Context: During the COVID-19 pandemic, communities of practice (CoPs) supported state, Tribal, local, and territorial (STLT) public health agencies. No studies have examined the collective role of these CoPs in helping STLT public health agencies translate guidance into practice. Objectives: This qualitative study examines the types of CoPs that supported STLT public health agencies during the COVID-19 response, how CoPs assisted in translating guidance into practice, and the characteristics of CoPs that made them valuable to STLT public health members. We report lessons for future public health emergencies (PHEs) for STLT public health agencies and membership organizations that represent them. Design: We conducted 21 in-depth interviews with CoP leaders, STLT public health participants, and federal agency sponsors and collaborators. Participants: We interviewed 9 CoP leads, 6 STLT participants, and 6 federal agency representatives. Results: Three types of CoPs, each with unique advantages, supported STLT public health agencies during the COVID-19 pandemic: (1) CoPs led by federal agencies, (2) CoPs led by membership organizations or associations that represent STLT public health agencies, and (3) CoPs led by other nonfederal organizations, such as philanthropic organizations and academic institutions. The most valuable CoPs to STLT public health agencies had a clear focus on issues of significance to their members, strong connections between members, and a structure tailored to the group’s goals. STLT public health agencies valued CoP support with implementing guidance-based policies and practices and facilitating bidirectional communication with federal agencies. STLT public health agencies also benefitted from tailored and implementation-focused resources developed through CoPs. Conclusion: Our study affirms the importance of CoPs in facilitating collaboration and information-sharing among multiple actors during PHEs. During the COVID-19 pandemic, CoPs helped STLT public health agencies implement guidance, tailor approaches to specific contexts, and generate practice-based discoveries to advance the field.
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