Utilization Patterns of Oral Disease-Modifying Drugs in Commercially Insured Patients with Multiple Sclerosis.

BACKGROUND: The approval of new oral disease-modifying drugs (DMDs), such as fingolimod, dimethyl fumarate (DMF), and teriflunamide, has considerably expanded treatment options for relapsing forms of multiple sclerosis (MS). However, data describing the use of these agents in routine clinical practice are limited. OBJECTIVE: To describe time trends and identify factors associated with oral DMD treatment initiation and switching among individuals with MS. METHODS: Using data from a large sample of commercially insured patients, we evaluated changes over time in the proportion of MS patients who initiated treatment with an oral DMD and who switched from an injectable DMD to an oral DMD between 2009 and 2014 in the United States. We evaluated predictors of oral DMD use using conditional logistic regression in 2 groups matched on calendar time: oral DMD initiators matched to injectable DMDs initiators and oral DMD switchers matched to those who switched to a second injectable DMD. RESULTS: Our cohort included 7,576 individuals who initiated a DMD and 1,342 who switched DMDs, of which oral DMDs accounted for 6% and 39%, respectively. Oral DMD initiation and switching steadily increased from 5% to 16% and 35% to 84%, respectively, between 2011 and 2014, with DMF being the most commonly used agent. Of the potential predictors with clinical significance, a recent neurologist consultation (OR = 1.60; 95% CI = 1.20-2.15) and emergency department visit (OR = 1.43; 95% CI = 1.01-2.01) were significantly associated with oral DMD initiation. History of depression was noted to be a potential predictor of oral DMD initiation; however, the estimate for this predictor did not reach statistical significance (OR = 1.35; 95% CI = 0.99-1.84). No clinically relevant factors measured in our data were associated with switching to an oral DMD. CONCLUSIONS: Oral DMDs were found to be routinely used as second-line treatment. However, we identified few factors predictive of oral DMD initiation or switching, which implies that their selection is driven by patient and/or physician preferences. DISCLOSURES: This study was funded by CVS Caremark through an unrestricted research grant to Brigham and Women's Hospital. Shrank and Matlin were employees of, and shareholders in, CVS Health at the time of the study; they report no financial interests in products or services that are related to the subject of this study. Spettell is an employee of, and shareholder in, Aetna. Chitnis serves on clinical trial advisory boards for Novartis and Genzyme-Sanofi; has consulted for Bayer, Biogen Idec, Celgene, Novartis, Merck-Serono, and Genentech-Roche; and has received research support from NIH, National Multiple Sclerosis Society, Peabody Foundation, Consortium for MS Centers, Guthy Jackson Charitable Foundation, EMD-Serono, Novartis Biogen, and Verily. Desai reports receiving a research grant from Merck for unrelated work. Gagne is principal investigator of a research grant from Novartis Pharmaceuticals Corporation to the Brigham and Women's Hospital and has received grant support from Eli Lilly, all for unrelated work. He is also a consultant to Aetion and Optum. Minden reports grants from Biogen and other fees from Genentech, EMD Serano, Avanir, and Novartis, unrelated to this study. The other authors have no conflicts to report. This study was presented as a poster at the International Society for Pharmacoepidemiology 32nd Annual Meeting; August 25-28, 2016; Dublin, Ireland.

