at Mount Sinai Hospital (Colwill, Gingras), Sinai Health System, Toronto, Ont.; Canadian Blood Services (Drews); Department of Laboratory Medicine and Pathology (O'Brien, Pambrun, Drews), University of Alberta, Edmonton, Alta.; Department of Molecular Genetics (Gingras, Skead), University of Toronto; Centre for Global Health Research (Jha), Unity Health Toronto and University of Toronto, Toronto, Ont.; Public Health Laboratory (Kanji), Alberta Precision Laboratories, Foothills Medical Centre, and Section of Medical Microbiology (Kanji), Department of Pathology and Laboratory Medicine, and Division of Infectious Diseases, Department of Medicine, University of Calgary, Calgary, Alta.; Ontario Health Study (Kirsh, Skead), Ontario Institute for Cancer Research; Department of Molecular Genetics (Kirsh, Skead), and Dalla Lana School of Public Health (Kirsh), University of Toronto, Toronto, Ont.; Roy Romanow Provincial Lab (Lang), Saskatchewan Health Authority; College of Medicine (Lang), University of Saskatchewan, Saskatoon, Sask.; Department of Biochemistry, Microbiology and Immunology (Langlois), and Centre for Infection, Immunity and Inflammation (Langlois), University of Ottawa, Ottawa, Ont.; Division of Infectious Diseases-Regina (Lee), University of Saskatchewan; Saskatchewan Health Authority (Lee), Saskatoon, Sask.; Medical Affair and Innovation (Lewin), Héma-Québec, Montréal, Que.; Departments of Epidemiology and Community Medicine (O'Brien), and Pathology and Laboratory Medicine (Pambrun), Faculty of Medicine, University of Ottawa, Ottawa, Ont.; Department of Mathematics & Statistics (Stephens), McGill University, Montréal, Que.; Department of Medical Microbiology (Stein, Van Caeseele), University of Manitoba, and Cadham Provincial Laboratory, Winnipeg, Man.; School of Population and Global Health (Evans), McGill University; The Research Institute of the McGill University Health Centre (Mazer, Buckeridge), Montréal, Que
| | - Anne-Claude Gingras
- COVID-19 Immunity Task Force (Murphy, Swail, Jain, Evans, Oxlade, Mazer, Buckeridge), School of Population and Global Health, McGill University, Montréal, Que.; Department of Community Health and Epidemiology (Anderson, Behl), University of Saskatchewan; Saskatchewan Health Authority (Anderson), Population Health, Saskatoon, Sask.; Department of Molecular Genetics (Awadalla), University of Toronto; Department of Computational Biology (Awadalla), Ontario Institute for Cancer Research; Centre for Global Health Research (Brown), Unity Health Toronto and University of Toronto, Toronto, Ont.; Public Health Laboratory (Charlton, Hinshaw, Tipples), Alberta Precision Laboratories, University of Alberta Hospital; Department of Laboratory Medicine and Pathology (Charlton, Tipples), and Li Ka Shing Institute of Virology, University of Alberta, Edmonton, Alta.; Lunenfeld-Tanenbaum Research Institute at Mount Sinai Hospital (Colwill, Gingras), Sinai Health System, Toronto, Ont.; Canadian Blood Services (Drews); Department of Laboratory Medicine and Pathology (O'Brien, Pambrun, Drews), University of Alberta, Edmonton, Alta.; Department of Molecular Genetics (Gingras, Skead), University of Toronto; Centre for Global Health Research (Jha), Unity Health Toronto and University of Toronto, Toronto, Ont.; Public Health Laboratory (Kanji), Alberta Precision Laboratories, Foothills Medical Centre, and Section of Medical Microbiology (Kanji), Department of Pathology and Laboratory Medicine, and Division of Infectious Diseases, Department of Medicine, University of Calgary, Calgary, Alta.; Ontario Health Study (Kirsh, Skead), Ontario Institute for Cancer Research; Department of Molecular Genetics (Kirsh, Skead), and Dalla Lana School of Public Health (Kirsh), University of Toronto, Toronto, Ont.; Roy Romanow Provincial Lab (Lang), Saskatchewan Health Authority; College of Medicine (Lang), University of Saskatchewan, Saskatoon, Sask.; Department of Biochemistry, Microbiology and Immunology (Langlois), and Centre for Infection, Immunity and Inflammation (Langlois), University of Ottawa, Ottawa, Ont.; Division of Infectious Diseases-Regina (Lee), University of Saskatchewan; Saskatchewan Health Authority (Lee), Saskatoon, Sask.; Medical Affair and Innovation (Lewin), Héma-Québec, Montréal, Que.; Departments of Epidemiology and Community Medicine (O'Brien), and Pathology and Laboratory Medicine (Pambrun), Faculty of Medicine, University of Ottawa, Ottawa, Ont.; Department of Mathematics & Statistics (Stephens), McGill University, Montréal, Que.; Department of Medical Microbiology (Stein, Van Caeseele), University of Manitoba, and Cadham Provincial Laboratory, Winnipeg, Man.; School of Population and Global Health (Evans), McGill University; The Research Institute of the McGill University Health Centre (Mazer, Buckeridge), Montréal, Que
| | - Deena Hinshaw
- COVID-19 Immunity Task Force (Murphy, Swail, Jain, Evans, Oxlade, Mazer, Buckeridge), School of Population and Global Health, McGill University, Montréal, Que.; Department of Community Health and Epidemiology (Anderson, Behl), University of Saskatchewan; Saskatchewan Health Authority (Anderson), Population Health, Saskatoon, Sask.; Department of Molecular Genetics (Awadalla), University of Toronto; Department of Computational Biology (Awadalla), Ontario Institute for Cancer Research; Centre for Global Health Research (Brown), Unity Health Toronto and University of Toronto, Toronto, Ont.; Public Health Laboratory (Charlton, Hinshaw, Tipples), Alberta Precision Laboratories, University of Alberta Hospital; Department of Laboratory Medicine and Pathology (Charlton, Tipples), and Li Ka Shing Institute of Virology, University of Alberta, Edmonton, Alta.; Lunenfeld-Tanenbaum Research Institute at Mount Sinai Hospital (Colwill, Gingras), Sinai Health System, Toronto, Ont.; Canadian Blood Services (Drews); Department of Laboratory Medicine and Pathology (O'Brien, Pambrun, Drews), University of Alberta, Edmonton, Alta.; Department of Molecular Genetics (Gingras, Skead), University of Toronto; Centre for Global Health Research (Jha), Unity Health Toronto and University of Toronto, Toronto, Ont.; Public Health Laboratory (Kanji), Alberta Precision Laboratories, Foothills Medical Centre, and Section of Medical Microbiology (Kanji), Department of Pathology and Laboratory Medicine, and Division of Infectious Diseases, Department of Medicine, University of Calgary, Calgary, Alta.; Ontario Health Study (Kirsh, Skead), Ontario Institute for Cancer Research; Department of Molecular Genetics (Kirsh, Skead), and Dalla Lana School of Public Health (Kirsh), University of Toronto, Toronto, Ont.; Roy Romanow Provincial Lab (Lang), Saskatchewan Health Authority; College of Medicine (Lang), University of Saskatchewan, Saskatoon, Sask.; Department of Biochemistry, Microbiology and Immunology (Langlois), and Centre for Infection, Immunity and Inflammation (Langlois), University of Ottawa, Ottawa, Ont.; Division of Infectious Diseases-Regina (Lee), University of Saskatchewan; Saskatchewan Health Authority (Lee), Saskatoon, Sask.; Medical Affair and Innovation (Lewin), Héma-Québec, Montréal, Que.; Departments of Epidemiology and Community Medicine (O'Brien), and Pathology and Laboratory Medicine (Pambrun), Faculty of Medicine, University of Ottawa, Ottawa, Ont.; Department of Mathematics & Statistics (Stephens), McGill University, Montréal, Que.; Department of Medical Microbiology (Stein, Van Caeseele), University of Manitoba, and Cadham Provincial Laboratory, Winnipeg, Man.; School of Population and Global Health (Evans), McGill University; The Research Institute of the McGill University Health Centre (Mazer, Buckeridge), Montréal, Que
| | - Prabhat Jha
- COVID-19 Immunity Task Force (Murphy, Swail, Jain, Evans, Oxlade, Mazer, Buckeridge), School of Population and Global Health, McGill University, Montréal, Que.; Department of Community Health and Epidemiology (Anderson, Behl), University of Saskatchewan; Saskatchewan Health Authority (Anderson), Population Health, Saskatoon, Sask.; Department of Molecular Genetics (Awadalla), University of Toronto; Department of Computational Biology (Awadalla), Ontario Institute for Cancer Research; Centre for Global Health Research (Brown), Unity Health Toronto and University of Toronto, Toronto, Ont.; Public Health Laboratory (Charlton, Hinshaw, Tipples), Alberta Precision Laboratories, University of Alberta Hospital; Department of Laboratory Medicine and Pathology (Charlton, Tipples), and Li Ka Shing Institute of Virology, University of Alberta, Edmonton, Alta.; Lunenfeld-Tanenbaum Research Institute at Mount Sinai Hospital (Colwill, Gingras), Sinai Health System, Toronto, Ont.; Canadian Blood Services (Drews); Department of Laboratory Medicine and Pathology (O'Brien, Pambrun, Drews), University of Alberta, Edmonton, Alta.; Department of Molecular Genetics (Gingras, Skead), University of Toronto; Centre for Global Health Research (Jha), Unity Health Toronto and University of Toronto, Toronto, Ont.; Public Health Laboratory (Kanji), Alberta Precision Laboratories, Foothills Medical Centre, and Section of Medical Microbiology (Kanji), Department of Pathology and Laboratory Medicine, and Division of Infectious Diseases, Department of Medicine, University of Calgary, Calgary, Alta.; Ontario Health Study (Kirsh, Skead), Ontario Institute for Cancer Research; Department of Molecular Genetics (Kirsh, Skead), and Dalla Lana School of Public Health (Kirsh), University of Toronto, Toronto, Ont.; Roy Romanow Provincial Lab (Lang), Saskatchewan Health Authority; College of Medicine (Lang), University of Saskatchewan, Saskatoon, Sask.; Department of Biochemistry, Microbiology and Immunology (Langlois), and Centre for Infection, Immunity and Inflammation (Langlois), University of Ottawa, Ottawa, Ont.; Division of Infectious Diseases-Regina (Lee), University of Saskatchewan; Saskatchewan Health Authority (Lee), Saskatoon, Sask.; Medical Affair and Innovation (Lewin), Héma-Québec, Montréal, Que.; Departments of Epidemiology and Community Medicine (O'Brien), and Pathology and Laboratory Medicine (Pambrun), Faculty of Medicine, University of Ottawa, Ottawa, Ont.; Department of Mathematics & Statistics (Stephens), McGill University, Montréal, Que.; Department of Medical Microbiology (Stein, Van Caeseele), University of Manitoba, and Cadham Provincial Laboratory, Winnipeg, Man.; School of Population and Global Health (Evans), McGill University; The Research Institute of the McGill University Health Centre (Mazer, Buckeridge), Montréal, Que
| | - Jamil N Kanji
- COVID-19 Immunity Task Force (Murphy, Swail, Jain, Evans, Oxlade, Mazer, Buckeridge), School of Population and Global Health, McGill University, Montréal, Que.; Department of Community Health and Epidemiology (Anderson, Behl), University of Saskatchewan; Saskatchewan Health Authority (Anderson), Population Health, Saskatoon, Sask.; Department of Molecular Genetics (Awadalla), University of Toronto; Department of Computational Biology (Awadalla), Ontario Institute for Cancer Research; Centre for Global Health Research (Brown), Unity Health Toronto and University of Toronto, Toronto, Ont.; Public Health Laboratory (Charlton, Hinshaw, Tipples), Alberta Precision Laboratories, University of Alberta Hospital; Department of Laboratory Medicine and Pathology (Charlton, Tipples), and Li Ka Shing Institute of Virology, University of Alberta, Edmonton, Alta.; Lunenfeld-Tanenbaum Research Institute at Mount Sinai Hospital (Colwill, Gingras), Sinai Health System, Toronto, Ont.; Canadian Blood Services (Drews); Department of Laboratory Medicine and Pathology (O'Brien, Pambrun, Drews), University of Alberta, Edmonton, Alta.; Department of Molecular Genetics (Gingras, Skead), University of Toronto; Centre for Global Health Research (Jha), Unity Health Toronto and University of Toronto, Toronto, Ont.; Public Health Laboratory (Kanji), Alberta Precision Laboratories, Foothills Medical Centre, and Section of Medical Microbiology (Kanji), Department of Pathology and Laboratory Medicine, and Division of Infectious Diseases, Department of Medicine, University of Calgary, Calgary, Alta.; Ontario Health Study (Kirsh, Skead), Ontario Institute for Cancer Research; Department of Molecular Genetics (Kirsh, Skead), and Dalla Lana School of Public Health (Kirsh), University of Toronto, Toronto, Ont.; Roy Romanow Provincial Lab (Lang), Saskatchewan Health Authority; College of Medicine (Lang), University of Saskatchewan, Saskatoon, Sask.; Department of Biochemistry, Microbiology and Immunology (Langlois), and Centre for Infection, Immunity and Inflammation (Langlois), University of Ottawa, Ottawa, Ont.; Division of Infectious Diseases-Regina (Lee), University of Saskatchewan; Saskatchewan Health Authority (Lee), Saskatoon, Sask.; Medical Affair and Innovation (Lewin), Héma-Québec, Montréal, Que.; Departments of Epidemiology and Community Medicine (O'Brien), and Pathology and Laboratory Medicine (Pambrun), Faculty of Medicine, University of Ottawa, Ottawa, Ont.; Department of Mathematics & Statistics (Stephens), McGill University, Montréal, Que.; Department of Medical Microbiology (Stein, Van Caeseele), University of Manitoba, and Cadham Provincial Laboratory, Winnipeg, Man.; School of Population and Global Health (Evans), McGill University; The Research Institute of the McGill University Health Centre (Mazer, Buckeridge), Montréal, Que
| | - Victoria A Kirsh
- COVID-19 Immunity Task Force (Murphy, Swail, Jain, Evans, Oxlade, Mazer, Buckeridge), School of Population and Global Health, McGill University, Montréal, Que.; Department of Community Health and Epidemiology (Anderson, Behl), University of Saskatchewan; Saskatchewan Health Authority (Anderson), Population Health, Saskatoon, Sask.; Department of Molecular Genetics (Awadalla), University of Toronto; Department of Computational Biology (Awadalla), Ontario Institute for Cancer Research; Centre for Global Health Research (Brown), Unity Health Toronto and University of Toronto, Toronto, Ont.; Public Health Laboratory (Charlton, Hinshaw, Tipples), Alberta Precision Laboratories, University of Alberta Hospital; Department of Laboratory Medicine and Pathology (Charlton, Tipples), and Li Ka Shing Institute of Virology, University of Alberta, Edmonton, Alta.; Lunenfeld-Tanenbaum Research Institute at Mount Sinai Hospital (Colwill, Gingras), Sinai Health System, Toronto, Ont.; Canadian Blood Services (Drews); Department of Laboratory Medicine and Pathology (O'Brien, Pambrun, Drews), University of Alberta, Edmonton, Alta.; Department of Molecular Genetics (Gingras, Skead), University of Toronto; Centre for Global Health Research (Jha), Unity Health Toronto and University of Toronto, Toronto, Ont.; Public Health Laboratory (Kanji), Alberta Precision Laboratories, Foothills Medical Centre, and Section of Medical Microbiology (Kanji), Department of Pathology and Laboratory Medicine, and Division of Infectious Diseases, Department of Medicine, University of Calgary, Calgary, Alta.; Ontario Health Study (Kirsh, Skead), Ontario Institute for Cancer Research; Department of Molecular Genetics (Kirsh, Skead), and Dalla Lana School of Public Health (Kirsh), University of Toronto, Toronto, Ont.; Roy Romanow Provincial Lab (Lang), Saskatchewan Health Authority; College of Medicine (Lang), University of Saskatchewan, Saskatoon, Sask.; Department of Biochemistry, Microbiology and Immunology (Langlois), and Centre for Infection, Immunity and Inflammation (Langlois), University of Ottawa, Ottawa, Ont.; Division of Infectious Diseases-Regina (Lee), University of Saskatchewan; Saskatchewan Health Authority (Lee), Saskatoon, Sask.; Medical Affair and Innovation (Lewin), Héma-Québec, Montréal, Que.; Departments of Epidemiology and Community Medicine (O'Brien), and Pathology and Laboratory Medicine (Pambrun), Faculty of Medicine, University of Ottawa, Ottawa, Ont.; Department of Mathematics & Statistics (Stephens), McGill University, Montréal, Que.; Department of Medical Microbiology (Stein, Van Caeseele), University of Manitoba, and Cadham Provincial Laboratory, Winnipeg, Man.; School of Population and Global Health (Evans), McGill University; The Research Institute of the McGill University Health Centre (Mazer, Buckeridge), Montréal, Que
| | - Amanda L S Lang
- COVID-19 Immunity Task Force (Murphy, Swail, Jain, Evans, Oxlade, Mazer, Buckeridge), School of Population and Global Health, McGill University, Montréal, Que.; Department of Community Health and Epidemiology (Anderson, Behl), University of Saskatchewan; Saskatchewan Health Authority (Anderson), Population Health, Saskatoon, Sask.; Department of Molecular Genetics (Awadalla), University of Toronto; Department of Computational Biology (Awadalla), Ontario Institute for Cancer Research; Centre for Global Health Research (Brown), Unity Health Toronto and University of Toronto, Toronto, Ont.; Public Health Laboratory (Charlton, Hinshaw, Tipples), Alberta Precision Laboratories, University of Alberta Hospital; Department of Laboratory Medicine and Pathology (Charlton, Tipples), and Li Ka Shing Institute of Virology, University of Alberta, Edmonton, Alta.; Lunenfeld-Tanenbaum Research Institute at Mount Sinai Hospital (Colwill, Gingras), Sinai Health System, Toronto, Ont.; Canadian Blood Services (Drews); Department of Laboratory Medicine and Pathology (O'Brien, Pambrun, Drews), University of Alberta, Edmonton, Alta.; Department of Molecular Genetics (Gingras, Skead), University of Toronto; Centre for Global Health Research (Jha), Unity Health Toronto and University of Toronto, Toronto, Ont.; Public Health Laboratory (Kanji), Alberta Precision Laboratories, Foothills Medical Centre, and Section of Medical Microbiology (Kanji), Department of Pathology and Laboratory Medicine, and Division of Infectious Diseases, Department of Medicine, University of Calgary, Calgary, Alta.; Ontario Health Study (Kirsh, Skead), Ontario Institute for Cancer Research; Department of Molecular Genetics (Kirsh, Skead), and Dalla Lana School of Public Health (Kirsh), University of Toronto, Toronto, Ont.; Roy Romanow Provincial Lab (Lang), Saskatchewan Health Authority; College of Medicine (Lang), University of Saskatchewan, Saskatoon, Sask.; Department of Biochemistry, Microbiology and Immunology (Langlois), and Centre for Infection, Immunity and Inflammation (Langlois), University of Ottawa, Ottawa, Ont.; Division of Infectious Diseases-Regina (Lee), University of Saskatchewan; Saskatchewan Health Authority (Lee), Saskatoon, Sask.; Medical Affair and Innovation (Lewin), Héma-Québec, Montréal, Que.; Departments of Epidemiology and Community Medicine (O'Brien), and Pathology and Laboratory Medicine (Pambrun), Faculty of Medicine, University of Ottawa, Ottawa, Ont.; Department of Mathematics & Statistics (Stephens), McGill University, Montréal, Que.; Department of Medical Microbiology (Stein, Van Caeseele), University of Manitoba, and Cadham Provincial Laboratory, Winnipeg, Man.; School of Population and Global Health (Evans), McGill University; The Research Institute of the McGill University Health Centre (Mazer, Buckeridge), Montréal, Que
| | - Marc-André Langlois
- COVID-19 Immunity Task Force (Murphy, Swail, Jain, Evans, Oxlade, Mazer, Buckeridge), School of Population and Global Health, McGill University, Montréal, Que.; Department of Community Health and Epidemiology (Anderson, Behl), University of Saskatchewan; Saskatchewan Health Authority (Anderson), Population Health, Saskatoon, Sask.; Department of Molecular Genetics (Awadalla), University of Toronto; Department of Computational Biology (Awadalla), Ontario Institute for Cancer Research; Centre for Global Health Research (Brown), Unity Health Toronto and University of Toronto, Toronto, Ont.; Public Health Laboratory (Charlton, Hinshaw, Tipples), Alberta Precision Laboratories, University of Alberta Hospital; Department of Laboratory Medicine and Pathology (Charlton, Tipples), and Li Ka Shing Institute of Virology, University of Alberta, Edmonton, Alta.; Lunenfeld-Tanenbaum Research Institute at Mount Sinai Hospital (Colwill, Gingras), Sinai Health System, Toronto, Ont.; Canadian Blood Services (Drews); Department of Laboratory Medicine and Pathology (O'Brien, Pambrun, Drews), University of Alberta, Edmonton, Alta.; Department of Molecular Genetics (Gingras, Skead), University of Toronto; Centre for Global Health Research (Jha), Unity Health Toronto and University of Toronto, Toronto, Ont.; Public Health Laboratory (Kanji), Alberta Precision Laboratories, Foothills Medical Centre, and Section of Medical Microbiology (Kanji), Department of Pathology and Laboratory Medicine, and Division of Infectious Diseases, Department of Medicine, University of Calgary, Calgary, Alta.; Ontario Health Study (Kirsh, Skead), Ontario Institute for Cancer Research; Department of Molecular Genetics (Kirsh, Skead), and Dalla Lana School of Public Health (Kirsh), University of Toronto, Toronto, Ont.; Roy Romanow Provincial Lab (Lang), Saskatchewan Health Authority; College of Medicine (Lang), University of Saskatchewan, Saskatoon, Sask.; Department of Biochemistry, Microbiology and Immunology (Langlois), and Centre for Infection, Immunity and Inflammation (Langlois), University of Ottawa, Ottawa, Ont.; Division of Infectious Diseases-Regina (Lee), University of Saskatchewan; Saskatchewan Health Authority (Lee), Saskatoon, Sask.; Medical Affair and Innovation (Lewin), Héma-Québec, Montréal, Que.; Departments of Epidemiology and Community Medicine (O'Brien), and Pathology and Laboratory Medicine (Pambrun), Faculty of Medicine, University of Ottawa, Ottawa, Ont.; Department of Mathematics & Statistics (Stephens), McGill University, Montréal, Que.; Department of Medical Microbiology (Stein, Van Caeseele), University of Manitoba, and Cadham Provincial Laboratory, Winnipeg, Man.; School of Population and Global Health (Evans), McGill University; The Research Institute of the McGill University Health Centre (Mazer, Buckeridge), Montréal, Que
| | - Stephen Lee
- COVID-19 Immunity Task Force (Murphy, Swail, Jain, Evans, Oxlade, Mazer, Buckeridge), School of Population and Global Health, McGill University, Montréal, Que.; Department of Community Health and Epidemiology (Anderson, Behl), University of Saskatchewan; Saskatchewan Health Authority (Anderson), Population Health, Saskatoon, Sask.; Department of Molecular Genetics (Awadalla), University of Toronto; Department of Computational Biology (Awadalla), Ontario Institute for Cancer Research; Centre for Global Health Research (Brown), Unity Health Toronto and University of Toronto, Toronto, Ont.; Public Health Laboratory (Charlton, Hinshaw, Tipples), Alberta Precision Laboratories, University of Alberta Hospital; Department of Laboratory Medicine and Pathology (Charlton, Tipples), and Li Ka Shing Institute of Virology, University of Alberta, Edmonton, Alta.; Lunenfeld-Tanenbaum Research Institute at Mount Sinai Hospital (Colwill, Gingras), Sinai Health System, Toronto, Ont.; Canadian Blood Services (Drews); Department of Laboratory Medicine and Pathology (O'Brien, Pambrun, Drews), University of Alberta, Edmonton, Alta.; Department of Molecular Genetics (Gingras, Skead), University of Toronto; Centre for Global Health Research (Jha), Unity Health Toronto and University of Toronto, Toronto, Ont.; Public Health Laboratory (Kanji), Alberta Precision Laboratories, Foothills Medical Centre, and Section of Medical Microbiology (Kanji), Department of Pathology and Laboratory Medicine, and Division of Infectious Diseases, Department of Medicine, University of Calgary, Calgary, Alta.; Ontario Health Study (Kirsh, Skead), Ontario Institute for Cancer Research; Department of Molecular Genetics (Kirsh, Skead), and Dalla Lana School of Public Health (Kirsh), University of Toronto, Toronto, Ont.; Roy Romanow Provincial Lab (Lang), Saskatchewan Health Authority; College of Medicine (Lang), University of Saskatchewan, Saskatoon, Sask.; Department of Biochemistry, Microbiology and Immunology (Langlois), and Centre for Infection, Immunity and Inflammation (Langlois), University of Ottawa, Ottawa, Ont.; Division of Infectious Diseases-Regina (Lee), University of Saskatchewan; Saskatchewan Health Authority (Lee), Saskatoon, Sask.; Medical Affair and Innovation (Lewin), Héma-Québec, Montréal, Que.; Departments of Epidemiology and Community Medicine (O'Brien), and Pathology and Laboratory Medicine (Pambrun), Faculty of Medicine, University of Ottawa, Ottawa, Ont.; Department of Mathematics & Statistics (Stephens), McGill University, Montréal, Que.; Department of Medical Microbiology (Stein, Van Caeseele), University of Manitoba, and Cadham Provincial Laboratory, Winnipeg, Man.; School of Population and Global Health (Evans), McGill University; The Research Institute of the McGill University Health Centre (Mazer, Buckeridge), Montréal, Que
| | - Antoine Lewin
- COVID-19 Immunity Task Force (Murphy, Swail, Jain, Evans, Oxlade, Mazer, Buckeridge), School of Population and Global Health, McGill University, Montréal, Que.; Department of Community Health and Epidemiology (Anderson, Behl), University of Saskatchewan; Saskatchewan Health Authority (Anderson), Population Health, Saskatoon, Sask.; Department of Molecular Genetics (Awadalla), University of Toronto; Department of Computational Biology (Awadalla), Ontario Institute for Cancer Research; Centre for Global Health Research (Brown), Unity Health Toronto and University of Toronto, Toronto, Ont.; Public Health Laboratory (Charlton, Hinshaw, Tipples), Alberta Precision Laboratories, University of Alberta Hospital; Department of Laboratory Medicine and Pathology (Charlton, Tipples), and Li Ka Shing Institute of Virology, University of Alberta, Edmonton, Alta.; Lunenfeld-Tanenbaum Research Institute at Mount Sinai Hospital (Colwill, Gingras), Sinai Health System, Toronto, Ont.; Canadian Blood Services (Drews); Department of Laboratory Medicine and Pathology (O'Brien, Pambrun, Drews), University of Alberta, Edmonton, Alta.; Department of Molecular Genetics (Gingras, Skead), University of Toronto; Centre for Global Health Research (Jha), Unity Health Toronto and University of Toronto, Toronto, Ont.; Public Health Laboratory (Kanji), Alberta Precision Laboratories, Foothills Medical Centre, and Section of Medical Microbiology (Kanji), Department of Pathology and Laboratory Medicine, and Division of Infectious Diseases, Department of Medicine, University of Calgary, Calgary, Alta.; Ontario Health Study (Kirsh, Skead), Ontario Institute for Cancer Research; Department of Molecular Genetics (Kirsh, Skead), and Dalla Lana School of Public Health (Kirsh), University of Toronto, Toronto, Ont.; Roy Romanow Provincial Lab (Lang), Saskatchewan Health Authority; College of Medicine (Lang), University of Saskatchewan, Saskatoon, Sask.; Department of Biochemistry, Microbiology and Immunology (Langlois), and Centre for Infection, Immunity and Inflammation (Langlois), University of Ottawa, Ottawa, Ont.; Division of Infectious Diseases-Regina (Lee), University of Saskatchewan; Saskatchewan Health Authority (Lee), Saskatoon, Sask.; Medical Affair and Innovation (Lewin), Héma-Québec, Montréal, Que.; Departments of Epidemiology and Community Medicine (O'Brien), and Pathology and Laboratory Medicine (Pambrun), Faculty of Medicine, University of Ottawa, Ottawa, Ont.; Department of Mathematics & Statistics (Stephens), McGill University, Montréal, Que.; Department of Medical Microbiology (Stein, Van Caeseele), University of Manitoba, and Cadham Provincial Laboratory, Winnipeg, Man.; School of Population and Global Health (Evans), McGill University; The Research Institute of the McGill University Health Centre (Mazer, Buckeridge), Montréal, Que
