Iron deficiency, anemia and association with refugee camp exposure among recently resettled refugees: A Canadian retrospective cohort study
Bibliographic record
Abstract
Malnutrition and poor health are common among recently resettled refugees and may be differentially associated with pre-migration exposure to refugee camp versus non-camp dwelling. We aimed to investigate the associations of iron deficiency (ID), anemia, and ID anemia (IDA) with pre-migration refugee camp exposure among recently arrived refugees to Canada. To this end, we conducted a retrospective cohort study of 1032 adult refugees who received care between January 1, 2011, and December 31, 2015, within a specialized refugee health clinic in Calgary, Canada. We evaluated the prevalence, severity, and predictors of ID, anemia, and IDA, stratified by sex. Using multivariable logistic regression, we estimated the association of refugee camp exposure with these outcomes, adjusting for age, months in Canada prior to investigations, global region of origin, and parity. Among female refugees, the prevalence of ID, anemia, and IDA was 25% (134/534), 21% (110/534), and 14% (76/534), respectively; among males, 0.8% (4/494), 1.8% (9/494), and 0% (0/494), respectively. Anemia was mild, moderate, and severe in 55% (60/110), 44% (48/110) and 1.8% (2/110) of anemic females. Refugee camp exposure was not associated with ID, anemia, or IDA while age by year (ID OR = 0.96, 95% CI 0.93-0.98; anemia OR = 0.98, 95% CI 0.96-1.00; IDA OR = 0.96, 95% CI 0.94-0.99) and months in Canada prior to investigations (ID OR = 0.85, 95% CI 0.72-1.01; anemia OR = 0.81, 95% CI 0.67-0.97; IDA OR = 0.80, 95% CI 0.64-1.00) were inversely correlated with these outcomes. ID, anemia, and IDA are common among recently arrived refugee women irrespective of refugee camp exposure. Our findings suggest these outcomes likely improve after resettlement; however, given proportionally few refugees are resettled globally, likely millions of refugee women and girls are affected.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".