To examine whether adherence to osteoporosis medications can be improved by educational interventions targeted at primary care physicians (PCPs) and patients.

Post hoc analysis of data collected as part of a prospective randomized controlled trial to improve initiation of osteoporosis management such as bone mineral density testing or osteoporosis drug initiation.

The trial was conducted among patients at risk for osteoporosis enrolled in Horizon Blue Cross Blue Shield of New Jersey. For a 3-month period, randomly selected PCPs and their patients received education about osteoporosis diagnosis and treatment. The PCPs received face-to-face education by trained pharmacists, while patients received letters and automated telephone calls. The control group received no education. We assessed medication adherence during 10 months following the start of the intervention using the

medication possession ratio (MPR), the ratio of available medication to the total number of days studied.

These analyses included 1867 patients (972 randomized to the intervention group and 875 to the control group) and their 436 PCPs. During 10 months following the intervention, the median MPRs were 74% (interquartile range [IQR], 19%-93%) for the intervention group and 73% (IQR, 0%-93%) for the control group (

 = .18). The median times until medication discontinuation after the intervention were 85 days (IQR, 58-174 days) for the intervention group and 79 days (IQR, 31-158 days) for the control group.

The educational intervention did not significantly improve medication compliance or persistence with osteoporosis drugs.

Medication adherence for osteoporosis is poor, and there are few proven effective interventions.

In a post hoc analysis of a randomized controlled trial of brief education, medication adherence was no better in the intervention group compared with the control group.

Interventions focused on initiation of chronic disease management may not enhance medication adherence.

Adherence interventions should focus on patients, not physicians.

Improving patient adherence with osteoporosis medications is an important challenge. Higher levels of adherence may be associated with reduced fracture rates

 demonstrate suboptimal adherence among patients in the community. Oft-cited barriers to achieving adequate adherence include insufficient patient education, specific patient health beliefs, complex medication regimens, polypharmacy, poor provider-patient relations, patient forgetfulness, and medication costs.

 Strategies targeting these barriers, as well as patient monitoring systems and feedback based on clinical markers, have been proposed to improve medication adherence for osteoporosis treatments.

Successful medication adherence interventions for other chronic diseases such as hypertension and asthma have been multifactorial and focused on the patient.

 A small randomized trial attempted to enhance adherence to raloxifene hydrochloride among 75 women with osteopenia; a nurse-run patient monitoring program with clinic appointments every 12 weeks improved adherence to raloxifene at 1 year.

 of patients with osteoporosis who sustained distal forearm fractures showed that timely provision of educational brochures, primary care provider appointments, and bone mineral density testing appointments improved adherence over 6 months of follow-up. However, many of the most adherent patients had the best bone mineral density.

While medication adherence may be primarily a patient behavior, it is unclear whether physician-directed interventions can influence this behavior. To our knowledge, no prior intervention for osteoporosis medication adherence has focused on the physician. In this study, we performed a post hoc analysis of data from a randomized controlled trial for improving osteoporosis management to determine whether a brief physician-oriented intervention improved compliance or persistence with osteoporosis medications.

These analyses are based on a cluster randomized controlled trial conducted in Horizon Blue Cross Blue Shield of New Jersey (HBCBSNJ) that has been described in detail elsewhere.

 Briefly, we randomly assigned primary care physicians (PCPs) and their patients at risk for osteoporosis (defined herein) to receive a multifaceted intervention or usual care. Randomization was clustered so that all patients of a particular physician were assigned to the same arm, either intervention or usual care. Patients were randomized with their PCP to reduce any contamination within a given physician’s practice. The trial was aimed at improving the management of osteoporosis among at-risk patients, including initiation of bone mineral density testing and pharmacotherapy for osteoporosis. The brief intervention proved effective for the primary outcome: initiation of osteoporosis management was enhanced by 45% (95% confidence interval [CI], 9%-93%) in the intervention group compared with the control group.

While initiation of testing and treatment are the necessary first steps for osteoporosis management, we pursued the present analyses to determine whether this intervention also enhanced adherence with medications used for osteoporosis. We examined 2 aspects of adherence, compliance and persistence. These analyses are post hoc and should be considered in light of all the known limitations of such analyses.

We ascertained baseline patient and physician characteristics during the period from July 1, 2002, through August 31, 2004. The intervention occurred during a 3-month interval between September 1, 2004, and December 1, 2004. Adherence was assessed by prescription filling data from September 1, 2004, through June 24, 2005. All aspects of the study were approved by the Partners Healthcare Institutional Review Board.

Eligible participants for the parent trial were insured by HBCBSNJ and were at risk for osteoporosis. At-risk status was defined as the following: (1) women 65 years or older, (2) men or women 45 years or older with a prior fragility fracture (including hip, wrist, humerus, or spine), or (3) men or women 45 years or older who took oral glucocorticoids for at least 90 days during the baseline period.

All subjects who filled at least 1 prescription during the baseline or follow-up period for a medication used for osteoporosis were included. Osteoporosis medications were alendronate sodium, calcitonin, estrogen, raloxifene hydrochloride, risedronate sodium, and teriparatide (there was no use of ibandronate sodium in the study population). Estrogen therapy was included, although we cannot determine the indication for use in the study database. Subjects were further classified into the following 3 groups: new users of osteoporosis medications during the intervention period, current users at the time the intervention started, and past users based on prescription filling during the baseline period. New users had never filled a prescription for an osteoporosis medication during the baseline period. Current users filled at least 1 prescription for an osteoporosis medication during the baseline period and filled at least 1 prescription in the 90 days before the start of the study period. Ninety days represents the longest prescription duration allowed. This definition intended to capture those patients who were filling prescriptions for osteoporosis medications on a somewhat regular basis immediately before the intervention. Past users filled at least 1 prescription for an osteoporosis medication during the baseline period but did not fill in the 90 days before the start of the study period. We assumed that these patients used osteoporosis medications in the past but stopped before the intervention.

The intervention consisted of education about osteoporosis diagnosis and appropriate treatment. This information was delivered to physicians in a single one-on-one educational encounter during a 3-month intervention period. Physician education was accomplished with a visit from a pharmacist-educator. These pharmacists had participated in a 1-day training session and several follow-up teleconferences about osteoporosis and the principles of one-on-one physician education (“academic detailing”).

