Thesis Thursday: Frank Sandmann

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 Frank Sandmann who has a PhD from the London School of Hygiene & Tropical Medicine. If you would like to suggest a candidate for an upcoming Thesis Thursday, get in touch.

Title
The true cost of epidemic and outbreak diseases in hospitals
Supervisors
Mark Jit, Sarah Deeny, Julie Robotham, John Edmunds
Repository link
http://researchonline.lshtm.ac.uk/4648208/

Do you refer to the ‘true’ cost because some costs are hidden in this context?

That’s a good observation. Economists use the term “true cost” as a synonym for “opportunity cost”, which can be defined as the net value of the forgone second-best use of a resource. The true value of a hospital bed is therefore determined by its second-best use, which may indeed be less easily observed and less obvious, or somewhat hidden.

In the context of infectious disease outbreaks in hospital, the most visible costs are the direct expenditures on treatments of infected cases and any measures of containment. However, they do not capture the full extent of the “alternative” costs and therefore cannot equal opportunity costs. Slightly less visible are the potential knock-on effects for visitors to the hospital who, unbeknown to them, may get infected and contribute to sustained transmission in the community. Least seen are the externalities borne by patients who have not been admitted so far but who are awaiting admission, and for whom there is no space in hospital yet due to the ongoing outbreak.

In my thesis, I provided a general overview of the historical development of the concept of opportunity costs of resources before I looked in detail at bed-days and the application for hospitals.

How should the opportunity cost of hospital stays be determined?

That depends on for whom you want to determine these costs.

For individual patients, it depends on the very subjective decision of how else they would spend their time instead, and how urgent it is to receive hospital care.

From the perspective of hospital administrators, it is straightforward to calculate the opportunity costs based on the revenues and expenditures of the inpatients, their length of stays, and the existing demand of care from the community. This is quite important because whether there are opportunity costs from forgone admissions will depend on whether there are other patients actually waiting to be admitted, which is somewhat reflected in occupancy rates and of course waiting lists.

Any other decision maker who is acting as an agent on behalf of a collective group or the public should look into the forgone health impact of patients who cannot be admitted when the beds are unavailable to them. In my thesis, I proposed a method for quantifying the opportunity costs of bed-days with the net benefit of the second-best patients forgone, which I illustrated with the example of norovirus-associated gastroenteritis.

How important are differences in methods for costing in the context of gastroenteritis and norovirus?

The results can differ quite substantially when using different costing methods. Norovirus is an ideal illness to illustrate this issue given that otherwise healthy people with gastrointestinal symptoms and no further comorbidities or complications shouldn’t be admitted to hospital in order to minimise the risk of an outbreak. Patients with norovirus are therefore often not the patient group that is benefitting the most from a hospital stay.

In one of the studies of my PhD, I was able to show that the annual burden of norovirus in public hospitals in England amounts to a mean £110 million using conventional costing methods, while the opportunity costs were two-to-three times higher of up to £300 million.

This means that there is the potential for a situation where an intervention is disadvantaged when using conventional methods for costing and ignoring the opportunity costs. When evaluating such an intervention against established decision rules of cost-effectiveness, this may lead to an incorrect decision.

What were some of the key challenges that you encountered in estimating the cost of norovirus to hospitals, and how did you overcome them?

There were at least four key challenges:

First was the number of admissions. Many inpatients with norovirus won’t get recorded as such if they haven’t been laboratory-confirmed. That is why I regressed national inpatient episodes of gastroenteritis against laboratory surveillance reports for ten different gastrointestinal pathogens to estimate the norovirus-attributable proportion.

Second was the number of bed-days used by inpatients that were infected with norovirus during their hospital stay. Using their total length of stay, or some form of propensity matching, suffers from time-dependent biases and overestimates the number of bed-days. Instead, I used a multi-state model and patient-level data from a local hospital.

Third was the bed-days that were left unoccupied for infection control. One of the datasets tracked them mandatorily for acute hospitals during winters, while another surveillance system was voluntary, but recorded outbreaks throughout the year. For a more accurate estimate, I compared both datasets with each other to explore their potential overlap.

Fourth was the forgone health of alternative admissions who had otherwise occupied the beds. I had to make assumptions about the disease progression with and without hospital treatment, for which I used health-state utilities that accounted for age, sex, and the primary medical condition.

If you could have wished for one additional set of data that wasn’t available, what would it have been?

