Chris Sampson’s journal round-up for 11th June 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.

End-of-life healthcare expenditure: testing economic explanations using a discrete choice experiment. Journal of Health Economics Published 7th June 2018

People incur a lot of health care costs at the end of life, despite the fact that – by definition – they aren’t going to get much value from it (so long as we’re using QALYs, anyway). In a 2007 paper, Gary Becker and colleagues put forward a theory for the high value of life and high expenditure on health care at the end of life. This article sets out to test a set of hypotheses derived from this theory, namely: i) higher willingness-to-pay (WTP) for health care with proximity to death, ii) higher WTP with greater chance of survival, iii) societal WTP exceeds individual WTP due to altruism, and iv) societal WTP may exceed individual WTP due to an aversion to restricting access to new end-of-life care. A further set of hypotheses relating to the ‘pain of risk-bearing’ is also tested. The authors conducted an online discrete choice experiment (DCE) with 1,529 Swiss residents, which asked respondents to suppose that they had terminal cancer and was designed to elicit WTP for a life-prolonging novel cancer drug. Attributes in the DCE included survival, quality of life, and ‘hope’ (chance of being cured). Individual WTP – using out-of-pocket costs – and societal WTP – based on social health insurance – were both estimated. The overall finding is that the hypotheses are on the whole true, at least in part. But the fact is that different people have different preferences – the authors note that “preferences with regard to end-of-life treatment are very heterogeneous”. The findings provide evidence to explain the prevailing high level of expenditure in end of life (cancer) care. But the questions remain of what we can or should do about it, if anything.

Valuation of preference-based measures: can existing preference data be used to generate better estimates? Health and Quality of Life Outcomes [PubMed] Published 5th June 2018

The EuroQol website lists EQ-5D-3L valuation studies for 27 countries. As the EQ-5D-5L comes into use, we’re going to see a lot of new valuation studies in the pipeline. But what if we could use data from one country’s valuation to inform another’s? The idea is that a valuation study in one country may be able to ‘borrow strength’ from another country’s valuation data. The author of this article has developed a Bayesian non-parametric model to achieve this and has previously applied it to UK and US EQ-5D valuations. But what about situations in which few data are available in the country of interest, and where the country’s cultural characteristics are substantially different. This study reports on an analysis to generate an SF-6D value set for Hong Kong, firstly using the Hong Kong values only, and secondly using the UK value set as a prior. As expected, the model which uses the UK data provided better predictions. And some of the differences in the valuation of health states are quite substantial (i.e. more than 0.1). Clearly, this could be a useful methodology, especially for small countries. But more research is needed into the implications of adopting the approach more widely.

Can a smoking ban save your heart? Health Economics [PubMed] Published 4th June 2018

Here we have another Swiss study, relating to the country’s public-place smoking bans. Exposure to tobacco smoke can have an acute and rapid impact on health to the extent that we would expect an immediate reduction in the risk of acute myocardial infarction (AMI) if a smoking ban reduces the number of people exposed. Studies have already looked at this effect, and found it to be large, but mostly with simple pre-/post- designs that don’t consider important confounding factors or prevailing trends. This study tests the hypothesis in a quasi-experimental setting, taking advantage of the fact that the 26 Swiss cantons implemented smoking bans at different times between 2007 and 2010. The authors analyse individual-level data from Swiss hospitals, estimating the impact of the smoking ban on AMI incidence, with area and time fixed effects, area-specific time trends, and unemployment. The findings show a large and robust effect of the smoking ban(s) for men, with a reduction in AMI incidence of about 11%. For women, the effect is weaker, with an average reduction of around 2%. The evidence also shows that men in low-education regions experienced the greatest benefit. What makes this an especially nice paper is that the authors bring in other data sources to help explain their findings. Panel survey data are used to demonstrate that non-smokers are likely to be the group benefitting most from smoking bans and that people working in public places and people with less education are most exposed to environmental tobacco smoke. These findings might not be generalisable to other settings. Other countries implemented more gradual policy changes and Switzerland had a particularly high baseline smoking rate. But the findings suggest that smoking bans are associated with population health benefits (and the associated cost savings) and could also help tackle health inequalities.

