Chris Sampson’s journal round-up for 15th 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.

Reliability and validity of the contingent valuation method for estimating willingness to pay: a case of in vitro fertilisation. Applied Health Economics and Health Policy [PubMed] Published 13th October 2018

In vitro fertilisation (IVF) is a challenge for standard models of valuation in health economics. Mostly, that’s because, despite it falling within the scope of health care, and despite infertility being a health problem, many of the benefits of IVF can’t be considered health-specific. QALYs can’t really do the job, so there’s arguably a role for cost-benefit analysis, and for using stated preference methods to determine the value of IVF. This study adds to an existing literature studying willingness to pay for IVF, but differs in that it tries to identify willingness to pay (WTP) from the general population. This study is set in Australia, where IVF is part-funded by universal health insurance, so asking the public is arguably the right thing to do.

Three contingent valuation surveys were conducted online with 1,870 people from the general public. The first survey used a starting point bid of $10,000, and then, 10 months later, two more surveys were conducted with starting point bids of $4,000 and $10,000. Each included questions for a 10%, 20%, and 50% success rate. Respondents were asked to adopt an ex-post perspective, assuming that they were infertile and could conceive by IVF. Individuals could respond to starting bids with ‘yes’, ‘no’, ‘not sure’, or ‘I am not willing to pay anything’. WTP for one IVF cycle with a 20% success rate ranged from $6,353 in the $4,000 survey to $11,750 in the first $10,000 survey. WTP for a year of treatment ranged from $18,433 to $28,117. The method was reliable insofar as there were no differences between the first and second $10,000 surveys. WTP values corresponded to the probability of success, providing support for the internal construct validity of the survey. However, the big difference between values derived using the alternative starting point bids indicates a strong anchoring bias. The authors also tested the external criterion validity by comparing the number of respondents willing to pay more than $4,000 for a cycle with a 20% success rate (roughly equivalent to the out of pocket cost in Australia) with the number of people who actually choose to pay for IVF in Australia. Around 63% of respondents were willing to pay at that price, which is close to the estimated 60% in Australia.

This study provides some support for the use of contingent valuation methods in the context of IVF, and for its use in general population samples. But the anchoring effect is worrying and justifies further research to identify appropriate methods to counteract this bias. The exclusion of the “not sure” and “I will not pay anything” responses from the analysis – as ‘non-demanders’ – arguably undermines the ‘societal valuation’ aspect of the estimates.

Pharmaceutical expenditure and gross domestic product: evidence of simultaneous effects using a two‐step instrumental variables strategy. Health Economics [PubMed] Published 10th October 2018

The question of how governments determine spending on medicines is pertinent in the UK right now, as the Pharmaceutical Price Regulation Scheme approaches its renewal date. The current agreement includes a cap on pharmaceutical expenditure. It should go without saying that GDP ought to have some influence on how much public spending is dedicated to medicines. But, when medicines expenditure might also influence GDP, the actual relationship is difficult to estimate. In this paper, the authors seek to identify both effects: the income elasticity of government spending on pharmaceuticals and the effect of that spending on income.

The authors use a variety of data sources from the World Health Organization, World Bank, and International Monetary Fund to construct an unbalanced panel for 136 countries from 1995 to 2006. To get around the challenge of two-way causality, the authors implement a two-step instrumental variable approach. In the first step of the procedure, a model estimates the impact of GDP per capita on government spending on pharmaceuticals. International tourist receipts are used as an instrument that is expected to correlate strongly with GDP per capita, but which is expected to be unrelated to medicines expenditure (except through its correlation with GDP). The model attempts to control for health care expenditure, life expectancy, and other important country-specific variables. In the second step, a reverse causality model is used to assess the impact of pharmaceutical expenditure on GDP per capita, with pharmaceutical expenditure adjusted to partial-out the response to GDP estimated in the first step.

