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.

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Paul Mitchell’s journal round-up for 26th December 2016

Every Monday (even if it’s Boxing Day here in the UK) 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.

Out-migration and attrition of physicians and dentists before and after EU accession (2003 and 2011): the case of Hungary. European Journal of Health Economics [PubMedPublished 2nd December 2016

Medical staff migration is an important cross-national policy issue given the international shortage of supply of doctors to meet healthcare demand. This study uses a large administrative survey collected in Hungary from 2004-2011 and focuses on the trends of medical doctors (GPs, specialists, dentists) since Hungary joined the EU in 2004 and the introduction of full freedom of movement between Hungary with Austria and Germany in 2011. The author conducted a time-to-event analysis with monthly collection of data on a person’s occupation used as a guide for outward-migration. A competing-risks model was used to also consider medical doctors exiting the profession, becoming inactive or dying. From the 18,266 medical doctors found in this sample over the nine year period, 12% migrated, 17% exited the profession and 14% became inactive. A five-fold increase in migration was seen when the restrictions on freedom of movement between Hungary and Austria/Germany were lifted, a worrying sign of brain drain from Hungary. For those who stayed but exited the profession, relative income is argued to have been a contributory factor, with incomes increasing by on average 40% in their new line of work (although this does not account for the “thank you money” received by doctors in Hungary for healthcare access). Generous maternity leave was argued to play a key role in absence from employment. A recognised limitation in this study is the inability to conduct robust analysis on the migration patterns of new medical graduates who are likely to be more prone to migration than their established colleagues (estimated to be 40% of all medical graduates in Hungary between 2007-2010 who migrated, before restrictions on freedom of movement between Austria and Germany were lifted). Nonetheless, the study still manages to shine a light on the external (competing against countries with larger economies) but also the internal (“attrition and feminisation of workforce”) challenges to national doctor staffing policy.

Does the proportion of pay linked to performance affect the job satisfaction of general practitioners? Social Science & Medicine [PubMedPublished 24th November 2016

The impact of pay for performance (P4P) on healthcare practice has been subject to much debate surrounding the pros and cons of incentives for medical staff to achieve specific goals. This study focuses on the impact that the introduction of the Quality and Outcomes Framework (QOF) for GPs in the UK in 2004 had on their subsequent job satisfaction. Job satisfaction for GPs is argued to be an important topic area due to it having an important role in retaining GPs and the quality of care they provide to their patients. Using linked data from the the GP Worklife Survey and the QOF, that rewards GPs performance based on clinical, organisation, additional services and patient experience indicators, across three time points (2004, 2005 and 2008), the authors model the relationship between P4P exposure (i.e. the proportion of income related to performance) and job satisfaction. Using a continuous difference-in-difference model with a random effects regression, the authors find that P4P exposure has no significant effect on job satisfaction after 1 and 4 years following the introduction of the QOF P4P system. The introduction of the QOF did lead to a large increase in GP life satisfaction; this is likely to be due to the large increase in average income for GPs following the introduction of QOF. The authors argue that their findings suggest GP job satisfaction is unlikely to be affected by changes in P4P exposure, so long as the final income the GP receives remains constant. Given the generous increases on GP final income from the initial QOF, it remains to be seen how generalisable these results would be to P4P systems that did not lead to such large increases in staff income.

Country-level cost-effectiveness thresholds: initial estimates and the need for further research. Value in Health [PubMed] Published 14th December 2016

National thresholds used to determine if a health intervention is cost-effective have been under scrutiny in the UK in recent years. It has been argued on the grounds of healthcare opportunity costs that the NICE £20,000-30,000 per QALY gained threshold is too high, with an estimate of £13,000 per QALY gain proposed instead. Until now, less attention has been paid to international cost-effectiveness thresholds recommended by the WHO, who have argued for a threshold between one and three times the GDP of a country. This study provides preliminary estimates of cost-effectiveness thresholds across a number of countries with varying levels of national income. Using estimates from the recent £13,000 per QALY gain threshold study in England, a ratio between the supply-side threshold with the consumption value of health was estimated and used as a basis to calculate other national thresholds. The authors use a range of income elasticity estimates for the value placed on a statistical life to take account of uncertainty around these values. The results suggest that even the lower end of the WHO recommended threshold range of 1x national GDP is likely to be an overestimate in most countries. It would appear something closer to 50% of GDP may be a better estimate, albeit with a great amount of uncertainty and variation between high and low income countries. The importance of these estimates according to the authors is that the application of the current WHO thresholds could lead to policies that reduce instead of increase population health. However, the threshold estimates from this study rely on a number of assumptions based on UK data that may not provide an accurate estimate when setting cost-effectiveness thresholds at an international level.

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Chris Sampson’s journal round-up for 26th 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.

Health economics as rhetoric: the limited impact of health economics on funding decisions in four European countries. Value in Health Published 19th September 2016

We start on a sombre note, with a paper that begs the question: why do we bother? A key purpose of health economic evaluation is to prevent the use of low-value, high-cost technologies. Influence on funding decisions is arguably a good basis on which to judge the impact of health economics. This study looks at funding decisions in England, Germany, the Netherlands and Sweden. The paper identifies key features of the HTA institutions and processes in each country. For all countries, there is very little evidence of economic evaluation having been the basis for the restriction of high-cost drugs. England found ways to support the funding of drugs for multiple sclerosis and cancer, despite high-cost and apparent low value. One positive impact might be in facilitating the negotiation of reduced prices – for example, through NICE’s patient access schemes. While the different countries have quite different processes, they have produced similar decisions in practice. The authors suggest that, despite having had limited impact on the outcome of funding decisions, health economics has influenced the process of decision making and the language of health care prioritisation. In this sense, health economics has value in rhetoric, increasing transparency and rational decision making. It’s an interesting idea that I’d like to see developed further, as the authors only provide a limited discussion of it. Personally, I think some distinction needs to be drawn between ‘health economics’ – as identified in the title – and ‘agency-mandated health technology assessment’. While many readers of this blog might do the former on a daily basis, I’d bet not many of us deal in the latter. I certainly don’t. So there’s a lot of ‘health economics’ that can’t – at least not directly – be judged on the basis of funding decisions. Yes, high-cost drugs backed by money-hungry Pharma evade HTA defences. But what about the other end of the spectrum? What about high-value interventions that have been commissioned because the economic evidence has been so compelling. Wishful thinking? Maybe not. Either way, we shouldn’t understate the value of health economics as rhetoric when dealing with capricious and myopic governments.

