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.

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Sam Watson’s journal round-up for 1st 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.

Systematic review of health economic impact evaluations of risk prediction models: stop developing, start evaluating. Value in Health [PubMed] Published April 2017

Risk prediction models are pervasive in clinical medicine. For example, one 2012 review of type 2 diabetes (T2DM) models identified 16 studies with 25 models. There was not much difference between the models in ability to predict T2DM and models including biomarkers were slightly better. But, obviously no model is perfect, the T2DM risk prediction tools generally overestimated the risk of development of diabetes. One could see parallels here with screening. When subjected to cost-benefit analyses, many screening programs become somewhat controversial. False positives can cause harm to patients both psychologically and through further procedures they may be subjected to. Such concerns thus may also apply to risk prediction models. This review surveys the literature on health economic evaluations of risk prediction models. Forty studies examining 60 risk models were included. Compare this number with the total of T2DM models above and you will see how the authors might arrive at the conclusion that economic evaluations of risk prediction models are rare. Another key finding, and one I empathize with as I am currently reviewing economic evaluations in another area of heath economics, is that there is a large amount of methodological heterogeneity and quality differences between studies. This makes comparisons difficult if not impossible. This limits the utility of these findings to decision makers. A routine, standardised approach to economic evaluation is needed.

The fading American dream: trends in absolute income mobility since 1940. Science [PubMed] [RePEc] Published 28th April 2017

This one is not strictly health. But it’s findings may have important implications for how we understand the relationship between income and health, and the inter-generational transmission of health. And, it’s not everyday an economics paper gets into Science. Economic mobility is a key goal for many societies – children should earn more than their parents. One way of examining this quantitatively is the proportion of children who earn more than their parents. This paper shows that this can be estimated using (i) the marginal income distribution of children, (ii) the marginal income distribution of parents, and (iii) the joint distribution of child and parent income ranks. The key finding is that mobility has declined over the 20th Century. While around 90% of children were earning more than their parents in 1940, by 1980 this is only around 40%. The authors look at what would happen to these estimates if GDP growth were more equally distributed and find much of the decline in mobility would be reversed.

Economic consequences of legal and illegal drugs: the case of social costs in Belgium. International Journal of Drug Policy [PubMed] Published 23rd April 2017

Put ten economists in a room and you’ll get 11 different opinions. Or so the saying goes. But while there is division on a number of topics in economics, some issues find a strong consensus. Drug prohibition is one of those issues many economists agree on. As a policy is has high costs and reasonably little benefit, especially when harm reduction is the goal. David Nutt, whose work we’ve discussed before, is a prominent critic of the UK government’s policy on drugs. Just this week he has discussed how the recent increase in the use of and health problems due to ‘spice’ (synthetic cannabinoids) may well be attributable to the prohibition of natural cannabis. However, recreational drug use, whether illegal or legal, does bear a societal cost. This paper attempts to quantify both the indirect and direct costs of drug use in Belgium. They take a ‘cost of illness’ approach, a term I think is a little unsuitable for the topic – most drug use causes no harm so could hardly be called illness. They also refer to the drugs as ‘addictive substances’, which is also a stretch for what they consider. Costs are further divided into health care and crime costs. The headline finding is that the total cost is 4.6 billion Euros annually. Interestingly, for illegal drugs, law enforcement expenditure was higher than the health care costs. In my mind this further undermines a prohibition policy. However, I think this study reveals the difficulty of taking an objective stance on these matters. Recreational substance use is an ‘illness’ and ‘addictive’ and bears a cost to society – the word ‘benefit’ is mentioned only once.

New metrics for economic evaluation in the presence of heterogeneity: focusing on evaluating policy alternatives rather than treatment alternatives. Medical Decision Making [PubMed] Published 25th April 2017

Cost-effectiveness analyses (CEA) are a key aspect of the evaluation of medical technologies and pharmaceutical products. Typically, the main output of these analyses is an incremental cost-effectiveness ratio (ICER) or other summary measure of incremental costs and benefits. However, these ICERs typically use an average treatment effect and complete adoption. This is unlikely to be realistic, though, from a policy perspective. Both effectiveness and adoption rates may differ between sub-groups. This paper proposes a ‘policy’ framework that takes this heterogeneity into account. In essence, the paper advocates a weighted average ICER taking into account adoption rates and heterogeneous effectiveness. It takes this idea a step further and considers uncertainty about all the parameters. Conceptually, the framework is a straightforward extension of CEA, but the paper is clear and lucid and it certainly makes sense to evaluate technologies on the basis of how they will actually be used. Similar ideas have been used to take forward clinical trial design: with more information patients will make different treatment choices, for example. The trouble is, innovative and sensible ideas can be very slow to catch on.

