Chris Sampson’s journal round-up for 3rd July 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.

Role of cost on failure to access prescribed pharmaceuticals: the case of statins. Applied Health Economics and Health Policy [PubMed] Published 28th June 2017

Outside work, I find that people often like to tell me how to solve health economics problems. A common one is the idea that the NHS could save a load of money by enforcing prescription charges. It’s a textbook life-ain’t-that-simple situation. One of the reasons it isn’t that simple is that, if you start charging for prescriptions, people will be less likely to take their meds. That’s probably bad news for patients and for doctors. “But it’s only a few quid”. Well… As in many countries, Australians have to cough up a co-payment to fill their prescriptions. The size of the copayment depends on i) whether or not the patient is concessional (e.g. a pensioner) and ii) whether or not a threshold has been reached for total family prescription expenditure in one year. Concessional patients have a lower co-payment, a lower threshold and no co-payment once the threshold is met. This study looks at statin use in this context for 94,000 over-45s in New South Wales from 2005-2011. Separate logistic regressions are run for each of the 4 groups (concessional/non-concessional, pre-threshold/post-threshold) to predict statin adherence, controlling for a good range of sociodemographic and health-related variables. The size of the copayment comes out as the biggest barrier to adherence. More than 75% of people who weren’t adherent before reaching their threshold became so after reaching it – that is, once their co-payment was either much-reduced or zero. Poorest adherence was observed in non-concessional low-income people who hadn’t reached the threshold, who faced the highest co-payment. Income, age group and holding private insurance were also important determinants. In short, charging people for their statins, even if it isn’t much money, reduces the likelihood that they will take them. There is the possibility that adherence is correlated with the likelihood of having reached the threshold, which could undermine these results. I’m not entirely convinced that the analysis cuts the mustard, but I’ll let the more econometrically minded amongst you figure that out.

Conceptualizations of the societal perspective within economic evaluations: a systematic review. International Journal of Technology Assessment in Health Care [PubMed] Published 23rd June 2017

In my last round-up, I included a study looking at resource use measures for intersectoral costs and benefits; costs and benefits that occur outside the health sector. This week we have a study looking at how the inclusion of intersectoral costs and benefits influences results, and how researchers have interpreted the ‘societal perspective’. A systematic review was conducted for economic evaluations purporting to use a societal perspective, published since the CHEERS statement was released, including 107 studies. Only 74 provided a conceptualisation of the societal perspective. Reported conceptualisations of the societal perspective were grouped according to the specificity of their definition – 18 general, 50 specific, 6 both – and assessed using content analysis. Of these, 25 referred to a guideline or other source in their conceptualisation. A total of 10 general and 56 specific clusters of conceptualisations were identified, demonstrating major inconsistency. For some studies – namely trial-based economic evaluations in musculoskeletal or mental disorders – the authors dug deeper and extracted additional information. In both cases, where data were adequately reported, the intersectoral costs tended to make up more than 50% of total costs. But in general the specific intersectoral items were not fully reported and relevant costs (e.g. in education or criminal justice) were not identified. It probably won’t come as a surprise that the general impression is that a lot of researchers interpret the societal perspective – in practice, if not in theory – as health costs plus productivity losses. And usually, that’s not really good enough.

Annual direct medical costs associated with diabetes-related complications in the event year and in subsequent years in Hong Kong. Diabetic Medicine [PubMed] Published 21st June 2017

There are a lot of high-quality decision models built for the evaluation of interventions in diabetes. See Mt Hood. But some are still a bit primitive when it comes to estimating the costs associated with the many clinical pathways and complications associated with diabetes, especially when multimorbidity can be important. So studies like this are very welcome. This study contributes cost estimates for a wide range of complications (13, to be precise) for what should be a representative sample of (Chinese) people with diabetes. It includes public health care expenditure for more than 120,000 people with diabetes in Hong Kong, with 5-year follow-up. For private health care costs, a cross-section of 1275 people was recruited through other studies and provided information about service use by telephone. Fixed effects panel data regressions were used for the public medical costs. During the follow-up, 17% developed at least one complication. The models estimate the impact on total cost of new disease and existing disease separately, in order to identify first-year and subsequent-year cost estimates. Generalised linear models were used for the private health care costs. The base case of a 65-year old with no complications was US$1500/year in costs to the public purse. The biggest effect on costs was a first-year multiplier of 9.38 for lower limb ulcer (1.62 in subsequent years). Other costly complications were stroke, heart failure, end-stage renal disease and acute myocardial infarction. Private costs were much smaller, at $187 for the base case. These figures may prove useful to decision modellers, even outside the Hong Kong setting.

