Evidence map›Paper›PMID 34100086›Full record

ReviewEpidemiologic reviews2022

Longitudinal Methods for Modeling Exposures in Pharmacoepidemiologic Studies in Pregnancy.

Mollie E Wood, Angela Lupattelli, Kristin Palmsten, Gretchen Bandoli, Caroline Hurault-Delarue, Christine Damase-Michel, Christina D Chambers, Hedvig M E Nordeng, Marleen M H J van Gelder

Abstract readReview
In one paragraph

Review in Epidemiologic reviews, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

20 citing papers in PubMed.

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  9. Individual differences in the evolution of causal illusions.British journal of psychology (London, England : 1953) · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Mollie E Wood
Angela Lupattelli
Kristin Palmsten
Gretchen Bandoli
Caroline Hurault-Delarue
Christine Damase-Michel
Christina D Chambers
Hedvig M E Nordeng
Marleen M H J van Gelder

Funding

CVD Epidemiology Training Program in Behavior, the Environment and Global HealthT32HL098048 · NHLBI · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI Goodarz Danaei, ERIC B RIMM · 2009 to 2026
$6.9M
Treatment Initiation for New Episodes of Depression in Pregnant WomenR01HD100579 · NICHD · HEALTHPARTNERS INSTITUTE · PI PALMSTEN, KRISTIN · 2021 to 2025
$3.1M
Maternal COVID-19 Vaccination and Lactation OutcomesR01HD107753 · NICHD · HEALTHPARTNERS INSTITUTE · PI PALMSTEN, KRISTIN · 2022 to 2024
$2.0M
Applying Machine Learning in the Prediction and Identification of Children Affected by Prenatal Alcohol ExposureK01AA027811 · NIAAA · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI BANDOLI, GRETCHEN E. · 2019 to 2023
$865k
Oral Corticosteroid Use During Pregnancy and Risk for Preterm BirthR00HD082412 · NICHD · HEALTHPARTNERS INSTITUTE · PI PALMSTEN, KRISTIN · 2017 to 2019
$716k
NHLBI NIH HHS T32 HL098048NIAAA NIH HHS K01 AA027811NICHD NIH HHS R00 HD082412NICHD NIH HHS R01 HD100579NICHD NIH HHS R01 HD107753
6 · The paper itself

Abstract

In many perinatal pharmacoepidemiologic studies, exposure to a medication is classified as "ever exposed" versus "never exposed" within each trimester or even over the entire pregnancy. This approach is often far from real-world exposure patterns, may lead to exposure misclassification, and does not to incorporate important aspects such as dosage, timing of exposure, and treatment duration. Alternative exposure modeling methods can better summarize complex, individual-level medication use trajectories or time-varying exposures from information on medication dosage, gestational timing of use, and frequency of use. We provide an overview of commonly used methods for more refined definitions of real-world exposure to medication use during pregnancy, focusing on the major strengths and limitations of the techniques, including the potential for method-specific biases. Unsupervised clustering methods, including k-means clustering, group-based trajectory models, and hierarchical cluster analysis, are of interest because they enable visual examination of medication use trajectories over time in pregnancy and complex individual-level exposures, as well as providing insight into comedication and drug-switching patterns. Analytical techniques for time-varying exposure methods, such as extended Cox models and Robins' generalized methods, are useful tools when medication exposure is not static during pregnancy. We propose that where appropriate, combining unsupervised clustering techniques with causal modeling approaches may be a powerful approach to understanding medication safety in pregnancy, and this framework can also be applied in other areas of epidemiology.

Indexed as

PharmacoepidemiologyCluster AnalysisFemaleHumansPregnancyPregnancy Trimestersclustering methodsconfounding factors (epidemiology)Cox modelsepidemiologic methodslongitudinal studiesmedicationpregnancytime-varying exposure methods

Identifiers

PMID34100086
PMCPMC8763114

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.