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

BACKGROUND: Efforts at predicting long-term adherence to medications have been focused on patients filling typical month-long supplies of medication. However, prediction remains difficult for patients filling longer initial supplies, a practice that is becoming increasingly common as a method to enhance medication adherence. OBJECTIVES: To (a) extend methods involving short-term filling behaviors and (b) develop novel variables to predict adherence in a cohort of patients receiving longer initial prescriptions. METHODS: In this retrospective cohort study, we used claims from a large national insurer to identify patients initiating a 90-day supply of oral medications for diabetes, hypertension, and hyperlipidemia (i.e., statins). Patients were included in the cohort if they had continuous database enrollment in the 180 days before and 365 days after medication initiation. Adherence was measured in the subsequent 12 months using the proportion of days covered metric. In total, 125 demographic, clinical, and medication characteristics at baseline and in the first 30-120 days after initiation were used to predict adherence using logistic regression models. We used 10-fold cross-validation to assess predictive accuracy by discrimination (c-statistic) measures. RESULTS: In total, 32,249 patients met the inclusion criteria, including 14,930 patients initiating statins, 12,887 patients initiating antihypertensives, and 4,432 patients initiating oral hypoglycemics. Prediction using only baseline variables was relatively poor (cross-validated c-statistic = 0.644). Including indicators of acute clinical conditions, health resource utilization, and short-term medication filling in the first 120 days greatly improved predictive ability (0.823). A model that incorporated all baseline characteristics and predictors within the first 120 days after medication initiation more accurately predicted future adherence (0.832). The best performing model that included all 125 baseline and postbaseline characteristics had strong predictive ability (0.837), suggesting the utility of measuring these novel postbaseline variables in this population. CONCLUSIONS: We demonstrate that long-term, 12-month adherence in patients filling longer supplies of medication can be strongly predicted using a combination of clinical, health resource utilization, and medication filling characteristics before and after treatment initiation. DISCLOSURES: This work was supported by an unrestricted grant from CVS Health to Brigham and Women's Hospital. Shrank and Matlin were employees and shareholders at CVS Health at the time of this study; they report no financial interests in products or services that are related to this subject. Spettell is an employee of, and shareholder in, Aetna. This research was previously presented at the 2016 Annual Conference of the International Society for Pharmacoepidemiology; August 25-28, 2016; Dublin, Ireland.

Predicting 1-Year Statin Adherence Among Prevalent Users: A Retrospective Cohort Study.

BACKGROUND: Attempts to predict who is at risk of future nonadherence have largely focused on predictions at the time of therapy initiation; however, these users are only a small proportion of all patients on therapy at any point in time. Methods to predict nonadherence for established medication users, which have not been previously described in the literature, would be helpful to guide efforts to enhance the use of evidence-based therapies. OBJECTIVE: To test approaches for adherence prediction among prevalent statin users, namely the use of short-term filling behavior, investigator-specified predictors from medical and pharmacy administrative claims, and the empirical selection of potential predictors using the high-dimensional propensity score variable selection algorithm. METHODS: Medical and prescription claims data from a large national health insurer were used to create a cohort of patients who filled statin medication prescriptions in January 2012. We defined 6 groups of adherence predictors and estimated 10 main models to predict medication adherence in the full cohort. The same was done for the population stratified based on the days supply of the index statin prescription (≤ 30 days vs. > 30 days). RESULTS: The study cohort consisted of 93,777 individuals, 58.4% of which were adherent to statins during follow-up. The use of 3 pre-index adherence predictors alone achieved a c-statistic of 0.70. Investigator-specified and empirically selected pharmacy, medical, and demographic variables did substantially worse (0.57-0.60). The use of 3 indicators of post-index adherence achieved a higher c-statistic than the best-performing model using pre-index information (0.74 vs. 0.72). The addition of 3 pre-index adherence predictors further improved discrimination (0.78). CONCLUSIONS: This analysis demonstrated the ability to predict adherence among medication users using filling behavior before and immediately after an index prescription fill. DISCLOSURES: This work was supported by an unrestricted grant from CVS Health to Brigham and Women's Hospital. Shrank, Brennan, and Matlin were employees and shareholders at CVS Health at the time of this manuscript preparation; they report no financial interests in products or services that are related to the subject of the manuscript. Franklin has received consulting fees from Aetion. Chourdry has received grants from the National Heart, Lung, and Blood Institute, PhRMA Foundation, Merck, Sanofi, AstraZeneca, and MediSafe. Spettell is an employee of, and shareholder in, Aetna. The other authors have nothing to disclose. Krumme, Choudhry, Tong, and Franklin contributed to the study design, interpretation of results, and manuscript drafting. Tong prepared and analyzed the data. Isaman, Spettell, Shrank, Brennan, and Matlin provided interpretation of results and critical manuscript revisions.

Effect of Reminder Devices on Medication Adherence: The REMIND Randomized Clinical Trial.

