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

Competition and quality indicators in the health care sector: empirical evidence from the Dutch hospital sector. The European Journal of Health Economics [PubMed] Published 3rd January 2017

In case you weren’t already convinced, this paper presents more evidence to support the notion that (non-price) competition between health care providers is good for quality. The Dutch system is based on compulsory insurance and information on quality of hospital care is made public. One feature of the Dutch health system is that – for many elective hospital services – prices are set following a negotiation between insurers and hospitals. This makes the setting of the study a bit different to some of the European evidence considered to date, because there is scope for competition on price. The study looks at claims data for 3 diagnosis groups – cataract, adenoid/tonsils and bladder tumor – between 2008 and 2011. The authors’ approach to measuring competition is a bit more sophisticated than some other studies’ and is based on actual market share. A variety of quality indicators are used for the 3 diagnosis groups relating mainly to the process of care (rather than health outcomes). Fixed and random effects linear regression models are used to estimate the impact of market share upon quality. Casemix was only controlled for in relation to the proportion of people over 65 and the proportion of women. Where a relationship was found, it tended to be in favour of lower market share (i.e. greater competition) being associated with higher quality. For cataract and for bladder tumor there was a ‘significant’ effect. So in this setting at least, competition seems to be good news for quality. But the effect sizes are neither huge nor certain. A look at each of the quality indicators separately showed plenty of ‘non-significant’ relationships in both directions. While a novelty of this study is the liberalised pricing context, the authors find that there is no relationship between price and quality scores. So even if we believe the competition-favouring results, we needn’t abandon the ‘non-price competition only’ mantra.

Cost-effectiveness thresholds in global health: taking a multisectoral perspective. Value in Health Published 3rd January 2017

We all know health care is not the only – and probably not even the most important – determinant of health. We call ourselves health economists, but most of us are simply health care economists. Rarely do we look beyond the domain of health care. If our goal as researchers is to help improve population health, then we should probably be allocating more of our mental resource beyond health care. The same goes for public spending. Publicly provided education might improve health in a way that the health service would be willing to fund. Likewise, health care might improve educational attainment. This study considers resource allocation decisions using the familiar ‘bookshelf approach’, but goes beyond the unisectoral perspective. The authors discuss a two-sector world of health and education, and demonstrate the ways in which there may be overlaps in costs and outcomes. In short, there are likely to be situations in which the optimal multisectoral decision would be for individual sectors to increase their threshold in order to incorporate the spillover benefits of an intervention in another sector. The authors acknowledge that – in a perfect world – a social-welfare-maximising government would have sufficient information to allocate resources earmarked for specific purposes (e.g. health improvement) across sectors. But this doesn’t happen. Instead the authors propose the use of a cofinancing mechanism, whereby funds would be transferred between sectors as needed. The paper provides an interesting and thought-provoking discussion, and the idea of transferring funds between sectors seems sensible. Personally I think the problem is slightly misspecified. I don’t believe other sectors face thresholds in the same way, because (generally speaking) they do not employ cost-effectiveness analysis. And I’m not sure they should. I’m convinced that for health we need to deviate from welfarism, but I’m not convinced of it for other sectors. So from my perspective it is simply a matter of health vs everything else, and we can incorporate the ‘everything else’ into a cost-effectiveness analysis (with a societal perspective) in monetary terms. Funds can be reallocated as necessary with each budget statement (of which there seem to be a lot nowadays).

Is the Rational Addiction model inherently impossible to estimate? Journal of Health Economics [RePEc] Published 28th December 2016

Saddle point dynamics. Something I’ve never managed to get my head around, but here goes… This paper starts from the problem that empirical tests of the Rational Addiction model serve up wildly variable and often ridiculous (implied) discount rates. That may be part of the reason why economists tend to support the RA model but at the same time believe that it has not been empirically proven. The paper sets out the basis for saddle point dynamics in the context of the RA model, and outlines the nature of the stable and unstable root within the function that determines a person’s consumption over time. The authors employ Monte Carlo estimation of RA-type equations, simulating panel data observations. These simulations demonstrate that the presence of the unstable root may make it very difficult to estimate the coefficients. So even if the RA model can truly represent behaviour, empirical estimation may contradict it. This raises the question of whether the RA model is essentially untestable. A key feature of the argument relates to use of the model where a person’s time horizon is not considered to be infinite. Some non-health economists like to assume it is, which, as the authors wryly note, is not particularly ‘rational’.