The headline average results are that GDP increases pharmaceutical expenditure and that pharmaceutical expenditure reduces GDP. A 1% increase in GDP per capita increases public pharmaceutical expenditure per capita by 1.4%, suggesting that pharmaceuticals are a luxury good. A 1% increase in public pharmaceutical expenditure is associated with a 0.09% decrease in GDP per capita. But the results are more nuanced than that. The authors outline various sources of heterogeneity. The positive effect of GDP on pharmaceutical expenditure only holds for high-income countries and the negative effect of pharmaceutical expenditure on GDP only holds for low-income countries. Quantile regressions show that income elasticity decreases for higher quantiles of expenditure. GDP only influences pharmaceutical spending in countries classified as ‘free’ on the index of Economic Freedom of the World, and pharmaceutical expenditure only has a negative impact on GDP in countries that are ‘not free’.

I’ve never come across this kind of two-step approach before, so I’m still trying to get my head around whether the methods and the data are adequate. But a series of robustness checks provide some reassurance. In particular, an analysis of intertemporal effects using lagged GDP and lagged pharmaceutical expenditure demonstrates the robustness of the main findings. Arguably, the findings of this study are more important for policymaking in low- and middle-income countries, where pharmaceutical expenditures might have important consequences for GDP. In high-income (and ‘free’) economies that spend a lot on medicines, like the UK, there is probably less at stake. This could be because of effective price regulation and monitoring, and better adherence, ensuring that pharmaceutical expenditure is not wasteful.

Parental health spillover in cost-effectiveness analysis: evidence from self-harming adolescents in England. PharmacoEconomics [PubMed] [RePEc] Published 8th October 2018

Any intervention has the potential for spillover effects, whereby people other than the recipient of care are positively or negatively affected by the consequences of the intervention. Where a child is the recipient of care, it stands to reason that any intervention could affect the well-being of the parents and that these impacts should be considered in economic evaluation. But how should parental spillovers be incorporated? Are parental utilities additive to that of the child patient? Or should a multiplier effect be used with reference to the effect of an intervention on the child’s utility?

The study reports on a trial-based economic evaluation of family therapy for self-harming adolescents aged 11-17. Data collection included EQ-5D-3L for the adolescents and HUI2 for the main caregiver (86% mothers) at baseline, 6-month follow-up, and 12-month follow-up, collected from 731 patient-parent pairs. The authors outline six alternative methods for including parental health spillovers: i) relative health spillover, ii) relative health spillover per treatment arm, iii) absolute health spillover, iv) absolute global health spillover per treatment arm, v) additive accrued health benefits, and vi) household equivalence scales. These differ according to whether parental utility is counted as depending on adolescent’s utility, treatment allocation, the primary outcome of the study, or some combination thereof. But the authors’ primary focus (and the main contribution of this study) is the equivalence scale option. This involves adding together the spillover effects for other members of the household and using alternative weightings depending on the importance of parental utility compared with adolescent utility.

Using Tobit models, controlling for a variety of factors, the authors demonstrate that parental utility is associated with adolescent utility. Then, economic evaluations are conducted using each of the alternative spillover accounting methods. The base case of including only adolescents’ utility delivers an ICER of around £40,453. Employing the alternative methods gives quite different results, with the intervention dominated in two of the cases and an ICER below £30,000 per QALY in others. For the equivalence scale approach, the authors employ several elasticities for spillover utility, ranging from 0 (where parental utility is of equivalent value to adolescent utility and therefore additive) to 1 (where the average health spillover per household member is estimated for each patient). The ICER estimates using the equivalence scale approach ranged from £27,166 to £32,504. Higher elasticity implied lower cumulated QALYs.

The paper’s contribution is methodological, and I wouldn’t read too much into the magnitude of the results. For starters, the use of HUI2 (a measure for children) in adults and the use of EQ-5D-3L (a measure for adults) in the children is somewhat confusing. The authors argue that health gains should only be aggregated at the household level if the QALY gain for the patient is greater or equal to zero, because the purpose of treatment is to benefit the adolescents, not the parents. And they argue in favour of using an equivalence scale approach. By requiring an explicit judgement to set the elasticity within the estimation, the method provides a useful and transparent approach to including parental spillovers.

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Sam Watson’s journal round-up for 15th January 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.

