Jacki Chou, MPP, MPL

 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.

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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.

Evaluating HCV Screening, Linkage to Care, and Treatment Across Insurers

To model the impacts of restrictive formulary designs on outcomes for patients with HIV and to demonstrate the costs of restricting access to novel HIV regimens with better safety and efficacy profiles. 

We modified an epidemiological model of HIV incidence, progression, and treatment to simulate the effects of 5 formulary scenarios on patient outcomes in the United States. 

Using a cohort of HIV-susceptible individuals, we followed patients through HIV infection, disease progression, and death. Patients transitioned in and out of treatment states once infected. Treatment discontinuation, efficacy, and the rate of adverse events (AEs; renal failure and bone fracture) in each formulary scenario depended on the treatment path and regimens included. Outcomes of interest included all-cause cumulative deaths, annual rates of AEs, and costs associated with treating those AEs. 

All outcomes of interest were more favorable in less restrictive formulary scenarios that provided fewer barriers to appropriate treatments. By 2025, more restrictive formularies would have resulted in 171,500 more cumulative bone and renal events among treated patients with HIV compared with an open formulary. This corresponds to AE treatment costs of $3.65 billion in more restrictive formularies compared with $1.43 billion in an open formulary. Finally, compared with an open formulary, there would be an additional 16,200 cumulative deaths in more restrictive formularies. 

Less restrictive formulary designs, which allow patients with HIV to initiate potentially safer and more efficacious regimens based on their proclivity to AEs, yield better outcomes and reduce costs. 

Am J Manag Care. 2018;24(Spec Issue No. 8):SP322-SP328

This study evaluated the impact of formulary designs on HIV patient outcomes, specifically the frequency and costs of certain adverse events and excess mortality. Findings are relevant for policy makers developing formulary management approaches for HIV treatments.

Compared with an open formulary, more restrictive formularies would have 171,500 more cumulative bone and renal events among treated patients by 2025.

Cumulative costs from these events in the more restrictive formularies would total $3.65 billion by 2025 compared with $1.43 billion in an open formulary.

In more restrictive formulary scenarios, there would be 16,200 more cumulative deaths by 2025 compared with an open formulary.

The past 20 years have seen a rapid development of innovative antiretroviral therapies allowing patients with HIV to live longer and healthier lives and reducing the risk of transmission.

 Molecules approved since the early 2000s are more effective and associated with fewer and less serious adverse events (AEs) than earlier HIV therapies.

 The combination of these therapies into single-tablet regimens (STRs) has also simplified treatment administration, enhancing adherence and viral suppression.

Despite the availability of these improved treatment options, fewer than half of the 1.1 million Americans living with HIV in 2015 were engaged in care and achieved viral suppression.

Many factors impact engagement in care and viral suppression. Formulary and utilization management policies, such as tiering and step therapy, that require patients to initiate therapy on cheaper regimens before moving to potentially more effective alternatives can reduce access to drugs that receive less desirable formulary placement.

 For patients with HIV, formulary restrictions may have a number of repercussions, such as reduced adherence and viral suppression.

Further, requiring patients to initiate one regimen before accessing others, without consideration of patient heterogeneity, may adversely impact those with comorbid conditions.

 Healthcare costs and utilization may also increase because of reduced viral suppression or increased AEs.

New therapies, such as those based on tenofovir alafenamide (TAF), continue to reduce the likelihood and severity of AEs, decreasing the possibility of a patient switching or discontinuing treatment.

 Although common AEs like nausea and headaches still occur across all HIV regimens, more serious events like renal failure or bone fractures are mitigated with the newest options.

Still, despite demonstrated improvements in reducing AEs, new regimens are not always immediately available for patients because of formulary and utilization management policies.

We aimed to model the impacts of restrictive formulary designs on outcomes for patients with HIV and to demonstrate the costs of restricting access to novel HIV regimens that have better efficacy and safety profiles.

We modified a previously developed epidemiological model of HIV incidence, progression, and treatment to simulate the effects of 5 HIV formulary scenarios on patient outcomes in the United States (

 Our model incorporated incoming cohorts of HIV-susceptible individuals, natural progression of HIV for treated and untreated individuals, and death. Individuals transitioned to different disease stages at rates based on the HIV-infected population size and infectivity in treated and untreated populations. The HIV natural history progression was defined according to the CDC’s clinical classifications of HIV disease stages

: Patients in stage 1 had CD4+ cell counts greater than 500 cells/mcL; stage 2, 350 to 499 cells/mcL; stage 3, 200 to 349 cells/mcL; and stage 4, fewer than 200 cells/mcL. We used data from the CDC to estimate the distribution of recently infected individuals by stage.

