# Alastair Canaway’s journal round-up for 10th July 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.

Use-of-time and health-related quality of life in 10- to 13-year-old children: not all screen time or physical activity minutes are the same. Quality of life Research [PubMedPublished 3rd July 2017

“If you watch too much TV, it’ll make your eyes square” – something I heard a lot as a child. This first paper explores whether this is true (sort of) by examining associations between aspects of time use and HRQL in children aged 10-13 (disclaimer: I peer reviewed it and was pleased to see them incorporate my views). This paper aims to examine how different types of time use are linked to HRQL. Time use was examined by the Multimedia Activity Recall for Children and Adolescents (MARCA) which separates out time into physical activity (sport, active transport, and play), screen time (TV, videogames, computer use), and sleep. The PedsQL was used to assess HRQL, whilst dual x-ray absorptiometry was used to accurately assess fatness. There were a couple of novel aspects to this study, first, the use of absorptiometry to accurately measure body fat percentage rather than the problematic BMI/skin folds in children; second, separating time out into specific components rather than just treating physical activity or screen time as homogeneous components. The primary findings were that for both genders, fatness (negative), sport (positive) and development stage (negative) were associated with HRQL. For boys, the most important other predictor of HRQL was videogames (negative) whilst predictors for girls included television (negative), active transport (negative) and household income (positive). With the exception of ‘active travel’ for girls, I don’t think any of these findings are particularly surprising. As with all cross-sectional studies of this nature, the authors give caution to the results: inability to demonstrate causality. Despite this, it opens the door for various possibilities for future research, and ideas for shaping future interventions in children this age.

Raise the bar, not the threshold value: meeting patient preferences for palliative and end-of-life care. PharmacoEconomics – Open Published 27th June 2017

Health care ≠ end of life care. Whilst health care seeks to maximise health, can the same be said for end of life care? Probably not. This June saw an editorial elaborating on this issue. Health is an important facet of end of life care. However, there are other substantial objects of value in this context e.g. preferences for place of care, preparedness, reducing family burdens etc. Evidence suggests that people at end of life can value these ‘other’ objects more than health status or life extension. Thus there is value beyond that captured by health. This is an issue for the QALY framework where health and length of life are the sole indicators of benefit. The editorial highlights that this is not people wishing for higher cost-per-QALY thresholds at end of life, instead, it is supporting the valuation of key elements of palliative care within the end of life context. It argues that palliative care interventions often are not amenable to integration with survival time in a QALY framework, this effectively implies that end of life care interventions should be evaluated in a separate framework to health care interventions altogether. The editorial discusses the ICECAP-Supportive Care Measure (designed for economic evaluation of end of life measures) as progress within this research context. An issue with this approach is that it doesn’t address allocative efficiency issues (and comparability) with ‘normal’ health care interventions. However, if end of life care is evaluated separately to regular healthcare, it will lead to better decisions within the EoL context. There is merit to this justification, after all, end of life care is often funded via third parties and arguments could, therefore, be made for adopting a separate framework. This, however, is a contentious area with lots of ongoing interest. For balance, it’s probably worth pointing out Chris’s (he did not ask me to put this in!) book chapter which debates many of these issues, specifically in relation to defining objects of value at end of life and whether the QALY should be altogether abandoned at EoL.

Investigating the relationship between costs and outcomes for English mental health providers: a bi-variate multi-level regression analysis. European Journal of Health Economics [PubMedPublished 24th June 2017

Payment systems that incentivise cost control and quality improvements are increasingly used. In England, until recently, mental health services have been funded via block contracts that do not necessarily incentivise cost control and payment has not been linked to outcomes. The National Tariff Payment System for reimbursement has now been introduced to mental health care. This paper harnesses the MHMDS (now called MHSDS) using multi-level bivariate regression to investigate whether it is possible to control costs without negatively affecting outcomes. It does this by examining the relationship between costs and outcomes for mental health providers. Due to the nature of the data, an appropriate instrumental variable was not available, and so it is important to note that the results do not imply causality. The primary results found that after controlling for key variables (demographics, need, social and treatment) there was a minuscule negative correlation between residual costs and outcomes with little evidence of a meaningful relationship. That is, the data suggest that outcome improvements could be made without incurring a lot more cost. This implies that cost-containment efforts by providers should not undermine outcome-improving efforts under the new payment systems. Something to bear in mind when interpreting the results is that there was a rather large list of limitations associated with the analysis, most notably that the analysis was conducted at a provider level. Although it’s continually improving, there still remain issues with the MHMDS data: poor diagnosis coding, missing outcome data, and poor quality of cost data. As somebody who is yet to use MHMDS data, but plans to in the future, this was a useful paper for generating ideas regarding what is possible and the associated limitations.

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# Chris Sampson’s journal round-up for 19th 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.

