Chris Sampson’s journal round-up for 14th October 2019

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.

Transparency in health economic modeling: options, issues and potential solutions. PharmacoEconomics [PubMed] Published 8th October 2019

Reading this paper was a strange experience. The purpose of the paper, and its content, is much the same as a paper of my own, which was published in the same journal a few months ago.

The authors outline what they see as the options for transparency in the context of decision modelling, with a focus on open source models and a focus on for whom the details are transparent. Models might be transparent to a small number of researchers (e.g. in peer review), to HTA agencies, or to the public at large. The paper includes a figure showing the two aspects of transparency, termed ‘reach’ and ‘level’, which relate to the number of people who can access the information and the level of detail made available. We provided a similar figure in our paper, using the terms ‘breadth’ and ‘depth’, which is at least some validation of our idea. The authors then go on to discuss five ‘issues’ with transparency: copyright, model misuse, confidential data, software, and time/resources. These issues are framed as questions, to which the authors posit some answers as solutions.

Perhaps inevitably, I think our paper does a better job, and so I’m probably over-critical of this article. Ours is more comprehensive, if nothing else. But I also think the authors make a few missteps. There’s a focus on models created by academic researchers, which oversimplifies the discussion somewhat. Open source modelling is framed as a more complete solution than it really is. The ‘issues’ that are discussed are at points framed as drawbacks or negative features of transparency, which they aren’t. Certainly, they’re challenges, but they aren’t reasons not to pursue transparency. ‘Copyright’ seems to be used as a synonym for intellectual property, and transparency is considered to be a threat to this. The authors’ proposed solution here is to use licensing fees. I think that’s a bad idea. Levying a fee creates an incentive to disregard copyright, not respect it.

It’s a little ironic that both this paper and my own were published, when both describe the benefits of transparency in terms of reducing “duplication of efforts”. No doubt, I read this paper with a far more critical eye than I normally would. Had I not published a paper on precisely the same subject, I might’ve thought this paper was brilliant.

If we recognize heterogeneity of treatment effect can we lessen waste? Journal of Comparative Effectiveness Research [PubMed] Published 1st October 2019

This commentary starts from the premise that a pervasive overuse of resources creates a lot of waste in health care, which I guess might be true in the US. Apparently, this is because clinicians have an insufficient understanding of heterogeneity in treatment effects and therefore assume average treatment effects for their patients. The authors suggest that this situation is reinforced by clinical trial publications tending to only report average treatment effects. I’m not sure whether the authors are arguing that clinicians are too knowledgable and dependent on the research, or that they don’t know the research well enough. Either way, it isn’t a very satisfying explanation of the overuse of health care. Certainly, patients could benefit from more personalised care, and I would support the authors’ argument in favour of stratified studies and the reporting of subgroup treatment effects. The most insightful part of this paper is the argument that these stratifications should be on the basis of observable characteristics. It isn’t much use to your general practitioner if personalisation requires genome sequencing. In short, I agree with the authors’ argument that we should do more to recognise heterogeneity of treatment effects, but I’m not sure it has much to do with waste.

No evidence for a protective effect of education on mental health. Social Science & Medicine Published 3rd October 2019

When it comes to the determinants of health and well-being, I often think back to my MSc dissertation research. As part of that, I learned that a) stuff that you might imagine to be important often isn’t and b) methodological choices matter a lot. Though it wasn’t the purpose of my study, it seemed from this research that higher education has a negative effect on people’s subjective well-being. But there isn’t much research out there to help us understand the association between education and mental health in general.

This study add to a small body of literature on the impact of changes in compulsory schooling on mental health. In (West) Germany, education policy was determined at the state level, so when compulsory schooling was extended from eight to nine years, different states implemented the change at different times between 1949 and 1969. This study includes 5,321 people, with 20,290 person-year observations, from the German Socio-Economic Panel survey (SOEP). Inclusion was based on people being born seven years either side of the cutoff birth year for which the longer compulsory schooling was enacted, with a further restriction to people aged between 50 and 85. The SOEP includes the SF-12 questionnaire, which includes a mental health component score (MCS). There is also an 11-point life satisfaction scale. The authors use an instrumental variable approach, using the policy change as an instrument for years of schooling and estimating a standard two-stage least squares model. The MCS score, life satisfaction score, and a binary indicator for MCS score lower than or equal to 45.6, are all modelled as separate outcomes.

Estimates using an OLS model show a positive and highly significant effect of years of schooling on all three outcomes. But when the instrumental variable model is used, this effect disappears. An additional year of schooling in this model is associated with a statistically and clinically insignificant decrease in the MCS score. Also insignificant was the finding that more years of schooling increases the likelihood of developing symptoms of a mental health disorder (as indicated by the MCS threshold of 45.6) and that life satisfaction is slightly lower. The same model shows a positive effect on physical health, which corresponds with previous research and provides some reassurance that the model could detect an effect if one existed.

