Rita Faria’s journal round-up for 15th April 2019

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.

Emulating a trial of joint dynamic strategies: an application to monitoring and treatment of HIV‐positive individuals. Statistics in Medicine [PubMed] Published 18th March 2019

Have you heard about the target trial approach? This is a causal inference method for using observational evidence to compare strategies. This outstanding paper by Ellen Caniglia and colleagues is a great way to get introduced to it!

The question is: what is the best test-and-treat strategy for HIV-positive individuals? Given that patients weren’t randomised to each of the 4 alternative strategies, chances are that their treatment was informed by their prognostic factors. And these also influence their outcome. It’s a typical situation of bias due to confounding. The target trial approach consists of designing the RCT which would estimate the causal effect of interest, and to think through how its design can be emulated by the observational data. Here, it would be a trial in which patients would be randomly assigned to one of the 4 joint monitoring and treatment strategies. The goal is to estimate the difference in outcomes if all patients had followed their assigned strategies.

The method is fascinating albeit a bit complicated. It involves censoring individuals, fitting survival models, estimating probability weights, and replicating data. It is worthy of a detailed read! I’m very excited about the target trial methodology for cost-effectiveness analysis with observational data. But I haven’t come across any application yet. Please do get in touch via comments or Twitter if you know of a cost-effectiveness application.

Achieving integrated care through commissioning of primary care services in the English NHS: a qualitative analysis. BMJ Open [PubMed] Published 1st April 2019

Are you confused about the set-up of primary health care services in England? Look no further than Imelda McDermott and colleagues’ paper.

The paper starts by telling the story of how primary care has been organised in England over time, from its creation in 1948 to current times. For example, I didn’t know that there are new plans to allow clinical commissioning groups (CCGs) to design local incentive schemes as an alternative to the Quality and Outcomes Framework pay-for-performance scheme. The research proper is a qualitative study using interviews, telephone surveys and analysis of policy documents to understand how the CCGs commission primary care services. CCG Commissioning is intended to make better and more efficient use of resources to address increasing demand for health care services, staff shortage and financial pressure. The issue is that it is not easy to implement in practice. Furthermore, there seems to be some “reinvention of the wheel”. For example, from one of the interviewees: “…it’s no great surprise to me that the three STPs that we’ve got are the same as the three PCT clusters that we broke up to create CCGs…” Hum, shall we just go back to pre-2012 then?

Even if CCG commissioning does achieve all it sets out to do, I wonder about its value for money given the costs of setting it up. This paper is an exceptional read about the practicalities of implementing this policy in practice.

The dark side of coproduction: do the costs outweight the benefits for health research? Health Research Policy and Systems [PubMed] Published 28th March 2019

Last month, I covered the excellent paper by Kathryn Oliver and Paul Cairney about how to get our research to influence policy. This week I’d like to suggest another remarkable paper by Kathryn, this time with Anita Kothari and Nicholas Mays, on the costs and benefits of coproduction.

If you are in the UK, you have certainly heard about public and patient involvement or PPI. In this paper, coproduction refers to any collaborative working between academics and non-academics, of which PPI is one type, but it includes working with professionals, policy makers and any other people affected by the research. The authors discuss a wide range of costs to coproduction. From the direct costs of doing collaborative research, such as organising meetings, travel arrangements, etc., to the personal costs on an individual researcher to manage conflicting views and disagreements between collaborators, of having research products seen to be of lower quality, of being seen as partisan, etc., and costs to the stakeholders themselves

As a detail, I loved the term “hit-and-run research” to describe the current climate: get funding, do research, achieve impact, leave. Indeed, the way that research is funded, with budgets only available for the period that the research is being developed, does not help academics to foster relationships.

This paper reinforced my view that there may well be benefits to coproduction, but that there are also quite a lot of costs. And there tends to be not much attention to the magnitude of those costs, in whom they fall, and what’s displaced. I found the authors’ advice about the questions to ask oneself when thinking about coproduction to be really useful. I’ll keep it to hand when writing my next funding application, and I recommend you do too!


Chris Sampson’s journal round-up for 25th July 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.

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 [RePEcPublished 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)