Wealth and Happiness

The CLAHRC WM Director has written before about happiness. Not his own mood, but that of people living under different conditions! His previous reading of the literature is that increasing wealth has a rather small effect on happiness, both at the individual and population levels. However, he may have underestimated the hedonic effect of wealth, at least at the national level, according to researchers from the Pew Research Center.[1] It transpires that self-reported well-being in high-income countries is considerably higher than in middle-income countries. This difference is diminishing as their economies converge. Of course there is an obvious assumption here, namely that the wealth is causing the happiness, not the other way around.

— Richard Lilford, CLAHRC WM Director

Reference:

  1. Pew Research Center. People in Emerging Markets Catch Up to Advanced Economies in Life Satisfaction. 2014.
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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

References:

  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.

Anti-Obesity Interventions

The CLAHRC WM Director spotted an article on obesity prevention in a recent issue of the Economist.[1] It was based on a systematic review and quantitative analysis of the literature covering 74 anti-obesity interventions, classified into four groups according to the mechanism of action.[2] The main findings are very much in line with current opinion:

  • Highest impact interventions rely on restricting choices (through regulation or structuring the environment differently), rather than individual will-power.
  • Structural solutions, such as provision of healthy food at schools, apply to wider populations and tend to be more enduring than those targeting behaviour on individual / small group basis.
  • However, there is no magic bullet and investing in the lower impact measures is still worthwhile; we cannot rely only on regulation and structural solutions, and a number of CLAHRCs are investigating methods to change individual behaviour.
  • Strategies relying on conscious effort have ephemeral effects, but some more so than others. Exercise alone is least effective in reducing weight in the short-term and these minimal effects are not enduring. Diet and exercise is more effective than diet alone in the short-term, but they end up about the same (mean weight loss of 5kg) at 50 months. Of course, exercise has benefits apart from weight-loss.
  • Advertising campaigns that address social norms and self-image are particularly effective in primary prevention – for example, stigmatising drunk drivers. However, the CLAHRC WM Director thinks that such messages would have to be carefully framed to avoid “victim blaming” in the context of obesity.

The authors have a provocative message for researchers that is relevant to the prevention themes within CLAHRCs. They make two points:

  1. It is difficult to measure the effect of some interventions, such as making cycle lanes available.
  2. Some worthwhile effects are very small and hence hard to measure.

These are important points to which the Director makes the following responses:

  1. It is crucially important not to conflate “no evidence of effect” with “evidence of no effect” – a lack of precise and accurate evidence is, by itself, a prescription for neither action nor inaction.
  2. Evidence of take-up of healthy behaviour can be used to model downstream effects and hence can help in deciding whether, on balance, a certain intervention is worthwhile. It is possible to model, for example, the potential effects on health of cycle lanes on the basis of changes in cycling behaviour.
  3. Because such models must be populated with a wide range of parameters, many of which are very uncertain, Bayesian methods should be used to calibrate effects and their credible ranges.[3] [4] [5]
  4. It is possible that the effect of introducing a wide range of interventions in parallel is more than the sum of each individual intervention effect. To put this another way, multiple interventions across society may generate a change in attitude – a culture change.

— Richard Lilford, CLAHRC WM Director

References:

  1. The Economist. The War on Obesity: Heavy Weapons. The Economist. 2014.
  2. McKinsey Global Institute. Overcoming Obesity: An Initial Economic Analysis. New York, NY: McKinsey & Company. 2014.
  3. Yao GL, Novielli N, Manaseki-Holland S, Chen YF, van der Klink M, Barach P, Chilton PJ, Lilford RJ; European HANDOVER Research Collaborative. Evaluation of a predevelopment service delivery intervention: an application to improve clinical handovers. BMJ Qual Saf. 2012; 21 (s1): i29-38.
  4. Lilford RJ, Chilton PJ, Hemming K, Girling AJ, Taylor CA, Barach P. Evaluating policy and service interventions: framework to guide selection and interpretation of study end points. BMJ. 2010; 341: c4413.
  5. Lilford RJ, Girling AJ, Sheikh A, Coleman JJ, Chilton PJ, Burn SL, Jenkinson DJ, Blake L, Hemming K. 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.

Conflict of Interest in NICE, CLAHRCs and Other Independent Organisations

In a previous blog the CLAHRC WM Director hailed the creation of NICE as one of the crowning achievements of the previous Labour administration – up there with granting independence to the reserve bank. NICE epitomised enlightenment values by bringing a highly rationalist approach to bear on NHS procurement decisions. Interventions had first to be effective and, if effective, they had also to be a better buy than the (nominal) activities displaced within a fixed budget. However, that was before the 2008 crash. The government, desperate to kick-start the economy, became susceptible to arguments to make NICE more responsive to the needs of industry. Companies would produce their own models, ‘single technology reviews’, to be critiqued by the NICE ecosystem, rather than the other way around. NICE would support industrial innovation for devices through a separate system of External Assessment Centres.

