Sam Watson’s journal round-up for 12th November 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.

Estimating health opportunity costs in low-income and middle-income countries: a novel approach and evidence from cross-country data. BMJ Global Health. Published November 2017.

The relationship between health care expenditure and population health outcomes is a topic that comes up often on this blog. Understanding how population health changes in response to increases or decreases in the health system budget is a reasonable way to set a cost-effectiveness threshold. Purchasing things above this threshold will, on average, displace activity with greater benefits. But identifying this effect is hard. Commonly papers use some kind of instrumental variable method to try to get at the causal effect with aggregate, say country-level, data. These instruments, though, can be controversial. Years ago I tried to articulate why I thought using socio-economic variables as instruments was inappropriate. I also wrote a short paper a few years ago, which remains unpublished, that used international commodity price indexes as an instrument for health spending in Sub-Saharan Africa, where commodity exports are a big driver of national income. This was rejected from a journal because of the choice of instruments. Commodity prices may well influence other things in the country that can influence population health. And a similar critique could be made of this article here, which uses consumption:investment ratios and military expenditure in neighbouring countries as instruments for national health expenditure in low and middle income countries.

I remain unconvinced by these instruments. The paper doesn’t present validity checks on them, which is forgiveable given medical journal word limitations, but does mean it is hard to assess. In any case, consumption:investment ratios change in line with the general macroeconomy – in an economic downturn this should change (assuming savings = investment) as people switch from consumption to investment. There are a multitude of pathways through which this will affect health. Similarly, neighbouring military expenditure would act by displacing own-country health expenditure towards military expenditure. But for many regions of the world, there has been little conflict between neighbours in recent years. And at the very least there would be a lag on this effect. Indeed, in all the models of health expenditure and population health outcomes I’ve seen, barely a handful take into account dynamic effects.

Now, I don’t mean to let the perfect be the enemy of the good. I would never have suggested this paper should not be published as it is, at the very least, important for the discussion of health care expenditure and cost-effectiveness. But I don’t feel there is strong enough evidence to accept these as causal estimates. I would even be willing to go as far to say that any mechanism that affects health care expenditure is likely to affect population health by some other means, since health expenditure is typically decided in the context of the broader public sector budget. That’s without considering what happens with private expenditure on health.

Strategic Patient Discharge: The Case of Long-Term Care Hospitals. American Economic Review. [RePEcPublished November 2018.

An important contribution of health economics has been to undermine people’s trust that doctors act in their best interest. Perhaps that’s a little facetious, nevertheless there has been ample demonstration that health care providers will often act in their own self-interest. Often this is due to trying to maximise revenue by gaming reimbursement schemes, but also includes things like doctors acting differently near the end of their shift so they can go home on time. So when I describe a particular reimbursement scheme that Medicare in the US uses, I don’t think there’ll be any doubt about the results of this study of it.

In the US, long-term acute care hospitals (LTCHs) specialise in treating patients with chronic care needs who require extended inpatient stays. Medicare reimbursement typically works on a fixed rate for each of many diagnostic related groups (DRGs), but given the longer and more complex care needs in LTCHs, they get a higher tariff. To discourage admitting patients purely to get higher levels of reimbursement, the bulk of the payment only kicks in after a certain length of stay. Like I said – you can guess what happened.

This article shows 26% of patients are discharged in the three days after the length of stay threshold compared to just 7% in the three days prior. This pattern is most strongly observed in discharges to home, and is not present in patients who die. But this may still be just by chance that the threshold and these discharges coincide. Fortunately for the authors the thresholds differ between DRGs and even move around within a DRG over time in a way that appears unrelated to actual patient health. They therefore estimate a set of decision models for patient discharge to try to estimate the effect of different reimbursement policies.

Estimating misreporting in condom use and its determinants among sex workers: Evidence from the list randomisation method. Health Economics. Published November 2018.

Working on health and health care research, especially if you conduct surveys, means you often want to ask people about sensitive topics. These could include sex and sexuality, bodily function, mood, or other ailments. For example, I work a fair bit on sanitation, where frequently self-reported diarrhoea in under fives (reported by the mother that is) is the primary outcome. This could be poorly reported particularly if an intervention includes any kind of educational component that suggests it could be the mother’s fault for, say, not washing her hands, if the child gets diarrhoea. This article looks at condom use among female sex workers in Senegal, another potentially sensitive topic, since unprotected sex is seen as risky. To try and get at the true prevalence of condom use, the authors use a ‘list randomisation’ method. This randomises survey participants to two sets of questions: a set of non-sensitive statements, or the same set of statements with the sensitive question thrown in. All respondents have to do is report the number of the statements they agree with. This means it is generally not possible to distinguish the response to the sensitive question, but the difference in average number of statements reported between the two groups gives an unbiased estimator for the population proportion. Neat, huh? Ultimately the authors report an estimate of 80% of sex workers using condoms, which compares to the 97% who said they used a condom when asked directly.

