Neighborhood Income and Health Outcomes in Infants
Bibliographic record
Abstract
OBJECTIVE: To quantify the effect of socioeconomic status (SES) on health outcomes during the first year after newborn discharge among infants with complex chronic conditions (CCCs) insured through a universal health plan. DESIGN: Longitudinal, population-based cohort study. SETTING: Ontario, Canada. PARTICIPANTS: Infants born in hospitals from April 1, 1996, through March 31, 2000. Infants with CCCs were identified from their newborn discharge records. Neighborhood income quintiles were obtained by linking participants' postal codes to census data. MAIN OUTCOME MEASURES: Mortality and hospital admissions in the first year after newborn discharge. Logistic and Poisson regression analyses were used to examine the relationship between neighborhood income quintiles and outcomes, adjusting for important covariates such as low birth weight and rural residence. RESULTS: A total of 512 768 infants were included, of whom 2.3% had CCCs at newborn discharge. Infants with CCCs accounted for 37.8% of deaths and 11.0% of hospitalizations during the first year after the newborn discharge. Infants with CCCs living in the lowest-income neighborhoods had a 1.26-fold higher mortality risk (95% confidence interval, 0.83-1.90; P = .28) and a 1.24-fold higher hospitalization rate (1.09-1.40; P < .001) compared with those living in the highest-income neighborhoods. Although the income gradients associated with mortality and hospitalization were less pronounced among infants with CCCs compared with infants without CCCs, the absolute interquintile risk differences attributable to SES were higher among infants with CCCs. CONCLUSIONS: Despite universal health insurance, SES-related inequality affects hospitalization and, possibly, mortality rates among medically vulnerable infants.
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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 | 0.000 | 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".