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

Background: Medication adherence measures are often dichotomized to classify patients into those with good or poor adherence using a cut-off value ⩾80%, but this cut-off may not be universal across diseases or medication classes. This study aimed to examine the cut-off value that optimally distinguish good and poor adherence by using the medication possession ratio (MPR) and proportion of days covered (PDC) as adherence measures and glycated hemoglobin (HbA1c) as outcome measure among type 2 diabetes mellitus (T2DM) patients. Method: We used pharmacy dispensing data of 1461 eligible T2DM patients from public primary care clinics in Malaysia treated with oral antidiabetic drugs between January 2018 and May 2019. Adherence rates were calculated during the period preceding the HbA1c measurement. Adherence cut-off values for the following conditions were compared: adherence measure (MPR versus PDC), assessment period (90-day versus 180-day), and HbA1c target (⩽7.0% versus ⩽8.0%). Results: The optimal adherence cut-offs for MPR and PDC in predicting HbA1c ⩽7.0% ranged between 86.1% and 98.3% across the two assessment periods. In predicting HbA1c ⩽8.0%, the optimal adherence cut-offs ranged from 86.1% to 92.8%. The cut-off value was notably higher with PDC as the adherence measure, shorter assessment period, and a stricter HbA1c target (⩽7.0%) as outcome. Conclusion: We found that optimal adherence cut-off appeared to be slightly higher than the conventional value of 80%. The adherence thresholds may vary depending on the length of assessment period and outcome definition but a reasonably wise cut-off to distinguish good versus poor medication adherence to be clinically meaningful should be at 90%.

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HIV Medication Adherence Measure

PsycTESTS Dataset ◽

10.1037/t31339-000 ◽

2012 ◽

Author(s):

Eva N. Woodward ◽

David W. Pantalone

Keyword(s):

Medication Adherence ◽

Adherence Measure ◽

Hiv Medication

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Abstract 015: Importance of Balanced Follow-up Time and Other Study Design Considerations When Evaluating Adherence Using Two Novel Oral Anticoagulants

Circulation Cardiovascular Quality and Outcomes ◽

10.1161/circoutcomes.10.suppl_3.015 ◽

2017 ◽

Vol 10 (suppl_3) ◽

Author(s):

Craig Coleman ◽

Zhong Yuan ◽

Jeffrey Schein ◽

Concetta Crivera ◽

Veronica Ashton ◽

...

Keyword(s):

Medication Adherence ◽

Real World ◽

Oral Anticoagulant Therapy ◽

Oral Anticoagulants ◽

Real World Data ◽

Adherence Measure ◽

Adherence Measurement ◽

Minimum Number ◽

The Impact

Background: Medication adherence rates decline over time, especially after the first dispensing. Comparing adherence rates for medications that have been on the market for differing period of time may distort real differences in medication adherence. Other analysis factors such as minimum number of dispensing criteria and Pharmacy Quality Alliance (PQA) adherence measures can also affect adherence measurement. Objectives: To use one real world example (rivaroxaban vs apixaban) in non-valvular atrial fibrillation (NVAF) patients to quantify the impact of adjusting for imbalances in follow-up periods, minimum number of dispensing, and use of the PQA adherence measure. Methods: Using IMS Health Real-World Data Adjudicated Claims and Truven MarketScan claims databases, we included adult patients with ≥1 rivaroxaban or apixaban dispensing (index date), ≥1 year of pre-index eligibility, ≥1 AF diagnosis pre-index, newly initiated on oral anticoagulant therapy, and no valvular involvement. Adherence was evaluated using proportion of days covered (PDC) ≥0.8 for cohorts with (1) unbalanced follow-up (2) balanced follow-up (by matching on month and year of follow-up since fill-date) (3) ≥2 rivaroxaban or apixaban dispensings and a balanced follow-up, and using (4) the PQA adherence measure. Results: Rivaroxaban users had significantly longer mean (SD) follow-up than apixaban (408 [300] versus 254 [196] days, respectively). While apixaban users appeared to be more adherent in unadjusted analyses, this finding was reversed after 1) adjusting for unbalanced follow-up 2) excluding single-time users; and 3) applying the PQA-endorsed adherence measure (Figure). Similar results were found using the Truven databases. Conclusion: Comparisons of the adherence rates among medications need to account for the period of time each have been on the market, number of dispensing and PQA measures. Retrospective analyses of adherence that do not adjust for such differences could produce spurious findings.

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