Sam Watson’s journal round-up for 26th November 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.

Alcohol and self-control: a field experiment in India. American Economic Review Forthcoming

Addiction is complex. For many people it is characterised by a need or compulsion to take something, often to prevent withdrawal, often in conflict with a desire to not take it. This conflicts with Gary Becker’s much-maligned rational theory of addiction, which views the addiction as a choice to maximise utility in the long term. Under Becker’s model, one could use market-based mechanisms to end repeated, long-term drug or alcohol use. By making the cost of continuing to use higher then people would choose to stop. This has led to the development of interventions like conditional payment or cost mechanisms: a user would receive a payment on condition of sobriety. Previous studies, however, have found little evidence people would be willing to pay for such sobriety contracts. This article reports a randomised trial among rickshaw drivers in Chennai, India, a group of people with a high prevalence of high alcohol use and dependency. The three trial arms consisted of a control arm who received an unconditional daily payment, a treatment arm who received a small payment plus extra if they passed a breathalyser test, and a third arm who had the choice between either of the two payment mechanisms. Two findings are of much interest. First, the incentive payments significantly increased daytime sobriety, and second, over half the participants preferred the conditional sobriety payments over the unconditional payments when they were weakly dominated, and a third still preferred them even when the unconditional payments were higher than the maximum possible conditional payment. This conflicts with a market-based conception of addiction and its treatment. Indeed, the nature of addiction means it can override all intrinsic motivation to stop, or do anything else frankly. So it makes sense that individuals are willing to pay for extrinsic motivation, which in this case did make a difference.

Heterogeneity in long term health outcomes of migrants within Italy. Journal of Health Economics [PubMed] [RePEc] Published 2nd November 2018

We’ve discussed neighbourhood effects a number of times on this blog (here and here, for example). In the absence of a randomised allocation to different neighbourhoods or areas, it is very difficult to discern why people living there or who have moved there might be better or worse off than elsewhere. This article is another neighbourhood effects analysis, this time framed through the lens of immigration. It looks at those who migrated within Italy in the 1970s during a period of large northward population movements. The authors, in essence, identify the average health and mental health of people who moved to different regions conditional on duration spent in origin destinations and a range of other factors. The analysis is conceptually similar to that of two papers we discussed at length on internal migration in the US and labour market outcomes in that it accounts for the duration of ‘exposure’ to poorer areas and differences between destinations. In the case of the labour market outcomes papers, the analysis couldn’t really differentiate between a causal effect of a neighbourhood increasing human capital, differences in labour market conditions, and unobserved heterogeneity between migrating people and families. Now this article examining Italian migration looks at health outcomes, such as the SF-12, which limit the explanations since one cannot ‘earn’ more health by moving elsewhere. Nevertheless, the labour market can still impact upon health strongly.

The authors carefully discuss the difficulties in identifying causal effects here. A number of model extensions are also estimated to try to deal with some issues discussed. This includes a type of propensity score weighting approach, although I would emphasize that this categorically does not deal with issues of unobserved heterogeneity. A finite mixture model is also estimated. Generally a well-thought-through analysis. However, there is a reliance on statistical significance here. I know I do bang on about statistical significance a lot, but it is widely used inappropriately. A rule of thumb I’ve adopted for reviewing papers for journals is that if the conclusions would change if you changed the statistical significance threshold then there’s probably an issue. This article would fail that test. They use a threshold of p<0.10 which seems inappropriate for an analysis with a sample size in the tens of thousands and they build a concluding narrative around what is and isn’t statistically significant. This is not to detract from the analysis, merely its interpretation. In future, this could be helped by banning asterisks in tables, like the AER has done, or better yet developing submission guidelines around its use.

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Chris Sampson’s journal round-up for 5th November 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.

