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.

Rita Faria’s journal round-up for 13th August 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.

Analysis of clinical benefit, harms, and cost-effectiveness of screening women for abdominal aortic aneurysm. The Lancet [PubMed] Published 26th July 2018

This study is an excellent example of the power and flexibility of decision models to help inform decisions on screening policies.

In many countries, screening for abdominal aortic aneurysm is offered to older men but not to women. This is because screening was found to be beneficial and cost-effective, based on evidence from RCTs in older men. In contrast, there is no direct evidence for women. To inform this question, the study team developed a decision model to simulate the benefits and costs of screening women.

This study has many fascinating features. Not only does it simulate the outcomes of expanding the current UK screening policy for men to include women, but also of other policies with different age parameters, diagnostic thresholds and treatment thresholds.

Curiously, the most cost-effective policy for women is not the current UK policy for men. This shows the importance of including the full range of options in the evaluation, rather than just what is done now. Unfortunately, the paper is sparse on detail on how the various policies were devised and if other more cost-effective policies may have been left out.

The key cost-effectiveness driver is the probability of having the disease and its presentation (i.e. the distribution of the aortic diameter), which is quite frequent in cost-effectiveness analysis of diagnostic tests. Neither of these parameters requires an RCT to be estimated. This means that, in principle, we could reduce the uncertainty on which policy to fund by conducting a study on the prevalence of the disease, rather than an RCT on whether a specific policy works.

An exciting aspect is that treatment itself could be better targeted, in particular, that lowering the threshold for treatment could reduce non-intervention rates and operative mortality. The implication is that there may be scope to improve the cost-effectiveness of management, which in turn will leave greater scope for investment in screening. Could this be the next question to be tackled by this remarkable model?

Establishing the value of diagnostic and prognostic tests in health technology assessment. Medical Decision Making [PubMed] Published 13th March 2018

Keeping on the topic of the cost-effectiveness of screening and diagnostic tests, this is a paper on how to evaluate tests in a manner consistent with health technology assessment principles. This paper has been around for a few months, but it’s only now that I’ve had the chance to give it the careful read that such a well thought out paper deserves.

Marta Soares and colleagues lay out an approach to determine the most cost-effective way to use diagnostic and prognostic tests. They start by explaining that the value of the test is mostly in informing better management decisions. This means that the cost-effectiveness of testing necessarily depends on the cost-effectiveness of management.

The paper also spells out that the cost-effectiveness of testing depends on the prevalence of the disease, as we saw in the paper above on screening for abdominal aortic aneurysm. Clearly, the cost-effectiveness of testing depends on the accuracy of the test.

Importantly, the paper highlights that the evaluation should compare all possible ways of using the test. A decision problem with 1 test and 1 treatment yields 6 strategies, of which 3 are relevant: no test and treat all; no test and treat none; test and treat if positive. If the reference test is added, another 3 strategies need to be considered. This shows how complex a cost-effectiveness analysis of a test can quickly become! In my paper with Marta and others, for example, we ended up with 383 testing strategies.

The discussion is excellent, particularly about the limitations of end-to-end studies (which compare testing strategies in terms of their end outcomes e.g. health). End-to-end studies can only compare a limited subset of testing strategies and may not allow for the modelling of the outcomes of strategies beyond those compared in the study. Furthermore, end-to-end studies are likely to be inefficient given the large sample sizes and long follow-up required to detect differences in outcomes. I wholeheartedly agree that primary studies should focus on the prevalence of the disease and the accuracy of the test, leaving the evaluation of the best way to use the test to decision modelling.

Reasonable patient care under uncertainty. Health Economics [PubMed] Published 22nd August 2018

And for my third paper for the week, something completely different. But so worth reading! Charles Manski provides an overview of his work on how to use the available evidence to make decisions under uncertainty. It is accompanied by comments from Karl Claxton, Emma McIntosh, and Anirban Basu, together with Manski’s response. The set is a superb read and great food for thought.

