Sam Watson’s journal round-up for 12th June 2017

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.

Machine learning: an applied econometric approach. Journal of Economic Perspectives [RePEcPublished Spring 2017

Machine learning tools have become ubiquitous in the software we use on a day to day basis. Facebook can identify faces in photos; Google can tell you the traffic for your journey; Netflix can recommend you movies based on what you’ve watched before. Machine learning algorithms provide a way to estimate an unknown function f that predicts an outcome Y given some data x: Y = f(x) + \epsilon. The potential application of these algorithms to many econometric problems is clear. This article outlines the principles of machine learning methods. It divides econometric problems into prediction, \hat{y}, and parameter estimation, \hat{\beta} and suggests machine learning is a useful tool for the former. However, this distinction is a false one, I believe. Parameters are typically estimated because they represent an average treatment effect, say E(y|x=1) - E(y|x=0). But, we can estimate these quantities in ‘\hat{y} problems’ since f(x) = E(y|x). Machine learning algorithms, therefore, represent a non-parametric (or very highly parametric) approach to the estimation of treatment effects. In cases where functional form is unknown, where there may be nonlinearities in the response function, and interactions between variables, this approach can be very useful. They do not represent a panacea to estimation problems of course, since interpretation rests on the assumptions. For example, as Jennifer Hill discusses, additive regression tree methods can be used to estimate conditional average treatment effects if we can assume the treatment is ignorable conditional on the covariates. This article, while providing a good summary of methods, doesn’t quite identify the right niche where these approaches might be useful in econometrics.

Incorporating equity in economic evaluations: a multi-attribute equity state approach. European Journal of Health Economics [PubMedPublished 1st June 2017

Efficiency is a key goal for the health service. Economic evaluation provides evidence to support investment decisions, whether displacing resources from one technology to another can produce greater health benefits. Equity is generally not formally considered except through the final investment decision-making process, which may lead to different decisions by different commissioning groups. One approach to incorporating equity considerations into economic evaluation is the weighting of benefits, such as QALYs, by group. For example, a number of studies have estimated that benefits of end-of-life treatments have a greater social valuation than other treatments. One way of incorporating this into economic evaluation is to raise the cost-effectiveness threshold by an appropriate amount for end-of-life treatments. However, multiple attributes may be relevant for equity considerations, negating a simplistic approach like this. This paper proposed a multi-attribute equity state approach to incorporating equity concerns formally in economic evaluation. The basic premise of this approach is to firstly define a set of morally relevant attributes, to secondly derive a weighting scheme for each set of characteristics (similarly to how QALY weights are derived from the EQ-5D questionnaire), and thirdly to apply these weights to economic evaluation. A key aspect of the last step is to weight both the QALYs gained by a population from a new technology and those displaced from another. Indeed, identifying where resources are displaced from is perhaps the biggest limitation to this approach. This displacement problem has also come up in other discussions revolving around the estimation of the cost-effectiveness threshold. This seems to be an important area for future research.

Financial incentives, hospital care, and health outcomes: evidence from fair pricing laws. American Economic Journal: Economic Policy [RePEcPublished May 2017

There is a not-insubstantial literature on the response of health care providers to financial incentives. Generally, providers behave as expected, which can often lead to adverse outcomes, such as overtreatment in cases where there is potential for revenue to be made. But empirical studies of this behaviour often rely upon the comparison of conditions with different incentive schedules; rarely is there the opportunity to study the effects of relative shifts in incentive within the same condition. This paper studies the effects of fair pricing laws in the US, which limited the amount uninsured patients would have to pay hospitals, thus providing the opportunity to study patients with the same conditions but who represent different levels of revenue for the hospital. The introduction of fair pricing laws was associated with a reduction in total billing costs and length of stay for uninsured patients but little association was seen with changes in quality. A similar effect was not seen in the insured suggesting the price ceiling introduced by the fair pricing laws led to an increase in efficiency.

Credits

Chris Sampson’s journal round-up for 8th May 2017

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.

