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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Chris Sampson’s journal round-up for 19th February 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.

Value of information methods to design a clinical trial in a small population to optimise a health economic utility function. BMC Medical Research Methodology [PubMed] Published 8th February 2018

Statistical significance – whatever you think of it – and the ‘power’ of clinical trials to detect change, is an important decider in clinical decision-making. Trials are designed to be big enough to detect ‘statistically significant’ differences. But in the context of rare diseases, this can be nigh-on impossible. In theory, the required sample size could exceed the size of the whole population. This paper describes an alternative method for determining sample sizes for trials in this context, couched in a value of information framework. Generally speaking, power calculations ignore the ‘value’ or ‘cost’ associated with errors, while a value of information analysis would take this into account and allow accepted error rates to vary accordingly. The starting point for this study is the notion that sample sizes should take into account the size of the population to which the findings will be applicable. As such, sample sizes can be defined on the basis of maximising the expected (societal) utility associated with the conduct of the trial (whether the intervention is approved or not). The authors describe the basis for hypothesis testing within this framework and specify the utility function to be maximised. Honestly, I didn’t completely follow the stats notation in this paper, but that’s OK – the trial statisticians will get it. A case study application is presented from the context of treating children with severe haemophilia A, which demonstrates the potential to optimise utility according to sample size. The key point is that the power is much smaller than would be required by conventional methods and the sample size accordingly reduced. The authors also demonstrate the tendency for the optimal trial sample size to increase with the size of the population. This Bayesian approach at least partly undermines the frequentist basis on which ‘power’ is usually determined. So one issue is whether regulators will accept this as a basis for defining a trial that will determine clinical practice. But then regulators are increasingly willing to allow for special cases, and it seems that the context of rare diseases could be a way-in for Bayesian trial design of this sort.

EQ-5D-5L: smaller steps but a major step change? Health Economics [PubMed] Published 7th February 2018

This editorial was doing the rounds on Twitter last week. European (and Canadian) health economists love talking about the EQ-5D-5L. The editorial features in the edition of Health Economics that hosts the 5L value set for England, which – 2 years on – has finally satisfied the vagaries of academic publication. The authors provide a summary of what’s ‘new’ with the 5L, and why it matters. But we’ve probably all figured that out by now anyway. More interestingly, the editorial points out some remaining concerns with the use of the EQ-5D-5L in England (even if it is way better than the EQ-5D-3L and its 25-year old value set). For example, there is some clustering in the valuations that might reflect bias or problems with the technique and – even if they’re accurate – present difficulties for analysts. And there are also uncertain implications for decision-making that could systematically favour or disfavour particular treatments or groups of patients. On this basis, the authors support NICE’s decision to ‘pause’ and await independent review. I tend to disagree, for reasons that I can’t fit in this round-up, so come back tomorrow for a follow-up blog post.

Factors influencing health-related quality of life in patients with Type 1 diabetes. Health and Quality of Life Outcomes [PubMed] Published 2nd February 2018

Diabetes and its complications can impact upon almost every aspect of a person’s health. It isn’t clear what aspects of health-related quality of life might be amenable to improvement in people with Type 1 diabetes, or which characteristics should be targeted. This study looks at a cohort of trial participants (n=437) and uses regression analyses to determine which factors explain differences in health-related quality of life at baseline, as measured using the EQ-5D-3L. Age, HbA1c, disease duration and being obese all significantly influenced EQ-VAS values, while self-reported mental illness and unemployment status were negatively associated with EQ-5D index scores. People who were unemployed were more likely to report problems in the mobility, self-care, and pain/discomfort domains. There are some minor misinterpretations in the paper (divining a ‘reduction’ in scores from a cross-section, for example). And the use of standard linear regression models is questionable given the nature of EQ-5D-3L index values. But the findings demonstrate the importance of looking beyond the direct consequences of a disease in order to identify the causes of reduced health-related quality of life. Getting people back to work could be more effective than most health care as a means of improving health-related quality of life.

Financial incentives for chronic disease management: results and limitations of 2 randomized clinical trials with New York Medicaid patients. American Journal of Health Promotion [PubMed] Published 1st February 2018

Chronic diseases require (self-)management, but it isn’t always easy to ensure that patients adhere to the medication or lifestyle changes that could improve health outcomes. This study looks at the effectiveness of financial incentives in the context of diabetes and hypertension. The data are drawn from 2 RCTs (n=1879) which, together, considered 3 types of incentive – process-based, outcome-based, or a combination of the two – compared with no financial incentives. Process-based incentives rewarded participants for attending primary care or endocrinologist appointments and filling their prescriptions, up to a maximum of $250. Outcome-based incentives rewarded up to $250 for achieving target reductions in systolic blood pressure or blood glucose levels. The combined arms could receive both rewards up to the same maximum of $250. In short, none of the financial incentives made any real difference. But generally speaking, at 6-month follow-up, the movement was in the right direction, with average blood pressure and blood glucose levels tending to fall in all arms. It’s not often that authors include the word ‘limitations’ in the title of a paper, but it’s the limitations that are most interesting here. One key difficulty is that most of the participants had relatively acceptable levels of the target outcomes at baseline, meaning that they may already have been managing their disease well and there may not have been much room for improvement. It would be easy to interpret these findings as showing that – generally speaking – financial incentives aren’t effective. But the study is more useful as a way of demonstrating the circumstances in which we can expect financial incentives to be ineffective, and support a better-informed targeting for future programmes.

