Rita Faria’s journal round-up for 22nd 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.

Economically efficient hepatitis C virus treatment prioritization improves health outcomes. Medical Decision Making [PubMed] Published 22th August 2018

Hepatitis C treatment was in the news a couple of years ago when the new direct-acting antivirals first appeared on the scene. These drugs are very effective but also incredibly expensive. This prompted a flurry of cost-effectiveness analyses and discussions of the role of affordability in cost-effectiveness (my views here).

This compelling study by Lauren Cipriano and colleagues joins the debate by comparing various strategies to prioritise patients for treatment when the budget is not enough to meet patient demand. This is a clear example of the health losses due to the opportunity cost.

The authors compare the costs and health outcomes of various prioritisation schedules in terms of the number of patients treated, the distribution by severity and age, time to treatment, impact on end-stage liver disease, QALYs, costs and net benefit.

The differences between prioritisation schedules in terms of these various outcomes were remarkable. Reassuringly, the optimal prioritisation schedule on the basis of net benefit (the “optimisation” schedule) was the one that achieved the most QALYs and the greatest net benefit. This was even though the cost-effectiveness threshold did not reflect the opportunity cost, as it was set at $100,000 per QALY gained.

This study is fascinating. It shows how the optimal policy depends on what we are trying to maximise. The “first come first serve” schedule treats the most patients, but it is the “optimisation” schedule that achieves the most health benefits net of the opportunity cost.

Since their purpose was not to compare treatments, the authors used a representative price depending on whether patients had progressed to cirrhosis. A future study could include a comparison between drugs, as our previous work found that there are clear differences in cost-effectiveness between treatment strategies. The more cost-effective the treatment strategies, the more patients can be treated with a given budget.

The authors made the Excel model available as supporting material, together with documentation. This is excellent practice! It disseminates the work and shows openness to independent validation. Well done!

Long-term survival and value of chimeric antigen receptor T-cell therapy for pediatric patients with relapsed or refractory leukemia. JAMA Pediatrics [PubMed] Published 8th October 2018

This fascinating study looks at the cost-effectiveness of tisagenlecleucel in the treatment of children with relapsed or refractory leukaemia compared to chemotherapy.

Tisagenlecleucel is the first chimeric antigen receptor T-cell (CAR-T) therapy. CAR-T therapy is the new kid on the block in cancer treatment. It involves modifying the patient’s own immune system cells to recognise and kill the patient’s cancer (see here for details). Such high-tech treatment comes with a hefty price tag. Tisagenlecleucel is listed at $475,000 for a one-off administration.

The key challenge was to obtain the effectiveness inputs under the chemotherapy option. This was because tisagenlecleucel has only been studied in single-arm trials and individual level data was not available to the research team. The research team selected a single-arm study on the outcomes with clofarabine monotherapy, since its patients at baseline were most similar in terms of demographics and number of prior therapies to the tisagenlecleucel study.

This study is brilliant in approaching a difficult decision problem and conducting extensive sensitivity analysis. In particular, it tests the impact of common drivers of the cost-effectiveness of potentially curative therapies in children, such as the discount rate, duration of benefit, treatment initiation, and the inclusion of future health care costs. Ideally, the sensitivity analysis should also have tested the assumption that the studies informing the effectiveness inputs for tisagenlecleucel and clofarabine monotherapy were comparable or if clofarabine monotherapy does not represent the current standard of care, although it would be difficult to parameterise.

This outstanding study highlights the challenges posed by the approval of treatments based on single-arm studies. Had individual-level data been available, an adjusted comparison may have been possible, which would improve the degree of confidence in the cost-effectiveness of tisagenlecleucel. Regulators and trial sponsors should work together to make anonymised individual level data available to bonafide researchers.

Researcher requests for inappropriate analysis and reporting: a U.S. survey of consulting biostatisticians. Annals of Internal Medicine [PubMed] Published 10th October 2018

This study reports a survey of biostatisticians on the frequency and severity of requests for inappropriate analysis and reporting. The results are stunning!

The top 3 requests in terms of severity were to falsify statistical significance to support a desired result, change data to achieve the desired outcome and remove/alter data records to better support the research hypothesis. Fortunately, this sort of requests appears to be rare.

The top 3 requests in terms of frequency seem to be not showing a plot because it does not show an effect as strong as it had been hoped; to stress only the significant findings but under-reporting non-significant ones, and report results before data have been cleaned and validated.

Given the frequency and severity of the requests, the authors recommend that researchers should be better educated in good statistical practice and research ethics. I couldn’t agree more and would suggest that cost-effectiveness analysis is included, given that it informs policy decisions and it is generally conducted by multidisciplinary teams.

I’m now wondering what the responses would be if we did a similar survey to health economists, particularly those working in health technology assessment! Something for HESG, iHEA or ISPOR to look at for the future?

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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 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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