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.

 The MAUT framework has been extended to a number of other applications such as differentiating among medication interventions for medical conditions, including migraine,

	The objective of this research was to develop a MADM to differentiate mood-stabilizing medications for the treatment of bipolar disorder. We synthesized data from a variety of sources, including focus group discussions, an online survey of psychiatric pharmacist specialists, and relevant clinical information in construction of our MADM.

	Development of a MADM involves determining the perspective of the model, identifying the treatment alternatives, ascertaining the relevant attributes to be considered, quantifying each attribute for each for this MADM was determined to be a hypothetical psychiatric hospital. Lithium, anticonvulsant medications, atypical antipsychotics, and the olanzapine-fluoxetine combination were identified as the possible treatment alternatives. The attributes relevant to a comparison of mood-stabilizing medications were determined by a focus group discussion involving clinical psychiatric pharmacist specialists and academic pharmacy practice  researchers. The attributes important to differentiating the various mood-stabilizing medications identified by the focus group were effectiveness, safety and tolerability, cost, monitoring burden, and dosing frequency. The next step in developing our MADM was to determine the relative weight or relative importance of each of these attributes and to assign a score for each medication on each of these attributes. To complete these steps of the analysis, we developed a survey instrument to be administered to a group of psychiatric pharmacist specialists.

	The survey instrument was developed as a self-administered online survey. The target respondent population was psychiatric pharmacist specialists. A psychiatric pharmacist specialist is a pharmacist who has received specialized training or practices in the area of clinical psychopharmacology. Survey respondents were recruited from among the membership of the College of Psychiatric and Neurological Pharmacists, an organization representing more than 700 healthcare professionals in the United States and Canada. Subjects were recruited via the organization’s e-mail Listserv. The initial invitation to participate in the survey was followed by a second e-mail invitation 1 week later. Online responses were collected for 14 days. The survey instrument consisted of 3 parts, namely, demographic information, attribute weights, and attribute factor scores. Only completed surveys were included in this analysis.

	Survey questions in this section were designed to obtain demographic information about survey participants. Demographic information collected included the following: sex, number of years in practice, academic degrees obtained, pharmacy residency information, board certifications obtained, and current practice setting.

	Attribute weights were determined by asking respondents to weight how important each of the attributes (effectiveness, safety and tolerability, cost, monitoring burden, and dosing frequency) is when considering which medication should be used for a patient with bipolar disorder. Responses were scored on a 10-point scale (10 indicates very important, and 1 indicates not important at all) and were averaged for each attribute. The raw attribute weightings were converted to ratio weights and finally to percentage scores for use in the final model.

	For each attribute, factors were identified that could be used to quantify the attribute. In light of the differential effectiveness of medications in the different phases of bipolar disorder, we asked respondents to score each medication on the following 3 separate factors related to effectiveness: effectiveness in acute mania, effectiveness in acute bipolar depression, and effectiveness in maintenance treatment. For the safety and tolerability attribute, respondents were asked to score each medication separately on the factors of safety and tolerability. Monitoring burden and costs were each evaluated as a single factor. When rating each of the medications on the cost factor, respondents were asked to rate their “perception of the overall cost” associated with each medication. Dosing frequency was also evaluated as a single factor. Dosing frequency was included in the model to incorporate adherence as a component in the model, as the frequency of medication treatment alternative, and calculating the total utility score for each treatment alternative.

 The perspective administration has been found to be closely related to medication adherence.

	Raw factor scores were determined by asking the respondents to score each medication on all of the factors using a 5-point Likert-type scale. The responses were anchored for each factor such that a higher raw factor score indicated a more favorable performance on each attribute. If respondents did not know how to score a particular medication on a given factor, there was a “not sure” response available for them to select. “Not sure” responses were excluded from subsequent calculations on a casewise basis. The mean response scores were then calculated for each of the medications, and this mean response score was used as the raw factor score for each factor. For the dosing attribute, a single factor score based on consensus dosage recommendations and the US Food and Drug Administration—approved dosing frequency was used. The dosing factor score for each medication was assigned a value on a scale from 0 to 100 as given in 

. This method for attributing dosage factor scores was based on techniques used in a MADM analysis of medication treatments for schizophrenia.

