Karen Van Nuys, PhD

 To identify differences in biosimilar uptake across providers and to examine the association between provider biosimilar uptake and observable practice-level characteristics.

 A retrospective analysis of 100% of a commercial medical claims database from June 2015 to June 2018.

 We focused on providers of biologic (Neupogen) and biosimilar (Zarxio) filgrastim. We compared trends in biosimilar uptake across 2 dimensions: provider’s place of service and provider’s prescribing exclusivity. We then used multivariate regression analysis to estimate the association between any biosimilar uptake and practice-level characteristics, controlling for geography and time fixed effects.

 Relative to hospital-based providers, office-based providers were earlier and quicker adopters of the biosimilar filgrastim. Across all places of service, providers predominantly prescribed either the biosimilar or biologic, exclusively, for all their patients. Any biosimilar uptake was more common among providers in office-based settings, providers with larger practice sizes, and providers with a higher share of health maintenance organization patients, nonwhite patients, and younger patients.

 This study uncovers important associations between provider practice characteristics and biosimilar uptake. Our findings suggest that provider awareness and incentives can be important levers to strengthen US biosimilar market penetration and competition.

After 3 years, the share of filgrastim claims that are biosimilar rose to 60% among office-based providers and 49% among providers at outpatient hospitals.

Very few providers simultaneously offered both biologic and biosimilar filgrastim; providers generally prescribed only 1 version.

Office-based settings and health maintenance organization contracting are associated with increased biosimilar uptake and should be further examined.

Accounting for 38% of all prescription drug spending in 2015 and 70% of drug spending growth from 2010 to 2015, biologic drugs are fueling interest in policies to develop and use biosimilars.

 Biosimilars are clinically equivalent therapeutic alternatives to biologics, and some policy makers have conjectured that biosimilars can reduce drug spending, as generics have done for small molecules.

 Although biosimilars are widely prescribed in Europe, they are less common in the United States, in part because of regulatory and developmental differences. As of May 2019, the FDA had approved 19 biosimilars, whereas the European Medicines Agency had approved 61.

 Of the 19 US-approved biosimilars, only 7 had been launched by May 2019.

The penetration of approved biosimilars has also disappointed some observers. Many approved biosimilars in the United States experience delayed launches because of patent litigation. For example, 3 approved biosimilars to trastuzumab (Herceptin), a therapy for early-stage breast cancer, faced patent infringement litigation before coming to market. Early surveys suggest that uptake has been hampered by factors such as insufficient provider awareness and lackluster price competition.

 Although biosimilars appear to generate some reductions in net prices, prices may not decline as quickly or significantly as some expect.

 Recently, some investigators have even suggested that biosimilars should be abandoned altogether in favor of government-imposed price controls on originator biologics once their patent protection has lapsed.

Assessing the viability of the US biosimilar market requires a better understanding of factors that have affected biosimilar adoption. Once a biosimilar has regulatory approval, providers play a key role in the decision to prescribe it or its reference biologic.

 In many cases, biologics and their biosimilars are physician administered, and physicians have discretion over which drug to prescribe. Even when a biologic is filled at a retail pharmacy, pharmacists cannot automatically substitute an approved biosimilar for a biologic prescription, as they can do for generic versions of small molecule drugs, because the FDA has not yet established that any biosimilars and biologics on the market are interchangeable; pharmacists must dispense only the drug prescribed by the provider.

 In July 2018, then—FDA Commissioner Scott Gottlieb, MD, concluded that “the ability for [biosimilars] to penetrate clinical practice, and gain acceptance, is still not firm,” and the FDA’s Biosimilar Action Plan consequently prioritized resources to improve provider understanding of biosimilar drugs.

In this study, we explore the dynamics of biosimilar uptake. Our study represents the first provider-specific analysis of the US market uptake of a biosimilar drug relative to its biologic.

Although our analysis cannot uncover causal relationships between provider characteristics and biosimilar uptake, it identifies factors that are associated with biosimilar use. Uncovering any such relationship is a necessary first step in highlighting whether provider incentives can be used to increase biosimilar adoption.

