Effects of a prior-authorization policy for celecoxib on medical service and prescription drug use in a managed care medicaid population

CLINICAL THERAPEUTICS®/VoL.2 6 , N o . 9, 2 0 0 4

Effects of a Prior Authorization Policy for Celecoxib on Medical Service and Prescription Drug Use in a Managed Care Medicaid Population Daniel M. Hartung, PharmD, ] Daniel R. Touchette, PharmD, MA, ] Kathy L. Ketchum, RPh, MPA, ] Dean G. Haxby, PharmD, ] and Bruce W. Goldberg, MD 2

~Oregon State University Collegeof Pharmacy, Portland, and 2State of Oregon, Officefor Oregon Health Policy and Research, Salem, Oregon

ABSTRACT

Background: Prior authorization (PA) is a poorly studied but commonly employed policy used by health care payers to manage the rising costs of pharmacy benefits. Objective: The aim of this study was to evaluate the intended and unintended effects of a PA policy for celecoxib on pharmacy and medical-service utilization in a Medicaid managed-care organization. Methods: This was a retrospective, interrupted time-series analysis of 22 monthly health-related utilization rates from January 1, 1999, to October 31, 2000. All Medicaid claims for CareOregon (a managed-care organization) and a fee-for-service program were reviewed. A model was constructed to evaluate changes in utilization of therapeutically related drug classes (eg, conventional nonsteroidal anti-inflammatory drugs [NSAIDs], gastrointestinal agents), office and emergency-department encounters, and hospitalizations before and after the PA policy was implemented on November 16, 1999. A secondary analysis evaluated these changes among a sample of prior NSAID users. Results: After the PA policy was implemented, use of celecoxib was immediately reduced from 1.07 to 0.53 days' supply per person-year (58.9%; 95% CI, 50.0%-67.9%). The monthly rate of increase was also reduced (P < 0.001). Utilization changes were not observed in other drug classes. Similar changes were observed in the secondary analysis. An 18% (95% CI, 2.2%-33.9%) nonsignificant increase in emergency-department visits was observed in the entire sample after the PA policy was implemented. However, a similar change was not observed in the secondary analysis of prior NSAID users. No other changes in medical service encounters were noted after the PA policy was activated. Conclusions: This observational study found that celecoxib use was substantially reduced after the implementation of a PA policy. No important changes in use of other drug classes were detected. The overall increase in emergency-department visits--although not observed among previous NSAID users--should be explored on the individual level. (Clin 7her. 2004;26:1518-1532) Copyright © 2004 Excerpta Medica, Inc. Key words: Medicaid, reimbursement, drug policy, pharmacy benefits, quasi-experimental design, prior authorization, celecoxib. This work was presented as an abstract at the 8th Annual International Meeting of the International Society of Pharmacoecononfics and Outcomes Research, May 18-21, 2003, Arlington, Virginia.

AcceptedJot publication July 19, 2004. Printed in the USA. Reproduction in whole or part is not permitted.

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doi: I O. I016/i.clinthera.2004.09.013 0149 2918/04/%19.00

Copyright © 2004 Excerpta Nedica, Inc.

D.M. Hartung et al.

INTRODUCTION

Since the mid-1990s, annual increases in expenditures for prescription drugs as a proportion of total health care spending have consistently outpaced all other components of health care spending in the United States. 1 In 2001 alone, spending on prescription drugs in the United States rose by 15.7% in contrast to 8.7% for total health care costs. 1 Increased use of branded medications, aggressive promotion, and the growing prevalence of treatable chronic diseases have all been cited as causes for the sustained increase in prescription drug expenditures. 2,3 Given the aging of the baby boom generation, as well as advances in biotechnology and genomics, it seems likely that these patterns of growth will persist. In response to what is perceived as unsustainable growth in costs, health care payers have developed many different benefit management strategies to help curtail these trends. To influence utilization and control the costs of pharmaceuticals, pharmacy benefit managers employ a variety of tools such as formularies, cost sharing, mandatory generic substitution, prior authorization (PA), and therapeutic interchange. PA policies, one of the most commonly employed prescription drug management tools, are used by >85% of managed care organizations (MCOs). ~ The premise behind PA is that prescribers must obtain preapproval from the insurer before a particular medication will be reimbursed and dispensed to the patient. Typically, the prescriber must provide clinical justification that satisfies drug-specific use criteria set by the MCO. Despite the widespread use of PA as a practical method for managing pharmacy benefits, little is known about health outcomes associated with its use. Many fear that delayed or reduced access to certain therapies might threaten patient care and safety. 5 Furthermore, there are also concerns that PA policies for medications might result in excessive administrative burden and inconvenience for already overworked physicians and pharmacists. 6 Finally, others object to PA policies because they restrict clinical discretion and are perceived as yet another threat to physician autonomy, r,8 In light of the widespread use and apparent concerns about unintended adverse consequences of PA policies, the volume of research evaluating such policies is strikingly sparse. In general, published descriptions of PA policies have shown them to be effective for

reducing pharmaceutical costs. 5 However, many have not evaluated their impact in terms of clinical or humanistic outcomes. 5 In addition, studies to evaluate the impact of PA policies have generally suffered from a lack of methodologic rigor. 5,9,1° In a July 2004 search of MEDLINE using the search term prior authorization, the only published study to report the effect of a PA on clinical outcomes evaluated an inpatient PA policy for parenteral antibiotics. 11 In addition to documenting substantial savings on antimicrobials, there were significant increases in bacterial susceptibilities, and no evidence of reduced survival rates.m Although generally well designed, this study used a pre-post method without a concomitant control group and therefore was unable to evaluate potentially time-related confounding variables. Smalley et al m conducted a timeseries analysis of a PA policy for branded nonsteroidal anti-inflammatory drugs (NSAIDs) in the Tennessee Medicaid program. In their analysis, the study team attributed a substantial reduction in expenditures for branded NSAIDs to the implemented PA policy without observing increases in the use of other health services, m The study also did not include a concurrent control group; however, the use of an interrupted timeseries design generally allows for better control of secular utilization trends before implementation. 13 A year2001 review of the literature by MacKinnon and Kumar 5 found that a majority of studies evaluating PA policies did not include a control arm. It has been strongly recommended that policy and program evaluations be conducted using methods that include multiple measurements before and after implementation, a well-chosen comparison control group, metrics that incorporate appropriate denominators, specific outcomes related to the policy or population affected, and appropriate statistical techniques, lo Cyclooxygenase-2 (COX-2) selective inhibitors are a relatively new class of NSAIDs that have been shown to reduce the risk of serious gastrointestinal (GI)-related adverse events. 1~ However, like many new drugs, they are relatively costly, and uncertainties about their safety and efficacy advantages remain unresolved. 15 On November 16, 1999, CareOregon, a not-for-profit Medicaid MCO in the Portland, Oregon, metropolitan area, instituted a PA policy for celecoxib in an effort to control costs and improve the appropriate use of this medication. When implemented, the PA policy required that patients had a 1519

