Chris Sampson’s journal round-up for 12th 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.

Developing open-source models for the US health system: practical experiences and challenges to date with the Open-Source Value Project. PharmacoEconomics [PubMed] Published 7th August 2019

PharmacoEconomics will soon publish a themed issue on transparency in decision modelling (to which I’ve contributed), and this paper – I assume – is one that will feature. At least one output from the Open-Source Value Project has featured in these round-ups before. The purpose of this paper is to describe the experiences of the initiative in developing and releasing two open-source models, one in rheumatoid arthritis and one in lung cancer.

The authors outline the background to the project and its goal to develop credible models that are more tuned-in to stakeholders’ needs. By sharing the R and C++ source code, developing interactive web applications, and providing extensive documentation, the models are intended to be wholly transparent and flexible. The model development process also involves feedback from experts and the public, followed by revision and re-release. It’s a huge undertaking. The paper sets out the key challenges associated with this process, such as enabling stakeholders with different backgrounds to understand technical models and each other. The authors explain how they have addressed such difficulties along the way. The resource implications of this process are also challenging, because the time and expertise required are much greater than for run-of-the-mill decision models. The advantages of the tools used by the project, such as R and GitHub, are explained, and the paper provides some ammunition for the open-source movement. One of the best parts of the paper is the authors’ challenge to those who question open-source modelling on the basis of intellectual property concerns. For example, they state that, “Claiming intellectually property on the implementation of a relatively common modeling approach in Excel or other programming software, such as a partitioned survival model in oncology, seems a bit pointless.” Agreed.

The response to date from the community has been broadly positive, though there has been a lack of engagement from US decision-makers. Despite this, the initiative has managed to secure adequate funding. This paper is a valuable read for anyone involved in open-source modelling or in establishing a collaborative platform for the creation and dissemination of research tools.

Incorporating affordability concerns within cost-effectiveness analysis for health technology assessment. Value in Health Published 30th July 2019

The issue of affordability is proving to be a hard nut to crack for health economists. That’s probably because we’ve spent a very long time conducting incremental cost-effectiveness analyses that pay little or no attention to the budget constraint. This paper sets out to define a framework that finally brings affordability into the fold.

The author sets up an example with a decision-maker that seeks to maximise population health with a fixed budget – read, HTA agency – and the motivating example is new medicines for hepatitis C. The core of the proposal is an alternative decision rule. Rather than simply comparing the incremental cost-effectiveness ratio (ICER) to a fixed threshold, it incorporates a threshold that is a function of the budget impact. At it’s most basic, a bigger budget impact (all else equal) means a greater opportunity cost and thus a lower threshold. The author suggests doing away with the ICER (which is almost impossible to work with) and instead using net health benefits. In this framework, whether or not net health benefit is greater than zero depends on the size of the budget impact at any given ICER. If we accept the core principle that budget impact should be incorporated into the decision rule, it raises two other issues – time and uncertainty – which are also addressed in the paper. The framework moves us beyond the current focus on net present value, which ignores the distribution of costs over time beyond simply discounting future expenditure. Instead, the opportunity cost ‘threshold’ depends on the budget impact in each time period. The description of the framework also addresses uncertainty in budget impact, which requires the estimation of opportunity costs in each iteration of a probabilistic analysis.

The paper is thorough in setting out the calculations needed to implement this framework. If you’re conducting an economic evaluation of a technology that could have a non-marginal (big) budget impact, you should tag this on to your analysis plan. Once researchers start producing these estimates, we’ll be able to understand how important these differences could be for resource allocation decision-making and determine whether the likes of NICE ought to incorporate it into their methods guide.

Did UberX reduce ambulance volume? Health Economics [PubMed] [RePEc] Published 24th June 2019

In London, you can probably – at most times of day – get an Uber quicker than you can get an ambulance. That isn’t necessarily a bad thing, as ambulances aren’t there to provide convenience. But it does raise an interesting question. Could the availability of super-fast, low-cost, low-effort taxi hailing reduce pressure on ambulance services? If so, we might anticipate the effect to be greatest where people have to actually pay for ambulances.

This study combines data on Uber market entry in the US, by state and city, with ambulance rates. Between Q1 2012 and Q4 2015, the proportion of the US population with access to Uber rose from 0% to almost 25%. The authors are also able to distinguish ‘lights and sirens’ ambulance rides from ‘no lights and sirens’ rides. A difference-in-differences model estimates the ambulance rate for a given city by quarter-year. The analysis suggests that there was a significant decline in ambulance rates in the years following Uber’s entry to the market, implying an average of 1.2 fewer ambulance trips per 1,000 population per quarter.

