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Original Research

www.ajpb.com Vol. 8, No. 2 • The American Journal of Pharmacy Benefits e17

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 implementa-tion 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 signifi-cant 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 implementa-tion 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

H ealth 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 con-trol pharmacy expenditures on higher-cost branded medica-tions. 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. Ga-bapentin, an anticonvulsant with the same mechanism of ac-tion 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 fibromy-algia, 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 as-sociated 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 differ-ence was observed in total healthcare costs.9

Another study compared members in a state Medicaid pro-gram that implemented a pregabalin prior authorization with

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Web exclusive eAppendix

Trends Associated with Implementing and Lifting a Pregabalin Step Therapy Policy

Kyle D. Null, PharmD, PhD; Keran Moll, PhD; Alesia Sadosky, PhD, MPH, MBA; Margaret K. Pasquale, PhD; Joseph C. Cappelleri, PhD, MPH, MS; Bruce Parsons, MD, PhD

e18 The American Journal of Pharmacy Benefits • March/April 2016 www.ajpb.com

n Null • Moll • Sadosky • Pasquale

members in state Medicaid programs with no restriction, and found decreased pregabalin use in the restricted pop-ulation, 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 increas-es 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 assess-ment 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 pregaba-lin 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.

METHODSStudy Data

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

Study DesignAn interrupted time series design was used to evalu-

ate the longitudinal effects of the ST restriction for prega-balin 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 re-searchers 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) health-care 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 commer-cial members; (b) January 2009 to April 2013, while the ST policy was in effect for Medicare and commercial mem-bers; and (c) May 2013 to April 2014, after the ST policy was lifted for Medicare members (Figure 1).

Study PopulationHumana members were included if they filled pharma-

ceutical claims under the Medicare Advantage and Phar-macy 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 commer-cial 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, opi-oids, 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 re-corded diagnoses for any condition of interest (DPN, FM or PHN), in any position, at least 30 days apart, were exam-ined. Medical costs include total condition-related medical costs (Humana-allowed amounts and member out-of-pocket expenditures) for hospital inpatient stays, emer-gency 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.

P R A C T I C A L I M P L I C A T I O N S

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

n 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 prerequi-site step therapy gabapentin.

n The step therapy edit shifted Humana members away from pregabalin with-out 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.


Pregabalin Step Therapy Time Series

Statistical AnalysisSegmented regression analysis was used to mea-

sure the degree to which the step edit impacted the utilization of pregabalin, the utilization of alterna-tive 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 alterna-tives was examined at the drug class level. The seg-mented 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*time

t + β

2*ST policy restriction

t +β

3*time dur-

ing ST policy restrictiont + β

4*lifting of ST policy restriction

t

+ β5* time since lifting of ST policy restriction

t + e

t

where Y

t is the mean number of claims per 100,000 members

for month t;β

0 estimates the baseline level at time zero, mean num-

ber 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;

β2 estimates the level change in the mean monthly num-

ber of prescriptions per 100,000 members immediately af-ter the ST policy restriction was implemented;

β3 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 be-fore the step edit;

β4 estimates the level change in the mean monthly num-

ber of prescriptions per 100,000 members immediately af-ter the ST policy restriction was lifted;

β5 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 co-efficients, standard errors, 95% CIs, and P values from the segmented regression model were reported. All data analy-ses were conducted using SAS version 9.3 (SAS Institute Inc, Cary, North Carolina), as well as STATA version 12.

RESULTSVisual inspection of the utilization trends in tandem

with interpretation of the segmented regression model is necessary to fully appreciate the results of studies us-ing interrupted time series. Table 1 reports the segmented regression results for the number of prescriptions P100K-MPM 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 eAppen-dix Figure 2 and eAppendix Table 3.

