Tag Archives: Economics

Another Excellent Paper on Economic and Mortality Inequality from Currie & Schwandt

In the latest News Blog [1] (before the election purdah) I covered Case and Deaton’s monumental study of death rates among white people in the US.[2] I briefly mentioned the idea that childhood (and even pre-natal) exposure can ‘programme’ the body, leading to mortality differences in later life. This can lead to exaggerated estimates of the effects of economic conditions and behaviours in later life on health and life expectancy. There is strong evidence that patterns of behaviour in adulthood are laid down by the age of three.[3] Failure to give due consideration to prior conditions can also lead to poor interpretation of life expectancy statistics. Life expectancy (say at birth) is derived, perforce, from the current age-specific mortality rates at all (subsequent) ages.[4] So there is an assumption that when a baby born in 2017 reaches age 40, she or he will be subject to the current mortality rates for 40 year olds, and so on. That is a massive assumption, given the above point concerning early childhood effects on adult health.

The subject of wealth and health is replete with academic bear traps. Mortality is rising among poor white people,[5] as we pointed out in a previous News Blog.[6] But then the composition of poor white people changes over time. So the mortality of poor white 40 year old women cannot automatically be ascribed to any recent change in the behaviours or exposures of such women. It could be attributable to their early life exposures. Likewise, Hispanic children have been dropping out of school in the US at progressively lower rates. So any observation comparing the health of drop-outs over time is highly biased – the same types of people are not being compared. And when it comes to ethnicity, things get harder still because the way ethnic groups are classified is ephemeral.

Currie and Schwandt use counties in the US as the basis for comparative statistics.[7] They use three year averages to reduce noise, and they measure the socio-economic standards of counties in different ways – poverty rates, high-school completion rates, and median income. They look at age-specific death rates, life expectancy (as a consolidated measure of death rates over all ages), and age/sex adjusted differences by race.

What do they find in their study covering the years 1990-2010?

  1. Life expectancy is increasing across the US, but us doing so to a greater extent in poorer areas than in richer ones.
  2. This relative improvement in poor counties compared to rich counties is seen particularly among women.
  3. And in children under the age of five (see a previous News Blog [6]).
  4. Inequalities in death rates in young adults are also declining.
  5. But over age 50, inequalities in mortality increased for women while remaining unchanged in men.
  6. For black children, inequalities narrowed compared to white children.
  7. The increased health inequality of white adults cited in our last News Blog is confirmed (phew!).

There are other interesting findings. I would have thought that immigrants would have worse outcomes than age and race matched residents, but the opposite is the case – at least for Hispanic people. A massive study of identical twins separated at birth would be needed to sort out cause and effect relationships (and even that would not be perfect). However, taken in the round, the news from the USA is good regarding inequalities; poor white people aside. Let me therefore end with a quote from the article – you can make of it what you will:

It sometimes seems as if the research literature on mortality is compelled in some way to emphasize a negative message, either about a group that is doing less well or about some aspect of inequality that is rising.

— Richard Lilford, CLAHRC WM Director


  1. Lilford RJ. Ever Increasing Life Expectancies Come to an Abrupt End Among American Whites. NIHR CLAHRC West Midlands News Blog. 5 May 2017.
  2. Case A, & Deaton A. Mortality and morbidity in the 21st century. Brookings Papers on Economic Activity. BPEA Conference Drafts. March 23-24, 2017.
  3. Suzuki E, & Fantom N. What does “life expectancy at birth” really mean? The DATA Blog. 11 November 2013.
  4. Lilford RJ. More on Brain Health in Young Children and Effect on Life Course. NIHR CLAHRC West Midlands News Blog. 24 February 2017.
  5. Chetty R, Stepner M, Abraham S, et al. The Association Between Income and Life Expectancy in the United States, 2001-2014. JAMA. 2016; 315(6):1750-66.
  6. Lilford RJ. Relative Wealth and Health. NIHR CLAHRC West Midlands News Blog. 6 May 2016.
  7. Currie J & Schwandt H. Mortality Inequality: The Good News from a County-Level Approach. J Econ Perspect. 2016; 30(2): 29-52.