| | - Sheila F O'Brien
- COVID-19 Immunity Task Force (Murphy, Swail, Jain, Evans, Oxlade, Mazer, Buckeridge), School of Population and Global Health, McGill University, Montréal, Que.; Department of Community Health and Epidemiology (Anderson, Behl), University of Saskatchewan; Saskatchewan Health Authority (Anderson), Population Health, Saskatoon, Sask.; Department of Molecular Genetics (Awadalla), University of Toronto; Department of Computational Biology (Awadalla), Ontario Institute for Cancer Research; Centre for Global Health Research (Brown), Unity Health Toronto and University of Toronto, Toronto, Ont.; Public Health Laboratory (Charlton, Hinshaw, Tipples), Alberta Precision Laboratories, University of Alberta Hospital; Department of Laboratory Medicine and Pathology (Charlton, Tipples), and Li Ka Shing Institute of Virology, University of Alberta, Edmonton, Alta.; Lunenfeld-Tanenbaum Research Institute at Mount Sinai Hospital (Colwill, Gingras), Sinai Health System, Toronto, Ont.; Canadian Blood Services (Drews); Department of Laboratory Medicine and Pathology (O'Brien, Pambrun, Drews), University of Alberta, Edmonton, Alta.; Department of Molecular Genetics (Gingras, Skead), University of Toronto; Centre for Global Health Research (Jha), Unity Health Toronto and University of Toronto, Toronto, Ont.; Public Health Laboratory (Kanji), Alberta Precision Laboratories, Foothills Medical Centre, and Section of Medical Microbiology (Kanji), Department of Pathology and Laboratory Medicine, and Division of Infectious Diseases, Department of Medicine, University of Calgary, Calgary, Alta.; Ontario Health Study (Kirsh, Skead), Ontario Institute for Cancer Research; Department of Molecular Genetics (Kirsh, Skead), and Dalla Lana School of Public Health (Kirsh), University of Toronto, Toronto, Ont.; Roy Romanow Provincial Lab (Lang), Saskatchewan Health Authority; College of Medicine (Lang), University of Saskatchewan, Saskatoon, Sask.; Department of Biochemistry, Microbiology and Immunology (Langlois), and Centre for Infection, Immunity and Inflammation (Langlois), University of Ottawa, Ottawa, Ont.; Division of Infectious Diseases-Regina (Lee), University of Saskatchewan; Saskatchewan Health Authority (Lee), Saskatoon, Sask.; Medical Affair and Innovation (Lewin), Héma-Québec, Montréal, Que.; Departments of Epidemiology and Community Medicine (O'Brien), and Pathology and Laboratory Medicine (Pambrun), Faculty of Medicine, University of Ottawa, Ottawa, Ont.; Department of Mathematics & Statistics (Stephens), McGill University, Montréal, Que.; Department of Medical Microbiology (Stein, Van Caeseele), University of Manitoba, and Cadham Provincial Laboratory, Winnipeg, Man.; School of Population and Global Health (Evans), McGill University; The Research Institute of the McGill University Health Centre (Mazer, Buckeridge), Montréal, Que
| | - Chantale Pambrun
- COVID-19 Immunity Task Force (Murphy, Swail, Jain, Evans, Oxlade, Mazer, Buckeridge), School of Population and Global Health, McGill University, Montréal, Que.; Department of Community Health and Epidemiology (Anderson, Behl), University of Saskatchewan; Saskatchewan Health Authority (Anderson), Population Health, Saskatoon, Sask.; Department of Molecular Genetics (Awadalla), University of Toronto; Department of Computational Biology (Awadalla), Ontario Institute for Cancer Research; Centre for Global Health Research (Brown), Unity Health Toronto and University of Toronto, Toronto, Ont.; Public Health Laboratory (Charlton, Hinshaw, Tipples), Alberta Precision Laboratories, University of Alberta Hospital; Department of Laboratory Medicine and Pathology (Charlton, Tipples), and Li Ka Shing Institute of Virology, University of Alberta, Edmonton, Alta.; Lunenfeld-Tanenbaum Research Institute at Mount Sinai Hospital (Colwill, Gingras), Sinai Health System, Toronto, Ont.; Canadian Blood Services (Drews); Department of Laboratory Medicine and Pathology (O'Brien, Pambrun, Drews), University of Alberta, Edmonton, Alta.; Department of Molecular Genetics (Gingras, Skead), University of Toronto; Centre for Global Health Research (Jha), Unity Health Toronto and University of Toronto, Toronto, Ont.; Public Health Laboratory (Kanji), Alberta Precision Laboratories, Foothills Medical Centre, and Section of Medical Microbiology (Kanji), Department of Pathology and Laboratory Medicine, and Division of Infectious Diseases, Department of Medicine, University of Calgary, Calgary, Alta.; Ontario Health Study (Kirsh, Skead), Ontario Institute for Cancer Research; Department of Molecular Genetics (Kirsh, Skead), and Dalla Lana School of Public Health (Kirsh), University of Toronto, Toronto, Ont.; Roy Romanow Provincial Lab (Lang), Saskatchewan Health Authority; College of Medicine (Lang), University of Saskatchewan, Saskatoon, Sask.; Department of Biochemistry, Microbiology and Immunology (Langlois), and Centre for Infection, Immunity and Inflammation (Langlois), University of Ottawa, Ottawa, Ont.; Division of Infectious Diseases-Regina (Lee), University of Saskatchewan; Saskatchewan Health Authority (Lee), Saskatoon, Sask.; Medical Affair and Innovation (Lewin), Héma-Québec, Montréal, Que.; Departments of Epidemiology and Community Medicine (O'Brien), and Pathology and Laboratory Medicine (Pambrun), Faculty of Medicine, University of Ottawa, Ottawa, Ont.; Department of Mathematics & Statistics (Stephens), McGill University, Montréal, Que.; Department of Medical Microbiology (Stein, Van Caeseele), University of Manitoba, and Cadham Provincial Laboratory, Winnipeg, Man.; School of Population and Global Health (Evans), McGill University; The Research Institute of the McGill University Health Centre (Mazer, Buckeridge), Montréal, Que
| | - Kimberly Skead
- COVID-19 Immunity Task Force (Murphy, Swail, Jain, Evans, Oxlade, Mazer, Buckeridge), School of Population and Global Health, McGill University, Montréal, Que.; Department of Community Health and Epidemiology (Anderson, Behl), University of Saskatchewan; Saskatchewan Health Authority (Anderson), Population Health, Saskatoon, Sask.; Department of Molecular Genetics (Awadalla), University of Toronto; Department of Computational Biology (Awadalla), Ontario Institute for Cancer Research; Centre for Global Health Research (Brown), Unity Health Toronto and University of Toronto, Toronto, Ont.; Public Health Laboratory (Charlton, Hinshaw, Tipples), Alberta Precision Laboratories, University of Alberta Hospital; Department of Laboratory Medicine and Pathology (Charlton, Tipples), and Li Ka Shing Institute of Virology, University of Alberta, Edmonton, Alta.; Lunenfeld-Tanenbaum Research Institute at Mount Sinai Hospital (Colwill, Gingras), Sinai Health System, Toronto, Ont.; Canadian Blood Services (Drews); Department of Laboratory Medicine and Pathology (O'Brien, Pambrun, Drews), University of Alberta, Edmonton, Alta.; Department of Molecular Genetics (Gingras, Skead), University of Toronto; Centre for Global Health Research (Jha), Unity Health Toronto and University of Toronto, Toronto, Ont.; Public Health Laboratory (Kanji), Alberta Precision Laboratories, Foothills Medical Centre, and Section of Medical Microbiology (Kanji), Department of Pathology and Laboratory Medicine, and Division of Infectious Diseases, Department of Medicine, University of Calgary, Calgary, Alta.; Ontario Health Study (Kirsh, Skead), Ontario Institute for Cancer Research; Department of Molecular Genetics (Kirsh, Skead), and Dalla Lana School of Public Health (Kirsh), University of Toronto, Toronto, Ont.; Roy Romanow Provincial Lab (Lang), Saskatchewan Health Authority; College of Medicine (Lang), University of Saskatchewan, Saskatoon, Sask.; Department of Biochemistry, Microbiology and Immunology (Langlois), and Centre for Infection, Immunity and Inflammation (Langlois), University of Ottawa, Ottawa, Ont.; Division of Infectious Diseases-Regina (Lee), University of Saskatchewan; Saskatchewan Health Authority (Lee), Saskatoon, Sask.; Medical Affair and Innovation (Lewin), Héma-Québec, Montréal, Que.; Departments of Epidemiology and Community Medicine (O'Brien), and Pathology and Laboratory Medicine (Pambrun), Faculty of Medicine, University of Ottawa, Ottawa, Ont.; Department of Mathematics & Statistics (Stephens), McGill University, Montréal, Que.; Department of Medical Microbiology (Stein, Van Caeseele), University of Manitoba, and Cadham Provincial Laboratory, Winnipeg, Man.; School of Population and Global Health (Evans), McGill University; The Research Institute of the McGill University Health Centre (Mazer, Buckeridge), Montréal, Que
| | - David A Stephens
- COVID-19 Immunity Task Force (Murphy, Swail, Jain, Evans, Oxlade, Mazer, Buckeridge), School of Population and Global Health, McGill University, Montréal, Que.; Department of Community Health and Epidemiology (Anderson, Behl), University of Saskatchewan; Saskatchewan Health Authority (Anderson), Population Health, Saskatoon, Sask.; Department of Molecular Genetics (Awadalla), University of Toronto; Department of Computational Biology (Awadalla), Ontario Institute for Cancer Research; Centre for Global Health Research (Brown), Unity Health Toronto and University of Toronto, Toronto, Ont.; Public Health Laboratory (Charlton, Hinshaw, Tipples), Alberta Precision Laboratories, University of Alberta Hospital; Department of Laboratory Medicine and Pathology (Charlton, Tipples), and Li Ka Shing Institute of Virology, University of Alberta, Edmonton, Alta.; Lunenfeld-Tanenbaum Research Institute at Mount Sinai Hospital (Colwill, Gingras), Sinai Health System, Toronto, Ont.; Canadian Blood Services (Drews); Department of Laboratory Medicine and Pathology (O'Brien, Pambrun, Drews), University of Alberta, Edmonton, Alta.; Department of Molecular Genetics (Gingras, Skead), University of Toronto; Centre for Global Health Research (Jha), Unity Health Toronto and University of Toronto, Toronto, Ont.; Public Health Laboratory (Kanji), Alberta Precision Laboratories, Foothills Medical Centre, and Section of Medical Microbiology (Kanji), Department of Pathology and Laboratory Medicine, and Division of Infectious Diseases, Department of Medicine, University of Calgary, Calgary, Alta.; Ontario Health Study (Kirsh, Skead), Ontario Institute for Cancer Research; Department of Molecular Genetics (Kirsh, Skead), and Dalla Lana School of Public Health (Kirsh), University of Toronto, Toronto, Ont.; Roy Romanow Provincial Lab (Lang), Saskatchewan Health Authority; College of Medicine (Lang), University of Saskatchewan, Saskatoon, Sask.; Department of Biochemistry, Microbiology and Immunology (Langlois), and Centre for Infection, Immunity and Inflammation (Langlois), University of Ottawa, Ottawa, Ont.; Division of Infectious Diseases-Regina (Lee), University of Saskatchewan; Saskatchewan Health Authority (Lee), Saskatoon, Sask.; Medical Affair and Innovation (Lewin), Héma-Québec, Montréal, Que.; Departments of Epidemiology and Community Medicine (O'Brien), and Pathology and Laboratory Medicine (Pambrun), Faculty of Medicine, University of Ottawa, Ottawa, Ont.; Department of Mathematics & Statistics (Stephens), McGill University, Montréal, Que.; Department of Medical Microbiology (Stein, Van Caeseele), University of Manitoba, and Cadham Provincial Laboratory, Winnipeg, Man.; School of Population and Global Health (Evans), McGill University; The Research Institute of the McGill University Health Centre (Mazer, Buckeridge), Montréal, Que.
| | - Derek R Stein
- COVID-19 Immunity Task Force (Murphy, Swail, Jain, Evans, Oxlade, Mazer, Buckeridge), School of Population and Global Health, McGill University, Montréal, Que.; Department of Community Health and Epidemiology (Anderson, Behl), University of Saskatchewan; Saskatchewan Health Authority (Anderson), Population Health, Saskatoon, Sask.; Department of Molecular Genetics (Awadalla), University of Toronto; Department of Computational Biology (Awadalla), Ontario Institute for Cancer Research; Centre for Global Health Research (Brown), Unity Health Toronto and University of Toronto, Toronto, Ont.; Public Health Laboratory (Charlton, Hinshaw, Tipples), Alberta Precision Laboratories, University of Alberta Hospital; Department of Laboratory Medicine and Pathology (Charlton, Tipples), and Li Ka Shing Institute of Virology, University of Alberta, Edmonton, Alta.; Lunenfeld-Tanenbaum Research Institute at Mount Sinai Hospital (Colwill, Gingras), Sinai Health System, Toronto, Ont.; Canadian Blood Services (Drews); Department of Laboratory Medicine and Pathology (O'Brien, Pambrun, Drews), University of Alberta, Edmonton, Alta.; Department of Molecular Genetics (Gingras, Skead), University of Toronto; Centre for Global Health Research (Jha), Unity Health Toronto and University of Toronto, Toronto, Ont.; Public Health Laboratory (Kanji), Alberta Precision Laboratories, Foothills Medical Centre, and Section of Medical Microbiology (Kanji), Department of Pathology and Laboratory Medicine, and Division of Infectious Diseases, Department of Medicine, University of Calgary, Calgary, Alta.; Ontario Health Study (Kirsh, Skead), Ontario Institute for Cancer Research; Department of Molecular Genetics (Kirsh, Skead), and Dalla Lana School of Public Health (Kirsh), University of Toronto, Toronto, Ont.; Roy Romanow Provincial Lab (Lang), Saskatchewan Health Authority; College of Medicine (Lang), University of Saskatchewan, Saskatoon, Sask.; Department of Biochemistry, Microbiology and Immunology (Langlois), and Centre for Infection, Immunity and Inflammation (Langlois), University of Ottawa, Ottawa, Ont.; Division of Infectious Diseases-Regina (Lee), University of Saskatchewan; Saskatchewan Health Authority (Lee), Saskatoon, Sask.; Medical Affair and Innovation (Lewin), Héma-Québec, Montréal, Que.; Departments of Epidemiology and Community Medicine (O'Brien), and Pathology and Laboratory Medicine (Pambrun), Faculty of Medicine, University of Ottawa, Ottawa, Ont.; Department of Mathematics & Statistics (Stephens), McGill University, Montréal, Que.; Department of Medical Microbiology (Stein, Van Caeseele), University of Manitoba, and Cadham Provincial Laboratory, Winnipeg, Man.; School of Population and Global Health (Evans), McGill University; The Research Institute of the McGill University Health Centre (Mazer, Buckeridge), Montréal, Que
| | - Graham Tipples
- COVID-19 Immunity Task Force (Murphy, Swail, Jain, Evans, Oxlade, Mazer, Buckeridge), School of Population and Global Health, McGill University, Montréal, Que.; Department of Community Health and Epidemiology (Anderson, Behl), University of Saskatchewan; Saskatchewan Health Authority (Anderson), Population Health, Saskatoon, Sask.; Department of Molecular Genetics (Awadalla), University of Toronto; Department of Computational Biology (Awadalla), Ontario Institute for Cancer Research; Centre for Global Health Research (Brown), Unity Health Toronto and University of Toronto, Toronto, Ont.; Public Health Laboratory (Charlton, Hinshaw, Tipples), Alberta Precision Laboratories, University of Alberta Hospital; Department of Laboratory Medicine and Pathology (Charlton, Tipples), and Li Ka Shing Institute of Virology, University of Alberta, Edmonton, Alta.; Lunenfeld-Tanenbaum Research Institute at Mount Sinai Hospital (Colwill, Gingras), Sinai Health System, Toronto, Ont.; Canadian Blood Services (Drews); Department of Laboratory Medicine and Pathology (O'Brien, Pambrun, Drews), University of Alberta, Edmonton, Alta.; Department of Molecular Genetics (Gingras, Skead), University of Toronto; Centre for Global Health Research (Jha), Unity Health Toronto and University of Toronto, Toronto, Ont.; Public Health Laboratory (Kanji), Alberta Precision Laboratories, Foothills Medical Centre, and Section of Medical Microbiology (Kanji), Department of Pathology and Laboratory Medicine, and Division of Infectious Diseases, Department of Medicine, University of Calgary, Calgary, Alta.; Ontario Health Study (Kirsh, Skead), Ontario Institute for Cancer Research; Department of Molecular Genetics (Kirsh, Skead), and Dalla Lana School of Public Health (Kirsh), University of Toronto, Toronto, Ont.; Roy Romanow Provincial Lab (Lang), Saskatchewan Health Authority; College of Medicine (Lang), University of Saskatchewan, Saskatoon, Sask.; Department of Biochemistry, Microbiology and Immunology (Langlois), and Centre for Infection, Immunity and Inflammation (Langlois), University of Ottawa, Ottawa, Ont.; Division of Infectious Diseases-Regina (Lee), University of Saskatchewan; Saskatchewan Health Authority (Lee), Saskatoon, Sask.; Medical Affair and Innovation (Lewin), Héma-Québec, Montréal, Que.; Departments of Epidemiology and Community Medicine (O'Brien), and Pathology and Laboratory Medicine (Pambrun), Faculty of Medicine, University of Ottawa, Ottawa, Ont.; Department of Mathematics & Statistics (Stephens), McGill University, Montréal, Que.; Department of Medical Microbiology (Stein, Van Caeseele), University of Manitoba, and Cadham Provincial Laboratory, Winnipeg, Man.; School of Population and Global Health (Evans), McGill University; The Research Institute of the McGill University Health Centre (Mazer, Buckeridge), Montréal, Que
| | - Paul G Van Caeseele
- COVID-19 Immunity Task Force (Murphy, Swail, Jain, Evans, Oxlade, Mazer, Buckeridge), School of Population and Global Health, McGill University, Montréal, Que.; Department of Community Health and Epidemiology (Anderson, Behl), University of Saskatchewan; Saskatchewan Health Authority (Anderson), Population Health, Saskatoon, Sask.; Department of Molecular Genetics (Awadalla), University of Toronto; Department of Computational Biology (Awadalla), Ontario Institute for Cancer Research; Centre for Global Health Research (Brown), Unity Health Toronto and University of Toronto, Toronto, Ont.; Public Health Laboratory (Charlton, Hinshaw, Tipples), Alberta Precision Laboratories, University of Alberta Hospital; Department of Laboratory Medicine and Pathology (Charlton, Tipples), and Li Ka Shing Institute of Virology, University of Alberta, Edmonton, Alta.; Lunenfeld-Tanenbaum Research Institute at Mount Sinai Hospital (Colwill, Gingras), Sinai Health System, Toronto, Ont.; Canadian Blood Services (Drews); Department of Laboratory Medicine and Pathology (O'Brien, Pambrun, Drews), University of Alberta, Edmonton, Alta.; Department of Molecular Genetics (Gingras, Skead), University of Toronto; Centre for Global Health Research (Jha), Unity Health Toronto and University of Toronto, Toronto, Ont.; Public Health Laboratory (Kanji), Alberta Precision Laboratories, Foothills Medical Centre, and Section of Medical Microbiology (Kanji), Department of Pathology and Laboratory Medicine, and Division of Infectious Diseases, Department of Medicine, University of Calgary, Calgary, Alta.; Ontario Health Study (Kirsh, Skead), Ontario Institute for Cancer Research; Department of Molecular Genetics (Kirsh, Skead), and Dalla Lana School of Public Health (Kirsh), University of Toronto, Toronto, Ont.; Roy Romanow Provincial Lab (Lang), Saskatchewan Health Authority; College of Medicine (Lang), University of Saskatchewan, Saskatoon, Sask.; Department of Biochemistry, Microbiology and Immunology (Langlois), and Centre for Infection, Immunity and Inflammation (Langlois), University of Ottawa, Ottawa, Ont.; Division of Infectious Diseases-Regina (Lee), University of Saskatchewan; Saskatchewan Health Authority (Lee), Saskatoon, Sask.; Medical Affair and Innovation (Lewin), Héma-Québec, Montréal, Que.; Departments of Epidemiology and Community Medicine (O'Brien), and Pathology and Laboratory Medicine (Pambrun), Faculty of Medicine, University of Ottawa, Ottawa, Ont.; Department of Mathematics & Statistics (Stephens), McGill University, Montréal, Que.; Department of Medical Microbiology (Stein, Van Caeseele), University of Manitoba, and Cadham Provincial Laboratory, Winnipeg, Man.; School of Population and Global Health (Evans), McGill University; The Research Institute of the McGill University Health Centre (Mazer, Buckeridge), Montréal, Que
| | - Timothy G Evans
- COVID-19 Immunity Task Force (Murphy, Swail, Jain, Evans, Oxlade, Mazer, Buckeridge), School of Population and Global Health, McGill University, Montréal, Que.; Department of Community Health and Epidemiology (Anderson, Behl), University of Saskatchewan; Saskatchewan Health Authority (Anderson), Population Health, Saskatoon, Sask.; Department of Molecular Genetics (Awadalla), University of Toronto; Department of Computational Biology (Awadalla), Ontario Institute for Cancer Research; Centre for Global Health Research (Brown), Unity Health Toronto and University of Toronto, Toronto, Ont.; Public Health Laboratory (Charlton, Hinshaw, Tipples), Alberta Precision Laboratories, University of Alberta Hospital; Department of Laboratory Medicine and Pathology (Charlton, Tipples), and Li Ka Shing Institute of Virology, University of Alberta, Edmonton, Alta.; Lunenfeld-Tanenbaum Research Institute at Mount Sinai Hospital (Colwill, Gingras), Sinai Health System, Toronto, Ont.; Canadian Blood Services (Drews); Department of Laboratory Medicine and Pathology (O'Brien, Pambrun, Drews), University of Alberta, Edmonton, Alta.; Department of Molecular Genetics (Gingras, Skead), University of Toronto; Centre for Global Health Research (Jha), Unity Health Toronto and University of Toronto, Toronto, Ont.; Public Health Laboratory (Kanji), Alberta Precision Laboratories, Foothills Medical Centre, and Section of Medical Microbiology (Kanji), Department of Pathology and Laboratory Medicine, and Division of Infectious Diseases, Department of Medicine, University of Calgary, Calgary, Alta.; Ontario Health Study (Kirsh, Skead), Ontario Institute for Cancer Research; Department of Molecular Genetics (Kirsh, Skead), and Dalla Lana School of Public Health (Kirsh), University of Toronto, Toronto, Ont.; Roy Romanow Provincial Lab (Lang), Saskatchewan Health Authority; College of Medicine (Lang), University of Saskatchewan, Saskatoon, Sask.; Department of Biochemistry, Microbiology and Immunology (Langlois), and Centre for Infection, Immunity and Inflammation (Langlois), University of Ottawa, Ottawa, Ont.; Division of Infectious Diseases-Regina (Lee), University of Saskatchewan; Saskatchewan Health Authority (Lee), Saskatoon, Sask.; Medical Affair and Innovation (Lewin), Héma-Québec, Montréal, Que.; Departments of Epidemiology and Community Medicine (O'Brien), and Pathology and Laboratory Medicine (Pambrun), Faculty of Medicine, University of Ottawa, Ottawa, Ont.; Department of Mathematics & Statistics (Stephens), McGill University, Montréal, Que.; Department of Medical Microbiology (Stein, Van Caeseele), University of Manitoba, and Cadham Provincial Laboratory, Winnipeg, Man.; School of Population and Global Health (Evans), McGill University; The Research Institute of the McGill University Health Centre (Mazer, Buckeridge), Montréal, Que
| | - Olivia Oxlade
- COVID-19 Immunity Task Force (Murphy, Swail, Jain, Evans, Oxlade, Mazer, Buckeridge), School of Population and Global Health, McGill University, Montréal, Que.; Department of Community Health and Epidemiology (Anderson, Behl), University of Saskatchewan; Saskatchewan Health Authority (Anderson), Population Health, Saskatoon, Sask.; Department of Molecular Genetics (Awadalla), University of Toronto; Department of Computational Biology (Awadalla), Ontario Institute for Cancer Research; Centre for Global Health Research (Brown), Unity Health Toronto and University of Toronto, Toronto, Ont.; Public Health Laboratory (Charlton, Hinshaw, Tipples), Alberta Precision Laboratories, University of Alberta Hospital; Department of Laboratory Medicine and Pathology (Charlton, Tipples), and Li Ka Shing Institute of Virology, University of Alberta, Edmonton, Alta.; Lunenfeld-Tanenbaum Research Institute at Mount Sinai Hospital (Colwill, Gingras), Sinai Health System, Toronto, Ont.; Canadian Blood Services (Drews); Department of Laboratory Medicine and Pathology (O'Brien, Pambrun, Drews), University of Alberta, Edmonton, Alta.; Department of Molecular Genetics (Gingras, Skead), University of Toronto; Centre for Global Health Research (Jha), Unity Health Toronto and University of Toronto, Toronto, Ont.; Public Health Laboratory (Kanji), Alberta Precision Laboratories, Foothills Medical Centre, and Section of Medical Microbiology (Kanji), Department of Pathology and Laboratory Medicine, and Division of Infectious Diseases, Department of Medicine, University of Calgary, Calgary, Alta.; Ontario Health Study (Kirsh, Skead), Ontario Institute for Cancer Research; Department of Molecular Genetics (Kirsh, Skead), and Dalla Lana School of Public Health (Kirsh), University of Toronto, Toronto, Ont.; Roy Romanow Provincial Lab (Lang), Saskatchewan Health Authority; College of Medicine (Lang), University of Saskatchewan, Saskatoon, Sask.; Department of Biochemistry, Microbiology and Immunology (Langlois), and Centre for Infection, Immunity and Inflammation (Langlois), University of Ottawa, Ottawa, Ont.; Division of Infectious Diseases-Regina (Lee), University of Saskatchewan; Saskatchewan Health Authority (Lee), Saskatoon, Sask.; Medical Affair and Innovation (Lewin), Héma-Québec, Montréal, Que.; Departments of Epidemiology and Community Medicine (O'Brien), and Pathology and Laboratory Medicine (Pambrun), Faculty of Medicine, University of Ottawa, Ottawa, Ont.; Department of Mathematics & Statistics (Stephens), McGill University, Montréal, Que.; Department of Medical Microbiology (Stein, Van Caeseele), University of Manitoba, and Cadham Provincial Laboratory, Winnipeg, Man.; School of Population and Global Health (Evans), McGill University; The Research Institute of the McGill University Health Centre (Mazer, Buckeridge), Montréal, Que
