Statistical analyses
To investigate possible associations of maternal country and region of
birth, reason for immigration and length of residence with placental
abruption, we estimated odds ratios (ORs) with 95% confidence intervals
(CIs) using binary logistics regression analysis. Maternal country of
birth, maternal region of birth, reason for immigration and length of
residence were included in the regression models as categorical
variables using non-immigrant women as the reference group.
Adjustments were made for year of birth, maternal age, parity, multiple
pregnancies, chronic hypertension and level of education. Year of birth
and maternal age at the birth were included as polynomial quadratic
terms in the regression. To account for dependency among pregnancies to
the same woman, we used robust standard errors that allowed for
within-mother clustering (18).
Missing values were imputed with the mi suite of commands in Stata,
using the multivariate normal model with five imputations (19). The
imputations were performed for each exposure-outcome association and
included the same variables as in the analytic regression models. To
obtain ORs with 95% CI across the five imputed datasets, we used
Rubin’s combination rules, adjusted for the variability between
imputation sets.
To investigate the possible impact of smoking on study results, we
performed analyses for the sub-period 1999-2016 for which smoking data
were available. Adjustment for smoking had little impact on the reported
results (data not shown). Similarly, adjustments for consanguinity
between mother and father or Norwegian health region for the birth did
not change the results and were therefore not included in the models.
All analyses were performed using Stata IC version 16 (Stata Statistical
Software, College Station, TX, USA).