 They learned specific teaching techniques and included the continuing medical education curriculum during their visits with the physicians. At the visits, they provided the physicians with the following educational material: a list of the physician’s patients who were deemed at risk for osteoporosis; written summaries of osteoporosis epidemiology, diagnosis, and treatment; an algorithm for diagnosis and treatment of osteoporosis that was laminated as a card to fit in a coat pocket; a guide to osteoporosis pharmacotherapy; “tear sheets” resembling prescription pads with check boxes for fall prevention, calcium and vitamin D use, bone mineral density testing, and treatment; and patient pamphlets on fall prevention (available from the author on request). Finally, the study paid for physicians to apply for continuing medical education credit on completion of a postvisit test.

In addition to the physician-directed education, patients in the intervention group received an introductory letter outlining the importance of osteoporosis, its diagnosis, and appropriate treatment. This letter was followed by an automated telephone call inviting them to undergo bone mineral density testing. This call used interactive voice response technology that has been used for other screening tests but which was ineffective in that trial.

This study measured medication compliance as the primary end point and persistence as a secondary end point. Compliance was expressed as the medication possession ratio (MPR), defined as number of days for which medication is available divided by number of days in the follow-up period.

 A patient who consistently fills prescriptions and has medication available for each day is 100% adherent. For new and past users (who did not have osteoporosis medications available in the 90 days before the intervention), the follow-up period started on the date of the first filled prescription for an osteoporosis medication on or after September 1, 2004 (the first day of the study period). For current users, the follow-up period started on September 1, 2004, with “rollover” of available osteoporosis medications from previously filled prescriptions. The end of follow-up occurred at the first of the following: death, loss of HBCBSNJ eligibility, or the end of the study period.

As a secondary end point, we examined persistence. Persistence measures complement the MPR in that they provide insight into a patient’s medication use over time.

 Persistence was expressed as days until discontinuation, where discontinuation was defined as at least 30 days without any medication available. Prior analyses found that 30 days without any medication available strongly correlated with permanent discontinuation.

 For new, current, and past users, the followup period started on the date of the first filled prescription for osteoporosis medications on or after September 1, 2004. As already defined, the end of follow-up occurred at the first of the following: death, loss of HBCBSNJ eligibility, or the end of the study period.

Two different patients could have the same MPR but different persistence values. For example, a patient who has medication available for the first 150 days of a 300-day study period has an MPR of 50%. Likewise, a patient who takes osteoporosis medications every other day for all 300 days also has an MPR of 50%. The first patient was persistent for 150 days, whereas the second patient was persistent for 300 days.

We used insurance claims as our source of outcomes and baseline patient data. Patient baseline data included demographics, comorbidities associated with increased risk of osteoporosis and fractures, health-seeking behaviors such as immunizations and cancer screening, and types of preintervention osteoporosis medications. We also collected data regarding filling of prescriptions for osteoporosis medications, including types of medication, dates filled, and amount of medication dispensed. This source of data does not contain indications for medication use; thus, some of the estrogen therapy was likely used for nonosteoporosis indications.

We compared the characteristics of patients randomized to intervention versus control groups. The outcome analyses considered new users of osteoporosis medications separately from current or past users. All analyses used an intent-to-treat approach, including physicians and patients in whom the educational interventions were not successfully completed. The median MPR and days until discontinuation were calculated and compared for intervention and control groups. We modeled MPRs using Poisson distribution regression analysis adjusting for the correlation with generalized estimating equations. Time until discontinuation or persistence was modeled with Cox proportional hazards regression analysis using sandwich estimators to adjust for the clustering within physician practice. For both adjusted models, we included all variables using the inverse probability treatment weighting method.

 Analyses were performed using SAS version 8.02 (SAS Institute, Cary, NC).

A total of 1867 patients and 436 PCPs were randomized and included in this study. There were 126 new osteoporosis medication users, 1157 current users, and 584 past users. Demographic characteristics, medical history, risk factors for osteoporosis, and health behavior were not notably different between the intervention and control groups (

). Most patients were between 65 and 74 years old, and almost all were female. Few patients had documented osteoporosis by diagnosis codes during the baseline period, and approximately 1 in 10 had sustained a fracture. Baseline osteoporosis treatments included all of the major categories, with bisphosphonates predominating (

Across all patients studied, new and prevalent users, the median MPRs were 74% (interquartile range [IQR], 19%-93%) for the intervention group and 73% (IQR, 0%-93%) for the control group (

 = .18). The median times until first refill after the intervention were 57 days (IQR, 22-298 days) for the intervention group and 61 days (IQR, 23-298 days) for the control group (

Among new users, the unadjusted median MPR was 88.6% for the intervention group compared with 76.4% for the control group (

). After adjusting for baseline characteristics listed in Table 1, there was no difference in MPRs (relative risk [RR], 1.05; 95% CI, 0.89-1.23). Before adjustment, persistence (measured as the median days until discontinuation) was 85 days for the intervention group compared with 79 days for the control group (

). The intervention group exhibited a trend toward higher number of days until discontinuation; however, this increase was not statistically significant in the adjusted model (RR, 0.85; 95% CI, 0.48-1.49).

As with new users, the median MPR and median persistence were similar between current users in the intervention and control groups. The median MPRs were 87.6% for the intervention group and 88.8% for the control group (

 = .60) (Table 3). The median persistence durations were 227 days for the intervention group and 229 days for the control group (

 = .31) (Table 4). Among current users, only 0.84% in the intervention group and 0.53% in the control group did not fill any osteoporosis medication prescriptions during the study period. Adjusted analyses showed no difference in the MPR or persistence.

Past users in both groups did not frequently begin to fill prescriptions for osteoporosis medications again during the study follow-up. The median MPR for the intervention group was 0.0 (IQR, 0.0-36.0), while the median for the control group was also 0.0 but with a much narrower IQR of 0.0 to 10.4 (Table 3). The adjusted analysis showed an increase in the MPR for the intervention group (RR, 1.35; 95% CI, 1.00-1.81). The median times until medication discontinuation (for those who filled ≥1 prescription) were 130 days for the intervention group and 104 days for the control group (

= .21). The adjusted analysis suggested a trend toward a reduced risk of discontinuation among the intervention group (RR, 0.81; 95% CI, 0.56-1.18). Among past users, 62.7% of the intervention group and 73.7% of the control group never filled osteoporosis medication prescriptions during the study period.

