Rita Faria’s journal round-up for 4th November 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.

The marginal benefits of healthcare spending in the Netherlands: estimating cost-effectiveness thresholds using a translog production function. Health Economics [PubMed] Published 30th August 2019

The marginal productivity of the healthcare sector or, as commonly known, the supply-side cost-effectiveness threshold, is a hot topic right now. A few years ago, we could only guess at the magnitude of health that was displaced by reimbursing expensive and not-that-beneficial drugs. Since the seminal work by Karl Claxton and colleagues, we have started to have a pretty good idea of what we’re giving up.

This paper by Niek Stadhouders and colleagues adds to this literature by estimating the marginal productivity of hospital care in the Netherlands. Spoiler alert: they estimated that hospital care generates 1 QALY for around €74,000 at the margin, with 95% confidence intervals ranging from €53,000 to €94,000. Remarkably, it’s close to the Dutch upper reference value for the cost-effectiveness threshold at €80,000!

The approach for estimation is quite elaborate because it required building QALYs and costs, and accounting for the effect of mortality on costs. The diagram in Figure 1 is excellent in explaining it. Their approach is different from the Claxton et al method, in that they corrected for the cost due to changes in mortality directly rather than via an instrumental variable analysis. To estimate the marginal effect of spending on health, they use a translog function. The confidence intervals are generated with Monte Carlo simulation and various robustness checks are presented.

This is a fantastic paper, which will be sure to have important policy implications. Analysts conducting cost-effectiveness analysis in the Netherlands, do take note.

Mixed-effects models for health care longitudinal data with an informative visiting process: a Monte Carlo simulation study. Statistica Neerlandica Published 5th September 2019

Electronic health records are the current big thing in health economics research, but they’re not without challenges. One issue is that the data reflects the clinical management, rather than a trial protocol. This means that doctors may test more severe patients more often. For example, people with higher cholesterol may get more frequent cholesterol tests. The challenge is that traditional methods for longitudinal data assume independence between observation times and disease severity.

Alessandro Gasparini and colleagues set out to solve this problem. They propose using inverse intensity of visit weighting within a mixed-methods model framework. Importantly, they provide a Stata package that includes the method. It’s part of the wide ranging and super-useful merlin package.

It was great to see how the method works with the directed acyclic graph. Essentially, after controlling for confounders, the longitudinal outcome and the observation process are associated through shared random effects. By assuming a distribution for the shared random effects, the model blocks the path between the outcome and the observation process. It makes it sound easy!

The paper goes through the method, compares it with other methods in the literature in a simulation study, and applies to a real case study. It’s a brilliant paper that deserves a close look by all of those using electronic health records.

Alternative approaches for confounding adjustment in observational studies using weighting based on the propensity score: a primer for practitioners. BMJ [PubMed] Published 23rd October 2019

Would you like to use a propensity score method but don’t know where to start? Look no further! This paper by Rishi Desai and Jessica Franklin provides a practical guide to propensity score methods.

They start by explaining what a propensity score is and how it can be used, from matching to reweighting and regression adjustment. I particularly enjoyed reading about the importance of conceptualising the target of inference, that is, what treatment effect are we trying to estimate. In the medical literature, it is rare to see a paper that is clear on whether it is average treatment effect or average treatment effect among the treated population.

I found the algorithm for method selection really useful. Here, Rishi and Jessica describe the steps in the choice of the propensity score method and recommend their preferred method for each situation. The paper also includes the application of each method to the example of dabigatran versus warfarin for atrial fibrillation. Thanks to the graphs, we can visualise how the distribution of the propensity score changes for each method and depending on the target of inference.

This is an excellent paper to those starting their propensity score analyses, or for those who would like a refresher. It’s a keeper!

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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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Thesis Thursday: Kevin Momanyi

On the third Thursday of every month, we speak to a recent graduate about their thesis and their studies. This month’s guest is Dr Kevin Momanyi who has a PhD from the University of Aberdeen. If you would like to suggest a candidate for an upcoming Thesis Thursday, get in touch.

Title
Enhancing quality in social care through economic analysis
Supervisors
Paul McNamee
Repository link
http://digitool.abdn.ac.uk/webclient/DeliveryManager?pid=240815

What are reablement and telecare services and why should economists study them?

