Comparison of Pharmacy Refill Data With Chemical Adherence Testing in Assessing Medication Nonadherence in a Safety Net Hospital Setting

Abstract

Background Pharmacy fill data are a practical tool for assessing medication nonadherence. However, previous studies have not compared the accuracy of pharmacy fill data to measurement of plasma drug levels, or chemical adherence testing (CAT). Methods and Results We performed a cross‐sectional study in patients with uncontrolled hypertension in outpatient clinics in a safety net health system. Plasma samples were obtained for measurement of common cardiovascular drugs, including calcium channel blockers, thiazide diuretics, beta blockers, angiotensin‐converting enzyme inhibitors/angiotensin receptor blockers, and statins, using liquid chromatography mass spectrometry. Proportion of days covered (PDC), a method for tracking pharmacy fill data, was calculated via linkages with Surescripts, and its diagnostic test characteristics were compared with CAT. Among 77 patients with uncontrolled hypertension, 13 (17%) were nonadherent to at least 1 antihypertensive drug and 23 (37%) were nonadherent to statins by CAT. PDC was significantly lower in the nonadherent versus the adherent group by CAT only among patients prescribed an angiotensin‐converting enzyme inhibitor/angiotensin receptor blocker or statin (all P <0.05) but not in patients prescribed other drug classes. The sensitivity and specificity of PDC in detecting nonadherence to angiotensin‐converting enzyme inhibitors/angiotensin receptor blockers and statin drugs by CAT were 75% to 82% and 56% to 79%, respectively. The positive predictive value of PDC in detecting nonadherence was only 11% to 27% for antihypertensive drugs and 45% for statins. Conclusions PDC is useful in detecting nonadherence to angiotensin‐converting enzyme inhibitors/angiotensin receptor blockers and statins but has limited usefulness in detecting nonadherence to calcium channel blockers, beta blockers, or thiazide diuretics and has a low positive predictive value for all drug classes.