The Voice of Epidemiology


    Web EpiMonitor

► Home ► About ► News ► Job Bank Events ► Resources ► Contact
Richard Doll Prize Lecturer George Davey Smith Focuses On The Role Of Epidemiology In The Age Of Big Data

Is there a role for epidemiology in the age of big data? That is the question posed and answered in the affirmative by the University of Bristol’s George Davey Smith, the recipient of this year’s Richard Doll prize, in his lecture delivered by video to the International Epidemiological Association (IEA) at its triennial meeting in Japan last month.

 Davey Smith did not hold his audience in suspense for very long as he confessed early in the lecture that there is an exciting future for epidemiologists in the era of big data and it is important for epidemiologists to enthusiastically embrace the opportunities afforded.

He outlined his talk to cover five topics, namely:

·       Epidemiology and causation--role of genetics and biomarkers

·       Exposure and outcome assessment

·       Levels of causation

·       Limits of prediction

·       A role for epidemiology

Early Career

Davey Smith began by recounting his experiences in studying cardiovascular disease in the 1980’s when observational studies suggested that elevated levels of HDL cholesterol could be protective against cardiovascular heart disease (CVD). He described a study demonstrating the challenges in disentangling HDL and triglyceride measurements in studying CVD. He called these and other CVD measurement issues “intractable problems” which influenced him to study other more epidemiologically tractable questions, for example ones around HIV/AIDS and diarrhoeal disease in childhood. These appeared less susceptible to measurement issues and the results had more direct implications for public health.

New Approaches

Davey Smith recounted how during the 2,000’s the incorporation of data on molecular genetic variation into observational epidemiological studies - and in particular Mendelian randomization (MR) - could be used to overcome some biases in studies and help strengthen causal inference.

He highlighted the benefits of triangulation or comparing different study approaches to obtain more reliable or accurate answers to research questions, including those around the role of elevated levels of HDL. Large scale randomized controlled trials and MR studies converged on providing strong evidence that modifying circulating HDL levels did not improve cardiovascular outcomes. It is difficult to think of how any other developments in epidemiological methodology had made a serious contribution to understanding this crucial issue. 

In discussing other transformational changes that have taken place around exposure and outcome assessment, Davey Smith used DNA methylation and how it indexes exposure to smoking more accurately than self-reported smoking data. It can even assess in utero exposures that took place decades earlier. Other innovations he described include the use of cameras which can be worn by infants to collect digital data on parent-child interactions, something Davey Smith described as “ a complete transformation” and one small indicator of the opportunities that can be afforded by digital and big data.

Levels of Causation

In discussing levels of causation, he reminded the audience that much of the big data collected is at the individual level and of itself may not be of particular relevance to interpreting the effects of broader, underlying social and economic influences on levels and trends in populations. He warned epidemiologists that it is important not to become distracted by the excitement around big data from the main objective of epidemiology to impact population health by creating population level interventions. While MR is a powerful tool, it does not in itself help us develop interventions at the population level, said Davey Smith.

Doll on Luck

He quoted from Richard Doll to highlight the role of luck or chance in determining which individuals actually come down with disease, and to point out that the influence of stochastic factors limits the accuracy of predictions that will be possible about individual health outcomes.

According to Doll, “Whether [any particular] exposed subject does or does not develop a cancer is largely a matter of luck; bad luck if the several necessary changes all occur in the same stem cell when there are several thousand such cells at risk, good luck if they don’t. Personally, I find that makes good sense, but many people apparently do not.” Davey Smith called this fact “good news” for epidemiologists because it means that population level interventions – which is what epidemiologists study – are crucial to improving public health.

Guiding Lights for Epi

In enthusiastically embracing big data, Davey Smith exhorted his audience not to forget that it is the factors at the population level that should be the focus for epidemiologists and that attention to basic principles will pay off. He encouraged epidemiologists to embrace the important and exciting role of grasping the opportunities provided by big data, whilst not being distracted from the major task of the discipline.

The lecture is available at:

with a 2 minute animation outlining Mendelian randomization that it contains also available at:


Reader Comments:
Have a thought or comment on this story ?  Fill out the information below and we'll post it on this page once it's been reviewed by our editors.

  Name:        Phone:   



      ©  2011 The Epidemiology Monitor

Privacy  Terms of Use  Sitemap

Digital Smart Tools, LLC