Tag NHS

Rachel Houten’s journal round-up for 22nd April 2019

Rachel Houten Leave a comment

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.

To HTA or not to HTA: identifying the factors influencing the rapid review outcome in Ireland. Value in Health [PubMed] Published 6th March 2019

National health services are constantly under pressure to provide access to new medicines as soon as marketing authorisation is granted. The NCPE in the Republic of Ireland has a rapid review process for selecting medicines that require a full health technology assessment (HTA), and the rest, approximately 45%, are able to be reimbursed without such an in-depth analysis.

Formal criteria do not exist. However, it has previously been suggested that the robustness of clinical evidence of at least equivalence; a drug that costs the same or less; an annual (or estimated) budget impact of less than €0.75 million to €1 million; and the ability of the current health systems to restrict usage are some of what is considered when making the decision.

The authors of this paper used the allocation over the past eight years to explore the factors that drive the decision to embark on a full HTA. They found, unsurprisingly, that first-in-class medicines are more likely to require an HTA as too are those with orphan status. Interestingly, the clinical area influenced the requirement for a full HTA, but the authors consider all of these factors to indicate that high-cost drugs are more likely to require a full assessment. Drug cost information is not publicly available and so the authors used the data available on the Scottish Medicine Consortium website as a surrogate for costs in Ireland. In doing so, they were able to establish a relationship between the cost per person for each drug and the likelihood of the drug having a full HTA, further supporting the idea that more expensive drugs are more likely to require HTA. On the face of it, this seems eminently sensible. However, my concern is that, in a system that is designed to deliberately measure cost per unit of health care (usually QALYs), there is the potential for lower-cost but ineffective drugs to become commonplace while more expensive medicines are subject to more rigor.

The paper provides some insight into what drives a decision to undertake a full HTA in Ireland. The NICE fast-track appraisal system operates as an opt-in system where manufacturers can ask to follow this shorter appraisal route if their drug is likely to produce an ICER of £10,000 or less. As my day job is for an Evidence Review Group (opinions my own), how things are done elsewhere – unsurprisingly – captured my attention. The desire to speed up the HTA process is obvious but the most appropriate mechanisms in which to do so are far from it. Whether or not the same decision is ultimately made is what concerns me.

NHS joint working with industry is out of public sight. BMJ [PubMed] Published 27th March 2019

This paper suggests that ‘joint working arrangements’ – a government-supported initiative between pharmaceutical companies and the NHS – are not being implemented according to guidelines on transparency. These arrangements are designed to promote collaborative research between the NHS and industry and help advance NHS provision of services.

The authors used freedom of information requests to obtain details on how many trusts were involved in joint working arrangements in 2016 and 2017. The declarations of payments made by drug companies are disclosed but the corresponding information from trusts is less readily accessible, and in some cases access to any details was prevented. Theoretically, the joint working arrangements are supposed to be void of any commercial influence on what is prescribed, but my thoughts are echoed in this paper when it asks “what’s in it for the private sector?” The sheer fact that some NHS trusts were unwilling to provide the BMJ with the information requested due to ‘commercial interest’ rings huge alarm bells.

I’m not completely cynical of these arrangements in principle, though, and the paper cites a couple of projects that involved building new facilities for age-related macular generation, which likely offer benefits to patients, and possibly much faster than could have been achieved with NHS funding alone. Some of the arrangements intend to push the implementation of national guidance, which, as a small cog in the guidance generation machine, I unashamedly (and predictably) think is a good thing.

Does it matter to us? As economists, it means that any work based on national practice and costs is likely to be unrepresentative of what actually happens. This, however, has always been the case to some extent, with variations in local service provision and the negotiation power of trusts with large volumes of patients. A national register of the arrangements would have the potential to feed into economic analysis, even if just as a statement of awareness.

Can the NHS survive without getting into bed with industry? Probably not. I think the paper does a good job of presenting the arguments on all sides and pushing for increasing availability of what is happening.

Estimating joint health condition utility values. Value in Health [PubMed] Published 22nd February 2019

I’m really interested in how this area is developing. Multi-morbidity is the norm, especially as we age. Single condition models are criticised for their lack of representation of patients in the real world. Appropriately estimating the quality of life of people with several chronic conditions, when only individual condition data are available, is incredibly difficult.

In this paper, parametric and non-parametric methods were tested on a dataset from a large primary care patient survey in the UK. The multiplicative approach was the best performing for two conditions. When more than two conditions were considered, the linear index (which incorporates additive, multiplicative, and minimum models with the use of linear regression and parameter weights derived from the underlying data) achieved the best results.

Including long-term mental health within the co-morbidities for which utility was estimated produced biased estimates. The authors discuss some possible explanations for this, including the fact that the anxiety and depression question in the EQ-5D is the only one which directly maps to an individual condition, and that mental health may have a causal effect on physical health. This is a fascinating finding, which has left me somewhat scratching my head as to how this oddity could be addressed and if separate methods of estimation will need to be used for any population with multi-morbidity including mental health conditions.

It did make me wonder if more precise EQ-5D data could be helpful to uncover the true interrelationships between joint health conditions and quality of life. The EQ-5D asks patients to think about their health state ‘today’. Although the primary care dataset used includes 16 chronic health conditions, it doesn’t, as far as I know, contain any information on the symptoms apparent on the day of quality of life assessment, which could be flaring or absent at any given time. This is a common problem with the EQ-5D and I don’t think a readily available data source of this type exists, so it’s a thought on ideals. Unsurprisingly, the more joint health conditions to be considered, the larger the error in terms of estimation from individual conditions. This may be due to the increasing likelihood of overlap in the symptoms experienced across conditions and thus a violation of the assumption that quality of life for an individual condition is independent of any other condition.

Whether the methodology remains robust for populations outside of the UK or for other measures of utility would need to be tested, and the authors are keen to highlight the need for caution before running away and using the methods verbatim. The paper does present a nice summary of the evidence to date in this area, what the authors did, and what it adds to the topic, so worth a read.

Credits

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

Rita Faria 1 Comment

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!

Credits

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

Chris Sampson Leave a comment

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.

Credits