Sam Watson’s journal round-up for 3rd June 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.

Limits to human life span through extreme value theory. Journal of the American Statistical Association [RePEc] Published 2nd April 2019

The oldest verified person to have ever lived was Jeanne Calment who died in 1997 at the superlative age of 122. No-one else has ever been recorded as living longer than 120, but there have been perhaps a few hundred supercentarians over 110. Whenever someone reaches such a stupendous age, some budding reporter will ask them what the secret was. They will reply that they have stuck to a regimen of three boiled eggs and a glass of scotch every day for 80 years. And this information is of course completely meaningless due to survivorship bias. But as public health and health care improves and with it life expectancy, there remains the question of whether people will ever exceed these extreme ages or whether there is actually a limit to human longevity.

Some studies have attempted to address the question of maximum human longevity by looking at how key biological systems, like getting oxygen to the muscles or vasculature, degrade. They suggest that there would be an upper limit as key systems of the body just cannot last, which is not to say medicine might not find a way to fix or replace them in the future. Another way of addressing this question is to take a purely statistical approach and look at the distribution of the ages of the oldest people alive and try to make inferences about its upper limit. Such an analysis relies on extreme value theory.

There are two types of extreme value data. The first type consists of just the series of maximum values from the distribution. The Fisher-Tippett-Gnedenko theorem shows that these maxima can only be distributed according to one of three distributions. The second type of data are all of the most extreme observations above a certain threshold, and wonderfully there is another triple-barrelled theorem that shows that these data are distributed as a generalised Pareto distribution – the Pickand-Balkema-de Haan theorem. This article makes use of this latter type of data and theorem to estimate: (i) is there an upper limit to the distribution of human life spans? (ii) What is it, if so? And (iii) does it change over time?

The authors use a dataset of the ages of death in days of all Dutch residents who died over the age of 92 between 1986 and 2015. Using these data to estimate the parameters of the generalised Pareto distribution, they find strong evidence to suggest that, statistically at least, it has an upper limit and that this limit is probably around 117-124. Over the years of the study there did not appear to be any change in this limit. This is not to say that it couldn’t change in the future if some new miraculous treatment appeared, but for now, we humans must put up with a short and finite existence.

Infant health care and long-term outcomes. Review of Economics and Statistics [RePEc] Published 13th May 2019

I haven’t covered an article on infant health and economic conditions and longer term outcomes for a while. It used to be that there would be one in every round-up I wrote. I could barely keep up with the literature, which I tried to summarise in a different blog post. Given that it has been a while, I thought I would include a new one. This time we are looking at the effect of mother and child health centres in Norway in the 1930s on the outcomes of adults later in the 20th Century.

Fortunately the health centres were built in different municipalities at different times. The authors note that the “key identifying assumption” is that they were not built at a time related to the health of infants in those areas (well, this and that the model is linear and additive, time trends are linear, etc. etc. something that economists often forget). They don’t go into too much detail on this, but it seems plausible. Another gripe of mine with most empirical economic papers, and indeed in medical and public health fields, is that plotting the data is a secondary concern or doesn’t happen at all. It should be the most important thing. Indeed, in this article much of the discussion can be captured by the figure buried two thirds through. The figure shows that the centres likely led to a big reduction in diarrhoeal disease, probably due to increased rates of breast feeding, but on other outcomes effects are more ambiguous and probably quite small if they exist. Some evidence is provided to suggest that these differences were associated with very modest increases in educational attainment and adult wages. However, a cost-benefit calculation suggests that on the basis of these wage increases the intervention had a annualised rate of return of about 5%.

I should say that this study is well-conducted and fairly solid so any gripes with it are fairly minor. It certainly fits neatly into the wide literature on the topic, and I don’t think anyone would doubt that investing in childhood interventions is likely to have a number of short and long term benefits.

Relationship between poor olfaction and mortality among community-dwelling older adults: a cohort study. Annals of Internal Medicine [PubMed] Published 21st May 2019

I included this last study, not because of any ground-breaking economics or statistics, but because it is interesting. This is one of a number of studies to have looked at the relationship between smell ability and risk of death. These studies have generally found a strong direct relationship between poor olfaction and risk of death in the following years (summarised briefly in this editorial). This study examines a cohort of a couple of thousand older people whose smell was rigourously tested at baseline, among other things. If they died then their death was categorised by a medical examiner into one of four categories: dementia or Parkinson disease, cardiovascular disease, cancer, and respiratory illness.

