Simon McNamara’s journal round-up for 8th April 2019

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.

National Institute for Health and Care Excellence, social values and healthcare priority setting. Journal of the Royal Society of Medicine [PubMed] Published 2nd April 2019

As is traditional, this week’s round-up starts with an imaginary birthday party. After much effort, we have finally managed to light the twenty candles, have agreed our approach to the distribution of the cake, and are waiting in anticipation of the entrance of the birthday “quasi-autonomous non-governmental body”. The door opens. You clear your throat. Here we go…

Happy Birthday to you,

Happy Birthday to you,

Happy Birthday dear National Institute for Health and Care Excellence,

Happy Birthday to you.

NICE smiles happily. It is no longer a teenager. It has made it to 20 – despite its parents challenging it a few times (cough, Cancer Drug Fund, cough). After the candles have been blown out, someone at the back shouts: “Speech! Speech!”. NICE coughs, thanks everyone politely, and (admittedly slight strangely) takes the opportunity to announce that they are revising their “Social Value Judgements” paper – a document that outlines the principles they use to develop guidance. They then proceed to circle the room, proudly handing out draft copies of the new document- “The principles that guide the development of NICE guidance and standards” (PDF). They look excited. Your fellow guests start to read.

“Surely not?”, “What the … ?”, “Why?” – they don’t seem pleased. You jump into the document. All of this is about process. Where are all the bits about justice, and inequalities, and bioethics, and the rest? “Why have you taken out loads of the good stuff?” you ask. “This is too vague, too procedural”. Your disappointment is obvious to those in the room.

Your phone pings – it’s your favourite WhatsApp group. One of the other guests has already started drafting a “critical friend” paper in the corner of the room. They want to know if you want to be involved. “I’m in”, you respond, “This is important, we need to make sure NICE knows what we think”. Your phone pings again. Another guest is in: “I want to be involved, this matters. Also, this is exactly the kind of paper that will get picked up by the AHE blog. If we are lucky, we might even be the first paper in one of their journal round-ups”. You pause, think, and respond hopefully: “Fingers crossed”.

I don’t know if NICE had an actual birthday party – if they did I certainly wasn’t invited. I also highly doubt that the authors of this week’s first paper, or indeed any paper, had the AHE blog in mind when writing. What I do know, is that the first article is indeed a “critical friend” paper which outlines the authors’ concerns with NICE’s proposal to “revise” (read: delete) their social value judgements guidance. This paper is relatively short, so if you are interested in these changes I suggest you read it, rather than relying on my imaginary birthday party version of their concerns.

I am highly sympathetic to the views expressed in this paper. The existing “social value judgements” document is excellent, and (to me at least) seems to be the gold standard in setting the values by which an HTA body should develop guidance. Reducing this down to solely procedural elements seems unnecessary, and potentially harmful if the other core values are forgotten, or deprioritised.

As I reflect on this paper, I can’t help think of the old adage: “If it ain’t broke, don’t fix it”. NICE – this ain’t broke.

Measuring survival benefit in health technology assessment in the presence of nonproportional hazards. Value in Health Published 22nd March 2019

Dear HTA bodies that don’t routinely look for violations of proportional hazards in oncology data: 2005 called, they want their methods back.

Seriously though, it’s 2019. Things have moved on. If a new drug has a different mode of action to its comparator, is given for a different duration, or has differing levels of treatment effect in different population subgroups, there are good reasons to think that the trial data for that drug might violate proportional hazards. So why not look? It’s easy enough, and could change the way you think about both the costs and the benefits of that medicine.

If you haven’t worked in oncology before, there is a good chance you are currently asking yourself two questions: “what does proportional hazards mean?” and “why does it matter?”. In massively simplified terms, when we say the hazards in a trial are “proportional” we mean that the treatment effect of the new intervention (typically on survival) is constant over time. If a treatment takes some time to work (e.g. immunotherapies), or is given for only a few weeks before being stopped (e.g. some chemotherapies), there are good reasons to think that the treatment effect of that intervention may vary over time. If this is the case, there will be a violation of proportional hazards (they will be “nonproportional”).

