David L Buckeridge
Biographic Data
| ID | 1727472 |
|---|---|
| NAME | David L Buckeridge |
| GIVEN NAMES | David L |
| FAMILY NAME | Buckeridge |
| SIGNATURE | BUCKERIDGE D L |
| AFFILIATIONS | McGill University |
| ORCID | 0000-0003-1817-5047 |
| VERIFIED | Yes |
| TOTAL WORKS | 32 |
| TOTAL CITATIONS | 12 |
| AUTHOR COUNT | 32 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2001 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 2 |
Machine learning used to study risk factors for chronic diseases: A scoping review
OBJECTIVES: Machine learning (ML) has received significant attention for its potential to process and learn from vast amounts of data. Our aim was to perform a scoping review to identify studies that used ML to study risk factors for chronic diseases at a population level, notably those that incorporated methods to mitigate algorithmic bias. We focused on ML applications for the most common risk factors for chronic disease: tobacco use, alcohol u…
Sociodemographic characteristics of Sars-CoV-2 serosurveillance studies with diverse recruitment strategies, Canada, 2020 to 2023
While no study had adequate representation of all subgroups, less traditional recruitment strategies were more representative of some population dimensions. Understanding demographic representativeness and barriers to recruitment are important considerations when designing population health surveillance studies
Comparability of Canadian Sars-CoV-2 seroprevalence estimates with statistical adjustment for socio-demographic representation
Behavioral interventions—past, present, and future: Proceedings of the 5th International Behavioural Trials Network International Hybrid Meeting
Canada’s provincial Covid-19 pandemic modelling efforts: A review of mathematical models and their impacts on the responses
Canada’s approach to Sars-CoV-2 sero-surveillance: Lessons learned for routine surveillance and future pandemics
Sars-CoV-2 infection in Africa: A systematic review and meta-analysis of standardised seroprevalence studies, from January 2020 to December 2021
Estimating the lagged effect of price discounting: A time-series study on sugar sweetened beverage purchasing in a supermarket
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
Staying Ahead of the Epidemiologic Curve: Evaluation of the British Columbia Asthma Prediction System (BCAPS) During the Unprecedented 2018 Wildfire Season
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 (PM 2.5 ) concentrations; (2) historic data on relevant health indicator counts; …
Generating community measures of food purchasing activities using store-level electronic grocery transaction records: An ecological study in Montreal, Canada
Objective: Geographic measurement of diets is generally not available at areas smaller than a national or provincial (state) scale, as existing nutrition surveys cannot achieve sample sizes needed for an acceptable statistical precision for small geographic units such as city subdivisions. Design: Using geocoded Nielsen grocery transaction data collected from supermarket, supercentre and pharmacy chains combined with a gravity model that transfor…
Price discounting as a hidden risk factor of energy drink consumption
Glossary for public health surveillance in the age of data science
Public health surveillance is the ongoing systematic collection, analysis and interpretation of data, closely integrated with the timely dissemination of the resulting information to those responsible for preventing and controlling disease and injury. With the rapid development of data science, encompassing big data and artificial intelligence, and with the exponential growth of accessible and highly heterogeneous health-related data, from health…
Defining ‘actionable’ high- costhealth care use: Results using the Canadian Institute for Health Information population grouping methodology
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; informi…
Why public health matters today and tomorrow: The role of applied public health research
Public health is critical to a healthy, fair, and sustainable society. Realizing this vision requires imagining a public health community that can maintain its foundational core while adapting and responding to contemporary imperatives such as entrenched inequities and ecological degradation. In this commentary, we reflect on what tomorrow's public health might look like, from the point of view of our collective experiences as researchers in Cana…
The effect of socio-demographic factors on mental health and addiction high-cost use: A retrospective, population-based study in Saskatchewan
Despite constituting only 5% of the study cohort, persistent high-cost MHA clients (n = 6455) accounted for ~ 35% of total costs. Efforts to reduce high-cost use should focus on reduction of multimorbidity, connection to a primary care provider (particularly for those with more than one MHA), young patients with schizophrenia, and adequately addressing housing stability
Comparing Twitter data to routine data sources in public health surveillance for the 2015 Pan/Parapan American Games: An ecological study
Validation of Diagnostic Groups Based on Health Care Utilization Data Should Adjust for Sampling Strategy
OBJECTIVE: Valid measurement of outcomes such as disease prevalence using health care utilization data is fundamental to the implementation of a "learning health system." Definitions of such outcomes can be complex, based on multiple diagnostic codes. The literature on validating such data demonstrates a lack of awareness of the need for a stratified sampling design and corresponding statistical methods. We propose a method for validating the mea…
An Innovative Approach to Addressing Childhood Obesity: A Knowledge-Based Infrastructure for Supporting Multi-Stakeholder Partnership Decision-Making in Quebec, Canada
Multi-stakeholder partnerships (MSPs) have become a widespread means for deploying policies in a whole of society strategy to address the complex problem of childhood obesity. However, decision-making in MSPs is fraught with challenges, as decision-makers are faced with complexity, and have to reconcile disparate conceptualizations of knowledge across multiple sectors with diverse sets of indicators and data. These challenges can be addressed by …
A qualitative study of health information technology in the Canadian public health system