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Sam Watson’s journal round-up for 10th 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.

Expertise versus Bias in Evaluation: Evidence from the NIH. American Economic Journal: Applied Economics. Published April 2017.

As an academic’s career progresses, she learns two things: patience and learning to deal with rejection. Getting a paper accepted by a top journal is hard. Obtaining funding for what seems like a good idea similarly so. We sometimes convince ourselves that the system is rigged, or at least biased. Research funding bodies may make poor decisions. This paper considers this question in great deal. While reviewers may have an informational advantage that allows them to assess quality, they may also be biased towards projects in their own domain of expertise. More funding for health economics blogs! To assess this, this paper examines 100,000 applications to the US National Institutes of Health. The proximity of the reviewer to the subject area of the application is judged by the number of times the reviewer has cited the work of the applicant. Quality is judged by the number of publications and citations the research produces – an attempt is made to adapt this to judge unfunded work. The principle finding is that reviewers are both more informed and more biased about work in their own field. Each additional permanent reviewer in a applicant’s area is estimated to increase the chance of funding by 2.2 percent, an equivalent effect to increasing quality by one quarter standard deviation. These effects seem small, as the author notes, and what strikes me is how little variation these measures in explain in funding decisions. Perhaps I will find some solace in the fact that there is quite a lot of apparent randomness in what gets funded. Nevertheless, the author suggests that the findings suggest that by trying to reduce bias by using impartial reviewers, the ability to judge quality will also decline.

Long-term effects of youth unemployment on mental health: does an economic crisis make a difference? Journal of Epidemiology and Community Health. [PubMedPublished April 2017.

Unemployment is related to mental health issues. The effect is appears to be particularly acute among young people for whom the transition to adult life can be difficult. Indeed, at this vulnerable period young people also transition from youth to adult mental health services, which breaks their continuity of care. Many become lost in the system. Services in many areas are being redesigned in light of this. This paper asks if the effect of unemployment on youth mental health is different depending on the economic conditions. Do period of high unemployment nationally exacerbate the effects of becoming unemployed? Surprisingly, the paper concludes, no, there is no difference. I say ‘surprisingly’ since I cannot recall finding a paper in this area or one that has featured on this blog with a negative finding. The analyses seem careful, and the authors concentrate on the magnitude of the effects, rather than statistical significance. Large sample sizes are required for adequate power to test a hypothesis on an interaction; this study does have a large sample size. The interactive effect is likely to be very small, not necessarily non-existent. But in comparison with the large effects of unemployment on youth mental health in general, the effect of economic conditions is of little importance. Nevertheless, Simpson’s paradox may rear its head here: during times of high unemployment, the cohort of the unemployed will be different. If those who only become unemployed during economic downturns have lower risk of mental health issues, then this may attenuate the estimated effect of unemployment on mental health. This issue is not addressed unfortunately, but I don’t want that to detract from a sensible use of statistics.

The Distortionary Effects of Incentives in Government: Evidence from China’s ‘Death Ceiling’ Program. American Economic Journal: Applied Economics. [RePEcPublished April 2017.

Targets and incentives to achieve those targets can distort the actions of agents. This is especially true of difficult to observe outcomes. People may be more inclined to manipulate the data than to actually achieve the target. Gaming and other similar behaviours have been noted in health services, for example. This article examines a policy in China designed to reduce the high rates of accidental deaths. In 2004 the State Administration of Work Safety announced that provinces would have to reduce their rate of accidental deaths by 2.5% per year. The provinces were set a so-called ‘death ceiling’. In 2012, the policy was declared a success; accidental death rates had come down by 45% since 2005. But further examination of the data, which were made publicly available in the state newspaper the People’s Daily, suggests this may not be the case. First of all, there was a sharp discontinuity of accidental deaths right below the death ceiling. This discontinuity was not consistent with a continuous variable. Provinces had much discretion about how to achieve the reductions. Those that used significant incentives for local officials were more likely to be successful. The authors also consider why, if the data were manipulated, deaths weren’t made to look significantly below the death ceiling rather than just below the death ceiling. They speculate that this would have the effect of making next year’s death ceiling even lower and more difficult to achieve. This paper provides a nice narrative that adds to our understanding of the perverse effects of incentives. For health services this is important. For many of the difficult to observe outcomes, like patient health, merely incentivising doctors and hospitals to improve may have little actual benefit.

 

 

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