Cozumel S Pruette1, Shayna S Coburn2, Cyd K Eaton3, Tammy M Brady4, Shamir Tuchman5, Susan Mendley6, Barbara A Fivush4, Michelle N Eakin3, Kristin A Riekert3. 1. Pediatrics, Johns Hopkins University, 200 N. Wolfe Street, Room 3055, Baltimore, MD, 21287, USA. csouthe1@jhmi.edu. 2. Psychiatry & Behavioral Sciences, George Washington School of Medicine, Washington, DC, USA. 3. Medicine, Johns Hopkins University, Baltimore, MD, USA. 4. Pediatrics, Johns Hopkins University, 200 N. Wolfe Street, Room 3055, Baltimore, MD, 21287, USA. 5. Nephrology, Children's National Health System, Washington, DC, USA. 6. Kidney, Urologic, and Hematologic Diseases, National Institutes of Health, Washington, DC, USA.
Abstract
BACKGROUND: Medical provider assessment of nonadherence is known to be inaccurate. Researchers have suggested using a multimethod assessment approach; however, no study has demonstrated how to integrate different measures to improve accuracy. This study aimed to determine if using additional measures improves the accurate identification of nonadherence beyond provider assessment alone. METHODS: Eighty-seven adolescents and young adults (AYAs), age 11-19 years, with chronic kidney disease (CKD) [stage 1-5/end-stage renal disease (ESRD)] and prescribed antihypertensive medication, their caregivers, and 17 medical providers participated in the multisite study. Five adherence measures were obtained: provider report, AYA report, caregiver report, electronic medication monitoring (MEMS), and pharmacy refill data [medication possession ratio (MPR)]. Concordance was calculated using kappa statistic. Sensitivity, specificity, positive predictive power, and negative predictive power were calculated using MEMS as the criterion for measuring adherence. RESULTS: There was poor to fair concordance (kappas = 0.12-0.54), with 35-61% of AYAs classified as nonadherent depending on the measure. While both providers and MEMS classified 35% of the AYAs as nonadherent, sensitivity (0.57) and specificity (0.77) demonstrated poor agreement between the two measures on identifying which AYAs were nonadherent. Combining provider report of nonadherence and MPR < 75% resulted in the highest sensitivity for identifying nonadherence (0.90) and negative predictive power (0.88). CONCLUSIONS: Nonadherence is prevalent in AYAs with CKD. Providers inaccurately identify nonadherence, leading to missed opportunities to intervene. Our study demonstrates the benefit to utilizing a multimethod approach to identify nonadherence in patients with chronic disease, an essential first step to reduce nonadherence.
BACKGROUND: Medical provider assessment of nonadherence is known to be inaccurate. Researchers have suggested using a multimethod assessment approach; however, no study has demonstrated how to integrate different measures to improve accuracy. This study aimed to determine if using additional measures improves the accurate identification of nonadherence beyond provider assessment alone. METHODS: Eighty-seven adolescents and young adults (AYAs), age 11-19 years, with chronic kidney disease (CKD) [stage 1-5/end-stage renal disease (ESRD)] and prescribed antihypertensive medication, their caregivers, and 17 medical providers participated in the multisite study. Five adherence measures were obtained: provider report, AYA report, caregiver report, electronic medication monitoring (MEMS), and pharmacy refill data [medication possession ratio (MPR)]. Concordance was calculated using kappa statistic. Sensitivity, specificity, positive predictive power, and negative predictive power were calculated using MEMS as the criterion for measuring adherence. RESULTS: There was poor to fair concordance (kappas = 0.12-0.54), with 35-61% of AYAs classified as nonadherent depending on the measure. While both providers and MEMS classified 35% of the AYAs as nonadherent, sensitivity (0.57) and specificity (0.77) demonstrated poor agreement between the two measures on identifying which AYAs were nonadherent. Combining provider report of nonadherence and MPR < 75% resulted in the highest sensitivity for identifying nonadherence (0.90) and negative predictive power (0.88). CONCLUSIONS: Nonadherence is prevalent in AYAs with CKD. Providers inaccurately identify nonadherence, leading to missed opportunities to intervene. Our study demonstrates the benefit to utilizing a multimethod approach to identify nonadherence in patients with chronic disease, an essential first step to reduce nonadherence.
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