of confounders. If they are known and measurable, they can be eliminated in the design of the study, for instance by excluding smokers and observing whether non-smoking coffee drinkers have a higher prevalence of periodontitis than non-smoking non-coffee drinkers.
Another method of minimising the effects of confounding is using a stratified analysis. If our fictional research was conducted in smokers as well as non-smokers, the results could be analysed separately in both groups. If the association was then found to be of a similar magnitude in both, smoking would be unlikely to be a confounder of the relationship. A further method of accounting for the potential presence of confounders is in the statistical analyses by employing regression modelling techniques, sometimes referred to as ‘adjusted analyses’. The basic premise of all these techniques, however, is that the confounder needs to be known, and needs to be easily, accurately and reproducibly measurable. This can sometimes be difficult, for example in the case of determining the socio-economic status of study participants. The ideal way of eliminating the problem of confounding is the process of randomisation. If the randomisation is robust, the arms should be equivalent in both known and unknown confounders, hence comparisons between the arms are devoid of these problems. Thus, randomised controlled trials and meta-analyses thereof form the pinnacle of the evidence-based medicine pyramid.
Fig 0-2 Bradford-Hill criteria: the nine aspects of association providing epidemiological evidence of a causal relationship between a presumed cause and an observed effect.
The criteria set out by Austin Bradford Hill, in 1965, represent a useful tool in guiding from association to causation (Fig 0-2). In this classic essay, Bradford Hill set out some aspects of association, which should be considered “before deciding that the most likely interpretation of it is causation”13. These included:
● The strength of the association. If the magnitude of the association is large, for example as measured by a high odds ratio or relative risk, it is more likely that the association will not be attenuated by some unmeasured or imperfectly measured variable. This makes the association more likely to be causal.
● Consistency. If an association has only been observed by one group at one time, it is likely to be artefactual. Reproducibility of the association from different populations at different times and or locations lends credence to the association.
● Specificity. This property relates to the association seen between two, very specific, conditions. The more specific an association between a factor and an effect, the greater the probability that it is causal. If this criterion is not met, however, it does not imply a lack of causation.
● Temporality. This criterion implies that the cause of a disease must precede the development of the disease itself. This condition is fulfilled in infection models, where exposure to a single pathogen causes a specific disease and where the exposure always precedes the disease. In complex disease processes, detecting this condition is more challenging, as the exposures are often subclinical for a period of time and may not be the sole cause, but a contributory factor.
● Biological gradient/dose–response relationship. It stands to reason that if exposure to a risk factor, pathogen or condition causes or contributes to another disease, greater exposure should be linked to poorer outcomes of that disease.
● Plausibility. Fundamental to any step from association to causation is the ability to postulate the underlying biologically plausible mechanism, by which the causal relationship is expressed. In the absence of such an explanation, implying causality becomes challenging.
● Coherence. This criterion is an extension of the plausibility criterion, stating that the plausible explanation should fit with what is currently known of the biology of the disease. Again, not meeting this criterion is not necessarily a barrier to causality, as the knowledge base is subject to evolution and change.
● Experiment. Experimentally intervening to alter the exposure to an agent suspected of contributing to a disease, and then monitoring changes in the onset or progression of that disease further strengthens the causal hypothesis.
● Analogy. If the biological mechanism from one established causal relationship is accepted, other associations employing similar biological mechanisms have a lesser burden of proof before they are accepted as causal.
The GRADE (Grading of Recommendations, Assessment, Development and Evaluations) framework is another useful tool for rating the quality of evidence in systematic reviews and other evidence syntheses, but it can also be applied also to individual studies14. It provides a systematic approach and transparent tool for generating clinical practice recommendations. Evidence from randomised controlled trials (RCTs) begins as high-quality evidence but can be downgraded according to five factors: risk of bias, inconsistency, indirectness, imprecision and publication bias. Evidence from non-randomised studies begins as low-quality evidence, but its rating can be upgraded, if no other limitations have been identified, according to three reasons: large magnitude of effect, evidence of a dose–response effect and all plausible confounding taken into account. After the process of downgrading or upgrading, the quality of the evidence for each outcome is indicated as high, moderate, low or very low15. GRADE has adopted most of Bradford Hill’s criteria, some implicitly, others explicitly. However, it has been proposed that GRADE should be adapted to consider the Bradford Hill criteria more extensively. The reason is that evidence from non-randomised studies may provide a more adequate or best available measure of a health-related research question, but that such evidence might be graded as lower quality in the GRADE framework16.
The expert authors of the following book chapters have taken into account the above criteria for critically appraising the existing evidence on the associations or causal relationships between periodontitis and systemic diseases. This book therefore provides comprehensive, contemporary and well- considered insights into the clinical evidence and biological plausibility of each condition. This is underpinned by the body of scientific literature published to date, which has been critically discussed throughout the book. The reader will be provided with an understanding of how periodontitis impacts on the health of other organ systems and vice versa, but also of the limitations of existing studies and how these can be overcome in the future.
References
1. Kassebaum NJ, Smith AGC, Bernabé E, et al. Global, regional, and national prevalence, incidence, and disability-adjusted life years for oral conditions for 195 countries, 1990–2015: A systematic analysis for the global burden of diseases, injuries, and risk factors. J Dent Res 2017;96:380–387.
2. Kassebaum NJ, Bernabé E, Dahiya M, Bhandari B, Murray CJL, Marcenes W. Global burden of severe periodontitis in 1990–2010: a systematic review and meta-regression. J Dent Res 2014;93:1045–1053.
3. Listl S, Galloway J, Mossey PA, Marcenes W. Global economic impact of dental diseases. J Dent Res 2015;94: 1355–1361.
4. Tonetti MS, Jepsen S, Jin L, Otomo-Corgel J. Impact of the global burden of periodontal diseases on health, nutrition and wellbeing of mankind: A call for global action. J Clin Periodontol 2017;44:456–462.
5. Genco RJ, Borgnakke WS. Risk factors for periodontal disease. Periodontology 2000 2013;62:59–94.