Manski starts with the premise that we make decisions about which course of action to take without having full information about what is best; i.e. under uncertainty. This is uncontroversial and well accepted, ever since Arrow’s seminal paper.

Less consensual is Manski’s view that clinicians’ decisions for individual patients may be better than the recommendations of guidelines to the ‘average’ patient because clinicians can take into account more information about the specific individual patient. I would contend that it is unrealistic to expect that clinicians keep pace with new knowledge in medicine given how fast and how much it is generated. Furthermore, clinicians, like all other people, are unlikely to be fully rational in their decision-making process.

Most fascinating was Section 6 on decision theory under uncertainty. Manski focussed on the minimax-regret criterion. I had not heard about these approaches before, so Manski’s explanations were quite the eye-opener.

Manksi concludes by recommending that central health care planners take a portfolio approach to their guidelines (adaptive diversification), coupled with the minimax criterion to update the guidelines as more information emerges (adaptive minimax-regret). Whether the minimax-regret criterion is the best is a question that I will leave to better brains than mine. A more immediate question is how feasible it is to implement this adaptive diversification, particularly in instituting a process in that data are systematically collected and analysed to update the guideline. In his response, Manski suggests that specialists in decision analysis should become members of the multidisciplinary clinical team and to teach decision analysis in Medicine courses. This resonates with my own view that we need to do better in helping people using information to make better decisions.

Credits

Simon McNamara’s journal round-up for 6th August 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.

Euthanasia, religiosity and the valuation of health states: results from an Irish EQ5D5L valuation study and their implications for anchor values. Health and Quality of Life Outcomes [PubMed] Published 31st July 2018

Do you support euthanasia? Do you think there are health states worse than death? Are you religious? Don’t worry – I am not commandeering this week’s AHE journal round-up just to bombard you with a series of difficult questions. These three questions form the foundation of the first article selected for this week’s round-up.

The paper is based upon the hypothesis that your religiosity (“adherence to religious beliefs”) is likely to impact your support for euthanasia and, subsequently, the likelihood of you valuing severe health states as worse than death. This seems like a logical hypothesis. Religions tend to be anti-euthanasia, and so it appears likely that religious people will have lower levels of support for euthanasia than non-religious people. Equally, if you don’t support the principle of euthanasia, it stands to reason that you are likely to be less willing to choose immediate death over living in a severe health state – something you would need to do for a health state to be considered as being worse than death in a time trade-off (TTO) study.

The authors test this hypothesis using a sub-sample of data (n=160) collected as part of the Irish EQ-5D-5L TTO valuation study. Perhaps unsurprisingly, the authors find evidence in support of the above hypotheses. Those that attend a religious service weekly were more likely to oppose euthanasia than those who attend a few times a year or less, and those who oppose euthanasia were less likely to give “worse than death” responses in the TTO than those that support it.

I found this paper really interesting, as it raises a number of challenging questions. If a society is made up of people with heterogeneous beliefs regarding religion, how should we balance these in the valuation of health? If a society is primarily non-religious is it fair to apply this valuation tariff to the lives of the religious, and vice versa? These certainly aren’t easy questions to answer, but may be worth reflecting on.

E-learning and health inequality aversion: A questionnaire experiment. Health Economics [PubMed] [RePEc] Published 22nd July 2018

Moving on from the cheery topic of euthanasia, what do you think about socioeconomic inequalities in health? In my home country, England, if you are from the poorest quintile of society, you can expect to experience 62 years in full health in your lifetime, whilst if you are from the richest quintile, you can expect to experience 74 years – a gap of 12 years.

In the second paper to be featured in this round-up, Cookson et al. explore the public’s willingness to sacrifice incremental population health gains in order to reduce these inequalities in health – their level of “health inequality aversion”. This is a potentially important area of research, as the vast majority of economic evaluation in health is distributionally-naïve and effectively assumes that members of the public aren’t at all concerned with inequalities in health.

