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Journal round-up: PharmacoEconomics 42(3)

For my second contribution to the blog, I will provide a short review of the March 2024 issue of PharmacoEconomics. There are 8 articles in this issue covering a range of topics, including articles about stakeholder involvement in economic model development, improving health disparity evidence gaps in value assessment, using generalised linear models to estimate costs, informing value-based contracts for cell and gene therapies using real-world data, a modelling study examining the impact of common pathogens in the United States, two systematic reviews, and a report providing recommendations on incorporating spillovers in economic evaluations. I will focus on three of these articles below.


Making decision models fit for purpose: the importance of ensuring stakeholder involvement

This editorial argues that in order for models to be fit for purpose, relevant stakeholders must be involved in their co-production. Several articles are cited to support the author’s views about the positive benefits that stakeholder involvement brings to decision-making. The editorial is unbalanced as it focuses solely on the positives, with no mention of the negatives. The author ends with a statement that models with no stakeholder involvement should at a minimum come with a disclaimer.

In principle, I agree; relevant stakeholders should be involved in the development of economic models, especially when they are being developed to inform public decision-making. However, this article makes some strong claims, including that a lack of stakeholder involvement “leaves a role to be played by modellers’ own values, which we might argue are influenced by their beliefs, experience and disposition, or otherwise determined by their social circumstances.” This seems a very strong claim on the basis that not only would it be extremely difficult to find an economic model which has been developed by a lone modeller or two, I find it difficult to argue that modellers’ incorporate their own values into model development given the standard methodological processes in place. Further, I suspect there are no models used for decision making developed by one or two modellers as these are consistently subject to input and validation from multiple cross-disciplinary stakeholders. 

To quote a famous man, ‘all models are wrong, but some are useful’. This article also seems to infer that in order to make a model useful, all you would need to do is ensure stakeholder involvement and they might just be useful. This is unlikely to be the case as even models which have been developed through multi-disciplinary teams – as is common – are not always helpful for decision-making.

Overall, although the premise of the editorial is reasonable, I feel that it offers a fairly negative view of economic modellers.


A research framework to improve health disparity evidence gaps in value assessments

This opinion piece argues that economic models do not currently capture differences experienced by health disparity populations. This is by and large true. The author states that these differences are not captured because current methods focus on cost and quality of life data with the author then going on to propose a framework informed by three distinct phases through which to address this gap. These are: (1) contextualisation of lived experiences for disadvantaged communities; (2) individual-level quantification of health disparities for cost and quality-of-life measures; and (3) quantifying community-level impacts. It would be helpful to see the author expand on this research to demonstrate the use of this framework in practice as the use of individual-level data could lead to very complex value assessment.


Using real-world data to inform value-based contracts for cell and gene therapies in Medicaid

As some of you may know, I love an advanced therapy medicinal product. So much so that they form the basis of my doctoral research. This study uses real-world Medicaid data to determine actual cost-offsets for two gene therapies, one in haemophilia A and the other in haemophilia B, from the perspective of Colorado Medicaid. The cost analysis used 2018–2022 data from the Colorado Department of Health Care Policy & Financing to determine standard-of-care (factor replacement) costs and employed simulation models to estimate the cost of Medicaid if patients switched to gene therapy versus if they did not. The main measures were annual standard-of-care costs, cost offset, and breakeven time when using gene therapies. The methods are not clearly described, but essentially they developed a partitioned survival analysis comparing treatment with gene therapy versus treatment with standard of care, focusing solely on costs. Although the title implies that the authors use these data to inform value-based contracts, they do not. The findings are not original: there is substantial uncertainty and extended payback periods for gene therapy costs. Implicitly, this article argues that all relevant value elements for cell and gene therapies are considered in Medicaid which may not be the case and that calculations of cost offsets using these data can determine the value of cell and gene therapies. True, but only with value restricted to certain factors.

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