Reablement and telecare are two types of services within homecare that enable individuals to live independently in their own homes with little or no assistance from other people. Reablement focuses on helping individuals relearn the skills needed for independent living after an illness or injury. It is a short term intervention that lasts for about 6 to 12 weeks and usually involves several health care professionals and social care workers working together to meet some set objectives. Telecare, on the other hand, entails the use of devices (e.g. community alarms and linked pill dispensers) to facilitate communication between homecare clients and their care providers in the event of an accident or negative health shock. Economists should study reablement and telecare so as to determine whether or not the services have value for money and also develop policies that would reduce social care costs without compromising the welfare of the populace.

In what ways did your study reach beyond the scope of previous research?

My study extended the previous studies in three main ways. Firstly, I estimated the treatment effects in a non-experimental setting unlike the previous studies that used either randomised controlled trials or quasi-experiments. Secondly, I used linked administrative health and social care data in Scotland for the 2010/2011 financial year. The data covered the administrative records for the entire Scottish population and was larger and more robust than the data used by the previous studies. Thirdly, the previous studies were simply concerned with quantifying the treatment effects and thus did not provide a rationale as to how the interventions affect the outcomes of interest. My thesis addressed this knowledge gap by formulating an econometric model that links the demand for reablement/telecare to several outcomes.

How did you go about trying to estimate treatment effects from observational data?

I used a theory driven approach combined with specialised econometric techniques in order to estimate the treatment effects. The theoretical model drew from the Almost Ideal Demand System (AIDS), Andersen’s Behavioural Model of Health Services Use, the Grossman Model of the demand for health capital, and Samuelson’s Revealed Preference Theory; whereas the estimation strategy simultaneously controlled for unexplained trend variations, potential endogeneity of key variables, potential sample selection bias and potential unobserved heterogeneity. For a more substantive discussion of the theoretical model and estimation strategy, see Momanyi, 2018. Although the majority of the studies in the econometric literature advocate for the use of quasi-experimental study designs in estimating treatment effects using observational data, I provided several proofs in my thesis showing that these designs do not always yield consistent results, and that estimating the econometric models in the way that I did is preferable since it nests several study designs and estimation strategies as special cases.

Are there key groups of people that could benefit from greater use of reablement and telecare services?

According to the empirical results of my thesis, there is sufficient evidence to conclude that there are certain groups within the population that could benefit from greater use of telecare. For instance, one empirical study investigating the effect of telecare use on the expected length of stay in hospital showed that the community alarm users with physical disabilities are more likely than the other community alarm users to have a shorter length of stay in hospital, holding other factors constant. Correspondingly, the results also showed that the individuals who use more advanced telecare devices than the community alarm and who are also considered to be frail elderly are expected to have a relatively shorter length of stay in hospital as compared to the other telecare users in the population, all else equal. A discussion of various econometric models that can be used to link telecare use to the length of stay in hospital can be found in Momanyi, 2017.

What would be your main recommendation for policymakers in Scotland?

The main recommendation for policymakers is that they ought to subsidise the cost of telecare services, especially in regions that currently have relatively low utilisation levels, so as to increase the uptake of telecare in Scotland. This was informed by a decomposition analysis that I conducted in the first empirical study to shed light on what could be driving the observed direct relationship between telecare use and independent living at home. The analysis showed that the treatment effect was in part due to the underlying differences (both observable and unobservable) between telecare users and non-users, and thus policymakers could stimulate telecare use in the population by addressing these differences. In addition to that, policymakers should advise the local authorities to target telecare services at the groups of people that are most likely to benefit from them as well as sensitise the population on the benefits of using community alarms. This is because the econometric analyses in my thesis showed that the treatment effects are not homogenous across the population, and that the use of a community alarm is expected to reduce the likelihood of unplanned hospitalisation, whereas the use of the other telecare devices has the opposite effect all else equal.

Can you name one thing that you wish you could have done as part of your PhD, which you weren’t able to do?

I would have liked to include in my thesis an empirical study on the effects of reablement services. My analyses focused only on telecare use as the treatment variable due to data limitations. This additional study would have been vital in validating the econometric model that I developed in the first chapter of the thesis as well as addressing the gaps in knowledge that were identified by the literature review. In particular, it would have been worthwhile to determine whether reablement services should be offered to individuals discharged from hospital or to individuals who have been selected into the intervention directly from the community.