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.
The income-health relationship ‘beyond the mean’: new evidence from biomarkers. Health Economics [PubMed] Published 15th July 2016
Going ‘beyond the mean’ is becoming a big deal in health economics, as we get better data and develop new tools for analysis. In economic evaluation we’re finding our feet in the age of personalised medicine. As this new study shows, analogous changes are taking place in the econometrics literature. We all know that income correlates with measures of health, but we know a lot less about the nature of this correlation. If we want to target policy in the most cost-effective way, simply asserting that higher income (on average) improves health is not that useful. This study uses a new econometric technique known as the recentered influence function (RIF) to look at the income-health relationship ‘beyond the mean’. It considers blood-based biomarkers with known disease associations as indicators of health, specifically: cholesterol, HbA1c, Fibrinogen and Ferritin. Even for someone with limited willingness to engage with econometrics (e.g. me) the methods are surprisingly elegant and intuitive. In short, the analysis divides people (in terms of each biomarker) into quantiles. So, for example, we can look at the people with high HbA1c (related to diabetes) and see if the relationship with income is different to that for people with a low HbA1c. The study finds that the income-health relationship is non-linear across the health distribution, thus proving the merit of the RIF approach. Generally, the income gradients were higher at the top quintiles. This suggests that income may be more important in tipping a person over the edge – in terms of clinical cut-offs – than in affecting the health of people who are closer to the average. The analysis for cholesterol showed that looking only at the mean (i.e. income increases cholesterol) might hide a positive relationship for most of the distribution but a negative relationship at the top end. This could translate into very different policy implications. The study carried out further decomposition analyses to look at gender differences, which support further differentiation in policy. This kind of analysis will become increasingly important in policy development and evaluation. We might start to see public interventions being exposed as useless for most people, and perhaps actively harmful for some, even if they look good on average.
Using patient-reported outcomes for economic evaluation: getting the timing right. Value in Health Published 15th July 2016
The estimation of QALYs involves an ‘area under the curve’ approach to outcome measurement. How accurately the estimate represents the ‘true’ number of QALYs (if there is such a thing) depends both on where the dots (i.e. data collection points) are and how we connect them. This study looks at the importance of these methodological decisions. Most of us (I think) would use linear interpolation between time points, but the authors also consider an alternative assumption that the health state utility value applies to the whole of the preceding period. The study looks at data for total knee arthroplasty with SF-12 data at 6 weeks, 3 and 6 months and then annually up to 5 years after the operation. The authors evaluated the use of alternative single postoperative SF-6D scores compared with using all of the data, and both linear and immediate interpolation. This gave 12 alternative scenarios. Collecting only at 3 months and using linear interpolation gave a surprisingly similar profile to the ‘true’ number of QALYs, at only about 5% too high. Collecting only at 6 weeks would underestimate QALY gain by 41%, while 6 months and 12 months would be 18% too high and 8% too low, respectively. It’s easy to see that the more data you can collect, the more accurate will be your results. This study shows how important it can be to collect health state data at the most appropriate time. 3 months seems to be the figure for total knee arthroplasty, but it will likely differ for other interventions.
Should the NHS abolish the purchaser-provider split? BMJ [PubMed] Published 12th July 2016
The NHS in England (notably not Scotland or Wales) operates with what’s known as the ‘internal market’, which separates the NHS’s functions as purchasers of health care and as providers of health care. In this BMJ ‘Head to Head’, Alan Maynard argues that it ought to be abolished, while Michael Dixon (a GP) defends its maintenance. Maynard argues that the internal market has been an expensive experiment, and that the results of the experiment have not been well-recorded. The Care Quality Commission and Monitor – organisations supporting the internal market – cost around £300 million to run in 2014/15. Dixon argues that the purchaser-provider split offered “refreshingly new accountability” to local commissioners with front-line experience rather than to the Department of Health. Though Dixon seems to be defending an idealised version of commissioning, rather than what is actually observed in practice. Neither party’s argument is particularly compelling because neither draws on any strong empirical findings. That’s because convincing evidence doesn’t exist either way.
The impact of women’s health clinic closures on preventive care. American Economic Journal: Applied Economics [RePEc] Published July 2016
More than the UK, the US has a problem with anti-abortion campaigns having political influence to the extent that they affect the availability of health services for women. This study is interested in cancer screenings and routine check-ups, which aren’t politically contentious. The authors obtain data that include clinic locations and survey responses from the Behavioural Risk Factor Surveillance System. The analysis relates to Texas and Wisconsin, which are states that implemented major funding cuts to family planning services and women’s health centres between 2007 and 2012. 25% of clinics in Texas closed during this period. As centres close, and women are required to travel further, we’d expect use of services to decline. There might also be knock-on effects in terms of waiting times and prices at the remaining centres. The analyses focus on the effect of distance to the nearest facility on use of preventive services, controlling for demographics and fixed effects relating to location and time. The principal finding is that an increase in distance to a woman’s nearest facility is likely to reduce use of preventive care, namely Pap tests and clinical breast exams. A 100-mile increase in the distance to the nearest centre was associated with a 7.4% percentage point drop in propensity to receive a breast exam in the past year, and 8.7% for Pap tests. Furthermore, the analysis shows that the impact is greater for individuals with lower educational attainment, particularly in the case of mammography. These findings demonstrate the threat to women’s health posed by political posturing.
Photo credit: Antony Theobald (CC BY-NC-ND 2.0)
[…] health economic analysis’. It was an informative talk that related to a paper I covered in a recent journal round-up. For me, it set up the theme that ran through the whole conference. How can we better acknowledge […]
I liked that “Beyond the Mean” study, although I’d have to sit down to get my head around the method. But at first glance I think what they actually had was a negative result – their results are consistent with it being more noisy in the tails of the distribution where there are fewer people (depending on the distribution of course). This is as useful as a positive result of course, but I would want a convincing physiological or economic reason why income would have a bigger slope in the tails before agreeing with that conclusion.