We focus on the uptake of the first US-approved biosimilar drug, Zarxio (filgrastim-sndz), the only biosimilar in the United States with more than 2 years of data by 2018. Zarxio is a biosimilar to Neupogen (filgrastim), a man-made granulocyte colony-stimulating factor (G-CSF) used to treat conditions such as febrile neutropenia. G-CSF drugs are predominantly administered by physicians in an outpatient setting to patients with cancer who are also receiving chemotherapy or undergoing bone marrow transplants or to patients with severe chronic neutropenia.

First approved in the United States in 1991, Neupogen’s US patent expired in December 2013. Granix (tbo-filgrastim), which is not considered a biosimilar to Neupogen in the United States, entered the US market in November 2013. Although Granix’s structure, formulation, and mechanism do not differ significantly from Neupogen’s, Granix was reviewed prior to the adoption of the Biologics Price Competition and Innovation Act of 2009.

 As such, it was approved through the biologics license pathway, and it was approved for only 1 of the 5 filgrastim indications.

 Therefore, Granix covers a different on-label market than Neupogen. In contrast, Zarxio—approved in March 2015 and launched in September 2015&mdash;was the first drug approved through the biosimilar pathway, and it was approved for all 5 of the filgrastim indications. Because our focus is on the US biosimilar market, our analysis focuses on Neupogen (the biologic) and Zarxio (its biosimilar). However, in the 

), we show that including Granix in our sample does not materially change the results of our analysis.

Our primary data came from the 100% sample of a commercial medical claims database from July 1, 2015, to June 30, 2018, covering approximately 9 million commercial and Medicare Advantage members. We used only claims for injection or infusion of filgrastim products (Neupogen and Zarxio). We also examined data on the average sales price (ASP) of filgrastim products from CMS ASP Drug Pricing Files.

We assessed trends in biosimilar filgrastim uptake in relation to trends in the relative biosimilar ASP. We considered 2 dimensions of provider biosimilar uptake. In each quarter, we first categorized filgrastim providers as being based in a physician office or outpatient hospital. We omitted subgroup analyses for other facilities, which constituted only 2% to 4% of the sample. Second, we examined separate trends for providers who, in a given quarter, administered (1) only the biologic Neupogen, (2) only the biosimilar Zarxio, or (3) both Neupogen and Zarxio.

Next, with the data at the provider-year level, we used univariate linear regressions to test for mean differences in provider practice characteristics between providers who administered the biosimilar Zarxio (either partly or exclusively) and those who exclusively provided the biologic. We weighted each provider by the number of filgrastim claims administered to adjust for those with larger market shares. Provider practice characteristics included an indicator equal to 1 if the provider was office based, the total number of filgrastim claims administered, the share of each practice’s filgrastim claims reimbursed under a health maintenance organization (HMO) contract, and the average sociodemographic characteristics of each provider’s patients receiving filgrastim, including patient age, share male, and share black, Hispanic, or Asian.

We estimated ordinary least squares regressions in which the dependent variables described provider biosimilar uptake. Our key independent variables were the aforementioned provider practice characteristics, plus Census division and year fixed effects to account for variation across geographies and over time. By controlling for geographic variation, we can identify whether differences across regions—due to heterogeneity in negotiated rebates and discounts, physician preferences, disease prevalence, or other factors&mdash;can explain some of the variation in biosimilar uptake.

Using data through mid-2018, we found that the uptake of Zarxio has increased steadily since its launch in September 2015 (

). After 3 years, the share of filgrastim claims that are biosimilar (ie, Zarxio as a share of Zarxio and Neupogen claims) rose to 60% among office-based providers and 49% among providers at outpatient hospitals. When additionally considering Granix in the filgrastim market, we still find that the share of biosimilar filgrastim was higher among office-based than outpatient hospital—based providers, at 54% and 35%, respectively (

). This biosimilar adoption rate is within the range of Europe’s experience: After 3 years, biosimilar filgrastim garnered between 45% and 87% of European market share, depending on the country.