I have been very fortunate to work with a number of colleagues at Public Health England and University College London who provided me with much of the epidemiological data that I needed. My research could have benefitted though from a dataset that tracked the time of infection for a larger patient population and for longer observation periods, and a dataset that included more robust estimates for the health gain from hospital care.

If I could make a wish about the existing datasets on norovirus that I have used, I would wish for a higher rate of reporting given that it became clear from our comparison of datasets that there is a highly-correlated trend, but the number of outbreaks reported and the details of reporting leave room for improvement. Another wish of mine for daily reporting of bed-days during winter became reality only recently; during my PhD, I had to impute missing values that were non-randomly missing at weekends and over the Christmas period. This was changed in winter 2016, and I have recently shown that the mean of our lowest-to-highest imputation scenarios is surprisingly close to the daily number of bed-days recorded since then.

Parts of your thesis are made up of journal articles that you published before submission. Was this always your intention and how did you find the experience?

I always wanted to publish parts of my thesis in separate journal articles as I believe this to be a great chance to reach different audiences. That is because my theoretical research on opportunity costs may be of broader interest than just to those who work on norovirus or bed-days given that my findings are generalisable to other diseases as well as other resources. At the same time, others may be more interested in my results for norovirus, and still others in my application of the various statistical, economic, and mathematical modelling techniques.

After all, I honestly suspect that some people may place a higher value on their next-best alternative use of time than reading my thesis from cover to cover.

Writing up my thoughts early on also helped me refine them, and the peer-review process was a great opportunity to get some additional feedback. It did require good time management skills though to keep coming back to previous studies to address the peer-reviewers’ comments while I was already busy working on the next studies.

All in all, I can recommend others to consider it and, looking back, I’d do it again this way.

Chris Sampson’s journal round-up for 23rd 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.

Quantifying life: understanding the history of quality-adjusted life-years (QALYs). Social Science & Medicine [PubMed] Published 3rd July 2018

We’ve had some fun talking about the history of the QALY here on this blog. The story of how the QALY came to be important in health policy has been obscured. This paper seeks to address that. The research adopts a method called ‘multiple streams analysis’ (MSA) in order to explain how QALYs caught on. The MSA framework identifies three streams – policy, politics, and problems – and considers the ‘policy entrepreneurs’ involved. For this study, archival material was collected from the National Archives, Department of Health files, and the University of York. The researchers also conducted 44 semi-structured interviews with academics and civil servants.

The problem stream highlights shocks to the UK economy in the late 1960s, coupled with growth in health care costs due to innovations and changing expectations. Cost-effectiveness began to be studied and, increasingly, policymaking was meant to be research-based and accountable. By the 80s, the likes of Williams and Maynard were drawing attention to apparent inequities and inefficiencies in the health service. The policy stream gets going in the 40s and 50s when health researchers started measuring quality of life. By the early 60s, the idea of standardising these measures to try and rank health states was on the table. Through the late 60s and early 70s, government economists proliferated and proved themselves useful in health policy. The meeting of Rachel Rosser and Alan Williams in the mid-70s led to the creation of QALYs as we know them, combining quantity and quality of life on a 0-1 scale. Having acknowledged inefficiencies and inequities in the health service, UK politicians and medics were open to new ideas, but remained unconvinced by the QALY. Yet it was a willingness to consider the need for rationing that put the wheels in motion for NICE, and the politics stream – like the problem and policy stream – characterises favourable conditions for the use of the QALY.

The MSA framework also considers ‘policy entrepreneurs’ who broker the transition from idea to implementation. The authors focus on the role of Alan Williams and of the Economic Advisers’ Office. Williams was key in translating economic ideas into forms that policymakers could understand. Meanwhile, the Economic Advisers’ Office encouraged government economists to engage with academics at HESG and later the QoL Measurement Group (which led to the creation of EuroQol).

The main takeaway from the paper is that good ideas only prevail in the right conditions and with the right people. It’s important to maintain multi-disciplinary and multi-stakeholder networks. In the case of the QALY, the two-way movement of economists between government and academia was crucial.

I don’t completely understand or appreciate the MSA framework, but this paper is an enjoyable read. My only reservation is with the way the authors describe the QALY as being a dominant aspect of health policy in the UK. I don’t think that’s right. It’s dominant within a niche of a niche of a niche – that is, health technology assessment for new pharmaceuticals. An alternative view is that the QALY has in fact languished in a quiet corner of British policymaking, and been completely excluded in some other countries.

