Brent Gibbons’s journal round-up for 10th February 2020

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.

Impact of comprehensive smoking bans on the health of infants and children. American Journal of Health Economics [RePEc] Published 15th January 2020

While debates on tobacco control policies have recently focused on the rising use of e-cigarettes and vaping devices, along with recent associated lung injuries in the U.S., there is still much to learn on the effectiveness of established tobacco control options. In the U.S., while strategies to increase cigarette taxes and to promote smoke-free public spaces have contributed to a decline in smoking prevalence, more stringent policies such as plain packaging, pictorial warning labels, and no point-of-sale advertising have generally not been implemented. Furthermore, comprehensive smoking bans that include restaurants, bars, and workplaces have only been implemented in approximately 60 percent of localities. This article fills an important gap in the evidence on comprehensive smoking bans, answering how this policy affects the health of children. It also provides interesting evidence on the effect of comprehensive smoking bans on smoking behavior in private residences.

There is ample evidence to support the conclusion that smoking bans reduce smoking prevalence and the exposure of nonsmoking adults to second-hand smoke. This reduced second-hand smoke exposure has been linked to reductions in related health conditions for adults, but has not been studied for infants and children. Of particular concern is that smoking bans may have the unintended ‘displacement’ effect of increasing smoking in private residences, potentially increasing exposure for some children and pregnant women.

For their analyses, the authors use nationally representative data from the US Vital Statistics Natality Data and the National Health Interview Survey (NHIS), coupled with detailed local and state tobacco policy data. The policy data allows the authors to look at partial smoking bans (e.g. limited smoking bans in bars and restaurants) versus comprehensive smoking bans, which are defined as 100 percent smoke-free environments in restaurants, bars, and workplaces in a locale. For their main analyses, a difference-in-difference model is used, comparing locales with comprehensive smoking bans to locales with no smoking bans; a counter factual of no smoking bans or partial bans is also used. Outcomes for infants are low birth weight and gestation, while smoke-related adverse health conditions (e.g. asthma) are used for children under 18.

Results support the conclusion that comprehensive smoking bans are linked to positive health effects for infants and children. The authors included local geographic fixed effects, controlled for excise taxes, and tested an impressive array of sensitivity analyses, all of which support the positive findings. For birth outcomes, the mechanism of effect is explored, using self-reported smoking status. The authors find that a majority of the birth outcome effects are likely due to pregnant mothers’ second-hand smoke exposure (80-85 percent), as opposed to a reduction in prenatal smoking. And regarding displacement concerns, the authors examine NHIS data and find no evidence that smoking bans were associated with displacement of smoking to private residences.

This paper is worth a deep dive. The authors have made an important contribution to the evidence on smoking bans, addressing a possible unintended consequence and adding further weight to arguments for extending comprehensive smoking bans nationwide in the U.S. The health implications are non-trivial, where impacts on birth outcomes alone “can prevent between approximately 1,100 and 1,750 low birth weight births among low-educated mothers, resulting in economic cost savings of about $71-111 million annually.”

Europeans’ willingness to pay for ending homelessness: a contingent valuation study. Social Science & Medicine Published 15th January 2020

Housing First (HF) is a social program that originates from a program in the U.S. to address homelessness in Los Angeles. Over time, it has been adapted particularly for individuals with unstable housing who have long-term behavioral health disorders, including mental health and substance use disorders. Similar to other community mental health services, HF has incorporated a philosophy of not requiring conditions before providing services. For example, with supported employment services, to help those with persistent behavioral health disorders gain employment, the currently accepted approach is to ‘place’ individuals in jobs and then provide training and other support; this is opposed to traditional models of ‘train, then place’. Similarly, for housing, the philosophy is to provide housing first, with various wraparound supports available, whether those wraparound services are accepted or not, and whether the person has refrained from substance use or not. The model is based on the logic that without stable housing, other health and social services will be less effective. It is also based on the assertion that stable housing is a basic human right.

