Chris Sampson’s journal round-up for 5th 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.

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 for health outcomes. Health Economics [PubMed] [RePEc] Published 19th June 2019

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.

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Alastair Canaway’s journal round-up for 30th July 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.

Is there an association between early weight status and utility-based health-related quality of life in young children? Quality of Life Research [PubMed] Published 10th July 2018

Childhood obesity is an issue which has risen to prominence in recent years. Concurrently, there has been an increased interest in measuring utility values in children for use in economic evaluation. In the obesity context, there are relatively few studies that have examined whether childhood weight status is associated with preference-based utility and, following, whether such measures are useful for the economic evaluation of childhood obesity interventions. This study sought to tackle this issue using the proxy version of the Health Utilities Index Mark 3 (HUI-3) and weight status data in 368 children aged five years. Associations between weight status and HUI-3 score were assessed using various regression techniques. No statistically significant differences were found between weight status and preference-based health-related quality of life (HRQL). This adds to several recent studies with similar findings which imply that young children may not experience any decrements in HRQL associated with weight status, or that the measures we have cannot capture these decrements. When considering trial-based economic evaluation of childhood obesity interventions, this highlights that we should not be solely relying on preference-based instruments.

Time is money: investigating the value of leisure time and unpaid work. Value in Health Published 14th July 2018

For those of us who work on trials, we almost always attempt to do some sort of ‘societal’ perspective incorporating benefits beyond health. When it comes to valuing leisure time and unpaid work there is a dearth of literature and numerous methodological challenges which has led to a bit of a scatter-gun approach to measuring and valuing (usually by ignoring) this time. The authors in the paper sought to value unpaid work (e.g. household chores and voluntary work) and leisure time (“non-productive” time to be spent on one’s likings, nb. this includes lunch breaks). They did this using online questionnaires which included contingent valuation exercises (WTP and WTA) in a sample of representative adults in the Netherlands. Regression techniques following best practice were used (two-part models with transformed data). Using WTA they found an additional hour of unpaid work and leisure time was valued at €16 Euros, whilst the WTP value was €9.50. These values fall into similar ranges to those used in other studies. There are many issues with stated preference studies, which the authors thoroughly acknowledge and address. These costs, so often omitted in economic evaluation, have the potential to be substantial and there remains a need to accurately value this time. Capturing and valuing these time costs remains an important issue, specifically, for those researchers working in countries where national guidelines for economic evaluation prefer a societal perspective.

The impact of depression on health-related quality of life and wellbeing: identifying important dimensions and assessing their inclusion in multi-attribute utility instruments. Quality of Life Research [PubMed] Published 13th July 2018

At the start of every trial, we ask “so what measures should we include?” In the UK, the EQ-5D is the default option, though this decision is not often straightforward. Mental health disorders have a huge burden of impact in terms of both costs (economic and healthcare) and health-related quality of life. How we currently measure the impact of such disorders in economic evaluation often receives scrutiny and there has been recent interest in broadening the evaluative space beyond health to include wellbeing, both subjective wellbeing (SWB) and capability wellbeing (CWB). This study sought to identify which dimensions of HRQL, SWB and CWB were most affected by depression (the most common mental health disorder) and to examine the sensitivity of existing multi-attribute utility instruments (MAUIs) to these dimensions. The study used data from the “Multi-Instrument Comparison” study – this includes lots of measures, including depression measures (Depression Anxiety Stress Scale, Kessler Psychological Distress Scale); SWB measures (Personal Wellbeing Index, Satisfaction with Life Scale, Integrated Household Survey); CWB (ICECAP-A); and multi-attribute utility instruments (15D, AQoL-4D, AQoL-8D, EQ-5D-5L, HUI-3, QWB-SA, and SF-6D). To identify dimensions that were important, the authors used the ‘Glass’s Delta effect size’ (the difference between the mean scores of healthy and self-reported groups divided by the standard deviation of the healthy group). To investigate the extent to which current MAUIs capture these dimensions, each MAUI was regressed on each dimension of HRQL, CWB and SWB. There were lots of interesting findings. Unsurprisingly, the most important dimensions were in the psychosocial dimensions of HRQL (e.g. the ‘coping’, ‘happiness’, and ‘self-worth’ dimensions of the AQoL-8D). Interestingly, the ICECAP-A proved to be the best measure for distinguishing between healthy individuals and those with depression. The SWB measures, on the other hand, were less impacted by depression. Of the MAUIs, the AQoL-8D was the most sensitive, whilst our beloved EQ-5D-5L and SF-6D were the least sensitive at distinguishing dimensions. There is a huge amount to unpack within this study, but it does raise interesting questions regarding measurement issues and the impact of broadening the evaluative space for decision makers. Finally, it’s worth noting that a new MAUI (ReQoL) for mental health has been recently developed – although further testing is needed, this is something to consider in future.

