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EDITORIALS: George Davey Smith and Shah Ebrahim Data dredging, bias, or confounding BMJ 2002; 325: 1437-1438 [Full text]

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Informing the public about controversies

Anthony Lwegaba   (6 January 2003)

Epidemiology needs to be taken seriously

Ralf Reintjes   (7 January 2003)

Informing the public about controversies 6 January 2003
Anthony Lwegaba, lecturer in Social and preventive Medicine UWI School of Clinical Medicine and Reseach, QEHosp, Barbados, W.I. Send response to journal: Re: Informing the public about controversies	Dear Editor, Informing the public about controversies The editorial by Smith GW and Ebrahim S, though written with seasonal humour, calls for serious consideration of two issues addressed; validity and public implications (1). First, data dredging, bias or confounding grouped together is as old as epidemiology. This is engrained in what my teacher James Lee at National University of Singapore called the grand equation of truth. All observations are subject to errors. What we Observe is equal to the Truth plus or minus errors; random, bias, and confounding, O=T ± e. In addition, low risks are more difficult to resolve, (do electric fields cause disease?); what makes epidemiological sense, does not necessarily make good public policy, (high fertility reduces breast cancer!), and public health action need not wait break through evidence, (AIDS prevention preceded the discovery of the HIV) (2). Second, controversies to the investigators are the engine of growth, leading to the refining of methods to yield better studies that minimize but do not eradicate errors. To the general public, they are causes of confusion and disputes. The mechanisms to address the first issue are available but need to be practiced, continuously updated and circulated as research methods, guidelines, CONSORT, and in all learning (3,4). Some journals do well on this aspect and others should be encouraged. The second issue has not been adequately examined and overlaps several areas that include research and public communication ethics (5). Because, the latter issue can arise without the former, newsworthy information to the public may be different and undesirable. Newscasters avoid ambiguities, because the general public loves rationalization, yes or no clear statements that are easy to understand and apply to self. So, the mass media broadcasted that older adults tolerate more alcohol and that breast self-examination is useless. Days are gone when the journals and scientific advances were a preserve of the profession and so should the response. What and who should communicate to the general public? The author and the editor should give a take home message to the general public. The present set up in BMJ for the caption: what is known and what the study adds, is a good attempt and should be universally adopted by authors and journals but should as well include a cautiously crafted evidence-based message for the general public. Evidence is an appropriate term both in science and general usage. In both, it has implied probability. No conflicting interest declared. Anthony Lwegaba, Lecturer, UWI School of Clinical Medicine and Research, QE Hosp., Barbados, W.I. Lwegaba@lycos.com. 1 Smith GW, Ebrahim S. Data dredging, bias or confounding. They can all get into the BMJ and the Friday Papers. BMJ 2002; 325:1435-8 2.Savitz DP, Poole C, Miller WC. Reassessing the role of epidemiology in public health. Am J Pub Health 1999; 89: 1158-61 3.Moher D, Schulz KF, Altman DG. The CONSORT statement: revised recommendations for improving the quality of reports of parallel-group randomised trials. Lancet 2001; 357; 1191-94 4.Moher D, Jones A, Lepage L. Use of the Consort statement and quality of reports of randomized trials; a comparative before-and-after evaluation. J Am Med Ass 2001; 285: 2006-7. PMID 11308436 [PubMed]. 5.Nelkin D. Scientific journal and public disputes. Lancet 1998; 352 s2: 8 -12 Competing interests:   None declared
Epidemiology needs to be taken seriously 7 January 2003
Ralf Reintjes, professor of epidemiology and public health surveillance Hamburg University of Applied Sciences, Germany Send response to journal: Re: Epidemiology needs to be taken seriously	Dear Sir, In their editorial "Data dredging, bias, or confounding" George Davey Smith and Shah Ebrahim (1) describe a common problem in Epidemiology and Public Health. Only too often even experienced epidemiologists do not resist from using existing (large) data sets for further or new analysis’s (often called “fishing expeditions”) which provide them with significant associations. Results of every epidemiological study can either show, what often is assumed, a causal relation between exposure and outcome or it can only show the effects of chance, bias or confounding. As Smith and Ebrahim rightly state, the effect of chance (using p <0.05) is in this kind of data dredging exercises often underestimated. Following their suggestions for the use of more stringent significance levels this problem might be reduced or controlled. But what about bias and confounding? Studies used for these re-analysis studies were generally not designed with the new study question in mind. This can be a problem especially when controlling for possible confounding in the new analysis. Experience shows, that in fact it is often very difficult to control for all known confounders when a study is systematically planned and all relevant variables known are included in the data collection. Using existing data sets which were collected for a study with different aims and objectives than for the new data dredging exercise does not allow to include any previously not collected variable (e.g. a possible confounder) in the study. This can open the door for effects of strong forms of confounding which might even reverse the measured association (e.g. Simpsons’ paradox) (2). This can and will ultimately lead to the loss of trust in epidemiology by the public. Therefore, epidemiological research needs to be taken seriously by those who use the results for decision making, but also by those who conduct and analyse the studies. References: 1) Smith GD, Ebrahim S. Data dredging, bias, or confounding. BMJ 2002; 325(7378):1437-8 2) Reintjes R, de Boer A, van Pelt W, Mintjes-de Groot J. Simpson's paradox: an example from hospital epidemiology. Epidemiology. 2000;11(1):81-3. Competing interests:   None declared