Sam Watson’s journal round-up for 15th January 2018

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.

Cost-effectiveness of publicly funded treatment of opioid use disorder in California. Annals of Internal Medicine [PubMed] Published 2nd January 2018

Deaths from opiate overdose have soared in the United States in recent years. In 2016, 64,000 people died this way, up from 16,000 in 2010 and 4,000 in 1999. The causes of public health crises like this are multifaceted, but we can identify two key issues that have contributed more than any other. Firstly, medical practitioners have been prescribing opiates irresponsibly for years. For the last ten years, well over 200,000,000 opiate prescriptions were issued per year in the US – enough for seven in every ten people. Once prescribed, opiate use is often not well managed. Prescriptions can be stopped abruptly, for example, leaving people with unexpected withdrawal syndromes and rebound pain. It is estimated that 75% of heroin users in the US began by using legal, prescription opiates. Secondly, drug suppliers have started cutting heroin with its far stronger but cheaper cousin, fentanyl. Given fentanyl’s strength, only a tiny amount is required to achieve the same effects as heroin, but the lack of pharmaceutical knowledge and equipment means it is often not measured or mixed appropriately into what is sold as ‘heroin’. There are two clear routes to alleviating the epidemic of opiate overdose: prevention, by ensuring responsible medical use of opiates, and ‘cure’, either by ensuring the quality and strength of heroin, or providing a means to stop opiate use. The former ‘cure’ is politically infeasible so it falls on the latter to help those already habitually using opiates. However, the availability of opiate treatment programs, such as opiate agonist treatment (OAT), is lacklustre in the US. OAT provides non-narcotic opiates, such as methadone or buprenorphine, to prevent withdrawal syndromes in users, from which they can slowly be weaned. This article looks at the cost-effectiveness of providing OAT for all persons seeking treatment for opiate use in California for an unlimited period versus standard care, which only provides OAT to those who have failed supervised withdrawal twice, and only for 21 days. The paper adopts a previously developed semi-Markov cohort model that includes states for treatment, relapse, incarceration, and abstinence. Transition probabilities for the new OAT treatment were determined from treatment data for current OAT patients (as far as I understand it). Although this does raise the question about the generalisability of this population to the whole population of opiate users – given the need to have already been through two supervised withdrawals, this population may have a greater motivation to quit, for example. In any case, the article estimates that the OAT program would be cost-saving, through reductions in crime and incarceration, and improve population health, by reducing the risk of death. Taken at face value these results seem highly plausible. But, as we’ve discussed before, drug policy rarely seems to be evidence-based.

The impact of aid on health outcomes in Uganda. Health Economics [PubMed] Published 22nd December 2017

Examining the response of population health outcomes to changes in health care expenditure has been the subject of a large and growing number of studies. One reason is to estimate a supply-side cost-effectiveness threshold: the health returns the health service achieves in response to budget expansions or contractions. Similarly, we might want to know the returns to particular types of health care expenditure. For example, there remains a debate about the effectiveness of aid spending in low and middle-income country (LMIC) settings. Aid spending may fail to be effective for reasons such as resource leakage, failure to target the right population, poor design and implementation, and crowding out of other public sector investment. Looking at these questions at an aggregate level can be tricky; the link between expenditure or expenditure decisions and health outcomes is long and causality flows in multiple directions. Effects are likely to therefore be small and noisy and require strong theoretical foundations to interpret. This article takes a different, and innovative, approach to looking at this question. In essence, the analysis boils down to a longitudinal comparison of those who live near large, aid funded health projects with those who don’t. The expectation is that the benefit of any aid spending will be felt most acutely by those who live nearest to actual health care facilities that come about as a result of it. Indeed, this is shown by the results – proximity to an aid project reduced disease prevalence and work days lost to ill health with greater effects observed closer to the project. However, one way of considering the ‘usefulness’ of this evidence is how it can be used to improve policymaking. One way is in understanding the returns to investment or over what area these projects have an impact. The latter is covered in the paper to some extent, but the former is hard to infer. A useful next step may be to try to quantify what kind of benefit aid dollars produce and its heterogeneity thereof.

