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 barriers and facilitators to model replication within health economics. Value in Health Published 16th July 2019
Replication is a valuable part of the scientific process, especially if there are uncertainties about the validity of research methods. When it comes to cost-effectiveness modelling, there are endless opportunities for researchers to do things badly, even with the best intentions. Attempting to replicate modelling studies can therefore support health care decision-making. But replication studies are rarely conducted, or, at least, rarely reported. The authors of this study sought to understand the factors that can make replication easy or difficult, with a view to informing reporting standards.
The authors attempted to replicate five published cost-effectiveness modelling studies, with the aim of recreating the key results. Each replication attempt was conducted by a different author and we’re even given a rating of the replicator’s experience level. The characteristics of the models were recorded and each replicator detailed – anecdotally – the things that helped or hindered their attempt. Some replications were a resounding failure. In one case, the replicated cost per patient was more than double the original, at more than £1,000 wide of the mark. Replicators reported that having a clear diagram of the model structure was a big help, as was the provision of example calculations and explicit listing of the key assumptions. Various shortcomings made replication difficult, all relating to a lack of clarity or completeness in reporting. The impact of this on the validation attempt was exacerbated if the model either involved lots of scenarios that weren’t clearly described or if the model had a long time horizon.
The quality of each study was assessed using the Philips checklist, and all did pretty well, suggesting that the checklist is not sufficient for ensuring replicability. If you develop and report cost-effectiveness models, this paper could help you better understand how end-users will interpret your reporting and make your work more replicable. This study focusses on Markov models. They’re definitely the most common approach, so perhaps that’s OK. It might be useful to produce prescriptive guidance specific to Markov models, informed by the findings of this study.
US integrated delivery networks perspective on economic burden of patients with treatment-resistant depression: a retrospective matched-cohort study. PharmacoEconomics – Open [PubMed] Published 28th June 2019
Treatment-resistant depression can be associated high health care costs, as multiple lines of treatment are tried, with patients experiencing little or no benefit. New treatments and models of care can go some way to addressing these challenges. In the US, there’s some reason to believe that integrated delivery networks (IDNs) could be associated with lower care costs, because IDNs are based on collaborative care models and constitute a single point of accountability for patient costs. They might be particularly useful in the case of treatment-resistant depression, but evidence is lacking. The authors of this study investigated the difference in health care resource use and costs for patients with and without treatment-resistant depression, in the context of IDNs.
The researchers conducted a retrospective cohort study using claims data for people receiving care from IDNs, with up to two years follow-up from first antidepressant use. 1,582 people with treatment-resistant depression were propensity score matched to two other groups – patients without depression and patients with depression that was not classified as treatment-resistant. Various regression models were used to compare the key outcomes of all-cause and specific categories of resource use and costs. Unfortunately, there is no assessment of whether the selected models are actually any good at estimating differences in costs.
The average costs and resource use levels in the three groups ranked as you would expect: $25,807 per person per year for the treatment-resistant group versus $13,701 in the non-resistant group and $8,500 in the non-depression group. People with treatment-resistant depression used a wider range of antidepressants and for a longer duration. They also had twice as many inpatient visits as people with depression that wasn’t treatment-resistant, which seems to have been the main driver of the adjusted differences in costs.
We don’t know (from this study) whether or not IDNs provide a higher quality of care. And the study isn’t able to compare IDN and non-IDN models of care. But it does show that IDNs probably aren’t a full solution to the high costs of treatment-resistant depression.
Rabin’s paradox arises from the theoretical demonstration that a risk-averse individual who turns down a 50:50 gamble of gaining £110 or losing £100 would, if expected utility theory is correct, turn down a 50:50 gamble of losing £1,000 or gaining millions. This is because of the assumed concave utility function over wealth that is used to model risk aversion and it is probably not realistic. But we don’t know about the relevance of this paradox in the health domain… until now.
A key contribution of this paper is that it considers both decision-making about one’s own health and decision-making from a societal perspective. Three different scenarios are set-up in each case, relating to gains and losses in life expectancy with different levels of health functioning. 201 students were recruited as part of a larger study on preferences and each completed all six gamble-pairs (three individual, three societal). To test for Rabin’s paradox, the participants were asked whether they would accept each gamble involving a moderate stake and a large stake.
In short, the authors observe Rabin’s proposed failure of expected utility theory. Many participants rejected small gambles but did not reject the larger gambles. The effect was more pronounced for societal preferences. Though there was a large minority for whom expected utility theory was not violated. The upshot of all this is that our models of health preferences that are based on expected utility may be flawed where uncertain outcomes are involved – as they often are in health. This study adds to a growing body of literature supporting the relevance of alternative utility theories, such as prospect theory, to health and health care.
My only problem here is that life expectancy is not health. Life expectancy is everything. It incorporates the monetary domain, which this study did not want to consider, as well as every other domain of life. When you die, your stock of cash is as useful to you as your stock of health. I think it would have been more useful if the study focussed only on health status and outcomes and excluded all considerations of death.