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Brent Gibbons’s journal round-up for 9th April 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.

The effect of Medicaid on management of depression: evidence from the Oregon Health Insurance Experiment. The Milbank Quarterly [PubMed] Published 5th March 2018

For the first journal article of this week’s AHE round-up, I selected a follow-up study on the Oregon health insurance experiment. The Oregon Health Insurance Experiment (OHIE) used a lottery system to expand Medicaid to low-income uninsured adults (and their associated households) who were previously ineligible for coverage. Those interested in being part of the study had to sign up. Individuals were then randomly selected through the lottery, after which individuals needed to take further action to complete enrollment in Medicaid, which included showing that enrollment criteria were satisfied (e.g. income below 100% of poverty line). These details are important because many who were selected for the lottery did not complete enrollment in Medicaid, though being selected through the lottery was associated with a 25 percentage point increase in the probability of having insurance (which the authors confirm was overwhelmingly due to Medicaid and not other insurance). More details on the study and data are publicly available. The OHIE is a seminal study in that it allows researchers to study the effects of having insurance in an experimental design – albeit in the U.S. health care system’s context. The other study that comes to mind is of course the famous RAND health insurance experiment that allowed researchers to study the effects of different levels of health insurance coverage. For the OHIE, the authors importantly point out that it is not necessarily obvious what the impact of having insurance is. While we would expect increases in health care utilization, it is possible that increases in primary care utilization could result in offsetting reductions in other settings (e.g. hospital or emergency department use). Also, while we would expect increases in health as a result of increases in health care use, it is possible that by reducing adverse financial consequences (e.g. of unhealthy behavior), health insurance could discourage investments in health. Medicaid has also been criticized by some as not very good insurance – though there are strong arguments to the contrary. First-year outcomes were detailed in another paper. These included increased health care utilization (across all settings), decreased out-of-pocket medical expenditures, decreased medical debt, improvements in self-reported physical and mental health, and decreased probability of screening positive for depression. In the follow-up paper on management of depression, the authors further explore the causal effect and causal pathway of having Medicaid on depression diagnosis, treatment, and symptoms. Outcomes of interest are the effect of having Medicaid on the prevalence of undiagnosed and untreated depression, the use of depression treatments including medication, and on self-reported depressive symptoms. Where possible, outcomes are examined for those with a prior depression diagnosis and those without. In order to examine the effect of Medicaid insurance (vs. being uninsured), the authors needed to control for the selection bias introduced from uncompleted enrollment into Medicaid. Instrumental variable 2SLS was used with lottery selection as the sole instrument. Local average treatment effects were reported with clustered standard errors on the household. The effect of Medicaid on the management of depression was overwhelmingly positive. For those with no prior depression diagnosis, it increased the chance of receiving a diagnosis and decreased the prevalence of undiagnosed depression (those who scored high on study survey depression instrument but with no official diagnosis). As far as treatment, Medicaid reduced the share of the population with untreated depression, virtually eliminating untreated depression among those with pre-lottery depression. There was a large reduction in unmet need for mental health treatment and an increased share who received specific mental health treatments (i.e. prescription drugs and talk therapy). For self-reported symptoms, Medicaid reduced the overall rate screened for depression symptoms in the post-lottery period. All effects were relatively strong in magnitude, giving an overall convincing picture that Medicaid increased access to treatment, which improved depression symptoms. The biggest limitation of this study is its generalizability. Much of the results were focused on the city of Portland, which may not represent more rural parts of the state. More importantly, this was limited to the state of Oregon for low-income adults who not only expressed interest in signing up, but who were able to follow through to complete enrollment. Other limitations were that the study only looked at the first two years of outcomes and that there was limited information on the types of treatments received.

