OHE Lunchtime Seminar: What Can NHS Trusts Do to Reduce Cancer Waiting Times?

OHE Lunchtime Seminar with Sarah Karlsberg, Steve Paling, and Júlia Esquerré on ‘What can NHS trusts do to reduce cancer waiting times?’ To be held on 14th November 2018 from 12 p.m. to 2 p.m.

Rapid diagnosis and access to treatment for cancer are vital for both clinical outcomes and patient experience of care. The NHS Constitution contains several waiting times targets, including that 85% of patients diagnosed with cancer should receive treatment within 62 days of referral. However, waiting times are increasing in England: the 62-day target has not been met since late 2013 and, in July 2018, the NHS recorded its worst performance since records began in October 2009.

This seminar will present evidence on where NHS trusts can take practical steps to reduce cancer waiting times. The work uses patient-level data (Hospital Episode Statistics) from 2016/17 and an econometric model to quantify the potential effects of several recommendations on the average length of patients’ cancer pathways. The project won the 2018 John Hoy Memorial Award for the best piece of economic analysis produced by government economists.

Sarah Karlsberg, Steven Paling, and Júlia González Esquerré work in the NHS Improvement Economics Team, which provides economics expertise to NHS Improvement (previously Monitor and the Trust Development Authority) and the provider sector. Their work covers all aspects of provider policy, including operational and financial performance, quality of care, leadership and strategic change. Sarah is also a Visiting Fellow at OHE.

Download the full seminar invite here.

The seminar will be held in the Sir Alexander Fleming Room, Southside, 7th Floor, 105 Victoria Street, London SW1E 6QT. A buffet lunch will be available from 12 p.m. The seminar will start promptly at 12:30 p.m. and finish promptly at 2 p.m.

If you would like to attend this seminar, please reply to ohegeneral@ohe.org.

James Altunkaya’s journal round-up for 3rd September 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.

Sensitivity analysis for not-at-random missing data in trial-based cost-effectiveness analysis: a tutorial. PharmacoEconomics [PubMed] [RePEc] Published 20th April 2018

Last month, we highlighted a Bayesian framework for imputing missing data in economic evaluation. The paper dealt with the issue of departure from the ‘Missing at Random’ (MAR) assumption by using a Bayesian approach to specify a plausible missingness model from the results of expert elicitation. This was used to estimate a prior distribution for the unobserved terms in the outcomes model.

For those less comfortable with Bayesian estimation, this month we highlight a tutorial paper from the same authors, outlining an approach to recognise the impact of plausible departures from ‘Missingness at Random’ assumptions on cost-effectiveness results. Given poor adherence to current recommendations for the best practice in handling and reporting missing data, an incremental approach to improving missing data methods in health research may be more realistic. The authors supply accompanying Stata code.

The paper investigates the importance of assuming a degree of ‘informative’ missingness (i.e. ‘Missingness not at Random’) in sensitivity analyses. In a case study, the authors present a range of scenarios which assume a decrement of 5-10% in the quality of life of patients with missing health outcomes, compared to multiple imputation estimates based on observed characteristics under standard ‘Missing at Random’ assumptions. This represents an assumption that, controlling for all observed characteristics used in multiple imputation, those with complete quality of life profiles may have higher quality of life than those with incomplete surveys.

Quality of life decrements were implemented in the control and treatment arm separately, and then jointly, in six scenarios. This aimed to demonstrate the sensitivity of cost-effectiveness judgements to the possibility of a different missingness mechanism in each arm. The authors similarly investigate sensitivity to higher health costs in those with missing data than predicted based on observed characteristics in imputation under ‘Missingness at Random’. Finally, sensitivity to a simultaneous departure from ‘Missingness at Random’ in both health outcomes and health costs is investigated.

The proposed sensitivity analyses provide a useful heuristic to assess what degree of difference between missing and non-missing subjects on unobserved characteristics would be necessary to change cost-effectiveness decisions. The authors admit this framework could appear relatively crude to those comfortable with more advanced missing data approaches such as those outlined in last month’s round-up. However, this approach should appeal to those interested in presenting the magnitude of uncertainty introduced by missing data assumptions, in a way that is easily interpretable to decision makers.

The impact of waiting for intervention on costs and effectiveness: the case of transcatheter aortic valve replacement. The European Journal of Health Economics [PubMed] [RePEc] Published September 2018

This paper appears in print this month and sparked interest as one of comparatively few studies on the cost-effectiveness of waiting lists. Given interest in using constrained optimisation methods in health outcomes research, highlighted in this month’s editorial in Value in Health, there is rightly interest in extending the traditional sphere of economic evaluation from drugs and devices to understanding the trade-offs of investing in a wider range of policy interventions, using a common metric of costs and QALYs. Rachel Meacock’s paper earlier this year did a great job at outlining some of the challenges involved broadening the scope of economic evaluation to more general decisions in health service delivery.

