On the commensurability of efficiency

In this week’s round-up, I highlighted a recent paper in the journal Cambridge Quarterly of Healthcare Ethics. There are some interesting ideas presented regarding the challenge of decision-making at the individual patient level, and in particular a supposed trade-off between achieving efficiency and satisfying health need.

The gist of the argument is that these two ‘values’ are incommensurable in the sense that the comparative value of two choices is ambiguous where the achievement of efficiency and need satisfaction needs to be traded. In the journal round-up, I highlighted 2 criticisms. First, I suggested that efficiency and health need satisfaction are commensurable. Second, I suggested that the paper did not adequately tackle the special nature of microlevel decision-making. The author – Anders Herlitz – was gracious enough to respond to my comments with several tweets.

Here, I’d like to put forth my reasoning on the subject (albeit with an ignorance of the background literature on incommensurability and other matters of ethics).

Consider a machine gun

A machine gun is far more efficient than a pistol, right? Well, maybe. A machine gun can shoot more bullets than a pistol over a sustained period. Likewise, a doctor who can treat 50 patients per day is more efficient than a doctor who can treat 20 patients per day.

However, the premise of this entire discussion, as established by Herlitz, is values. Herlitz introduces efficiency as a value and not as some dispassionate indicator of return on input. When we are considering values – as we necessarily are when we are discussing decision-making and more generally ‘what matters’ – we cannot take the ‘more bullets’ approach to assessing efficiency.

That’s because ‘more bullets’ is not what we mean when we talk about the value of efficiency. The production function is fundamental to our understanding of efficiency as a value. Once values are introduced, it is plain to see that in the context of war (where value is attached to a greater number of deaths) a machine gun may very well be considered more efficient. However, bearing a machine gun is far less efficient than bearing a pistol in a civilian context because we value a situation that results in fewer deaths.

In this analogy, bullets are health care and deaths are (somewhat confusingly, I admit) health improvement. Treating more people is not better because we want to provide more health care, but because we want to improve people’s health (along with some other basket of values).

Efficiency only has value with respect to the outcome in whose terms it is defined, and is therefore always commensurable with that outcome. That is, the production function is an inherent and necessary component of an efficiency to which we attach value.

I believe that Herlitz’s idea of incommensurability could be a useful one. Different outcomes may well be incommensurable in the way described in the paper. But efficiency has no place in this discussion. The incommensurability Herlitz describes in his paper seems to be a simple conflict between utilitarianism and prioritarianism, though I don’t have the wherewithal to pursue that argument so I’ll leave it there!

Microlevel efficiency trade-offs

Having said all that, I do think there could be a special decision-making challenge regarding efficiency at the microlevel. And that might partly explain Herlitz’s suggestion that efficiency is incommensurable with other outcomes.

There could be an incommensurability between values that can be measured in their achievement at the individual level (e.g. health improvement) and values that aren’t measured with individual-level outcomes (e.g. prioritisation of more severe patients). Those two outcomes are incommensurable in the way Herlitz described, but the simple fact that we tend to think about the former as an efficiency argument and the latter as an equity argument is irrelevant. We could think about both in efficiency terms (for example, treating n patients of severity x is more efficient than treating n-1 patients of severity x, or n patients of severity x-1), we just don’t. The difficulty is that this equity argument is meaningless at the individual level because it relies on information about outcomes outside the microlevel. The real challenge at the microlevel, therefore, is to acknowledge scope for efficiency in all outcomes of value. The incommensurability that matters is between microlevel and higher-level assessments of value.

As an aside, I was surprised that the Rule of Rescue did not get a mention in the paper. This is a perfect example of a situation in which arguments that tend to be made on efficiency grounds are thrown out and another value (the duty to save an immediately endangered life) takes over. One doesn’t need to think very hard about how Rule of Rescue decision-making could be framed as efficient.

In short, efficiency is never incommensurable because it is never an end in itself. If you’re concerned with being more efficient for the sake of being more efficient then you are probably not making very efficient decisions.

Credit

Chris Sampson’s journal round-up for 4th December 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.

Funding breakthrough therapies: a systematic review and recommendation. Health Policy Published 2nd December 2017

One of the (numerous) financial pressures on health care funders in the West is the introduction of innovative (and generally very expensive) new therapies. Some of these can be considered curative, which isn’t necessarily the best way for manufacturers to create a steady income. New funding arrangements have been proposed to facilitate patient access while maintaining financial sustainability. This article focuses on a specific group of innovative therapies known as ‘Advanced Therapy Medicinal Products’ (ATMPs), which includes gene therapies. The authors conducted a systematic review of papers proposing funding models and considered their appropriateness for ATMPs. There were 48 papers included in the review that proposed payment mechanisms for high-cost therapies. Three top-level groups were identified: i) financial agreements, ii) performance-based agreements, and iii) healthcoin (a tradable currency representing the value of outcomes). The different mechanisms are compared in terms of their feasibility, acceptability, burden, ‘financial attractiveness’ and their appeal to payers and manufacturers. Annuity payments are identified as relatively attractive compared to other options, but each mechanism is summarily shown to be imperfect in the ATMP context. So, instead, the authors propose an ATMP-specific fund. For UK readers, this will likely smell a bit too much like the disastrous Cancer Drugs Fund. It isn’t clear why such a programme would be superior to annuity payments or more inventive mechanisms, or even whether it would be theoretically sound. Thus, the proposal is not convincing.

