Thesis Thursday: Cheryl Jones

On the third Thursday of every month, we speak to a recent graduate about their thesis and their studies. This month’s guest is Dr Cheryl Jones who has a PhD from the University of Manchester. If you would like to suggest a candidate for an upcoming Thesis Thursday, get in touch.

Title
The economics of presenteeism in the context of rheumatoid arthritis, ankylosing spondylitis and psoriatic arthritis
Supervisors
Katherine Payne, Suzanne Verstappen, Brenda Gannon
Repository link
https://www.research.manchester.ac.uk/portal/en/theses/the-economics-of-presenteeism-in-the-context-of-rheumatoid-arthritis-ankylosing-spondylitis-and-psoriatic-arthritis%288215e79a-925e-4664-9a3c-3fd42d643528%29.html

What attracted you to studying health-related presenteeism?

I was attracted to study presenteeism because it gave me a chance to address both normative and positive issues. Presenteeism, a concept related to productivity, is a controversial topic in the economic evaluation of healthcare technologies and is currently excluded from health economic evaluations, following the recommendation made by the NICE reference case. The reasons why productivity is excluded from economic evaluations are important and valid, however, there are some circumstances where excluding productivity is difficult to defend. Presenteeism offered an opportunity for me to explore and question the social value judgements that underpin economic evaluation methods with respect to productivity. In terms of positive issues related to presenteeism, research into the development of methods that can be used to measure and value presenteeism was (and still is) limited. This provided an opportunity to think creatively about the types of methods we could use, both quantitative and qualitative, to address and further methods for quantifying presenteeism.

Are existing tools adequate for measuring and valuing presenteeism in inflammatory arthritic conditions?

That is the question! Research into methods that can be used to quantify presenteeism is still in its infancy. Presenteeism is difficult to measure accurately because there are a lack of objective measures that can be used, for example, the number of cars assembled per day. As a consequence, many methods rely on self-report surveys, which tend to suffer from bias, such as reporting or recall bias. Methods that have been used to value presenteeism have largely focused on valuing presenteeism as a cost using the human capital approach (HCA: volume of presenteeism multiplied by a monetary factor). The monetary factor typically used to convert the volume of presenteeism into a cost value is wages. Valuing productivity using wages risks taking account of discriminatory factors that are associated with wages, such as age. There are also economic arguments that question whether the value of the wage truly reflects the value of productivity. My PhD focused on developing a method that values presenteeism as a non-monetary benefit, thereby avoiding the need to value it as a cost using wages. Overall, methods to measure and value presenteeism still have some way to go before a ‘gold standard’ can be established, however, there are many experts from many disciplines who are working to improve these methods.

Why was it important to conduct qualitative interviews as part of your research?

The quantitative component of my PhD was to develop an algorithm, using mapping methods, that links presenteeism with health status and capability measures. A study by Connolly et al. recommend conducting qualitative interviews to provide some evidence of face/content validity to establish whether a quantitative link between two measures (or concepts) is feasible and potentially valid. The qualitative study I conducted was designed to understand the extent to which the EQ-5D-5L, SF6D and ICECAP-C were able to capture those aspects of rheumatic conditions that negatively impact presenteeism. The results suggested that all three measures were able to capture those important aspects of rheumatic conditions that affect presenteeism; however, the results indicated that the SF6D would most likely be the most appropriate measure. The results from the quantitative mapping study identified the SF6D as the most suitable outcome measure able to predict presenteeism in working populations with rheumatic conditions. The advantage of the qualitative results was that it provided some evidence that explained why the SF6D was the more suitable measure rather than relying on speculation.

Is it feasible to predict presenteeism using outcome measures within economic evaluation?

I developed an algorithm that links presenteeism, measured using the Work Activity Productivity Impairment (WPAI) questionnaire, with health and capability. Health status was measured using the EQ-5D-5L and SF6D, and capability was measured using the ICECAP-A. The SF6D was identified as the most suitable measure to predict presenteeism in a population of employees with rheumatoid arthritis or ankylosing spondylitis. The results indicate that it is possible to predict presenteeism using generic outcome measures; however, the results have yet to be externally validated. The qualitative interviews provided evidence as to why the SF6D was the better predictor for presenteeism and the result gave rise to questions about the suitability of outcome measures given a specific population. The results indicate that it is potentially feasible to predict presenteeism using outcome measures.

