Prior authorization for biologic DMARDs: A description of United States Medicaid programs

doi:10.1002/art.24191

Arthritis Rheum. Author manuscript; available in PMC 2011 May 23.

Published in final edited form as:

Arthritis Rheum. 2008 November 15; 59(11): 1611–1617.

doi: 10.1002/art.24191

PMCID: PMC3099624

NIHMSID: NIHMS293558

Prior authorization for biologic DMARDs: A description of United States Medicaid programs

Michael A. Fischer, MD, MS, Jennifer M. Polinski, MPH, MS, Amber D. Servi, Jessica Agnew-Blais, Liljana Kaci, and Daniel H. Solomon, MD, MPH

Division of Pharmacoepidemiology and Pharmacoeconomics (all authors), Division of Rheumatology (DHS), Brigham and Women’s Hospital, Harvard Medical School, Boston, MA

Contact information: Michael A. Fischer, M.D., M.S., Brigham and Women’s Hospital, Division of Pharmacoepidemiology and Pharmacoeconomics, 1620 Tremont St., Suite 3030, Boston, MA 02120, Phone: 617-278-0930, Fax: 617-232-8602, Email: mfischer/at/partners.org

The publisher's final edited version of this article is available free at Arthritis Rheum

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Abstract

Objective

To evaluate state Medicaid prior authorization programs for biologic disease-modifying anti-rheumatic drugs (DMARDs).

Methods

We obtained biologic DMARD prior authorization policy information from state Medicaid programs. Using aggregate Medicaid drug spending data, we calculated the proportion of DMARD prescriptions and spending attributed to adalimumab and etanercept in 1999 and 2005 and compared the changes in these proportions in states with and without prior authorization policies. Infliximab and other infused DMARDs were not included because of substantial missing data.

Results

Thirty-two states required prior authorization for one or more biologic DMARDS, with wide variation in the specific agents covered and the criteria required for a drug to be authorized. There were 18 states with prior authorization requirements for adalimumab or etanercept. States that implemented prior authorization for these agents initially had lower use of the targeted medications, but use increased over time to a level similar to that in states that did not have prior authorization requirements.

Conclusion

States vary widely in their implementation of prior authorization policies to limit use of biologic DMARDs. While it appears that these policies may have a short-term effect on the use of targeted medications, this effect does not appear to be sustained. The clinical impact and appropriateness of such policies is not clear from our data and should be studied further.

Introduction

Patients with inflammatory diseases now have many more treatment options than were available in the past. Biologic disease-modifying anti-rheumatic drugs (DMARDs) have been widely used for rheumatoid arthritis (RA) and increasingly for other rheumatic diseases. However, there are not clear guidelines for when such medications should be started. The cost of biologic DMARDs is a major concern for payors, since one month of a biologic DMARD may cost a hundred times more than a year’s supply of an older DMARD such as methotrexate or hydroxychloroquine. Cost concerns are especially salient for programs facing budget constraints, such as Medicaid.¹

Medicaid regulations do not allow states to have closed formularies, under which only a subset of medications are available and some drugs are completely excluded.² Individual states do have discretion to implement policies to control the use of selected prescription medications.³ Prior authorization is one commonly used tool for containing drug spending.^4–11 Under prior authorization policies, patients must meet specific clinical criteria prior to reimbursement for a prescription. Ideally, these policies attempt to target medications to appropriate patients while avoiding inappropriate use. Although prior authorization has been used in many public and private programs for a large range of medications, relatively little is known about how such policies are developed or about the effects that these policies have on drug use.

The state-level variations in Medicaid prior authorization policies create natural experiments that can be used to study the impact of various policies. Given the complexity of treating rheumatic diseases, and the expense of many of the biologic DMARDs, policies to control utilization of these medications can provide an illustrative case study of drug cost-control policy.

We reviewed Medicaid prior authorization policies for biologic DMARDs and examined aggregate use of selected agents before and after policy implementation.

