Chris Sampson’s journal round-up for 17th December 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.

Health related quality of life aspects not captured by EQ-5D-5L: results from an international survey of patients. Health Policy Published 14th December 2018

Generic preference-based measures, such as the EQ-5D, cannot capture all aspects of health-related quality of life. They’re not meant to. Rather, their purpose is to capture just enough information to be able to adequately distinguish between health states with respect to the domains deemed normatively relavent to decisionmakers. The stated aim of this paper is to determine whether people with a variety of chronic conditions believe that their experiences can be adequately represented by the EQ-5D-5L.

The authors conducted an online survey, identifying participants through 320 patient associations across 47 countries. Participants were asked to complete the EQ-5D-5L and then asked if any aspects of their illness, which had a “big impact” on their health, were not captured by the EQ-5D-5L. 1,031 people started the survey and 767 completed it. More than half were from the UK. 51% of respondents said that there was some aspect of health not captured by the EQ-5D-5L. Of them, 19% mentioned fatigue, 12% mentioned medication side effects, 9.5% mentioned co-morbid conditions, and then a bunch of others in smaller proportions.

It’s nice to know what people think, but I have a few concerns about the usefulness of this study. One of the main problems is that it doesn’t seem safe to assume that respondents interpret “big impact” as meaning “an impact that is independently important in determining your overall level of quality of life”. So, even if we accept that people judging something to be important makes it important (which I’m not sure it does), then we still can’t be sure whether what they are identifying is within the scope of what we’re trying to measure. For starters, I can see no justification for including a ‘medication side effects’ domain. There’s also some concern about selection and attrition. I’d guess that people with more complicated or less common health concerns would be more likely to start and finish a survey about more complicated or less common health concerns.

The main thing I took from this study is that half of respondents with chronic diseases thought that the EQ-5D-5L captured every single aspect of health that had a “big impact”, and that there wasn’t broad support for any other specific dimension.

Reducing drug wastage in pharmaceuticals dosed by weight or body surface areas by optimising vial sizes. Applied Health Economics and Health Policy [PubMed] Published 5th December 2018

It’s common for pharmaceuticals to be wasted. Not just those out-of-date painkillers you threw in the bin, but also the expensive stuff being used in hospitals. One of the main reasons that waste occurs is that vials are made to specific sizes and, often, dosage varies from patient to patient – according to weight, for example – and doesn’t match the vial size. Suppose that vials are available as 50mg and 80mg and that an individual requires a 60mg dose. One way to address this might be to allow for vial sharing, whereby the leftovers are given to the next patient. But that isn’t always possible. So, we might like to consider what the best combination of available vial sizes should be, given the characteristics of the population.

In this paper, the authors set out the problem mathematically. Essentially, the optimisation problem is to minimise cost across the population subject to the vial sizes. An example is presented for two drugs (pembrolizumab and cabazitaxel), simulating patients based on samples drawn from the Health Survey for England. Simplifications are applied to the examples, such as setting a constraint of 8 vials per patient and assuming that prices are linear (i.e. fixed per milligram).

Pembrolizumab is currently available in 50mg and 100mg vials, and the authors estimate current wastage to be 13.2%. The simulations show that switching the 50mg to a 70mg would cut wastage to 8.6%. Cabazitaxel is available in 60mg vials, resulting in 19.4% wastage. Introducing a 12.5mg vial would cut wastage by around two thirds. An important general finding, which should be self-evident, is that vial sizes should not be divisible by each other, as this limits the number of possible combinations.

Depending on when vial sizes are determined (e.g. pre- or post-authorisation), pharmaceutical companies might use it to increase profit margins, or health systems might use it to save costs. Regardless, wastage isn’t useful. Evidence-based manufacture is an example of one of those best ideas; the sort that is simple and seems obvious once it’s spelt out. It’s a rare opportunity to benefit patients, health care providers, and manufacturers, with no significant burden on policymakers.

Death or debt? National estimates of financial toxicity in persons with newly-diagnosed cancer. The American Journal of Medicine [PubMed] Published October 2018

If you’re British, what’s the scariest thing about an ‘Americanised’ (/Americanized) health care system? Expensive inhalers? A shortened life expectancy? My guess is that the prospect of having to add financial ruin to terminal illness looms pretty large. You should make sure your fear is evidence-based. Here’s a paper to shake in the face of anyone who doesn’t support universal health care.

The authors use data from the Health and Retirement Study from 1998-2014, which includes people over 50 years of age and includes new (self-reported) diagnoses of cancer. This was the basis for inclusion in the study, with over 9.5 million new diagnoses of cancer. Up to two years pre-diagnosis was taken as a baseline. The data set also includes information on participants’ assets and debts, allowing the authors to use change in net worth as the primary outcome. Generalised linear models were used to assess various indicators of financial toxicity, including change or incurrence of consumer debt, mortgage debt, and home equity debt at two- and four-year follow-up. In addition to cancer diagnosis, various chronic comorbidities and socio-demographic variables were included in the models.

Shockingly, after two years following diagnosis, 42.4% of people had depleted their entire life’s assets. Average net worth had dropped $92,000. After four years, 38.2% were still insolvent. Women, older people, people who weren’t White, people with Medicaid, and those with worsening cancer status were among those more likely to have completely depleted their assets within two years. Having private insurance and being married had protective effects, as we might expect. There were some interesting findings associated with the 2008 financial crisis, which also seemed to be protective. And a protective effect associated with psychiatric comorbidity deserves more thought.

