Geographical disparities in temporal trends of low birth weight in Saskatchewan from 2002/2003 to 2021/2022: insights from a joinpoint regression analysis
Bibliographic record
Abstract
Low birth weight (LBW) is an important public health indicator that is associated with various negative health outcomes in infants. To effectively implement interventions that would improve health outcomes in children, it is important to understand both the historical trends and current levels of LBW rates. In this study, trends and regional differences in LBW rates in Saskatchewan from 2002/2003 to 2021/2022 were assessed. A joinpoint regression analysis was conducted using historical LBW rates, obtained from the Canadian Institute for Health Information database. Data were analysed using average percent change and average annual percent change. Spatial patterns and trends were identified using a choropleth map. From a provincial and national rate of 5.2% in 2002/2003, the LBW rate in Saskatchewan increased to 6.5% in 2021/2022, approaching the national rate of 6.8%. Over the 20-year period, average annual changes for Canada were 1.4% and 1.0% for Saskatchewan. There was a turning point in the study: 2004/2005 for Canada and 2011/2012 for Saskatchewan. Initially, Saskatchewan had stable LBW rates, increasing yearly by 0.1%, while the national rate was 5.7%. However, in recent years, Saskatchewan's rate increased to 1.8% annually, surpassing the national rate of 0.9%. Geographical differences were also observed within Saskatchewan, with the Far North region having the highest LBW rate (9.2%), and the Central West region having the lowest rate (4.3%) in 2021/2022. The Central East, Regina Qu'Appelle, and southern Saskatchewan saw significant upwards trends in LBW rates between 2015/2016 and 2021/2022. There is an increasing trend in LBW rates in Canada and Saskatchewan, as well as geographical disparities within the province. The geographical disparities in LBW rates underscore the need for tailored interventions in high-risk regions in the province.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".