Introduction The persisting and evolving COVID‐19 pandemic has made apparent that no singular policy of mitigation at a regional, national or global level has achieved satisfactory and universally acceptable results. In the United States, carefully planned and executed pandemic policies have been neither effective nor popular and COVID‐19 risk management decisions have been relegated to individual citizens and communities. In this paper, we argue that a more effective approach is to equip and strengthen community coalitions to become local learning health communities (LLHCs) that use data over time to make adaptive decisions that can optimize the equity and well‐being in their communities. Methods We used data from the North Carolina (NC) county and zip code levels from May to August 2020 to demonstrate how a LLHC could use statistical process control (SPC) charts and simple statistical analysis to make local decisions about how to respond to COVID‐19. Results We found many patterns of COVID‐19 progression at the local (county and zip code) levels during the same time period within the state that were completely different from the aggregate NC state level data used for policy making. Conclusions Systematic approaches to learning from local data to support effective decisions have promise well beyond the current pandemic. These tools can help address other complex public health issues, and advance outcomes and equity. Building this capacity requires investment in data infrastructure and the strengthening of data competencies in community coalitions to better interpret data with limited need for advanced statistical expertise. Additional incentives that build trust, support data transparency, encourage truth‐telling and promote meaningful teamwork are also critical. These must be carefully designed, contextually appropriate and multifaceted to motivate citizens to create and sustain an effective learning system that works for their communities.

Objective: To determine if individuals newly diagnosed with opioid use disorder (OUD) who saw a primary care provider (PCP) before or on the date of diagnosis had higher rates of medication treatment for OUD (MOUD). Methods: Observational study using logistic regression with claims data from Medicaid and a large private insurer in North Carolina from January 2014 to July 2017. Key Results: Between 2014 and 2017, the prevalence of diagnosed OUD increased by 47% among Medicaid enrollees and by 76% among the privately insured. Over the same time period, the percent of people with an OUD who received MOUD fell among both groups, while PCP involvement in treatment increased. Of Medicaid enrollees receiving buprenorphine, the percent receiving buprenorphine from a PCP increased from 32% in 2014 to 39% in 2017. Approximately 82% of people newly diagnosed with OUD had a PCP visit in the 12 months before diagnosis in Medicaid and private insurance. Those with a prior PCP visit were not more likely to receive MOUD. Seeing a PCP at diagnosis was associated with a higher probability of receiving MOUD than seeing an emergency provider but a lower probability than seeing a behavioral health specialist or other provider type. Conclusions: People newly diagnosed with OUD had high rates of contact with PCPs before diagnosis, supporting the importance of PCPs in diagnosing OUD and connecting people to MOUD. Policies and programs to increase access to MOUD and improve PCPs' ability to connect people to evidence-based treatment are needed.

Introduction The United States has been unsuccessful in containing the rapid spread of COVID‐19. The complex epidemiology of the disease and the fragmented response to it has resulted in thousands of ways in which spread has occurred, creating a situation where each community needs to create its own local, context‐specific learning model while remaining compliant to county or state mandates. Methods In this paper, we demonstrate how cross sector collaborations can use the Cynefin Framework, a tool for decision‐making in complex systems, to guide community response to the COVID‐19 pandemic. Results We explore circumstances under which communities can inhabit each of the four domains of systems complexity represented in the Cynefin framework: simple, complicated, chaotic, and complex, and describe the decision‐making process in each domain that balances health, economic, and social well‐being. Conclusion This paper serves as a call to action for the creation of community learning systems to improve community resilience and capacity to make better‐informed decisions to address complex public health problems during the pandemic and beyond.

Introduction This paper explores the capabilities that contribute to community transformation and the common pathways followed by communities in the 100 Million Healthier Lives SCALE (Spreading Community Accelerators through Learning and Evaluation) initiative in their transformation journeys towards a “Culture of Health”. Methods Funded by the Robert Wood Johnson Foundation (RWJF), from 2016 to 2020, between 18 to 24 community coalitions nationwide participated in SCALE, the goal of which was to co‐design, implement, test, and scale up a model called the Community of Solutions (COS) Framework, that built community capacity around a set of skills and behaviors to advance culture change and create sustainable improvement in health, well‐being, and equity. We adapted and applied two qualitative research techniques, meta‐ethnography and participatory action synthesis, to evaluate SCALE initiative data. Results Eight concepts emerged that represent the knowledge, capabilities and practices commonly acquired and utilized across the communities. Overall, these concepts emphasize individual and team leadership, quality improvement skills, an intentional focus on equity, and partnerships for spread and sustainment. Concepts were linked into lines of arguments which were unique storylines explaining the transformation pathways. Three stories of the transformation process emerged from the data. Causal Loop Diagrams (CLDs) were created to represent non‐linear system relationships and visually capture some of the most important dynamics of the process of transformation. Even with vast heterogeneity among the SCALE communities and the diversity of activities that the communities undertook, our analysis showed there were a few basic principles that undergirded the process of building capability for transformation. Conclusions The knowledge from our findings should be useful to expand further research and practice in community learning systems.