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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Thesis Thursday: Matthew Quaife

On the third Thursday of every month, we speak to a recent graduate about their thesis and their studies. This month’s guest is Dr Matthew Quaife who has a PhD from the London School of Hygiene and Tropical Medicine. If you would like to suggest a candidate for an upcoming Thesis Thursday, get in touch.

Title
Using stated preferences to estimate the impact and cost-effectiveness of new HIV prevention products in South Africa
Supervisors
Fern Terris-Prestholt, Peter Vickerman
Repository link
http://researchonline.lshtm.ac.uk/4646708

Stated preferences for what?

Our main study looked at preferences for new HIV prevention products in South Africa – estimating the uptake and cost-effectiveness of multi-purpose prevention products, which protect against HIV, pregnancy and STIs. You’ll notice that condoms do this, so why even bother? Condom use needs both partners to agree (for the duration of a given activity) and, whilst female partners tend to prefer condom-protected sex, there is lots of evidence that male partners – who also have greater bargaining power in many contexts – do not.

Oral pre-exposure prophylaxis (PrEP), microbicide gels, and vaginal rings are new products which prevent HIV infection. More importantly, they are female-initiated and can generally be used without a male partner’s knowledge. But trials and demonstration projects among women at high risk of HIV in sub-Saharan Africa have shown low levels of uptake and adherence. We used a DCE to inform the development of attractive and usable profiles for these products, and also estimate how much additional demand – and therefore protection – would be gained from adding contraceptive or STI-protective attributes.

We also elicited the stated preferences of female sex workers for client risk, condom use, and payments for sex. Sex workers can earn more for risky unprotected sex, and we used a repeated DCE to predict risk compensation (i.e. how much condom use would change) if they were to use HIV prevention products.

What did you find most influenced people’s preferences in your research?

Unsurprisingly for products, HIV protection was most important to people, followed by STI and then pregnancy protection. But digging below these averages with a latent class analysis, we found some interesting variation within female respondents: over a third were not concerned with HIV protection at all, instead strongly caring about pregnancy and STI protection. Worryingly, these were more likely to be respondents from high-incidence adolescent and sex worker groups. The remainder of the sample overwhelmingly chose based on HIV protection.

In the second sex worker DCE, we found that using a new HIV prevention product made condoms become less important and price more important. We predict that the price premium for unprotected sex would reduce by two thirds, and the amount of condomless sex would double. This is an interesting labour market/economic finding, but – if true – also has real public health implications. Since economic changes mean sex workers move from multi-purpose condoms to single-purpose products which need high levels of adherence, we thought this would be interesting to model.

How did you use information about people’s preferences to inform estimates of cost-effectiveness?

In two ways. First, we used simple uptake predictions from DCEs to parameterise an HIV transmission model, allowing for condom substitution uptake to vary by condom users and non-users (it was double in the latter). We were also able to model the potential uptake of multipurpose products which don’t exist yet – e.g. a pill protecting from HIV and pregnancy. We predict that this combination, in particular, would double uptake among high-risk young women.

Second, we predicted risk compensation among sex workers who chose new products instead of condoms. We were also able to calculate the price elasticity of supply of unprotected sex, which we built into a dynamic transmission model as a determinant of behaviour.

Can discrete choice experiments accurately predict the kinds of behaviours that you were looking at?

To be honest, when I started the PhD I was really sceptical – and I still am to an extent. But two things make me think DCEs can be useful in predicting behaviours.

First is the data. We published a meta-analysis of how well DCEs predict real-world health choices at an individual level. We only found six studies with individual-level data, but these showed DCEs predict with an 88% sensitivity but just a 34% specificity. If a DCE says you’ll do something, you more than likely will – which is important for modelling heterogeneity in uptake. We desperately need more studies following up DCE participants making real-world choices.

Second is the lack of alternative inputs. Where products are new and potential users are inexperienced, modellers pick an uptake number/range and hope for the best. Where we don’t know efficacy, we may assume that uptake and efficacy are linearly related – but they may not be (e.g. if proportionately more people use a 95% effective product than a 45% effective one). Instead, we might assume uptake and efficacy are independent, but that might sound even less realistic. I think that DCEs can tell us something about these behaviours that are useful for the parameters and structures of models, even if they are not perfect predictors.

Your tread the waters of infectious disease modelling in your research – was the incorporation of economic factors a challenge?

It was pretty tricky, though not as challenging as building the simple dynamic transmission model as a first exposure to R. In general, behaviours are pretty crudely modelled in transmission models, largely due to assumptions like random mixing and other population-level dynamics. We made a simple mechanistic model of sex work based on the supply elasticities estimated in the DCE, and ran a few scenarios, each time estimating the impact of prevention products. We simulated the price of unprotected sex falling and quantity rising as above, but also overlaid a few behavioural rules (e.g. Camerer’s constant income hypothesis) to simulate behavioural responses to a fall in overall income. Finally, we thought about competition between product users and non-users, and how much the latter may be affected by the market behaviours of the former. Look out for the paper at Bristol HESG!

How would you like to see research build on your work to improve HIV prevention?

