Agency of Human Services, 280 State Dr, Waterbury, VT 05671. E-mail:
Am J Manag Care. 2017 Oct 1;23(10):e331-e339.
To understand how a statewide data infrastructure, including clinical and multipayer claims data, can inform preventive care and reduce medical expenditures for patients with diabetes.
A retrospective 1-year cross-sectional analysis of claims linked to clinical data for 6719 patients with diabetes in 2014 to evaluate impacts of comorbidities on the total cost of care.
Initially, variation in healthcare expenditures was examined versus a measure of disease control (most recent glycated hemoglobin [A1C] test results). Multivariable linear regression calculated the relative impact of a series of risk factors on medical expenditures. Poisson regression estimated the relative impact on inpatient hospital admissions. Possible savings were estimated with a reduction in potentially avoidable hospital admissions.
No linear relationship was found between A1C and same-year medical expenditures. Comorbidities in the population with diabetes with the largest relative impact on expenditures and inpatient hospital admissions were renal failure, congestive heart failure, chronic obstructive pulmonary disease, and discordant blood pressure. Diabetes plus congestive heart failure had the highest cost per inpatient admission; diabetes plus body mass index (BMI) ≥35 had the highest aggregate costs and potential savings.
A statewide data infrastructure can be used to identify criteria for outreach and population management of diabetes. The selection criteria are applicable across a statewide population and are associated with a higher relative impact on near-term expenditures than recent A1C test results. Whole-population data aggregation can be used to develop actionable information that is particularly relevant as independent organizations work together under alternative payment model arrangements.
了解全州范围内的数据基础设施（包括临床和多付款人索赔数据）如何为糖尿病患者提供预防保健并降低医疗支出。
对 2014 年 6719 名糖尿病患者的索赔数据与临床数据进行的回顾性 1 年横断面分析，以评估合并症对总护理费用的影响。
最初，根据疾病控制指标（最近的糖化血红蛋白[A1C]检测结果），比较了医疗保健支出的变化。多变量线性回归计算了一系列风险因素对医疗支出的相对影响。泊松回归估计了对住院入院的相对影响。通过减少潜在可避免的住院入院来估计可能的节省。
未发现 A1C 与同年医疗支出之间存在线性关系。对糖尿病患者的支出和住院入院具有最大相对影响的合并症是肾衰竭、充血性心力衰竭、慢性阻塞性肺疾病和血压不一致。糖尿病加充血性心力衰竭的每次住院入院费用最高；糖尿病加 BMI（体重指数）≥35 的总费用和潜在节省最高。
全州范围的数据基础设施可用于确定糖尿病外展和人群管理的标准。选择标准适用于全州范围内的人群，与近期 A1C 检测结果相比，对近期支出的相对影响更大。全人群数据汇总可用于制定切实可行的信息，这在独立组织根据替代支付模式安排共同合作时特别相关。
