Scenario Analysis When Conducting Budget Impact Analyses for Rare Diseases

The American Journal of Managed CareOctober 2022
Volume 28
Issue 10

The authors propose conducting a scenario analysis for interventions to treat rare diseases by varying health plan size to demonstrate the variability of potential budget impact.


Pharmacoeconomic analyses are an important and useful guide for understanding a pharmacotherapeutic intervention’s financial impact for relevant stakeholders. One type of pharmacoeconomic analysis that assesses a pharmacotherapeutic intervention’s short-term financial implications is a budget impact analysis. Although methodology guidelines for budget impact analyses in the United States currently exist, not much guidance is available for analyses that are being conducted of rare or ultrarare disease states. In this article, we propose conducting a scenario analysis for pharmacotherapeutic interventions to treat rare diseases by varying health plan sizes to indicate what the potential plan impact would be if 1 member in said health plan received treatment. We then walk through an illustrative example and discuss the rationale for it.

Am J Manag Care. 2022;28(10):e351-e354.


Takeaway Points

Scenario analyses of varying health plan size should be conducted in budget impact models for rare diseases to make the results fully transparent and more useful for all relevant stakeholders.

  • Most budget impact analyses are conducted from the perspective of third-party payers with large hypothetical populations (100,000-1 million members).
  • Most health plans have much lower enrollment sizes.
  • For rare disease states, the prevalence of a condition is often lower than the enrollment size of some smaller health plans in the United States.
  • Generic budget impact models may severely underestimate the potential budget impact of a medication for treating a rare condition in a smaller health plan.


Health plans and payers in the United States have the multifaceted task of determining formulary coverage for medications. The complexity of this task is exacerbated by the substantial increases in medication costs in recent years.1-4 With only a finite budget to cover all members, payers typically rely on pharmacoeconomic analyses to help guide their decision-making on which medications represent the best value for money in their respective disease states. Pharmacy and therapeutics committees rely on a multitude of factors when determining formulary decisions and utilization management, including efficacy and safety data, clinical and contextual considerations, place in treatment, and pharmacoeconomic analyses.

Pharmacoeconomic analyses are an important and useful guide for understanding a pharmacotherapeutic intervention’s financial impact for relevant stakeholders. A budget impact analysis (BIA) in health care calculates the financial consequences of introducing a new intervention in a specific setting in the short term, and published BIAs are a great tool for relevant stakeholders. They are usually calculated for a short time horizon—ranging from 1 to 5 years—and include direct medical and pharmacy costs incurred before and after the introduction of the intervention of interest. Methodology guidelines for BIAs in the United States are currently available,5,6 but not much guidance exists for when analyses are being conducted in rare disease states.7 A rare disease is defined in the United States as a disease or condition that affects fewer than 200,000 individuals.8 In the United States, most BIAs are conducted from the perspective of third-party payers (ie, commercial, Medicare, Medicaid, broad US payer) with large hypothetical populations, usually anywhere from 100,000 to 1 million members. However, most health plans have much lower enrollment sizes, with the average Medicare health plan in January 2021 having an enrollment size of fewer than 10,000 beneficiaries.9

A search of PubMed/MEDLINE for BIAs of pharmaceutical interventions published in 2020 and 2021 using the terms budget impact model or budget impact analysis found 8 BIAs from the payer perspective in the United States with a population of 1 million or more members and fewer than 10 patients receiving the treatment of interest.10-17 Of these published BIAs, 3 of them had fewer than 5 patients receiving said treatment10-12 and none performed scenario analyses to assess the impact of varying plan sizes.10-17 For rare diseases, the prevalence of a condition is often lower than the enrollment size of some smaller health plans in the United States, and generic BIAs with population sizes of 100,000 or more may severely underestimate the potential budget impact of a medication for treating said condition in a smaller health plan. However, some health plans may have no patients with a specific rare disease, so the estimated impact on these plans would be zero. Adjustments should be made to render the model and results more useful for all relevant stakeholders.

Methodologic Proposal

When conducting a BIA for a medication to treat a rare disease, scenario analyses should be conducted of varying health plan sizes to estimate the potential plan impact if 1 member in said health plan received treatment.

Illustrative Example

Let’s say a new medication, HypoDrug, is approaching FDA approval for Condition X, which affects 1 in 100,000 patients in the United States. If approved, HypoDrug would be the first medication on the market indicated for the treatment of Condition X. After considering all costs associated with HypoDrug (eg, medication cost, cost of administration, cost of managing toxicities, medical cost offsets), HypoDrug has an incremental additive cost of $100,000 per patient per year (PPPY) treated. Analysts expect that HypoDrug will have 50% market uptake in year 1.

A BIA was conducted for this new drug with 50% market uptake for a hypothetical third-party payer with 1 million members. Five patients in the model were estimated to receive HypoDrug (based upon a prevalence of 1 in 100,000 plan members equaling 10 patients in a 1 million–member plan having Condition X and 50% market uptake indicating half of these patients would receive the treatment). The analysis found that based upon the incremental additive cost of $100,000 PPPY, the impact of this new drug for the health plan would be $0.04 per member per month (PMPM). This was derived as the number of patients receiving HypoDrug (n = 5) multiplied by the incremental total cost associated with HypoDrug ($100,000) divided by the number of members in the plan (1,000,000) divided by the number of months in a year (12).

