Sam Watson’s journal round-up for 26th November 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.

Alcohol and self-control: a field experiment in India. American Economic Review Forthcoming

Addiction is complex. For many people it is characterised by a need or compulsion to take something, often to prevent withdrawal, often in conflict with a desire to not take it. This conflicts with Gary Becker’s much-maligned rational theory of addiction, which views the addiction as a choice to maximise utility in the long term. Under Becker’s model, one could use market-based mechanisms to end repeated, long-term drug or alcohol use. By making the cost of continuing to use higher then people would choose to stop. This has led to the development of interventions like conditional payment or cost mechanisms: a user would receive a payment on condition of sobriety. Previous studies, however, have found little evidence people would be willing to pay for such sobriety contracts. This article reports a randomised trial among rickshaw drivers in Chennai, India, a group of people with a high prevalence of high alcohol use and dependency. The three trial arms consisted of a control arm who received an unconditional daily payment, a treatment arm who received a small payment plus extra if they passed a breathalyser test, and a third arm who had the choice between either of the two payment mechanisms. Two findings are of much interest. First, the incentive payments significantly increased daytime sobriety, and second, over half the participants preferred the conditional sobriety payments over the unconditional payments when they were weakly dominated, and a third still preferred them even when the unconditional payments were higher than the maximum possible conditional payment. This conflicts with a market-based conception of addiction and its treatment. Indeed, the nature of addiction means it can override all intrinsic motivation to stop, or do anything else frankly. So it makes sense that individuals are willing to pay for extrinsic motivation, which in this case did make a difference.

Heterogeneity in long term health outcomes of migrants within Italy. Journal of Health Economics [PubMed] [RePEc] Published 2nd November 2018

We’ve discussed neighbourhood effects a number of times on this blog (here and here, for example). In the absence of a randomised allocation to different neighbourhoods or areas, it is very difficult to discern why people living there or who have moved there might be better or worse off than elsewhere. This article is another neighbourhood effects analysis, this time framed through the lens of immigration. It looks at those who migrated within Italy in the 1970s during a period of large northward population movements. The authors, in essence, identify the average health and mental health of people who moved to different regions conditional on duration spent in origin destinations and a range of other factors. The analysis is conceptually similar to that of two papers we discussed at length on internal migration in the US and labour market outcomes in that it accounts for the duration of ‘exposure’ to poorer areas and differences between destinations. In the case of the labour market outcomes papers, the analysis couldn’t really differentiate between a causal effect of a neighbourhood increasing human capital, differences in labour market conditions, and unobserved heterogeneity between migrating people and families. Now this article examining Italian migration looks at health outcomes, such as the SF-12, which limit the explanations since one cannot ‘earn’ more health by moving elsewhere. Nevertheless, the labour market can still impact upon health strongly.

The authors carefully discuss the difficulties in identifying causal effects here. A number of model extensions are also estimated to try to deal with some issues discussed. This includes a type of propensity score weighting approach, although I would emphasize that this categorically does not deal with issues of unobserved heterogeneity. A finite mixture model is also estimated. Generally a well-thought-through analysis. However, there is a reliance on statistical significance here. I know I do bang on about statistical significance a lot, but it is widely used inappropriately. A rule of thumb I’ve adopted for reviewing papers for journals is that if the conclusions would change if you changed the statistical significance threshold then there’s probably an issue. This article would fail that test. They use a threshold of p<0.10 which seems inappropriate for an analysis with a sample size in the tens of thousands and they build a concluding narrative around what is and isn’t statistically significant. This is not to detract from the analysis, merely its interpretation. In future, this could be helped by banning asterisks in tables, like the AER has done, or better yet developing submission guidelines around its use.

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Sam Watson’s journal round-up for Monday 20th August 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 Randomized Trial of Epinephrine in Out-of-Hospital Cardiac Arrest. New England Journal of Medicine. Published July 2018.

Adrenaline (epinephrine) is often administered to patients in cardiac arrest in order to increase blood flow and improve heart rhythm. However, there had been some concern about the potential adverse effects of using adrenaline, and a placebo controlled trial was called for. This article presents the findings of this trial. While there is little economics in this article, it is an interesting example of what I believe to be erroneous causal thinking, especially in the way it was reported in the media. For example, The Guardian‘s headline was,

Routine treatment for cardiac arrest doubles risk of brain damage – study

while The Telegraph went for the even more inflammatory

Cardiac arrest resuscitation drug has needlessly brain-damaged thousands

But what did the study itself say about their findings:

the use of epinephrine during resuscitation for out-of-hospital cardiac arrest resulted in a significantly higher rate of survival at 30 days than the use of placebo. […] although the rate of survival was slightly better, the trial did not show evidence of a between-group difference in the rate of survival with a favorable neurologic outcome. This result was explained by a higher proportion of patients who survived with severe neurologic disability in the epinephrine group.

