# 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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# Chris Sampson’s journal round-up for 29th 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.

“Naming and framing”: The impact of labeling on health state values for multiple sclerosis. Medical Decision Making [PubMedPublished 21st May 2017

Tell someone that the health state that they’re valuing is actually related to cancer, and they’ll give you a different value than if you hadn’t mentioned cancer. A lower value, probably. There’s a growing amount of evidence that ‘labelling’ health state descriptions with the name of a particular disease can influence the resulting values. Generally, the evidence is that mentioning the disease will lower values, though that’s probably because researchers have been selecting diseases that they think will show this. (Has anyone tried it for hayfever?) The jury is out on whether labelling is a good thing or a bad thing, so in the meantime, we need evidence for particular diseases to help us understand what’s going on. This study looks at MS. Two UK-representative samples (n = 1576; n = 1641) completed an online TTO valuation task for states defined using the condition-specific preference-based MSIS-8D. Participants were first asked to complete the MSIS-8D to provide their own health state, and then to rank three MSIS-8D states and also complete a practice TTO task. For the preference elicitation proper, individuals were presented with a set of 5 MSIS-8D health states. One group were asked to imagine that they had MS and were provided with some information and a link to the NHS Choices website. The authors’ first analysis tests for a difference due to labelling. Their second analysis creates two alternative tariffs for the MSIS-8D based on the two surveys. People in the label group reported lower health state values on average. The size of this labelling-related decrement was greater for less severe health states. The creation of the tariffs seemed to show that labelling does not have a consistent impact across dimensions. This means that, in practice, the two tariffs could favour different types of interventions, depending on for which dimensions benefits might be observed. The tariff derived from the label group demonstrated slightly poorer predictive performance. This study tells us that label-or-not is a decision that will influence the relative cost-effectiveness of interventions for MS. But we still need a sound basis for making that choice.

Nudges in a post-truth world. Journal of Medical Ethics [PubMed] Published 19th May 2017

Not everyone likes the idea of nudges. They can be used to get people to behave in ways that are ‘better’… but who decides what is better? Truth, surely, we can all agree, is better. There are strong forces against the truth, whether they be our own cognitive biases, the mainstream media (FAKE NEWS!!!), or Nutella trying to tell us they offer a healthy breakfast option thanks to all that calcium. In this essay, the author outlines a special kind of nudge, which he refers to as a ‘nudge to reason’. The paper starts with a summary of the evidence regarding the failure of people to change their minds in response to evidence, and the backfire effect, whereby false beliefs become even more entrenched in light of conflicting evidence. Memory failures, and the ease with which people can handle the information, are identified as key reasons for perverse responses to evidence. The author then goes on to look at the evidence in relation to the conditions in which people do respond to evidence. In particular, where people get their evidence matters (we still trust academics, right?). The persuasiveness of evidence can be influenced by the way it is delivered. So why not nudge towards the truth? The author focuses on a key objection to nudges; that they do not protect freedom in a substantive sense because they bypass people’s capacities for deliberation. Nudges take advantage of non-rational features of human nature and fail to treat people as autonomous agents deserving of respect. One of the reasons I’ve never much like nudges is that they could promote ignorance and reinforce biases. Nudges to reason, on the other hand, influence behaviour indirectly via beliefs: changing behaviour by changing minds by improving responses to genuine evidence. The author argues that nudges to reason do not bypass the deliberative capacities of agents at all, but rather appeal to them, and are thus permissible. They operate by appealing to mechanisms that are partially constitutive of rationality and this is itself part of what defines our substantive freedom. We could also extend this to argue that we have a moral responsibility to frame arguments in a way that is truth-conducive, in order to show respect to individuals. I think health economists are in a great position to contribute to these debates. Our subfield exists principally because of uncertainty and asymmetry of information in health care. We’ve been studying these things for years. I’m convinced by the author’s arguments about the permissibility of nudges to reason. But they’d probably make for flaccid public policy. Nudges to reason would surely be dominated by nudges to ignorance. Either people need coercing towards the truth or those nudges to ignorance need to be shut down.

