Chris Sampson’s journal round-up for 14th May 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.

A practical guide to conducting a systematic review and meta-analysis of health state utility values. PharmacoEconomics [PubMed] Published 10th May 2018

I love articles that outline the practical application of a particular method to solve a particular problem, especially when the article shares analysis code that can be copied and adapted. This paper does just that for the case of synthesising health state utility values. Decision modellers use utility values as parameters. Most of the time these are drawn from a single source which almost certainly introduces some kind of bias to the resulting cost-effectiveness estimates. So it’s better to combine all of the relevant available information. But that’s easier said than done, as numerous researchers (myself included) have discovered. This paper outlines the various approaches and some of the merits and limitations of each. There are some standard stages, for which advice is provided, relating to the identification, selection, and extraction of data. Those are by no means simple tasks, but the really tricky bit comes when you try and pool the utility values that you’ve found. The authors outline three strategies: i) fixed effect meta-analysis, ii) random effects meta-analysis, and iii) mixed effects meta-regression. Each is illustrated with a hypothetical example, with Stata and R commands provided. Broadly speaking, the authors favour mixed effects meta-regression because of its ability to identify the extent of similarity between sources and to help explain heterogeneity. The authors insist that comparability between sources is a precondition for pooling. But the thing about health state utility values is that they are – almost by definition – never comparable. Different population? Not comparable. Different treatment pathway? No chance. Different utility measure? Ha! They may or may not appear to be similar statistically, but that’s totally irrelevant. What matters is whether the decision-maker ‘believes’ the values. If they believe them then they should be included and pooled. If decision-makers have reason to believe one source more or less than another then this should be accounted for in the weighting. If they don’t believe them at all then they should be excluded. Comparability is framed as a statistical question, when in reality it is a conceptual one. For now, researchers will have to tackle that themselves. This paper doesn’t solve all of the problems around meta-analysis of health state utility values, but it does a good job of outlining methodological developments to date and provides recommendations in accordance with them.

Unemployment, unemployment duration, and health: selection or causation? The European Journal of Health Economics [PubMed] Published 3rd May 2018

One of the major socioeconomic correlates of poor health is unemployment. It appears not to be very good for you. But there’s an obvious challenge here – does unemployment cause ill-health, or are unhealthy people just more likely to be unemployed? Both, probably, but that answer doesn’t make for clear policy solutions. This paper – following a large body of literature – attempts to explain what’s going on. Its novelty comes in the way the author considers timing and distinguishes between mental and physical health. The basis for the analysis is that selection into unemployment by the unhealthy ought to imply time-constant effects of unemployment on health. On the other hand, the negative effect of unemployment on health ought to grow over time. Using seven waves of data from the German Socio-economic Panel, a sample of 17,000 people (chopped from 48,000) is analysed, of which around 3,000 experienced unemployment. The basis for measuring mental and physical health is summary scores from the SF-12. A fixed-effects model is constructed based on the dependence of health on the duration and timing of unemployment, rather than just the occurrence of unemployment per se. The author finds a cumulative effect of unemployment on physical ill-health over time, implying causation. This is particularly pronounced for people unemployed in later life, and there was essentially no impact on physical health for younger people. The longer people spent unemployed, the more their health deteriorated. This was accompanied by a strong long-term selection effect of less physically healthy people being more likely to become unemployed. In contrast, for mental health, the findings suggest a short-term selection effect of people who experience a decline in mental health being more likely to become unemployed. But then, following unemployment, mental health declines further, so the balance of selection and causation effects is less clear. In contrast to physical health, people’s mental health is more badly affected by unemployment at younger ages. By no means does this study prove the balance between selection and causality. It can’t account for people’s anticipation of unemployment or future ill-health. But it does provide inspiration for better-targeted policies to limit the impact of unemployment on health.

