The statistical evaluation of the data was carried out with using SPSS Statistics Version 25. The data were first assessed for normal distribution applying a Kolmogorov-Smirnov test. For normally distributed metric data a t-test was used for independent variables and a Mann-Whitney-U-test for non-normally distributed metric data. The χ2 test was used for the analysis of nominal or ordinary data. P-value < 0.05 (*) were considered to be significant. Birth percentiles for height, weight and head circumference were calculated according to Voigt et al. [14]. In the univariate regression model, known and unknown factors that could influence the birth weight percentile were evaluated. Individual confounders with a potential influence, which had a p-value < 0.05 in the univariate linear regression model, were checked for multicollinearity using Kendall-Tau-B correlation analysis and included in the multiple linear regression analysis if no correlation (r < 0.5) was present. The results of the multiple linear regression analysis were considered valid if the Durbin-Watson value was between 1 and 3, Variance Inflation Factor (VIF) was < 5, the largest condition index was < 30 and p < 0.05.

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