Stratified treatment recommendation or one-size-fits-all? A health economic insight based on graphical exploration. The European Journal of Health Economics [PubMed] Published 29th October 2018

Health care is increasingly personalised. This creates the need to evaluate interventions for smaller and smaller subgroups as patient heterogeneity is taken into account. And this usually means we lack the statistical power to have confidence in our findings. The purpose of this paper is to consider the usefulness of a tool that hasn’t previously been employed in economic evaluation – the subpopulation treatment effect pattern plot (STEPP). STEPP works by assessing the interaction between treatments and covariates in different subgroups, which can then be presented graphically. Imagine your X-axis with the values defining the subgroups and your Y-axis showing the treatment outcome. This information can then be used to determine which subgroups exhibit positive outcomes.

This study uses data from a trial-based economic evaluation in heart failure, where patients’ 18-month all-cause mortality risk was estimated at baseline before allocation to one of three treatment strategies. For the STEPP procedure, the authors use baseline risk to define subgroups and adopt net monetary benefit at the patient level as the outcome. The study makes two comparisons (between three alternative strategies) and therefore presents two STEPP figures. The STEPP figures are used to identify subgroups, which the authors apply in a stratified cost-effectiveness analysis, estimating net benefit in each defined risk subgroup.

Interpretation of the STEPPs is a bit loose, with no hard decision rules. The authors suggest that one of the STEPPs shows no clear relationship between net benefit and baseline risk in terms of the cost-effectiveness of the intervention (care as usual vs basic support). The other STEPP shows that, on average, people with baseline risk below 0.16 have a positive net benefit from the intervention (intensive support vs basic support), while those with higher risk do not. The authors evaluate this stratification strategy against an alternative stratification strategy (based on the patient’s New York Heart Association class) and find that the STEPP-based approach is expected to be more cost-effective. So the key message seems to be that STEPP can be used as a basis for defining subgroups as cost-effectively as possible.

I’m unsure about the extent to which this is a method that deserves to have its own name, insofar as it is used in this study. I’ve seen plenty of studies present a graph with net benefit on the Y-axis and some patient characteristic on the X-axis. But my main concern is about defining subgroups on the basis of net monetary benefit rather than some patient characteristic. Is it OK to deny treatment to subgroup A because treatment costs are higher than in subgroup B, even if treatment is cost-effective for the entire population of A+B? Maybe, but I think that creates more challenges than stratification on the basis of treatment outcome.

Using post-market utilisation analysis to support medicines pricing policy: an Australian case study of aflibercept and ranibizumab use. Applied Health Economics and Health Policy [PubMed] Published 25th October 2018

The use of ranibizumab and aflibercept has been a hot topic in the UK, where NHS providers feel that they’ve been bureaucratically strong-armed into using an incredibly expensive drug to treat certain eye conditions when a cheaper and just-as-effective alternative is available. Seeing how other countries have managed prices in this context could, therefore, be valuable to the NHS and other health services internationally. This study uses data from Australia, where decisions about subsidising medicines are informed by research into how drugs are used after they come to market. Both ranibizumab (in 2007) and aflibercept (in 2012) were supported for the treatment of age-related macular degeneration. These decisions were based on clinical trials and modelling studies, which also showed that the benefit of ~6 aflibercept prescriptions equated to the benefit of ~12 ranibizumab prescriptions, justifying a higher price-per-injection for aflibercept.

In the UK and US, aflibercept attracts a higher price. The authors assume that this is because of the aforementioned trial data relating to the number of doses. However, in Australia, the same price is paid for aflibercept and ranibizumab. This is because a post-market analysis showed that, in practice, ranibizumab and aflibercept had a similar dose frequency. The purpose of this study is to see whether this is because different groups of patients are being prescribed the two drugs. If they are, then we might anticipate heterogenous treatment outcomes and thus a justification for differential pricing. Data were drawn from an administrative claims database for 208,000 Australian veterans for 2007-2017. The monthly number of aflibercept and ranibizumab prescriptions was estimated for each person, showing that total prescriptions increased steadily over the period, with aflibercept taking around half the market within a year of its approval. Ranibizumab initiators were slightly older in the post-aflibercept era but, aside from that, there were no real differences identified. When it comes to the prescription of ranibizumab or aflibercept, gender, being in residential care, remoteness of location, and co-morbidities don’t seem to be important. Dispensing rates were similar, at around 3 prescriptions during the first 90 days and around 9 prescriptions during the following 12 months.