What would be your key recommendation to a researcher hoping to capture the impact of an intervention on presenteeism?

Due to the lack of a ‘gold standard’ method for capturing the impact of presenteeism, I would recommend that the researcher reports and justifies their selection of the following:

  1. Provide a rationale that explains why presenteeism is an important factor that needs to be considered in the analysis.
  2. Explain how and why presenteeism will be captured and included in the analysis; as a cost, monetary benefit, or non-monetary benefit.
  3. Justify the methods used to measure and value presenteeism. It is important that the research clearly reports why specific tools, such as presenteeism surveys, have been selected for use.

Because there is no ‘gold standard’ method for measuring and valuing presenteeism and guidelines do not exist to inform the reporting of methods used to quantify presenteeism, it is important that the researcher reports and justifies their selection of methods used in their analysis.

Chris Sampson’s journal round-up for 15th October 2018

Every Monday our authors provide a round-up of some of the most recently published peer reviewed articles from the field. We don’t cover everything, or even what’s most important – just a few papers that have interested the author. Visit our Resources page for links to more journals or follow the HealthEconBot. If you’d like to write one of our weekly journal round-ups, get in touch.

Reliability and validity of the contingent valuation method for estimating willingness to pay: a case of in vitro fertilisation. Applied Health Economics and Health Policy [PubMed] Published 13th October 2018

In vitro fertilisation (IVF) is a challenge for standard models of valuation in health economics. Mostly, that’s because, despite it falling within the scope of health care, and despite infertility being a health problem, many of the benefits of IVF can’t be considered health-specific. QALYs can’t really do the job, so there’s arguably a role for cost-benefit analysis, and for using stated preference methods to determine the value of IVF. This study adds to an existing literature studying willingness to pay for IVF, but differs in that it tries to identify willingness to pay (WTP) from the general population. This study is set in Australia, where IVF is part-funded by universal health insurance, so asking the public is arguably the right thing to do.

Three contingent valuation surveys were conducted online with 1,870 people from the general public. The first survey used a starting point bid of $10,000, and then, 10 months later, two more surveys were conducted with starting point bids of $4,000 and $10,000. Each included questions for a 10%, 20%, and 50% success rate. Respondents were asked to adopt an ex-post perspective, assuming that they were infertile and could conceive by IVF. Individuals could respond to starting bids with ‘yes’, ‘no’, ‘not sure’, or ‘I am not willing to pay anything’. WTP for one IVF cycle with a 20% success rate ranged from $6,353 in the $4,000 survey to $11,750 in the first $10,000 survey. WTP for a year of treatment ranged from $18,433 to $28,117. The method was reliable insofar as there were no differences between the first and second $10,000 surveys. WTP values corresponded to the probability of success, providing support for the internal construct validity of the survey. However, the big difference between values derived using the alternative starting point bids indicates a strong anchoring bias. The authors also tested the external criterion validity by comparing the number of respondents willing to pay more than $4,000 for a cycle with a 20% success rate (roughly equivalent to the out of pocket cost in Australia) with the number of people who actually choose to pay for IVF in Australia. Around 63% of respondents were willing to pay at that price, which is close to the estimated 60% in Australia.

This study provides some support for the use of contingent valuation methods in the context of IVF, and for its use in general population samples. But the anchoring effect is worrying and justifies further research to identify appropriate methods to counteract this bias. The exclusion of the “not sure” and “I will not pay anything” responses from the analysis – as ‘non-demanders’ – arguably undermines the ‘societal valuation’ aspect of the estimates.

