A refill adherence algorithm for multiple short intervals to estimate refill compliance (ReComp).

Bryson CL, Au DH, Young B, McDonell MB, Fihn SD. A refill adherence algorithm for multiple short intervals to estimate refill compliance (ReComp). Medical care. 2007 Jun 1; 45(6):497-504.

Related HSR&D Project(s)

• SDR 96-002 – Ambulatory Care Quality Improvement (ACQUIP)
• IIR 99-376 – Population-Based Assessments of Health Status and Satisfaction Using ACQUIP

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Abstract:

BACKGROUND: There are many measures of refill adherence available, but few have been designed or validated for use with repeated measures designs and short observation periods. OBJECTIVE: To design a refill-based adherence algorithm suitable for short observation periods, and compare it to 2 reference measures. METHODS: A single composite algorithm incorporating information on both medication gaps and oversupply was created. Electronic Veterans Affairs pharmacy data, clinical data, and laboratory data from routine clinical care were used to compare the new measure, ReComp, with standard reference measures of medication gaps (MEDOUT) and adherence or oversupply (MEDSUM) in 3 different repeated measures medication adherence-response analyses. These analyses examined the change in low density lipoprotein (LDL) with simvastatin use, blood pressure with antihypertensive use, and heart rate with beta-blocker use for 30- and 90-day intervals. Measures were compared by regression based correlations (R2 values) and graphical comparisons of average medication adherence-response curves. RESULTS: In each analysis, ReComp yielded a significantly higher R2 value and more expected adherence-response curve regardless of the length of the observation interval. For the 30-day intervals, the highest correlations were observed in the LDL-simvastatin analysis (ReComp R2 = 0.231; [95% CI, 0.222-0.239]; MEDSUM R2 = 0.054; [95% CI, 0.049-0.059]; MEDOUT R2 = 0.053; [95% CI, 0.048-0.058]). CONCLUSIONS: ReComp is better suited to shorter observation intervals with repeated measures than previously used measures.