Accuracy of patient recall for self‐reported doctor visits: is shorter recall better? Health Economics [PubMed] Published 2nd July 2018

In designing observational studies, such as clinical trials, I have always recommended that self-reported resource use be collected no less frequently than every 3 months. This is partly based on something I once read somewhere that I can’t remember, but partly also on some logic that the accuracy of people’s recall decays over time. This paper has come to tell me how wrong I’ve been.

The authors start by highlighting that recall can be subject to omission, whereby respondents forget relevant information, or commission, whereby respondents include events that did not occur. A key manifestation of the latter is ‘telescoping’, whereby events are included from outside the recall period. We might expect commission to be more likely in short recalls and omission to be more common for long recalls. But there’s very little research on this regarding health service use.

This study uses data from a large trial in diabetes care in Australia, in which 5,305 participants were randomised to receive either 2-week, 3-month, or 12-month recall for how many times they had seen a doctor. Then, the trial data were matched with Medicare data to identify the true levels of resource use.

Over 92% of 12-month recall participants made an error, 76% of the 3-month recall, and 46% of the 2-week recall. The patterns of errors were different. There was very little under-reporting in the 2-week recall sample, with 3-month giving the most over-reporting and 12-month giving the most under-reporting. 12-month recall was associated with the largest number of days reported in error. However, when the authors account for the longer period being considered, and estimate a relative error, the impact of misreporting is smallest for the 12-month recall and greatest for the 2-week recall. This translates into a smaller overall bias for the longest recall period. The authors also find that older, less educated, unemployed, and low‐income patients exhibit higher measurement errors.

Health surveys and comparative studies that estimate resource use over a long period of time should use 12-month recall unless they can find a reason to do otherwise. The authors provide some examples from economic evaluations to demonstrate how selecting shorter recall periods could result in recommending the wrong decisions. It’s worth trying to understand the reasons why people can more accurately recall service use over 12 months. That way, data collection methods could be designed to optimise recall accuracy.

Who should receive treatment? An empirical enquiry into the relationship between societal views and preferences concerning healthcare priority setting. PLoS One [PubMed] Published 27th June 2018

Part of the reason the QALY faces opposition is that it has been used in a way that might not reflect societal preferences for resource allocation. In particular, the idea that ‘a QALY is a QALY is a QALY’ may conflict with notions of desert, severity, or process. We’re starting to see more evidence for groups of people holding different views, which makes it difficult to come up with decision rules to maximise welfare. This study considers some of the perspectives that people adopt, which have been identified in previous research – ‘equal right to healthcare’, ‘limits to healthcare’, and ‘effective and efficient healthcare’ – and looks at how they are distributed in the Netherlands. Using four willingness to trade-off (WTT) exercises, the authors explore the relationship between these views and people’s preferences about resource allocation. Trade-offs are between quality vs quantity of life, health maximisation vs equality, children vs the elderly, and lifestyle-related risk vs adversity. The authors sought to test several hypotheses: i) that ‘equal right’ respondents have a lower WTT; ii) ‘limits to healthcare’ people express a preference for health gains, health maximisation, and treating people with adversity; and iii) ‘effective and efficient’ people support health maximisation, treating children, and treating people with adversity.

A representative online sample of adults in the Netherlands (n=261) was recruited. The first part of the questionnaire collected socio-demographic information. The second part asked questions necessary to allocate people to one of the three perspectives using Likert scales based on a previous study. The third part of the questionnaire consisted of the four reimbursement scenarios. Participants were asked to identify the point (in terms of the relevant quantities) at which they would be indifferent between two options.

The distribution of the viewpoints was 65% ‘equal right’, 23% ‘limits to healthcare’, and 7% ‘effective and efficient’. 6% couldn’t be matched to one of the three viewpoints. In each scenario, people had the option to opt out of trading. 24% of respondents were non-traders for all scenarios and, of these, 78% were of the ‘equal right’ viewpoint. Unfortunately, a lot of people opted out of at least one of the trades, and for a wide variety of reasons. Decisionmakers can’t opt out, so I’m not sure how useful this is.

The authors describe many associations between individual characteristics, viewpoints, and WTT results. But the tested hypotheses were broadly supported. While the findings showed that different groups were more or less willing to trade, the points of indifference for traders within the groups did not vary. So while you can’t please everyone in health care priority setting, this study shows how policies might be designed to satisfy the preferences of people with different perspectives.

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.