 The observed increase in Zarxio adoption reflects increases in both relative and absolute levels; the total count of filgrastim claims (ignoring the cyclicality of claims across quarters) has been increasing across both places of service since mid-2016 (

Notably, although the biosimilar’s ASP had fallen to 67% of the biologic’s ASP by mid- 2018, office-based providers began adopting biosimilar filgrastim prior to any significant reduction in ASP. Office-based providers were also earlier and more rapid adopters than hospital-based providers, whose adoption of the biosimilar more closely mirrored reductions in ASP. As office-based adoption rates have continued to rise, adoption rates in the hospital setting have plateaued at around 50%. These differential adoption patterns suggest a nuanced and dynamic market in which factors besides price play an important role in encouraging or discouraging biosimilar uptake.

Next, we consider differences in biosimilar uptake by the provider’s prescribing practices. 

 shows that the uptake of biosimilar filgrastim occurred predominantly through providers who treated 

 their patients receiving filgrastim with the biosimilar version. Very few providers simultaneously offered both biologic and biosimilar filgrastim. By quarter 2 of 2018, approximately 61% (742 of 1223) of providers were offering some Zarxio; of this group, 88% (651 of 742) were offering exclusively Zarxio. This pattern is evident across both office- and hospital-based providers (

 reports summary statistics and univariate differences in practice characteristics across providers who did not adopt Zarxio and those who provided some Zarxio. Providers averaged 67 filgrastim claims per year, with those using some Zarxio prescribing more filgrastim (82 claims) than those prescribing no Zarxio (53 claims) (Table 1). On average, 18% of any provider’s filgrastim claims were associated with an HMO contract, with that share rising to 22% among those providing some Zarxio compared with 14% among those providing only Neupogen. The average patient receiving filgrastim was 70 years old; 22% were nonwhite, and 42% were male. Relative to providers of only Neupogen, those who administered some biosimilar tended to have patient populations who were younger (by 2 years), more nonwhite (4.9 percentage points), and slightly more male (1.8 percentage points).

The magnitude and significance of these differences persist when controlling for variation across geographic regions and years (

). Relative to hospital-based providers, office-based providers were 10.5 percentage points, or 22% (calculated from 0.105/0.479), more likely to have adopted the biosimilar version. This effect size decreases slightly to 21% (0.0993/0.479) after we control for other observables. Larger providers were more likely to adopt Zarxio: An increase of 10 filgrastim claims in the year was associated with a 1.5% ([10 × 0.0007]/0.479) increase in biosimilar adoption. A 10% increase in the share of HMO claims was associated with a 0.9 percentage point, or 1.9% ([10% × 0.0899]/0.479), increase in biosimilar adoption.

A practice’s average patient characteristics were also associated with biosimilar adoption. A 10-year increase in average patient age and a 10% increase in the share of nonwhite patients were associated with a 6.4% ([10 × 0.0031]/0.479) decrease and a 2.5% ([10% × 0.122]/0.479) increase in the likelihood of biosimilar adoption, respectively. A 10% increase in the share of male patients was associated with a negligibly small, although statistically significant, 0.6% ([10% × 0.0272]/0.479) increase in the likelihood of biosimilar adoption. The full regression results are shown in 

. A variance inflation factor analysis displayed in 

 confirms that the predictors examined are not multicollinear.

After 3 years on the market, biosimilar filgrastim was being used in more than 50% of the filgrastim claims in our sample. This adoption rate is markedly higher than what has been documented using less recent Medicare or sales data: Kozlowski et al

 found 32% and 15% biosimilar penetration, respectively, by quarter 4 of 2016.

We found that at any point in time, most providers were using either the biosimilar or the biologic for all their patients receiving filgrastim. This pattern suggests that, at least within a provider group, patients were unlikely to have been presented with the option to choose between the biologic and the biosimilar, which highlights the importance of the provider’s role in biosimilar adoption. It also suggests that, relative to practice-level decisions, patient-specific factors play a considerably smaller role in biosimilar filgrastim adoption decisions.

 In addition, although lack of information and widespread physician awareness may be a barrier to biosimilar adoption, our results suggest that, once providers 

 comfortable with biosimilar use, they tend to adopt biosimilars exclusively, discontinuing any use of the biologic.

Several factors are correlated with provider uptake. In particular, providers with more HMO-based reimbursements were more likely to use the biosimilar, perhaps because capitation strengthens incentives to reduce the costs of care. Providers with patients who might be more burdened by the costs of care, such as younger patients who do not meet the age-eligibility requirement for Medicare coverage or nonwhite patients of likely lower socioeconomic status, were more likely to use the biosimilar.

