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.
Verification of decision-analytic models for health economic evaluations: an overview. PharmacoEconomics [PubMed] Published 29th April 2017
Increasingly, it’s expected that model-based economic evaluations can be validated and shown to be fit-for-purpose. However, up to now, discussions have focussed on scientific questions about conceptualisation and external validity, rather than technical questions, such as whether the model is programmed correctly and behaves as expected. This paper looks at how things are done in the software industry with a view to creating guidance for health economists. Given that Microsoft Excel remains one of the most popular software packages for modelling, there is a discussion of spreadsheet errors. These might be errors in logic, simple copy-paste type mistakes and errors of omission. A variety of tactics is discussed. In particular, the authors describe unit testing, whereby individual parts of the code are demonstrated to be correct. Unit testing frameworks do not exist for application to spreadsheets, so the authors recommend the creation of a ‘Tests’ spreadsheet with tests for parameter assignments, functions, equations and exploratory items. Independent review by another modeller is also recommended. Six recommendations are given for taking model verification forward: i) the use of open source models, ii) standardisation in model storage and communication (anyone for a registry?), iii) style guides for script, iv) agency and journal mandates, v) training and vi) creation of an ISPOR/SMDM task force. This is a worthwhile read for any modeller, with some neat tactics that you can build into your workflow.
How robust are value judgments of health inequality aversion? Testing for framing and cognitive effects. Medical Decision Making [PubMed] Published 25th April 2017
Evidence shows that people are often extremely averse to health inequality. Sometimes these super-egalitarian responses imply such extreme preferences that monotonicity is violated. The starting point for this study is the idea that these findings are probably influenced by framing effects and cognitive biases, and that they may therefore not constitute a reliable basis for policy making. The authors investigate 4 hypotheses that might indicate the presence of bias: i) realistic small health inequality reductions vs larger one, ii) population- vs individual-level descriptions, iii) concrete vs abstract intervention scenarios and iv) online vs face-to-face administration. Two samples were recruited: one with a face-to-face discussion (n=52) and the other online (n=83). The questionnaire introduced respondents to health inequality in England before asking 4 questions in the form of a choice experiment, with 20 paired choices. Responses are grouped according to non-egalitarianism, prioritarianism and strict egalitarianism. The main research question is whether or not the alternative strategies resulted in fewer strict egalitarian responses. Not much of an effect was found with regard to large gains or to population-level descriptions. There was evidence that the abstract scenarios resulted in a greater proportion of people giving strong egalitarian responses. And the face-to-face sample did seem to exhibit some social desirability bias, with more egalitarian responses. But the main take-home message from this study for me is that it is not easy to explain-away people’s extreme aversion to health inequality, which is heartening. Yet, as with all choice experiments, we see that the mode of administration – and cognitive effects induced by the question – can be very important.
Adaptation to health states: sick yet better off? Health Economics [PubMed] Published 20th April 2017
Should patients or the public value health states for the purpose of resource allocation? It’s a question that’s cropped up plenty of times on this blog. One of the trickier challenges is understanding and dealing with adaptation. This paper has a pretty straightforward purpose – to look for signs of adaptation in a longitudinal dataset. The authors’ approach is to see whether there is a positive relationship between the length of time a person has an illness and the likelihood of them reporting better health. I did pretty much the same thing (for SF-6D and satisfaction with life) in my MSc dissertation, and found little evidence of adaptation, so I’m keen to see where this goes! The study uses 4 waves of data from the British Cohort Study, looking at self-assessed health (on a 4-point scale) and self-reported chronic illness and health shocks. Latent self-assessed health is modelled using a dynamic ordered probit model. In short, there is evidence of adaptation. People who have had a long-standing illness for a greater duration are more likely to report a higher level of self-assessed health. An additional 10 years of illness is associated with an 8 percentage point increase in the likelihood of reporting ‘excellent’ health. The study is opaque about sample sizes, but I’d guess that finding is based on not-that-many people. Further analyses are conducted to show that adaptation seems to become important only after a relatively long duration (~20 years) and that better health before diagnosis may not influence adaptation. The authors also look at specific conditions, finding that some (e.g. diabetes, anxiety, back problems) are associated with adaptation, while others (e.g. depression, cancer, Crohn’s disease) are not. I have a bit of a problem with this study though, in that it’s framed as being relevant to health care resource allocation and health technology assessment. But I don’t think it is. Self-assessed health in the ‘how healthy are you’ sense is very far removed from the process by which health state utilities are obtained using the EQ-5D. And they probably don’t reflect adaptation in the same way.
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