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.
Individualized glycemic control for U.S. adults with type 2 diabetes: a cost-effectiveness analysis. Annals of Internal Medicine [PubMed] Published 12th December 2017
The nature of diabetes – that it affects a lot of people and is associated with a wide array of physiological characteristics and health impacts – has given rise to recommendations for individualisation of care. This paper evaluates individualisation of glycemic control targets. Specifically, the individualised programme allocated people to one of 3 HbA1c targets (<6.5%, <7%, <8%) according to their characteristics, while the comparator was based on a single fixed target (<7%). The researchers used a patient-level simulation model. Risk equations developed by the UKPDS study were used to predict diabetes complications and mortality. The baseline population was derived from the NHANES study for 2011-12 and constitutes people who self-reported as having diabetes and who were at least 30 years old at diagnosis (to try and isolate type 2 diabetes). It’s not much of a surprise that the individualised approach dominated uniform intensive control, saving $13,547 on average per patient with a slight improvement in QALY outcomes. But the findings are not all in favour of individualisation. Quality of life improvements due to the benefits of medication were partially counteracted by a slight decrease in life years gained due to a higher rate of (mortality-increasing) complications. The absolute lifetime risk of myocardial infarction was 1.39% higher with individualisation. A key outstanding question is how much the individualisation process would actually cost to get right. Granted, it probably wouldn’t cost as much as the savings estimated in this study, but the difficulty of ensuring adequate data quality to consistently inform individualisation should not be underestimated.
This article concerns the ethical challenges of decision-making at the microlevel. For example, decisions may need to be made about allocating resources between 2 or more identifiable patients, perhaps within a particular clinic or amongst an individual clinician’s patients. The author asserts two relevant values: health need satisfaction and efficiency. Health need satisfaction is defined in terms of severity (regardless of capacity to benefit from available treatments), while efficiency is defined in terms of the maximisation of health benefit (subject to the effectiveness of treatment). The author then argues that these two values are incommensurable in the sense that we can have situations in which health need satisfaction is greater (or less) for a given choice over another, while efficiency could be lower (or higher). Thus, it is not always possible to rank choices given two non-cardinally-comparable values. It might not be clear whether it is better to treat patient A or patient B if the implications of doing so are different in terms of need and efficiency. The author then goes on to suggest some solutions to this apparent problem, starting by highlighting the need for decision makers (in this case clinicians) to recognise different decision paths. The first solution is to generate some guidelines that offer complete ordering of possible choices. These might be based on a process of weighting the different values (e.g. health need satisfaction and efficiency). The other ‘solution’ is to leave the decision to medical practitioners, who can create reasons for choices that may be unique to the case at hand. In this case, certain decision paths should be avoided, such as those that would entail discrimination. I have a lot of problems with this assessment of decision-making at the individual level. Mainly, the discussion is undermined by the fact that efficiency and health need satisfaction are entirely commensurable insofar as we care about either of them in relation to prioritisation in health care. We tend to understand both health need satisfaction and opportunity cost (the basis for estimating efficiency) in terms of health outcomes. The essay also fails to clearly identify the uniqueness of the challenge of microlevel decision-making as distinct from the process of creating clinical guidelines. This may call for a follow-up blog post…
EQ-5D: moving from three levels to five. Value in Health Published 6th December 2017
If you work on economic evaluation, the move from using the EQ-5D-3L to the EQ-5D-5L – in terms of the impact on our results – is one of the biggest methodological step changes in recent memory. We all know that the 5L (and associated value set for England) is better than the 3L. Don’t we? So it is perhaps a bit disappointing that the step to the 5L has been so tentative. This editorial articulates the challenge. NICE makes standards. EuroQoL does research. NICE was (relatively) satisfied with the 3L. EuroQoL wasn’t. We have a clash between an inherently (perhaps necessarily) conservative institution and an inherently progressive institution. Hopefully, their interaction will put us on a sustainable path that achieves both methodological consistency and scientific rigour. This editorial also provides us with a DOI-citable account of the saga that includes the development of the 5L value set for England and NICE’s subsequent memorandum.
Current UK practices on health economics analysis plans (HEAPs): are we using heaps of them? PharmacoEconomics [PubMed] Published 6th December 2017
You could get by for years in economic evaluation without even hearing about ‘health economics analysis plans’ (HEAPs). It probably depends on the policies set by the clinical trials unit (CTU) that you’re working with. The idea is that HEAPs are an equivalent standard operating procedure (SOP) to a statistical analysis plan – setting out how the trial data will be analysed before the analysis begins. This could aid transparency and consistency, and prevent dodgy practices. In this study, the researchers sought to find out whether HEAPs are actually being used, and their perceived role in clinical trials research. A survey targeted 46 UK CTUs, asking about the role of health economists in the unit and whether they used HEAP SOPs. Of 28 respondents, 11 reported having an embedded health economics team. A third of CTUs reported always having a HEAP in place. Most said they only used HEAPs ‘sometimes’, and publicly funded trials were said to be more likely to use a HEAP. The majority of respondents agreed it was acceptable to produce the HEAP at any point prior to a lockdown of the data. The findings demonstrate inconsistency in who writes HEAPs and who is perceived to be the audience. I agree with the premise that we need HEAPs. Though I’m not sure what they should look like, except that statistical analysis plans probably should not be used as a template. It would be good if some of these researchers took things a step further and figured out what ought to go into a HEAP, so that we can consistently employ their recommendations. If you’re on the HEALTHECON-ALL mailing list, you’ll know that they’re already on the case.