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.

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Sam Watson’s journal round-up for 8th October 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 cost‐effectiveness threshold based on the marginal returns of cardiovascular hospital spending. Health Economics [PubMed] Published 1st October 2018

There are two types of cost-effectiveness threshold of interest to researchers. First, there’s the societal willingness-to-pay for a given gain in health or quality of life. This is what many regulatory bodies, such as NICE, use. Second, there is the actual return on medical spending achieved by the health service. Reimbursement of technologies with a lesser return for every pound or dollar would reduce the overall efficiency of the health service. Some refer to this as the opportunity cost, although in a technical sense I would disagree that it is the opportunity cost per se. Nevertheless, this latter definition has seen a growth in empirical work; with some data on health spending and outcomes, we can start to estimate this threshold.

This article looks at spending on cardiovascular disease (CVD) among elderly age groups by gender in the Netherlands and survival. Estimating the causal effect of spending is tricky with these data: spending may go up because survival is worsening, external factors like smoking may have a confounding role, and using five year age bands (as the authors do) over time can lead to bias as the average age in these bands is increasing as demographics shift. The authors do a pretty good job in specifying a Bayesian hierarchical model with enough flexibility to accommodate these potential issues. For example, linear time trends are allowed to vary by age-gender groups and  dynamic effects of spending are included. However, there’s no examination of whether the model is actually a good fit to the data, something which I’m growing to believe is an area where we, in health and health services research, need to improve.

Most interestingly (for me at least) the authors look at a range of priors based on previous studies and a meta-analysis of similar studies. The estimated elasticity using information from prior studies is more ‘optimistic’ about the effect of health spending than a ‘vague’ prior. This could be because CVD or the Netherlands differs in a particular way from other areas. I might argue that the modelling here is better than some previous efforts as well, which could explain the difference. Extrapolating using life tables the authors estimate a base case cost per QALY of €40,000.

Early illicit drug use and the age of onset of homelessness. Journal of the Royal Statistical Society: Series A Published 11th September 2018

How the consumption of different things, like food, drugs, or alcohol, affects life and health outcomes is a difficult question to answer empirically. Consider a recent widely-criticised study on alcohol published in The Lancet. Among a number of issues, despite including a huge amount of data, the paper was unable to address the problem that different kinds of people drink different amounts. The kind of person who is teetotal may be so for a number of reasons including alcoholism, interaction with medication, or other health issues. Similarly, studies on the effect of cannabis consumption have shown among other things an association with lower IQ and poorer mental health. But are those who consume cannabis already those with lower IQs or at higher risk of psychoses? This article considers the relationship between cannabis and homelessness. While homelessness may lead to an increase in drug use, drug use may also be a cause of homelessness.

The paper is a neat application of bivariate hazard models. We recently looked at shared parameter models on the blog, which factorise the joint distribution of two variables into their marginal distribution by assuming their relationship is due to some unobserved variable. The bivariate hazard models work here in a similar way: the bivariate model is specified as the product of the marginal densities and the individual unobserved heterogeneity. This specification allows (i) people to have different unobserved risks for both homelessness and cannabis use and (ii) cannabis to have a causal effect on homelessness and vice versa.

Despite the careful set-up though, I’m not wholly convinced of the face validity of the results. The authors claim that daily cannabis use among men has a large effect on becoming homeless – as large an effect as having separated parents – which seems implausible to me. Cannabis use can cause psychological dependency but I can’t see people choosing it over having a home as they might with something like heroin. The authors also claim that homelessness doesn’t really have an effect on cannabis use among men because the estimated effect is “relatively small” (it is the same order of magnitude as the reverse causal effect) and only “marginally significant”. Interpreting these results in the context of cannabis use would then be difficult, though. The paper provides much additional material of interest. However, the conclusion that regular cannabis use, all else being equal, has a “strong effect” on male homelessness, seems both difficult to conceptualise and not in keeping with the messiness of the data and complexity of the empirical question.

How could health care be anything other than high quality? The Lancet: Global Health [PubMed] Published 5th September 2018

Tedros Adhanom Ghebreyesus, or Dr Tedros as he’s better known, is the head of the WHO. This editorial was penned in response to the recent Lancet Commission on Health Care Quality and related studies (see this round-up). However, I was critical of these studies for a number of reasons, in particular, the conflation of ‘quality’ as we normally understand it and everything else that may impact on how a health system performs. This includes resourcing, which is obviously low in poor countries, availability of labour and medical supplies, and demand side choices about health care access. The empirical evidence was fairly weak; even in countries like in the UK in which we’re swimming in data we struggle to quantify quality. Data are also often averaged at the national level, masking huge underlying variation within-country. This editorial is, therefore, a bit of an empty platitude: of course we should strive to improve ‘quality’ – its goodness is definitional. But without a solid understanding of how to do this or even what we mean when we say ‘quality’ in this context, we’re not really saying anything at all. Proposing that we need a ‘revolution’ without any real concrete proposals is fairly meaningless and ignores the massive strides that have been made in recent years. Delivering high-quality, timely, effective, equitable, and integrated health care in the poorest settings means more resources. Tinkering with what little services already exist for those most in need is not going to produce a revolutionary change. But this strays into political territory, which UN organisations often flounder in.

Editorial: Statistical flaws in the teaching excellence and student outcomes framework in UK higher education. Journal of the Royal Statistical Society: Series A Published 21st September 2018

As a final note for our academic audience, we give you a statement on the Teaching Excellence Framework (TEF). For our non-UK audience, the TEF is a new system being introduced by the government, which seeks to introduce more of a ‘market’ in higher education by trying to quantify teaching quality and then allowing the best-performing universities to charge more. No-one would disagree with the sentiment that improving higher education standards is better for students and teachers alike, but the TEF is fundamentally statistically flawed, as discussed in this editorial in the JRSS.

Some key points of contention are: (i) TEF doesn’t actually assess any teaching, such as through observation; (ii) there is no consideration of uncertainty about scores and rankings; (iii) “The benchmarking process appears to be a kind of poor person’s propensity analysis” – copied verbatim as I couldn’t have phrased it any better; (iv) there has been no consideration of gaming the metrics; and (v) the proposed models do not reflect the actual aims of TEF and are likely to be biased. Economists will also likely have strong views on how the TEF incentives will affect institutional behaviour. But, as Michael Gove, the former justice and education secretary said, Britons have had enough of experts.

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