Recommendations for conduct, methodological practices, and reporting of cost-effectiveness analyses: Second Panel on Cost-Effectiveness in Health and Medicine. JAMA [PubMed] Published 13th September 2016

What do you mean you haven’t yet pre-ordered the new edition of the ‘Gold’ book from the famous Panel on Cost-Effectiveness in Health and Medicine? The original Panel was a big deal (not that I remember it, of course, as I was 8 years old), and so, presumably is the Second Panel. Maybe less so as relative consensus has developed in the use of health technology assessment in practice around the world. But we still need guidance. It’s ironic that the Panel was convened and funded by US organisations in a country that lags far behind in its use of economic evaluation in health technology assessment. This article in JAMA outlines the Panel’s recommendations. I can’t summarise them all here, so you probably need to go and read it all yourself. But know that there isn’t anything radical or unexpected. This Panel updated the original recommendations and created new ones where necessary. Threatening the validity of many a joke at economists’ expense, the Panel was able to reach consensus on all recommendations. Readers are chastised for not appropriately adopting a societal perspective as recommended by the first Panel, but then we are offered a compromise: “All studies should report a reference case analysis based on a health care sector perspective and another reference case analysis based on a societal perspective”. The Panel also recommend use of an “impact inventory”. This is a nice suggestion and I like the terminology. Including a disaggregated list of costs (and outcomes) improves transparency and makes studies more useful to future researchers. One new recommendation is that we should include unrelated future costs, which is something we saw discussed in a recent journal round-up. Another departure from the first Panel is that we are told to include productivity costs in the cost side of our equation. A suggestion that’s dropped in is that protocols should be written in advance of a study. I wish the panel had been more forceful with this one, as published protocols could go a long way in improving consistency, transparency and quality.

The Load Model: an alternative to QALY. Journal of Medical Economics [PubMedPublished 7th September 2016

OK, I admit it: I went into this paper with a lot of scepticism. The QALY – that is, the combination of the quality and quantity of life – fundamentally makes sense. I’m not sure we need ‘an alternative’. The paper introduces some interesting ideas, but they aren’t as revolutionary as the author suggests and I’m not sure that it gets us anywhere. There are some problems from the outset. The article jumbles up positive and normative matters, criticising the QALY on the basis of its capacity to indicate what we might consider to be inequitable results. The author hints that the need for a new model derives from the QALY’s inappropriate combination of quality and duration of life. The most obvious criticism is that the constant proportional trade-off assumption does not hold. But then there’s no discussion of CPTO. The Load Model is presented as “radically different”, but it isn’t. Equations are shuffled so that we’re dealing in rates rather than time, but this adjustment appears to be inconsequential. It might be a more useful way to think about morbidity and mortality, but no argument to that end is presented. The main difference in the Load Model is that a ‘load’ is added for the negative impact of death (as opposed to being dead). Now, I have big problems with the way we handle ‘dead’ in health state valuation. I think it’s a more serious issue than we know (and we know quite a bit), so I am always glad to see attempts to fix it. Once you get past the superficial adjustments to the QALY, what’s really going on is that the Load Model is adding a third dimension to the valuation process; in addition to length of life and quality of life (in the Load Model it’s disease burden) we also have quality (or rather the burden) of death. But this could be incorporated into a QALY framework; I’ve spoken before about the notion of a 3- or otherwise multi-dimensional QALY. Given that death is so key to the distinction between the Load Model and the QALY, it’s unfortunate that in the worked example an entirely arbitrary value of questionable meaning is attributed to it. So the subsequent comparison between the two approaches seems meaningless. There may be more merit in the Load Model than I can see – perhaps I lack the immagination. But it seems to solve none of the problems associated with the QALY framework, while introducing new ones.

Associations between extending access to primary care and emergency department visits: a difference-in-differences analysis. PLOS Medicine [PubMedPublished 6th September 2016

We’ve had quite a bit of discussion of 7 day services here on the blog. But the papers continue to flood in, much to the chagrin of Jeremy Hunt. This study doesn’t look at the most controversial case of extending hospital services, but investigates whether extended (evening and weekend) opening of GP practices reduces hospital attendance. The context is that providers in Manchester (England) were invited to bid for funding to roll out extended hours from December 2013. In total we’re looking at 56 practices who succeeded in the bid and 469 practices who provided services as normal. The analysis uses routinely collected hospital administrative data for almost 3 million patients from 2011 to 2014. A difference-in-differences OLS regression was used with propensity score matching to try and deal with the obvious selection problem. Of course, there was an increase in the number of GP visits: 33,519 in total. The main finding is that patients registered at practices with extended hours exhibited a 26.4% relative reduction in attendances for minor problems at A&E. So in this sense, extending opening hours seems to have satisfied its purpose. Though each emergency attendance ‘avoided’ corresponded to around 3 additional GP appointments. Unfortunately the study wasn’t able to determine the set-up and running costs of the extended GP services, so couldn’t carry out a proper cost-effectiveness analysis. And as we’ve discussed before in this context, that’s the question that really matters.

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