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

Thesis Thursday: Caroline Chuard

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 Caroline Chuard who has a PhD from the University of Zurich. If you would like to suggest a candidate for an upcoming Thesis Thursday, get in touch.

Title
Three essays on the health effects of family policies
Supervisors
Hannes Schwandt, Josef Zweimüller
Repository link
https://www.zora.uzh.ch/id/eprint/172853/

Is there a strong health economics evidence base on family policies?

The literature on parental leave and family health is relatively young. This literature emphasises that the returns depend on several key features. First, the timing of measurement matters. Therefore, the effects differ according to whether they are measured in the short- versus long-run. Second, the initial level of parental leave and the extent to which parental leave is increased are both key influencing factors. As such, an introduction is more beneficial than an increase at an already generous level of parental leave. Third, the results depend on the targeted group.

But keep in mind that the effects of family policies on health outcomes are just one part of a large literature that studies the effect on other outcomes such as maternal labour market outcomes, fertility, and child cognitive and non-cognitive development (e.g. Ruhm (2000), Lalive and Zweimüller (2009), Baker and Milligan (2008), Dustmann and Schönberg (2012), Lalive et al. (2014), Carneiro et al. (2015), Dahl et al. (2016), Danzer and Lavy (2018), Butikofer et al. (2018) and many more which have recently been reviewed by Olivetti and Petrongolo (2017) and Rossin-Slater (2018)).

What policy changes were you able to evaluate in your research?

I exploit two types of family policy changes in two countries. On the one hand, I use three changes in parental leave duration in Austria and, on the other hand, I use cantonal variation in family allowances across Switzerland.

More specifically, Austria increased parental leave by 1 year to 2 years in July 1990. This was partially reversed again in July 1996, by exclusively reserving 6 months to fathers so that maternal leave was essentially reduced to 1.5 years. Finally, in July 2000, there was another large extension in paid parental leave by 1 year to 2.5 years. Enforcement of all these changes was very strict, changing from one day to another depending on giving birth in June or July. This sharp discontinuity allows me to employ a regression discontinuity design.

In the case of Switzerland, I analyse the impact of birth allowances (so-called baby bonuses) on fertility, newborn health and birth scheduling. I exploit a unique quasi-experimental setting of Switzerland’s family allowances system. In this system, cantons are free to choose whether they want to implement birth allowances and how much they want to pay. During the last 50 years, 11 cantons have introduced a baby bonus, all increase the amount paid thereafter, and two cantons even abolished the baby bonus after all. This gives rise to a lot of cantonal variation. Thus, I use a difference-in-differences setting where I can analyse both the introduction and the intensity of the treatment.

What were the key strengths of the data sets that you used?

For all my studies I rely on administrative data. Thus, I can use the universe of observations delivered with high quality, as both Austria and Switzerland have very reliable administrative data.

In the Austrian case, I can even combine several different data sets. Namely, I use the Austrian Social Security Database (ASSD), which covers the complete working history of every worker in Austria. The ASSD covers every birth of employed mothers and their actual duration of parental leave. I can link the ASSD to the Austrian Birth Register (ABR) recording newborn health outcomes and additional individual-level characteristics of the mother. Finally, for a part of Austria, I additionally merge the data to health outcomes recorded in the health insurance data. This data set records every outpatient doctor visit, prescribed medication, and hospital stays including diagnosis code.

All of this, together, gives a huge variety of different variables on an individual basis allowing me to study a broad set of outcomes (such as health outcomes next to the directly targeted labour market outcomes). Furthermore, the detailed level of information allows me to study the impact of labour market behaviour on two margins—the extensive margin of mothers who choose to work or not and the intensive margin of how much mothers choose to work. The richness of the data also makes it possible to analyse heterogeneous effects across mothers and by work environment.

Did the policies achieve what they were designed to achieve?

This is a little hard to tell from looking at my results only. For example, in Austria the initial increase of parental leave duration by 1 year was introduced so that fathers could take up to 6 months of the full duration. This policy reform was a result of parliamentary procedural requests which wanted to introduce paternal leave. Due to the flat benefit structure almost no fathers were taking up parental leave, which essentially resulted in an increase of maternal leave from 1 to 2 years and, ultimately, led to the second policy change by exclusively reserving 6 months out of the total 2 years for fathers.

