Sam Watson’s journal round-up for 30th April 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.

The Millennium Villages Project: a retrospective, observational, endline evaluation. The Lancet Global Health [PubMedPublished May 2018

There are some clinical researchers who would have you believe observational studies are completely useless. The clinical trial is king, they might say, observation studies are just too biased. And while it’s true that observational studies are difficult to do well and convincingly, they can be a reliable and powerful source of evidence. Similarly, randomised trials are frequently flawed, for example there’s often missing data that hasn’t been dealt with, or a lack of allocation concealment, and many researchers forget that randomisation does not guarantee a balance of covariates, it merely increases the probability of it. I bring this up, as this study is a particularly carefully designed observational data study that I think serves as a good example to other researchers. The paper is an evaluation of the Millennium Villages Project, an integrated intervention program designed to help rural villages across sub-Saharan Africa meet the Millennium Development Goals over ten years between 2005 and 2015. Initial before-after evaluations of the project were criticised for inferring causal “impacts” from before and after data (for example, this Lancet paper had to be corrected after some criticism). To address these concerns, this new paper is incredibly careful about choosing appropriate control villages against which to evaluate the intervention. Their method is too long to summarise here, but in essence they match intervention villages to other villages on the basis of district, agroecological zone, and a range of variables from the DHS – matches were they reviewed for face validity and revised until a satisfactory matching was complete. The wide range of outcomes are all scaled to a standard normal and made to “point” in the same direction, i.e. so an increase indicated economic development. Then, to avoid multiple comparisons problems, a Bayesian hierarchical model is used to pool data across countries and outcomes. Costs data were also reported. Even better, “statistical significance” is barely mentioned at all! All in all, a neat and convincing evaluation.

Reconsidering the income‐health relationship using distributional regression. Health Economics [PubMed] [RePEcPublished 19th April 2018

The relationship between health and income has long been of interest to health economists. But it is a complex relationship. Increases in income may change consumption behaviours and a change in the use of time, promoting health, while improvements to health may lead to increases in income. Similarly, people who are more likely to make higher incomes may also be those who look after themselves, or maybe not. Disentangling these various factors has generated a pretty sizeable literature, but almost all of the empirical papers in this area (and indeed all empirical papers in general) use modelling techniques to estimate the effect of something on the expected value, i.e. mean, of some outcome. But the rest of the distribution is of interest – the mean effect of income may not be very large, but a small increase in income for poorer individuals may have a relatively large effect on the risk of very poor health. This article looks at the relationship between income and the conditional distribution of health using something called “structured additive distribution regression” (SADR). My interpretation of SADR is that, one would model the outcome y ~ g(a,b) as being distributed according to some distribution g(.) indexed by parameters a and b, for example, a normal or Gamma distribution has two parameters. One would then specify a generalised linear model for a and b, e.g. a = f(X’B). I’m not sure this is a completely novel method, as people use the approach to, for example, model heteroscedasticity. But that’s not to detract from the paper itself. The findings are very interesting – increases to income have a much greater effect on health at the lower end of the spectrum.

Ask your doctor whether this product is right for you: a Bayesian joint model for patient drug requests and physician prescriptions. Journal of the Royal Statistical Society: Series C Published April 2018.

When I used to take econometrics tutorials for undergraduates, one of the sessions involved going through coursework about the role of advertising. To set the scene, I would talk about the work of Alfred Marshall, the influential economist from the late 1800s/early 1900s. He described two roles for advertising: constructive and combative. The former is when advertising grows the market as a whole, increasing everyone’s revenues, and the latter is when ads just steal market share from rivals without changing the size of the market. Later economists would go on to thoroughly develop theories around advertising, exploring such things as the power of ads to distort preferences, the supply of ads and their complementarity with the product they’re selling, or seeing ads as a source of consumer information. Nevertheless, Marshall’s distinction is still a key consideration, although often phrased in different terms. This study examines a lot of things, but one of its key objectives is to explore the role of direct to consumer advertising on prescriptions of brands of drugs. The system is clearly complex: drug companies advertise both to consumers and physicians, consumers may request the drug from the physician, and the physician may or may not prescribe it. Further, there may be correlated unobservable differences between physicians and patients, and the choice to advertise to particular patients may not be exogenous. The paper does a pretty good job of dealing with each of these issues, but it is dense and took me a couple of reads to work out what was going on, especially with the mix of Bayesian and Frequentist terms. Examining the erectile dysfunction drug market, the authors reckon that direct to consumer advertising reduces drug requests across the category, while increasing the proportion of requests for the advertised drug – potentially suggesting a “combative” role. However, it’s more complex than that patient requests and doctor’s prescriptions seem to be influenced by a multitude of factors.


How do you solve a problem like obesity?


Making headlines this morning (Thursday 20th November) has been the report by McKinsey Global Institute, an offshoot of the management consultancy McKinsey, on the global economic impact of obesity. This report estimates that $2.0 trillion is spent annually worldwide as a result of obesity, which it compares to the global burden of smoking and armed conflict; the quoted figure is comprised of various elements such as productivity losses and spending to mitigate obesity. Certainly, the magnitude of the burden is in part due to the fact that obesity is generally a developed nation problem, and these nations typically spend many orders of magnitude more on healthcare than their developing nation counterparts. The claim then that obesity represents a problem as serious as armed conflict and violence may therefore end up being somewhat spurious if global issues were measured on a scale other than total financial expenditure. Nonetheless, the report acknowledges such issues, and provides a comprehensive summary of obesity related statistics to demonstrate them.

One of the main aims of the report is to identify interventions that may be used to tackle obesity in order to reduce expenditure resulting from obesity. To credit the McKinsey report, it recognises the complex nature of obesity and reproduces the above figure, asking if it is possible to tackle obesity given its complex aetiology. The report even provides some evidence that various social and cultural factors are at play. However, the authors write that while the background may be complex, the proximal causes are well known, and that interventions that target these proximal causes are both more feasible and simpler to implement and ought to be the ones they consider. This expression of a certain public health ideology, I would argue, is an issue with many discussions about population and global health issues.

This is the notion that public health and healthcare should be focussed on targeting individuals and modifying their behaviour, through such things as technological innovation, divorced from social, economic, or political contexts. For example, the McKinsey report suggests calorie labelling, advertising restrictions, and public health campaigns. However, if we want to tackle health issues such as obesity at the aggregate level then we should probably consider asking aggregate level questions, such as why markets are producing inefficient outcomes in terms of the health of the labour force, and why there is an oversupply of calories in some countries and an undersupply elsewhere. Policies that result from such analyses are likely to be more complex but are also more likely to be efficacious.

Historically, public health progress has been the result of a convergence of a wide range of social, economic, and political projects. Countries have adopted various strategies, historically, to reduce mortality including: better income distribution; improved diet; public health; medicine; changes in household education – however, none of these policies have been universally successful on its own and real progress requires integration of various social, medical, political, and economic strategies (Brin, 2005The Lancet—University of Oslo Commission on Global Governance for Health, 2014). The interventions in the report seem to me to be somewhat limp in the face of what they call a problem with a ‘global burden’.