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Method of the month: Eye-tracking

Once a month we discuss a particular research method that may be of interest to people working in health economics. We’ll consider widely used key methodologies, as well as more novel approaches. Our reviews are not designed to be comprehensive but provide an introduction to the method, its underlying principles, some applied examples, and where to find out more. If you’d like to write a post for this series, get in touch. This month’s method is eye-tracking.

Principles

Eye-tracking methods can be used to analyse how individuals acquire information and how they make decisions. The method has been extensively used by psychologists in a variety of applications, from identifying cases of dyslexia in children to testing aviation pilots’ awareness. It was made popular by Keith Rayner, but its growing use reflects changes in the availability and affordability of technology. The textbook ‘Eye Tracking: A Comprehensive Guide to Methods and Measures’ provides a great introduction and complements the course offered by Lund University.

Eye-tracking analyses typically depend on the ‘eye-mind hypothesis’ which states “there is no appreciable lag between what is fixated on and what is processed”. In addition to fixing their gaze, individuals make rapid eye movements called ‘saccades’ when they are searching for information or items of interest. There is also research into ‘pupilometery’ which relates pupil size to cognitive burden, where ‘hard’ tasks are hypothesised to cause dilation.

There is a growing interest in using quantitative elicitation methods to understand individuals’ preferences for healthcare goods or services. Quantitative methods, such as the standard gamble, time trade-off, contingent valuation or discrete choice experiments, often employ surveys which are increasingly self-completed and administered online. These valuation methods are often underpinned by economic theories either for utility (random utility theory, expected utility theory) or, in the case of attribute-based approaches, Lancaster’s Theory of consumer demand. If respondents do not answer in line with the supporting theories, the valuations derived from the survey data may be biased. Therefore, various approaches have been employed to understand whether people complete surveys in line with the analysts’ expectations – from restricted models to test for attribute non-attendance to qualitative ‘think-aloud’ interviews. Eye-tracking offers an alternative method of testing these hypotheses.

Implementation

The research question will dictate the eye-tracking study design, if it’s a reading study or a survey then accuracy will be key. If the experiment seeks to understand how participants respond to large visual stimuli, then it may be preferable to have comfortable equipment which can be used in a setting the participant is familiar with.

Equipment

In its most basic form, eye-tracking research has involved researcher-individual observation of a participants’ eyes and manual notes on pupil dilation. However, more sophisticated methods have since developed in line with changes to, and availability of, technology. In the 1950s, magnetic search coils were used to track people’s eye movements which involved placing two coils on the eye, with one circling the iris on a contact lens. Nowadays, most eye-tracking involves less invasive equipment, commonly with a camera recording data on a computer and complex algorithms to calculate the location of the individual’s gaze.

To track eyes, almost all modern devices record the corneal reflection on a camera positioned towards the individual’s pupil. The corneal reflection is a glint, usually in the iris, which allows the machine to calculate the direction of the gaze using the distance from 1) the camera to the eye and; 2) the eye to the screen. From the corneal reflection, the X and Y (horizontal and vertical) coordinates, which provide the location of current focus on the screen, are then recorded. The number of times this is logged a second is referred to as the speed (or ‘frequency’) of the tracker. As the eye moves from one position to another, the magnitude of the movement is measured in visual degrees (θ), rather than millimetres, as studies may involve moving stimulus and so the distance between eye and object could change.

