%0 Journal Article
%T Considering pregnancies as repeated vs independent events: an empirical comparison of common approaches across selected perinatal outcomes.
%A Bane S
%A Carmichael SL
%A Mathur MB
%A Simard JF
%J Am J Obstet Gynecol MFM
%V 6
%N 8
%D 2024 Jul 10
%M 38996915
%F 8.679
%R 10.1016/j.ajogmf.2024.101434
%X <B>BACKGROUND: </B>In population-based research, pregnancy may be a repeated event. Despite published guidance on how to address repeated pregnancies to the same individual, a variety of approaches are observed in perinatal epidemiological studies. While some of these approaches are supported by the chosen research question, others are consequences of constraints inherent to a given dataset (eg, missing parity information). These decisions determine how appropriately a given research question can be answered and overall generalizability.<BR><B>OBJECTIVE: </B>To compare common cohort selection and analytic approaches used for perinatal epidemiological research by assessing the prevalence of two perinatal outcomes and their association with a clinical and a social independent variable.<BR><B>METHODS: </B>Using vital records linked to maternal hospital discharge records for singleton births, we created four cohorts: (1) all-births (2) randomly selected one birth per individual (3) first-observed birth per individual (4) primiparous-births (parity 1). Sampling of births was not conditional on cluster (ie, we did not sample all births by a given mother, but rather sampled individual births). Study outcomes were severe maternal morbidity (SMM) and preeclampsia/eclampsia, and the independent variables were self-reported race/ethnicity (as a social factor) and systemic lupus erythematosus. Comparing the four cohorts, we assessed the distribution of maternal characteristics, the prevalence of outcomes, overall and stratified by parity, and risk ratios (RR) for the associations of outcomes with independent variables. Among all-births, we then compared RR from three analytic strategies: with standard inference that assumes independently sampled births to the same mother in the model, with cluster-robust inference, and adjusting for parity.<BR><B>RESULTS: </B>We observed minor differences in the population characteristics between the all-birth (N=2736,693), random-selection, and first-observed birth cohorts (both N=2284,660), with more substantial differences between these cohorts and the primiparous-births cohort (N=1054,684). Outcome prevalence was consistently lowest among all-births and highest among primiparous-births (eg, SMM 18.9 per 1000 births among primiparous-births vs 16.6 per 1000 births among all-births). When stratified by parity, outcome prevalence was always the lowest in births of parity 2 and highest among births of parity 1 for both outcomes. RR differed for study outcomes across all four cohorts, with the most pronounced differences between the primiparous-birth cohort and other cohorts. Among all-births, robust inference minimally impacted the confidence bounds of estimates, compared to the standard inference, that is, crude estimates (eg, lupus-SMM association: 4.01, 95% confidence intervals [CI] 3.54-4.55 vs 4.01, 95% CI 3.53-4.56 for crude estimate), while adjusting for parity slightly shifted estimates, toward the null for SMM and away from the null for preeclampsia/eclampsia.<BR><B>CONCLUSIONS: </B>Researchers should consider the alignment between the methods they use, their sampling strategy, and their research question. This could include refining the research question to better match inference possible for available data, considering alternative data sources, and appropriately noting data limitations and resulting bias, as well as the generalizability of findings. If parity is an established effect modifier, stratified results should be presented.