Inadequate Diet Is Associated with Acquiring <i>Mycobacterium tuberculosis</i> Infection in an Inuit Community. A Case–Control Study
Bibliographic record
Abstract
BACKGROUND: Tuberculosis predominantly affects socioeconomically disadvantaged communities. The extent to which specific dietary and lifestyle factors contribute to tuberculosis susceptibility has not been established. METHODS: A total of 200 residents of a village in Northern Quebec were investigated during a tuberculosis outbreak and identified to have active tuberculosis, latent tuberculosis infection, or neither. Participants completed questionnaires about their intake of food from traditional and commercial sources, and provided blood samples. Adults were asked about recent smoking and drug and alcohol intake. Nutritional adequacy was evaluated with reference to North American standards. Multiple dietary, lifestyle, and housing factors were combined in a logistic regression model evaluating the contributions of each to disease and infection. FINDINGS: After adjusting for potential confounding, new infection was associated with inadequate intake of fruit and vegetables (odds ratio [OR], 2.1; 95% confidence interval [CI], 1.03-4.3), carbohydrates (OR, 4.4; 95% CI, 1.2-16.3), and certain vitamins and minerals. A multivariable model, combining nutrition, housing, and lifestyle factors, found associations between new infection and inadequate fruit and vegetable intake (OR, 2.3; 95% CI, 1.0-5.1), living in the same house as a person with smear-positive tuberculosis (OR, 14.7; 95% CI, 1.6-137.3), and visiting a community gathering house (OR, 3.7; 95% CI, 1.7-8.3). Current smoking was associated with new infection (OR, 9.4; 95% CI, 1.2-72) among adults completing a detailed lifestyle survey. INTERPRETATION: Inadequate nutrition was associated with increased susceptibility to infection, but not active tuberculosis. Interventions addressed at improving nutrition may reduce susceptibility to infection in settings where access to healthy foods is limited.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".