Sam Watson’s journal round-up for 16th 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 impact of NHS expenditure on health outcomes in England: alternative approaches to identification in all‐cause and disease specific models of mortality. Health Economics [PubMedPublished 2nd April 2018

Studies looking at the relationship between health care expenditure and patient outcomes have exploded in popularity. A recent systematic review identified 65 studies by 2014 on the topic – and recent experience from these journal round-ups suggests this number has increased significantly since then. The relationship between national spending and health outcomes is important to inform policy and health care budgets, not least through the specification of a cost-effectiveness threshold. Karl Claxton and colleagues released a big study looking at all the programmes of care in the NHS in 2015 purporting to estimate exactly this. I wrote at the time that: (i) these estimates are only truly an opportunity cost if the health service is allocatively efficient, which it isn’t; and (ii) their statistical identification method, in which they used a range of socio-economic variables as instruments for expenditure, was flawed as the instruments were neither strong determinants of expenditure nor (conditionally) independent of population health. I also noted that their tests would be unlikely to be any good to detect this problem. In response to the first, Tony O’Hagan commented to say that that they did not assume NHS efficiency, nor even that it was assumed that the NHS is trying to maximise health. This may well have been the case, but I would still, perhaps pedantically, argue then that this is therefore not an opportunity cost. For the question of instrumental variables, an alternative method was proposed by Martyn Andrews and co-authors, using information that feeds into the budget allocation formula as instruments for expenditure. In this new article, Claxton, Lomas, and Martin adopt Andrews’s approach and apply it across four key programs of care in the NHS to try to derive cost-per-QALY thresholds. First off, many of my original criticisms I would also apply to this paper, to which I’d also add one: (Statistical significance being used inappropriately complaint alert!!!) The authors use what seems to be some form of stepwise regression by including and excluding regressors on the basis of statistical significance – this is a big no-no and just introduces large biases (see this article for a list of reasons why). Beyond that, the instruments issue – I think – is still a problem, as it’s hard to justify, for example, an input price index (which translates to larger budgets) as an instrument here. It is certainly correlated with higher expenditure – inputs are more expensive in higher price areas after all – but this instrument won’t be correlated with greater inputs for this same reason. Thus, it’s the ‘wrong kind’ of correlation for this study. Needless to say, perhaps I am letting the perfect be the enemy of the good. Is this evidence strong enough to warrant a change in a cost-effectiveness threshold? My inclination would be that it is not, but that is not to deny it’s relevance to the debate.

Risk thresholds for alcohol consumption: combined analysis of individual-participant data for 599 912 current drinkers in 83 prospective studies. The Lancet Published 14th April 2018

“Moderate drinkers live longer” is the adage of the casual drinker as if to justify a hedonistic pursuit as purely pragmatic. But where does this idea come from? Studies that have compared risk of cardiovascular disease to level of alcohol consumption have shown that disease risk is lower in those that drink moderately compared to those that don’t drink. But correlation does not imply causation – non-drinkers might differ from those that drink. They may be abstinent after experiencing health issues related to alcohol, or be otherwise advised to not drink to protect their health. If we truly believed moderate alcohol consumption was better for your health than no alcohol consumption we’d advise people who don’t drink to drink. Moreover, if this relationship were true then there would be an ‘optimal’ level of consumption where any protective effect were maximised before being outweighed by the adverse effects. This new study pools data from three large consortia each containing data from multiple studies or centres on individual alcohol consumption, cardiovascular disease (CVD), and all-cause mortality to look at these outcomes among drinkers, excluding non-drinkers for the aforementioned reasons. Reading the methods section, it’s not wholly clear, if replicability were the standard, what was done. I believe that for each different database a hazard ratio or odds ratio for the risk of CVD or mortality for eight groups of alcohol consumption was estimated, these ratios were then subsequently pooled in a random-effects meta-analysis. However, it’s not clear to me why you would need to do this in two steps when you could just estimate a hierarchical model that achieves the same thing while also propagating any uncertainty through all the levels. Anyway, a polynomial was then fitted through the pooled ratios – again, why not just do this in the main stage and estimate some kind of hierarchical semi-parametric model instead of a three-stage model to get the curve of interest? I don’t know. The key finding is that risk generally increases above around 100g/week alcohol (around 5-6 UK glasses of wine per week), below which it is fairly flat (although whether it is different to non-drinkers we don’t know). However, the picture the article paints is complicated, risk of stroke and heart failure go up with increased alcohol consumption, but myocardial infarction goes down. This would suggest some kind of competing risk: the mechanism by which alcohol works increases your overall risk of CVD and your proportional risk of non-myocardial infarction CVD given CVD.

