Statistical analyses
Prior to analyses, multiple imputations were performed to replace the
missing values in the predictors (ranging from 1.1% to 36.2%) and
timing of pregnancy recognition (25.5%) using multivariate imputation
by chained equations.35 We created 50 imputed datasets
with 100 iterations. Besides the study variables, we used body mass
index at intake, gestational age at intake, and apgar score 5 minutes
after birth as predictors for imputation.36
First, the inequalities, i.e. the associations between the different
predictors and timing of antenatal care initiation, were estimated using
linear regression models. The reference level was the level of the
predictor with the earliest antenatal care initiation, e.g. ‘30-35
years’ in age, or ‘high’ in educational attainment, to ensure that the
inequality was always positive because that simplified the
interpretation. Second, the association between early pregnancy
recognition and timing of antenatal care initiation was estimated using
linear regression analyses adjusted for all predictors (age, migration
background, relationship status, pregnancy intention, mental illness,
Dutch language skills, parity, education, employment household income,
housing, neighborhood deprivation, and cognitive functioning). This
model was subsequently used to estimate the timing of antenatal care
initiation if the intervention was set to pregnancy recognition within 6
weeks. By comparing the timing of antenatal care initiation with the
hypothetical intervention (i.e. had all participants recognized the
pregnancy within 6 weeks) and without the hypothetical intervention
(i.e. timing of pregnancy recognition as it appears in the data), the
reduction in the inequalities driven by the predictors was
estimated.32 Bootstrapping with 1000 iterations was
used to calculate the 95% confidence intervals. All analyses were
conducted in IBM SPSS version 28 and R statistical software version
4.2.1.