A randomized brief educational intervention targeting initiation of testing and treatment for osteoporosis did not improve compliance or persistence with osteoporosis medications over a 10-month follow-up period. There was a suggestion for some improvement among patients who had used osteoporosis medications in the past, but there was no demonstrable improvement among current or new users of these medications. The intervention was successful at improving the frequency of initiating of bone mineral density testing and osteoporosis medication prescription.

 Our ability to observe a difference in compliance or persistence may have been affected by our 10-month follow-up and a high rate of baseline adherence in the control group. In general, an MPR of 80% or higher is regarded as adequate. Furthermore, results of some studies

 suggest that an MPR of 80% or higher is associated with reduced rates of fracture.

The analyses we present are secondary analyses of a trial designed to improve initiation of osteoporosis management. We pursued these post hoc analyses because the intervention produced a positive effect on initiation of medication use, suggesting that there might have been benefits to adherence. However, the intervention (focused more on PCPs than on patients) did not produce substantial gains in medication adherence. This implies that medication adherence is a specific behavior that likely requires potent interventions designed specifically for adherence.

Several limitations of this intervention need mention. Our source of data for this study was a large healthcare utilization database that does not contain information on the reasons for prescribing a given medication. Some of the estrogen therapy users were likely using this treatment for other indications. We followed up patients for 10 months to determine their compliance. This limits our ability to detect clinical outcomes such as fractures. The compliance level of the control arm was high, limiting our ability to detect an incremental effect due to the intervention. Finally, our intervention was brief; a more sustained intervention might have been more effective at enhancing adherence.

Strengths of this study include its randomized nature and the use of a typical community population versus a clinical trial population, where volunteers may be different from usual patients. Much of the literature on medication adherence is based on observational findings

 rather than randomized findings and suggests that adherent patients have improved outcomes such as reduced fractures.

 It is critical to recognize that adherence is not a random behavior; adherent patients may have improved outcomes for many reasons, only one of them being consuming more osteoporosis medication. In fact, randomized trials in other areas have found that adherent patients receiving placebo often fare better than nonadherent patients in the intervention arm.

Our negative results, using a brief intervention focused on physicians, suggest that adherence interventions for osteoporosis might be more effective when focused on patients. A recent successful intervention for other chronic medications targeted patients with education and the use of medication blister packs.

 Prior interventions to improve osteoporosis medication adherence have examined the effects of simpler regimens, patient monitoring, and education. Several studies

 demonstrate that oral bisphosphonates dosed weekly versus daily may improve adherence. An observational study

 of 2741 postmenopausal American women starting bisphosphonate therapy showed a significantly higher mean MPR for weekly dosing (69.2%) compared with 57.6% for daily dosing. Over 365 days, the median persistence was significantly increased for weekly dosing versus daily dosing (269 vs 134 days). Another observational study

 involving 2124 postmenopausal Dutch women starting bisphosphonate therapy also demonstrated better persistence at the end of 1 year for weekly versus daily dosing. Despite these encouraging results, all study authors pointed out that, even for the patients taking bisphosphonates dosed weekly, adherence and persistence were suboptimal. Although it is possible that a less frequently dosed bisphosphonate might further improve adherence,

 the literature suggests that enhancing medication adherence is much more complicated than implementing less frequent dosing.

 conducted a randomized controlled trial using a monitoring intervention. In that study, 75 postmenopausal women with osteopenia were newly prescribed raloxifene and assigned to nurse monitoring or usual care (no monitoring). Those patients who were randomized to the monitoring group attended 3 appointments (spaced 12 weeks apart) with nursing staff, during which they were asked 6 questions related to well-being, problems with medication, and adverse events. No information about osteoporosis per se was offered, unless patients happened to ask about it. Monitored patients demonstrated a statistically significant increase in rates of adherence: 65% were adherent (defined as taking >75% of prescribed raloxifene tablets) at 1 year compared with 42% in the nonmonitored group. These study findings suggest that a patient-directed program of ongoing intensive counseling improves adherence.

 suggests that patient education is an important strategy for improving adherence. Additional studies

 of patients with chronic diseases such as rheumatoid arthritis and asthma show that face-to-face counseling from healthcare providers can improve adherence. Other than our study, there are few studies investigating the effect of education on adherence with osteoporosis medications. Cuddihy and colleagues

 describe a prospective nonrandomized population-based study involving women with osteoporosis having recent distal forearm fractures. Soon after the fracture, patients were provided educational pamphlets, a PCP appointment, and a bone mineral density testing appointment. For 38 women advised to begin therapy for osteoporosis, the intervention improved self-reported medication adherence at 6 months (36% were adherent compared with 9% in a historical cohort). Nielsen and colleagues

 describe preliminary results from a prospective randomized study of 280 patients with osteoporosis undergoing an educational intervention consisting of “school” comprising 16 days of classes over 4 weeks. Self-reported persistence with pharmacotherapy was higher in the school group compared with the control group. However, the high persistence rates (93%-99%) for the 61 patients who reached 24 months of follow-up suggest that this extraordinarily motivated study population may not be representative of the wider community.

The brief intervention we tested that targeted PCPs to improve their initiation of management of osteoporosis did not enhance medication adherence, primarily a patient behavior. We have since conducted several focus groups with patients that suggest the following: it is critical to convince patients of the importance of treating a largely asymptomatic condition at the outset of treatment; most patients would rather not take medications chronically, and thus any adverse experience or concern is often enough to interfere with adherence; and persistent use of treatments requires reminder systems that are often idiosyncratic to a given patient. These observations and recent review articles

 have helped us to design new adherence interventions for osteoporosis that we anticipate will be more effective than what we describe herein. We will not realize the full potential of our new treatments for osteoporosis without better adherence.

Division of Pharmacoepidemiology (AD-HS, MRS, JMP, DHS) and Division of Rheumatology (DHS), Brigham and Women’s Hospital, Boston, MA; Division of Endocrinology (AD-HS), Columbia Presbyterian Hospital, New York, NY; Horizon Blue Cross Blue Shield (SAJ, MP, CT, LB), Newark, NJ; and Outcomes Research Group (Y-TC, TWW), Merck & Co, Inc, West Point, PA. Ms Truppo is now with Care Management International, Hoboken, NJ. Ms Breiner is now with NYC Department of Health, New York, NY.

Portions of this educational trial were supported by Merck & Co, Inc. Dr Solomon’s work on osteoporosis is also supported by the Arthritis Foundation and the National Institutes of Health (National Institute of Arthritis and Musculoskeletal and Skin Diseases [NIAMS] K24 AR055989 and NIAMS P60 AR 047782).