The economy is generally conceptualised in terms of the demand for, and supply of, products and services. The economy can be strengthened on both sides – providing better information strengthens the demand side, while innovations to meet demand strengthens the supply side. Can one organisation really do both simultaneously? Not according to a recent BMJ article reporting ‘insiders’ concerns that the new minister with responsibility for NICE will have dual appointments across the Department of Business, Innovation and Skills, and the Department of Health.[1] According to the BMJ article the new minister, George Freeman, is aware of the potential risk and will take steps to mitigate it.

So how may NICE manage this putative conflict of interest, thereby preserving its currently colossal international reputation? Well, it so happens that CLAHRCs also have a responsibility to work with industry and applicants had to say how they would do so on the application form. Three separate “ways of working” can be distinguished in which an independent organisation (such as NICE or CLAHRCs) may contribute to the national wealth agenda:

  1. Strengthening the demand side of the health economy by evaluating cost-effectiveness of interventions – in the case of NICE there are particular clinical treatments, while in the case of CLAHRCs they are the services that support individual treatments.[2]
  2. At the supply side, by strengthening industry generally – the industry, as opposed to any particular industry. One way of doing this is by supplying knowledge and tools that might be helpful to commercial enterprises in a certain sector. For example, CLAHRC WM has developed methods for health economic evaluations at the design and development stages of a new technology.[3] [4] [5]
  3. At the supply side, by collaborating with a particular commercial organisation.

It is only in the third of these “ways of working” that the potential conflict arises. To manage the risk we propose that:

  1. The potential risk should be acknowledged, not ‘pushed under the carpet’, since it is based on extensive empirical evidence.[6] [7] [8] [9] [10]
  2. The “way of working” should be crystal clear for any project.
  3. There should be no overlap between personnel involved in supply or demand side evaluations of a particular product at any time in its life cycle.
  4. Ideally an organisation should not be involved in supply and demand side evaluation of a particular product at the same time (but this criterion may be difficult to meet in a large organisation, such as a university or NICE).

These ideas have been “road-tested” in a presentation to the NIHR Office for Clinical Research Infrastructure (NOCRI) and to the NIHR Biomedical Centres and new CLAHRC Directors, but we would welcome comments and feedback.

— Richard Lilford, CLAHRC WM Director

References:

  1. Cohen D. Insiders say NICE is being encouraged to be more favourable to industry. BMJ. 2014; 349: g6387.
  2. Lilford RJ, Chilton PJ, Hemming K, Girling AJ, Taylor CA, Barach P. Evaluating policy and service interventions: framework to guide selection and interpretation of study end points. BMJ. 2010; 341: c4413.
  3. Girling AJ, Lilford RJ, Young TP. Pricing of medical devices under coverage uncertainty – a modelling approach. Health Econ. 2012; 21(12): 1502-7.
  4. 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.
  5. Vallejo-Torres L, Steuten L, Buxton M, 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.
  6. Ethical Standards in Health & Life Sciences Group. Guidance on collaboration between healthcare professionals and the pharmaceutical industry. [Online]. 2012.
  7. Fletcher SW. Continuing Education in the Health Professions: Improving Healthcare Through Lifelong Learning. Chairman’s Summary of the Conference. New York: Josiah Macy, Jr Foundation. 2008. pp. 13-23.
  8. Spurling GK, Mansfield PR, Montgomery BD, Lexchin J, Doust J, Othman N, Vitry AI. Information from Pharmaceutical Companies and the Quality, Quantity, and Cost of Physicians’ Prescribing: A Systematic Review. PLoS Med. 2010; 7(10): e1000352.
  9. Steuten L, Buxton M. Economic evaluation of healthcare safety: which attributes of safety do healthcare professionals consider most important in resource allocation decisions? Qual Saf Health Care. 2010; 19: 1-6.
  10. Wang AT, McCoy CP, Murad MH, Montori VM. Association between industry affiliation and position on cardiovascular risk with rosiglitazone: cross sectional systematic review. BMJ. 2010; 340: c1344.