Pregabalin UtilizationVisual 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 contin-ued after the ST edit was lifted (Figure 2a). For the com-mercial population, the initial decline was followed by a

Figure 1. Study Design

ST indicates step therapy. Intercept β0 (a); Baseline trend β1 (b); Level β2 (c); Trend β3 (d); Level β4 (e); Trend β5 (f)

ST Policy Implemented(Medicare and Commercial Plan)

Intercept (a)

Pre-ST trend (b)

January 1,2007

January 1,2007

April 26,2013

April 30,2014

Post ST level Δ (c)

Post ST trend Δ (d)

Post ST lift trend Δ (f)

Post ST lift level Δ (e)

ST Policy Lifted(Medicare Plan Only)

Util

izat

ion

Study Period: January 1, 2007, to April 30, 2014



continued tapered decline. There was no visible upsurge throughout the remainder of the study period for commer-cial 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 ob-tained 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 pre-scriptions 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 β0 (a),

level β2 (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 β1 (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 popula-tion 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 UtilizationThe number of prescription claims for anti-

convulsants (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 in-dicated a rising trend in the number of anticonvulsant pre-scription fills for both Medicare and commercial members throughout the study time period.

Table 1. Segmented Regression Model for the Number of Claims per 100,000 Members per Month for Medications Used for DPN, FM, or PHN Among Medicare and Commercial Members

Segment

Medicare Commercial

Coef 95% CI P value Coef 95% CI P value

Pregabalin

a 719.3 346.1-1092.5 <.001 111.7 98.4-125.1 <.001

b 11.9 –4.2 to 28.1 .148 3.5 2.7-4.3 <.001

c –198.2 –247.1 to –149.3 <.001 –107.3 –115.3 to –99.4 <.001

d –16.2 –37.8 to 5.5 .143 –3.6 –4.7 to –2.5 <.001

e 50.5 –4.4 to 105.5 .072 8.0 –37.3 to 53.3 .730

f 8.2 –18.4 to 34.7 .546 –1.3 –5.9 to 3.2 .568

Anticonvulsants (not including pregabalin)

a 6381.4 6177.0-6585.8 <.001 1066.5 1025.8-1107.1 <.001

b 26.1 9.7-42.5 .002 3.0 0.2-5.8 .039

c –985.3 –1299.7 to –671.0 <.001 53.4 6.5-100.2 .026

d 44.1 27.4-60.9 <.001 2.8 –0.2 to 5.8 .064

e 612.4 306.5-918.3 <.001 18.0 –47.0 to 83.0 .583

f –62.6 –100.1 to –25.1 .001 5.1 –3.0 to 13.2 .212

Opioids

a 9844.5 9452.9-10,236.1 <.001 1742.5 1675.0-1809.9 <.001

b 73.6 46.2-101.0 <.001 7.5 2.8-12.3 .002

c –963.5 –1414.9 to –512.2 <.001 107.7 29.9-185.4 .007

d –52.7 –81.5 to –24.0 <.001 –9.1 –14.1 to –4.2 <.001

e 751.8 125.9-1377.7 .019 –41.9 –149.7 to 65.9 .442

f –83.4 –161.6 to –5.2 .037 9.7 –3.8 to 23.2 .155

SNRIs

a 2151.5 2022.2-2280.8 <.001 978.6 942.2-1015.1 <.001

b 22.1 13.0-31.1 <.001 4.3 1.8-6.9 .001

c –408.0 –557.1 to –259.0 <.001 10.4 –31.6 to 52.4 .623

d –7.2 –16.7 to 2.3 .136 –3.9 –6.6 to –1.3 .004

e 194.9 –11.8 to 401.6 .064 –39.5 –97.7 to 18.8 .181

f –12.6 –38.4 to 13.3 .336 0.5 –6.8 to 7.8 .888

SSRIs

a 9145.1 8950.7-9339.4 <.001 3356.2 3313.8-3398.5 <.001

b 62.3 45.9-78.7 <.001 0.4 –2.8 to 3.6 .803

c –597.5 –941.7 to –253.3 .001 73.7 9.1-138.3 .025

d –34.0 –51.8 to –16.1 <.001 10.1 6.8-13.5 <.001

e 399.3 50.0-748.5 .025 180.5 86.6-274.4 <.001

f –62.8 –106.1 to –19.6 .004 –19.4 –31.4 to –7.4 .002

Coef indicates coefficient; DPN, diabetic peripheral neuropathy; FM, fibromyalgia; PHN, post herpetic neu-ralgia; SNRI, serotonin-norepinephrine reuptake inhibitor; SSRI, selective serotonin reuptake inhibitor.



