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.
Higher mortality rates amongst emergency patients admitted to hospital at weekends reflect a lower probability of admission. Journal of Health Services Research & Policy Published 6th May 2016
The ‘weekend effect‘ is the hot topic in health policy in the UK right now. Whether or not it exists, and whether or not it can be corrected by steamrollering junior doctors’ contracts, has major implications for the NHS. In this study the authors used data on 12.7 million A&E attendances and 4.7 million emergency admissions in England in 2013-14. It’s possible to be admitted to hospital via A&E or directly from a community service. A&E is available 24/7, while community services are more limited at the weekend. The analyses mainly use logistic regressions with the usual case-mix adjustments to estimate the probability of death within 30 days. Weekend attendance at A&E was not associated with a significantly higher probability of death than attendance during the week. On Saturday or Sunday, there were 7% fewer admissions via A&E than on weekdays. The number of direct admissions via referral from community services was a whopping 61% lower at weekends. For both groups of people admitted, the mortality rate at the weekend was higher than on weekdays; we see the familiar weekend effect. The 7% difference in A&E admission rates could not be explained by the patient characteristics available in the data, suggesting that a higher admission threshold is used at weekends. There was no weekend effect associated with A&E attendances, which is perhaps what a lot of people have in mind when they think about this issue. Only those admitted at the weekend have a higher mortality rate, and in particular those referred from community services. The implication is that mortality rates hide the true story by combining the number of people dying (the numerator) with the number of people being admitted (the denominator). Increasing the number of doctors available at weekends might increase the number of people being admitted (at great cost) but with no reduction in the number of deaths. Patients who are admitted to hospital at the weekend are a different group of people, and different in a way that has not yet been adequately captured by risk-adjustment.
People are living longer. This contributes to health care expenditure growth as people require more treatment to keep them alive. In this paper, the author argues that we should not focus only on the role of life-prolonging treatments but also on life-enhancing treatments. How people age and the ways in which the chances of becoming ill vary with age ought to be considered in resource allocation decisions. Social context is important in this respect; for example, the availability of public toilets may influence an older person’s willingness to engage in their usual activities. The arguments presented focus mainly on Norman Daniels’s prudential lifespan approach, which essentially considers whether or not a person would choose to purchase insurance for a particular health problem. We would expect an ageing population to insure more against the health problems of later life, and so proportionally greater resources ought to be allocated to older people. But the paper does not pursuade me that this requires any departure from current practice or thought. When Alan Williams described the fair innings approach to just allocations of resources in old age, he was expressly concerned with the quality of life. I’m not clear on what this paper adds, aside from further criticism of Harris’s view that life-extending treatment should always trump life-enhancing treatment. But I know of nobody who actually supports that view. Nevertheless, it’s an interesting discussion with which I hope health economists will engage.
An elicitation of utility for quality of life under prospect theory. Journal of Health Economics [RePEc] Published 2nd May 2016
Back in 1979, Kahneman and Tversky introduced prospect theory. Simply, this deviation from expected utility theory demonstrates that people value gains and losses from a given reference point differently, and that people’s decisions relate to probabilities in a nonlinear way. One of the key aspects of prospect theory is that it allows for loss aversion, which has been observed in the health context. We may therefore wish to develop methods for the estimation of QALYs that are based on prospect theory. This study demonstrates the limited validity of expected utility in estimating QALYs and shows how to estimate utility using prospect theory. A representative Dutch sample of 500 people was recruited for 2 experiments carried out online. Demographic and health state data were collected and participants were presented with possible gains and losses in quality of life within a 20%-100% interval associated with a specified reference point. Loss aversion was observed in both experiments, with evidence that responses were reference-dependent. Furthermore, there was risk aversion associated with both gains and losses. This undermines expected utility as a reasonable basis on which to estimate QALYs. Furthermore, the study found utility to be concave, such that a loss from 60% to 40% was perceived as smaller than a loss from 40% to 20%. This not only differs from the way in which we estimate QALYs, but also from the nature of prospect theory in the valuation of monetary outcomes. Expect to hear plenty more about PT-QALYs in the future.
Efficiency of health investment: education or intelligence? Health Economics [PubMed] Published 3rd May 2016
People with better education are healthier and live longer. But is this due to their education, or simply due to intelligence? It should go without saying that measuring intelligence, let alone separating it from the effects of education, is not straightforward. This study looks at whether education is associated with a higher efficiency of health investment. Health outcome is measured as survival and health investment as hospitalisation for a given condition. The authors then go on to consider the extent to which any benefit is due to intelligence. The data include 2570 Dutch individuals surveyed in 1952 in their final year of primary school and then followed up again in 1983 and 1993. The sample includes those people with hospitalisation records for 1995-2005 and mortality data for 1995-2011. A structural equation model is estimated to capture the impact of intelligence with the states ‘healthy’, ‘hospitalised’ and ‘dead’. Intelligence is modelled as a latent variable based on an IQ test and a vocabulary test at the age of 12. The analysis treats education choice as exogenous but controls for numerous socioeconomic and school-specific variables. People with higher education were less likely to die after a hospitalisation, though this relationship disappears once intelligence is accounted for. This suggests that the health investment advantage of the better educated is due to intelligence. There are plenty of limitations to the study in terms of the available data, but the findings nevertheless suggest that education per se might not be as beneficial to health as previous studies have shown.