Popular manufacturers include Tobii, SensoMotoric Instruments (SMI) and SR Research. Eye-trackers are usually distinguished by their speed and, as a general rule, a ‘good’ eye-tracker has a high frequency and high-resolution camera. A higher frequency allows a more accurate estimation of the fixation duration, as the start of the fixation is revealed earlier and the end revealed later. There is some consensus that a sampling frequency of 500 Hz is sufficiently powerful to accurately determine fixations and saccades. Another determinant of a ‘good’ eye-tracking device is its ‘latency’, which is the time taken for the computer to make a recording. A substantial volume of processing from the headset to screen to recording is required and, for some devices, there is a measurable delay in this process.

There are three broad categories of modern eye-tracking devices.

Head-mounted

Head-mounted eye-trackers, such as smart glasses or helmet cameras, offer participants some freedom but are harder to calibrate and can be cumbersome to wear. These eye-trackers are often used to understand how objects are attended to in a dynamic situation, for example, whilst the participant is engaged in a shopping activity.

Remote

Remote eye-trackers let the participant move freely but in front of a screen, with algorithms used to detect non-eye movements [PDF]. However, the additional calculations to distinguish head and eye movements are a burden to the processing capacity of the computer and, generally, result in a lower frequency and, as a consequence, have decreased precision.

Head-supported towers

Head-supported towers involve the use of a forehead and chin-rest. Whilst being contactless, these can be uncomfortable and unnatural for some participants. These devices are also often immobile, due to their heavy processing power, and require stability of the head because of their high frequency. However, head-supported towers are the most accurate and precise equipment available for researchers. For studies where the individual is not required to move and the stimuli are stationary (such as a survey), a head-supported tower eye-tracker provides the best quality data. Head-supported towers also offer the most accurate recording of pupil size.

Data collection

Data collection will likely occur in a university lab if a head-supported tower tracker is chosen. Head-mounted and remote trackers are generally mobile and can, therefore, travel to people of interest.

Tracking devices can either record both eyes (binocular) or a single eye (monocular). When both eyes are recorded, an average of the horizontal and vertical coordinates from each eye are taken. However, most people generally have an ‘active’ and ‘lazy’ eye and literature suggests that the active, dominant eye should only be tracked. If a participant performs poorly in the calibration, then an alternative eye should be tried.

It is crucial that the eye-tracker is calibrated for each individual to ensure the eye-tracker is recording correctly. The calibration procedure involves collecting fixation data from simple points on the screen in order to ascertain the true gaze position of the individual before the experiment begins. The points are often shown as dots or crosses which move around the screen whilst fixation data are collected. A test of the calibration can be conducted by re-running the sequence and comparing the secondary fixations to the tracker’s prediction based on the first calibration data.

The calibration should involve points in all corners of the screen to ensure that the tracker is able to record in all areas. In the corners and edges, the corneal reflection can disappear, which therefore invalidates the computer’s calculations as well as resulting in missing data. Similarly, for individuals with visual aids (glasses, contact lenses) or heavy eye-makeup, the far corners can often induce another reflection which may confuse the recording and create anomalous data.

If a respondent is completing a survey, between-page calibration called ‘drift correction’ can also be completed. In this procedure, a small dot is presented in the centre of the screen and the next page appears once the participant has focussed on the spot. If there has been too much movement, the experiment will not progress and the tracker must be recalibrated.

Data analysis

Saccades are easily identifiable as the eye moves quickly in response to or in search of visual ‘stimuli’ or objects of interest. Saccadic behaviour rarely indicates information processing as the movements are so rapid that the brain is unable to consciously realise everything that is scanned, a process known as ‘saccadic suppression’. Instead, saccades most often represent a search for information. Saccades are distinctly different to ‘micro-saccades’, which are involuntary movements whilst an individual is attempting to fixate, and the involuntary movements which occur when an individual blinks. Blinks are quite easily identifiable from regular saccades as they are immediately followed by a missing pupil image on the camera as the eyelid closes.

What constitutes a fixation varies from study to study and is dependent on the stimulus presented. For example, a familiar picture may be processed quicker than text, and a new diagram may be somewhere in between. Although complex algorithms exist for the identification of fixations in eye-tracking data, most studies define a threshold for a fixation as less than one degree of movement (a measure of distance) for between 50 to 200 milliseconds. Aggregation of the total time spent fixating, including recurrent fixations, is defined as the ‘dwell time’ to a stimulus.