 Drs Chen and Weiss are full-time employees of Merck & Co, Inc, the funder of this study, and report owning stock in that company. Dr Solomon reports receiving research grants pending from Novartis and Amgen. The other authors (AD-HS, MRS, JMP, SAJ, MP, CT) report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

 Concept and design (AD-HS, SAJ, MP, Y-TC, TWW, DHS); acquisition of data (JMP, CT, TWW, DHS); analysis and interpretation of data (AD-HS, MRS, Y-TC, TWW, DHS); drafting of the manuscript (AD-HS, DHS); critical revision of the manuscript for important intellectual content (JMP, SAJ, MP, Y-TC); statistical analysis (MRS); provision of study materials or patients (CT); obtaining funding (MP, Y-TC); and administrative, technical, or logistic support (JMP, CT).

 Daniel H. Solomon, MD, MPH, Division of Pharmacoepidemiology, Brigham and Women’s Hospital, 75 Francis St, Boston, MA 02115. E-mail: dhsolomon@partners.org.

1. Caro JJ, Ishak KJ, Huybrechts KF, Raggio G, Naujoks C.

 The impact of compliance with osteoporosis therapy on fracture rates in actual practice. 

2. McCombs JS, Thiebaud P, McLaughlin-Miley C, Shi J. 

Compliance with drug therapies for the treatment and prevention of osteoporosis. 

Compliance with osteoporosis medications. 

 Improving compliance and persistence with bisphosphonate therapy for osteoporosis. 

 Interventions to enhance patient adherence to medication prescriptions: scientific review [published correction appears in 

 Persistence with bisphosphonate treatment for osteoporosis: finding the root of the problem. 

 Approaches to patient education: emphasizing the long-term value of compliance and persistence. 

Articles

CLINICAL

Post hoc analysis of a randomized controlled trial found that a 1-session educational intervention targeted at patients and primary care physicians did not improve osteoporosis medication adherence.


Adherence to Osteoporosis Medications After Patient and Physician Brief Education: Post Hoc Analysis of a Randomized Controlled Trial

To examine the association of medication adherence with healthcare use and costs among Medicaid beneficiaries with congestive heart failure (CHF), to investigate whether the association was a graded one, and to estimate the potential savings due to improved adherence.

Using Medicare and Medicaid data for 4 states, adherence was estimated using the medication possession ratio (MPR).

Multivariate logistic and 2-part general linear models were estimated to study the primary objectives. The MPR was specified in multiple ways to examine its association with healthcare use and costs.

Adherent beneficiaries were less likely to have a hospitalization (0.4 percentage points), had fewer hospitalizations (13%), had in excess of 2 fewer inpatient days (25%), were less likely to have an emergency department (ED) visit (3%), and had fewer ED visits (10%) than nonadherent beneficiaries. Total healthcare costs were $5910 (23%) less per year for adherent beneficiaries compared with nonadherent beneficiaries. The relationship between medication adherence and healthcare costs was graded. For example, beneficiaries with adherence rates of 95% or higher had about 15% lower healthcare costs than those with adherence rates between 80% and less than 95% ($17,665 vs $20,747, P <.01). The relationship between adherence and total healthcare costs was even more stark when the most adherent beneficiaries were segmented into finer subgroups.

Healthcare costs among Medicaid beneficiaries with CHF would be lower if more patients were adherent to prescribed medication regimens. Researchers should reconsider whether a binary threshold for adherence is sufficient to examine the association of adherence with outcomes and healthcare costs.

Higher adherence to congestive heart failure (CHF) medications was associated with lower healthcare utilization and lower costs among Medicaid beneficiaries.

The relationship between medication adherence and healthcare costs was a graded one. Beneficiaries with near-perfect drug adherence had lower healthcare costs than beneficiaries with only slightly lower adherence. 

Overall Medicare outlays could be considerably lower if more enrollees with CHF were adherent.

Researchers should reconsider whether a simple binary threshold for adherence (eg, a medication possession ratio of 80%) is sufficient for examining the association of drug adherence with outcomes and healthcare costs.

Congestive heart failure (CHF) is a leading cause of hospitalization and mortality in the United States, affecting more than 5 million people at an expected cost of $34.8 billion in 2008.

 The Centers for Medicare & Medicaid Services (CMS) has prioritized improved treatment of CHF, among other chronic conditions, through demonstrations and pilot programs for its beneficiaries.

 The prevalence of CHF is as high as 2.6% among Medicaid beneficiaries and 10.7% among those dually enrolled in Medicare and Medicaid (dual eligibles).

 Patients with CHF account for a disproportionate share of CMS spending. In 1999, 14% of fee-for-service Medicare beneficiaries with CHF accounted for 43% of total spending.

Patients with CHF are generally at increased risk for heart attack, stroke, emergency department (ED) visits, hospitalization, and death.

 To minimize their risk, most patients with CHF should use 1 or more drugs from different therapeutic subclasses, including loop diuretics, angiotensin-converting enzyme (ACE) inhibitors, angiotensin II receptor blockers, and β-blockers.

 However, medication nonadherence is common among patients with CHF, and Medicaid beneficiaries’ drug use is often inconsistent with practice guidelines.

Despite evidence that poor adherence leads to higher hospitalization rates, few studies

 have examined the relationship between adherence and healthcare costs for patients with CHF, although hospitalization accounts for their highest share of expenditures. If higher CHF drug adherence is associated with lower hospitalization risk, it stands to reason that it is also associated with lower healthcare costs.

This study had 3 primary objectives. The first objective was to examine the association of CHF medication adherence with healthcare use and costs in a Medicaid population. The second objective was to investigate whether the association between drug adherence and outcomes was a graded one. Throughout the literature, the primary threshold used to represent adherent behavior is a medication possession ratio (MPR) of 80%, but we hypothesized that the relationship was more likely graded. The third objective was to estimate the potential savings to Medicaid based on any findings that suggested an association between CHF medication adherence and healthcare costs.

This study used medical and pharmacy claims data from the 1998 State Medicaid Research Files, the 1999 Medicaid Analytic eXtract, and the 1999 Medicare Standard Analytic File for Medicaid beneficiaries residing in Arkansas, California, Indiana, and New Jersey. We selected these states because they are geographically and demographically diverse and because they had limited or no capitated managed care for the disabled and older Medicaid population in 1998 and 1999. In addition, CHF drug utilization differences across these states were not due to differences in copayments or benefit designs.

