Use of hierarchical models to analyze European trends in congenital anomaly prevalence.

Background: Surveillance of congenital anomalies is important to identify potential Teratcgens. Despite known associations between different anomalies, current surveillance methods examine trends witin each subgroup separately. We aimed to evaluate whether hierarchical statistical methods that combine information from several subgroups simultaneously would enhance current surveillance methods using data collected by EUROCAT, a European network of population based congenital anomaly registries. Methods: Ten year trends (2003 2012) in 18 EUROCAT registries over 11 countries were analyzed for the following, groups of anomalies: neural tube defects, congenital heart defects, digestive system, and chromosomal anomalies. Hierarchical Pcisson regression models that combined related subgroups together according to EUROCAT's hierarchy of subgroup coding were applied. Results from hierarchical models were compared with :hose from Poisson models tat consider each congenital anomaly separately. Results: Hierarchical models gave similar results as thcse obtained when considering each anomaly subgroup in a separate analysis. Hierarchical models that included only around three subgroups showed poor convergence and were generally found tc be over parameterized. Larger sets of anomaly subgroups were found to too he;erogeneous: x; group 'oge.Ther in This way. Conclusion: There were no substantial differences be Ateen independent analyses of each subgroup and hierarchical models when using the EUROCAT anomaly subgroups. Considering each anomaly separately, therefore, remains an appropriate method for the detection of potential changes in prevalence by surveillance systems. Hierarchical models do, however, remain an interesting alternative method of analysis when considering the risks ol specific exposures in relation to the prevalence of congenital anomalies, which could be investigated in other studies.