Importance: Forgetfulness is a major contributor to nonadherence to chronic disease medications and could be addressed with medication reminder devices. Objective: To compare the effect of 3 low-cost reminder devices on medication adherence. Design, Setting, and Participants: This 4-arm, block-randomized clinical trial involved 53 480 enrollees of CVS Caremark, a pharmacy benefit manager, across the United States. Eligible participants were aged 18 to 64 years and taking 1 to 3 oral medications for long-term use. Participants had to be suboptimally adherent to all of their prescribed therapies (with a medication possession ratio of 30% to 80%) in the 12 months before randomization. Participants were stratified on the basis of the medications they were using at randomization: medications for cardiovascular or other nondepression chronic conditions (the chronic disease stratum) and antidepressants (the antidepressant stratum). In each stratum, randomization occurred within blocks defined by whether all of the patient's targeted medications were dosed once daily. Patients were randomized to receive in the mail a pill bottle strip with toggles, digital timer cap, or standard pillbox. The control group received neither notification nor a device. Data were collected from February 12, 2013, through March 21, 2015, and data analyses were on the intention-to-treat population. Main Outcomes and Measures: The primary outcome was optimal adherence (medication possession ratio ≥80%) to all eligible medications among patients in the chronic disease stratum during 12 months of follow-up, ascertained using pharmacy claims data. Secondary outcomes included optimal adherence to cardiovascular medications among patients in the chronic disease stratum as well as optimal adherence to antidepressants. Results: Of the 53 480 participants, mean (SD) age was 45 (12) years and 56% were female. In the primary analysis, 15.5% of patients in the chronic disease stratum assigned to the standard pillbox, 15.1% assigned to the digital timer cap, 16.3% assigned to the pill bottle strip with toggles, and 15.1% assigned to the control arm were optimally adherent to their prescribed treatments during follow-up. There was no statistically significant difference in the odds of optimal adherence between the control and any of the devices (standard pillbox: odds ratio [OR], 1.03 [95% CI, 0.95-1.13]; digital timer cap: OR, 1.00 [95% CI, 0.92-1.09]; and pill bottle strip with toggles: OR, 0.94 [95% CI, 0.85-1.04]). In direct comparisons, the odds of optimal adherence were higher with a standard pillbox than with the pill bottle strip (OR, 1.10 [95% CI, 1.00-1.21]). Secondary analyses yielded similar results. Conclusions and Relevance: Low-cost reminder devices did not improve adherence among nonadherent patients who were taking up to 3 medications to treat common chronic conditions. The devices may have been more effective if coupled with interventions to ensure consistent use or if targeted to individuals with an even higher risk of nonadherence. Trial Registration: clinicaltrials.gov Identifier: NCT02015806.

Longitudinal Patterns of Spending Enhance the Ability to Predict Costly Patients: A Novel Approach to Identify Patients for Cost Containment.

BACKGROUND: With rising health spending, predicting costs is essential to identify patients for interventions. Many of the existing approaches have moderate predictive ability, which may result, in part, from not considering potentially meaningful changes in spending over time. Group-based trajectory modeling could be used to classify patients into dynamic long-term spending patterns. OBJECTIVES: To classify patients by their spending patterns over a 1-year period and to assess the ability of models to predict patients in the highest spending trajectory and the top 5% of annual spending using prior-year predictors. SUBJECTS: We identified all fully insured adult members enrolled in a large US nationwide insurer and used medical and prescription data from 2009 to 2011. RESEARCH DESIGN: Group-based trajectory modeling was used to classify patients by their spending patterns over a 1-year period. We assessed the predictive ability of models that categorized patients in the top fifth percentile of annual spending and in the highest spending trajectory, using logistic regression and split-sample validation. Models were estimated using investigator-specified variables and a proprietary risk-adjustment method. RESULTS: Among 998,651 patients, in the best-performing model, prediction was strong for patients in the highest trajectory group (C-statistic: 0.86; R: 0.47). The C-statistic of being in the top fifth percentile of spending in the best-performing model was 0.82 (R: 0.26). Approaches using nonproprietary investigator-specified methods performed almost as well as other risk-adjustment methods (C-statistic: 0.81 vs. 0.82). CONCLUSIONS: Trajectory modeling may be a useful way to predict costly patients that could be implementable by payers to improve cost-containment efforts.

Association Between Patient-Centered Medical Homes and Adherence to Chronic Disease Medications: A Cohort Study.