	The final steps of our MAUT analysis involved conversion of the raw factor scores to utility scores, calculation of the attribute utility scores, and calculation of the total utility score for each medication. The mean raw factor scores were converted to a common utility scale ranging from 0 (the worst plausible value for a factor) to 100 (the best plausible value for a factor) to obtain the factor utility score. 

 was used to convert the raw factor scores to the factor utility scores. For all of the individual factors, except for the dosing factor, values of 

 = 1 were used. For the dosing factor, values of 

	Factor utility scores were weighted and summed, when appropriate, to determine the attribute utility score (see 

 herein). For example, because the safety and tolerability attribute score was related to 2 factor scores, the safety and tolerability factor scores (usafety and utolerability, respectively) were each weighted equally (ie, wsafety and wtolerability were both set to 0.50) to calculate the unified safety and tolerability attribute score (

). A similar procedure was used to calculate the effectiveness attribute utility score (

), as discussed in the following “Model Iterations” subsection. For the monitoring burden, cost, and dosing frequency attributes, the attribute utility score (

 , respectively) is equivalent to the factor utility score, because only a single factor contributes to the attribute. Finally, attribute utility scores were scaled by their respective attribute weights and were summed to calculate the total utility score. The total utility score is represented in Equation 2.

	Four main iterations of the model were performed to compare the total utility scores when effectiveness in acute mania, effectiveness in acute bipolar depression, effectiveness in maintenance treatment, and overall effectiveness were considered. Each of these iterations was performed by adjusting the effectiveness factor weightings (

 in equation 2). For example, for the iteration of the model evaluating effectiveness in acute mania, 

 were set to 0. For the iteration evaluation of overall effectiveness, each of the effectiveness factors was weighted equally.

	Sensitivity Analysis We also evaluated the stability of our model by varying the methods of calculating the monitoring and cost based on factor scores. Namely, we calculated the monitoring factor score using the number of monitoring interventions recommended instead of the survey response scores. We also calculated the cost factor score by inputting the average wholesale price for a 30-day supply of a typical dosage of each medication instead of using the perception of cost item from the survey. We then compared the results of these models with those of the original model to evaluate the model stability. To clarify the reporting of results, in cases where a medication is available in both generic and branded forms, we indicate brand in parentheses next to the chemical name for branded versions of the medication. In cases where a medication was only available in a branded formulation at the time of this research, the chemical name is used exclusively. All calculations were performed using SPSS version 14.0 (SPSS Inc, Chicago, IL) and Excel 2007 (Microsoft, Redmond, WA). This research project was approved by the University of Texas at Austin Institutional Review Board.

	The total membership of the College of Psychiatric and Neurological Pharmacists at the time of study recruitment was 735, with 643 individuals subscribed to the organization’s Listserv. The online survey was accessed by 145 individuals, 116 (80%) of whom submitted a completed survey. The overall response rate was 116 of 643 (18%). The demographics of the survey respondents and available overall membership are given in 

	The mean raw attribute weights, as well as the corresponding ratio weights and percentage weights, are given in 

. Effectiveness was identified as the most important attribute to consider during the medication selection process, followed closely by the safety and tolerability attribute. The dosing frequency, monitoring burden, and cost attributes were considered important as well and clustered closely together. The mean raw factor scores and factor utility scores for the effectiveness, safety and tolerability, cost, monitoring burden, and dosing frequency factors are available in Appendix Tables 1, 2, and 3 to this article.

	Total utility scores for each iteration of the model are given in 

. When only effectiveness in acute mania was considered in the effectiveness attribute score, lithium carbonate was the decision alternative with the highest total score. Among the atypical antipsychotics, aripiprazole was the medication with the highest utility score.

	When only effectiveness in acute bipolar depression was considered for the effectiveness attribute score, lamotrigine was the decision alternative with the highest total utility score (Table 4). In this iteration, aripiprazole was the atypical antipsychotic with the highest total utility score.

	Results for the effectiveness in maintenance treatment iteration of the MADM reveal that lithium carbonate is the medication with the highest total utility score (Table 4). The most highly rated atypical antipsychotic in this iteration of the model was aripiprazole.

	The final model constructed was an overall effectiveness model, where each of the 3 effectiveness factors (effectiveness in acute mania, effectiveness in acute bipolar depression, and effectiveness in maintenance treatment) was equally weighted to calculate the attribute utility score (Table 4). In this iteration, lithium carbonate was associated with the highest total utility score. As in the other iterations of the model, aripiprazole was associated with the highest score among the atypical antipsychotics.

	A sensitivity analysis was also performed. Results showed that the alternate methods of calculating the monitoring and cost factor scores did not affect the overall results of any iteration of the MADM.