CLINICALTHERAPEUTICS®

diagnosis of osteoarthritis, rheumatoid arthritis, or other chronic pain condition, plus any of the following: history of GI bleeding, peptic ulcer, or corticosteroid therapy; age >60 years; or therapeutic failure or contraindication to _>2 generic NSAIDs. To obtain authorization, the prescribing clinician was required to contact the pharmacy benefits company (by phone or fax) and provide documentation verifying that the patient met the established criteria. The objective of this study was to evaluate the intended and unintended effects of a PA policy for celecoxib on pharmacy and medical-service utilization in a Medicaid MCO. The present study adds to and improves on the current body of literature evaluating PA programs by using interrupted time-series analysis, a powerful analytic technique, and by having a reference group of similar patients for comparison of observed trends. METHODS

This study was a retrospective, interrupted timeseries analysis of 22 monthly health-related utilization rates from January 1, 1999 (the date that celecoxib was made commercially available in the United States), to October 31, 2000 (1 year after the celecoxib PA policy was activated). All Medicaid claims for patients in CareOregon and in a Medicaid fee-forservice program were reviewed. On November 3, 2000, CareOregon activated a more comprehensive formulary that restricted the use of a number of medications, including branded NSAIDs (eg, rofecoxib) and certain proton p u m p inhibitors (PPIs). Physicians and patients were not formally notified of the celecoxib PA policy before its implementation. No other substantial drug policy changes were made before the formulary was implemented. To evaluate the indirect impact of the PA policy in other areas, utilization of the following prescription drugs and drug classes was evaluated before and after the policy was implemented: celecoxib, rofecoxib, NSAIDs, GI protectants (eg, PPIs, histamin% antagonists, misoprostol), long-acting opioids (eg, long-acting oxycodone, long-acting morphine sulfate, transdermal fentanyl), short-acting opioids and opioidcontaining combination products (eg, hydrocodone/ acetaminophen, oxycodone/acetaminophen), diseasemodifying antirheumatic drugs (DMARDs) (eg, methotrexate, hydroxychloroquine), and oral corticosteroids 1520

(eg, prednisone, dexamethasone). Trends in the use of medical services were evaluated in 3 broad categories of claims: office visits, emergency department (ED) encounters, and hospitalizations. In addition, medical claims for musculoskeletal conditions and GI ulceration were analyzed to assess the impact of the PA policy on conditions related specifically to efficacy and toxicity of NSAIDs. Medical services for these conditions were determined using the coding system established by the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM). 16 GI ulcer-related claims were identified using the ICD-9-CM codes 531 (gastric ulcer), 532 (duodenal ulcer), 533 (peptic ulcer), and 534 (gastrojejunal ulcer), lr Musculoskeletal conditions were identified using ICD-9-CM codes 710 through 739, which include diseases of the musculoskeletal system and connective tissue. All outcomes were quantified as either a sum of total days' supply (DS) dispensed for medications or a count of medical encounters for office or ED visits or for hospitalizations. DS is a data element entered by the pharmacist at the point of service and reflects the estimated number of days provided by the prescription directions and quantity (ie, 30 tablets of a QD medication would be a 30-day supply). To our knowledge, there were no quantity limits or DS limits <31 days for any of the studied drugs. All outcomes for each month were annualized by dividing the volume of use (DS dispensed or count of encounters) by the combined person-years (PY) of healthplan enrollment for that month. This was performed to accurately quantify the cumulative patient health plan experience (denominator), taking into account any lapses or terminations in eligibility during that month. It was recognized that many MCO enrollees would not be affected by the celecoxib PA policy. Therefore, a secondary analysis was performed for a subgroup of members with a history of NSAID use. Based on our clinical experience, we concluded that this group of patients would more likely represent patients with chronic conditions requiring treatment with NSAIDs, such as arthritis and other chronic pain, and were more likely to be affected by the PA policy. Patients were selected for the subgroup analysis if they had a history of use of traditional NSAIDs or COX-2 selective inhibitors and continuous eligibility for 18

D.M. Hartung et al.

months. Continuous eligibility was defined as having >1 prescription or medical claim every 6 months between January 1, 1999, and June 30, 2000. This criterion was used to minimize losses to follow-up for individuals whose eligibility was suspended temporarily, which frequently occurs in Medicaid populationsJ 8 A history of NSAID use was defined as _>1 claim for a nonselective NSAID or COX-2 selective inhibitor before the celecoxib PA-policy implementation date (November 1999). The same analyses of prescription drug and medical-service utilization were performed for this control sample as for the primary study groups. To help control for confounding secular changes in health-service utilization, the same model was applied to a control population of Oregon Medicaid patients enrolled in the fee-for-service (FFS) program. In Oregon, FFS enrollment is typically reserved for patients logistically unable to enroll in one of the state's contracted fully capitated health plans. For example, some areas in rural Oregon have low MCO penetration; therefore, Medicaid patients would be more likely to receive care on an FFS basis. Patients enrolled in the FFS program were not subject to any notable prescription drug restrictions and therefore represented a similar sample that could serve as an unexposed control. Statistical Analysis

A time-series segmented linear regression model was fit with monthly utilization estimates to evaluate changes after the celecoxib PA-policy implementation date (November 1999): Y = {~o + ~1xl + ~2(xl - 9)x2 + {~3x2+ ei where ~o = intercept; [31 = secular trend before implementation of PA policy; x 1 = month number (eg, January 1999 = 0 and February 1999 = 1); [32 = secular trend after implementation of PA policy; x 2 = design variable (ie, before PA = 0, after PA = 1); [33 = one-time effect of the month of PA-policy activation; g~ = error term.

