Accounting for the Distribution of Adverse Birth Outcomes in Ontario: A Hierarchical Analysis of Provincial and Local Outcomes
Bibliographic record
Abstract
Background: Adverse birth outcomes present a difficult and chronic challenge in Ontario, in Canada and in developed countries in general. Increasing proportions of preterm births, significant regional disparities and the high cost of treating all adverse birth outcomes have focused attention on explaining them and developing effective treatments. Methods: Birth outcomes and maternal characteristics for approximately 626,000 births, about 90% of births in 2005–2009, were linked to small geographic areas throughout Ontario. For each of four adverse outcomes: late preterm, moderate to very preterm, small for gestation age and still births, proportions of total births were calculated for the full province and for each small geographic area. Geographic hotspots of elevated rates were identified for each of the different adverse birth outcomes using the local Moran’s I statistic. Data for nine known ecologic and individual risk factors were then linked to the areas. Hierarchical regression analysis was used to model each of the outcomes for the full province and for dispersed local areas. The resulting models for the different outcomes were contrasted. Results: Significant geographic hotspots exist for each of the four outcomes. Hotspots for the different outcomes were found to be largely spatially exclusive. For like outcomes, predictive models differed markedly between local areas (i.e. local groups of hotspots) as well as between full-province and local areas. Ecologic level variables played a strong role in all models; the influence of individual level risk factors was consistently modified by ecologic risk factors except for small for gestational births. Conclusions: The finding of significant hotspots for different adverse birth outcomes indicates that certain geographic areas have aetiologies or patterns of predictors sufficient to create significantly elevated levels of particular outcomes. The finding that hotspots for the different adverse outcomes are largely exclusive implies that the aetiologies are specific; i.e., those that are sufficient to create significantly higher levels for one outcome do not also create significantly higher levels of others. The consistently strong role of ecologic level risk factors in modifying individual level risk factors implies that contextual characteristics are an important part of the aetiology of adverse birth outcomes. Differences in local area models suggest the existence of location-specific (rather than universal) aetiologies. The findings support the need for more careful attention to local context when explaining birth outcomes.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".