How should hospital reimbursement be refined to support concentration of complex care services? Health Economics [PubMed] Published 19th May 2017

Treating rare and complex conditions in specialist centres may be good for patients. We might expect these patients to be especially expensive to treat compared with people treated in general hospitals. Therefore, unless reimbursement mechanisms are able to account for this, specialist hospitals will be financially disadvantaged and concentration might not be sustainable. Healthcare Resource Groups (HRGs) – the basis for current payments – only work if variation in cost is not related to any differences in the types of patients treated at particular hospitals. This study looks at hospitals that might be at risk of financial disadvantage due to differences in casemix complexity. Individual-level Hospital Episode Statistics for 2013-14 were matched to hospital-level Reference Costs and a set of indicators for the use of specialist services were applied. The data included 12.4 million patients of whom 766,204 received complex care. The authors construct a random effects model estimating the cost difference associated with complex care, by modelling the impact of a set of complex care markers on individual-level cost estimates. The Gini coefficient is estimated to look at the concentration of complex care across hospitals. Most of the complex care markers were associated with significantly higher costs. 26 of 69 types of complex care were associated with costs more than 10% higher. What’s more, complex care was concentrated among relatively few hospitals with a mean Gini coefficient of 0.88. Two possible approaches to fixing the payment system are considered: i) recalculation of the HRG price to include a top-up or ii) a more complex refinement of the allocation of patients to different HRGs. The second option becomes less attractive as more HRGs are subject to this refinement as we could end up with just one hospital reporting all of the activity for a particular HRG. Based on the expected impact of these differences – in view of the size of the cost difference and the extent of distribution across different HRGs and hospitals – the authors are able to make recommendations about which HRGs might require refinement. The study also hints at an interesting challenge. Some of the complex care services were associated with lower costs where care was concentrated in very few centres, suggesting that concentration could give rise to cost savings. This could imply that some HRGs may need refining downwards with complexity, which feels a bit counterintuitive. My only criticism of the paper? The references include at least 3 web pages that are no longer there. Please use WebCite, people!

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# Paul Mitchell’s journal round-up for 17th 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.

Is foreign direct investment good for health in low and middle income countries? An instrumental variable approach. Social Science & Medicine [PubMed] Published 28th March 2017

Foreign direct investment (FDI) is considered a key benefit of globalisation in the economic development of countries with developing economies. The effect FDI has on the population health of countries is less well understood. In this paper, the authors draw from a large panel of data, primarily World Bank and UN sources, for 85 low and middle income countries between 1974 and 2012 to assess the relationship between FDI and population health, proxied by life expectancy at birth, as well as child and adult mortality data. They explain clearly the problem of using basic regression analysis in trying to explain this relationship, given the problem of endogeneity between FDI and health outcomes. By introducing two instrumental variables, using grossed fixed capital formation and volatility of exchange rates in FDI origin countries, as well as controlling for GDP per capita, education, quality of institutions and urban population, the study shows that FDI is weakly statistically associated with life expectancy, estimated to amount to 4.15 year increase in life expectancy during the study period. FDI also appears to have an effect on reducing adult mortality, but a negligible effect on child mortality. They also produce some evidence that FDI linked to manufacturing could lead to reductions in life expectancy, although these findings are not as robust as the other findings using instrumental variables, so they recommend this relationship between FDI type and population health to be explored further. The paper also clearly shows the benefit of robust analysis using instrumental variables, as the results without the introduction of these variables to the regression would have led to misleading inferences, where no relationship between life expectancy and FDI would have been found if the analysis did not adjust for the underlying endogeneity bias.