The time series consisted of 2 segments, before and after the policy activation date, that could be evaluated for both changes in the linear slope (the rate of utilization) and an instantaneous change in magnitude immediately after the PA-policy time interval (November 1999). {3o indicated the y intercept of the regression model and represented the initial utilization level at time 0 (January 1999). [31 indicated the initial monthly rate of utilization occurring from January 1, 1999, through the celecoxib PA-policy implementation date (November 1999). {32 indicated the change in utilization rate (ie, slope change) after implementation of the PA policy. The instantaneous changes occurring immediately after implementation of the PA policy were evaluated by [33. One of the main assumptions of linear regression is that the error terms are uncorrelated.19,20 Correlation of error terms leads to underestimation of model coefficient standard errors, producing artificially low P values. > Because each value in a chronologic time sequence variable is likely to be correlated to the next, their error terms are also likely to be correlated. >,2° To deal with this problem, the model was corrected for first-degree autocorrelated errors using the SAS PROC AUTOREG procedure. 2° The DurbanWatson statistic produced by this procedure is used to gauge serial autocorrelation of error terms. Values near or greater than 2.00 are evidence of no autocorrelation. Because of the large data sets studied and the numerous regression models fit, coefficients were considered statistically significant if P < 0 . 0 1 . 21 Finally, 2 prediction models were constructed in order to estimate cost-savings for the MCO achieved after the celecoxib PA policy was enacted. Utilization patterns of celecoxib (expenditures/PY) before implementation of the PA policy were extrapolated for 12 months using a linear model and compared with actual use patterns to estimate savings accrued. However, preliminary celecoxib prescribing trends from both the MCO and the control FFS group suggest that although small segments fit well in a linear model, extended linear extrapolation is likely to lead to an overestimate of future utilization. Therefore, a second model estimating MCO celecoxib expenditures for 12 months after implementation of the PA policy was based on a natural logarithmic model, y = [30 + [31ex, where eXis the natural logarithmic function. Potential savings to the MCO were estimated by calcu1521

CLINICAL THERAPEUTICS®

lating the difference between the actual and estimated utilizations after implementation of the PA policy and multiplying by the enrollment for each month. Data extraction and manipulation were carried out with Microsoft Access 2000 (Microsoft Inc., Redmond, Washington). All statistical analyses were performed using SAS statistical software, version 8.1 GAS Institute Inc., Cary, North Carolina), and SPSS, version 12.0 (SPSS Inc., Chicago, Illinois). Graphic figures were constructed using Excel 2000, version 9.0.3821 SR1 (Microsoft Inc.). The study was submitted to and approved by the Oregon State University Institutional Review Board.

(19.5) in 2000; a majority of MCO enrollees were female and white (53.2% [39,829/74,866] and 65.8% [49,262/74,866], respectively, in 1999, and 53.2% [48,846/91,816] and 66.5% [61,058/91,816], respectively, in 2000). Mean monthly prescription drug exposure, as measured by the total DS of prescriptions dispensed per PY of enrollment excluding mental health-related medications (reimbursed through a separate statewide carve-out p r o g r a m ) was used as a gauge of overall population health. The mean (SD) level of medication exposure among MCO enrollees was 269 (22) DS/PY in 1999 and 289 (14) DS/PY in 2000 compared with 382 (14) DS/PY and 376 (13) DS/PY for the FFS in 1999 and 2000, respectively.

RESULTS Demographics

As shown in Table I, MCO enrollment grew from 74,866 in 1999 to 91,816 in 2000. Despite this marked increase, the measured demographics remained relatively stable. The mean (SD) age of MCO enrollees was 23.6 (19.5) years in 1999 and 23.8

Prescription D r u g Utilization

The segmented linear regression model equation shown previously was used for all of the models. The Durbin-Watson test statistics produced by the SAS PROC AUTOREG procedure for each of the models

Table I. Demographics of patients in a retrospective, Medicaid claims-based, interrupted time-series analysis of 22 monthly health-related utilization rates from January I, 1999, to October 3 I, 2000, to assess the effect of the implementation of a prior-authorization policy for celecoxib on November 16, 1999. HCO Primary*

FFS

1999 (n = 74,866)

2000 (n = 91,816)

Secondar~t January 1999 June 2000 (n = 5140)

23.6 (19.5)

23.8 (19.5)

38.6 (19.1)

33.1 (27.0)

29.9 (25.4)

47.7 (21.3)

Sex, no. (%) Female Hale

39,829 (53.2) 35,037 (46.8)

48,846 (53.2) 42,970 (46.8)

3416 (66.5) 1724 (33.5)

63,126 (57.3) 46,949 (42.7)

87,808 (57.1) 65,976 (42.9)

8140 (72.9) 3023 (27. I)

Race, no. (%) White Hispanic Black Other

49,262 (65.8) 15,572 (20.8) 4941 (6.6) 5090 (6.8)

61,058 (66.5) 18,363(20.0) 6243 (6.8) 6152 (6.7)

3356 617 447 520

88,202 11,371 3055 7448

269 (22)

289 (14)

Chara~eri~ic Age, mean (SO),y

Honthly Rx exposure,* mean (SD), DS/PY

(69.3) (12.0) (8.7) (I 0. I)

766 (66)

Primary* 1999 (n = 110,076)

2000 (n = 153,784)

(80.1) (10.3) (2.8) (6.8)

382 (14)

II 1,167 (72.3) 29,635 (19.3) 3487 (2.3) 9495 (6.2) 376 (13)

Secondar~t January 1999 June 2000 (n = 11,164)

9872 323 239 730

(88.4) (2.9) (2.1) (6.5)

1270 (77)

Iv]CO = managed care organization; FFS = fee-for-service; Rx = prescription; DS = number of days' supply; PY = person-year ~The primary analysis included all patients enrolled in CareOregon, a not-for-profit Medicaid MCO or in a FFS Medicaid program in Oregon. tThe secondary analysis included a selecLed group of patients with a history of use of traditional non~eroidal anti-inflammatory drugs or cyclooxygenase-2 selecLive inhibitors who were continuously enrolled in an Oregon Medicaid program from January I, 1999, through June 30, 2000. ~Excluded psychoacLive medications.

