Maternal immigrant status and survival among children with congenital heart disease
Bibliographic record
Abstract
ObjectiveTo examine associations between maternal immigrant status and survival of children with congenital heart disease (CHD). ApproachA retrospective population-based cohort study of hospital live births between April 2002 to September 2020 in Ontario, Canada was conducted at ICES. We identified children aged 0-17 years with a diagnosis of CHD from inpatient and health services datasets. The exposure was maternal immigrant status obtained from the IRCC permanent resident database. The outcome was time-to-death from after birth to <18 years olds. Multilevel Cox hazard regression models generated hazard ratios (HR) for associations between maternal immigrant status and children’s deaths while accounting for hospitals as a cluster factor and adjusting for maternal age at birth, neighbourhood income and education quantiles, comorbidities, a composite of severe maternal morbidity, gestational age at birth, birth weight, and infant sex. ResultsRelative to children born to non-immigrant mothers, the adjusted HR for death was 1.17 (95% CI:1.06-1.30) in children whose mothers were immigrants, and 1.33 (95% CI:1.07-1.65) in those from refugees. Moreover, compared to children residing in neighbourhoods with the highest income and educational levels, the adjusted HR of death for children who lived in neighbourhoods with the lowest income and educational level was 1.52 (95% CI: 1.24-1.88) and 1.47 (95% CI: 1.18-1.83) respectively. ConclusionChildren with CHD born to immigrant and refugee mothers, and those living in low socioeconomic status neighbourhoods had worse mortality. Health policy decision makers should target children with CHD from immigrant/refugee families and disadvantaged neighbourhoods to improve their survival.
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 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 | 0.000 | 0.000 |
| 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) | 0.002 | 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".