Chris Sampson’s journal round-up for 22nd May 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.

The effect of health care expenditure on patient outcomes: evidence from English neonatal care. Health Economics [PubMed] Published 12th May 2017

Recently, people have started trying to identify opportunity cost in the NHS, by assessing the health gains associated with current spending. Studies have thrown up a wide range of values in different clinical areas, including in neonatal care. This study uses individual-level data for infants treated in 32 neonatal intensive care units from 2009-2013, along with the NHS Reference Cost for an intensive care cot day. A model is constructed to assess the impact of changes in expenditure, controlling for a variety of variables available in the National Neonatal Research Database. Two outcomes are considered: the in-hospital mortality rate and morbidity-free survival. The main finding is that a £100 increase in the cost per cot day is associated with a reduction in the mortality rate of 0.36 percentage points. This translates into a marginal cost per infant life saved of around £420,000. Assuming an average life expectancy of 81 years, this equates to a present value cost per life year gained of £15,200. Reductions in the mortality rate are associated with similar increases in morbidity. The estimated cost contradicts a much higher estimate presented in the Claxton et al modern classic on searching for the threshold.

A comparison of four software programs for implementing decision analytic cost-effectiveness models. PharmacoEconomics [PubMed] Published 9th May 2017

Markov models: TreeAge vs Excel vs R vs MATLAB. This paper compares the alternative programs in terms of transparency and validation, the associated learning curve, capability, processing speed and cost. A benchmarking assessment is conducted using a previously published model (originally developed in TreeAge). Excel is rightly identified as the ‘ubiquitous workhorse’ of cost-effectiveness modelling. It’s transparent in theory, but in practice can include cell relations that are difficult to disentangle. TreeAge, on the other hand, includes valuable features to aid model transparency and validation, though the workings of the software itself are not always clear. Being based on programming languages, MATLAB and R may be entirely transparent but challenging to validate. The authors assert that TreeAge is the easiest to learn due to its graphical nature and the availability of training options. Save for complex VBA, Excel is also simple to learn. R and MATLAB are equivalently more difficult to learn, but clearly worth the time saving for anybody expecting to work on multiple complex modelling studies. R and MATLAB both come top in terms of capability, with Excel falling behind due to having fewer statistical facilities. TreeAge has clearly defined capabilities limited to the features that the company chooses to support. MATLAB and R were both able to complete 10,000 simulations in a matter of seconds, while Excel took 15 minutes and TreeAge took over 4 hours. For a value of information analysis requiring 1000 runs, this could translate into 6 months for TreeAge! MATLAB has some advantage over R in processing time that might make its cost ($500 for academics) worthwhile to some. Excel and TreeAge are both identified as particularly useful as educational tools for people getting to grips with the concepts of decision modelling. Though the take-home message for me is that I really need to learn R.

Economic evaluation of factorial randomised controlled trials: challenges, methods and recommendations. Statistics in Medicine [PubMed] Published 3rd May 2017

Factorial trials randomise participants to at least 2 alternative levels (for example, different doses) of at least 2 alternative treatments (possibly in combination). Very little has been written about how economic evaluations ought to be conducted alongside such trials. This study starts by outlining some key challenges for economic evaluation in this context. First, there may be interactions between combined therapies, which might exist for costs and QALYs even if not for the primary clinical endpoint. Second, transformation of the data may not be straightforward, for example, it may not be possible to disaggregate a net benefit estimation with its components using alternative transformations. Third, regression analysis of factorial trials may be tricky for the purpose of constructing CEACs and conducting value of information analysis. Finally, defining the study question may not be simple. The authors simulate a 2×2 factorial trial (0 vs A vs B vs A+B) to demonstrate these challenges. The first analysis compares A and B against placebo separately in what’s known as an ‘at-the-margins’ approach. Both A and B are shown to be cost-effective, with the implication that A+B should be provided. The next analysis uses regression, with interaction terms demonstrating the unlikelihood of being statistically significant for costs or net benefit. ‘Inside-the-table’ analysis is used to separately evaluate the 4 alternative treatments, with an associated loss in statistical power. The findings of this analysis contradict the findings of the at-the-margins analysis. A variety of regression-based analyses is presented, with the discussion focussed on the variability in the estimated standard errors and the implications of this for value of information analysis. The authors then go on to present their conception of the ‘opportunity cost of ignoring interactions’ as a new basis for value of information analysis. A set of 14 recommendations is provided for people conducting economic evaluations alongside factorial trials, which could be used as a bolt-on to CHEERS and CONSORT guidelines.