The impact of social expenditure on health inequalities in Europe. Social Science & Medicine Published 11th January 2018

Let us consider for a moment how we might explore empirically whether social expenditure (e.g. unemployment support, child support, housing support, etc) affects health inequalities. First, we establish a measure of health inequality. We need a proxy measure of health – this study uses self-rated health and self-rated difficulty in daily living – and then compare these outcomes along some relevant measure of socioeconomic status (SES) – in this study they use level of education and a compound measure of occupation, income, and education (the ISEI). So far, so good. Data on levels of social expenditure are available in Europe and are used here, but oddly these data are converted to a percentage of GDP. The trouble with doing this is that this variable can change if social expenditure changes or if GDP changes. During the financial crisis, for example, social expenditure shot up as a proportion of GDP, which likely had very different effects on health and inequality than when social expenditure increased as a proportion of GDP due to a policy change under the Labour government. This variable also likely has little relationship to the level of support received per eligible person. Anyway, at the crudest level, we can then consider how the relationship between SES and health is affected by social spending. A more nuanced approach might consider who the recipients of social expenditure are and how they stand on our measure of SES, but I digress. In the article, the baseline category for education is those with only primary education or less, which seems like an odd category to compare to since in Europe I would imagine this is a very small proportion of people given compulsory schooling ages unless, of course, they are children. But including children in the sample would be an odd choice here since they don’t personally receive social assistance and are difficult to compare to adults. However, there are no descriptive statistics in the paper so we don’t know and no comparisons are made between other groups. Indeed, the estimates of the intercepts in the models are very noisy and variable for no obvious reason other than perhaps the reference group is very small. Despite the problems outlined so far though, there is a potentially more serious one. The article uses a logistic regression model, which is perfectly justifiable given the binary or ordinal nature of the outcomes. However, the authors justify the conclusion that “Results show that health inequalities measured by education are lower in countries where social expenditure is higher” by demonstrating that the odds ratio for reporting a poor health outcome in the groups with greater than primary education, compared to primary education or less, is smaller in magnitude when social expenditure as a proportion of GDP is higher. But the conclusion does not follow from the premise. It is entirely possible for these odds ratios to change without any change in the variance of the underlying distribution of health, the relative ordering of people, or the absolute difference in health between categories, simply by shifting the whole distribution up or down. For example, if the proportions of people in two groups reporting a negative outcome are 0.3 and 0.4, which then change to 0.2 and 0.3 respectively, then the odds ratio comparing the two groups changes from 0.64 to 0.58. The difference between them remains 0.1. No calculations are made regarding absolute effects in the paper though. GDP is also shown to have a positive effect on health outcomes. All that might have been shown is that the relative difference in health outcomes between those with primary education or less and others changes as GDP changes because everyone is getting healthier. The question of the article is interesting, it’s a shame about the execution.

Credits

 

Meeting round-up: The Role of the University of York in the Development of Health Economics

By Eleanor MacKillop and Sally Sheard

On 27th October 2017, some key British health economists were reunited to discuss the origins and development of their discipline. The event, held at the Centre for Health Economics at the University of York, formed part of the ‘Governance of Health’ project, led by Professor Sally Sheard at the University of Liverpool. Health economics (HE) now dominates British and foreign health policy and decision-making, as illustrated by the resource allocation formula, the formulation of the quality-adjusted life year (QALY), or the introduction of quasi-markets in the NHS, and NICE.

Witness seminars provide an opportunity for collective and public oral history. The event chronicled a history that has rarely been examined, and then only by economists. Using open questions, the witnesses explored the origins of British HE, relationships with the Department of Health (DH) and how it infiltrated other areas such as the NHS – the word ‘infiltrating’ was often repeated by witnesses.

