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.

Credits

Chris Sampson’s journal round-up for 5th September 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 effect of complementary private health insurance on the use of health care services. International Journal of Health Economics and Management Published 31st August 2016

Moral hazard is one of the key ideas taught to fledgling health economists, but having taken flight you don’t hear all that much about it. That’s because most of us live in Europe, enjoying our universal publicly funded health care systems. But I quite like papers that remind me that moral hazard is still a going concern and that my MSc was relevant. This paper looks at the impact of complementary private health insurance – that is, alongside a national health service. There aren’t so many studies of moral hazard in this setting. Private health insurance (let’s call it PHI) might decrease use of public health care (let’s call it NHS), but it might also increase pressure on the NHS by creating additional demand. For example, people might need a referral from an NHS GP in order to qualify for PHI coverage. This study uses cross-sectional questionnaire data from Denmark, collected from 5447 individuals for the purpose of this study. The questionnaire collected all sorts of data relating to health care use and sociodemographics. People who gave ‘Don’t know’ or ‘Other’ responses were dropped, meaning that only 4362 were analysed. 49% of the sample had PHI – the ‘treatment’ of interest. The authors use a bivariate probit model with propensity score matching to predict health care use. Furthermore, an instrumental variable is used to improve identification. Having PHI seems to increase use of services, with strong effects for prescription medicine, dentist visits and chiropractors. This suggests that PHI coverage may contribute to increasing national health care costs. There are some major limitations to this study, which the authors acknowledge. The response rate was 41%, and the sample wasn’t particularly representative. The one thing I can’t get my head around is the authors’ identification strategy. The instrumental variable chosen was whether or not an individual wears glasses, as in this case PHI is particularly favourable. Even controlling for the covariates used in this analysis, I cannot see (no pun intended) how this could be unrelated to health care use.

The value of disease prevention vs treatment. Journal of Health Economics Published 29th August 2016

The public’s view of pharma just keeps getting worse“, apparently. One probably-entirely-made-up-but-sort-of-reasonable-sounding thing I’ve heard Joe Public say in the past is that Pharma would like us all to remain sickly cash cows. New treatments = milk. Prevention is just… soya. That analogy made no sense, but there are also more reasoned arguments that we spend too much on treatment and too little on prevention. There are also numerous studies characterising people’s preferences regarding prevention and treatment under different conditions. This study builds on this background by developing a utility model of disease valuation in order to derive willingness-to-pay values for reductions in incidence (prevention), mortality (treatment) or deterioration in quality of life (palliative care). The basis for the model is 3 possible states – healthy, ill and dead – through which people can progress in only one direction (i.e. there is no cure). The ‘ill’ state relates to a specific disease and has a value somewhere between 1 (healthy) and 0 (dead). The authors use the model to determine – for example – how willingness to pay for improvement in the ‘ill’ state might be affected by the mortality rate. Two key implications of the model are that i) when the risk of dying from a disease is greater than the incidence rate, prevention is more valuable than treatment and ii) when the incidence rate is greater than the decline in quality of life, prevention is more valuable than palliative care. The model is also used to incorporate probability weighting to give a more realistic characterisation of people’s risk preferences. In most cases, the two previous findings will hold. An interesting finding of this part of the analysis is that it seems to partly explain people’s disproportionately strong preferences for treating more severe diseases. The model suggests that prevention is more valuable than treatment for most real-world situations, and so we’ve probably got the balance all wrong.

Does one size fit all? Assessing the preferences of older and younger people for attributes of quality of life. Quality of Life Research [PubMed] Published 23rd August 2016

There’s plenty of talk nowadays about the idea that QALYs don’t reflect the most important objects of value for particular groups of people, especially older people. Non-health improvements in quality of life might be more important. Whether we’re using EQ-5D, SF-6D, HUI3 or your personally preferred multi-attribute utility measure, the idea is that they’re measuring the same thing. But they’re not. They consistently give systematically different results. This study sought to find out if older people value quality of life attributes used in these measures differently to younger people. The authors elicit preferences for different domains using a web-based survey of two groups of 500 people: over 65s and 18-64 year olds. Individuals were presented with 12 descriptors from the EQ-5D, AQoL and ASCOT and asked to complete both a ranking and a best worst exercise. Socioeconomic data were also collected. The two cohorts ranked the domains differently, but perhaps not as differently as we might expect. ‘Independence’ was important to both groups, with 36% of over 65s and 20% of 18-64 year olds ranking it first. Physical mobility, mental health and pain also ranked highly for both groups. Older people ranked control, self-care and vision more highly than younger people, who in turn ranked safety, social relationships, dignity, sleep and hearing more highly. The results from the ranking exercise and the best worst exercise were similar. So, non-health attributes matter to everyone and older people’s preferences differ to younger people’s. But so what? We could probably find differences between a sample of men and a sample of women, or between an urban and a rural population. The question is: which differences matter? Studies like this are useful, but they can’t tell us how we ought to handle heterogeneous preferences.

From representing views to representativeness of views: Illustrating a new (Q2S) approach in the context of health care priority setting in nine European countries. Social Science & Medicine [PubMedPublished 22nd August 2016

Asking the public what they think; it’s a dangerous game (nb Brexit, Boaty McBoatface, Mrs Brown’s Boys). But there are good grounds for doing so when it comes to health care resource allocation. This paper comes from an ongoing research project that I’ve written about on a couple of occasions. A previous paper used Q methodology and identified 5 viewpoints regarding the fundamental basis for the allocation of resources in health care, titled: 1) ‘egalitarianism, entitlement and equality of access’, 2) ‘severity and the magnitude of health gains’, 3) ‘fair innings, young people and maximising health benefits’, 4) ‘the intrinsic value of life and healthy living’ and 5) ‘quality life is more important than simply staying alive’. This study developed a new methodology called Q2S, designed to extract features from the viewpoints elicited through the original Q study and create a survey to find out how these different viewpoints are represented in society. Data were collected from 39,560 respondents from 9 European countries. Participants were presented with a series of descriptions with which to identify agreement on a 7-point Likert scale from “very unlike my point of view” to “very much like my point of view”. 41% of respondents gave their highest score to a single viewpoint, while the rest tied across two or more viewpoints and were subsequently asked to identify which one would best reflect their view. 43% of respondents were allocated to Viewpoint 1. This viewpoint asserts that health care is a basic right, that treatment effectiveness is essentially irrelevant because all life has the same value, and that scarcity is not a concern. It was predominant in all 9 countries. Gulp! Next up with 17% was Viewpoint 2, which is a bit closer to health maximisation but with a preference for allocation to life-saving treatment and more severe health states. Viewpoint 3 was not popular, with only 4% of people identifying it as most like their point of view. The authors identify various associations between sociodemographic variables and likelihood of particular viewpoints. There’s a lot of food for thought in this paper. Where do you sit? My position changes depending on how revolutionary I’m feeling.

Photo credit: Antony Theobald (CC BY-NC-ND 2.0)