 Uptake of Zarxio also increased with practice size. Because the associations between these factors and provider uptake remained after including region and year fixed effects, it suggests that these factors are not explained by variation over geography or time.

Of note, the largest factor associated with uptake was the provider’s office setting. Weaker uptake among hospital-based providers compared with their office-based counterparts suggests that healthcare costs for filgrastim treatments will be higher in hospital-based outpatient settings, a result that is consistent with existing literature documenting higher cancer-related costs in such settings due to differences in product prices.

 Several factors may explain the differential adoption pattern between office- and hospital-based providers. First, reimbursement formulas differ between the 2 groups. Compared with office-based providers, hospital-based providers are more likely to be reimbursed as a percentage of hospital charges, where the charges are set by the hospitals themselves and include significant profit margins on physician-administered drugs.

 If hospitals charge more for biologic drugs than for biosimilars, their reimbursement will also be higher for biologics, leading to higher profits from using biologics rather than biosimilars in the treatment of commercially insured patients.

The federal 340B program, which provides discounts on outpatient drugs for hospitals that qualify for 340B status, may also play a role in these differential uptake patterns. Depending on the prevalence of 340B program participation among the hospitals versus physician offices in our sample and the changing 340B reimbursement rules over this period, 340B discounts may have weakened hospitals’ incentives to adopt biosimilars relative to physician offices, particularly in the early part of our sample. This dynamic would be similar to the observation that providers with fewer HMO contracts have weaker incentives to use a less expensive substitute.

It is also possible that hospital-based providers have slower adoption rates because of institutional barriers such as strict protocols set by hospital pharmacy and therapy committees that may delay more immediate responses to biosimilar availability.

Our study has several limitations. First, we are limited to the observable factors in the data and cannot account for unobserved determinants, such as provider reimbursements and drug rebates. As such, we cannot pinpoint which of the aforementioned factors are most responsible for the observed difference between office- and hospital-based provider uptake patterns. Second, our analysis focused on Zarxio adoption within hospitals and offices, and our results may not generalize to Zarxio adoption in other places of service or to the adoption of other biosimilar drugs, particularly those administered in predominantly inpatient settings or those for chronic conditions. Third, our results are based on a subset of commercial claims data. Although our data capture a geographically diverse population, they come from a single large insurer, which may not be representative of the commercially or publicly insured populations in the United States.

Our study is the first to highlight the provider’s role in the uptake of biosimilars. We find that providers who adopt the biosimilar tend to convert all their patients receiving filgrastim to the biosimilar, and we demonstrate that place of service, and its attendant incentives and insurer contracts, are important determinants of biosimilar uptake. These results suggest that provider awareness and incentives can be important levers to increase biosimilar market penetration.

Although frustrations over the slow pace of biosimilar introduction and adoption in the United States have led some to abandon hope that a competitive biosimilar market will produce lower prices, our study findings underscore the need for additional research on the mechanisms of uptake as promising policy targets to strengthen competition in US biosimilar markets.

The authors would like to thank participants at the 2018 Value of Life Sciences Innovation Steering Committee meeting and 2019 Association for Public Policy Analysis & Management Fall Research Conference for helpful suggestions.

 Leonard D. Schaeffer Center for Health Policy & Economics (AJC, RR, KVN) and Sol Price School of Public Policy (AJC, KVN), University of Southern California, Los Angeles, CA.

 Research reported in this publication was supported by the Leonard D. Schaeffer Center for Health Policy & Economics and the National Institute on Aging of the National Institutes of Health under award number 3R01AG055401-02S2. The findings and conclusions expressed are solely those of the authors and do not represent the views of the National Institutes of Health.

 Drs Chen, Ribero, and Van Nuys report that 21% of Schaeffer Center funding comes from corporations, some of which are life sciences companies. More information is available on the Schaeffer Center website at healthpolicy.usc.edu/our-funding-sources.

 Concept and design (AJC, RR, KVN); acquisition of data (RR); analysis and interpretation of data (AJC, RR, KVN); drafting of the manuscript (AJC, RR, KVN); critical revision of the manuscript for important intellectual content (AJC, KVN); statistical analysis (AJC, RR); obtaining funding (AJC, KVN); and supervision (AJC, KVN).

