Chris Sampson’s journal round-up for 2nd July 2018

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.

Choice in the presence of experts: the role of general practitioners in patients’ hospital choice. Journal of Health Economics [PubMed] [RePEc] Published 26th June 2018

In the UK, patients are in principle free to choose which hospital they use for elective procedures. However, as these choices operate through a GP referral, the extent to which the choice is ‘free’ is limited. The choice set is provided by the GP and thus there are two decision-makers. It’s a classic example of the principal-agent relationship. What’s best for the patient and what’s best for the local health care budget might not align. The focus of this study is on the applied importance of this dynamic and the idea that econometric studies that ignore it – by looking only at patient decision-making or only at GP decision-making – may give bias estimates. The author outlines a two-stage model for the choice process that takes place. Hospital characteristics can affect choices in three ways: i) by only influencing the choice set that the GP presents to the patient, e.g. hospital quality, ii) by only influencing the patient’s choice from the set, e.g. hospital amenities, and iii) by influencing both, e.g. waiting times. The study uses Hospital Episode Statistics for 30,000 hip replacements that took place in 2011/12, referred by 4,721 GPs to 168 hospitals, to examine revealed preferences. The choice set for each patient is not observed, so a key assumption is that all hospitals to which a GP made referrals in the period are included in the choice set presented to patients. The main findings are that both GPs and patients are influenced primarily by distance. GPs are influenced by hospital quality and the budget impact of referrals, while distance and waiting times explain patient choices. For patients, parking spaces seem to be more important than mortality ratios. The results support the notion that patients defer to GPs in assessing quality. In places, it’s difficult to follow what the author did and why they did it. But in essence, the author is looking for (and in most cases finding) reasons not to ignore GPs’ preselection of choice sets when conducting econometric analyses involving patient choice. Econometricians should take note. And policymakers should be asking whether freedom of choice is sensible when patients prioritise parking and when variable GP incentives could give rise to heterogeneous standards of care.

Using evidence from randomised controlled trials in economic models: what information is relevant and is there a minimum amount of sample data required to make decisions? PharmacoEconomics [PubMed] Published 20th June 2018

You’re probably aware of the classic ‘irrelevance of inference’ argument. Statistical significance is irrelevant in deciding whether or not to fund a health technology, because we ought to do whatever we expect to be best on average. This new paper argues the case for irrelevance in other domains, namely multiplicity (e.g. multiple testing) and sample size. With a primer on hypothesis testing, the author sets out the regulatory perspective. Multiplicity inflates the chance of a type I error, so regulators worry about it. That’s why triallists often obsess over primary outcomes (and avoiding multiplicity). But when we build decision models, we rely on all sorts of outcomes from all sorts of studies, and QALYs are never the primary outcome. So what does this mean for reimbursement decision-making? Reimbursement is based on expected net benefit as derived using decision models, which are Bayesian by definition. Within a Bayesian framework of probabilistic sensitivity analysis, data for relevant parameters should never be disregarded on the basis of the status of their collection in a trial, and it is up to the analyst to properly specify a model that properly accounts for the effects of multiplicity and other sources of uncertainty. The author outlines how this operates in three settings: i) estimating treatment effects for rare events, ii) the number of trials available for a meta-analysis, and iii) the estimation of population mean overall survival. It isn’t so much that multiplicity and sample size are irrelevant, as they could inform the analysis, but rather that no data is too weak for a Bayesian analyst.

Life satisfaction, QALYs, and the monetary value of health. Social Science & Medicine [PubMed] Published 18th June 2018

One of this blog’s first ever posts was on the subject of ‘the well-being valuation approach‘ but, to date, I don’t think we’ve ever covered a study in the round-up that uses this method. In essence, the method is about estimating trade-offs between (for example) income and some measure of subjective well-being, or some health condition, in order to estimate the income equivalence for that state. This study attempts to estimate the (Australian) dollar value of QALYs, as measured using the SF-6D. Thus, the study is a rival cousin to the Claxton-esque opportunity cost approach, and a rival sibling to stated preference ‘social value of a QALY’ approaches. The authors are trying to identify a threshold value on the basis of revealed preferences. The analysis is conducted using 14 waves of the Australian HILDA panel, with more than 200,000 person-year responses. A regression model estimates the impact on life satisfaction of income, SF-6D index scores, and the presence of long-term conditions. The authors adopt an instrumental variable approach to try and address the endogeneity of life satisfaction and income, using an indicator of ‘financial worsening’ to approximate an income shock. The estimated value of a QALY is found to be around A$42,000 (~£23,500) over a 2-year period. Over the long-term, it’s higher, at around A$67,000 (~£37,500), because individuals are found to discount money differently to health. The results also demonstrate that individuals are willing to pay around A$2,000 to avoid a long-term condition on top of the value of a QALY. The authors apply their approach to a few examples from the literature to demonstrate the implications of using well-being valuation in the economic evaluation of health care. As with all uses of experienced utility in the health domain, adaptation is a big concern. But a key advantage is that this approach can be easily applied to large sets of survey data, giving powerful results. However, I haven’t quite got my head around how meaningful the results are. SF-6D index values – as used in this study – are generated on the basis of stated preferences. So to what extent are we measuring revealed preferences? And if it’s some combination of stated and revealed preference, how should we interpret willingness to pay values?