Financing and distribution of pharmaceuticals in the United States. JAMA [PubMed] Published 15th May 2017

The purpose of this article seems to be to demonstrate the complexity of the financing and distribution of pharmaceuticals in the US. It describes distributors, retailers and patients on the distribution side, and pharmacy benefit managers and health insurers on the financing side, with manufacturers in the middle. But the system that is shown in the article’s figure strikes me as surprisingly simple for an industry in which such vast amounts of money are sloshing around. It’s far more straightforward than any diagram you might see relating to the organisation of NHS services. I would imagine that a freer market would be associated with more complexity as upstarts might muscle-in on smaller corners of the market and become new intermediaries. But the article is still enlightening. It outlines some of the features of the market, particularly the high levels of concentration, characteristics of the key players and the staggering sums of money changing hands.

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Paul Mitchell’s journal round-up for 17th April 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.

Is foreign direct investment good for health in low and middle income countries? An instrumental variable approach. Social Science & Medicine [PubMed] Published 28th March 2017

Foreign direct investment (FDI) is considered a key benefit of globalisation in the economic development of countries with developing economies. The effect FDI has on the population health of countries is less well understood. In this paper, the authors draw from a large panel of data, primarily World Bank and UN sources, for 85 low and middle income countries between 1974 and 2012 to assess the relationship between FDI and population health, proxied by life expectancy at birth, as well as child and adult mortality data. They explain clearly the problem of using basic regression analysis in trying to explain this relationship, given the problem of endogeneity between FDI and health outcomes. By introducing two instrumental variables, using grossed fixed capital formation and volatility of exchange rates in FDI origin countries, as well as controlling for GDP per capita, education, quality of institutions and urban population, the study shows that FDI is weakly statistically associated with life expectancy, estimated to amount to 4.15 year increase in life expectancy during the study period. FDI also appears to have an effect on reducing adult mortality, but a negligible effect on child mortality. They also produce some evidence that FDI linked to manufacturing could lead to reductions in life expectancy, although these findings are not as robust as the other findings using instrumental variables, so they recommend this relationship between FDI type and population health to be explored further. The paper also clearly shows the benefit of robust analysis using instrumental variables, as the results without the introduction of these variables to the regression would have led to misleading inferences, where no relationship between life expectancy and FDI would have been found if the analysis did not adjust for the underlying endogeneity bias.

Uncovering waste in US healthcare: evidence from ambulance referral patterns. Journal of Health Economics [PubMed] Published 22nd March 2017

This study looks to unpick some of the reasons behind the estimated waste in US healthcare spending, by focusing on mortality rates across the country following an emergency admission to hospital through ambulances. The authors argue that patients admitted to hospital for emergency care using ambulances act as a good instrument to assess hospital quality given the nature of emergency admissions limiting the selection bias of what type of patients end up in different hospitals. Using linear regressions, the study primarily measures the relationship between patients assigned to certain hospitals and the 90-day spending on these patients compared to mortality. They also consider one-year mortality and the downstream payments post-acute care (excluding pharmaceuticals outside the hospital setting) has on this outcome. Through a lengthy data cleaning process, the study looks at over 1.5 million admissions between 2002-2011, with a high average age of patients of 82 who are predominantly female and white. Approximately $27,500 per patient was spent in the first 90 days post-admission, with inpatient spending accounting for the majority of this amount (≈$16,000). The authors argue initially that the higher 90-day spending in some hospitals only produces modestly lower mortality rates. Spending over 1 year is estimated to cost more than $300,000 per life year, which the authors use to argue that current spending levels do not lead to improved outcomes. But when the authors dig deeper, it seems clear there is an association between hospitals who have higher spending on inpatient care and reduced mortality, approximately 10% lower. This leads to the authors turning their attention to post-acute care as their main target of reducing waste and they find an association between mortality and patients receiving specialised nursing care. However, this target seems somewhat strange to me, as post-acute care is not controlled for in the same way as their initial, insightful approach to randomising based on ambulatory care. I imagine those in such care are likely to be a different mix from those receiving other types of care post 90 days after the initial event. I feel there really is not enough to go on to make recommendations about specialist nursing care being the key waste driver from their analysis as it says nothing, beyond mortality, about the quality of care these elderly patients are receiving in the specialist nurse facilities. After reading this paper, one way I would suggest in reducing inefficiency related to their primary analysis could be to send patients to the most appropriate hospital for what the patient needs in the first place, which seems difficult given the complexity of the private and hospital provided mix of ambulatory care offered in the US currently.

