Predicting Adherence to Chronic Disease Medications in Patients with Long-term Initial Medication Fills Using Indicators of Clinical Events and Health Behaviors

Lauffenburger, Julie C.; Franklin, Jessica M.; Krumme, Alexis A.; Shrank, William H.; Matlin, Olga S.; Spettell, Claire M.; Brill, Gregory E.; Choudhry, Niteesh K.

doi:10.18553/jmcp.2018.24.5.469

Cited by 33 publications

(31 citation statements)

References 29 publications

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“…As noted in a recent review, many of the factors identified as having an association with medication adherence are inconsistent across studies and are not modifiable . More recent studies have started to examine the relative contribution of different risk factors for non‐adherence across a range of treatments for long‐term conditions: prior non‐adherence and trajectories of use of treatment appear to be consistent and clinically relevant factors …”

Section: Discussionmentioning

confidence: 99%

“…A wide range of factors have been shown to be associated with poor adherence to diabetes medications including clinical features (female sex, younger age, non‐white ethnicity), other comorbidities such as depression, the class of medication and other prescriptions (medications after first‐line metformin, total daily pill burden), healthcare system (insurance, medication costs) and psychosocial factors (medication beliefs, physician trust) . In addition, adherence to previous medications has been shown to help improve predictive ability for determining adherence to statins and, more recently, in patients with cardiometabolic disorders . Adherence differs by medication class, with adherence being poorer with first‐line metformin therapy .…”

Section: Introductionmentioning

confidence: 99%

“…6,7 In addition, adherence to previous medications has been shown to help improve predictive ability for determining adherence to statins 8,9 and, more recently, in patients with cardiometabolic disorders. 10 Adherence differs by medication class, with adherence being poorer with first-line metformin therapy. 4 The time of first intensification, is therefore an appropriate time point to review adherence when considering optimal second-line treatment.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Identifying routine clinical predictors of non‐adherence to second‐line therapies in type 2 diabetes: A retrospective cohort analysis in a large primary care database

Shields

Hattersley

Farmer

2019

Diabetes Obesity Metabolism

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Aims To investigate whether combinations of routinely available clinical features can predict which patients are likely to be non‐adherent to diabetes medication. Materials and Methods A total of 67 882 patients with prescription records for their first and second oral glucose‐lowering therapies were identified from electronic healthcare records (Clinical Practice Research Datalink). Non‐adherence was defined as a medical possession ratio (MPR) ≤80%. Potential predictors were examined, including age at diagnosis, sex, body mass index, duration of diabetes, glycated haemoglobin, Charlson index and other recent prescriptions. Results Routine clinical features were poor at predicting non‐adherence to the first diabetes therapy (c‐statistic = 0.601 for all in combined model). Non‐adherence to the second drug was better predicted for all combined factors (c‐statistic =0.715) but this improvement was predominantly a result of including adherence to the first drug (c‐statistic =0.695 for this alone). Patients with an MPR ≤80% for their first drug were 3.6 times (95% confidence interval 3.3,3.8) more likely to be non‐adherent to their second drug (32% vs. 9%). Conclusions Although certain clinical features were associated with poor adherence, their performance for predicting who is likely to be non‐adherent, even when combined, was weak. The strongest predictor of adherence to second‐line therapy was adherence to the first therapy. Examining previous prescription records could offer a practical way for clinicians to identify potentially non‐adherent patients and is an area warranting further research.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Identifying routine clinical predictors of non‐adherence to second‐line therapies in type 2 diabetes: A retrospective cohort analysis in a large primary care database

Shields

Hattersley

Farmer

2019

Diabetes Obesity Metabolism

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“…Fifth, our definition of medication adherence assumes observed medication supply and/or prescription fills equate with actual medication‐taking behavior. While this definition may not be entirely accurate, use of PDC and administrative claims are well‐documented in the scientific literature . Lastly, our analytic models did not account for the effect of the coverage gap on predicting adherence.…”

Section: Discussionmentioning

confidence: 99%

“…Second, predicting issues of nonadherence, especially those related to access and convenience, could help determine which patients are eligible for long‐term medication use. Increasing medication supply per prescription (eg, 90‐day supply) has become a common method used by payers to reduce refill requests and address adherence problems . Third, predicting adherence problems also has potential reimbursement implications.…”

Section: Discussionmentioning

confidence: 99%

Novel application of approaches to predicting medication adherence using medical claims data