1522

D.M. Hartung et al.

were >2, indicating that serial autocorrelation was not detected. Celecoxib utilization experienced a marked decrease immediately after the PA policy was implemented on November 16, 1999, whereas the FFS group exhibited continued growth, as illustrated by similar slopes before and after the PA-policy implementation shown in Figure 1. After the celecoxib PA policy was activated, a large and immediate 0.66 DS/PY decrease in utilization (58.9% change; 95% CI, 50.0%67.9%; P < 0.001) was observed among MCO patients. The PA policy was also associated with a statistically significant decline in the monthly rate (ie, slope) of celecoxib utilization (P < 0.001). An 11.9% drop (95% CI, 2.9%-20.9%; P = 0.012) in celecoxib use was also detected in the control model. However, this drop was not significant at the P < 0.01 level and was not followed by a significant decrease in the rate of celecoxib uptake, suggesting that it may have been an anomaly. When the model was applied to the other related drug classes (rofecoxib, NSAIDs, GI protectants,

long-acting opioids, short-acting opioids, DMARDs, and oral corticosteroids), significant immediate changes in overall drug utilization for the MCO during the same period were not detected. There was a concomitant 4.9% (95% CI, -47.3% to 57.2%; P = NS) immediate reduction in the use of rofecoxib and no change in the trend. Figure 2 shows the changes in the use of rofecoxib during the celecoxib PA-policy implementation. Utilization changes for other related medication classes for the MCO and FFS groups are shown in Table II. No significant immediate or slope changes for nonselective NSAIDs, which would be the most likely substitutes, were observed after the PA policy for celecoxib was implemented. To determine whether the PA policy had an impact on overall anti-inflammatory drug use, the trend of combined COX-2 selective inhibitors and nonspecific NSAIDs was evaluated. As shown in Figure 3, when these data were analyzed in aggregate, no statistically significant changes were observed in relation to the celecoxib PA policy, either in terms of sudden changes or slope changes. Medication utilization within the FFS

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Figure I. Comparison of celecoxib use before and after implementation of a prior-authorization (PA) policy among patients enrolled in CareOregon, a not-for-profit Medicaid managed care organization (MCO), and among patients enrolled in a fee-for-service (FFS) Medicaid program in Oregon that did not have a PA policy.*P < 0.001 for onetime decline in M C O group; fp < 0.001 for change in celecoxib use slope from first period in M C O group.

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CLINICAL THERAPEUTICS @

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Month Figure 2. Comparison of rofecoxib use before and after implementation of a prior-authorization (PA) policy for celecoxib among patients enrolled in CareOregon, a not-for-profit Medicaid managed care organization ( M C O ) , and among patients enrolled in a fee-for-service (FFS) Medicaid program in Oregon that did not have a PA policy.

control arm was relatively stable and did not exhibit any significant changes during the same time period.

Table III shows the mean monthly expenditures for the relevant drug classes. Mean monthly expenditures for nonselective NSAIDS increased by 8.2% (P = 0.004) in the MCO group compared with a decrease of 7.1% (P = 0.009) in the FFS group. After the PA policy was activated, spending for GI protectants, DMARDs, longacting opioids, and short-acting opioids increased significantly for the MCO group. Monthly prescription drug spending for the FFS group increased for all of these drug classes except DMARDs.

musculoskeletal-related encounters in the ED and found similar results: no immediate increase in utilization but a statistically significant increase in the slope (P < 0.001). Concomitant changes in the FFS ED utilization were not observed. The analysis also showed an immediate decline in the use of GI-related medical services (P = 0.003) without a concomitant change in the rate of use from the pre-period to the post-period. However, the absolute number of GI-related events per month was small and sporadic, thus complicating fit in the linear model (adjusted r 2 = 0.35). There were no other notable changes in the use of other medical services (office visits or hospitalizations) within the MCO or FFS groups (Table II).

Medical Claims

Secondary

After the celecoxib PA policy activation, the model detected an immediate 18.0% increase (95% CI, 2.2%33.9%) that was not significant at the predetermined level of P < 0.01, as well as a statistically significant increase in slope (P < 0.001) (Figure 4). To further explore this increase, we evaluated the trend in

In the secondary subgroup analysis, a total of 5140 and 11,164 patients in the MCO and FFS groups, respectively, were evaluated. The demographics of these groups are shown in Table I. The mean age of enrollees was 38.6 (19.1) years in the secondary MCO sample and 47.7 (21.3) years in the secondary

Prescription Drug Expenditures

1524

Analysis

D.M. Hartung et al.

Table II. Pharmacy and medical service utilization changes before and after the implementation of a prior-authorization (PA) policy for celecoxib on November 16, 1999, among all patients enrolled in CareOregon, a not-for-profit Medicaid managed care organization (MCO), or in a fee-for-service (FFS) Medicaid program in Oregon that did not have a PA policy, based on 22 monthly health-related utilization rates from January I, 1999, to O c t o b e r 31,2000. HCO

FFS

Change, % (95% CI)

P forTrend Difference

Pharmacy utilization Celecoxib Rofecoxib NSAIDs GI protecLants DIARDs Long-acting narcotics Short-acting narcotics Oral corticosteroids

~8.9" (~0.0 to 67.9 ~i.94 (~i7.3 to 57.2 6.3 (~.3 to 14.8) 3.5 ( 3.4 to 10.3) 17.6 (I.6 to 33.6) 6. I ( I 0. I to 22.2) 8.3 (q).4 to 17.0) 5.3 ( 16.3 to 5.6)

