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.

Credits

Sam Watson’s journal round-up for 10th April 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.

Expertise versus Bias in Evaluation: Evidence from the NIH. American Economic Journal: Applied Economics. Published April 2017.

As an academic’s career progresses, she learns two things: patience and learning to deal with rejection. Getting a paper accepted by a top journal is hard. Obtaining funding for what seems like a good idea similarly so. We sometimes convince ourselves that the system is rigged, or at least biased. Research funding bodies may make poor decisions. This paper considers this question in great deal. While reviewers may have an informational advantage that allows them to assess quality, they may also be biased towards projects in their own domain of expertise. More funding for health economics blogs! To assess this, this paper examines 100,000 applications to the US National Institutes of Health. The proximity of the reviewer to the subject area of the application is judged by the number of times the reviewer has cited the work of the applicant. Quality is judged by the number of publications and citations the research produces – an attempt is made to adapt this to judge unfunded work. The principle finding is that reviewers are both more informed and more biased about work in their own field. Each additional permanent reviewer in a applicant’s area is estimated to increase the chance of funding by 2.2 percent, an equivalent effect to increasing quality by one quarter standard deviation. These effects seem small, as the author notes, and what strikes me is how little variation these measures in explain in funding decisions. Perhaps I will find some solace in the fact that there is quite a lot of apparent randomness in what gets funded. Nevertheless, the author suggests that the findings suggest that by trying to reduce bias by using impartial reviewers, the ability to judge quality will also decline.

Long-term effects of youth unemployment on mental health: does an economic crisis make a difference? Journal of Epidemiology and Community Health. [PubMedPublished April 2017.

Unemployment is related to mental health issues. The effect is appears to be particularly acute among young people for whom the transition to adult life can be difficult. Indeed, at this vulnerable period young people also transition from youth to adult mental health services, which breaks their continuity of care. Many become lost in the system. Services in many areas are being redesigned in light of this. This paper asks if the effect of unemployment on youth mental health is different depending on the economic conditions. Do period of high unemployment nationally exacerbate the effects of becoming unemployed? Surprisingly, the paper concludes, no, there is no difference. I say ‘surprisingly’ since I cannot recall finding a paper in this area or one that has featured on this blog with a negative finding. The analyses seem careful, and the authors concentrate on the magnitude of the effects, rather than statistical significance. Large sample sizes are required for adequate power to test a hypothesis on an interaction; this study does have a large sample size. The interactive effect is likely to be very small, not necessarily non-existent. But in comparison with the large effects of unemployment on youth mental health in general, the effect of economic conditions is of little importance. Nevertheless, Simpson’s paradox may rear its head here: during times of high unemployment, the cohort of the unemployed will be different. If those who only become unemployed during economic downturns have lower risk of mental health issues, then this may attenuate the estimated effect of unemployment on mental health. This issue is not addressed unfortunately, but I don’t want that to detract from a sensible use of statistics.

The Distortionary Effects of Incentives in Government: Evidence from China’s ‘Death Ceiling’ Program. American Economic Journal: Applied Economics. [RePEcPublished April 2017.

Targets and incentives to achieve those targets can distort the actions of agents. This is especially true of difficult to observe outcomes. People may be more inclined to manipulate the data than to actually achieve the target. Gaming and other similar behaviours have been noted in health services, for example. This article examines a policy in China designed to reduce the high rates of accidental deaths. In 2004 the State Administration of Work Safety announced that provinces would have to reduce their rate of accidental deaths by 2.5% per year. The provinces were set a so-called ‘death ceiling’. In 2012, the policy was declared a success; accidental death rates had come down by 45% since 2005. But further examination of the data, which were made publicly available in the state newspaper the People’s Daily, suggests this may not be the case. First of all, there was a sharp discontinuity of accidental deaths right below the death ceiling. This discontinuity was not consistent with a continuous variable. Provinces had much discretion about how to achieve the reductions. Those that used significant incentives for local officials were more likely to be successful. The authors also consider why, if the data were manipulated, deaths weren’t made to look significantly below the death ceiling rather than just below the death ceiling. They speculate that this would have the effect of making next year’s death ceiling even lower and more difficult to achieve. This paper provides a nice narrative that adds to our understanding of the perverse effects of incentives. For health services this is important. For many of the difficult to observe outcomes, like patient health, merely incentivising doctors and hospitals to improve may have little actual benefit.

Credits

Sam Watson’s journal round-up for 6th March 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.