The specification of the model seems reasonable and a host of robustness checks are reported. The only potential issue I could spot is that a person’s state of residence at the time of schooling is not observed, and so their location at entry into the sample is used. Given that education is associated with mobility, this could be a problem, and I would have liked to see the authors subject it to more testing. The overall finding – that an additional year of school for people who might otherwise only stay at school for eight years does not improve mental health – is persuasive. But the extent to which we can say anything more general about the impact of education on well-being is limited. What if it had been three years of additional schooling, rather than one? There is still much work to be done in this area.

Scientific sinkhole: the pernicious price of formatting. PLoS One [PubMed] Published 26th September 2019

This study is based on a survey that asked 372 researchers from 41 countries about the time they spent formatting manuscripts for journal submission. Let’s see how I can frame this as health economics… Well, some of the participants are health researchers. The time they spend on formatting journal submissions is time not spent on health research. The opportunity cost of time spent formatting could be measured in terms of health.

The authors focused on the time and wage costs of formatting. The results showed that formatting took a median time of 52 hours per person per year, at a cost of $477 per manuscript or $1,908 per person per year. Researchers spend – on average – 14 hours on formatting a manuscript. That’s outrageous. I have never spent that long on formatting. If you do, you only have yourself to blame. Or maybe it’s just because of what I consider to constitute formatting. The survey asked respondents to consider formatting of figures, tables, and supplementary files. Improving the format of a figure or a table can add real value to a paper. A good figure or table can change a bad paper to a good paper. I’d love to know how the time cost differed for people using LaTeX.

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Alastair Canaway’s journal round-up for 28th May 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.

Information, education, and health behaviours: evidence from the MMR vaccine autism controversy. Health Economics [PubMed] Published 2nd May 2018

In 1998, Andrew Wakefield published (in the Lancet) his infamous and later retracted research purportedly linking the measles-mumps-rubella (MMR) vaccine and autism. Despite the thorough debunking and exposure of academic skulduggery, a noxious cloud of misinformation remained in the public mind, particularly in the US. This study examined several facets of the MMR fake news including: what impact did this have on vaccine uptake in the US (both MMR and other vaccines); how did state level variation in media coverage impact uptake; and what role did education play in subsequent decisions about whether to vaccinate or not. This study harnessed the National Immunization Survey from 1995 to 2006 to answer these questions. This is a yearly dataset of over 200,000 children aged between 19 to 35 months with detailed information on not just immunisation, but also maternal education, income and other sociodemographics. The NewsLibrary database was used to identify stories published in national and state media relating to vaccines and autism. Various regression methods were implemented to examine these data. The paper found that, unsurprisingly, for the year following the Wakefield publication the MMR vaccine take-up declined by between 1.1%-1.5% (notably less than 3% in the UK), likewise this fall in take-up spilled over into other vaccines take-up. The most interesting finding related to education: MMR take-up for children of college-educated mothers declined significantly compared to those without a degree. This can be explained by the education gradient where more-educated individuals absorb and respond to health information more quickly. However, in the US, this continued for many years beyond 2003 despite proliferation of research refuting the autism-MMR link. This contrasts to the UK where educational link closed soon after the findings were refuted, that is, in the UK, the educated responded to the new information refuting the MMR-Autism link. In the US, despite the research being debunked, MMR uptake was lower in the children of those with higher levels of education for many more years. The author speculates that this contrast to the UK may be a result of the media influencing parents’ decisions. Whilst the media buzz in the UK peaked in 2002, it had largely subsided by 2003. In the US however, the media attention was constant, if not increasing till 2006, and so this may have been the reason the link remained within the US. So, we have Andrew Wakefield and arguably fearmongering media to blame for causing a long-term reduction in MMR take-up in the US. Overall, an interesting study leaning on multiple datasets that could be of interest for those working with big data.