Health-related resource-use measurement instruments for intersectoral costs and benefits in the education and criminal justice sectors. PharmacoEconomics [PubMed] Published 8th June 2017

Increasingly, people are embracing a societal perspective for economic evaluation. This often requires the identification of costs (and benefits) in non-health sectors such as education and criminal justice. But it feels as if we aren’t as well-versed in capturing these as we are in the health sector. This study reviews the measures that are available to support a broader perspective. The authors search the Database of Instruments for Resource Use Measurement (DIRUM) as well as the usual electronic journal databases. The review also sought to identify the validity and reliability of the instruments. From 167 papers assessed in the review, 26 different measures were identified (half of which were in DIRUM). 21 of the instruments were only used in one study. Half of the measures included items relating to the criminal justice sector, while 21 included education-related items. Common specifics for education included time missed at school, tutoring needs, classroom assistance and attendance at a special school. Criminal justice sector items tended to include legal assistance, prison detainment, court appearances, probation and police contacts. Assessments of the psychometric properties was found for only 7 of the 26 measures, with specific details on the non-health items available for just 2: test-retest reliability for the Child and Adolescent Services Assessment (CASA) and validity for the WPAI+CIQ:SHP,V2 (there isn’t room on the Internet for the full name). So there isn’t much evidence of any validity for any of these measures in the context of intersectoral (non-health) costs and benefits. It’s no doubt the case that health-specific resource use measures aren’t subject to adequate testing, but this study has identified that the problem may be even greater when it comes to intersectoral costs and benefits. Most worrying, perhaps, is the fact that 1 in 5 of the articles identified in the review reported using some unspecified instrument, presumably developed specifically for the study or adapted from an off-the-shelf instrument. The authors propose that a new resource use measure for intersectoral costs and benefits (RUM ICB) be developed from scratch, with reference to existing measures and guidance from experts in education and criminal justice.

Use of large-scale HRQoL datasets to generate individualised predictions and inform patients about the likely benefit of surgery. Quality of Life Research [PubMed] Published 31st May 2017

In the NHS, EQ-5D data are now routinely collected from patients before and after undergoing one of four common procedures. These data can be used to see how much patients’ health improves (or deteriorates) following the operations. However, at the individual level, for a person deciding whether or not to undergo the procedure, aggregate outcomes might not be all that useful. This study relates to the development of a nifty online tool that a prospective patient can use to find out the expected likelihood that they will feel better, the same or worse following the procedure. The data used include EQ-5D-3L responses associated with almost half a million unilateral hip or knee replacements or groin hernia repairs between April 2009 and March 2016. Other variables are also included, and central to this analysis is a Likert scale about improvement or worsening of hip/knee/hernia problems compared to before the operation. The purpose of the study is to group people – based on their pre-operation characteristics – according to their expected postoperative utility scores. The authors employed a recursive Classification and Regression Tree (CART) algorithm to split the datasets into strata according to the risk factors. The final set of risk variables were age, gender, pre-operative EQ-5D-3L profile and symptom duration. The CART analysis grouped people into between 55 and 60 different groups for each of the procedures, with the groupings explaining 14-27% of the variation in postoperative utility scores. Minimally important (positive and negative) differences in the EQ-5D utility score were estimated with reference to changes in the Likert scale for each of the procedures. These ranged in magnitude from 0.041 to 0.106. The resulting algorithms are what drive the results delivered by the online interface (you can go and have a play with it). There are a few limitations to the study, such as the reliance on complete case analysis and the fact that the CART analysis might lack predictive ability. And there’s an interesting problem inherent in all of this, that the more people use the tool, the less representative it will become as it influences selection into treatment. The validity of the tool as a precise risk calculator is quite limited. But that isn’t really the point. The point is that it unlocks some of the potential value of PROMs to provide meaningful guidance in the process of shared decision-making.

Can present biasedness explain early onset of diabetes and subsequent disease progression? Exploring causal inference by linking survey and register data. Social Science & Medicine [PubMed] Published 26th May 2017

The term ‘irrational’ is overused by economists. But one situation in which I am willing to accept it is with respect to excessive present bias. That people don’t pay enough attention to future outcomes seems to be a fundamental limitation of the human brain in the 21st century. When it comes to diabetes and its complications, there are lots of treatments available, but there is only so much that doctors can do. A lot depends on the patient managing their own disease, and it stands to reason that present bias might cause people to manage their diabetes poorly, as the value of not going blind or losing a foot 20 years in the future seems less salient than the joy of eating your own weight in carbs right now. But there’s a question of causality here; does the kind of behaviour associated with time-inconsistent preferences lead to poorer health or vice versa? This study provides some insight on that front. The authors outline an expected utility model with quasi-hyperbolic discounting and probability weighting, and incorporate a present bias coefficient attached to payoffs occurring in the future. Postal questionnaires were collected from 1031 type 2 diabetes patients in Denmark with an online discrete choice experiment as a follow-up. These data were combined with data from a registry of around 9000 diabetes patients, from which the postal/online participants were identified. BMI, HbA1c, age and year of diabetes onset were all available in the registry and the postal survey included physical activity, smoking, EQ-5D, diabetes literacy and education. The DCE was designed to elicit time preferences using the offer of (monetary) lottery wins, with 12 different choice sets presented to all participants. Unfortunately, despite the offer of a real-life lottery award for taking part in the research, only 79 of 1031 completed the online DCE survey. Regression analyses showed that individuals with diabetes since 1999 or earlier, or who were 48 or younger at the time of onset, exhibited present bias. And the present bias seems to be causal. Being inactive, obese, diabetes illiterate and having lower quality of life or poorer glycaemic control were associated with being present biased. These relationships hold when subject to a number of control measures. So it looks as if present bias explains at least part of the variation in self-management and health outcomes for people with diabetes. Clearly, the selection of the small sample is a bit of a concern. It may have meant that people with particular risk preferences (given that the reward was a lottery) were excluded, and so the sample might not be representative. Nevertheless, it seems that at least some people with diabetes could benefit from interventions that increase the salience of future health-related payoffs associated with self-management.

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# 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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