Different domains – different time preferences? Social Science & Medicine [PubMed] Published 30th April 2018

Economists are often criticised by non-economists. Usually, the criticisms are unfounded, but one of the ways in which I think some (micro)economists can have tunnel vision is in thinking that preferences elicited with respect to money exhibit the same characteristics as preferences about things other than money. My instinct tells me that – for most people – that isn’t true. This study looks at one of those characteristics of preferences – namely, time preferences. Unfortunately for me, it suggests that my instincts aren’t correct. The authors outline a quasi-hyperbolic discounting model, incorporating both short-term present bias and long-term impatience, to explain gym members’ time preferences in the health and monetary domains. A survey was conducted with members of a chain of fitness centres in Denmark, of which 1,687 responded. Half were allocated to money-related questions and half to health-related questions. Respondents were asked to match an amount of future gains with an amount of immediate gains to provide a point of indifference. Health problems were formulated as back pain, with an EQ-5D-3L level 2 for usual activities and a level 2 for pain or discomfort. The findings were that estimates for discount rates and present bias in the two domains are different, but not by very much. On average, discount rates are slightly higher in the health domain – a finding driven by female respondents and people with more education. Present bias is the same – on average – in each domain, though retired people are more present biased for health. The authors conclude by focussing on the similarity between health and monetary time preferences, suggesting that time preferences in the monetary domain can safely be applied in the health domain. But I’d still be wary of this. For starters, one would expect a group of gym members – who have all decided to join the gym – to be relatively homogenous in their time preferences. Findings are similar on average, and there are only small differences in subgroups, but when it comes to health care (even public health) we’re never dealing with average people. Targeted interventions are increasingly needed, which means that differential discount rates in the health domain – of the kind identified in this study – should be brought into focus.

Credits

 

Sam Watson’s journal round-up for 18th July 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.

Mortality inequality: the good news from a county-level approach. Journal of Economic Perspectives [RePEcPublished Spring 2016

Research on mortality trends always focuses on the bad news. For example, in a well publicized article Anne Case and Angus Deaton report on finding significant increases in mortality for middle-aged white non-Hispanic men and women in the US.  (Although this article did attract some criticism for bias due to aggregation of age groups.) This essay by Janet Currie and Hannes Schwandt takes an altogether different line: it suggests that there is good news on the whole. Examining life expectancy at birth it is shown that mortality inequality between rich and poor counties declined significantly between 1990 and 2010. However, mortality rates and inequality in life expectancy have shifted a lot less for older age groups – a factor many previous ‘bad news’ type studies have focussed on. One explanation for such a trend is that there has been more smoking cessation in wealthier areas.The authors conclude then that for the youngest people, inequality is likely to remain low, while for older generations positive health behaviours such as smoking cessation are also likely to spread, improving inequality in mortality. However, one might suggest such conclusions are overly optimistic. Poverty and low socio-economic status have a complex relationship with health; reductions in mortality at lower ages may create a survivor bias so that the overall cohort has worse health on average now as those in poor health who may have died a number of years ago now survive to older ages. Nevertheless, Currie and Schwandt are right to suggest that policy makers should be made aware that improvements in mortality are possible and that evidence such as this should be used to mobilise efforts to improve the health of high risk groups.

The tax-free year in Iceland: A natural experiment to explore the impact of a short-term increase in labor supply on the risk of heart attacks. Journal of Health Economics [PubMedPublished 23rd June 2016

In 1987, owing to a change in the tax system in Iceland, no-one had to pay income tax. As a result labour supply increased substantially, which provides a neat natural experiment. In this study, the authors aim to examine whether increased labour market participation increases the risk of acute myocardial infarction (AMI). There is a growing literature of the relationship between macroeconomic conditions and health; a seminal article was Christopher Ruhm’s 2000 study that showed that economic downturns are associated with decreases in the overall mortality rate. However, the mechanisms that mediate such an effect remain elusive. Using panel data on individuals from 1982-92 linked to data on coronary events the authors show an increase in the risk of AMI in both 1987 and 1988 among men. However, some of the results seem improbably large, e.g. a 149% increase in the probability of AMI among self-employed men aged 45-64. While taken as a whole I think the evidence does suggest an increase in AMI risk in 1987, I was left with a number of questions: why no individual effect in the specification?; could the errors be serially correlated?; why wasn’t an instrumental variable approach used if the motivation is that the 1987 policy exogenously shifted labour market participation?; aside from having lower average risk, is there any reason to separately analyse men and women? These results also contradict an earlier study, also from Christopher Ruhm, that showed unemployment was associated with increases in deaths from coronary heart disease. At the very least, this study shows us that we just don’t really understand the complex interplay between economy, society, and health.