<0.001 0.628 0.684 0.761 0.869 0.429 0.63 0.209

I 1.9 (~.9 to 20.9) 3.98 (~3.6 to 31.6) 2.3 (~i.4 to 9.0) 0.9 ( 6.5 to 8.3) 3.2 (~.0 to 11.4) -0.8 (~.1 to 7.5) I. I (~.4 to 7.7) 2.9 ( 3.5 to 9.3)

Medical service utilization Office visits ED encounters Hospitalizations G-related encounters Musculoskeletal-related encounters

19.5 (q).6 to 8.1) 18.0 (2.2 to 33.9) 12.0 (0.2 to 23.9) ~i9.6" ( 80.6 to 18.6 9. I (~7.3 to 9.2)

0.092 <0.001 0.086 0.764 0.043

7.7 (~i.7 to 20.2) 7.5 (~.8 to 20.8) 5.4 ( 10.8 to 0.02) 10.2 (~9.8 to 9.3) 3.5 ( 8.2 to 15.2)

Measure

Change, % (95% CI)

P forTrend Difference

<0.001 0.8 0.821 0.939 0.207 0.717 0.428 0.5 0.904 0.087 0.079 0.806 0.478

Change = immediate changein utilizationafter PA-policyimplementation;trend difference = overalltrend in utilization (ie,slope of the time-series regression); NSAIDs = nonsteroidalanti-inflammatorydrugs; GI = ga~rointe~inal;DMARDs = disease-modifyingantirheumaticdrugs; ED = emergencydepartment. ~P< 0.0 I.

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Month Figure 3. Aggregated nonsteroidal anti-inflammatory drug (NSAID) use before and after implementation of a priorauthorization (PA) policy for celecoxib among patients enrolled in CareOregon, a not-for-profit Medicaid managed care organization ( M C O ) , and among patients enrolled in a fee-for-service (FFS) Medicaid program in Oregon that did not have a PA policy. ]525

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THERAPEUTICS

@)

Table III. Mean monthly prescription expenditures before and after the November 16, 1999, implementation of a priorauthorization (PA) policy by CareOregon, a not-for-profit Medicaid managed care organization, and percent change in expenditures after the PA-policy implementation. Monetary values are given in year-1999 US dollars. MCO Baseline Monthly Expenditures, $/PY

Drug Class Nonselective NSAIDs GI protectants DMARDs Long-acting opioids Short-acting opioids Oral corticosteroids

FFS

Change, $ (%)

7.91 32.66 2.75 10.23 9.82 0.44

0.65 5.23 0.95 4.70 2.05 0.01

(8.2) (I 6.0) (34.6) (45.9) (20.9) (3.2)

P

Baseline Monthly Expenditures, $/PY

Change, $ (%)

P

0.004 <0.001 <0.001 <0.001 <0.001 0.415

14.37 39.50 7.14 25.16 13.28 1.28

1.02 ~7. I) 3.16 (8.0) 1.04 (14.6) $7.70 (30.6) 1.03 (7.7) 0.00 ~0.3)

0.009 <0.001 0.029 <0.001 <0.001 0.917

NSAIDs = non~eroidal anti-inflammatorydrugs;GI = ga~rointe~inal;DMARDs = disease-modifyingantirheumaticdrugs.

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2000

v

Month Figure 4. Comparison of overall emergency department (ED) encounters and ED encounters related to musculoskeletal conditions before and after implementation of a prior-authorization (PA) policy for celecoxib among patients enrolled in CareOregon, a not-for-profit Medicaid managed care organization (MCO), and among patients enrolled in a fee-for-service (FFS) Medicaid program in Oregon that did not have a PA policy. *P < 0.001 for change in slope of all ED encounters in M C O group; tp < 0.001 for increase in slope of ED encounters for musculoskeletal conditions in M C O group.

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FFS sample. Most patients in both the MCO and FFS groups were female (66.5% [3416/5140] and 72.9% [8140/11,164], respectively). The mean (SD) monthly prescription drug exposures were 766 (66) DS/PY and 1270 (77) DS/PY in the MCO and FFS groups, respectively. The mean (SD) ages were 38.6 (19.1) and 47.7 (21.3) years in the MCO and FFS groups, respectively. Analysis of medication use within these samples followed similar patterns to those observed among all enrolled patients. Table IV shows utilization trends for drug classes and medical claims among the secondary analysis sample. The use of celecoxib immediately after initiation of the PA policy was observed to decrease by 59.8% (95% CI, -69.2% to -50.4%) and be significantly suppressed (P < 0.001) thereafter. No other statistically significant changes in medication use coincided with the celecoxib PA-policy implementation date. The model failed to detect a significant one-time or rate (slope) change in the utilization of ED services within the MCO sample. Specifically, there was a nonsignificant, immediate reduction in ED services of

7.3% (95% CI, -20.8% to 6.3%) accompanied by a nonsignificant change in rate of use. Data from the FFS sample demonstrated a similar secular trend. The use of other medical services (eg, office visits, hospitalizations) did not exhibit any significant increases in utilization after the PA policy was activated. Too few GI-related encounters were generated in the MCO sample to adequately populate the model and produce stable beta-coefficients. There was a mean of only 4.3 GI-related events per month among patients in this sample. It is recommended to have >100 observations at each regression data point (time point) to achieve stable estimates of utilization. 2° Prediction Model

Finally, the 2 prediction models were constructed for the purposes of estimating potential savings attributable to the celecoxib PA policy. The linear model fit the pre-PA policy celecoxib data in the MCO sample better than did the logarithmic model (r 2 = 0.95 vs r 2 = 0.83, respectively). Because it seems reasonable to assume that indefinite linear growth in celecoxib use is unlikely, we decided to present both

Table IV. Pharmacy and medical service utilization changes after the implementation of a prior-authorization (PA) policy for celecoxib on November 16, 1999, among patients with a history of use of traditional nonsteroidal antiinflammatory drugs (NSAIDs) or cyclooxygenase-2 selective inhibitors who were enrolled in CareOregon, a notfor-profit Medicaid managed care organization (MCO). MCO

Measure

Change, % (95% CI)