 Alice J. Chen, PhD, Leonard D. Schaeffer Center for Health Policy & Economics, University of Southern California, 635 Downey Way, Los Angeles, CA 90089-3333. Email: alicejc@price.usc.edu.

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2. Conti RM. Biosimilars: reimbursement issues in your oncology practice. 

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3. Brill A, Ippolito B. Biologics are not natural monopolies. 

 website. healthaffairs.org/do/10.1377/hblog20190701.349559/full/. Published July 2, 2019. Accessed September 5, 2019.

Articles

The speed and extent of biosimilar penetration differ across provider types. Provider awareness and incentives are significantly associated with biosimilar uptake.

Provider Differences in Biosimilar Uptake in the Filgrastim Market

 We examined how a population susceptible to hepatitis C virus (HCV) moves through the HCV screening and linkage-to-care (SLTC) continuum across insurance providers (Medicare, Medicaid, commercial) and identified opportunities for increasing the number of patients who complete the SLTC process and receive treatment.

 A cohort of 10,000 HCV-susceptible patients was simulated through the HCV SLTC process using a Markov model with parameters from published literature. Three scenarios were explored: baseline, in which each step required a separate visit and all infected saw a specialist; reflex, which reflexed antibody and RNA testing; and consolidated, which reflexed antibody, RNA, fibrosis staging, and genotype testing into 1 step, with an optional specialist visit. For each scenario, we estimated the number of patients lost at each stage, yield, and cost.

 Streamlining the SLTC process by reducing the number of required visits results in more patients completing the process and receiving treatment. Among antibody-positive patients, 76% of those with Medicaid and 71% of those with Medicare and commercial insurance are lost to follow-up in baseline. In reflex and consolidated, these proportions fall to 26% and 27% and 4% and 5%, respectively. The cost to identify and link 1 additional infected patient to care ranges from $1586 to $2546 in baseline and $212 to $548 in consolidated. Total cost, inclusive of treatment, ranges from $1.0 million to $3.1 million in baseline and increases to $3.8 million to $15.1 million in reflex and $5.3 million to $21.0 million in consolidated.

 Reducing steps in the HCV SLTC process increases the number of patients who learn their HCV status, receive appropriate care, and initiate treatment.

This study evaluated the impact of streamlining the hepatitis C virus (HCV) screening and linkage-to-care (SLTC) process on costs, yield, and patients lost to follow-up by integrating reflex testing for early steps in the process. The findings are relevant for clinicians and managed care decision makers involved in HCV SLTC programs.

Reducing the number of required visits during the SLTC process decreases the number of patients lost to follow-up by 62% to 95%.

Streamlining the HCV SLTC process results in more patients who are aware of their HCV status, receive appropriate care, and are ultimately treated.

Up to 3.5 million people in the United States are infected with chronic hepatitis C virus (HCV), and half are unaware of their infection status.

 Current guidelines recommend 1-time HCV screening for individuals born between 1945 and 1965 and individuals with increased risk of infection, but initial screening represents only the first stage in the screening and linkage-to-care (SLTC) process.

 To detect chronic infection, patients with a positive HCV antibody test must have confirmatory RNA testing, and for those with chronic HCV, additional diagnostics, including genotype testing and fibrosis staging, are recommended before treatment.

Reflex testing, in which RNA is tested immediately following a reactive antibody test using the same blood draw, represents a simplified SLTC process and allows patients to definitively know their HCV status following 1 visit.

 Without reflex testing, an estimated 33% to 47% of patients who receive a positive antibody test do not receive confirmatory RNA testing, highlighting the importance of a streamlined process for patient awareness.

Although fewer visits in the SLTC process may result in fewer patients lost to follow-up, other barriers may result in patients dropping out of the process prior to initiating treatment. For example, HCV guidelines still recommend subspecialty consultation for patients with advanced fibrosis or cirrhosis.

 The need for specialty care may disproportionately impact patients with less access to care, such as those who use community health centers.

 Even if patients successfully complete all screening and diagnostic testing and receive a prescription for treatment, they still may not be treated if their payer policy includes coverage restrictions, such as prior authorization (PA).