Population health and the economy: mortality and the Great Recession in Europe. Health Economics [PubMed] Published 27th March 2017

Understanding how economic recessions affect population health is of great research interest given the recent global financial crisis that led to the worst downturn in economic performance in the West since the 1930s. This study uses data from 27 European countries between 2004 and 2010 collected by WHO and the World Bank to study the relationship between economic performance and population health by comparing national unemployment and mortality rates before and after 2007. Regression analyses appropriate for time-series data are applied with a number of different specifications applied. The authors find that the more severe the economic downturn, the greater the increase in life expectancy at birth. Additional specific health mortality rates follow a similar trend in their analysis, with largest improvements observed in countries where the severity of the recession was the highest. The only exception the authors note is data on suicide, where they argue the relationship is less clear, but points towards higher rates of suicide with greater unemployment. The message the authors were trying to get across in this study was not very clear throughout most of the paper and some lay readers of the abstract alone could easily be misled in thinking recessions themselves were responsible for better population health. Mortality rates fell across all six years, but at a faster rate in the recession years. Although the results appeared consistent across all models, question marks remain for me in terms of their initial variable selection. Although the discussion mentions evidence that suggests health care may not have a short-term effect on mortality, they did not consider any potential lagged effect record investment in healthcare as a proportion of GDP up until 2007 may have had on the initial recession years. The authors rule out earlier comparisons with countries in the post-Soviet era but do not consider the effect of recent EU accession for many of the countries and more regulated national policies as a consequence. Another issue is the potential of countries’ mortality rates to improve, where countries with existing lower life expectancy have more room for moving in the right direction. However, one interesting discussion point raised by the authors in trying to explain their findings is the potential impact of economic activity on pollution levels and knock-on health impacts from this (and to a lesser extent occupational health levels), that may have some plausibility in better mortality rates linked to physical health during recessions.

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How to cite The Academic Health Economists’ Blog

Occasionally we get emails from people who would like to cite our blog posts. Usually, these requests are framed as ‘is this going to be published in a journal?’. It’s no surprise that people are more comfortable citing the traditional academic literature. But researchers are increasingly citing blog posts. Indeed, some of our blog posts have been cited in published academic literature.

There are plenty of guides out there for citing blog posts. You may like to refer to them for specific formatting styles. Cite This For Me is a useful tool for generating references in a variety of styles. Here I’d like to provide a few specific recommendations for citing posts from this blog.

1. Cite the author

Our blog posts are written by lots of different authors, not by ‘the blog’. The author’s name – assuming they have not claimed anonymity – will appear at the top of the blog post. Let’s take a recent example. To start with, your citation should look something like:

Watson, S. (2017). Variations in NHS admissions at a glance. The Academic Health Economists’ Blog. Available at: https://aheblog.com/2017/01/25/variations-in-nhs-admissions-at-a-glance/ [Accessed 8 Mar. 2017].

2. Use our ISSN

As of this week, the blog now has its own International Standard Serial Number (ISSN). This number uniquely identifies and distinguishes the blog. Our ISSN is 2514-3441. You can find it at the bottom of the sidebar and on our About page. So your citation could become:

Watson, S. (2017). Variations in NHS admissions at a glance. The Academic Health Economists’ Blog (ISSN 2514-3441). Available at: https://aheblog.com/2017/01/25/variations-in-nhs-admissions-at-a-glance/ [Accessed 8 Mar. 2017].

3. Use WebCite

Unlike journal articles, websites can change. One of our authors could (in principle) completely change the content of their blog post after publishing it. More importantly, it is possible that our URLs may change in the future. If this were to happen, the link in the reference above would become redundant and the citation would not be useful to readers. What needs to be cited, therefore, is the blog post at the time at which you accessed it. Enter WebCite. WebCite is a service that archives a webpage and provides a permanent link for citation. This can be achieved by completing an archiving form. Our citation becomes:

Watson, S. (2017). Variations in NHS admissions at a glance. The Academic Health Economists’ Blog (ISSN 2514-3441). Available at: https://aheblog.com/2017/01/25/variations-in-nhs-admissions-at-a-glance/ [Accessed 8 Mar. 2017]. (Archived by WebCite® at http://www.webcitation.org/6ooALaGyF)

4. Check the comments

Finally, authors may choose to subsequently publish their blog post elsewhere in another format or to upload it to a service such as figshare in order to obtain a DOI. Check the comments below a blog post to see if this is the case as there may be an alternative source that you might prefer to cite.

But as ever, if you’re struggling, get in touch.

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