There was a very strong relationship between poor ability to smell and all-cause death. They found that cumulative risk for death was 46% and 30% higher in persons with a loss of smelling ability at 10 and 13 years respectively. Delving into death by cause, they found that this relationship was most important among those who died of dementia or Parkinson disease, which makes sense as smell is one of the oldest limbic structures and linked to many parts of the brain. Some relationship was seen with cardiovascular disease but not cancer or respiratory illness. They then use a ‘mediation analysis’, i.e. conditioning on post-treatment variables to ‘block’ causal pathways, to identify how much variation is explained and conclude that dementia, Parkinson disease, and weight loss account for about 30% of the observed relationship. However, I am usually suspicious of mediation analyses, and standard arguments would suggest that model parameters would be biased.

Interestingly, olfaction is not normally used as a diagnostic test among the elderly despite sense of smell being one of the strongest predictors of mortality. People do not generally notice their sense of smell waning as it is gradual, so would not likely remark on it to a doctor. Perhaps it is time to start testing it routinely?

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Brent Gibbons’s journal round-up for 22nd January 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.

Is retirement good for men’s health? Evidence using a change in the retirement age in Israel. Journal of Health Economics [PubMed] Published January 2018

This article is a tour de force from one chapter of a recently completed dissertation from the Hebrew University of Jerusalem. The article focuses on answering the question of what are the health implications of extending working years for older adults. As many countries are faced with critical decisions on how to adjust labor policies to solve rising pension costs (or in the case of the U.S., Social Security insolvency) in the face of aging populations, one obvious potential solution is to change the retirement age. Most OECD countries appear to have retirement ages in the mid-60’s with a number of countries on track to increase that threshold. Israel is one of these countries, having changed their retirement age for men from age 65 to age 67 in 2004. The author capitalizes on this exogenous change in retirement incentives, as workers will be incentivized to keep working to receive full pension benefits, to measure the causal effect of working in these later years, compared to retiring. As the relationship between employment and health is complicated by the endogenous nature of the decision to work, there is a growing literature that has attempted to deal with this endogeneity in different ways. Shai details the conflicting findings in this literature and describes various shortcomings of methods used. He helpfully categorizes studies into those that compare health between retirees and non-retirees (does not deal with selection problem), those that use variation in retirement age across countries (retirement ages could be correlated with individual health across countries), those that exploit variation in specific sector retirement ages (problem of generalizing to population), and those that use age-specific retirement eligibility (health may deteriorate at specific age regardless of eligibility for retirement). As this empirical question has amounted conflicting evidence, the author suggests that his methodology is an improvement on prior papers. He uses a difference-in-difference model that estimates the impact on various health outcomes, before and after the law change, comparing those aged 65-66 years after 2004 with both older and younger cohorts unaffected by the law. The assumption is that any differences in measured health between the age 65-66 group and the comparison group are a result of the extended work in later years. There are several different datasets used in the study and quite a number of analyses that attempt to assuage threats to a causal interpretation of results. Overall, results are that delaying the retirement age has a negative effect on individual health. The size of the effect found is in the ballpark of 1 standard deviation; outcome measures included a severe morbidity index, a poor health index, and the number of physician visits. In addition, these impacts were stronger for individuals with lower levels of education, which the author relates to more physically demanding jobs. Counterfactuals, for example number of dentist visits, which are not expected to be related to employment, are not found to be statistically different. Furthermore, there are non-trivial estimated effects on health care expenditures that are positive for the delayed retirement group. The author suggests that all of these findings are important pieces of evidence in retirement age policy decisions. The implication is that health, at least for men, and especially for those with lower education, may be negatively impacted by delaying retirement and that, furthermore, savings as a result of such policies may be tempered by increased health care expenditures.