Clearly, a slightly more nuanced view, but nevertheless it leaves room for the implication that the adrenaline is causing the neurological damage. Indeed the authors go on to say that “the use of epinephrine did not improve neurologic outcome.” But a counterfactual view of causation should lead us to ask what would have happened to those who survived with brain damage had they not been given adrenaline.

We have a competing risks set up: (A) survival with favourable neurologic outcome, (B) survival with neurologic impairment, and (C) death. The proportion of patients with outcome (A) was slightly higher in the adrenaline group (although not statistically significant so apparently no effect eyes roll), the proportion of patients with outcome (B) was a lot higher in the adrenaline group, and the proportion of patients with outcome (C) was lower in the adrenaline group. This all suggests to me that the adrenaline caused patients who would have otherwise died to mostly survive with brain damage, and a few to survive impairment free, not that adrenaline caused those who would have otherwise been fine to have brain damage. So the question in response to the above quotes is then, is death a preferable neurologic outcome to brain damage? As trite as this may sound, it is a key health economics question – how do we value these health states?

Incentivizing Safer Sexual Behavior: Evidence from a Lottery Experiment on HIV Prevention. American Economic Review: Applied Economics. [RePEcPublished July 2018. 

This article presents a randomised trial testing an interesting idea. People who are at high risk of HIV and other sexually transmitted infections (STIs) and often those who engage in riskier sexual behaviour. A basic decision theoretic conception would be that those individuals don’t consider the costs to be high enough relative to the benefits (although there is clearly some divide between this explanation and how people actually think in terms of risky sexual behaviour, much like any other seemingly irrational behaviour). Conditional cash transfers can change the balance of the decision to incentivise people to act differently, what this study looks at is using a conditional lottery with the chance of high winnings instead, since this should be more attractive still to risk-seeking individuals. While the trial was designed to reduce HIV prevalance, entry into the lottery in the treatment arm was conditional on being free of two curable STIs at each round – this enabled people who fail to be eligible again, and also allowed the entry of HIV-positive individuals whose sexual behaviour is perhaps the most important to reducing HIV transmission. The lottery arm of the trial was found to have 20% lower incidence over the study period compared to the control arm – quite impressive. However, the cost-effectiveness of the program was estimated to be $882 per HIV infection averted on the basis of lottery payments alone, and around $3,300 per case averted all in. This seems quite high to me. Despite a plethora of non-comparable outcomes in cost-effectiveness studies of HIV public health interventions other studies have reported costs per cases averted an order of magnitude lower than this. The conclusions seems to be then that the idea works well – it’s just too costly to be of much use.

Monitoring equity in universal health coverage with essential services for neglected tropical diseases: an analysis of data reported for five diseases in 123 countries over 9 years. The Lancet: Global Health. [PubMedPublished July 2018. 

Universal health coverage (UHC) is one the key parts of Sustainable Development Goal (SDG) 3, good health and well-being. The text of the SDG identifies UHC as being about access to services – but this word “access” in the context of health care is often vague and nebulous. Many people mistakenly treat access to health services as synonymous with use of health services, but having access to something is not dependent on whether you actually use it or not. Barriers to a person’s ability to use health care for a given complaint are numerous: financial cost, time cost, lack of education, language barrier, and so forth. It is therefore difficult to quantify and measure access. Hogan and co-authors proposed an index to quantify and monitor UHC across the world that was derived from a number of proxies such as women with four or more antenatal visits, children with vaccines, blood pressure, and health worker density. Their work is useful but of course flawed – these proxies all capture something different, either access, use, or health outcomes – and it is unclear that they are all sensitive to the same underlying construct. Needless to say, we should still be able to diagnose access issues from some combination of these data. This article extends the work of Hogan et al to look at neglected tropical diseases, which affect over 1.5 billion, yet which are, obviously, neglected. The paper uses ‘preventative chemotherapy coverage’ as its key measure, which is the proportion of those needed the chemotherapy who actually receive it. This is a measure of use and not access (although they should be related), for example, there may be near universal availability of the chemotherapy, but various factors on the demand side limiting use. Needless to say, the measure should still be a useful diagnostic tool and it is interesting to see how much worse countries perform on this metric for neglected tropical diseases than general health care.

 

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Chris Sampson’s journal round-up for 2nd 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.

