Division of General Internal Medicine, Meharry Medical College, 1005 Dr DB Todd Jr Blvd, Nashville, TN 37208, USA.
Am J Manag Care. 2010 Jan;16(1):42-8.
To examine the relationships among patient characteristics, labor inputs, and improvement in glycosylated hemoglobin (A1C) level in a successful primary care-based diabetes disease management program (DDMP).
We performed subanalyses to examine the relationships among patient characteristics, labor inputs, and improvement in A1C level within a randomized controlled trial. Control patients received usual care, while intervention patients received usual care plus a comprehensive DDMP.
The primary outcome was improvement in A1C level over 12 months stratified by intervention status and patient characteristics. Process outcomes included the number of actions or contacts with patients, time spent with patients, and number of glucose medication titrations or additions.
One hundred ninety-three of 217 enrolled patients (88.9%) had complete 12-month followup data. Patients in the intervention group had significantly greater improvement in A1C level than the control group (-2.1% vs -1.2%, P = .007). In multivariate analysis, no significant differences were observed in improvement in A1C level when stratified by age, race/ethnicity, income, or insurance status, and no interaction effect was observed between any covariate and intervention status. Among intervention patients, we observed similar labor inputs regardless of age, race/ethnicity, sex, education, or whether goal A1C level was achieved.
Among intervention patients in a successful DDMP, improvement in A1C level was achieved regardless of age, race/ethnicity, sex, income, education, or insurance status. Labor inputs were similar regardless of age, race/ethnicity, sex, or education and may reflect the nondiscriminatory nature of providing algorithm-based disease management care.
在一项成功的基于初级保健的糖尿病疾病管理计划（DDMP）中，研究患者特征、劳动力投入与糖化血红蛋白（A1C）水平改善之间的关系。
我们进行了亚分析，以检查随机对照试验中患者特征、劳动力投入与 A1C 水平改善之间的关系。对照组患者接受常规护理，而干预组患者则在常规护理的基础上接受全面的 DDMP。
主要结局是根据干预状态和患者特征，将 A1C 水平在 12 个月内的改善情况进行分层。过程结局包括与患者的行动或接触次数、花费在患者身上的时间以及葡萄糖药物滴定或添加的次数。
在 217 名入组患者中，有 193 名（88.9%）患者完成了 12 个月的完整随访数据。干预组患者的 A1C 水平改善显著大于对照组（-2.1%对-1.2%，P=0.007）。在多变量分析中，按年龄、种族/族裔、收入或保险状况分层时，A1C 水平的改善没有显著差异，且没有观察到任何协变量与干预状态之间的交互作用。在干预患者中，无论年龄、种族/族裔、性别、教育程度或是否达到目标 A1C 水平，我们观察到的劳动力投入相似。
在一项成功的 DDMP 中，干预患者的 A1C 水平改善与年龄、种族/族裔、性别、收入、教育程度或保险状况无关。劳动力投入与年龄、种族/族裔、性别或教育程度无关，这可能反映了提供基于算法的疾病管理护理的非歧视性。