Eye-tracking data provide a highly detailed record of all the locations that a user has looked at, so reducing these data to a level that can be easily analysed is challenging. One common approach in the analysis of eye-tracking data involves segmenting coordinates to defined regions or ‘areas of interest’ (AOI). AOI can be defined either prior to the experiment or post-experimentally once eye-movement data have been collected.

Another approach to reducing the data is the generation of a ‘scan path’ describing the overall sequence of movements in terms of both saccades and fixations of a respondent, either imposed on a background image of the stimulus or as a colour-coded heat map.

Pupil size can be more difficult to interpret and analyse. Measurement of the pupil differs by equipment, some use an ellipse whereas others count the number of black pixels on the camera image of the eye. Pupil dilation can be calculated as the difference in pupil size, however, analysing this as a percentage increase can cause inflated estimates when the baseline pupil size is small. Pupil size can also rarely be compared across studies as it highly affected by equipment set-up and setting luminosity.

Software

Many eye-trackers come with manufacturer written software for programming the experiment (such as the EyeLink Experiment Builder). However, access to eye-trackers and related software may be restricted. PsychoPy is an open-source software, written in Python, for eye-tracking (and other neuroscience) experiments. Similarly, data can be analysed either in specialist software such as EyeLink’s Data Viewer (which is useful for creating scan paths) or the data can be exported to other statistical programmes such as Matlab, Stata or R.

Applications

Orquin & Loose review how eye-tracking methods have been used in decision-making generally. In health, there are a few examples of survey-based choice experiments which have employed eye-tracking methods to understand more about respondents’ decision-making. Spinks & Mortimer used a remote eye-tracker to identify attribute non-attendance. Krucien et al combined eye-tracking and choice data to model information processing; and Ryan extended this analysis to focus on presentation biases in a subsequent publication. Vass et al use eye-tracking to understand how respondents complete choice experiments and if this differed with the presentation of risk. A forthcoming publication by Rigby et al. will provide an overview of approaches, including eye-tracking, to capturing decision-making in health care choice experiments.

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Thesis Thursday: Matthew Quaife

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 Matthew Quaife who has a PhD from the London School of Hygiene and Tropical Medicine. If you would like to suggest a candidate for an upcoming Thesis Thursday, get in touch.

Title
Using stated preferences to estimate the impact and cost-effectiveness of new HIV prevention products in South Africa
Supervisors
Fern Terris-Prestholt, Peter Vickerman
Repository link
http://researchonline.lshtm.ac.uk/4646708

Stated preferences for what?

Our main study looked at preferences for new HIV prevention products in South Africa – estimating the uptake and cost-effectiveness of multi-purpose prevention products, which protect against HIV, pregnancy and STIs. You’ll notice that condoms do this, so why even bother? Condom use needs both partners to agree (for the duration of a given activity) and, whilst female partners tend to prefer condom-protected sex, there is lots of evidence that male partners – who also have greater bargaining power in many contexts – do not.

Oral pre-exposure prophylaxis (PrEP), microbicide gels, and vaginal rings are new products which prevent HIV infection. More importantly, they are female-initiated and can generally be used without a male partner’s knowledge. But trials and demonstration projects among women at high risk of HIV in sub-Saharan Africa have shown low levels of uptake and adherence. We used a DCE to inform the development of attractive and usable profiles for these products, and also estimate how much additional demand – and therefore protection – would be gained from adding contraceptive or STI-protective attributes.

We also elicited the stated preferences of female sex workers for client risk, condom use, and payments for sex. Sex workers can earn more for risky unprotected sex, and we used a repeated DCE to predict risk compensation (i.e. how much condom use would change) if they were to use HIV prevention products.

What did you find most influenced people’s preferences in your research?