| | - Bruce D Mazer
- COVID-19 Immunity Task Force (Murphy, Swail, Jain, Evans, Oxlade, Mazer, Buckeridge), School of Population and Global Health, McGill University, Montréal, Que.; Department of Community Health and Epidemiology (Anderson, Behl), University of Saskatchewan; Saskatchewan Health Authority (Anderson), Population Health, Saskatoon, Sask.; Department of Molecular Genetics (Awadalla), University of Toronto; Department of Computational Biology (Awadalla), Ontario Institute for Cancer Research; Centre for Global Health Research (Brown), Unity Health Toronto and University of Toronto, Toronto, Ont.; Public Health Laboratory (Charlton, Hinshaw, Tipples), Alberta Precision Laboratories, University of Alberta Hospital; Department of Laboratory Medicine and Pathology (Charlton, Tipples), and Li Ka Shing Institute of Virology, University of Alberta, Edmonton, Alta.; Lunenfeld-Tanenbaum Research Institute at Mount Sinai Hospital (Colwill, Gingras), Sinai Health System, Toronto, Ont.; Canadian Blood Services (Drews); Department of Laboratory Medicine and Pathology (O'Brien, Pambrun, Drews), University of Alberta, Edmonton, Alta.; Department of Molecular Genetics (Gingras, Skead), University of Toronto; Centre for Global Health Research (Jha), Unity Health Toronto and University of Toronto, Toronto, Ont.; Public Health Laboratory (Kanji), Alberta Precision Laboratories, Foothills Medical Centre, and Section of Medical Microbiology (Kanji), Department of Pathology and Laboratory Medicine, and Division of Infectious Diseases, Department of Medicine, University of Calgary, Calgary, Alta.; Ontario Health Study (Kirsh, Skead), Ontario Institute for Cancer Research; Department of Molecular Genetics (Kirsh, Skead), and Dalla Lana School of Public Health (Kirsh), University of Toronto, Toronto, Ont.; Roy Romanow Provincial Lab (Lang), Saskatchewan Health Authority; College of Medicine (Lang), University of Saskatchewan, Saskatoon, Sask.; Department of Biochemistry, Microbiology and Immunology (Langlois), and Centre for Infection, Immunity and Inflammation (Langlois), University of Ottawa, Ottawa, Ont.; Division of Infectious Diseases-Regina (Lee), University of Saskatchewan; Saskatchewan Health Authority (Lee), Saskatoon, Sask.; Medical Affair and Innovation (Lewin), Héma-Québec, Montréal, Que.; Departments of Epidemiology and Community Medicine (O'Brien), and Pathology and Laboratory Medicine (Pambrun), Faculty of Medicine, University of Ottawa, Ottawa, Ont.; Department of Mathematics & Statistics (Stephens), McGill University, Montréal, Que.; Department of Medical Microbiology (Stein, Van Caeseele), University of Manitoba, and Cadham Provincial Laboratory, Winnipeg, Man.; School of Population and Global Health (Evans), McGill University; The Research Institute of the McGill University Health Centre (Mazer, Buckeridge), Montréal, Que
| | - David L Buckeridge
- COVID-19 Immunity Task Force (Murphy, Swail, Jain, Evans, Oxlade, Mazer, Buckeridge), School of Population and Global Health, McGill University, Montréal, Que.; Department of Community Health and Epidemiology (Anderson, Behl), University of Saskatchewan; Saskatchewan Health Authority (Anderson), Population Health, Saskatoon, Sask.; Department of Molecular Genetics (Awadalla), University of Toronto; Department of Computational Biology (Awadalla), Ontario Institute for Cancer Research; Centre for Global Health Research (Brown), Unity Health Toronto and University of Toronto, Toronto, Ont.; Public Health Laboratory (Charlton, Hinshaw, Tipples), Alberta Precision Laboratories, University of Alberta Hospital; Department of Laboratory Medicine and Pathology (Charlton, Tipples), and Li Ka Shing Institute of Virology, University of Alberta, Edmonton, Alta.; Lunenfeld-Tanenbaum Research Institute at Mount Sinai Hospital (Colwill, Gingras), Sinai Health System, Toronto, Ont.; Canadian Blood Services (Drews); Department of Laboratory Medicine and Pathology (O'Brien, Pambrun, Drews), University of Alberta, Edmonton, Alta.; Department of Molecular Genetics (Gingras, Skead), University of Toronto; Centre for Global Health Research (Jha), Unity Health Toronto and University of Toronto, Toronto, Ont.; Public Health Laboratory (Kanji), Alberta Precision Laboratories, Foothills Medical Centre, and Section of Medical Microbiology (Kanji), Department of Pathology and Laboratory Medicine, and Division of Infectious Diseases, Department of Medicine, University of Calgary, Calgary, Alta.; Ontario Health Study (Kirsh, Skead), Ontario Institute for Cancer Research; Department of Molecular Genetics (Kirsh, Skead), and Dalla Lana School of Public Health (Kirsh), University of Toronto, Toronto, Ont.; Roy Romanow Provincial Lab (Lang), Saskatchewan Health Authority; College of Medicine (Lang), University of Saskatchewan, Saskatoon, Sask.; Department of Biochemistry, Microbiology and Immunology (Langlois), and Centre for Infection, Immunity and Inflammation (Langlois), University of Ottawa, Ottawa, Ont.; Division of Infectious Diseases-Regina (Lee), University of Saskatchewan; Saskatchewan Health Authority (Lee), Saskatoon, Sask.; Medical Affair and Innovation (Lewin), Héma-Québec, Montréal, Que.; Departments of Epidemiology and Community Medicine (O'Brien), and Pathology and Laboratory Medicine (Pambrun), Faculty of Medicine, University of Ottawa, Ottawa, Ont.; Department of Mathematics & Statistics (Stephens), McGill University, Montréal, Que.; Department of Medical Microbiology (Stein, Van Caeseele), University of Manitoba, and Cadham Provincial Laboratory, Winnipeg, Man.; School of Population and Global Health (Evans), McGill University; The Research Institute of the McGill University Health Centre (Mazer, Buckeridge), Montréal, Que
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4
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McVittie JH, Best AF, Wolfson DB, Stephens DA, Wolfson J, Buckeridge DL, Gadalla SM. Survival Modelling For Data From Combined Cohorts: Opening the Door to Meta Survival Analyses and Survival Analysis using Electronic Health Records. Int Stat Rev 2023; 91:72-87. [PMID: 37193196 PMCID: PMC10181797 DOI: 10.1111/insr.12510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Accepted: 05/26/2022] [Indexed: 11/27/2022]
Abstract
Non-parametric estimation of the survival function using observed failure time data depends on the underlying data generating mechanism, including 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 analyze 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.
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Affiliation(s)
| | - Ana F Best
- Biostatistics Branch, Biometrics Research Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health
| | | | | | - Julian Wolfson
- School of Public Health, Division of Biostatistics, University of Minnesota
| | - David L Buckeridge
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University
| | - Shahinaz M Gadalla
- Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health
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Gupta P, Maharaj T, Weiss M, Rahaman N, Alsdurf H, Minoyan N, Harnois-Leblanc S, Merckx J, Williams A, Schmidt V, St-Charles PL, Patel A, Zhang Y, Buckeridge DL, Pal C, Schölkopf B, Bengio Y. Proactive Contact Tracing. PLOS Digit Health 2023; 2:e0000199. [PMID: 36913342 PMCID: PMC10010527 DOI: 10.1371/journal.pdig.0000199] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 01/25/2023] [Indexed: 03/14/2023]
Abstract
The COVID-19 pandemic has spurred an unprecedented demand for interventions that can reduce disease spread without excessively restricting daily activity, given negative impacts on mental health and economic outcomes. Digital contact tracing (DCT) apps have emerged as a component of the epidemic management toolkit. Existing DCT apps typically recommend quarantine to all digitally-recorded contacts of test-confirmed cases. Over-reliance on testing may, however, impede the effectiveness of such apps, since by the time cases are confirmed through testing, onward transmissions are likely to have occurred. Furthermore, most cases are infectious over a short period; only a subset of their contacts are likely to become infected. These apps do not fully utilize data sources to base their predictions of transmission risk during an encounter, leading to recommendations of quarantine to many uninfected people and associated slowdowns in economic activity. This phenomenon, commonly termed as "pingdemic," may additionally contribute to reduced compliance to public health measures. In this work, we propose a novel DCT framework, Proactive Contact Tracing (PCT), which uses multiple sources of information (e.g. self-reported symptoms, received messages from contacts) to estimate app users' infectiousness histories and provide behavioral recommendations. PCT methods are by design proactive, predicting spread before it occurs. We present an interpretable instance of this framework, the Rule-based PCT algorithm, designed via a multi-disciplinary collaboration among epidemiologists, computer scientists, and behavior experts. Finally, we develop an agent-based model that allows us to compare different DCT methods and evaluate their performance in negotiating the trade-off between epidemic control and restricting population mobility. Performing extensive sensitivity analysis across user behavior, public health policy, and virological parameters, we compare Rule-based PCT to i) binary contact tracing (BCT), which exclusively relies on test results and recommends a fixed-duration quarantine, and ii) household quarantine (HQ). Our results suggest that both BCT and Rule-based PCT improve upon HQ, however, Rule-based PCT is more efficient at controlling spread of disease than BCT across a range of scenarios. In terms of cost-effectiveness, we show that Rule-based PCT pareto-dominates BCT, as demonstrated by a decrease in Disability Adjusted Life Years, as well as Temporary Productivity Loss. Overall, we find that Rule-based PCT outperforms existing approaches across a varying range of parameters. By leveraging anonymized infectiousness estimates received from digitally-recorded contacts, PCT is able to notify potentially infected users earlier than BCT methods and prevent onward transmissions. Our results suggest that PCT-based applications could be a useful tool in managing future epidemics.
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Affiliation(s)
- Prateek Gupta
- Montréal Institute of Learning Algorithms (Mila), Montréal, Québec, Canada
- The Alan Turing Institute, London, United Kingdom
- Department of Engineering Science, University of Oxford, Oxford, United Kingdom
- * E-mail:
| | - Tegan Maharaj
- Montréal Institute of Learning Algorithms (Mila), Montréal, Québec, Canada
- Department of Computer Science and Operations Research, Université de Montréal, Montréal, Québec, Canada
| | - Martin Weiss
- Montréal Institute of Learning Algorithms (Mila), Montréal, Québec, Canada
- Department of Computer Science and Operations Research, Université de Montréal, Montréal, Québec, Canada
| | - Nasim Rahaman
- Montréal Institute of Learning Algorithms (Mila), Montréal, Québec, Canada
- Max Planck Institute for Intelligent Systems, Tübingen, Germany
| | - Hannah Alsdurf
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, Ontario, Canada
| | - Nanor Minoyan
- Department of Social and Preventive Medicine, School of Public Health, Université de Montréal, Canada
| | - Soren Harnois-Leblanc
- Department of Social and Preventive Medicine, School of Public Health, Université de Montréal, Canada
| | - Joanna Merckx
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Canada
| | - Andrew Williams
- Montréal Institute of Learning Algorithms (Mila), Montréal, Québec, Canada
- Department of Computer Science and Operations Research, Université de Montréal, Montréal, Québec, Canada
| | - Victor Schmidt
- Montréal Institute of Learning Algorithms (Mila), Montréal, Québec, Canada
- Department of Computer Science and Operations Research, Université de Montréal, Montréal, Québec, Canada
| | | | - Akshay Patel
- Cheriton School of Computer Science, University of Waterloo, Waterloo, Ontario, Canada
| | - Yang Zhang
- Montréal Institute of Learning Algorithms (Mila), Montréal, Québec, Canada
| | - David L. Buckeridge
- Montréal Institute of Learning Algorithms (Mila), Montréal, Québec, Canada
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Canada
| | - Christopher Pal
- Montréal Institute of Learning Algorithms (Mila), Montréal, Québec, Canada
- Department of Computer Science and Operations Research, Université de Montréal, Montréal, Québec, Canada
| | - Bernhard Schölkopf
- Max Planck Institute for Intelligent Systems, Tübingen, Germany
- Fellow of the Canadian Institute for Advanced Research (CIFAR), Canada
| | - Yoshua Bengio
- Montréal Institute of Learning Algorithms (Mila), Montréal, Québec, Canada
- Department of Computer Science and Operations Research, Université de Montréal, Montréal, Québec, Canada
- Fellow of the Canadian Institute for Advanced Research (CIFAR), Canada
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Bobrovitz N, Ware H, Ma X, Li Z, Hosseini R, Cao C, Selemon A, Whelan M, Premji Z, Issa H, Cheng B, Abu Raddad LJ, Buckeridge DL, Van Kerkhove MD, Piechotta V, Higdon MM, Wilder-Smith A, Bergeri I, Feikin DR, Arora RK, Patel MK, Subissi L. Protective effectiveness of previous SARS-CoV-2 infection and hybrid immunity against the omicron variant and severe disease: a systematic review and meta-regression. Lancet Infect Dis 2023; 23:556-567. [PMID: 36681084 PMCID: PMC10014083 DOI: 10.1016/s1473-3099(22)00801-5] [Citation(s) in RCA: 192] [Impact Index Per Article: 192.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 11/01/2022] [Accepted: 11/21/2022] [Indexed: 01/20/2023]
Abstract
BACKGROUND The global surge in the omicron (B.1.1.529) variant has resulted in many individuals with hybrid immunity (immunity developed through a combination of SARS-CoV-2 infection and vaccination). We aimed to systematically review the magnitude and duration of the protective effectiveness of previous SARS-CoV-2 infection and hybrid immunity against infection and severe disease caused by the omicron variant. METHODS For this systematic review and meta-regression, we searched for cohort, cross-sectional, and case-control studies in MEDLINE, Embase, Web of Science, ClinicalTrials.gov, the Cochrane Central Register of Controlled Trials, the WHO COVID-19 database, and Europe PubMed Central from Jan 1, 2020, to June 1, 2022, using keywords related to SARS-CoV-2, reinfection, protective effectiveness, previous infection, presence of antibodies, and hybrid immunity. The main outcomes were the protective effectiveness against reinfection and against hospital admission or severe disease of hybrid immunity, hybrid immunity relative to previous infection alone, hybrid immunity relative to previous vaccination alone, and hybrid immunity relative to hybrid immunity with fewer vaccine doses. Risk of bias was assessed with the Risk of Bias In Non-Randomized Studies of Interventions Tool. We used log-odds random-effects meta-regression to estimate the magnitude of protection at 1-month intervals. This study was registered with PROSPERO (CRD42022318605). FINDINGS 11 studies reporting the protective effectiveness of previous SARS-CoV-2 infection and 15 studies reporting the protective effectiveness of hybrid immunity were included. For previous infection, there were 97 estimates (27 with a moderate risk of bias and 70 with a serious risk of bias). The effectiveness of previous infection against hospital admission or severe disease was 74·6% (95% CI 63·1-83·5) at 12 months. The effectiveness of previous infection against reinfection waned to 24·7% (95% CI 16·4-35·5) at 12 months. For hybrid immunity, there were 153 estimates (78 with a moderate risk of bias and 75 with a serious risk of bias). The effectiveness of hybrid immunity against hospital admission or severe disease was 97·4% (95% CI 91·4-99·2) at 12 months with primary series vaccination and 95·3% (81·9-98·9) at 6 months with the first booster vaccination after the most recent infection or vaccination. Against reinfection, the effectiveness of hybrid immunity following primary series vaccination waned to 41·8% (95% CI 31·5-52·8) at 12 months, while the effectiveness of hybrid immunity following first booster vaccination waned to 46·5% (36·0-57·3) at 6 months. INTERPRETATION All estimates of protection waned within months against reinfection but remained high and sustained for hospital admission or severe disease. Individuals with hybrid immunity had the highest magnitude and durability of protection, and as a result might be able to extend the period before booster vaccinations are needed compared to individuals who have never been infected. FUNDING WHO COVID-19 Solidarity Response Fund and the Coalition for Epidemic Preparedness Innovations.
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Affiliation(s)
- Niklas Bobrovitz
- Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada; Department of Critical Care Medicine, University of Calgary, Calgary, AB, Canada; Centre for Health Informatics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
| | - Harriet Ware
- Centre for Health Informatics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Xiaomeng Ma
- Institute of Health Policy Management and Evaluation, University of Toronto, Toronto, ON, Canada
| | - Zihan Li
- Centre for Health Informatics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; Department of Bioengineering, University of California, Berkeley, CA, USA
| | - Reza Hosseini
- Centre for Health Informatics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada
| | - Christian Cao
- Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada; Centre for Health Informatics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Anabel Selemon
- Centre for Health Informatics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Mairead Whelan
- Centre for Health Informatics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Zahra Premji
- Libraries, University of Victoria, Victoria, BC, Canada
| | - Hanane Issa
- Institute of Health Informatics, University College London, London, UK
| | - Brianna Cheng
- Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Laith J Abu Raddad
- Infectious Disease Epidemiology Group, Weill Cornell Medicine-Qatar, Cornell University, Doha, Qatar
| | - David L Buckeridge
- Department of Epidemiology and Biostatistics, School of Population and Global Health, McGill University, Montreal, QC, Canada
| | | | - Vanessa Piechotta
- Department of Infectious Disease Epidemiology, Robert Koch Institute, Berlin, Germany
| | - Melissa M Higdon
- International Vaccine Access Center, Department of International Health, John Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Annelies Wilder-Smith
- Department of Immunizations, Vaccines and Biologicals, World Health Organization, Geneva, Switzerland; Heidelberg Institute of Global Health, University of Heidelberg, Germany
| | - Isabel Bergeri
- Health Emergencies Programme, World Health Organization, Geneva, Switzerland
| | - Daniel R Feikin
- Department of Immunizations, Vaccines and Biologicals, World Health Organization, Geneva, Switzerland
| | - Rahul K Arora
- Centre for Health Informatics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; Institute of Biomedical Engineering, University of Oxford, Oxford, UK
| | - Minal K Patel
- Department of Immunizations, Vaccines and Biologicals, World Health Organization, Geneva, Switzerland
| | - Lorenzo Subissi
- Health Emergencies Programme, World Health Organization, Geneva, Switzerland
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Osmanlliu E, Burstein B, Tamblyn R, Buckeridge DL. Assessing the potential for virtualizable care in the pediatric emergency department. J Telemed Telecare 2022:1357633X221133415. [PMID: 36408736 DOI: 10.1177/1357633x221133415] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/17/2024]
Abstract
INTRODUCTION There is increasing interest for patient-to-provider telemedicine in pediatric acute care. The suitability of telemedicine (virtualizability) for visits in this setting has not been formally assessed. We estimated the proportion of in-person pediatric emergency department (PED) visits that were potentially virtualizable, and identified factors associated with virtualizable care. METHODS This was a retrospective analysis of in-person visits at the PED of a Canadian tertiary pediatric hospital (02/2018-12/2019). Three definitions of virtualizable care were developed: (1) a definition based on "resource use" classifying visits as virtualizable if they resulted in a home discharge, no diagnostic testing, and no return visit within 72 h; (2) a "diagnostic definition" based on primary ED diagnosis; and (3) a stringent "combined definition" by which visits were classified as virtualizable if they met both the resource use and diagnostic definitions. Multivariable logistic regression was used to identify factors associated with telemedicine suitability. RESULTS There were 130,535 eligible visits from 80,727 individual patients during the study period. Using the most stringent combined definition of telemedicine suitability, 37.9% (95% confidence interval (CI) 37.6%-38.2%) of in-person visits were virtualizable. Overnight visits (adjusted odds ratio (aOR) 1.16-1.37), non-Canadian citizenship (aOR 1.10-1.18), ethnocultural vulnerability (aOR 1.14-1.22), and a consultation for head trauma (aOR 3.50-4.60) were associated with higher telemedicine suitability across definitions. DISCUSSION There is a high potential for patient-to-provider telemedicine in the PED setting. Local patient and visit-level characteristics must be considered in the design of safe and inclusive telemedicine models for pediatric acute care.
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Affiliation(s)
- Esli Osmanlliu
- Department of Epidemiology, Biostatistics and Occupational Health, Faculty of Medicine and Health Sciences, 10040McGill University, Montréal, Canada
- Pediatric Emergency Medicine Division, 12367McGill University Health Center, McGill University, Montréal, Canada
- 507266McGill Clinical & Health Informatics (MCHI) Research Group, McGill University, Montréal, Canada
| | - Brett Burstein
- Department of Epidemiology, Biostatistics and Occupational Health, Faculty of Medicine and Health Sciences, 10040McGill University, Montréal, Canada
- Pediatric Emergency Medicine Division, 12367McGill University Health Center, McGill University, Montréal, Canada
| | - Robyn Tamblyn
- Department of Epidemiology, Biostatistics and Occupational Health, Faculty of Medicine and Health Sciences, 10040McGill University, Montréal, Canada
- 507266McGill Clinical & Health Informatics (MCHI) Research Group, McGill University, Montréal, Canada
| | - David L Buckeridge
- Department of Epidemiology, Biostatistics and Occupational Health, Faculty of Medicine and Health Sciences, 10040McGill University, Montréal, Canada
- 507266McGill Clinical & Health Informatics (MCHI) Research Group, McGill University, Montréal, Canada
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Rajotte JF, Bergen R, Buckeridge DL, El Emam K, Ng R, Strome E. Synthetic data as an enabler for machine learning applications in medicine. iScience 2022; 25:105331. [DOI: 10.1016/j.isci.2022.105331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022] Open
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Shaban-Nejad A, Michalowski M, Bianco S, Brownstein JS, Buckeridge DL, Davis RL. Applied artificial intelligence in healthcare: Listening to the winds of change in a post-COVID-19 world. Exp Biol Med (Maywood) 2022; 247:1969-1971. [PMID: 36426683 PMCID: PMC9703021 DOI: 10.1177/15353702221140406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
Abstract
This editorial article aims to highlight advances in artificial intelligence (AI) technologies in five areas: Collaborative AI, Multimodal AI, Human-Centered AI, Equitable AI, and Ethical and Value-based AI in order to cope with future complex socioeconomic and public health issues.
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Affiliation(s)
- Arash Shaban-Nejad
- UTHSC-ORNL Center for Biomedical Informatics, Department of Pediatrics, College of Medicine, University of Tennessee Health Science Center, Memphis, TN 38103, USA,Arash Shaban-Nejad.
| | - Martin Michalowski
- School of Nursing, University of Minnesota Twin Cities, Minneapolis, MN 55455, USA
| | - Simone Bianco
- Altos Labs – Bay Area Institute of Science, Redwood City, CA 94065, USA
| | | | - David L Buckeridge
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, QC H3A 1G1, Canada
| | - Robert L Davis
- UTHSC-ORNL Center for Biomedical Informatics, Department of Pediatrics, College of Medicine, University of Tennessee Health Science Center, Memphis, TN 38103, USA
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Zou Y, Pesaranghader A, Song Z, Verma A, Buckeridge DL, Li Y. Modeling electronic health record data using an end-to-end knowledge-graph-informed topic model. Sci Rep 2022; 12:17868. [PMID: 36284225 PMCID: PMC9596500 DOI: 10.1038/s41598-022-22956-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 10/21/2022] [Indexed: 01/20/2023] Open
Abstract
The rapid growth of electronic health record (EHR) datasets opens up promising opportunities to understand human diseases in a systematic way. However, effective extraction of clinical knowledge from EHR data has been hindered by the sparse and noisy information. We present Graph ATtention-Embedded Topic Model (GAT-ETM), an end-to-end taxonomy-knowledge-graph-based multimodal embedded topic model. GAT-ETM distills latent disease topics from EHR data by learning the embedding from a constructed medical knowledge graph. We applied GAT-ETM to a large-scale EHR dataset consisting of over 1 million patients. We evaluated its performance based on topic quality, drug imputation, and disease diagnosis prediction. GAT-ETM demonstrated superior performance over the alternative methods on all tasks. Moreover, GAT-ETM learned clinically meaningful graph-informed embedding of the EHR codes and discovered interpretable and accurate patient representations for patient stratification and drug recommendations. GAT-ETM code is available at https://github.com/li-lab-mcgill/GAT-ETM .