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Sharing the burden of healthcare: providing care to our sickest patients

One of the major challenges to affordable, universal health insurance is the high cost of providing care to the sickest patients. According to Roy Vaughn, senior vice president at BlueCross BlueShield of Tennessee, “just 5 percent of the company’s marketplace customers had accounted for nearly 75 percent of its claims costs.” What is the cost of healthcare for the typical person in the United States?Distribution of per capita US health expenditures 2012

Data from 2012, the last year for which a full analysis is available, presents a complex and confusing picture. The graph above shows per capita expenditures by percentile starting with the highest per capita expenditure. 10% face expenditures of at least $10,250. The median per capita expenditure was $854. The mean average per capita expenditure was $4309 – five times the median – and “the top 1 percent ranked by their healthcare expenses accounted for 22.7 percent of total healthcare expenditures with an annual mean expenditure of $97,956″. In brief, there is no typical person: since the bottom 50% accounted for 2.7% of total expenditures, the average per capita expenditure of the top 1% was 420 times that of the bottom 50%. There really is no typical person in terms of healthcare expenditures.

Pareto/ power law distribution of healthcare costs

This extreme distribution of healthcare costs (approximately an “80/20”, Pareto/ power law distribution) poses a major challenge to providing universal healthcare through traditional insurance models based upon risk pooling. Prior to the Affordable Care Act (ACA), the US health insurance industry addressed these challenges with risk selection – adjusting premiums or denying insurance to patients with high predicted risks, such as those with pre-existing conditions, and imposing caps on annual and/or lifetime benefits, much like the way the auto insurance industry sets premiums and limits benefits to address extreme differences in projected driver risks. Come back tomorrow for another blog post with more technical details about the Pareto distribution and healthcare costs.

Risk selection is illegal but prevalent

The ACA makes both caps on benefits and risk selection based upon pre-existing conditions illegal. In particular, US insurance carriers are required to provide coverage to all, at rates independent of pre-existing conditions, a requirement which President-Elect Donald Trump would like to keep.

However, the extreme distribution of healthcare costs means that “Targeting the highest spenders represents the greatest opportunity to have a significant impact on overall spending”; an opportunity for insurance carriers as well as for public policy. Moreover, there are good predictors for high spending: age and end of life, chronic conditions, and high spending in a previous year. For example 44.8% of the top decile in 2008 healthcare expenditures “retained this top decile ranking with respect to their 2009 healthcare expenditures”; a fact cited in an extensive Forbes report. Swiss and Dutch experience found risk selection prevalent and persistent. However, with every adult paying the same premium – within a given fund for the same type of contract – but expected healthcare expenditure (HCE) varying widely, strong incentives for risk selection are created in the absence of an adequate risk adjustment scheme. Although risk selection is illegal, it is prevalent. Swiss conglomerates of insurance carriers have been reported to achieve risk selection by assigning applicants to “specific carriers based on their risk profiles.”

Removing the economic incentives for risk selection

There is one clear way to avoid built-in economic incentives for risk selection (incentives which seem to drive insurance company behavior); that is, a single payer system, universally or as excess coverage for significant, predictable expenses. The United States now has several parallel single payer systems, namely Medicare for the elderly, Medicaid for the very poor and CHIP for children; thus, in effect, a public/private partnership in healthcare. These pre-existing single-payer systems might serve as models for a more inclusive US single payer system. Alternatively, the United States might act as an insurer of last resort, providing umbrella insurance covering individual expenses above some relatively high limit, or for costly but treatable conditions using the End Stage Renal Disease (ESRD) Program, passed in 1972 as a model. This approach would also remove extreme costs from the health insurance risk pool, as both Medicare and the ESRD Program do now, by providing near-universal coverage for our sickest patients outside the private insurance system (elderly US citizens and those with severe chronic kidney disease, respectively).

Tomorrow I will return to the Pareto-like distribution of healthcare expenditures and its consequences for any competitive insurance program. But for now, a few conclusions. Medicare and the ESRD program provide models for a smooth transition from health insurance pre-ACA with its caps and limitations to a more universal system. Medicare can be expanded to a broader public alternative. Universal coverage for additional treatable but high-risk conditions can be modeled on the ESRD program. These steps should provide the basis for further evolution of the present public/private partnership into a more universal, more cost-effective system.

In my opinion, the extreme distribution of healthcare expenditures and the ability to perform risk selection, even though illegal, present a strong, essentially irrefutable argument for a single payer system; either overall, or for chronic conditions and expenditures predictable through risk selection. In the US, Medicare and the ESRD program provide illustrative, successful and useful models.

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