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Sam Watson’s journal round-up for October 24th 2016

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.

Mortality decrease according to socioeconomic groups during the economic crisis in Spain: a cohort study of 36 million people. The Lancet [PubMed] Published 13th October 2016

There is no shortage of studies examining the relationship between macroeconomic conditions and population health. Papers have come up on the journal round-up here, here, and here, and we previously discussed economic conditions and baby health. So what does this study add? Using data from the 2011 Spanish census on 36 million individuals, the study compares age-adjusted mortality rates for different socioeconomic groups before and after the economic crisis in Spain. The socioeconomic status of households was classified on the basis of household wealth, household floor space, and number of cars. The study compares the annual change in mortality rates for 2004-7 to the annual percentage change in the post-crisis period 2008-11. In essence the authors are looking for a structural break. The article reports that mortality rates declined faster post-crisis than before and that this effect was more pronounced in low socioeconomic status households. However, this conclusion is based on observed differences in estimated changes of rate: differences between the socioeconomic groups are not directly tested. The authors seem to fall foul of the problem that the difference between “significant” and “not significant” is not itself statistically significant. The plots in the paper illustrate strong differences in age-adjusted mortality rates by socioeconomic status, but a structural break in changes in rates is not so clearly evident. A large econometric literature has arisen around measuring structural breaks in macroeconomic series, many of these methods may have been of use. Indeed, there have been a number of sophisticated and careful analyses of the effect of macroeconomic conditions and health previous published, including the seminal study by Christopher Ruhm. Why this study landed in The Lancet therefore seems somewhat mysterious.

The ambiguous effect of GP competition: the case of hospital admissions. Health Economics [PubMedPublished 14th October 2016

Another mainstay of this blog: competition in healthcare. We’ve covered papers on this topic in previous journal round-ups here and here, and critically discussed a paper on the topic here. It seems to be one of those topics with important implications for healthcare policy but one which becomes less certain the more is known. Indeed, this paper recognises this in its title. The ambiguity to which it refers is the effect of GP competition on hospital admissions: if GPs retain more patients due to increased competition then admissions go down; if they recruit new patients due to increased competition then admissions go up. Typically studies in this area either compare outcomes before and after a pro-competitive policy change, or compare outcomes between areas with different densities (and hence competition) between GPs. This study adopts a variant of the latter approach using the number of open list practices in an area as their proxy for competition. They find that increased competition reduces inpatient attendances and increases outpatient attendances. I’ve often been skeptical of the use of GP density as a proxy for competition. Do people really compare GP practices before choosing them or do they just go to the nearest one? If a person is already registered at one practice, how often do they search around to choose another if care isn’t that bad? An observed effect of a change in GP density could be attributable to entry into or exit from the ‘market’ of differently performing providers, which may have little to do with competition, more the type of GP, GP age, and differences in medical training. Nevertheless, this article does present a well-considered analysis, the difficulty is in the interpretation in light of all the previous studies.

Modeling the economic burden of adult vaccine-preventable diseases in the United States. Health Affairs [PubMed] Published 12th October 2016

Andrew Wakefield, disbarred doctor and disgraced author of the fraudulent Lancet paper on MMR and autism, is currently promoting his new anti-vaccine film. His work and a cabal of conspiracy theorists have led many parents to refuse to get their children vaccinated. All this despite vaccines being one of the safest and most cost-effective of health interventions. This new paper seeks to determine the economic burden of vaccine-preventable diseases is in the US. The diseases considered include hepatitis A and B; measles, mumps, and rubella; and shingles (herpes zoster). Epidemiological models were developed in conjunction with experts; economic costs were assessed using both cost-of-illness and full income methodologies; and, parameters were specified on the basis of a literature review. Taking into account healthcare costs and productivity losses, the burden of the considered diseases was estimated at $9 billion annually. The authors also discuss taking into account social welfare losses using the value of a statistical life, however I think I may be misinterpreting the results when it states

The current-dollar value of statistical life calculated from each source was $5.9 billion from the FDA; $6.3 billion from the NHTSA; and $8.3 billion from the EPA. The full income value of death as a result of vaccine-preventable diseases is estimated to be $176 billion annually (plausibility range: $166 billion–$231 billion).

That seems way too large to me so I’m not sure what to make of that. Nevertheless, the study illustrates the potentially massive burden that vaccine-preventable diseases may present.

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