BACKGROUND: Despite the widespread adoption of patient-centered medical homes into primary care practice, the evidence supporting their effect on health care outcomes has come primarily from geographically localized and well-integrated health systems. OBJECTIVE: To assess the association between medication adherence and medical homes in a national patient and provider population, given the strong ties between adherence to chronic disease medications and health care quality and spending. DESIGN: Retrospective cohort study. SETTING: Claims from a large national health insurer. PATIENTS: Patients initiating therapy with common medications for chronic diseases (diabetes, hypertension, and hyperlipidemia) between 2011 and 2013. MEASUREMENTS: Medication adherence in the 12 months after treatment initiation was compared among patients cared for by providers practicing in National Committee for Quality Assurance-recognized patient-centered medical homes and propensity score-matched control practices in the same Primary Care Service Areas. Linear mixed models were used to examine the association between medical homes and adherence. RESULTS: Of 313 765 patients meeting study criteria, 18 611 (5.9%) received care in patient-centered medical homes. Mean rates of adherence were 64% among medical home patients and 59% among control patients. Among 4660 matched control and medical home practices, medication adherence was significantly higher in medical homes (2.2% [95% CI, 1.5% to 2.9%]). The association between medical homes and better adherence did not differ significantly by disease state (diabetes, 3.0% [CI, 1.5% to 4.6%]; hypertension, 3.2% [CI, 2.2% to 4.2%]; hyperlipidemia, 1.5% [CI, 0.6% to 2.5%]). LIMITATION: Clinical outcomes related to medication adherence were not assessed. CONCLUSION: Receipt of care in a patient-centered medical home is associated with better adherence, a vital measure of health care quality, among patients initiating treatment with medications for common high-cost chronic diseases. PRIMARY FUNDING SOURCE: CVS Health.

Association among change in medical costs, level of comorbidity, and change in adherence behavior.

OBJECTIVES: Interventions to improve medication adherence are effective, but resource intensive. Interventions must be targeted to those who will potentially benefit most. We examined what heterogeneity exists in the value of adherence based on levels of comorbidity, and the changes in spending on medical services that followed changes in adherence behavior. STUDY DESIGN: Retrospective cohort study examining medical spending for 2 years (April 1, 2011, to March 31, 2013) in commercial insurance beneficiaries. METHODS: Multivariable linear modeling was used to adjust for differences in patient characteristics. Analyses were performed at the patient/condition level in 2 cohorts: adherent at baseline and nonadherent at baseline. RESULTS: We evaluated 857,041 patients, representing 1,264,797 patient therapies consisting of 40% high cholesterol, 48% hypertension, and 12% diabetes. Among those with 3 or more conditions, annual savings associated with becoming adherent were $5341, $4423, and $2081 for patients with at least diabetes, hypertension, and high cholesterol, respectively. The increased costs for patients in this group who became nonadherent were $4653, $7946, and $4008, respectively. Depending on the condition and the direction of behavior change, savings were 2 to 7 times greater than the value for individuals with fewer than 3 conditions. In most cases, the value of preventing nonadherence (ie, persistence) was greater than the value of moving people who are nonadherent to an adherent state. CONCLUSIONS: There is important heterogeneity in the impact of medication adherence on medical spending. Clinicians and policy makers should consider this when promoting the change of adherence behavior.

An Insurer's Care Transition Program Emphasizes Medication Reconciliation, Reduces Readmissions And Costs.

Adverse drug events and the challenges of clarifying and adhering to complex medication regimens are central drivers of hospital readmissions. Medication reconciliation programs can reduce the incidence of adverse drug events after discharge, but evidence regarding the impact of medication reconciliation on readmission rates and health care costs is less clear. We studied an insurer-initiated care transition program based on medication reconciliation delivered by pharmacists via home visits and telephone and explored its effects on high-risk patients. We examined whether voluntary program participation was associated with improved medication use, reduced readmissions, and savings net of program costs. Program participants had a 50 percent reduced relative risk of readmission within thirty days of discharge and an absolute risk reduction of 11.1 percent. The program saved $2 for every $1 spent. These results represent real-world evidence that insurer-initiated, pharmacist-led care transition programs, focused on but not limited to medication reconciliation, have the potential to both improve clinical outcomes and reduce total costs of care.

Prescriber and pharmacy variation in patient adherence to five medication classes measured using implementation during persistent episodes.