Pharmacy utilization Celecoxib Rofecoxib NSAIDs GI protectants DMARDs Long-acting narcotics Short-acting narcotics Oral corticosteroids

39.8 ~69.2 ~ 2 . I ~67.4 6.4 ~20.7 1.2 ~1 I.I 7. I ~13.8 5.1 ~26.0 2.2 ~10. I 3.8 ~14.3

to 50.4) to 23. I) to 8.0) d o 8.8) to 28.0) to 15.7) to 9.7) to 21.9)

Medical service utilization Office visits ED encounters Hospitalizations Musculoskeletal encounters

2.2 7.3 3.7 ~3.6

to to to to

~12.1 ~20.8 ~18.7 (~i0.7

16.4) 6.3) I 1.3) 6.6)

FFS P for-Trend Difference

Change, % (95% CI)

P for-Trend Difference

<0.001 0.032 0.31 0.27 0.265 0.3 0.265 0.047

I 1.3 (0.0 to 22.6) 3.9 ~16.5 to 8.6) 1.7 ~9.5 to 6.2) 6.9 ~2.3 to 16.1) 1.2 ~9.2 to I 1.5) 3.6 ~6.3 to 13.5) 0.2 ~6.6 to 7. I ) 6.6 ~3.1 to 16.3)

<0.001 0.058 0.207 0.24 0.642 0.009 0.077 0.99

0.781 0.26 0.062 0.154

0.1 ~12.1 3.9 ~16.7 1.8 ~22.3 10.2 ~23.7

to to to to

12.0) 9.0) 25.8) 3.2)

0.133 0.056 0.126 0.669

FFS = fee for service; change = immediate change in utilization after the PA-policy implementation; trend difference = overall trend in utilization (ie, slope o f time-series regression); CI = gastrointestinal; DMARDs = disease-modifying antirheumatic drugs; ED = emergency deparment.

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models. Figure 5 depicts MCO expenditures per PY projected for 12 months beyond the PA-policy activation date. The mean projected savings attributed to the PA policy was $2.87/PY using a linear extrapolation and $1.40/PY using the logarithmic extrapolation. Health care payers commonly quantify utilization of services in terms of per-member, per-month (PMPM) use. The measurement unit used in this study was converted into the PMPM format by dividing the PY estimate by 12. Thus, the PMPM savings accrued by the PA policy were $0.24 and $0.12 using the linear and logarithmic extrapolation models, respectively. Given enrollment estimates for the MCO during this time period, 22 the savings attributable to the PA policy were projected to be approximately $10,402 (linear model) and $4999 (logarithmic model) per month.

that may be effective for reigning in escalating prescription drug costs, very few have been evaluated to ensure that they do not harm patients or drive health care costs in other areas. 5,9,1° In this observational study of a large Medicaid MCO, a PA policy was effective for rapidly reducing the utilization of celecoxib by 59%. In addition to causing a sharp decline in overall celecoxib use, the PA policy was also effective for reducing the rate of celecoxib use (Figure 1). This reduction in celecoxib use represented a change from -9% of the total NSAID share before implementation of the PA policy to -4% of the total NSAID share after implementation. Celecoxib use did not return to the pre-PA policy implementation level by 1 year after implementation. In contrast, the FFS group saw continued increases in the use of celecoxib, with no appreciable decline in the rate of growth. Our model estimated a mean monthly savings of about $1.40/PY, or roughly $0.12 PMPM, directly attributable to the celecoxib PA policy. In the context of pre-PA policy implementation spending on COX-2 selective inhibitors and nonselective NSAIDs of approximately $0.79 PMPM, the celecoxib PA policy alone repre-

DISCUSSION

Prescription drug spending has become an increasingly important component responsible for the dramatic rise in health care costs experienced since the beginning of the 1990s. 23 Although many tools exist

Actual utilization

- Linear prediction .....................Logarithmic prediction -

7-

6

S

S

S

S

S

5

o

S

S

j,

4

L.

3 L.

/

2 OX I.U

Y

I

0

I

an 1999

PAinitiate I

I

I

I

I

I

I

I

I

Mar

May

July

Sep

Nov

Jan

Mar

May

July

Sep

1999

1999

1999

1999

1999

2000

2000

2000

2000

2000

I

Nov 2000

Month Figure 5. Projected expenditures per person-year before and after implementation of a prior-authorization (PA) policy for celecoxib among patients enrolled in CareOregon, a not-for-profit Medicaid managed care organization.

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D.M. Hartung et al.

sented savings o f - 1 5 % on aggregate NSAID (COX-2 selective/nonselective) expenditures. Using monthly patient enrollment figures to convert these data into absolute costs (absolute costs = [costs/PY] × [MCO PYs of enrollment]), the PA policy would be expected to reduce expenditures for celecoxib by $59,988 to $124,824 over a 12-month period ($4999 to $10,402 per month for 12 months) for the MCO. However, this estimate does not take into account the costs to the MCO for administering the celecoxib PA policy. Interestingly, a nonsignificant, immediate decline of 11.9% was observed in celecoxib use in the fitted lines of the control group after implementation of the PA policy. A concomitant immediate, nonsignificant 4.9% drop in rofecoxib use was also observed in the MCO group when the PA policy was implemented. Neither of these potential events was followed by a subsequent change in the rate of utilization (slope in the second segment of the model) after November 1999 (the month that the PA policy was implemented). It is possible that these were merely artifacts in the data. It is also possible that these were unintended effects of the PA policy on physicians who treated patients in both the treatment and control groups. In either case, these events were neither significant nor sustained. Large fluctuations in the use of other medications were not observed coincident to the celecoxib PApolicy implementation. The abrupt and sustained reduction in celecoxib use, in the absence of increased use of other NSAIDs, is worrisome because it suggests potential undertreatment of patients with musculoskeletal conditions. However, when patterns of use of aggregate NSAIDs (nonselective NSAIDs and COX-2 selective inhibitors) were evaluated, no significant changes were observed. This suggests that although substantial reductions in the use of celecoxib were achieved after activation, the overall level of treatment with all NSAIDs remained stable after the celecoxib PA policy was put into place. It also suggests that our model may have been insensitive to small changes in NSAID use. In our analyses, medication utilization patterns in a subgroup of previous NSAID users were consistent with those observed for the aggregate sample. Although the use of rofecoxib, the most probable substitute drug available during the PA-policy implementation period, increased before and after celecoxib prescriptions were restricted,

