Health Services Research UK conference

The 12th Annual Health Services Research UK (HSRUK) Conference on 2 and 3 July 2019 at the University of Manchester will present the leading-edge of health services and systems research.

HSRUK is dedicated to the promotion of health services research in policy and practice. Our annual conference is open to everyone interested in health services and systems research, and offers you the opportunity to:

Hear about cutting-edge research: The HSRUK19 programme is multidisciplinary, with research presentations, posters, themed sessions and interactive workshops alongside plenaries from key opinion leaders in research, policy and practice.

Build your network and meet the agenda-setters: The conference offers the opportunity to build your network across research, policy and practice, form new collaborations and engage with key influencers and decision makers. Delegates attend from universities and research institutes, government, NHS and social care bodies, industry, consultancy and the third sector.

Rita Faria’s journal round-up for 28th January 2019

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.

Appraising the value of evidence generation activities: an HIV modelling study. BMJ Global Health [PubMed] Published 7th December 2018

How much should we spend on implementing our health care strategy versus getting more information to devise a better strategy? Should we devolve budgets to regions or administer the budget centrally? These are difficult questions and this new paper by Beth Woods et al has a brilliant stab at answering them.

The paper looks at the HIV prevention and treatment policies in Zambia. It starts by finding the most cost-effective strategy and the corresponding budget in each region, given what is currently known about the prevalence of the infection, the effectiveness of interventions, etc. The idea is that the regions receive a cost-effective budget to implement a cost-effective strategy. The issue is that the cost-effective strategy and budget are devised according to what we currently know. In practice, regions might face a situation on the ground which is different from what was expected. Regions might not have enough budget to implement the strategy or might have some leftover.

What if we spend some of the budget to get more information to make a better decision? This paper considers the value of perfect information given the costs of research. Depending on the size of the budget and the cost of research, it may be worthwhile to divert some funds to get more information. But what if we had more flexibility in the budgetary policy? This paper tests 2 more budgetary options: a national hard budget but with the flexibility to transfer funds from under- to overspending regions, and a regional hard budget with a contingency fund.

The results are remarkable. The best budgetary policy is to have a national budget with the flexibility to reallocate funds across regions. This is a fascinating paper, with implications not only for prioritisation and budget setting in LMICs but also for high-income countries. For example, the 2012 Health and Social Care Act broke down PCTs into smaller CCGs and gave them hard budgets. Some CCGs went into deficit, and there are reports that some interventions have been cut back as a result. There are probably many reasons for the deficit, but this paper shows that hard regional budgets clearly have negative consequences.

Health economics methods for public health resource allocation: a qualitative interview study of decision makers from an English local authority. Health Economics, Policy and Law [PubMed] Published 11th January 2019

Our first paper looked at how to use cost-effectiveness to allocate resources between regions and across health care services and research. Emma Frew and Katie Breheny look at how decisions are actually made in practice, but this time in a local authority in England. Another change of the 2012 Health and Social Care Act was to move public health responsibilities from the NHS to local authorities. Local authorities are now given a ring-fenced budget to implement cost-effective interventions that best match their needs. How do they make decisions? Thanks to this paper, we’re about to find out.

This paper is an enjoyable read and quite an eye-opener. It was startling that health economics evidence was not much used in practice. But the barriers that were cited are not insurmountable. And the suggestions by the interviewees were really useful. There were suggestions about how economic evaluations should consider the local context to get a fair picture of the impact of the intervention to services and to the population, and to move beyond the trial into the real world. Equity was mentioned too, as well as broadening the outcomes beyond health. Fortunately, the health economics community is working on many of these issues.

Lastly, there was a clear message to make economic evidence accessible to lay audiences. This is a topic really close to my heart, and something I’d like to help improve. We have to make our work easy to understand and use. Otherwise, it may stay locked away in papers rather than do what we intended it for. Which is, at least in my view, to help inform decisions and to improve people’s lives.

I found this paper reassuring in that there is clearly a need for economic evidence and a desire to use it. Yes, there are some teething issues, but we’re working in the right direction. In sum, the future for health economics is bright!

Survival extrapolation in cancer immunotherapy: a validation-based case study. Value in Health Published 13th December 2018

Often, the cost-effectiveness of cancer drugs hangs in the method to extrapolate overall survival. This is because many cancer drugs receive their marketing authorisation before most patients in the trial have died. Extrapolation is tested extensively in the sensitivity analysis, and this is the subject of many discussions in NICE appraisal committees. Ultimately, at the point of making the decision, the correct method to extrapolate is a known unknown. Only in hindsight can we know for sure what the best choice was.

