Exposures
Data on medication exposures were obtained from the three prenatal
questionnaires and the first postpartum questionnaire. For a wide range
of indications, including heartburn and acid reflux, women reported the
name of the medication taken, time period of use, frequency of use, and
quantity taken. Missing data on the duration of treatment, frequency, or
quantity were replaced with the median cohort value for that variable
specific for the medication of interest. Medications were coded
according to the Anatomical Therapeutic Chemical (ATC) Classification
System.21 Exposure to calcium-based antacids was
defined as reported use of antacids (ATC code A02) containing any amount
of calcium carbonate. The dose of calcium on each day was calculated by
multiplying the amount of calcium carbonate in mg per medication unit by
the number of units taken per day. The doses for multiple calcium-based
antacids per day were summed. PPI exposure was defined as report of
medication belonging to ATC group A02BC. Dosage was converted to Defined
Daily Dose (DDD) per day. Following, daily doses were expressed as the
average daily dose (milligrams per day for calcium-based antacids and
DDDs per day for PPIs) per week. For calcium-based antacids, we
considered a daily dose of ≥1 g calcium as high,6while a high dose for PPIs was >1 DDD. The sensitivity of
the questionnaires was 0.89 (95% CI 0.86-0.93) for gastroesophageal
reflux medication.22
We adhered to a recently published guidance on longitudinal methods for
modelling medication exposures in pregnancy.23 We
evaluated exposure binarily (any versus none) in the first 237 days of
pregnancy, further subdivided into early pregnancy (gestational weeks
0-16) and mid-pregnancy (gestational weeks 17-33), reflecting the
temporality of data collection in both sources (in weeks 17 and 34).
Furthermore, we clustered women with similar individual trajectories of
calcium dose or DDDs of PPIs in gestational weeks 0-33 usingk -means clustering with the R statistical software package
“kml ”.24 This unsupervised learning approach
makes no a priori assumptions about trajectory shape or
membership.25 We considered daily and cumulative dose
in each gestational week allowing for k = 2 to k = 8
clusters. We selected the number of clusters based on (a) optimization
of three statistical quality criteria,25 (b) clinical
relevance of the clusters, and (c) at least 100 pregnancies per cluster.
K -means clustering requires all pregnancies to have the same
gestational length to avoid including exposure after the diagnosis of
preeclampsia and on postpartum days.26 Moreover,
immortal time bias could be introduced if we would apply the binary
exposure categories after gestational day 237, as pregnancies without
preeclampsia and pregnancies with longer gestations have more
opportunity for exposure.27,28 Therefore, we modelled
time-dependent changes in dose on each gestational day between 238 and
the end of follow-up, defined as diagnosis of preeclampsia or delivery,
allowing for daily changes in use (none/any) and dose (none/low/high).
We determined exposure time by dividing the average number of
person-weeks in each exposure category by the number of women with any
exposure to each level after day 237. Women could contribute
person-weeks to multiple dose levels.29