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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Sam Watson’s journal round-up for 2nd October 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 path to longer and healthier lives for all Africans by 2030: the Lancet Commission on the future of health in sub-Saharan Africa. The Lancet [PubMedPublished 13th September 2017

The African continent has the highest rates of economic growth, the fastest growing populations and rates of urbanisation, but also the highest burden of disease. The challenges for public health and health care provision are great. It is no surprise then that this Lancet commission on the future of health in Sub-Saharan Africa runs to 57 pages yet still has some notable absences. In the space of a few hundred words, it would be impossible to fully discuss the topics in this tome, these will appear in future blog posts. For now, I want to briefly discuss a lack of consideration of the importance of political economy in the Commission’s report. For example, the report notes the damaging effects of IMF and World Bank structural adjustment programs in the 70s and 80s. These led to a dismantling of much of the public sector in indebted African nations in order for them to qualify for further loans. However, these issues have not gone away. Despite strongly emphasizing that countries in Africa must increase their health spending, it does not mention that many countries spend much more servicing debt than on public health and health care. Kenya, for example, will soon no longer qualify for aid as it becomes a middle-income country, and yet it spends almost double (around $6 billion) servicing its debt than it does on health care (around $3 billion). Debt reform and relief may be a major step towards increasing health expenditure. The inequalities in access to basic health services reflect the disparities in income and wealth both between and within countries. The growth of slums across the continent is stark evidence of this. Residents of these communities, despite often facing the worst exposure to major disease risk factors, are often not recognised by authorities and cannot access health services. Even where health services are available there are still difficulties with access. A lack of regulation and oversight can lead the growth of a rentier class within slums as those with access to small amounts of capital, land, or property act as petty landlords. So while some in slum areas can afford the fees for basic health services, the poorest still face a barrier even when services are available. These people are also those who have little access to decent water and sanitation or education and have the highest risk of disease. Finally, the lack of incentives for trained doctors and medical staff to work in poor or rural areas is also identified as a key problem. Many doctors either leave for wealthier countries or work in urban areas. Doctors are often a powerful interest group and can influence macro health policy, distorting it to favour richer urban areas. Political solutions are required, as well as the public health interventions more widely discussed. The Commission’s report is extensive and worth the time to read for anyone with an interest in the subject matter. What also becomes clear upon reading it is the lack of solid evidence on health systems and what works and does not work. From an economic perspective, much of the evidence pertaining to health system functioning and efficiency is still just the results from country-level panel data regressions, which tell us very little about what is actually happening. This results in us being able to identify areas needed for reform with very little idea of how.

The relationship of health insurance and mortality: is lack of insurance deadly? Annals of Internal Medicine [PubMedPublished 19th September 2017

One sure-fire way of increasing your chances of publishing in a top-ranked journal is to do something on a hot political topic. In the UK this has been seven-day services, as well as other issues relating to deficiencies of supply. In the US, health insurance is right up there with the Republicans trying to repeal the Affordable Care Act, a.k.a. Obamacare. This paper systematically reviews the literature on the relationship between health insurance coverage and the risk of mortality. The theory being that health insurance permits access to medical services and therefore treatment and prevention measures that reduce the risk of death. Many readers will be familiar with the Oregon Health Insurance Experiment, in which the US state of Oregon distributed access to increased Medicaid expansion by lottery, therein creating an RCT. This experiment, which takes a top spot in the review, estimated that those who had ‘won’ the lottery had a mortality rate 0.032 percentage points lower than the ‘losers’, whose mortality rate was 0.8%; a relative reduction of around 4%. Similar results were found for the quasi-experimental studies included, and slightly larger effects were found in cohort follow-up studies. These effects are small. But then so is the baseline. Most of these studies only examined non-elderly, non-disabled people, who would otherwise not qualify for any other public health insurance. For people under 45 in the US, the leading cause of death is unintentional injury, and its only above this age that cancer becomes the leading cause of death. If you suffer major trauma in the US you will (for the most part) be treated in an ER insured or uninsured, even if you end up with a large bill afterwards. So it’s no surprise that the effects of insurance coverage on mortality are very small for these people. This is probably the inappropriate endpoint to be looking at for this study. Indeed, the Oregon experiment found that the biggest differences were in reduced out-of-pocket expenses and medical debt, and improved self-reported health. The review’s conclusion that, “The odds of dying among the insured relative to the uninsured is 0.71 to 0.97,” is seemingly unwarranted. If they want to make a political point about the need for insurance, they’re looking in the wrong place.

Smoking, expectations, and health: a dynamic stochastic model of lifetime smoking behavior. Journal of Political Economy [RePEcPublished 24th August 2017

I’ve long been sceptical of mathematical models of complex health behaviours. The most egregious of which is often the ‘rational addiction’ literature. Originating with the late Gary Becker, the rational addiction model, in essence, assumes that addiction is a rational choice made by utility maximising individuals, whose preferences alter with use of a particular drug. The biggest problem I find with this approach is that it is completely out of touch with the reality of addiction and drug dependence, and makes absurd assumptions about the preferences of addicts. Nevertheless, it has spawned a sizable literature. And, one may argue that the model is useful if it makes accurate predictions, regardless of the assumptions underlying it. On this front, I have yet to be convinced. This paper builds a rational addiction-type model for smoking to examine whether learning of one’s health risks reduces smoking. As an illustration of why I dislike this method of understanding addictive behaviours, the authors note that “…the model cannot explain why individuals start smoking. […] The estimated preference parameters in the absence of a chronic illness suggest that, for a never smoker under the age of 25, there is no incentive to begin smoking because the marginal utility of smoking is negative.” But for many, social and cultural factors simply explain why young people start smoking. The weakness of the deductive approach to social science seems to rear its head, but like I said, the aim here may be the development of good predictive models. And, the model does appear to predict smoking behaviour well. However, it is all in-sample prediction, and with the number of parameters it is not surprising it predicts well. This discussion is not meant to be completely excoriating. What is interesting is the discussion and attempt to deal with the endogeneity of smoking – people in poor health may be more likely to smoke and so the estimated effects of smoking on longevity may be overestimated. As a final point of contention though, I’m still trying to work out what the “addictive stock of smoking capital” is.

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