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

Advertisements

Thesis Thursday: Till Seuring

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 Till Seuring who graduated with a PhD from the University of East Anglia. If you would like to suggest a candidate for an upcoming Thesis Thursday, get in touch.

Title
The economics of type 2 diabetes in middle-income countries
Supervisors
Marc Suhrcke, Max Bachmann, Pieter Serneels
Repository link
https://ueaeprints.uea.ac.uk/63278/

What made you want to study the economics of diabetes?

I was diagnosed with type 1 diabetes when I was 18. So while looking for a topic for my master’s thesis in development economics, I was wondering about how big of a problem diabetes – in particular, type 2 diabetes – would be in low- and middle-income countries (LMICs), because I had never heard about it during my studies. Looking for data I found some on Mexico, where, as it turned out, diabetes was a huge problem and ended up writing my master’s thesis on the labour market effects of diabetes in Mexico. After that, I worked at the International Diabetes Federation as a health economist in a junior position for about a year and a half and at one of their conferences met Prof Marc Suhrcke, who is doing a lot of global health and non-communicable disease related work. We stayed in contact and in the end he offered me the possibility to pursue a PhD on diabetes in LMICs. So this is how I ended up at the University of East Anglia in Norwich studying the economics of diabetes.

Which sources of data did you use for your analyses, and how was your experience of using them?

I exclusively used household survey data that was publicly available. In my master’s thesis, I had already worked with the Mexican Family Life Survey, which is quite an extensive household survey covering many socioeconomic as well as health-related topics. I ended up using it for two of my thesis chapters. The nice thing about it is that it has a panel structure now with three waves, and the last waves also included information on HbA1c levels – a biomarker used to infer on blood glucose levels over the last three months – that I could use to detect people with undiagnosed diabetes in the survey. The second source of data was the China Health and Nutrition Survey, which has many of the same qualities, with even more waves of data. There are more and more surveys with high-quality data coming out so it will be exciting to explore them further in the future.

How did you try to identify the effects of diabetes as separate from other influences?

As in many other fields, there is great worry that diabetes might be endogenous when trying to investigate its relationship with economic outcomes. For example, personal characteristics (such as ambition) could affect your likelihood to be employed or your wage, but maybe also your exercise levels and consequently your risk to develop diabetes. Unfortunately, such things are very difficult to measure so that they often remain unobserved. Similarly, changes in income or job status could affect lifestyles that in turn could change the risk to develop diabetes, making estimates prone to selection biases and reverse causality. To deal with this, I used several strategies. In my first paper on Mexico, I used a commonly used instrumental variable strategy. My instrument was parental diabetes and we argued that, given our control variables, it was unrelated to employment status but predicted diabetes in the children due to the genetic component of diabetes. In the second paper on Mexico, I used fixed effects estimation to control for any time-invariant confounding. This strategy does not need an instrument, however, unobserved time-variant confounding or reverse causality may still be a problem. I tackled the latter in my last paper on the effect of diabetes on employment and behavioural outcomes in China, using a methodology mainly used in epidemiology called marginal structural models, which uses inverse probability weighting to account for the selection into diabetes on previous values of the outcomes of interest, e.g. changes in employment status or weight. Of course, in the absence of a true experiment, it still remains difficult to truly establish causality using observational data, so one still needs to be careful to not over-interpret these findings.

The focus of your PhD was on middle-income countries. Does diabetes present particular economic challenges in this setting?

Well, over the last 30 years many middle-income countries, especially in Asia but also Latin America, have gone from diabetes rates much below high-income countries to surpassing them. China today has about 100 million people with diabetes, sporting the largest diabetes population worldwide. While, as countries become richer, first the economically better-off populations tend to have a higher diabetes prevalence, in many middle-income countries diabetes is now affecting, in particular, the middle class and the poor, who often lack the financial resources to access treatment or to even be diagnosed. Consequently, many remain poorly treated and develop diabetes complications that can lead to amputations, loss of vision and cardiovascular problems. Once these complications appear, the associated medical expenditures can represent a very large economic burden, and as I have shown in this thesis, can also lead to income losses because people lose their jobs.

What advice would you give to policymakers looking to minimise the economic burden of diabetes?

The policy question is always the most difficult one, but I’ll try to give some answers. The results of the thesis suggest that there is a considerable economic burden of diabetes which disproportionately affects the poor, the uninsured and women. Further, many people remain undiagnosed and some of the results of the biomarker analysis I conducted in one of my papers suggest that diagnosis likely often happens too late to prevent adverse health outcomes. Therefore, earlier diagnosis may help to reduce the burden, the problem is that once people are diagnosed they will also need treatment, and it appears that even now many do not receive appropriate treatment. Therefore, simply aiming to diagnose more people will not be sufficient. Policymakers in these countries will need to make sure that they will also be able to offer treatment to everybody, in particular the disadvantaged groups. Otherwise, inequities will likely become even greater and healthcare systems even more overburdened. How this can be achieved is another question and more research will be needed. Promising areas could be a greater integration of diabetes treatment into the existing health care systems specialised in treating communicable diseases such as tuberculosis, which often are related to diabetes. This would both improve treatment and likely limit the amount of additional costs. Of course, investments in early life health, nutrition and education will also help to reduce the burden by improving health and thereby economic possibilities, so that people may never become diabetic or at least have better possibilities to cope with the disease.

