Sam Watson’s journal round-up for October 24th 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 decrease according to socioeconomic groups during the economic crisis in Spain: a cohort study of 36 million people. The Lancet [PubMed] Published 13th October 2016

There is no shortage of studies examining the relationship between macroeconomic conditions and population health. Papers have come up on the journal round-up here, here, and here, and we previously discussed economic conditions and baby health. So what does this study add? Using data from the 2011 Spanish census on 36 million individuals, the study compares age-adjusted mortality rates for different socioeconomic groups before and after the economic crisis in Spain. The socioeconomic status of households was classified on the basis of household wealth, household floor space, and number of cars. The study compares the annual change in mortality rates for 2004-7 to the annual percentage change in the post-crisis period 2008-11. In essence the authors are looking for a structural break. The article reports that mortality rates declined faster post-crisis than before and that this effect was more pronounced in low socioeconomic status households. However, this conclusion is based on observed differences in estimated changes of rate: differences between the socioeconomic groups are not directly tested. The authors seem to fall foul of the problem that the difference between “significant” and “not significant” is not itself statistically significant. The plots in the paper illustrate strong differences in age-adjusted mortality rates by socioeconomic status, but a structural break in changes in rates is not so clearly evident. A large econometric literature has arisen around measuring structural breaks in macroeconomic series, many of these methods may have been of use. Indeed, there have been a number of sophisticated and careful analyses of the effect of macroeconomic conditions and health previous published, including the seminal study by Christopher Ruhm. Why this study landed in The Lancet therefore seems somewhat mysterious.

The ambiguous effect of GP competition: the case of hospital admissions. Health Economics [PubMedPublished 14th October 2016

Another mainstay of this blog: competition in healthcare. We’ve covered papers on this topic in previous journal round-ups here and here, and critically discussed a paper on the topic here. It seems to be one of those topics with important implications for healthcare policy but one which becomes less certain the more is known. Indeed, this paper recognises this in its title. The ambiguity to which it refers is the effect of GP competition on hospital admissions: if GPs retain more patients due to increased competition then admissions go down; if they recruit new patients due to increased competition then admissions go up. Typically studies in this area either compare outcomes before and after a pro-competitive policy change, or compare outcomes between areas with different densities (and hence competition) between GPs. This study adopts a variant of the latter approach using the number of open list practices in an area as their proxy for competition. They find that increased competition reduces inpatient attendances and increases outpatient attendances. I’ve often been skeptical of the use of GP density as a proxy for competition. Do people really compare GP practices before choosing them or do they just go to the nearest one? If a person is already registered at one practice, how often do they search around to choose another if care isn’t that bad? An observed effect of a change in GP density could be attributable to entry into or exit from the ‘market’ of differently performing providers, which may have little to do with competition, more the type of GP, GP age, and differences in medical training. Nevertheless, this article does present a well-considered analysis, the difficulty is in the interpretation in light of all the previous studies.

Modeling the economic burden of adult vaccine-preventable diseases in the United States. Health Affairs [PubMed] Published 12th October 2016

Andrew Wakefield, disbarred doctor and disgraced author of the fraudulent Lancet paper on MMR and autism, is currently promoting his new anti-vaccine film. His work and a cabal of conspiracy theorists have led many parents to refuse to get their children vaccinated. All this despite vaccines being one of the safest and most cost-effective of health interventions. This new paper seeks to determine the economic burden of vaccine-preventable diseases is in the US. The diseases considered include hepatitis A and B; measles, mumps, and rubella; and shingles (herpes zoster). Epidemiological models were developed in conjunction with experts; economic costs were assessed using both cost-of-illness and full income methodologies; and, parameters were specified on the basis of a literature review. Taking into account healthcare costs and productivity losses, the burden of the considered diseases was estimated at $9 billion annually. The authors also discuss taking into account social welfare losses using the value of a statistical life, however I think I may be misinterpreting the results when it states

The current-dollar value of statistical life calculated from each source was $5.9 billion from the FDA; $6.3 billion from the NHTSA; and $8.3 billion from the EPA. The full income value of death as a result of vaccine-preventable diseases is estimated to be $176 billion annually (plausibility range: $166 billion–$231 billion).

That seems way too large to me so I’m not sure what to make of that. Nevertheless, the study illustrates the potentially massive burden that vaccine-preventable diseases may present.

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…