Family ruptures, stress, and the mental health of the next generation [comment] [reply]. American Economic Review [RePEc] Published April 2018

I’m not sure I will write out the full blurb again about studies of in utero exposure to difficult or stressful conditions and later life outcomes. There are a lot of them and they continue to make the top journals. Admittedly, I continue to cover them in these round-ups – so much so that we could write a literature review on the topic on the basis of the content of this blog. Needless to say, exposure in the womb to stressors likely increases the risk of low birth weight birth, neonatal and childhood disease, poor educational outcomes, and worse labour market outcomes. So what does this new study (and the comments) contribute? Firstly, it uses a new type of stressor – maternal stress caused by a death in the family and apparently this has a dose-response as stronger ties to the deceased are more stressful, and secondly, it looks at mental health outcomes of the child, which are less common in these sorts of studies. The identification strategy compares the effect of the death on infants who are in the womb to those infants who experience it shortly after birth. Herein lies the interesting discussion raised in the above linked comment and reply papers: in this paper the sample contains all births up to one year post birth and to be in the ‘treatment’ group the death had to have occurred between conception and the expected date of birth, so those babies born preterm were less likely to end up in the control group than those born after the expected date. This spurious correlation could potentially lead to bias. In the authors’ reply, they re-estimate their models by redefining the control group on the basis of expected date of birth rather than actual. They find that their estimates for the effect of their stressor on physical outcomes, like low birth weight, are much smaller in magnitude, and I’m not sure they’re clinically significant. For mental health outcomes, again the estimates are qualitatively small in magnitude, but remain similar to the original paper but this choice phrase pops up (Statistical significance being used inappropriately complaint alert!!!): “We cannot reject the null hypothesis that the mental health coefficients presented in panel C of Table 3 are statistically the same as the corresponding coefficients in our original paper.” Statistically the same! I can see they’re different! Anyway, given all the other evidence on the topic I don’t need to explain the results in detail – the methods discussion is far more interesting.

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Sam Watson’s journal round-up for 11th December 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.

Can incentives improve survey data quality in developing countries?: results from a field experiment in India. Journal of the Royal Statistical Society: Series A Published 17th November 2017

I must admit a keen interest in the topic of this paper. As part of a large project looking at the availability of health services in slums and informal settlements around the world, we are designing a household survey. Much like the Demographic and Health Surveys, which are perhaps the Gold standard of household surveys in low-income countries, interviewers will go door to door to sampled households to complete surveys. One of the issues with household surveys is that they take a long time, and so non-response can be an issue. A potential solution is to offer respondents incentives, cash or otherwise, either before the survey or conditionally on completing it. But any change in survey response as a result of an incentive might create suspicion around data quality. Work in high-income countries suggests incentives to participate have little or no effect on data quality. But there is little evidence about these effects in low-income countries. We might suspect the consequences of survey incentives to differ in poorer settings. For a start, many surveys are conducted on behalf of the government or an NGO, and respondents may misrepresent themselves if they believe further investment in their area might be forthcoming if they are sufficiently badly-off. There may also be larger differences between the interviewer and interviewee in terms of education or cultural background. And finally, incentives can affect the balance between a respondent’s so-called intrinsic and extrinsic motivations for doing something. This study presents the results of a randomised trial where the ‘treatment’ was a small conditional payment for completing a survey, and the ‘control’ was no incentive. In both arms, the response rate was very high (>96%), but it was higher in the treatment arm. More importantly, the authors compare responses to a broad range of socioeconomic and demographic questions between the study arms. Aside from the frequent criticism that statistical significance is interpreted here as the existence of a difference, there are some interesting results. The key observed difference is that in the incentive arm respondents reported having lower wealth consistently across a number of categories. This may result from any of the aforementioned effects of incentives, but may be evidence that incentives can affect data quality and should be used with caution.

Association of US state implementation of newborn screening policies for critical congenital heart disease with early infant cardiac deaths. JAMA [PubMedPublished 5th December 2017

Writing these journal round-ups obviously requires reading the papers that you choose. This can be quite an undertaking for papers published in economics journals, which are often very long, but they provide substantial detail allowing for a thorough appraisal. The opposite is true for articles in medical journals. They are pleasingly concise, but often at the expense of including detail or additional analyses. This paper falls into the latter camp. Using detailed panel data on infant deaths by cause by year and by state in the US, it estimates the effect of mandated screening policies for infant congenital heart defects on deaths from this condition. Given these data and more space, one might expect to see more flexible models than the differences in differences type analysis presented here, such as allowing for state-level correlated time trends. The results seem clear and robust – the policies were associated with a reduction in death from congenital heart conditions by around a third. Given this, one might ask: if it’s so effective, why weren’t doctors doing it anyway? Additional analyses reveal little to no association of the policies with death from other conditions, which may suggest that doctors didn’t have to reallocate their time from other beneficial functions. Perhaps then the screening bore other costs. In the discussion, the authors mention that a previous economic evaluation showed that universal screening was relatively costly (approximately $40,000 per life year saved), but that this may be an overestimate in light of these new results. Certainly then an updated economic evaluation is warranted. However, the models used in the paper may lead one to be cautious about causal interpretations and hence using the estimates in an evaluation. Given some more space the authors may have added additional analyses, but then I might not have read it…

