Meeting round-up: 7th annual Vancouver Health Economics Methodology (VanHEM) meeting

The 7th annual Vancouver Health Economics Methodology (VanHEM) meeting took place on June 16 in Vancouver, Canada. This one-day conference brings together health economists from across the Pacific Northwest, including Vancouver, Washington State, and Calgary. This has always been more than a Vancouver meeting, which led Anirban Basu from Washington State to suggest changing the name of the meeting to the Cascadia Health Economics Workshop (CHEW) ‚Äď a definite improvement.

This year’s event began a day early, with Richard Grieve from the London School of Hygiene and Tropical Medicine, Stephen O’Neill from NUI Galway, and Jasjeet Sekhon from the University of California Berkeley, delivering a workshop titled Methods for Addressing Confounding in Comparative Effectiveness and Cost-effectiveness Studies. This provided both theoretical and practical examples of propensity score matching, genetic matching, difference-in-difference estimation and the synthetic control method. I was fortunate enough to be one of the 16 attendees (it was oversubscribed) to participate after being unable to attend when the course was offered at the Society for Medical Decision Making conference this past October. The course was an excellent introduction to these methodologies, including both theoretical and empirical examples of their use. I was particularly interested to have R and Stata code provided, to work through real-world examples. Being able to see the data and code and explore different analyses provided an incredibly rich learning experience.

The following morning, Prof Grieve delivered the plenary address to the more than 80 attendees. This talk discussed the potential for causal inference and large-scale data to influence policy, and outlined how observational data can complement evidence from randomized controlled trials (the slides are available here [PDF]). Since the expertise of our health economics community centres on other methods, primarily economic evaluation and stated preference methods, Prof Grieve’s plenary catalyzed a lot of discussion, which continued throughout the day. After the plenary, there were eight papers discussed over four parallel sessions, in addition to ten posters presented over lunch. This included an interesting paper by Nathaniel Hendrix from Washington state on a mapping algorithm between a generic and condition-specific quality-of-life measure for epilepsy, and two papers using discrete choice methodology. One by Tracey-Lea Laba evaluated cost sharing for long-acting beta-agonists in Australia, and another by Dean Regier, Verity Watson and Jonathon Sicsic explored choice certainty and choice consistency in DCEs using Kahneman’s dual processing theory.

Having been to three HESG meetings, there are lots of similarities with the format of VanHEM. For instance, papers are discussed for 20 minutes by another attendee, and the author has 5-minutes for clarification. What is different is that before a wider discussion, members of the audience break into small groups for 5 minutes. In my experience, this addition has been very effective at increasing participation during the final 25 minutes of the session, which is an open discussion amongst all attendees. It also gave attendees the opportunity to swap tips on where to find the best deals on plaid shirts.

I was fortunate enough to have my paper accepted and discussed by Prof Larry Lynd from the UBC Faculty of Pharmaceutical Science. Prof Lynd provided a number of excellent suggestions. Of particular note was a much simpler and more intuitive description of the marginal rate of substitution.

VanHEM also afforded an opportunity for discussion and reflection within the local health economics community. Recently, the Canadian Institutes for Health Research launched the Strategy for Patient-Oriented Research (SPOR). In BC, this involves an $80 million investment to “foster evidence-informed health care by bringing innovative approaches to the point of care, so as to ensure greater quality, accountability, and access of care”. One innovative approach is the creation of a new health economics methods cluster in the province, which is co-led by David Whitehurst (Simon Fraser University) and Nick Bansback (University of British Columbia). It receives SPOR funds to help support the health economics community as a whole, and specific research projects that focus on novel methods. At VanHEM, one hour was dedicated to determining how the cluster could help support the community that sees many health economists located at different sites throughout the region. Participants suggested having a number of dedicated academic half-days throughout the year that aim to provide an opportunity for members of the community to see each other face-to-face and engage in activities that support professional development. The theme of great titles continued with the suggestion of a “HEck-a-thon”.

Overall, this year‚Äôs VanHEM meeting was a great success. The addition of a pre-meeting workshop provided an excellent opportunity for our community to gain practical experience in causal methods, and we continue to see increased numbers of participants from outside our local region. I’m looking forward to doing this again in 2018, and I would encourage anyone visiting our region to be in touch!

Credits

Chris Sampson’s journal round-up for 11th April 2016

Every Monday¬†our authors provide a round-up of some of the most recently published peer reviewed articles from the field. We don‚Äôt cover everything, or even what‚Äôs most important ‚Äď just a few papers that have¬†interested the author. Visit our Resources page for links to more journals or follow the HealthEconBot. If you‚Äôd like to write one of our weekly¬†journal round-ups, get in touch.