Gender roles and medical progress. Journal of Political Economy [RePEcPublished 3rd May 2016

Over the past century female labour market participation has improved as restrictive female gender roles have shifted and technological innovations have reduced the burden of many tasks traditionally assigned to women. Ha-Joon Chang posits that the invention of the washing machine was a more important invention than the internet in the way it revolutionised the labour market. This paper argues that the reduction in maternity conditions as a result of medical progress over the 20th century had a significant impact on female labour market participation. Indeed, they estimate that medical progress can account for 50% of the rise in female labour market participation between 1930 and 1960.

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

Hidden costs of the recession

In a previous post I considered whether the current Great Recession had been good for your health. Evidence suggests that temporary reductions in income may improve your health for a number of reasons. In part, when I lose my job I may have expectations of finding work again in the short term, my skills may not depreciate in the short term, and I may be able to smooth my consumption with access to credit or savings and do more time-consuming, health-promoting things. But, the longer my spell of unemployment, the less access to health promoting goods I have and the greater the effects of socioeconomic deprivation. A number of studies have remarked on the link between income inequality and poor health (e.g. see here and here).

In the last post, I looked at a cross section of data from the 2011 census. I presented some correlations between the proportion of individuals who were unemployed and the proportion reporting bad health. I, and I am certainly not alone, may argue that myriad other factors could cause this observed relationship. I can’t prove or disprove any hypothesis in the space that this blog permits but I will add the following figure in support of the relationship. Here, I took data from both the 2001 and 2011 censuses for all lower super output areas (LSOAs; geographical areas of approximately 1,500 people) and looked at the relationship between the difference in the proportion unemployed and the difference in the proportion reporting bad health between 2001 and 2011:

change in prop bad health vs change unemployed

Given the long lag between 2001 and 2011, the arguments from the previous post, that this represents changes to structural unemployment rather than short term cyclical unemployment, may still stand. But, for whatever reason, there is a correlation between unemployment and self-reported bad health.

I should mention that the questions about health differed between the two censuses from three options in 2001: ‘good health’, ‘fair health’, or ‘bad health’, compared to five options in 2011: ‘very good health’, ‘good health’, ‘fair health’, ‘bad health’, and ‘very bad health’. I have compared here the percentage reporting the 2001 option ‘bad health’ to the combined ‘bad health’ and ‘very bad health’ option. You may think this is an affront to good data analysis, so to allay your fears I have provided versions of the following two figures that use only 2011 data. You will see that they tell the same story.

The increase to poor health as a result of increased socioeconomic deprivation is costly for a number of reasons. Considering healthcare, direct costs such as hospital admissions for physical and mental health problems may increase, along with the accompanying costs of providing pharmaceuticals and other treatments. One cost that is not well reported in the media is that of unpaid care. One study in the UK estimated the costs of services provided by unpaid carers to be as much as £87 billion per year. Now, those in poor health require care. The following figure shows the relationship between the change in the proportion of people reporting bad health and the change in the proportion of people providing more than 20 hours a week of unpaid care between 2001 and 2011 in each LSOA:

bad health vs unpaid care

bad health vs unpaid care 2011

2011 data only

I am not surprised by this relationship, and I doubt you are either. Then, it should also come as no surprise, given the previous two figures, that when I plot the relationship between the difference in the proportion unemployed and the difference in the proportion providing more than 20 hours unpaid care per week that there is also a strong relationship:

unemployed vs unpaid care

2011 data only

2011 data only

The relationship between health and economic conditions is complicated to say the least. What these data may indicate is that the cost due to increased unemployment may be far more than just reduced growth and output. Unpaid carers often have to leave employment to provide their services. Cutting back on health and social care funding in real terms will only shift the growing burden to individuals in poor areas, where health is worse, rather than to the state.

I would like to point out as a final note, and perhaps one of optimism, that the percentage of people reporting bad health has on average declined between 2001 and 2011. Although this may just be a case of hedonic adaptation…