Methods

Prior authorization policy data

We contacted all state Medicaid agencies between April and July 2006 to determine whether the state Medicaid program had a prior authorization policy for biologic DMARDs. We gathered information about policies affecting abatacept, adalimumab, anakinra, etanercept, infliximab, and rituximab. Arizona has a decentralized Medicaid program and does not provide state-wide data or policy information; we collected data from the other 49 states and the District of Columbia. We gathered and reviewed all manuals, instructions, bulletins, and submission forms for the prior authorization process, including information specific to the biologic DMARDs. Historical information, beginning with the inception of the prior authorization policy for biologic DMARDs in each state, as well as current information, was examined.

For states that had prior authorization policies in effect, we assessed the policy characteristics, determining which biologic DMARDs required authorization, when such requirements had been implemented, and what criteria had to be met before payment for the biologic DMARD could be approved. We recorded several specific data elements, including whether the prior authorization policy had detailed clinical criteria, whether the criteria were clearly spelled out in the forms used, whether prescriptions for biologic DMARDs had to be written by a rheumatologist, and whether a skin test for mycobacterium tuberculosis (i.e. PPD) was required before starting patients on biologic DMARDs.

Medication use data

Data on drug utilization by Medicaid programs were obtained from the Center for Medicare and Medicaid Services (CMS).¹² These aggregate quarterly data are compiled at the state level and include all outpatient prescriptions for which Medicaid provides reimbursement. Variables available include total prescriptions filled, total units of medication (tablets, capsules, etc.) dispensed, and reimbursement for each medication. The data do not include information on manufacturer rebates provided to state Medicaid programs. No person-level data were available for analysis.

For this study, we used data from the first quarter of 1999 through the fourth quarter of 2005. We did not include 2006 data since as of January 1, 2006 many patients were transitioned from Medicaid drug coverage to Medicare part D programs. Tennessee’s drug utilization data had excessive missing values, so Tennessee was not included in any of the quantitative analysis. Not all states include all biologic DMARDs in the drug reimbursement data. Specifically, many states do not include consistent data on infliximab, abatacept, and rituximab, presumably categorizing payment for these infused medications with other fees associated with the infusion, not in drug reimbursement files. For anakinra, which was more recently approved, only limited data were available. However, both etanercept and adalimumab were consistently included in all states for the time period that we studied.

Data analysis

For each state, in each calendar quarter, we calculated the total number of DMARD prescriptions covered by Medicaid and the total number of dollars reimbursed for those prescriptions. These totals included the data on the biologic DMARDs listed above as well as all synthetic DMARDs (azathioprine, cyclophosphamide, cyclosporine, D-penicillamine, gold, hydroxychloroquine, leflunomide, methotrexate, minocycline, mycophenolate mofetil, sulfasalazine, tacrolimus). We then calculated the proportion of DMARD prescriptions and reimbursement that were accounted for by adalimumab and etanercept. We compared the proportion of DMARD utilization and spending accounted for by etanercept in 1999 (adalimumab was not available in that year), at the beginning of our study period, and the proportion accounted for by adalimumab and etanercept combined in 2005, at the end of our study period.

To understand the changes in drug use attributable to the implementation of prior authorization we developed interrupted time-series models, using techniques that we have previously employed in the study of other drug classes.^{5, 6} We identified the implementation date of prior authorization programs for biologic DMARDs in all states that had such policies. We used as our intervention group the 9 states that implemented prior authorization between 2000 and 2004. The time frame for each state’s data was standardized relative to the quarter in which the prior authorization policy was initiated. The weighted average of the biologic DMARD utilization trend in control states without prior authorization programs was used as a comparator.