Uncovering waste in US healthcare: evidence from ambulance referral patterns. Journal of Health Economics [PubMed] Published 22nd March 2017

This study looks to unpick some of the reasons behind the estimated waste in US healthcare spending, by focusing on mortality rates across the country following an emergency admission to hospital through ambulances. The authors argue that patients admitted to hospital for emergency care using ambulances act as a good instrument to assess hospital quality given the nature of emergency admissions limiting the selection bias of what type of patients end up in different hospitals. Using linear regressions, the study primarily measures the relationship between patients assigned to certain hospitals and the 90-day spending on these patients compared to mortality. They also consider one-year mortality and the downstream payments post-acute care (excluding pharmaceuticals outside the hospital setting) has on this outcome. Through a lengthy data cleaning process, the study looks at over 1.5 million admissions between 2002-2011, with a high average age of patients of 82 who are predominantly female and white. Approximately $27,500 per patient was spent in the first 90 days post-admission, with inpatient spending accounting for the majority of this amount (≈$16,000). The authors argue initially that the higher 90-day spending in some hospitals only produces modestly lower mortality rates. Spending over 1 year is estimated to cost more than \$300,000 per life year, which the authors use to argue that current spending levels do not lead to improved outcomes. But when the authors dig deeper, it seems clear there is an association between hospitals who have higher spending on inpatient care and reduced mortality, approximately 10% lower. This leads to the authors turning their attention to post-acute care as their main target of reducing waste and they find an association between mortality and patients receiving specialised nursing care. However, this target seems somewhat strange to me, as post-acute care is not controlled for in the same way as their initial, insightful approach to randomising based on ambulatory care. I imagine those in such care are likely to be a different mix from those receiving other types of care post 90 days after the initial event. I feel there really is not enough to go on to make recommendations about specialist nursing care being the key waste driver from their analysis as it says nothing, beyond mortality, about the quality of care these elderly patients are receiving in the specialist nurse facilities. After reading this paper, one way I would suggest in reducing inefficiency related to their primary analysis could be to send patients to the most appropriate hospital for what the patient needs in the first place, which seems difficult given the complexity of the private and hospital provided mix of ambulatory care offered in the US currently.

Population health and the economy: mortality and the Great Recession in Europe. Health Economics [PubMed] Published 27th March 2017

Understanding how economic recessions affect population health is of great research interest given the recent global financial crisis that led to the worst downturn in economic performance in the West since the 1930s. This study uses data from 27 European countries between 2004 and 2010 collected by WHO and the World Bank to study the relationship between economic performance and population health by comparing national unemployment and mortality rates before and after 2007. Regression analyses appropriate for time-series data are applied with a number of different specifications applied. The authors find that the more severe the economic downturn, the greater the increase in life expectancy at birth. Additional specific health mortality rates follow a similar trend in their analysis, with largest improvements observed in countries where the severity of the recession was the highest. The only exception the authors note is data on suicide, where they argue the relationship is less clear, but points towards higher rates of suicide with greater unemployment. The message the authors were trying to get across in this study was not very clear throughout most of the paper and some lay readers of the abstract alone could easily be misled in thinking recessions themselves were responsible for better population health. Mortality rates fell across all six years, but at a faster rate in the recession years. Although the results appeared consistent across all models, question marks remain for me in terms of their initial variable selection. Although the discussion mentions evidence that suggests health care may not have a short-term effect on mortality, they did not consider any potential lagged effect record investment in healthcare as a proportion of GDP up until 2007 may have had on the initial recession years. The authors rule out earlier comparisons with countries in the post-Soviet era but do not consider the effect of recent EU accession for many of the countries and more regulated national policies as a consequence. Another issue is the potential of countries’ mortality rates to improve, where countries with existing lower life expectancy have more room for moving in the right direction. However, one interesting discussion point raised by the authors in trying to explain their findings is the potential impact of economic activity on pollution levels and knock-on health impacts from this (and to a lesser extent occupational health levels), that may have some plausibility in better mortality rates linked to physical health during recessions.

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