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.
Hubert Department of Global Health, Emory University, 1518 Clifton Road NE, Ste 7041 CNR Building, Atlanta, GA, 30322, USA.
Curr Diab Rep. 2018 Nov 20;18(12):146. doi: 10.1007/s11892-018-1112-3.
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.
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.
为了增进我们对美国旨在提高糖尿病检测、参与度、预防和临床管理水平的政策和项目影响的理解,我们综合了利用自然实验评估糖尿病健康政策和项目的网络研究的结果。
自然实验促进糖尿病转化(NEXT-D)网络中的研究采用严格的纵向准实验研究设计(例如,中断时间序列)和分析方法(例如,差异中的差异)来增强因果推断。研究人员与卫生系统利益相关者合作,评估在诊所干预(例如,在纽约市整合电子筛查决策提示)前后,葡萄糖检测率是否发生变化,或雇主计划(例如,针对高危员工的定向信息传递和免除共付额)。其他研究则考察了低强度(例如,健康教练)或高强度生活方式改变计划(例如,类似于糖尿病预防计划的干预措施)的参与度和行为变化,这些计划由支付者或雇主提供。最后,研究评估了雇主健康保险福利如何影响糖尿病患者的医疗保健利用、依从性和结果。NEXT-D 表明,促进葡萄糖检测和增强生活方式改变参与度的低强度干预措施与体重的微小改善相关,但在得到电子健康记录决策支持支持时,与筛查和检测的大幅改善相关。关于支付者或雇主提供的类似于高强度糖尿病预防计划的生活方式项目,入组人数适中,导致体重减轻和短期医疗支出略有降低。激励患者行为的健康计划与药物依从性的增加有关。同时,将患者转移到高免赔额健康计划与药物使用和预防筛查没有变化相关,但糖尿病患者延迟了急性并发症(例如蜂窝织炎)的医疗保健。在收入较低的患者中,这些发现更为明显,他们因糖尿病并发症和其他高严重程度疾病的急诊就诊率和严重程度增加。NEXT-D 研究的结果提供了有启发性的数据,可以指导计划和政策,以促进糖尿病的实际检测、预防和治疗。