COVID-19 changed everyday life and how people access healthcare, like going to the doctor, getting vaccinated, or receiving check-ups. However, life changed for different people in different ways. Different countries faced different types of the virus and had different rules about staying home and wearing masks. Many researchers use information on healthcare from around the world to find out what makes people healthy or sick. So, when things like COVID-19 happen, they need to know how people and countries are different in terms of their health. This is why we are making visual tools to see how healthcare is different in different countries and people. As an example, we are looking at what happens after people start a new medication depending on where they are in the world and if they started before or after COVID-19 started spreading. We chose to look at medications for problems with blood sugar, blood pressure, and depression. We also chose to look at three things: events like a heart attack, being told you have new health problems, and diseases requiring doctors to check in person if you have them. Our visual tools will be used to compare people in Ontario, British Columbia, and the United Kingdom through graphs.
This project will help people by creating tools scientists can use to see how different groups are affected by different diseases. This will make it easier to put together results from different countries to understand how drugs help or cause diseases.
The COVID-19 pandemic had major impacts on healthcare systems worldwide that varied greatly in their timing and magnitude. Different countries (and different jurisdictions within countries) also pursued responses to the pandemic that varied greatly. The resulting heterogeneity has major implications for public health research that aims to combine and synthesize treatment effect estimates from different jurisdictions. We previously developed visualizations showing and evaluating heterogeneity in treatment distributions, outcome rates, covariates distributions, covariate imbalances, and subgroup treatment effect estimates across a multi-site study by separating a cohort of CPRD patients with diabetes initiating treatment with metformin or sulfonylurea prior to the pandemic into distinct regions. In this project, we plan to apply these tools to visualize heterogeneity in multi-site active comparator new user studies of commonly studied medication classes from 2019 through the most available CPRD data. Our primary exposure will be exposure to ¬¬SSRIs and SNRIS for the treatment of depression and our primary outcome will be all-cause mortality. Similar analyses will be performed for other outcomes related to COVID-19 to further display the potential for these visualization tools. We will use inverse probability weights based on propensity scores estimated using multivariable logistic regression to estimate treatment effects on the outcome. After conducting the initial analyses and understanding the trends specific to the patients within CPRD, the calendar time trends in treatment, outcome rates, covariates, and treatment effect estimates from the UK will be compared with results from separately analysed British Columbia and Ontario cohorts to understand the utility of these visualizations in a real-world case study and illustrate the potential for rapid applications of these visualizations to capture the impacts of heterogeneous responses to future shocks to healthcare system.
Incidence rates for the first occurrence of three different types of outcomes, with some outcomes specific to specific active comparator/new user cohorts:
1) Event-based outcomes (stroke, all-cause mortality, myocardial infarction, hospitalization for heart failure, lower extremity amputation, hospitalization for hypoglycemia, chronic obstructive pulmonary disease [COPD] exacerbation, hospitalization for suicidal ideation)
2) New onset of comorbidities related to COVID-19 (type 2 diabetes, hypertension, depression, hyperlipidemia, COPD, heart failure, end stage renal disease)
3) Procedures and diagnoses requiring in-person procedures or office visits (diabetic retinopathy, breast cancer screening, colon cancer screening)
Samy Suissa - Chief Investigator - Sir Mortimer B Davis Jewish General Hospital
Samy Suissa - Corresponding Applicant - Sir Mortimer B Davis Jewish General Hospital
Gwen Aubrac - Collaborator - McGill University
Kristian Filion - Collaborator - McGill University
Laurent Azoulay - Collaborator - McGill University
Michael Webster-Clark - Collaborator - McGill University
Qi Zhang - Collaborator - Sir Mortimer B Davis Jewish General Hospital
Robert Platt - Collaborator - McGill University
HES Admitted Patient Care;ONS Death Registration Data;Patient Level Index of Multiple Deprivation