Cost-effectiveness of publicly funded treatment of opioid use disorder in California. Annals of Internal Medicine [PubMed] Published 2nd January 2018

Deaths from opiate overdose have soared in the United States in recent years. In 2016, 64,000 people died this way, up from 16,000 in 2010 and 4,000 in 1999. The causes of public health crises like this are multifaceted, but we can identify two key issues that have contributed more than any other. Firstly, medical practitioners have been prescribing opiates irresponsibly for years. For the last ten years, well over 200,000,000 opiate prescriptions were issued per year in the US – enough for seven in every ten people. Once prescribed, opiate use is often not well managed. Prescriptions can be stopped abruptly, for example, leaving people with unexpected withdrawal syndromes and rebound pain. It is estimated that 75% of heroin users in the US began by using legal, prescription opiates. Secondly, drug suppliers have started cutting heroin with its far stronger but cheaper cousin, fentanyl. Given fentanyl’s strength, only a tiny amount is required to achieve the same effects as heroin, but the lack of pharmaceutical knowledge and equipment means it is often not measured or mixed appropriately into what is sold as ‘heroin’. There are two clear routes to alleviating the epidemic of opiate overdose: prevention, by ensuring responsible medical use of opiates, and ‘cure’, either by ensuring the quality and strength of heroin, or providing a means to stop opiate use. The former ‘cure’ is politically infeasible so it falls on the latter to help those already habitually using opiates. However, the availability of opiate treatment programs, such as opiate agonist treatment (OAT), is lacklustre in the US. OAT provides non-narcotic opiates, such as methadone or buprenorphine, to prevent withdrawal syndromes in users, from which they can slowly be weaned. This article looks at the cost-effectiveness of providing OAT for all persons seeking treatment for opiate use in California for an unlimited period versus standard care, which only provides OAT to those who have failed supervised withdrawal twice, and only for 21 days. The paper adopts a previously developed semi-Markov cohort model that includes states for treatment, relapse, incarceration, and abstinence. Transition probabilities for the new OAT treatment were determined from treatment data for current OAT patients (as far as I understand it). Although this does raise the question about the generalisability of this population to the whole population of opiate users – given the need to have already been through two supervised withdrawals, this population may have a greater motivation to quit, for example. In any case, the article estimates that the OAT program would be cost-saving, through reductions in crime and incarceration, and improve population health, by reducing the risk of death. Taken at face value these results seem highly plausible. But, as we’ve discussed before, drug policy rarely seems to be evidence-based.

The impact of aid on health outcomes in Uganda. Health Economics [PubMed] Published 22nd December 2017

Examining the response of population health outcomes to changes in health care expenditure has been the subject of a large and growing number of studies. One reason is to estimate a supply-side cost-effectiveness threshold: the health returns the health service achieves in response to budget expansions or contractions. Similarly, we might want to know the returns to particular types of health care expenditure. For example, there remains a debate about the effectiveness of aid spending in low and middle-income country (LMIC) settings. Aid spending may fail to be effective for reasons such as resource leakage, failure to target the right population, poor design and implementation, and crowding out of other public sector investment. Looking at these questions at an aggregate level can be tricky; the link between expenditure or expenditure decisions and health outcomes is long and causality flows in multiple directions. Effects are likely to therefore be small and noisy and require strong theoretical foundations to interpret. This article takes a different, and innovative, approach to looking at this question. In essence, the analysis boils down to a longitudinal comparison of those who live near large, aid funded health projects with those who don’t. The expectation is that the benefit of any aid spending will be felt most acutely by those who live nearest to actual health care facilities that come about as a result of it. Indeed, this is shown by the results – proximity to an aid project reduced disease prevalence and work days lost to ill health with greater effects observed closer to the project. However, one way of considering the ‘usefulness’ of this evidence is how it can be used to improve policymaking. One way is in understanding the returns to investment or over what area these projects have an impact. The latter is covered in the paper to some extent, but the former is hard to infer. A useful next step may be to try to quantify what kind of benefit aid dollars produce and its heterogeneity thereof.

