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Record W3196672079 · doi:10.1080/09603123.2021.1970116

Association between residential proximity to municipal solid waste incinerator sites and birth outcomes in Shanghai: a retrospective cohort study of births during 2014–2018

2021· article· en· W3196672079 on OpenAlexaff
Yanhui Hao, Weibin Wu, William D. Fraser, He‐Feng Huang

Bibliographic record

VenueInternational Journal of Environmental Health Research · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicEnvironmental Justice and Health Disparities
Canadian institutionsLunenfeld-Tanenbaum Research InstituteMount Sinai Hospital
FundersScience and Technology Commission of Shanghai MunicipalityNational Natural Science Foundation of China
KeywordsMedicineUnderweightSmall for gestational ageOdds ratioEnvironmental healthRetrospective cohort studyPregnancyConfidence intervalCohort studyCohortBirth weightDemographyBody mass indexOverweight

Abstract

fetched live from OpenAlex

We tested the hypothesis of whether maternal residential proximity to municipal solid waste incinerator (MSWI) sites could significantly affect birth outcomes. This retrospective birth cohort study conducted at the International Peace Maternity and Infant Hospital, Shanghai, China, included 59,606 mothers with singleton live births during 2014-2018. Multivariate generalized linear models were used to examine associations between residential proximity to MSWI sites and birth outcomes. Small for gestational age (SGA) was significantly more common among children with maternal residential proximity to MSWI sites (odds ratio [OR]=1.20, 95% confidence interval [CI]: 1.07-1.34). Maternal prepregnancy body mass index (BMI) influenced this association. Infants of underweight mothers (prepregnancy BMI <18.5 kg/m2) with MSWI exposure (OR=2.00, 95% CI: 1.58-2.52) had higher risks of SGA than their counterparts. Our findings underscore the need to prevent adverse environmental effects of MSWI on birth outcomes; improved exposure assessment measures are warranted in future studies.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.030
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.059
GPT teacher head0.425
Teacher spread0.366 · 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 teacher head, 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

Citations2
Published2021
Admission routes1
Has abstractyes

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