The Language of Potential Outcomes. In: Berzuini C, Dawid P, Bernardinelli L, editors. Causality. Chichester: John Wiley & Sons, Ltd; 2012. pp. 6-14. [DOI: 10.1002/9781119945710.ch2]

Diet-Induced Overweight and Obesity and Periodontitis Risk: An Application of the Parametric G-Formula in the 1982 Pelotas Birth Cohort

We aimed to estimate hypothetical effects of habits (smoking, alcohol consumption, and fat and carbohydrates consumption) combined with diet-induced overweight/obesity on the risk of periodontitis. The risk of any periodontitis, moderate/severe periodontitis, and the combination of bleeding on probing (BOP) and clinical attachment loss (CAL) was estimated using the parametric g-formula in adults aged 31 years from the 1982 Pelotas Birth Cohort in Brazil. Individuals in this cohort have been followed since birth. Hypothetical conditions were set independently for each risk factor and in combination for the entire population. A total of 539 participants had oral examinations in 2013. The cumulative 31-year risk under no intervention was 33.3% for any periodontitis, 14.3%, for moderate/severe periodontitis, and 14.7%, for BOP and CAL. According to our statistical approach, diet-induced overweight/obesity increased the risk of all outcomes: 11% (overweight) and 22% (obesity) higher risk of periodontitis; 12% (overweight) and 27% (obesity) higher risk of moderate/severe periodontitis; 21% (overweight) and 57% (obesity) higher risk of CAL and BOP. When overweight/obesity was combined with other unhealthy habits, the risk was even greater. Our findings suggest that the combination of diet-induced obesity with other risk factors may increase the risk of periodontitis. Further research in the field is required to corroborate our study.

A counterfactual approach to bias and effect modification in terms of response types

Background

The counterfactual approach provides a clear and coherent framework to think about a variety of important concepts related to causation. Meanwhile, directed acyclic graphs have been used as causal diagrams in epidemiologic research to visually summarize hypothetical relations among variables of interest, providing a clear understanding of underlying causal structures of bias and effect modification. In this study, the authors aim to further clarify the concepts of bias (confounding bias and selection bias) and effect modification in the counterfactual framework.

Methods

The authors show how theoretical data frequencies can be described by using unobservable response types both in observational studies and in randomized controlled trials. By using the descriptions of data frequencies, the authors show epidemiologic measures in terms of response types, demonstrating significant distinctions between association measures and effect measures. These descriptions also demonstrate sufficient conditions to estimate effect measures in observational studies. To illustrate the ideas, the authors show how directed acyclic graphs can be extended by integrating response types and observed variables.

Results

This study shows a hitherto unrecognized sufficient condition to estimate effect measures in observational studies by adjusting for confounding bias. The present findings would provide a further understanding of the assumption of conditional exchangeability, clarifying the link between the assumptions for making causal inferences in observational studies and the counterfactual approach. The extension of directed acyclic graphs using response types maintains the integrity of the original directed acyclic graphs, which allows one to understand the underlying causal structure discussed in this study.

Conclusions

The present findings highlight that analytic adjustment for confounders in observational studies has consequences quite different from those of physical control in randomized controlled trials. In particular, the present findings would be of great use when demonstrating the inherent distinctions between observational studies and randomized controlled trials.