Verification of decision-analytic models for health economic evaluations: an overview. PharmacoEconomics [PubMed] Published 29th April 2017

Increasingly, it’s expected that model-based economic evaluations can be validated and shown to be fit-for-purpose. However, up to now, discussions have focussed on scientific questions about conceptualisation and external validity, rather than technical questions, such as whether the model is programmed correctly and behaves as expected. This paper looks at how things are done in the software industry with a view to creating guidance for health economists. Given that Microsoft Excel remains one of the most popular software packages for modelling, there is a discussion of spreadsheet errors. These might be errors in logic, simple copy-paste type mistakes and errors of omission. A variety of tactics is discussed. In particular, the authors describe unit testing, whereby individual parts of the code are demonstrated to be correct. Unit testing frameworks do not exist for application to spreadsheets, so the authors recommend the creation of a ‘Tests’ spreadsheet with tests for parameter assignments, functions, equations and exploratory items. Independent review by another modeller is also recommended. Six recommendations are given for taking model verification forward: i) the use of open source models, ii) standardisation in model storage and communication (anyone for a registry?), iii) style guides for script, iv) agency and journal mandates, v) training and vi) creation of an ISPOR/SMDM task force. This is a worthwhile read for any modeller, with some neat tactics that you can build into your workflow.

How robust are value judgments of health inequality aversion? Testing for framing and cognitive effects. Medical Decision Making [PubMed] Published 25th April 2017

Evidence shows that people are often extremely averse to health inequality. Sometimes these super-egalitarian responses imply such extreme preferences that monotonicity is violated. The starting point for this study is the idea that these findings are probably influenced by framing effects and cognitive biases, and that they may therefore not constitute a reliable basis for policy making. The authors investigate 4 hypotheses that might indicate the presence of bias: i) realistic small health inequality reductions vs larger one, ii) population- vs individual-level descriptions, iii) concrete vs abstract intervention scenarios and iv) online vs face-to-face administration. Two samples were recruited: one with a face-to-face discussion (n=52) and the other online (n=83). The questionnaire introduced respondents to health inequality in England before asking 4 questions in the form of a choice experiment, with 20 paired choices. Responses are grouped according to non-egalitarianism, prioritarianism and strict egalitarianism. The main research question is whether or not the alternative strategies resulted in fewer strict egalitarian responses. Not much of an effect was found with regard to large gains or to population-level descriptions. There was evidence that the abstract scenarios resulted in a greater proportion of people giving strong egalitarian responses. And the face-to-face sample did seem to exhibit some social desirability bias, with more egalitarian responses. But the main take-home message from this study for me is that it is not easy to explain-away people’s extreme aversion to health inequality, which is heartening. Yet, as with all choice experiments, we see that the mode of administration – and cognitive effects induced by the question – can be very important.

Adaptation to health states: sick yet better off? Health Economics [PubMed] Published 20th April 2017

Should patients or the public value health states for the purpose of resource allocation? It’s a question that’s cropped up plenty of times on this blog. One of the trickier challenges is understanding and dealing with adaptation. This paper has a pretty straightforward purpose – to look for signs of adaptation in a longitudinal dataset. The authors’ approach is to see whether there is a positive relationship between the length of time a person has an illness and the likelihood of them reporting better health. I did pretty much the same thing (for SF-6D and satisfaction with life) in my MSc dissertation, and found little evidence of adaptation, so I’m keen to see where this goes! The study uses 4 waves of data from the British Cohort Study, looking at self-assessed health (on a 4-point scale) and self-reported chronic illness and health shocks. Latent self-assessed health is modelled using a dynamic ordered probit model. In short, there is evidence of adaptation. People who have had a long-standing illness for a greater duration are more likely to report a higher level of self-assessed health. An additional 10 years of illness is associated with an 8 percentage point increase in the likelihood of reporting ‘excellent’ health. The study is opaque about sample sizes, but I’d guess that finding is based on not-that-many people. Further analyses are conducted to show that adaptation seems to become important only after a relatively long duration (~20 years) and that better health before diagnosis may not influence adaptation. The authors also look at specific conditions, finding that some (e.g. diabetes, anxiety, back problems) are associated with adaptation, while others (e.g. depression, cancer, Crohn’s disease) are not. I have a bit of a problem with this study though, in that it’s framed as being relevant to health care resource allocation and health technology assessment. But I don’t think it is. Self-assessed health in the ‘how healthy are you’ sense is very far removed from the process by which health state utilities are obtained using the EQ-5D. And they probably don’t reflect adaptation in the same way.

Credits

Chris Sampson’s journal round-up for 3rd April 2017

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.

