Using Community Learning Through Data-Driven Discovery to Monitor Substance Use in Rural Communities

Thomas M. Menino Convention & Exhibition Center 

Rural communities often lack public health surveillance systems due to small populations, limited staffing and resources, unstable rates, and data suppression rules, despite being significantly impacted by substance use outbreaks. Using the Community Learning Through Data-Driven Discovery (CLD3) framework, we partner with rural stakeholders to co-identify priority substance use concerns, relevant data sources, and feasible strategies for local monitoring and prevention.

Our presentation describes CLD3 implementation in data-constrained rural contexts, including integrating nontraditional and administrative data, interpreting trends under uncertainty, and converting insights into actionable local knowledge. We highlight how participatory data design and interpretation address statistical limitations while enhancing the legitimacy and usability of surveillance outputs.

This work provides rural communities with a sustainable, community-governed approach to substance use monitoring that supports prevention, treatment, and recovery, aligns with local data capacity and literacy, and offers statisticians practical insights into community-engaged decision-making with sparse data.

Community Learning Through Data-Driven Discovery

Rural communities

Substance misuse

Data visualization

Administrative data 

Health Policy Statistics Section