Point-counterpoint. The triumph of the null hypothesis: epidemiology in an age of change

Is uncertainty in complex disease epidemiology resolvable?

The imposed limitations on what we can know about nature have been long recognized. Yet in the field of epidemiology a futile search for lifestyle-related risk factors for common chronic diseases continues unabated. This has led to the production of a growing body of evidence about potential lifestyle risk factors that tend to be marginal, contradictory, irreproducible, or hard to interpret. While epidemiologists are calling for a more refined methodology, I argue that our limitation in studying complex diseases is insurmountable. This is because the study of lifestyle-related small risks requires accurate measurement of multiple behaviors-exposures over a long period of time. It is also because in complex systems such as population's health, the effect of rich interactions between its parts cannot be predicted based on traditional causal models of epidemiology. Within complex systems, understanding the interactions between system components can be more important than the contribution of each to disease risk.

Epidemiology in a changing world: implications for population-based research on mental disorders

Introduction and objectives. Population-based research on mental disorders needs to keep pace with trends in general epidemiology. At present, this requirement is complicated by uncertainty within the parent discipline about its future development. The present study examines proposals for new directions in strategy and methods and considers their significance for psychiatric epidemiology. Method. Narrative review, cross-checked by search of English-language journals of epidemiology for new trends and developments reported in the years from 2000 onwards. Results. The proposals reviewed here are divided into three groups: 1. A new research paradigm of 'eco-epidemiology', which includes both individual risk factors and macro-environmental systems that mediate population levels of health and sickness. 2. Improved 'translation' of research findings - i.e. more rapid and effective implementation of epidemiological evidence into health policy and practice. 3. Adaptation of epidemiology to a globalised economy, with firmer regulation of funding and resources. Conclusions. Each of these proposals has implications for psychiatric epidemiology. Workers in this field, however, are still preoccupied by relatively specific problems of definition, measurement and classification, and so far the current debates in general epidemiology are scarcely reflected. The proposals outlined above call for: • a working model of eco-epidemiology as it relates to psychiatric disorders; • implementation strategies to encourage more active participation in epidemiological research by community health services and caregiver organisations; • international collaborative projects that offer practical benefits in training and service facilities for the countries taking part.

False alarms and pseudo-epidemics: the limitations of observational epidemiology

Most reported associations in observational clinical research are false, and the minority of associations that are true are often exaggerated. This credibility problem has many causes, including the failure of authors, reviewers, and editors to recognize the inherent limitations of these studies. This issue is especially problematic for weak associations, variably defined as relative risks (RRs) or odds ratios (ORs) less than 4. Such associations, commonly reported in the medical literature, are more likely to be attributable to bias than to causal association. All observational research has bias (which can include selection, information, and confounding bias). Hence, detection of small associations falls below the discriminatory ability of observational studies. In general, unless RRs in cohort studies exceed 2 to 3 or ORs in case-control studies exceed 3 or 4, associations in observational research findings should not be considered credible. However, these guidelines are not foolproof: strong (yet spurious) associations can result when large amounts of bias are present. Only in a properly performed randomized controlled trial, free of bias, should small associations merit attention. Better training and more circumspection on the part of investigators, tougher editorial standards on the part of journals, and hefty skepticism on the part of referees and readers are necessary to avoid the dangers of false alarms, pseudo-epidemics, and their unfortunate consequences.