For the number of anticonvulsant prescrip-tions 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 anticon-vulsants following the pregabalin ST (–985.3; 95% CI, –1299.7 to –671.0; P <.001), the average num-ber 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 coeffi-cients were statistically significant except the co-efficients 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 UtilizationFollowing the ST implementation, the average

number of opioid prescriptions P100KMPM de-creased 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 P100K-MPM 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 aver-age 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 statis-tically 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 in-creased 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 through-out the study period, with no apparent change in utiliza-tion resulting from the pregabalin step therapy initiation or

lifting in the Medicare population. Following implementa-tion of the pregabalin ST, there was a significant decrease in the average number of SSRI prescriptions in the Medi-care 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 prescrip-tions (180.5; 95% CI, 86.6-274.4; P <.001), with a statistically

Figure 2. Number of Prescription Claims per 100,000 Members per Month by Drug Class for Medicare and Commercial Members, January 2007 through April 2014.

SNRI indicates serotonin-norepinephrine reuptake inhibitor; SSRI, selective serotonin reuptake inhibitor; ST, step therapy. Note: Anticonvulsant: gabapentin; SNRIs: duloxetine and milnacipran; opioids: alfentanil, buprenor-phine, butorphanol, codeine, dihydrocodeine, fentanyl, hydrocodone, hydromorphone, levorphanol tar-trate, meperidine, methadone, morphine sulfate, nalbuphine, oxycodone, oxymorphone, pentazocine, propoxyphene, remifentanil, tramadol, and all combinations (with acetaminophen, aspirin, etc). Lines were obtained by applying locally weighted scatterplot smoothing methods to observed values, and the smoothing parameter was set as 0.2.

ST b

egin

s

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nds

for M

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are

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clai

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Number of Prescription Claims per 100,000 Eligible Commercial Members

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egin

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nds

for M

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m4

Month

Number of Prescription Claims per 100,000 Eligible Medicare Members

Opioids SSRIs Anticonvulsants SNRIs Pregabalin



significant change in month-over-month trend (–19.4 pre-scriptions; 95% CI, –31.4 to –7.4; P = .002).

Medical and Total Healthcare CostsTable 2 reports the medical and total healthcare costs

related to DPN, FM, and PHN in the Medicare and commer-cial populations. Month-over-month Medicare and com-mercial costs increased across the entire study period. For medical costs, the coefficients for baseline trend were pos-itive 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 prega-balin ST in the Medicare population was not significant (P for level change after step edit = .081). The trend change

coefficient for commercial plans following re-moval of the ST in Medicare plans was statisti-cally significant (3498.7; 95% CI, 1165.4-5831.9; P = .003). For total healthcare costs, several vari-able coefficients indicated statistical significance (Table 2). However, Figure 3 illustrates a steady rise in total healthcare costs over the time period.

DISCUSSIONThe ST restriction placed on pregabalin had a

marked, statistically significant decrease in prega-balin utilization in both the Medicare and com-mercial populations in 2009. The lifting of the ST in April 2013 for the Medicare population resulted in a nonsignificant increase in utilization of pre-gabalin for Medicare members. As a comparator, pregabalin utilization in the commercial popula-tion remained relatively flat following the step edit lift in the Medicare population. The pregaba-lin ST policy required a prior trial of gabapentin before use of pregabalin was approved.18 As a re-sult, 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-recom-mended 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 al-ternatives 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 com-mercial 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 imple-mentation 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 unre-stricted members outside of that health plan.

Table 2. Segmented Regression Model for Condition-related (DPN, FM, and PHN) Medical and Total Healthcare Costs per 1000 Members per Month among Medicare and Commercial Members