The impact of social expenditure on health inequalities in Europe. Social Science & Medicine Published 11th January 2018

Let us consider for a moment how we might explore empirically whether social expenditure (e.g. unemployment support, child support, housing support, etc) affects health inequalities. First, we establish a measure of health inequality. We need a proxy measure of health – this study uses self-rated health and self-rated difficulty in daily living – and then compare these outcomes along some relevant measure of socioeconomic status (SES) – in this study they use level of education and a compound measure of occupation, income, and education (the ISEI). So far, so good. Data on levels of social expenditure are available in Europe and are used here, but oddly these data are converted to a percentage of GDP. The trouble with doing this is that this variable can change if social expenditure changes or if GDP changes. During the financial crisis, for example, social expenditure shot up as a proportion of GDP, which likely had very different effects on health and inequality than when social expenditure increased as a proportion of GDP due to a policy change under the Labour government. This variable also likely has little relationship to the level of support received per eligible person. Anyway, at the crudest level, we can then consider how the relationship between SES and health is affected by social spending. A more nuanced approach might consider who the recipients of social expenditure are and how they stand on our measure of SES, but I digress. In the article, the baseline category for education is those with only primary education or less, which seems like an odd category to compare to since in Europe I would imagine this is a very small proportion of people given compulsory schooling ages unless, of course, they are children. But including children in the sample would be an odd choice here since they don’t personally receive social assistance and are difficult to compare to adults. However, there are no descriptive statistics in the paper so we don’t know and no comparisons are made between other groups. Indeed, the estimates of the intercepts in the models are very noisy and variable for no obvious reason other than perhaps the reference group is very small. Despite the problems outlined so far though, there is a potentially more serious one. The article uses a logistic regression model, which is perfectly justifiable given the binary or ordinal nature of the outcomes. However, the authors justify the conclusion that “Results show that health inequalities measured by education are lower in countries where social expenditure is higher” by demonstrating that the odds ratio for reporting a poor health outcome in the groups with greater than primary education, compared to primary education or less, is smaller in magnitude when social expenditure as a proportion of GDP is higher. But the conclusion does not follow from the premise. It is entirely possible for these odds ratios to change without any change in the variance of the underlying distribution of health, the relative ordering of people, or the absolute difference in health between categories, simply by shifting the whole distribution up or down. For example, if the proportions of people in two groups reporting a negative outcome are 0.3 and 0.4, which then change to 0.2 and 0.3 respectively, then the odds ratio comparing the two groups changes from 0.64 to 0.58. The difference between them remains 0.1. No calculations are made regarding absolute effects in the paper though. GDP is also shown to have a positive effect on health outcomes. All that might have been shown is that the relative difference in health outcomes between those with primary education or less and others changes as GDP changes because everyone is getting healthier. The question of the article is interesting, it’s a shame about the execution.

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Lazaros Andronis’s journal round-up for 4th September 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.

The effect of spending cuts on teen pregnancy. Journal of Health Economics [PubMed] Published July 2017

High teenage pregnancy rates are an important concern that features high in many countries’ social policy agendas. In the UK, a country which has one of the highest teen pregnancy rates in the world, efforts to tackle the issue have been spearheaded by the Teenage Pregnancy Strategy, an initiative aiming to halve under-18 pregnancy rates by offering access to sex education and contraception. However, the recent spending cuts have led to reductions in grants to local authorities, many of which have, in turn, limited or cut a number of teenage pregnancy-related programmes. This has led to vocal opposition by politicians and organisations, who argue that cuts jeopardise the reductions in teenage pregnancy rates seen in previous years. In this paper, Paton and Wright set out to examine whether this is the case; that is, whether cuts to Teenage Pregnancy Strategy-related services have had an impact on teenage pregnancy rates. To do so, the authors used panel data from 149 local authorities in England collected between 2009 and 2014. To capture changes in teenage pregnancy rates across local authorities over the specified period, the authors used a fixed effects model which assumed that under-18 conception rates are a function of annual expenditure on teenage pregnancy services per 13-17 year female in the local authority, and a set of other socioeconomic variables acting as controls. Area and year dummies were also included in the model to account for unobservable effects that relate to particular years and localities and a number of additional analysis were run to get around spurious correlations between expenditure and pregnancy rates. Overall, findings showed that areas which implemented bigger cuts to teenage pregnancy-targeting programmes have, on average, seen larger drops in teenage pregnancy rates. However, these drops are, in absolute terms, small (e.g. a 10% reduction in expenditure is associated with a 0.25% decrease in teenage conception rates). Various explanations can be put forward to interpret these findings, one of which is that cuts might have trimmed off superfluous or underperforming elements of the programme. If this is the case, Paton and Wright’s findings offer some support to arguments that spending cuts may not always be bad for the public.