The findings seem to support Australia’s decision to treat ranibizumab and aflibercept as substitutes at the same price. More generally, they support the idea that post-market utilisation assessments can (and perhaps should) be used as part of the health technology assessment and reimbursement process.

Do political factors influence public health expenditures? Evidence pre- and post-great recession. The European Journal of Health Economics [PubMed] Published 24th October 2018

There is mixed evidence about the importance of partisanship in public spending, and very little relating specifically to health care. I’d be worried if political factors didn’t influence public spending on health, given that that’s a definitively political issue. How the situation might be different before and after a recession is an interesting question.

The authors combined OECD data for 34 countries from 1970-2016 with the Database of Political Institutions. This allowed for the creation of variables relating to the ideology of the government and the proximity of elections. Stationary panel data models were identified as the most appropriate method for analysis of these data. A variety of political factors were included in the models, for which the authors present marginal effects. The more left-wing a government, the higher is public spending on health care, but this is only statistically significant in the period before the crisis of 2007. Before the crisis, coalition governments tended to spend more, while governments with more years in office tended to spend less. These effects also seem to disappear after 2007. Throughout the whole period, governing parties with a stronger majority tended to spend less on health care. Several of the non-political factors included in the models show the results that we would expect. GDP per capita is positively associated with health care expenditures, for example. The findings relating to the importance of political factors appear to be robust to the inclusion of other (non-political) variables and there are similar findings when the authors look at public health expenditure as a percentage of total health expenditure. In contradiction with some previous studies, proximity to elections does not appear to be important.

The most interesting finding here is that the effect of partisanship seems to have mostly disappeared – or, at least, reduced – since the crisis of 2007. Why did left-wing parties and right-wing parties converge? The authors suggest that it’s because adverse economic circumstances restrict the extent to which governments can make decisions on the basis of ideology. Though I dare say readers of this blog could come up with plenty of other (perhaps non-economic) explanations.

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Thesis Thursday: Frank Sandmann

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

Title
The true cost of epidemic and outbreak diseases in hospitals
Supervisors
Mark Jit, Sarah Deeny, Julie Robotham, John Edmunds
Repository link
http://researchonline.lshtm.ac.uk/4648208/

Do you refer to the ‘true’ cost because some costs are hidden in this context?

That’s a good observation. Economists use the term “true cost” as a synonym for “opportunity cost”, which can be defined as the net value of the forgone second-best use of a resource. The true value of a hospital bed is therefore determined by its second-best use, which may indeed be less easily observed and less obvious, or somewhat hidden.

In the context of infectious disease outbreaks in hospital, the most visible costs are the direct expenditures on treatments of infected cases and any measures of containment. However, they do not capture the full extent of the “alternative” costs and therefore cannot equal opportunity costs. Slightly less visible are the potential knock-on effects for visitors to the hospital who, unbeknown to them, may get infected and contribute to sustained transmission in the community. Least seen are the externalities borne by patients who have not been admitted so far but who are awaiting admission, and for whom there is no space in hospital yet due to the ongoing outbreak.

In my thesis, I provided a general overview of the historical development of the concept of opportunity costs of resources before I looked in detail at bed-days and the application for hospitals.

How should the opportunity cost of hospital stays be determined?

That depends on for whom you want to determine these costs.

For individual patients, it depends on the very subjective decision of how else they would spend their time instead, and how urgent it is to receive hospital care.

From the perspective of hospital administrators, it is straightforward to calculate the opportunity costs based on the revenues and expenditures of the inpatients, their length of stays, and the existing demand of care from the community. This is quite important because whether there are opportunity costs from forgone admissions will depend on whether there are other patients actually waiting to be admitted, which is somewhat reflected in occupancy rates and of course waiting lists.

