Chris Sampson’s journal round-up for 30th May 2016

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 [RePEcPublished 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 [RePEcPublished 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.

Chris Sampson’s journal round-up for 28th March 2016

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.

How did medicaid expansions affect labor supply and welfare enrollment? Evidence from the early 2000s. Health Economics Review [PubMed] Published 22nd March 2016

In the early 2000s, a number of states in the USA expanded Medicaid while others did not. These expansions covered similar populations to those that are likely to benefit from the Affordable Care Act. This study combines county-level statistics on unemployment, wages and enrollment in welfare programmes and uses a difference-in-differences regression to look at the effects of the expansion. The study is based on the assumption that some counties would be more affected than others, and that these groups can be identified based on the level of poverty before the expansion. Counties above the 75th percentile of poverty rates are considered the ‘treatment group’. The poorer counties saw a decrease in labour force participation rate, a decrease in working hours, an increase in wages and an increase in the use of food stamps. The main identification strategy isn’t very convincing, but the author argues that the findings are robust to alternatives. If we believe it, then the Affordable Care Act might have some modest negative consequences for employment and welfare enrollment.

Extrapolating survival from randomized trials using external data: a review of methods. Medical Decision Making [PubMed] Published 22nd March 2016

Modelling is built on assumptions. Many of these will be about how we extrapolate outcomes beyond what is observed in a trial. This study reviews the ways in which cost-effectiveness studies have extrapolated survival data using external information to inform the extrapolation. Such external information might come from national statistics such as life-tables, or from large cohort studies. The authors started with NIHR HTA reports and adopted a ‘pearl growing’ approach to identifying relevant studies. Based on their findings, the authors present a framework for the assumption process. This can be followed to determine which kind of approach to extrapolation should be adopted; for example, depending on whether the control group is assumed to have the same mortality as the external population or whether this differs in the short term. The authors describe the approach that should be taken in each circumstance. It’s also important to think about uncertainty. In particular, there is likely to be uncertainty regarding the effectiveness of the treatment. No studies were identified that formally used external data to quantify future changes in treatment effects. The authors discuss the potential for the use of expert elicitation to inform survival extrapolation using Bayesian inference. If you’re building a model that requires survival data from a trial to be extrapolated, you’ll find this review to be very helpful.

Harsh parenting, physical health, and the protective role of positive parent-adolescent relationships. Social Science & Medicine Published 21st March 2016

There’s plenty of evidence showing that being loved by one’s parents is crucial to development. A new study of 451 adolescents followed into adulthood supports this. 12 year olds and their families were recruited for a study in Iowa, and data were collected at multiple time points up to age 20. Harsh parenting was identified by coders who watched videotapes of parent behaviour. Harsh parenting behaviours were hostility, angry coercion, physical attacks and antisocial behaviour. Adolescents’ self-assessed health and BMI were recorded throughout the study, as was their own judgment of parental warmth for each parent. The authors use a latent change score model to investigate associations between these variables. Harsh parenting was associated with a negative impact on future self-reported health and BMI. In terms of self-reported health, a positive relationship with the father helped mitigate the health impact of having a harsh mother. But then the effect on BMI seemed to increase with warmth from the other parent. The evidence suggests that as well as preventing harsh parenting, it may be worthwhile focussing on a child’s relationship with the less harsh parent as a means of buffering against the negative effects.

Analyzing health-related quality of life in the EVOLVE trial: the joint impact of treatment and clinical events. Medical Decision Making [PubMed] Published 17th March 2016

This study reports on the EQ-5D data from a clinical trial of cinacalcet for secondary hyperparathyroidism. Using the normal approach of estimating QALYs based on the area under the curve, no difference was identified between the two arms of the trial. But then trials like this aren’t designed to identify differences in health-related quality of life. This study explores an alternative approach. EQ-5D-3L was collected at baseline and 6 follow-up points from 3547 subjects. It was additionally collected after particular clinical events. A regression model using a generalised estimating equation (GEE) approach was fitted with EQ-5D index scores as the dependent variable and clinical events as explanatory variables along with baseline utility and trial allocation. The analysis looked at acute effects (on utility within 13 weeks) and chronic effects (on utility in all subsequent months). A regression analysis with just trial allocation as an explanatory variable only found a non-significant treatment effect. However, the GEE regression that controlled for the acute and chronic effects of clinical events was able to identify a small but significant beneficial effect of the treatment. The effect could be observed independent of the effect of clinical events. Whether such results will be as convincing as traditional trial comparisons will remain to be seen, but adopting an approach of this sort could be far more informative when determining parameters for a decision model.