Advancing Health Policy and Program Research in Diabetes: Findings from the Natural Experiments for Translation in Diabetes (NEXT-D) Network.

PURPOSE OF REVIEW: To advance our understanding of the impacts of policies and programs aimed at improving detection, engagement, prevention, and clinical diabetes management in the USA, we synthesized findings from a network of studies that used natural experiments to evaluate diabetes health policies and programs. FINDINGS: Studies from the Natural EXperiments for Translation in Diabetes (NEXT-D) network used rigorous longitudinal quasi-experimental study designs (e.g., interrupted time series) and analytical methods (e.g., difference-in-differences) to augment causal inference. Investigators partnered with health system stakeholders to evaluate whether glucose testing rates changed from before-to-after clinic interventions (e.g., integrating electronic screening decision prompts in New York City) or employer programs (e.g., targeted messaging and waiving copayments for at-risk employees). Other studies examined participation and behavior change in low- (e.g., wellness coaching) or high-intensity lifestyle modification programs (e.g., diabetes prevention program-like interventions) offered by payers or employers. Lastly, studies assessed how employer health insurance benefits impacted healthcare utilization, adherence, and outcomes among people with diabetes. NEXT-D demonstrated that low-intensity interventions to facilitate glucose testing and enhance engagement in lifestyle modification were associated with small improvements in weight but large improvements in screening and testing when supported by electronic health record-based decision-support. Regarding high-intensity diabetes prevention program-like lifestyle programs offered by payers or employers, enrollment was modest and led to weight loss and marginally lower short-term health expenditures. Health plans that incentivize patient behaviors were associated with increases in medication adherence. Meanwhile, shifting patients to high-deductible health plans was associated with no change in medication use and preventive screenings, but patients with diabetes delayed accessing healthcare for acute complications (e.g., cellulitis). Findings were more pronounced among lower-income patients, who experienced increased rates and acuity of emergency department visits for diabetes complications and other high-severity conditions. Findings from NEXT-D studies provide informative data that can guide programs and policies to facilitate detection, prevention, and treatment of diabetes in practice.

Evaluating diabetes health policies using natural experiments: the natural experiments for translation in diabetes study.

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.