How Does Drug Coverage Vary by Insurance Type? Analysis of Drug Formularies in the United States | Page 2
Published Online: April 21, 2014
Stephane A. Regnier, PhD, MBA
Three different therapeutic classes with different levels of generic competition and disease burden were analyzed in this paper: (1) statins (3-hydroxy-3-methylglutaryl coenzyme A reductase inhibitors; HMGs), used to lower serum cholesterol levels; (2) ARBs, used to reduce hypertension; and (3) P-TKIs, treatments for chronic myeloid leukemia (CML) and gastrointestinal stromal tumor. At the time of the data download, the HMG class comprised 4 off-patent products that had generic equivalents and 8 on-patent products. Six ARBs had patent protection and 1 ARB was available off patent and as a generic equivalent. Azilsartan medoxomil (Edarbi) had only recently been launched and was not included in the analysis, as a number of plans had no information on the drug. All available P-TKIs were still within patent, and no generic versions were available. Coinsurance copays were much higher for P-TKIs than for HMGs and ARBs. For instance, if the coinsurance rate was 30%, the average 2011 copay for a 30-day supply was $1820 for imatinib mesylate Gleevec), $56 for atorvastatin 80 mg (Lipitor) and $48 for valsartan/hydrochlorothiazide 320 mg/25 mg (Diovan HCT).24
The objective of the model was to explain how the number of on-patent drugs in a low tier varies by insurance type (eg, commercial, municipal, Medicare) and therapeutic area. The level of drug coverage could be defined in several ways: number of reimbursed drugs, number of reimbursed on-patent drugs, number of drugs with a lowtier copay, number of on-patent drugs with a low-tier copay, etc. Because generics are typically reimbursed and/ or are affordable, the focus was on on-patent products. Therefore, coverage was defined as the number of drugs with patent protection that were in a low copay tier (ie, tiers 1 or 2), and is referred to as “on-patent drug coverage” hereafter. Medicare PDPs, MA, and SN plans were grouped into a Medicare variable.
A logit regression was used to explain drug coverage. Tobit and count models were also considered to ensure that the findings did not depend on the methodology. More details on the models can be found in eAppendix C. Plans without coverage information for all products in a therapeutic area were excluded for such analyses. On-patent drugs used in the analysis are shown in eAppendix D.
Generalized Linear Model With Logit Link
A generalized linear model (GLM) with a logit link function was used to examine the relationship between the percentage of available on-patent drugs in a therapeutic area that are covered in tiers 1 or 2 and the variables of interest: insurance type and therapeutic areas. Hosmer- Lemeshow and Pregibon link tests were used to ensure that the model adequately ﬁt the data.
Use of Poisson or negative binomial regressions was also considered, as the number of on-patent drugs with favorable tier coverage was a count variable. However, the Poisson distribution was not deemed relevant because the distributions were overdispersed (eAppendix E). Therefore, only a negative binomial model was analyzed.
Drug coverage data can be considered right-censored, as the number of covered drugs was, by definition, bound by the number of approved brands. Therefore, applying a censored model (Tobit regression) would be appropriate. Intuitively, a censored model should be more relevant for therapeutic classes with few or no interchangeable options. In this case, one could assume that insurance companies would be willing to cover more drugs if more were available. The high number of plans covering all 4 P-TKIs was consistent with this hypothesis. As the number of approved drugs was specific to each therapeutic area, a Tobit model was run separately for HMGs, ARBs, and P-TKIs. As the assumption of normality of the residuals was rejected (ie, the maximum-likelihood estimator was inconsistent), a bootstrap approach was used (with 1000 replications at 95% CI).
All analyses were performed using the statistical analysis program Stata, version 12.1 (StataCorp LP, College Station, Texas).