Medical Costs

Segment

Medicare Commercial


a 41,863.136,721.7-47,004.6

<.001 46,157.428,214.6-64,100.2

<.001

b 544.4 184.6-904.3 .003 433.8–678.7 to 1546.3

.445

c 5268.9–657.2 to 11,194.9

.081 –2245.0–17,058.4

to 12,568.5.766

d –3.3–380.3 to

373.8.986 –187.8

–1335.8 to 960.2

.748

e 5010.2–3207.5 to 13,227.9

.229 –12,026.7–29,277.1 to 5223.7

.172

f 637.2–389.4 to 1663.8

.220 3498.71165.4-5831.9

.003

Total Healthcare Costs

Segment

Medicare Commercial


a 53,249.744,360.7-62,138.6

<.001 57,908.750,616.3-65,201.1

<.001

b 4170.33548.2-4792.4

<.001 972.7462.4-1483.1

<.001

c –20,483.2–30,728.6,to –10,237.9

<.001 –7335.6–15,740.8 to 1069.6

.086

d –2356.4–3008.4 to –1704.5

<.001 –685.1–1219.9 to

–150.3.013

e 8236.0–5971.3 to 22,443.3

.252 –11,087.6–22,743.1 to 567.9

.062

f 2155.9381.0-3930.8

.018 3719.02262.9-5175.1

<.001

Coef indicates coefficient; DPN, diabetic peripheral neuropathy; FM, fibromyalgia; PHN, post herpetic neuralgia.



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

LimitationsAn 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 as-sociated with a lag, was not known a priori.

Limitations, common to studies using admin-istrative claims data, also apply to this study, in-cluding 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 general-ized to the general population. However, Huma-na is a large national health plan with members residing in a broad array of geographic regions.

CONCLUSIONSAlthough Humana’s ST edit policy shifted mem-

bers away from pregabalin, there was no substantial impact on medical or total healthcare costs. These results suggest cost savings may be minimal as a re-sult of restricting medication access to patients.

Acknowledgments

The authors would like to thank Patrick Hlavacek for as-sisting 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 em-ployees and shareholders of Pfizer; Dr Pasquale is an employee of Compre-hensive 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.

Figure 3. DPN-, FM-, and PHN-related Total Healthcare Costs per 1000 Eligible Medicare and Commercial Members

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Note: The segmented regression model in an interrupted time series analysis indicated statistical signi�cance for several variable coef�cients: baseline trend (P < .001); level change after step edit (P < .001); trend change after step edit (P < .001); level change after step edit was lifted (P = .252); trend change after step edit was lifted (P = .018).

Total Healthcare Costs Related to FM, DPN, and PHN per 1000 Medicare Members(monthly data points and LOWESS smoothing lines)

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Note: The segmented regression model in an interrupted time series analysis indicated statistical signi�cance for several variable coef�cients: baseline trend (P < .001); level change after step edit (P = .086); trend change after step edit (P = .013); level change after step edit was lifted (P = .062); trend change after step edit was lifted (P < .001).

Total Healthcare Costs Melated to FM, DPN, and PHN per 1000 Commercial Members(monthly data points and LOWESS smoothing lines)

DPN indicates diabetic peripheral neuropathy; FM, fibromyalgia; LOWESS, locally weighted scatterplot smoothing; PHN, post herpetic neuralgia; ST, step therapy. Note: Notable outliers during the month of December 2010 for both Medicare and commercial plans were likely due to a retrospective billing adjustment. The outlier was also observed for total costs for the entire nonstudy population in that month. The authors elected to leave the outlier rather than employing an imputation technique for that data point. Smooth lines were obtained by applying LOW-ESS methods to observed values, and the smoothing parameter was set as 0.2. Jagged lines were obtained by simply connecting the adjacent observed values.



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: [email protected].

REFERENCES1. Lyrica [package insert]. New York, NY: Pfizer, Inc; 2013.2. Bril V, England J, Franklin G, et al; American Academy of Neurology; American Association of Neuromuscular and Electrodiagnostic Medicine; American Academy of Physical Medicine and Rehabilitation. Evidence-based guideline: treatment of painful diabetic neuropathy: report of the American Academy of Neurology, the American Association of Neuromuscular and Electrodiagnostic Medicine, and the American Academy of Physical Medicine and Rehabilitation. Neurology. 2011;76(20):1758-1765.3. Carville SF, Arendt-Nielsen L, Bliddal H, et al; EULAR. EULAR evidence-based recommendations for the management of fibromyalgia syndrome. Ann Rheum Dis. 2008;67(4):536-541.4. Häuser W, Walitt B, Fitzcharles MA, Sommer C. Review of pharmacologi-cal therapies in fibromyalgia syndrome. Arthritis Res Ther. 2014;16(1):201. doi: 10.1186/ar4441.5. Gore M, Tai KS, Chandran A, Zlateva G, Leslie D. Clinical comorbidities, treatment patterns, and healthcare costs among patients with fibromyalgia newly prescribed pregabalin or duloxetine in usual care. J Med Econ. 2012;15(1):19-31. doi: 10.3111/13696998.2011.629262.6. Gore M, Tai KS, Zlateva G, Bala Chandran A, Leslie D. Clinical character-istics, pharmacotherapy, and healthcare resource use among patients with diabetic neuropathy newly prescribed pregabalin or gabapentin. Pain Pract. 2011;11(6):528-539.7. Berger A, Dukes E, Martin S, Edelsberg J, Oster G. Characteristics and healthcare costs of patients with fibromyalgia syndrome. Int J Clin Pract. 2007;61(9):1498-1508.

