Social determinants of health among residential areas with a high tuberculosis incidence in a remote Inuit community
Bibliographic record
Abstract
BACKGROUND: Tuberculosis (TB) remains a significant health burden among Inuit in Canada. Social determinants of health (SDH) play a key role in TB infection, disease and ongoing transmission in this population. The objective of this research was to estimate the prevalence of social determinants of Inuit health as they relate to latent TB infection (LTBI) among people living in residential areas at high risk for TB in Iqaluit, Nunavut. METHODS: Inperson home surveys were conducted among those who lived in predetermined residential areas at high risk for TB identified in a door-to-door TB prevention campaign in Iqaluit, Nunavut in 2011. Risk ratios for SDH and LTBI were estimated, and multiple imputation was used to address missing data. RESULTS: 261 participants completed the questionnaire. Most participants identified as Inuit (82%). Unadjusted risk ratios demonstrated that age, education, smoking tobacco, crowded housing conditions and Inuit ethnicity were associated with LTBI. After adjusting for other SDH, multivariable analysis showed an association between LTBI with increasing age (relative risk, RR 1.07, 95% CI 1.04 to 1.11), crowded housing (RR 1.48, 95% CI 1.10 to 2.00) and ethnicity (RR 2.76, 95% CI 1.33 to 5.73) after imputing missing data. CONCLUSION: Among high-risk residential areas for TB in a remote Arctic region of Canada, crowded housing and Inuit ethnicity were associated with LTBI after adjusting for other SDH. In addition to strong screening and treatment programmes, alleviating the chronic housing shortage will be a key element in the elimination of TB in the Canadian Inuit Nunangat.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".