Estimating the medical care costs of obesity in the United States: systematic review, meta-analysis, and empirical analysis. Value in Health Published 6th April 2016

I’m always a little wary of the “[insert disease] costs the economy $[insert big number]¬†per year” studies. There is just too much up for debate: whether a cost can be attributed to the disease; who bears the cost; whether in fact it should be considered a cost at all. A second look through the lense of a critical review is just what these studies need. Obesity is a big deal, but there is¬†wide variation¬†in estimates of its cost to the¬†US economy. This study¬†includes a¬†systematic review and meta-analysis looking at the medical costs of obesity estimated by studies between 2008 and 2012. Twelve studies were included in the review. The annual cost of obesity per person that was reported in the studies ranged from $227 to $7269. Wow! The pooled estimate from the meta-analysis was $1910; around $150 billion for the US as a whole. The authors looked at the methods used in the studies, but due to the variation in methods chosen and data used they weren’t able to learn that much about how¬†this might affect estimates. The studies aren’t entirely comparable to one another. So the authors also carry out an¬†original analysis using¬†data from the Medical Expenditure Panel Survey to explore the impact on estimates of alternative modelling strategies. The¬†analysis was¬†varied¬†by 4 age groups, 5 statistical models and 4 sets of confounders to give 80 estimates in total. The alternative statistical models didn’t make much difference, but the authors found that their extended estimating equation had the best goodness of fit. This analysis found an average cost of $1343 per person. Age groups and confounders were important. Costs were especially high in the over 65s. Older obese people have a lot of¬†obesity-related diseases, while obese children have very few and have relatively low costs. Controlling for obesity-related disease explained away most of the incremental cost. This brings us back to the question of what should and what shouldn’t be considered a cost of the disease.¬†What we really want to know is the counterfactual cost of the presence of obesity; what if these people weren’t obese? It remains unclear how studies might even go about¬†defining this, let alone actually estimating it.

Introduction of a national minimum wage reduced depressive symptoms in low-wage workers: a quasi-natural experiment in the UK. Health Economics [PubMed] Published 4th April 2016

The introduction to this paper¬†states that “no study has investigated the health effects of the UK National Minimum Wage”. That took me by surprise. So – apparently – here is the first, and it’s particularly relevant given the recent¬†introduction of the so-called ‘National Living Wage’. The authors use data from the BHPS to test whether the increase in wages for low earners associated with the introduction of the minimum wage resulted in a positive health effect. A difference-in-differences analysis was performed using data from just before and just after the introduction of the minimum wage.¬†Health effect is measured using the General Health Questionnaire (GHQ), which asks about current mental health problems relative to what the respondent normally feels.¬†The intervention group was those earning less than ¬£3.60 per hour in 1998 and between ¬£3.60 and ¬£4.00 per hour in 1999. There are 2 alternative control groups; one consisting¬†those earning just above the minimum wage in 1998 and another for people whose employer did not comply. Plenty of effort is made to try and isolate the effect by incorporating physical health changes into the model and exploring the role of financial strain as a mediating effect. The results show a (statistically significant) positive impact on the GHQ. But the results aren’t quite as compelling as they might at first seem. There are a lot of exclusions that might not stand up to scrutiny, and the intervention group was made up of just 63 people. It would be good¬†to see the analysis adapted into an economic evaluation of the policy.

An econometric model of healthcare demand with nonlinear pricing. Health Economics [PubMed] Published 4th April 2016

In Germany, health insurance is mandatory and most people receive their coverage through a public system. Between 2004 and 2013 it operated an interesting policy: the first visit to a doctor in each¬†calendar quarter was subject to a co-payment of ‚ā¨10, with no copayment for subsequent visits. That’s not a lot of money for most people, but instinct would tell me that at least some people would probably avoid a single visit within a quarter and perhaps bunch-up visits if possible. This study tests that instinct. The authors develop a model of health care demand based on health shocks arriving as a Poisson process. It assumes that the co-payment increases the probability of no visit taking place and that if one does take place then this is more likely to be later in the quarter. A joint¬†analysis of¬†two difference-in-differences experiments is used,¬†based on both the introduction and the repeal of the policy. The control group consists the people with private health insurance who were not affected by the policy changes. Data come from the German Socio-Economic Panel and the main analysis included over 30,000 observations. This was in part thanks to the development and successful implementation of a method to address mismatching between observation¬†date and calendar quarters. None of the various model specifications identified a statistically significant effect of the policy on the number of doctor visits, so I suspect it won’t be reintroduced any time soon.

Diagnosing the causes of rising health-care expenditure in Canada: does Baumol’s cost disease loom large? American Journal of Health Economics Published 31st March 2016

Baumol’s cost disease is a neat idea: health care costs¬†will rise faster than most others because health care is labour intensive and – while wages will grow in line with other industries – productivity growth cannot keep up. There’s some evidence that Baumol’s cost disease does exist, but there is less evidence about how big a deal it is compared to other non-observable drivers of rising health care expenditure. As for many other countries, Canada’s health care spending has grown at a much faster rate than the¬†consumer price index. This new study looks at national and provincial data from Canada¬†for 1982-2011 and decomposes the growth rate into that driven by the cost disease, technological progress and observable factors. Observable variables include population ageing, per capita income growth, economic recession and social determinants of health. The¬†analysis uses a recently developed method, referred to as the¬†Hartwig-Colombier test, to evaluate the impact of Baumol’s cost disease. In line with previous research, growth in per capita income is shown to be the most important driver of health care spending growth. For all provinces, the analysis finds that the cost disease is relatively unimportant. Technological progress appears to have a far greater influence, accounting for at least 31% of spending increases. Furthermore, the authors find that population ageing is not such a big concern and that the spending increases resulting from it are manageable. The implication is that if Canada¬†wants to control spending growth then it should focus on managing¬†the adoption of new technologies.