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Affiliation(s)
- Yuesong Zou
- grid.14709.3b0000 0004 1936 8649School of Computer Science, McGill University, Montreal, Canada
| | - Ahmad Pesaranghader
- grid.14709.3b0000 0004 1936 8649School of Computer Science, McGill University, Montreal, Canada
| | - Ziyang Song
- grid.14709.3b0000 0004 1936 8649School of Computer Science, McGill University, Montreal, Canada
| | - Aman Verma
- grid.14709.3b0000 0004 1936 8649School of Population and Global Health, McGill University, Montreal, Canada
| | - David L. Buckeridge
- grid.14709.3b0000 0004 1936 8649School of Population and Global Health, McGill University, Montreal, Canada
| | - Yue Li
- grid.14709.3b0000 0004 1936 8649School of Computer Science, McGill University, Montreal, Canada
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Blanchard AC, Desforges M, Labbé AC, Nguyen CT, Petit Y, Besner D, Zinszer K, Séguin O, Laghdir Z, Adams K, Benoit MÈ, Leduc G, Longtin J, Ragoussis J, Buckeridge DL, Quach C. Evaluation of Real-life Use of Point-of-care Rapid Antigen Testing for SARS-CoV-2 in Schools (EPOCRATES): a cohort study. CMAJ Open 2022; 10:E1027-E1033. [PMID: 36622324 PMCID: PMC9744263 DOI: 10.9778/cmajo.20210327] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND SARS-CoV-2 transmission has an impact on education. In this study, we assessed the performance of rapid antigen detection tests (RADTs) versus polymerase chain reaction (PCR) for the diagnosis of SARS-CoV-2 infection in school settings, and RADT use for monitoring exposed contacts. METHODS In this real-world, prospective observational cohort study, high-school students and staff were recruited from 2 high schools in Montréal, Canada, and followed from Jan. 25 to June 10, 2021. Twenty-five percent of asymptomatic participants were tested weekly by RADT (nasal) and PCR (gargle). Class contacts of cases were tested. Symptomatic participants were tested by RADT (nasal) and PCR (nasal and gargle). The number of cases and outbreaks were compared with those of other high schools in the same area. RESULTS Overall, 2099 students and 286 school staff members consented to participate. The overall specificity of RADTs varied from 99.8% to 100%, with a lower sensitivity, varying from 28.6% in asymptomatic to 83.3% in symptomatic participants. Secondary cases were identified in 10 of 35 classes. Returning students to school after a 7-day quarantine, with a negative PCR result on days 6-7 after exposure, did not lead to subsequent outbreaks. Of cases for whom the source was known, 37 of 51 (72.5%) were secondary to household transmission, 13 (25.5%) to intraschool transmission, and 1 to community contacts between students in the same school. INTERPRETATION Rapid antigen detection tests did not perform well compared with PCR in asymptomatic individuals. Reinforcing policies for symptom screening when entering schools and testing symptomatic individuals with RADTs on the spot may avoid subsequent substantial exposures in class. Preprint: medRxiv - doi.org/10.1101/2021.10.13.21264960.
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Affiliation(s)
- Ana C Blanchard
- Division of Infectious Diseases (Blanchard), Department of Paediatrics, CHU Sainte-Justine, Université de Montréal; Clinical Department of Laboratory Medicine (Desforges, Quach), CHU Sainte-Justine; Department of Microbiology, Infectious Diseases and Immunology (Desforges, Labbé, Quach), Université de Montréal; Division of Infectious Diseases (Labbé), Department of Medicine, Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Est-de-l'Île-de-Montréal; Direction régionale de santé publique (Nguyen, Séguin), CIUSSS du Centre-Sud-de-l'île-de-Montréal; Pensionnat du Saint-Nom-de-Marie (Petit); École secondaire Calixa-Lavallée (Besner); École de santé publique de l'Université de Montréal (Zinszer), Université de Montréal; CHU Sainte-Justine Research Center (Laghdir, Adams, Benoit, Leduc), Montréal, Que.; Clinical Department of Laboratory Medicine (Longtin), CHU de Québec, Québec, Que.; McGill Genome Centre (Ragoussis), and Department of Epidemiology, Biostatistics and Occupational Health (Buckeridge), McGill University, Montréal, Que
| | - Marc Desforges
- Division of Infectious Diseases (Blanchard), Department of Paediatrics, CHU Sainte-Justine, Université de Montréal; Clinical Department of Laboratory Medicine (Desforges, Quach), CHU Sainte-Justine; Department of Microbiology, Infectious Diseases and Immunology (Desforges, Labbé, Quach), Université de Montréal; Division of Infectious Diseases (Labbé), Department of Medicine, Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Est-de-l'Île-de-Montréal; Direction régionale de santé publique (Nguyen, Séguin), CIUSSS du Centre-Sud-de-l'île-de-Montréal; Pensionnat du Saint-Nom-de-Marie (Petit); École secondaire Calixa-Lavallée (Besner); École de santé publique de l'Université de Montréal (Zinszer), Université de Montréal; CHU Sainte-Justine Research Center (Laghdir, Adams, Benoit, Leduc), Montréal, Que.; Clinical Department of Laboratory Medicine (Longtin), CHU de Québec, Québec, Que.; McGill Genome Centre (Ragoussis), and Department of Epidemiology, Biostatistics and Occupational Health (Buckeridge), McGill University, Montréal, Que
| | - Annie-Claude Labbé
- Division of Infectious Diseases (Blanchard), Department of Paediatrics, CHU Sainte-Justine, Université de Montréal; Clinical Department of Laboratory Medicine (Desforges, Quach), CHU Sainte-Justine; Department of Microbiology, Infectious Diseases and Immunology (Desforges, Labbé, Quach), Université de Montréal; Division of Infectious Diseases (Labbé), Department of Medicine, Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Est-de-l'Île-de-Montréal; Direction régionale de santé publique (Nguyen, Séguin), CIUSSS du Centre-Sud-de-l'île-de-Montréal; Pensionnat du Saint-Nom-de-Marie (Petit); École secondaire Calixa-Lavallée (Besner); École de santé publique de l'Université de Montréal (Zinszer), Université de Montréal; CHU Sainte-Justine Research Center (Laghdir, Adams, Benoit, Leduc), Montréal, Que.; Clinical Department of Laboratory Medicine (Longtin), CHU de Québec, Québec, Que.; McGill Genome Centre (Ragoussis), and Department of Epidemiology, Biostatistics and Occupational Health (Buckeridge), McGill University, Montréal, Que
| | - Cat Tuong Nguyen
- Division of Infectious Diseases (Blanchard), Department of Paediatrics, CHU Sainte-Justine, Université de Montréal; Clinical Department of Laboratory Medicine (Desforges, Quach), CHU Sainte-Justine; Department of Microbiology, Infectious Diseases and Immunology (Desforges, Labbé, Quach), Université de Montréal; Division of Infectious Diseases (Labbé), Department of Medicine, Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Est-de-l'Île-de-Montréal; Direction régionale de santé publique (Nguyen, Séguin), CIUSSS du Centre-Sud-de-l'île-de-Montréal; Pensionnat du Saint-Nom-de-Marie (Petit); École secondaire Calixa-Lavallée (Besner); École de santé publique de l'Université de Montréal (Zinszer), Université de Montréal; CHU Sainte-Justine Research Center (Laghdir, Adams, Benoit, Leduc), Montréal, Que.; Clinical Department of Laboratory Medicine (Longtin), CHU de Québec, Québec, Que.; McGill Genome Centre (Ragoussis), and Department of Epidemiology, Biostatistics and Occupational Health (Buckeridge), McGill University, Montréal, Que
| | - Yves Petit
- Division of Infectious Diseases (Blanchard), Department of Paediatrics, CHU Sainte-Justine, Université de Montréal; Clinical Department of Laboratory Medicine (Desforges, Quach), CHU Sainte-Justine; Department of Microbiology, Infectious Diseases and Immunology (Desforges, Labbé, Quach), Université de Montréal; Division of Infectious Diseases (Labbé), Department of Medicine, Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Est-de-l'Île-de-Montréal; Direction régionale de santé publique (Nguyen, Séguin), CIUSSS du Centre-Sud-de-l'île-de-Montréal; Pensionnat du Saint-Nom-de-Marie (Petit); École secondaire Calixa-Lavallée (Besner); École de santé publique de l'Université de Montréal (Zinszer), Université de Montréal; CHU Sainte-Justine Research Center (Laghdir, Adams, Benoit, Leduc), Montréal, Que.; Clinical Department of Laboratory Medicine (Longtin), CHU de Québec, Québec, Que.; McGill Genome Centre (Ragoussis), and Department of Epidemiology, Biostatistics and Occupational Health (Buckeridge), McGill University, Montréal, Que
| | - Dominic Besner
- Division of Infectious Diseases (Blanchard), Department of Paediatrics, CHU Sainte-Justine, Université de Montréal; Clinical Department of Laboratory Medicine (Desforges, Quach), CHU Sainte-Justine; Department of Microbiology, Infectious Diseases and Immunology (Desforges, Labbé, Quach), Université de Montréal; Division of Infectious Diseases (Labbé), Department of Medicine, Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Est-de-l'Île-de-Montréal; Direction régionale de santé publique (Nguyen, Séguin), CIUSSS du Centre-Sud-de-l'île-de-Montréal; Pensionnat du Saint-Nom-de-Marie (Petit); École secondaire Calixa-Lavallée (Besner); École de santé publique de l'Université de Montréal (Zinszer), Université de Montréal; CHU Sainte-Justine Research Center (Laghdir, Adams, Benoit, Leduc), Montréal, Que.; Clinical Department of Laboratory Medicine (Longtin), CHU de Québec, Québec, Que.; McGill Genome Centre (Ragoussis), and Department of Epidemiology, Biostatistics and Occupational Health (Buckeridge), McGill University, Montréal, Que
| | - Kate Zinszer
- Division of Infectious Diseases (Blanchard), Department of Paediatrics, CHU Sainte-Justine, Université de Montréal; Clinical Department of Laboratory Medicine (Desforges, Quach), CHU Sainte-Justine; Department of Microbiology, Infectious Diseases and Immunology (Desforges, Labbé, Quach), Université de Montréal; Division of Infectious Diseases (Labbé), Department of Medicine, Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Est-de-l'Île-de-Montréal; Direction régionale de santé publique (Nguyen, Séguin), CIUSSS du Centre-Sud-de-l'île-de-Montréal; Pensionnat du Saint-Nom-de-Marie (Petit); École secondaire Calixa-Lavallée (Besner); École de santé publique de l'Université de Montréal (Zinszer), Université de Montréal; CHU Sainte-Justine Research Center (Laghdir, Adams, Benoit, Leduc), Montréal, Que.; Clinical Department of Laboratory Medicine (Longtin), CHU de Québec, Québec, Que.; McGill Genome Centre (Ragoussis), and Department of Epidemiology, Biostatistics and Occupational Health (Buckeridge), McGill University, Montréal, Que
| | - Olivier Séguin
- Division of Infectious Diseases (Blanchard), Department of Paediatrics, CHU Sainte-Justine, Université de Montréal; Clinical Department of Laboratory Medicine (Desforges, Quach), CHU Sainte-Justine; Department of Microbiology, Infectious Diseases and Immunology (Desforges, Labbé, Quach), Université de Montréal; Division of Infectious Diseases (Labbé), Department of Medicine, Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Est-de-l'Île-de-Montréal; Direction régionale de santé publique (Nguyen, Séguin), CIUSSS du Centre-Sud-de-l'île-de-Montréal; Pensionnat du Saint-Nom-de-Marie (Petit); École secondaire Calixa-Lavallée (Besner); École de santé publique de l'Université de Montréal (Zinszer), Université de Montréal; CHU Sainte-Justine Research Center (Laghdir, Adams, Benoit, Leduc), Montréal, Que.; Clinical Department of Laboratory Medicine (Longtin), CHU de Québec, Québec, Que.; McGill Genome Centre (Ragoussis), and Department of Epidemiology, Biostatistics and Occupational Health (Buckeridge), McGill University, Montréal, Que
| | - Zineb Laghdir
- Division of Infectious Diseases (Blanchard), Department of Paediatrics, CHU Sainte-Justine, Université de Montréal; Clinical Department of Laboratory Medicine (Desforges, Quach), CHU Sainte-Justine; Department of Microbiology, Infectious Diseases and Immunology (Desforges, Labbé, Quach), Université de Montréal; Division of Infectious Diseases (Labbé), Department of Medicine, Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Est-de-l'Île-de-Montréal; Direction régionale de santé publique (Nguyen, Séguin), CIUSSS du Centre-Sud-de-l'île-de-Montréal; Pensionnat du Saint-Nom-de-Marie (Petit); École secondaire Calixa-Lavallée (Besner); École de santé publique de l'Université de Montréal (Zinszer), Université de Montréal; CHU Sainte-Justine Research Center (Laghdir, Adams, Benoit, Leduc), Montréal, Que.; Clinical Department of Laboratory Medicine (Longtin), CHU de Québec, Québec, Que.; McGill Genome Centre (Ragoussis), and Department of Epidemiology, Biostatistics and Occupational Health (Buckeridge), McGill University, Montréal, Que
| | - Kelsey Adams
- Division of Infectious Diseases (Blanchard), Department of Paediatrics, CHU Sainte-Justine, Université de Montréal; Clinical Department of Laboratory Medicine (Desforges, Quach), CHU Sainte-Justine; Department of Microbiology, Infectious Diseases and Immunology (Desforges, Labbé, Quach), Université de Montréal; Division of Infectious Diseases (Labbé), Department of Medicine, Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Est-de-l'Île-de-Montréal; Direction régionale de santé publique (Nguyen, Séguin), CIUSSS du Centre-Sud-de-l'île-de-Montréal; Pensionnat du Saint-Nom-de-Marie (Petit); École secondaire Calixa-Lavallée (Besner); École de santé publique de l'Université de Montréal (Zinszer), Université de Montréal; CHU Sainte-Justine Research Center (Laghdir, Adams, Benoit, Leduc), Montréal, Que.; Clinical Department of Laboratory Medicine (Longtin), CHU de Québec, Québec, Que.; McGill Genome Centre (Ragoussis), and Department of Epidemiology, Biostatistics and Occupational Health (Buckeridge), McGill University, Montréal, Que
| | - Marie-Ève Benoit
- Division of Infectious Diseases (Blanchard), Department of Paediatrics, CHU Sainte-Justine, Université de Montréal; Clinical Department of Laboratory Medicine (Desforges, Quach), CHU Sainte-Justine; Department of Microbiology, Infectious Diseases and Immunology (Desforges, Labbé, Quach), Université de Montréal; Division of Infectious Diseases (Labbé), Department of Medicine, Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Est-de-l'Île-de-Montréal; Direction régionale de santé publique (Nguyen, Séguin), CIUSSS du Centre-Sud-de-l'île-de-Montréal; Pensionnat du Saint-Nom-de-Marie (Petit); École secondaire Calixa-Lavallée (Besner); École de santé publique de l'Université de Montréal (Zinszer), Université de Montréal; CHU Sainte-Justine Research Center (Laghdir, Adams, Benoit, Leduc), Montréal, Que.; Clinical Department of Laboratory Medicine (Longtin), CHU de Québec, Québec, Que.; McGill Genome Centre (Ragoussis), and Department of Epidemiology, Biostatistics and Occupational Health (Buckeridge), McGill University, Montréal, Que
| | - Geneviève Leduc
- Division of Infectious Diseases (Blanchard), Department of Paediatrics, CHU Sainte-Justine, Université de Montréal; Clinical Department of Laboratory Medicine (Desforges, Quach), CHU Sainte-Justine; Department of Microbiology, Infectious Diseases and Immunology (Desforges, Labbé, Quach), Université de Montréal; Division of Infectious Diseases (Labbé), Department of Medicine, Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Est-de-l'Île-de-Montréal; Direction régionale de santé publique (Nguyen, Séguin), CIUSSS du Centre-Sud-de-l'île-de-Montréal; Pensionnat du Saint-Nom-de-Marie (Petit); École secondaire Calixa-Lavallée (Besner); École de santé publique de l'Université de Montréal (Zinszer), Université de Montréal; CHU Sainte-Justine Research Center (Laghdir, Adams, Benoit, Leduc), Montréal, Que.; Clinical Department of Laboratory Medicine (Longtin), CHU de Québec, Québec, Que.; McGill Genome Centre (Ragoussis), and Department of Epidemiology, Biostatistics and Occupational Health (Buckeridge), McGill University, Montréal, Que
| | - Jean Longtin
- Division of Infectious Diseases (Blanchard), Department of Paediatrics, CHU Sainte-Justine, Université de Montréal; Clinical Department of Laboratory Medicine (Desforges, Quach), CHU Sainte-Justine; Department of Microbiology, Infectious Diseases and Immunology (Desforges, Labbé, Quach), Université de Montréal; Division of Infectious Diseases (Labbé), Department of Medicine, Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Est-de-l'Île-de-Montréal; Direction régionale de santé publique (Nguyen, Séguin), CIUSSS du Centre-Sud-de-l'île-de-Montréal; Pensionnat du Saint-Nom-de-Marie (Petit); École secondaire Calixa-Lavallée (Besner); École de santé publique de l'Université de Montréal (Zinszer), Université de Montréal; CHU Sainte-Justine Research Center (Laghdir, Adams, Benoit, Leduc), Montréal, Que.; Clinical Department of Laboratory Medicine (Longtin), CHU de Québec, Québec, Que.; McGill Genome Centre (Ragoussis), and Department of Epidemiology, Biostatistics and Occupational Health (Buckeridge), McGill University, Montréal, Que
| | - Jiannis Ragoussis
- Division of Infectious Diseases (Blanchard), Department of Paediatrics, CHU Sainte-Justine, Université de Montréal; Clinical Department of Laboratory Medicine (Desforges, Quach), CHU Sainte-Justine; Department of Microbiology, Infectious Diseases and Immunology (Desforges, Labbé, Quach), Université de Montréal; Division of Infectious Diseases (Labbé), Department of Medicine, Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Est-de-l'Île-de-Montréal; Direction régionale de santé publique (Nguyen, Séguin), CIUSSS du Centre-Sud-de-l'île-de-Montréal; Pensionnat du Saint-Nom-de-Marie (Petit); École secondaire Calixa-Lavallée (Besner); École de santé publique de l'Université de Montréal (Zinszer), Université de Montréal; CHU Sainte-Justine Research Center (Laghdir, Adams, Benoit, Leduc), Montréal, Que.; Clinical Department of Laboratory Medicine (Longtin), CHU de Québec, Québec, Que.; McGill Genome Centre (Ragoussis), and Department of Epidemiology, Biostatistics and Occupational Health (Buckeridge), McGill University, Montréal, Que
| | - David L Buckeridge
- Division of Infectious Diseases (Blanchard), Department of Paediatrics, CHU Sainte-Justine, Université de Montréal; Clinical Department of Laboratory Medicine (Desforges, Quach), CHU Sainte-Justine; Department of Microbiology, Infectious Diseases and Immunology (Desforges, Labbé, Quach), Université de Montréal; Division of Infectious Diseases (Labbé), Department of Medicine, Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Est-de-l'Île-de-Montréal; Direction régionale de santé publique (Nguyen, Séguin), CIUSSS du Centre-Sud-de-l'île-de-Montréal; Pensionnat du Saint-Nom-de-Marie (Petit); École secondaire Calixa-Lavallée (Besner); École de santé publique de l'Université de Montréal (Zinszer), Université de Montréal; CHU Sainte-Justine Research Center (Laghdir, Adams, Benoit, Leduc), Montréal, Que.; Clinical Department of Laboratory Medicine (Longtin), CHU de Québec, Québec, Que.; McGill Genome Centre (Ragoussis), and Department of Epidemiology, Biostatistics and Occupational Health (Buckeridge), McGill University, Montréal, Que
| | - Caroline Quach
- Division of Infectious Diseases (Blanchard), Department of Paediatrics, CHU Sainte-Justine, Université de Montréal; Clinical Department of Laboratory Medicine (Desforges, Quach), CHU Sainte-Justine; Department of Microbiology, Infectious Diseases and Immunology (Desforges, Labbé, Quach), Université de Montréal; Division of Infectious Diseases (Labbé), Department of Medicine, Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Est-de-l'Île-de-Montréal; Direction régionale de santé publique (Nguyen, Séguin), CIUSSS du Centre-Sud-de-l'île-de-Montréal; Pensionnat du Saint-Nom-de-Marie (Petit); École secondaire Calixa-Lavallée (Besner); École de santé publique de l'Université de Montréal (Zinszer), Université de Montréal; CHU Sainte-Justine Research Center (Laghdir, Adams, Benoit, Leduc), Montréal, Que.; Clinical Department of Laboratory Medicine (Longtin), CHU de Québec, Québec, Que.; McGill Genome Centre (Ragoussis), and Department of Epidemiology, Biostatistics and Occupational Health (Buckeridge), McGill University, Montréal, Que.
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Mamiya H, Schmidt AM, Moodie EEM, Buckeridge DL. Estimating the lagged effect of price discounting: a time-series study on sugar sweetened beverage purchasing in a supermarket. BMC Public Health 2022; 22:1502. [PMID: 35932051 PMCID: PMC9356513 DOI: 10.1186/s12889-022-13928-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 07/29/2022] [Indexed: 11/28/2022] Open
Abstract
Background Price discount is an unregulated obesogenic environmental risk factor for the purchasing of unhealthy food, including Sugar Sweetened Beverages (SSB). Sales of price discounted food items are known to increase during the period of discounting. However, the presence and extent of the lagged effect of discounting, a sustained level of sales after discounting ends, is previously unaccounted for. We investigated the presence of the lagged effect of discounting on the sales of five SSB categories, which are soda, fruit juice, sport and energy drink, sugar-sweetened coffee and tea, and sugar-sweetened drinkable yogurt. Methods We fitted distributed lag models to weekly volume-standardized sales and percent discounting generated by a supermarket in Montreal, Canada between January 2008 and December 2013, inclusive (n = 311 weeks). Results While the sales of SSB increased during the period of discounting, there was no evidence of a prominent lagged effect of discounting in four of the five SSB; the exception was sports and energy drinks, where a posterior mean of 28,459 servings (95% credible interval: 2661 to 67,253) of excess sales can be attributed to the lagged effect in the target store during the 6 years study period. Conclusion Our results indicate that studies that do not account for the lagged effect of promotions may not fully capture the effect of price discounting for some food categories. Supplementary Information The online version contains supplementary material available at 10.1186/s12889-022-13928-w.
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Affiliation(s)
- Hiroshi Mamiya
- School of Global and Population Health, Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Suite 1200, 2001 McGill College Avenue, Montreal, QC, H3A1G1, Canada.
| | - Alexandra M Schmidt
- School of Global and Population Health, Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Suite 1200, 2001 McGill College Avenue, Montreal, QC, H3A1G1, Canada
| | - Erica E M Moodie
- School of Global and Population Health, Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Suite 1200, 2001 McGill College Avenue, Montreal, QC, H3A1G1, Canada
| | - David L Buckeridge
- School of Global and Population Health, Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Suite 1200, 2001 McGill College Avenue, Montreal, QC, H3A1G1, Canada
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13
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Mamiya H, Schmidt AM, Moodie EEM, Buckeridge DL. Revisiting Transfer Functions: Learning About a Lagged Exposure-Outcome Association in Time-Series Data. Int J Public Health 2022; 67:1604841. [PMID: 35910431 PMCID: PMC9336681 DOI: 10.3389/ijph.2022.1604841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 06/17/2022] [Indexed: 11/20/2022] Open
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Wen Z, Zhang J, Powell G, Chafi I, Buckeridge DL, Li Y. EpiTopics: A dynamic machine learning model to predict and inform non-pharmacological public health interventions from global news reports. STAR Protoc 2022; 3:101463. [PMID: 35712009 PMCID: PMC9189439 DOI: 10.1016/j.xpro.2022.101463] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Non-pharmacological interventions (NPIs) are important for controlling infectious diseases such as COVID-19, but their implementation is currently monitored in an ad hoc manner. To address this issue, we present a three-stage machine learning framework called EpiTopics to facilitate the surveillance of NPI. In this protocol, we outline the use of transfer-learning to address the limited number of NPI-labeled documents and topic modeling to support interpretation of the results. For complete details on the use and execution of this protocol, please refer to Wen et al. (2022). Automated prediction of public health intervention from COVID-19 news reports Inferring 42 country-specific temporal topic trends to monitor interventions Learning interpretable topics that predict interventions from news reports Transfer-learning to predict interventions for each country on weekly basis
Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.
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Okhmatovskaia A, Shen Y, Ganser I, Collier N, King NB, Meng Z, Buckeridge DL. A Conceptual Framework for Representing Events Under Public Health Surveillance. Stud Health Technol Inform 2022; 294:387-391. [PMID: 35612102 DOI: 10.3233/shti220480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Information integration across multiple event-based surveillance (EBS) systems has been shown to improve global disease surveillance in experimental settings. In practice, however, integration does not occur due to the lack of a common conceptual framework for encoding data within EBS systems. We aim to address this gap by proposing a candidate conceptual framework for representing events and related concepts in the domain of public health surveillance.
