Improving tuberculosis case detection rate with a lay informant questionnaire: an experience from the Lao People's Democratic Republic
Bibliographic record
Abstract
PROBLEM: In many countries, the tuberculosis (TB) annual case detection rate is below the target of 70%. In the Lao People's Democratic Republic in 2005, it did not exceed 55% APPROACH: The DOTS strategy promotes passive case detection of TB. In order to increase the detection rate, we validated a questionnaire targeting lay informants at village level to notify patients with chronic cough and assessed the relevance for TB case-finding. A three-item questionnaire was sent through the district health departments to all villages in six districts in six provinces. The village headmen were asked to notify chronic cough patients. Answers were validated in a door-to-door survey (20 villages/district). In a sub-sample (four villages/district) all confirmed patients were screened for TB and paragonimiasis. LOCAL SETTING: Attapeu, Luang Namtha, Luang Prabang, Saravane, Savanakhet and Vientiane provinces in the Lao People's Democratic Republic. RELEVANT CHANGES: Lay informant questionnaires sent from district health offices to villages are cost-effective and foster interaction between the health services and remote and underserved communities. Although the correct detection of patients is highly dependent on direct respondents, a substantial number of new TB and paragonimiasis cases were consistently diagnosed in chronic cough patients. LESSONS LEARNED: Out of 456 questionnaires, 295 were returned (65%). Return rates were highly variable between districts (48-87%), questionnaires' sensitivity (56-98%), positive predictive value (34-88%) and correlation between number of notified and confirmed patients (r: 0.26-0.78). In sub-sampled villages (13,541 population) 19 (5.1%) TB and 26 (7.0%) paragonimiasis cases were detected in 374 chronic cough patients. This quick questionnaire approach proved motivating for district authorities and village key informants, although no incentives were provided. The highly operator-dependent approach yielded a consistent detection rate of TB and paragonimiasis cases. This approach brings health services and populations in need in close contact, which is particularly crucial in remote and underserved areas.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 teacher head, 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".