Determinants of iron deficiency and anemia among Nunavimmiut: results from the Qanuilirpitaa? 2017 Nunavik Health Survey
Bibliographic record
Abstract
OBJECTIVE: To estimate the prevalence of iron deficiency (ID) and anemia and study their main distal and proximal protective and risk factors among Nunavimmiut 16 years and older in 2017. METHODS: In a cross-sectional participatory survey of 831 women and 436 men from the Qanuilirpitaa? 2017 Nunavik Inuit Health Survey, venous blood samples were collected to measure various indicators of iron status and anemia as well as biomarkers of nutritional and inflammatory status and contaminant exposures. Sociodemographic, food security status, anthropometric, lifestyle, dietary, and health data were collected using questionnaires, clinical sessions, and a medical chart review. ID and anemia diagnoses were based on serum ferritin (SF) and hemoglobin (Hb), respectively. Multiple regressions were used to assess correlates of anemia and iron status. RESULTS: Prevalence of ID was highest among women of childbearing age (16-49 years old, 33%) and anemia among adults aged 50 years and older (31%). These estimates are prone to biases due to the relatively low participation rate (37%). Serum vitamin D, omega-3 polyunsaturated fatty acid content of erythrocyte membranes, blood selenium, inflammation, higher socioeconomic status (SES), obesity, and alcohol consumption were all positively associated with SF, while Helicobacter pylori infection and a recent pregnancy were negatively associated with Hb among women of childbearing age. Among older adults, food insecurity was associated with lower SF. CONCLUSION: While data reported here provide some indication of an improvement since the previous survey conducted in 2004, additional efforts should be devoted to further increasing the SES and access to country foods and nutritious market foods in this population, the two main protective factors against ID and anemia identified 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".