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Affiliation(s)
| | - Yannan Shen
- School of Population and Global Health, McGill University, Canada
| | - Iris Ganser
- School of Population and Global Health, McGill University, Canada
- Bordeaux Population Health Research Center, Bordeaux University, France
| | - Nigel Collier
- Department of Theoretical & Applied Linguistics, University of Cambridge, UK
| | - Nicholas B King
- School of Population and Global Health, McGill University, Canada
| | - Zaiqiao Meng
- Department of Theoretical & Applied Linguistics, University of Cambridge, UK
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16
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Vickers DM, Baral S, Mishra S, Kwong JC, Sundaram M, Katz A, Calzavara A, Maheu-Giroux M, Buckeridge DL, Williamson T. Stringency of containment and closures on the growth of SARS-CoV-2 in Canada prior to accelerated vaccine roll-out. Int J Infect Dis 2022; 118:73-82. [PMID: 35202783 PMCID: PMC8863413 DOI: 10.1016/j.ijid.2022.02.030] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 02/12/2022] [Accepted: 02/14/2022] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Many studies have examined the effectiveness of non-pharmaceutical interventions (NPIs) on SARS-CoV-2 transmission worldwide. However, less attention has been devoted to understanding the limits of NPIs across the course of the pandemic and along a continuum of their stringency. In this study, we explore the relationship between the growth of SARS-CoV-2 cases and an NPI stringency index across Canada before the accelerated vaccine roll-out. METHODS We conducted an ecological time-series study of daily SARS-CoV-2 case growth in Canada from February 2020 to February 2021. Our outcome was a back-projected version of the daily growth ratio in a stringency period (i.e., a 10-point range of the stringency index) relative to the last day of the previous period. We examined the trends in case growth using a linear mixed-effects model accounting for stringency period, province, and mobility in public domains. RESULTS Case growth declined rapidly by 20-60% and plateaued within the first month of the first wave, irrespective of the starting values of the stringency index. When stringency periods increased, changes in case growth were not immediate and were faster in the first wave than in the second. In the first wave, the largest decreasing trends from our mixed effects model occurred in both early and late stringency periods, depending on the province, at a geometric mean index value of 30⋅1 out of 100. When compared with the first wave, the stringency periods in the second wave possessed little association with case growth. CONCLUSIONS The minimal association in the first wave, and the lack thereof in the second, is compatible with the hypothesis that NPIs do not, per se, lead to a decline in case growth. Instead, the correlations we observed might be better explained by a combination of underlying behaviors of the populations in each province and the natural dynamics of SARS-CoV-2. Although there exist alternative explanations for the equivocal relationship between NPIs and case growth, the onus of providing evidence shifts to demonstrating how NPIs can consistently have flat association, despite incrementally high stringency.
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Affiliation(s)
- David M. Vickers
- Centre for Health Informatics and Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada,Corresponding author: David M. Vickers, PhD, Centre for Health Informatics, 5th Floor, TRW Building, University of Calgary, 3280 Hospital Drive NW, Calgary, AB, T2N 4Z6, Canada, Phone: +001 403 771 6893
| | - Stefan Baral
- Department of Epidemiology, Johns Hopkins School of Public Health, Baltimore, United States
| | - Sharmistha Mishra
- MAP Centre for Urban Health Solutions, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada,Division of Infectious Diseases, Department of Medicine, University of Toronto, Toronto, ON, Canada
| | - Jeffrey C. Kwong
- ICES, Toronto, ON, Canada,Public Health Ontario, ON, Canada,Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada,Centre for Vaccine Preventable Diseases, University of Toronto, Toronto, ON, Canada,Department of Family and Community Medicine, University of Toronto, ON, Canada
| | - Maria Sundaram
- ICES, Toronto, ON, Canada,Centre for Clinical Epidemiology and Population Health, Marshfield Clinic Research Institute, Marshfield, WI, USA
| | - Alan Katz
- Departments of Community Health Sciences and Family Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
| | | | - Mathieu Maheu-Giroux
- Department of Epidemiology, Biostatistics, and Occupational Health, School of Population and Global Health, McGill University, Montréal, QC, Canada
| | - David L. Buckeridge
- Department of Epidemiology, Biostatistics, and Occupational Health, School of Population and Global Health, McGill University, Montréal, QC, Canada
| | - Tyler Williamson
- Centre for Health Informatics and Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
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Xu H, Buckeridge DL, Wang F, Tarczy-Hornoch P. Novel informatics approaches to COVID-19 Research: From methods to applications. J Biomed Inform 2022; 129:104028. [PMID: 35181495 PMCID: PMC8847074 DOI: 10.1016/j.jbi.2022.104028] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 02/10/2022] [Indexed: 10/30/2022]
Affiliation(s)
- Hua Xu
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - David L Buckeridge
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada
| | - Fei Wang
- Department of Population Health Sciences, Cornell University, New York, NY, USA
| | - Peter Tarczy-Hornoch
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA
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18
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Xia Y, Ma H, Buckeridge DL, Brisson M, Sander B, Chan A, Verma A, Ganser I, Kronfli N, Mishra S, Maheu-Giroux M. Mortality trends and lengths of stay among hospitalized COVID-19 patients in Ontario and Québec (Canada): a population-based cohort study of the first three epidemic waves. Int J Infect Dis 2022; 121:1-10. [PMID: 35477050 PMCID: PMC9040412 DOI: 10.1016/j.ijid.2022.04.048] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 03/08/2022] [Accepted: 04/20/2022] [Indexed: 12/15/2022] Open
Abstract
Background Epidemics of COVID-19 strained hospital resources. We describe temporal trends in mortality risk and length of stays in hospital and intensive care units (ICUs) among patients with COVID-19 hospitalized through the first three epidemic waves in Canada. Methods We used population-based provincial hospitalization data from the epicenters of Canada's epidemics (Ontario and Québec). Adjusted estimates were obtained using marginal standardization of logistic regression models, accounting for patient-level and hospital-level determinants. Results Using all hospitalizations from Ontario (N = 26,538) and Québec (N = 23,857), we found that unadjusted in-hospital mortality risks peaked at 31% in the first wave and was lowest at the end of the third wave at 6–7%. This general trend remained after adjustments. The odds of in-hospital mortality in the highest patient load quintile were 1.2-fold (95% CI: 1.0–1.4; Ontario) and 1.6-fold (95% CI: 1.3–1.9; Québec) that of the lowest quintile. Mean hospital and ICU length of stays decreased over time but ICU stays were consistently higher in Ontario than Québec. Conclusions In-hospital mortality risks and length of ICU stays declined over time despite changing patient demographics. Continuous population-based monitoring of patient outcomes in an evolving epidemic is necessary for health system preparedness and response.
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Affiliation(s)
- Yiqing Xia
- Department of Epidemiology and Biostatistics, School of Population and Global Health, McGill University, Montréal, QC, Canada
| | - Huiting Ma
- MAP Centre for Urban Health Solutions, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada
| | - David L Buckeridge
- Department of Epidemiology and Biostatistics, School of Population and Global Health, McGill University, Montréal, QC, Canada
| | - Marc Brisson
- Département de médecine sociale et préventive, Faculté de médecine, Université Laval, Québec, QC, Canada
| | - Beate Sander
- Management and Evaluation (IHPME), Dalla Lana School of Public Health, Institute of Health Policy, University of Toronto; Toronto Health Economics and Technology Assessment (THETA) collaborative, University Health Network; Public Health Ontario, Toronto, ON, Canada; ICES, Toronto, ON, Canada
| | - Adrienne Chan
- Management and Evaluation (IHPME), Dalla Lana School of Public Health, Institute of Health Policy, University of Toronto; Division of Infectious Diseases, Department of Medicine, University of Toronto, Toronto, ON, Canada
| | - Aman Verma
- Department of Epidemiology and Biostatistics, School of Population and Global Health, McGill University, Montréal, QC, Canada
| | - Iris Ganser
- Department of Epidemiology and Biostatistics, School of Population and Global Health, McGill University, Montréal, QC, Canada
| | - Nadine Kronfli
- Centre for Outcomes Research and Evaluation (CORE), Research Institute of the McGill University Health Centre, Montréal, QC, Canada; Department of Medicine, Division of Infectious Diseases and Chronic Viral Illness Service, McGill University Health Centre, Montréal, QC, Canada
| | - Sharmistha Mishra
- Division of Infectious Diseases, Department of Medicine, University of Toronto, Toronto, ON, Canada; MAP Centre for Urban Health Solutions, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada; Management and Evaluation (IHPME), Dalla Lana School of Public Health, Institute of Health Policy, University of Toronto; Institute of Medical Sciences, University of Toronto
| | - Mathieu Maheu-Giroux
- Department of Epidemiology and Biostatistics, School of Population and Global Health, McGill University, Montréal, QC, Canada.
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Wen Z, Powell G, Chafi I, Buckeridge DL, Li Y. Inferring global-scale temporal latent topics from news reports to predict public health interventions for COVID-19. Patterns 2022; 3:100435. [PMID: 35128492 PMCID: PMC8805211 DOI: 10.1016/j.patter.2022.100435] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 11/19/2021] [Accepted: 01/05/2022] [Indexed: 12/02/2022]
Abstract
The COVID-19 pandemic has highlighted the importance of non-pharmacological interventions (NPIs) for controlling epidemics of emerging infectious diseases. Despite their importance, NPIs have been monitored mainly through the manual efforts of volunteers. This approach hinders measurement of the NPI effectiveness and development of evidence to guide their use to control the global pandemic. We present EpiTopics, a machine learning approach to support automation of NPI prediction and monitoring at both the document level and country level by mining the vast amount of unlabeled news reports on COVID-19. EpiTopics uses a 3-stage, transfer-learning algorithm to classify documents according to NPI categories, relying on topic modeling to support result interpretation. We identified 25 interpretable topics under 4 distinct and coherent COVID-related themes. Importantly, the use of these topics resulted in significant improvements over alternative automated methods in predicting the NPIs in labeled documents and in predicting country-level NPIs for 42 countries. Automated prediction of public health intervention from COVID-19 news reports Inferred 42 country-specific temporal topic trends to monitor interventions Learned interpretable topics that predict interventions from news reports Transfer learning to predict interventions for each country on weekly basis
Accurate, scalable detection of the timing of changes to public health interventions for COVID-19 is an important step toward automating evaluation of the effectiveness of interventions. We show that it is possible to train an interpretable deep-learning model called EpiTopics on media news data to predict (1) the interventions mentioned in individual news articles and (2) the temporal change of intervention status at the country level. We addressed a main challenge of label scarcity among the news reports. Using EpiTopics, we modeled the latent semantics from 1.2 million unlabeled news reports on COVID-19 over 42 countries recorded from November 1, 2019 to July 31, 2020, identifying 25 interpretable topics under 4 COVID-related themes. Using the learned topic model, we inferred topic mixture membership for each labeled article, which allowed us to learn an accurate connection between the topics and the public health interventions at both the document level and country level.
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Xia Y, Ma H, Moloney G, Velásquez García HA, Sirski M, Janjua NZ, Vickers D, Williamson T, Katz A, Yiu K, Kustra R, Buckeridge DL, Brisson M, Baral SD, Mishra S, Maheu-Giroux M. Geographic concentration of SARS-CoV-2 cases by social determinants of health in metropolitan areas in Canada: a cross-sectional study. CMAJ 2022; 194:E195-E204. [PMID: 35165131 PMCID: PMC8900797 DOI: 10.1503/cmaj.211249] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/07/2022] [Indexed: 12/27/2022] Open
Abstract
Background: Understanding inequalities in SARS-CoV-2 transmission associated with the social determinants of health could help the development of effective mitigation strategies that are responsive to local transmission dynamics. This study aims to quantify social determinants of geographic concentration of SARS-CoV-2 cases across 16 census metropolitan areas (hereafter, cities) in 4 Canadian provinces, British Columbia, Manitoba, Ontario and Quebec. Methods: We used surveillance data on confirmed SARS-CoV-2 cases and census data for social determinants at the level of the dissemination area (DA). We calculated Gini coefficients to determine the overall geographic heterogeneity of confirmed cases of SARS-CoV-2 in each city, and calculated Gini covariance coefficients to determine each city’s heterogeneity by each social determinant (income, education, housing density and proportions of visible minorities, recent immigrants and essential workers). We visualized heterogeneity using Lorenz (concentration) curves. Results: We observed geographic concentration of SARS-CoV-2 cases in cities, as half of the cumulative cases were concentrated in DAs containing 21%–35% of their population, with the greatest geographic heterogeneity in Ontario cities (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 and educational attainment, and in areas with a higher proportion of visible minorities, recent immigrants, high-density housing and essential workers. Although a consistent feature across cities was concentration by the proportion of visible minorities, the magnitude of concentration by social determinant varied across cities. Interpretation: Geographic concentration of SARS-CoV-2 cases was observed in all of the included cities, 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 the resurgence of SARS-CoV-2.
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Affiliation(s)
- Yiqing Xia
- Department of Epidemiology and Biostatistics (Xia, Buckeridge, Maheu-Giroux), School of Population and Global Health, McGill University, Montréal, Que.; MAP Centre for Urban Health Solutions (Xia, Ma, Moloney, Yiu, Mishra), St. Michael's Hospital, Unity Health Toronto, Toronto, Ont.; School of Population and Public Health (Velásquez García, Janjua), University of British Columbia; British Columbia Centre for Disease Control (Velásquez García, Janjua), Vancouver, BC; Departments of Community Health Sciences and Family Medicine (Sirski, Katz), Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Man.; Department of Community Health Sciences (Vickers, Williamson) and Centre for Health Informatics (Williamson), University of Calgary, Calgary, Alta.; Dalla Lana School of Public Health (Kustra), University of Toronto, Toronto, Ont.; Département de médecine sociale et préventive (Brisson), Faculté de médecine, Université Laval, Québec, Que.; Department of Epidemiology (Baral), Johns Hopkins School of Public Health, Baltimore, Md.; Division of Infectious Diseases (Mishra), Department of Medicine, University of Toronto, Toronto, Ont
| | - Huiting Ma
- Department of Epidemiology and Biostatistics (Xia, Buckeridge, Maheu-Giroux), School of Population and Global Health, McGill University, Montréal, Que.; MAP Centre for Urban Health Solutions (Xia, Ma, Moloney, Yiu, Mishra), St. Michael's Hospital, Unity Health Toronto, Toronto, Ont.; School of Population and Public Health (Velásquez García, Janjua), University of British Columbia; British Columbia Centre for Disease Control (Velásquez García, Janjua), Vancouver, BC; Departments of Community Health Sciences and Family Medicine (Sirski, Katz), Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Man.; Department of Community Health Sciences (Vickers, Williamson) and Centre for Health Informatics (Williamson), University of Calgary, Calgary, Alta.; Dalla Lana School of Public Health (Kustra), University of Toronto, Toronto, Ont.; Département de médecine sociale et préventive (Brisson), Faculté de médecine, Université Laval, Québec, Que.; Department of Epidemiology (Baral), Johns Hopkins School of Public Health, Baltimore, Md.; Division of Infectious Diseases (Mishra), Department of Medicine, University of Toronto, Toronto, Ont
| | - Gary Moloney
- Department of Epidemiology and Biostatistics (Xia, Buckeridge, Maheu-Giroux), School of Population and Global Health, McGill University, Montréal, Que.; MAP Centre for Urban Health Solutions (Xia, Ma, Moloney, Yiu, Mishra), St. Michael's Hospital, Unity Health Toronto, Toronto, Ont.; School of Population and Public Health (Velásquez García, Janjua), University of British Columbia; British Columbia Centre for Disease Control (Velásquez García, Janjua), Vancouver, BC; Departments of Community Health Sciences and Family Medicine (Sirski, Katz), Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Man.; Department of Community Health Sciences (Vickers, Williamson) and Centre for Health Informatics (Williamson), University of Calgary, Calgary, Alta.; Dalla Lana School of Public Health (Kustra), University of Toronto, Toronto, Ont.; Département de médecine sociale et préventive (Brisson), Faculté de médecine, Université Laval, Québec, Que.; Department of Epidemiology (Baral), Johns Hopkins School of Public Health, Baltimore, Md.; Division of Infectious Diseases (Mishra), Department of Medicine, University of Toronto, Toronto, Ont
| | - Héctor A Velásquez García
- Department of Epidemiology and Biostatistics (Xia, Buckeridge, Maheu-Giroux), School of Population and Global Health, McGill University, Montréal, Que.; MAP Centre for Urban Health Solutions (Xia, Ma, Moloney, Yiu, Mishra), St. Michael's Hospital, Unity Health Toronto, Toronto, Ont.; School of Population and Public Health (Velásquez García, Janjua), University of British Columbia; British Columbia Centre for Disease Control (Velásquez García, Janjua), Vancouver, BC; Departments of Community Health Sciences and Family Medicine (Sirski, Katz), Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Man.; Department of Community Health Sciences (Vickers, Williamson) and Centre for Health Informatics (Williamson), University of Calgary, Calgary, Alta.; Dalla Lana School of Public Health (Kustra), University of Toronto, Toronto, Ont.; Département de médecine sociale et préventive (Brisson), Faculté de médecine, Université Laval, Québec, Que.; Department of Epidemiology (Baral), Johns Hopkins School of Public Health, Baltimore, Md.; Division of Infectious Diseases (Mishra), Department of Medicine, University of Toronto, Toronto, Ont
| | - Monica Sirski
- Department of Epidemiology and Biostatistics (Xia, Buckeridge, Maheu-Giroux), School of Population and Global Health, McGill University, Montréal, Que.; MAP Centre for Urban Health Solutions (Xia, Ma, Moloney, Yiu, Mishra), St. Michael's Hospital, Unity Health Toronto, Toronto, Ont.; School of Population and Public Health (Velásquez García, Janjua), University of British Columbia; British Columbia Centre for Disease Control (Velásquez García, Janjua), Vancouver, BC; Departments of Community Health Sciences and Family Medicine (Sirski, Katz), Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Man.; Department of Community Health Sciences (Vickers, Williamson) and Centre for Health Informatics (Williamson), University of Calgary, Calgary, Alta.; Dalla Lana School of Public Health (Kustra), University of Toronto, Toronto, Ont.; Département de médecine sociale et préventive (Brisson), Faculté de médecine, Université Laval, Québec, Que.; Department of Epidemiology (Baral), Johns Hopkins School of Public Health, Baltimore, Md.; Division of Infectious Diseases (Mishra), Department of Medicine, University of Toronto, Toronto, Ont
| | - Naveed Z Janjua
- Department of Epidemiology and Biostatistics (Xia, Buckeridge, Maheu-Giroux), School of Population and Global Health, McGill University, Montréal, Que.; MAP Centre for Urban Health Solutions (Xia, Ma, Moloney, Yiu, Mishra), St. Michael's Hospital, Unity Health Toronto, Toronto, Ont.; School of Population and Public Health (Velásquez García, Janjua), University of British Columbia; British Columbia Centre for Disease Control (Velásquez García, Janjua), Vancouver, BC; Departments of Community Health Sciences and Family Medicine (Sirski, Katz), Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Man.; Department of Community Health Sciences (Vickers, Williamson) and Centre for Health Informatics (Williamson), University of Calgary, Calgary, Alta.; Dalla Lana School of Public Health (Kustra), University of Toronto, Toronto, Ont.; Département de médecine sociale et préventive (Brisson), Faculté de médecine, Université Laval, Québec, Que.; Department of Epidemiology (Baral), Johns Hopkins School of Public Health, Baltimore, Md.; Division of Infectious Diseases (Mishra), Department of Medicine, University of Toronto, Toronto, Ont
| | - David Vickers
- Department of Epidemiology and Biostatistics (Xia, Buckeridge, Maheu-Giroux), School of Population and Global Health, McGill University, Montréal, Que.; MAP Centre for Urban Health Solutions (Xia, Ma, Moloney, Yiu, Mishra), St. Michael's Hospital, Unity Health Toronto, Toronto, Ont.; School of Population and Public Health (Velásquez García, Janjua), University of British Columbia; British Columbia Centre for Disease Control (Velásquez García, Janjua), Vancouver, BC; Departments of Community Health Sciences and Family Medicine (Sirski, Katz), Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Man.; Department of Community Health Sciences (Vickers, Williamson) and Centre for Health Informatics (Williamson), University of Calgary, Calgary, Alta.; Dalla Lana School of Public Health (Kustra), University of Toronto, Toronto, Ont.; Département de médecine sociale et préventive (Brisson), Faculté de médecine, Université Laval, Québec, Que.; Department of Epidemiology (Baral), Johns Hopkins School of Public Health, Baltimore, Md.; Division of Infectious Diseases (Mishra), Department of Medicine, University of Toronto, Toronto, Ont
| | - Tyler Williamson
- Department of Epidemiology and Biostatistics (Xia, Buckeridge, Maheu-Giroux), School of Population and Global Health, McGill University, Montréal, Que.; MAP Centre for Urban Health Solutions (Xia, Ma, Moloney, Yiu, Mishra), St. Michael's Hospital, Unity Health Toronto, Toronto, Ont.; School of Population and Public Health (Velásquez García, Janjua), University of British Columbia; British Columbia Centre for Disease Control (Velásquez García, Janjua), Vancouver, BC; Departments of Community Health Sciences and Family Medicine (Sirski, Katz), Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Man.; Department of Community Health Sciences (Vickers, Williamson) and Centre for Health Informatics (Williamson), University of Calgary, Calgary, Alta.; Dalla Lana School of Public Health (Kustra), University of Toronto, Toronto, Ont.; Département de médecine sociale et préventive (Brisson), Faculté de médecine, Université Laval, Québec, Que.; Department of Epidemiology (Baral), Johns Hopkins School of Public Health, Baltimore, Md.; Division of Infectious Diseases (Mishra), Department of Medicine, University of Toronto, Toronto, Ont
| | - Alan Katz
- Department of Epidemiology and Biostatistics (Xia, Buckeridge, Maheu-Giroux), School of Population and Global Health, McGill University, Montréal, Que.; MAP Centre for Urban Health Solutions (Xia, Ma, Moloney, Yiu, Mishra), St. Michael's Hospital, Unity Health Toronto, Toronto, Ont.; School of Population and Public Health (Velásquez García, Janjua), University of British Columbia; British Columbia Centre for Disease Control (Velásquez García, Janjua), Vancouver, BC; Departments of Community Health Sciences and Family Medicine (Sirski, Katz), Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Man.; Department of Community Health Sciences (Vickers, Williamson) and Centre for Health Informatics (Williamson), University of Calgary, Calgary, Alta.; Dalla Lana School of Public Health (Kustra), University of Toronto, Toronto, Ont.; Département de médecine sociale et préventive (Brisson), Faculté de médecine, Université Laval, Québec, Que.; Department of Epidemiology (Baral), Johns Hopkins School of Public Health, Baltimore, Md.; Division of Infectious Diseases (Mishra), Department of Medicine, University of Toronto, Toronto, Ont
| | - Kristy Yiu
- Department of Epidemiology and Biostatistics (Xia, Buckeridge, Maheu-Giroux), School of Population and Global Health, McGill University, Montréal, Que.; MAP Centre for Urban Health Solutions (Xia, Ma, Moloney, Yiu, Mishra), St. Michael's Hospital, Unity Health Toronto, Toronto, Ont.; School of Population and Public Health (Velásquez García, Janjua), University of British Columbia; British Columbia Centre for Disease Control (Velásquez García, Janjua), Vancouver, BC; Departments of Community Health Sciences and Family Medicine (Sirski, Katz), Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Man.; Department of Community Health Sciences (Vickers, Williamson) and Centre for Health Informatics (Williamson), University of Calgary, Calgary, Alta.; Dalla Lana School of Public Health (Kustra), University of Toronto, Toronto, Ont.; Département de médecine sociale et préventive (Brisson), Faculté de médecine, Université Laval, Québec, Que.; Department of Epidemiology (Baral), Johns Hopkins School of Public Health, Baltimore, Md.; Division of Infectious Diseases (Mishra), Department of Medicine, University of Toronto, Toronto, Ont
| | - Rafal Kustra
- Department of Epidemiology and Biostatistics (Xia, Buckeridge, Maheu-Giroux), School of Population and Global Health, McGill University, Montréal, Que.; MAP Centre for Urban Health Solutions (Xia, Ma, Moloney, Yiu, Mishra), St. Michael's Hospital, Unity Health Toronto, Toronto, Ont.; School of Population and Public Health (Velásquez García, Janjua), University of British Columbia; British Columbia Centre for Disease Control (Velásquez García, Janjua), Vancouver, BC; Departments of Community Health Sciences and Family Medicine (Sirski, Katz), Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Man.; Department of Community Health Sciences (Vickers, Williamson) and Centre for Health Informatics (Williamson), University of Calgary, Calgary, Alta.; Dalla Lana School of Public Health (Kustra), University of Toronto, Toronto, Ont.; Département de médecine sociale et préventive (Brisson), Faculté de médecine, Université Laval, Québec, Que.; Department of Epidemiology (Baral), Johns Hopkins School of Public Health, Baltimore, Md.; Division of Infectious Diseases (Mishra), Department of Medicine, University of Toronto, Toronto, Ont
| | - David L Buckeridge
- Department of Epidemiology and Biostatistics (Xia, Buckeridge, Maheu-Giroux), School of Population and Global Health, McGill University, Montréal, Que.; MAP Centre for Urban Health Solutions (Xia, Ma, Moloney, Yiu, Mishra), St. Michael's Hospital, Unity Health Toronto, Toronto, Ont.; School of Population and Public Health (Velásquez García, Janjua), University of British Columbia; British Columbia Centre for Disease Control (Velásquez García, Janjua), Vancouver, BC; Departments of Community Health Sciences and Family Medicine (Sirski, Katz), Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Man.; Department of Community Health Sciences (Vickers, Williamson) and Centre for Health Informatics (Williamson), University of Calgary, Calgary, Alta.; Dalla Lana School of Public Health (Kustra), University of Toronto, Toronto, Ont.; Département de médecine sociale et préventive (Brisson), Faculté de médecine, Université Laval, Québec, Que.; Department of Epidemiology (Baral), Johns Hopkins School of Public Health, Baltimore, Md.; Division of Infectious Diseases (Mishra), Department of Medicine, University of Toronto, Toronto, Ont
| | - Marc Brisson
- Department of Epidemiology and Biostatistics (Xia, Buckeridge, Maheu-Giroux), School of Population and Global Health, McGill University, Montréal, Que.; MAP Centre for Urban Health Solutions (Xia, Ma, Moloney, Yiu, Mishra), St. Michael's Hospital, Unity Health Toronto, Toronto, Ont.; School of Population and Public Health (Velásquez García, Janjua), University of British Columbia; British Columbia Centre for Disease Control (Velásquez García, Janjua), Vancouver, BC; Departments of Community Health Sciences and Family Medicine (Sirski, Katz), Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Man.; Department of Community Health Sciences (Vickers, Williamson) and Centre for Health Informatics (Williamson), University of Calgary, Calgary, Alta.; Dalla Lana School of Public Health (Kustra), University of Toronto, Toronto, Ont.; Département de médecine sociale et préventive (Brisson), Faculté de médecine, Université Laval, Québec, Que.; Department of Epidemiology (Baral), Johns Hopkins School of Public Health, Baltimore, Md.; Division of Infectious Diseases (Mishra), Department of Medicine, University of Toronto, Toronto, Ont
| | - Stefan D Baral
- Department of Epidemiology and Biostatistics (Xia, Buckeridge, Maheu-Giroux), School of Population and Global Health, McGill University, Montréal, Que.; MAP Centre for Urban Health Solutions (Xia, Ma, Moloney, Yiu, Mishra), St. Michael's Hospital, Unity Health Toronto, Toronto, Ont.; School of Population and Public Health (Velásquez García, Janjua), University of British Columbia; British Columbia Centre for Disease Control (Velásquez García, Janjua), Vancouver, BC; Departments of Community Health Sciences and Family Medicine (Sirski, Katz), Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Man.; Department of Community Health Sciences (Vickers, Williamson) and Centre for Health Informatics (Williamson), University of Calgary, Calgary, Alta.; Dalla Lana School of Public Health (Kustra), University of Toronto, Toronto, Ont.; Département de médecine sociale et préventive (Brisson), Faculté de médecine, Université Laval, Québec, Que.; Department of Epidemiology (Baral), Johns Hopkins School of Public Health, Baltimore, Md.; Division of Infectious Diseases (Mishra), Department of Medicine, University of Toronto, Toronto, Ont
| | - Sharmistha Mishra
- Department of Epidemiology and Biostatistics (Xia, Buckeridge, Maheu-Giroux), School of Population and Global Health, McGill University, Montréal, Que.; MAP Centre for Urban Health Solutions (Xia, Ma, Moloney, Yiu, Mishra), St. Michael's Hospital, Unity Health Toronto, Toronto, Ont.; School of Population and Public Health (Velásquez García, Janjua), University of British Columbia; British Columbia Centre for Disease Control (Velásquez García, Janjua), Vancouver, BC; Departments of Community Health Sciences and Family Medicine (Sirski, Katz), Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Man.; Department of Community Health Sciences (Vickers, Williamson) and Centre for Health Informatics (Williamson), University of Calgary, Calgary, Alta.; Dalla Lana School of Public Health (Kustra), University of Toronto, Toronto, Ont.; Département de médecine sociale et préventive (Brisson), Faculté de médecine, Université Laval, Québec, Que.; Department of Epidemiology (Baral), Johns Hopkins School of Public Health, Baltimore, Md.; Division of Infectious Diseases (Mishra), Department of Medicine, University of Toronto, Toronto, Ont.