The research sample included noninstitutionalized beneficiaries with at least 1 CHF drug claim in 1999, medical claims for CHF, and continuous enrollment in fee-for-service Medicaid with pharmacy benefit coverage. The CHF medications were identified using First DataBank’s Master Drug Data Base

 therapeutic classification system and included the following drug groups: antianginals, β-blockers, calcium channel blockers, antiarrhythmics, antihypertensives, and diuretics. Beneficiaries were identified as having CHF if they were hospitalized with a CHF diagnosis in 1998 or had at least 2 ambulatory visits in 1998 with a CHF diagnosis (

International Classification of Diseases, Ninth Revision, Clinical Modification

We used the MPR to measure CHF medication adherence in 1999.

 Using all CHF drug claims, the MPR was calculated by dividing a patient’s total days’ supply of medication by the number of days between the date of the patient’s first fill and the last day on which the patient had medication available. Days during which a patient stayed in a hospital are excluded from the calculation, and days for which more than 1 CHF drugs were available are counted only once. Using multiple CHF drug subclasses to examine adherence is more lenient than focusing on 1 subclass and is appropriate for a Medicaid population, as research indicates considerable underutilization of CHF drugs from any single subclass.

This study considers multiple MPR specifications. First, the threshold of 80% is used to deem patients as adherent (≥80%) or as nonadherent (<80%). This value is borrowed from established literature on cardiovascular disease and from previous adherence research.

 However, there is no clinical evidence to support using this ratio or any other value as the threshold for medication adherence. Second, in a sensitivity analysis we also specified the MPR as a continuous variable. Third, we specified the MPR as 4 different ordinal variables. The first ordinal variable has 3 levels, segmenting patients with an MPR of 95% or higher from patients with an MPR between 80% and less than 95% and from patients with an MPR below 80%. We also specified 3 different 5-level ordinal variables. First, we examined the MPR by adherence quintile (eg, the first quintile is 0%-20%, and the last quintile is 80%-100%). Second, we segmented the MPR by quintile such that roughly 20% of the sample fell into each group. Third, we specified an ordinal variable to examine adherence for patients with a nearperfect MPR (with each subgroup containing ≥10% of the sample). The 5 levels were 99% or higher, 95% to less than 99%, 80% to less than 95%, 50% to less than 80%, and less than 50%.

Outcome Variables and Regression Analyses

We examined healthcare costs and utilization in 1999. Cost outcomes included total healthcare (including and excluding drug costs) and drug, inpatient, outpatient, and other medical costs (skilled nursing facility, hospice, ED, and durable medical equipment). Utilization included any hospital use, the number of hospital admissions, the number of hospital days, any ED use, and the number of ED visits. Regression analyses examined the association in 1999 between CHF drug adherence and outcomes.

The distribution of costs dictated regression specifications for models in which costs were the dependent variables. For cost data with only nonzero values (total costs, including drug costs), we estimated a generalized linear model (GLM). For skewed data with many zero values, we used a 2-stage procedure.

 We first estimated a logistic regression to model the likelihood of having a nonzero cost and then estimated costs with a GLM, multiplying cost estimates by the predicted probability of having nonzero costs to obtain final cost estimates. For all GLM equations, we used the modified Park test to determine the appropriate link function.

 We estimated costs through the method of recycled predictions, setting all sample members as adherent or as nonadherent, while keeping all other individual characteristics constant. For models in which adherence was specified as a 3-level or 5-level variable, we estimated costs for each of the 3 to 5 subgroups separately.

We estimated logit models for hospital admissions and ED visits and least squares regressions for the number of hospitalizations, the number of hospital days, and the number of ED visits. All utilization outcomes were estimated through recycled predictions. We estimated all regressions using commercially available statistical software (STATA, release 9; StataCorp LP, College Station, TX).

The independent variable of interest was the MPR. Regression analyses also included demographic characteristics, health risk factors, and CHF comorbidities.  demographic characteristics included a dual-eligible indicator, age (≤64, 65-74, 75-84, and ≥85 years), sex, state of residence, and race/ethnicity (white, African American, or other). Health risk factors and comorbidities included whether the beneficiary also had diagnosed coronary artery disease or diabetes mellitus and whether the beneficiary had any hospitalizations related or unrelated to CHF during 1998, as well as a diagnostic risk adjustor based on the Chronic Illness and Disability Payment System

To estimate the potential savings to Medicare from higher CHF medication adherence, we extrapolated study findings on an aggregate level. This was based on published estimates of the number of beneficiaries with CHF and on assumptions about their mean medication adherence.

In the 4 study states, 37,408 of Medicaid beneficiaries met the inclusion criteria (

). About 36% were younger than 65 years, and 15% were 85 years or older. Slightly more than half were white, and roughly a quarter were African American. Almost three-fourths were female, 72% were dual eligibles, and about half were classified as disabled (and about half of these were also dual eligibles [data not shown]). Many beneficiaries had medical claims for other cardiovascular conditions in addition to CHF, including coronary artery disease (29%) and diabetes (30%). In 1998, 38% of the sample had a hospitalization for CHF, and 38% had a hospitalization for other conditions. In 1999, beneficiaries averaged 1.2 CHF drug claims per month. The most common CHF drug subclasses in the sample were diuretics (59% of patients), ACE inhibitors (45%), and antianginals (35%) (data not shown). Among adherent sample members as specified by an MPR threshold of 80%, the demographic profile was similar to that of the entire research sample, but the number of drug fills was higher.

Hospital and ED outcomes were always lower for adherent beneficiaries compared with nonadherent beneficiaries, and all differences were significant at 

). Adherent beneficiaries were less likely to have a hospitalization (0.4 percentage points), had fewer hospitalizations per beneficiary (13%), had in excess of 2 fewer days spent in the hospital (25%), were less likely to have an ED visit (3%), and had fewer ED visits per beneficiary (10%). When medication adherence was specified as an ordinal variable (at 3 or 5 levels), all healthcare utilization outcomes were generally least likely or lowest for beneficiaries with the highest MPR (data not shown).

