Rita Faria’s journal round-up for 2nd September 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.

RoB 2: a revised tool for assessing risk of bias in randomised trials. BMJ [PubMed] Published 28th August 2019

RCTs are the gold standard primary study to estimate the effect of treatments but are often far from perfect. The question is the extent to which their flaws make a difference to the results. Well, RoB 2 is your new best friend to help answer this question.

Developed by a star-studded team, the RoB 2 is the update to the original risk of bias tool by the Cochrane Collaboration. Bias is assessed by outcome, rather than for the whole RCT. For me, this makes sense.  For example, the primary outcome may be well reported, yet the secondary outcome, which may be the outcome of interest for a cost-effectiveness model, much less so.

Bias is considered in terms of 5 domains, with the overall risk of bias usually corresponding to the worst risk of bias in any of the domains. This overall risk of bias is then reflected in the evidence synthesis, with, for example, a stratified meta-analysis.

The paper is a great read! Jonathan Sterne and colleagues explain the reasons for the update and the process that was followed. Clearly, there was quite a lot of thought given to the types of bias and to develop questions to help reviewers assess it. The only downside is that it may require more time to apply, given that it needs to be done by outcome. Still, I think that’s a price worth paying for more reliable results. Looking forward to seeing it in use!

Characteristics and methods of incorporating randomised and nonrandomised evidence in network meta-analyses: a scoping review. Journal of Clinical Epidemiology [PubMed] Published 3rd May 2019

In keeping with the evidence synthesis theme, this paper by Kathryn Zhang and colleagues reviews how the applied literature has been combining randomised and non-randomised evidence. The headline findings are that combining these two types of study designs is rare and, when it does happen, naïve pooling is the most common method.

I imagine that the limited use of non-randomised evidence is due to its risk of bias. After all, it is difficult to ensure that the measure of association from a non-randomised study is an estimate of a causal effect. Hence, it is worrying that the majority of network meta-analyses that did combine non-randomised studies did so with naïve pooling.

This scoping review may kick start some discussions in the evidence synthesis world. When should we combine randomised and non-randomised evidence? How best to do so? And how to make sure that the right methods are used in practice? As a cost-effectiveness modeller, with limited knowledge of evidence synthesis, I’ve grappled with these questions myself. Do get in touch if you have any thoughts.

A cost-effectiveness analysis of shortened direct-acting antiviral treatment in genotype 1 noncirrhotic treatment-naive patients with chronic hepatitis C virus. Value in Health [PubMed] Published 17th May 2019

Rarely we see a cost-effectiveness paper where the proposed intervention is less costly and less effective, that is, in the controversial southwest quadrant. This exceptional paper by Christopher Fawsitt and colleagues is a welcome exception!

Christopher and colleagues looked at the cost-effectiveness of shorter treatment durations for chronic hepatitis C. Compared with the standard duration, the shorter treatment is not as effective, hence results in fewer QALYs. But it is much cheaper to treat patients over a shorter duration and re-treat those patients who were not cured, rather than treat everyone with the standard duration. Hence, for the base-case and for most scenarios, the shorter treatment is cost-effective.

I’m sure that labelling a less effective and less costly option as cost-effective may have been controversial in some quarters. Some may argue that it is unethical to offer a worse treatment than the standard even if it saves a lot of money. In my view, it is no different from funding better and more costlier treatments, given that the savings will be borne by other patients who will necessarily have access to fewer resources.

The paper is beautifully written and is another example of an outstanding cost-effectiveness analysis with important implications for policy and practice. The extensive sensitivity analysis should provide reassurance to the sceptics. And the discussion is clever in arguing for the value of a shorter duration in resource-constrained settings and for hard to reach populations. A must read!

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Rita Faria’s journal round-up for 26th August 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.

Vaccine hesitancy and (fake) news: quasi‐experimental evidence from Italy. Health Economics [PubMed] [RePEc] Published 20th August 2019

Has fake news led to fewer children being vaccinated? At least in Italy, the answer seems to be yes.

