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.
Solving shortage in a priceless market: insights from blood donation. Journal of Health Economics [RePEc] Published 16th May 2016
I’m drawn to studies about blood donation. As a regular donor, I’d like to better understand why I bother. This study is only loosely about individual donors’ behaviour; looking at ways of increasing donations in times of shortage. In the UK, there is no (legal) market for (human) blood. This means that a shortage can’t simply be solved by increasing prices. This study evaluates two approaches to increasing donations: i) sending out a shortage message to past donors and ii) family replacement, whereby a patient in need of blood is given the opportunity to recruit a family member for donation. Data are taken from a blood bank in China, with information on 330,000 donors and 447,357 donations from 2005-2013. Large scale shortage messages were sent out on 2 occasions to 7,858 and 3,102 past donors. Family replacement was introduced in 2010 and is associated with around 4% of donations. The identification strategy for shortage messages relies on the fact that these related to specific blood groups, and a difference-in-differences analysis with matching is used. It’s less clean for family replacement because the data cannot identify people who were invited on the basis of family replacement but did not donate. The study finds that both methods are effective, but with differing short- and long-term implications and with heterogeneous impact across different populations. This is a mammoth paper that presents a lot of analysis and provides a lot of discussion about the trade-offs between alternative strategies. While probably of huge value to blood donation policy-makers, I found myself at a loss trying to identify the main conclusions.
A comprehensive algorithm for approval of health technologies with, without, or only in research: the key principles for informing coverage decisions. Value in Health Published 11th May 2016
When NICE consider new treatments, it isn’t simply a matter of approve or reject. There are two other important options: ‘only in research’ and ‘approve with research’. The first means that the treatment can be used in a research setting, while the second means that the treatment can be used (more broadly) while research is being carried out. This study presents a decision process to determine whether a new technology should be approved, rejected, approved only in research or approved with research. The authors argue that the starting point for the decision process is whether or not the technology has been shown to be cost-effective, but that this is not the end of the story. The other factors that should determine the decision (from the 4 options) relate to whether there are significant irrecoverable costs associated with introducing the technology (e.g. set-up costs), whether further research seems valuable and could be carried out with/without approval, and expectations about how uncertainty might change over time. A complete process is presented that outlines the necessary assessments and decisions that need to be implemented along the way to achieve the best (i.e. health maximising) approach. I expect this will represent an extremely useful tool for determining guidance outcomes from assessments by the likes of NICE, and the transparency that it facilitates in the process could make for some very legitimate decision-making. My only query is whether it is worth it. Following the process is likely to lead to a lot of research, and the kinds of research that is not usually carried out. It might be that in practice there is a lot to be said for ‘muddling through’, which might lead to the same outcomes without the same research burden. I expect real practice will fall somewhere in the between.
Health insurance and income inequality. Journal of Economic Perspectives [RePEc] Published May 2016
Part of the reason we have the NHS in the UK is that we expect it to reduce (or prevent increases in) inequality, which might arise if health care was funded through voluntary insurance. The same goes for Medicaid and (to a lesser extent, perhaps) Medicare in the US. But when we think about inequality we often focus on income, and the benefits ‘in-kind’ from the likes of Medicaid are overlooked. This study considers whether – and the extent to which – Medicaid and Medicare reduce inequality. The authors review the literature and use data from a number of national surveys to estimate medical expenditure in different income groups. Key to the question is how we value Medicaid and insurance coverage: is $1 of insurance or Medicaid coverage worth $1 to recipients? If the average cost of the programmes is added to people’s incomes, then inequality is reduced by about 25-30%, but if individual expenditures are instead used then the results would be quite different. It’s also important to consider how the tax (or lack thereof) levied on health insurance affects inequality. This will lead to an increase in inequality because coverage increases with income and marginal tax rates. The authors find that taxing employer-provided insurance in line with income would reduce the ratio of the 90th to the 10th income percentile by about 4%. On net the effect of Medicaid dominates and overall health policy in the US probably reduces inequality. But from where I’m sitting, this is really the tip of the iceberg. The definition of inequality being discussed here is a narrow one. The authors rightly identify the importance of the health impact of these programmes on a broader interpretation of well-being, but note that health spending is not strongly related to health outcomes. But if we are more concerned with inequality in health (specifically) rather than income inequality, then that may not matter.
