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!

Credits

Brendan Collins’s journal round-up for 3rd December 2018

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.

A framework for conducting economic evaluations alongside natural experiments. Social Science & Medicine Published 27th November 2018

I feel like Social Science & Medicine is publishing some excellent health economics papers lately and this is another example. Natural experiment methods, like instrumental variables, difference in difference, and propensity matching, are increasingly used to evaluate public health policy interventions. This paper provides a review and a framework for how to incorporate economic evaluation alongside this. And even better, it has a checklist! It goes into some detail in describing each item in the checklist which I think will be really useful. A couple of the items seemed a bit peculiar to me, like talking about “Potential behavioural responses (e.g. ‘nudge effects’)” – I would prefer a more general term like causal mechanism. And it has multi-criteria decision analysis (MCDA) as a potential method. I love MCDA but I think that using MCDA would surely require a whole new set of items on the checklist, for instance, to record how MCDA weights have been decided. (For me, saying that CEA is insufficient so we should use MCDA instead is like saying I find it hard to put IKEA furniture together so I will make my own furniture from scratch.) My hope with checklists is that they actually improve practice, rather than just being used in a post hoc way to include a few caveats and excuses in papers.

Autonomy, accountability, and ambiguity in arm’s-length meta-governance: the case of NHS England. Public Management Review Published 18th November 2018

It has been said that NICE in England serves a purpose of insulating politicians from the fallout of difficult investment decisions, for example recommending that people with mild Alzheimers disease do not get certain drugs. When the coalition government gained power in the UK in 2010, there was initially talk that NICE’s role of approving drugs may be reduced. But the government may have realised that NICE serve a useful role of being a focus of public and media anger when new drugs are rejected on cost-effectiveness grounds. And so it may be with NHS England (NHSE), which according to this paper, as an arms-length body (ALB), has powers that exceed what was initially planned.

This paper uses meta-governance theory, examining different types of control mechanisms and the relationship between the ALB and the sponsor (Department for Health and Social Care), and how they impact on autonomy and accountability. It suggests that NHSE is operating at a macro, policy-making level, rather than an operational, implementation level. Policy changes from NHSE are presented by ministers as coming ‘from’ the NHS but, in reality, the NHS is much bigger than NHSE. NHSE was created to take political interference out of decision-making and let civil servants get on with things. But before reading this paper, it had not occurred to me how much power NHSE had accrued, and how this may create difficulties in terms of accountability for reasonableness. For instance, NHSE have a very complicated structure and do not publish all of their meeting minutes so it is difficult to understand how investment decisions are made. It may be that the changes that have happened in the NHS since 2012 were intended to involve healthcare professionals more in local investment decisions. But actually, a lot of power in terms of shaping the balance of hierarchies, markets and networks has ended up in NHSE, sitting in a hinterland between politicians in Whitehall and local NHS organisations. With a new NHS Plan reportedly delayed because of Brexit chaos, it will be interesting to see what this plan says about accountability.

How health policy shapes healthcare sector productivity? Evidence from Italy and UK. Health Policy [PubMed] Published 2nd November 2018

This paper starts with an interesting premise: the English and Italian state healthcare systems (the NHS and the SSN) are quite similar (which I didn’t know before). But the two systems have had different priorities in the time period from 2004-2011. England focused on increasing activity, reducing waiting times and quality improvements while Italy focused on reducing hospital beds as well as reducing variation and unnecessary treatments. This paper finds that productivity increased more quickly in the NHS than the SSN from 2004-2011. This paper is ambitious in its scope and the data the authors have used. The model uses input-specific price deflators, so it includes the fact that healthcare inputs increase in price faster than other industries but treats this as exogenous to the production function. This price inflation may be because around 75% of costs are staff costs, and wage inflation in other industries produces wage inflation in the NHS. It may be interesting in future to analyse to what extent the rate of inflation for healthcare is inevitable and if it is linked in some way to the inputs and outputs. We often hear that productivity in the NHS has not increased as much as other industries, so it is perhaps reassuring to read a paper that says the NHS has performed better than a similar health system elsewhere.

Credits

Thesis Thursday: Cheryl Jones

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 Cheryl Jones who has a PhD from the University of Manchester. If you would like to suggest a candidate for an upcoming Thesis Thursday, get in touch.

Title
The economics of presenteeism in the context of rheumatoid arthritis, ankylosing spondylitis and psoriatic arthritis
Supervisors
Katherine Payne, Suzanne Verstappen, Brenda Gannon
Repository link
https://www.research.manchester.ac.uk/portal/en/theses/the-economics-of-presenteeism-in-the-context-of-rheumatoid-arthritis-ankylosing-spondylitis-and-psoriatic-arthritis%288215e79a-925e-4664-9a3c-3fd42d643528%29.html

What attracted you to studying health-related presenteeism?