there were no sudden changes detected to indicate whether patients were merely being switched from one COX-2 selective inhibitor to the other. However, this study did not specifically explore medication use in unique patients; therefore, definitive statements cannot be made. From a prescriber's perspective, if PA for celecoxib for a particular patient were denied, one of the other many generic or low-cost NSAIDs would be recommended as a substitute. In one large Medicaid-based administrative claims analysis, Smalley et al 2. investigated the effects of a PA policy for branded NSAIDs in the state of Tennessee. The authors found that the PA policy was associated with a 53% decrease in the mean annual expenditures for NSAIDs. No evidence was found suggesting that the policy was associated with increased use of other drug classes or health-related service claims. Similar results were found when subgroups of chronic NSAID users were assessed. The authors concluded that the PA policy was responsible for saving an estimated US $12.8 million during the 2 years after its 1989 implementation. In our analysis, utilization of medical services was also evaluated. ED visits were the only medical service for which utilization increased during the period coinciding with the celecoxib PA policy in the MCO population. When limited to musculoskeletal-related ED visits, a similar trend was observed. This pattern was not echoed in either the control FFS population or the NSAID users. Although it is conceivable that an increase in ED encounters could be attributed to reduced patient access to celecoxib in the MCO group, the absence of a similar increase in ED visits among previous NSAID users (the most susceptible patients) does not corroborate this. The celecoxib response to the PA policy in this sample was otherwise similar to the general MCO population (rapid, sustained decline). A possible explanation for the increased ED utilization in the general MCO population (but not among previous NSAID users) is that it was caused by some unmeasured, uncontrolled-for event. However, this trend was not observed in the control FFS group, making it seem less likely that this increase was the result of a confounding event. An alternative explanation is that the increase in ED encounters is the result of primary care access problems after the marked increase in enrollment that occurred from 1999 to 2000. The ED is frequently a primary point of contact 1529

CLINICALTHERAPEUTICS®

for nonurgent health care when all other options are unavailable or require long waiting periods. 25 The theory that increased ED visitation was related to reduced primary care access is substantiated by the observation made in the secondary analysis of previous NSAID users with continuous enrollment from January 1, 1999, through June 30, 2000. As mentioned previously, no increased utilization of the ED was observed among these previous NSAID users. It is possible they did not need to seek health care in the ED because they already had an established primary care provider. This refutes the notion that the increased utilization of ED services that we observed can be attributed to the celecoxib PA policy. However, without a formal investigation of individual experiences among patients using celecoxib before implementation of the PA policy, definitive conclusions about the precise impact of the policy on ED encounters cannot be made. Several limitations of this analysis merit consideration. First, this study was a retrospective, observational, claims-based evaluation of drug and medicalservice utilization coincident to a medication policy change. As such, formal inferences about the intervention of interest--in this case, CareOregon's celecoxib PA policy--cannot be made. In the absence of random assignment to exposure groups (PA policy, no PA policy), causality of events is difficult to establish conclusively. The randomized controlled trial, a paradigm for establishing comparative efficacy, is rarely used for health services research because of logistic and political reasons. 13 When controlled, prospective trials are not possible, quasi-experimental techniques are a recommended method of assessing policy changes. 10,26-29 Interrupted time series, of which segmented regression analysis is an example, is currently the strongest quasi-experimental design to employ in this situation. 2° Although a control arm of patients not exposed to the PA policy (FFS patients) was used in the present study, it was evident that the groups were not completely comparable, leading to a potential selection bias. Demographic data suggest that the FFS control population consisted of patients who were generally older and likely to be more severely ill. Another limitation is that this analysis did not have a large enough sample size to estimate the impact of the PA policy on medical claims for GI-related conditions. Recommendations call for _>100 observations

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of the event of interest for each time period to minimize the variability in the series2°; we observed <20 GI-related claims each month. Finally, the analysis used pharmacy and medical claims data from one of Oregong MCOs and the FFS Medicaid programs. The fields in these data have not been validated for research such as this. However, misclassification of data would introduce only systematic error into the analyses if it were to occur at differential rates in the pre- and post-periods. The intent of this analysis was to evaluate global changes in the use of different drugs and health services, and not to follow and measure the outcomes associated with individual patients who were affected by the PA policy. Therefore, it is not possible to determine whether individual patients were harmed by this policy; this limitation is sometimes termed the ecolo~cfallacy. 3° In addition, the claims-based nature of this study precluded any formal evaluation of health-related quality of life, functional status, or patient satisfaction. However, other studies in similar patient samples have found that a PA policy for branded NSAIDs had no appreciable impact on quality of life. 31 The fact that this study involved Medicaid patients also limits the external validity of the results. Medicaid is a joint state/federal health insurance program that provides care to specific categories of economically disadvantaged and disabled patients, and therefore may not be comparable with the general population. 32 However, if it is believed that patients in the present study were more severely ill than the general population, then it may be conservative and appropriate to extrapolate these results to a less ill population. Although controversy exists regarding the clinical equivalence of the currently available COX-2 selective inhibitors, it is probably appropriate to consider them comparable for the purposes of pharmacy benefits management. 33,3~ CONCLUSIONS

This observational study found that celecoxib use was substantially reduced after implementation of a PA policy We did not find evidence that the PA policy was responsible for rises in the use of other drug classes or for overall undertreatment. The temporally related rise in ED encounters overall and specific to musculoskeletal-related claims is a concern and requires further investigation on the individual level.

D.M. Hartung et al.

