Chris Sampson’s journal round-up for 14th August 2017

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.

Does paying service providers by results improve recovery outcomes for drug misusers in treatment in England? Addiction [PubMedPublished 10th August 2017

‘Getting what you pay for’ is a fundamentally attractive funding model, which is why we see lots of pay for performance (P4P) initiatives cropping up in the NHS. But P4P plans can go awry. This study considers an experimental setting in which 8 areas participated in P4P pilots for drug misuse treatment, from 2012-2014. Payments were aligned with 3 national priorities: i) abstinence, ii) reduced offending and iii) improved health and well-being. The participating areas allocated differing proportions of payments to the P4P model, between 10% and 100%. Data were drawn from the National Drug Treatment Monitoring System, which includes information on drug use, assessment and interventions received. Other national sources were used to identify criminal activity and mortality rates. Drug misusers attending treatment services during the 2 years before and after the introduction of the P4P scheme were included in the study. Using a difference-in-differences analysis, the researchers compared outcomes in the 8 participating areas with those in 143 non-participating areas. Separate multilevel regression models were used for a set of outcomes, each controlling for a variety of individual-level characteristics. The authors analysed ‘treatment journeys’, of which there were around 20,000 for those in participating areas and 280,000 for those in non-participating areas; roughly half before the introduction and half after. The results don’t look good for P4P. Use of opiates, crack cocaine and injecting increased. Treatment initiation increased in non-participating areas but decreased in participating areas. Moreover, longer waiting times were observed in participating areas as well as more unplanned discharges. P4P was associated with people being less likely to successfully complete treatment within 12 months. In P4P’s favour, there was evidence that abstinence increased. I’d’ve liked to have seen some attempt at matching between the areas, given that there was an element of self-selection into the scheme. Or at least, better control for the characteristics of the areas before P4P was introduced. This paper isn’t quite the final nail in the coffin. I don’t see P4P disappearing anytime soon. There’s a lot to be learnt from the paper’s discussion, which outlines some of the likely reasons and mechanisms underlying the findings. Commissioners should take note.

The short- and long-run effects of smoking cessation on alcohol consumption. International Journal of Health Economics and Management [PubMedPublished 7th August 2017

Anecdotally, it seems as if smoking and drinking are complementary behaviours. Generally, the evidence suggests that this is true. Smoking cessation programmes may, therefore, have value in their ability to reduce alcohol consumption (and vice versa). But only if the relationship is causal. This study seeks to add to that causal evidence. Using data from 5887 individuals in the Lung Health Study, the author runs a two-stage least squares estimation, with randomisation to smoking cessation treatment as an instrumental variable for smoking status. In the short term, there is some evidence that smokers tend to drink more (especially men). But findings in the longer term, up to 5 years, are more persuasive. It’s unfortunate that the (largely incoherent) rational addiction theory makes an appearance and that the findings are presented as supportive of it. A stopped clock is right twice a day. In line with rational addiction theory, the long-term relationship is measured in terms of a ‘smoking stock’, which is an aggregate measure of smoking behaviour over the 5 year period. Smoking and drinking are found to be complementary in the long term. Crucially, the extent of their complementarity is associated with particular factors. For example, people who smoke more cigarettes or who abstain for longer exhibit larger reductions in alcohol consumption when they stop smoking. People who smoke relatively few cigarettes per day do not drink more alcohol. Those smoking 6-10 per day consume around 1 extra drink per week compared with non-smokers. Quitting for 5 years can reduce alcohol consumption by more than 50%. In the long run, the effect is more pronounced for women and for people who are married. This highlights important opportunities for targeted public policy, which could achieve a win-win in terms of reducing both cigarette and alcohol consumption.

Time for a change in how new antibiotics are reimbursed: development of an insurance framework for funding new antibiotics based on a policy of risk mitigation. Health Policy Published 5th August 2017