In-Vehicle Technology to Reduce Road Traffic Incidents

A 2013 report from the World Health Organization (WHO) highlighted that 1.24 million people die every year from road traffic incidents (RTI) and 50 million are injured.[1] Traffic accidents are the second biggest killer of children (5-14 years) and young people (15-29 years).[2] Projections suggest that they will be the seventh largest cause of all death by 2030,[3] and third in the league table for burden of disease.[4] In response, the General Assembly of the United Nations called for “effective international collaboration on road safety issues”. The Global Status Report on Road Safety (2013) identifies an inverted U-shaped curve for road traffic fatalities by stage of economic development: 20.1 per 100,000 population in middle-income countries, 8.3 per 100,000 in high-income countries, and 18.3 per 100,000 in low-income countries.[1]

A number of interventions have been shown to reduce the frequency and severity of crashes. Cars can be made safer by standard engineering solutions, such as seat belts and airbags.[5] Civil engineering projects include ‘traffic calming’ and street lighting.[6] Enforcement diminishes driving while intoxicated and reduces speed. Various behavioural techniques, such as roadside speedometers and encouraging passenger activism are also effective.[7] [8] However, many of these interventions, such as road improvement, are expensive and cannot be implemented quickly.

In-vehicle technologies offer considerable promise in many ways. First, they can be used to report crash data autonomously to a remote computer and hence identify crash ‘hot spots’ and contribute to the evaluation of interventions. Second, they can alert the police and emergency services that a crash has occurred, where it occurred, and expectations of serious injuries.[9] Third, they can provide a record of driver behaviour for driver feedback or for rewards or sanctions, such as decreased/increased insurance premiums. Fourth, they may incorporate driver warning functions to alert the driver when speed limits are breached or when potential hazards, such as a cyclist in the driver’s blind spot, are present. Fifth, they can take control of a vehicle in an emergency, for example to rectify drift towards opposing traffic when the driver is fatigued or distracted. The first three uses are telematic and the fourth and fifth are generally referred to as driver assistance functions.

We hypothesise that the dismal extrapolation in the Global Status Report cited above can be ameliorated if telematic and driver assistance technology is implemented in a way that is psychologically, socially, and technically appropriate. We have therefore formed an international collaboration with the University of Michigan Research Institute in Ann Arbor, Michigan, The Indian Institute of Technology in New Delhi, and the DY Patil University in Mumbai to adapt and evaluate this technology in middle-income countries.

— Richard Lilford, CLAHRC WM Director
— Chetan Trivedy, Academic Clinical Lecturer in Emergency Medicine, University of Warwick

References:

  1. World Health Organization. Global Status Report on Road Safety 2013: Supporting a Decade of Action. Geneva: World Health Organization. 2013
  2. World Health Organization. World Report on road traffic in jury prevention. Ed. Peden M, Scurfield R, Sleet D, Mohan D, Hyder AA, Jarawan E, Mathers C. Geneva: World Health Organization. 2004
  3. World Health Organization. Projections of mortality and causes of death, 2015 and 2030. [Online]. 2011.
  4. World Health Organization. The Global Burden of Disease. 2004 Update. Geneva: World Health Organization. 2008.
  5. Cummings P, McKnight B, Rivara FP, Grossman DC. Association of driver airbags with driver fatality: a matched cohort study. BMJ. 2002; 324: 1119-22.
  6. Kjemtrup K, Herrdtedt L. Speed management and traffic calming in urban areas in Europe: a historical view. Accident Anal Prev. 1992; 24(1): 57-65.
  7. Pilkington P, Kinra S. Effectiveness of speed cameras in preventing road traffic collisions and related road casualties. BMJ. 2005; 330: 331-4.
  8. Habyarimana J, Jack W. Heckle and Chide: Results of a randomized road safety intervention in Kenya. J Public Econ. 2011: 95; 1438-46.
  9. Road Safety Observatory Review. Synthesis title: Telematics. 2013.

Simpson’s Paradox and Discrimination

Readers of the News Blog will have encountered an example of Simpson’s paradox in a previous blog, applied first to base-ball strikers’ averages and then to the beguilingly appealing issue of Standardised Mortality Rates. Prof. Tony Belli, director of the NIHR Surgical Reconstruction and Microbiology Research Centre in Birmingham, recently drew the CLAHRC WM Director’s attention to another fascinating example; this time arising from discrimination cases in American courts.[1] [2] You will remember that Simpson’s paradox can arise by aggregating data across strata where the strata vary in size and where outcome rates differ across strata. The departments of English and History attract large numbers of applicants, a high proportion of whom are women, and rejection rates are high. Mathematics and Physics, by contrast, attract fewer applicants, a high proportion of whom are male, and rejection rates are low. Simple aggregation, ignoring the interaction between acceptance rates and applicant numbers, results in the mistaken conclusion that there is discrimination against women, rather than the correct conclusion that women favour popular subjects with high rejection rates. To avoid this problem it is necessary to use a method of aggregation based on weighted averages of strata specific estimates.

