Portrait of socio‐economic inequality in childhood morbidity and mortality over time, Québec, 1990–2005
Bibliographic record
Abstract
AIM: To determine the age and cause groups contributing to absolute and relative socio-economic inequalities in paediatric mortality, hospitalisation and tumour incidence over time. METHODS: Deaths (n= 9559), hospitalisations (n= 834,932) and incident tumours (n= 4555) were obtained for five age groupings (<1, 1-4, 5-9, 10-14, 15-19 years) and four periods (1990-1993, 1994-1997, 1998-2001, 2002-2005) for Québec, Canada. Age- and cause-specific morbidity and mortality rates for males and females were calculated across socio-economic status decile based on a composite deprivation score for 89 urban communities. Absolute and relative measures of inequality were computed for each age and cause. RESULTS: Mortality and morbidity rates tended to decrease over time, as did absolute and relative socio-economic inequalities for most (but not all) causes and age groups, although precision was low. Socio-economic inequalities persisted in the last period and were greater on the absolute scale for mortality and hospitalisation in early childhood, and on the relative scale for mortality in adolescents. Four causes (respiratory, digestive, infectious, genito-urinary diseases) contributed to the majority of absolute inequality in hospitalisation (males 85%, females 98%). Inequalities were not pronounced for cause-specific mortality and not apparent for tumour incidence. CONCLUSIONS: Socio-economic inequalities in Québec tended to narrow for most but not all outcomes. Absolute socio-economic inequalities persisted for children <10 years, and several causes were responsible for the majority of inequality in hospitalisation. Public health policies and prevention programs aiming to reduce socio-economic inequalities in paediatric health should account for trends that differ across age and cause of disease.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".