I did a public engagement event last year based on one statistic: if you are a 16-year old girl living in Durban, you have an 80% lifetime risk of acquiring HIV. I find it unbelievable that, in 2018, when millions have been spent on HIV prevention and we have a range of interventions that can prevent HIV, incidence among some groups is still so dramatically and persistently high.

I think research has a really important role in understanding how people want to protect themselves from HIV, STIs, and pregnancy. In addition to highlighting the populations where interventions will be most cost-effective, we show that variation in preferences drives impact. I hope we can keep banging the drum to make attractive and effective options available to those at high risk.

Sam Watson’s journal round-up for 7th August 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.

Financing transformative health systems towards achievement of the health Sustainable Development Goals: a model for projected resource needs in 67 low-income and middle-income countries. Lancet: Global Health [PubMedPublished 17th July 2017

Achieving universal health coverage is a key aspect of the UN’s sustainable development goals. However, what this means in practice is complicated. People need to be able to access health services free at the point of use, but once those services are accessed there needs to be sufficient labour, capital, skill, and quality to correctly diagnose and treat them. For many health systems worldwide, this will require large investments in infrastructure and staffing, but the potential cost of achieving these goals is unclear. This article sets out to estimate these costs. Clearly, this is a complicated task – health care systems are incredibly complex. From a basic microeconomic standpoint, one might need some understanding of the production function of different health care systems, and the marginal productivity of labour and capital inputs to these systems. There is generally good evidence of what is effective and cost-effective for the treatment of different diseases, and so given the amenable disease burden for a particular country, we could begin to understand what would be required to combat it. This is how this article tackles this question, more or less. They take a bottom-up costing approach to a wide range of interventions, governance requirements, and, where required, other interventions such as water and sanitation. However, there are other mechanisms at play. At national levels, economies of scale and scope play a role. Integration of care programs can reduce the costs, improve the quality, or both, of the individual programs. Similarly, the levels of investment considered are likely to have relevant macroeconomic effects, boosting employment, income, and subsequent socioeconomic indicators. Credit is due to the authors, they do consider financing and health impacts of investment, and their paper is the most comprehensive to date on the topic. However, their projections (~$300 billion annually) are perhaps more uncertain than they let on, a criticism I made of similar papers recently. While I should remind myself not to let the perfect be the enemy of the good, detailed case studies of particular countries may help me to see how the spreadsheet model may actually translate into real-world changes.

Precommitment, cash transfers, and timely arrival for birth: evidence from a randomized controlled trial in Nairobi Kenya. American Economic Review [RePEcPublished May 2017

A great proportion of the gains in life expectancy in recent years has been through the reduction of childhood mortality. The early years of life are some of the most precarious. A newborn child, if she survives past five years of age, will not face the same risk of dying until late adulthood. Many of the same risk factors that contribute to childhood mortality also contribute to maternal death rates and many low-income countries still face unacceptably high rates of dying for both mother and child. One way of tackling this is to ensure mothers have access to adequate antenatal and postnatal care. In Kenya, for example, the government legislated to provide free delivery services in government health facilities in 2013. However, Kenya still has some of the highest death rates for mother and child in the world. It is speculated that one reason for this is the delay in receiving services in the case of complications with a pregnancy. A potential cause of this delay in Nairobi is a lack of adequate planning from women who face a large number of heterogeneous treatment options for birth. This study presents an RCT in which pregnant women were offered a “precommitment transfer package”, which consisted of a cash transfer of 1000 KSh (~£7) during pregnancy and a further 1000 KSh if women stuck to a delivery plan they had earlier committed to. The transfer was found to increase the proportion of women arriving early to delivery facilities. The study was a fairly small pilot study and the results somewhat uncertain, but the intervention appears promising. Cost-effectiveness comparisons are warranted with other interventions aiming to achieve the same ends.

Bans on electronic cigarette sales to minors and smoking among high school students. Journal of Health Economics [PubMedPublished July 2017

E-cigarettes have provoked quite a debate among public health researchers and campaigners as we’ve previously discussed. E-cigarettes are a substitute for tobacco smoking and are likely to be significantly less harmful. They may have contributed to large declines in the use of tobacco in the UK in the last few years. However, some have taken a “think of the children!” position. While e-cigarette use per se among adolescents may not be a significant public health issue, it could lead to increased use of tobacco. Others have countered that those young people using e-cigarettes would have been those that used tobacco anyway, so banning e-cigarettes among minors may lead them to go back to the tobacco. This paper takes data from repeated surveys of high school students in the US to estimate the effects of banning the sale of e-cigarettes to minors on the prevalence of tobacco smoking. Interestingly, bans appear to reduce tobacco smoking prevalence; the results appear fairly robust and the modelling is sensible. This conflicts with other recent similar studies. The authors argue that this shows that e-cigarettes and tobacco smoking are complements, so reducing one reduces the other. But I am not sure this explains the decline since no increase in youth smoking was observed as e-cigarettes became more popular. Certainly, such a ban would not have reduced smoking prevalence years ago. At the very least e-cigarettes have clearly had a significant effect on attitudes towards smoking. Perhaps smoking was on the decline anyway – but the authors estimate a model with state-specific time trends, and no declines were seen in control states. Whatever our prior beliefs about the efficacy of regulating or banning e-cigarettes, the evidence is complex, reflecting the complex behaviour of people towards drugs, alcohol, and tobacco.

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