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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ICU triage: a challenge and an opportunity

In a well-publicized snapshot of the challenge of ICU triage, Chang and colleagues wrote:

Critical care services can be life-saving, but many patients admitted to intensive care units (ICUs) are too sick or, conversely, not sick enough to benefit. Intensive care unit overutilization can produce more costly and invasive care without improving outcomes.

Emphasis added. Hyder provides an interesting critique to which Chang and Shapiro respond. In this post, I shall consider over-utilization by those “not sick enough to benefit”: 23.4% of the 808 patients admitted to the UCLA Medical Center in the study by Chang et al. This over-utilization provides both a challenge and a win-win opportunity (better outcomes at lower cost) if we can meet the challenge.

In a forward-looking vision, which some may regard as optimistic, Anesi et al wrote:

In the year 2050 we will unambiguously reimburse healthcare based on value, and so there is good reason to suspect that we will have targeted and reduced many services that provide little or no benefit to patients…

It can be argued that ICU over-utilization, on average, provides no overall benefit, while significantly increasing costs. Gooch and Kahn observed that US spending on critical care represents nearly 3% of GDP, while:

In contrast, the United Kingdom spends only 0.1% of its gross domestic product on critical care services, with no evidence of worse patient outcomes and similar life expectancies as in the United States. Although there are many differences between these 2 countries, one significant difference is intensive care unit (ICU) bed supply. The United States has 25 ICU beds per 100 000 people, as compared with 5 per 100 000 in the United Kingdom. As a result, ICU case-mix differs substantially. In the United Kingdom, the majority of ICU patients are at high risk for death, whereas in the United States, many patients are admitted to the ICU for observation.

As observed by Halpern, these differences come at a significant cost in the US:

The number of intensive care unit (ICU) beds in the United States has continued to increase over the last 3 decades, as have ICU utilization rates and costs, and this despite the lack of any federal, regional, or critical care society mandates to justify these increases. Some experts believe that the increase in the number of ICU beds has led to inappropriate use of these beds by patients who are either too healthy or too sick to benefit from intensive care. This may in part explain the stable national ICU occupancy rate of approximately 68% between 1985 and 2010 and suggests that ICU utilization has simply risen to meet the increased number of beds.

Emphasis added. I shall consider here only ICU usage by patients too healthy to benefit. Although the economics behind reducing ICU over-utilization by “those not sick enough to benefit” appears simple, the underlying cause is in fact likely complex.

icu-costs-fig-1

This one appears easy: lower costs and potentially better outcomes

At the same time, I recall several caveats, well known to health economists, but important in planning and communication:

  1. We expect ICUs to be available when needed, including for emergencies and disasters,
  2. ICUs have high fixed costs,
  3. Decision-making is critical: incremental costs of adding capacity become fixed costs in the future.

Chris Sampson recently reviewed a study aimed at overconsumption or misconsumption (a consequence of over-utilization). The authors of that paper suggest that “cultural change might be required to achieve significant shifts in clinical behaviour.” Chris laments that this study did not ‘dig deeper’; here we aim to dig deeper in one specific area: ICU triage for patients “not sick enough to benefit.” More questions than answers at this stage, but hopefully the questions will ultimately lead to answers.

I begin by stepping back: economic decisions frequently involve compromises in allocating scarce resources. Decisions in health economics are frequently no different. How scarce are ICU resources? What happens if they are less scarce? What are the costs? Increasing availability can frequently lead to increased utilization, a phenomenon called “demand elasticity”. For example, increasing expressway/motorway capacity “can lead to increased traffic as new drivers seize the opportunity to travel on the larger road”, and thus no reduction in travel time. Gooch and Kahn further note that:

The presence of demand elasticity in decisions regarding ICU care has major implications for health care delivery and financing. Primarily, this indicates it is possible to reduce the costs of US hospital care by constraining ICU bed supply, perhaps through certificate of need laws or other legislation.

I offer a highly simplified sketch of how ICU over-utilization by those “not sick enough to benefit” is one driver of a vicious cycle in ICU cost growth.

icu-costs-fig-2

ICU over-utilization by patients “not sick enough to benefit” as a driver for ICU demand elasticity

Who (if anyone) is at fault for this ICU vicious cycle? Chang and Shapiro offer one suggestion:

For medical conditions where ICU care is frequently provided, but may not always be necessary, institutions that utilize ICUs more frequently are more likely to perform invasive procedures and have higher costs but have no improvement in hospital mortality. Hospitals had similar ICU utilization patterns across the 4 medical conditions, suggesting that systematic institutional factors may influence decisions to potentially overutilize ICU care.

Emphasis added. I note that demand elasticity is not in itself bad; it must simply be recognized, controlled and used appropriately. As part of a discussion in print on the role of cost considerations in medical decisions, Du and Kahn write:

Although we argue that costs should not be factored into medical decision-making in the ICU, this does not mean that we should not strive toward healthcare cost reduction in other ways. One strategy is to devise systems of care that prevent unnecessary or unwanted ICU admissions—given the small amount of ICU care that is due to discretionary spending, the only real way to reduce ICU costs is to prevent ICU admissions in the first place.

Du and Kahn also argue for careful cost-effectiveness analyses, such as that supported by NICE in the UK:

These programs limit use of treatments that are not cost-effective, taking cost decisions out of the hands of physicians and putting them where they belong: in the hands of society at large… We will achieve real ICU savings only by encouraging a society committed to system-based reforms.