We developed general linear models, using generalized estimating equations to adjust for repeated observations, drawing on modeling techniques developed in our prior studies of anti-inflammatories and antihypertensives.^{5, 6} We assumed autoregressive correlation structure with three months lag time. The models included terms indicating the temporal relationship of each quarter to the implementation of the state’s prior authorization policy. Data from up to 6 quarters before and after the implementation of prior authorization were included. Interaction terms between the level and slope indicators and the prior authorization indicator were included to estimate the time-trend adjusted effects of the prior authorization policies. The full specification of the model is provided as Appendix I. We used z-test results based on regression beta coefficients and standard errors from the generalized estimating equations to determine statistical significance at the p<0.05 level. All analyses were done using Stata.¹³

Results

The characteristics of state Medicaid prior authorization policies for DMARDs are summarized in Table 1. Overall, 32 states required prior authorization for one or more biologic DMARDs. States varied in the amount of clinical detail required, with prior authorization policy in 20 (63%) asking for detailed clinical criteria, such as counts of swollen or painful joints, rheumatoid factors levels, radiologic findings, or other clinical data. Distinct from the amount of clinical detail required, 14 (44%) states were very clear about which criteria needed to be met in order for a drug to be approved. In the remaining states, the policy left ambiguity as to how the prior authorization requests would be determined. Figure 1 shows examples of states with and without clear approval criteria for biologic DMARDs. Six (19%) states required that a rheumatologist prescribe the biologic DMARD and only two (6%) states required that a PPD be checked before starting treatment with a biologic. Programs varied as to which agents required prior authorization, and none of the six biologic DMARDs required prior authorization in all 32 states that had policies. States began implementing prior authorization policies for biologic DMARDs slowly from 1999 through 2003, with increasing numbers of states initiating policies in 2005 and 2006.

Table 1

Summary characteristics of state Medicaid prior authorization policies for DMARDs

Figure 1

Examples of clinical criteria for prior authorization from two representative states.

We examined the trends in utilization of DMARDs over the study period. As noted in the preceding section, data on infused DMARDs was often missing, so they were not included. Figure 2 shows the increasing use of synthetic DMARDs, with the number of prescriptions shown in panel 2a and spending shown in panel 2b. In 1999 state Medicaid programs paid for 1.14 million prescriptions for DMARDs, and etanercept accounted for 30,460 (2.7%) of those prescriptions. The total spending on DMARDs in 1999 was just over $200 million, of which $27.7 million (13.8%) was spent on etanercept. By 2005 total Medicaid DMARD use had increased to 2.11 million prescriptions, of which 187,000 (8.8%) were for etanercept or adalimumab. The total DMARD spending in 2005 was $567 million dollars, with $255 million (44.8%) spent on etanercept and adalimumab.

Figure 2

Trends in DMARD prescriptions and spending in Medicaid from 1999 to 2005. Figure 2a shows, for each calendar quarter, the number of Medicaid-covered prescriptions for etanercept, for adalimumab, and for all other DMARDs. Figure 2b shows the same trends (more ...)

We next examined the proportion of DMARD prescriptions and spending that were accounted for by adalimumab and etanercept in states with and without prior authorization policies. Table 2 provides the details on when states implemented policies for these two medications. Tennessee implemented prior authorization for both adalimumab and etanercept in 2005 but, as noted in the methods section, the drug use data for Tennessee were incomplete, so although that state appears in Table 2, it is not included in the quantitative analyses.Figure 3a shows the percentage of prescriptions accounted for by these agents in 1999 and in 2005 and Figure 3b shows the reimbursement percentages for these same years.

Table 2

Years of prior authorization initiation for adalimumab and etanercept, specific states indicated in parentheses

Figure 3

Proportion of Medicaid DMARD prescriptions (figure 3a) and spending (figure 3b) accounted for by etanercept and adalimumab in 1999 and 2005, according to prior authorization policy status. In each panel of the figure, the left-most pair of bars shows (more ...)

Two states started prior authorization policies at the beginning of 1999, when etanercept was introduced on the market. In these states, etanercept accounted for 2.1% of DMARD prescriptions and 11.6% of spending in 1999, about one third lower than in other states. By 2005 use of biologic DMARDs in these two states had more than tripled (3.8-fold increase), to 7.8% of DMARD prescriptions and 39.7% of DMARD spending (3.4-fold increase); biologic DMARD use and spending in these two states in 2005 was similar to use in other states.

