Rita Faria’s journal round-up for 15th April 2019

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.

Emulating a trial of joint dynamic strategies: an application to monitoring and treatment of HIV‐positive individuals. Statistics in Medicine [PubMed] Published 18th March 2019

Have you heard about the target trial approach? This is a causal inference method for using observational evidence to compare strategies. This outstanding paper by Ellen Caniglia and colleagues is a great way to get introduced to it!

The question is: what is the best test-and-treat strategy for HIV-positive individuals? Given that patients weren’t randomised to each of the 4 alternative strategies, chances are that their treatment was informed by their prognostic factors. And these also influence their outcome. It’s a typical situation of bias due to confounding. The target trial approach consists of designing the RCT which would estimate the causal effect of interest, and to think through how its design can be emulated by the observational data. Here, it would be a trial in which patients would be randomly assigned to one of the 4 joint monitoring and treatment strategies. The goal is to estimate the difference in outcomes if all patients had followed their assigned strategies.

The method is fascinating albeit a bit complicated. It involves censoring individuals, fitting survival models, estimating probability weights, and replicating data. It is worthy of a detailed read! I’m very excited about the target trial methodology for cost-effectiveness analysis with observational data. But I haven’t come across any application yet. Please do get in touch via comments or Twitter if you know of a cost-effectiveness application.

Achieving integrated care through commissioning of primary care services in the English NHS: a qualitative analysis. BMJ Open [PubMed] Published 1st April 2019

Are you confused about the set-up of primary health care services in England? Look no further than Imelda McDermott and colleagues’ paper.

The paper starts by telling the story of how primary care has been organised in England over time, from its creation in 1948 to current times. For example, I didn’t know that there are new plans to allow clinical commissioning groups (CCGs) to design local incentive schemes as an alternative to the Quality and Outcomes Framework pay-for-performance scheme. The research proper is a qualitative study using interviews, telephone surveys and analysis of policy documents to understand how the CCGs commission primary care services. CCG Commissioning is intended to make better and more efficient use of resources to address increasing demand for health care services, staff shortage and financial pressure. The issue is that it is not easy to implement in practice. Furthermore, there seems to be some “reinvention of the wheel”. For example, from one of the interviewees: “…it’s no great surprise to me that the three STPs that we’ve got are the same as the three PCT clusters that we broke up to create CCGs…” Hum, shall we just go back to pre-2012 then?

Even if CCG commissioning does achieve all it sets out to do, I wonder about its value for money given the costs of setting it up. This paper is an exceptional read about the practicalities of implementing this policy in practice.

The dark side of coproduction: do the costs outweight the benefits for health research? Health Research Policy and Systems [PubMed] Published 28th March 2019

Last month, I covered the excellent paper by Kathryn Oliver and Paul Cairney about how to get our research to influence policy. This week I’d like to suggest another remarkable paper by Kathryn, this time with Anita Kothari and Nicholas Mays, on the costs and benefits of coproduction.

If you are in the UK, you have certainly heard about public and patient involvement or PPI. In this paper, coproduction refers to any collaborative working between academics and non-academics, of which PPI is one type, but it includes working with professionals, policy makers and any other people affected by the research. The authors discuss a wide range of costs to coproduction. From the direct costs of doing collaborative research, such as organising meetings, travel arrangements, etc., to the personal costs on an individual researcher to manage conflicting views and disagreements between collaborators, of having research products seen to be of lower quality, of being seen as partisan, etc., and costs to the stakeholders themselves

As a detail, I loved the term “hit-and-run research” to describe the current climate: get funding, do research, achieve impact, leave. Indeed, the way that research is funded, with budgets only available for the period that the research is being developed, does not help academics to foster relationships.

This paper reinforced my view that there may well be benefits to coproduction, but that there are also quite a lot of costs. And there tends to be not much attention to the magnitude of those costs, in whom they fall, and what’s displaced. I found the authors’ advice about the questions to ask oneself when thinking about coproduction to be really useful. I’ll keep it to hand when writing my next funding application, and I recommend you do too!

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Chris Sampson’s journal round-up for 4th February 2019

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.

Patient choice and provider competition – quality enhancing drivers in primary care? Social Science & Medicine Published 29th January 2019

There’s no shortage of studies in economics claiming to identify the impact (or lack of impact) of competition in the market for health care. The evidence has brought us close to a consensus that greater competition might improve quality, so long as providers don’t compete on price. However, many of these studies aren’t able to demonstrate the mechanism through which competition might improve quality, and the causality is therefore speculative. The research reported in this article was an attempt to see whether the supposed mechanisms for quality improvement actually exist. The authors distinguish between the demand-side mechanisms of competition-increasing quality-improving reforms (i.e. changes in patient behaviour) and the supply-side mechanisms (i.e. changes in provider behaviour), asserting that the supply-side has been neglected in the research.

The study is based on primary care in Sweden’s two largest cities, where patients can choose their primary care practice, which could be a private provider. Key is the fact that patients can switch between providers as often as they like, and with fewer barriers to doing so than in the UK. Prospective patients have access to some published quality indicators. With the goal of maximum variation, the researchers recruited 13 primary health care providers for semi-structured interviews with the practice manager and (in most cases) one or more of the practice GPs. The interview protocol included questions about the organisation of patient visits, information received about patients’ choices, market situation, reimbursement, and working conditions. Interview transcripts were coded and a framework established. Two overarching themes were ‘local market conditions’ and ‘feedback from patient choice’.