Evidence for HF has generally supported its advantage over more traditional policies, especially in its effectiveness in improving stable housing. Other cost offsets have been reported, including health service use reductions, however, the literature is more inconclusive on the existence and amount of cost offsets. The Substance Abuse and Mental Health Services Administration (SAMHSA) has identified HF as an evidence-based model and a number of countries, including the U.S., Canada, and several European countries, have begun incorporating HF into their homelessness policies. Yet the cost effectiveness of HF is not firmly addressed in the literature. At present, results appear favorable towards HF in comparison to other housing policies, though there are considerable difficulties in HF CEAs, most notably that there are multiple measures of effectiveness (e.g. stable housing days and QALYs). More research needs to be done to better establish the cost-effectiveness of HF.

I’ve chosen to highlight this background because Loubiere et al., in this article, have pushed a large contingent valuation (CV) study to assess willingness to pay (WTP) for HF, which the title implies is commensurate with “ending homelessness”. Contingent valuation is generally accepted as one method for valuing resources where no market is available, though not without considerable past criticism. Discrete choice experiments are favored (though not with their own criticism), but the authors decided on CV as the survey was embedded in a longer questionnaire. The study is aimed at policy makers who must take into account broader public preferences for either increased taxation or for a shifting of resources. The intention is laudable in the respect that it attempts to highlight how much the average person would be willing to give up to not have homelessness exist in her country; this information may help policy makers to act. But more important, I would argue, is to have more definitive information on HF’s cost-effectiveness.

As far as the rigor of the study, I was disappointed to see that the survey was performed through telephone, which goes against recommendations to use personal interviews in CV. An iterative bidding process was used which helps to mitigate overvaluation, though there is still the threat of anchoring bias, which was not randomly allocated. There was limited description of what was conveyed to respondents, including what efficacy results were used for HF. This information is important to make appropriate sense of the results. Aside from other survey limitations such as acquiescence bias and non-response bias, the authors did attempt to deal with the issue of ‘protest’ answers by performing alternative analyses with and without protest answers, where protest answers were assigned a €0 value. WTP ranged from an average of €23 (€16 in Poland to €57 in Sweden) to €28 Euros. Analyses were also conducted to understand factors related to reported WTP. The results suggest that Europeans are supportive of reducing homelessness and will give up considerable hard earned cash toward this cause. This reader for one is not convinced. However, I would hope that policy makers, armed with better cost effectiveness research, could make policy decisions for a marginalized group, even without a more rigorous WTP estimate.

Credits

Sam Watson’s journal round-up for 3rd June 2019

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.

Limits to human life span through extreme value theory. Journal of the American Statistical Association [RePEc] Published 2nd April 2019

The oldest verified person to have ever lived was Jeanne Calment who died in 1997 at the superlative age of 122. No-one else has ever been recorded as living longer than 120, but there have been perhaps a few hundred supercentarians over 110. Whenever someone reaches such a stupendous age, some budding reporter will ask them what the secret was. They will reply that they have stuck to a regimen of three boiled eggs and a glass of scotch every day for 80 years. And this information is of course completely meaningless due to survivorship bias. But as public health and health care improves and with it life expectancy, there remains the question of whether people will ever exceed these extreme ages or whether there is actually a limit to human longevity.

Some studies have attempted to address the question of maximum human longevity by looking at how key biological systems, like getting oxygen to the muscles or vasculature, degrade. They suggest that there would be an upper limit as key systems of the body just cannot last, which is not to say medicine might not find a way to fix or replace them in the future. Another way of addressing this question is to take a purely statistical approach and look at the distribution of the ages of the oldest people alive and try to make inferences about its upper limit. Such an analysis relies on extreme value theory.

There are two types of extreme value data. The first type consists of just the series of maximum values from the distribution. The Fisher-Tippett-Gnedenko theorem shows that these maxima can only be distributed according to one of three distributions. The second type of data are all of the most extreme observations above a certain threshold, and wonderfully there is another triple-barrelled theorem that shows that these data are distributed as a generalised Pareto distribution – the Pickand-Balkema-de Haan theorem. This article makes use of this latter type of data and theorem to estimate: (i) is there an upper limit to the distribution of human life spans? (ii) What is it, if so? And (iii) does it change over time?

