Prevalence and risk factors for self‐reported chronic disease amongst Inuvialuit populations
Bibliographic record
Abstract
BACKGROUND: Chronic disease prevalence amongst Canadian Arctic populations is increasing, but the literature amongst Inuvialuit is limited. The present study aimed to provide baseline data that could be used to monitor changes in chronic disease risk factors and long-term health in the Arctic by determining prevalence and risk factors of self-reported chronic disease amongst adult Inuvialuit in remote communities. METHODS: Self-reported demographics and history of chronic disease (hypertension, heart disease, diabetes and cancer) were collected in three communities between July 2007 and July 2008 in the Northwest Territories. Food frequency questionnaires recorded dietary intake, International Physical Activity Questionnaires recorded physical activity and anthropometric measures of height and weight were obtained. RESULTS: Response rates ranged from 65-85%. More than 20% of the 228 participants aged 19-84 years reported having a chronic disease. Age-adjusted prevalence was 28, 9, 9 and 6 per 100 for hypertension, heart disease, diabetes and cancer, respectively. Compared with non-cases, participants reporting hypertension were more likely to have a higher body mass index and a lower level of education. Hypertension was more common amongst participants reporting heart disease and diabetes than Inuvialuit not reporting these morbidities. CONCLUSIONS: Inuvialuit participants in this study were most affected by hypertension and diabetes compared with heart disease or any cancer. Female participants had a higher prevalence of heart disease compared with the Canadian average. Primary preventive strategies are necessary to mitigate the increasing rates of chronic disease risk factors in this population. Further studies with a larger sample size and measured chronic disease are necessary to confirm the findings obtained in the present study.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".