	This article presents an implementation of MAUT analysis in bipolar psychopharmacology. According to the model we developed, lithium carbonate is the medication with the highest total utility score when considering medication treatments for effectiveness in acute mania, effectiveness in maintenance treatment, and overall effectiveness. Lamotrigine is the medication associated with the highest total utility score for bipolar depression.

	In light of the high prevalence of atypical antipsychotic use and the high costs associated with the atypical antipsychotics, we also evaluated the results within this class of medications separately. According to our model, aripiprazole is the antipsychotic associated with the highest total utility score for all iterations of this MADM. Evaluations of the individual factor utility scores reveal that aripiprazole scored only moderately well on the effectiveness and cost factors; however, aripiprazole scored highest among all of the antipsychotics on the safety and tolerability and monitorn ing burden factors. The contribution of the latter attributes to the overall utility score seems to drive the differences in total utility scores seen between aripiprazole and the other antipsychotics.

	It is also notable to highlight the medications with the lowest total utility scores for each of the model iterations. Gabapentin (brand and generic formulations) and topiramate scored poorly on all iterations of the MADM. The low scores for these agents were driven in large part by low effectiveness ratings. These agents are not recommended as mood-stabilizing agents in the Texas Medication Algorithm Project

 treatment guidelines. These results suggest that the model is able to discriminate between medications with different levels of consensually recognized utility, even while accounting for factors not explicitly considered in these treatment guidelines (ie, cost, dosing frequency, etc).

	The response rate to our online survey using e-mail recruitment was modest (18%). This is a limitation frequently seen with online surveys

 and especially with online surveys of health professionals.

 Surveys of health professionals using multiple follow-up recruitment e-mails,

 have shown higher response rates than studies

 that did not use these recruitment strategies. Our study used only a single follow-up e-mail for study recruitment. A number of other factors, including technical problems accessing the Web-based survey, the timing of the survey, lack of compensation, concerns regarding confidentiality, length of the survey, and misidentification of the survey recruitment e-mail as spam, may have adversely affected our response rate. We did not account for e-mails that “bounced” or were returned with “out of office” replies in the denominator of the response rate calculation. Despite the modest response rate, our sample was generally representative of the membership of the College of Psychiatric and Neurological Pharmacists with regard to academic degree, board certification, and nationality. Our sample seems to be underrepresentative of members from the northeastern United States and overrepresentative of members from the southern United States. The percentage deviation for the northeastern US region in our sample was −53.9% from the expected value. The percentage deviation for the southern US region was 20.4%. The overrepresentation of membership from the South may be attributable to the authors’ academic affiliations at a southern university.

	A limitation of this analysis is that we chose to survey only psychiatric pharmacist specialists. We did not seek to obtain attribute or factor scores from physicians, nurses, psychologists, or other medical professionals. Psychiatric pharmacist specialists were chosen for the sampling frame in this study because of their specialized knowledge in the area of psychopharmacology and their broad knowledge base regarding the attributes included in this MADM. As evidenced by the demographics of our survey respondents, psychiatric pharmacist specialists typically have received advanced postgraduate training in psychopharmacology or pharmacy practice and frequently hold board certification. In addition to a variety of clinical, academic, and industry roles, pharmacists often participate on pharmacy and therapeutics committees in healthcare systems and provide input on medication formulary decisions. Among our survey respondents, 85% (99 respondents) indicate that they practice in a setting with direct patient contact (Table 2). In some states and practice settings, pharmacists may operate under collaborative practice agreements and have prescriptive authority within their defined scope of practice; however, we did not specifically ask our respondents whether they had a collaborative practice agreement in place or had prescriptive authority.

	Although it is possible that other healthcare professionals would assign different attribute weights, research has shown that physicians and pharmacists rate the importance of the attributes included in this model similarly with regard to their influence on medication decision making. Schumock et al20 surveyed a group of pharmacists, physicians, and formulary decision makers to determine the influence that a variety of factors have on medication decision making. Among the drug-related attributes assessed, pharmacists and physicians rated the importance of effectiveness, safety, ease of dosing, monitoring requirements, and cost similarly. The relative importance of each attribute also corresponds to the findings in our survey. Schumock et al found that physicians and pharmacists both rate effectiveness and safety as the most highly influential attributes. Monitoring requirements, ease of dosing, and cost attribute scores clustered closely together, and, as in our study, these attributes were scored lower than the effectiveness and safety attributes. Schumock et al also found that there was a statistically significant difference in the importance that pharmacists and physicians placed on “personal experience” when making medication decisions, with physicians rating personal experience as having a greater influence. Perhaps reflecting these findings, this attribute was not identified for inclusion in our model by our focus group of psychiatric pharmacist specialists.

