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The high prevalence and costs of type 2 diabetes makes it a rapidly evolving focus of policy action. Health systems, employers, community organizations, and public agencies have increasingly looked to translate the benefits of promising research interventions into innovative policies intended to prevent or control diabetes. Though guided by research, these health policies provide no guarantee of effectiveness and may have opportunity costs or unintended consequences. Natural experiments use pragmatic and available data sources to compare specific policies to other policy alternatives or predictions of what would likely have happened in the absence of any intervention. The Natural Experiments for Translation in Diabetes (NEXT-D) Study is a network of academic, community, industry, and policy partners, collaborating to advance the methods and practice of natural experimental research, with a shared aim of identifying and prioritizing the best policies to prevent and control diabetes. This manuscript describes the NEXT-D Study group's multi-sector natural experiments in areas of diabetes prevention or control as case examples to illustrate the selection, design, analysis, and challenges inherent to natural experimental study approaches to inform development or evaluation of health policies.

To address the increasing burden of diabetes in New York City, we designed 2 electronic health records (EHRs)-facilitated diabetes management systems to be implemented in 6 primary care practices on the West Side of Manhattan, a standard system and an enhanced system. The standard system includes screening for diabetes. The enhanced system includes screening and ensures close patient follow-up; it applies principles of the chronic care model, including community–clinic linkages, to the management of patients newly diagnosed with diabetes and prediabetes through screening. We will stagger implementation of the enhanced system across the 6 clinics allowing comparison, through a quasi-experimental design (pre–post difference with a control group), of patients treated in the enhanced system with similar patients treated in the standard system. The findings could inform health system practices at multiple levels and influence the integration of community resources into routine diabetes care.