Origins of health economics in the UK

For Tony Culyer, his personal experience of health economics began in 1964 when working with Mike Cooper at Exeter University. Mike Cooper had previously worked with Dennis Lees at Keele University and the Institute of Economic Affairs (IEA). For other witnesses, their first brush with economics as applied to health – the term ‘health economics’ was not used until much later – came through the MSc and PhD programmes at York. The creation of the University of York in 1963 allowed new disciplines such as economics to develop in a less rigid environment as compared to Oxbridge. A similar pattern emerged at the University of Aberdeen and Brunel University.

Dr Alan Haycox and Professor Karen Bloor sharing their stories

Why York rather than other more established centres? Several witnesses explained that there was a ‘snobbery’ and that HE was seen as ‘a waste of time’ and ‘not proper economics’. Another crucial event was the ‘coup’ of York of recruiting two leading economists – Alan Peacock and Jack Wiseman – to start an economics department which was first inaugurated as the Institute of Social and Economics Research (ISER) in 1964. However, as noted by some witnesses, the political inclinations of these two economists, who were close to the free-market-leaning IEA, may have hindered York’s early relationship with Government. The hiring of Alan Williams in 1968 – seen by many as, ‘inspirational and fascinating’ and equipped with ‘a relentless logic’ – was also a defining event in the development of economics at York. It is important to note that the first health economics centre was established at Aberdeen – the Health Economics Research Unit (HERU) in 1977 – and that the Centre for Health Economics (CHE) at York wasn’t inaugurated until 1983. More generally, the financial and economic context of the 1970s, with the oil shocks, devaluation of the pound and beginning of years of budget restrictions, was an obvious factor in making HE a helpful discipline for successive governments.

Health economics in government

David Pole, first Chief Economist for health in the Department of Health and Social Security (DHSS), was a key individual, entering the Department’s Economic Advisers’ Office (EAO) as a senior economic adviser in 1970, shortly followed by Jeremy Hurst. They tried to convince some hostile medical professionals and administrators of the merits of their approach. Ron Akehurst explained how the attempt by administrators to hide from the then Minister of State for Health – David Owen – a report on the geographical inequalities in the allocation of resources in the mid-1970s led the latter to liaise directly with David Pole and the EAO, and the emergence of the resource allocation formula. A former economic adviser in DH, Andrew Burchell, reminisced how David Pole and his successor Clive Smee were successful in identifying academic research that could be grafted onto policy – such as QALYs from the mid-1970s – and seizing opportunities as they arose.

Photograph of schema fusing painfulness and restriction of activity into a single dimension (TNA, MH166/927, Economics of Medical Care, ‘Health Indicators’ report submitted by Culyer, A., Lavers, R. and Williams, A. to the DHSS, p. 29, 1971)

The inauguration of CHE in 1983 and its funding as a DHSS research unit forged a closer relationship between York and government. Public Health England’s Chief Economist, Brian Ferguson, noted how Alan Maynard would often speak of ‘infiltrating the field and government’. More generally, York was successful at delivering research and reports that DH could use, such as Peter Smith’s work on the cost of teaching hospitals in the early 1970s and Ken Wright’s work on ambulances and social care.

Health economics in other contexts and key achievements

From the 1980s, witnesses Anne Ludbrook, Alan Haycox and Ron Akehurst worked as economists in Regional Health Authorities in England and Scotland. They talked about the difficulty of getting the HE message across to doctors and managers, and making decisions more transparent.

Ron Akehurst spoke about how he was commissioned by DH to run HE training courses for doctors. A number of other contributions were mentioned, but most witnesses agreed that NICE was the ‘single most important impact of health economics on policy’.

What we have learnt: three key messages from the Witness Seminar

  1. Opportunities created: Witnesses highlighted the importance of chance and the unpredictability of events which led to health economics playing an important role. The resource allocation formula or research on teaching hospitals’ costs provide examples of chance and the ways in which economists were prepared for playing a greater role in policy development.
  2. Role of charismatic individuals: David Pole, Clive Smee, Alan Williams, Tony Culyer and Alan Maynard were all seen as individuals – maybe even ‘policy entrepreneurs’ – who were capable of presenting convincing arguments to different audiences, be they politicians, administrators or the NHS, and able to negotiate between policy communities.
  3. An ongoing project: Although the panel noted the importance of Health Technology Assessment (HTA) today, Karen Bloor and others reminded us that HE isn’t a battle ‘won’ but instead an ongoing phenomenon developing into a multiplicity of branches.