8. Udall M, Harnett J, Mardekian J. Costs of pregabalin or gabapentin for painful diabetic peripheral neuropathy. J Med Econ. 2012;15(2):361-370.9. Suehs BT, Louder A, Udall M, Cappelleri JC, Joshi AV, Patel NC. Impact of a pregabalin step therapy policy among Medicare Advantage beneficiaries. Pain Pract. 2014;14(5):419-426. doi: 10.1111/papr.12073.10. Johnston SS, Udall M, Alvir J, et al. Association between pregabalin access restrictions and pain-related health care utilization and expenditures in medicare supplemental health plans. Poster presented at: Academy of Managed Care Phar-macy’s 2012 Educational Conference; October 3-5, 2012; Cincinnati, OH. 11. Margolis JM, Johnston SS, Chu BC, et al. Effects of a Medicaid prior authori-zation policy for pregabalin. Am J Manag Care. 2009;15(9):e95-e102.12. Margolis JM, Cao Z, Onukwugha E, et al. Healthcare utilization and cost ef-fects of prior authorization for pregabalin in commercial health plans. Am J Manag Care. 2010;16(6):447-456.13. Udall M, Louder A, Suehs BT, Cappelleri JC, Joshi AV, Patel NC. Impact of a step-therapy protocol for pregabalin on healthcare utilization and expen-ditures in a commercial population. J Med Econ. 2013;16(6):784-792. doi: 10.3111/13696998.2013.793692.14. Wagner AK, Soumerai SB, Zhang F, Ross-Degnan D. Segmented regression analysis of interrupted time series studies in medication use research. J Clin Pharm Ther. 2002;27(4):299-309.15. Shadish WR, Cook TD, Campbell DT. Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Boston, MA: Houghton Mifflin Company; 2002.16. Jandoc R, Burden AM, Mamdani M, Lévesque LE, Cadarette SM. Interrupted times series analysis in drug utilization research is increasing: systematic review and recommendations. J Clin Epidemiol. 2015;68(8):950-956. doi: 10.1016/j.jclinepi.2014.12.018.17. Gujarati D, Porter D. Basic Econometrics. 5th ed. New York, NY: McGraw Hill / Irwin; 2008.18. Humana. Lyrica (pregabalin) Pharmacy Coverage Policy. 2014. http://apps.humana.com/tad/tad_new/Search.aspx?searchtype=beginswith&docbegin=L&policyType=pharmacy. 19. Cox ER, Henderson R, Motheral BR. Health plan member experi-ence with point-of-service prescription step therapy. J Manag Care Pharm. 2004;10(4):291-298.

Running Title: Pregabalin Step Therapy Time Series

1

Supplemental Tables and Figures

Supplemental Table 1. Pregabalin utilization among Medicare and commercial members during study period, Jan 2007-Apr 2014

Segment Time Point Medicare Commercial

Denominator Numerator

Number of

claims per

100,000

members Denominator Numerator

Number of claims

per 100,000

members

Segment 1:

Pre-Step Edit

Start Month 1/2007 4,543,476 35,046 771 1,579,850 1,876 119

Midpoint:1/2008 4,348,960 46,250 1063 1,717,968 2,837 165

End Month 12/2008 4,370,404 47,720 1092 1,784,914 3,229 181

Segment 2:

During Step

Edit

Start Month 1/2009 3,464,829 30,324 875 1,710,817 767 45

Midpoint:2/2011 4,215,721 21,691 515 1,469,685 1,181 80

End Month 4/2013 5,553,699 36,609 659 1,579,757 1,311 83

Segment 3:

Post Step Edit

Start Month 5/2013 5,553,686 39,560 712 1,585,505 1,383 87

Midpoint: 11/2013 5,642,456 40,427 716 1,621,654 1,384 85


2

End Month 4/2014 6,557,014 47,733 728 1,987,894 1,364 69

Numerator: total number of pregabalin claims (standardized to the number of 30 days of supply) in the month.


