Evidence map›Paper›PMID 42063395›Full record

ArticleMultivariate behavioral research

Beyond Linear Risk: A Machine Learning Approach to Understanding Perinatal Depression in Context.

Phillip Sherlock, Maxwell Mansolf, Julie Hofheimer, Christine W Hockett, Thomas G O'Connor, Danielle Roubinov, J Carolyn Graff, Jin-Shei Lai, Nicole R Bush, Rosalind J Wright and 2 more

Abstract read
In one paragraph

Article in Multivariate behavioral research. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

12 authors.

Phillip SherlockAnita Zucker Center for Excellence in Early Childhood Studies, College of Education, University of Florida, Gainesville, FL, USA.
Maxwell MansolfDepartment of Medical Social Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
Julie HofheimerDepartment of Pediatrics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Christine W HockettDepartment of Pediatrics, University of South Dakota School of Medicine, Sioux Falls, SD, USA.
Thomas G O'ConnorDepartment of Psychiatry, University of Rochester School of Medicine and Dentistry, Rochester, NY, USA.
Danielle RoubinovDepartment of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
J Carolyn GraffDepartment of Health Promotion and Disease Prevention and Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN, USA.
Jin-Shei LaiDepartment of Medical Social Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
Nicole R BushDepartment of Psychiatry and Behavioral Sciences, University of California San Francisco, CA, USA.
Rosalind J WrightDepartment of Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Yueh-Hsiu Mathilda ChiuDepartment of Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
program collaborators for Environmental Influences on Child Health Outcomes

Funding

Project-005U2COD023375 · OD · DUKE UNIVERSITY · PI Linda S Adair, Phillip Brian Smith · 2016 to 2026
$215.9M
ECHODAC (Environmental Influences on Child Health Outcomes Data Analysis Center)U24OD023382 · OD · JOHNS HOPKINS UNIVERSITY · PI Diane J Catellier, LISA P JACOBSON · 2016 to 2026
$148.4M
Wisconsin Infant Study Cohort (WISC) ECHO Pediatric Follow-UpUH3OD023282 · OD · UNIVERSITY OF WISCONSIN-MADISON · PI James E. Gern · 2018 to 2026
$64.8M
The NYU Children’s Health & Environment Study: an ECHO CohortUH3OD023305 · OD · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI Akhgar Ghassabian, Leonardo Trasande · 2018 to 2026
$59.5M
Mount Sinai ECHO site for Perinatal Environment and Development StudiesUH3OD023337 · OD · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI KECIA Nicole CARROLL, Robert O Wright · 2018 to 2026
$54.1M
The ECHO Measurement Core: A Framework and Resource for Assessing Environmental Exposure and Child HealthU24OD023319 · OD · NORTHWESTERN UNIVERSITY AT CHICAGO · PI Courtney K Blackwell, RICHARD GERSHON · 2016 to 2026
$43.7M
Prenatal and Early Childhood Pathways To Health:  An Integrated Model of Chemical and Social Exposures, Biological Mechanisms, and Sex-Specific Effects on Neurodevelopment and Respiratory OutcomesUH3OD023271 · OD · UNIVERSITY OF WASHINGTON · PI BUSH, NICOLE RENEE, KARR, CATHERINE J · 2018 to 2022
$37.3M
Environmental Influences on Childhood Outcomes in the Northern Plains Safe Passage Study CohortsUH3OD023279 · OD · AVERA MCKENNAN · PI Amy J Elliott, Christine W Hockett · 2018 to 2026
$31.7M
Lifecourse Approach to Developmental Repercussions of Environmental Agents on Metabolic and Respiratory Health (LA DREAMERs)UH3OD023287 · OD · UNIVERSITY OF SOUTHERN CALIFORNIA · PI BASTAIN, THERESA M, BRETON, CARRIE VAN DOREN · 2018 to 2022
$28.4M
Understanding Breastfeeding Practices Among ECHO Cohort Participants Before and During/After the COVID-19 PandemicUH3OD023285 · OD · MICHIGAN STATE UNIVERSITY · PI Charles James Barone, MICHAEL R. ELLIOTT · 2018 to 2026
$24.5M
Environmental Influences on Child Health Outcomes in Puerto Rico (ECHO-PRO)UH3OD023251 · OD · NORTHEASTERN UNIVERSITY · PI Akram N Alshawabkeh · 2018 to 2026
$24.0M
The Developing Brain: Influences and OutcomesUH3OD023313 · OD · WOMEN AND INFANTS HOSPITAL-RHODE ISLAND · PI Viren Andrew D'Sa, Daphne Koinis Mitchell · 2018 to 2026
$20.7M
NIH HHS U24 OD023319NIH HHS U24 OD023382NIH HHS U2C OD023375NIH HHS UH3 OD023244NIH HHS UH3 OD023251NIH HHS UH3 OD023271NIH HHS UH3 OD023279NIH HHS UH3 OD023282NIH HHS UH3 OD023285NIH HHS UH3 OD023286NIH HHS UH3 OD023287NIH HHS UH3 OD023305NIH HHS UH3 OD023313NIH HHS UH3 OD023328NIH HHS UH3 OD023337NIH HHS UH3 OD023349
6 · The paper itself

Abstract

The goal of this study was to investigate the contextual nature of prenatal depression (PND) and postpartum depression (PPD). We report an investigation of maternal PND and PPD using nonrandomly clustered data from 8,936 mothers in 16 cohorts in the Environmental influences on Child Health Outcomes (ECHO) Cohort. We used a recursive partitioning algorithm (ctree) to account for differential effects arising from predictor and effect heterogeneity across cohort to identify contexts, and the characteristics comprising these contexts, associated with PND and PPD. Consistent with Bronfenbrenner's bioecological systems framework, risk factors for PND and PPD are heterogeneously and synergistically present in context: the same variables did not consistently present as risk factors across groups. Findings from this study support the idea that the presence or absence of medical (and sociodemographic) risks do not have simple associations with perinatal depression. To that end, no univariate predictors of PND and PPD can be said to constitute risk absent the broader context of mothers' lives. Thus, a more useful research question than "which variables increase risk for PPD?" is "in what contexts, or in which subgroups, does a particular risk or set of risks contribute to perinatal depression?"

Indexed as

DepressionDepression, PostpartumMachine LearningPregnancy ComplicationsAdultAlgorithmsCohort StudiesFemaleHumansMothersPrediction AlgorithmsPredictive Learning ModelsPregnancyRisk Factorsconditional inferencePostpartum depressionrecursive partitioningregression

Identifiers

PMID42063395
PMCPMC13221726

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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.