The fifth IRDES Workshop on Applied Health Economics and Policy Evaluation, will take place in Paris, France, on June 20th-21st 2019. The workshop is organized by IRDES, Institute for Research and Information in Health Economics, and the Chaire Santé Dauphine.
Submission and selection of papers. You are invited to submit a full paper before January 14th 2019. Papers will be selected by the scientific committee on the basis of a full or advanced draft papers, written in English. Papers should include empirical material, and only unpublished papers at the time of the submission will be accepted. The submission should contain author’s name(s) and affiliation(s), a structured abstract and keywords (up to five).
Registration and fees. Registration fees are 200 euros. Only authors or coauthors can apply for registration. PhD students or early career researchers may benefit from free registration upon request.
Program. The workshop will cover the following topics, with an emphasis on Public Policies analysis and evaluation: Social Health Inequalities, Health Services Utilization, Insurance, Health Services Delivery and Organization, Specific Populations: The Elderly, Migrants, High Needs-High Costs Patients, Low Income Households…. About 16 papers will be selected. Each paper will be allocated 20 minutes for presentation and 20 minutes for discussion (introduced by a participant or a member of the scientific committee).
Scientific committee. Damien Bricard (IRDES), Andrew Clark (Paris School of Economics), Brigitte Dormont (Paris Dauphine University and Chaire santé Dauphine), Paul Dourgnon (IRDES), Agnès Gramain (Université Lorraine), Julien Mousquès (IRDES), Aurélie Pierre (IRDES), Erin Strumpf (McGill University, Montreal), Matt Sutton (University of Manchester)
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.
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.
In previous posts (here and here) the comprehensive work undertaken by Claxton et al on the returns to medical expenditure in the NHS was discussed. Claxton and colleagues estimated the average change in quality adjusted life years (QALY) that have resulted from marginal budget changes in the NHS. Their estimate was £9,000 per QALY, much lower than the current threshold of £20,000 to £30,000 per QALY, so they argued that the threshold should be reduced. While we discussed some possible criticisms of the research it nevertheless remains the best empirical work on the topic and does provide some evidence for a reduction in the threshold. Many national news outlets picked up on the story when the paper was published. Nevertheless, changes to NHS reimbursement decisions and associated policies may have effects beyond that of altering the cost-effectiveness of the portfolio of treatments provided by the NHS.
A recent paper by Koijen, Philipson, and Uhlig argues that uncertainty about government healthcare policy in the United States has reduced medical research and development (R&D) and as a consequence reduced overall medical spending as a share of GDP. The equity returns of firms in the health care sector are higher than in other sectors: they find there is a ‘premium’ of around 4-6%. This, they argue, is a consequence of there being higher risk in the health care sector.
Figure from Koijen, Philipson, and Uhlig (2016)
Koijen, Philipson, and Uhlig suggest that this higher risk is a result of uncertainty about government regulation in the health care sector. The figure above demonstrates a large drawdown in the healthcare sector, not related to other sectors, at the time when the Clinton health care reform was being discussed. However, the figure does not show an equivalent decrease specific to the health care sector for Obamacare (around 2008-10). Obamacare likely improves health care sector revenues by adding a large number of health care consumers to the market. This quantity effect outweighs any reduction in markup. Indeed, the healthcare sector spent around $150 million dollars lobbying in support of the Affordable Care Act. However, the effect of the Clinton health reform would have been to impose price controls, thus reducing returns to the health care sector.
That medical innovation is directed towards the areas with the highest returns is an uncontroversial idea. It is the reason so few new treatments are developed for tropical diseases and tuberculosis, for example. Changes in government policy that affect returns of investment are likely to affect investment decisions. A reduction in the threshold for reimbursement will reduce return on investment in the UK. Uncertainty about future health care policy may also do so for the above reasons. The health care sector is particularly sensitive to American health care policy since the US accounts for 48% of global medical spending, so such changes in the UK may not lead to large effects. But for firms whose primary customer is the NHS, this may well be an issue.
A potential solution is to increase subsidies for medical R&D in the event of a reduction in the cost-effectiveness threshold. Indeed, this is one potential solution to encouraging the development of treatments for those diseases that predominantly affect people in the global South. However, what the article above demonstrates is that if there is a large amount of uncertainty about health care policies then any R&D stimuli may not have their intended effects.