Given that only 16% of chronically infected patients are eventually prescribed treatment,

 it is important to identify steps in the SLTC process where patient retention is lowest and improve retention at those points. To examine this issue, we developed a model that simulates the HCV SLTC process from antibody testing through treatment initiation. The minimum number of visits required prior to a treatment decision varied from 2 to 4, and the resulting costs, yield, and patients lost to follow-up were estimated depending on patients’ insurance provider (Medicaid, Medicare, or commercial).

A discrete-time Markov model was developed to simulate the HCV SLTC process and was stratified by insurance type: Medicaid, Medicare, and commercial. The model follows 10,000 patients from antibody screening through treatment initiation until 1 of 5 conditions is met: (1) they are found not to have chronic HCV, (2) a “no treatment recommended” decision is made, (3) PA is denied, (4) treatment is initiated, or (5) they drop out before meeting any of the prior conditions and are lost to follow-up (henceforth, “lost”).

 Patients enter the model and receive an antibody test. Those who are antibody-negative are not infected with HCV, require no additional testing, and have completed the SLTC process. Patients who are antibody-positive (Ab+) continue to confirmatory RNA testing or are lost.

RNA testing assesses the presence of chronic infection. Patients who are RNA-negative have no active infection and have completed the screening process. Patients who test RNA-positive and have been infected longer than 6 months are chronically infected and continue to a specialist for further testing or are lost.

Patients with chronic HCV receive genotype testing and noninvasive liver fibrosis staging at a specialist visit; results determine their treatment regimen and duration. Biopsies account for fewer than 10% of fibrosis staging tests

 and were excluded from our model. At this stage, patients either receive a “no treatment recommended” decision, are prescribed treatment, or are lost. Patients who receive a decision of no treatment recommended have completed the screening process.

If treatment is recommended, patients transition through additional stages before receiving therapy. We do not explicitly model additional tests that may follow the treatment recommendation, such as NS5A resistance testing or renal function testing; however, such tests could be conducted during the specialist visit, which avoids additional visits during the SLTC process. At least 29 states require some duration of sobriety for Medicaid patients.

 Therefore, Medicaid enrollees in our model must meet sobriety requirements and obtain PA before initiating treatment. Commercial and Medicare enrollees must obtain PA.

Model parameters were drawn from the published literature and stratified by insurance type where possible. Key parameters for the model include transition probabilities, average timing values, and per visit costs. HCV prevalence plays a key role in model outcomes because it determines the number of Ab+ patients who progress beyond the initial state. Of our 3 insurance strata, Medicaid has the highest prevalence of Ab+ patients (16%), followed by Medicare (9%) and commercial (5%).

 The proportion of Ab+ patients with chronic HCV is the same for all strata (79.7%).

) provides a full description of the model assumptions and parameters.

The baseline scenario previously described requires at least 4 visits before chronically infected patients receive a treatment recommendation (Figure 1). We also estimated reflex and consolidated scenarios in which chronically infected patients require a minimum of 3 or 2 visits, respectively, before treatment recommendation. All stages of the SLTC process in baseline are included in reflex and consolidated, but they are condensed to varying degrees. Beyond the treatment decision step, the 3 scenarios are identical.

Baseline includes the SLTC steps recommended in current HCV guidelines, with each step in the process requiring a separate visit and only specialists providing genotype testing, fibrosis staging, and treatment decisions. Baseline thus requires 4 visits for a chronically infected patient to receive a treatment recommendation and is the least efficient scenario.

Reflex also includes the SLTC steps recommended in current HCV guidelines, but reflexes antibody and RNA testing so that 2 blood samples are collected from a single draw at the first visit and if the first is Ab+, the second is automatically tested for HCV RNA.

 Thus, the process can be completed in 3 visits rather than 4.

Reflex also eliminates the specialist visit for less clinically complex patients who can be effectively managed by primary care physicians (PCPs). To be conservative, we assumed that only patients with fibrosis scores below F2 can be managed by PCPs, which resulted in 60% of patients requiring a specialist visit.

Consolidated represents a hypothetical best-case scenario in which all tests are reflexed and a specialist visit is not required for patients with fibrosis scores below F2. This scenario requires at least 2 visits for chronically infected patients to receive a treatment decision and provides the fewest opportunities for patients to be lost.

