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Record W4400363819 · doi:10.5194/ems2024-938

Exploring vulnerability patterns in heat-related preterm birth: a multi-country multi-city analysis

2024· preprint· en· W4400363819 on OpenAlexaff
Coral Salvador, Carmen Íñiguez, Yoonhee Kim, Éric Lavigne, Hans Orru, Martina S. Ragettli, Dominic Royé, Francesca de’Donato, Yue Leon Guo, Howard Chang, Christofer Åström, Shoko Konishi, Aurelio Tobı́as, Keren Agay‐Shay, Noa Scovronick, Tanya Singh, Nicolás Valdés Ortega, Ana M. Vicedo‐Cabrera

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsHealth Canada
Fundersnot available
KeywordsDemographySocioeconomic statusMedicinePoisson regressionPercentilePremature birthGestational ageMarital statusParity (physics)PregnancyPopulationEnvironmental health

Abstract

fetched live from OpenAlex

Evidence suggests that high temperatures may trigger preterm birth (PTB), which is associated with a higher risk of infant mortality and morbidity during childhood and adult life. However, there is limited evidence on the role of sociodemographic factors on the vulnerability of pregnant women. In a multi-location setting, we aimed to assess 1) the effect of heat on PTB of different gestational ages (extreme, very, late, standard preterm births) and at-term births and 2) how mother and child characteristics (sex, ethnicity, parity, age, education, marital status, socioeconomic class) influence the association between heat and PTB.The analysis included all singleton births born in the warm season (5 warmer months) in 243 cities in 13 countries between 1979-2019. A two-stage design was applied with conditional quasi-Poisson regression with distributed lag nonlinear models to estimate the association between daily mean temperature and PTB or at-term birth (lags 0-4 days) in each location and for each category of characteristic of the mother and child. Then, a random-effects multilevel metaanalytical model was applied to report overall effects and by country level. Extreme heat effects were measured as the percentage change (ch%, 95%CI) in the outcome when the mean temperature increased from 1% to the 95% percentile.Heat was positively associated with all endpoints, except for extreme or very PTB whose risks were very imprecise, with larger risks for late-PTB (5%, 0.8-9.3) and smaller in at term births (2%, 1-3.1). Younger, Caucasian mothers and those with low socioeconomic class seemed to be more vulnerable to heat. Female fetal gender was associated with higher risk than males.This is the largest multi-location study assessing vulnerability patterns of heat-related PTB. It emphasises the need to integrate evidence from vulnerability assessments in designing public health interventions to face climate change effects.

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)0.018
metaresearch head score (Gemma)0.018
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 score0.020
Threshold uncertainty score0.093

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.018
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.030
Bibliometrics0.0050.008
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.227
GPT teacher head0.360
Teacher spread0.134 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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