The authors use a dataset of the ages of death in days of all Dutch residents who died over the age of 92 between 1986 and 2015. Using these data to estimate the parameters of the generalised Pareto distribution, they find strong evidence to suggest that, statistically at least, it has an upper limit and that this limit is probably around 117-124. Over the years of the study there did not appear to be any change in this limit. This is not to say that it couldn’t change in the future if some new miraculous treatment appeared, but for now, we humans must put up with a short and finite existence.

Infant health care and long-term outcomes. Review of Economics and Statistics [RePEc] Published 13th May 2019

I haven’t covered an article on infant health and economic conditions and longer term outcomes for a while. It used to be that there would be one in every round-up I wrote. I could barely keep up with the literature, which I tried to summarise in a different blog post. Given that it has been a while, I thought I would include a new one. This time we are looking at the effect of mother and child health centres in Norway in the 1930s on the outcomes of adults later in the 20th Century.

Fortunately the health centres were built in different municipalities at different times. The authors note that the “key identifying assumption” is that they were not built at a time related to the health of infants in those areas (well, this and that the model is linear and additive, time trends are linear, etc. etc. something that economists often forget). They don’t go into too much detail on this, but it seems plausible. Another gripe of mine with most empirical economic papers, and indeed in medical and public health fields, is that plotting the data is a secondary concern or doesn’t happen at all. It should be the most important thing. Indeed, in this article much of the discussion can be captured by the figure buried two thirds through. The figure shows that the centres likely led to a big reduction in diarrhoeal disease, probably due to increased rates of breast feeding, but on other outcomes effects are more ambiguous and probably quite small if they exist. Some evidence is provided to suggest that these differences were associated with very modest increases in educational attainment and adult wages. However, a cost-benefit calculation suggests that on the basis of these wage increases the intervention had a annualised rate of return of about 5%.

I should say that this study is well-conducted and fairly solid so any gripes with it are fairly minor. It certainly fits neatly into the wide literature on the topic, and I don’t think anyone would doubt that investing in childhood interventions is likely to have a number of short and long term benefits.

Relationship between poor olfaction and mortality among community-dwelling older adults: a cohort study. Annals of Internal Medicine [PubMed] Published 21st May 2019

I included this last study, not because of any ground-breaking economics or statistics, but because it is interesting. This is one of a number of studies to have looked at the relationship between smell ability and risk of death. These studies have generally found a strong direct relationship between poor olfaction and risk of death in the following years (summarised briefly in this editorial). This study examines a cohort of a couple of thousand older people whose smell was rigourously tested at baseline, among other things. If they died then their death was categorised by a medical examiner into one of four categories: dementia or Parkinson disease, cardiovascular disease, cancer, and respiratory illness.

There was a very strong relationship between poor ability to smell and all-cause death. They found that cumulative risk for death was 46% and 30% higher in persons with a loss of smelling ability at 10 and 13 years respectively. Delving into death by cause, they found that this relationship was most important among those who died of dementia or Parkinson disease, which makes sense as smell is one of the oldest limbic structures and linked to many parts of the brain. Some relationship was seen with cardiovascular disease but not cancer or respiratory illness. They then use a ‘mediation analysis’, i.e. conditioning on post-treatment variables to ‘block’ causal pathways, to identify how much variation is explained and conclude that dementia, Parkinson disease, and weight loss account for about 30% of the observed relationship. However, I am usually suspicious of mediation analyses, and standard arguments would suggest that model parameters would be biased.

Interestingly, olfaction is not normally used as a diagnostic test among the elderly despite sense of smell being one of the strongest predictors of mortality. People do not generally notice their sense of smell waning as it is gradual, so would not likely remark on it to a doctor. Perhaps it is time to start testing it routinely?

Credits

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.

Credits