Although pangenotypic treatment is now available, guidelines still recommend genotype testing, and all 3 scenarios include it. Genotype testing can also be used to guide treatment for patients who do not receive pangenotypic treatment. To reflex noninvasive fibrosis staging, diagnostic tests using a blood draw (eg, AST [aspartate aminotransferase] to Platelet Ratio Index or FibroTest) would be required.

 Although these may be uncommon as the primary means for fibrosis staging in current practice, they are feasible. Current HCV treatment guidelines recommend combined blood- and image-based fibrosis testing, so our consolidated scenario should be considered exploratory, as an estimate of potential benefits from an SLTC process that minimizes patient visits.

We estimated several outcomes for each scenario and insurance type: number of patients lost at each stage, yield (percentage of patients entering the model who complete the process and initiate treatment), conditional yield (percentage of patients with chronic HCV who complete the process and initiate treatment), and several cost outcomes. Total screening costs include the cost of antibody testing, RNA testing, genotype testing, fibrosis staging, and, when relevant, specialist and sobriety costs. Total costs include screening costs plus the cost of treatment. We assumed that treatment cost equals the wholesale acquisition cost of sofosbuvir 400 mg/velpatasvir 100 mg (Epclusa) discounted by 46%.

 To estimate the cost to identify and link 1 additional patient to care, we calculated the number of patients screened to yield 1 patient in the genotype/fibrosis step of the model and calculated the cost of antibody and RNA testing for those patients. This outcome provides an additional measure of efficiency of the SLTC process prior to receiving a treatment recommendation and is independent of the number of patients treated. Other outcomes are presented in the eAppendix, including cost per person screened, timing-related results, and yield conditional on the number of Ab+ patients.

SLTC results for each strata and scenario are presented in 

. The figures show the number of patients lost after each step; fewer patients lost indicates a more efficient process. In baseline, 12% of Medicaid, 6% of Medicare, and 4% of commercial patients are lost before treatment. Across all insurance types, most patients are lost after RNA testing. For reflex and consolidated, 4.0% and 0.7%, respectively, are lost before treatment for Medicaid, 2.0% and 0.5% for Medicare, and 1.0% and 0.3% for commercial patients.

Yield and total lost results are presented in the 

. Yield incorporates both HCV prevalence in the screened population and the likelihood of loss. High yields therefore result from high prevalence, screening process efficiency, or both. Baseline yields are 0.5%, 0.7%, and 0.2% for Medicaid, Medicare, and commercial patients, respectively. The higher efficiency of the reflex and consolidated models translates into higher yields; reflex yields are 3.5%, 3.1%, and 0.9%, respectively, and 4.9%, 4.4%, and 1.2% in consolidated.

Conditional yield describes the efficiency of the screening process for chronically infected patients. Baseline conditional yields are 4%, 9%, and 5% for Medicaid, Medicare, and commercial, respectively, increasing to 28%, 44%, and 22% in reflex and 31%, 49%, and 24% in consolidated.

The Table also presents total costs, total screening costs, screening costs per patient treated, and the cost to identify 1 additional patient and link them to care. We used undiscounted costs in our model; therefore, our cost results represent an upper bound.

Total costs are driven by the total treated, and in baseline are highest for Medicare ($3.1 million) and lowest for commercial ($1.0 million); total costs increase substantially for reflex and consolidated, ranging from $3.8 million to $15.1 million and $5.3 million to $21.0 million, respectively. Although commercial has the lowest total screening cost, it also treats the fewest patients, leading to higher per person costs. In baseline, the cost per person treated is $7843 for Medicaid, $4833 for Medicare, and $14,176 for commercial.

For baseline, the cost to identify 1 additional chronically infected patient and link them to care is highest in the commercial population ($2546) and lowest in the Medicare population ($1539), but Medicaid sees the largest reductions in that cost when the process is collapsed from baseline to reflex or consolidated.

Three alternative analyses were conducted: (1) “fixed prevalence,” which assumed the same HCV Ab+ prevalence across insurance types; (2) “no genotype,” which removed genotype testing; and (3) “no sobriety requirements,” which removed sobriety requirements. Results for alternative analyses are presented in the eAppendix.

