Socioeconomic gradients of adverse birth outcomes and related maternal factors in rural and urban Alberta, Canada: a concentration index approach
Bibliographic record
Abstract
Objective Using a summary measure of health inequalities, this study evaluated the distribution of adverse birth outcomes (ABO) and related maternal risk factors across area-level socioeconomic status (SES) gradients in urban and rural Alberta, Canada. Design Cross-sectional study using a validated perinatal clinical registry and an area-level SES. Setting The study was conducted in Alberta, Canada. Data about ABO and related maternal risk factors were obtained from the Alberta Perinatal Health Program between 2006 and 2012. An area-level SES index derived from census data (2006) was linked to the postal code at delivery. Participants Women (n=3 30 957) having singleton live births with gestational age ≥22 weeks. Primary and secondary outcome measures We estimated concentration indexes to assess inequalities across SES gradients in both rural and urban areas (CIdx R and CIdx U , respectively) for spontaneous preterm birth (PTB), small for gestational age (SGA), large for gestational age (LGA), gestational hypertension, gestational diabetes, smoking and substance use during pregnancy and pre-pregnancy weight >91 kg. Results The highest health inequalities disfavouring low SES groups were identified for substance abuse and smoking in rural areas (CIdx R −0.38 and −0.23, respectively). Medium inequalities were identified for LGA (CIdx R −0.08), pre-pregnancy weight >91 kg (CIdx R −0.07), substance use (CIdx U −0.15), smoking (CIdx U −0.14), gestational diabetes (CIdx U −0.10) and SGA (CIdx U −0.07). Low inequalities were identified for PTB (CIdx R −0.05; CIdx U −0.05) and gestational diabetes (CIdx R −0.04). Inequalities disfavouring high SES groups were identified for gestational hypertension (CIdx R +0.04), SGA (CIdx R +0.03) and LGA (CIdx U +0.03). Conclusions ABO and related maternal risk factors were unequally distributed across the socioeconomic gradient in urban–rural settings, with the greatest concentrations in lower SES groups of rural areas. Future research is needed on underlying mechanisms driving SES gradients in perinatal health across the rural–urban spectrum.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".