3

Supplemental Table 2. Linear combinations for the segmented regression model for number of claims per 100,000 members per month by drug class for Medicare and commercial members

Medicare Commercial

Segment Coef. 95% CI P-value Coef. 95% CI P-value

Pregabalin

Intercept β0 (a) + Level β2 (c) 521.1 (154.2, 887.9) .005 4.4 (-12.1, 20.9) .599

Baseline trend β1 (b) + Trend β3 (d) -4.3 (-12.8, 4.3) .331 -.1 (-.7, .4) .653

Intercept β0 (a) + Level β2 (c) + Level β4 (e) 571.6 (196.7, 946.5) .003 12.4 (-35.9, 60.7) .616

Baseline trend β1 (b) + Trend β3 (d) + Trend β5 (f)

3.9 (-16.0, 23.8) .700 -1.4 (-5.9, 3.0) .529

Anticonvulsants (not including pregabalin)

Intercept β0 (a) + Level β2 (c) 5396.1 (4957.5, 5834.6) <.001 1119.8 (1046.4, 1193.3) <.001

Baseline trend β1 (b) + Trend β3 (d) 70.3 (65.5, 75.0) <.001 5.8 (4.9, 6.7) <.001

Intercept β0 (a) + Level β2 (c) + Level β4 (e) 6008.5 (5444.0, 6572.9) <.001 1137.8 (1036.2, 1239.5) <.001


7.7 (-29.5, 44.9) .686 10.9 (2.9, 19.0) =.008

Opioids


Baseline trend β1 (b) + Trend β3 (d) 20.8 (12.3, 29.4) <.001 -1.6 (-3.1, -.1) .034



-62.5 (-140.3, 15.2) .113 8.1 (-5.3, 21.5) .231

SNRIs


4


Baseline trend β1 (b) + Trend β3 (d) 14.9 (12.1, 17.7) <.001 .4 (-.4, 1.2) .339



2.3 (-23.3, 28.0) .857 .9 (-6.3, 8.1) .804

SSRIs


Baseline trend β1 (b) + Trend β3 (d) 28.3 (21.4, 35.3) <.001 10.5 (9.4, 11.7) <.001



-34.5 (-77.4, 8.5) .115 -8.9 (-20.9, 3.2) .148

Intercept β0 (a) + Level β2 (c) = Post ST level estimate; Baseline trend β1 (b) + Trend β3 (d) = Post ST trend estimate; Intercept β0 (a) + Level β2 (c) + Level β4

(e) = Post ST lift level estimate; Baseline trend β1 (b) + Trend β3 (d) + Trend β5 (f) = Post ST lift trend estimate


5

Supplemental Table 3. Segmented regression model for the number of claims per 100,000 members per month for medications used for DPN, FM, or PHN among Medicare and commercial members (all drug classes)