 We also included transitions between treated and untreated states according to treatment initiation and discontinuation rates and the possibility of switching HIV regimens (described later).

Our model started with 1.2 million people infected with HIV in 2016.

 We used published estimates from the CDC’s Medical Monitoring Report and HIV Surveillance reports to obtain the distribution of the initial population by disease stage and treatment status (eAppendix Table 2).

 We modeled HIV incidence through 2 categories of HIV transmission: (1) between untreated HIV-infected patients and susceptible individuals and (2) between treated HIV-infected patients and susceptible individuals. A lower rate of transmission was used for the latter category, reflecting the lower transmission rates observed for treated versus untreated infected patients.

eAppendix A presents the details of the model and parameters.

Treatment Regimens and Formulary Scenarios

Our model included 4 STRs: emtricitabine/rilpivirine/tenofovir disoproxil fumarate (FTC/RPV/TDF), elvitegravir/cobicistat/emtricitabine/tenofovir disoproxil fumarate (EVG/COBI/FTC/TDF), elvitegravir/ cobicistat/emtricitabine/tenofovir alafenamide (EVG/COBI/FTC/TAF), and abacavir/dolutegravir/lamivudine (ABC/DTG/3TC). A fifth regimen, darunavir/ritonavir (protease inhibitor [PI]) plus 2 or more nucleoside reverse transcriptase inhibitors (PI/2NRTIs), was included as a second-line therapy in the event of virologic failure in certain formularies. 

To encapsulate a range of formulary treatment policies, we developed 5 formulary scenarios that differed in access to HIV treatment regimens (

). Within each formulary, patients faced AEs (specifically, renal toxicity or bone fracture) associated with certain HIV medications. Patients who experienced AEs or virologic failure switched to a different treatment regimen, determined by the rules of the particular formulary. Different efficacy and AE rates were used depending on pre-existing clinical conditions for patients. Treatment discontinuation rates were defined as the number of patients randomized to treatment who discontinued because of death, pregnancy, or study withdrawal, but did not include patients who discontinued treatment due to an AE. All parameter estimates were derived from the published literature or clinical trials. Detailed schematics for each formulary scenario can be found in eAppendix B.

The first formulary scenario, “most restrictive,” distributed patients equally across the TDF-based regimens and only allowed access to the initial treatment, regardless of AEs. In the event of a virologic failure, however, patients were transitioned to a PI/2NRTIs regimen.

Under the second and third formulary scenarios, “step (renal)”and “step (any AEs),” respectively, patients were distributed equally across the TDF-based regimens and were transitioned to an alternative regimen (either ABC/DTG/3TC or EVG/COBI/FTC/TAF) after the occurrence of an AE. The step (renal) scenario only allowed patients to transition to EVG/COBI/FTC/TAF if they experienced a renal AE. Patients with a bone fracture event were moved to ABC/DTG/3TC. In contrast, the step (any AEs) scenario allowed for transitions to EVG/COBI/FTC/TAF if a patient experienced either of the AEs. Under both step scenarios, patients with virologic failure on their first-line regimen were moved to a second-line treatment of PI/2NRTIs.

The fourth scenario, “presorted” formulary, allowed pre-existing clinical conditions to determine the initial treatment regimen. Under this scenario, patients with an existing bone disease, osteopenia, started on ABC/DTG/3TC or EVG/COBI/FTC/TAF; patients with a disposition to a renal event started on EVG/COBI/FTC/TAF; and all other patients started on a TDF-based regimen. Switching regimens occurred in the case of an AE or treatment failure, as in the step formularies.

The last scenario, “open” formulary, was the least restrictive and reflected current market trends among a population facing minimal or no co-pays. Patients with osteopenia (35.1%) were started on ABC/DTG/3TC or EVG/COBI/FTC/TAF, whereas patients with a disposition to a renal AE (6.2%) were started on EVG/COBI/FTC/TAF. Additional patients were started on the TAF-based regimen such that this overall percentage matched the market distribution of treatment-naïve patients in the Ryan White program, a government program that provides cost-sharing assistance (and other services) to low-income people with HIV, as of the second quarter of 2016 (46%).

 All other patients were started on a TDF-based regimen. Patients switched to an alternative regimen after treatment failure or AE.

An estimated 3% to 8% of individuals have allele HLA-B*5701, which is associated with hypersensitivity reactions from the abacavir component in ABC/DTG/3TC.

 Based on those estimates, we assumed that 5.5% of the population had the allele. In the relevant formularies, we respected the hypersensitivity of those patients by using EVG/COBI/FTC/TAF, where ABC/DTG/3TC would have otherwise been chosen.

