Impact of Socioeconomic Status and Residence Distance on Infant Heart Disease Outcomes in Canada
Bibliographic record
Abstract
Background Socioeconomic status (SES) impacts clinical outcomes associated with severe congenital heart disease (sCHD). We examined the impact of SES and remoteness of residence (RoR) on congenital heart disease (CHD) outcomes in Canada, a jurisdiction with universal health insurance. Methods and Results All infants born in Canada (excluding Quebec) from 2008 to 2018 and hospitalized with CHD requiring intervention in the first year were identified. Neighborhood level SES income quintiles were calculated, and RoR was categorized as residing <100 km, 100 to 299 km, or >300 km from the closest of 7 cardiac surgical programs. In‐hospital mortality at <1 year was the primary outcome, adjusted for preterm birth, low birth weight, and extracardiac pathology. Among 7711 infants, 4485 (58 . 2%) had moderate CHD (mCHD) and 3226 (41.8%) had sCHD. Overall mortality rate was 10.5%, with higher rates in sCHD than mCHD (13.3% versus 8.5%, respectively). More CHD infants were in the lowest compared with the highest SES category (27.1% versus 15.0%, respectively). The distribution of CHD across RoR categories was 52.3%, 21.3%, and 26.4% for <100 km, 100 to 299 km, and >300 km, respectively. Although SES and RoR had no impact on sCHD mortality, infants with mCHD living >300 km had a higher risk of mortality relative to those living <100 km (adjusted odds ratio [aOR], 1.43 [95% CI, 1.11–1.84]). Infants with mCHD within the lowest SES quintile and living farthest away had the highest risk for mortality (aOR, 1.74 [95% CI, 1.08–2.81]). Conclusions In Canada, neither RoR nor SES had an impact on outcomes of infants with sCHD. Greater RoR, however, may contribute to higher risk of mortality among infants with mCHD.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".