PURPOSE: The objective of this study was to determine the fraction of variance in patient-level medication adherence accounted for by prescribers and pharmacies. METHODS: We used prescription drug claims paid between January 2010 and July 2011 to a national pharmacy benefits manager to define implementation during persistent episodes. Patients in Massachusetts or Rhode Island covered by Blue Cross Blue Shield of Rhode Island and their prescribers were included. Five drug classes were analyzed: angiotensin converting enzyme (ACE) inhibitors, antihyperglycemics (ANHGs), drugs for prostatic hyperplasia (PH), statins, and levothyroxine (THYR). We performed mixed models with random intercepts (drug, patient, prescriber, and pharmacy) and examined the fraction of variance explained at each level using intraclass correlations. RESULTS: Overall implementation ranged from 87 to 91%. The fraction of the explained variance in implementation to ACEs, ANHG, PH, statins, and THYR accounted for by prescribers was 16.4%, 12.6%, 14.6%, 15.6%, and 15% respectively; and for pharmacies 20.4%, 20%, 15.2%, 10.6%, and 9.4%, respectively. CONCLUSIONS: Prescriber and pharmacy effects accounted for a substantial amount of the explained variance in implementation across all five drug classes. Adherence interventions for chronic conditions that target prescribers and pharmacies, in addition to patients, could be effective and efficient. Copyright © 2016 John Wiley & Sons, Ltd.

Brief Report: Intensification to Triple Therapy After Treatment With Nonbiologic Disease-Modifying Antirheumatic Drugs for Rheumatoid Arthritis in the United States From 2009 to 2014.

OBJECTIVE: Several trials suggest that triple therapy (methotrexate, sulfasalazine, and hydroxychloroquine) and biologic disease-modifying antirheumatic drugs (DMARDs) have similar efficacy in patients with rheumatoid arthritis (RA). This study was undertaken to investigate intensification to triple therapy after initial nonbiologic prescription among patients with RA. METHODS: The use of triple therapy among patients with RA in 2009-2014 was evaluated using US insurance claims data. Patients with a health care visit for RA and an initial nonbiologic DMARD prescription were included. Frequencies of intensification to triple therapy or a biologic DMARD and rates of intensification per 6-month time period were calculated. Using Cox regression, we evaluated whether sociodemographic, temporal, geographic, clinical, and health care utilization factors were associated with intensification to triple therapy. Among those patients whose therapy was intensified, we investigated factors associated with triple therapy use by logistic regression. Hazard ratios (HRs) with 95% confidence intervals (95% CIs) for intensification to triple therapy in relation to various clinical and demographic factors were calculated. RESULTS: There were 24,576 patients with a mean ± SD age of 50.3 ± 12.3 years, and 78% were female. During the study period, treatment was intensified to biologic DMARDs in 2,739 patients (11.1%) compared to 181 patients (0.7%) whose treatment was intensified to triple therapy. There was no significant change in triple therapy use across calendar years. Patients whose treatment was intensified to triple therapy were more likely to receive glucocorticoids (HR 1.91 [95% CI 1.41-2.60]) compared to patients who did not use glucocorticoids and were more likely to use nonsteroidal antiinflammatory drugs (NSAIDs) (HR 1.48, 95% CI 1.10-1.99 versus no NSAID use). Among those patients whose treatment was intensified to triple therapy or biologic DMARDs, factors significantly associated with triple therapy use included older age, US region (with the highest odds for triple therapy use in the West and lowest odds for triple therapy use in the Northeast), glucocorticoid use, and lower number of outpatient visits within 180 days of initial nonbiologic DMARD prescription. CONCLUSION: Despite reports published during the study period suggesting equivalent efficacy of triple therapy and biologic DMARDs for RA, the use of triple therapy was infrequent and did not increase over time in this large nationwide study.

The adherence impact of a program offering specialty pharmacy services to patients using retail pharmacies.

