Time series analysis of congenital anomalies based on Hungarian Congenital Anomalies Registry’s data

Abstract

Introduction: Two distinct categories of disease registries exist based on their data usage. The Hungarian Congenital Anomalies Registry (HCAR) is responsible for managing and processing the mandatory reporting of data on individuals affected by congenital anomalies. The registry can be classified as an epidemiological register. Through statistical analysis, it examines the national prevalence and trends of congenital anomalies for public health interest. It also filters out possible accumulations of cases and offers data for care planning and prevention.
Methodology: The subject of our study was the data of HCAR, which operates on the basis of mandatory and population-wide data provision, registered in the years 2015-2021 and associated with significant anatomical irregularities and abnormalities. In order to identify and separate changes that can be found in the prevalence of congenital anomalies from the effects of potential foetal harm during this period, we analysed changes in the prevalence of congenital anomalies over time.
Results: Based on HCAR analyses, the annual incidence of congenital anomalies identified at birth is 50 per thousand live births, with an irregular monthly distribution, which indicates a minor seasonal fluctuation. Out of the recorded notifications during the analysis period, a total of 22,384 cases were selected for the sample group, which represented a frequency of 35.6 per thousand, with a seasonal dissimilarity of 5.07, equivalent to approximately 14% of the mean. The highest values occur in the months of May-June-July, the lowest in January-February-March. The Kruskall-Wallis test revealed a significant seasonality (p=0.007) in the results.
Conclusions: The incidence of congenital malformations shows a slight, yet statistically significant variation throughout the seasons, which can be attributed to the considerable fluctuation in gestational period. Due to larger variations caused by random fluctuations and seasonal effects, the importance of this study lies in the creation of a methodology that can be employed to investigate specific pathologies in the future. Furthermore, the approach developed can be extended to analyses that combine spatial and temporal occurrences.