— Richard Lilford, CLAHRC WM Director

References

  1. Borhani H. Bias in Measuring Bias. American Bar Association Labour and Employment Section’s Annual CLE. Washington D.C. November 4–7 2009.
  2. Bickel PJ, Hammel EA, O’Connell JW. Sex Bias in Graduate Admissions: Data from Berkeley. Science. 1975. 187(4175): 398-404.

Most C. diff Infections Are Not Hospital-Acquired

Derrick Crook and others have explored the cause of the dramatic drop in incidence in the colonic infection, Clostridium difficile, in English hospitals since 2010.[1] They found that only 20% of cases of hospital-acquired C. diff infections result from patient to patient transmission of the organism – the rest are acquired from reservoirs outside the hospital. So the great majority of C. diff infections are not hospital-acquired.

The dramatic drop in incidence of C. diff enteritis was preceded by a precipitous drop in prescribing of quinolone antibiotics in both general practice and in hospitals. Moreover, the drop in incidence can be entirely explained by the decline in quinolone-resistant species of C. diff; the incidence of quinolone-sensitive C. diff infections remains unchanged.

The drop in C. diff infections in English hospitals turns out to have nothing to do with hand-washing and hospital hygiene.[2] Moreover, washing hands in alcohol is of no value as far as this organism is concerned because C. diff spores are resistant to this disinfectant.[3]

— Richard Lilford, CLAHRC WM Director

References:

  1. Eyre DW, Cule ML, Wilson DJ, et al. Diverse Sources of C. difficile Infection Identified on Whole-Genome Sequencing. New Eng J Med. 2013; 369(13): 1195-205.
  2. Benning A, Dixon-Woods M, Nwulu U, et al. Multiple component patient safety intervention in English hospitals: controlled evaluation of second phase. BMJ. 2011; 342: d199.
  3. Jabbar U, Leischner J, Kasper D, et al. Effectiveness of alcohol-based hand rubs for removal of Clostridium difficile spores from hands. Infect Control Hosp Epidemiol. 2010; 31(6): 565-70.

Network Meta-Analysis Can Correct For “Bias” In Head-To-Head Treatment Comparisons

When we talk of bias we tend to mean bias due to factors that affect the “answer” to a given question. But a type of bias can arise when a question is posed in a way that it predisposes to a certain result, say by comparing an optimal dose of medicine A with a sub-optimal dose of medicine B, or comparing medicine A with medicine C when it is likely to fare less well against medicine B. Conventional tools for the assessment of methodological quality of individual trials are adept at picking up the former type of bias, but discerning the latter usually requires a broader view based on medical knowledge. The CLAHRC WM Director co-authored a paper, led by Fujian Song of East Anglia showing how network meta-analysis can explore this second type of bias.[1]

A further example of use of network meta-analysis to explore this form of bias due to choice of a sub-optimal comparator comes from a recent study comparing reductions in LDL cholesterol in industry-funded versus publicly-funded RCTs of various statins.[2] The combined result across 183 RCTs failed to show a difference in end-points between statins funded in these two ways when the playing field was levelled by using network meta-analysis to compare optimal doses. The authors observed no obvious effects of various attributes of methodological quality on study outcome.

— Richard Lilford, CLAHRC WM Director

References

  1. Song F, Harvey I, Lilford R. Adjusted indirect comparison may be less biased than direct comparison for evaluating new pharmaceutical interventions. J Clin Epidemiol. 2008; 61(5): 455-63.
  2. Naci H, Dias S, Ades AE. Industry sponsorship bias in research findings: a network meta-analysis of LDL cholesterol reduction in randomised trials of statins. BMJ. 2014; 349: g5741.

Measuring Subjective Well-Being

No subject can mature without finding a way to quantify its core constructs. Improving subjective well-being is at the heart of health care, economics, social services, and more. Measurement of this ontologically and epistemologically subjective construct is obviously not straightforward; two people may record pain seven on a ten-point scale, but this does not mean their experiences were identical. However, at the population level it is not necessary for measures to correspond – it is sufficient that they correlate, so that on average people scoring seven have more pain than those scoring six.[1] One way to determine whether a measure is valid in this sense is to see whether it correlates with other variables that are part of the same latent (underlying) construct. Pain, for example, correlates with various neurophysiological and endocrine changes and with other health outcomes. Comparing groups of people can, however, be problematic if there are systematic differences between them in how they record the same experience. Various techniques can be used to mitigate bias when groups are compared – use of rich verbal descriptions of scenarios rather than more abstract descriptions, for example. People also adapt to a new state over time, but it is hard to separate genuine change in experience from “re-calibration” in how the scale is used.

