Periodontal disease and preterm birth: do the data have enough teeth to recommend screening and preventive treatment?

In recent years there has been a mounting body of evidence that indicates an association between periodontal disease and preterm birth. Drs Jean-Noel and Michel, in the current issue of the Journal, report the results of a comprehensive and well-executed meta-analysis that summarizes the available observational studies that explore this relationship. Corroborating prior studies, their meta-analysis indicates a “likely association” between periodontitis and preterm birth or low birthweight, with approximately a 2-fold increase risk for preterm birth in patients with periodontal disease. We applaud the authors for explicitly identifying the unavoidable limitations of the analysis and caution the reader to consider these limitations before using these results to alter clinical practice. Three issues limit our ability to clinically apply findings from this area of research. First, the validity of the meta-analytic results is limited by the quality of the studies that comprise the analysis. Second, as the authors mention, association does not always correspond to causation, and third, we can never leap from observation of an association to proof of efficacy of an intervention. The first issue restricting the clinical application of these findings is the inherent methodologic limitations of metaanalysis of observational studies. In meta-analysis, results from independent studies that are considered “combinable” are integrated in a statistical procedure to arrive at conclusions about a body of research. However, if heterogeneity across studies exists, combining the results may underestimate the variance and the size of the confidence interval, thereby overestimating the precision of the pooled estimate (odds ratio or relative risk) or producing a biased pooled estimate. Jean-Noel and Michel carefully considered the role of heterogeneity in their metaanalysis and found significant heterogeneity across studies. Among the 17 studies included in this meta-analysis, there are great variations in the study designs (ie, case-control, cohort, and cross-sectional studies), assessment of exposure (ie, periodontitis) and adverse pregnancy outcome (ie, preterm birth, low birthweight, preterm low birthweight, and intrauterine growth restriction), and control of confounding. In an attempt to delineate factors that may account for the heterogeneity, the authors conducted subgroup analysis and meta-regression by study characteristics, including study design and measurement of exposure and outcomes. On the basis of statistically nonsignificant test results, the authors concluded that differences in study designs and assessment of exposures and outcomes could not have accounted for the heterogeneity. But, among the 17 observational studies included in the meta-analysis, a standardized outcome measure was clearly lacking. Hence, concluding that heterogeneity is not creating a biased pooled odds ratio may not be warranted until further exploration of variations is conducted. A major limitation with statistical testing of heterogeneity is that the statistical tests have notoriously weak power and often fail to reject the null hypothesis of homogeneity even if substantial differences between studies exist. Small P values suggest that the heterogeneity should not be ignored, but large P values should not be unequivocally interpreted as justifying the homogeneity assumption. Regarding subgroup analysis, if there is important within-group heterogeneity (heterogeneity not accounted for by the grouping variable), the standard error tends to be underestimated; consequently, statistical tests based on standard error will not be valid. Therefore, when heterogeneity is suspected, it should not simply be ignored after a statistical test is applied; rather, it should be scrutinized, with an attempt to explain it. In this meta-analysis, Jean-Noel and Michel suggested that 1 possible source of heterogeneity was the quality of studies based on statistical testing results. As a common practice, the authors weighted studies included in the meta-analysis based on a quality score. Quality scoring is subjective; it submerges important information by combining unequal study features into a single score. Item-specific analyses of the impact of each factor (eg, study design) would allow the data, rather than the investigators, to indicate the importance of each item in determining the estimated effect. Notwithstanding limitations, we commend Jean-Noel and Michel for their efforts in such a comprehensive and well considered meta-analysis and we strongly agree with their conclusion that the pooled estimate from their meta-analysis may be biased. The later 2 issues, proving causation and demonstrating treatment efficacy, can be interrelated or they can function independently. The most definitive conclusion that we can From the Division of Maternal Fetal Medicine, Department of Obstetrics and Gynecology, University of Pennsylvania Health System, and Center for Clinical Epidemiology and Biostatistics, Philadelphia, PA (Dr Stamilio); the Department of Community Health in Epidemiology, School of Public Health, Saint Louis University (Dr Chang); and the Department of Obstetrics and Gynecology, Washington University (Dr Macones), St Louis, MO. 0002-9378/free © 2007 Mosby, Inc. All rights reserved. doi: 10.1016/j.ajog.2006.09.029