Medical Technology – Separating the Wheat from the Chaff

Scientists often come up with elegant inventions that they wish to exploit commercially. However, an elegant invention may not be cost-effective. The world is experiencing a period of massive growth in investments in high-tech start-up companies.[1] Many of these achieve high values in short time periods, despite not generating any sales  – Silicon Valley is home to 142 ‘unicorns’ (unlisted start-up companies valued at more than $1 billion).[1] It is no secret that investments of this type are highly speculative, driven more by sentiment than analysis. Currently a ‘bubble’ appears to be forming.[2] A company’s market value is theoretically equal to the net present value of future cash flows. Problem – if there are no present cash flows on which to base future projections, then the company’s value is a wild guess. But is it so wild or could it be tamed?

A substantial proportion of start-ups are based on medical technology – for example, the fabled ‘unicorn’, Theranos, which makes (or rather is attempting to make) revolutionary blood spot testing equipment. Medical innovations are increasingly procured on the rational grounds that they are cost-effective.[3] [4] Health economics provides a set of techniques to calculate the cost-effectiveness of new treatments to help decide whether a treatment or diagnostic method should be supported in a service [5]; in England, the National Institute for Health and Care Excellence (NICE) makes procurement decisions on this basis. A recent article from our CLAHRC [6] provides a synopsis of techniques to inform procurement decisions by calculating cost-effectiveness of a technology when it is still at the idea or design stage.[7] [8] This, in turn, can be used to determine the optimal price.[9] Then future cash flows can be calculated taking into account repayment of the initial investment. Health economics at the supply side (i.e. to inform investment decisions) has two fundamental differences from health economics at the demand side (i.e. to inform procurement decisions):

  1. Uncertainties are greater.
  2. Uncertainties can be resolved or reduced during development of the technology. This means that the option to develop the technology can be kept alive until more information has been collected.

The corollaries of those two fundamental points are that:

  1. Parameter estimates for supply-side economic models are ‘Bayesian’ in the sense that they are prior probability estimates derived from experts (rather than observed frequencies) and;
  2. The calculations must include the present value of holding an option that may, or may not, be pursued at some future date.

Economic models cannot give a definitive answer to investment decisions. However, human judgement is clouded by all sorts of faulty heuristics (mental processes),[10] [11] and models direct decision-makers to look closely at assumptions and provide at least a partial antidote to ‘optimism bias’.[12] They are a guide for the savvy investor and should strengthen the supply side of the medical technology industry, thereby mitigating the risk of boom and bust.

— Richard Lilford, Professor of Public Health, University of Warwick


  1. The Economist. Theranos: The fable of the unicorn. The Economist. 31 October 2015. 69-70.
  2. Mahmood T. The Tech Industry is in Denial, but the Bubble is About to Burst. Tech Crunch. 26 June 2015.
  3. Cleemput I, Neyt M, Thiry N, De Laet C, Leys M. Using threshold values for cost per quality-adjusted life-year gained in healthcare decisions. Int J Technol Assess Health Care. 2011; 27(1): 71-6.
  4. Schwarzer R, Rochau U, Saverno K, et al. Systematic overview of cost-effectiveness thresholds in ten countries across four continents. J Comp Eff Res. 2015; 4(5): 485-504.
  5. Drummond MF. Methods for the economic evaluation of health care programmes. Oxford: Oxford University Press. 2005.
  6. Girling A, Young T, Chapman A, Lilford R. Economic assessment in the commercial development cycle for medical devices. Intl J Technol Assess in Health Care. 2015. [ePub].
  7. Girling A, Young T, Brown C, Lilford R. Early-Stage Valuation of Medical Devices: The Role of Developmental Uncertainty. Value Health. 2010;13(5):585-91.
  8. Vallejo-Torres L, Steuten LMG, Buxton MJ, Girling AJ, Lilford RJ, Young T. Integrating health economics modelling in the product development cycle of medical devices: A Bayesian approach. Int J Technol Assess Health Care. 2008; 24(4): 459-64.
  9. Girling A, Lilford R, Young T. Pricing of medical devices under coverage uncertainty – a modelling approach. Health Econ. 2012. 21(12): 1502-7.
  10. Kahneman D. Thinking, Fast and Slow. London: Penguin Group. 2012.
  11. Kahneman D, Slovic P, Tversky A. Judgement under Uncertainty: Heuristics and Biases. Cambridge: Cambridge University Press. 1982.
  12. Sharot T. The Optimism Bias. Curr Biol. 2011; 21(23): R941-5.