Pharmaceutical expenditure and gross domestic product: evidence of simultaneous effects using a two‐step instrumental variables strategy. Health Economics [PubMed] Published 10th October 2018

The question of how governments determine spending on medicines is pertinent in the UK right now, as the Pharmaceutical Price Regulation Scheme approaches its renewal date. The current agreement includes a cap on pharmaceutical expenditure. It should go without saying that GDP ought to have some influence on how much public spending is dedicated to medicines. But, when medicines expenditure might also influence GDP, the actual relationship is difficult to estimate. In this paper, the authors seek to identify both effects: the income elasticity of government spending on pharmaceuticals and the effect of that spending on income.

The authors use a variety of data sources from the World Health Organization, World Bank, and International Monetary Fund to construct an unbalanced panel for 136 countries from 1995 to 2006. To get around the challenge of two-way causality, the authors implement a two-step instrumental variable approach. In the first step of the procedure, a model estimates the impact of GDP per capita on government spending on pharmaceuticals. International tourist receipts are used as an instrument that is expected to correlate strongly with GDP per capita, but which is expected to be unrelated to medicines expenditure (except through its correlation with GDP). The model attempts to control for health care expenditure, life expectancy, and other important country-specific variables. In the second step, a reverse causality model is used to assess the impact of pharmaceutical expenditure on GDP per capita, with pharmaceutical expenditure adjusted to partial-out the response to GDP estimated in the first step.

The headline average results are that GDP increases pharmaceutical expenditure and that pharmaceutical expenditure reduces GDP. A 1% increase in GDP per capita increases public pharmaceutical expenditure per capita by 1.4%, suggesting that pharmaceuticals are a luxury good. A 1% increase in public pharmaceutical expenditure is associated with a 0.09% decrease in GDP per capita. But the results are more nuanced than that. The authors outline various sources of heterogeneity. The positive effect of GDP on pharmaceutical expenditure only holds for high-income countries and the negative effect of pharmaceutical expenditure on GDP only holds for low-income countries. Quantile regressions show that income elasticity decreases for higher quantiles of expenditure. GDP only influences pharmaceutical spending in countries classified as ‘free’ on the index of Economic Freedom of the World, and pharmaceutical expenditure only has a negative impact on GDP in countries that are ‘not free’.

I’ve never come across this kind of two-step approach before, so I’m still trying to get my head around whether the methods and the data are adequate. But a series of robustness checks provide some reassurance. In particular, an analysis of intertemporal effects using lagged GDP and lagged pharmaceutical expenditure demonstrates the robustness of the main findings. Arguably, the findings of this study are more important for policymaking in low- and middle-income countries, where pharmaceutical expenditures might have important consequences for GDP. In high-income (and ‘free’) economies that spend a lot on medicines, like the UK, there is probably less at stake. This could be because of effective price regulation and monitoring, and better adherence, ensuring that pharmaceutical expenditure is not wasteful.

Parental health spillover in cost-effectiveness analysis: evidence from self-harming adolescents in England. PharmacoEconomics [PubMed] [RePEc] Published 8th October 2018

Any intervention has the potential for spillover effects, whereby people other than the recipient of care are positively or negatively affected by the consequences of the intervention. Where a child is the recipient of care, it stands to reason that any intervention could affect the well-being of the parents and that these impacts should be considered in economic evaluation. But how should parental spillovers be incorporated? Are parental utilities additive to that of the child patient? Or should a multiplier effect be used with reference to the effect of an intervention on the child’s utility?

The study reports on a trial-based economic evaluation of family therapy for self-harming adolescents aged 11-17. Data collection included EQ-5D-3L for the adolescents and HUI2 for the main caregiver (86% mothers) at baseline, 6-month follow-up, and 12-month follow-up, collected from 731 patient-parent pairs. The authors outline six alternative methods for including parental health spillovers: i) relative health spillover, ii) relative health spillover per treatment arm, iii) absolute health spillover, iv) absolute global health spillover per treatment arm, v) additive accrued health benefits, and vi) household equivalence scales. These differ according to whether parental utility is counted as depending on adolescent’s utility, treatment allocation, the primary outcome of the study, or some combination thereof. But the authors’ primary focus (and the main contribution of this study) is the equivalence scale option. This involves adding together the spillover effects for other members of the household and using alternative weightings depending on the importance of parental utility compared with adolescent utility.