If you are an HTA body, this is important for at least three reasons. First, if hazards are non-proportional, this can mean that the average hazard ratio (treatment effect) from the trial is a poor representation of what is likely to happen beyond the trial period – a big issue if you are extrapolating data in an economic model. Second, if hazards are non-proportional, this can mean that the median survival benefit from the trial is a poor representation of the mean benefit (e.g. in the case of a curve with a “big tail”). If you don’t account for this, and rely on medians (as some HTA bodies do), this can result in your evaluation under-estimating, or over-estimating, the true benefits and costs of the medicine. Third, most approaches to including indirect comparison in economic models rely on proportionality so, if this doesn’t hold, your model might be a poor representation of reality. Given these issues, it makes sense that HTA bodies should be looking for violations in proportional hazards when evaluating oncology data.

In this week’s second paper, the authors review the way different HTA bodies approach the issue of non-proportionality in their methods guides, and in a sample of their appraisals. Of the HTA bodies considered, they find that only NICE (UK), CADTH (Canada), and PBAC (Australia) recommend testing for proportional hazards. Notably, the authors report that the Transparency Committee (France), IQWiG (Germany), and TLV (Sweden) don’t recommend testing for proportionality. Interestingly, despite these recommendations, the authors find that solely the majority of NICE appraisals they reviewed included these tests, and that only 20% of the PBAC appraisals and 8% of the CADTH appraisals did. This suggests that the vast majority of oncology drug evaluations do not include consideration of non-proportionality – a big concern given the issues outlined above.

I liked this paper, although I was a bit shocked at the results. If you work for an HTA body that doesn’t recommend testing for non-proportionality, or doesn’t enforce their existing recommendations, I suggest you think very carefully about this issue – particularly if you rely on the extrapolation of survival curves in your assessments. If you aren’t looking for violations of proportional hazards, there is a good chance that you aren’t reflecting the true costs and benefits of many medicines in your evaluations. So, why not look for them?

The challenge of antimicrobial resistance: what economics can contribute. Science Published 5th April 2019

Health Economics doesn’t normally make it into Science (the journal). If it does, it probably means the paper is an important one. This one certainly is.

Antimicrobial resistance (AMR) is scary – really scary. One source cited in this paper predicts that by 2050, 10 million people a year will die due to AMR. I don’t know about you, but I find this pretty worrying (how’s that for a bit of British understatement?). Given these predicted consequences, you would think that there would be quite a lot of work from economists on this issue. Well, there isn’t. According to this article, there are only 55 papers on EconLit that “broadly relate” to AMR.

This paper contributes to this literature in two important ways. First, it is a call to arms to economists to do more work on AMR. If there are only 55 papers on this topic, this suggests we are only scratching the surface of the issue and could do more as a field contribute to helping solve the problem. Second, it neatly demonstrates how economics could be applied to the problem of AMR – including analysis of both the supply side (not enough new antibiotics being developed) and demand side (too much antibiotic use) of the problem.

In the main body of the paper, the authors draw parallels between the economics of AMR and the economics of climate change: both are global instances of the ‘tragedy of the commons’, both are subject to significant uncertainty about the future, and both are highly sensitive to inter-temporal discounting. They then go on to suggest that many of the ideas developed in the context of climate change could be applied to AMR – including the potential for use of antibiotic prescribing quotas (analogous to carbon quotas) and taxation of antibiotic prescriptions (analogous to the idea of a carbon tax). There are many other ideas in the paper, and if you are interested in these I suggest you take the time to read it in full.

I think this is an important paper and one that has made me think more about the economics of both AMR and, inadvertently, climate change. With both issues, I can’t help but think we might be sleepwalking into a world where we have royally screwed over future generations because we didn’t take the actions we needed to take. If economists can help stop these things happening, we need to act. If we don’t, what will you say in 2050 when you turn on the news and see that 10 million people are dying from AMR each year? That is, assuming you aren’t one of those who has died as a result. Scary stuff indeed.