Can social care needs and well-being be explained by the EQ-5D? Analysis of the Health Survey for England. Value in Health Published 23rd May 2018

There is increasing discussion about integrating health and social care to provide a more integrated approach to fulfilling health and social care needs. This creates challenges for health economists and decision makers when allocating resources, particularly when comparing benefits from different sectors. NICE itself recognises that the EQ-5D may be inappropriate in some situations. With the likes of ASCOT, ICECAP and WEMWBS frequenting the health economics world this isn’t an unknown issue. To better understand the relationship between health and social care measures, this EuroQol Foundation funded study examined the relationship between social care needs as measured by the Barthel Index, well-being measured using WEMWBS and also the GGH-12, and the EQ-5D as the measure of health. Data was obtained through the Health Survey for England (HSE) and contained 3354 individuals aged over 65 years. Unsurprisingly the authors found that higher health and wellbeing scores were associated with an increased probability of no social care needs. Those who are healthier or at higher levels of wellbeing are less likely to need social care. Of all the instruments, it was the self-care and the pain/discomfort dimensions of the EQ-5D that were most strongly associated with the need for social care. No GHQ-12 dimensions were statistically significant, and for the WEMWBS only the ‘been feeling useful’ and ‘had energy to spare’ were statistically significantly associated with social care need. The authors also investigated various other associations between the measures with many unsurprising findings e.g. EQ-5D anxiety/depression dimension was negatively associated with wellbeing as measured using the GHQ-12. Although the findings are favourable for the EQ-5D in terms of it capturing to some extent social care needs, there is clearly still a gap whereby some outcomes are not necessarily captured. Considering this, the authors suggest that it might be appropriate to strap on an extra dimension to the EQ-5D (known as a ‘bolt on’) to better capture important ‘other’ dimensions, for example, to capture dignity or any other important social care outcomes. Of course, a significant limitation with this paper relates to the measures available in the data. Measures such as ASCOT and ICECAP have been developed and operationalised for economic evaluation with social care in mind, and a comparison against these would have been more informative.

The health benefits of a targeted cash transfer: the UK Winter Fuel Payment. Health Economics [PubMed] [RePEc] Published 9th May 2018

In the UK, each winter is accompanied by an increase in mortality, often known as ‘excess winter mortality’ (EWM). To combat this, the UK introduced the Winter Fuel Payment (WFP), the purpose of the WFP is an unconditional cash transfer to households containing an older person (those most vulnerable to EWM) above the female state pension age with the intent for this to used to help the elderly deal with the cost of keeping their dwelling warm. The purpose of this paper was to examine whether the WFP policy has improved the health of elderly people. The authors use the Health Surveys for England (HSE), the Scottish health Survey (SHeS) and the English Longitudinal Study of Ageing (ELSA) and employ a regression discontinuity design to estimate causal effects of the WFP. To measure impact (benefit) they focus on circulatory and respiratory illness as measured by: self-reports of chest infection, nurse measured hypertension, and two blood biomarkers for infection and inflammation. The authors found that for those living in a household receiving the payment there was a 6% point reduction (p<0.01) in the incidence of high levels of serum fibrinogen (biomarker) which are considered to be a marker of current infection and are associated with chronic pulmonary disease. For the other health outcomes, although positive, the estimated effects were less robust and not statistically significant. The authors investigated the impact of increasing the age of eligibility for the WFP (in line with the increase of women’s pension age). Their findings suggest there may be some health cost associated with the increase in age of eligibility for WFP. To surmise, the paper highlights that there may be some health benefits from the receipt of the WFP. What it doesn’t however consider is opportunity cost. With WFP costing about £2 billion per year, as a health economist, I can’t help but wonder if the money could have been better spent through other avenues.

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Chris Sampson’s journal round-up for 19th June 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.

Health-related resource-use measurement instruments for intersectoral costs and benefits in the education and criminal justice sectors. PharmacoEconomics [PubMed] Published 8th June 2017

Increasingly, people are embracing a societal perspective for economic evaluation. This often requires the identification of costs (and benefits) in non-health sectors such as education and criminal justice. But it feels as if we aren’t as well-versed in capturing these as we are in the health sector. This study reviews the measures that are available to support a broader perspective. The authors search the Database of Instruments for Resource Use Measurement (DIRUM) as well as the usual electronic journal databases. The review also sought to identify the validity and reliability of the instruments. From 167 papers assessed in the review, 26 different measures were identified (half of which were in DIRUM). 21 of the instruments were only used in one study. Half of the measures included items relating to the criminal justice sector, while 21 included education-related items. Common specifics for education included time missed at school, tutoring needs, classroom assistance and attendance at a special school. Criminal justice sector items tended to include legal assistance, prison detainment, court appearances, probation and police contacts. Assessments of the psychometric properties was found for only 7 of the 26 measures, with specific details on the non-health items available for just 2: test-retest reliability for the Child and Adolescent Services Assessment (CASA) and validity for the WPAI+CIQ:SHP,V2 (there isn’t room on the Internet for the full name). So there isn’t much evidence of any validity for any of these measures in the context of intersectoral (non-health) costs and benefits. It’s no doubt the case that health-specific resource use measures aren’t subject to adequate testing, but this study has identified that the problem may be even greater when it comes to intersectoral costs and benefits. Most worrying, perhaps, is the fact that 1 in 5 of the articles identified in the review reported using some unspecified instrument, presumably developed specifically for the study or adapted from an off-the-shelf instrument. The authors propose that a new resource use measure for intersectoral costs and benefits (RUM ICB) be developed from scratch, with reference to existing measures and guidance from experts in education and criminal justice.