Using Tobit models, controlling for a variety of factors, the authors demonstrate that parental utility is associated with adolescent utility. Then, economic evaluations are conducted using each of the alternative spillover accounting methods. The base case of including only adolescents’ utility delivers an ICER of around £40,453. Employing the alternative methods gives quite different results, with the intervention dominated in two of the cases and an ICER below £30,000 per QALY in others. For the equivalence scale approach, the authors employ several elasticities for spillover utility, ranging from 0 (where parental utility is of equivalent value to adolescent utility and therefore additive) to 1 (where the average health spillover per household member is estimated for each patient). The ICER estimates using the equivalence scale approach ranged from £27,166 to £32,504. Higher elasticity implied lower cumulated QALYs.

The paper’s contribution is methodological, and I wouldn’t read too much into the magnitude of the results. For starters, the use of HUI2 (a measure for children) in adults and the use of EQ-5D-3L (a measure for adults) in the children is somewhat confusing. The authors argue that health gains should only be aggregated at the household level if the QALY gain for the patient is greater or equal to zero, because the purpose of treatment is to benefit the adolescents, not the parents. And they argue in favour of using an equivalence scale approach. By requiring an explicit judgement to set the elasticity within the estimation, the method provides a useful and transparent approach to including parental spillovers.

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Sam Watson’s journal round-up for 8th October 2018

Every Monday our authors provide a round-up of some of the most recently published peer reviewed articles from the field. We don’t cover everything, or even what’s most important – just a few papers that have interested the author. Visit our Resources page for links to more journals or follow the HealthEconBot. If you’d like to write one of our weekly journal round-ups, get in touch.

A cost‐effectiveness threshold based on the marginal returns of cardiovascular hospital spending. Health Economics [PubMed] Published 1st October 2018

There are two types of cost-effectiveness threshold of interest to researchers. First, there’s the societal willingness-to-pay for a given gain in health or quality of life. This is what many regulatory bodies, such as NICE, use. Second, there is the actual return on medical spending achieved by the health service. Reimbursement of technologies with a lesser return for every pound or dollar would reduce the overall efficiency of the health service. Some refer to this as the opportunity cost, although in a technical sense I would disagree that it is the opportunity cost per se. Nevertheless, this latter definition has seen a growth in empirical work; with some data on health spending and outcomes, we can start to estimate this threshold.

This article looks at spending on cardiovascular disease (CVD) among elderly age groups by gender in the Netherlands and survival. Estimating the causal effect of spending is tricky with these data: spending may go up because survival is worsening, external factors like smoking may have a confounding role, and using five year age bands (as the authors do) over time can lead to bias as the average age in these bands is increasing as demographics shift. The authors do a pretty good job in specifying a Bayesian hierarchical model with enough flexibility to accommodate these potential issues. For example, linear time trends are allowed to vary by age-gender groups and  dynamic effects of spending are included. However, there’s no examination of whether the model is actually a good fit to the data, something which I’m growing to believe is an area where we, in health and health services research, need to improve.

Most interestingly (for me at least) the authors look at a range of priors based on previous studies and a meta-analysis of similar studies. The estimated elasticity using information from prior studies is more ‘optimistic’ about the effect of health spending than a ‘vague’ prior. This could be because CVD or the Netherlands differs in a particular way from other areas. I might argue that the modelling here is better than some previous efforts as well, which could explain the difference. Extrapolating using life tables the authors estimate a base case cost per QALY of €40,000.

Early illicit drug use and the age of onset of homelessness. Journal of the Royal Statistical Society: Series A Published 11th September 2018

How the consumption of different things, like food, drugs, or alcohol, affects life and health outcomes is a difficult question to answer empirically. Consider a recent widely-criticised study on alcohol published in The Lancet. Among a number of issues, despite including a huge amount of data, the paper was unable to address the problem that different kinds of people drink different amounts. The kind of person who is teetotal may be so for a number of reasons including alcoholism, interaction with medication, or other health issues. Similarly, studies on the effect of cannabis consumption have shown among other things an association with lower IQ and poorer mental health. But are those who consume cannabis already those with lower IQs or at higher risk of psychoses? This article considers the relationship between cannabis and homelessness. While homelessness may lead to an increase in drug use, drug use may also be a cause of homelessness.