Evaluating community-based health improvement programs. Health Affairs [PubMed] Published January 2018

For article 2, I see that the lead author is a doctoral student in health policy at Harvard, working with colleagues at Vanderbilt. Without intention, this round-up is highlighting two very impressive studies from extremely promising young investigators. This study takes on the challenge of evaluating community-based health improvement programs, which I will call CBHIPs. CBHIPs take a population-based approach to public health for their communities and often focus on issues of prevention and health promotion. Investment in CBHIPs has increased in recent years, emphasizing collaboration between the community and public and private sectors. At the heart of CBHIPs are the ideas of empowering communities to self-assess and make needed changes from within (in collaboration with outside partners) and that CBHIPs allow for more flexibility in creating programs that target a community’s unique needs. Evaluations of CBHIPs, however, suffer from limited resources and investment, and often use “easily-collectable data and pre-post designs without comparison or control communities.” Current overall evidence on the effectiveness of CBHIPs remains limited as a result. In this study, the authors attempt to evaluate a large set of CBHIPs across the United States using inverse propensity score weighting and a difference-in-difference analysis. Health outcomes on poor or fair health, smoking status, and obesity status were used at the county level from the BRFSS (Behavioral Risk Factor Surveillance System) SMART (Selected Metropolitan/Micropolitan Area Risk Trends) data. Information on counties implementing CBHIPs was compiled through a series of systematic web searches and through interviews with leaders in population health efforts in the public and private sector. With information on the exact years of implementation of CBHIPs in each county, a pre-post design was used that identified county treatment and control groups. With additional census data, untreated counties were weighted to achieve better balance on pre-implementation covariates. Importantly, treated counties were limited to those with CBHIPs that implemented programs related to smoking and obesity. Results showed little to no evidence that CBHIPs improved population health outcomes. For example, CBHIPs focusing on tobacco prevention were associated with a 0.2 percentage point reduction in the rate of smoking, which was not statistically significant. Several important limitations of the study were noted by the authors, such as limited information on the intensity of programs and resources available. It is recognized that it is difficult to improve population-level health outcomes and that perhaps the study period of 5-years post-implementation may not have been long enough. The researchers encourage future CBHIPs to utilize more rigorous evaluation methods, while acknowledging the uphill battle CBHIPs face to do this.

Through the looking glass: estimating effects of medical homes for people with severe mental illness. Health Services Research [PubMed] Published October 2017

The third article in this round-up comes from a publication from October of last year, however, it is from the latest issue of Health Services Research so I deem it fair play. The article uses the topic of medical homes for individuals with severe mental illness to critically examine the topic of heterogeneous treatment effects. While specifically looking to answer whether there are heterogeneous treatment effects of medical homes on different portions of the population with a severe mental illness, the authors make a strong case for the need to examine heterogeneous treatment effects as a more general practice in observational studies research, as well as to be more precise in interpretations of results and statements of generalizability when presenting estimated effects. Adults with a severe mental illness were identified as good candidates for medical homes because of complex health care needs (including high physical health care needs) and because barriers to care have been found to exist for these individuals. Medicaid medical homes establish primary care physicians and their teams as the managers of the individual’s overall health care treatment. The authors are particularly concerned with the reasons individuals choose to participate in medical homes, whether because of expected improvements in quality of care, regional availability of medical homes, or symptomatology. Very clever differences in estimation methods allow the authors to estimate treatment effects associated with these different enrollment reasons. As an example, an instrumental variables analysis, using measures of regional availability as instruments, estimated local average treatment effects that were much smaller than the fixed effects estimates or the generalized estimating equation model’s effects. This implies that differences in county-level medical home availability are a smaller portion of the overall measured effects from other models. Overall results were that medical homes were positively associated with access to primary care, access to specialty mental health care, medication adherence, and measures of routine health care (e.g. screenings); there was also a slightly negative association with emergency room use. Since unmeasured stable attributes (e.g. patient preferences) do not seem to affect outcomes, results should be generalizable to the larger patient population. Finally, medical homes do not appear to be a good strategy for cost-savings but do promise to increase access to appropriate levels of health care treatment.

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Thesis Thursday: Koonal Shah

On the third Thursday of every month, we speak to a recent graduate about their thesis and their studies. This month’s guest is Dr Koonal Shah who has a PhD from the University of Sheffield. If you would like to suggest a candidate for an upcoming Thesis Thursday, get in touch.

Title
Valuing health at the end of life
Supervisors
Aki Tsuchiya, Allan Wailoo
Repository link
http://etheses.whiterose.ac.uk/17579

What were the key questions you wanted to answer with your research?