| | - Mathieu Maheu-Giroux
- Department of Epidemiology and Biostatistics (Xia, Buckeridge, Maheu-Giroux), School of Population and Global Health, McGill University, Montréal, Que.; MAP Centre for Urban Health Solutions (Xia, Ma, Moloney, Yiu, Mishra), St. Michael's Hospital, Unity Health Toronto, Toronto, Ont.; School of Population and Public Health (Velásquez García, Janjua), University of British Columbia; British Columbia Centre for Disease Control (Velásquez García, Janjua), Vancouver, BC; Departments of Community Health Sciences and Family Medicine (Sirski, Katz), Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Man.; Department of Community Health Sciences (Vickers, Williamson) and Centre for Health Informatics (Williamson), University of Calgary, Calgary, Alta.; Dalla Lana School of Public Health (Kustra), University of Toronto, Toronto, Ont.; Département de médecine sociale et préventive (Brisson), Faculté de médecine, Université Laval, Québec, Que.; Department of Epidemiology (Baral), Johns Hopkins School of Public Health, Baltimore, Md.; Division of Infectious Diseases (Mishra), Department of Medicine, University of Toronto, Toronto, Ont
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21
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Dick DW, Childs L, Feng Z, Li J, Röst G, Buckeridge DL, Ogden NH, Heffernan JM. COVID-19 Seroprevalence in Canada Modelling Waning and Boosting COVID-19 Immunity in Canada a Canadian Immunization Research Network Study. Vaccines (Basel) 2021; 10:17. [PMID: 35062678 PMCID: PMC8779812 DOI: 10.3390/vaccines10010017] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2021] [Revised: 10/23/2021] [Accepted: 11/24/2021] [Indexed: 11/16/2022] Open
Abstract
COVID-19 seroprevalence changes over time, with infection, vaccination, and waning immunity. Seroprevalence estimates are needed to determine when increased COVID-19 vaccination coverage is needed, and when booster doses should be considered, to reduce the spread and disease severity of COVID-19 infection. We use an age-structured model including infection, vaccination and waning immunity to estimate the distribution of immunity to COVID-19 in the Canadian population. This is the first mathematical model to do so. We estimate that 60-80% of the Canadian population has some immunity to COVID-19 by late Summer 2021, depending on specific characteristics of the vaccine and the waning rate of immunity. Models results indicate that increased vaccination uptake in age groups 12-29, and booster doses in age group 50+ are needed to reduce the severity COVID-19 Fall 2021 resurgence.
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Affiliation(s)
- David W. Dick
- Mathematics and Statistics, Centre for Disease Modelling, York University, Toronto, ON M3J 1P3, Canada;
| | - Lauren Childs
- Department of Mathematics, Virginia Tech, Blacksburg, VA 24061, USA;
| | - Zhilan Feng
- Department of Mathematics, Purdue University, West Lafayette, IN 46202, USA;
- National Science Foundation, Alexandria, VA 22314, USA
| | - Jing Li
- Department of Mathematics, California State University, Northridge, CA 91330, USA;
| | - Gergely Röst
- Department of Mathematics, University of Szeged, 6720 Szeged, Hungary;
| | - David L. Buckeridge
- Epidemiology and Biostatistics, School of Population and Global Health, McGill University, Montreal, QC H3A 0G4, Canada;
| | - Nick H. Ogden
- National Microbiology Laboratory, Public Health Risk Sciences Division, Public Health Agency of Canada, St. Hyacinthe, QC J2S 2M2, Canada;
| | - Jane M. Heffernan
- Mathematics and Statistics, Centre for Disease Modelling, York University, Toronto, ON M3J 1P3, Canada;
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22
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Luo Y, Stephens DA, Buckeridge DL. Bayesian clustering for continuous‐time hidden Markov models. CAN J STAT 2021. [DOI: 10.1002/cjs.11671] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Yu Luo
- Department of Mathematics Imperial College London London U.K
| | - David A. Stephens
- Department of Mathematics and Statistics McGill University Montreal Canada
| | - David L. Buckeridge
- Department of Epidemiology, Biostatistics and Occupational Health McGill University Montreal Canada
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23
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Jani M, Girard N, Bates DW, Buckeridge DL, Sheppard T, Li J, Iqbal U, Vik S, Weaver C, Seidel J, Dixon WG, Tamblyn R. Opioid prescribing among new users for non-cancer pain in the USA, Canada, UK, and Taiwan: A population-based cohort study. PLoS Med 2021; 18:e1003829. [PMID: 34723956 PMCID: PMC8601614 DOI: 10.1371/journal.pmed.1003829] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 11/18/2021] [Accepted: 09/30/2021] [Indexed: 01/12/2023] Open
Abstract
BACKGROUND The opioid epidemic in North America has been driven by an increase in the use and potency of prescription opioids, with ensuing excessive opioid-related deaths. Internationally, there are lower rates of opioid-related mortality, possibly because of differences in prescribing and health system policies. Our aim was to compare opioid prescribing rates in patients without cancer, across 5 centers in 4 countries. In addition, we evaluated differences in the type, strength, and starting dose of medication and whether these characteristics changed over time. METHODS AND FINDINGS We conducted a retrospective multicenter cohort study of adults who are new users of opioids without prior cancer. Electronic health records and administrative health records from Boston (United States), Quebec and Alberta (Canada), United Kingdom, and Taiwan were used to identify patients between 2006 and 2015. Standard dosages in morphine milligram equivalents (MMEs) were calculated according to The Centers for Disease Control and Prevention. Age- and sex-standardized opioid prescribing rates were calculated for each jurisdiction. Of the 2,542,890 patients included, 44,690 were from Boston (US), 1,420,136 Alberta, 26,871 Quebec (Canada), 1,012,939 UK, and 38,254 Taiwan. The highest standardized opioid prescribing rates in 2014 were observed in Alberta at 66/1,000 persons compared to 52, 51, and 18/1,000 in the UK, US, and Quebec, respectively. The median MME/day (IQR) at initiation was highest in Boston at 38 (20 to 45); followed by Quebec, 27 (18 to 43); Alberta, 23 (9 to 38); UK, 12 (7 to 20); and Taiwan, 8 (4 to 11). Oxycodone was the first prescribed opioid in 65% of patients in the US cohort compared to 14% in Quebec, 4% in Alberta, 0.1% in the UK, and none in Taiwan. One of the limitations was that data were not available from all centers for the entirety of the 10-year period. CONCLUSIONS In this study, we observed substantial differences in opioid prescribing practices for non-cancer pain between jurisdictions. The preference to start patients on higher MME/day and more potent opioids in North America may be a contributing cause to the opioid epidemic.
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Affiliation(s)
- Meghna Jani
- Centre for Epidemiology Versus Arthritis, Centre for Musculoskeletal Research, University of Manchester, Manchester, United Kingdom
- Department of Rheumatology, Salford Royal Foundation Trust, Salford, United Kingdom
| | - Nadyne Girard
- Department of Epidemiology, Biostatistics & Occupational Health, University of McGill, Montreal, Canada
- Clinical and Health Informatics Research Group, McGill University, Montreal, Canada
| | - David W. Bates
- Harvard Medical School, Boston, Massachusetts, United States of America
- Division of General Internal Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
- Department of Health Policy and Management, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - David L. Buckeridge
- Department of Epidemiology, Biostatistics & Occupational Health, University of McGill, Montreal, Canada
- Clinical and Health Informatics Research Group, McGill University, Montreal, Canada
| | - Therese Sheppard
- Centre for Epidemiology Versus Arthritis, Centre for Musculoskeletal Research, University of Manchester, Manchester, United Kingdom
| | - Jack Li
- International Centre for Health Information Technology (ICHIT), Taipei Medical University, Taipei City, Taiwan
- Graduate Institute of Biomedical Informatics, College of Medicine Science and Technology, Taipei Medical University, Taipei City, Taiwan
| | - Usman Iqbal
- International Centre for Health Information Technology (ICHIT), Taipei Medical University, Taipei City, Taiwan
- Graduate Institute of Biomedical Informatics, College of Medicine Science and Technology, Taipei Medical University, Taipei City, Taiwan
| | - Shelly Vik
- Applied Research and Evaluation Services, Alberta Health Services, Calgary, Canada
- Department of Community Health Science, Cumming School of Medicine, University of Calgary, Calgary, Canada
| | - Colin Weaver
- Applied Research and Evaluation Services, Alberta Health Services, Calgary, Canada
- Department of Community Health Science, Cumming School of Medicine, University of Calgary, Calgary, Canada
| | - Judy Seidel
- Applied Research and Evaluation Services, Alberta Health Services, Calgary, Canada
- Department of Community Health Science, Cumming School of Medicine, University of Calgary, Calgary, Canada
| | - William G. Dixon
- Centre for Epidemiology Versus Arthritis, Centre for Musculoskeletal Research, University of Manchester, Manchester, United Kingdom
- Department of Rheumatology, Salford Royal Foundation Trust, Salford, United Kingdom
| | - Robyn Tamblyn
- Department of Epidemiology, Biostatistics & Occupational Health, University of McGill, Montreal, Canada
- Clinical and Health Informatics Research Group, McGill University, Montreal, Canada
- * E-mail:
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24
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Panagiotoglou D, Abrahamowicz M, Buckeridge DL, Caro JJ, Latimer E, Maheu-Giroux M, Strumpf EC. Evaluating Montréal's harm reduction interventions for people who inject drugs: protocol for observational study and cost-effectiveness analysis. BMJ Open 2021; 11:e053191. [PMID: 34702731 PMCID: PMC8549659 DOI: 10.1136/bmjopen-2021-053191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
INTRODUCTION The main harm reduction interventions for people who inject drugs (PWID) are supervised injection facilities, needle and syringe programmes and opioid agonist treatment. Current evidence supporting their implementation and operation underestimates their usefulness by excluding skin, soft tissue and vascular infections (SSTVIs) and anoxic/toxicity-related brain injury from cost-effectiveness analyses (CEA). Our goal is to conduct a comprehensive CEA of harm reduction interventions in a setting with a large, dispersed, heterogeneous population of PWID, and include prevention of SSTVIs and anoxic/toxicity-related brain injury as measures of benefit in addition to HIV, hepatitis C and overdose morbidity and mortalities averted. METHODS AND ANALYSIS This protocol describes how we will develop an open, retrospective cohort of adult PWID living in Québec between 1 January 2009 and 31 December 2020 using administrative health record data. By complementing this data with non-linkable paramedic dispatch records, regional monthly needle and syringe dispensation counts and repeated cross-sectional biobehavioural surveys, we will estimate the hazards of occurrence and the impact of Montréal's harm reduction interventions on the incidence of drug-use-related injuries, infections and deaths. We will synthesise results from our empirical analyses with published evidence to simulate infections and injuries in a hypothetical population of PWID in Montréal under different intervention scenarios including current levels of use and scale-up, and assess the cost-effectiveness of each intervention from the public healthcare payer's perspective. ETHICS AND DISSEMINATION This study was approved by McGill University's Institutional Review Board (Study Number: A08-E53-19B). We will work with community partners to disseminate results to the public and scientific community via scientific conferences, a publicly accessible report, op-ed articles and open access peer-reviewed journals.
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Affiliation(s)
- Dimitra Panagiotoglou
- Department of Epidemiology, Biostatistics and Occupational Health, School of Population and Global Health, McGill University, Montréal, Québec, Canada
| | - Michal Abrahamowicz
- Department of Epidemiology, Biostatistics and Occupational Health, School of Population and Global Health, McGill University, Montréal, Québec, Canada
- Research Institute, McGill University Health Centre, Montréal, Québec, Canada
| | - David L Buckeridge
- Department of Epidemiology, Biostatistics and Occupational Health, School of Population and Global Health, McGill University, Montréal, Québec, Canada
- Clinical and Health Informatics Research Group, Department of Medicine, McGill University, Montréal, Québec, Canada
| | - J Jaime Caro
- Department of Epidemiology, Biostatistics and Occupational Health, School of Population and Global Health, McGill University, Montréal, Québec, Canada
- Evidera, Boston, Massachusetts, USA
- London School of Economics and Political Science, London, UK
| | - Eric Latimer
- Douglas Research Institute, Montréal, Québec, Canada
- Department of Psychiatry, McGill University, Montréal, Québec, Canada
| | - Mathieu Maheu-Giroux
- Department of Epidemiology, Biostatistics and Occupational Health, School of Population and Global Health, McGill University, Montréal, Québec, Canada
| | - Erin C Strumpf
- Department of Epidemiology, Biostatistics and Occupational Health, School of Population and Global Health, McGill University, Montréal, Québec, Canada
- Department of Economics, McGill University, Montréal, Québec, Canada
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25
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Dhaliwal B, Neil-Sztramko SE, Boston-Fisher N, Buckeridge DL, Dobbins M. Assessing the Electronic Evidence System Needs of Canadian Public Health Professionals: Cross-sectional Study. JMIR Public Health Surveill 2021; 7:e26503. [PMID: 34491205 PMCID: PMC8456326 DOI: 10.2196/26503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Revised: 05/26/2021] [Accepted: 06/02/2021] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND True evidence-informed decision-making in public health relies on incorporating evidence from a number of sources in addition to traditional scientific evidence. Lack of access to these types of data as well as ease of use and interpretability of scientific evidence contribute to limited uptake of evidence-informed decision-making in practice. An electronic evidence system that includes multiple sources of evidence and potentially novel computational processing approaches or artificial intelligence holds promise as a solution to overcoming barriers to evidence-informed decision-making in public health. OBJECTIVE This study aims to understand the needs and preferences for an electronic evidence system among public health professionals in Canada. METHODS An invitation to participate in an anonymous web-based survey was distributed via listservs of 2 Canadian public health organizations in February 2019. Eligible participants were English- or French-speaking individuals currently working in public health. The survey contained both multiple-choice and open-ended questions about the needs and preferences relevant to an electronic evidence system. Quantitative responses were analyzed to explore differences by public health role. Inductive and deductive analysis methods were used to code and interpret the qualitative data. Ethics review was not required by the host institution. RESULTS Respondents (N=371) were heterogeneous, spanning organizations, positions, and areas of practice within public health. Nearly all (364/371, 98.1%) respondents indicated that an electronic evidence system would support their work. Respondents had high preferences for local contextual data, research and intervention evidence, and information about human and financial resources. Qualitative analyses identified several concerns, needs, and suggestions for the development of such a system. Concerns ranged from the personal use of such a system to the ability of their organization to use such a system. Recognized needs spanned the different sources of evidence, including local context, research and intervention evidence, and resources and tools. Additional suggestions were identified to improve system usability. CONCLUSIONS Canadian public health professionals have positive perceptions toward an electronic evidence system that would bring together evidence from the local context, scientific research, and resources. Elements were also identified to increase the usability of an electronic evidence system.
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Affiliation(s)
- Bandna Dhaliwal
- National Collaborating Centre for Methods and Tools, McMaster University, Hamilton, ON, Canada
| | - Sarah E Neil-Sztramko
- National Collaborating Centre for Methods and Tools, McMaster University, Hamilton, ON, Canada
- Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, ON, Canada
| | | | - David L Buckeridge
- School of Population and Global Health, McGill University, Montreal, QC, Canada
| | - Maureen Dobbins
- National Collaborating Centre for Methods and Tools, McMaster University, Hamilton, ON, Canada
- School of Nursing, McMaster University, Hamilton, ON, Canada
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26
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Shen Y, Powell G, Ganser I, Zheng Q, Grundy C, Okhmatovskaia A, Buckeridge DL. Monitoring non-pharmaceutical public health interventions during the COVID-19 pandemic. Sci Data 2021; 8:225. [PMID: 34429423 PMCID: PMC8385050 DOI: 10.1038/s41597-021-01001-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Accepted: 07/27/2021] [Indexed: 11/24/2022] Open
Abstract
Measuring and monitoring non-pharmaceutical interventions is important yet challenging due to the need to clearly define and encode non-pharmaceutical interventions, to collect geographically and socially representative data, and to accurately document the timing at which interventions are initiated and changed. These challenges highlight the importance of integrating and triangulating across multiple databases and the need to expand and fund the mandate for public health organizations to track interventions systematically.
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Affiliation(s)
- Yannan Shen
- McGill University, School of Population and Global Health, Department of Epidemiology, Biostatistics and Occupational Health, Montreal, H3A 1A2, Canada
| | - Guido Powell
- McGill University, School of Population and Global Health, Department of Epidemiology, Biostatistics and Occupational Health, Montreal, H3A 1A2, Canada
| | - Iris Ganser
- McGill University, School of Population and Global Health, Department of Epidemiology, Biostatistics and Occupational Health, Montreal, H3A 1A2, Canada
| | - Qulu Zheng
- Johns Hopkins Bloomberg School of Public Health, Department of Epidemiology, Baltimore, 21205, USA
| | - Chris Grundy
- London School of Hygiene and Tropical Medicine, Department of Epidemiology and Population Health, London, WC1E 7HT, UK
| | - Anya Okhmatovskaia
- McGill University, School of Population and Global Health, Department of Epidemiology, Biostatistics and Occupational Health, Montreal, H3A 1A2, Canada
| | - David L Buckeridge
- McGill University, School of Population and Global Health, Department of Epidemiology, Biostatistics and Occupational Health, Montreal, H3A 1A2, Canada.
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27
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Márquez Fosser S, Mahmoud N, Habib B, Weir DL, Chan F, El Halabieh R, Vachon J, Thakur M, Tran T, Bustillo M, Beauchamp C, Bonnici A, Buckeridge DL, Tamblyn R. Smart about medications (SAM): a digital solution to enhance medication management following hospital discharge. JAMIA Open 2021; 4:ooab037. [PMID: 34159299 PMCID: PMC8211568 DOI: 10.1093/jamiaopen/ooab037] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 03/16/2021] [Accepted: 04/15/2021] [Indexed: 11/21/2022] Open
Abstract
Objective To outline the development of a software solution to improve medication management after hospital discharge, including its design, data sources, intrinsic features, and to evaluate the usability and the perception of use by end-users. Materials and Methods Patients were directly involved in the development using a User Center Design (UCD) approach. We conducted usability interviews prior to hospital discharge, before a user started using the application. A technology acceptance questionnaire was administered to evaluate user self-perception after 2 weeks of use. Results The following features were developed; pill identification, patient-friendly drug information leaflet, side effect checker, and interaction checker, adherence monitoring and alerts, weekly medication schedule, daily pill reminders, messaging service, and patient medication reviews. The usability interviews show a 98.3% total success rate for all features, severity (on a scale of 1–4) 1.4 (SD 0.79). Regarding the self-perception of use (1–7 agreement scale) the 3 highest-rated domains were: (1) perceived ease of use 5.65 (SD 2.02), (2) output quality 5.44 (SD 1.65), and (3) perceived usefulness 5.29 (SD 2.11). Discussion Many medication management apps solutions have been created and most of them have not been properly evaluated. SAM (Smart About Medications) includes the user perspective, integration between a province drug database and the pharmacist workflow in real time. Its features are not limited to maintaining a medication list through manual entry. Conclusion We can conclude after evaluation that the application is usable and has been self-perceived as easy to use by end-users. Future studies are required to assess the health benefits associated with its use.
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Affiliation(s)
- Santiago Márquez Fosser
- Clinical and Health Informatics Research Group, Department of Medicine, McGill University, Montreal, Canada.,Department of Epidemiology and Biostatistics, McGill University, Montreal, Canada
| | - Nadar Mahmoud
- Clinical and Health Informatics Research Group, Department of Medicine, McGill University, Montreal, Canada
| | - Bettina Habib
- Clinical and Health Informatics Research Group, Department of Medicine, McGill University, Montreal, Canada
| | - Daniala L Weir
- Clinical and Health Informatics Research Group, Department of Medicine, McGill University, Montreal, Canada
| | - Fiona Chan
- Clinical and Health Informatics Research Group, Department of Medicine, McGill University, Montreal, Canada
| | - Rola El Halabieh
- Clinical and Health Informatics Research Group, Department of Medicine, McGill University, Montreal, Canada
| | - Jeanne Vachon
- Clinical and Health Informatics Research Group, Department of Medicine, McGill University, Montreal, Canada
| | - Manish Thakur
- Clinical and Health Informatics Research Group, Department of Medicine, McGill University, Montreal, Canada
| | - Thai Tran
- Clinical and Health Informatics Research Group, Department of Medicine, McGill University, Montreal, Canada
| | - Melissa Bustillo
- Clinical and Health Informatics Research Group, Department of Medicine, McGill University, Montreal, Canada
| | | | - André Bonnici
- Pharmacy Department, McGill University Health Centre, Montreal, Canada
| | - David L Buckeridge
- Clinical and Health Informatics Research Group, Department of Medicine, McGill University, Montreal, Canada.,Department of Epidemiology and Biostatistics, McGill University, Montreal, Canada
| | - Robyn Tamblyn
- Clinical and Health Informatics Research Group, Department of Medicine, McGill University, Montreal, Canada.,Department of Epidemiology and Biostatistics, McGill University, Montreal, Canada.,Department of Medicine, McGill University, Montréal, Canada
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28
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Lasry O, Dendukuri N, Marcoux J, Buckeridge DL. Recurrent Traumatic Brain Injury Surveillance Using Administrative Health Data: A Bayesian Latent Class Analysis. Front Neurol 2021; 12:664631. [PMID: 34054707 PMCID: PMC8160293 DOI: 10.3389/fneur.2021.664631] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Accepted: 04/09/2021] [Indexed: 11/13/2022] Open
Abstract
Background: The initial injury burden from incident TBI is significantly amplified by recurrent TBI (rTBI). Unfortunately, research assessing 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.
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Affiliation(s)
- Oliver Lasry
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, QC, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Nandini Dendukuri
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, QC, Canada
| | - Judith Marcoux
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - David L Buckeridge
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, QC, Canada
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29
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Aenishaenslin C, Häsler B, Ravel A, Parmley EJ, Mediouni S, Bennani H, Stärk KDC, Buckeridge DL. Evaluating the Integration of One Health in Surveillance Systems for Antimicrobial Use and Resistance: A Conceptual Framework. Front Vet Sci 2021; 8:611931. [PMID: 33842569 PMCID: PMC8024545 DOI: 10.3389/fvets.2021.611931] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Accepted: 02/08/2021] [Indexed: 11/26/2022] Open
Abstract
It is now widely acknowledged that surveillance of antimicrobial resistance (AMR) must adopt a "One Health" (OH) approach to successfully 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.
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Affiliation(s)
- Cécile Aenishaenslin
- Centre de recherche en santé publique de l'Université de Montréal et du CIUSSS du Centre-Sud-de-l'Île-de-Montréal, Montreal, QC, Canada
- Research Group on Epidemiology of Zoonoses and Public Health, Faculty of Veterinary Medicine, Université de Montréal, Saint-Hyacinthe, QC, Canada
| | - Barbara Häsler
- Veterinary Epidemiology Economics and Public Health Group, Department of Pathobiology and Population Sciences, Royal Veterinary College, Hatfield, United Kingdom
| | - André Ravel
- Centre de recherche en santé publique de l'Université de Montréal et du CIUSSS du Centre-Sud-de-l'Île-de-Montréal, Montreal, QC, Canada
- Research Group on Epidemiology of Zoonoses and Public Health, Faculty of Veterinary Medicine, Université de Montréal, Saint-Hyacinthe, QC, Canada
| | - E. Jane Parmley
- Centre for Foodborne, Environmental and Zoonotic Infectious Diseases, Public Health Agency of Canada, Guelph, ON, Canada
- Department of Population Medicine, University of Guelph, Guelph, ON, Canada
| | - Sarah Mediouni
- Centre de recherche en santé publique de l'Université de Montréal et du CIUSSS du Centre-Sud-de-l'Île-de-Montréal, Montreal, QC, Canada
- Research Group on Epidemiology of Zoonoses and Public Health, Faculty of Veterinary Medicine, Université de Montréal, Saint-Hyacinthe, QC, Canada
| | - Houda Bennani
- Veterinary Epidemiology Economics and Public Health Group, Department of Pathobiology and Population Sciences, Royal Veterinary College, Hatfield, United Kingdom
| | - Katharina D. C. Stärk
- Department of Animal Health, Federal Office for Food Safety and Veterinary Affairs, Bern, Switzerland
| | - David L. Buckeridge
- Department of Epidemiology, Biostatistics and Occupational Health, Faculty of Medicine, McGill University, Montreal, QC, Canada
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30
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Mamiya H, Moodie EEM, Schmidt AM, Ma Y, Buckeridge DL. Price discounting as a hidden risk factor of energy drink consumption. Can J Public Health 2021; 112:638-646. [PMID: 33725331 DOI: 10.17269/s41997-021-00479-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Accepted: 01/25/2021] [Indexed: 11/17/2022]
Abstract
OBJECTIVES Global consumption of caffeinated energy drinks (CED) has been increasing dramatically despite increasing evidence of their adverse health effects. 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. METHODS 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. RESULTS 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. CONCLUSION Price discounting is an important driver of CED consumption and has a varying impact across community education and income.