Thesis Thursday: Raymond Oppong

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 Raymond Oppong who graduated with a PhD from the University of Birmingham. If you would like to suggest a candidate for an upcoming Thesis Thursday, get in touch.

Title
Economic analysis alongside multinational studies
Supervisors
Sue Jowett, Tracy Roberts
Repository link
http://etheses.bham.ac.uk/7288/

What attracted you to studying economic evaluation in the context of multinational studies?

One of the first projects that I was involved in when I started work as a health economist was the Genomics to combat Resistance against Antibiotics in Community-acquired lower respiratory tract infections (LRTI) in Europe (GRACE) project. This was an EU-funded study aimed at integrating and coordinating the activities of physicians and scientists from institutions in 14 European countries to combat antibiotic resistance in community-acquired lower respiratory tract infections.

My first task on this project was to undertake a multinational costing study to estimate the costs of treating acute cough/LRTI in Europe. I faced quite a number of challenges including the lack of unit cost data across countries. Conducting a full economic evaluation alongside the interventional studies in GRACE also brought up a number of issues with respect to methods of analysis of multinational trials which needed to be resolved. The desire to understand and resolve some of these issues led me to undertake the PhD to investigate the implications of conducting economic evaluations alongside multinational studies.

Your thesis includes some case studies from a large multinational project. What were the main findings of your empirical work?

I used three main case studies for my empirical work. The first was an observational study aimed at describing the current presentation, investigation, treatment and outcomes of community-acquired lower respiratory tract infections and analysing the determinants of antibiotic use in Europe. The other 2 were RCTs. The first was aimed at studying the effectiveness of antibiotic therapy (amoxicillin) in community-acquired lower respiratory tract infections, whilst the second was aimed at assessing training interventions to improve antibiotic prescribing behaviour by general practitioners. The observational study was used to explore issues relating to costing and outcomes in multinational studies whilst the RCTs explored the various analytical approaches (pooled and split) to economic evaluation alongside multinational studies.

The results from the observational study revealed large variations in costs across Europe and showed that contacting researchers in individual countries was the most effective way of obtaining unit costs. Results from both RCTs showed that the choice of whether to pool or split data had an impact on the cost-effectiveness of the interventions.

What were the key analytical methods used in your analysis?

The overall aim of the thesis was to study the implications of conducting economic analysis alongside multinational studies. Specific objectives include: i) documenting challenges associated with economic evaluations alongside multinational studies, ii) exploring various approaches to obtaining and estimating unit costs, iii) exploring the impact of using different tariffs to value EQ-5D health state descriptions, iv) comparing methods that have been used to conduct economic evaluation alongside multinational studies and v) making recommendations to guide the design and conduct of future economic evaluations carried out alongside multinational studies.

A number of approaches were used to achieve each of the objectives. A systematic review of the literature identified challenges associated with economic evaluations alongside multinational studies. A four-stage approach to obtaining unit costs was assessed. The UK, European and country-specific EQ-5D value sets were compared to determine which is the most appropriate to use in the context of multinational studies. Four analytical approaches – fully pooled one country costing, fully pooled multicountry costing, fully split one country costing and fully split multicountry costing – were compared in terms of resource use, costs, health outcomes and cost-effectiveness. Finally, based on the findings of the study, a set of recommendations were developed.

You completed your PhD part-time while working as a researcher. Did you find this a help or a hindrance to your studies?

I must say that it was both a help and a hindrance. Working in a research environment was really helpful. There was a lot of support from supervisors and colleagues which kept me motivated. I might have not gotten this support if I was not working in a research/academic environment. However, even though some time during the week was allocated to the PhD, I had to completely put it on hold for long periods of time in order to deal with the pressures of work/research. Consequently, I always had to struggle to find my bearings when I got back to the PhD. I also spent most weekends working on the PhD especially when I was nearing submission.

On the whole, it should be noted that a part-time PhD requires a lot of time management skills. I personally had to go on time management courses which were really helpful.

What advice would you give to a health economist conducting an economic evaluation alongside a multinational study?

For a health economist conducting an economic evaluation alongside a multinational trial, it is important to plan ahead and understand the challenges that are associated with economic evaluations alongside multinational studies. A lot of the problems such as those related to the identification of unit costs can be avoided by ensuring adequate measures are put in place at the design stage of the study. An understanding of the various health systems of the countries involved in the study is important in order to make a judgement about the differences and similarities in resource use across countries. Decision makers are interested in results that can be applied to their jurisdiction; therefore it is important to adopt transparent methods e.g. state the countries that participated in the study, state the sources of unit costs and make it clear whether data from all countries (pooling) or from a subset (splitting) were used. To ensure that the results of the study are generalisable to a number of countries it may be advisable to present country-specific results and probably conduct the analysis from different perspectives.