The application of HIT in public health should focus on automating core processes and identifying innovative applications of HIT to advance public health outcomes. The Public Health Agency of Canada should develop the expertise to lead public health HIT policy and should establish a mechanism for coordinating public health stakeholder input on HIT policy
The impact of geographical location of residence on disease outcomes among Canadian First Nations populations during the 2009 influenza A(H1N1) pandemic
Age Distribution of Infection and Hospitalization Among Canadian First Nations Populations During the 2009 H1N1 Pandemic
Objectives. We estimated age-standardized ratios of infection and hospitalization among Canadian First Nations (FN) populations and compared their distributions with those estimated for non-FN populations in Manitoba, Canada. Methods. For the spring and fall 2009 waves of the H1N1 pandemic, we obtained daily numbers of laboratory-confirmed and hospitalized cases of H1N1 infection, stratified by 5-year age groups and FN status. We calculated age-s…
Patient, physician, encounter, and billing characteristics predict the accuracy of syndromic surveillance case definitions
Many physician, patient, encounter, and billing characteristics associated with the PPV of surveillance case definition are accessible to public health, and could be used to reduce false-positive alerts by surveillance systems, either by focusing on the data most likely to be accurate, or by adjusting the observed data for known biases in diagnosis reporting and performing surveillance using the adjusted values
Accuracy of syndrome definitions based on diagnoses in physician claims
Future research should identify physician, patient, and encounter characteristics associated with the accuracy of diagnostic codes in physician claims. This would enable public health to improve syndromic surveillance, either by focusing on physician claims whose diagnostic code is more likely to be accurate, or by using all physician claims and weighing each according to the likelihood that its diagnostic code is accurate
Vaccination against 2009 pandemic H1N1 in a population dynamical model of Vancouver, Canada: Timing is everything
Delays in vaccine production due to technological or logistical barriers may reduce potential benefits of vaccination for pandemic influenza, and these temporal effects can outweigh any additional theoretical benefits from population targeting. Careful modeling may provide decision makers with estimates of these effects before the epidemic peak to guide production goals and inform policy. Integration of real-time surveillance data with mathematic…
Physician privacy concerns when disclosing patient data for public health purposes during a pandemic influenza outbreak
The uncertainty surrounding a pandemic of a new strain of influenza has not changed the privacy concerns of physicians about disclosing patient data. It is important to address these concerns to ensure reliable reporting during future outbreaks
Glossary for public health surveillance in the age of data science
Public health surveillance is the ongoing systematic collection, analysis and interpretation of data, closely integrated with the timely dissemination of the resulting information to those responsible for preventing and controlling disease and injury. With the rapid development of data science, encompassing big data and artificial intelligence, and with the exponential growth of accessible and highly heterogeneous health-related data, from health…
Making health data maps: A Case Study of a Community/University Research Collaboration
Why public health matters today and tomorrow: The role of applied public health research
Public health is critical to a healthy, fair, and sustainable society. Realizing this vision requires imagining a public health community that can maintain its foundational core while adapting and responding to contemporary imperatives such as entrenched inequities and ecological degradation. In this commentary, we reflect on what tomorrow's public health might look like, from the point of view of our collective experiences as researchers in Cana…
Health Informatics Education: An Opportunity for Public Health in Canada
Making health data maps: A Case Study of a Community/University Research Collaboration
A knowledge-based method for surveillance
Tools to facilitate the interchange and analysis of nontraditional health surveillance data
A knowledge-based approach to defining syndromes
Perceptions of immunization information systems for collecting pandemic H1N1 immunization data within Canada's public health community: A qualitative study
IISs were perceived as valuable by key informants for strengthening management capacity and improving evaluation of both seasonal and pandemic influenza vaccination campaigns. However, certain implementation restrictions may need to be overcome for these benefits to be achieved
Accuracy of syndrome definitions based on diagnoses in physician claims
Future research should identify physician, patient, and encounter characteristics associated with the accuracy of diagnostic codes in physician claims. This would enable public health to improve syndromic surveillance, either by focusing on physician claims whose diagnostic code is more likely to be accurate, or by using all physician claims and weighing each according to the likelihood that its diagnostic code is accurate
Vaccination against 2009 pandemic H1N1 in a population dynamical model of Vancouver, Canada: Timing is everything
Delays in vaccine production due to technological or logistical barriers may reduce potential benefits of vaccination for pandemic influenza, and these temporal effects can outweigh any additional theoretical benefits from population targeting. Careful modeling may provide decision makers with estimates of these effects before the epidemic peak to guide production goals and inform policy. Integration of real-time surveillance data with mathematic…
Physician privacy concerns when disclosing patient data for public health purposes during a pandemic influenza outbreak
The uncertainty surrounding a pandemic of a new strain of influenza has not changed the privacy concerns of physicians about disclosing patient data. It is important to address these concerns to ensure reliable reporting during future outbreaks