There were nine states that implemented prior authorization policies between 2000 and 2004. In 1999, these nine states had the highest relative use of etanercept, both in terms of prescriptions (3.7%) and reimbursement (21.1%). Between 1999 and 2005, the combined use of etanercept and adalimumab in these states almost doubled, to 7.2% of DMARD prescriptions and 40.7% of DMARD spending (1.9-fold increase for both measures). In the states that had prior authorization policies, the percentage of DMARDs accounted for by etanercept and adalimumab did not differ between states with clearly defined and non-clearly defined approval criteria.

In the 31 states that did not implement prior authorization policies, use of etanercept in 1999 accounted for 2.7% of DMARD prescriptions and 14.4% of DMARD spending. By 2005, use of etanercept and adalimumab in these states had increased to 8.4% of DMARD prescriptions and 44.6% of DMARD spending (3.1-fold increase for both measures). There were seven states that implemented prior authorization in 2005 or 2006. In those states the baseline use of etanercept accounted for 2.8% of DMARD prescriptions and 16.5% of DMARD spending in 1999. By 2005, combined use of adalimumab and etanercept in these states had more than tripled to 9.1% of DMARD prescriptions and 49.4% of DMARD spending, the highest rates for both measures.

The interrupted time series models suggested an initial decrease in use of adalimumab after prior authorization, followed by increasing use, although the effects were small. The immediate decrease in adalimumab spending after prior authorization started was 2.4% (p=0.07), but adalimumab use then increased in subsequent time periods by 1.6% per quarter (p=0.002). For etanercept, the effects were much smaller and did not approach statistical significance (immediate decrease 0.6%, p=0.79; subsequent increase 0.4%, p=0.59). Full results of both models are provided in Appendix I.

Discussion

We gathered data on state Medicaid prior authorization policies for biologic DMARDs used to treat RA and other rheumatic diseases. Of the 50 states studied, we identified 32 states that had implemented, or planned to implement, such policies. There was significant heterogeneity in the drugs that were included in the prior authorization policies and states also varied widely in the criteria required, both in terms of the amount of detail requested and in terms of the clarity of how authorization was determined.

Complete Medicaid utilization data allowed us to perform quantitative data analyses for adalimumab and etanercept. Combined use of these two drugs in Medicaid increased sharply between 1999 (etanercept alone) and 2005, both in absolute terms and as a proportion of all DMARDs. We found that states with prior authorization programs in place at the beginning of the study period had relatively low use of the targeted biologic DMARDs initially, with a sharp increase over the years studied. Many of the states with the highest levels of use of etanercept in 1999 went on to implement prior authorization policies between 2000 and 2004 and had a smaller increase in use of biologic DMARDs compared to the other states. States with the highest levels of use in 2005 were those that were in the process of implementing prior authorization requirements.

Previous studies have evaluated prior authorization for a variety of medication classes. Evaluation of Medicaid policies for selective cyclooxygenase-2 inhibitors (coxibs) found wide variability in the degree to which prior authorization criteria adhered to clinical evidence.⁴ Likewise, studies of psychiatric medications found considerable heterogeneity in how states approached prior authorization for these medications.^{7, 8} The state-to-state variability that we observed in prior authorization policies for biologic DMARDs is consistent with these prior findings.