Unsurprisingly for products, HIV protection was most important to people, followed by STI and then pregnancy protection. But digging below these averages with a latent class analysis, we found some interesting variation within female respondents: over a third were not concerned with HIV protection at all, instead strongly caring about pregnancy and STI protection. Worryingly, these were more likely to be respondents from high-incidence adolescent and sex worker groups. The remainder of the sample overwhelmingly chose based on HIV protection.

In the second sex worker DCE, we found that using a new HIV prevention product made condoms become less important and price more important. We predict that the price premium for unprotected sex would reduce by two thirds, and the amount of condomless sex would double. This is an interesting labour market/economic finding, but – if true – also has real public health implications. Since economic changes mean sex workers move from multi-purpose condoms to single-purpose products which need high levels of adherence, we thought this would be interesting to model.

How did you use information about people’s preferences to inform estimates of cost-effectiveness?

In two ways. First, we used simple uptake predictions from DCEs to parameterise an HIV transmission model, allowing for condom substitution uptake to vary by condom users and non-users (it was double in the latter). We were also able to model the potential uptake of multipurpose products which don’t exist yet – e.g. a pill protecting from HIV and pregnancy. We predict that this combination, in particular, would double uptake among high-risk young women.

Second, we predicted risk compensation among sex workers who chose new products instead of condoms. We were also able to calculate the price elasticity of supply of unprotected sex, which we built into a dynamic transmission model as a determinant of behaviour.

Can discrete choice experiments accurately predict the kinds of behaviours that you were looking at?

To be honest, when I started the PhD I was really sceptical – and I still am to an extent. But two things make me think DCEs can be useful in predicting behaviours.

First is the data. We published a meta-analysis of how well DCEs predict real-world health choices at an individual level. We only found six studies with individual-level data, but these showed DCEs predict with an 88% sensitivity but just a 34% specificity. If a DCE says you’ll do something, you more than likely will – which is important for modelling heterogeneity in uptake. We desperately need more studies following up DCE participants making real-world choices.

Second is the lack of alternative inputs. Where products are new and potential users are inexperienced, modellers pick an uptake number/range and hope for the best. Where we don’t know efficacy, we may assume that uptake and efficacy are linearly related – but they may not be (e.g. if proportionately more people use a 95% effective product than a 45% effective one). Instead, we might assume uptake and efficacy are independent, but that might sound even less realistic. I think that DCEs can tell us something about these behaviours that are useful for the parameters and structures of models, even if they are not perfect predictors.

Your tread the waters of infectious disease modelling in your research – was the incorporation of economic factors a challenge?

It was pretty tricky, though not as challenging as building the simple dynamic transmission model as a first exposure to R. In general, behaviours are pretty crudely modelled in transmission models, largely due to assumptions like random mixing and other population-level dynamics. We made a simple mechanistic model of sex work based on the supply elasticities estimated in the DCE, and ran a few scenarios, each time estimating the impact of prevention products. We simulated the price of unprotected sex falling and quantity rising as above, but also overlaid a few behavioural rules (e.g. Camerer’s constant income hypothesis) to simulate behavioural responses to a fall in overall income. Finally, we thought about competition between product users and non-users, and how much the latter may be affected by the market behaviours of the former. Look out for the paper at Bristol HESG!

How would you like to see research build on your work to improve HIV prevention?

I did a public engagement event last year based on one statistic: if you are a 16-year old girl living in Durban, you have an 80% lifetime risk of acquiring HIV. I find it unbelievable that, in 2018, when millions have been spent on HIV prevention and we have a range of interventions that can prevent HIV, incidence among some groups is still so dramatically and persistently high.

I think research has a really important role in understanding how people want to protect themselves from HIV, STIs, and pregnancy. In addition to highlighting the populations where interventions will be most cost-effective, we show that variation in preferences drives impact. I hope we can keep banging the drum to make attractive and effective options available to those at high risk.