Co-PIs: John Maguire, Mary Alipay* (DLSPH)

Collaborators: Jeff Kwong (DLSPH), Rahul Krishnan (Department of Computer Science, UofT), Charles Keowan-Stoneman (DLSPH Biostatistics), Ashleigh Tuite (DSLPH), Muhammad Mamdani (IHPME)               

Project Description: Over 83,000 children in Ontario have tested positive for SARS-CoV-2. Infection has been associated with increased hospitalizations in children with little data on long-term morbidity. The ‘Swiss Cheese’ model of pandemic defense is an approach to preventing SARS-CoV-2 infection by layering protections, including individual interventions (e.g., handwashing, wearing masks) and community interventions (e.g., school closures, lockdowns). While some layers may provide more protection than others, there are differing costs to children and families associated with each individual intervention. Nearly 2 years into the pandemic, it remains unknown which individual and community interventions are the most effective for preventing infection among children. Using linked cohort (TARGet Kids!) and administrative (ICES) data, this team will use advanced machine learning techniques to determine the most effective interventions for preventing SARS-Cov-2 infection among children. Results will offer parents and policymakers with the evidence needed to balance the benefits of interventions against the costs to families.

Project funded by the Edwin S.H. Leong Centre for Healthy Children.

Co-PIs: Peter Smith, Andrea Portt* (DLSPH)           

Collaborators: Hong Chen (Public Health Ontario, DLSPH), Antonio Gasparrini (London School of Hygiene & Tropical Medicine), Erjia Ge (DLSPH Biostatistics), Christine Lay (Women’s College Hospital)

Project Description: Migraine causes debilitating head pain, nausea, and sensitivity to light and/or sound for more than 1 billion people worldwide. Migraine drugs can be expensive and under-prescribed, and sometimes have side effects, which leaves many people reliant upon avoiding triggers (e.g. flashing lights, foods, or odours) to avoid migraine attacks. Some research suggests that air pollution such as particulate matter (PM2.5), ozone (O3), and nitrogen dioxide (NO2) may cause migraine attacks. The majority of the research used clinic and hospital records. These include imprecise attack start times, so it is hard to know which of the many air pollutants we breathe are important for migraines. Smartphone migraine application (app) data provides more precise information about when migraine attacks start. This study will determine the association of local air pollution with migraine attack onset via both ER and smartphone data. The overarching goal of the interdisciplinary team is to validate the use of self-reported smartphone app data compared to administrative data in the context of environmental exposures and health outcomes.

PI: Michael Colacci* (IHPME/DLSPH), Michael Fralick (General Internal Medicine), Fahad Razak (General Internal Medicine)

Collaborators: Alanna Weisman (Mount Sinai), Amol Verma (General Internal Medicine), Bruce Perkins (UHN), Muhammad Mamdani (Unity Health Toronto), Chloe Pou-Prom (St. Michael’s Hospital), Patrick O’Brien (University Health Network), Esmerelda Carson (St. Michael’s Hospital)

Project description: DLSPH Master’s student Michael Colacci, together with clinician scientists Michael Fralick and Fahad Razak from University of Toronto’s Faculty of Medicine and eight co-investigators representing five different affiliate hospitals, will link data from the Ontario Diabetes Database and the General Medicine Inpatient Initiative platform (GEMINI database) to create a cohort of approximately 200,000 people who have been hospitalized with diabetes in Canada.  The new cohort will offer an incredibly rich source of data to evaluate the nature of inpatient care for diabetes – a disease affecting one in seven adults in this country.  The team will use the new database to evaluate which proportion of patients received the most up-to-date care, and apply an artificial intelligence-based prediction model to identify which groups of patients might be at highest risk of negative outcomes.