#HEJC for 26/02/2015

The next #HEJC discussion will take place Thursday 26th February, at 11pm London time on Twitter. To see what this means for your time zone visit Time.is or join the Facebook event. For more information about the Health Economics Journal Club and how to take part, click here.

The paper for discussion is a working paper published by the Canadian Centre for Health Economics (CCHE). The authors are Koffi-Ahoto Kpelitse, Rose Anne Devlin and Sisira Sarma. The title of the paper is:

The effect of income on obesity among Canadian adults

Following the meeting, a transcript of the Twitter discussion can be downloaded here.

Links to the article

Direct: http://www.canadiancentreforhealtheconomics.ca/wp-content/uploads/2014/08/Sisira-et-al.pdf

RePEc: https://ideas.repec.org/p/cch/wpaper/14c002.html

Summary of the paper

This is the first paper to examine the causal relationship between income and obesity in the Canadian context. To do so, they examined data from five biennial Canadian Community Health Survey (from 2000/01 to 2009/10), a nationally representative survey collecting information on over 100,000 individuals each survey.

Initially, the paper explored the Grossman model, which suggested increasing income would promote healthy lifestyle investments, and thus lead to a negative relationship between income and obesity. Previous studies that examined this link were discussed, some (eg. Lindahl (2005)) demonstrating a negative relationship; some (eg. Schmeiser (2009)) demonstrating a positive relationship; some (eg. Cawley (2010)) finding no evidence of a causal relationship.

Additionally, education and employment were explored. Again, the Grossman model was used as a basis, predicting i) a¬†negative relationship between education level and obesity with a greater income effect amongst educated people and ii) a¬†negative relationship between employment level and obesity.¬†However, regarding education, prior studies discussed have shown “mixed results”, and regarding employment, the authors were not aware of any study to examine this causal relationship, but suggested the relationship was ambiguous.

Finally, the relationship between gender and obesity were discussed. Numerous studies have shown negative association between income and BMI amongst women, but for men, the relationship is unclear (some showing positive relationship, some negative, and some no significant relationship at all). The importance of the effect of obesity on labour market outcomes (outlining the “large” empirical literature showing obese women more likely to suffer discrimination in the labour market) was outlined.

In this study, the authors found that:

  • From 2000/01 to 2009/10, BMI and obesity rates amongst both men and women have risen.
    • For¬†men, the obesity rate rises¬†from 19.48% for those with income below $10k to 26.09% for those with income over $80k.
    • For women¬†obesity falls¬†from 26.71% for those below $10k to 17.38% for those with income over $80k.
  • For men, a 1% rise in household income leads to 0.027 point decrease in BMI (2SLS estimate); 0.084kg reduction and 0.27% point decrease in probability of being obese (linear IV procedure).
  • For¬†women, a 1% rise in household income leads to 0.113 point decrease in BMI (much higher than for men; this used a 2SLS estimate); 0.300kg reduction; and 0.76% point decrease in probability of being obese (linear IV procedure).
  • For¬†men the effect of income on BMI was only¬†demonstrated at higher BMI distribution, while for¬†women the effect of income on BMI was found throughout¬†with a larger¬†effect at higher BMI.
  • Education had a¬†variable relationship amongst both men and women,¬†not consistent with the theoretical prediction that the effect would be larger amongst educated people.
  • The effect of employment for men was mixed, with a¬†negative effect of income on BMI only in employed men¬†and a¬†negative effect of income on obesity probability only in unemployed men.
  • The effect of employment for women was more consistent with theoretical predictions, showing negative effects of income on both BMI and on the probability of being obese across employment status.
  • Higher BMI and probability of obesity was associated with older age, marriage (much greater effect in women), household size (much greater effect in women) and home ownership.
  • Lower BMI and probability of obesity was associated with being¬†widowed/separated/divorced, being an immigrant and living in urban area (in men).

In summary, this study supports the findings of Lindahl, and stands in contrast to Schmeiser, Cawley and other related studies.

Discussion points

  • Why might there be significant variation in findings between the different studies¬†discussed?
  • Are there ways in which unemployment and neighbourhood income might directly influence BMI?
  • Is the set of control variables used in the authors’ models satisfactory?
  • Is it of concern that policies to increase household income could be regarded a pure, explicit public health policy?
  • Are there relevant studies¬†from other countries?
  • To what extent are these findings generalisable?

Can’t join in with the Twitter discussion? Add your thoughts on the paper in the comments below.