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Affiliation(s)
- Hiroshi Mamiya
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, 1120 Ave Pine, Montreal, QC, H3G 1A1, Canada.
| | - Erica E M Moodie
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, 1120 Ave Pine, Montreal, QC, H3G 1A1, Canada
| | - Alexandra M Schmidt
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, 1120 Ave Pine, Montreal, QC, H3G 1A1, Canada
| | - Yu Ma
- Desautels Faculty of Management, McGill University, 1001 Ave Sherbrooke West, Montreal, QC, H3G 1G5, Canada
| | - David L Buckeridge
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, 1120 Ave Pine, Montreal, QC, H3G 1A1, Canada
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Henderson SB, Morrison KT, McLean KE, Ding Y, Yao J, Shaddick G, Buckeridge DL. Staying Ahead of the Epidemiologic Curve: Evaluation of the British Columbia Asthma Prediction System (BCAPS) During the Unprecedented 2018 Wildfire Season. Front Public Health 2021; 9:499309. [PMID: 33777871 PMCID: PMC7994359 DOI: 10.3389/fpubh.2021.499309] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2019] [Accepted: 02/15/2021] [Indexed: 11/13/2022] Open
Abstract
Background: The modular British Columbia Asthma Prediction System (BCAPS) is designed to reduce information burden during wildfire smoke events by automatically gathering, integrating, generating, and visualizing data for public health users. The BCAPS framework comprises five flexible and geographically scalable modules: (1) historic data on fine particulate matter (PM2.5) concentrations; (2) historic data on relevant health indicator counts; (3) PM2.5 forecasts for the upcoming days; (4) a health forecasting model that uses the relationship between (1) and (2) to predict the impacts of (3); and (5) a reporting mechanism. Methods: The 2018 wildfire season was the most extreme in British Columbia history. Every morning BCAPS generated forecasts of salbutamol sulfate (e.g., Ventolin) inhaler dispensations for the upcoming days in 16 Health Service Delivery Areas (HSDAs) using random forest machine learning. These forecasts were compared with observations over a 63-day study period using different methods including the index of agreement (IOA), which ranges from 0 (no agreement) to 1 (perfect agreement). Some observations were compared with the same period in the milder wildfire season of 2016 for context. Results: The mean province-wide population-weighted PM2.5 concentration over the study period was 22.0 μg/m3, compared with 4.2 μg/m3 during the milder wildfire season of 2016. The PM2.5 forecasts underpredicted the severe smoke impacts, but the IOA was relatively strong with a population-weighted average of 0.85, ranging from 0.65 to 0.95 among the HSDAs. Inhaler dispensations increased by 30% over 2016 values. Forecasted dispensations were within 20% of the observed value in 71% of cases, and the IOA was strong with a population-weighted average of 0.95, ranging from 0.92 to 0.98. All measures of agreement were correlated with HSDA population, where BCAPS performance was better in the larger populations with more moderate smoke impacts. The accuracy of the health forecasts was partially dependent on the accuracy of the PM2.5 forecasts, but they were robust to over- and underpredictions of PM2.5 exposure. Conclusions: Daily reports from the BCAPS framework provided timely and reasonable insight into the population health impacts of predicted smoke exposures, though more work is necessary to improve the PM2.5 and health indicator forecasts.
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Affiliation(s)
- Sarah B Henderson
- Environmental Health Services, British Columbia Centre for Disease Control (BCCDC), Vancouver, BC, Canada
| | - Kathryn T Morrison
- Department of Epidemiology and Biostatistics, McGill University, Montreal, QC, Canada
| | - Kathleen E McLean
- Environmental Health Services, British Columbia Centre for Disease Control (BCCDC), Vancouver, BC, Canada
| | - Yue Ding
- Environmental Health Services, British Columbia Centre for Disease Control (BCCDC), Vancouver, BC, Canada
| | - Jiayun Yao
- Environmental Health Services, British Columbia Centre for Disease Control (BCCDC), Vancouver, BC, Canada
| | - Gavin Shaddick
- Department of Mathematical Sciences, University of Exeter, Exeter, United Kingdom
| | - David L Buckeridge
- Department of Epidemiology and Biostatistics, McGill University, Montreal, QC, Canada
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Liu EY, Tamblyn R, Filion KB, Buckeridge DL. Concurrent prescriptions for opioids and benzodiazepines and risk of opioid overdose: protocol for a retrospective cohort study using linked administrative data. BMJ Open 2021; 11:e042299. [PMID: 33602708 PMCID: PMC7896580 DOI: 10.1136/bmjopen-2020-042299] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
INTRODUCTION Opioid overdoses have increased substantially over the last 20 years, with over 400 000 deaths in North America. While opioid prescribing has been a target of research, benzodiazepine and opioid co-intoxication has emerged as a potential risk factor. Our aim was to assess the risk of opioid overdose associated with concurrent use of opioids and benzodiazepines relative to opioids alone. METHODS AND ANALYSIS A retrospective cohort study will be conducted using medical claims data from adult residents of Montréal, Canada. We will create a cohort of new users of opioids (ie, no opioid dispensations in prior year) in 2000-2014 from people with at least 2 years of continuous health insurance. Those with any diagnosis or hospitalisation for cancer or palliative care in the 2 years before their first opioid dispensation will be excluded. On each person-day of follow-up, exposure status will be classified into one of four mutually exclusive categories: (1) opioid-only, (2) benzodiazepine-only, (3) both opioid and benzodiazepine (concurrent use) or (4) neither. Opioid overdose will be measured using diagnostic codes documented in the hospital discharge abstract database, physician billing claims from emergency department visits and death records. Using a marginal structural Cox proportional hazards model, we will compare the hazard of overdose during intervals of concurrent opioid and benzodiazepine use to intervals of opioid use alone, adjusted for sociodemographics, medical and psychiatric comorbidities, and substance use disorders. ETHICS AND DISSEMINATION This study is approved by the McGill Faculty of Medicine Institutional Review Board and the Commission d'access à l'information (Québec privacy commission). Results will be relevant to clinicians, policymakers and other researchers interested in co-prescribing practices of opioids and benzodiazepines. Study findings will be disseminated at relevant conferences and published in biomedical and epidemiological peer-reviewed journals.
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Affiliation(s)
- Erin Y Liu
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, Quebec, Canada
- McGill Clinical and Health Informatics, McGill University, Montréal, Quebec, Canada
| | - Robyn Tamblyn
- McGill Clinical and Health Informatics, McGill University, Montréal, Quebec, Canada
- Departments of Medicine and of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, Quebec, Canada
| | - Kristian B Filion
- Departments of Medicine and of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, Quebec, Canada
- Centre for Clinical Epidemiology, Lady Davis Institute, Jewish General Hospital, Montréal, Quebec, Canada
| | - David L Buckeridge
- McGill Clinical and Health Informatics, McGill University, Montréal, Quebec, Canada
- Departments of Medicine and of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, Quebec, Canada
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Lapointe A, Laramée C, Belanger-Gravel A, Buckeridge DL, Desroches S, Garriguet D, Gauvin L, Lemieux S, Plante C, Lamarche B. NutriQuébec: a unique web-based prospective cohort study to monitor the population's eating and other lifestyle behaviours in the province of Québec. BMJ Open 2020; 10:e039889. [PMID: 33115902 PMCID: PMC7594370 DOI: 10.1136/bmjopen-2020-039889] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/23/2023] Open
Abstract
INTRODUCTION The epidemic of non-communicable diseases including cardiovascular diseases and type 2 diabetes is attributable in large part to unhealthy eating and physical inactivity. In the fall of 2016, the Québec government launched its first-ever Government Health Prevention Policy (Politique gouvernementale de prévention en santé (PGPS)) to influence factors that lead to improved health status and quality of life as well as reduced social inequalities in health in the population of Québec. NutriQuébec is a web-based prospective open cohort study whose primary aim is to provide essential data for the evaluation of the PGPS on the Québec population's eating and other lifestyle behaviours over time. METHODS AND ANALYSIS Over a first phase of 3 years, NutriQuébec will enrol 20 000 adults living in the province of Québec in Canada through a multimedia campaign designed to reach different segments of the population, including subgroups with lower socioeconomic status. Participants will be invited to complete on a web platform nine core questionnaires on a yearly basis. Questionnaires will assess several dimensions related to lifestyle, including eating and physical activity behaviours, as well as a large number of personal characteristics and global health status. Temporal trends in eating and lifestyle behaviours will be analysed in relation to the implementation of the PGPS to provide essential data for its evaluation at a population level. Data analyses will use sociodemographic weights to adjust responses of participants to achieve, so far as is possible, representativeness of the adult Québec population. ETHICS AND DISSEMINATION Université Laval Research Ethics Board approved the NutriQuébec project. Data analysis, presentations in conferences and publication of manuscripts are scheduled to start in 2020. TRIAL REGISTRATION NUMBER NCT04140071.
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Affiliation(s)
- Annie Lapointe
- Centre NUTRISS, INAF, Université Laval, Quebec City, Quebec, Canada
| | | | - Ariane Belanger-Gravel
- Centre NUTRISS, INAF, Université Laval, Quebec City, Quebec, Canada
- Department of Information and Communication, Université Laval, Quebec city, Quebec, Canada
- Quebec Heart and Lung Institute, Quebec city, Quebec, Canada
| | - David L Buckeridge
- School of Population and Global Health, McGill University, Montreal, Quebec, Canada
| | - Sophie Desroches
- Centre NUTRISS, INAF, Université Laval, Quebec City, Quebec, Canada
- School of Nutrition, Université Laval, Quebec City, Quebec, Canada
| | - Didier Garriguet
- Health Analysis Division, Statistics Canada, Ottawa, Ontario, Canada
| | - Lise Gauvin
- Centre de recherche du CHUM, Centre Hospitalier de l'Université de Montréal, Montreal, Quebec, Canada
- Department of Social and Preventive Medicine, Université de Montréal, Montreal, Quebec, Canada
| | - Simone Lemieux
- Centre NUTRISS, INAF, Université Laval, Quebec City, Quebec, Canada
- School of Nutrition, Université Laval, Quebec City, Quebec, Canada
| | - Céline Plante
- Institut national de santé publique du Québec, Quebec City, Quebec, Canada
| | - Benoit Lamarche
- Centre NUTRISS, INAF, Université Laval, Quebec City, Quebec, Canada
- School of Nutrition, Université Laval, Quebec City, Quebec, Canada
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Godin A, Xia Y, Buckeridge DL, Mishra S, Douwes-Schultz D, Shen Y, Lavigne M, Drolet M, Schmidt AM, Brisson M, Maheu-Giroux M. The role of case importation in explaining differences in early SARS-CoV-2 transmission dynamics in Canada-A mathematical modeling study of surveillance data. Int J Infect Dis 2020; 102:254-259. [PMID: 33115683 PMCID: PMC7585716 DOI: 10.1016/j.ijid.2020.10.046] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Revised: 10/15/2020] [Accepted: 10/21/2020] [Indexed: 11/18/2022] Open
Abstract
Objective The North American coronavirus disease-2019 (COVID-19) epidemic exhibited distinct early trajectories. In Canada, Quebec had the highest COVID-19 burden and its earlier March school break, taking place two weeks before those in other provinces, could have shaped early transmission dynamics. Methods We combined a semi-mechanistic model of SARS-CoV-2 transmission with detailed surveillance data from Quebec and Ontario (initially accounting for 85% of Canadian cases) to explore the impact of case importation and timing of control measures on cumulative hospitalizations. Results A total of 1544 and 1150 cases among returning travelers were laboratory-confirmed in Quebec and Ontario, respectively (symptoms onset ≤03-25-2020). Hospitalizations could have been reduced by 55% (95% CrI: 51%–59%) if no cases had been imported after Quebec’s March break. However, if Quebec had experienced Ontario’s number of introductions, hospitalizations would have only been reduced by 12% (95% CrI: 8%–16%). Early public health measures mitigated the epidemic spread as a one-week delay could have resulted in twice as many hospitalizations (95% CrI: 1.7–2.1). Conclusion Beyond introductions, factors such as public health preparedness, responses and capacity could play a role in explaining interprovincial differences. In a context where regions are considering lifting travel restrictions, coordinated strategies and proactive measures are to be considered.
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Affiliation(s)
- Arnaud Godin
- Department of Epidemiology, Biostatistics, and Occupational Health, School of Population and Global Health, McGill University, Montréal, Quebec, Canada.
| | - Yiqing Xia
- Department of Epidemiology, Biostatistics, and Occupational Health, School of Population and Global Health, McGill University, Montréal, Quebec, Canada.
| | - David L Buckeridge
- Department of Epidemiology, Biostatistics, and Occupational Health, School of Population and Global Health, McGill University, Montréal, Quebec, Canada.
| | - Sharmistha Mishra
- Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Department of Medicine, Toronto, Ontario, Canada; St. Michael's Hospital, University of Toronto, Institute of Health Policy, Management and Evaluation, Toronto, Ontario, Canada.
| | - Dirk Douwes-Schultz
- Department of Epidemiology, Biostatistics, and Occupational Health, School of Population and Global Health, McGill University, Montréal, Quebec, Canada.
| | - Yannan Shen
- Department of Epidemiology, Biostatistics, and Occupational Health, School of Population and Global Health, McGill University, Montréal, Quebec, Canada.
| | - Maxime Lavigne
- Department of Epidemiology, Biostatistics, and Occupational Health, School of Population and Global Health, McGill University, Montréal, Quebec, Canada.
| | - Mélanie Drolet
- Centre de Recherche du CHU de Quebec and Département de Médecine Sociale et Préventive, Université Laval, Ville de Quebec, Quebec, Canada.
| | - Alexandra M Schmidt
- Department of Epidemiology, Biostatistics, and Occupational Health, School of Population and Global Health, McGill University, Montréal, Quebec, Canada.
| | - Marc Brisson
- Centre de Recherche du CHU de Quebec and Département de Médecine Sociale et Préventive, Université Laval, Ville de Quebec, Quebec, Canada.
| | - Mathieu Maheu-Giroux
- Department of Epidemiology, Biostatistics, and Occupational Health, School of Population and Global Health, McGill University, Montréal, Quebec, Canada.
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Arora RK, Joseph A, Van Wyk J, Rocco S, Atmaja A, May E, Yan T, Bobrovitz N, Chevrier J, Cheng MP, Williamson T, Buckeridge DL. SeroTracker: a global SARS-CoV-2 seroprevalence dashboard. Lancet Infect Dis 2020; 21:e75-e76. [PMID: 32763195 PMCID: PMC7402646 DOI: 10.1016/s1473-3099(20)30631-9] [Citation(s) in RCA: 117] [Impact Index Per Article: 29.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Accepted: 07/17/2020] [Indexed: 11/28/2022]
Affiliation(s)
- Rahul K Arora
- Institute of Biomedical Engineering, University of Oxford, Oxford OX3 7DQ, UK; Centre for Health Informatics, Cumming School of Medicine, University of Calgary, AB, Canada.
| | - Abel Joseph
- Faculty of Engineering, University of Waterloo, Waterloo, ON, Canada
| | - Jordan Van Wyk
- Faculty of Engineering, University of Waterloo, Waterloo, ON, Canada
| | - Simona Rocco
- Faculty of Engineering, University of Waterloo, Waterloo, ON, Canada
| | - Austin Atmaja
- Faculty of Engineering, University of Waterloo, Waterloo, ON, Canada
| | - Ewan May
- Centre for Health Informatics, Cumming School of Medicine, University of Calgary, AB, Canada; Schulich School of Engineering, University of Calgary, AB, Canada
| | - Tingting Yan
- Faculty of Medicine, University of Toronto, Toronto, ON, Canada; Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Niklas Bobrovitz
- Centre for Evidence-Based Medicine, University of Oxford, Oxford OX3 7DQ, UK; Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Jonathan Chevrier
- Department of Epidemiology, Biostatistics, and Occupational Health, Faculty of Medicine, McGill University, Montreal, QC, Canada
| | - Matthew P Cheng
- McGill Interdisciplinary Initiative in Infection and Immunity, Faculty of Medicine, McGill University, Montreal, QC, Canada
| | - Tyler Williamson
- Centre for Health Informatics, Cumming School of Medicine, University of Calgary, AB, Canada
| | - David L Buckeridge
- Department of Epidemiology, Biostatistics, and Occupational Health, Faculty of Medicine, McGill University, Montreal, QC, Canada
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Abstract
OBJECTIVES This scoping review synthesizes the recent literature on precision public health and the influence of predictive models on health equity with the intent to highlight central concepts for each topic and identify research opportunities for the biomedical informatics community. METHODS Searches were conducted using PubMed for publications between 2017-01-01 and 2019-12-31. RESULTS Precision public health is defined as the use of data and evidence to tailor interventions to the characteristics of a single population. It differs from precision medicine in terms of its focus on populations and the limited role of human genomics. High-resolution spatial analysis in a global health context and application of genomics to infectious organisms are areas of progress. Opportunities for informatics research include (i) the development of frameworks for measuring non-clinical concepts, such as social position, (ii) the development of methods for learning from similar populations, and (iii) the evaluation of precision public health implementations. Just as the effects of interventions can differ across populations, predictive models can perform systematically differently across subpopulations due to information bias, sampling bias, random error, and the choice of the output. Algorithm developers, professional societies, and governments can take steps to prevent and mitigate these biases. However, even if the steps to avoid bias are clear in theory, they can be very challenging to accomplish in practice. CONCLUSIONS Both precision public health and predictive modelling require careful consideration in how subpopulations are defined and access to data on subpopulations can be challenging. While the theory for both topics has advanced considerably, there is much work to be done in understanding how to implement and evaluate these approaches in practice.
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Okhmatovskaia A, Buckeridge DL. Intelligent Tools for Precision Public Health. Stud Health Technol Inform 2020; 270:858-863. [PMID: 32570504 DOI: 10.3233/shti200283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
The idea of "precision public health" (PPH) was proposed as an alternative to a one-size-fits-all approach to improving population health, which is not always effective. PPH aims to develop and apply interventions in a customized way, taking into account the detailed information about the target group. To enable the implementation of PPH in practice, we are developing an ontology-driven software platform that provides: a) access to detailed up-to-date information about population health, b) a structured machine-readable repository of evidence about public health interventions, and c) a set of intelligent tools to facilitate the assessment of evidence transferability, i.e. to determine how well certain interventions fit a given population.
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Weir DL, Motulsky A, Abrahamowicz M, Lee TC, Morgan S, Buckeridge DL, Tamblyn R. Failure to follow medication changes made at hospital discharge is associated with adverse events in 30 days. Health Serv Res 2020; 55:512-523. [PMID: 32434274 PMCID: PMC7376001 DOI: 10.1111/1475-6773.13292] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2019] [Revised: 03/02/2020] [Accepted: 04/04/2020] [Indexed: 11/30/2022] Open
Abstract
OBJECTIVE To evaluate the hypothesis that nonadherence to medication changes made at hospital discharge is associated with an increased risk of adverse events in the 30 days postdischarge. STUDY SETTING Patients admitted to hospitals in Montreal, Quebec, between 2014 and 2016. STUDY DESIGN Prospective cohort study. DATA COLLECTION Nonadherence to medication changes was measured by comparing medications dispensed in the community with those prescribed at hospital discharge. Patient, health system, and drug regimen-level covariates were measured using medical services and pharmacy claims data as well as data abstracted from the patient's hospital chart. Multivariable Cox models were used to determine the association between nonadherence to medication changes and the risk of adverse events. PRINCIPAL FINDINGS Among 2655 patients who met our inclusion criteria, mean age was 69.5 years (SD 14.7) and 1581 (60%) were males. Almost half of patients (n = 1161, 44%) were nonadherent to at least one medication change, and 860 (32%) were readmitted to hospital, visited the emergency department, or died in the 30 days postdischarge. Patients who were not adherent to any of their medication changes had a 35% higher risk of adverse events compared to those who were adherent to all medication changes (1.41 vs 1.27 events/100 person-days, adjusted hazard ratio: 1.35, 95% CI: 1.06-1.71). CONCLUSIONS Almost half of all patients were not adherent to some or all changes made to their medications at hospital discharge. Nonadherence to all changes was associated with an increased risk of adverse events. Interventions addressing barriers to adherence should be considered moving forward.
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Affiliation(s)
- Daniala L Weir
- Department of Epidemiology and Biostatistics, Department of Medicine,, McGill University, Montreal, Quebec, Canada.,Clinical and Health Informatics Research Group, Department of Medicine, McGill University, Montreal, Quebec, Canada
| | - Aude Motulsky
- Research Center, Centre hospitalier de l'Université de Montréal, Montreal, Quebec, Canada.,Department of Management, Evaluation & Health Policy, School of Public Health, Université de Montréal, Montreal, Quebec, Canada
| | - Michal Abrahamowicz
- Department of Epidemiology and Biostatistics, Department of Medicine,, McGill University, Montreal, Quebec, Canada.,Research Institute of the McGill University Health Centre, Montreal, Canada
| | - Todd C Lee
- Department of Epidemiology and Biostatistics, Department of Medicine,, McGill University, Montreal, Quebec, Canada.,Research Center, Centre hospitalier de l'Université de Montréal, Montreal, Quebec, Canada
| | - Steven Morgan
- School of Population and Public Health, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - David L Buckeridge
- Department of Epidemiology and Biostatistics, Department of Medicine,, McGill University, Montreal, Quebec, Canada.,Clinical and Health Informatics Research Group, Department of Medicine, McGill University, Montreal, Quebec, Canada
| | - Robyn Tamblyn
- Department of Epidemiology and Biostatistics, Department of Medicine,, McGill University, Montreal, Quebec, Canada.,Clinical and Health Informatics Research Group, Department of Medicine, McGill University, Montreal, Quebec, Canada
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Luo Y, Stephens DA, Verma A, Buckeridge DL. Bayesian latent multi-state modeling for nonequidistant longitudinal electronic health records. Biometrics 2020; 77:78-90. [PMID: 32162300 DOI: 10.1111/biom.13261] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2019] [Accepted: 03/04/2020] [Indexed: 11/30/2022]
Abstract
Large amounts of longitudinal health records are now available for dynamic monitoring of the underlying processes governing the observations. However, the health status progression across time is not typically observed directly: records are observed only when a subject interacts with the system, yielding irregular and often sparse observations. This suggests that the observed trajectories should be modeled via a latent continuous-time process potentially as a function of time-varying covariates. We develop a continuous-time hidden Markov model to analyze longitudinal data accounting for irregular visits and different types of observations. By employing a specific missing data likelihood formulation, we can construct an efficient computational algorithm. We focus on Bayesian inference for the model: this is facilitated by an expectation-maximization algorithm and Markov chain Monte Carlo methods. Simulation studies demonstrate that these approaches can be implemented efficiently for large data sets in a fully Bayesian setting. We apply this model to a real cohort where patients suffer from chronic obstructive pulmonary disease with the outcome being the number of drugs taken, using health care utilization indicators and patient characteristics as covariates.
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Affiliation(s)
- Yu Luo
- Department of Mathematics and Statistics, McGill University, Quebec, Canada
| | - David A Stephens
- Department of Mathematics and Statistics, McGill University, Quebec, Canada
| | - Aman Verma
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Quebec, Canada
| | - David L Buckeridge
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Quebec, Canada
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Tamblyn R, Bates DW, Buckeridge DL, Dixon WG, Girard N, Haas JS, Habib B, Iqbal U, Li J, Sheppard T. Multinational Investigation of Fracture Risk with Antidepressant Use by Class, Drug, and Indication. J Am Geriatr Soc 2020; 68:1494-1503. [PMID: 32181493 PMCID: PMC7383967 DOI: 10.1111/jgs.16404] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Revised: 02/04/2020] [Accepted: 02/11/2020] [Indexed: 02/01/2023]
Abstract
OBJECTIVES Antidepressants increase the risk of falls and fracture in older adults. However, risk estimates vary considerably even in comparable populations, limiting the usefulness of current evidence for clinical decision making. Our aim was to apply a common protocol to cohorts of older antidepressant users in multiple jurisdictions to estimate fracture risk associated with different antidepressant classes, drugs, doses, and potential treatment indications. DESIGN Retrospective (2009–2014) cohort study. SETTING Five jurisdictions in the United States, Canada, United Kingdom, and Taiwan. PARTICIPANTS Older antidepressant users—subjects were followed from first antidepressant prescription or dispensation to first fracture or until the end of follow‐up. MEASUREMENTS The risk of fractures with antidepressants was estimated by multivariable Cox proportional hazards models using time‐varying measures of antidepressant dose and use vs nonuse, adjusting for patient characteristics. RESULTS Between 42.9% and 55.6% of study cohorts were 75 years and older, and 29.3% to 45.4% were men. Selective serotonin reuptake inhibitors (SSRIs) (48.4%‐60.0%) were the predominant class used in North America compared with tricyclic antidepressants (TCAs) in the United Kingdom and Taiwan (49.6%‐53.6%). Fracture rates varied from 37.67 to 107.18 per 1,000. The SSRIs citalopram (hazard ratio [HR] = 1.23; 95% confidence interval [CI] = 1.11‐1.36 to HR = 1.43; 95% CI = 1.11‐1.84) and sertraline (HR = 1.36; 95% CI = 1.10‐1.68), the SNRI duloxetine (HR = 1.41; 95% CI = 1.06‐1.88), TCAs doxepin (HR = 1.36; 95% CI = 1.00‐1.86) and imipramine (HR = 1.16; 95% CI = 1.05‐1.28), and atypicals (HR = 1.34; 95% CI = 1.14‐1.58) increased fracture risk in some but not all jurisdictions. In the United States and the United Kingdom, fracture risk with all classes was higher when prescribed for depression than chronic pain, a trend that is likely explained by drug choice. CONCLUSION The fracture risk for patients may be reduced by selecting paroxetine, an SSRI with lower risk than citalopram, the SNRI venlafaxine over duloxetine, and the TCA amitriptyline over imipramine or doxepin. There is uncertainty about the risk associated with the atypical antidepressants. J Am Geriatr Soc 68:1494‐1503, 2020.
