Household expenditure and tuberculosis prevalence in VietNam: prediction by a set of household indicators.
Bibliographic record
Abstract
OBJECTIVE: To study the association between TB and household expenditure in a nationwide TB prevalence survey in Viet Nam using nine household characteristics. METHOD: To assess the prevalence of TB in Viet Nam, a nationwide stratified cluster sample survey was conducted from 2006 to 2007. Nine household characteristics used in the second Viet Nam Living Standards Survey (VLSS) were scored per household. In the VLSS dataset, we regressed these nine characteristics against household expenditure per capita, and used the coefficients to predict household expenditure level (in quintiles) in our survey and assess its relation with TB prevalence. RESULTS: The prevalence of bacteriologically confirmed TB was 307 per 100,000 population in persons aged ≥ 15 years (95%CI 249-366). After adjustment for confounders, prevalence was found to be associated with household expenditure level: the rate was 2.5 times higher for those in the lowest household expenditure quintile (95%CI 1.6-3.9) than those in the highest quintile. CONCLUSION: With a set of nine household characteristics, we were able to predict household expenditure level fairly accurately. There was a significant association between TB prevalence rates and estimated household expenditure level, showing that TB is related to poverty in Viet Nam.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".