Seven mistakes and potential solutions in epidemiology, including a call for a World Council of Epidemiology and Causality

Abstract

All sciences make mistakes, and epidemiology is no exception. I have chosen 7 illustrative mistakes and derived 7 solutions to avoid them. The mistakes (Roman numerals denoting solutions) are: 1. Failing to provide the context and definitions of study populations. (I Describe the study population in detail) 2. Insufficient attention to evaluation of error. (II Don't pretend error does not exist.) 3. Not demonstrating comparisons are like-for-like. (III Start with detailed comparisons of groups.) 4. Either overstatement or understatement of the case for causality. (IV Never say this design cannot contribute to causality or imply causality is ensured by your design.) 5. Not providing both absolute and relative summary measures. (V Give numbers, rates and comparative measures, and adjust summary measures such as odds ratios appropriately.) 6. In intervention studies not demonstrating general health benefits. (VI Ensure general benefits (mortality/morbidity) before recommending application of cause-specific findings.) 7. Failure to utilise study data to benefit populations. (VII Establish a World Council on Epidemiology to help infer causality from associations and apply the work internationally.) Analysis of these and other common mistakes is needed to benefit from the increasing discovery of associations that will be multiplying as data mining, linkage, and large-scale scale epidemiology become commonplace.