Sam Watson’s journal round-up for 24th 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.

Ten years after the financial crisis: the long reach of austerity and its global impacts on health. Social Science & Medicine [PubMedPublished 22nd June 2017

The subject of austerity and its impact on health has generated its own subgenre in the academic literature. We have covered a number of papers on these journal round-ups on this topic, which, given the nature of economic papers, are generally quantitative in nature. However, while quantitative studies are necessary for generation of knowledge of the social world, they are not sufficient. At aggregate levels, quantitative studies may often rely on a black box approach. We may reasonably conclude a policy caused a change in some population-level indicator on the basis of a causal inference type paper, but we often need other types of evidence to answer why or how this occurred. A realist philosophy of social science may see this as a process of triangulation; at the very least it’s a process of abduction to develop theory that best explains what we observe. In clinical research, Bradford-Hill’s famous criteria can be used as a heuristic for causal inference: a cause can be attributed to an effect if it demonstrates a number of criteria including dose-response and reproducibility. For social science, we can conceive of a similar set of criteria. Effects must follow causes, there has to be a plausible mechanism, and so forth. This article in Social Science & Medicine introduces a themed issue on austerity and its effects on health. The issue contains a number of papers examining experiences of people with respect to austerity and how these may translate into changes in health. One example is a study in a Mozambican hospital and how health outcomes change in response to continued restructuring programs due to budget shortfalls. Another study explores the narrative of austerity in Guyana and it has long been sold as necessary for future benefits which never actually materialise. It is not immediately clear how austerity is being defined here, but it is presumably something like ‘a fiscal contraction that causes a significant increase in aggregate unemployment‘. In any case, it makes for interesting reading and complements economics research on the topic. It is a refreshing change from the bizarre ravings we featured a couple of weeks ago!

Home-to-home time — measuring what matters to patients and payers. New England Journal of Medicine [PubMedPublished 6th July 2017

Length of hospital stay is often used as a metric to evaluate hospital performance: for a given severity of illness, a shorter length of hospital stay may suggest higher quality care. However, hospitals can of course game these metrics, and they are further complicated by survival bias. Hospitals are further incentivised to reduce length of stay. For example, the move from per diem reimbursement to per episode had the effect of dramatically reducing length of stay in hospitals. As a patient recovers, they may no longer need hospital based care, the care they require may be adequately provided in other institutional settings. Although, in the UK there has been a significant issue with many patients convalescing in hospital for extended periods as they wait for a place in residential care homes. Thus from the perspective of the whole health system, length of stay in hospital may no longer be the right metric to evaluate performance. This article makes this argument and provides some interesting statistics. For example, between 2004 and 2011 the average length of stay in hospital among Medicare beneficiaries in the US decreased from 6.3 to 5.7 days; post-acute care stays increased from 4.8 to 6.0 days. Thus, the total time in care actually increased from 11.1 to 11.7 days over this period. In the post-acute care setting, Medicare still reimburses providers on a per diem basis, so total payments adjusted for inflation also increased. This article makes the argument that we need to structure incentives and reimbursement schemes across the whole care system if we want to ensure efficiency and equity.

The population health benefits of a healthy lifestyle: life expectancy increased and onset of disability delayed. Health Affairs [PubMedPublished July 2017

Obesity and tobacco smoking increase the risk of ill health and in so doing reduce life expectancy. The same goes for alcohol, although the relationship between alcohol consumption and risk of illness is less well understood. One goal of public health policy is to mitigate these risks. One successful way of communicating the risks of different behaviours is as changes to life expectancy, or conversely ‘effective age‘. From a different perspective, understanding how different risk factors affect life expectancy and disability-free life expectancy is important for cost-benefit analyses of different public health interventions. This study estimates life expectancy and disability-free life expectancy associated with smoking, obesity, and moderate alcohol consumption using the US-based Health and Retirement Study. However, I struggle to see how this study adds much; while it communicates its results well, it is, in essence, a series of univariate comparisons followed by a multivariate comparison. This has been done widely before, such as here and here. Nevertheless, the results reinforce those previous studies. For example, obesity reduced disability-free life expectancy by 3 years for men and 6 years for women.

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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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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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