ICU triage: a challenge and an opportunity

In a well-publicized snapshot of the challenge of ICU triage, Chang and colleagues wrote:

Critical care services can be life-saving, but many patients admitted to intensive care units (ICUs) are too sick or, conversely, not sick enough to benefit. Intensive care unit overutilization can produce more costly and invasive care without improving outcomes.

Emphasis added. Hyder provides an interesting critique to which Chang and Shapiro respond. In this post, I shall consider over-utilization by those “not sick enough to benefit”: 23.4% of the 808 patients admitted to the UCLA Medical Center in the study by Chang et al. This over-utilization provides both a challenge and a win-win opportunity (better outcomes at lower cost) if we can meet the challenge.

In a forward-looking vision, which some may regard as optimistic, Anesi et al wrote:

In the year 2050 we will unambiguously reimburse healthcare based on value, and so there is good reason to suspect that we will have targeted and reduced many services that provide little or no benefit to patients…

It can be argued that ICU over-utilization, on average, provides no overall benefit, while significantly increasing costs. Gooch and Kahn observed that US spending on critical care represents nearly 3% of GDP, while:

In contrast, the United Kingdom spends only 0.1% of its gross domestic product on critical care services, with no evidence of worse patient outcomes and similar life expectancies as in the United States. Although there are many differences between these 2 countries, one significant difference is intensive care unit (ICU) bed supply. The United States has 25 ICU beds per 100 000 people, as compared with 5 per 100 000 in the United Kingdom. As a result, ICU case-mix differs substantially. In the United Kingdom, the majority of ICU patients are at high risk for death, whereas in the United States, many patients are admitted to the ICU for observation.

As observed by Halpern, these differences come at a significant cost in the US:

The number of intensive care unit (ICU) beds in the United States has continued to increase over the last 3 decades, as have ICU utilization rates and costs, and this despite the lack of any federal, regional, or critical care society mandates to justify these increases. Some experts believe that the increase in the number of ICU beds has led to inappropriate use of these beds by patients who are either too healthy or too sick to benefit from intensive care. This may in part explain the stable national ICU occupancy rate of approximately 68% between 1985 and 2010 and suggests that ICU utilization has simply risen to meet the increased number of beds.

Emphasis added. I shall consider here only ICU usage by patients too healthy to benefit. Although the economics behind reducing ICU over-utilization by “those not sick enough to benefit” appears simple, the underlying cause is in fact likely complex.

icu-costs-fig-1

This one appears easy: lower costs and potentially better outcomes

At the same time, I recall several caveats, well known to health economists, but important in planning and communication:

  1. We expect ICUs to be available when needed, including for emergencies and disasters,
  2. ICUs have high fixed costs,
  3. Decision-making is critical: incremental costs of adding capacity become fixed costs in the future.

Chris Sampson recently reviewed a study aimed at overconsumption or misconsumption (a consequence of over-utilization). The authors of that paper suggest that “cultural change might be required to achieve significant shifts in clinical behaviour.” Chris laments that this study did not ‘dig deeper’; here we aim to dig deeper in one specific area: ICU triage for patients “not sick enough to benefit.” More questions than answers at this stage, but hopefully the questions will ultimately lead to answers.

I begin by stepping back: economic decisions frequently involve compromises in allocating scarce resources. Decisions in health economics are frequently no different. How scarce are ICU resources? What happens if they are less scarce? What are the costs? Increasing availability can frequently lead to increased utilization, a phenomenon called “demand elasticity”. For example, increasing expressway/motorway capacity “can lead to increased traffic as new drivers seize the opportunity to travel on the larger road”, and thus no reduction in travel time. Gooch and Kahn further note that:

The presence of demand elasticity in decisions regarding ICU care has major implications for health care delivery and financing. Primarily, this indicates it is possible to reduce the costs of US hospital care by constraining ICU bed supply, perhaps through certificate of need laws or other legislation.