Credits

Chris Sampson’s journal round-up for 14th August 2017

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.

Does paying service providers by results improve recovery outcomes for drug misusers in treatment in England? Addiction [PubMedPublished 10th August 2017

‘Getting what you pay for’ is a fundamentally attractive funding model, which is why we see lots of pay for performance (P4P) initiatives cropping up in the NHS. But P4P plans can go awry. This study considers an experimental setting in which 8 areas participated in P4P pilots for drug misuse treatment, from 2012-2014. Payments were aligned with 3 national priorities: i) abstinence, ii) reduced offending and iii) improved health and well-being. The participating areas allocated differing proportions of payments to the P4P model, between 10% and 100%. Data were drawn from the National Drug Treatment Monitoring System, which includes information on drug use, assessment and interventions received. Other national sources were used to identify criminal activity and mortality rates. Drug misusers attending treatment services during the 2 years before and after the introduction of the P4P scheme were included in the study. Using a difference-in-differences analysis, the researchers compared outcomes in the 8 participating areas with those in 143 non-participating areas. Separate multilevel regression models were used for a set of outcomes, each controlling for a variety of individual-level characteristics. The authors analysed ‘treatment journeys’, of which there were around 20,000 for those in participating areas and 280,000 for those in non-participating areas; roughly half before the introduction and half after. The results don’t look good for P4P. Use of opiates, crack cocaine and injecting increased. Treatment initiation increased in non-participating areas but decreased in participating areas. Moreover, longer waiting times were observed in participating areas as well as more unplanned discharges. P4P was associated with people being less likely to successfully complete treatment within 12 months. In P4P’s favour, there was evidence that abstinence increased. I’d’ve liked to have seen some attempt at matching between the areas, given that there was an element of self-selection into the scheme. Or at least, better control for the characteristics of the areas before P4P was introduced. This paper isn’t quite the final nail in the coffin. I don’t see P4P disappearing anytime soon. There’s a lot to be learnt from the paper’s discussion, which outlines some of the likely reasons and mechanisms underlying the findings. Commissioners should take note.

The short- and long-run effects of smoking cessation on alcohol consumption. International Journal of Health Economics and Management [PubMedPublished 7th August 2017

Anecdotally, it seems as if smoking and drinking are complementary behaviours. Generally, the evidence suggests that this is true. Smoking cessation programmes may, therefore, have value in their ability to reduce alcohol consumption (and vice versa). But only if the relationship is causal. This study seeks to add to that causal evidence. Using data from 5887 individuals in the Lung Health Study, the author runs a two-stage least squares estimation, with randomisation to smoking cessation treatment as an instrumental variable for smoking status. In the short term, there is some evidence that smokers tend to drink more (especially men). But findings in the longer term, up to 5 years, are more persuasive. It’s unfortunate that the (largely incoherent) rational addiction theory makes an appearance and that the findings are presented as supportive of it. A stopped clock is right twice a day. In line with rational addiction theory, the long-term relationship is measured in terms of a ‘smoking stock’, which is an aggregate measure of smoking behaviour over the 5 year period. Smoking and drinking are found to be complementary in the long term. Crucially, the extent of their complementarity is associated with particular factors. For example, people who smoke more cigarettes or who abstain for longer exhibit larger reductions in alcohol consumption when they stop smoking. People who smoke relatively few cigarettes per day do not drink more alcohol. Those smoking 6-10 per day consume around 1 extra drink per week compared with non-smokers. Quitting for 5 years can reduce alcohol consumption by more than 50%. In the long run, the effect is more pronounced for women and for people who are married. This highlights important opportunities for targeted public policy, which could achieve a win-win in terms of reducing both cigarette and alcohol consumption.