Medicare Commercial

Segment Coefficient 95% CI P-value Coefficient 95% CI P-value

Pregabalin

a 719.3 (346.1, 1092.5) <.001 111.7 (98.4, 125.1) <.001

b 11.9 (-4.2, 28.1) .148 3.5 (2.7, 4.3) <.001

c -198.2 (-247.1, -149.3) <.001 -107.3 (-115.3, -99.4) <.001

d -16.2 (-37.8, 5.5) .143 -3.6 (-4.7, -2.5) <.001

e 50.5 (-4.4, 105.5) .072 8.0 (-37.3, 53.3) .730

f 8.2 (-18.4, 34.7) .546 -1.3 (-5.9, 3.2) .568

Anesthetics

a 414.1 (373.1, 455.0) <.001 34.9 (27.6, 42.2) <.001

b 1.7 (-0.7, 4.0) .173 0.2 (-0.3, 0.7) .385

c -358.4 (-381.6, -335.2) <.001 -3.1 (-10.3, 4.1) .401

d -2.0 (-4.9, 0.8) .166 -0.4 (-0.9, 0.1) .108

e -1.9 (-516.8, 513.1) .994 9.0 (2.4, 15.5) .007

f 0.8 (-46.8, 48.4) .973 -1.9 (-2.8, -1.1) <.001


6

Anticonvulsants

a 6381.4 (6177.0, 6585.8) <.001 1066.5 (1025.8, 1107.1) <.001

b 26.1 (9.7, 42.5) .002 3.0 (0.2, 5.8) .039

c -985.3 (-1299.7, -671.0) <.001 53.4 (6.5, 100.2) .026

d 44.1 (27.4, 60.9) <.001 2.8 (-0.2, 5.8) .064

e 612.4 (306.5, 918.3) <.001 18.0 (-47.0, 83.0) .583

f -62.6 (-100.1, -25.1) .001 5.1 (-3.0, 13.2) .212

NSAIDS

a 6064.5 (5911.0, 6218.0) <.001 1544.1 (1488.0, 1600.2) <.001

b 35.3 (23.1, 47.4) <.001 0.6 (-3.3, 4.6) .746

c -376.8 (-607.5, -146.2) .001 54.6 (-10.0, 119.3) .097

d -28.1 (-41.1, -15.0) <.001 2.2 (-2.0, 6.3) .300

e 440.5 (179.8, 701.2) .001 -2.3 (-91.9, 87.4) .960

f -50.4 (-81.7, -19.1) .002 -0.2 (-11.4, 11.0) .973

Opioids

a 9844.5 (9452.9, 10236.1) <.001 1742.5 (1675.0, 1809.9) <.001

b 73.6 (46.2, 101.0) <.001 7.5 (2.8, 12.3) .002

c -963.5 (-1414.9, -512.2) <.001 107.7 (29.9, 185.4) .007

d -52.7 (-81.5, -24.0) <.001 -9.1 (-14.1, -4.2) <.001

e 751.8 (125.9, 1377.7) .019 -41.9 (-149.7, 65.9) .442

f -83.4 (-161.6, -5.2) .037 9.7 (-3.8, 23.2) .155


7

SNRIs

a 2151.5 (2022.2, 2280.8) <.001 978.6 (942.2, 1015.1) <.001

b 22.1 (13.0, 31.1) <.001 4.3 (1.8, 6.9) .001

c -408.0 (-557.1, -259.0) <.001 10.4 (-31.6, 52.4) .623

d -7.2 (-16.7, 2.3) .136 -3.9 (-6.6, -1.3) .004

e 194.9 (-11.8, 401.6) .064 -39.5 (-97.7, 18.8) .181

f -12.6 (-38.4, 13.3) .336 0.5 (-6.8, 7.8) .888

SSRIs

a 9145.1 (8950.7, 9339.4) <.001 3356.2 (3313.8, 3398.5) <.001

b 62.3 (45.9, 78.7) <.001 0.4 (-2.8, 3.6) .803

c -597.5 (-941.7, -253.3) .001 73.7 (9.1, 138.3) .025

d -34.0 (-51.8, -16.1) <.001 10.1 (6.8, 13.5) <.001

e 399.3 (50.0, 748.5) .025 180.5 (86.6, 274.4) <.001

f -62.8 (-106.1, -19.6) .004 -19.4 (-31.4, -7.4) .002

TCAs

a 2508.3 (2454.4, 2562.2) <.001 421.9 (411.7, 432.2) <.001

b 4.0 (-0.2, 8.3) .064 -1.2 (-1.8, -0.5) .001

c -61.1 (-142.0, 19.8) .139 12.0 (1.8, 22.3) .022

d -10.2 (-14.9, -5.5) <.001 1.7 (0.9, 2.6) <.001

e -65.1 (-195.4, 65.3) .328 -4.4 (-15.8, 7.0) .454

f -12.7 (-29.3, 4.0) .135 0.0 (-2.0, 2.0) .983


8


9

Supplemental Figure 1. Number of pregabalin claims per 100,000-members, Jan 2007 through Apr 2014

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Supplemental Figure 2. Number of prescription claims per 100,000 members per month by drug class for Medicare and commercial members, January 2007 through April 2014

Lines were obtained by applying locally weighted scatterplot smoothing methods (LOWESS) to observed values and the smoothing parameter was

set as 0.2.

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