The costs of bone and renal AEs were calculated using data from the 2014 Medical Expenditure Panel Survey (

 Costs included inpatient, outpatient, emergency department, prescription, and office-based costs for each medical condition.

Our model projected the annual number of renal AEs and bone fractures for the treated population, the cumulative value of treatment costs associated with these events over the next 10 years, and cumulative all-cause deaths. Costs are represented in 2016 US$ and discounted at an annual rate of 3%.

We calibrated the model by comparing the percentage of patients with HIV treated by 2020, cumulative deaths from 2016 to 2025, and number of prevalent HIV cases in 2025 with published estimates from Shah et al

 by adjusting HIV infectivity and the rate of disease progression across treated and untreated stages.

All outcomes of interest were most favorable for patients in the open formulary, followed by the presorted formulary. By 2025, the number of renal and bone AEs, costs associated with those events, and cumulative all-cause deaths were considerably lower in the open formulary than in the other scenarios (

Our calibrated parameters produced estimates of HIV cases by 2025 and the percentage of individuals on treatment in 2020 that aligned with those reported in Shah et al (eAppendix D).

 Our model showed an increase in the HIV-infected population from 1.2 million in 2016 to approximately 1.53 million in 2025 under the 3 most restrictive formulary scenarios and 1.52 million in the open scenario. Those results fell within Shah et al’s CI of 1.24 million to 1.57 million. Across the formulary scenarios, our model produced a range of 515,000 to 532,000 cumulative deaths from 2016 to 2025, all of which fall within Shah et al’s CI of 364,000 to 578,000.

The open scenario led to 171,500 and 68,500 fewer cumulative bone and renal events among treated patients with HIV by 2025 compared with the average of the 3 more restrictive scenarios and the presorted formulary scenario, respectively (Figure 1 and 

). Although the number of AEs was the greatest under the most restrictive formulary scenario, both step therapy formulary scenarios resulted in more AEs than the presorted and open formularies.

From 2016 to 2025, cumulative costs associated with both bone fractures and renal disease were $2.23 billion higher in the step (renal) formulary, $2.19 billion higher in the step (any AEs) formulary, and $648.7 million higher in the presorted formulary, relative to the open formulary (

). Although there were fewer renal AEs across all scenarios, treatment costs for renal events were much higher than for bone fractures, leading to greater overall total costs.

Inpatient costs incurred by a renal event or bone fracture were the largest component of the cumulative costs in 2016-2025, representing 52% and 48%, respectively (

). Office-based provider costs were the second largest component, comprising 35% of renal AE costs and 19% of bone fracture AE costs. These 2 components alone represented $1.2 billion in cumulative costs in the open formulary, $1.7 billion in the presorted formulary, and slightly more than $3.0 billion in the step (any AEs) and step (renal) scenarios over 10 years (Table).

In more restrictive scenarios, considerably more people switched to a second-line treatment of PI/2NRTIs compared with the less restrictive formularies. In 2025, about 58,000 patients in the 3 most restrictive formularies were on a second-line regimen because of virologic failure. In contrast, an estimated 41,000 patients in the presorted formulary and 27,000 patients in the open formulary were on the second-line regimen due to virologic failure.

Compared with the open formulary, restricting access to HIV treatments led to 16,200 more deaths, on average, in the more restrictive formularies by 2025 (Figure 1). The presorted formulary resulted in about 7300 more cumulative deaths by 2025 than the open formulary.

Our findings illustrate that a scenario that matches the distribution of treatments observed in a treatment-naÏve HIV population that obtains a high degree of access to available treatments—Ryan White patients&mdash;significantly reduces AE treatment costs and mortality rates relative to more restrictive formulary designs. These results are driven by a greater initial utilization of therapies with better efficacy and AE profiles in our open design, along with sorting patients who are predisposed to certain AEs to more tolerable regimens. The open design outperforms the presorted design because even more patients are started on the newer TAF-based therapy, which has demonstrated lower rates of serious AEs and improved efficacy.

 Compared with the open formulary, we estimated that the average restrictive formulary design, which initiates fewer people on therapies with better efficacy and safety profiles and allows switching to those therapies only under particular circumstances, would resultin $2.28 billion in additional healthcare costs over 10 years and 3.5% more deaths of patients with HIV.

Constraining access to HIV regimens can have significant implications for patients. This study examined the economic and health impacts of restrictive HIV formulary designs.

Jacki Chou, MPP, MPL

Articles

Evaluating HCV Screening, Linkage to Care, and Treatment Across Insurers

Modeling the Impacts of Restrictive Formularies on Patients With HIV