Articles

WEB EXCLUSIVE

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

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

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. 

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 review the fasting lipid panel changes that occurred after removing higher potency statins from a prescription formulary. 

 Researchers compiled data for patients in a medical clinic receiving pharmacy benefits from a particular managed care plan. Patients enrolled in the benefits program at least between June 2006 and June 2007 were evaluated for atorvastatin use before January 1, 2007. After January 1, patients on atorvastatin were prescribed new statins according to the program’s preferred drug list. Patients treated with atorvastatin were reviewed for fasting lipid panel results while on atorvastatin; these results then were compared with fasting lipid panels after the formulary change took place. Total cholesterol, low-density lipoprotein, high-density lipoprotein, and triglyceride values were examined for changes. Appropriateness of the replacement statin also was evaluated using statin equivalency charts.

Values for total cholesterol, low-density lipoprotein, and triglycerides were not significantly different from baseline to follow-up. High-density lipoprotein values did significantly increase over the study period. Only 34% of statin prescriptions were considered appropriately converted to equipotent or higher potency doses. The mean time to documentation of statin conversion was 2.9 months.

 Removing higher potency statins from the formulary did not  significantly change a population’s fasting lipid panel except for a significant increase in high-density lipoprotein. Although multiple factors may have contributed to this effect, the results of this investigation suggest that changing formulary statins will not alter the surrogate lipoprotein markers associated with poor cardiovascular outcomes.

The higher potency agents (atorvastatin and rosuvastatin) are available only under their proprietary names, whereas the moderate potency statins (simvastatin, lovastatin, and pravastatin) are more readily available through multiple generic manufacturers.

Removing higher potency statins from the formulary did not significantly influence a population’s fasting lipid panel except for a significant increase in high-density lipoprotein.

This lack of difference was seen with only 34% of statin prescriptions considered appropriately converted to equipotent or higher potency doses.

Spending in the United States for prescription drugs was $216.7 billion in 2006, and it has been one of the fastest growing components of the healthcare budget.

 In an effort to provide comprehensive healthcare services while trying to decrease the rising costs of prescription drugs to society, managed care plans and most thirdparty payers have instituted formularies to help control the utilization of pharmaceuticals within their respective plans. Formularies, limited lists of approved pharmaceuticals, are the most prevalent means of containing drug costs and are utilized by most managed care plans.

 Health Maintenance Organizations, Preferred Provider Organizations, and some traditional plans use the strategy of offering lower copayments for formulary generic medications and increasing costs more than 3 times higher for a brand name drug not on the formulary.

In 2007, approximately 13 million persons were treated with atorvastatin, accounting for $6.16 billion in sales. Although the dollar amount has remained relatively unchanged over the last 5 years, third-party payers and managed care plans continue to look for ways to decrease cost.

 This is commonly done by selecting several preferred cheaper generic substitutions and/or brand name options that are then included on the company’s prescription drug formulary. A managed care plan’s formulary selection affects many aspects of care, including the provider’s choice of medication and the patient’s satisfaction with therapy, as well as the patient’s adherence to lipid-lowering therapy.

 The development of a medication formulary gives these third-party payers a cost-effective way to provide quality care. When a generic and/or preferred option is chosen, both patients and payers benefit. Adhering to a formulary will decrease cost to payers, resulting in discounted pricing for the patient.

 Also, research has shown that when patients are prescribed medications included on their prescription insurance formulary, their likelihood of remaining adherent to the medication regimen is increased.

Although patients may remain adherent to the medication regimen, these drugs are selected on the basis of average patient outcome, not individual effectiveness.

 It is important that managed care plans be careful to not compromise individual patient health when decreasing the cost of prescription drugs. In the case of prescription formularies, limiting access to higher potency statins may affect a patient’s and a provider’s ability to reach therapeutic fasting lipid panel levels in a reasonable and safe amount of time.

In addition, very strict limitations could hinder patients’ access to the medication during a critical point in their therapy such as immediately post—myocardial infarction.

Although most statins may be considered equivalent with regard to primary and secondary prevention of cardiovascular disease, the lipid-lowering potential of each individual statin is based on the agent’s potency and dose.

 These differences can potentially affect patients’ ability to reach their fasting lipid panel goals. Since December 2001, 3 of the 6 available statins have been approved in generic formulations, thereby offering a less expensive option for third-party payers and managed care plans.