Earlier research has also examined the quantitative impact of Medicaid prior authorization policies. A relatively consistent reduction in use of the targeted medications has been observed in studies of coxibs and other anti-inflammatories^{6, 9, 10} and angiotensin-receptor blockers.⁵ In both of those cases, the drug classes studied have straightforward substitution patterns that can be anticipated and built into policies (using non-selective NSAIDs instead of coxibs; using angiotensin-converting enzyme inhibitors instead of angiotensin-receptor blockers). Management of rheumatic diseases is more difficult and the medication choices are more complicated. Patients may receive a biologic agent together with a synthetic DMARD, after failing one or more non-biologic DMARDs, or without trying a non-biologic DMARD and these subtleties cannot easily be measured in our data. In addition, the utilization data for biologic DMARDs do not include important and frequently used agents such as infliximab. Some of the non-biologic DMARDs may be used for non-rheumatologic indications, and their inclusion in the denominator of our outcome measure could make the measure less precise. The variability that we see in the drug utilization data may both limit our ability to measure a precise impact of prior authorization policy and also reflects the difficulty of applying and implementing these kinds of policies to complicated conditions such as rheumatic diseases. Nevertheless, interpretation of our results does provide some insight into these policies.

Our data suggest that states implement prior authorization for biologic DMARDs when the use of and spending on these agents reaches a high level, as can be seen in the last two sections of Figures 3a and 3b. States with the highest levels of etanercept use in 1999 implemented prior authorization policies for etanercept and adalimumab in the following years and these states did have a lower rate of increase than other groups of states. The seven states with the highest levels of adalimumab and etanercept use in 2005 were in the process of implementing prior authorization policies at that time. On the other hand, it is notable that the two states with prior authorization in place as of early 1999 had the lowest levels of biologic DMARD use in the first year, but showed a sharp increase during the study period. By 2005, the level of biologic DMARD use in these two states was similar to the level in the nine states that implemented prior authorization between 2000 and 2004. Results for these eleven states suggest that the initial impact of prior authorization may blunt the growth in the use of targeted agents, but the rapid growth in the two states with early prior authorization raises question about the sustainability of these effects. The interrupted time series analyses provide additional support for this interpretation, since we observed an initial decrease in use of biologic DMARDs when prior authorization was implemented that was offset by a subsequent increase in use of biologic DMARDs over time. Most of the time-series results were not statistically significant, so we must interpret them with caution.

There are limitations that must be considered in interpreting this study. We obtained data on prior authorization policies for biologic DMARDs by contacting state Medicaid agencies directly. It is possible that the information provided was incomplete, and it may not have reflected changes in the policies over time or subtle aspects of the policies not included in the written documents. Local officials may have discretion in approving individual requests when the criteria are ambiguous, or the actual implementation of the policy may differ from the written rules, and these discretionary elements would not be captured in our data. States may use other policy tools besides prior authorization, such as drug utilization review and dispensing limits, and the impact of these interventions could alter our results. The data available for research contain the actual amount paid by state Medicaid programs, while information on pricing policy for Medicaid programs, any discounts negotiated by programs, and the amount rebated to states by manufacturers are not publicly available. These additional factors may affect spending more than interventions such as prior authorization, but we cannot determine that from our analyses.

The data for the quantitative analyses did not include infused medications and our inability to evaluate these agents, especially infliximab, may limit the generalizability of these findings. If etanercept or adalimumab are administered in doctor’s office the billing may be similar to that for infused medications and may not be fully captured. The drug use data are aggregated at the state level and thus cannot capture clinical details of individual drug use patterns or prior authorization decisions. The non-biologic agents that we used as a comparator group can be used to treat a variety of inflammatory and rheumatic diseases, and changes in the prescribing patterns for these medications may impair our ability to measure true changes in the use of biologic DMARDs. Biologic and non-biologic DMARDs may be used together in treating rheumatic disease, so prior authorization policies do not necessarily target a simple trade-off between biologic and non-biologic DMARDs. Since we only have data on overall drug use, and not on population numbers or characteristics, we used total volume of DMARD use as the denominator of our outcome measure to adjust for changes in the Medicaid population, but this technique is imprecise. This limitation may cause us to underestimate the impact of prior authorization policy on biologic DMARD prescribing for specific conditions. If prior authorization policies are developed in response to increasing drug spending, then the presence of a policy may be endogenous, introducing bias into our effect estimates. These aggregate data cannot provide insight into the impact of these policies on clinical endpoints, just on drug use. Future research using patient-level data would be helpful for illuminating the effect of these policies on patterns of care and patient outcomes.