However, what I want to mention here, note that I explicitly evaluated side effects. All three chapters of my dissertation highlight the importance of studying alternative and indirect outcome measures in addition to the direct measures targeted by policymakers.

For example, in the Swiss study, we only find little fertility effects, the directly targeted outcome measure of birth allowances, but a sizable and significant reduction in the stillbirth rate as well as a positive impact on birth weight. A policymaker, who would now only study fertility, would argue that birth allowances were expensive to implement with little to no result, which, however, does not capture the full story.

Is there heterogeneity in how family policy reforms affect families?

The answer depends on the person affected and the studied outcome. For example, the Austrian parental leave duration reform affects maternal work behaviour during pregnancy regardless of the mother’s socioeconomic background and the industry. This change in prenatal maternal work status doesn’t affect newborn health at all.

However, when I study the same reforms with respect to maternal health, there is substantial heterogeneity. The initial increase in leave length is especially good for low-wage and unmarried mothers. Reducing leave duration harms mothers with unhealthy babies, proxied by a preterm birth or low birth weight baby. Substantially increasing leave duration is, though, especially bad for maternal health of those mothers who already suffered from mental diseases pre-birth. Also, for the paper on the Swiss baby bonus, we find a more beneficial impact in the decline of stillbirths for low socioeconomic status mothers.

Based on your research, how would you design parental leave policies?

With my research, I tried to give a more complete picture on the impact of family policies by taking into account health outcomes which have vastly been neglected so far. Nevertheless, for a policy recommendation it is crucial to take the findings from the previous literature into account.

Firstly, introducing parental leave has generally been shown to be very beneficial for the cognitive development of children (Carneiro et al., 2015). Secondly, these returns are, however, quickly declining (Butikofer et al., 2018). In combination with my findings of no impact of working during pregnancy on child health and a negative impact of too long parental leave policies for maternal health (Chuard, 2018), I would clearly put the focus on mandatory leave in the first months of a newborn’s life. While this might seem obvious for many European countries, this is still not the case in the US. And even Europe might face the risk on the other end of the parental leave duration scale. Many European countries tend to expand leave rather generously both pre- and post-natal, which seems from my research not necessary (always keep in mind, these policies are extremely expensive) and could potentially even be harmful in the long-run.

Chris Sampson’s journal round-up for 16th December 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.

MCDA-based deliberation to value health states: lessons learned from a pilot study. Health and Quality of Life Outcomes [PubMed] Published 1st July 2019

The rejection of the EQ-5D-5L value set for England indicates something of a crisis in health state valuation. Evidently, there is a lack of trust in the quantitative data and methods used. This is despite decades of methodological development. Perhaps we need a completely different approach. Could we instead develop a value set using qualitative methods?

A value set based on qualitative research aligns with an idea forwarded by Daniel Hausman, who has argued for the use of deliberative approaches. This could circumvent the problems associated with asking people to give instant (and possibly ill-thought-out) responses to preference elicitation surveys. The authors of this study report on the first ever (pilot) attempt to develop a consensus value set using methods of multi-criteria decision analysis (MCDA) and deliberation. The study attempts to identify a German value set for the SF-6D.

The study included 34 students in a one-day conference setting. A two-step process was followed for the MCDA using MACBETH (the Measuring Attractiveness by a Categorical Based Evaluation Technique), which uses pairwise comparisons to derive numerical scales without quantitative assessments. First, a scoring procedure was conducted for each of the six dimensions. Second, a weighting was identified for each dimension. After an introductory session, participants were allocated into groups of five or six and each group was tasked with scoring one SF-6D dimension. Within each group, consensus was achieved. After these group sessions, all participants were brought together to present and validate the results. In this deliberation process, consensus was achieved for all domains except pain. Then the weighting session took place, but resulted in no consensus. Subsequent to the one-day conference, a series of semi-structured interviews were conducted with moderators. All the sessions and interviews were recorded, transcribed, and analysed qualitatively.

In short, the study failed. A consensus value set could not be identified. Part of the problem was probably in the SF-6D descriptive system, particularly in relation to pain, which was interpreted differently by different people. But the main issue was that people had different opinions and didn’t seem willing to move towards consensus with a societal perspective in mind. Participants broadly fell into three groups – one in favour of prioritising pain and mental health, one opposed to trading-off SF-6D dimensions and favouring equal weights, and another group that was not willing to accept any trade-offs.