Multiple clinical trials have demonstrated that changing to a more potent statin is associated with additional low-density lipoprotein (LDL) reduction.

 However, because most of the higher potency statins still are available only under their brand name, their managed care availability may be limited.

When a provider is prompted to change from a nonformulary medication to a formulary medication, the opportunity arises to evaluate the patient’s current lipid levels and reassess the appropriateness of statin choice and dose. Miller et al recently found that the “usual care” associated with switching from atorvastatin during a formulary change resulted in a significant increase in LDL, non—high-density lipoprotein (HDL), and total cholesterol.

 These results imply that the practice of therapeutic statin conversion during a formulary change may need more guidance. Although prescribers may be aware of the potency differences between the available statins, we cannot conclude that they are aware of the necessary dose changes when changing to a different statin. An inappropriate, suboptimal dose change could result in a worsened

lipid profile and increased risk for cardiovascular event.

Our objective was to investigate the changes in fasting lipid panels based on individual effectiveness in a population that underwent a similar formulary change and needed statin conversion.

This retrospective chart review was conducted with St. Luke’s Hospital and Health Network in Bethlehem, Pennsylvania, in an internal medicine primary care clinic. Each patient evaluated underwent a statin conversion after a managed care plan and prescription insurance provider removed atorvastatin from the preferred drug formulary. All information was gathered to ensure patient privacy and the protocol was reviewed and approved (deemed exempt) by the institutional review board of the St. Luke’s Hospital and Health Network.

Patients were identified using a member census provided monthly by the managed care plan. Patients were included if they were age 18 years or older and were enrolled in the managed care plan from at least June 2006 to June 2007. Patients who had been enrolled before June 2006 and/or continued beyond June 2007 also were eligible. Finally, patients had to have started taking atorvastatin (Lipitor, Parke-Davis & Pfizer Inc, New York, NY) at any dose prior to June 2006. Patients taking atorvastatin after the formulary change date because of prior authorization approval were included in the analysis.

Patients were excluded if they were not taking atorvastatin prior to June 2006, did not have a baseline lipid panel documented at least 6 weeks after starting the last documented atorvastatin dose, or did not have a follow-up lipid panel documented at least 6 weeks after starting the latest replacement statin dose. Patients also were excluded if they had baseline or follow-up triglycerides greater than 400 mg/dL without a direct LDL value, or if their atorvastatin dose or replacement statin dose was not documented in the medical chart. Patients were not excluded if they were using concurrent nonstatin lipid-lowering agents such as nicotinic acid, fibrates, bile acid

The primary end point was the observed change in fasting lipid panel components after converting from atorvastatin to another formulary statin. Follow-up assessments used the first fasting lipid panel at least 3 months after the medication change was prescribed. The population also was subdivided and compared as “high risk” patients, requiring LDL cholesterol to be less than 100 mg/dL, and moderate- to low-risk patients, requiring LDL cholesterol to be less than 130 mgdL. As a secondary end point, we evaluated the same criteria used in the primary end point for each baseline atorvastatin dose. In addition, we reviewed the data for appropriateness of the replacement statin based on LDL-lowering potential (

For this research, appropriateness was defined as being at least equipotent to the last documented atorvastatin dose with regard to LDL-lowering potential. Given that we did not interview or assess the patient for adherence, appropriateness did not include evaluating whether a dose adjustment was needed based on the baseline fasting lipid panel. If the patient was continued on atorvastatin at the same or greater dose with prior authorization approval, this treatment was considered appropriate. We also evaluated the time elapsed from the date of the formulary change (January 1, 2007) to documented statin change in the patient’s medical record. During this time, patients were assumed to be without statin therapy as the pharmacy benefits manager did not offer a formulary change grace period. Changes made during January 2007 were valued as zero months.

All data collection and statistical analyses were performed using Microsoft’s Access and Excel (Access 2004 and Excel 2004 for Macintosh; Microsoft Corp, Redmond, WA). The differences in fasting lipid profiles (total cholesterol, LDL, HDL, and triglycerides) from baseline with atorvastatin treatment to follow-up with a replacement statin were evaluated using the 

 test. Appropriateness and mean time to statin change were not evaluated for statistical significance and are reported as descriptive statistics only. All 

tests were performed using a 2-sided alpha of .05 unless otherwise noted.

