• Am J Manag Care · Jan 2010

    Randomized Controlled Trial

    Leveling the field: addressing health disparities through diabetes disease management.

    • Richard O White, Darren A DeWalt, Robert M Malone, Chandra Y Osborn, Michael P Pignone, and Russell L Rothman.
    • Division of General Internal Medicine, Meharry Medical College, 1005 Dr DB Todd Jr Blvd, Nashville, TN 37208, USA. rwhite@mmc.edu
    • Am J Manag Care. 2010 Jan 1; 16 (1): 42-8.

    ObjectivesTo 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).Study DesignWe 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.MethodsThe 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.ResultsOne 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.ConclusionsAmong 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.

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