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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Sam Watson’s journal round up for 16th October 2017

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.

Effect of forced displacement on health. Journal of the Royal Statistical Society: Series A [RePEcPublished October 2017

History, as they say, is doomed to repeat itself. Despite repeated cries of ‘never again’, war and conflict continue to harm and displace people around the world. The mass displacement of the Rohingya in Myanmar is leading to the formation of the world’s largest refugee camp in Bangladesh. Aside from the obvious harm from conflict itself, displacement is likely to have pernicious effects on health. Livelihoods and well-being is lost as well as access to basic amenities and facilities. The conflict in Croatia and wider Eastern Europe created a mass displacement of people, however, many went to relatively wealthy neighbouring countries across Europe. Thus, the health effects of displacement in this conflict should provide an understanding of the lower bound of what happens. This paper looks into this question using a health survey of Croatians from 2003. An empirical issue the authors spend a substantial amount of time addressing is that displacement status is likely to be endogenous: beyond choices about protecting their household and possessions, health and the ability to travel may play a large role in decisions to move. The mortality rate from conflict is used as an instrument for displacement, being a proxy for the intensity of war. However, conflict intensity is obviously likely to have a effect itself on health status. A method of relaxing the exclusion restriction is used, which tempers the estimates somewhat. Nevertheless, there is evidence that displacement impacts upon hypertension, self-assessed health, and emotional and physical dimensions of the SF-36. However, it seems to me that there may be another empirical issue not dealt with – the sample selection problem. While the number of casualties was low relative to the size of the population and numbers of displaced people, those who died obviously don’t feature in the sample. And those who died may have also been more likely or not to be displaced and be in worse health. Maybe only a bias of second order but a point it seems is left unconsidered.

Can variation in subgroups’ average treatment effects explain treatment effect heterogeneity? Evidence from a social experiment. Review of Economics and Statistics [RePEcPublished October 2017

A common approach to explore treatment effect heterogeneity is to estimate mean impacts by subgroups. In applied health economics studies I have most often seen this done by pooling data and adding interactions of the treatment with subgroups of interest to a regression model. For example, there is a large interest in differences in access to care across socioeconomic groups – in the UK we often use quintiles, or other division, of the Index of Multiple Deprivation, which is estimated at small area level, to look at this. However, this paper looks at the question of whether this approach to estimating heterogeneity is any good. Using data from a large jobs treatment program, they compare estimates of quantile treatment effects, which are considered to fully capture treatment effect heterogeneity, to results from various specifications of models that assume constant treatment effects within subgroups. If they found there was little difference in the two methods, I doubt the paper would have been published in such a good journal, so it’s no surprise that their conclusions are that the subgroup models perform poorly. Even allowing for more flexibility, such as by allowing effects to vary over time, and adding submodels for a point mass at zero, they still don’t do that well. Interestingly, subgroups defined according to different variables, e.g. education or pre-treatment earnings, fare differently – so comparisons across types of subgroups is important when the analyst is looking at heterogeneity. The takeaway message though is that constant effects subgroups models aren’t that good – more flexible semi or nonparametric methods may be preferred.

The hidden costs of terrorism: The effects on health at birth. Journal of Health Economics [PubMedPublished October 2017

We here at the blog have covered a long series of papers on the effects of in utero stressors on birth and later life health and economic outcomes. The so-called fetal-origins hypothesis posits that the nine months in the womb are some of the most important in predicting later life health outcomes. This may be one of the main mechanisms explaining intergenerational transmission of health. Some of these previous studies have covered reduced maternal nutrition, exposure to conditions of famine, or unemployment shocks in the household. This study examines the effect of the mother being pregnant in a province in Spain during which a terrorist attack by ETA occurred. At first glance, one might be forgiven for being sceptical at first, given (i) terrorist attacks were rare, (ii) the chances of actually being affected by an attack in a province if an attack occurred is low, so (iii) the chances are that the effect of feeling stressed on birth weight is small and likely to be swamped by a multitude of other factors (see all the literature we’ve covered on the topic!) All credit to the authors for doing a thorough job of trying to address all these concerns, but I’ll admit I remain sceptical. The effect sizes are very small indeed, as we suspected, and unfortunately there is not enough evidence to examine whether those women who had low birth weight live births were stressed or demonstrating adverse health behaviours.

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