Use of large-scale HRQoL datasets to generate individualised predictions and inform patients about the likely benefit of surgery. Quality of Life Research [PubMed] Published 31st May 2017

In the NHS, EQ-5D data are now routinely collected from patients before and after undergoing one of four common procedures. These data can be used to see how much patients’ health improves (or deteriorates) following the operations. However, at the individual level, for a person deciding whether or not to undergo the procedure, aggregate outcomes might not be all that useful. This study relates to the development of a nifty online tool that a prospective patient can use to find out the expected likelihood that they will feel better, the same or worse following the procedure. The data used include EQ-5D-3L responses associated with almost half a million unilateral hip or knee replacements or groin hernia repairs between April 2009 and March 2016. Other variables are also included, and central to this analysis is a Likert scale about improvement or worsening of hip/knee/hernia problems compared to before the operation. The purpose of the study is to group people – based on their pre-operation characteristics – according to their expected postoperative utility scores. The authors employed a recursive Classification and Regression Tree (CART) algorithm to split the datasets into strata according to the risk factors. The final set of risk variables were age, gender, pre-operative EQ-5D-3L profile and symptom duration. The CART analysis grouped people into between 55 and 60 different groups for each of the procedures, with the groupings explaining 14-27% of the variation in postoperative utility scores. Minimally important (positive and negative) differences in the EQ-5D utility score were estimated with reference to changes in the Likert scale for each of the procedures. These ranged in magnitude from 0.041 to 0.106. The resulting algorithms are what drive the results delivered by the online interface (you can go and have a play with it). There are a few limitations to the study, such as the reliance on complete case analysis and the fact that the CART analysis might lack predictive ability. And there’s an interesting problem inherent in all of this, that the more people use the tool, the less representative it will become as it influences selection into treatment. The validity of the tool as a precise risk calculator is quite limited. But that isn’t really the point. The point is that it unlocks some of the potential value of PROMs to provide meaningful guidance in the process of shared decision-making.

Can present biasedness explain early onset of diabetes and subsequent disease progression? Exploring causal inference by linking survey and register data. Social Science & Medicine [PubMed] Published 26th May 2017

The term ‘irrational’ is overused by economists. But one situation in which I am willing to accept it is with respect to excessive present bias. That people don’t pay enough attention to future outcomes seems to be a fundamental limitation of the human brain in the 21st century. When it comes to diabetes and its complications, there are lots of treatments available, but there is only so much that doctors can do. A lot depends on the patient managing their own disease, and it stands to reason that present bias might cause people to manage their diabetes poorly, as the value of not going blind or losing a foot 20 years in the future seems less salient than the joy of eating your own weight in carbs right now. But there’s a question of causality here; does the kind of behaviour associated with time-inconsistent preferences lead to poorer health or vice versa? This study provides some insight on that front. The authors outline an expected utility model with quasi-hyperbolic discounting and probability weighting, and incorporate a present bias coefficient attached to payoffs occurring in the future. Postal questionnaires were collected from 1031 type 2 diabetes patients in Denmark with an online discrete choice experiment as a follow-up. These data were combined with data from a registry of around 9000 diabetes patients, from which the postal/online participants were identified. BMI, HbA1c, age and year of diabetes onset were all available in the registry and the postal survey included physical activity, smoking, EQ-5D, diabetes literacy and education. The DCE was designed to elicit time preferences using the offer of (monetary) lottery wins, with 12 different choice sets presented to all participants. Unfortunately, despite the offer of a real-life lottery award for taking part in the research, only 79 of 1031 completed the online DCE survey. Regression analyses showed that individuals with diabetes since 1999 or earlier, or who were 48 or younger at the time of onset, exhibited present bias. And the present bias seems to be causal. Being inactive, obese, diabetes illiterate and having lower quality of life or poorer glycaemic control were associated with being present biased. These relationships hold when subject to a number of control measures. So it looks as if present bias explains at least part of the variation in self-management and health outcomes for people with diabetes. Clearly, the selection of the small sample is a bit of a concern. It may have meant that people with particular risk preferences (given that the reward was a lottery) were excluded, and so the sample might not be representative. Nevertheless, it seems that at least some people with diabetes could benefit from interventions that increase the salience of future health-related payoffs associated with self-management.

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