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Thesis Thursday: Francesco Longo

On the third Thursday of every month, we speak to a recent graduate about their thesis and their studies. This month’s guest is Dr Francesco Longo who has a PhD from the University of York. If you would like to suggest a candidate for an upcoming Thesis Thursday, get in touch.

Title
Essays on hospital performance in England
Supervisor
Luigi Siciliani
Repository link
http://etheses.whiterose.ac.uk/18975/

What do you mean by ‘hospital performance’, and how is it measured?

The concept of performance in the healthcare sector covers a number of dimensions including responsiveness, affordability, accessibility, quality, and efficiency. A PhD does not normally provide enough time to investigate all these aspects and, hence, my thesis mostly focuses on quality and efficiency in the hospital sector. The concept of quality or efficiency of a hospital is also surprisingly broad and, as a consequence, perfect quality and efficiency measures do not exist. For example, mortality and readmissions are good clinical quality measures but the majority of hospital patients do not die and are not readmitted. How well does the hospital treat these patients? Similarly for efficiency: knowing that a hospital is more efficient because it now has lower costs is essential, but how is that hospital actually reducing costs? My thesis tries to answer also these questions by analysing various quality and efficiency indicators. For example, Chapter 3 uses quality measures such as overall and condition-specific mortality, overall readmissions, and patient-reported outcomes for hip replacement. It also uses efficiency indicators such as bed occupancy, cancelled elective operations, and cost indexes. Chapter 4 analyses additional efficiency indicators, such as admissions per bed, the proportion of day cases, and proportion of untouched meals.

You dedicated a lot of effort to comparing specialist and general hospitals. Why is this important?

The first part of my thesis focuses on specialisation, i.e. an organisational form which is supposed to generate greater efficiency, quality, and responsiveness but not necessarily lower costs. Some evidence from the US suggests that orthopaedic and surgical hospitals had 20 percent higher inpatient costs because of, for example, higher staffing levels and better quality of care. In the English NHS, specialist hospitals play an important role because they deliver high proportions of specialised services, commonly low-volume but high-cost treatments for patients with complex and rare conditions. Specialist hospitals, therefore, allow the achievement of a critical mass of clinical expertise to ensure patients receive specialised treatments that produce better health outcomes. More precisely, my thesis focuses on specialist orthopaedic hospitals which, for instance, provide 90% of bone and soft tissue sarcomas surgeries, and 50% of scoliosis treatments. It is therefore important to investigate the financial viability of specialist orthopaedic hospitals relative to general hospitals that undertake similar activities, under the current payment system. The thesis implements weighted least square regressions to compare profit margins between specialist and general hospitals. Specialist orthopaedic hospitals are found to have lower profit margins, which are explained by patient characteristics such as age and severity. This means that, under the current payment system, providers that generally attract more complex patients such as specialist orthopaedic hospitals may be financially disadvantaged.

In what way is your analysis of competition in the NHS distinct from that of previous studies?

The second part of my thesis investigates the effect of competition on quality and efficiency under two different perspectives. First, it explores whether under competitive pressures neighbouring hospitals strategically interact in quality and efficiency, i.e. whether a hospital’s quality and efficiency respond to neighbouring hospitals’ quality and efficiency. Previous studies on English hospitals analyse strategic interactions only in quality and they employ cross-sectional spatial econometric models. Instead, my thesis uses panel spatial econometric models and a cross-sectional IV model in order to make causal statements about the existence of strategic interactions among rival hospitals. Second, the thesis examines the direct effect of hospital competition on efficiency. The previous empirical literature has studied this topic by focusing on two measures of efficiency such as unit costs and length of stay measured at the aggregate level or for a specific procedure (hip and knee replacement). My thesis provides a richer analysis by examining a wider range of efficiency dimensions. It combines a difference-in-difference strategy, commonly used in the literature, with Seemingly Unrelated Regression models to estimate the effect of competition on efficiency and enhance the precision of the estimates. Moreover, the thesis tests whether the effect of competition varies for more or less efficient hospitals using an unconditional quantile regression approach.

Where should researchers turn next to help policymakers understand hospital performance?

