Nermina Ferizovic | Health Economics Expert

For my latest blog post, I am delighted to review a paper recently published in Pharmacoeconomics titled Issues, Challenges and Opportunities for Economic Evaluations of Orphan Drugs in Rare Diseases: An Umbrella Review by Grand et al., 2024. This study aimed to identify the challenges for economic evaluations of rare diseases with the aim of identifying solutions to the challenges with the ultimate aim of improving patient access to interventions in under-served disease areas. 

For this umbrella review (a fancy term for a synthesis of systematic reviews which the authors argue here was the best approach to understand the literature in this area based on an initial scoping review), Medline, Embase and Cochrane were searched focussing on economic evaluations, models and systematic literature reviews of orphan drugs and rare diseases. Thirty-six reviews were identified in total following the database search and subsequent hand search. Identified studies underwent a critical appraisal using the Joanna Briggs Institute critical appraisal checklist. This methodology enabled the identification of the challenges of economic evaluations of orphan drugs and subsequent tabulation according to themes. Although, the authors do acknowledge that “it was not possible, with confidence, to assert whether all issues for orphan drugs applied less to other drugs, which was part of the original research objective”. 

The majority of the included studies were of ‘moderate’ quality. Data extraction was inductively split into three broad themes: issues with health economic parameters, issues with health economic evaluations and issues with estimating value/reimbursement, with subtopics further developed for each theme with a subtopic for each category (Table 1). The table describes all issues identified in relation to challenges for the economic evaluation of rare diseases. There is also a MindMap which provides a visual overview of the three themes and their respective subtopics found here: https://tobiasgrand.github.io/-data-extraction-themes.github.io/.

In summary, the key issues are as follows. Data are lacking for both clinical and economic parameters (costs, quality of life) and where available are often of low quality which impacts on the number of cost-effectiveness analyses and budget impact assessments. Where cost-effectiveness and budget impact assessments are available, these are subject to a publication bias. Finally, lack of good quality data impacts the value assessment of interventions for rare diseases with multi-criteria decision making (MCDA) being commonly used albeit with inconsistent use. 

The authors summarised it well with: “Challenges are abundant, and solutions are not plentiful and rarely forthcoming.” To this end, they proceed to provide us with recommendations as to how to bridge the gap. Their key recommendations include the following: 

• Comprehensive and flexible cost-effectiveness models
• Addressing publication bias to meet cost-effectiveness thresholds
• Identifying data gaps
• Use of patient organisations to support reimbursement efforts 
• Risk-sharing agreements

Overall, this is a well-conducted study with important findings – and thus a key first step in a research workstream. It will be interesting to see how the authors use these findings in subsequent research including whether they demonstrate the use of their recommendations and assess the extent to which they alleviate issues around the economic evaluation of orphan drugs. 

Table 1. Identified issues on the challenges of economic evaluations in rare diseases

Themes/Subtopic	Identified Issue
Issues with health economic parameters
Natural history of disease	Missing data on the natural history of the disease or unknown rare disease trajectories
Clinical effectiveness	Clinical trials may suffer from short durations, small sample sizes, premature termination, inadequate power, missing data or missing control arms. Long-term studies providing post-marketing data on safety and efficacy are rarely available. Missing treatment guidelines. Missing data to predict treatment responses. Concerns on the patient relevance and the use of surrogate endpoints. Missing data on comparators. Low quality of clinical evidence.
Costs	Cost-of-illness or burden-of-disease studies are scarce in rare diseases; most are retrospective and only a small proportion of studies report indirect, non-medical or informal-care costs. Aggregated primary data are rarely available, hence, studies tend to report patient-reported claims or registry data. A multitude of factors influence transferability such as data sources, geographical perspective, nomenclature, assumptions, discount rates, unit costs, treatment guidelines and value frameworks.
Quality of life	Quality-of-life studies in rare diseases are limited. Data limitations on the quality of life of caregivers. Studies tend to be small, not randomised or controlled, which decreases the reliability of conclusions; this scarcity of evidence may lead to the use of assumptions, for example, assumption of equal utility values across treatment arms or a linearity assumption of utilities between different timepoints. Shortcomings in methods and reporting, for example, the failure to include utility values or mapping algorithms, and insufficiently describing the elicitation of utility weights.
Issues with health economic evaluations
Cost effectiveness	Health economic evaluations for rare diseases are scarce; missing patient-level data, high drug costs and the inability to measure effects for clinical or quality-of-life outcomes. The difficulties for economic evaluations are driving factors for the use of assumptions to overcome challenges for cost-effectiveness modelling. Commonplace to use modelling techniques such as mapping algorithms or long-term extrapolation for outcomes because of data limitations. Limited patient numbers coupled with unreliable estimates of effects, symptoms and complications suggest that methods such as patient-level simulation modelling may have limited applicability in rare diseases. Publication bias in relation to positive results or industry-sponsorship bias seems to be prominent in rare diseases. Most studies were industry funded; incremental cost-utility ratios were significantly lower when published by industry compared with foundations and academia. Most economic evaluations have moderate quality, and the failure to reach high quality may be partly attributed to a lack of good-quality model inputs (e.g. utility values that do not account for patient characteristics and disease severity) or because they omit lifetime horizons for chronic rare diseases. Problems with reporting are frequently highlighted as another factor that may contribute to insufficient quality. Cost-effectiveness analyses are heterogenous because of modelling variations in treatments, patient populations, time horizons, countries, cost-effectiveness thresholds, settings, year of analysis, comparators and assumptions. This impacts transferability.
Budget impact	Studies on budget impact modelling are few, mostly from high-income or native English-speaking countries. Studies are low quality and show poor adherence to guidelines. A proportion of budget-impact studies fail to report side effects, drug-related services, life-extension costs, savings from mortality reductions and validation methods. Assumptions are commonly used including regarding target populations, population sizes, interventions, comparators, costs and market uptake.
Issues with estimating value or reimbursement
Value frameworks and thresholds	Whilst evidence may be scarce, input parameters on prevalence, incidence, number of treatment-eligible patients, and clinical benefits are nonetheless needed when estimating the budget impact and cost effectiveness for rare diseases. Reference pricing further adds to the complexity and may prevent launches of orphan drugs in low-income countries. Value frameworks may suffer from transparency and consistency issues. This largely makes budget-impact and cost-effectiveness analyses country specific.
Multiple criteria decision analyses (MCDA)	A multiple criteria decision analysis (MCDA) is an emerging value framework for orphan drugs because it offers an opportunity to include a broad range of value criteria, for example, societal, disease or treatment criteria. Critics highlight variations in scoring functions for value criteria as a significant limitation and for decision making it is difficult to observe consistent recommendations. Friedmann and colleagues suggested that traditional value aspects used in HTAs (budget impact and cost effectiveness) were considered unimportant by stakeholders involved in orphan drug appraisal processes. The most cited value criterion was disease severity (n = 10), cost effectiveness (n = 7) and budget impact (n = 3) were cited ten times, collectively. By contrast, Mohammadshahi and colleagues found in their review an equal citation frequency for the value criteria: disease severity (n = 8), cost effectiveness (n = 8) and budget effect (n = 8).