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Affiliation(s)
- Robyn Tamblyn
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada.,Department of Medicine, McGill University Health Center, Montreal, Quebec, Canada.,Clinical and Health Informatics Research Group, McGill University, Montreal, Quebec, Canada
| | | | - David L Buckeridge
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada.,Clinical and Health Informatics Research Group, McGill University, Montreal, Quebec, Canada
| | - William G Dixon
- Centre for Epidemiology versus Arthritis, Centre for Musculoskeletal Research, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK
| | - Nadyne Girard
- Clinical and Health Informatics Research Group, McGill University, Montreal, Quebec, Canada
| | | | - Bettina Habib
- Clinical and Health Informatics Research Group, McGill University, Montreal, Quebec, Canada
| | - Usman Iqbal
- International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei, Taiwan.,Master's Program in Global Health and Development, PhD Program in Global Health and Health Security, College of Public Health, Taipei Medical University, Taipei, Taiwan
| | - Jack Li
- International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei, Taiwan.,Graduate Institute of Biomedical Informatics, College of Medicine Science and Technology, Taipei Medical University, Taipei, Taiwan.,Department of Dermatology, Taipei Wanfang Hospital, Taipei, Taiwan
| | - Therese Sheppard
- Centre for Epidemiology versus Arthritis, Centre for Musculoskeletal Research, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK
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Soucy JPR, Schmidt AM, Quach C, Buckeridge DL. Fluoroquinolone Use and Seasonal Patterns of Ciprofloxacin Resistance in Community-Acquired Urinary Escherichia coli Infection in a Large Urban Center. Am J Epidemiol 2020; 189:215-223. [PMID: 31665215 DOI: 10.1093/aje/kwz239] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2019] [Revised: 10/03/2019] [Accepted: 10/07/2019] [Indexed: 11/14/2022] Open
Abstract
Urinary tract infections caused by the bacterium Escherichia coli are among the most frequently encountered infections and are a common reason for antimicrobial prescriptions. Resistance to fluoroquinolone antimicrobial agents, particularly ciprofloxacin, has increased in recent decades. It is intuitive that variation in fluoroquinolone resistance is driven by changes in antimicrobial use, but careful study of this association requires the use of time-series methods. Between April 2010 and December 2014, we studied seasonal variation in resistance to ciprofloxacin, trimethoprim-sulfamethoxazole, and ampicillin in community-acquired urinary E. coli isolates in Montreal, Quebec, Canada. Using dynamic linear models, we investigated whether seasonal variation in resistance could be explained by seasonal variation in community antimicrobial use. We found a positive association between total fluoroquinolone use lagged by 1 and 2 months and the proportion of isolates resistant to ciprofloxacin. Our results suggest that resistance to ciprofloxacin is responsive to short-term variation in antimicrobial use. Thus, antimicrobial stewardship campaigns to reduce fluoroquinolone use, particularly in the winter when use is highest, are likely to be a valuable tool in the struggle against antimicrobial resistance.
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42
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Shaban-Nejad A, Michalowski M, Peek N, Brownstein JS, Buckeridge DL. Seven pillars of precision digital health and medicine. Artif Intell Med 2020; 103:101793. [PMID: 32143798 DOI: 10.1016/j.artmed.2020.101793] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Accepted: 01/03/2020] [Indexed: 01/07/2023]
Affiliation(s)
- Arash Shaban-Nejad
- The University of Tennessee Health Science Center - Oak-Ridge National Lab (UTHSC-ORNL) Center for Biomedical Informatics, Department of Pediatrics, College of Medicine, R492-50 N. Dunlap Street, Memphis, TN 38103, USA.
| | - Martin Michalowski
- School of Nursing, University of Minnesota - Twin Cities, 5-140 Weaver-Densford Hall, 308 Harvard Street SE, Minneapolis, MN, 55455, United States
| | - Niels Peek
- Centre for Health Informatics, Division of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK
| | - John S Brownstein
- Boston Children's Hospital and Harvard Medical School, Harvard University, Boston, MA, USA
| | - David L Buckeridge
- McGill Clinical and Health Informatics, School of Population and Global Health, McGill University, Montreal, Quebec H3A 1A3, Canada
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43
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Anderson M, Revie CW, Stryhn H, Neudorf C, Rosehart Y, Li W, Osman M, Buckeridge DL, Rosella LC, Wodchis WP. Defining 'actionable' high- costhealth care use: results using the Canadian Institute for Health Information population grouping methodology. Int J Equity Health 2019; 18:171. [PMID: 31707981 PMCID: PMC6842471 DOI: 10.1186/s12939-019-1074-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Accepted: 10/09/2019] [Indexed: 11/15/2022] Open
Abstract
Background A small proportion of the population consumes the majority of health care resources. High-cost health care users are a heterogeneous group. We aim to segment a provincial population into relevant homogenous sub-groups to provide actionable information on risk factors associated with high-cost health care use within sub-populations. Methods The Canadian Institute for Health Information (CIHI) Population Grouping methodology was used to define mutually exclusive and clinically relevant health profile sub-groups. High-cost users (> = 90th percentile of health care spending) were defined within each sub-group. Univariate analyses explored demographic, socio-economic status, health status and health care utilization variables associated with high-cost use. Multivariable logistic regression models were constructed for the costliest health profile groups. Results From 2015 to 2017, 1,175,147 individuals were identified for study. High-cost users consumed 41% of total health care resources. Average annual health care spending for individuals not high-cost were $642; high-cost users were $16,316. The costliest health profile groups were ‘long-term care’, ‘palliative’, ‘major acute’, ‘major chronic’, ‘major cancer’, ‘major newborn’, ‘major mental health’ and ‘moderate chronic’. Both ‘major acute’ and ‘major cancer’ health profile groups were largely explained by measures of health care utilization and multi-morbidity. In the remaining costliest health profile groups modelled, ‘major chronic’, ‘moderate chronic’, ‘major newborn’ and ‘other mental health’, a measure of socio-economic status, low neighbourhood income, was statistically significantly associated with high-cost use. Interpretation Model results point to specific, actionable information within clinically meaningful subgroups to reduce high-cost health care use. Health equity, specifically low socio-economic status, was statistically significantly associated with high-cost use in the majority of health profile sub-groups. Population segmentation methods, and more specifically, the CIHI Population Grouping Methodology, provide specificity to high-cost health care use; informing interventions aimed at reducing health care costs and improving population health.
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Affiliation(s)
- Maureen Anderson
- Department of Health Management, University of Prince Edward Island, Charlottetown, Prince Edward Island, Canada. .,Department of Community Health and Epidemiology, College of Medicine, University of Saskatchewan, Saskatoon, Saskatchewan, Canada.
| | - Crawford W Revie
- Department of Health Management, University of Prince Edward Island, Charlottetown, Prince Edward Island, Canada.,Department of Computing and Information Sciences, University of Strathclyde, Glasgow, Scotland
| | - Henrik Stryhn
- Department of Health Management, University of Prince Edward Island, Charlottetown, Prince Edward Island, Canada
| | - Cordell Neudorf
- Department of Community Health and Epidemiology, College of Medicine, University of Saskatchewan, Saskatoon, Saskatchewan, Canada.,Population and Public Health, Saskatchewan Health Authority, Saskatoon, Saskatchewan, Canada
| | - Yvonne Rosehart
- Canadian Institute for Health Information, Ottawa, Ontario, Canada
| | - Wenbin Li
- Saskatchewan Health Quality Council, Saskatoon, Saskatchewan, Canada.,Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada
| | - Meriç Osman
- Saskatchewan Health Quality Council, Saskatoon, Saskatchewan, Canada
| | - David L Buckeridge
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec, Canada
| | - Laura C Rosella
- Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada.,Public Health Ontario, Toronto, Ontario, Canada.,Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.,Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada
| | - Walter P Wodchis
- Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada.,Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.,Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada
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44
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Weir DL, Motulsky A, Abrahamowicz M, Lee TC, Morgan S, Buckeridge DL, Tamblyn R. Challenges at Care Transitions: Failure to Follow Medication Changes Made at Hospital Discharge. Am J Med 2019; 132:1216-1224.e5. [PMID: 31145881 DOI: 10.1016/j.amjmed.2019.05.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/10/2018] [Revised: 04/10/2019] [Accepted: 05/02/2019] [Indexed: 01/01/2023]
Abstract
BACKGROUND In-hospital medication reconciliation has not demonstrated reductions in adverse health outcomes, possibly because patients do not follow the changes made to their preadmission medications. Our objective was to determine the incidence of and variables associated with failure to follow newly prescribed therapies, discontinued medications, and dose changes. METHODS A prospective cohort study of patients admitted to hospitals in Montreal, Quebec between 2014 and 2016 was conducted. Failure to follow medication changes 30 days post discharge was measured by comparing prescribed and dispensed medications. Multivariable logistic regression was used to determine characteristics associated with failure to follow changes. RESULTS Among 2655 patients, mean age was 69.5 years (SD 14.7), and 1581 (60%) were males. There were 10,068 medication changes made at hospital discharge and 24% were not followed in the 30 days post discharge. Thirty percent of dose modifications were filled at the incorrect dose, 27% of new medications were not filled, and 12% of discontinued medications were filled. A number of factors increased the risk of failure to follow medication changes, including increasing out-of-pocket medication costs (adjusted odds ratio [aOR] 1.12; 95% confidence interval [CI], 1.07-1.18), discharge to long-term care facility (aOR 2.29; 95% CI, 1.63-3.08), and not having medications dispensed prior to admission (aOR 4.67; 95% CI, 3.75-5.90). CONCLUSION One in 4 hospital medication changes was not followed post discharge. Health policy aimed at eliminating out-of-pocket medication costs and investigation of factors influencing failure to follow changes for those not dispensed medications prior to admission and for long-term care residents are important next steps to address this issue.
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Affiliation(s)
- Daniala L Weir
- Department of Epidemiology and Biostatistics, McGill University, Montreal, Que, Canada; Clinical and Health Informatics Research Group, Department of Medicine, McGill University, Montreal, Que, Canada.
| | - Aude Motulsky
- Research Center, Centre hospitalier de l'Université de Montréal, Que, Canada; Department of Management, Evaluation & Health Policy, School of Public Health, Université de Montréal, Que, Canada
| | - Michal Abrahamowicz
- Department of Epidemiology and Biostatistics, McGill University, Montreal, Que, Canada; Research Institute of the McGill University Health Centre, Montreal, Que, Canada
| | - Todd C Lee
- Department of Epidemiology and Biostatistics, McGill University, Montreal, Que, Canada; Department of Medicine, McGill University, Montreal, Que, Canada
| | - Steven Morgan
- Faculty of Medicine, School of Population and Public Health, University of British Columbia, Vancouver, Canada
| | - David L Buckeridge
- Department of Epidemiology and Biostatistics, McGill University, Montreal, Que, Canada; Clinical and Health Informatics Research Group, Department of Medicine, McGill University, Montreal, Que, Canada
| | - Robyn Tamblyn
- Department of Epidemiology and Biostatistics, McGill University, Montreal, Que, Canada; Clinical and Health Informatics Research Group, Department of Medicine, McGill University, Montreal, Que, Canada
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45
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Tamblyn R, Abrahamowicz M, Buckeridge DL, Bustillo M, Forster AJ, Girard N, Habib B, Hanley J, Huang A, Kurteva S, Lee TC, Meguerditchian AN, Moraga T, Motulsky A, Petrella L, Weir DL, Winslade N. Effect of an Electronic Medication Reconciliation Intervention on Adverse Drug Events: A Cluster Randomized Trial. JAMA Netw Open 2019; 2:e1910756. [PMID: 31539073 PMCID: PMC6755531 DOI: 10.1001/jamanetworkopen.2019.10756] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
IMPORTANCE Adverse drug events (ADEs) account for up to 16% of emergency department (ED) visits and 7% of hospital admissions. Medication reconciliation is required for hospital accreditation because it can reduce medication discrepancies, but there is no evidence that reducing discrepancies reduces ADEs or other adverse outcomes. OBJECTIVE To evaluate whether electronic medication reconciliation reduces ADEs, medication discrepancies, and other adverse outcomes compared with usual care. DESIGN, SETTING, AND PARTICIPANTS This cluster randomized trial involved 3491 patients who were discharged from 2 medical units and 2 surgical units at the McGill University Health Centre, Montreal, Quebec, Canada, between October 2014 and November 2016. Data analysis took place from July 2017 to July 2019. INTERVENTION The RightRx intervention electronically retrieved community drugs from the provincial insurer and aligned them with in-hospital drugs to facilitate reconciliation and communication at care transitions. MAIN OUTCOMES AND MEASURES The primary outcome was ADEs in 30 days after discharge. Secondary outcomes included medication discrepancies, ED visits, hospital readmissions, and a composite outcome of ED visits, readmissions, and death up to 90 days after discharge. RESULTS Of 4656 eligible patients, 3567 (76.6%) consented to participate (2060 [57.8%] men; mean [SD] age, 69.8 [14.9] years). Overall, 76 patients died during the hospital stay, so 3491 patients were included in the analysis. There was no significant difference in the risk of ADEs between intervention and control groups (76 [4.6%] vs 73 [4.0%]; OR, 0.97; 95% CI, 0.33-1.48), ED visits (433 [26.2%] vs 488 [26.6%]; OR, 0.83; 95% CI, 0.36-1.42), hospital readmission (170 [10.3%] vs 261 [14.2%]; OR, 0.22; 95% CI, 0.06-1.14), or the composite outcome (447 [27.0%] vs 506 [27.6%]; OR, 0.75; 95% CI, 0.34-1.27) at 30 days. Medication discrepancies were significantly reduced in the intervention group compared with the control group (437 [26.4%] vs 1029 [56.0%]; OR, 0.24; 95% CI, 0.12-0.57). Changes made to community medications (OR, 1.05; 95% CI, 1.01-1.10) and new medications (OR, 1.09; 95% CI, 1.01-1.18) were significant risk factors for ADEs. CONCLUSIONS AND RELEVANCE Electronic medication reconciliation reduced medication discrepancies but did not reduce ADEs or other adverse outcomes. Hospital accreditation should focus on interventions that reduce the risk of adverse events for patients with multiple changes to community medications. TRIAL REGISTRATION ClinicalTrials.gov identifier: NCT01179867.
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Affiliation(s)
- Robyn Tamblyn
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada
- Clinical and Health Informatics Research Group, McGill University, Montreal, Quebec, Canada
- Department of Medicine, McGill University Health Center, Montreal, Quebec, Canada
| | - Michal Abrahamowicz
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada
| | - David L. Buckeridge
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada
- Clinical and Health Informatics Research Group, McGill University, Montreal, Quebec, Canada
| | - Melissa Bustillo
- Clinical and Health Informatics Research Group, McGill University, Montreal, Quebec, Canada
| | | | - Nadyne Girard
- Clinical and Health Informatics Research Group, McGill University, Montreal, Quebec, Canada
| | - Bettina Habib
- Clinical and Health Informatics Research Group, McGill University, Montreal, Quebec, Canada
| | - James Hanley
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada
| | - Allen Huang
- Division of Geriatric Medicine, University of Ottawa, Ottawa, Ontario, Canada
| | - Siyana Kurteva
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada
| | - Todd C. Lee
- Department of Medicine, McGill University Health Center, Montreal, Quebec, Canada
- McGill University Health Centre, Montreal, Quebec, Canada
| | - Ari N. Meguerditchian
- Clinical and Health Informatics Research Group, McGill University, Montreal, Quebec, Canada
- Department of Medicine, McGill University Health Center, Montreal, Quebec, Canada
- McGill University Health Centre, Montreal, Quebec, Canada
| | - Teresa Moraga
- Clinical and Health Informatics Research Group, McGill University, Montreal, Quebec, Canada
| | - Aude Motulsky
- Centre de Recherche du Centre Hospitalier de l’Université de Montréal, School of Public Health, University of Montreal, Montreal, Quebec, Canada
| | - Lina Petrella
- McGill University Health Centre, Montreal, Quebec, Canada
| | - Daniala L. Weir
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada
| | - Nancy Winslade
- Clinical and Health Informatics Research Group, McGill University, Montreal, Quebec, Canada
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46
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Mamiya H, Schmidt AM, Moodie EEM, Ma Y, Buckeridge DL. An Area-Level Indicator of Latent Soda Demand: Spatial Statistical Modeling of Grocery Store Transaction Data to Characterize the Nutritional Landscape in Montreal, Canada. Am J Epidemiol 2019; 188:1713-1722. [PMID: 31063186 DOI: 10.1093/aje/kwz115] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2018] [Revised: 04/24/2019] [Accepted: 04/29/2019] [Indexed: 12/26/2022] Open
Abstract
Measurement of neighborhood dietary patterns at high spatial resolution allows public health agencies to identify and monitor communities with an elevated risk of nutrition-related chronic diseases. Currently, data on diet are obtained primarily through nutrition surveys, which produce measurements at low spatial resolutions. The availability of store-level grocery transaction data provides an opportunity to refine the measurement of neighborhood dietary patterns. We used these data to develop an indicator of area-level latent demand for soda in the Census Metropolitan Area of Montreal in 2012 by applying a hierarchical Bayesian spatial model to data on soda sales from 1,097 chain retail food outlets. The utility of the indicator of latent soda demand was evaluated by assessing its association with the neighborhood relative risk of prevalent type 2 diabetes mellitus. The indicator improved the fit of the disease-mapping model (deviance information criterion: 2,140 with the indicator and 2,148 without) and enables a novel approach to nutrition surveillance.
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Affiliation(s)
- Hiroshi Mamiya
- Surveillance Lab, McGill Clinical and Health Informatics, McGill University, Montreal, Quebec, Canada
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec, Canada
| | - Alexandra M Schmidt
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec, Canada
| | - Erica E M Moodie
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec, Canada
| | - Yu Ma
- Desautels Faculty of Management, McGill University, Montreal, Quebec, Canada
| | - David L Buckeridge
- Surveillance Lab, McGill Clinical and Health Informatics, McGill University, Montreal, Quebec, Canada
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec, Canada
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47
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Habib B, Fosser SM, Buckeridge DL, Weir DL, Bustillo M, Thakur M, Tran T, Motulsky A, Bonnici A, McDonald EG, Lee TC, Tamblyn R. Evaluation of a Mobile Application to Enhance Medication Management Following Hospital Discharge: Study Protocol for a Pilot Randomized Controlled Trial. Stud Health Technol Inform 2019; 264:1929-1930. [PMID: 31438412 DOI: 10.3233/shti190718] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Over 3 million hospitalizations and 17 million ER visits occur in Canada each year. A substantial proportion of these encounters are preventable and attributable to medication non- adherence. Non-adherence to medication changes during discharge increases the risk of adverse events post-discharge. A mobile application was developed to improve medication management of post-discharge patients. A pilot randomized controlled trial was conducted to assess the application's usability and efficacy in decreasing non-adherence to medication changes made at discharge.
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Affiliation(s)
- Bettina Habib
- Clinical and Health Informatics Research Group, McGill University, Montréal, Québec, Canada
| | | | - David L Buckeridge
- Clinical and Health Informatics Research Group, McGill University, Montréal, Québec, Canada
- Department of Epidemiology and Biostatistics, McGill University, Montréal, Québec, Canada
| | - Daniala L Weir
- Clinical and Health Informatics Research Group, McGill University, Montréal, Québec, Canada
- Department of Epidemiology and Biostatistics, McGill University, Montréal, Québec, Canada
| | - Melissa Bustillo
- Clinical and Health Informatics Research Group, McGill University, Montréal, Québec, Canada
| | - Manish Thakur
- Clinical and Health Informatics Research Group, McGill University, Montréal, Québec, Canada
| | - Thai Tran
- Clinical and Health Informatics Research Group, McGill University, Montréal, Québec, Canada
| | - Aude Motulsky
- Department of Medicine, McGill University, Montréal, Québec, Canada
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Montréal, Québec, Canada
- School of Public Health, Université de Montréal, Montréal, Québec, Canada
| | - André Bonnici
- McGill University Health Centre, Montréal, Québec, Canada
| | - Emily G McDonald
- Department of Medicine, McGill University, Montréal, Québec, Canada
- McGill University Health Centre, Montréal, Québec, Canada
| | - Todd C Lee
- Department of Medicine, McGill University, Montréal, Québec, Canada
- McGill University Health Centre, Montréal, Québec, Canada
| | - Robyn Tamblyn
- Clinical and Health Informatics Research Group, McGill University, Montréal, Québec, Canada
- Department of Epidemiology and Biostatistics, McGill University, Montréal, Québec, Canada
- Department of Medicine, McGill University, Montréal, Québec, Canada
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48
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Lu XH, Mamiya H, Vybihal J, Ma Y, Buckeridge DL. Application of Machine Learning and Grocery Transaction Data to Forecast Effectiveness of Beverage Taxation. Stud Health Technol Inform 2019; 264:248-252. [PMID: 31437923 DOI: 10.3233/shti190221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Sugar Sweetened Beverages (SSB) are the primary source of artificially added sugar and have a casual association with chronic diseases. Taxation of SSB has been proposed, but limited evidence exists to guide this public health policy. Grocery transaction data, with price, discounting and other information for beverage products, present an opportunity to evaluate the likely effects of taxation policy. Sales are often non-linearly associated with price and are affected by the prices of multiple competing brands. We evaluated the predictive performance of Boosted Decision Tree Regression (B-DTR) and Deep Neural Networks (DNN) that account for the non-linearity and competition across brands, and compared their performance to a benchmark regression, the Least Absolute Shrinkage and Selection Operator (LASSO). B-DTR and DNN showed a lower Mean Squared Error (MSE) of prediction in the sales of most major SSB brands in comparison to LASSO, indicating a superior accuracy in predicting the effectiveness of SSB taxation. We demonstrated the application of machine learning methods and large transactional data from grocery stores to forecast the effectiveness food taxation.
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Affiliation(s)
- Xing Han Lu
- Surveillance Lab, McGill Clinical and Health Informatics, McGill University, Montreal, Quebec, Canada
- School of Computer Science, McGill University, Montreal, Quebec, Canada
| | - Hiroshi Mamiya
- Surveillance Lab, McGill Clinical and Health Informatics, McGill University, Montreal, Quebec, Canada
| | - Joseph Vybihal
- School of Computer Science, McGill University, Montreal, Quebec, Canada
| | - Yu Ma
- Desautels Faculty of Management, McGill University, Montreal, Quebec, Canada
| | - David L Buckeridge
- Surveillance Lab, McGill Clinical and Health Informatics, McGill University, Montreal, Quebec, Canada
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49
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Mamiya H, Moodie EEM, Ma Y, Buckeridge DL. Susceptibility to price discounting of soda by neighbourhood educational status: an ecological analysis of disparities in soda consumption using point-of-purchase transaction data in Montreal, Canada. Int J Epidemiol 2019; 47:1877-1886. [PMID: 29939286 DOI: 10.1093/ije/dyy108] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/18/2018] [Indexed: 12/25/2022] Open
Abstract
Introduction Price discounting is a marketing tactic used frequently by food industries and retailers, but the extent to which education modifies the effect of discounting on the purchasing of unhealthy foods has received little attention. We investigated whether there was a differential association of price discounting of soda with store-level soda purchasing records between 2008 and 2013 by store-neighbourhood education in Montreal, Canada. Methods Using data on grocery purchase transactions from a sample of supermarkets, pharmacies, supercentres and convenience stores, we performed an ecological time-series analysis, modelling weekly store-level sales of soda as a function of store-level price discounting, store- and neighbourhood-level confounders and an interaction term between discounting and categorical education in the neighbourhood of each store. Results Analysis by store type (n = 18 743, 12 437, 3965 and 49 533 store-weeks for superstores, pharmacies, supercentres and convenience stores, respectively) revealed that the effect measure modification of discounting by neighbourhood education on soda purchasing was lower in stores in the more educated neighbourhoods, most notably in pharmacies: -0.020 [95% confidence interval (CI): -0.028, -0.012] and -0.038 (95% CI: -0.051, -0.025), for middle- and high-education categories, respectively). Weaker effect modification was observed in convenience stores. There was no evidence of effect modification in supercentres or superstores. Conclusions Price discounting is an important environmental risk factor for soda purchasing and can widen education inequalities in excess sugar intake across levels of education. Interventions to regulate price discounting warrant further investigation as a public health strategy to improve population nutrition, particularly in lower-education neighbourhoods.
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Affiliation(s)
- Hiroshi Mamiya
- Department of Epidemiology, Biostatistics, and Occupational Health.,Surveillance Laboratory, McGill Clinical and Health Informatics
| | - Erica E M Moodie
- Department of Epidemiology, Biostatistics, and Occupational Health
| | - Yu Ma
- Desautels Faculty of Management, McGill University, Montréal, QC, Canada
| | - David L Buckeridge
- Department of Epidemiology, Biostatistics, and Occupational Health.,Surveillance Laboratory, McGill Clinical and Health Informatics
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Abstract
The burgeoning field of Artificial Intelligence (AI) has the potential to profoundly
impact the public’s health. Yet, to make the most of this opportunity, decision-makers
must understand AI concepts. In this article, we describe approaches and fields within AI
and illustrate through examples how they can contribute to informed decisions, with a
focus on population health applications. We first introduce core concepts needed to
understand modern uses of AI and then describe its sub-fields. Finally, we examine four
sub-fields of AI most relevant to population health along with examples of available tools
and frameworks. Artificial intelligence is a broad and complex field, but the tools that
enable the use of AI techniques are becoming more accessible, less expensive, and easier
to use than ever before. Applications of AI have the potential to assist clinicians,
health system managers, policy-makers, and public health practitioners in making more
precise, and potentially more effective, decisions.
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Affiliation(s)
- Maxime Lavigne
- 1 Surveillance Lab, McGill Clinical and Health Informatics, McGill University, Montreal, Quebec, Canada.,2 Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec, Canada
| | - Fatima Mussa
- 3 CIHR Institute of Population and Public Health, Canadian Institutes of Health Research, Toronto, Ontario, Canada
| | - Maria I Creatore
- 3 CIHR Institute of Population and Public Health, Canadian Institutes of Health Research, Toronto, Ontario, Canada.,4 Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Steven J Hoffman
- 3 CIHR Institute of Population and Public Health, Canadian Institutes of Health Research, Toronto, Ontario, Canada.,5 Global Strategy Lab, York University, Toronto, Canada.,6 Dahdaleh Institute for Global Health Research, Faculty of Health and Osgoode Hall Law School, York University, Toronto, Ontario, Canada
| | - David L Buckeridge
- 1 Surveillance Lab, McGill Clinical and Health Informatics, McGill University, Montreal, Quebec, Canada.,2 Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec, Canada
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