Hyperendemic pulmonary tuberculosis in peri-urban areas of Karachi, Pakistan
Bibliographic record
Abstract
BACKGROUND: Currently there are very limited empirical data available on the prevalence of pulmonary tuberculosis among residents of marginalized settings in Pakistan. This study assessed the prevalence of pulmonary tuberculosis through active case detection and evaluated predictors of pulmonary tuberculosis among residents of two peri-urban neighbourhoods of Karachi, Pakistan. METHODS: A cross-sectional study was conducted in two peri-urban neighbourhoods from May 2002 to November 2002. Systematic sampling design was used to select households for inclusion in the study. Consenting subjects aged 15 years or more from selected households were interviewed and, whenever possible, sputum samples were obtained. Sputum samples were subjected to direct microscopy by Ziehl-Neelson method, bacterial culture and antibiotic sensitivity tests. RESULTS: The prevalence (per 100,000) of pulmonary tuberculosis among the subjects aged 15 years or more, who participated in the study was 329 (95% confidence interval (CI): 195-519). The prevalence (per 100,000) of pulmonary tuberculosis adjusted for non-sampling was 438 (95% CI: 282-651). Other than cough, none of the other clinical variables was significantly associated with pulmonary tuberculosis status. Analysis of drug sensitivity pattern of 15 strains of Mycobacterium tuberculosis revealed that one strain was resistant to isoniazid alone, one to streptomycin alone and one was resistant to isoniazid and streptomycin. The remaining 12 strains were susceptible to all five drugs including streptomycin, isoniazid, rifampicin, ethambutol, and pyrazinamide. CONCLUSION: This study of previously undetected tuberculosis cases in an impoverished peri-urban setting reveals the poor operational performance of Pakistan's current approach to tuberculosis control; it also demonstrates a higher prevalence of pulmonary tuberculosis than current national estimates. Public health authorities may wish to augment health education efforts aimed at prompting health-seeking behaviour to facilitate more complete and earlier case detection. Such efforts to improve passive case-finding, if combined with more accessible DOTS infra-structure for treatment of detected cases, may help to diminish the high tuberculosis-related morbidity and mortality in marginalized populations. The economics of implementing a more active approach to case finding in resource-constrained setting also deserve further 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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".