The Weekend Effect

It is well known that the mortality rate of patients admitted to hospitals over the weekend is higher than that for patients admitted during the week. Whether, or to what extent, this ‘weekend effect’ is caused by case-mix factors vs. care quality factors is one of the big unknowns. This is being investigated by a CLAHRC WM-associated HS&DR grant led by Prof Julian Bion with economic support from Sam Watson, the CLAHRC WM Director and Jo Lord. We were thus provoked by a recent article by Meacock at al [1] investigating the health economics of providing increased consultant support over the weekend. The health gain is calculated on the basis of avoiding all of the excess in deaths and this is offset against the cost of providing a seven-day service. Based on their calculation, the authors find that even if the weekend effect could be eliminated, it would not justify the cost of the service at the NICE willingness-to-pay threshold. In other words, the opportunity cost is such that it would be better to leave the money doing what it is currently doing (if no new money), or to allocate it elsewhere (if new money). However, preventable deaths are merely the top of the adverse event severity pyramid and if the adverse events come down roughly in proportion to deaths, then the gains are much greater and the cost much lower than estimated in the paper. CLAHRC WM collaborators have produced a model to estimate the costs and benefits of reducing adverse events.[2] [3] We hope to collaborate with the authors of the Meacock paper in developing this research.

–Richard Lilford, CLAHRC WM Director


  1. Meacock R, Doran T, Sutton M. What are the costs and benefits of providing comprehensive seven-day services for emergency hospital admissions? Health Economics. 2015. [ePub].
  2. Yao GL, Novielli N, Manaseki-Holland S, Chen Y-F, van der Klink M, Barach P, Chilton PJ, Lilford RJ. Evaluation of a predevelopment service delivery intervention: an application to improve clinical handovers. BMJ Qual Saf. 2012; 21(s1):i29-38.
  3. Lilford RJ, Girling AJ, Sheikh A, et al. Protocol for evaluation of the cost-effectiveness of ePrescribing systems and candidate prototype for other related health information technologies. BMC Health Serv Res. 2014; 14: 314.

Paying for Health

The CLAHRC WM director has long been fascinated by the link between how health is paid for and access, quality, and satisfaction. The famous RAND RCT showed that fee-for-service systems resulted in more satisfied patients, but at the cost of over-servicing, compared with capitation payment.[1] This is consistant with economic theory. No changes in quality were detected. Subsequent sharp improvements in care quality in the public Veterans Affairs system vs. other American institutions has led many to speculate that the profit motive is inimical to quality.

So what happens when hospitals convert from not-for-profit to for-profit status? Joynt, Orav and Jha, conducted a controlled before and after study among no less than 237 converting hospitals and 631 matched control hospitals.[2] While the converting hospitals improved their financial margins, no significant changes were observed for adherence to quality standards, nurse to patient ratios, access for poor or minority patients, or mortality. The primary outcomes of interest were expressed as differences in differences, meaning that each hospital acted as its own control, thereby mitigating bias. The authors did not find any effect of time since conversion.