Sam Watson’s journal round-up for 19th September 2016

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.

Health losses at the end of life: a Bayesian mixed beta regression approach. Journal of the Royal Statistical Society Series A Published 2nd September 2016

The growth in healthcare expenditure is and has long been a concern for policy makers worldwide. Many factors contribute to this increase, for example it may be a consequence of economic growth, but perhaps the most widely cited determinant is an ageing population. A growing literature is questioning the simplicity of this assumption though: is it age per se that leads to increased healthcare costs or is it proximity to death? This study presents a new analysis of this question. More specifically, the authors propose that the observed decline in health related quality of life (HRQoL) associated with age is due to the increased age-specific mortality and the lower HRQoL associated with being close to death, and not age itself. The implication of this is that increased longevity is unlikely to have a large effect on overall healthcare expenditure. To examine this empirically the authors use longitudinal data on HRQoL from 356 individuals over 16 years. Issues such as the skewness of the outcome measure and it being bounded between zero and one, along with the correlation within individuals over time and the relationship between the mean and variance are accommodated using a Bayesian beta regression. Estimation using MCMC methods provides great flexibility in terms of complex models that may be intractable using classical maximum likelihood methods, and the inclusion of previous evidence through the prior reduces uncertainty that may arise due to smaller sample sizes. A wide range of sensitivity analyses are also conducted. The authors’ key finding is that when time to death is included as a variable the effect of age is almost negligible. This journal round-up author’s interest in Bayesian methods has grown exponentially over the last few years and most of his analyses are now in the Bayesian paradigm. Articles such as this demonstrate the power and flexibility of such methods and, importantly, they show how the emphasis is on the estimation problem rather than arbitrary hypothesis testing and estimation of p-values.

Group-based microfinance for collective empowerment: a systematic review of health impacts. Bulletin of the World Health Organisation Published September 2016

Microfinance initiatives have become a popular method to promote development and a sizable empirical literature has grown around it. Indeed, the 2006 Nobel Peace Prize was awarded to a Bangladeshi microfinance program. Many microfinance schemes are founded on the principle of collective empowerment by providing capital to support female autonomy and entrepreneurship. As such, there is an increasing interest in whether these schemes can also improve health. However, the enthusiasm for microfinance initiatives is often said to outstrip evidence of their effectiveness. This systematic review considers the evidence for whether microfinance can improve health. The studies identified in this review provided evidence that microfinance schemes can reduce maternal and infant mortality, improve sexual health, and in some cases lower interpersonal violence. However, even in the higher quality studies, there was potential for bias and unfortunately publication bias was not assessed. Nevertheless, such a review of the effectiveness of microfinance has been long overdue.

The impact of changing economic conditions on overweight risk among children in California from 2008 to 2012. Journal of Epidemiology and Community Health [PubMedPublished September 2016