Emphasis added. One can debate “taking cost decisions out of the hands of physicians”, though Guidet & Beale‘s and Capuzzo & Rhodes‘s argument for more physician awareness of cost might provide a good intermediate position in this debate.

Finally, increasing ICU supply (that is, ICU beds) in response to well-conceived increases in ICU demand is not in itself bad; ICU supply must be able to respond to demands imposed by disasters or other emergencies. We need to seek out novel ways to provide this capacity without incurring potentially unnecessary fixed costs, perhaps from region-wide stockpiling of supplies and equipment, and region-wide pools of on-call physicians and other ICU personnel. In summary, current health-related literature offers a wide-ranging discussion of the growing costs of intensive care; in my opinion: more questions than answers at this stage, but hopefully the questions will ultimately lead to answers.

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Alastair Canaway’s journal round-up for 20th February 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 estimation and inclusion of presenteeism costs in applied economic evaluation: a systematic review. Value in Health Published 30th January 2017

Presenteeism is one of those issues that you hear about from time to time, but rarely see addressed within economic evaluations. For those who haven’t come across it before, presenteeism refers to being at work, but not working at full capacity, for example, due to your health limiting your ability to work. The literature suggests that given presenteeism can have large associated costs which could significantly impact economic evaluations, it should be considered. These impacts are rarely captured in practice. This paper sought to identify studies where presenteeism costs were included, examined how valuation was approached and the degree of impact of including presenteeism on costs. The review included cost of illness studies as well as economic evaluations, just 28 papers had attempted to capture the costs of presenteeism, these were in a wide variety of disease areas. A range of methods was used, across all studies, presenteeism costs accounted for 52% (range from 19%-85%) of the total costs relating to the intervention and disease. This is a vast proportion and significantly outweighed absenteeism costs. Presenteeism is clearly a significant issue, yet widely ignored within economic evaluation. This in part may be due to the health and social care perspective advised within the NICE reference case and compounded by the lack of guidance in how to measure and value productivity costs. Should an economic evaluation pursue a societal perspective, the findings suggest that capturing and valuing presenteeism costs should be a priority.

Priority to end of life treatments? Views of the public in the Netherlands. Value in Health Published 5th January 2017

Everybody dies, and thus, end of life care is probably something that we should all have at least a passing interest in. The end of life context is an incredibly tricky research area with methodological pitfalls at every turn. End of life care is often seen as ‘different’ to other care, and this is reflected in NICE having supplementary guidance for the appraisal of end of life interventions. Similarly, in the Netherlands, treatments that do not meet typical cost per QALY thresholds may be provided should public support be sufficient. There, however, is a dearth of such evidence, and this paper sought to elucidate this issue using the novel Q methodology. Three primary viewpoints emerged: 1) Access to healthcare as a human right – all have equal rights regardless of setting, that is, nobody is more important. Viewpoint one appeared to reject the notion of scarce resources when it comes to health: ‘you can’t put a price on life’. 2) The second group focussed on providing the ‘right’ care for those with terminal illness and emphasised that quality of life should be respected and unnecessary care at end of life should be avoided. This second group did not place great importance on cost-effectiveness but did acknowledge that costly treatments at end of life might not be the best use of money. 3) Finally, the third group felt there should be a focus on care which is effective and efficient, that is, those treatments which generate the most health should be prioritised. There was a consensus across all three groups that the ultimate goal of the health system is to generate the greatest overall health benefit for the population. This rejects the notion that priority should be given to those at end of life and the study concludes that across the three groups there was minimal support for the possibility of the terminally ill being treated with priority.

Methodological issues surrounding the use of baseline health-related quality of life data to inform trial-based economic evaluations of interventions within emergency and critical care settings: a systematic literature review. PharmacoEconomics [PubMed] Published 6th January 2017

Catchy title. Conducting research within emergency and critical settings presents a number of unique challenges. For the health economist seeking to conduct a trial based economic evaluation, one such issue relates to the calculation of QALYs. To calculate QALYs within a trial, baseline and follow-up data are required. For obvious reasons – severe and acute injuries/illness, unplanned admission – collecting baseline data on those entering emergency and critical care is problematic. Even when patients are conscious, there are ethical issues surrounding collecting baseline data in this setting, the example used relates to somebody being conscious after cardiac arrest, is it appropriate to be getting them to complete HRQL questionnaires? Probably not. Various methods have been used to circumnavigate this issue; this paper sought to systematically review the methods that have been used and provide guidance for future studies. Just 19 studies made it through screening, thus highlighting the difficulty of research in this context. Just one study prospectively collected baseline HRQL data, and this was restricted to patients in a non-life threatening state. Four different strategies were adopted in the remaining papers. Eight studies adopted a fixed health utility for all participants at baseline, four used only the available data, that is, from the first time point where HRQL was measured. One asked patients to retrospectively recall their baseline state, whilst one other used Delphi methods to derive EQ-5D states from experts. The paper examines the implications and limitations of adopting each of these strategies. The key finding seems to relate to whether or not the trial arms are balanced with respect to HRQL at baseline. This obviously isn’t observed, the authors suggest trial covariates should instead be used to explore this, and adjustments made where applicable. If, and that’s a big if, trial arms are balanced, then all of the four methods suggested should give similar answers. It seems the key here is the randomisation, however, even the best randomisation techniques do not always lead to balanced arms and there is no guarantee of baseline balance. The authors conclude trials should aim to make an initial assessment of HRQL at the earliest opportunity and that further research is required to thoroughly examine how the different approaches will impact cost-effectiveness results.

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