The paper is a neat application of bivariate hazard models. We recently looked at shared parameter models on the blog, which factorise the joint distribution of two variables into their marginal distribution by assuming their relationship is due to some unobserved variable. The bivariate hazard models work here in a similar way: the bivariate model is specified as the product of the marginal densities and the individual unobserved heterogeneity. This specification allows (i) people to have different unobserved risks for both homelessness and cannabis use and (ii) cannabis to have a causal effect on homelessness and vice versa.

Despite the careful set-up though, I’m not wholly convinced of the face validity of the results. The authors claim that daily cannabis use among men has a large effect on becoming homeless – as large an effect as having separated parents – which seems implausible to me. Cannabis use can cause psychological dependency but I can’t see people choosing it over having a home as they might with something like heroin. The authors also claim that homelessness doesn’t really have an effect on cannabis use among men because the estimated effect is “relatively small” (it is the same order of magnitude as the reverse causal effect) and only “marginally significant”. Interpreting these results in the context of cannabis use would then be difficult, though. The paper provides much additional material of interest. However, the conclusion that regular cannabis use, all else being equal, has a “strong effect” on male homelessness, seems both difficult to conceptualise and not in keeping with the messiness of the data and complexity of the empirical question.

How could health care be anything other than high quality? The Lancet: Global Health [PubMed] Published 5th September 2018

Tedros Adhanom Ghebreyesus, or Dr Tedros as he’s better known, is the head of the WHO. This editorial was penned in response to the recent Lancet Commission on Health Care Quality and related studies (see this round-up). However, I was critical of these studies for a number of reasons, in particular, the conflation of ‘quality’ as we normally understand it and everything else that may impact on how a health system performs. This includes resourcing, which is obviously low in poor countries, availability of labour and medical supplies, and demand side choices about health care access. The empirical evidence was fairly weak; even in countries like in the UK in which we’re swimming in data we struggle to quantify quality. Data are also often averaged at the national level, masking huge underlying variation within-country. This editorial is, therefore, a bit of an empty platitude: of course we should strive to improve ‘quality’ – its goodness is definitional. But without a solid understanding of how to do this or even what we mean when we say ‘quality’ in this context, we’re not really saying anything at all. Proposing that we need a ‘revolution’ without any real concrete proposals is fairly meaningless and ignores the massive strides that have been made in recent years. Delivering high-quality, timely, effective, equitable, and integrated health care in the poorest settings means more resources. Tinkering with what little services already exist for those most in need is not going to produce a revolutionary change. But this strays into political territory, which UN organisations often flounder in.

Editorial: Statistical flaws in the teaching excellence and student outcomes framework in UK higher education. Journal of the Royal Statistical Society: Series A Published 21st September 2018

As a final note for our academic audience, we give you a statement on the Teaching Excellence Framework (TEF). For our non-UK audience, the TEF is a new system being introduced by the government, which seeks to introduce more of a ‘market’ in higher education by trying to quantify teaching quality and then allowing the best-performing universities to charge more. No-one would disagree with the sentiment that improving higher education standards is better for students and teachers alike, but the TEF is fundamentally statistically flawed, as discussed in this editorial in the JRSS.

Some key points of contention are: (i) TEF doesn’t actually assess any teaching, such as through observation; (ii) there is no consideration of uncertainty about scores and rankings; (iii) “The benchmarking process appears to be a kind of poor person’s propensity analysis” – copied verbatim as I couldn’t have phrased it any better; (iv) there has been no consideration of gaming the metrics; and (v) the proposed models do not reflect the actual aims of TEF and are likely to be biased. Economists will also likely have strong views on how the TEF incentives will affect institutional behaviour. But, as Michael Gove, the former justice and education secretary said, Britons have had enough of experts.

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