Tobacco regulation and cost-benefit analysis: how should we value foregone consumer surplus? American Journal of Health Economics [PubMed] [RePEcPublished 23rd January 2018

This second article addresses a very interesting theoretical question in cost-benefit analysis, that has emerged in the context of tobacco regulation. The general question is how should foregone consumer surplus, in the form of reduced smoking, be valued? The history of this particular question in the context of recent FDA efforts to regulate smoking is quite fascinating. I highly recommend reading the article just for this background. In brief, the FDA issued proposed regulations to implement graphic warning labels on cigarettes in 2010 and more recently proposed that cigars and e-cigarettes should also be subject to FDA regulation. In both cases, an economic impact analysis was required and debates ensued on if, and how, foregone consumer surplus should be valued. Economists on both sides weighed-in, some arguing that the FDA should not consider foregone consumer surplus because smoking behavior is irrational, others arguing consumers are perfectly rational and informed and the full consumer surplus should be valued, and still others arguing that some consumer surplus should be counted but there is likely bounded rationality and that it is methodologically unclear how to perform a valuation in such a case. The authors helpfully break down the debate into the following questions: 1) if we assume consumers are fully informed and rational, what is the right approach? 2) are consumers fully informed and rational? and 3) if consumers are not fully informed and rational, what is the right approach? The reason the first question is important is that the FDA was conducting the economic impact analysis by examining health gains and foregone consumer surplus separately. However, if consumers are perfectly rational and informed, their preferences already account for health impacts, meaning that only changes in consumer surplus should be counted. On the second question, the authors explore the literature on smoking behavior to understand “whether consumers are rational in the sense of reflecting stable preferences that fully take into account the available information on current and expected future consequences of current choices.” In general, the literature shows that consumers are pretty well aware of the risks, though they may underestimate the difficulty of quitting. On whether consumers are rational is a much harder question. The authors explore different rational addiction models, including quasi-rational addiction models that take into account more recent developments in behavioral economics, but declare that the literature at this point provides no clear answer and that no empirical test exists to distinguish between rational and quasi-rational models. Without answering whether consumers are fully informed and rational, the authors suggest that welfare analysis – even in the face of bounded rationality – can still use a similar valuation approach to consumer surplus as was recommended for when consumers are fully informed and rational. A series of simple supply and demand curves are presented where there is a biased demand curve (demand under bounded rationality) and an unbiased demand curve (demand where fully informed and rational) and different regulations are illustrated. The implication is that rather than trying to estimate health gains as a result of regulations, what is needed is to understand the amount of demand bias as result of bounded rationality. Foregone consumer surplus can then be appropriately measured. Of course, more research is needed to estimate if, and how much, ‘demand bias’ or bounded rationality exists. The framework of the paper is extremely useful and it pushes health economists to consider advances that have been made in environmental economics to account for bounded rationality in cost-benefit analysis.

2SLS versus 2SRI: appropriate methods for rare outcomes and/or rare exposures. Health Economics [PubMed] Published 26th March 2018

This third paper I will touch on only briefly, but I wanted to include it as it addresses an important methodological topic. The paper explores several alternative instrumental variable estimation techniques for situations when the treatment (exposure) variable is binary, compared to the common 2SLS (two-stage least squares) estimation technique which was developed for a linear setting with continuous endogenous treatments and outcome measures. A more flexible approach, referred to as 2SRI (two-stage residual inclusion) allows for non-linear estimation methods in the first stage (and second stage), including logit or probit estimation methods. As the title suggests, these alternative estimation methods may be particularly useful when treatment (exposure) and/or outcomes are rare (e.g below 5%). Monte Carlo simulations are performed on what the authors term ‘the simplest case’ where the outcome, treatment, and instrument are binary variables and a range of results are considered as the treatment and/or outcome become rarer. Model bias and consistency are assessed in the ability to produce average treatment effects (ATEs) and local average treatment effects (LATEs), comparing the 2SLS, several forms of probit-probit 2SRI models, and a bivariate probit model. Results are that the 2SLS produced biased estimates of the ATE, especially as treatment and outcomes become rarer. The 2SRI models had substantially higher bias than the bivariate probit in producing ATEs (though the bivariate probit requires the assumption of bivariate normality). For LATE, 2SLS always produces consistent estimates, even if the linear probability model produces out of range predictions. Estimates for 2SRI models and the bivariate probit model were biased in producing LATEs. An empirical example was also tested with data on the impact of long-term care insurance on long-term care use. Conclusions are that 2SRI models do not dependably produce unbiased estimates of ATEs. Among the 2SRI models though, there were varying levels of bias and the 2SRI model with generalized residuals appeared to produce the least ATE bias. For more rare treatments and outcomes, the 2SRI model with Anscombe residuals generated the least ATE bias. Results were similar to another simulation study by Chapman and Brooks. The study enhances our understanding of how different instrumental variable estimation methods may function under conditions where treatment and outcome variables have nonlinear distributions and where those same treatments and outcomes are rare. In general, the authors give a cautionary note to say that there is not one perfect estimation method in these types of conditions and that researchers should be aware of the potential pitfalls of different estimation methods.

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