My key research question was: Do members of the general public wish to place greater weight on a unit of health gain for end of life patients than on that for other types of patients? Or put more concisely: Is there evidence of public support for an end of life premium?

The research question was motivated by a policy introduced by NICE in 2009 [PDF], which effectively gives special weighting to health gains generated by life-extending end of life treatments. This represents an explicit departure from the Institute’s reference case position that all equal-sized health gains are of equal social value (the ‘a QALY is a QALY’ rule). NICE’s policy was justified in part by claims that it represented the preferences of society, but little evidence was available to either support or refute that premise. It was this gap in the evidence that inspired my research question.

I also sought to answer other questions, such as whether the focus on life extensions (rather than quality of life improvements) in NICE’s policy is consistent with public preferences, and whether people’s stated end of life-related preferences depend on the ways in which the preference elicitation tasks are designed, framed and presented.

Which methodologies did you use to elicit people’s preferences?

All four of my empirical studies used hypothetical choice exercises to elicit preferences from samples of the UK general public. NICE’s policy was used as the framework for the designs in each case. Three of the studies can be described as having used simple choice tasks, while one study specifically applied the discrete choice experiment methodology. The general approach was to ask survey respondents which of two hypothetical patients they thought should be treated, assuming that the health service had only enough funds to treat one of them.

In my final study, which focused on framing effects and study design considerations, I included attitudinal questions with Likert item responses alongside the hypothetical choice tasks. The rationale for including these questions was to examine the consistency of respondents’ views across two different approaches (spoiler: most people are not very consistent).

Your study included face-to-face interviews. Did these provide you with information that you weren’t able to obtain from a more general survey?

The surveys in my first two empirical studies were both administered via face-to-face interviews. In the first study, I conducted the interviews myself, while in the second study the interviews were subcontracted to a market research agency. I also conducted a small number of face-to-face interviews when pilot testing early versions of the surveys for my third and fourth studies. The piloting process was useful as it provided me with first-hand information about which aspects of the surveys did and did not work well when administered in practice. It also gave me a sense of how appropriate my questions were. The subject matter – prioritising between patients described as having terminal illnesses and poor prognoses – had the potential to be distressing for some people. My view was that I shouldn’t be including questions that I did not feel comfortable asking strangers in an interview setting.

The use of face-to-face interviews was particularly valuable in my first study as it allowed me to ask debrief questions designed to probe respondents and elicit qualitative information about the thinking behind their responses.

What factors influence people’s preferences for allocating health care resources at the end of life?

My research suggests that people’s preferences regarding the value of end of life treatments can depend on whether the treatment is life-extending or quality of life-improving. This is noteworthy because NICE’s end of life criteria accommodate life extensions but not quality of life improvements.

I also found that the amount of time that end of life patients have to ‘prepare for death’ was a consideration for a number of respondents. Some of my results suggest that observed preferences for prioritising the treatment of end of life patients may be driven by concern about how long the patients have known their prognosis rather than by concern about how long they have left to live, per se.

The wider literature suggests that the age of the end of life patients (which may act as a proxy for their role in their household or in society) may also matter. Some studies have reported evidence that respondents become less concerned about the number of remaining life years when the patients in question are relatively old. This is consistent with the ‘fair innings’ argument proposed by Alan Williams.

Given the findings of your study, are there any circumstances under which you would support an end of life premium?

My findings offer limited support for an end of life premium (though it should be noted that the wider literature is more equivocal). So it might be considered appropriate for NICE to abandon its end of life policy on the grounds that the population health losses that arise due to the policy are not justified by the evidence on societal preferences. However, there may be arguments for retaining some form of end of life weighting irrespective of societal preferences. For example, if the standard QALY approach systematically underestimates the benefits of end of life treatments, it may be appropriate to correct for this (though whether this is actually the case would itself need investigating).

Many studies reporting that people wish to prioritise the treatment of the severely ill have described severity in terms of quality of life rather than life expectancy. And some of my results suggest that support for an end of life premium would be stronger if it applied to quality of life-improving treatments. This suggests that weighting QALYs in accordance with continuous variables capturing quality of life as well as life expectancy may be more consistent with public preferences than the current practice of applying binary cut-offs based only on life expectancy information, and would address some of the criticisms of the arbitrariness of NICE’s policy.