Our results have implications for prescription drug reimbursement policy, both for Medicaid and for other programs. In terms of Medicaid programs, although the clinical decisions about use of biologic DMARDs for inflammatory diseases are unquestionably complex, the heterogeneity across states in authorization criteria reveals limitations in policy development. It is not clear how state agencies determine which clinical factors are included in the prior authorization rules or how closely these rules adhere to clinical evidence. This finding may be a particular concern with the transition of many patients to the Medicare part D drug benefit in 2006. If the heterogeneity that we observe across Medicaid programs is also found across various Medicare part D plans, clinicians treating patients with RA and other inflammatory diseases are likely to face many different sets of criteria when prescribing biologic DMARDs, further complicating care for these already vulnerable patients. The transition to Medicare Part D may also complicate the attempts of state Medicaid programs to control prescribing of targeted medications.

In conclusion, we found wide variation in state Medicaid prior authorization policies for biologic DMARDs. Quantitative data analyses suggested that these policies may have a sentinel effect on use of biologic DMARDs, but we could not demonstrate a consistently measurable effect as has been seen for some other drug classes. Policy-makers must weight the costs imposed by these policies in terms of professional time and patient delays of therapy against potential savings on these expensive medications. Further examination of these policy approaches including their impact on patterns of care and patient outcomes will be critical to foster the development of more rational policies in the future, for Medicaid and for all drug insurance programs.

Acknowledgments

Financial support: No specific financial support for this research. Dr. Fischer is supported by AHRQ grant R18-HS017151. Dr. Solomon receives grant support from the Arthritis Foundation's Engalitcheff Arthritis Outcomes Initiative and the NIH (P60 AR047782, K24 AR055989) for related work.

Appendix I

A. Specification of the linear regression model

BioDMARD% = β₀ + β₁time + β₂PA_indicator + β₃Post_indicator + β₄Post_trend + β₅PA_indicator*Post_indicator + β₆PA_indicator *Post_trend + ε

Explanation of terms

Time: Time variable to measure secular trend throughout the study period (0 to 12)
PA_indicator: Indicator variable for states with prior authorization (1 vs. 0)
Post_indicator: Indicator variable for quarter falling after intervention (1 vs. 0)
Post_trend: Time variable measuring trend after intervention (1 to 6)
PA_indicator*Post_indicator: Interaction term, measures the level effect of intervention adjusted for utilization time trends for the states without prior authorization PA_indicator*Post_trend: Interaction term, measures the slope effect of intervention adjusted for utilization time trends for the states without prior authorization

B. Table of model results for etanercept

Parameter	Coefficient	Standard Error^*	z- score	P- value	95% Confidence Interval
Time	.0010	.00332	0.29	0.77	−.00552	.00747
PA_ind	.0068	.02190	−0.31	0.76	−.03609	. 04975
Post_ind	.0046	.01525	0.30	0.76	−.02526	.03453
Post_trend	.0039	.00663	0.58	0.56	−.00913	.01687
PA_ind_x Post ind	−.0058	.02180	−0.27	0.79	−.04851	. 03692
PA_ind_x Post trend	.0040	.00742	0.55	0.59	−.01049	.01859

^*Generalized estimating equation estimates assuming an autoregressive correlation structure

C. Table of model results for adalimumab

Parameter	Coefficient	Standard Error^*	z- score	P- value	95% Confidence Interval
Time	.0030	.00212	1.44	0.15	−.00111	.00720
PA_ind	−.0095	.01565	−0.60	0.55	−.04013	. 02122
Post_ind	−.0088	.00933	−0.94	0.35	−.02710	.00951
Post_trend	.0111	.00421	2.63	0.008	.00283	.019357
PA_ind_x Post ind	−.0242	.01346	−1.80	0.0729	−.05055	.00220
PA_ind_x Post trend	.0157	.00505	3.11	0.002	.00581	.02563

^*Generalized estimating equation estimates assuming an autoregressive correlation structure

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