ACKNOWLEDGMENTS

Dr. Hartung's work on this study was supported through a fellowship partly funded by Pharmacia Corporation (now Pfizer Inc, New York, New York). The fellowship was also partially supported through a contract with the State of Oregon Office of Medical Assistance Programs. REFERENCES

1. Levit K, Smith C, Cowan C, et al. Trends in U.S. health care spending, 2001. Health Aft (Millwood). 2003;22: 154-164. 2. Altman SH, Parks-Thomas C. Controlling spending for prescription drugs. N Engld Med. 2002;346:855-856. 3. Rosenthal MB, Bern& ER, Donohue JM, et al. Promotion of prescription drugs to consumers. N Engl d Med. 2002;346:498-505. 4. Novartis Pharmacy Ben@t Report. Parsippany, NJ: Novartis Pharmaceutical Corp; 2000. 5. MacKinnon NJ, Kumar R. Prior authorization programs: A critical review of the literature, d Manage Care Pharm. 2001;7:297-302. 6. Olson BM. Approaches to pharmacy benefit management and the impact of consumer cost sharing. Clin Ther. 2003;25:250-272. 7. Eddy DM. Clinical decision making: From theory to practice. Principles for making difficult decisions in difficult times. JAMA. 1994;271:1792-1798. 8. Reinertsen JL. Zen and the art of physician autonomy maintenance. Ann Intern Med. 2003;138:992-995. 9. Soumerai SB. Benefits and risks of increasing restrictions on access to costly drugs in Medicaid. Health Aft (Millwood). 2004;23:135-146. 10. Soumerai SB, Ross-Degnan D, Fortess EE, Abelson J. A critical analysis of studies of state drug reimbursement policies: Research m need of discipline. Milbank Q. 1993;71:217-252. 11. White AC Jr, Atmar RL, Wilson J, et al. Effects of requiring prior authorization for selected antimicrobials: Expenditures, susceptibilities, and clinical outcomes. Clin Infect Dis. 1997;25:230-239. 12. Smalley WE, Griffin MR, Fought RL, et al. Effect of a prior-authorization requirement on the use of nonsteroidal antiinflammatory drugs by Medicaid patients. N gnglJ Med. 1995;332:1612-1617. 13. Ray WA. Policy and program analysis using administrative databases. Ann Intern Med. 1997;127:712718.

14. FitzGerald GA, Patrono C. The combs, selective inhibitots of cydooxygenase-2. N Engl J Med. 2001;345:433442. 15. Spiegel BM, Targownik L, Dulai GS, Gralnek IM. The cost-effectiveness of cyclooxygenase-2 selective inhibitors in the management of chronic arthritis. Ann Intern Med. 2003;138:795-806. 16. International Classification of Diseases, Ninth Revision, Clinical Modification. 6th ed. Washington, DC: US Department of Health and Human Services, Centers for Disease Control and Prevention, Centers for Medicare and Medicaid Services; 2003. 17. Raiford DS, Perez Gutthann S, Garcia Rodriguez LA. Positive predictive value of ICD-9 codes in the identification of cases of complicated peptic ulcer disease in the Saskatchewan hospital automated database. gpidemiology. 1996;7:101-104. 18. Bright RA, Avorn J, Everitt DE. Medicaid data as a resource for epidemiologic studies: Strengths and limitations. J Clin gpidemiol. 1989;42:937-945. 19. Neter J, Kutner MH, Nachtsheim CJ, Wasserman W Applied Linear Regression Models. 3rd ed. Chicago, Ill: Irwin; 1996. 20. Wagner AN, Soumerai SB, Zhang F, Ross-Degnan D. Segmented regression analysis of interrupted time series studies in medication use research. J Clin Pharm Ther. 2002;27:299-309. 21. Strom BL, Carson JL. Use of automated databases for pharmacoepidemiology research. Epidemiol Rev. 1990; 12:87-107. 22. Oregon Department of Human Services, Oregon Health Plan. OHP managed care enrollment reports. Available at: http://www.dhs.state.or.us/healthplar2 data_pubs/enrollment. Accessed July 23, 2004. 23. Steinbrook R. The prescription-drug problem. N EnglJ Med. 2002;346:790. 24. Smalley WE, Griffin MR, Fought RL, Ray WA. Excess costs from gastrointestinal disease associated with nonsteroidal anti-inflammatory drugs. J Gen Intern Med. 1996; 11:461-469. 25. Petersen LA, Burstin HR, O'Neil AC, et al. Nonurgent emergency department visits: The effect of having a regular doctor. Med Care. 1998;36:1249-1255. 26. Soumerai SB, McLaughlin TJ, Ross-Degnan D, et al. Effects of a limit on Medicaid drug-reimbursement benefits on the use of psychotropic agents and acute mental health services by patients with schizophrenia. N EnglJ Med. 1994;331:650-655.

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27. Gillings D, Makuc D, Siegel E. Analysis of interrupted time series mortality trends: An example to evaluate regionalized perinatal care. AmJ Public Health. 1981;71:38-46. 28. Cook TD, Campbell DT. Quasi-Experimentation: Design and Analysis Issuesfor Field 5ettings. Chicago, Ill: Rand McNally College Publishing; 1979. 29. Veney JE, Kaluzny AD. Evaluation and Decision Making for Health 5ervices. Chicago, Ill: Health Administration Press; 1998. 30. Hearst N, Grady D, Barton HV, Kerlikowske K. Research using existing data: Secondary data analysis, ancillary studies, and systematic reviews. In: Hully SB, Cummings SR, Browner WS, et al, eds. Designing Clinical Research.

31.

32.

33.

34.

2nd ed. Philadelphia, Pa: Lippincott Williams & Wilkins; 2001:195-210. Momani AA, Madhavan SS, Nau DP Impact of NSAIDs prior authorization policy on patients' QoL. Ann Pharmatother. 2002;36:1686-1691. Carson JL, Ray WA, Strom BL. Medicaid databases. In: Strom BL, ed. Pharmacoepidemiolog;y. 3rd ed. New York: Wiley; 2000:303-324. Sonnenblick EH. Differences between COX-2-specific inhibitors: Clinical and economic implications. Am J Manag Care. 2002;8(Suppl 15):$428-$430. Wright JM. The double-edged sword of COX-2 selective NSAIDs. CMAJ. 2002;167:1131-1137.

Address correspondence to: Daniel R. Touchette, PharmD, MA, 840 SW Gaines Street, Mail Code GH212, Portland, OR 97239. E-mail: [email protected]

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