PI: Abby Emdin* (DLSPH), Susan Bronskill, (DLSPH)

Collaborators: Peter Austin (DLSPH/IHPME), Colleen Maxwell (University of Waterloo – School of Pharmacy), Jennifer Watt, (Department of Medicine), Aaron Jones (ICES), Daniel Harris* (DLSPH), Jennifer Bethell, (DLSPH/IHPME)

Project description: DLSPH PhD student Abby Emdin and Professor Susan Bronskill (DLSPH, IHPME and ICES), together with six co-investigators with expertise across geriatric medicine, epidemiology and rehabilitation sciences are collaborating on a project that will apply network analysis to linked health administration data to understand prescription patterns for older adults newly diagnosed with dementia.  Using a study population of approximately 25,000 people in Ontario diagnosed between 2015 and 2019, the work will aim to identify the prevalence and circumstances related to patterns of over-medication.  The findings may be used to improve health system efficiency by detecting medication overuse, and also prevent inappropriate prescribing – and associated negative health outcomes – among people with dementia.  The use of network analysis methodology to patterns of drug prescribing is virtually unexplored, and this project will help identify the utility of this tool for future research in the field of pharmacoepidemiology.

PI: Sean Hill (CAMH), Daniel Buchman (DLSPH)

Collaborators: Zoe Findlay* (CAMH), Katrina Hui (Department of Psychiatry), Marta Maslej (CAMH), Laura Sikstrom (CAMH), Juveria Zaheer (CAMH)

Project description: Sean Hill, Director of CAMH’s Krembil Center for Neuroinformatics, together with DLSPH Assistant Professor Daniel Buchman and five co-investigators with expertise across multiscale modeling, experimental and emergency psychology, translational science and bioethics, will evaluate whether biases might be built into the way risk assessment takes place in the context of psychiatric care.  Recognizing that both conventional risk assessment and different machine learning tools may be subject to bias, the team will use a mixed methods approach that combines an analysis of Electronic Health Records, participant observation, and interviews with clinicians and patients, to identify how ML tools might amplify existing inequities, such as racial bias, because they are trained on biased datasets.  Findings from the project will provide critical context and pilot data for future work using machine learning to redress these biases through fairer models.

PI: Melissa McCradden (DLSPH)

Collaborators: Dolly Menna-Dack (Holland Bloorview Kids Rehabilitation Hospital), Randi Zlotnik Shaul (SickKids), James Anderson (SickKids), Elizabeth Stephenson (SickKids)

Project description: In this project supported by the Edwin S.H. Leong Centre for Healthy Children, DLSPH Assistant Professor Melissa McCradden, together with co-investigators Dolly Menna-Dack (Holland Bloorview Kids Rehabilitation), Randi Zlotnik Shaul (SickKids), James Anderson (SickKids) and Elizabeth Stephenson (SickKids), will explore how children and youth understand and feel about the use of artificial intelligence in healthcare. The work aims to remedy the fact that there currently exist no explorations of the views of children and youth about the tools employed in their own care. The group brings expertise across qualitative health research, youth engagement, explainable AI, Bioethics, health law and research ethics. The project will generate new guidelines for clinical discussions with children about AI-assisted decision-making, create template language for informed consent forms for AI research studies, and launch a new agenda for healthcare AI engagement for children and youth.

Project funded by the Edwin S.H. Leong Centre for Healthy Children.

PI: Steven Narod (DLSPH)

Collaborators: Vasily Giannakeas* (DLSPH), Jennifer Brooks (DLSPH); Victoria Sopik* (Institute of Medical Science)  

Project description: DLSPH Professor Steven Narod, together with PhD candidates Vasily Giannakeas (DLSPH) and Victoria Sopik (Institute of Medical Sciences), and Assistant Professor Jennifer Brooks (DLSPH) aim to generate findings that might help develop a tool which can be used to predict the presence of cancer based on routine complete blood count tests that can be performed in any family doctor’s office.  In earlier work, this group linked data from the Ontario Laboratory Information System and the Ontario Cancer Registry to analyse the risk of cancer occurrences in over nine million adults in Ontario; they found that high platelet counts were associated with a more than five-fold risk of a solid cancer diagnosed within six months of the blood test.  In this new project, the team will evaluate more measures from original blood tests, incorporate additional variables such as age, sex and ethnicity, and apply statistical learning to improve the predictive values of their findings.  The goal will be to create a tool that researchers might subsequently evaluate in prospective studies.