Trends Associated with Implementing and Lifting a Pregabalin Step Therapy Policy

ABSTRACT

Objectives: Humana implemented a step therapy (ST) policy on pregabalin for Medicare and commercial plans effective January 2009, and lifted the policy for Medicare plans in April 2013. This study examined the impact of ST on pregabalin utilization and therapeutic alternatives, as well as medical and total healthcare costs for FDA-approved pain indications for pregabalin.

Study Design: A retrospective, interrupted time series analysis was conducted using Humana Research Database’s Medicare and commercial claims from January 2007 to April 2014.

Methods: Segmented regression analyses with 2 change points, January 2009 and April 2013, accounted for policy implementation and lift. Utilization was reported as the mean number of claims per 100,000 members per month for time trend and level change following ST changes. Medical and total healthcare costs were reported in dollars per 1000 members per month.

Results: The ST implementation resulted in a statistically significant decrease in pregabalin use by Medicare members (P <.001), while its lift resulted in a nonsignificant increase in pregabalin use by Medicare members (P = .072). There was no change in trend after the ST implementation or lift. Utilization of gabapentin in Medicare saw a consistent increase following the implementation of the pregabalin ST. Medical and total healthcare costs for diabetic peripheral neuropathy, fibromyalgia, and post herpetic neuralgia among the Medicare and commercial populations increased throughout the study period, but did not appear to be impacted by the pregabalin ST.

Conclusions: Although Humana’s ST edit policy shifted members away from pregabalin, no significant cost savings were achieved by restricting medication access to patients.

Am J Pharm Benefits. 2016;8(2):e17-e24

PRACTICAL IMPLICATIONS

This study examined the impact of a pregabalin step therapy policy on utilization and medical costs in Humana’s Medicare and commercial plans.

• The pregabalin step edit resulted in a significant decrease in pregabalin use in both the Medicare and commercial plans, with an expected increase in utilization in the anticonvulsant category, which contained the prerequisite step therapy gabapentin.
• The step therapy edit shifted Humana members away from pregabalin without a statistically significant impact on disease-related medical costs or total healthcare costs, which increased throughout the study period for both commercial and Medicare plans.

Health plan payers utilize formulary management strategies like prior authorizations, step therapy (ST) policies, cost sharing, mandatory generic substitution, and therapeutic interchange in an effort to control pharmacy expenditures on higher-cost branded medications. Pregabalin is a branded medication indicated for pain conditions including diabetic peripheral neuropathy (DPN), fibromyalgia (FM), and post herpetic neuralgia (PHN).¹

Humana Inc implemented a ST policy for pregabalin for its Medicare and commercial plans effective January 2009. Beneficiaries, new to pregabalin therapy, were required to have previous treatment, intolerance, or contraindication to gabapentin before pregabalin therapy would be covered. Gabapentin, an anticonvulsant with the same mechanism of action as pregabalin, is a therapeutic alternative used by health plans to treat neuropathic pain.

In addition to gabapentin and pregabalin, there are several other guideline-recommended therapies for the treatment of neuropathic pain and fibromyalgia, including opioids, serotonin-norepinephrine reuptake inhibitors (SNRIs), selective serotonin reuptake inhibitors (SSRIs), and tricyclic antidepressants (TCAs).^2-4 The ST policy for pregabalin was lifted in April 2013 for all Medicare plans, but remained in effect for commercial plans, which enabled comparison between Medicare and commercial members.

Studies have examined healthcare resource use and costs related to pregabalin use for the treatment of pain associated with DPN, FM, and PHN.^5-13 A previous study examining the impact of a pregabalin ST restriction on pregabalin utilization and costs among Medicare members in a large United States health plan found that after controlling for differences in age and comorbidity burden between restricted and unrestricted groups, implementation of a pregabalin ST restriction was associated with increased medical costs.⁹ However, increased medical costs were offset by lower prescription drug costs of initiating therapy with generic gabapentin, and no net difference was observed in total healthcare costs.⁹

Another study compared members in a state Medicaid program that implemented a pregabalin prior authorization with members in state Medicaid programs with no restriction, and found decreased pregabalin use in the restricted population, with increased opioid utilization compared with the unrestricted population.¹¹ Other restriction studies comparing commercial and Medicare plan data similarly observed decreases in pregabalin utilization with increases in gabapentin utilization and no statistically significant impact on DPN- or PHN-related medical costs.^12,13

This study is unique in that it allowed for the assessment and impact of implementing and lifting a restriction on pregabalin use in the same health plan, rather than comparison across different plans as has been previously described. We employed an interrupted time series design: Separate time series models were estimated for pregabalin utilization, the utilization of therapeutic alternatives, medical costs, and total healthcare costs related to DPN, FM and PHN, conditions for which pregabalin has FDA-approved indications.

