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