A refill adherence algorithm for multiple short intervals to estimate refill compliance (ReComp).
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.
Bryson
Chris L
CL
Health Services Research and Development Northwest Center of Excellence, Seattle, Washington 98101, USA. cbryson@u.washington.edu
Au
David H
DH
Young
Bessie
B
McDonell
Mary B
MB
Fihn
Stephan D
SD
D000465:Algorithms; D016303:Clinical Pharmacy Information Systems / Q000706:statistics & numerical data*; D003625:Data Collection / Q000379:methods*; D011307:Drug Prescriptions / Q000706:statistics & numerical data*; D004358:Drug Therapy / Q000706:statistics & numerical data*; D005260:Female; D006331:Heart Diseases / Q000188:drug therapy; D006801:Humans; D006937:Hypercholesterolemia / Q000188:drug therapy; D006973:Hypertension / Q000188:drug therapy; D008297:Male; D008875:Middle Aged; D010349:Patient Compliance / Q000706:statistics & numerical data*; D012044:Regression Analysis; D015203:Reproducibility of Results; D013997:Time Factors; D016896:Treatment Outcome; D014481:United States