There are some questionable results in here, including the fact that a larger effect was found for the ‘lights and sirens’ ambulance rate, so it’s not entirely clear what’s going on. The authors describe a variety of robustness checks for our consideration. Unfortunately, the discussion of the results is lacking in detail and insight, so readers need to figure it out themselves. I’d be very interested to see a similar analysis in the UK. I suspect that I would be inclined to opt for an Uber over an ambulance in many cases. And I wouldn’t have the usual concern about Uber exploiting its drivers, as I dare say ambulance drivers aren’t treated much better.

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Rita Faria’s journal round-up for 18th June 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.

Objectives, budgets, thresholds, and opportunity costs—a health economics approach: an ISPOR Special Task Force report. Value in Health [PubMedPublished 21st February 2018

The economic evaluation world has been discussing cost-effectiveness thresholds for a while. This paper has been out for a few months, but it slipped under my radar. It explains the relationship between the cost-effectiveness threshold, the budget, opportunity costs and willingness to pay for health. My take-home messages are that we should use cost-effectiveness analysis to inform decisions both for publicly funded and privately funded health care systems. Each system has a budget and a way of raising funds for that budget. The cost-effectiveness threshold should be specific for each health care system, in order to reflect its specific opportunity cost. The budget can change for many reasons. The cost-effectiveness threshold should be adjusted to reflect these changes and hence reflect the opportunity cost. For example, taxpayers can increase their willingness to pay for health through increased taxes for the health care system. We are starting to see this in the UK with the calls to raise taxes to increase the NHS budget. It is worth noting that the NICE threshold may not warrant adjustment upwards since research suggests that it does not reflect the opportunity cost. This is a welcome paper on the topic and a must read, particularly if you’re arguing for the use of cost-effectiveness analysis in settings that traditionally were reluctant to embrace it, such as the US.

Basic versus supplementary health insurance: access to care and the role of cost effectiveness. Journal of Health Economics [RePEc] Published 31st May 2018

Using cost-effectiveness analysis to inform coverage decisions not only for the public but also for the privately funded health care is also a feature of this study by Jan Boone. I’ll admit that the equations are well beyond my level of microeconomics, but the text is good at explaining the insights and the intuition. Boone grapples with the question about how the public and private health care systems should choose which technologies to cover. Boone concludes that, when choosing which technologies to cover, the most cost-effective technologies should be prioritised for funding. That the theory matches the practice is reassuring to an economic evaluator like myself! One of the findings is that cost-effective technologies which are very cheap should not be covered. The rationale being that everyone can afford them. The issue for me is that people may decide not to purchase a highly cost-effective technology which is very cheap. As we know from behaviour economics, people are not rational all the time! Boone also concludes that the inclusion of technologies in the universal basic package should consider the prevalence of the conditions in those people at high risk and with low income. The way that I interpreted this is that it is more cost-effective to include technologies for high-risk low-income people in the universal basic package who would not be able to afford these technologies otherwise, than technologies for high-income people who can afford supplementary insurance. I can’t cover here all the findings and the nuances of the theoretical model. Suffice to say that it is an interesting read, even if you avoid the equations like myself.

Surveying the cost effectiveness of the 20 procedures with the largest public health services waiting lists in Ireland: implications for Ireland’s cost-effectiveness threshold. Value in Health Published 11th June 2018

As we are on the topic of cost-effectiveness thresholds, this is a study on the threshold in Ireland. This study sets out to find out if the current cost-effectiveness threshold is too high given the ICERs of the 20 procedures with the largest waiting lists. The idea is that, if the current cost-effectiveness threshold is correct, the procedures with large and long waiting lists would have an ICER of above the cost-effectiveness threshold. If the procedures have a low ICER, the cost-effectiveness threshold may be set too high. I thought that Figure 1 is excellent in conveying the discordance between ICERs and waiting lists. For example, the ICER for extracapsular extraction of crystalline lens is €10,139/QALY and the waiting list has 10,056 people; the ICER for surgical tooth removal is €195,155/QALY and the waiting list is smaller at 833. This study suggests that, similar to many other countries, there are inefficiencies in the way that the Irish health care system prioritises technologies for funding. The limitation of the study is in the ICERs. Ideally, the relevant ICER compares the procedure with the standard care in Ireland whilst on the waiting list (“no procedure” option). But it is nigh impossible to find ICERs that meet this condition for all procedures. The alternative is to assume that the difference in costs and QALYs is generalisable from the source study to Ireland. It was great to see another study on empirical cost-effectiveness thresholds. Looking forward to knowing what the cost-effectiveness threshold should be to accurately reflect opportunity costs.

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James Lomas’s journal round-up for 21st May 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.

