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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