Zullig

Jazowski

Wang

et al. 2019

Health Services Research

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Objective To compare predictive analytic approaches to characterize medication nonadherence and determine under which circumstances each method may be best applied. Data Sources/Study Setting Medicare Parts A, B, and D claims from 2007 to 2013. Study Design We evaluated three statistical techniques to predict statin adherence (proportion of days covered [PDC ≥ 80 percent]) in the year following discharge: standard logistic regression with backward selection of covariates, least absolute shrinkage and selection operator (LASSO), and random forest. We used the C‐index to assess model discrimination and decile plots comparing predicted values to observed event rates to evaluate model performance. Data Extraction We identified 11 969 beneficiaries with an acute myocardial infarction (MI)‐related admission from 2007 to 2012, who filled a statin prescription at, or shortly after, discharge. Principal Findings In all models, prior statin use was the most important predictor of future adherence (OR = 3.65, 95% CI: 3.34‐3.98; OR = 3.55). Although the LASSO regression model selected nearly 90 percent of all candidate predictors, all three analytic approaches had moderate discrimination (C‐index ranging from 0.664 to 0.673). Conclusions Although none of the models emerged as clearly superior, predictive analytics could proactively determine which patients are at risk of nonadherence, thus allowing for timely engagement in adherence‐improving interventions.

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Statins, Mortality, and Major Adverse Cardiovascular Events Among US Veterans With Chronic Kidney Disease

Barayev,

Hawley,

Wellman

et al. 2023

JAMA Netw Open

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ImportanceThere are limited data for the utility of statins for primary prevention of atherosclerotic cardiovascular disease (ASCVD) and death in adults with chronic kidney disease (CKD).ObjectiveTo evaluate the association of statin use with all-cause mortality and major adverse cardiovascular events (MACE) among US veterans older than 65 years with CKD stages 3 to 4.Design, Setting, and ParticipantsThis cohort study used a target trial emulation design for statin initiation among veterans with moderate CKD (stages 3 or 4) using nested trials with a propensity weighting approach. Linked Veterans Affairs (VA) Healthcare System, Medicare, and Medicaid data were used. This study considered veterans newly diagnosed with moderate CKD between 2005 and 2015 in the VA, with follow-up through December 31, 2017. Veterans were older than 65 years, within 5 years of CKD diagnosis, had no prior ASCVD or statin use, and had at least 1 clinical visit in the year prior to trial baseline. Eligibility criteria were assessed for each nested trial, and Cox proportional hazards models with bootstrapping were run. Analysis was conducted from July 2021 to October 2023.ExposureStatin initiation vs none.Main Outcomes and MeasuresPrimary outcome was all-cause mortality; secondary outcome was time to first MACE (myocardial infarction, transient ischemic attack, stroke, revascularization, or mortality).ResultsIncluded in the analysis were 14 828 veterans. Mean (SD) age at CKD diagnosis was 76.9 (8.2) years, 14 616 (99%) were men, 10 539 (72%) White, and 2568 (17%) Black. After expanding to person-trials and assessing eligibility at each baseline, there were 151 243 person-trials (14 685 individuals) of nonstatin initiators and 2924 person-trials (2924 individuals) of statin initiators included. Propensity score adjustment via overlap weighting with nonparametric bootstrapping resulted in covariate balance, with mean (SD) follow-up of 3.6 (2.7) years. The hazard ratio for all-cause mortality was 0.91 (95% CI, 0.85-0.97) comparing statin initiators to noninitiators. The hazard ratio for MACE was 0.96 (95% CI, 0.91-1.02). Results remained consistent in prespecified subgroup analyses.Conclusions and RelevanceIn this target trial emulation of statin initiation in US veterans older than 65 years with CKD stages 3 to 4 and no prior ASCVD, statin initiation was significantly associated with a lower risk of all-cause mortality but not MACE. Results should be confirmed in a randomized clinical trial.

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Predicting Adherence to Chronic Disease Medications in Patients with Long-term Initial Medication Fills Using Indicators of Clinical Events and Health Behaviors

Cited by 33 publications

References 29 publications

Identifying routine clinical predictors of non‐adherence to second‐line therapies in type 2 diabetes: A retrospective cohort analysis in a large primary care database

Identifying routine clinical predictors of non‐adherence to second‐line therapies in type 2 diabetes: A retrospective cohort analysis in a large primary care database

Novel application of approaches to predicting medication adherence using medical claims data

Statins, Mortality, and Major Adverse Cardiovascular Events Among US Veterans With Chronic Kidney Disease

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