Decision making for healthcare resource allocation: joint v. separate decisions on interacting interventions. Medical Decision Making [PubMed] Published 23rd April 2018

While it may be uncontroversial that including all of the relevant comparators in an economic evaluation is crucial, a careful examination of this statement raises some interesting questions. Which comparators are relevant? For those that are relevant, how crucial is it that they are not excluded? The answer to the first of these questions may seem obvious, that all feasible mutually exclusive interventions should be compared, but this is in fact deceptive. Dakin and Gray highlight inconsistency between guidelines as to what constitutes interventions that are ‘mutually exclusive’ and so try to re-frame the distinction according to whether interventions are ‘incompatible’ – when it is physically impossible to implement both interventions simultaneously – and, if not, whether interventions are ‘interacting’ – where the costs and effects of the simultaneous implementation of A and B do not equal the sum of these parts. What I really like about this paper is that it has a very pragmatic focus. Inspired by policy arrangements, for example single technology appraisals, and the difficulty in capturing all interactions, Dakin and Gray provide a reader-friendly flow diagram to illustrate cases where excluding interacting interventions from a joint evaluation is likely to have a big impact, and furthermore propose a sequencing approach that avoids the major problems in evaluating separately what should be considered jointly. Essentially when we have interacting interventions at different points of the disease pathway, evaluating separately may not be problematic if we start at the end of the pathway and move backwards, similar to the method of backward induction used in sequence problems in game theory. There are additional related questions that I’d like to see these authors turn to next, such as how to include interaction effects between interventions and, in particular, how to evaluate system-wide policies that may interact with a very large number of interventions. This paper makes a great contribution to answering all of these questions by establishing a framework that clearly distinguishes concepts that had previously been subject to muddied thinking.

When cost-effective interventions are unaffordable: integrating cost-effectiveness and budget impact in priority setting for global health programs. PLoS Medicine [PubMed] Published 2nd October 2017

In my opinion, there are many things that health economists shouldn’t try to include when they conduct cost-effectiveness analysis. Affordability is not one of these. This paper is great, because Bilinski et al shine a light on the worldwide phenomenon of interventions being found to be ‘cost-effective’ but not affordable. A particular quote – that it would be financially impossible to implement all interventions that are found to be ‘very cost-effective’ in many low- and middle-income countries – is quite shocking. Bilinski et al compare and contrast cost-effectiveness analysis and budget impact analysis, and argue that there are four key reasons why something could be ‘cost-effective’ but not affordable: 1) judging cost-effectiveness with reference to an inappropriate cost-effectiveness ‘threshold’, 2) adoption of a societal perspective that includes costs not falling upon the payer’s budget, 3) failing to make explicit consideration of the distribution of costs over time and 4) the use of an inappropriate discount rate that may not accurately reflect the borrowing and investment opportunities facing the payer. They then argue that, because of this, cost-effectiveness analysis should be presented along with budget impact analysis so that the decision-maker can base a decision on both analyses. I don’t disagree with this as a pragmatic interim solution, but – by highlighting these four reasons for divergence of results with such important economic consequences – I think that there will be further reaching implications of this paper. To my mind, Bilinski et al essentially serves as a call to arms for researchers to try to come up with frameworks and estimates so that the conduct of cost-effectiveness analysis can be improved in order that paradoxical results are no longer produced, decisions are more usefully informed by cost-effectiveness analysis, and the opportunity costs of large budget impacts are properly evaluated – especially in the context of low- and middle-income countries where the foregone health from poor decisions can be so significant.

Patient cost-sharing, socioeconomic status, and children’s health care utilization. Journal of Health Economics [PubMed] Published 16th April 2018

This paper evaluates a policy using a combination of regression discontinuity design and difference-in-difference methods. Not only does it do that, but it tackles an important policy question using a detailed population-wide dataset (a set of linked datasets, more accurately). As if that weren’t enough, one of the policy reforms was actually implemented as a result of a vote where two politicians ‘accidentally pressed the wrong button’, reducing concerns that the policy may have in some way not been exogenous. Needless to say I found the method employed in this paper to be a pretty convincing identification strategy. The policy question at hand is about whether demand for GP visits for children in the Swedish county of Scania (Skåne) is affected by cost-sharing. Cost-sharing for GP visits has occurred for different age groups over different periods of time, providing the basis for regression discontinuities around the age threshold and treated and control groups over time. Nilsson and Paul find results suggesting that when health care is free of charge doctor visits by children increase by 5-10%. In this context, doctor visits happened subject to telephone triage by a nurse and so in this sense it can be argued that all of these visits would be ‘needed’. Further, Nilsson and Paul find that the sensitivity to price is concentrated in low-income households, and is greater among sickly children. The authors contextualise their results very well and, in addition to that context, I can’t deny that it also particularly resonated with me to read this approaching the 70th birthday of the NHS – a system where cost-sharing has never been implemented for GP visits by children. This paper is clearly also highly relevant to that debate that has surfaced again and again in the UK.

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