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Record W4366220783 · doi:10.1503/cmaj.221711

Morbidity and mortality of newborns born to immigrant and nonimmigrant females residing in low-income neighbourhoods

2023· article· en· W4366220783 on OpenAlexaffvenueabout
Jennifer A. Jairam, Simone N. Vigod, Arjumand Siddiqi, Jun Guan, Alexa Boblitz, Xuesong Wang, Patricia O’Campo, Joel G. Ray

Bibliographic record

VenueCanadian Medical Association Journal · 2023
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsWomen's College HospitalSt. Michael's Hospital
Fundersnot available
KeywordsMedicinePoisson regressionDemographyPopulationRelative riskResidencePediatricsCohortImmigrationCohort studyConfidence intervalEnvironmental healthGeographyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Living in low-income neighbourhoods and being an immigrant are each independently associated with adverse neonatal outcomes, but it is unknown if disparities exist in the neonatal period for children of immigrant and nonimmigrant females living in low-income areas. We sought to compare the risk of severe neonatal morbidity and mortality (SNMM) between newborns of immigrant and nonimmigrant mothers who resided in low-income neighbourhoods. METHODS: This population-based cohort study used administrative data for females residing in low-income urban neighbourhoods in Ontario, who had an in-hospital, singleton live birth at 20-42 weeks' gestation, from 2002 to 2019. We defined immigrant status as nonrefugee immigrant or nonimmigrant, further detailed by country of birth and duration of residence in Ontario. The primary outcome was a SNMM composite (with 16 diagnoses, including neonatal death and 7 neonatal procedures as indicators), arising within 0-27 days after birth. We estimated relative risks (RRs) and 95% confidence intervals (CIs) using modified Poisson regression with generalized estimating equations. RESULTS: Our cohort included 148 050 and 266 191 live births among immigrant and nonimmigrant mothers, respectively. Compared with newborns of non-immigrant females, SNMM was less frequent among newborns of immigrant females (49.7 v. 65.6 per 1000 live births), with an adjusted RR of 0.76 (95% CI 0.74 to 0.79). The most frequent SNMM indicator was receipt of ventilatory support. Relative to neonates of nonimmigrant females, the risk of SNMM was highest among those of immigrants from Jamaica (adjusted RR 1.14, 95% CI 1.05 to 1.23) and Ghana (adjusted RR 1.20, 95% CI 1.05 to 1.38), and lowest among those of immigrants from China (adjusted RR 0.44, 95% CI 0.40 to 0.48). Among immigrants, the risk of SNMM declined with shorter duration of residence before the index birth. INTERPRETATION: Within low-income urban areas, newborns of immigrant females had an overall lower risk of SNMM than those of nonimmigrant females, with considerable variation by maternal birthplace and duration of residence. Initiatives should focus on improving preconception health and perinatal care within subgroups of females residing in low-income neighbourhoods.

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)0.000
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 score0.705
Threshold uncertainty score0.586

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.024
GPT teacher head0.325
Teacher spread0.301 · 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

Citations5
Published2023
Admission routes3
Has abstractyes

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