Thanks to Michael Lambert and Phil Begley for their help editing this post

Hawking is right, Jeremy Hunt does egregiously cherry pick the evidence

I’m beginning to think Jeremy Hunt doesn’t actually care what the evidence says on the weekend effect. Last week, renowned physicist Stephen Hawking criticized Hunt for ‘cherry picking’ evidence with regard to the ‘weekend effect’: that patients admitted at the weekend are observed to be more likely than their counterparts admitted on a weekday to die. Hunt responded by doubling down on his claims:

Some people have questioned Hawking’s credentials to speak on the topic beyond being a user of the NHS. But it has taken a respected public figure to speak out to elicit a response from the Secretary of State for Health, and that should be welcomed. It remains the case though that a multitude of experts do continue to be ignored. Even the oft-quoted Freemantle paper is partially ignored where it notes of the ‘excess’ weekend deaths, “to assume that [these deaths] are avoidable would be rash and misleading.”

We produced a simple tool to demonstrate how weekend effect studies might estimate an increased risk of mortality associated with weekend admissions even in the case of no difference in care quality. However, the causal model underlying these arguments is not always obvious. So here it is:

weekend

A simple model of the effect of the weekend on patient health outcomes. The dashed line represents unobserved effects

 

So what do we know about the weekend effect?

  1. The weekend effect exists. A multitude of studies have observed that patients admitted at the weekend are more likely to die than those admitted on a weekday. This amounts to having shown that E(Y|W,S) \neq E(Y|W',S). As our causal model demonstrates, being admitted is correlated with health and, importantly, the day of the week. So, this is not the same as saying that risk of adverse clinical outcomes differs by day of the week if you take into account propensity for admission, we can’t say E(Y|W) \neq E(Y|W'). Nor does this evidence imply care quality differs at the weekend, E(Q|W) \neq E(Q|W'). In fact, the evidence only implies differences in care quality if the propensity to be admitted is independent of (unobserved) health status, i.e. Pr(S|U,X) = Pr(S|X) (or if health outcomes are uncorrelated with health status, which is definitely not the case!).
  2. Admissions are different at the weekend. Fewer patients are admitted at the weekend and those that are admitted are on average more severely unwell. Evidence suggests that the better patient severity is controlled for, the smaller the estimated weekend effect. Weekend effect estimates also diminish in models that account for the selection mechanism.
  3. There is some evidence that care quality may be worse at the weekend (at least in the United States). So E(Q|W) \neq E(Q|W'). Although this has not been established in the UK (we’re currently investigating it!)
  4. Staffing levels, particularly specialist to patient ratios, are different at the weekend, E(X|W) \neq E(X|W').
  5. There is little evidence to suggest how staffing levels and care quality are related. While the relationship seems evident prima facie, its extent is not well understood, for example, we might expect a diminishing return to increased staffing levels.
  6. There is a reasonable amount of evidence on the impact of care quality (preventable errors and adverse events) on patient health outcomes.

But what are we actually interested in from a policy perspective? Do we actually care that it is the weekend per se? I would say no, we care that there is potentially a lapse in care quality. So, it’s a two part question: (i) how does care quality (and hence avoidable patient harm) differ at the weekend E(Q|W) - E(Q|W') = ?; and (ii) what effect does this have on patient outcomes E(Y|Q)=?. The first question answers to what extent policy may affect change and the second gives us a way of valuing that change and yet the vast majority of studies in the area address neither. Despite there being a number of publicly funded research projects looking at these questions right now, it’s the studies that are not useful for policy that keep being quoted by those with the power to make change.

Hawking is right, Jeremy Hunt has egregiously cherry picked and misrepresented the evidence, as has been pointed out again and again and again and again and … One begins to wonder if there isn’t some motive other than ensuring long run efficiency and equity in the health service.

Credits