The paper builds on prior work conducted by the authors in this area, in which they noted a high proportion of respondents in health inequality aversion elicitation studies appear to be so averse to inequalities that they violate monotonicity – they choose scenarios that reduce inequalities in health even if these scenarios reduce the health of the rich at no gain to the poor, or they reduce the health of the poor, or they may reduce the health of both groups. The authors hypothesise that these monotonicity violations may be due to incomplete thinking from participants, and suggest that the quality of their thinking could be improved by two e-learning educational interventions. The primary aim of the paper is to test the impact of these interventions in a sample of the UK public (n=60).

The first e-learning intervention was an animated video that described a range of potential positions that a respondent could take (e.g. health maximisation, or maximising the health of the worst off). The second was an interactive spreadsheet-based questionnaire that presented the consequences of the participant’s choices, prior to them confirming their selection. Both interventions are available online.

The authors found that the interactive tool significantly reduced the amount of extreme egalitarian (monotonicity-violating) responses, compared to a non-interactive, paper-based version of the study. Similarly, when the video was watched before completing the paper-based exercise, the number of extreme egalitarian responses reduced. However, when the video was watched before the interactive tool there was no further decrease in extreme egalitarianism. Despite this reduction in extreme egalitarianism, the median levels of inequality aversion remained high, with implied weights of 2.6 and 7.0 for QALY gains granted to someone from the poorest fifth of society, compared to the richest fifth of society for the interactive questionnaire and video groups respectively.

This is an interesting study that provides further evidence of inequality aversion, and raises further concern about the practical dominance of distributionally-naïve approaches to economic evaluation. The public does seem to care about distribution. Furthermore, the paper demonstrates that participant responses to inequality aversion exercises are shaped by the information given to them, and the way that information is presented. I look forward to seeing more studies like this in the future.

A new method for valuing health: directly eliciting personal utility functions. The European Journal of Health Economics [PubMed] [RePEc] Published 20th July 2018

Last, but not least, for this round-up, is a paper by Devlin et al. on a new method for valuing health.

The relative valuation of health states is a pretty important topic for health economists. If we are to quantify the effectiveness, and subsequently cost-effectiveness, of an intervention, we need to understand which health states are better than others, and how much better they are. Traditionally, this is done by asking members of the public to choose between different health profiles featuring differing levels of fulfilment of a range of domains of health, in order to ‘uncover’ the relative importance the respondent places on these domains, and levels. These can then be used in order to generate social tariffs that assign a utility value to a given health state for use in economic evaluation.

The authors point out that, in the modern day, valuation studies can be conducted rapidly, and at scale, online, but at the potential cost of deliberation from participants, and the resultant risk of heuristic dominated decision making. In response to this, the authors propose a new method – direct elicitation of personal utility functions, and pilot its use for the valuation of EQ-5D in a sample of the English public (n=76).

The proposed approach differs from traditional approaches in three key ways. Firstly, instead of simply attempting to infer the relative importance that participants place on differing domains based upon choices between health profiles, the respondents are asked directly about the relative importance they place on differing domains of health, prior to validating these with profile choices. Secondly, the authors place a heavy emphasis on deliberation, and the construction, rather than uncovering, of preferences during the elicitation exercises. Thirdly, a “personal utility function” for each individual is constructed (in effect a personal EQ-5D tariff), and these individual utility functions are subsequently aggregated into a social utility function.

In the pilot, the authors find that the method appears feasible for wider use, albeit with some teething troubles associated with the computer-based tool developed to implement it, and the skills of the interviewers.

This direct method raises an interesting question for health economics – should we be inferring preferences based upon choices that differ in terms of certain attributes, or should we just ask directly about the attributes? This is a tricky question. It is possible that the preferences elicited via these different approaches could result in different preferences – if they do, on what grounds should we choose one or other? This requires a normative judgment, and at present, it appears both are (potentially) as legitimate as each other.

Whilst the authors apply this direct method to the valuation of health, I don’t see why similar approaches couldn’t be applied to any multi-attribute choice experiment. Keep your eyes out for future uses of it in valuation, and perhaps beyond? It will be interesting to see how it develops.

Credits