Return on investment of public health interventions: a systematic review. Journal of Epidemiology & Community Health [PubMed] Published 29th March 2017

Cost-effectiveness analysis in the context of public health is tricky. Often the health benefits are small at the individual level and the returns to investment might be cross-sectoral. Lots of smart people believe that spending on public health is low in proportion to other health spending. Here we have a systematic review of studies reporting cost-benefit ratios (CBR) or return on investment (ROI) estimates for public health interventions. The stated aim of the paper is to demonstrate the false economy associated with cuts to public health spending. 52 titles were included from a search that identified 2957. The inclusion and exclusion criteria are not very clear, with some studies rejected on the basis of ‘poor generalisability to the UK’. There’s a bit too much subjectivity sneaking around in the methods for my liking.  Results for CBR and ROI estimates are presented according to local or national level and grouped by ‘specialism’. From all studies, the median CBR was 8.3 and the median ROI was 14.3. As we might have suspected, public health interventions are cost-saving in a big way. National health protection and legislative interventions offered the greatest return on investment. While there is wide variation in the results, all specialism groupings showed a positive return on average. I don’t doubt the truth of the study’s message – that cuts to public health spending are foolish. But the review doesn’t really demonstrate what the authors want it to demonstrate. We don’t know what (if any) disinvestment is taking place with respect to the interventions identified in the review. The results presented in the study represent a useful reference point for discussion and further analysis, but they aren’t a sufficient basis for supporting general increases in public health spending. That said, the study adds to an already resounding call and may help bring more attention to the issue.

Acceptable health and priority weighting: discussing a reference-level approach using sufficientarian reasoning. Social Science & Medicine Published 27th March 2017

In some ways, the moral principle of sufficiency is very attractive. It acknowledges a desire for redistribution from the haves to the have-nots and may also make for a more manageable goal than all-out maximisation. It may also be particularly useful in specific situations, such as evaluating health care for the elderly, for whom ‘full health’ is never achievable and not a meaningful reference point. This paper presents a discussion of the normative issues at play, drawing insights from the distributive justice literature. We’re reminded of the fair innings argument as a familiar sufficientarian flavoured allocation principle. The sufficientarian approach is outlined in contrast to egalitarianism and prioritarianism. Strict sufficientarian value weighting is not a good idea. If we suppose a socially ‘acceptable’ health state value of 0.7, such an approach would – for example – value an improvement from 0.69 to 0.71 for one person as infinitely more valuable than an improvement from 0.2 to 0.6 for the whole population. The authors go on to outline some more relaxed sufficiency weightings, whereby improvements below the threshold are attributed a value greater than 0 (though still less than those achieving sufficiency). The sufficientarian approach alone is (forgive me) an insufficient framework for the allocation of health care resources and cannot represent the kind of societal preferences that have been observed in the literature. Thus, hybrids are proposed. In particular, a sufficientarian-prioritarian weighting function is presented and the authors suggest that this may be a useful basis for priority setting. One can imagine a very weak form of the sufficientarian approach that corresponds to a prioritarian weighting function that is (perhaps) concave below the threshold and convex above it. Still, we have the major problem of identifying a level of acceptable health that is not arbitrary. The real question you need to ask yourself is this: do you really want health economists to start arguing about another threshold?

Emotions and scope effects in the monetary valuation of health. The European Journal of Health Economics [PubMed] Published 24th March 2017

It seems obvious that emotions could affect the value people attach to goods and services, but little research has been conducted with respect to willingness to pay for health services. This study considers the relationship between a person’s self-reported fear of being operated on and their willingness to pay for risk-reducing drug-eluting stents. A sample of 1479 people in Spain made a series of choices between bare-metal stents at no cost and drug-eluting stents with some out-of-pocket cost, alongside a set of sociodemographic questions and a fear of surgery Likert scale. Each respondent provided 8 responses with 4 different risk reductions and 2 different willingness to pay ‘bids’. The authors outline what they call a ‘cognitive-emotional random utility model’ including an ’emotional shift effect’. Four different models are presented to demonstrate the predictive value of the emotion levels interacting with the risk reduction levels. The sample was split roughly in half according to whether people reported high emotion (8, 9 or 10 on the fear Likert) or low emotion (<8). People who reported more fear of being operated on were willing to pay more for risk reductions, which is the obvious result. More interesting is that the high emotion group exhibited a lower sensitivity to scope – that is, there wasn’t much difference in their valuation of the alternative magnitudes of risk reduction. This constitutes a problem for willingness to pay estimates in this group as it may prevent the elicitation of meaningful values, and it is perhaps another reason why we usually go for collective approaches to health state valuation. The authors conclude that emotional response is a bias that needs to be corrected. I don’t buy this interpretation and would tend to the view that the bias that needs correcting here is that of the economist. Emotions may be a justifiable reflection of personality traits that ought to determine preferences, at least at the individual level. But I do agree with the authors that this is an interesting field for further research if only to understand possible sources of heterogeneity in health state valuation.

Credits