“Fixed prevalence” allows us to compare the efficiency of the SLTC process across insurance types while holding constant population disease prevalence. Commercial has the highest per person costs in the main analysis, but Medicaid and commercial costs are similar in the fixed prevalence analysis, suggesting that lower per person costs in Medicaid are driven primarily by higher prevalence.

Although genotype testing is still recommended in HCV treatment guidelines, “no genotype” explores the impact of removing genotype testing, which may be possible with pangenotypic therapies. Because genotype testing occurs during the same visit as fibrosis staging, removing it reduces per person costs by $351 but does not reduce visits.

“No sobriety requirements” removes a barrier to treatment initiation that occurs late in the SLTC process and affects only the Medicaid population. Removing sobriety testing reduces screening costs by $48 per drug test.

Comparing baseline, reflex, and consolidated results shows the value of streamlining the SLTC process. Collapsing baseline to reflex reduces the number of patients lost prior to receiving a treatment recommendation by 62% to 66%. Further streamlining to consolidated reduces the number lost by 92% to 95%. Because the Medicaid population has the most inefficient SLTC process to begin with, it experiences the largest improvements from streamlining the process.

Although our analysis focuses on the number of visits as a measure of SLTC process efficiency, the underlying prevalence in a given population also plays an important role in the process. For example, although the Medicaid subpopulation loses the most patients, it also has the highest Ab+ prevalence (16%), resulting in higher yield versus the commercial population. Moreover, in consolidated, only 20 additional Medicaid patients need to be screened on average to get 1 additional chronically infected patient into treatment compared with 100 additional patients screened to achieve the same result in the commercial population.

Our findings are consistent with those of a recently published study of the care continuum for patients with HCV diagnosed in 2 urban emergency departments.

 In the study, the Medicaid process was less efficient, with only 8.5% of RNA-positive patients initiating treatment compared with 12% of Medicare patients. Additionally, 82% of Ab+ patients completed viral load testing, similar to our finding of approximately 80% of patients progressing to RNA testing. Generally, the literature presents conditional yield rather than yield; conditional yield estimates range from 3.9% to 24%.

 Our baseline conditional yields (3.7%-9.5%) are on the lower end compared with recent studies. Our baseline Ab+ conditional yields (eAppendix) range from 2.7% to 7.5%, which is consistent with findings from 2 recent studies (3.3% and 4.0%).

Not surprisingly, total costs increase dramatically from baseline to reflex and consolidated, because more patients receive treatment. This paper is not intended as a cost-benefit exercise, nor do we model other medical expenditures for patients with HCV. However, the increased treatment costs are arguably of high value because identifying and treating more patients will provide benefits associated with reduced transmission rates, long-term cost savings on medical expenditures related to untreated HCV, and a reduction in liver transplants.

Reducing the number of patients lost decreases screening costs per person treated because the total system costs are spread among more patients. This aligns with prior literature showing that expanded HCV screening provides the most value when coupled with expanded treatment.

 Additionally, there are costs associated with the SLTC process that are difficult to measure (eg, patient navigation, social work) and are not included in our analysis. It is likely that Medicaid patients would benefit most from these services and incur additional costs, but this population also experiences the greatest gains from reflex and consolidated.

For all insurance types, a majority of patients are lost prior to visiting a specialist, which suggests that having insurance does not eliminate inefficiencies associated with multiple visits required in the SLTC process. Although our 3 scenarios focus on streamlining the SLTC process prior to treatment recommendation, barriers to treatment exist in later stages of the process. Specifically, PA poses a significant barrier for patients who are prescribed treatment, particularly in the Medicaid and commercial populations. Of patients who seek PA, 46% and 55% of Medicaid and commercial patients, respectively, are denied, whereas only 13% of Medicare patients are denied.

A patient who is denied PA is comparable with one who is lost, and for patients who are eventually denied, streamlining the process simply delays the point at which they are lost. This delay increases overall costs from screening and time spent in the process but does not change the disease outcome because treatment is not received. Consequently, to maximize the number of patients treated, barriers to treatment must be reduced.

An optimized hepatitis C virus screening and linkage-to-care process reduces the number of patients lost to follow-up and improves linkage to care for Medicare, Medicaid, and commercially insured patients.

Author | Karen Van Nuys, PhD

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