BACKGROUND: A new service model integrates the specialty pharmacy's comprehensive service with the retail pharmacy's patient contact, giving patients options for medication delivery to home, pharmacy, or doctor's office. OBJECTIVE: Evaluate the impact of the new service model on medication adherence. DESIGN: Retrospective cohort study SETTINGS: One hundred fifteen CVS retail stores in Philadelphia participated in a pilot from May 2012 to October 2013, and 115 matched CVS retail stores from around the nation served as controls. PATIENTS: All eligible patients from the intervention and control stores received specialty medications through CVS retail pharmacies prior to implementation of the new service model. INTERVENTION: The intervention patients were transitioned from retail pharmacy service to the specialty pharmacy with delivery options. The control patients received standard retail pharmacy services. MAIN OUTCOME MEASURES: Proportion of days covered and first fill persistence were tracked for 12 months before and after program implementation. RESULTS: Under the new service model, 228 patients new to therapy in the post period had a 17.5% increase in the rate of obtaining a second fill as compared to matched controls. Patients on therapy in both the pre- and the post-periods had a pre-post increase of 6.6% in average adherence rates and a pre-post increase of 10.8% in optimal adherence rates as compared to 326 matched controls. CONCLUSION: The study demonstrated significant improvement in both adherence to therapy and first-fill persistence among patients in the new service model integrating specialty pharmacy's comprehensive services with the retail pharmacy's patient contact and medication delivery choices.

Can purchasing information be used to predict adherence to cardiovascular medications? An analysis of linked retail pharmacy and insurance claims data.

OBJECTIVE: The use of retail purchasing data may improve adherence prediction over approaches using healthcare insurance claims alone. DESIGN: Retrospective. SETTING AND PARTICIPANTS: A cohort of patients who received prescription medication benefits through CVS Caremark, used a CVS Pharmacy ExtraCare Health Care (ECHC) loyalty card, and initiated a statin medication in 2011. OUTCOME: We evaluated associations between retail purchasing patterns and optimal adherence to statins in the 12 subsequent months. RESULTS: Among 11 010 statin initiators, 43% were optimally adherent at 12 months of follow-up. Greater numbers of store visits per month and dollar amount per visit were positively associated with optimal adherence, as was making a purchase on the same day as filling a prescription (p<0.0001 for all). Models to predict adherence using retail purchase variables had low discriminative ability (C-statistic: 0.563), while models with both clinical and retail purchase variables achieved a C-statistic of 0.617. CONCLUSIONS: While the use of retail purchases may improve the discriminative ability of claims-based approaches, these data alone appear inadequate for adherence prediction, even with the addition of more complex analytical approaches. Nevertheless, associations between retail purchasing behaviours and adherence could inform the development of quality improvement interventions.

Patient Use of Email, Facebook, and Physician Websites to Communicate with Physicians: A National Online Survey of Retail Pharmacy Users.

BACKGROUND: Patient-physician communication often occurs outside the clinic setting; many institutions discourage electronic communication outside of established electronic health record systems. Little empirical data are available on patient interest in electronic communication and Web-based health tools that are technically feasible but not widely available. RESEARCH OBJECTIVE: To explore patient behavior and interest in using the Internet to contact physicians. DESIGN: National cross-sectional online survey. PARTICIPANTS: A sample of 4,510 CVS customers with at least one chronic condition in the household was used to target patients with chronic conditions and their caregivers. Subjects were identified from a national panel of over 100,000 retail pharmacy customers. Of those sampled, 2,252 responded (50.0 % response rate). MAIN MEASURES: Survey measures included demographic and health information, patient use of email and Facebook to contact physicians, and patient interest in and use of Web-based tools for health. KEY RESULTS: A total of 37 % of patients reported contacting their physicians via email within the last six months, and 18 % via Facebook. Older age was negatively associated with contacting physicians using email (OR 0.57 [95 % CI 0.41-0.78]) or Facebook (OR 0.28 [0.17-0.45]). Non-white race (OR 1.61 [1.18-2.18] and OR 1.82 [1.24-2.67]) and caregiver status (OR 1.58 [1.27-1.96] and OR 1.71 [1.31- 2.23]) were positively associated with using email and Facebook, respectively. Patients were interested in using Web-based tools to fill prescriptions, track their own health, and access health information (37-57 %), but few were currently doing so (4-8 %). CONCLUSIONS: In this population of retail pharmacy users, there is strong interest among patients in the use of email and Facebook to communicate with their physicians. The findings highlight the gap between patient interest for online communication and what physicians may currently provide. Improving and accelerating the adoption of secure Web messaging systems is a possible solution that addresses both institutional concerns and patient demand.

Observing versus Predicting: Initial Patterns of Filling Predict Long-Term Adherence More Accurately Than High-Dimensional Modeling Techniques.