Using the managed care program enrollment time frame described in the Methods section, 328 patients were evaluated for inclusion in the study. Of these, 102 patients were actively receiving atorvastatin for primary and secondary prevention of coronary artery disease. Thirty-one patients were excluded from this group for various reasons including triglycerides greater than 400 mg/dL without a direct LDL value, no baseline lipid panel, no follow-up fasting lipid panel, or atorvastatin change not noted in the medical record.

A more detailed description is provided in 

. Demographics for patients included in the evaluation are reported in 

After comparing fasting lipid profiles for the primary end point, only HDL levels changed significantly. Average baseline HDL levels were measured as 42.5 mg/dL on atorvastatin compared with 44.4 mg/dL after starting the alternate statin (

 = .018). Though slightly improved, changes were not significant for LDL (104.0 mg/dL vs 98.9 mg/dL; 

 = .417) or triglycerides (145.8 mg/dL vs 142.5 mg/dL; 

 = .637). Total cholesterol was slightly increased (171.6 mg/dL vs 172.3 mg/dL; 

In the high-risk subgroup (n = 39), 64.1% of patients had an LDL of less than 100 mg/dL while taking atorvastatin at any dose. After the formulary change and statin conversion, 71.8% of patients had an LDL of less than 100 mg/dL. In the moderate- to low-risk subgroup (n = 32), 87.5% of patients had an LDL of less than 130 mg/dL on atorvastatin, which decreased to 81.3% after the formulary change and conversion. Though neither comparison resulted in a statistically significant difference, the small subgroup size did not allow for appropriate power.

For the secondary end point, we chose to evaluate whether changes in lipid panel parameters were significant with respect to specific atorvastatin doses (

). Statistically significant changes were noted only for total cholesterol in patients originally treated with atorvastatin 20 mg (n = 10; 158.9 mg/dL vs 181.9 mg/dL; 

 = .019) and for triglycerides in patients originally treated with atorvastatin 80 mg (n = 15; 180.5 mg/dL vs 150.1 mg/dL; 

 = .049). The change in HDL for patients treated with atorvastatin 20 mg trended toward significant (40.0 mg/dL vs 43.8 mg/dL; 

 = .054), but did not reach statistical significance.

Only 33.8% of statin changes were deemed appropriate (ie, an equipotent or greater potency agent was chosen as an alternate statin). For atorvastatin 10 mg, 59.1% of the conversions were considered appropriate; for atorvastatin 20 mg, 40% were considered appropriate; for atorvastatin 40 mg, 29.2% were considered appropriate; and for atorvastatin 80 mg, 0% were considered appropriate (

). The mean time after January 1, 2007, until an alternate statin and dose were chosen was 2.9 months. During this time, patients were perceived to be without statin therapy because the pharmacy benefits manager did not allow for a grace period.

In this study, patients taking atorvastatin were changed to an alternate statin because of formulary restrictions. As a result, patients’ total cholesterol, LDL, and triglyceride levels remained unchanged and HDL levels slightly increased, despite only 34% of conversions being considered therapeutically equivalent. Most data published regarding statin conversion evaluate changing to a uniform (usually more potent) statin, resulting in an improved lipid profile.

 The data presented here suggest that the formulary change from a higher potency statin (atorvastatin) to a moderate potency statin (simvastatin, pravastatin, or lovastatin) does not affect the lipid profile as one may expect. Taken in conjunction with the 2004 update to the National Cholesterol Education Program Adult Treatment Panel III, these data should suggest to providers that moderate doses of generically available statins are just as effective as higher doses of brand-only statins for controlling lipid panels. Additionally, our data are unique in that they challenge previously published data that indicated usual care for formulary statin changes results in significantly worse LDL.

High-density lipoprotein levels significantly increased for patients after switching statins (42.5 mg/dL to 44.4 mg/dL; 

= .018). Clinically, this increase carries with it the potential for a decrease in myocardial infarction or death from coronary heart disease, as a 1-mg/dL increase in HDL has been linked to a 4.5% decrease in these outcomes.

 This increase in HDL levels observed in the research results reported previously was expected. Previous research on the lipid-lowering potential of various statins suggests that as doses of atorvastatin approach 80 mg daily, the medication tends to decrease HDL rather than have a slightly positive or neutral effect.

 The increase in HDL in this patient population does not translate into a decreased risk of myocardial infarction or death from coronary heart disease, as this research was only designed to look at surrogate markers and did not assess clinical end points.

With the exception of high-density lipoprotein cholesterol, fasting lipid profiles were not significantly changed after higher potency statins were removed from prescription formularies.

July 2009

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