It’s shocking to read that the WHO has included the reluctance or refusal to vaccinate as one of the 10 threats to global health today. And many of us are asking: why has this happened and what can we do to address it? Vincenzo Carrieri, Leonardo Madio and Francesco Principe help answer this first question. They looked at how fake news affects the take-up of vaccines, assuming that exposure to fake news is proxied by access to broadband and within a difference-in-differences framework. They found that a 10% increase in broadband coverage is associated with a 1.2-1.6% reduction in vaccination rates.

The differences-in-differences method hinges on a court ruling in 2012 that accepted that the MMR vaccine causes autism. Following the ruling, fake news about vaccines spread across the internet. In parallel, broadband coverage increased over time due to a government programme, but it varied by region, depending on the existing infrastructure and geographical conditions. Broadband coverage, by itself, cannot lead to lower vaccination rates. So it makes sense to assume that broadband coverage leads to greater exposure to fake news about vaccines, which in turn leads to lower vaccination rates.

On the other hand, it may be that greater broadband coverage and lower vaccination rates are both caused by something else. The authors wrote a good introduction to justify the model assumptions and show a few robustness checks. Had they had more space, I would have like to read a bit more about the uncertainties around the model assumptions. This is a fantastic paper and good food for thought on the consequences of fake news. Great read!

The cost-effectiveness of one-time birth cohort screening for hepatitis C as part of the National Health Service Health Check programme in England. Value in Health Published 19th August 2019

Jack Williams and colleagues looked at the cost-effectiveness of one-time birth cohort screening for hepatitis C. As hepatitis C is usually asymptomatic before reaching its more advanced stages, people may not be aware that they are infected. Therefore, they may not get tested and treated, even though treatment is effective and cost-effective.

At the level of the individual eligible for testing, the ICERs were between £8k-£31k/QALY, with lower ICERs for younger birth cohorts. The ICERs also depended on the transition probabilities for the progression of the disease, with lower ICERs if progression is faster. Extensive sensitivity and value of information analyses indicate that the key cost-effectiveness drivers are the transition probabilities, probabilities of referral and of treatment post-referral, and the quality of life benefits of being cured.

This is a great example of a good quality applied cost-effectiveness analysis. The model is well justified, the results are thoroughly tested, and the discussion is meticulous. Well done!

NICE, in confidence: an assessment of redaction to obscure confidential information in Single Technology Appraisals by the National Institute for Health and Care Excellence. PharmacoEconomics [PubMed] Published 27th June 2019

NICE walks a fine line between making decisions transparent and protecting confidential information. Confidential information includes commercially sensitive information (e.g. discounts to the price paid by the NHS) and academic-in-confidence information, such as unpublished results of clinical trials. The problem is that the redacted information may preclude readers from understanding NICE decisions.

Ash Bullement and colleagues reviewed NICE appraisals of technologies with an approved price discount. Their goal was to understand the extent of redactions and their consequences on the transparency of NICE decisions. Of the 171 NICE appraisals, 118 had an approved commercial arrangement and 110 had a simple price discount. The type of redacted information varied. Some did not present the ICER, others presented ICERs but not the components of the ICERs, and others did not even present the estimates of life expectancy from the model. Remarkably, the confidential discount could be back-calculated in seven NICE appraisals! The authors also looked at the academic-in-confidence redactions. They found that 68 out of 86 appraisals published before 2018 still had academic-in-confidence information redacted. This made me wonder if NICE has a process to review these redactions and disclose them once the information is in the public domain.

As Ash and colleagues rightly conclude, this review shows that there does not seem to be a consistent process for redaction and disclosure. This is a compelling paper on the practicalities of the NICE process, and with useful reflections for HTA agencies around the world. The message for NICE is that it may be time to review the process to handle sensitive information.

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Rita Faria’s journal round-up for 13th May 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.