Supply-side effects from public insurance expansions: evidence from physician labor markets. Health Economics [PubMed] Published 1st December 2017

Crazy though American health care may be, its inconsistency in coverage can make for good research fodder. The Child Health Insurance Program (CHIP) was set up in 1997 and then, when the initial money ran out 10 years later, the program was (eventually) expanded. In this study, the authors use the changes in CHIP to examine the impact of expanded public coverage on provider behaviour, namely; subspecialty training (which could become more attractive with a well-insured customer base), practice setting and prevailing wage offers. The data for the study relate to the physician labour market for New York state for 2002-2013, as collected in the Graduate Medical Education survey. A simple difference-in-differences analysis is conducted with reference to the 2009 CHIP expansion, controlling for physician demographics. Paediatricians are the treatment group and the control group is adult physician generalists (mostly internal medicine). 2009 seems to be associated with a step-change in the proportion of paediatricians choosing to subspecialise – an increased probability of about 8 percentage points. There is also an upward shift in the proportion of paediatricians entering private practice, with some (weak) evidence that there is an increased preference for rural areas. These changes don’t seem to be driven by relative wage increases, with no major change in trends. So it seems that the expanded coverage did have important supply-side effects. But the waters are muddy here. In particular, we have the Great Recession and Obamacare as possible alternative explanations. Though it’s difficult to come up with good reasons for why these might better explain the observed changes.

Reflections on the NICE decision to reject patient production losses. International Journal of Technology Assessment in Health Care [PubMedPublished 20th November 2017

When people conduct economic evaluations ‘from a societal perspective’, this often just means a health service perspective with productivity losses added. NICE explicitly exclude the inclusion of these production losses in health technology appraisals. This paper reviews the issues at play, focussing on the normative question of why they should (or should not) be included. Findings from a literature review are summarised with reference to the ethical, theoretical and policy questions. Unethical discrimination potentially occurs if people are denied health care on the basis of non-health-related characteristics, such as the ability to work. All else equal, should health care for men be prioritised over health care for women because men have higher wages? Are the unemployed less of a priority because they’re unemployed? The only basis on which to defend the efficiency of an approach that includes productivity losses seems to be a neoclassical welfarist one, which is hardly tenable in the context of health care. If we adopt the extra-welfarist understanding of opportunity cost as foregone health then there is really no place for production losses. The authors also argue that including production losses may be at odds with policy objectives, at least in the context of the NHS in the UK. Health systems based on privately-funded care or social insurance may have different priorities. The article concludes that taking account of production losses is at odds with the goal of health maximisation and therefore the purpose of the NHS in the UK. Personally, I think priority setting in health care should take a narrow health perspective. So I agree with the authors that production losses shouldn’t be included. I’m not sure this article will convince those who disagree, but it’s good to have a reference to vindicate NICE’s position.

Credits

Variations in NHS admissions at a glance

Variations in admissions to NHS hospitals are the source of a great deal of consternation. Over the long-run, admissions and the volume of activity required of the NHS have increased, without equivalent increases in funding or productivity. Over the course of the year, there are repeated claims of crises as hospitals are ill-equipped for the increase in demand in the winter. While different patterns of admissions at weekends relative to weekdays may be the foundation of the ‘weekend effect’ as we recently demonstrated. And yet all these different sources of variation produce a singular time series of numbers of daily admissions. But, each of the different sources of variation are important for different planning and research aims. So let’s decompose the daily number of admissions into its various components.

Data

Daily number of emergency admissions to NHS hospitals between April 2007 and March 2015 from Hospital Episode Statistics.

Methods

A similar analysis was first conducted on variations in the number of births by day of the year. A full description of the model can be found in Chapter 21 of the textbook Bayesian Data Analysis (indeed the model is shown on the front cover!). The model is a sum of Gaussian processes, each one modelling a different aspect of the data, such as the long-run trend or weekly periodic variation. We have previously used Gaussian processes in a geostatistical model on this blog. Gaussian processes are a flexible class of models for which any finite dimensional marginal distribution is Gaussian. Different covariance functions can be specified for different models, such as the aforementioned periodic or long-run trends. The model was run using the software GPstuff in Octave (basically an open-source version of Matlab) and we have modified code from the GPstuff website.

Results

admit5-1

The four panels of the figure reveal to us things we may claim to already know. Emergency admissions have been increasing over time and were about 15% higher in 2015 than in 2007 (top panel). The second panel shows us the day of the week effects: there are about 20% fewer admissions on a Saturday or Sunday than on a weekday. The third panel shows a decrease in summer and increase in winter as we often see reported, although perhaps not quite as large as we might have expected. And finally the bottom panel shows the effects of different days of the year. We should note that the large dip at the end of March/beginning of April is an artifact of coding at the end of the financial year in HES and not an actual drop in admissions. But, we do see expected drops for public holidays such as Christmas and the August bank holiday.

While none of this is unexpected it does show that there’s a lot going on underneath the aggregate data. Perhaps the most alarming aspect of the data is the long run increase in emergency admissions when we compare it to the (lack of) change in funding or productivity. It suggests that hospitals will often be running at capacity so other variation, such as over winter, may lead to an excess capacity problem. We might also speculate on other possible ‘weekend effects’, such as admission on a bank holiday.

As a final thought, the method used to model the data is an excellent way of modelling data with an unknown structure without posing assumptions such as linearity that might be too strong. Hence their use in geostatistics. They are widely used in machine learning and artificial intelligence as well. We often encounter data with unknown and potentially complicated structures in health care and public health research so hopefully this will serve as a good advert for some new methods. See this book, or the one referenced in the methods section, for an in depth look.

Credits