METHODS

Study Data

This study utilized data from Humana Research Database (Humana, Louisville, Kentucky), containing enrollment, medical, and pharmacy claims data from January 2007 to April 2014. Data sources were merged using de-identified patient data. The finalized protocol was approved by an independent institutional review board.

Study Design

An interrupted time series design was used to evaluate the longitudinal effects of the ST restriction for pregabalin use.14,15 The use of interrupted time series design is very common in drug utilization research and its use has increased in recent years. When randomized controlled studies are not feasible, an interrupted time series analysis is the strongest quasi-experimental design available to researchers assessing the impact of an intervention.¹⁶

Separate time series models were estimated for pregabalin utilization, the utilization of therapeutic alternatives, and medical and total (pharmacy + medical) healthcare costs related to DPN, FM, and PHN, conditions for which pregabalin has an FDA-labeled indication. Although approved for treatment with pregabalin, spinal cord injury-related neuropathic pain (SCI-NeP) patients were not included in this study due to lack of consensus on identifying NeP in SCI in the data and the extremely low prevalence of the condition.

The availability of data counted on evenly spaced intervals allows for the measurement of changes in both level and trend. Monthly time series data were examined for any changes in level and trend (slope) across the following 3 time periods: (a) pre-January 2009, before the ST policy went into effect for Medicare and commercial members; (b) January 2009 to April 2013, while the ST policy was in effect for Medicare and commercial members; and (c) May 2013 to April 2014, after the ST policy was lifted for Medicare members (Figure 1).

Study Population

Humana members were included if they filled pharmaceutical claims under the Medicare Advantage and Pharmacy Benefits (MAPD) plan or Pharmacy Drug Plan for the Medicare population (hereafter, Medicare), and under the fully-insured or drug-plan-only plan for the commercial population. The unit of measure for this interrupted time series was claims per 100,000 members per month (P100KMPM). Utilization was measured for pregabalin, as well as for therapeutic alternatives used in the treatment of DPN, FM, and PHN, which included anticonvulsants, opioids, SNRIs, and SSRIs. Pharmacy claims for any particular month were excluded from the study if the member filling the claim did not have continuous enrollment for all days in that particular month.

For medical costs, all medical claims of Medicare and commercial members having at least 2 claims with recorded diagnoses for any condition of interest (DPN, FM or PHN), in any position, at least 30 days apart, were examined. Medical costs include total condition-related medical costs (Humana-allowed amounts and member out-of-pocket expenditures) for hospital inpatient stays, emergency department visits, outpatient and physician office visits, procedures and lab tests.

Members were included in the monthly assessments for the entire study period as long as they were enrolled for the full month. Medical costs were reported in dollars per 1000 members per month. Total healthcare costs summed the dollars associated with DPN-, FM- or PHN-related pharmacy and medical claims.

Statistical Analysis

Segmented regression analysis was used to measure the degree to which the step edit impacted the utilization of pregabalin, the utilization of alternative therapeutic classes, and medical costs related to DPN, FM, and PHN. Segmented regression models were estimated for pregabalin as well as for therapeutic alternatives used to treat these pain conditions. Utilization for the therapeutic alternatives was examined at the drug class level. The segmented regression analysis accounted for 2 change points: January 2009 (step-edit implementation for Medicare and commercial plans) and April 2013 (step edit lift for Medicare plan only).