This BIA indicates that HypoDrug would have a small financial impact on a health plan. However, this as the takeaway message may be misleading. Although it is true that for the health plan assessed with 1 million members, HypoDrug is projected to have a small financial impact at an incremental addition of $0.04 PMPM to the health plan’s budget, this is only generalizable to large health plans of similar size and prevalence estimates. The creators of this BIA knew this and, as a way to improve the generalizability and transparency of their study’s results, conducted scenario analyses to show what the financial impact would be if 1 patient with Condition X was present in health plans of varying sizes and required treatment with HypoDrug. Although a health plan with 200,000 members had the same impact of $0.04 PMPM as the base case, the results were drastically different for smaller plans. For example, if a health plan of 5000 members had 1 patient requiring HypoDrug, it would result in an incremental addition of $1.67 PMPM ([$100,000 cost × 1 member] / [5000 members × 12 months]) (Table and Figure). A health plan of 15,000 members with 1 patient requiring HypoDrug would have an increase in spend of $0.56 PMPM. A health plan of 50,000 members with 1 patient requiring HypoDrug would have an increase in spend of $0.17 PMPM.


This illustrative example shows the large variation in the impact on plans of varying membership size from a medication with an incremental additive cost of $100,000 PPPY. Although larger health plans of 200,000 members or more may have similar results to the base-case example, the impact for smaller health plans is drastically greater. No budget impact thresholds exist in the United States detailing what a high budget impact is. However, a survey of health care decision makers in 2019 found that respondents assessed a low impact as a median of $0.08 PMPM (IQR, $0.04-$0.15), a moderate impact as $0.10 PMPM (IQR, $0.09-$0.21), and a high impact as $0.37 PMPM (IQR, $0.20-$0.50).18 Using these thresholds, the budget impact of HypoDrug would range from being low impact for plans with at least 100,000 members to being high impact for plans with fewer than 25,000 members.

The $100,000 pricing was chosen for illustrative purposes, and although it may seem steep, some medications for rare and ultrarare diseases have come to market in recent years exceeding $750,000 PPPY.19-21 In addition, the costs of medications that treat orphan diseases have been found to be substantially higher than those of specialty and traditional drugs, and they have been increasing annually.22-24 For medications with an incremental net price greater than $100,000, the increase in budget impact for smaller plans would be even more profound. This adds to the importance of conducting BIAs for appropriate health plan sizes and performing scenario analyses showing the varying impact for differing plan sizes when applicable.

Although conducting scenario analyses of smaller health plan populations in these instances is not needed for all payers, as the population assessed may be representative for some, it does not tell the whole picture for all plans and may misrepresent what the actual financial impact may be. Additionally, other factors may influence and vary the budget impact found in the published analysis from a specific health plan. The market share of competitors in the model and/or expected uptake may differ from what is seen in specific plans. Also, prices used in analyses are publicly available reference prices and may not represent the true price paid by plans. Scenario analyses of varying health plan size may help address one particular limitation of BIAs in rare disease, but there are still other additional limitations that may need to be addressed. One limitation relates to how some therapies for rare diseases involve gene therapies, which may incur a very large up-front cost in year 1 but little to no additional costs the subsequent years; these may not be captured appropriately depending on how the impact is reported and on the model time horizon. Another limitation relates to value-based contracting, in which the PMPM cost may vary depending on the real-world effectiveness of the product. Several papers have been published in the past few years trying to address limitations in this setting and are a good resource to consider when conducting or assessing BIAs of rare diseases.25-27 An important consideration for BIAs is that focusing solely on the reporting of results may affect patient access, and it is important to consider the full and nuanced analysis.

Although smaller health plans may not end up having any beneficiaries with a given rare condition, these analyses can help them prepare for potential formulary and utilization management options in the event that a beneficiary develops this condition or a new beneficiary to the plan has it as a preexisting condition. For relevant stakeholders reading published BIAs, these scenario analyses help with the transparency of financial implications for smaller health plans of pharmacotherapeutic interventions treating rare diseases. The information provided from this scenario analysis would likely be beneficial for third-party payers, government agencies, and possibly patient advocacy organizations. We recommend that BIAs in the future, especially ones to be published in the peer-reviewed literature, conduct these scenario analyses to provide more transparency of the model’s results to readers and respective stakeholders.

Author Affiliations: MedImpact Healthcare Systems (EPB, RSL), San Diego, CA.

Source of Funding: None.

Author Disclosures: Drs Borrelli and Leslie were employees of MedImpact Healthcare Systems during the course of this study.

Authorship Information: Concept and design (EPB, RSL); analysis and interpretation of data (EPB); drafting of the manuscript (EPB); critical revision of the manuscript for important intellectual content (EPB, RSL); statistical analysis (EPB); administrative, technical, or logistic support (EPB, RSL); and supervision (EPB, RSL).

Address Correspondence to: Eric P. Borrelli, PhD, PharmD, MBA, MedImpact Healthcare Systems, 10181 Scripps Gateway Ct, San Diego, CA 92131. Email:


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