Except for total drug costs, healthcare costs were lower for adherent beneficiaries than for nonadherent beneficiaries (

 <.01 for most comparisons) (Table 2). Total healthcare costs (including drug costs) were $5910 (23%) less per year. When the MPR was specified with 3 or more levels, the relationship between adherence and healthcare costs was graded (

). For example, beneficiaries with adherence rates of 95% or higher had about 15% lower total healthcare costs, including drug costs, than those with adherence rates between 80% and less than 95% ($17,665 vs $20,747, 

 <.01). The same pattern was evident when the sample was split into quintiles by adherence level (20% intervals of the MPR) or by sample size (20% of the sample in each quintile). A specification by decile was also used, but the data are not shown.

The relationship between medication adherence rates and total healthcare costs was stronger when the most adherent beneficiaries were segmented into finer subgroups (Table 3). Beneficiaries with adherence rates of 99% or higher (nearperfect adherence) had 6% lower total healthcare costs, including drug costs, than patients with adherence rates between 95% and less than 99% ($16,989 vs $18,141, 

<.01). The association of CHF medication adherence with costs was higher in absolute US dollars for dual-eligible beneficiaries adherent patients had annual costs (

). Among dualeligible beneficiaries, adherent patients had annual costs (including drug costs) that were $7913 lower than annual costs of nonadherent patients, or 24% of the nonadherent mean (

 <.01). However, the difference between adherent and nonadherent beneficiaries among non—dual-eligible beneficiaries was only $2859 or 19% of the nonadherent mean (

Potential Savings to Medicare From Improved Adherence In 2002, approximately 13% of community-dwelling Medicare beneficiaries had CHF, and their mean healthcare costs were about $24,000.

 Because more than 90% of Medicare enrollees reside in the community and the total number of enrollees in 2002 was about 40 million, roughly 5 million community-dwelling beneficiaries had CHF. Based on the association between medication adherence and healthcare costs for dual-eligible beneficiaries in this study, we estimated total costs to Medicare assuming that a fixed proportion of enrollees were adherent (≥80% MPR).

Because the mean annual healthcare costs for nonadherent dual eligibles were 23% higher than those for adherent dual eligibles, we estimated that the mean annual healthcare costs among nonadherent beneficiaries were $28,374 compared with $21,750 for adherent beneficiaries. If 60% of enrollees with CHF were adherent and that percentage rose to 80%, Medicare costs would be $6.6 billion lower, or about 2% of total Medicare spending. This estimate is sensitive to the initial proportion of beneficiaries who are presumed to be adherent. If 65% are adherent, then savings are about $5 billion. Moreover, these savings assume that Medicare could achieve higher mean patient adherence at little or no cost. However, because we had only 1 year of data, it is impossible to estimate the effect of persistent medication adherence from one year to the next.

We conducted sensitivity analyses by specific drug subclass and by the number of distinct subclasses filled by beneficiaries. First, we specified the MPR as a continuous variable and estimated costs at various MPR levels (50%, 75%, 80%, 85%, 95%, and 99%). Consistent with our primary results, healthcare costs decrease monotonically as the MPR rises (

). Second, we estimated regressions for the top 4 CHF drug subclasses (ranked by the proportion of patients with ≥1 fill) in the sample (ACE inhibitors, antianginals, βblockers, and diuretics) using only the MPR calculated for that drug subclass. Results for this analysis were qualitatively similar to those of the main analysis.

A potential analytical limitation is that our measure of mean adherence depends on the number of CHF medications a patient fills. To test whether the association between healthcare costs and medication adherence varied among patients with differing numbers of unique drugs filled, we estimated regressions for the subgroups of patients with 1, 2, 3, and 4 or more unique CHF drugs filled. Across all 4 groups, results were qualitatively consistent with those of the main analysis (Table 5).

The final sensitivity analysis examined the decision to estimate the relationship between medication adherence and healthcare costs contemporaneously. Estimating models in this way cannot account for the potential of reverse causality that healthcare outcomes cause changes in medication adherence rather than vice versa. Although the results of this research do not suggest that better adherence results in fewer adverse health events and lower healthcare costs, the inclusion of healthy sample members who adhere regularly to medications and have few medical problems other than CHF might bias our results. To test this hypothesis, we examined 1998 healthcare use and estimated 4 separate models for patients with a (1) a Chronic Illness and Disability Payment System score of 1 or higher, (2) a diabetes diagnosis, (3) a diagnosis of coronary artery disease, and (4) hospitalization for any condition. For all 4 models, the association between the MPR and healthcare expenditures was qualitatively the same as that in the primary analysis.

In our study, higher medication adherence among Medicaid beneficiaries with CHF and those dually enrolled in Medicare was associated with a lower likelihood of hospitalization and ED use. This study’s finding that adherent patients were slightly less likely to have a hospitalization is lower in magnitude than previous results among patients with CHF in which magnitudes were 8 to 10 percentage points6 and 6.1% to 8.7%.

 Findings on ED use were also lower in magnitude, although qualitatively similar.

 Unlike other research on patients with CHF that did not find or did not examine other outcomes, this study also finds an association between CHF drug adherence and the number of hospitalizations, hospital days, and ED visits. That nonadherent beneficiaries are more likely than adherent beneficiaries to experience more of these adverse health events is likely important to state Medicaid agencies and to the federal government, as these events are expensive. Among patients in this research sample, the mean inpatient costs in 1999 among those with at least 1 visit were $19,432, or more than $6000 per visit. Given the persistent financial problems plaguing Medicare and the high mean cost of inpatient visits, improvement of CHF drug adherence among its beneficiaries (particularly dual eligibles) could result in considerable savings.

The relationship between CHF drug adherence and total costs was stark. When the MPR threshold of 80% was used, total costs for adherent patients were almost $6000 lower per year (Table 2). Although no other research has reported such a relationship for patients with CHF, one other study6 found differences for commercially insured patients with hypertension and hypercholesterolemia.

This study also finds that the association between total healthcare expenditures and patient adherence is a graded one, challenging the 80% threshold used throughout the literature on medication adherence. Total healthcare costs of patients with adherence rates of 95% or higher were more than $3000 lower (almost 15%) than those of patients with adherence rates between 80% and less than 95% (Table 3). This result suggests that Medicaid agencies and the CMS could benefit substantially from interventions that improve beneficiaries’ adherence to CHF drug therapy (as long as the cost of these interventions does not exceed their potential savings).

Total healthcare costs of patients with near-perfect medication adherence (≥99%) compared with patients whose medication adherence was slightly lower (95% to <99%) were about $1150 per year (6%) less than those of patients with slightly lower medication adherence (Table 3). Whether it would be cost-effective for Medicaid agencies or the CMS to encourage near-perfect adherence compared with adherence at least 95% of the time is dependent on how much more costly it is to these agencies to achieve near-perfect adherence rates among their beneficiaries. Future research should consider quantifying how much it might cost these agencies to improve medication adherence for patients with CHF who are already very adherent.