The basic equation was as follows:

Yt =

β

0 +

β

1*timet +

β

2*ST policy restrictiont

+β

3*time during ST policy restrictiont +

β

4*lifting of ST policy restrictiont +

β

5* time since lifting of ST policy restrictiont + et

where

Yt is the mean number of claims per 100,000 members for month t;

β

0 estimates the baseline level at time zero, mean number of prescriptions per 100,000 members per month;

β

1 estimates the baseline trend, the change in the mean number of prescriptions per 100,000 members that occurs in each month before the step edit;

β

2 estimates the level change in the mean monthly number of prescriptions per 100,000 members immediately after the ST policy restriction was implemented;

β

3 estimates the change in trend in the mean monthly number of prescriptions per 100,000 members after the ST policy restriction, compared with the monthly trend before the step edit;

β

4 estimates the level change in the mean monthly number of prescriptions per 100,000 members immediately after the ST policy restriction was lifted;

β

5 estimates the change in trend in the mean monthly number of prescriptions per 100,000 members after the ST policy restriction was lifted; and

et is an error term.

Autocorrelation was evaluated by visually inspecting the residual plot after fitting a simple linear regression model and by computing a Durbin-Watson statistic and P value.¹⁷ If autocorrelation was detected, an autoregressive model was estimated via the ARIMA procedure in STATA (StataCorp LP, College Station, Texas). Parameter estimates including coefficients, standard errors, 95% CIs, and P values from the segmented regression model were reported. All data analyses were conducted using SAS version 9.3 (SAS Institute Inc, Cary, North Carolina), as well as STATA version 12.

RESULTS

Visual inspection of the utilization trends in tandem with interpretation of the segmented regression model is necessary to fully appreciate the results of studies using interrupted time series. Table 1 reports the segmented regression results for the number of prescriptions P100KMPM for the drug classes of interest using segment labels that correspond with the labels found in the study design figure (Figure 1). Results for commercial members are also reported alongside the Medicare results for comparison.

The number of prescription claims per 100,000 eligible Medicare and commercial members is reported in Figure 2 for all months beginning in January 2007 and ending in April 2014. Two vertical, dotted lines mark the start and end dates of the pregabalin ST restriction policy (January 2009 and April 2013, respectively).

A closer examination of pregabalin utilization may be seen in eAppendix Table 1 and in eAppendix Figure 1 (eAppendices available at www.ajpb.com). Estimates of level and trend in each segment for the segmented regression models can be seen in eAppendix Table 2. In addition, the therapeutic categories of anesthetics, muscle relaxants, nonsteroidal anti-inflammatory drugs, and TCAs were included in the analysis and may be seen in eAppendix Figure 2 and eAppendix Table 3.

Pregabalin Utilization

Visual inspection of the 2 panels in Figure 2 indicated a decline in the number of pregabalin prescription fills for both Medicare (Figure 2a) and commercial members (Figure 2b), coinciding with the start of the ST restriction policy. For Medicare members, the decline was followed by a gradual rise before the ST edit was lifted, and continued after the ST edit was lifted (Figure 2a). For the commercial population, the initial decline was followed by a continued tapered decline. There was no visible upsurge throughout the remainder of the study period for commercial members (Figure 2b).

For the Medicare results, the average (mean) number of pregabalin prescriptions P100KMPM at the beginning of the baseline period was 719.3 (95% CI, 346.1-1092.5; P <.001). This number increased numerically on average by approximately 11.9 prescriptions per month (95% CI, —4.2 to 28.1; P = .148), until the ST policy was implemented.

Following the implementation of the ST policy, the average number of prescriptions P100KMPM decreased by 198.2 prescriptions (95% CI, —247.1 to –149.3; P <.001) to 521.1 prescriptions P100KMPM (95% CI, 154-888; P = .005). The number 521 was obtained by adding intercept

β

0 (a) to level

β

2 (c) for pregabalin (eAppendix Table 2).

The increasing trend reversed to a declining trend after the ST implementation by 4.3 prescriptions P100KMPM (95% CI, —12.8 to 4.3; P = .331), which was obtained by adding baseline trend

β

1 (b) to trend

β

3 (d). The trend change after the ST edit was nonsignificant (—16.2; 95% CI, –37.8 to 5.5; P = .143). After the ST policy was removed 5 years later, the average number of prescriptions P100KMPM increased by 50.5 prescriptions (95% CI, –4.4 to 105.5; P = .072) to 571.6 prescriptions (95% CI, 196.7-946.5; P <.001), obtained by summing intercept

β

0 (a), level

β

2 (c), and level

β

4 (e).