Despite its apparent failure, this seems like an extremely useful and important study. The authors provide a huge amount of detail regarding what they did, what went well, and what might be done differently next time. I’m not sure it will ever be possible to get a group of people to reach a consensus on a value set. The whole point of preference-based measures is surely that different people have different priorities, and they should be expected to disagree. But I think we should expect that the future of health state valuation lies in mixed methods. There might be more success in a qualitative and deliberative approach to scoring combined with a quantitative approach to weighting, or perhaps a qualitative approach informed by quantitative data that demands trade-offs. Whatever the future holds, this study will be a valuable guide.

Preference-based health-related quality of life outcomes associated with preterm birth: a systematic review and meta-analysis. PharmacoEconomics [PubMed] Published 9th December 2019

Premature and low birth weight babies can experience a whole host of negative health outcomes. Most studies in this context look at short-term biomedical assessments or behavioural and neurodevelopmental indicators. But some studies have sought to identify the long-term consequences on health-related quality of life by identifying health state utility values. This study provides us with a review and meta-analysis of such values.

The authors screened 2,139 articles from their search and included 20 in the review. Lots of data were extracted from the articles, which is helpfully tabulated in the paper. The majority of the studies included adolescents and focussed on children born very preterm or at very low birth weight.

For the meta-analysis, the authors employed a linear mixed-effects meta-regression, which is an increasingly routine approach in this context. The models were used to estimate the decrement in utility values associated with preterm birth or low birth weight, compared with matched controls. Conveniently, all but one of the studies used a measure other than the HUI2 or HUI3, so the analysis was restricted to these two measures. Preterm birth was associated with an average decrement of 0.066 and extremely low birth weight with a decrement of 0.068. The mean estimated utility scores for the study groups was 0.838, compared with 0.919 for the control groups.

Reviews of utility values are valuable as they provide modellers with a catalogue of potential parameters that can be selected in a meaningful and transparent way. Even though this is a thorough and well-reported study, it’s a bit harder to see how its findings will be used. Most reviews of utility values relate to a particular disease, which might be prevented or ameliorated by treatment, and the value of this treatment depends on the utility values selected. But how will these utility values be used? The avoidance of preterm or low-weight birth is not the subject of most evaluations in the neonatal setting. Even if it was, how valuable are estimates from a single point in adolescence? The authors suggest that future research should seek to identify a trajectory of utility values over the life course. But, even if we could achieve this, it’s not clear to me how this should complement utility values identified in relation to the specific health problems experienced by these people.

The new and non-transparent Cancer Drugs Fund. PharmacoEconomics [PubMed] Published 12th December 2019

Not many (any?) health economists liked the Cancer Drugs Fund (CDF). It was set-up to give special treatment to cancer drugs, which weren’t assessed on the same basis as other drugs being assessed by NICE. In 2016, the CDF was brought within NICE’s remit, with medicines available through the CDF requiring a managed access agreement. This includes agreements on data collection and on payments by the NHS during the period. In this article, the authors contend that the new CDF process is not sufficiently transparent.

Three main issued are raised: i) lack of transparency relating to the value of CDF drugs, ii) lack of transparency relating to the cost of CDF drugs, and iii) the amount of time that medicines remain on the CDF. The authors tabulate the reporting of ICERs according to the decisions made, showing that the majority of treatment comparisons do not report ICERs. Similarly, the time in the CDF is tabulated, with many indications being in the CDF for an unknown amount of time. In short, we don’t know much about medicines going through the CDF, except that they’re probably costing a lot.

I’m a fan of transparency, in almost all contexts. I think it is inherently valuable to share information widely. It seems that the authors of this paper do too. A lack of transparency in NICE decision-making is a broader problem that arises from the need to protect commercially sensitive pricing agreements. But what this paper doesn’t manage to do is to articulate why anybody who doesn’t support transparency in principle should care about the CDF in particular. Part of the authors’ argument is that the lack of transparency prevents independent scrutiny. But surely NICE is the independent scrutiny? The authors argue that it is a problem that commissioners and the public cannot assess the value of the medicines, but it isn’t clear why that should be a problem if they are not the arbiters of value. The CDF has quite rightly faced criticism over the years, but I’m not convinced that its lack of transparency is its main problem.

Credits