Trends in Gestational Diabetes in Manitoba From 1981 to 2019: A Descriptive Study With Geospatial Mapping
Bibliographic record
Abstract
OBJECTIVE: This study aimed to describe trends in incidence of gestational diabetes in Manitoba and within subgroups that often experience health inequities. METHODS: We leveraged provincial administrative health data to describe trends in gestational diabetes incidence between 1981 and 2019, stratified by subpopulations based on age, urbanicity, and neighbourhood-level average household income. We calculated yearly incidence across subgroups and annual percent change in incidence to assess trends over time. Geospatial mapping was used to visualize changes by neighbourhood cluster. RESULTS: Gestational diabetes incidence increased from 1.3% to 8.6% between 1981 and 2019, with an upward inflection occurring around 2010. The annual percent change (APC) between 1981 and 2009, prior to the inflection point, was 1.9% (95% confidence interval [CI] 1.4% to 2.5%), and it was 11.7% (95% CI 8.9% to 14.7%) postinflection---from 2010 to 2019. After 2010, gestational diabetes incidence increased most among urban residents (APC 18.1%, 95% CI 13.9% to 22.5%), among those >35 years of age (APC 12.0%, 95% CI 8.4% to 15.7%), and among individuals in the highest socioeconomic status (SES) group (APC 14.8%, 95% CI 9.4% to 20.4%). Geospatial mapping showed that incidence increased more in neighbourhoods with the highest proportion of recent immigrants to Canada. CONCLUSIONS: Incidence of gestational diabetes increased 6-fold in Manitoba over the past 20 years, particularly among those with high SES and higher age. Further research is required to clarify the role of screening practices in the trends observed in this work.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".