How Does Drug Coverage Vary by Insurance Type? Analysis of Drug Formularies in the United States | Page 4
Published Online: April 21, 2014
Stephane A. Regnier, PhD, MBA
Finally, a variable measuring the number of insured lives by plan was introduced to assess whether the size of a plan had an impact on on-patent drug coverage. The hypothesis was that larger plans had lower general marketing expenses (as percentage of sales) due to economies of scale, and could afford to cover more on-patent drugs in tiers 1 and 2. The variable measuring insured lives was significant and positive, meaning that plans with more enrollees covered, on average, more on-patent drugs in tiers 1 and 2. All variables remained significant after adding this variable and analyzing a subset of the original data set. One variable (commercial Medicaid) experienced a significant switch in sign. The regression did not include municipal plans or discount prescription programs since only 1 of 14 discount prescription programs and 79 of 235 municipal plans had information on lives covered. Since the data set with lives data is only a subset of the universe, care must be taken when comparing the results, in particular Akaike’s information criterion, of previous regressions.
Logit Model Specification Tests
The logistic model with interaction terms passed the Pregibon link test but not the Hosmer–Lemeshow test (F test P = .46 and P = .01, respectively). The model with interaction terms and lives covered variable passed both tests (F-test P = .85 and P = .23, respectively) and its goodness of fit was not rejected.
Finally, a bootstrap estimation (using 1000 bootstrap replications at 95% CI) was performed for the GLM, with interaction variables. The coefficients from the bootstrap estimates were found to be similar to those of the GLM. The only difference was that the commercial Medicaid variable became significant. Based on these results, the coefficients’ estimates and significance levels from the GLMs were not driven by outliers.
Tobit and Negative Binomial Models
The sign and significance of coefficients were similar for the Tobit and negative binomial models (Table 5). The sign and significance of coefficients were also similar to those of the logit model (Table 5).
To the author’s knowledge, this article is the first report of an attempt to analyze drug tier data in this way. Across all models and therapeutic areas, Medicare plans and discount prescription programs had consistently fewer on-patent drugs in the lowest copay tiers than did commercial plans. These results were expected. The revenue per enrollee of Medicare PDP and MA plans is capped, since these plan providers have to offer standard plan designs (or designs at least actuarially equivalent to the standard plan designs). Therefore, these plans may try to contain costs to increase profits. Discount prescription programs are basic drug insurance programs that mainly cover generics. Conversely, PBMs, employer, and union plans had higher on-patent drug coverage than commercial plans. Therefore, employees had access to more on-patent drugs in the low copay tiers when companies outsourced pharmacy benefits to a PBM and when companies created their own formulary. State Medicaid covered more on-patent drugs in a low-copay tier than any other types of plans. This result reflects the fact that 46 out of 49 Medicaid plans only had 2 tiers. In addition, only 1 Medicaid plan (Mi Salud, Puerto Rico Medicaid) did not reimburse all on-patent drugs. One explanation could be that 2 tiers may be enough to convince patients to use drugs in tier 1 since a marginal copay difference may dissuade patients on low incomes from using a drug. Alternatively, Medicaid plans may have high bargaining power, and/or pharmaceutical companies may be willing to offer substantial rebates to grant access to this segment of the population. Medicaid administrators may also limit cost-sharing for on-patent drugs so that cost is not a barrier to compliance with treatment regimens.
The significance of coefficients for commercial Medicaid and municipal plans varied by therapeutic area and/ or methods. Commercial Medicaid plans covered, on average, more P-TKIs (all within patent) than did commercial plans as a whole. This was not the case for HMGs (Table 5). One explanation may be that state Medicaid agencies, when negotiating with commercial Medicaid companies, include clauses regarding maximum allowable cost sharing for classes with generics.
Across all plans, the percentage of on-patent drugs covered in a low tier was substantially higher for P-TKIs than for HMGs and ARBs, despite P-TKIs being more expensive. One explanation might be that the high unmet medical need associated with CML justifies an affordable choice (ie, the clinical data outweigh the economics). On-patent ARBs were slightly less well covered than on-patent HMGs, despite a lower degree of generic competition. Plan providers might consider that other classes of antihypertensives (eg, calcium channel blockers or beta-blockers) are acceptable alternatives to ARBs, and thus do not feel compelled to place numerous drugs in tiers 1 or 2.
Plan competition had a negative impact on P-TKI coverage, but no significant impact on on-patent ARB or HMG coverage. In other words, when on-patent drugs were more scarce and expensive (such as P-TKIs), plans in competitive states limited the number of on-patent drugs with a low copay, possibly to reduce costs. The key findings were robust: the coefficients’ signs and significance levels remained similar for the core variables when: (1) additional covariates were added; (2) alternative models were applied; and (3) subsets of data were analyzed. Additionally, the most comprehensive logit model passed the goodness-of-fit tests.
The analysis presented in this paper could have some public health implications. Newhouse argued that the optimal cost sharing indicated by the RAND health insurance experiment (HIE) (25% coinsurance) may be suboptimal for medications for chronic diseases, especially medications whose benefits only become apparent in the long term.26,27 For instance, Goldman et al found that compliance with cholesterol-lowering therapy was associated with a reduction in the annual rate of hospitalization. However, the study authors also found that higher levels of cost sharing were associated with a reduction in compliance.28 As such, Medicare plan designs may be suboptimal because they rely heavily on cost-sharing (ie, tier 4) for expensive chronic therapies such as P-TKIs.
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