The average increase in the number of prescriptions P100KMPM (3.9; 95% CI, —16.0 to 23.8; P = .700), obtained by summing baseline trend

β

1 (b) with trend

β3

(d) and trend

β5

(f), and the change in trend compared with the period during the ST policy (8.2; 95% CI, —18.4 to 34.7; P = .546) was not statistically significant.

Results for the commercial member population were qualitatively similar to the Medicare member population for the implementation of the ST restriction policy (P <.001 for intercept, baseline trend, level change after ST, and trend change after ST). Neither coefficients for the level change (e) nor trend change (f) were statistically significant for commercial members following the step edit lift in the Medicare plans (Table 1).

Anticonvulsant Utilization

The number of prescription claims for anticonvulsants (excluding pregabalin) per 100,000 Medicare members per month, which contains the step edit prerequisite therapy gabapentin, is reported in Table 1 and in Figure 2. Visual inspection of Figure 2 panels indicated a rising trend in the number of anticonvulsant prescription fills for both Medicare and commercial members throughout the study time period.

For the number of anticonvulsant prescriptions P100KMPM in the Medicare population, all coefficients were statistically significant (Table 2), showing significant utilization changes, both for the trend and level, following the step therapy implementation and subsequent lift. Although there was a decrease in the utilization of anticonvulsants following the pregabalin ST (—985.3; 95% CI, –1299.7 to –671.0; P <.001), the average number of prescriptions P100KMPM increased by 44.1 (95% CI, 27.4-60.9; P <.001), as seen in Figure 2. Similarly, for commercial members, most coefficients were statistically significant except the coefficients for level (P = .583) and trend (P = .212) change after the ST was lifted in the Medicare plans (note the ST was not lifted for commercial plans) (Table 2).

Opioid Utilization

Following the ST implementation, the average number of opioid prescriptions P100KMPM decreased by 963.5 prescriptions (95% CI, —1414.9 to –512.2; P <.001) to 8881 in the Medicare plan, and the average number of prescriptions P100KMPM decreased by 52.7 prescriptions per month (95% CI, –81.5 to –24.0; P <.001). During the same period in the commercial plan, the average number of opioid prescriptions P100KMPM increased by 107.7 prescriptions (95% CI, 29.9-185.4; P = .007).

Serotonin Norepinephrine Reuptake Inhibitors (SNRIs)

In the Medicare population, there was a statistically significant decrease in the average number of SNRI prescriptions P100KMPM (—408; 95% CI, –557.1 to –259.0; P <.001) following the ST edit implementation. As depicted in Figure 2, the increased utilization in the SNRI category for the Medicare population may be partially explained by 2 expanded label indications for duloxetine in November 2009 and in October 2010. In January 2013, a prior authorization was placed on duloxetine in the commercial plans, which may have impacted utilization.¹⁸

Selective Serotonin Reuptake Inhibitors (SSRIs)

Visual inspection of SSRI utilization in Figure 2 revealed growth in both the Medicare and commercial plans throughout the study period, with no apparent change in utilization resulting from the pregabalin step therapy initiation or lifting in the Medicare population. Following implementation of the pregabalin ST, there was a significant decrease in the average number of SSRI prescriptions in the Medicare plan (—597.5; 95% CI, –941.7 to –253.3; P <.001), with a corresponding increase in the commercial plan (73.7; 95% CI, 9.1-138.3; P = .025). After the ST was lifted, there were significant increases in the average number of SSRI prescriptions (180.5; 95% CI, 86.6-274.4; P <.001), with a statistically significant change in month-over-month trend (–19.4 prescriptions; 95% CI, –31.4 to –7.4; P = .002).

Medical and Total Healthcare Costs

Table 2 reports the medical and total healthcare costs related to DPN, FM, and PHN in the Medicare and commercial populations. Month-over-month Medicare and commercial costs increased across the entire study period. For medical costs, the coefficients for baseline trend were positive and statistically significant for Medicare plans (P for baseline trend = .003) and nonsignificant for commercial plans (P = .445).

None of the coefficients for level change for Medicare or commercial populations were statistically significant, although the level change following the pregabalin ST in the Medicare population was not significant (P for level change after step edit = .081). The trend change coefficient for commercial plans following removal of the ST in Medicare plans was statistically significant (3498.7; 95% CI, 1165.4-5831.9; P = .003). For total healthcare costs, several variable coefficients indicated statistical significance (Table 2). However, Figure 3 illustrates a steady rise in total healthcare costs over the time period.

