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Curr Diab Rep. 2018 Nov 20;18(12):146. doi: 10.1007/s11892-018-1112-3.

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

Author information

1
Division of Diabetes Translation, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Mailstop K10, 4770 Buford Highway, Atlanta, GA, 30341, USA. ise1@cdc.gov.
2
Hubert Department of Global Health, Emory University, 1518 Clifton Road NE, Ste 7041 CNR Building, Atlanta, GA, 30322, USA. ise1@cdc.gov.
3
Harvard Pilgrim Health Care Institute, Department of Population Medicine, Harvard Medical School, 401 Park Drive, Suite 401 East, Boston, MA, 02215, USA.
4
Division of General Internal Medicine, University of California Los Angeles, 911 Broxton Ave., Los Angeles, CA, 90024, USA.
5
Division of Research, Kaiser Permanente Northern California, 2000 Broadway, Oakland, CA, 94612, USA.
6
Department of Medicine, General Medicine Division, Northwestern University, Rubloff Building 10th Floor 750 N Lake Shore, Chicago, IL, 60611, USA.
7
Division of Endocrinology, Diabetes and Bone Diseases, Icahn School of Medicine at Mount Sinai, 1111 Amsterdam Avenue Babcock Building - 10th Floor, New York, NY, 10025, USA.
8
Office of Behavioral and Social Sciences Research, National Institutes of Health, 31 Center Drive, Bethesda, MD, 20892, USA.
9
Division of General Internal Medicine, University of California Los Angeles, UCLA Med-GIM & HSR BOX 957394, 10940 Wilshire Blvd, Los Angeles, CA, 90095, USA.
10
Division of Diabetes Translation, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Mailstop K10, 4770 Buford Highway, Atlanta, GA, 30341, USA.

Abstract

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.

KEYWORDS:

Clinical management; Diabetes; Natural experiment; Policy; Prevention

PMID:
30456479
DOI:
10.1007/s11892-018-1112-3

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