Antibiotics have become a key component of health care, but antimicrobial resistance threatens their usefulness and we don’t see new antibiotics in the pipeline to help overcome this. It’s a fundamentally difficult problem; we want new antibiotics but we want to use them as sparingly as possible. Antibiotic development is relatively unattractive (financially) to pharmaceutical companies. Provision of research funding and regulatory changes haven’t solved the problem to date. This paper considers why this might be the case, and explores 2 alternative approaches: a premium price model and an insurance-type model. Essentially, the authors conduct a spreadsheet analysis to compare the alternative models with a base case of no incentives. The expected net present value of the base case was negative (to the tune of about $1.5 billion), demonstrating why much-needed new antibiotics aren’t being developed. Current incentives – including public-private funding partnerships and market exclusivity – are also shown to fail to reach a positive net present value. The premium price model, whereby there is an enhanced price per unit, is not particularly attractive. The daily cost of the resulting antibiotics would likely be too high, and manufacturers’ pursuit of profit would be at odds with conservative prescribing. Furthermore, it exposes areas experiencing outbreaks to serious financial risk. The insurance model, which involved an annual fee paid by each healthcare system (to manufacturers), is more promising. Pharmaceutical companies would be insured against low prices and variable use and health systems would be insured against a lack of antibiotics and the risk of an infection outbreak. The key feature here is that manufacturers’ revenues are de-linked from sales volume. This is important when we consider the need for conservative prescribing. The authors estimate that the necessary fee (for the global market) would be around $262 million per year, or $114 million if combined with current funding and regulatory incentives. Of course, these findings are based on major assumptions about infection rates, research costs and plenty besides. A number of sensitivity analyses are conducted that highlight uncertainty about what the insurance fee might need to be in the future. I think this uncertainty is somewhat understated – there are far more sensitivity and scenario analyses that would be warranted if such a policy were being seriously considered. Nevertheless, pooling risk in an insurance model looks like a promising strategy that’s worthy of further investigation and piloting.

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Sam Watson’s journal round-up for 24th July 2017

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.

Ten years after the financial crisis: the long reach of austerity and its global impacts on health. Social Science & Medicine [PubMedPublished 22nd June 2017

The subject of austerity and its impact on health has generated its own subgenre in the academic literature. We have covered a number of papers on these journal round-ups on this topic, which, given the nature of economic papers, are generally quantitative in nature. However, while quantitative studies are necessary for generation of knowledge of the social world, they are not sufficient. At aggregate levels, quantitative studies may often rely on a black box approach. We may reasonably conclude a policy caused a change in some population-level indicator on the basis of a causal inference type paper, but we often need other types of evidence to answer why or how this occurred. A realist philosophy of social science may see this as a process of triangulation; at the very least it’s a process of abduction to develop theory that best explains what we observe. In clinical research, Bradford-Hill’s famous criteria can be used as a heuristic for causal inference: a cause can be attributed to an effect if it demonstrates a number of criteria including dose-response and reproducibility. For social science, we can conceive of a similar set of criteria. Effects must follow causes, there has to be a plausible mechanism, and so forth. This article in Social Science & Medicine introduces a themed issue on austerity and its effects on health. The issue contains a number of papers examining experiences of people with respect to austerity and how these may translate into changes in health. One example is a study in a Mozambican hospital and how health outcomes change in response to continued restructuring programs due to budget shortfalls. Another study explores the narrative of austerity in Guyana and it has long been sold as necessary for future benefits which never actually materialise. It is not immediately clear how austerity is being defined here, but it is presumably something like ‘a fiscal contraction that causes a significant increase in aggregate unemployment‘. In any case, it makes for interesting reading and complements economics research on the topic. It is a refreshing change from the bizarre ravings we featured a couple of weeks ago!

Home-to-home time — measuring what matters to patients and payers. New England Journal of Medicine [PubMedPublished 6th July 2017

Length of hospital stay is often used as a metric to evaluate hospital performance: for a given severity of illness, a shorter length of hospital stay may suggest higher quality care. However, hospitals can of course game these metrics, and they are further complicated by survival bias. Hospitals are further incentivised to reduce length of stay. For example, the move from per diem reimbursement to per episode had the effect of dramatically reducing length of stay in hospitals. As a patient recovers, they may no longer need hospital based care, the care they require may be adequately provided in other institutional settings. Although, in the UK there has been a significant issue with many patients convalescing in hospital for extended periods as they wait for a place in residential care homes. Thus from the perspective of the whole health system, length of stay in hospital may no longer be the right metric to evaluate performance. This article makes this argument and provides some interesting statistics. For example, between 2004 and 2011 the average length of stay in hospital among Medicare beneficiaries in the US decreased from 6.3 to 5.7 days; post-acute care stays increased from 4.8 to 6.0 days. Thus, the total time in care actually increased from 11.1 to 11.7 days over this period. In the post-acute care setting, Medicare still reimburses providers on a per diem basis, so total payments adjusted for inflation also increased. This article makes the argument that we need to structure incentives and reimbursement schemes across the whole care system if we want to ensure efficiency and equity.

