Chris Sampson’s journal round-up for 20th November 2017

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.

Effects of health and social care spending constraints on mortality in England: a time trend analysis. BMJ Open [PubMed] Published 15th November 2017

I’d hazard a guess that I’m not the only one here who gets angry about the politics of austerity. Having seen this study’s title, it’s clear that the research could provide fuel for that anger. It doesn’t disappoint. Recent years have seen very low year-on-year increases in public expenditure on health in England. Even worse, between 2010 and 2014, public expenditure on social care actually fell in real terms. This is despite growing need for health and social care. In this study, the authors look at health and social care spending and try to estimate the impact that reduced expenditure has had on mortality in England. The analysis uses spending and mortality data from 2001 onwards and also incorporates mortality projections for 2015-2020. Time trend analyses are conducted using Poisson regression models. From 2001-2010, deaths decreased by 0.77% per year (on average). The mortality rate was falling. Now it seems to be increasing; from 2011-2014, the average number of deaths per year increased by 0.87%. This corresponds to 18,324 additional deaths in 2014, for example. But everybody dies. Extra deaths are really sooner deaths. So the question, really, is how much sooner? The authors look at potential years of life lost and find this figure to be 75,496 life-years greater than expected in 2014, given pre-2010 trends. This shouldn’t come as much of a surprise. Spending less generally achieves less. What makes this study really interesting is that it can tell us who is losing these potential years of life as a result of spending cuts. The authors find that it’s the over-60s. Care home deaths were the largest contributor to increased mortality. A £10 cut in social care spending per capita resulted in 5 additional care home deaths per 100,000 people. When the authors looked at deaths by local area, no association was found with the level of deprivation. If health and social care expenditure are combined in a single model, we see that it’s social care spending that is driving the number of excess deaths. The impact of health spending on hospital deaths was less robust. The number of nurses acted as a mediator for the relationship between spending and mortality. The authors estimate that current spending projections will result in 150,000 additional deaths compared with pre-2010 trends. There are plenty of limitations to this study. It’s pretty much impossible (though the authors do try) to separate the effects of austerity from the effect of a weak economy. Still, I’m satisfied with the conclusion that austerity kills older people (no jokes about turkeys and Christmas, please). For me, the findings also highlight the need for more research in the context of social care, and how we (as researchers) might effectively direct policy to prevent ‘excess’ deaths.

Should cost effectiveness analyses for NICE always consider future unrelated medical costs? BMJ [PubMed] Published 10th November 2017

The question of whether or not ‘unrelated’ future medical costs should be included in economic evaluation is becoming a hot topic. So much so that the BMJ has published this Head To Head, which introduces some of the arguments for and against. NICE currently recommends excluding unrelated future medical costs. An example given in this article is the case of the expected costs of dementia care having saved someone’s life by heart transplantation. The argument in favour of including unrelated costs is quite obvious – these costs can’t be ignored if we seek to maximise social welfare. Their inclusion is described as “not difficult” by the authors defending this move. By ignoring unrelated future costs (but accounting for the benefit of longer life), the relative cost-effectiveness of life-extending treatments, compared with life-improving treatments, is artificially inflated. The argument against including unrelated medical costs is presented as one of fairness. The author suggests that their inclusion could preclude access to health care for certain groups of people that are likely to have high needs in the future. So perhaps NICE should ignore unrelated medical costs in certain circumstances. I sympathise with this view, but I feel it is less a fairness issue and more a demonstration of the current limits of health-related quality of life measurement, which don’t reflect adaptation and coping. However, I tend to disagree with both of the arguments presented here. I really don’t think NICE should include or exclude unrelated future medical costs according to the context because that could create some very perverse incentives for certain stakeholders. But then, I do not agree that it is “not difficult” to include all unrelated future costs. ‘All’ is an important qualifier here because the capacity for analysts to pick and choose unrelated future costs creates the potential to pick and choose results. When it comes to unrelated future medical costs, NICE’s position needs to be all-or-nothing, and right now the ‘all’ bit is a high bar to clear. NICE should include unrelated future medical costs – it’s difficult to formulate a sound argument against that – but they should only do so once more groundwork has been done. In particular, we need to develop more valid methods for valuing quality of life against life-years in health technology assessment across different patient groups. And we need more reliable methods for estimating future medical costs in all settings.

Oncology modeling for fun and profit! Key steps for busy analysts in health technology assessment. PharmacoEconomics [PubMed] Published 6th November 2017

Quite a title(!). The subject of this essay is ‘partitioned survival modelling’. Honestly,  I never really knew what that was until I read this article. It seems the reason for my ignorance could be that I haven’t worked on the evaluation of cancer treatments, for which it’s a popular methodology. Apparently, a recent study found that almost 75% of NICE cancer drug appraisals were informed by this sort of analysis. Partitioned survival modelling is a simple means by which to extrapolate outcomes in a context where people can survive (or not) with or without progression. Often this can be on the basis of survival analyses and standard trial endpoints. This article seeks to provide some guidance on the development and use of partitioned survival models. Or, rather, it provides a toolkit for calling out those who might seek to use the method as a means of providing favourable results for a new therapy when data and analytical resources are lacking. The ‘key steps’ can be summarised as 1) avoiding/ignoring/misrepresenting current standards of economic evaluation, 2) using handpicked parametric approaches for extrapolation in order to maximise survival benefits, 3) creatively estimating relative treatment effects using indirect comparisons without adjustment, 4) make optimistic assumptions about post-progression outcomes, and 5) deny the possibility of any structural uncertainty. The authors illustrate just how much an analyst can influence the results of an evaluation (if they want to “keep ICERs in the sweet spot!”). Generally, these tactics move the model far from being representative of reality. However, the prevailing secrecy around most models means that it isn’t always easy to detect these shortcomings. Sometimes it is though, and the authors make explicit reference to technology appraisals that they suggest demonstrate these crimes. Brilliant!

