Journal round-up: PharmacoEconomics – Open 5(2)

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This June issue has a bumper 18 articles from systematic reviews to cost-effectiveness analyses and many more. The systematic reviews are both on health economic evidence, one on point-of-care testing and the other on mantle cell lymphoma. There is also a protocol for a review on the cost of revision of total knee replacement.

The cost-effectiveness papers include an analysis on a type of surgery for patients with sacroiliac joint pain compared to non-surgical management (they found that minimally invasive sacroiliac joint fusion was cost-effective); mental health treatment for older patients with HIV (cost-effective compared to no mental health treatment); and nivolumab for patients with advanced, previously treated squamous and non-squamous non-small-cell lung cancer (also found to be cost-effective).

There are five papers estimating costs: treatment costs of insulin for type 2 diabetes, costs of patients hospitalised for pneumonia beyond the acute phase of the disease, costs of next-generation sequencing compared to single-gene testing, treatment costs of metastatic castration-resistant prostate cancer, and operative costs of liver transplantation and normothermic machine perfusion.

The editorial is about value of information analysis (VoI). In quite a short piece, Haitham Tuffaha provides a whistle-stop tour on the rationale, methods, and new developments. The upshot is that VoI is used rarely, despite its utility in informing decisions about reimbursement, research funding. and study design. Interestingly, of the three cost-effectiveness analyses published in this issue, none conducted VoI. Haitham notes that the barriers are the complexity of the calculations and the lack of awareness among researchers and policymakers. I agree with these barriers but would add an additional one: how to deal with structural uncertainty. In my experience, often the assumptions underpinning the cost-effectiveness analysis have a large impact on the ICER and are not easy to parameterise and analyse with VoI. For example, what does the long-term mortality risk look like beyond the trial follow-up period? How to parameterise this uncertainty and include it in the VoI? The good news is that the VoI research world is continuously developing the methods and producing guidance – the future is bright!

Speaking of assumptions about long-term mortality risk in cost-effectiveness models, my highlight is a tutorial about a survival analysis method – mixture cure models. Mixture cure models assume that, at time zero (e.g. following treatment), a proportion of patients is cured and/or are long-term survivors – this is known as the ‘cure fraction’. The mortality risk of the cure fraction is the risk of the general population, with or without adjustments to account for greater risk. We’re likely to see mixture cure models more as new cell and gene therapies enter the scene. This is a really useful paper. It explains mixture cure models and how to implement them in practice, including R code. It’s a keeper!

Another paper that caught my eye is T. Joseph Mattingly II and colleagues’ study about crowdfunding campaigns for medical costs related to hepatitis C via GoFundMe. Crowdfunding for medical costs is becoming increasingly common, with GoFundMe alone reporting over 250,000 medical campaigns a year. By extracting data from the publicly available crowdfunding pages related to hepatitis C infection (N=690), the authors found that most people do not disclose the source of their infection and tend to mostly mention direct costs associated with the disease (e.g. medications). People who disclosed the source of the infection and those who described needing funds for both direct and indirect costs (e.g. transport, funeral costs) raised greater funds, although this was based on a descriptive analysis. Despite the many limitations, which the authors acknowledge, this study brings to the fore the potential of new platforms as data sources for health economics.

Finally, I’d like to highlight an unusual paper, but one in which I suspect many health economists will be interested, on the wages of health economics, outcomes research, and market access professionals across the world. The study used self-reported wage data from a 2017 survey of professionals via healtheconomics.com (N=372 out of 25,000 subscribers). Higher wages were earned by men (compared to women) and by professionals living in the US (compared to outside the US), although the authors did not adjust for purchasing power and there was no data on the number of hours worked. If you’re curious about the wages that colleagues across the world are earning (or at least those who answered this survey), Table 1 has the means and Table 3 has the regression results (although you’ll need to exponentiate the coefficients as the regression was on log wages). For example, having a PhD was associated with higher earnings, mostly for US women (25% higher earnings compared to US women without a PhD).

Completing the set of 18 papers, this issue includes a study examining whether working at private or publicly owned health centres affects the likelihood of prescribing antibiotics and of choosing broad- over narrow-spectrum antibiotics in Sweden (sadly, it does!). There is a study about attribute non-attendance when patients are asked to evaluate EQ-5D-5L health states in a discrete choice experiment (physical dimensions seem to be less likely to be considered). And a study about individuals’ willingness to pay for insulin in pens rather than in vials in India.


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  • Rita is a health economist at the University of York working mainly in economic evaluation. See https://tinyurl.com/y8ogvhjw for her academic profile.

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