Candidate predictors, variable selection, and coding
Demographic, medical and obstetric factors known to be associated with
an increased risk of severe maternal morbidity were considered as
candidate predictors. These included: estimated maternal age at
conception (continuous, categorical, and squared terms); residential
income quintile; world region of origin (Table S2); attendance at a
first-trimester prenatal care visit; pre-pregnancy body mass index
(BMI); parity; multiple gestation; infertility; infertility treatment;
placental disorders (e.g., placenta praevia, placenta accreta); and
pre-existing medical conditions coded within 12 months before the
estimated date of conception (Table S1). Substantial missing data was
noted only for the variable pre-pregnancy BMI (63.79%). We tested
models in which BMI was modelled as a continuous variable and where
missing values were assigned the median BMI (24.2
kg/m2). We further tested models in which BMI was
divided into the following categories: <18.5
kg/m2, 18.5-24.9 kg/m2 (reference
category), 25-29.9 kg/m2, >30
kg/m2, and missing. Certain categorical variables with
a low frequency in the cohort were combined with other similar variables
(e.g., pre-existing cardiovascular conditions; placental conditions and
anomalies). Variables were also assessed for collinearity by checking
the variance inflation factor (VIF), and where collinear (VIF
> 5), the most commonly reported variable was
selected.22
In the CPM restricted to the sub-cohort of parous women, in addition to
the above variables, we included complications coded in any previous
pregnancy as predictors (Table S1).
Possible interactions between variables were assessed and included if
statistically significant at alpha=0.10.23