Every Monday our authors provide a round-up of some of the most recently published peer reviewed articles from the field. We don’t cover everything, or even what’s most important – just a few papers that have interested the author. Visit our Resources page for links to more journals or follow the HealthEconBot. If you’d like to write one of our weekly journal round-ups, get in touch.
Talkin’ about a resolution: issues in the push for greater transparency of medicine prices. PharmacoEconomics [PubMed] [RePEc] Published 20th January 2020
Any paper with a pun title will naturally grab my attention, but this paper is well worth a read regardless as it gives a great overview of some of the issues and controversy surrounding drug price transparency.
Countries tend to publish ‘list prices’ of medicines that may or may not (usually not) reflect the actual price that is paid for the drug. ‘Real’ prices tend to be confidential, and there is much debate on whether this is a good or a bad thing. This paper provides a helpful overview of the debate.
At the 2019 World Health Assembly a resolution was agreed for countries to start sharing the real prices they pay for medicines in their health systems. The paper explains that policy makers are frustrated about rising drug prices without justification or insight into the reasons for these increases, and that assessing what a reasonable price would be is more difficult without insight into what other health systems are paying. Furthermore, concerns exist that pharmaceutical companies have too much power in setting prices due to the lack of price transparency. In price negotiations, this results in asymmetrical information that puts payers at a disadvantage.
However, the authors carefully set out the reasons why more price transparency for medicines might have some unwanted side effects. Affordability varies between countries, and confidential discounts are a mechanism to achieve differential prices for different markets. One would expect price convergence when prices in different markets are transparent, which in practice is likely to create access issues in countries where affordability is lower. Furthermore, if high income countries lose their ability to negotiate substantial discounts, they might also end up paying higher drug prices.
This paper gives a balanced overview of the reasons one might want to push for more transparency, or not. The authors rightfully point out that there are concerns about the long-term unintended consequences of pushing for more price transparency, including access issues for low and middle income countries if countries start sharing information on real prices. It seems that with the push for more transparency, there might be a price to pay.
Ranking the criteria used in the appraisal of drugs for reimbursement: a stated preferences elicitation with health technology assessment stakeholders across jurisdictional contexts. Value in Health Published 16th December 2019
This discrete choice experiment (DCE) looked at stakeholders’ preferences for cancer drugs when making reimbursement decisions. Their sample of 111 stakeholders included appraisal committee members, payer representatives, clinical and patient experts, and modelling experts from a bunch of different countries. Attributes included were survival benefit, added cost per patient, number of patients, other treatment options, and adverse events. They chose to use qualitative levels (for example, the survival benefit levels were superior to comparator; near identical to comparator; uncertain) rather than quantitative levels (e.g. four months survival) to increase the applicability of the study to more healthcare contexts. They also included a best-worst scaling exercise to measure how certain respondents were about their answers.
The findings are not very surprising: the respondents preferred drugs with survival benefits, low costs, and mild side effects. The number of patients attribute was not significant. The authors hypothesise that budget impact considerations do not have an impact on reimbursement decisions, but I wonder whether the ‘number of patients’ in this DCE being a positive or a negative really depends on the levels of the other attributes. For example, for a drug that is cost-saving and has superior survival, a high number of patients would be better than a low number of patients. Yet for a drug with uncertain benefits and high added cost per patient, a low number of patients would be good, and a high number of patients would be bad.
This study used very general attributes and included respondents from Germany, Poland, and the UK, who arguably have quite different systems. Although I understand the desire to design a study such that it is applicable in multiple decision contexts, I also question whether we then can still learn a lot of meaningful things from a study that general.
I think there might be a trade-off between applicability and relevance – a study so general it can apply to any healthcare setting might not hold a lot of valuable lessons for each specific decision context.
The interaction between price negotiations and heterogeneity: implications for economic evaluations. Medical Decision Making [PubMed] Published 2nd February 2020
This is another paper that talks about the specifics of the drug reimbursement process in a very general way. The paper presents a framework for taking strategic behaviour in price negotiations into account. According to the authors, if the price (or discount offered to a payer) of a drug in one population is dependent on achieving reimbursement in a different population, a failure to account for this ‘interaction effect’ could lead to sub-optimal resource allocation decisions.
The authors give two examples for a fictional cancer drug for two indications, oesophageal cancer and colon cancer. When these drugs would be considered for both indications separately, their ICERs would be €15,000 and €70,000, which would result in the fictional decision maker who has a threshold of €45,000 saying ‘yes’ to reimbursing the drug for oesophageal cancer but ‘no’ to colon cancer.
But what if the company offers a discount for the drug (price goes from €5,000 to €3,000) conditional on achieving reimbursement in both indications? If both indications are considered separately, the ICERs drop to €2,500 and €50,000, so the decision maker would make the same decisions. However, the author propose a hybrid approach. Here, rather than presenting two separate comparative analyses, they suggest instead comparing all possible scenarios (not reimbursing drug at all, drug reimbursed for colon cancer only, drug is reimbursed for oesophageal cancer only, drug is reimbursed for both indications) in one cost-effectiveness analysis. This way, the surplus that is generated due to the lower threshold for oesophageal cancer with the discount is essentially used to offset the higher ICER for the colon cancer indication.
I like some of the ideas in this paper, but I struggle to think of a real-life situation in which the fictitious decision problem would occur. In the example used in the article, two indications are considered simultaneously, but in practice it is much more common that one drug is considered for multiple indications sequentially. If the drug would already be reimbursed for oesophageal cancer, and a decision maker would only need to make a decision about the colon cancer indication, the hybrid approach wouldn’t apply. This type of scenario might be more applicable to a situation where a single indication contains clinically distinct subgroups.
The examples used in the paper assume both indications have identical numbers of patients, which is a pity. It might make sense for a company to offer a discount for the combined indications if the decrease in price would be compensated by a large enough increase in volume. If the framework would have used net health benefits for each scenario rather than ICERs, it would have been straightforward to take account of patient volumes.
There is some interesting stuff in this paper, but I think it is another example of a paper that presents a simplified decision problem that does not really reflect the reality of most healthcare contexts. Furthermore, for systems that allow blended pricing or indication based pricing, this type of problem will not exist.