There are some limitations related to the use of administrative claims data and reverse causality. First, using pharmacy data to measure adherence can inform us that a prescription was filled but cannot confirm that patients take medications as directed. As in other medication adherence studies, we cannot account for this bias. Further research is needed on the association of patients’ estimated medication adherence from claims with their reported adherence, possibly from surveys. Second, it was impossible to determine the severity of illness among patients with CHF by any means other than proxy measures. The association of medication adherence with healthcare utilization and costs might be different among patients having lower CHF severity compared with patients having higher severity. Future research should carefully address the association of CHF severity to inform policy makers of the risks of medication nonadherence among beneficiaries in the poorest of health. Third, our data did not allow us to account for important socioeconomic factors such as income or years of education. Because these factors are likely associated with drug adherence, their inclusion may have explained some of the variation in medication adherence across the sample. In particular, some research suggests that adherence to physician-recommended drug regimens (including adherence to a placebo) is associated with enhanced patient outcomes, indicating that researchers should attempt (whenever feasible) to examine as many factors as possible when estimating the association between adherence and patient outcomes.

POLICY

Higher medication adherence among Medicaid beneficiaries with congestive heart failure was associated with lower healthcare utilization and lower costs, and the relationship to costs was graded.

Medicaid Beneficiaries With Congestive Heart Failure: Association of Medication Adherence With Healthcare Use and Costs

 To propose standardized methods for measuring concurrent adherence to multiple related medications and to apply these definitions to a cohort of patients with diabetes mellitus.

Retrospective cohort study of 7567 subjects with diabetes prescribed 2 or more classes of oral hypoglycemic agents in 2005.

For each medication class, adherence for each patient was estimated using prescription-based and interval-based measures of proportion of days covered (PDC) from cohort entry until December 31, 2006. Concurrent adherence was calculated by applying these 2 measures in the following 3 ways: (1) the mean of each patient’s average PDC, (2) the proportion of days during which patients had at least 1 of their medications available to them, and (3) the proportion of patients with a PDC of at least 80% for all medication classes. Because patients taking multiple related medications have distinct patterns of use, the analysis was repeated after classifying patients into mutually exclusive groups.

Concurrent medication adherence ranged from 35% to 95% depending on the definition applied. Interval-based measures provide lower estimates than prescription-based techniques. Definitions that require the use of at least 1 drug class categorize virtually all patients as adherent. Requiring patients to have a PDC of at least 80% for each of their drugs results in only 30% to 40% of patients being defined as adherent. The variability in adherence is greatest for patients whose treatment regimen changed the most during follow-up.

The variability in adherence estimates derived from different definitions may substantially impact qualitative conclusions about concurrent adherence to related medications. Because the measures we propose have different underlying assumptions, the choice of technique should depend on why adherence is being evaluated.

Variability in adherence estimates derived from different definitions of concurrent adherence

to related medications may lead to very different qualitative conclusions about adherence in a

Therefore, it is important to establish how concurrent adherence to multiple related medications should be calculated.

This study found that concurrent medication adherence varied widely depending on the definitions applied because of the assumptions on which they are based.

The choice of adherence measurement technique should depend on the reason why adherence is being measured.

dherence (also called compliance) to prescribed medications is a key dimension of healthcare quality. Nonadherence is associated with poor health outcomes

 and threatens the gains in quality that have been made by appropriate pharmacotherapy over the past several decades.

 Efforts to accurately measure and improve adherence have received increasing attention from patients, physicians, payers, and other healthcare stakeholders. Moreover, the National Committee for Quality Assurance has recently included adherence among the measures by which it evaluates the quality of care provided by healthcare plans.

Adherence may be assessed in various ways, including surveying patients, directly observing medication taking, measuring drug or metabolite blood levels, and using electronic medication monitors.

 Pharmacy refill claims, which are highly correlated with these measures,

 provide an objective way to measure adherence for large populations of patients. Such data are frequently used by health services researchers and pharmacy benefit plan managers to calculate adherence by estimating the amount of medication available to a patient over a given interval.

These measures were developed and have been most widely used to evaluate adherence to individual medications or medication classes. For example, Benner et al

 assessed statin adherence by measuring the proportion of days covered (PDC) for patients newly prescribed 1 of these agents. However, it is common for patients with chronic conditions (such as hypertension, coronary artery disease, or congestive heart failure) to switch between classes of medications or to receive multiple medications to treat a single disease. Such behavior is clinically sensible and formalized in practice guidelines but is inadequately captured by measuring adherence for a single agent or medication class.

Studies that have measured concurrent adherence to multiple medication classes have used various techniques,

 and unlike adherence to single medications or single medication classes,

 there are no published definitions or guidelines about how to structure these measurements. Understanding the variability in adherence estimates obtained from different measures will assist in selecting appropriate definitions. In this article, we propose standardized methods for measuring concurrent adherence, apply them to a cohort of patients with diabetes mellitus receiving treatment with oral hypoglycemic agents, and compare their performances.

We assembled a cohort of patients who received pharmacy

 benefits through Horizon Blue Cross Blue Shield of New Jersey and who were prescribed an oral hypoglycemic agent between January 1, 2005, and December 31, 2005. We considered only those patients who had 1 or more inpatient or outpatient claims with a diagnosis of diabetes (

 code 250.x) and were prescribed 2 or more classes of oral hypoglycemic medications during this period. While all oral hypoglycemics may be considered members of 1 therapeutic class, we defined classes on a mechanistic basis (ie, sulfonylurea, metformin hydrochloride, glitazones, acarbose, and meglitinides), as many patients take agents belonging to these different classes concurrently. Furthermore, this strategy allows for the generalization of our methods to patients using noninterchangeable medication classes (eg, statins, angiotensin-converting enzyme inhibitors, antiplatelets, and 

We excluded patients who lost eligibility or did not fill any prescriptions or use any medical services in 2006. We defined an index date for each oral hypoglycemic class prescribed to each patient as the first prescription date for any member of the class during the accrual period.

