Epidemiology, epigenetics and the ‘Gloomy Prospect’:
embracing randomness in population health research and practice
George Davey Smith MRC Centre for Causal Analyses in Translational Epidemiology, School of Social and Community Medicine, University of Bristol, UK
International Journal of Epidemiology doi:10.1093/ije/dyr117
“….Implications of the ‘gloomy prospect’ for epidemiology, public health and personalized medicine
•A major component of inter-individual differences in risk of disease is accounted for by events that are not epidemiologically tractable, including stochastic events ranging across the sub-cellular and cellular level, to chance biographical events and idiosyncratic gene by environment interactions. These will fall into the ‘non-shared environmental’ component of variance identified in behavioural genetic analyses. Even if their causal contribution could be identified, there would often be no implications for disease prevention, as such events do not generally provide targets for intervention.
•At the level of populations, rather than individuals, a large proportion of cases of disease will often be attributable to modifiable influences that only account for a small proportion of inter-individual variation in risk. These may be elements of ‘the shared environment’ in childhood or the adulthood equivalent of such group-level exposures.
•Epidemiology is a group-level discipline. As Jerry Morris stated in his seminal ‘The Uses of Epidemiology’ over 50 years ago, ‘the unit of study in epidemiology is the population or group, not the individual’.1 Epidemiology relates to incidence rather than particular incidents.
•Ecological studies directly address causes of population disease burden but are subject to many well-known biases, and aetiological hypotheses they support require testing in different study designs. However, the fact that we collect data at the level of the individual does not detract from the fact that in most situations we can only make inferences to groups, and not to individuals.
•Genetic variants are borne by individuals but, like other exposures in an epidemiological context, must usually be analysed at a group level.
•We should not conflate individual- and group-level explanation. In an insightful paper David Coggon and Chris Martyn convincingly present the case for the highly stochastic nature of disease causation. However, they consider that substantial variation between populations in disease rates, or rapid changes in incidence over time, provide an exception to this rule. In fact chance processes at an individual level together with almost entirely explicable group level differences are in no way contradictory, indeed should be expected.
•The substantial stochastic element in disease causation and treatment response suggests that fully personalized medicine is an unlikely scenario. Indeed the move from personalized to stratified medicine reflects the fact that in most situations group-level rather than purely individual data contribute to appropriate treatment decisions, and provide the empirical basis for evidence – based medicine and best practice treatment guidelines.
The tension between more reliable estimates based on larger groups and the essentially individual nature of medical encounters is a long running one, highlighting the importance and difficulty of identifying the smallest coherent groups for which reliable treatment effects can be estimated….”
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