The authors set out to understand the cost-effectiveness of delaying a cardiac treatment (TVAR) using a waiting list of up to 12 months compared to a policy of immediate treatment. The effectiveness of treatment at 3, 6, 9 & 12 months after initial diagnosis, health decrements during waiting, and corresponding health costs during wait time and post-treatment were derived from a small observational study. As treatment is studied in an elderly population, a non-ignorable proportion of patients die whilst waiting for surgery. This translates to lower modelled costs, but also lower quality life years in modelled cohorts where there was any delay from a policy of immediate treatment. The authors conclude that eliminating all waiting time for TVAR would produce population health at a rate of ~€12,500 per QALY gained.

However, based on the modelling presented, the authors lack the ability to make cost-effectiveness judgements of this sort. Waiting lists exist for a reason, chiefly a lack of clinical capacity to treat patients immediately. In taking a decision to treat patients immediately in one disease area, we therefore need some judgement as to whether the health displaced in now untreated patients in another disease area is of greater, less or equal magnitude to that gained by treating TVAR patients immediately. Alternately, modelling should include the cost of acquiring additional clinical capacity (such as theatre space) to treat TVAR patients immediately, so as not to displace other treatments. In such a case, the ICER is likely to be much higher, due to the large cost of new resources needed to reduce waiting times to zero.

Given the data available, a simple improvement to the paper would be to reflect current waiting times (already gathered from observational study) as the ‘standard of care’ arm. As such, the estimated change in quality of life and healthcare resource cost from reducing waiting times to zero from levels observed in current practice could be calculated. This could then be used to calculate the maximum acceptable cost of acquiring additional treatment resources needed to treat patients with no waiting time, given current national willingness-to-pay thresholds.

Admittedly, there remain problems in using the authors’ chosen observational dataset to calculate quality of life and cost outcomes for patients treated at different time periods. Waiting times were prioritised in this ‘real world’ observational study, based on clinical assessment of patients’ treatment need. Thus it is expected that the quality of life lost during a waiting period would be lower for patients treated in the observational study at 12 months, compared to the expected quality of life loss of waiting for the group of patients judged to need immediate treatment. A previous study in cardiac care took on the more manageable task of investigating the cost-effectiveness of different prioritisation strategies for the waiting list, investigating the sensitivity of conclusions to varying a fixed maximum wait-time for the last patient treated.

This study therefore demonstrates some of the difficulties in attempting to make cost-effectiveness judgements about waiting time policy. Given that the cost-effectiveness of reducing waiting times in different disease areas is expected to vary, based on relative importance of waiting for treatment on short and long-term health outcomes and costs, this remains an interesting area for economic evaluation to explore. In the context of the current focus on constrained optimisation techniques across different areas in healthcare (see ISPOR task force), it is likely that extending economic evaluation to evaluate a broader range of decision problems on a common scale will become increasingly important in future.

Understanding and identifying key issues with the involvement of clinicians in the development of decision-analytic model structures: a qualitative study. PharmacoEconomics [PubMed] Published 17th August 2018

This paper gathers evidence from interviews with clinicians and modellers, with the aim to improve the nature of the working relationship between the two fields during model development.

Researchers gathered opinion from a variety of settings, including industry. The main report focusses on evidence from two case studies – one tracking the working relationship between modellers and a single clinical advisor at a UK university, with the second gathering evidence from a UK policy institute – where modellers worked with up to 11 clinical experts per meeting.

Some of the authors’ conclusions are not particularly surprising. Modellers reported difficulty in recruiting clinicians to advise on model structures, and further difficulty in then engaging recruited clinicians to provide relevant advice for the model building process. Specific comments suggested difficulty for some clinical advisors in identifying representative patient experiences, instead diverting modellers’ attention towards rare outlier events.

Study responses suggested currently only 1 or 2 clinicians were typically consulted during model development. The authors recommend involving a larger group of clinicians at this stage of the modelling process, with a more varied range of clinical experience (junior as well as senior clinicians, with some geographical variation). This is intended to help ensure clinical pathways modelled are generalizable. The experience of one clinical collaborator involved in the case study based at a UK university, compared to 11 clinicians at the policy institute studied, perhaps may also illustrate a general problem of inadequate compensation for clinical time within the university system. The authors also advocate the availability of some relevant training for clinicians in decision modelling to help enhance the efficiency of participants’ time during model building. Clinicians sampled were supportive of this view – citing the need for further guidance from modellers on the nature of their expected contribution.