It’s difficult to explain away any (let alone all) of the magnitude of these findings. The analysis seems robust. But, given all other evidence available about out-of-pocket costs for cancer patients in the US, it should be shocking but not unexpected. The authors describe financial toxicity as ‘unintended’. There’s nothing unintended about this. Policymakers in the US keep deciding that they’d prefer to destroy the lives of sick people than allow for the spreading of that financial risk.

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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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Chris Sampson’s journal round-up for 31st July 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.

An exploratory study on using principal-component analysis and confirmatory factor analysis to identify bolt-on dimensions: the EQ-5D case study. Value in Health Published 14th July 2017

I’m not convinced by the idea of using bolt-on dimensions for multi-attribute utility instruments. A state description with a bolt-on refers to a different evaluative space, and therefore is not comparable with the progenitor, thus undermining its purpose. Maybe this study will persuade me otherwise. The authors analyse data from the Multi Instrument Comparison database, including responses to EQ-5D-5L, SF-6D, HUI3, AQoL 8D and 15D questionnaires, as well as the ICECAP and 3 measures of subjective well-being. Content analysis was used to allocate items from the measures to underlying constructs of health-related quality of life. The sample of 8022 was randomly split, with one half used for principal-component analysis and confirmatory factor analysis, and the other used for validation. This approach looks at the underlying constructs associated with health-related quality of life and the extent to which individual items from the questionnaires influence them. Candidate items for bolt-ons are those items from questionnaires other than the EQ-5D that are important and not otherwise captured by the EQ-5D questions. The principal-component analysis supported a 9-component model: physical functioning, psychological symptoms, satisfaction, pain, relationships, speech/cognition, hearing, energy/sleep and vision. The EQ-5D only covered physical functioning, psychological symptoms and pain. Therefore, items from measures that explain the other 6 components represent bolt-on candidates for the EQ-5D. This study succeeds in its aim. It demonstrates what appears to be a meaningful quantitative approach to identifying items not fully captured by the EQ-5D, which might be added as bolt-ons. But it doesn’t answer the question of which (if any) of these bolt-ons ought to be added, or in what circumstances. That would at least require pre-definition of the evaluative space, which might not correspond to the authors’ chosen model of health-related quality of life. If it does, then these findings would be more persuasive as a reason to do away with the EQ-5D altogether.

Endogenous information, adverse selection, and prevention: implications for genetic testing policy. Journal of Health Economics Published 13th July 2017

If you can afford it, there are all sorts of genetic tests available nowadays. Some of them could provide valuable information about the risk of particular health problems in the future. Therefore, they can be used to guide individuals’ decisions about preventive care. But if the individual’s health care is financed through insurance, that same information could prove costly. It could reinforce that classic asymmetry of information and adverse selection problem. So we need policy that deals with this. This study considers the incentives and insurance market outcomes associated with four policy options: i) mandatory disclosure of test results, ii) voluntary disclosure, iii) insurers knowing the test was taken, but not the results and iv) complete ban on the use of test information by insurers. The authors describe a utility model that incorporates the use of prevention technologies, and available insurance contracts, amongst people who are informed or uninformed (according to whether they have taken a test) and high or low risk (according to test results). This is used to estimate the value of taking a genetic test, which differs under the four different policy options. Under voluntary disclosure, the information from a genetic test always has non-negative value to the individual, who can choose to only tell their insurer if it’s favourable. The analysis shows that, in terms of social welfare, mandatory disclosure is expected to be optimal, while an information ban is dominated by all other options. These findings are in line with previous studies, which were less generalisable according to the authors. In the introduction, the authors state that “ethical issues are beyond the scope of this paper”. That’s kind of a problem. I doubt anybody who supports an information ban does so on the basis that they think it will maximise social welfare in the fashion described in this paper. More likely, they’re worried about the inequities in health that mandatory disclosure could reinforce, about which this study tells us nothing. Still, an information ban seems to be a popular policy, and studies like this indicate that such decisions should be reconsidered in light of their expected impact on social welfare.

Returns to scientific publications for pharmaceutical products in the United States. Health Economics [PubMedPublished 10th July 2017

Publication bias is a big problem. Part of the cause is that pharmaceutical companies have no incentive to publish negative findings for their own products. Though positive findings may be valuable in terms of sales. As usual, it isn’t quite that simple when you really think about it. This study looks at the effect of publications on revenue for 20 branded drugs in 3 markets – statins, rheumatoid arthritis and asthma – using an ‘event-study’ approach. The authors analyse a panel of quarterly US sales data from 2003-2013 alongside publications identified through literature searches and several drug- and market-specific covariates. Effects are estimated using first difference and difference in first difference models. The authors hypothesise that publications should have an important impact on sales in markets with high generic competition, and less in those without or with high branded competition. Essentially, this is what they find. For statins and asthma drugs, where there was some competition, clinical studies in high-impact journals increased sales to the tune of $8 million per publication. For statins, volume was not significantly affected, with mediation through price. In rhematoid arthritis, where competition is limited, the effect on sales was mediated by the effect on volume. Studies published in lower impact journals seemed to have a negative influence. Cost-effectiveness studies were only important in the market with high generic competition, increasing statin sales by $2.2 million on average. I’d imagine that these impacts are something with which firms already have a reasonable grasp. But this study provides value to public policy decision makers. It highlights those situations in which we might expect manufacturers to publish evidence and those in which it might be worthwhile increasing public investment to pick up the slack. It could also help identify where publication bias might be a bigger problem due to the incentives faced by pharmaceutical companies.

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