Morbidity and mortality of newborns born to immigrant and nonimmigrant females residing in low-income neighbourhoods
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".