Time for a change in how new antibiotics are reimbursed: development of an insurance framework for funding new antibiotics based on a policy of risk mitigation. Health Policy Published 5th August 2017

Antibiotics have become a key component of health care, but antimicrobial resistance threatens their usefulness and we don’t see new antibiotics in the pipeline to help overcome this. It’s a fundamentally difficult problem; we want new antibiotics but we want to use them as sparingly as possible. Antibiotic development is relatively unattractive (financially) to pharmaceutical companies. Provision of research funding and regulatory changes haven’t solved the problem to date. This paper considers why this might be the case, and explores 2 alternative approaches: a premium price model and an insurance-type model. Essentially, the authors conduct a spreadsheet analysis to compare the alternative models with a base case of no incentives. The expected net present value of the base case was negative (to the tune of about $1.5 billion), demonstrating why much-needed new antibiotics aren’t being developed. Current incentives – including public-private funding partnerships and market exclusivity – are also shown to fail to reach a positive net present value. The premium price model, whereby there is an enhanced price per unit, is not particularly attractive. The daily cost of the resulting antibiotics would likely be too high, and manufacturers’ pursuit of profit would be at odds with conservative prescribing. Furthermore, it exposes areas experiencing outbreaks to serious financial risk. The insurance model, which involved an annual fee paid by each healthcare system (to manufacturers), is more promising. Pharmaceutical companies would be insured against low prices and variable use and health systems would be insured against a lack of antibiotics and the risk of an infection outbreak. The key feature here is that manufacturers’ revenues are de-linked from sales volume. This is important when we consider the need for conservative prescribing. The authors estimate that the necessary fee (for the global market) would be around $262 million per year, or $114 million if combined with current funding and regulatory incentives. Of course, these findings are based on major assumptions about infection rates, research costs and plenty besides. A number of sensitivity analyses are conducted that highlight uncertainty about what the insurance fee might need to be in the future. I think this uncertainty is somewhat understated – there are far more sensitivity and scenario analyses that would be warranted if such a policy were being seriously considered. Nevertheless, pooling risk in an insurance model looks like a promising strategy that’s worthy of further investigation and piloting.

Credits

#HEJC for 01/04/2013 (new time!)

This month’s meeting will take place Monday 1st April, at 5pm London time. That’ll be 6pm in Cape Town and 7pm in Riga. Join the Facebook event here. We’ll also hold an antipodal meeting on Tuesday 2nd April, at 5am London time. That’ll be 2pm in Brisbane and 9pm on Monday in Seattle. Join the Facebook event here. For more information about the Health Economics Twitter Journal Club and how to take part, click here.

The paper for discussion this month is a working paper published by the Research Institute of Industrial Economics in Sweden. The authors are Sara Fogelberg and Jonas Karlsson. The title of the paper is:

“Competition and antibiotics prescription”

Following the meeting, a transcript of the discussion can be downloaded here.

Links to the article

Direct: http://www.ifn.se/wfiles/wp/wp949.pdf

RePEc: http://ideas.repec.org/p/hhs/iuiwop/0949.html

Other: tbc

Summary of the paper

Antibiotics resistance is an increasingly apparent problem in medicine, with the prevalence of multi-resistant bacteria on the rise. Over-prescription of antibiotics has short- and long-term implications for public health. Furthermore, there is much debate about the role of competition in healthcare provision. This paper investigates the effect of increased competition between healthcare providers on the prescription of antibiotics. The authors hypothesise that, as a result of increased competition, doctors may be inclined to prescribe more antibiotics in order to meet patients’ demand. The study makes use of a natural experiment where competition-inducing reform was implemented in different counties in Sweden at different points in time during 2007 to 2010. The dataset contains monthly data on all prescribed antibiotics in Sweden, including those defined as narrow spectrum and broad spectrum antibiotics. The authors implement a difference in differences model. The results indicate that increased competition had a positive and significant effect on antibiotics prescription.

Discussion points

  • What is the significance of Swedish reimbursement processes?
  • What does this study tell us about patients’ and doctors’ preferences for antibiotics?
  • What are the implications for the UK and other countries?
  • How can this study inform the debate about competition in healthcare?

Missed the meeting? Add your thoughts on the paper in the comments below.