Inequalities in paediatric hospitalisations for costly and prevalent conditions in Ontario, Canada: a population-based cohort study
Bibliographic record
Abstract
Background: Identifying inequalities is important for informing research, and policy efforts to reduce health disparities. This study measured the inequalities in hospitalisations for the costly and prevalent conditions in hospitalised children using association estimates. Methods: Population-based cohort study using health administrative databases in Ontario, Canada between 2014 and 2019. The hospitalisation rate was determined for the costly and prevalent conditions in children. Hospitalisation inequalities by four equity stratifiers (material resources, rurality, sex, and immigrant status) were quantified using rate difference (RD), rate ratio (RR), and ratio of excess to total hospitalisation rate. Multivariable logistic regression analyses were also conducted. Findings: In a population of 3·7 million children (median age 7·0 years, Interquartile range: 1·0-12·0), there were 612,597 hospitalisations. Large inequalities comparing children among least versus most resourced quintile was observed in low birth weight (RD: 1,823·3 hospitalisations per 100,000 children, 95% CI: 1,662·7, 1,983·9). Conditions with large inequalities comparing rural versus urban areas included low birth weight (RD: -1,833·2 hospitalisations per 100,000, 95% CI: -2,012·8, -1,653·6); and drug withdrawal syndrome in newborn (RR: 1·9, 95% CI: 1·7, 2·1; adjusted odds ratio (aOR): 1·4, 95% CI: 1·2, 1·5). Conditions with large inequalities comparing males versus females included low birth weight (RD: -888·3 hospitalisations per 100,000, 95% CI: -992·5, -784·02); and anorexia nervosa (RR: 0·08, 95% CI: 0·07, 0·10; aOR: 0·1, 95% CI: 0.1, 0.1). Conditions with large inequalities comparing non-refugee immigrants versus non-immigrants included major depressive disorder (RR: 2·8, 95% CI: 2·7, 2·9), and comparing refugees versus non-immigrants included drug withdrawal syndrome in newborn (RR: 0·09, 95% CI: 0·05, 0·15). Results from multivariable analyses were similar. Interpretation: Newborn and mental health conditions had the largest inequalities in hospitalisations by the equity stratifiers. Findings from this study can be used to prioritise future health equity research to reduce health inequalities. Funding: PSI Foundation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".