Approaches to Immunization Data Collection Employed Across Canada During the Pandemic (H1N1) 2009 Influenza Vaccination Campaign
Patient, physician, encounter, and billing characteristics predict the accuracy of syndromic surveillance case definitions
Many physician, patient, encounter, and billing characteristics associated with the PPV of surveillance case definition are accessible to public health, and could be used to reduce false-positive alerts by surveillance systems, either by focusing on the data most likely to be accurate, or by adjusting the observed data for known biases in diagnosis reporting and performing surveillance using the adjusted values
A qualitative study of health information technology in the Canadian public health system
The application of HIT in public health should focus on automating core processes and identifying innovative applications of HIT to advance public health outcomes. The Public Health Agency of Canada should develop the expertise to lead public health HIT policy and should establish a mechanism for coordinating public health stakeholder input on HIT policy
The impact of geographical location of residence on disease outcomes among Canadian First Nations populations during the 2009 influenza A(H1N1) pandemic
Age Distribution of Infection and Hospitalization Among Canadian First Nations Populations During the 2009 H1N1 Pandemic
Objectives. We estimated age-standardized ratios of infection and hospitalization among Canadian First Nations (FN) populations and compared their distributions with those estimated for non-FN populations in Manitoba, Canada. Methods. For the spring and fall 2009 waves of the H1N1 pandemic, we obtained daily numbers of laboratory-confirmed and hospitalized cases of H1N1 infection, stratified by 5-year age groups and FN status. We calculated age-s…
An Innovative Approach to Addressing Childhood Obesity: A Knowledge-Based Infrastructure for Supporting Multi-Stakeholder Partnership Decision-Making in Quebec, Canada
Multi-stakeholder partnerships (MSPs) have become a widespread means for deploying policies in a whole of society strategy to address the complex problem of childhood obesity. However, decision-making in MSPs is fraught with challenges, as decision-makers are faced with complexity, and have to reconcile disparate conceptualizations of knowledge across multiple sectors with diverse sets of indicators and data. These challenges can be addressed by …
Validation of Diagnostic Groups Based on Health Care Utilization Data Should Adjust for Sampling Strategy
OBJECTIVE: Valid measurement of outcomes such as disease prevalence using health care utilization data is fundamental to the implementation of a "learning health system." Definitions of such outcomes can be complex, based on multiple diagnostic codes. The literature on validating such data demonstrates a lack of awareness of the need for a stratified sampling design and corresponding statistical methods. We propose a method for validating the mea…
The effect of socio-demographic factors on mental health and addiction high-cost use: A retrospective, population-based study in Saskatchewan
Despite constituting only 5% of the study cohort, persistent high-cost MHA clients (n = 6455) accounted for ~ 35% of total costs. Efforts to reduce high-cost use should focus on reduction of multimorbidity, connection to a primary care provider (particularly for those with more than one MHA), young patients with schizophrenia, and adequately addressing housing stability
Comparing Twitter data to routine data sources in public health surveillance for the 2015 Pan/Parapan American Games: An ecological study
Defining ‘actionable’ high- costhealth care use: Results using the Canadian Institute for Health Information population grouping methodology
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; informi…
Why public health matters today and tomorrow: The role of applied public health research
Public health is critical to a healthy, fair, and sustainable society. Realizing this vision requires imagining a public health community that can maintain its foundational core while adapting and responding to contemporary imperatives such as entrenched inequities and ecological degradation. In this commentary, we reflect on what tomorrow's public health might look like, from the point of view of our collective experiences as researchers in Cana…
Glossary for public health surveillance in the age of data science
Public health surveillance is the ongoing systematic collection, analysis and interpretation of data, closely integrated with the timely dissemination of the resulting information to those responsible for preventing and controlling disease and injury. With the rapid development of data science, encompassing big data and artificial intelligence, and with the exponential growth of accessible and highly heterogeneous health-related data, from health…
Staying Ahead of the Epidemiologic Curve: Evaluation of the British Columbia Asthma Prediction System (BCAPS) During the Unprecedented 2018 Wildfire Season
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 (PM 2.5 ) concentrations; (2) historic data on relevant health indicator counts; …
Generating community measures of food purchasing activities using store-level electronic grocery transaction records: An ecological study in Montreal, Canada
Objective: Geographic measurement of diets is generally not available at areas smaller than a national or provincial (state) scale, as existing nutrition surveys cannot achieve sample sizes needed for an acceptable statistical precision for small geographic units such as city subdivisions. Design: Using geocoded Nielsen grocery transaction data collected from supermarket, supercentre and pharmacy chains combined with a gravity model that transfor…
Price discounting as a hidden risk factor of energy drink consumption
Sars-CoV-2 infection in Africa: A systematic review and meta-analysis of standardised seroprevalence studies, from January 2020 to December 2021
Medicine (24 works) · Computer Science (15 works) · Public health (14 works) · Data-Driven Disease Surveillance (11 works) · Environmental health (10 works) · Pandemic (9 works) · Nursing (8 works) · Coronavirus disease 2019 (COVID-19 (7 works) · Disease (7 works) · Infectious disease (medical specialty (7 works)