Most interviewees did not see competitors in the local market as a threat – conversely, providers are encouraged to cooperate on matters such as public health. Where providers did talk about competing, it was in terms of (speed of) access for patients, or in competition to recruit and keep staff. None of the interviewees were automatically informed of patients being removed from their list, and some managers reported difficulties in actually knowing which patients on their list were still genuinely on it. Even where these data were more readily available, nobody had access to information on reasons for patients leaving. Managers saw greater availability of this information as useful for quality improvement, while GPs tended to think it could be useful in ensuring continuity of care. Still, most expressed no desire to expand their market share. Managers reported using marketing efforts in response to greater competition generally, rather than as a response to observed changes within their practice. But most relied on reputation. Some reported becoming more service-minded as a result of choice reforms.

It seems that practices need more information to be able to act on competitive pressures. But, most practices don’t care about it because they don’t want to expand and they face no risk of there being a shortage of patients (in cities, at least). And, even if they did want to act on the information, chances are it would just create an opportunity for them to improve access as a way of cherry-picking younger and healthier people who demand convenience. Primary care providers (in this study, at least) are not income maximisers, but satisficers (they want to break-even), so there isn’t much scope for reforms to encourage providers to compete for new patients. Patient choice reforms may improve quality, but it isn’t clear that this has anything to do with competitive pressure.

Maximising the impact of patient reported outcome assessment for patients and society. BMJ [PubMed] Published 24th January 2019

Patient-reported outcome measures (PROMs) have been touted as a way of improving patient care. Yet, their use around the world is fragmented. In this paper, the authors make some recommendations about how we might use PROMs to improve patient care. The authors summarise some of the benefits of using PROMs and discuss some of the ways that they’ve been used in the UK.

Five key challenges in the use of PROMs are specified: i) appropriate and consistent selection of the best measures; ii) ethical collection and reporting of PROM data; iii) data collection, analysis, reporting, and interpretation; iv) data logistics; and v) a lack of coordination and efficiency. To address these challenges, the authors recommend an ‘integrated’ approach. To achieve this, stakeholder engagement is important and a governance framework needs to be developed. A handy table of current uses is provided.

I can’t argue with what the paper proposes, but it outlines an idealised scenario rather than any firm and actionable recommendations. What the authors don’t discuss is the fact that the use of PROMs in the UK is flailing. The NHS PROMs programme has been scaled back, measures have been dropped from the QOF, the EQ-5D has been dropped from the GP Patient Survey. Perhaps we need bolder recommendations and new ideas to turn the tide.

Check your checklist: the danger of over- and underestimating the quality of economic evaluations. PharmacoEconomics – Open [PubMed] Published 24th January 2019

This paper outlines the problems associated with misusing methodological and reporting checklists. The author argues that the current number of checklists available in the context of economic evaluation and HTA (13, apparently) is ‘overwhelming’. Three key issues are discussed. First, researchers choose the wrong checklist. A previous review found that the Drummond, CHEC, and Philips checklists were regularly used in the wrong context. Second, checklists can be overinterpreted, resulting in incorrect conclusions. A complete checklist does not mean that a study is perfect, and different features are of varying importance in different studies. Third, checklists are misused, with researchers deciding which items are or aren’t relevant to their study, without guidance.

The author suggests that more guidance is needed and that a checklist for selecting the correct checklist could be the way to go. The issue of updating checklists over time – and who ought to be responsible for this – is also raised.

In general, the tendency seems to be to broaden the scope of general checklists and to develop new checklists for specific methodologies, requiring the application of multiple checklists. As methods develop, they become increasingly specialised and heterogeneous. I think there’s little hope for checklists in this context unless they’re pared down and used as a reminder of the more complex guidance that’s needed to specify suitable methods and achieve adequate reporting. ‘Check your checklist’ is a useful refrain, though I reckon ‘chuck your checklist’ can sometimes be a better strategy.

A systematic review of dimensions evaluating patient experience in chronic illness. Health and Quality of Life Outcomes [PubMed] Published 21st January 2019

Back to PROMs and PRE(xperience)Ms. This study sets out to understand what it is that patient-reported measures are being used to capture in the context of chronic illness. The authors conducted a systematic review, screening 2,375 articles and ultimately including 107 articles that investigated the measurement properties of chronic (physical) illness PROMs and PREMs.

29 questionnaires were about (health-related) quality of life, 19 about functional status or symptoms, 20 on feelings and attitudes about illness, 19 assessing attitudes towards health care, and 20 on patient experience. The authors provide some nice radar charts showing the percentage of questionnaires that included each of 12 dimensions: i) physical, ii) functional, iii) social, iv) psychological, v) illness perceptions, vi) behaviours and coping, vii) effects of treatment, viii) expectations and satisfaction, ix) experience of health care, x) beliefs and adherence to treatment, xi) involvement in health care, and xii) patient’s knowledge.

The study supports the idea that a patient’s lived experience of illness and treatment, and adaptation to that, has been judged to be important in addition to quality of life indicators. The authors recommend that no measure should try to capture everything because there are simply too many concepts that could be included. Rather, researchers should specify the domains of interest and clearly define them for instrument development.

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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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