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

Tuskegee and the health of black men. The Quarterly Journal of Economics [RePEc] Published February 2018

In 1932, a study often considered the most infamous and potentially most unethical in U.S. medical history began. Researchers in Alabama enrolled impoverished black men in a research program designed to examine the effects of syphilis under the guise of receiving government-funded health care. The study was known as the Tuskegee syphilis experiment. For 40 years the research subjects were not informed they had syphilis nor were they treated, even after penicillin was shown to be effective. The study was terminated in 1972 after its details were leaked to the press; numerous men died, 40 wives contracted syphilis, and a number of children were born with congenital syphilis. It is no surprise then that there is distrust among African Americans in the medical system. The aim of this article is to examine whether the distrust engendered by the Tuskegee study could have contributed to the significant differences in health outcomes between black males and other groups. To derive a causal estimate the study makes use of a number of differences: black vs non-black, for obvious reasons; male vs female, since the study targeted males, and also since women were more likely to have had contact with and hence higher trust in the medical system; before vs after; and geographic differences, since proximity to the location of the study may be informative about trust in the local health care facilities. A wide variety of further checks reinforce the conclusions that the study led to a reduction in health care utilisation among black men of around 20%. The effect is particularly pronounced in those with low education and income. Beyond elucidating the indirect harms caused by this most heinous of studies, it illustrates the importance of trust in mediating the effectiveness of public institutions. Poor reputations caused by negligence and malpractice can spread far and wide – the mid-Staffordshire hospital scandal may be just such an example.

The economic consequences of hospital admissions. American Economic Review [RePEcPublished February 2018

That this paper’s title recalls that of Keynes’s book The Economic Consequences of the Peace is to my mind no mistake. Keynes argued that a generous and equitable post-war settlement was required to ensure peace and economic well-being in Europe. The slow ‘economic privation’ driven by the punitive measures and imposed austerity of the Treaty of Versailles would lead to crisis. Keynes was evidently highly critical of the conference that led to the Treaty and resigned in protest before its end. But what does this have to do with hospital admissions? Using an ‘event study’ approach – in essence regressing the outcome of interest on covariates including indicators of time relative to an event – the paper examines the impact hospital admissions have on a range of economic outcomes. The authors find that for insured non-elderly adults “hospital admissions increase out-of-pocket medical spending, unpaid medical bills, and bankruptcy, and reduce earnings, income, access to credit, and consumer borrowing.” Similarly, they estimate that hospital admissions among this same group are responsible for around 4% of bankruptcies annually. These losses are often not insured, but they note that in a number of European countries the social welfare system does provide assistance for lost wages in the event of hospital admission. Certainly, this could be construed as economic privation brought about by a lack of generosity of the state. Nevertheless, it also reinforces the fact that negative health shocks can have adverse consequences through a person’s life beyond those directly caused by the need for medical care.

Is health care infected by Baumol’s cost disease? Test of a new model. Health Economics [PubMed] [RePEcPublished 9th February 2018

A few years ago we discussed Baumol’s theory of the ‘cost disease’ and an empirical study trying to identify it. In brief, the theory supposes that spending on health care (and other labour-intensive or creative industries) as a proportion of GDP increases, at least in part, because these sectors experience the least productivity growth. Productivity increases the fastest in sectors like manufacturing and remuneration increases as a result. However, this would lead to wages in the most productive sectors outstripping those in the ‘stagnant’ sectors. For example, salaries for doctors would end up being less than those for low-skilled factory work. Wages, therefore, increase in the stagnant sectors despite a lack of productivity growth. The consequence of all this is that as GDP grows, the proportion spent on stagnant sectors increases, but importantly the absolute amount spent on the productive sectors does not decrease. The share of the pie gets bigger but the pie is growing at least as fast, as it were. To test this, this article starts with a theoretic two-sector model to develop some testable predictions. In particular, the authors posit that the cost disease implies: (i) productivity is related to the share of labour in the health sector, and (ii) productivity is related to the ratio of prices in the health and non-health sectors. Using data from 28 OECD countries between 1995 and 2016 as well as further data on US industry group, they find no evidence to support these predictions, nor others generated by their model. One reason for this could be that wages in the last ten years or more have not risen in line with productivity in manufacturing or other ‘productive’ sectors, or that productivity has indeed increased as fast as the rest of the economy in the health care sector. Indeed, we have discussed productivity growth in the health sector in England and Wales previously. The cost disease may well then not be a cause of rising health care costs – nevertheless, health care need is rising and we should still expect costs to rise concordantly.

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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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Method of the month: Coding qualitative data

Once a month we discuss a particular research method that may be of interest to people working in health economics. We’ll consider widely used key methodologies, as well as more novel approaches. Our reviews are not designed to be comprehensive but provide an introduction to the method, its underlying principles, some applied examples, and where to find out more. If you’d like to write a post for this series, get in touch. This month’s method is coding qualitative data.

Principles

Health economists are increasingly stepping away from quantitative datasets and conducting interviews and focus groups, as well as collecting free text responses. Good qualitative analysis requires thought and rigour. In this blog post, I focus on coding of textual data – a fundamental part of analysis in nearly all qualitative studies. Many textbooks deal with this in detail. I have drawn on three in particular in this blog post (and my research): Coast (2017), Miles and Huberman (1994), and Ritchie and Lewis (2003).

Coding involves tagging segments of the text with salient words or short phrases. This assists the researcher with retrieving the data for further analysis and is, in itself, the first stage of analysing the data. Ultimately, the codes will feed into the final themes or model resulting from the research. So the codes – and the way they are applied – are important!

