Epidemiological methods to tackle causal questions.

It is a dull day when there is not at least one media report of a claim that research has identified some new environmental cause of disease. Such claimed causes concern a wide range of supposed hazards including medical interventions such as the measlesmumps-rubella (MMR) vaccine, the thimerasol (mercury) preservative in other vaccines, dietary factors of many different kinds (coffee, alcohol, food additives, etc.), prenatal stresses or the intra-uterine exposure to the effects of maternal smoking or ingestion of alcohol, use of mobile phones, and living near radiation sources—to mention just a few examples. It is problematic, however, that many of these claims are not confirmed by other research and some are even reversed. Unsurprisingly both professionals and the lay public have developed a scepticism about claims on environmental causes of disease. Because much of the evidence derives from epidemiological studies of one kind or another, epidemiological science itself has come under scrutiny both within and outside the profession. Some have argued that only laboratory experiments and randomized controlled trials (RCT) can provide acceptable evidence on causation. That cannot be a solution, however, because so many of the putative environmental causes are ones that cannot be manipulated in humans—for a mixture of both ethical and practical reasons. Because the identification of environmental causes is of such importance, and because the solution of the causal inference problem has no obvious single, simple solution, the Academy of Medical Sciences set up a working party (made up of clinical scientists, epidemiologists, statisticians, policy leaders and scientific media specialists) to answer the key questions of ‘how should we decide what to believe and when to take action?’ The report emphasized the need to focus on individual components in the causal process and not on some misleading abstract notion of a single basic cause. It also notes the many reasons why an observed association or correlation might not reflect an environmentally mediated causal effect. Such reasons include genetic mediation of the causal risk effect, social selection (allocation bias), and reverse causation to mention but three out of a much longer list of possibilities. It was also noted that the main problem in moving from an observed association to a causal inference did not lie in the effect of known, measured confounders, but rather in the effect of unknown, unmeasured confounders.