An article in Science provides a romp over the state of science in this topic.[2] It introduces the important idea that policy should aim to minimise distress rather than maximise well-being. Depending on how these constructs are conceptualised and measured, the latter is not necessarily a simple reciprocal of the former. Krueger and Stone [3] have recently come up with an index based on proportion of a person’s waking times spent in an unpleasant emotional state – the U index.[4] This line of argument appeals to the CLAHRC WM Director, who thinks that much more can be achieved by reliving distress than seeking the utopian goal of maximising everyone’s hedonistic experience of life. There are great dangers in using public policy as a tool to achieve the latter, as vividly portrayed in Aldous Huxley’s masterpiece.[5]

— Richard Lilford, CLAHRC WM Director

References:

  1. Searle JR. The Construction of Social Reality. London: Penguin Books. 1996.
  2. Kahneman D, Krueger AB, Schkade D, Schwarz N, Stone AA. Would you be happier if you were richer? A focusing illusion. Science. 2006; 312(5782): 1908-10.
  3. Krueger AB, Stone AA. Progress in measuring subjective well-being. Science. 2014; 346(6205): 42-3.
  4. Krueger AB. Measuring the subjective well-being of nations: National accounts of time use and well-being. Chicago, IL: University of Chicago Press; 2009.
  5. Huxley A. Brave New World. London: Chatto & Windus. 1932.

Social Change

Social change may have social origins, for example the increasing emphasis on involving service users in the design and quality assurance of services. However, social change may result from technical advances; think of the printing press in the 15th century, or social networking in this century. CLAHRCs have a duty to promote adoption of safe and cost-effective new technologies. Sometimes, the technology is a “bolt on” – it can simply be added to the existing repertoire of services. MRI scanning is an example of such a non-disruptive technology. Yes, it can improve diagnostic sensitivity and improve care, but it can be slotted into existing patient pathways and service schedules in a largely unproblematic way.

Contrast this with the molecular techniques to identify microbes. Out will go laborious processes of plating bacteria on a succession of nutrient media to make a diagnosis. What about conventional histological staining and examination? Molecular techniques, particularly those based on genetic signatures, will sweep away much previous technology. Radical changes can also be anticipated in radiology as increasing imaging techniques follow Moore’s law, becoming progressively smaller and less expensive.

All these advances will enable some low-income countries to bypass existing technology and leapfrog into the new era, as has happened with mobile phone technology. In richer countries, however, disruptive change will ensue as large numbers of relatively skilled jobs are replaced by a smaller cadre of highly-skilled technical and managerial workers. These advances will also “take diagnosis out of the cupboard,” making it directly accessible to clinicians on the ward and in the clinic. Imaging, for example, will become an extension of, and to some extent replacement of, normal “bedside” clinical skills All these advances in microbiology, pathology and diagnostic imaging are truly disruptive.

It is time to broaden our gaze from the technological and scientific aspects, intriguing and important as they are, and consider broader societal implications.[1] By anticipating these changes, the workforce can be gradually re-deployed and/or re-trained so that upheaval is minimised and disruption of the service kept to a minimum. We do not want the work force to be ambushed in the way dock-workers and printers seem to have been in the 1980s.

These changes also have massive educational implications as technology moves from the laboratory to the ward. Portable microchips and ultrasound machines the size of a mobile phone can do harm in poorly-educated hands; a point well understood by Health Education England who are promoting a campaign on education in the new genetics.[2] Yet patients and the public also need to understand the technology and its limitations – a project for Public Involvement in Science. Imaging specialists, micro-biologists and pathology staff should become educators, quality assurers and problem solvers, rather than guilds holding custody of their art.

CLAHRCs have a role in defining the role of new technology (especially determining cost-effectiveness [3]), in helping to design and implement new services and in evaluation. Our CLAHRC is collaborating with regional partners in the development and evaluation of new ways of working and is a partner in an application to NHS England’s 100,000 Genomes Project. Likewise, there is fascinating and important work to be done on the cost-effectiveness of new technologies in low- and middle-income countries – we will give an example in the next News Blog.

— Richard Lilford, CLAHRC WM Director
— Tim Jones, Executive Director of Delivery, University Hospitals Birmingham NHS Foundation Trust

Reference:

  1. Christensen CM, Grossman J, Hwang J. The Innovator’s Prescription. New York, NY: McGraw-Hill. 2009.
  2. NHS Health Education England. Genomics Education. 2014. [Online]
  3. 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.