Table 1 shows the average, across all plans, of the number of drugs in each tier. The numbers were calculated by first extracting, from the database, the number of drugs by tier for each plan. Then, for each tier, the average of the number of drugs was calculated across all 1768 plans. The results can be interpreted in terms of how the drugs are split across tiers. On average, 4.7 HMGs and 2.1 ARBs were not reimbursed. Most P-TKIs were reimbursed—an average of only 0.5 drugs out of 4 available drugs (12%) was not reimbursed. HMGs and ARBs were rarely assigned to tiers 4 and above (average 3% of available ARBs and HMGs were in such tiers), but P-TKIs were more frequently found in those tiers (average 31% of available P-TKIs). As expected, onpatent drugs were rarely in tier 1 because it is typically reserved for generics. On average, approximately 30% of on-patent ARBs and HMGs were not reimbursed by health insurance plans. The proportion of on-patent drugs in tier 2 was similar across therapeutic classes (34%-39%), but tier 3 was used much less for on-patent P-TKIs than for on-patent HMGs or ARBs (9% vs 26% and 30%, respectively).
On-patent drug coverage varied greatly by plan type (Table 2). Across therapeutic areas, discount prescription programs and Medicare plans offered the lowest number of on-patent drugs in tiers 1 and 2, especially for P-TKIs. P-TKIs were often in tier 4 or higher in Medicare plans, meaning that a coinsurance was required, likely due to their relatively high cost (eg, in 2011, the average wholesale price of the P-TKI imatinib mesylate [Gleevec] was $202 per day for patients with chronic-phase Philadelphia chromosome– positive CML24,25). Union and municipal plans had many on-patent HMGs in tier 1 or 2 (ie, the lowest copay tiers), which seems to corroborate the assumption that those plans offer generous drug benefits. Commercial Medicaid plans placed a high proportion of the on-patent drugs in the lowest copay tiers. The results for state Medicaid will be further discussed in the Summary and Discussion section.
Table 3 shows summary statistics for the independent variables used in the model specifications. Commercial plans were the most prevalent plans in the database. PBMs were the plans with the most enrollees, on average. The variable capturing the number of competitors is also called “plan competition” in the remainder of the article. Its calculation can be found in online eAppendix F. The categories of plans with the highest number of competitors were PBMs and Medicare PDP.
Generalized Linear Model
The results of the GLM regression are presented in Table 4. The reference categories for plan and therapeutic dummies were commercial plans and HMGs, respectively. A positive coefficient sign indicates an increased probability of on-patent drug coverage in a low tier. On average, 40.1% of on-patent drugs were in a low tier. All variables were highly significant and therefore had an impact on the probability that an on-patent drug had a low copay requirement. The average marginal effect of P-TKIs represents the incremental probability, relative to HMGs, of on-patent PTKIs to be covered in a low tier. The value was +5.6 points and the branded P-TKIs were predicted to be in a low tier in 44.8% of cases versus 39.0% for HMGs. The marginal effect was –2.4 points for on-patent ARBs, which were predicted to be in a low tier in 36.7% of cases. On average, the percentage of on-patent drugs in tiers 1 or 2 was 20 points higher for union plans and 74 points higher for state Medicaid than for commercial plans. All else being equal, PBMs, commercial Medicaid, employer, and municipal plans also covered more on-patent drugs with a low copay than did commercial plans. However, Medicare on-patent drug coverage in tiers 1 or 2 was substantially lower than that of commercial plans (–23 points). If we do not control for the differences between plans, the conclusion remains that P-TKIs (respective to ARBs) have higher (respective to lower) probability of on-patent drug coverage in a low tier than HMGs (P <.001).
Additional covariates. Medicare plans had relatively few on-patent drugs in tiers 1 and 2 for P-TKIs (Table 2). Conversely, commercial Medicaid and employer plans had a high number of P-TKIs with a low copay. Interaction variables were therefore introduced between P-TKIs and Medicare, commercial Medicaid, and employer plans. The number of competitors a plan faces was introduced, since high levels of competition could impact drug coverage in 2 opposing ways: by triggering a drug “arms race” to attract enrollees, or by leading to cost and drug control. Interaction variables between plans’ competition and therapeutic areas were introduced to investigate whether the competition impact varied by therapeutic area.
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