I offer a highly simplified sketch of how ICU over-utilization by those “not sick enough to benefit” is one driver of a vicious cycle in ICU cost growth.

icu-costs-fig-2

ICU over-utilization by patients “not sick enough to benefit” as a driver for ICU demand elasticity

Who (if anyone) is at fault for this ICU vicious cycle? Chang and Shapiro offer one suggestion:

For medical conditions where ICU care is frequently provided, but may not always be necessary, institutions that utilize ICUs more frequently are more likely to perform invasive procedures and have higher costs but have no improvement in hospital mortality. Hospitals had similar ICU utilization patterns across the 4 medical conditions, suggesting that systematic institutional factors may influence decisions to potentially overutilize ICU care.

Emphasis added. I note that demand elasticity is not in itself bad; it must simply be recognized, controlled and used appropriately. As part of a discussion in print on the role of cost considerations in medical decisions, Du and Kahn write:

Although we argue that costs should not be factored into medical decision-making in the ICU, this does not mean that we should not strive toward healthcare cost reduction in other ways. One strategy is to devise systems of care that prevent unnecessary or unwanted ICU admissions—given the small amount of ICU care that is due to discretionary spending, the only real way to reduce ICU costs is to prevent ICU admissions in the first place.

Du and Kahn also argue for careful cost-effectiveness analyses, such as that supported by NICE in the UK:

These programs limit use of treatments that are not cost-effective, taking cost decisions out of the hands of physicians and putting them where they belong: in the hands of society at large… We will achieve real ICU savings only by encouraging a society committed to system-based reforms.

Emphasis added. One can debate “taking cost decisions out of the hands of physicians”, though Guidet & Beale‘s and Capuzzo & Rhodes‘s argument for more physician awareness of cost might provide a good intermediate position in this debate.

Finally, increasing ICU supply (that is, ICU beds) in response to well-conceived increases in ICU demand is not in itself bad; ICU supply must be able to respond to demands imposed by disasters or other emergencies. We need to seek out novel ways to provide this capacity without incurring potentially unnecessary fixed costs, perhaps from region-wide stockpiling of supplies and equipment, and region-wide pools of on-call physicians and other ICU personnel. In summary, current health-related literature offers a wide-ranging discussion of the growing costs of intensive care; in my opinion: more questions than answers at this stage, but hopefully the questions will ultimately lead to answers.

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Alastair Canaway’s journal round-up for 20th February 2017

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.

The estimation and inclusion of presenteeism costs in applied economic evaluation: a systematic review. Value in Health Published 30th January 2017

Presenteeism is one of those issues that you hear about from time to time, but rarely see addressed within economic evaluations. For those who haven’t come across it before, presenteeism refers to being at work, but not working at full capacity, for example, due to your health limiting your ability to work. The literature suggests that given presenteeism can have large associated costs which could significantly impact economic evaluations, it should be considered. These impacts are rarely captured in practice. This paper sought to identify studies where presenteeism costs were included, examined how valuation was approached and the degree of impact of including presenteeism on costs. The review included cost of illness studies as well as economic evaluations, just 28 papers had attempted to capture the costs of presenteeism, these were in a wide variety of disease areas. A range of methods was used, across all studies, presenteeism costs accounted for 52% (range from 19%-85%) of the total costs relating to the intervention and disease. This is a vast proportion and significantly outweighed absenteeism costs. Presenteeism is clearly a significant issue, yet widely ignored within economic evaluation. This in part may be due to the health and social care perspective advised within the NICE reference case and compounded by the lack of guidance in how to measure and value productivity costs. Should an economic evaluation pursue a societal perspective, the findings suggest that capturing and valuing presenteeism costs should be a priority.