I was attracted to study presenteeism because it gave me a chance to address both normative and positive issues. Presenteeism, a concept related to productivity, is a controversial topic in the economic evaluation of healthcare technologies and is currently excluded from health economic evaluations, following the recommendation made by the NICE reference case. The reasons why productivity is excluded from economic evaluations are important and valid, however, there are some circumstances where excluding productivity is difficult to defend. Presenteeism offered an opportunity for me to explore and question the social value judgements that underpin economic evaluation methods with respect to productivity. In terms of positive issues related to presenteeism, research into the development of methods that can be used to measure and value presenteeism was (and still is) limited. This provided an opportunity to think creatively about the types of methods we could use, both quantitative and qualitative, to address and further methods for quantifying presenteeism.

Are existing tools adequate for measuring and valuing presenteeism in inflammatory arthritic conditions?

That is the question! Research into methods that can be used to quantify presenteeism is still in its infancy. Presenteeism is difficult to measure accurately because there are a lack of objective measures that can be used, for example, the number of cars assembled per day. As a consequence, many methods rely on self-report surveys, which tend to suffer from bias, such as reporting or recall bias. Methods that have been used to value presenteeism have largely focused on valuing presenteeism as a cost using the human capital approach (HCA: volume of presenteeism multiplied by a monetary factor). The monetary factor typically used to convert the volume of presenteeism into a cost value is wages. Valuing productivity using wages risks taking account of discriminatory factors that are associated with wages, such as age. There are also economic arguments that question whether the value of the wage truly reflects the value of productivity. My PhD focused on developing a method that values presenteeism as a non-monetary benefit, thereby avoiding the need to value it as a cost using wages. Overall, methods to measure and value presenteeism still have some way to go before a ‘gold standard’ can be established, however, there are many experts from many disciplines who are working to improve these methods.

Why was it important to conduct qualitative interviews as part of your research?

The quantitative component of my PhD was to develop an algorithm, using mapping methods, that links presenteeism with health status and capability measures. A study by Connolly et al. recommend conducting qualitative interviews to provide some evidence of face/content validity to establish whether a quantitative link between two measures (or concepts) is feasible and potentially valid. The qualitative study I conducted was designed to understand the extent to which the EQ-5D-5L, SF6D and ICECAP-C were able to capture those aspects of rheumatic conditions that negatively impact presenteeism. The results suggested that all three measures were able to capture those important aspects of rheumatic conditions that affect presenteeism; however, the results indicated that the SF6D would most likely be the most appropriate measure. The results from the quantitative mapping study identified the SF6D as the most suitable outcome measure able to predict presenteeism in working populations with rheumatic conditions. The advantage of the qualitative results was that it provided some evidence that explained why the SF6D was the more suitable measure rather than relying on speculation.

Is it feasible to predict presenteeism using outcome measures within economic evaluation?

I developed an algorithm that links presenteeism, measured using the Work Activity Productivity Impairment (WPAI) questionnaire, with health and capability. Health status was measured using the EQ-5D-5L and SF6D, and capability was measured using the ICECAP-A. The SF6D was identified as the most suitable measure to predict presenteeism in a population of employees with rheumatoid arthritis or ankylosing spondylitis. The results indicate that it is possible to predict presenteeism using generic outcome measures; however, the results have yet to be externally validated. The qualitative interviews provided evidence as to why the SF6D was the better predictor for presenteeism and the result gave rise to questions about the suitability of outcome measures given a specific population. The results indicate that it is potentially feasible to predict presenteeism using outcome measures.

What would be your key recommendation to a researcher hoping to capture the impact of an intervention on presenteeism?

Due to the lack of a ‘gold standard’ method for capturing the impact of presenteeism, I would recommend that the researcher reports and justifies their selection of the following:

  1. Provide a rationale that explains why presenteeism is an important factor that needs to be considered in the analysis.
  2. Explain how and why presenteeism will be captured and included in the analysis; as a cost, monetary benefit, or non-monetary benefit.
  3. Justify the methods used to measure and value presenteeism. It is important that the research clearly reports why specific tools, such as presenteeism surveys, have been selected for use.

Because there is no ‘gold standard’ method for measuring and valuing presenteeism and guidelines do not exist to inform the reporting of methods used to quantify presenteeism, it is important that the researcher reports and justifies their selection of methods used in their analysis.