This study ties into the general literature regarding structural uncertainty in decision analytic models. In advocating the early contribution of a larger, more diverse group of clinicians in model development, the authors advocate a degree of alignment between clinical involvement during model structuring, and guidelines for eliciting parameter estimates from clinical experts. Similar problems, however, remain for both fields, in recruiting clinical experts from sufficiently diverse backgrounds to provide a valid sample.

Credits

 

Thesis Thursday: Thomas Hoe

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 Thomas Hoe who has a PhD from University College London. If you would like to suggest a candidate for an upcoming Thesis Thursday, get in touch.

Title
Essays on the economics of health care provision
Supervisors
Richard Blundell, Orazio Attanasio
Repository link
http://discovery.ucl.ac.uk/10048627/

What data do you use in your analyses and what are your main analytical methods?

I use data from the English National Health Service (NHS). One of the great features of the NHS is the centralized data it collects, with the Hospital Episodes Statistics (HES) containing information on every public hospital visit in England.

In my thesis, I primarily use two empirical approaches. In my work on trauma and orthopaedic departments, I exploit the fact that the number of emergency trauma admissions to hospital each day is random. This randomness allows me to conduct a quasi-experiment to assess how hospitals perform when they are more or less busy.

The second approach I use, in my work on emergency departments with Jonathan Gruber and George Stoye, is based on bunching techniques that originated in the tax literature (Chetty et al, 2013; Kleven and Waseem, 2013; Saez, 2010). These techniques use interpolation to infer how discontinuities in incentive schemes affect outcomes. We apply and extend these techniques to evaluate the impact of the ‘4-hour target’ in English emergency departments.

How did you characterise and measure quality in your research?

Measuring the quality of health care outcomes is always a challenge in empirical research. Since my research primarily relies on administrative data from HES, I use the patient outcomes that can be directly constructed from this data: in-hospital mortality, and unplanned readmission.

Mortality is, of course, an outcome that is widely used, and offers an unambiguous interpretation. Readmission, on the other hand, is an outcome that has gained more acceptance as a measure of quality in recent years, particularly following the implementation of readmission penalties in the UK and the US.

What is ‘crowding’, and how can it affect the quality of care?

I use the term crowding to refer, in a fairly general sense, to how busy a hospital is. This could mean that the hospital is physically very crowded, with lots of patients in close proximity to one another, or that the number of patients outstrips the available resources.

In practice, I evaluate how crowding affects quality of care by comparing hospital performance and patient outcomes on days when hospitals deal with different levels of admissions (due to random spikes in the number of trauma admissions). I find that hospitals respond by not only cancelling some planned admissions, such as elective hip and knee replacements, but also discharge existing patients sooner. For these discharged patients, the shorter-than-otherwise stay in the hospital is associated with poorer health outcomes for patients, most notably an increase in subsequent hospital visits (unplanned readmissions).

How might incentives faced by hospitals lead to negative consequences?

One of the strongest incentives faced by public hospitals in England is to meet the government-set waiting time target for elective care. This target has been very successful at reducing wait times. In doing so, however, it may have contributed to hospitals shortening patient stays and increasing patient admissions.

My research shows that shorter hospitals stays, in turn, can lead to increases in unplanned readmissions. Setting strong wait time targets, then, is in effect trading off shorter waits (from which patients benefit) with crowding effects (which may harm patients).

Your research highlights the importance of time in the hospital production process. How does this play out?

I look at this from three dimensions, each a separate part of a patient’s journey through hospital.

The first two relate to waiting for treatment. For elective patients, this means waiting for an appointment, and previous work has shown that patients attach significant value to reductions in these wait times. I show that trauma and orthopaedic patients would be better off with further wait time reductions, even if that leads to more crowding.

Emergency patients, in contrast, wait for treatment while physically in a hospital emergency department. I show that these waiting times can be very harmful and that by shortening these wait times we can actually save lives.

The third dimension relates to how long a patient spends in hospital recovering from surgery. I show that, at least on the margin of care for trauma and orthopaedic patients, an additional day in hospital has tangible benefits in terms of reducing the likelihood of experiencing an unplanned readmission.

How could your findings be practically employed in the NHS to improve productivity?

I would highlight two areas of my research that speak directly to the policy debate about NHS productivity.

First, while the wait time targets for elective care may have led to some crowding problems and subsequently more readmissions, the net benefit of these targets to trauma and orthopaedic patients is positive. Second, the wait time target for emergency departments also appears to have benefited patients: it saved lives at a reasonably cost-effective rate.

From the perspective of patients, therefore, I would argue these policies have been relatively successful and should be maintained.