How Does Drug Coverage Vary by Insurance Type? Analysis of Drug Formularies in the United States | Page 3
Published Online: April 21, 2014
Stephane A. Regnier, PhD, MBA
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.
Table 4 shows the impact of introducing additional variables (see online Appendix 3 for additional details). As expected, the sums of the coefficients for P-TKIs and for the interaction between the Medicare/commercial Medicaid plans and P-TKIs were negative and positive respectively (P <.001), which means that Medicare (respective to commercial Medicaid) plans covered fewer (respective to more) P-TKIs than HMGs in a low tier. In other words, Medicare plans had a substantially lower proportion of P-TKIs than on-patent HMGs in tier 1 or 2. Commercial Medicaid plans were less restrictive for P-TKIs than for HMGs. The average number of competitor plans had a significant negative effect for P-TKIs (P = .05). The more competition faced by plans, the less likely they were to cover P-TKIs in the low copay tiers. However, the number of competitor plans did not impact coverage of on-patent ARBs differently from coverage of HMGs. Overall, the number of competitors was not significant across therapeutic areas (F test P = .15). The other variables remained significant, with similar coefficient values. The interaction variables added some information to the core model (Akaike’s information criterion of 0.99 vs 0.96, respectively).
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