Young adults’ experiences of neighbourhood smoking-related norms and practices: a qualitative study exploring place-based social inequalities in smoking. Social Science & Medicine [PubMed] Published September 2017

Smoking is a universal problem affecting millions of people around the world and Canada’s young adults are no exception. As in most countries, smoking prevalence and initiation is highest amongst young groups, which is bad news, as many people who start smoking at a young age continue to smoke throughout adulthood. Evidence suggests that there is a strong socioeconomic gradient in smoking, which can be seen in the fact that smoking prevalence is unequally distributed according to education and neighbourhood-level deprivation, being a greater problem in more deprived areas. This offers an opportunity for local-level interventions that may be more effective than national strategies. Though, to come up with such interventions, policy makers need to understand how neighbourhoods might shape, encourage or tolerate certain attitudes towards smoking. To understand this, Glenn and colleagues saw smoking as a practice that is closely related to local smoking norms and social structures, and sought to get young adult smokers’ views on how their neighbourhood affects their attitudes towards smoking. Within this context, the authors carried out a number of focus groups with young adult smokers who lived in four different neighbourhoods, during which they asked questions such as “do you think your neighbourhood might be encouraging or discouraging people to smoke?” Findings showed that some social norms, attitudes and practices were common among neighbourhoods of the same SES. Participants from low-SES neighbourhoods reported more tolerant and permissive local smoking norms, whereas in more affluent neighbourhoods, participants felt that smoking was more contained and regulated. While young smokers from high SES neighbourhoods expressed some degree of alignment and agency with local smoking norms and practices, smokers in low SES described smoking as inevitable in their neighbourhood. Of interest is how individuals living in different SES areas saw anti-smoking regulations: while young smokers in affluent areas advocate social responsibility (and downplay the role of regulations), their counterparts in poorer areas called for more protection and spoke in favour of greater government intervention and smoking restrictions. Glenn and colleagues’ findings serve to highlight the importance of context in designing public health measures, especially when such measures affect different groups in entirely different ways.

Cigarette taxes, smoking—and exercise? Health Economics [PubMed] Published August 2017

Evidence suggests that rises in cigarette taxes have a positive effect on smoking reduction and/or cessation. However, it is also plausible that the effect of tax hikes extends beyond smoking, to decisions about exercise. To explore whether this proposition is supported by empirical evidence, Conway and Niles put together a simple conceptual framework, which assumes that individuals aim to maximise the utility they get from exercise, smoking, health (or weight management) and other goods subject to market inputs (e.g. medical care, diet aids) and time and budget constraints. Much of the data for this analysis came from the Behavioral Risk Factor Surveillance System (BRFSS) in the US, which includes survey participants’ demographic characteristics (age, gender), as well as answers to questions about physical activities and exercise (e.g. intensity and time per week spent on activities) and smoking behaviour (e.g. current smoking status, number of cigarettes smoked per day). Survey data were subsequently combined with changes in cigarette taxes and other state-level variables. Conway and Niles’s results suggest that increased cigarette costs reduce both smoking and exercise, with the decline in exercise being more pronounced among heavy and regular smokers. However, the direction of the effect varied according to one’s age and smoking experience (e.g. higher cigarette cost increased physical activity among recent quitters), which highlights the need for caution in drawing conclusions about the exact mechanism that underpins this relationship. Encouraging smoking cessation and promoting physical exercise are important and desirable public health objectives, but, as Conway and Niles’s findings suggest, pursuing both of them at the same time may not always be plausible.

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