It’s good to be first: order bias in reading and citing NBER working papers. The Review of Economics and Statistics [RePEcPublished 23rd February 2017

Each week one of the authors at this blog choose three or four recently published studies to summarise and briefly discuss. Making this choice from the many thousands of articles published every week can be difficult. I browse those journals that publish in my area and search recently published economics papers on PubMed and Econlit for titles that pique my interest. But this strategy is not without its own flaws as this study aptly demonstrates. When making a choice among many alternatives, people aren’t typically presented with a set of choices, rather a list. This arises in healthcare as well. In an effort to promote competition, at least in the UK, patients are presented with a list of possible of providers and some basic information about those providers. We recently covered a paper that explored this expansion of choice ‘sets’ and investigated its effects on quality. We have previously criticised the use of such lists. People often skim these lists relying on simple heuristics to make choices. This article shows that for the weekly email of new papers published by the National Bureau of Economic Research (NBER), being listed first leads to an increase of approximately 30% in downloads and citations, despite the essentially random ordering of the list. This is certainly not the first study to illustrate the biases in human decision making, but it shows that this journal round-up may not be a fair reflection of the literature, and providing more information about healthcare providers may not have the impact on quality that might be hypothesised.

Economic conditions, illicit drug use, and substance use disorders in the United States. Journal of Health Economics [PubMed] Published March 2017

We have featured a large number of papers about the relationship between macroeconomic conditions and health and health-related behaviours on this blog. It is certainly one of the health economic issues du jour and one we have discussed in detail. Generally speaking, when looking at an aggregate level, such as countries or states, all-cause mortality appears to be pro-cyclical: it declines in economic downturns. Whereas an examination at individual or household levels suggest unemployment and reduced income is generally bad for health. It is certainly possible to reconcile these two effects as any discussion of Simpson’s paradox will reveal. This study takes the aggregate approach to looking at US state-level unemployment rates and their relationship with drug use. It’s relevant to the discussion around economic conditions and health; the US has seen soaring rates of opiate-related deaths recently, although whether this is linked to the prevailing economic conditions remains to be seen. Unfortunately, this paper predicates a lot of its discussion about whether there is an effect on whether there was statistical significance, a gripe we’ve contended with previously. And there are no corrections for multiple comparisons, despite the well over 100 hypothesis tests that are conducted. That aside, the authors conclude that the evidence suggests that use of ecstasy and heroin is procyclical with respect to unemployment (i.e increase with greater unemployment) and LSD, crack cocaine, and cocaine use is counter-cyclical. The results appear robust to the model specifications they compare, but I find it hard to reconcile some of the findings with the prior information about how people actually consume drugs. Many drugs are substitutes and/or compliments for one another. For example, many heroin users began using opiates through abuse of prescription drugs such as oxycodone but made the switch as heroin is generally much cheaper. Alcohol and marijuana have been shown to be substitutes for one another. All of this suggesting a lack of independence between the different outcomes considered. People may also lose their job because of drug use. Taken all together I remain a little sceptical of the conclusions from the study, but it is nevertheless an interesting and timely piece of research.

Child-to-adult neurodevelopmental and mental health trajectories after early life deprivation: the young adult follow-up of the longitudinal English and Romanian Adoptees study. The Lancet [PubMedPublished 22nd February 2017

Does early life deprivation lead to later life mental health issues? A question that is difficult to answer with observational data. Children from deprived backgrounds may be predisposed to mental health issues, perhaps through familial inheritance. To attempt to discern whether deprivation in early life is a cause of mental health issues this paper uses data derived from a cohort of Romanian children who spent time in one of the terribly deprived institutions of Ceaușescu’s Romania and who were later adopted by British families. These institutions were characterised by poor hygiene, inadequate food, and lack of social or educational stimulation. A cohort of British adoptees was used for comparison. For children who spent more than six months in one of the deprived institutions, there was a large increase in cognitive and social problems in later life compared with either British adoptees or those who spent less than six months in an institution. The evidence is convincing, with differences being displayed across multiple dimensions of mental health, and a clear causal mechanism by which deprivation acts. However, for this and many other studies that I write about on this blog, a disclaimer might be needed when there is significant (pun intended) abuse and misuse of p-values. Ziliak and McClosky’s damning diatribe on p-values, The Cult of Statistical Significance, presents examples of lists of p-values being given completely out of context, with no reference to the model or hypothesis test they are derived from, and with the implication that they represent whether an effect exists or not. This study does just that. I’ll leave you with this extract from the abstract:

Cognitive impairment in the group who spent more than 6 months in an institution remitted from markedly higher rates at ages 6 years (p=0·0001) and 11 years (p=0·0016) compared with UK controls, to normal rates at young adulthood (p=0·76). By contrast, self-rated emotional symptoms showed a late onset pattern with minimal differences versus UK controls at ages 11 years (p=0·0449) and 15 years (p=0·17), and then marked increases by young adulthood (p=0·0005), with similar effects seen for parent ratings. The high deprivation group also had a higher proportion of people with low educational achievement (p=0·0195), unemployment (p=0·0124), and mental health service use (p=0·0120, p=0·0032, and p=0·0003 for use when aged <11 years, 11–14 years, and 15–23 years, respectively) than the UK control group.

Credits