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
The data set used was the Fingertip Formulary (Fingertip Formulary LLC, Glen Rock, New Jersey), which is a collection of drug coverage data for 1768 health plans in the United States. Fingertip Formulary estimates that between 95% and 98% of the covered lives in the United States are in the database. The data were collected from health plans’ websites or by directly contacting the health plan providers. The information changes regularly; the data used in this paper were downloaded on May 15, 2011. While drug coverage typically changes once a year (before members enroll), it could change during the year if there is a market event (eg, an on-patent drug goes off patent) and/ or if the contract with a manufacturer was not renewed. The author accessed the website on May 15, 2011, and downloaded the information available that day. Since no angiotensin II receptor blockers (ARBs) and no proteintyrosine kinase inhibitors (P-TKIs) lost patent that year, it is likely that the conclusions would not have changed dramatically in 2011. The conclusions on HMG-CoA reductase inhibitors (HMGs) would have remained valid for most of 2011, since Lipitor lost its patent on November 30, 2011.
Fingertip Formulary data include a number of fields that are identical for all drugs: (1) health plan identifier; (2) provider (ie, company providing the health plan) identifier; (3) state(s) where the health plan operated; (4) lives covered (ie, number of enrollees in the plan); (5) plan/ insurance type (commercial, pharmacy benefit manager [PBM], employer, Medicare Advantage [MA], Medicare Part D plan [PDP], special needs plan [SNP], state Medicaid, commercial Medicaid plan [if the Medicaid plan was contracted to private health companies], union, municipal, discount prescription programs). In Fingertip Formulary, employer plans are those offered by organizations that arrange pharmaceutical benefits for workers as part of their total benefit plan and whose data. Fingertip Formulary can source in an ongoing basis. Commercial plans are set by non-Medicare and non-Medicaid organizations that offer pharmaceutical benefits to individuals and/or groups such as businesses and local governments. The database allows for information to be included on the tier status (whether a drug is in tiers 1-6, not reimbursed, or not available) for each drug within each plan. eAppendix B provides the number of plans and the number of insured individuals for each insurance type.
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).
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