Communicating uncertainty about facts, numbers and science. Royal Society Open Science Published 8th May 2019

This remarkable paper by Anne Marthe van der Bles and colleagues, including the illustrious David Spiegelhalter, covers two of my most favourite topics: communication and uncertainty. They focused on epistemic uncertainty. That is, the uncertainty about facts, numbers and science due to limited knowledge (rather than due to the randomness of the world). This is what we could know more about, if we spent more resources in finding it out.

The authors propose a framework for communicating uncertainty and apply it to two case studies, one in climate change and the other in economic statistics. They also review the literature on the effect of communicating uncertainty. It is so wide-ranging and exhaustive that, if I have any criticism, its 42 pages are not conducive to a leisurely read.

I found the distinction between direct and indirect uncertainty fascinating and incredibly relevant to health economics. Direct uncertainty is about the precision of the evidence whilst indirect uncertainty is about its quality. For example, evidence based on a naïve comparison of patients in a Phase 2 trial with historical controls in another country (yup, this happens!).

So, how should we communicate the uncertainty in our findings? I’m afraid that this paper is not a practical guide but rather a brilliant ground clearing exercise on how to start thinking about this. Nevertheless Box 5 (p35) does give some good advice! I do hope this paper kick-starts research on how to explain uncertainty beyond an academic audience. Looking forward to more!

Was Brexit triggered by the old and unhappy? Or by financial feelings? Journal of Economic Behavior & Organization [RePEc] Published 18th April 2019

Not strictly health economics – although arguably Brexit affects our health – is this impressive study about the factors that contributed to the Leave win in the Brexit referendum. Federica Liberini and colleagues used data from the Understanding Society survey to look at the predictors of people’s views about whether or not the UK should leave the EU. The main results are from a regression on whether or not a person was pro-Brexit, regressed on life satisfaction, their feelings on their financial situation, and other characteristics.

Their conclusions are staggering. They found that people’s views were generally unrelated to their age, their life satisfaction or their income. Instead, it was a person’s feelings about their financial situation that was the strongest predictor. For economists, it may be a bit cringe-worthy to see OLS used for a categorical dependent variable. But to be fair, the authors mention that the results are similar with non-linear models and they report extensive supplementary analyses. Remarkably, they’re making the individual level data available on the 18th of June here.

As the authors discuss, it is not clear if we’re looking at predictive estimates of characteristics related to pro-Brexit feeling or at causal estimates of factors that led to the pro-Brexit feeling. That is, if we could improve someone’s perceived financial situation, would we reduce their probability of feeling pro-Brexit? In any case, the message is clear. Feelings matter!

How does treating chronic hepatitis C affect individuals in need of organ transplants in the United Kingdom? Value in Health Published 8th March 2019

Anupam Bapu Jena and colleagues looked at the spillover benefits of curing hepatitis C given its consequences on the supply and demand of liver and other organs for transplant in the UK. They compare three policies: the status quo, in which there is no screening for hepatitis C and organ donation by people with hepatitis C is rare; universal screen and treat policy where cured people opt-in for organ donation; and similarly, but with opt-out for organ donation.

To do this, they adapted a previously developed queuing model. For the status quo, the model inputs were estimated by calibrating the model outputs to reported NHS performance. They then changed the model inputs to reflect the anticipated impact of the new policies. Importantly, they assumed that all patients with hepatitis C would be cured and no longer require a transplanted organ; conversely, that cured patients would donate organs at similar rates to the general population. They predict that curing hepatitis C would directly reduce the waiting list for organ transplants by reducing the number of patients needing them. Also, there would be an indirect benefit via increasing their availability to other patients. These consequences aren’t typically included in the cost-effectiveness analysis of treatments for hepatitis C, which means that their comparative benefits and costs may not be accurate.

Keeping in the theme of uncertainty, it was disappointing that the paper does not include some sort of confidence bounds on its results nor does it present sensitivity analysis to their assumptions, which in my view, were quite favourable towards a universal screen and test policy. This is an interesting application of a queuing model, which is something I don’t often see in cost-effectiveness analysis. It is also timely and relevant, given the recent drive by the NHS to eliminate hepatitis C. In a few years’ time, we’ll hopefully know to what extent the predicted spillover benefits were realised.

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