While on the subject of behavioural economics, a paper by Whaley and colleagues will also provoke readers.[3] This study concerns the effect of price transparency on utilisation rates for various services in an insurance-based system involving an element of cost-sharing with patients. The intervention was simple – making prices available online to prospective service users. Most people did not use the service, but given an insured population of half a million individuals, there were still plenty who did. These people were less likely to use a service (lab testing, imaging or clinician visit) than those who did not avail themselves of the pricing service.

Was this because they were already predisposed to parsimony? On the contrary: researchers looked at the behaviour of both groups before introduction of the online pricing service, showing that people who used the service had higher than average utilisation prior to the information service, and lower utilisation after it had been introduced. Making the service available seems to have made them more discriminating consumers.

— Richard Lilford, CLAHRC WM Director


  1. Davies AR, Ware Jr JE, Brook RH, Peterson JR, Newhouse JP. Consumer acceptance of prepaid and fee-for-service medical care: results from a randomized controlled trial. Health Services Research. 1986;21(3):429.
  2. Joynt KE, Orav E, Jha AK. Asociation between hospital conversions to for-profit status and clinical and economic outcomes. 2014; 312(16): 1644-52.
  3. Whaley C, Schneider Chafen J, Pinkard S, Kellerman G, Bravat D, Kocher R, Sood N. Association between availability of health service prices and payments for these services. 2014; 312(16): 1670-6.

Is Low Fertility a Problem for High-Income Countries, but a Boon For Low-Income Countries?

The perceived wisdom is that low fertility is bad for national wealth in high-income countries, but good news in low-income countries. A UN report found that 54 high- and middle-income nations are following pro-natal policies, at least in part, because of their putative economic advantages.[1]

So let’s start with the basics. The middle-aged (working) population supports the childhood and elderly population through public (e.g. education) and private (e.g. direct payment) transfers. A large elderly population supported by a relatively small working population is bad news for public finances.

But that’s not the end of the story according to a recent paper by Lee and Mason.[2] Public finances are only part of a country’s economy and it is important to consider also private inter-generational transfers. It is also important to factor in the costs of educating and bringing up children. As the proportion of older people rises, so private transfers from old to young increase and the costs of bringing up the next generation decrease.

The above study is based on detailed analysis in forty countries using standardised methods to estimate production and consumption of goods and services, along with public and private inter-generational transfers. The authors use the data to calculate the fertility rate that maximises material living standards overall. The results obtained from their model confirm the above point regarding the narrow issue of public finances in high-income countries. They are maximised by fertility rates of about 3 births per woman – well above the replacement rate. Similar effects are seen in middle-income countries, but in low-income countries low fertility rates (down to 1%) maximise public finances. This is because such a low replacement rate provides a big proportional reduction in the costs of rearing children.

So much for public finances, but what about the economy overall – is it true that living standards fall in high-income countries when fertility falls below the replacement rate of ~2.1%? In fact, the optimal fertility level is about 1.8 in high-income countries, falling to about 1.5 in low-income countries. To put this another way, the combined effects of inter-generational transfers and having a lower proportion of children to rear, exceed losses due to relatively smaller working-age populations, irrespective of whether the country has high or low per capita GDP.

What about immigration in high-income countries? To cut a long story short, us immigrants are chameleons, taking on the behaviour of our adoptive country. So we provide a short-term boost but fairly neutral effects in the long-term.

Of course there are many assumptions in these calculations notwithstanding the empirical source of data to populate the model. Nevertheless, the accepted wisdom that high fertility rates are bad news in low-income countries, is supported. However, in contrast to the prevailing view, modest reductions in population growth might actually benefit high-income countries. The paper quoted here is not an easy read but I strongly recommend it for your next long haul flight.

— Richard Lilford, CLAHRC WM Director


  1. Department of Economic and Social Affairs: Population Division. World Population Policies 2013. New York: United Nations. 2013.
  2. Lee R, Mason A, members of the NTA Network. Is low fertility really a problem? Population aging, dependency, and consumption. Science. 2014; 346(6206): 229-234.