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Alastair Canaway’s journal round-up for 31st October 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.

Ethical hurdles in the prioritization of oncology care. Applied Health Economics and Health Policy [PubMedPublished 21st October 2016

Recently between health economists, there has been significant scrutiny and disquiet directed towards the Cancer Drugs Fund with Professor Karl Claxton describing it as “an appalling, unfair use of NHS resources”. With the latest reorganization of the Cancer Drugs Fund in mind, this article examining the ethical issues surrounding prioritisation of cancer care was of particular interest. As all health economists will tell you, there is an opportunity cost with any allocation of scarce resources. Likewise, with prioritisation of specific disease groups, there may be equity issues with specific patients’ lives essentially being valued more greatly than those suffering other conditions. This article conducts a systematic review of the oncology literature to examine the ethical issues surrounding inequity in healthcare. The review found that public and political attention often focuses on ‘availability’ of pharmacological treatment in addition to factors that lead to good outcomes. The public and political focus on availability can have perverse consequences as highlighted by the Cancer Drugs Fund: resources are diverted towards availability and away from other more cost-effective areas, and in turn this may have had a detrimental effect on care for non-cancer patients. Additionally, by approving high cost, less cost-effective agents, strain will be placed upon health budgets and causing problems for existing cost-effectiveness thresholds. If prioritisation for cancer drugs is to be pursued then the authors suggest that the question of how to fund new therapies equitably will need to be addressed. Although the above issues will not be new to most, the paper is still worth reading as it: i) gives an overview of the different prioritisation frameworks used across Europe, ii) provides several suggestions for how, if prioritization is to be pursued, it can be done in a fairer manner rather than simply overriding typical HTA decision processes, iii) considers the potential legal consequences of prioritisation and iv) the impact of prioritisation on the sustainability of healthcare funding.

Doctor-patient differences in risk and time preferences: a field experiment. Journal of Health Economics Published 19th October 2016

The patient-doctor agency interaction, and associated issues due to asymmetrical information is something that was discussed often during my health economics MSc, but rarely during my day to day work. Despite being very familiar with supplier induced demand, differences in risk and time preferences in the patient-doctor dyad wasn’t something I’d considered in recent times. Upon reading, immediately, it is clear that if risk and time preferences do differ, then what is seen as the optimal treatment for the patient may be very different to that of the doctor. This may lead to poorer adherence to treatments and worse outcomes. This paper sought to investigate whether patients and their doctors had similar time and risk preferences using a framed field experiment with 300 patients and 67 doctors in Athens, Greece in a natural clinical setting. The authors claim to be the first to attempt this, and have three main findings: i) there were significant time preference differences between the patients and doctors – doctors discounted future health gains and financial outcomes less heavily than patients; ii) there were no significant differences in risk preferences for health with both doctors and patients being mildly risk averse; iii) there were however risk preference differences for financial impacts with doctors being more risk averse than patients. The implication of this paper is that there is potential for improvements in doctor-patient communication for treatments, and as agents for patients, doctors should attempts to gauge their patient’s preferences and attitudes before recommending treatment. For those who heavily discount the future it may be preferable to provide care that increases the short term benefits.

Hospital productivity growth in the English NHS 2008/09 to 2013/14 [PDF]. Centre for Health Economics Research Paper [RePEcPublished 21st October 2016

Although this is technically a ‘journal round-up’, this week I’ve chosen to include the latest CHE report as I think it is something which may be of wider interest to the AHEBlog community. Given limited resources, there is an unerring call for both productivity and efficiency gains within the NHS. The CHE report examines the extent to which NHS hospitals have improved productivity: have they made better use of their resources by increasing the number of patients they treat and the services they deliver for the same or fewer inputs. To assess productivity, the report uses established methods: Total Factor Productivity (TFP) which is the ratio of all outputs to all inputs. Growth in TFP is seen as being key to improving patient care with limited resources. The primary report finding was that TFP growth at the trust level exhibits ‘extraordinary volatility’. For example one year there maybe TFP growth followed by negative growth the next year, and then positive growth. The authors assert that much of the TFP growth measured is in fact implausible, and much of the changes are driven largely by nominal effects alongside some real changes. These nominal effects may be data entry errors or changes in accounting practices and data recording processes which results in changes to the timing of the recording of outputs and inputs. This is an important finding for research assessing productivity growth within the NHS. The TFP approach is an established methodology, yet as this research demonstrates, such methods do not provide credible measures of productivity at the hospital level. If hospital level productivity growth is to be measured credibly, then a new methodology will be required.

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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)