OBJECTIVE: Despite the proliferation of databases with increasingly rich patient data, prediction of medication adherence remains poor. We proposed and evaluated approaches for improved adherence prediction. DATA SOURCES: We identified Medicare beneficiaries who received prescription drug coverage through CVS Caremark and initiated a statin. STUDY DESIGN: A total of 643 variables were identified at baseline from prior claims and linked Census data. In addition, we identified three postbaseline predictors, indicators of adherence to statins during each of the first 3 months of follow-up. We estimated 10 models predicting subsequent adherence, using logistic regression and boosted logistic regression, a nonparametric data-mining technique. Models were also estimated within strata defined by the index days supply. RESULTS: In 77,703 statin initiators, prediction using baseline variables only was poor with maximum cross-validated C-statistics of 0.606 and 0.577 among patients with index supply ≤30 days and >30 days, respectively. Using only indicators of initial statin adherence improved prediction accuracy substantially among patients with shorter initial dispensings (C = 0.827/0.518), and, when combined with investigator-specified variables, prediction accuracy was further improved (C = 0.842/0.596). CONCLUSIONS: Observed adherence immediately after initiation predicted future adherence for patients whose initial dispensings were relatively short.

Community pharmacy automatic refill program improves adherence to maintenance therapy and reduces wasted medication.

OBJECTIVES: Automatic prescription refill programs are a popular means of improving medication adherence. A concern is the potential for prescription drug wastage and unnecessary healthcare spending. We evaluated the impact of an automatic refill program on patterns of medication use. STUDY DESIGN: Retrospective propensity score matched cohort study with multivariable generalized linear modeling. METHODS: The setting of the study was a pharmacy benefit manager administering benefits for patients of retail pharmacies. Participants included patients on medication for chronic conditions; those receiving a 30-day supply (n = 153,964) and a 90-day supply (n = 100,394) were analyzed separately. The intervention was the automatic prescription refill program. Measures included medication possession ratio (MPR) and average days excess at the time of refill. The results are reported across 11 therapeutic classes. RESULTS: Overall, patients receiving 30-day supplies of medication in the automatic refill program had an MPR that was 3 points higher than those not in the refill program; among those receiving 90-day fills and in the refill program, the MPR was 1.4 points higher (P < .001 for both 30- and 90-day fills). The MPR was higher for members in the refill program across all therapeutic classes. Limiting our analysis to members receiving more than 365 days of medication, we found that patients who received 30-day fills and enrolled in the automatic refill program had 2.5 fewer days' oversupply than those in the control group, whereas automatic refill patients receiving 90-day supplies had 2.18 fewer days' oversupply than the controls (P < .001 for both 30- and 90-day fills). CONCLUSIONS: For this pharmacy provider, automatic refill programs result in improved adherence without adding to medication oversupply.

Predicting adherence trajectory using initial patterns of medication filling.

OBJECTIVES: To evaluate the ability of initial medication dispensings to predict long-term patterns of adherence. STUDY DESIGN: A retrospective cohort study of statin initiators enrolled in a Medicare Part D drug plan from CVS Caremark from 2005 to 2008. METHODS: We used group-based trajectory models to classify patients into 6 adherence trajectories based on patterns of statin filling over the year following therapy initiation. Baseline clinical characteristics and indicators of statin filling during the first 2 to 4 months following initiation were used to predict adherence trajectory in logistic regression models, separately within strata of the days' supply of the initial statin dispensing. Cross-validation was used to measure predictive accuracy of models in data not used for model estimation. RESULTS: Among 77,703 statin initiators, prediction using baseline variables only was poor (cross-validated C statistic ≤ 0.61). When using 3 months of initial adherence to predict trajectory, prediction was greatly improved among patients with an index supply ≤30 days (0.62 ≤ C ≤ 0.91). With 4 months of initial adherence in the model, prediction was strong for all patients (C ≥ 0.72), especially for the best and worst trajectories (C = 0.90 and 0.94, respectively, in patients with an index supply ≤ 30 days; and C = 0.83 and 0.90, respectively, in patients with an index supply > 30 days). CONCLUSIONS: Initial filling behavior strongly predicted future adherence trajectory. Predicting adherence trajectories may facilitate better targeting of interventions to patients most likely to benefit.

Comparative effectiveness of generic versus brand-name antiepileptic medications.

