Perinatal Depression Risk Contexts from 8,936 Mothers in the ECHO Cohort

A 2026 study by Phillip Sherlock investigates prenatal and postpartum depression using data from 8,936 mothers across 16 cohorts in the Environmental influences on Child Health Outcomes (ECHO) Cohort. The dataset likely contains contextual and risk factor data analyzed with a recursive partitioning algorithm (ctree) to identify heterogeneous subgroups. The findings emphasize that risk factors for perinatal depression are synergistic and context-dependent, with no simple univariate predictors.

Use Cases

• Training context-aware risk prediction models based on the described cohort and sociodemographic data.
• Analyzing predictor and effect heterogeneity for perinatal depression across different subgroups as described in the study.
• Testing machine learning algorithms like recursive partitioning on clustered, nonrandom clinical data.
• Researching the application of Bronfenbrenner's bioecological systems framework to mental health data.

Strengths

• Data from 8,936 mothers provides a substantial sample size for analysis.
• Multi-cohort design includes 16 cohorts from the ECHO program, suggesting diverse sources.
• Analysis employs a specific machine learning method (ctree) to account for context and heterogeneity.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset is very small (512.8 KB), indicating limited raw data or a document-focused release.

Provenance

Source

Environmental influences on Child Health Outcomes (ECHO) Cohort

Collection Method

Nonrandomly clustered data from 16 cohorts, analyzed with a recursive partitioning algorithm.

Time Range

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Freshness

Last updated 2026-05-01 09:20:04; freshness should be verified.

Geography

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