Investigating the Potential for Bias When Using a Widely Accepted Medication Adherence Measure to Predict Mortality

Ward, Ralph; Taber, David J.; Axon, R. Neal; Gebregziabher, Mulugeta

doi:10.1002/phar.2178

Cited by 2 publications

(3 citation statements)

References 21 publications

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“…8 Our study advances the current understanding of methods for measuring drug exposure by taking common details such as overlaps in prescriptions and hospital stays into account which has not been well defined previously using administrative data. 6,30,31 As ESRD patients commonly take >7 prescription medications on a single day, are hospitalized frequently (78%) with a median length of hospital stay of 7 days, and have high mortality rate, our methods are particularly more comprehensive than previously reported adherence matrix. Therefore, if a study fails to incorporate frequent and prolonged hospitalizations in adherence measures, outcomes can be falsely misattributed to nonadherence.…”

Section: Discussionmentioning

confidence: 99%

“…32,33 Previous studies, which are used for calculating traditional MPR and PDC values, did not include full adjustments for overlapping refill periods, gaps in refill, and hospital stays. Some studies included hospitalization as a covariate in the final analyses only; 4,10,31,34,35 others adjusted for overlapping coverage days by moving the start date of overlapping prescriptions but failed to address the issue of refills of different strengths. [36][37][38] Only a select few studies attempted to account for hospital/institutional stays.…”

Section: Discussionmentioning

confidence: 99%

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Accurate Medication Adherence Measurement Using Administrative Data for Frequently Hospitalized Patients

et al. 2020

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Background: Pharmacy administrative claims data remain an accessible and efficient source to measure medication adherence for frequently hospitalized patient populations that are systematically excluded from the landmark drug trials. Published pharmacotherapy studies use medication possession ratio (MPR) and proportion of days covered (PDC) to calculate medication adherence and usually fail to incorporate hospitalization and prescription overlap/gap from claims data. To make the cacophony of adherence measures clearer, this study created a refined hospital-adjusted algorithm to capture pharmacotherapy adherence among patients with end-stage renal disease (ESRD). Methods: The United States Renal Data System (USRDS) registry of ESRD was used to determine prescription-filling patterns of those receiving new prescriptions for oral P2Y12 inhibitors (P2Y12-I) between 2011 and 2015. P2Y12-I-naïve patients were followed until death, kidney transplantation, discontinuing medications, or loss to follow-up. After flagging/censoring key variables, the algorithm adjusted for hospital length of stay (LOS) and medication overlap. Hospital-adjusted medication adherence (HA-PDC) was calculated and compared with traditional MPR and PDC methods. Analyses were performed with SAS software. Results: Hospitalization occurred for 78% of the cohort (N = 46 514). The median LOS was 12 (interquartile range [IQR] = 2-34) days. MPR and PDC were 61% (IQR = 29%-94%) and 59% (IQR = 31%-93%), respectively. After applying adjustments for overlapping coverage days and hospital stays independently, HA-PDC adherence values changed in 41% and 52.7% of the cohort, respectively. When adjustments for overlap and hospital stay were made concurrently, HA-PDC adherence values changed in 68% of the cohort by 5.8% (HA-PDC median = 0.68, IQR = 0.31-0.93). HA-PDC declined over time (3M-6M-9M-12M). Nearly 48% of the cohort had a ≥30 days refill gap in the first 3 months, and this increased over time ( P < .0001). Conclusions: Refill gaps should be investigated carefully to capture accurate pharmacotherapy adherence. HA-PDC measures increased adherence substantially when adjustments for hospital stay and medication refill overlaps are made. Furthermore, if hospitalizations were ignored for medications that are included in Medicare quality measures, such as Medicare STAR program, the apparent reduction in adherence might be associated with lower quality and health plan reimbursement.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Accurate Medication Adherence Measurement Using Administrative Data for Frequently Hospitalized Patients

et al. 2020

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“…Calculation methods include medication possession ratio (MPR), proportion of days covered (PDC) or daily polypharmacy possession ratio (DPPR) 16 . The observation window, needed to calculate the adherence rate, can be defined in two ways: a prescription‐based approach, with the observation window starting when the medication is first dispensed and ending before the last dispensing, and an interval‐based approach using a predefined observation window 17 . Overall, a wide variety of methods have been described in literature, but to date there seems to be no golden standard method to quantify adherence using refill data.…”

Section: Introductionmentioning

confidence: 99%

Does a pharmacist‐led medication use review improve medication adherence in polymedicated aged patients? Methodological choices matter!

Wuyts

Maesschalck

Hamelinck

et al. 2021

Evaluation Clinical Practice

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Rationale, aims and objectives The rise in chronic diseases urges for an identification of interventions that improve adherence. However, no golden standard exists for evaluating adherence in polymedicated patients. The objectives were to investigate the impact of a pharmacist‐led medication use review (MUR) service on adherence implementation rates for chronic medication, and to evaluate the appropriateness of two adherence calculation approaches in this regard. Method Before‐after study (SIMENON study) including ambulatory, aged, polymedicated patients. Refill data were used to calculate the proportion of days covered (PDC) per medication before and after the MUR. Adherence was assessed for chronic, solid, oral medication using a prescription‐based and interval‐based approach, and the performance of both approaches was compared. Results Adherence was evaluated for 1483 medications from 316 patients. The median baseline PDC per medication was 0.95 (prescription‐based method) and 0.91 (interval‐based approach). Pearson correlation between both approaches was 0.616 (P < .0001). The prescription‐based approach found less medication below the adherence threshold of 0.80 (319 medications; 21.5% vs 481 medications; 32.4%). Using the prescription‐based approach, the proportion of non‐adherent patients at baseline was 61.7% (195/316 patients), of which 54.9% (107/195 patients) was only non‐adherent for one medication. Median PDC scores increased significantly from 0.95 to 0.99 and 0.91 to 0.99 with the prescription‐based and interval‐based approach respectively (both P < .0001). Only considering the 319 medications with baseline PDC scores <0.8 using the prescription‐based approach, the median PDC scores significantly increased from 0.67 to 0.84 (P < .0001), with a median score improvement of 18.3%. Conclusions Although baseline adherence was high in this population of polymedicated aged patients, both calculation methods showed the MUR significantly improved adherence. The prescription‐based approach was considered the most appropriate method. Based on these findings and literature evidence, it is recommended to implement the MUR service in Belgium and target non‐adherent patients for maximal effect.

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Investigating the Potential for Bias When Using a Widely Accepted Medication Adherence Measure to Predict Mortality

Abstract: Investigators using PDC or similar proxy measures should carefully consider the temporal relationship between adherence exposure and clinical outcomes when the outcome event affects the adherence measurement.

Cited by 2 publications

References 21 publications

Accurate Medication Adherence Measurement Using Administrative Data for Frequently Hospitalized Patients

Accurate Medication Adherence Measurement Using Administrative Data for Frequently Hospitalized Patients

Does a pharmacist‐led medication use review improve medication adherence in polymedicated aged patients? Methodological choices matter!

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