Ash Bullement and colleagues take advantage of hindsight to know the best method for extrapolation of a clinical trial of an immunotherapy drug. Survival after treatment with immunotherapy drugs is more difficult to predict because some patients can survive for a very long time, while others have much poorer outcomes. They fitted survival models to the 3-year data cut, which was available at the time of the NICE technology appraisal. Then they compared their predictions to the observed survival in the 5-year data cut and to long-term survival trends from registry data. They found that the piecewise model and a mixture-cure model had the best predictions at 5 years.

This is a relevant paper for those of us who work in the technology appraisal world. I have to admit that I can be sceptical of piecewise and mixture-cure models, but they definitely have a role in our toolbox for survival extrapolation. Ideally, we’d have a study like this for all the technology appraisals hanging on the survival extrapolation so that we can take learnings across cancers and classes of drugs. With time, we would get to know more about what works best for which condition or drug. Ultimately, we may be able to get to a stage where we can look at the extrapolation with less inherent uncertainty.

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Brent Gibbons’s journal round-up for 9th April 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.

The effect of Medicaid on management of depression: evidence from the Oregon Health Insurance Experiment. The Milbank Quarterly [PubMed] Published 5th March 2018

For the first journal article of this week’s AHE round-up, I selected a follow-up study on the Oregon health insurance experiment. The Oregon Health Insurance Experiment (OHIE) used a lottery system to expand Medicaid to low-income uninsured adults (and their associated households) who were previously ineligible for coverage. Those interested in being part of the study had to sign up. Individuals were then randomly selected through the lottery, after which individuals needed to take further action to complete enrollment in Medicaid, which included showing that enrollment criteria were satisfied (e.g. income below 100% of poverty line). These details are important because many who were selected for the lottery did not complete enrollment in Medicaid, though being selected through the lottery was associated with a 25 percentage point increase in the probability of having insurance (which the authors confirm was overwhelmingly due to Medicaid and not other insurance). More details on the study and data are publicly available. The OHIE is a seminal study in that it allows researchers to study the effects of having insurance in an experimental design – albeit in the U.S. health care system’s context. The other study that comes to mind is of course the famous RAND health insurance experiment that allowed researchers to study the effects of different levels of health insurance coverage. For the OHIE, the authors importantly point out that it is not necessarily obvious what the impact of having insurance is. While we would expect increases in health care utilization, it is possible that increases in primary care utilization could result in offsetting reductions in other settings (e.g. hospital or emergency department use). Also, while we would expect increases in health as a result of increases in health care use, it is possible that by reducing adverse financial consequences (e.g. of unhealthy behavior), health insurance could discourage investments in health. Medicaid has also been criticized by some as not very good insurance – though there are strong arguments to the contrary. First-year outcomes were detailed in another paper. These included increased health care utilization (across all settings), decreased out-of-pocket medical expenditures, decreased medical debt, improvements in self-reported physical and mental health, and decreased probability of screening positive for depression. In the follow-up paper on management of depression, the authors further explore the causal effect and causal pathway of having Medicaid on depression diagnosis, treatment, and symptoms. Outcomes of interest are the effect of having Medicaid on the prevalence of undiagnosed and untreated depression, the use of depression treatments including medication, and on self-reported depressive symptoms. Where possible, outcomes are examined for those with a prior depression diagnosis and those without. In order to examine the effect of Medicaid insurance (vs. being uninsured), the authors needed to control for the selection bias introduced from uncompleted enrollment into Medicaid. Instrumental variable 2SLS was used with lottery selection as the sole instrument. Local average treatment effects were reported with clustered standard errors on the household. The effect of Medicaid on the management of depression was overwhelmingly positive. For those with no prior depression diagnosis, it increased the chance of receiving a diagnosis and decreased the prevalence of undiagnosed depression (those who scored high on study survey depression instrument but with no official diagnosis). As far as treatment, Medicaid reduced the share of the population with untreated depression, virtually eliminating untreated depression among those with pre-lottery depression. There was a large reduction in unmet need for mental health treatment and an increased share who received specific mental health treatments (i.e. prescription drugs and talk therapy). For self-reported symptoms, Medicaid reduced the overall rate screened for depression symptoms in the post-lottery period. All effects were relatively strong in magnitude, giving an overall convincing picture that Medicaid increased access to treatment, which improved depression symptoms. The biggest limitation of this study is its generalizability. Much of the results were focused on the city of Portland, which may not represent more rural parts of the state. More importantly, this was limited to the state of Oregon for low-income adults who not only expressed interest in signing up, but who were able to follow through to complete enrollment. Other limitations were that the study only looked at the first two years of outcomes and that there was limited information on the types of treatments received.