Any other decision maker who is acting as an agent on behalf of a collective group or the public should look into the forgone health impact of patients who cannot be admitted when the beds are unavailable to them. In my thesis, I proposed a method for quantifying the opportunity costs of bed-days with the net benefit of the second-best patients forgone, which I illustrated with the example of norovirus-associated gastroenteritis.

How important are differences in methods for costing in the context of gastroenteritis and norovirus?

The results can differ quite substantially when using different costing methods. Norovirus is an ideal illness to illustrate this issue given that otherwise healthy people with gastrointestinal symptoms and no further comorbidities or complications shouldn’t be admitted to hospital in order to minimise the risk of an outbreak. Patients with norovirus are therefore often not the patient group that is benefitting the most from a hospital stay.

In one of the studies of my PhD, I was able to show that the annual burden of norovirus in public hospitals in England amounts to a mean £110 million using conventional costing methods, while the opportunity costs were two-to-three times higher of up to £300 million.

This means that there is the potential for a situation where an intervention is disadvantaged when using conventional methods for costing and ignoring the opportunity costs. When evaluating such an intervention against established decision rules of cost-effectiveness, this may lead to an incorrect decision.

What were some of the key challenges that you encountered in estimating the cost of norovirus to hospitals, and how did you overcome them?

There were at least four key challenges:

First was the number of admissions. Many inpatients with norovirus won’t get recorded as such if they haven’t been laboratory-confirmed. That is why I regressed national inpatient episodes of gastroenteritis against laboratory surveillance reports for ten different gastrointestinal pathogens to estimate the norovirus-attributable proportion.

Second was the number of bed-days used by inpatients that were infected with norovirus during their hospital stay. Using their total length of stay, or some form of propensity matching, suffers from time-dependent biases and overestimates the number of bed-days. Instead, I used a multi-state model and patient-level data from a local hospital.

Third was the bed-days that were left unoccupied for infection control. One of the datasets tracked them mandatorily for acute hospitals during winters, while another surveillance system was voluntary, but recorded outbreaks throughout the year. For a more accurate estimate, I compared both datasets with each other to explore their potential overlap.

Fourth was the forgone health of alternative admissions who had otherwise occupied the beds. I had to make assumptions about the disease progression with and without hospital treatment, for which I used health-state utilities that accounted for age, sex, and the primary medical condition.

If you could have wished for one additional set of data that wasn’t available, what would it have been?

I have been very fortunate to work with a number of colleagues at Public Health England and University College London who provided me with much of the epidemiological data that I needed. My research could have benefitted though from a dataset that tracked the time of infection for a larger patient population and for longer observation periods, and a dataset that included more robust estimates for the health gain from hospital care.

If I could make a wish about the existing datasets on norovirus that I have used, I would wish for a higher rate of reporting given that it became clear from our comparison of datasets that there is a highly-correlated trend, but the number of outbreaks reported and the details of reporting leave room for improvement. Another wish of mine for daily reporting of bed-days during winter became reality only recently; during my PhD, I had to impute missing values that were non-randomly missing at weekends and over the Christmas period. This was changed in winter 2016, and I have recently shown that the mean of our lowest-to-highest imputation scenarios is surprisingly close to the daily number of bed-days recorded since then.

Parts of your thesis are made up of journal articles that you published before submission. Was this always your intention and how did you find the experience?

I always wanted to publish parts of my thesis in separate journal articles as I believe this to be a great chance to reach different audiences. That is because my theoretical research on opportunity costs may be of broader interest than just to those who work on norovirus or bed-days given that my findings are generalisable to other diseases as well as other resources. At the same time, others may be more interested in my results for norovirus, and still others in my application of the various statistical, economic, and mathematical modelling techniques.

After all, I honestly suspect that some people may place a higher value on their next-best alternative use of time than reading my thesis from cover to cover.

Writing up my thoughts early on also helped me refine them, and the peer-review process was a great opportunity to get some additional feedback. It did require good time management skills though to keep coming back to previous studies to address the peer-reviewers’ comments while I was already busy working on the next studies.

All in all, I can recommend others to consider it and, looking back, I’d do it again this way.