Recent epidemiologic trends of diabetes mellitus among status Aboriginal adults
Bibliographic record
Abstract
BACKGROUND: Little is known about longitudinal trends in diabetes mellitus among Aboriginal people in Canada. We compared the incidence and prevalence of diabetes, and its impact on mortality, among status Aboriginal adults and adults in the general population between 1995 and 2007. METHODS: We examined de-identified data from Alberta Health and Wellness administrative databases for status Aboriginal people (First Nations and Inuit people with treaty status) and members of the general public aged 20 years and older who received a diagnosis of diabetes mellitus from Apr. 1, 1995, to Mar. 31, 2007. We calculated the incidence and prevalence of diabetes and mortality rate ratios by sex and ethnicity in 2007. We examined the average relative changes per year for longitudinal trends. RESULTS: The average relative change per year in the prevalence of diabetes showed a smaller increase over time in the Aboriginal population than in the general population (2.39 v. 4.09, p < 0.001). A similar finding was observed for the incidence of diabetes. In the Aboriginal population, we found that the increase in the average relative change per year was greater among men than among women (3.13 v. 1.88 for prevalence, p < 0.001; 2.60 v. 0.02 for incidence, p = 0.001). Mortality among people with diabetes decreased over time to a similar extent in both populations. Among people without diabetes, mortality decreased in the general population but was unchanged in the Aboriginal population (-1.92 v. 0.11, p = 0.04). Overall, mortality was higher in the Aboriginal population than in the general population regardless of diabetes status. INTERPRETATION: The increases in the incidence and prevalence of diabetes over the study period appeared to be slower in the status Aboriginal population than in the general population in Alberta, although the overall rates were higher in the Aboriginal population. Mortality decreased among people with diabetes in both populations but was higher overall in the Aboriginal population regardless of diabetes status.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".