Priority to end of life treatments? Views of the public in the Netherlands. Value in Health Published 5th January 2017

Everybody dies, and thus, end of life care is probably something that we should all have at least a passing interest in. The end of life context is an incredibly tricky research area with methodological pitfalls at every turn. End of life care is often seen as ‘different’ to other care, and this is reflected in NICE having supplementary guidance for the appraisal of end of life interventions. Similarly, in the Netherlands, treatments that do not meet typical cost per QALY thresholds may be provided should public support be sufficient. There, however, is a dearth of such evidence, and this paper sought to elucidate this issue using the novel Q methodology. Three primary viewpoints emerged: 1) Access to healthcare as a human right – all have equal rights regardless of setting, that is, nobody is more important. Viewpoint one appeared to reject the notion of scarce resources when it comes to health: ‘you can’t put a price on life’. 2) The second group focussed on providing the ‘right’ care for those with terminal illness and emphasised that quality of life should be respected and unnecessary care at end of life should be avoided. This second group did not place great importance on cost-effectiveness but did acknowledge that costly treatments at end of life might not be the best use of money. 3) Finally, the third group felt there should be a focus on care which is effective and efficient, that is, those treatments which generate the most health should be prioritised. There was a consensus across all three groups that the ultimate goal of the health system is to generate the greatest overall health benefit for the population. This rejects the notion that priority should be given to those at end of life and the study concludes that across the three groups there was minimal support for the possibility of the terminally ill being treated with priority.

Methodological issues surrounding the use of baseline health-related quality of life data to inform trial-based economic evaluations of interventions within emergency and critical care settings: a systematic literature review. PharmacoEconomics [PubMed] Published 6th January 2017

Catchy title. Conducting research within emergency and critical settings presents a number of unique challenges. For the health economist seeking to conduct a trial based economic evaluation, one such issue relates to the calculation of QALYs. To calculate QALYs within a trial, baseline and follow-up data are required. For obvious reasons – severe and acute injuries/illness, unplanned admission – collecting baseline data on those entering emergency and critical care is problematic. Even when patients are conscious, there are ethical issues surrounding collecting baseline data in this setting, the example used relates to somebody being conscious after cardiac arrest, is it appropriate to be getting them to complete HRQL questionnaires? Probably not. Various methods have been used to circumnavigate this issue; this paper sought to systematically review the methods that have been used and provide guidance for future studies. Just 19 studies made it through screening, thus highlighting the difficulty of research in this context. Just one study prospectively collected baseline HRQL data, and this was restricted to patients in a non-life threatening state. Four different strategies were adopted in the remaining papers. Eight studies adopted a fixed health utility for all participants at baseline, four used only the available data, that is, from the first time point where HRQL was measured. One asked patients to retrospectively recall their baseline state, whilst one other used Delphi methods to derive EQ-5D states from experts. The paper examines the implications and limitations of adopting each of these strategies. The key finding seems to relate to whether or not the trial arms are balanced with respect to HRQL at baseline. This obviously isn’t observed, the authors suggest trial covariates should instead be used to explore this, and adjustments made where applicable. If, and that’s a big if, trial arms are balanced, then all of the four methods suggested should give similar answers. It seems the key here is the randomisation, however, even the best randomisation techniques do not always lead to balanced arms and there is no guarantee of baseline balance. The authors conclude trials should aim to make an initial assessment of HRQL at the earliest opportunity and that further research is required to thoroughly examine how the different approaches will impact cost-effectiveness results.

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Thesis Thursday: Ayesha Ali

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 Ayesha Ali who graduated with a PhD from Lancaster University. If you would like to suggest a candidate for an upcoming Thesis Thursday, get in touch.

Title
Essays on health economics: trans fat policies, commuting, physical activity, and body mass index in the US
Supervisors
Colin Green, Bruce Hollingsworth
Repository link
http://www.research.lancs.ac.uk/portal/en/publications/-(182eadeb-a873-4d45-93bc-a987899c6587).html

What drew you to this particular topic and made you want to dedicate your PhD to it?

I’ve always been very fascinated about how people make decisions when it comes to health-related behaviors. My Mom was a doctor, so health has always been a main driver in what our family ate and what sort of values my parents emphasized. Growing up, I think we were always kind of “the weird family” in the neighborhood and in our extended family, because we often put health before cultural norms (i.e. changing traditional recipes to be healthier, avoiding all of those colorful and fun children’s cereals and candies, etc.), so I’ve been very aware and very curious about what drives health-related choices for different people. I’ve also lived in a number of very different communities, where norms about behaviors related to health varied significantly from place to place, and that variation has always been something that I’ve sort of observed and wondered about as well. So, I think these observations are what drew me to economics, as a way of understanding choices. My thesis work was somewhat of an attempt to crack the surface.

