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Record W4405669135 · doi:10.1016/j.envint.2024.109222

Urban environment during pregnancy, cognitive abilities, motor function, and externalizing and internalizing symptoms at 2–5 years old in 3 Canadian birth cohorts

2024· article· en· W4405669135 on OpenAlexafffundabout
Anne-Claire Binter, Dany Doiron, Martine Shareck, Tona M. Pitt, Sheila McDonald, Padmaja Subbarao, Suzanne Tough, Jeffrey R. Brook, Mònica Guxens

Bibliographic record

VenueEnvironment International · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Green Space and Health
Canadian institutionsUniversity of TorontoUniversity of CalgaryPublic Health OntarioHospital for Sick ChildrenCalgary Laboratory ServicesProvincial Laboratory of Public HealthAlberta Health ServicesUniversité de SherbrookeCentre Hospitalier Universitaire de SherbrookeMcGill University Health Centre
FundersAgencia Estatal de InvestigaciónHorizon 2020Instituto de Salud Carlos IIICanadian Institutes of Health ResearchHORIZON EUROPE Framework ProgrammeHorizon 2020 Framework ProgrammeCentres de Recerca de CatalunyaAlberta InnovatesGeneralitat de CatalunyaEuropean CommissionMinisterio de Ciencia e Innovación
KeywordsPregnancyMotor functionCognitionDevelopmental psychologyPsychologyEnvironmental healthClinical psychologyMedicinePsychiatryPhysical medicine and rehabilitation

Abstract

fetched live from OpenAlex

• Effects of the urban environment on neurodevelopment are understudied. • We estimated 7 exposures of built environment, surrounding greenness, and air pollution. • We assessed cognitive and motor function, internalizing and externalizing symptoms. • Urban environment was not associated with neurodevelopment at ages 2 to 5. More than 80% of the Canadian population lives in urban settings. Urban areas usually bring exposure to poorer air quality, less access to green spaces, and higher building density. These environmental factors may endanger child development. To assess the relationship of urban environmental exposures during pregnancy with cognitive abilities, motor function, externalizing and internalizing symptoms in children. We included 6,279 mother–child pairs from 3 Canadian population-based birth-cohorts (3D Cohort Study in Montreal, Quebec City, and Sherbrooke, AOF Study in Calgary, CHILD Study in Edmonton, Vancouver, Toronto, and Winnipeg). We estimated 7 environmental exposures of the built environment, surrounding greenness, and air pollution, around participant’s home addresses during pregnancy. Validated neuropsychological tests were used to assess non-verbal and verbal abilities, gross and fine motor function, externalizing and internalizing symptoms at child’s age 2 to 5 years. We assessed associations of each environmental exposure indicator with each of the 6 outcomes, using multivariate linear regression models. We conducted analyses separately by city of recruitment and combined estimates in meta -analyses. Overall, urban environment during pregnancy was not associated with cognitive abilities (e.g., −0.81 non-verbal points 95 %CI [-2.10; 0.48] per 1 μg/m 3 increase in PM 2.5 ), motor function, or externalizing and internalizing symptoms. In individual cohorts, we found associations of some environmental exposures, in particular building density, fine particles, and nitrogen dioxide with non-verbal abilities, verbal abilities, and fine motor function, but overall confidence intervals in the meta -analyses included the null. We found no evidence of a relationship of prenatal built environment, surrounding greenness, and air pollution with cognitive abilities, motor functions or externalizing and internalizing symptoms in childhood. Urban environment has been shown to influence health across the lifecourse, however, specific exposures during pregnancy do not seem associated with poorer neurodevelopment in children of 2- to 5- year.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.095

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.003
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.006
GPT teacher head0.203
Teacher spread0.196 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2024
Admission routes3
Has abstractyes

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