Choice in the presence of experts: the role of general practitioners in patients’ hospital choice. Journal of Health Economics [PubMed] [RePEc] Published 26th June 2018

In the UK, patients are in principle free to choose which hospital they use for elective procedures. However, as these choices operate through a GP referral, the extent to which the choice is ‘free’ is limited. The choice set is provided by the GP and thus there are two decision-makers. It’s a classic example of the principal-agent relationship. What’s best for the patient and what’s best for the local health care budget might not align. The focus of this study is on the applied importance of this dynamic and the idea that econometric studies that ignore it – by looking only at patient decision-making or only at GP decision-making – may give bias estimates. The author outlines a two-stage model for the choice process that takes place. Hospital characteristics can affect choices in three ways: i) by only influencing the choice set that the GP presents to the patient, e.g. hospital quality, ii) by only influencing the patient’s choice from the set, e.g. hospital amenities, and iii) by influencing both, e.g. waiting times. The study uses Hospital Episode Statistics for 30,000 hip replacements that took place in 2011/12, referred by 4,721 GPs to 168 hospitals, to examine revealed preferences. The choice set for each patient is not observed, so a key assumption is that all hospitals to which a GP made referrals in the period are included in the choice set presented to patients. The main findings are that both GPs and patients are influenced primarily by distance. GPs are influenced by hospital quality and the budget impact of referrals, while distance and waiting times explain patient choices. For patients, parking spaces seem to be more important than mortality ratios. The results support the notion that patients defer to GPs in assessing quality. In places, it’s difficult to follow what the author did and why they did it. But in essence, the author is looking for (and in most cases finding) reasons not to ignore GPs’ preselection of choice sets when conducting econometric analyses involving patient choice. Econometricians should take note. And policymakers should be asking whether freedom of choice is sensible when patients prioritise parking and when variable GP incentives could give rise to heterogeneous standards of care.

Using evidence from randomised controlled trials in economic models: what information is relevant and is there a minimum amount of sample data required to make decisions? PharmacoEconomics [PubMed] Published 20th June 2018

You’re probably aware of the classic ‘irrelevance of inference’ argument. Statistical significance is irrelevant in deciding whether or not to fund a health technology, because we ought to do whatever we expect to be best on average. This new paper argues the case for irrelevance in other domains, namely multiplicity (e.g. multiple testing) and sample size. With a primer on hypothesis testing, the author sets out the regulatory perspective. Multiplicity inflates the chance of a type I error, so regulators worry about it. That’s why triallists often obsess over primary outcomes (and avoiding multiplicity). But when we build decision models, we rely on all sorts of outcomes from all sorts of studies, and QALYs are never the primary outcome. So what does this mean for reimbursement decision-making? Reimbursement is based on expected net benefit as derived using decision models, which are Bayesian by definition. Within a Bayesian framework of probabilistic sensitivity analysis, data for relevant parameters should never be disregarded on the basis of the status of their collection in a trial, and it is up to the analyst to properly specify a model that properly accounts for the effects of multiplicity and other sources of uncertainty. The author outlines how this operates in three settings: i) estimating treatment effects for rare events, ii) the number of trials available for a meta-analysis, and iii) the estimation of population mean overall survival. It isn’t so much that multiplicity and sample size are irrelevant, as they could inform the analysis, but rather that no data is too weak for a Bayesian analyst.

Life satisfaction, QALYs, and the monetary value of health. Social Science & Medicine [PubMed] Published 18th June 2018

One of this blog’s first ever posts was on the subject of ‘the well-being valuation approach‘ but, to date, I don’t think we’ve ever covered a study in the round-up that uses this method. In essence, the method is about estimating trade-offs between (for example) income and some measure of subjective well-being, or some health condition, in order to estimate the income equivalence for that state. This study attempts to estimate the (Australian) dollar value of QALYs, as measured using the SF-6D. Thus, the study is a rival cousin to the Claxton-esque opportunity cost approach, and a rival sibling to stated preference ‘social value of a QALY’ approaches. The authors are trying to identify a threshold value on the basis of revealed preferences. The analysis is conducted using 14 waves of the Australian HILDA panel, with more than 200,000 person-year responses. A regression model estimates the impact on life satisfaction of income, SF-6D index scores, and the presence of long-term conditions. The authors adopt an instrumental variable approach to try and address the endogeneity of life satisfaction and income, using an indicator of ‘financial worsening’ to approximate an income shock. The estimated value of a QALY is found to be around A$42,000 (~£23,500) over a 2-year period. Over the long-term, it’s higher, at around A$67,000 (~£37,500), because individuals are found to discount money differently to health. The results also demonstrate that individuals are willing to pay around A$2,000 to avoid a long-term condition on top of the value of a QALY. The authors apply their approach to a few examples from the literature to demonstrate the implications of using well-being valuation in the economic evaluation of health care. As with all uses of experienced utility in the health domain, adaptation is a big concern. But a key advantage is that this approach can be easily applied to large sets of survey data, giving powerful results. However, I haven’t quite got my head around how meaningful the results are. SF-6D index values – as used in this study – are generated on the basis of stated preferences. So to what extent are we measuring revealed preferences? And if it’s some combination of stated and revealed preference, how should we interpret willingness to pay values?

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