I don’t think I got the chance in my thesis to dig as deeply into the issue where health behaviors and culture meet as I had wanted, but I was lucky to get to use some of the types of data and some of the econometric tools that will come in handy as I continue to explore this area.

Your study focussed on US data, but you studied at a UK university. Was this a help or a hindrance?

I think it was definitely a challenge to work with non-UK data while studying at a UK University; for example, I didn’t find certain types of support (in terms of local workshops or having other colleagues using the same data) or familiarity that was often easily available to someone using UK data. However, my supervisors were very supportive of me and there were students in my cohort using other non-UK data so I was not alone in that regard.

One challenge that I had was that I couldn’t just ask someone who uses the data, but instead had to spend a bit of time trying to understand how other researchers in the literature used the data and then try to figure out whether or not their assumptions and ways of using the data were applicable to my work. I spent a lot of time reading data documentation files — both of my datasets (NHANES and ATUS) have really good online resources that I’m now very familiar with! I think that a lot of this being “on my own” with the data helped me to develop a feeling of confidence that I may not have had otherwise. I’m somewhat prone to second-guessing myself, so being able to learn to have confidence in my work was really valuable.

So, although challenging, overall I would say it was definitely more of a help than a hindrance.

Methodologically, what was the most challenging aspect of the research?

In my third chapter I use time use data, a two-part model, and a recursive bivariate probit model to estimate physical activity participation and duration decisions given an individual’s commuting time, with an instrument for commuting. There are some conflicting ideas on how best to use time-use data in the literature (see: Franzis and Stewart, 2010; Gershuny, 2012; Stewart, 2013) and few examples of instruments for commuting (see: Baum-Snow, 2007; Gimenez-Nadal and Molina, 2011).

I received a lot of different feedback on the best estimation approach to use. I wanted to estimate participation and intensity elasticities for individuals who do physical activity on a given day. There are a number of ways to deal with these two questions separately or simultaneously and also to deal with the large number of zeros in the data (i.e. individuals who did not participate in physical activity). At one point, I remember that I thought I had everything figured out with this chapter; and then following a conference presentation of this work, one commenter was dead set that I should be using a switching model. I hadn’t considered that approach and wasn’t familiar with it at all, so I had to go look it up, figure out what it was and whether or not it worked with what I was doing. So, just figuring out the best way to deal with my data and with the questions I was asking in this third chapter were probably the most challenging part of the thesis for me.

If you could have a decision maker implement one policy change supported by your work, what would it be?

If I could ideally have policy makers do one thing that is supported by my work, I think it would actually be a rather general thing, not related to one specific policy, but sort of related to the entire approach of policy-making. I would want policy makers to consider the groups they are targeting with more care. For example, my third chapter looks at time use among obese individuals and healthier-weight individuals and finds that many decisions, such as the decision of where to live and work, are often driven by different factors in these different groups. In general, my work suggests that different groups may be driven by different motivating factors and if we don’t understand what these are, policies might not successfully reach those who could most benefit. That being said, this probably isn’t as easy as it sounds, as there are a lot of political influences on how policy decisions are made.

If you had to do it all again (perish the thought), is there anything you would have done differently?

I think overall, I’m really happy with my experience; I had some good resources and Lancaster was a great environment for me. My PhD cohort was close-knit and faculty in my department were very approachable and supportive. If anyone is interested, I also found McCloskey’s Economical Writing and Thomson’s Guide for the Young Economist to be really helpful at various stages of the PhD.

If I could have changed one thing though, it was how I dealt with insecurity. Even in such a supportive environment, the competitive nature of academia can contribute to feelings of insecurity; I worked hard to recognize my own insecurities and fight through them, but I didn’t always succeed. So, if I could do something differently, I would like to have been less afraid to own up to not knowing something and to just keep asking questions until I understood.

I really enjoyed having the chance to talk with you about my experiences and motivation. And I hope that if anyone can find my experiences useful to them at their stage of the process, they do. Thanks again for inviting me.