Sam Watson’s journal round-up for Monday 20th August 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.

A Randomized Trial of Epinephrine in Out-of-Hospital Cardiac Arrest. New England Journal of Medicine. Published July 2018.

Adrenaline (epinephrine) is often administered to patients in cardiac arrest in order to increase blood flow and improve heart rhythm. However, there had been some concern about the potential adverse effects of using adrenaline, and a placebo controlled trial was called for. This article presents the findings of this trial. While there is little economics in this article, it is an interesting example of what I believe to be erroneous causal thinking, especially in the way it was reported in the media. For example, The Guardian‘s headline was,

Routine treatment for cardiac arrest doubles risk of brain damage – study

while The Telegraph went for the even more inflammatory

Cardiac arrest resuscitation drug has needlessly brain-damaged thousands

But what did the study itself say about their findings:

the use of epinephrine during resuscitation for out-of-hospital cardiac arrest resulted in a significantly higher rate of survival at 30 days than the use of placebo. […] although the rate of survival was slightly better, the trial did not show evidence of a between-group difference in the rate of survival with a favorable neurologic outcome. This result was explained by a higher proportion of patients who survived with severe neurologic disability in the epinephrine group.

Clearly, a slightly more nuanced view, but nevertheless it leaves room for the implication that the adrenaline is causing the neurological damage. Indeed the authors go on to say that “the use of epinephrine did not improve neurologic outcome.” But a counterfactual view of causation should lead us to ask what would have happened to those who survived with brain damage had they not been given adrenaline.

We have a competing risks set up: (A) survival with favourable neurologic outcome, (B) survival with neurologic impairment, and (C) death. The proportion of patients with outcome (A) was slightly higher in the adrenaline group (although not statistically significant so apparently no effect eyes roll), the proportion of patients with outcome (B) was a lot higher in the adrenaline group, and the proportion of patients with outcome (C) was lower in the adrenaline group. This all suggests to me that the adrenaline caused patients who would have otherwise died to mostly survive with brain damage, and a few to survive impairment free, not that adrenaline caused those who would have otherwise been fine to have brain damage. So the question in response to the above quotes is then, is death a preferable neurologic outcome to brain damage? As trite as this may sound, it is a key health economics question – how do we value these health states?

Incentivizing Safer Sexual Behavior: Evidence from a Lottery Experiment on HIV Prevention. American Economic Review: Applied Economics. [RePEcPublished July 2018. 

This article presents a randomised trial testing an interesting idea. People who are at high risk of HIV and other sexually transmitted infections (STIs) and often those who engage in riskier sexual behaviour. A basic decision theoretic conception would be that those individuals don’t consider the costs to be high enough relative to the benefits (although there is clearly some divide between this explanation and how people actually think in terms of risky sexual behaviour, much like any other seemingly irrational behaviour). Conditional cash transfers can change the balance of the decision to incentivise people to act differently, what this study looks at is using a conditional lottery with the chance of high winnings instead, since this should be more attractive still to risk-seeking individuals. While the trial was designed to reduce HIV prevalance, entry into the lottery in the treatment arm was conditional on being free of two curable STIs at each round – this enabled people who fail to be eligible again, and also allowed the entry of HIV-positive individuals whose sexual behaviour is perhaps the most important to reducing HIV transmission. The lottery arm of the trial was found to have 20% lower incidence over the study period compared to the control arm – quite impressive. However, the cost-effectiveness of the program was estimated to be $882 per HIV infection averted on the basis of lottery payments alone, and around $3,300 per case averted all in. This seems quite high to me. Despite a plethora of non-comparable outcomes in cost-effectiveness studies of HIV public health interventions other studies have reported costs per cases averted an order of magnitude lower than this. The conclusions seems to be then that the idea works well – it’s just too costly to be of much use.

Monitoring equity in universal health coverage with essential services for neglected tropical diseases: an analysis of data reported for five diseases in 123 countries over 9 years. The Lancet: Global Health. [PubMedPublished July 2018. 

Universal health coverage (UHC) is one the key parts of Sustainable Development Goal (SDG) 3, good health and well-being. The text of the SDG identifies UHC as being about access to services – but this word “access” in the context of health care is often vague and nebulous. Many people mistakenly treat access to health services as synonymous with use of health services, but having access to something is not dependent on whether you actually use it or not. Barriers to a person’s ability to use health care for a given complaint are numerous: financial cost, time cost, lack of education, language barrier, and so forth. It is therefore difficult to quantify and measure access. Hogan and co-authors proposed an index to quantify and monitor UHC across the world that was derived from a number of proxies such as women with four or more antenatal visits, children with vaccines, blood pressure, and health worker density. Their work is useful but of course flawed – these proxies all capture something different, either access, use, or health outcomes – and it is unclear that they are all sensitive to the same underlying construct. Needless to say, we should still be able to diagnose access issues from some combination of these data. This article extends the work of Hogan et al to look at neglected tropical diseases, which affect over 1.5 billion, yet which are, obviously, neglected. The paper uses ‘preventative chemotherapy coverage’ as its key measure, which is the proportion of those needed the chemotherapy who actually receive it. This is a measure of use and not access (although they should be related), for example, there may be near universal availability of the chemotherapy, but various factors on the demand side limiting use. Needless to say, the measure should still be a useful diagnostic tool and it is interesting to see how much worse countries perform on this metric for neglected tropical diseases than general health care.

 

Credits

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.