Tobacco regulation and cost-benefit analysis: how should we value foregone consumer surplus? American Journal of Health Economics [PubMed] [RePEcPublished 23rd January 2018

This second article addresses a very interesting theoretical question in cost-benefit analysis, that has emerged in the context of tobacco regulation. The general question is how should foregone consumer surplus, in the form of reduced smoking, be valued? The history of this particular question in the context of recent FDA efforts to regulate smoking is quite fascinating. I highly recommend reading the article just for this background. In brief, the FDA issued proposed regulations to implement graphic warning labels on cigarettes in 2010 and more recently proposed that cigars and e-cigarettes should also be subject to FDA regulation. In both cases, an economic impact analysis was required and debates ensued on if, and how, foregone consumer surplus should be valued. Economists on both sides weighed-in, some arguing that the FDA should not consider foregone consumer surplus because smoking behavior is irrational, others arguing consumers are perfectly rational and informed and the full consumer surplus should be valued, and still others arguing that some consumer surplus should be counted but there is likely bounded rationality and that it is methodologically unclear how to perform a valuation in such a case. The authors helpfully break down the debate into the following questions: 1) if we assume consumers are fully informed and rational, what is the right approach? 2) are consumers fully informed and rational? and 3) if consumers are not fully informed and rational, what is the right approach? The reason the first question is important is that the FDA was conducting the economic impact analysis by examining health gains and foregone consumer surplus separately. However, if consumers are perfectly rational and informed, their preferences already account for health impacts, meaning that only changes in consumer surplus should be counted. On the second question, the authors explore the literature on smoking behavior to understand “whether consumers are rational in the sense of reflecting stable preferences that fully take into account the available information on current and expected future consequences of current choices.” In general, the literature shows that consumers are pretty well aware of the risks, though they may underestimate the difficulty of quitting. On whether consumers are rational is a much harder question. The authors explore different rational addiction models, including quasi-rational addiction models that take into account more recent developments in behavioral economics, but declare that the literature at this point provides no clear answer and that no empirical test exists to distinguish between rational and quasi-rational models. Without answering whether consumers are fully informed and rational, the authors suggest that welfare analysis – even in the face of bounded rationality – can still use a similar valuation approach to consumer surplus as was recommended for when consumers are fully informed and rational. A series of simple supply and demand curves are presented where there is a biased demand curve (demand under bounded rationality) and an unbiased demand curve (demand where fully informed and rational) and different regulations are illustrated. The implication is that rather than trying to estimate health gains as a result of regulations, what is needed is to understand the amount of demand bias as result of bounded rationality. Foregone consumer surplus can then be appropriately measured. Of course, more research is needed to estimate if, and how much, ‘demand bias’ or bounded rationality exists. The framework of the paper is extremely useful and it pushes health economists to consider advances that have been made in environmental economics to account for bounded rationality in cost-benefit analysis.

2SLS versus 2SRI: appropriate methods for rare outcomes and/or rare exposures. Health Economics [PubMed] Published 26th March 2018

This third paper I will touch on only briefly, but I wanted to include it as it addresses an important methodological topic. The paper explores several alternative instrumental variable estimation techniques for situations when the treatment (exposure) variable is binary, compared to the common 2SLS (two-stage least squares) estimation technique which was developed for a linear setting with continuous endogenous treatments and outcome measures. A more flexible approach, referred to as 2SRI (two-stage residual inclusion) allows for non-linear estimation methods in the first stage (and second stage), including logit or probit estimation methods. As the title suggests, these alternative estimation methods may be particularly useful when treatment (exposure) and/or outcomes are rare (e.g below 5%). Monte Carlo simulations are performed on what the authors term ‘the simplest case’ where the outcome, treatment, and instrument are binary variables and a range of results are considered as the treatment and/or outcome become rarer. Model bias and consistency are assessed in the ability to produce average treatment effects (ATEs) and local average treatment effects (LATEs), comparing the 2SLS, several forms of probit-probit 2SRI models, and a bivariate probit model. Results are that the 2SLS produced biased estimates of the ATE, especially as treatment and outcomes become rarer. The 2SRI models had substantially higher bias than the bivariate probit in producing ATEs (though the bivariate probit requires the assumption of bivariate normality). For LATE, 2SLS always produces consistent estimates, even if the linear probability model produces out of range predictions. Estimates for 2SRI models and the bivariate probit model were biased in producing LATEs. An empirical example was also tested with data on the impact of long-term care insurance on long-term care use. Conclusions are that 2SRI models do not dependably produce unbiased estimates of ATEs. Among the 2SRI models though, there were varying levels of bias and the 2SRI model with generalized residuals appeared to produce the least ATE bias. For more rare treatments and outcomes, the 2SRI model with Anscombe residuals generated the least ATE bias. Results were similar to another simulation study by Chapman and Brooks. The study enhances our understanding of how different instrumental variable estimation methods may function under conditions where treatment and outcome variables have nonlinear distributions and where those same treatments and outcomes are rare. In general, the authors give a cautionary note to say that there is not one perfect estimation method in these types of conditions and that researchers should be aware of the potential pitfalls of different estimation methods.

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