Subsidies and structure: the lasting impact of the Hill-Burton program on the hospital industry. Review of Economics and Statistics [RePEcPublished 29th November 2017

As part of the Hospital Survey and Construction Act of 1946 in the United States, the Hill-Burton program was enacted. As a reaction to the perceived lack of health care services for workers during World War 2, the program provided subsidies of up to a third for building nonprofit and local hospitals. Poorer areas were prioritised. This article examines the consequences of this subsidy program on the structure of the hospital market and health care utilisation. The main result is that the program had the consequence of increasing hospital beds per capita and that this increase was lasting. More specific analyses are presented. Firstly, the increase in beds took a number of years to materialise and showed a dose-response; higher-funded counties had bigger increases. Secondly, the funding reduced private hospital bed capacity. The net effect on overall hospital beds was positive, so the program affected the composition of the hospital sector. Although this would be expected given that it substantially affected the relative costs of different types of hospital bed. And thirdly, hospital utilisation increased in line with the increases in capacity, indicating a previously unmet need for health care. Again, this was expected given the motivation for the program in the first place. It isn’t often that results turn out as neatly as this – the effects are exactly as one would expect and are large in magnitude. If only all research projects turned out this way.

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Is this recession good for your health?

There have been a good number of articles to document the phenomenon of a counter-cyclical relationship between unemployment and health. As unemployment rises, deaths from a number of causes have been found to decline. These include accidents, infant mortality, heart disease, and liver disease (Ruhm, 2000; Dehejia and Lleras-Muney, 2004). That such a relationship is observed may at first seem counterintuitive; the reduction in income must surely damage our health. But, there are a number of reasons why we may see this relationship:

  1. Opportunity cost of time: In economic upturns leisure time decreases and health improving behaviours such as exercise decrease. Thus, in an economic downturn, since our time is less precious we have more time to engage in time-intensive and health-promoting activities. We could even visit the doctor more.
  2. Health as an input to production: The production of goods and services requires healthy people. But this production may be hazardous or stress-inducing. Furthermore, some of the most hazardous sectors, such as construction, are the most affected by economic downturns.
  3. External sources of death: Less time spent commuting means less time on the road and so fewer vehicular accidents. We may also see less drink driving, which is more common in economic upturns.
  4. Income effect: Our consumption of alcohol and tobacco as well as other goods that damage our health may decline.

On the back of this evidence, I asked myself, has this effect been present in the UK during the current Great Recession? Overall, unemployment has risen over the last five years, and the average weekly wage has declined in real terms (thanks to @peterpannier for the graph):

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A proper analysis of the data would be a full paper, something that someone, somewhere, may be in the process of writing – but, for the purposes of a preliminary investigation, let’s just look at the raw data. The 2011 census asked people how they would rate their health and provided them with five possible responses from ‘very good’ through to ‘very bad’. The census also provides us with the number of economically active but unemployed individuals. All this information is aggregated at the level of lower super output area (LSOA); of which there are around 32,000 in the UK each with a population of around 1,500. The following figure shows a plot of the proportion of unemployed individuals (as a proportion of 16-74 year olds) against the proportion reporting ‘bad’ or ‘very bad’ health:

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Clearly, there is a strong upward trend; areas with more unemployed have more people reporting bad health. Does this contradict our initial hypothesis? One of the crucial points about the aforementioned arguments are that they are arguments to explain the relationship between a change in health and a change in economic circumstances. The papers cited above used a fixed effects analysis; an analysis to examine the effects of changes. Thus, the correlations in the figure above may be picking up structural unemployment: we may be seeing the relationship between health and unemployment for those for whom the recession doesn’t affect health behaviour because they don’t experience a change as they are already unemployed. So let’s look instead at the relationship between short-term unemployment and the proportion reporting ‘bad’ or ‘very bad’ health. I defined short term unemployed here as having last been employed in 2011, i.e. a maximum of three months prior to the census. I looked at this in two ways; firstly, by looking at the number of short term unemployed as a proportion of the total number of people between 16 and 74:

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As you can see, there is now a downward trend, albeit not very steep. One issue is that areas with high short-term unemployment may also have high long-term unemployment making it hard to distinguish their effects. Therefore, my second approach was to look at the proportion of short term unemployed as a proportion of the total unemployed:

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Now there is clearly a strong downward trend. At a superficial level, these data seem to preliminarily support the hypothesis that short-term changes to unemployment may improve health. However, we also see that long-term unemployment is related to negative health. This is certainly not unexpected.

It is well evidenced that longer spells of unemployment lead to a reduced probability of finding work. From the macroeconomic point of view, the longer a downturn in the economy lasts, the greater the structural unemployment. This, as the above data suggest, may therefore lead to a reduction in average population health. Reducing unemployment and the duration of employment spells is certainly important but an ambitious policy goal. A better understanding of how socioeconomic deprivation and poor health are related would identify other methods to combat this negative effect on health.

These data may also shine a different light on Keynes’s well quoted line that ‘In the long run we are all dead’.