The population health benefits of a healthy lifestyle: life expectancy increased and onset of disability delayed. Health Affairs [PubMedPublished July 2017

Obesity and tobacco smoking increase the risk of ill health and in so doing reduce life expectancy. The same goes for alcohol, although the relationship between alcohol consumption and risk of illness is less well understood. One goal of public health policy is to mitigate these risks. One successful way of communicating the risks of different behaviours is as changes to life expectancy, or conversely ‘effective age‘. From a different perspective, understanding how different risk factors affect life expectancy and disability-free life expectancy is important for cost-benefit analyses of different public health interventions. This study estimates life expectancy and disability-free life expectancy associated with smoking, obesity, and moderate alcohol consumption using the US-based Health and Retirement Study. However, I struggle to see how this study adds much; while it communicates its results well, it is, in essence, a series of univariate comparisons followed by a multivariate comparison. This has been done widely before, such as here and here. Nevertheless, the results reinforce those previous studies. For example, obesity reduced disability-free life expectancy by 3 years for men and 6 years for women.

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Sam Watson’s journal round-up for 27th March 2017

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 minimum legal drinking age and morbidity in the United States. Review of Economics and Statistics Published 23rd February 2017

Governments have tried multiple different policies to reduce the physical and social harms of alcohol consumption. In the United Kingdom, a minimum price per unit alcohol has been investigated recently, and in 2003 opening times for licensed premises were extended. Neither policy was overwhelmingly judged to be an effective way to reduce harms. In the United States, the legal minimum age for purchasing alcohol is 21, notably higher than other Western nations. This legal age resulted from the National Minimum Age Drinking Act of 1984, which threatened states with a reduction of 10% in their funding for federal highways if they did not raise the legal age to 21. The Act was ostensibly in response to evidence of increased traffic fatalities associated with a lower legal age. This study adds evidence to this ongoing debate. The legal cut-off provides a natural discontinuity for the authors to investigate. Regression discontinuity can be abused, with some researchers controlling inappropriately for high powers of the variable, ‘forcing’ a difference to appear. This paper takes a more sensible approach adopting a quadratic form. For some variables, such as ED admission for alcohol intoxication, the discontinuity is obvious, as you would expect. But for others, such as accidental injury or deliberate injury by another person, the difference is not so apparent if you ignore the fitted lines. One wonders then how much their effect size is driven by their functional form. The authors write that their model is to ‘determine if an increase in the morbidity rate visible in a figure is statistically significant’. Oh dear.  Theoretically, the effect makes sense, alcohol does lead to physical and social harms. But I’m not convinced by the magnitude of the effect they’ve estimated: some sensitivity analyses wouldn’t have gone amiss.

A re-evaluation of fixed effect(s) meta-analysis. Journal of the Royal Statistical Society: Series A Published 16th March 2017

Meta-analysis is the frequently used method to combine results from multiple studies. Evidence synthesis is frequently required in health economic analyses to estimate parameters for models. Practitioners typically either consider ‘fixed effects’ or ‘random effects’ meta-analysis. The latter is used when it is assumed the estimated effects differ between studies, leading many authors to shun fixed effects analyses if there’s any suspicion of heterogeneity. But, as this article argues, there are multiple interpretations of fixed effects analyses. They can provide useful results even in the presence of between study heterogeneity. There are three key assumptions about the parameters estimated in different studies. First, there could be the same common effect underlying all studies. Secondly, each study could have a its own separate fixed effect. Or thirdly, each estimate is a draw from an underlying sampling distribution, an exchangeable parameters assumption. This latter assumption is the basis of random effects meta-analysis. The fixed effects meta-analysis estimator is consistent for the common effect parameter. For the multiple fixed effects assumption the fixed effects meta-analysis is a consistent estimator for the parameter that would have been estimated if the samples in each study were amalgamated. The key point of the paper is that under both the common effect and fixed effects assumptions the fixed effects meta-analysis estimator is useful.

Insurer competition in health care markets. Econometrica. Published 21st March 2017.

Given the gestation length of an economics paper, it is perhaps somewhat fortuitous that this one should land just as major health care market legislation is being discussed in the US. Health care provision differs notably between the US and other high income countries. Health care is predominantly left up to the market with ‘consumers’ purchasing insurance or health care directly. This, despite it being long recognised that health care markets are likely to fail (see our recent piece on the late Kenneth Arrow). But a single payer system is politically unpalatable. The Affordable Care Act (ACA; Obamacare) aimed to ensure universal coverage of health care through a system of subsidies, regulations, and mandates. The ACA brought about changes to the insurance market with a number of providers merging and consolidating. The consequences of these mergers may be deliterious as increased monopoly power within states may lead to higher premiums, but equally increased monopsony power may mean lower prices negotiated with health care providers. This article attempts to simulate what will happen to premiums and health care prices when insurers of different sizes are removed from the market. I can’t give a fair review to the methods in the time I’ve had to read this paper as there is a lot going on including econometric models of household choice and game theoretic models of insurer bargaining. But I put it here as it appears at first glance to be a solid analysis of what is an incredibly large and complex market in the US and is likely worth more time to understand.

 

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