 

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Sam Watson’s journal round-up for 30th 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 Millennium Villages Project: a retrospective, observational, endline evaluation. The Lancet Global Health [PubMedPublished May 2018

There are some clinical researchers who would have you believe observational studies are completely useless. The clinical trial is king, they might say, observation studies are just too biased. And while it’s true that observational studies are difficult to do well and convincingly, they can be a reliable and powerful source of evidence. Similarly, randomised trials are frequently flawed, for example there’s often missing data that hasn’t been dealt with, or a lack of allocation concealment, and many researchers forget that randomisation does not guarantee a balance of covariates, it merely increases the probability of it. I bring this up, as this study is a particularly carefully designed observational data study that I think serves as a good example to other researchers. The paper is an evaluation of the Millennium Villages Project, an integrated intervention program designed to help rural villages across sub-Saharan Africa meet the Millennium Development Goals over ten years between 2005 and 2015. Initial before-after evaluations of the project were criticised for inferring causal “impacts” from before and after data (for example, this Lancet paper had to be corrected after some criticism). To address these concerns, this new paper is incredibly careful about choosing appropriate control villages against which to evaluate the intervention. Their method is too long to summarise here, but in essence they match intervention villages to other villages on the basis of district, agroecological zone, and a range of variables from the DHS – matches were they reviewed for face validity and revised until a satisfactory matching was complete. The wide range of outcomes are all scaled to a standard normal and made to “point” in the same direction, i.e. so an increase indicated economic development. Then, to avoid multiple comparisons problems, a Bayesian hierarchical model is used to pool data across countries and outcomes. Costs data were also reported. Even better, “statistical significance” is barely mentioned at all! All in all, a neat and convincing evaluation.

Reconsidering the income‐health relationship using distributional regression. Health Economics [PubMed] [RePEcPublished 19th April 2018

The relationship between health and income has long been of interest to health economists. But it is a complex relationship. Increases in income may change consumption behaviours and a change in the use of time, promoting health, while improvements to health may lead to increases in income. Similarly, people who are more likely to make higher incomes may also be those who look after themselves, or maybe not. Disentangling these various factors has generated a pretty sizeable literature, but almost all of the empirical papers in this area (and indeed all empirical papers in general) use modelling techniques to estimate the effect of something on the expected value, i.e. mean, of some outcome. But the rest of the distribution is of interest – the mean effect of income may not be very large, but a small increase in income for poorer individuals may have a relatively large effect on the risk of very poor health. This article looks at the relationship between income and the conditional distribution of health using something called “structured additive distribution regression” (SADR). My interpretation of SADR is that, one would model the outcome y ~ g(a,b) as being distributed according to some distribution g(.) indexed by parameters a and b, for example, a normal or Gamma distribution has two parameters. One would then specify a generalised linear model for a and b, e.g. a = f(X’B). I’m not sure this is a completely novel method, as people use the approach to, for example, model heteroscedasticity. But that’s not to detract from the paper itself. The findings are very interesting – increases to income have a much greater effect on health at the lower end of the spectrum.

Ask your doctor whether this product is right for you: a Bayesian joint model for patient drug requests and physician prescriptions. Journal of the Royal Statistical Society: Series C Published April 2018.

When I used to take econometrics tutorials for undergraduates, one of the sessions involved going through coursework about the role of advertising. To set the scene, I would talk about the work of Alfred Marshall, the influential economist from the late 1800s/early 1900s. He described two roles for advertising: constructive and combative. The former is when advertising grows the market as a whole, increasing everyone’s revenues, and the latter is when ads just steal market share from rivals without changing the size of the market. Later economists would go on to thoroughly develop theories around advertising, exploring such things as the power of ads to distort preferences, the supply of ads and their complementarity with the product they’re selling, or seeing ads as a source of consumer information. Nevertheless, Marshall’s distinction is still a key consideration, although often phrased in different terms. This study examines a lot of things, but one of its key objectives is to explore the role of direct to consumer advertising on prescriptions of brands of drugs. The system is clearly complex: drug companies advertise both to consumers and physicians, consumers may request the drug from the physician, and the physician may or may not prescribe it. Further, there may be correlated unobservable differences between physicians and patients, and the choice to advertise to particular patients may not be exogenous. The paper does a pretty good job of dealing with each of these issues, but it is dense and took me a couple of reads to work out what was going on, especially with the mix of Bayesian and Frequentist terms. Examining the erectile dysfunction drug market, the authors reckon that direct to consumer advertising reduces drug requests across the category, while increasing the proportion of requests for the advertised drug – potentially suggesting a “combative” role. However, it’s more complex than that patient requests and doctor’s prescriptions seem to be influenced by a multitude of factors.

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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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