Implementation

There is no ‘right way’ to code. However, I have increasingly found it useful to think of two phases of coding. First, ‘open coding’, which refers to the initial exploratory process of identifying pertinent phrases and concepts in the data. Second, formal or ‘axial’ coding, involving the application of a clear, pre-specified coding framework consistently across the source material.

Open coding

Any qualitative analysis should start with the researcher being very familiar with both the source material (such as interview transcripts) and the study objectives. This sounds obvious, but it is easy, as a researcher, to get drawn into the narrative of an interview and forget what exactly you are trying to get out of the research and, by extension, the coding. Open coding requires the researcher to go through the text, carefully, line-by-line, tagging segments with a code to denote its meaning. It is important to be inquisitive. What is being said? Does this relate to the research question and, if so, how?

Take, for example, the excerpt below from a speech by the Secretary of State for Health, Jeremy Hunt, on safety and efficiency in the NHS in 2015:

Let’s look at those challenges. And I think we have good news and bad news. If I start with the bad news it is that we face a triple whammy of huge financial pressures because of the deficit that we know we have to tackle as a country, of the ageing population that will mean we have a million more over 70s by 2020, and also of rising consumer expectations, the incredible excitement that people feel when they read about immunotherapy in the newspapers that gives a heart attack to me and Simon Stevens but is very very exciting for the country. The desire for 24/7 access to healthcare. These are expectations that we have to recognise in the NHS but all of these add to a massive pressure on the system.

This excerpt may be analysed, for example, as part of a study into demand pressures on the NHS. And, in this case, codes such as “ageing population” “consumer expectations” “immunotherapy” “24/7 access to healthcare” might initially be identified. However, if the study was investigating the nature of ministerial responsibility for the NHS, one might pull out very different codes, such as “tackle as a country”, “public demands vs. government stewardship” and “minister – chief exec shared responsibility”.

Codes can be anything – attitudes, behaviours, viewpoints – so long as they relate to the research question. It is very useful to get (at least) one other person to also code some of the same source material. Comparing codes will provide new ideas for the coding framework, a different perspective of the meaning of the source material and a check that key sections of the source material have not been missed. Researchers shouldn’t aim to code all (or even most) of the text of a transcript – there is always some redundancy. And, in general, initial codes should be as close to the source text as possible – some interpretation is fine but it is important to not get too abstract too quickly!

Formal or ‘axial’ coding

When the researcher has an initial list of codes, it is a good time to develop a formal coding framework. The aim here is to devise an index of some sort to tag all the data in a logical, systematic and comprehensive way, and in a way that will be useful for further analysis.

One way to start is to chart how the initial codes can be grouped and relate to one another. For example, in analysing NHS demand pressures, a researcher may group “immunotherapy” with other medical innovations mentioned elsewhere in the study. It’s important to avoid having many disconnected codes, and at this stage, many codes will be changed, subdivided, or combined. Much like an index, the resulting codes could be organised into loose chapters (or themes) such as “1. Consumer expectations”, “2. Access” and/or there might be a hierarchical relationship between codes, for example, with codes relating to national and local demand pressures. A proper axial coding framework has categories and sub-categories of codes with interdependencies formally specified.

There is no right number of codes. There could be as few as 10, or as many as 50, or more. It is crucial however that the list of codes are logically organised (not alphabetically listed) and sufficiently concise, so that the researcher can hold them in their head while coding transcripts. Alongside the coding framework itself – which may only be a page – it can be very helpful to put together an explanatory document with more detail on the meaning of each code and possibly some examples.

Software

Once the formal coding framework is finalised it can be applied to the source material. I find this a good stage to use software like Nvivo. While coding in Nvivo takes a similar amount of time to paper-based methods, it can help speed up the process of retrieving and comparing segments of the text later on. Other software packages are available and some researchers prefer to use computer packages earlier in the process or not all – it is a personal choice.

Again, it is a good idea to involve at least one other person. One possibility is for two researchers to apply the framework separately and code the first, say 5 pages of a transcript. Reliability between coders can then be compared, with any discrepancies discussed and used to adjust the coding framework accordingly. The researchers could then repeat the process. Once reliability is at an acceptable level, a researcher should be able to code the transcripts in a much more reproducible way.

Even at this stage, the formal coding framework does not need to be set in stone. If it is based on a subset of interviews, new issues are likely to emerge in subsequent transcripts and these may need to be incorporated. Additionally, analyses may be conducted with sub-samples of participants or the analysis may move from more descriptive to explanatory work, and therefore the coding needs may change.

Applications

Published qualitative studies will often mention that transcript data were coded, with few details to discern how this was done. In the study I worked on to develop the ICECAP-A capability measure, we coded to identify influences on quality of life in the first batch of interviews and dimensions of quality of life in later batches of interviews. A recent study into disinvestment decisions highlights how a second rater can be used in coding. Reporting guidelines for qualitative research papers highlight three important items related to coding – number of coders, description of the coding tree (framework), and derivation of the themes – that ought to be included in study write-ups.

Coding qualitative data can feel quite laborious. However, the real benefit of a well organised coding framework comes when reconstituting transcript data under common codes or themes. Codes that relate clearly to the research question, and one another, allow the researcher to reorganise the data with real purpose. Juxtaposing previously unrelated text and quotes sparks the discovery of exciting new links in the data. In turn, this spawns the interpretative work that is the fundamental value of the qualitative analysis. In economics parlance, good coding can improve both the efficiency of retrieving text for analysis and the quality of the analytical output itself.

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