DISCUSSION

The ST restriction placed on pregabalin had a marked, statistically significant decrease in pregabalin utilization in both the Medicare and commercial populations in 2009. The lifting of the ST in April 2013 for the Medicare population resulted in a nonsignificant increase in utilization of pregabalin for Medicare members. As a comparator, pregabalin utilization in the commercial population remained relatively flat following the step edit lift in the Medicare population. The pregabalin ST policy required a prior trial of gabapentin before use of pregabalin was approved.¹⁸

As a result, the anticonvulsant category, which contains gabapentin, experienced growth throughout the duration of the study period in both Medicare and commercial plans. The utilization of therapeutic alternatives to pregabalin, including other guideline-recommended medications to treat DPN, FM, and PHN, such as antidepressants (SNRIs and SSRIs) and opioids, did not yield consistent results. This finding is partially attributable to the majority of utilization being shifted to gabapentin, and other restrictions being placed on these therapeutic alternatives across the study period.

No substantial impact on DPN-, FM-, or PHN-related medical or total healthcare costs appeared to be attributed to the pregabalin ST in either the Medicare or the commercial populations. However, a continual increase in total costs was observed in both the Medicare and commercial plans throughout the study period, with the Medicare plan’s total costs increasing at a greater rate compared with the commercial plan’s (Figure 3).

This result differs from that of Suehs and colleagues,6 who reported that implementation of a pregabalin ST was associated with increased condition-related medical costs. A possible explanation for this difference is that our study assessed implementation and lift of the pregabalin ST among members in the same health plan. In contrast, Suehs et al compared restricted members with DPN, FM or PHN in 1 health plan with unrestricted members outside of that health plan.

While health plans may save in pharmacy costs, savings do not appear to be achieved in total medical costs, suggesting the burden of restricting medication access does not necessarily benefit the health plan or the members because physicians are limited by what they can prescribe.¹⁹

Limitations

An interrupted time series design was used to detect whether changes in the pregabalin ST had any effect greater than the underlying secular trend. It is possible that factors not fully accounted for in the model could have impacted the results, including exogenous variables, lagging effects, or nonlinear relationships among variables.

Policy changes unrelated to the pregabalin ST might have impacted utilization in these drug classes. Additionally, the duration of an effect following an intervention, and whether the effect was associated with a lag, was not known a priori.

Limitations, common to studies using administrative claims data, also apply to this study, including potential errors in claims coding and lack of information in the database (eg, lab results, weight, health behavior information, medication use not necessarily linked with diagnoses) that may influence outcomes.

No causal inference can be ascertained from claims data, as it is an observational study using retrospective claims data. Because this study uses data from Humana members only, the results may not be generalized to the general population. However, Humana is a large national health plan with members residing in a broad array of geographic regions.

CONCLUSIONS

Although Humana’s ST edit policy shifted members away from pregabalin, there was no substantial impact on medical or total healthcare costs. These results suggest cost savings may be minimal as a result of restricting medication access to patients.

Acknowledgments

The authors would like to thank Patrick Hlavacek for assisting with literature review and reviewing the manuscript, and Vishal Saundankar and Ibrahim Abbass for assistance with statistical analyses.

Author Affiliations: Comprehensive Health Insights (KDN, KM, MKP), Louisville, KY; Pfizer Inc (AS, JCC, BP), New York, NY.

Source of Funding: This study was funded jointly by Humana Inc and Pfizer Inc.

Author Disclosures: Drs Sadosky, Cappelleri, and Parsons are employees and shareholders of Pfizer; Dr Pasquale is an employee of Comprehensive Health Insights, a wholly owned subsidiary of Humana; Drs Null and Moll were employees of Comprehensive Health Insights at the time of the study. Comprehensive Health Insights was paid by Pfizer to conduct the study and develop this manuscript.

Authorship Information: Concept and design (KDN, KM, MKP, AS,JCC); acquisition of data (KM); analysis and interpretation of data (KDN, KM, MKP, AS, JCC, BP).

Address correspondence to:

Margaret K. Pasquale, PhD,

Comprehensive Health Insights, Inc.

515 W Market St,

Louisville, KY 40202. E-mail: mpasquale@humana.com.

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