The relationship between economic conditions and population health has emerged into a huge research area in the last 20 years. We’ve previously discussed it in a number of blog posts. As the literature grows and understanding and knowledge expand, research moves from generalities to specifics. This article explores whether childhood obesity was affected by changing economic conditions during the last recession using a huge dataset of over 1.7 million children. The proportion of children who are obese is larger among low socioeconomic status groups than higher status groups, so it may be expected that a worsening of economic conditions, measured here as unemployment, would lead to an increase in childhood obesity. However, the authors theorise that the association is ambiguous, citing the typical economic argument that through income and substitution effects households may both change their consumption patterns and the amount of time devoted to health promoting activities, which could either improve or worsen health. Using an individual and county level fixed-effects linear probability model (the justification for using the linear probability model over a perhaps more appropriate nonlinear model is not given), a one percentage point increase (around 10-15% relative increase) in the county level unemployment rate is estimated to lead to a 1.4 percentage point increase in the risk of being obese (around 4% increase in risk). Of course, the results are statistically significant. The results seem reasonable, but I’m left struggling to interpret them properly having had the same issue with an aggregate treatment and individual level outcomes previously. The results are consistent with the idea that the increase in obesity occurred among individuals whose families increased their income over the period, for example. Simpson’s paradox often rears its head in the economic conditions and health literature, as we have previously seen with infant health.

Photo credit: Antony Theobald (CC BY-NC-ND 2.0)

Economic conditions and the health of babies. You won’t believe what the literature says!


How do economic conditions affect a person’s health? We can think of three major mechanisms that researchers examine. Firstly, the absolute effect of wealth or income that affects your access to health-influencing goods and services such as healthcare, good housing, high quality food, and exercise. Secondly, the relative effect of your social position through psychosocial mechanisms as widely popularised by Michael Marmot. And thirdly, the fetal origins hypothesis. Initially brought to popular attention by David J. Barker in 1986, the fetal origins hypothesis posits that the nine months in utero influence health over the course of a life through effects on the development of ones body and its organs. This latter mechanism provides a strong reason, beyond improving maternal health, for enacting policies that assist expectant mothers.

There is a growing research interest in identifying how economic conditions and maternal well-being affect the health of the baby. This can be used to inform policies to improve infant health and could take the form of improved maternity leave or social assistance in the form of cash transfers or other goods and services.

At an aggregate level a shift in economic conditions such as a change in the unemployment rate could affect infant health in different ways. Through an income effect mothers may buy more or less goods that affect the health of the baby. For example, some mothers may reduce smoking in the face of a reduction in household income. A substitution effect may lead to mothers to change the amount of time spent on work and more time on leisure or doing more or less health promoting activities. Finally, households may choose to delay or bring forward their fertility decisions. Thus the ultimate effect of economic conditions on the health of the birth cohort remains theoretically ambiguous.

Dehejia and Llera-Muney looked at the relationship between the unemployment rate and the proportion of babies born at low birth weights, a marker of poor infant health, in US states. They found that increases in the unemployment rate reduced the low birth weigh birth rate, which on further investigation appeared to be attributable both to a change in the women who choose to have a baby (they are of higher socioeconomic status) and an improvement in health behaviours. While interesting, however, this does not reveal much to us about what is going on at the level of the individual mother. Lindo showed that this effect is dependent on the level of aggregation of the data; at a more disaggregated level the effect diminishes. Indeed, unemployment is not a policy choice for improving infant health.

A paper featured recently on the journal round-up perhaps provides more useful information for the policy-making context. It showed that mothers participating in a social assistance programme in Uruguay that provided cash transfers to mothers experienced a lower rate of low birth weight births. These mothers showed increased weight gain, reduction in labour supply, and a reduction in smoking, all potentially contributing to infant health. A further paper by Lindo shows a negative impact of husband’s job losses during pregnancy on infant health at birth.

The evidence appears perhaps contradictory at the individual and aggregate levels: a classic case of Simpson’s paradox. This paradox describes the situation where a trend observed at the individual level disappears or even reverses when the data are aggregated. For an individual mother, providing her with extra income, improves the likely health of her baby; but across society as average incomes move, different mothers are making decisions to have children – fewer women of lower incomes are giving birth in times of recession. The appropriate evidence would therefore be that of the individual level.

Better maternity leave and greater social assistance for mothers would seem to be supported by the evidence as not only improving maternal health but also the long term health of her child.