Available online 18 May 2023
Author links open overlay panel, , , , , ABSTRACTPurposeTo ascertain whether adverse pregnancy outcomes at first pregnancy influence subsequent interpregnancy intervals (IPIs) and whether the size of this effect varies with IPI distribution
MethodsWe included 251,892 mothers who gave birth to their first two singletons in Western Australia, from 1980–2015. Using quantile regression, we investigated whether gestational diabetes, hypertension, or preeclampsia in the first pregnancy influenced IPI to subsequent pregnancy and whether effects were consistent across the IPI distribution. We considered intervals at the 25th centile of the distribution as ‘short’ and the 75th centile as ‘long’.
ResultsThe average IPI was 26.6 months. It was 0.56 months (95% CI: 0.25-0.88 months) and 1.12 months (95% CI: 0.56 – 1.68 months) longer after preeclampsia, and gestational hypertension respectively. There was insufficient evidence to suggest that the association between previous pregnancy complications and IPI differed by the extent of the interval. However, associations with marital status, race/ethnicity and stillbirth contributed to either shortening or prolonging IPIs differently across the distribution of IPI.
ConclusionMothers with preeclampsia and gestational hypertension had slightly longer subsequent interpregnancy intervals than mothers whose pregnancies were not complicated by these conditions. However, the extent of the delay was small (<2 months).
Section snippetsINTRODUCTIONInterpregnancy interval (IPI), the period between the end of one pregnancy and conception of the next, has been associated with adverse pregnancy complications. [1], [2] Both short and long IPIs are associated with a greater risk of gestational diabetes and hypertensive complications of pregnancy (preeclampsia, gestational hypertension) in subsequent pregnancies.
It is well established that pregnancy complications have a higher tendency to recur. [3], [4] A recent meta-analysis reported that
Design, participants, and data sourcesWe conducted a longitudinal study of mothers who gave birth to their first two singletons (parity 0 and 1) at 20-44 weeks of gestation in Western Australia (WA) between 1980 - 2015. We obtained birth records from the Midwives Notification System (MNS), and maternal hospitalization records from Hospital Morbidity Data Collection. [6], [7] Data sources have been described in detail in our protocol. 8
Cohort characteristicsOf the eligible 258,037 women with their first two consecutive pregnancies between 1980 and 2015, 6,145 were excluded due to missing information on key covariates (gestational length, SES, maternal age, IPI, and infant weight), leaving 251,892 women eligible for analyses (Supplementary Figure 1). The mean (+ SD) age at study entry (first delivery) was 25.29 (5.2) and peaked between 25-29 years. The majority of mothers were married and Caucasian (Table 1). The prevalence of gestational diabetes,
DiscussionUsing data from a large population cohort of mothers with their first two births, we used quantile regression to ascertain whether pregnancy complications at first pregnancy influence subsequent IPI and verify whether changes occur along the IPI distribution. To our knowledge, no studies have investigated the influence of pregnancy complications on IPI or whether this effect is consistent across the IPI distribution. We observed that mothers with hypertensive complications had slightly longer
CRediT authorship contribution statementAmanuel Gebremedhin: Conceptualization, Methodology, Software, Formal analysis and Investigation, Writing – original draft, Visualization. Annette K REGAN: Methodology, Validation, Writing – Review & Editing, Supervision. Siri E HÅBERG: Validation, Writing – Review & Editing. M Luke MARINOVICH: Validation, Writing – Review & Editing, Supervision. Gizachew A Tessema: Validation, Writing – Review & Editing, Resources, Supervision. Gavin Pereira: Methodology, Validation, Writing – Review &
Declaration of Competing InterestThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
AcknowledgementsThe authors would like to thank the Data Linkage Branch (Department of Health WA) as well as the Data Custodians for the MNS and HMDC for providing data for this project.
Disclosure of conflicts of interestThe authors have no potential conflicts of interest to disclose.
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