Grant: Project Grant

The COVID-19 pandemic demonstrated the power and potential of wastewater-based surveillance (WBS). Governments and health jurisdictions across the world now routinely use this technology to predict COVID-19 disease and direct health policy. Our team of scientists and health and addiction specialists are adapting this technology to better understand and react to the tainted drug supply (i.e., opioid crisis), finding new ways to fight Canada's epidemic of drug poisonings. Since 2016, >50,000 Canadians have died from drug poisonings. A key feature of this crisis is the unpredictability of the drug supply, with novel agents (i.e., more toxic opioids such as carfentanil and additives such as xylazine) intermittently contaminating the drug supply. We are developing tools to monitor substances in wastewater collected from diverse populations - from small towns to large cities (and neighborhoods within), First Nations communities, and high-risk facilities (e.g., shelters) to understand the distribution and variation in substances across populations and over time. Aim 1: Develop and refine processes to identify and quantify key substances (and their metabolites) across diverse populations (ranging from cities to buildings). Machine learning will be used to identify and track new and emerging substances and how they correlate with clinical events. Aim 2: Determine how substance use differs over time and between populations. We will correlate socioeconomic characteristics of populations with substances in wastewater to understand how use differs between areas and the factors associated with regional variation. We will correlate how drug poisonings in different catchments correlate with substances and policies. Aim 3: Develop early warning, harm reduction tools in partnership with health providers, addiction experts, and substance users that enable relatable and understandable real-time data sharing on the changing nature of the drug supply.