Hospitals are complex organisations and the idea of performance within this context is multifaceted. Even when we focus on a single performance dimension such as quality or efficiency, it is difficult to identify a measure that could work as a comprehensive proxy. It is therefore important to decompose as much as possible the analysis by exploring indicators capturing complementary aspects of the performance dimension of interest. This practice is likely to generate findings that are readily interpretable by policymakers. For instance, some results from my thesis suggest that hospital competition improves efficiency by reducing admissions per bed. Such an effect is driven by a reduction in the number of beds rather than an increase in the number of admissions. In addition, competition improves efficiency by pushing hospitals to increase the proportion of day cases. These findings may help to explain why other studies in the literature find that competition decreases length of stay: hospitals may replace elective patients, who occupy hospital beds for one or more nights, with day case patients, who are instead likely to be discharged the same day of admission.

Chris Sampson’s journal round-up for 16th January 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.

Competition and quality indicators in the health care sector: empirical evidence from the Dutch hospital sector. The European Journal of Health Economics [PubMed] Published 3rd January 2017

In case you weren’t already convinced, this paper presents more evidence to support the notion that (non-price) competition between health care providers is good for quality. The Dutch system is based on compulsory insurance and information on quality of hospital care is made public. One feature of the Dutch health system is that – for many elective hospital services – prices are set following a negotiation between insurers and hospitals. This makes the setting of the study a bit different to some of the European evidence considered to date, because there is scope for competition on price. The study looks at claims data for 3 diagnosis groups – cataract, adenoid/tonsils and bladder tumor – between 2008 and 2011. The authors’ approach to measuring competition is a bit more sophisticated than some other studies’ and is based on actual market share. A variety of quality indicators are used for the 3 diagnosis groups relating mainly to the process of care (rather than health outcomes). Fixed and random effects linear regression models are used to estimate the impact of market share upon quality. Casemix was only controlled for in relation to the proportion of people over 65 and the proportion of women. Where a relationship was found, it tended to be in favour of lower market share (i.e. greater competition) being associated with higher quality. For cataract and for bladder tumor there was a ‘significant’ effect. So in this setting at least, competition seems to be good news for quality. But the effect sizes are neither huge nor certain. A look at each of the quality indicators separately showed plenty of ‘non-significant’ relationships in both directions. While a novelty of this study is the liberalised pricing context, the authors find that there is no relationship between price and quality scores. So even if we believe the competition-favouring results, we needn’t abandon the ‘non-price competition only’ mantra.

Cost-effectiveness thresholds in global health: taking a multisectoral perspective. Value in Health Published 3rd January 2017

We all know health care is not the only – and probably not even the most important – determinant of health. We call ourselves health economists, but most of us are simply health care economists. Rarely do we look beyond the domain of health care. If our goal as researchers is to help improve population health, then we should probably be allocating more of our mental resource beyond health care. The same goes for public spending. Publicly provided education might improve health in a way that the health service would be willing to fund. Likewise, health care might improve educational attainment. This study considers resource allocation decisions using the familiar ‘bookshelf approach’, but goes beyond the unisectoral perspective. The authors discuss a two-sector world of health and education, and demonstrate the ways in which there may be overlaps in costs and outcomes. In short, there are likely to be situations in which the optimal multisectoral decision would be for individual sectors to increase their threshold in order to incorporate the spillover benefits of an intervention in another sector. The authors acknowledge that – in a perfect world – a social-welfare-maximising government would have sufficient information to allocate resources earmarked for specific purposes (e.g. health improvement) across sectors. But this doesn’t happen. Instead the authors propose the use of a cofinancing mechanism, whereby funds would be transferred between sectors as needed. The paper provides an interesting and thought-provoking discussion, and the idea of transferring funds between sectors seems sensible. Personally I think the problem is slightly misspecified. I don’t believe other sectors face thresholds in the same way, because (generally speaking) they do not employ cost-effectiveness analysis. And I’m not sure they should. I’m convinced that for health we need to deviate from welfarism, but I’m not convinced of it for other sectors. So from my perspective it is simply a matter of health vs everything else, and we can incorporate the ‘everything else’ into a cost-effectiveness analysis (with a societal perspective) in monetary terms. Funds can be reallocated as necessary with each budget statement (of which there seem to be a lot nowadays).

Is the Rational Addiction model inherently impossible to estimate? Journal of Health Economics [RePEc] Published 28th December 2016

Saddle point dynamics. Something I’ve never managed to get my head around, but here goes… This paper starts from the problem that empirical tests of the Rational Addiction model serve up wildly variable and often ridiculous (implied) discount rates. That may be part of the reason why economists tend to support the RA model but at the same time believe that it has not been empirically proven. The paper sets out the basis for saddle point dynamics in the context of the RA model, and outlines the nature of the stable and unstable root within the function that determines a person’s consumption over time. The authors employ Monte Carlo estimation of RA-type equations, simulating panel data observations. These simulations demonstrate that the presence of the unstable root may make it very difficult to estimate the coefficients. So even if the RA model can truly represent behaviour, empirical estimation may contradict it. This raises the question of whether the RA model is essentially untestable. A key feature of the argument relates to use of the model where a person’s time horizon is not considered to be infinite. Some non-health economists like to assume it is, which, as the authors wryly note, is not particularly ‘rational’.

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