We combined filled prescription data for patients in our cohort with complete paid claims data and eligibility files to create a relational database consisting of data for all filled prescriptions, procedures, inpatient and outpatient physician encounters, hospitalizations, and deaths for the patients in our cohort. Prescription information in the claims data included drug name, dosage, date dispensed, quantity dispensed, and days supplied. All traceable person-specific identifying factors were transformed into anonymous coded study numbers. The institutional review board of Brigham and Women’s Hospital approved the study.

To measure adherence, we first created a supply diary for each patient-day by stringing together consecutive fills of each medication class being studied based on dispensing dates and reported days’ supply. All drugs dispensed within a therapeutic class (eg, glyburide and glipizide in the sulfonylurea class) were considered interchangeable. When a dispensing occurred before the previous dispensing should have run out, utilization of the new medication was assumed to begin the day after the end of the old dispensing. If a patient accumulated more than 180 days’ supply on a given day, the accumulated supply was truncated at 180 days.

We estimated adherence by calculating the PDC for each drug class prescribed to each patient from the index date to the end of our assessment period (December 31, 2006) using 2 different methods that differ in how the denominator of the adherence measure is calculated (

). We defined prescription-based adherence based on medication possession for all drugs within a class during the time between 2 prescriptions; that is, the number of days of medication supplied between the first and last prescriptions in a given period (numerator) was divided by the number of days between these 2 prescriptions plus the accumulated days supplied from the last prescription (denominator). In contrast, we defined interval-based adherence based on medication possession during the interval from the index date to December 31, 2006. In this way, the number of days of medication supplied throughout the period (numerator) was divided by the number of days in it (denominator).

Adherence to Multiple Medications Concurrently

We estimated simultaneous adherence to multiple medication classes for each patient by using the prescription-based adherence estimated for each class in 3 distinct ways (

 First, we calculated an average of the prescription-based PDC for each patient and then generated a group mean of these averages for the entire cohort. For example, for patients taking 2 classes of oral hypoglycemics, the PDC for each was calculated and then averaged, and this average was used to calculate the mean group PDC. Second, we calculated the number of days during which patients had at least 1 of their prescribed medications available to them beginning from the date they filled their first prescription for any oral hypoglycemic (ie, their earliest index date) until their latest prescription date for any of the medications they were using. For example, for a patient being treated with metformin and glyburide, the numerator of the adherence measure was the number of days during which he or she had either metformin or glyburide available. Third, we estimated the proportion of patients who had a prescription-based PDC of at least 80% for each medication they were using. For example, for patients treated with glyburide and metformin, each patient was considered adherent if his or her prescription-based PDC for each was at least 80%.

METHODS

This study compares the performance of several definitions of concurrent adherence to related medications.

Measuring Concurrent Adherence to Multiple Related Medications

develop a multiattribute decision model (MADM) to aid in the selection of preferred medications for the treatment of bipolar disorder.

Data were obtained via a selfadministered online survey among psychiatric pharmacist specialists. These survey data were used to construct a MADM based on multiattribute utility technology.

Anticonvulsant mood stabilizers, atypical antipsychotics, olanzapine-fluoxetine combination, and lithium carbonate were evaluated using a MADM. Attributes included in the model were effectiveness, safety and tolerability, cost, monitoring burden, and dosing frequency. A survey instrument was developed to score the relative importance of each attribute and the factor scores for each medication. Four iterations of the model were performed to ascertain the medication with the highest total utility score when the effectiveness factors were weighted to consider the following: overall effectiveness, effectiveness in acute mania, effectiveness in acute bipolar depression, and effectiveness in maintenance treatment. Sensitivity analyses were performed to evaluate the stability of the MADM results.

When overall effectiveness, effectiveness in acute mania, or effectiveness in maintenance treatment was considered, lithium carbonate had the highest total utility score. When effectiveness in acute bipolar depression was considered, lamotrigine had the highest total utility score. When considering only atypical antipsychotics, aripiprazole was associated with the highest total utility score for all iterations of the MADM.

The use of a MADM may be a beneficial tool to assist in making formulary or preferred therapeutic agent decisions.

	This article describes the process of developing a multiattribute decision model (MADM) to compare mood stabilizers for the treatment of bipolar disorder.

		A MADM takes multiple attributes (eg, effectiveness, safety and tolerability, and cost) into account when determining the most favorable decision alternative.

		According to this model, lithium carbonate and lamotrigine are the mood-stabilizing medications with the highest utility scores.

		Among the atypical antipsychotics, aripiprazole is associated with the highest utility score.

		MADMs based on multiattribute technology are a versatile method that may have applications in the formulary decision-making process.

	Bipolar disorder is a cyclical mood disorder characterized by periods of mania or depression punctuated by periods of euthymia. Bipolar disorder affects approximately 2% of Americans.

 There are various medication treatments aimed at minimizing the duration and severity of symptoms of bipolar disorder. Lithium carbonate, atypical antipsychotics, and mood-stabilizing anticonvulsants have all been shown to be effective in various phases of the disorder and are recommended agents for the treatment of bipolar disorder.

	Treatment of bipolar disorder is associated with significant costs, a large portion of which is related to the cost of chronic medication administration.

 Medication formularies are increasingly being used to help control the cost of treatment for various medical conditions, as well as for psychiatric disorders such as bipolar disorder. To thoroughly evaluate the utility of a medication, it is important that formulary decisions take into account factors other than just medication cost in the formulary development process. A number of factors such as safety and effectiveness should be considered in addition to acquisition cost. A multiattribute decision model (MADM) attempts to take multiple factors into account when determining what medication alternative has the highest utility.

	The MADM presented herein is a form of evaluation using multiattribute utility technology (MAUT). Multiattribute utility technology analysis is a broad technique originally developed to aid in making decisions regarding competing social programs.

This research develops a multiattribute decision model to aid in the selection of preferred mood-stabilizing agents for the treatment of bipolar disorder.

July 2009

Adherence to Osteoporosis Medications After Patient and Physician Brief Education: Post Hoc Analysis of a Randomized Controlled Trial

Medicaid Beneficiaries With Congestive Heart Failure: Association of Medication Adherence With Healthcare Use and Costs

Measuring Concurrent Adherence to Multiple Related Medications

A Multiattribute Decision Model for Bipolar Disorder: Identification of Preferred Mood-Stabilizing Medications

Impact of Workplace Health Services on Adherence to Chronic Medications

Automated Messaging to Improve Compliance With Diabetes Test Monitoring

Lipid Profile Changes Associated With Changing Available Formulary Statins: Removing Higher Potency Agents

Medication Adherence and Use of Generic Drug Therapies