OBJECTIVE: The objective of this study was to compare treatment persistence and rates of seizure-related events in patients who initiate antiepileptic drug (AED) therapy with a generic versus a brand-name product. METHODS: We used linked electronic medical and pharmacy claims data to identify Medicare beneficiaries who initiated one of five AEDs (clonazepam, gabapentin, oxcarbazepine, phenytoin, zonisamide). We matched initiators of generic versus brand-name versions of these drugs using a propensity score that accounted for demographic, clinical, and health service utilization variables. We used a Cox proportional hazards model to compare rates of seizure-related emergency room (ER) visit or hospitalization (primary outcome) and ER visit for bone fracture or head injury (secondary outcome) between the matched generic and brand-name initiators. We also compared treatment persistence, measured as time to first 14-day treatment gap, between generic and brand-name initiators. RESULTS: We identified 19,760 AED initiators who met study eligibility criteria; 18,306 (93%) initiated a generic AED. In the matched cohort, we observed 47 seizure-related hospitalizations and ER visits among brand-name initiators and 31 events among generic initiators, corresponding to a hazard ratio of 0.53 (95% confidence interval, 0.30 to 0.96). Similar results were observed for the secondary clinical endpoint and across sensitivity analyses. Mean time to first treatment gap was 124.2 days (standard deviation [sd], 125.8) for brand-name initiators and 137.9 (sd, 148.6) for generic initiators. SIGNIFICANCE: Patients who initiated generic AEDs had fewer adverse seizure-related clinical outcomes and longer continuous treatment periods before experiencing a gap than those who initiated brand-name versions.

Rationale and design of the Randomized Evaluation to Measure Improvements in Non-adherence from Low-Cost Devices (REMIND) trial.

BACKGROUND: Long-term adherence to prescription medications for the treatment of chronic disease remains low. While there are many contributors to suboptimal medication use, simple forgetfulness is widely believed to be central. Relatively simple devices may be a particularly cost-efficient and scalable way to promote adherence, however limited data exists about their ability to improve adherence in real-world settings. METHODS/DESIGN: The REMIND trial is a prospective, intent-to-treat randomized control trial to evaluate the impact on medication adherence of three simple, low-cost devices (Take-N-Slide(™), the RxTimerCap(™), and a standard pillbox). In March 2014, we enrolled 53,480 individuals 18 to 64 years old taking one to three medications to treat chronic disease whose prescription drug benefits were administered by CVS Caremark. The study's primary outcome is optimal adherence over the 12-month period after randomization. Using a randomization ratio of 1:2 between control and each intervention arm, the study has more than 80% power with an alpha of 5% to detect a 1% difference in the rate of optimal adherence between intervention and control groups and across intervention arms. DISCUSSION: The REMIND trial is the first randomized study to rigorously evaluate the impact of simple, low-cost reminder devices on medication adherence. The results will inform comparative cost effectiveness studies of reminder systems in improving medication adherence and clinical outcomes.

Association between trajectories of statin adherence and subsequent cardiovascular events.

PURPOSE: Trajectory models have been shown to (1) identify groups of patients with similar patterns of medication filling behavior and (2) summarize the trajectory, the average adherence in each group over time. However, the association between adherence trajectories and clinical outcomes remains unclear. This study investigated the association between 12-month statin trajectories and subsequent cardiovascular events. METHODS: We identified patients with insurance coverage from a large national insurer who initiated a statin during January 1, 2007 to December 31, 2010. We assessed medication adherence during the 360 days following initiation and grouped patients based on the proportion of days covered (PDC) and trajectory models. We then measured cardiovascular events during the year after adherence assessment. Cox proportional hazards models were used to evaluate the association between adherence measures and cardiovascular outcomes; strength of association was quantified by the hazard ratio, the increase in model C-statistic, and the net reclassification index (NRI). RESULTS: Among 519 842 statin initiators, 8777 (1.7%) had a cardiovascular event during follow-up. More consistent medication use was associated with a lower likelihood of clinical events, whether adherence was measured through trajectory groups or PDC. When evaluating the prediction of future cardiovascular events by including a measure of adherence in the model, the best model reclassification was observed when adherence was measured using three or four trajectory groups (NRI = 0.189; 95% confidence interval: [0.171, 0.210]). CONCLUSIONS: Statin adherence trajectory predicted future cardiovascular events better than measures categorizing PDC. Thus, adherence trajectories may be useful for targeting adherence interventions.