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.

Credits

Thesis Thursday: Angela Devine

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 Angela Devine who has a PhD from The Open University. If you would like to suggest a candidate for an upcoming Thesis Thursday, get in touch.

Title
The economics of vivax malaria treatment
Supervisors
Yoel Lubell, Ric Price, Ricardo Aguas, Shunmay Yeung
Repository link
https://thesiscommons.org/zsc6x/

What is vivax malaria and what are some of the key challenges that it presents for health economists?

One infectious bite from a mosquito carrying vivax malaria can lead to multiple episodes of malaria due to dormant liver parasites called hypnozoites. We can’t tell the difference between these relapse infections and new infections, which means that it’s challenging to model. Unlike falciparum malaria, which frequently results in severe outcomes and deaths, vivax malaria doesn’t often result in direct mortality. Instead, it likely causes indirect mortality through the malnutrition and anaemia that are caused by repeated malaria episodes. Unfortunately, the evidence of this is limited.

To prevent future relapses, patients need to be given a drug to treat the liver parasites (radical cure) in addition to treating the blood stage treatment. The only drug that is currently licensed for radical cure, primaquine, can cause potentially life-threatening haemolysis in individuals who have a genetic disorder called glucose-6-phosphate-dehydrogenase (G6PD) deficiency. While some countries are so concerned about haemolytic events that primaquine isn’t used at all, other settings prescribe primaquine to everyone. The evidence on the risk of primaquine-induced haemolysis and death is sparse, and expert opinion on this matter is fiercely divided.

How did you go about collecting the data needed for your study?

Not much has been done previously on vivax malaria costs, which meant that a lot of my work involved generating cost data. I started by analysing some fairly old data that my supervisors had from a study on treatment-seeking behaviour in Papua, Indonesia. The cost of illness study indicated that household costs were similar for both vivax and falciparum malaria in 2006. I also collected provider and patient-level cost data alongside a multi-country clinical trial on vivax malaria treatment. I wasn’t able to travel to the some of the study sites (e.g. Afghanistan) to collect the provider costs, so I had to create worksheets for local trial staff to fill out. It was an iterative process, particularly with the first site in Indonesia, but it got faster and easier to do each time. I’m very thankful that the local teams were enthusiastic about this work and patient with my many questions and requests.

What is the economic burden of vivax malaria and who bears the cost?

A lot of vivax malaria episodes occur in remote areas where access to care is limited. The highest incidence of the disease is in children, particularly those under the age of five. This often means that someone will need to take time off from usual activities, such as farming, attending school, or household chores, to care for the sick. I estimated the global economic burden to be US$330 million. These estimates don’t include mortality, malnutrition or anaemia. Since we know that repeated episodes can have a profound impact on a household’s income, I included productivity losses for those who were ill and their carers. We also know that malaria causes educational losses, so I included these productivity costs for children as well as adults to try to capture some of those losses. In total, productivity losses accounted for US$263 million, nearly 80% of the total costs. Since many who are affected by this disease aren’t paid for their work, I used one GDP per capita per day for every day lost to illness or caretaking. Other methods of valuing these losses would have a substantial impact on the total costs. While there’s a considerable amount of uncertainty around some of the numbers I used and assumptions that I made, my hope is that by identifying the issues, we will be able to generate the data needed for better estimates in the future.

What methods did you use to evaluate the cost-effectiveness of new treatment strategies?

Asia-Pacific malaria control programs stated that the cost of G6PD screening was an obstacle to its widespread use. My research addressed those concerns through a decision tree model in R that weighed up the costs, risks and benefits of screening using newly developed G6PD rapid diagnostic tests (RDTs) before prescribing primaquine. I wanted to make this work as relevant to policymakers as possible, so I did two separate comparisons. First I compared this strategy to not using primaquine, then I compared it to prescribing primaquine to everyone without screening. While this strayed from typical economic evaluation methods, it seemed unlikely that a setting where primaquine isn’t prescribed due to fear of haemolysis would switch to prescribing primaquine to everyone without screening, or that a setting where primaquine is prescribed to everyone would stop using it altogether.

As G6PD deficiency is X-linked, the risk of haemolysis varies by gender, so results need to be stratified by gender. The prevalence and severity of G6PD deficiency and the latency period and number of relapses for vivax malaria varies geographically. While I wanted to have more than one setting to explore how these might impact the results, four comparisons was already a lot of information to present. Instead, I used R-shiny with my model to create an interactive website where people can see how changes in the baseline model parameters impact the results. My goal was to provide a tool that policymakers could use to help make decisions about treatment strategies in their settings. This also provides an opportunity to explore the impact of parameter values that may be seen as contentious.

What are some of the issues you encountered in working with policymakers to ensure that cost-effective treatments become more widely used?

One issue is that patients, especially those who can afford to do so, seek treatment in the private sector, which is harder to control. Encouragingly, the follow-up survey in Papua, Indonesia indicated that changing treatment policy in the public sector also had an impact on how private sector providers diagnosed and treated malaria. As someone keen to influence policy, I benefited a lot from meetings with malaria control program officials from the Asia Pacific. These provided insights on the challenges that countries are facing. For example, the work I did on G6PD screening was aimed at addressing the cost issue that kept coming up in these meetings. Unfortunately, I’m not aware of settings where they have begun to routinely use G6PD RDTs. There are additional barriers, like getting the tests licensed so that malaria control programs can purchase them with a subsidy from The Global Fund. Another issue that I hadn’t fully appreciated before beginning my PhD is that funding for diseases like malaria is often siloed for specific purposes by the various donors. This can make it more challenging to ensure that countries are getting the best possible value for the money that is spent. There’s also been a lot of debate recently about what willingness to pay threshold should be used in poorly resourced settings. This is a debate that we need to have, but it also makes it more challenging to decide which treatments should be considered to be cost-effective.