Tuberculosis screening among cough suppressant buyers in pharmacies and drug outlets in Guinea: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Tuberculosis (TB) poses a significant public health challenge in Guinea, with an estimated 22 000 TB cases in 2020; an estimated 6125 (28%) cases went undetected. We evaluated an intensified TB case finding strategy in Guinea which targeted customers who bought cough suppressants from pharmacies or drug outlets. METHODS: We involved 25 pharmacies and 25 drug outlets in Matoto, Conakry, Guinea. Pharmacists or outlet owners identified and referred all customers with TB symptoms who were purchasing cough suppressants to healthcare workers for sputum collection either at the pharmacy or drug outlet or at a nearby TB diagnosis and treatment centre (CDT); sputum was subjected to bacteriological testing with acid fast bacilli smear or Xpert MTB/RIF. We assessed factors associated with eventual TB diagnosis using logistic regression and time to TB diagnosis using cox regression and used microcosting to estimate the cost of the intervention in 2020 US$. RESULTS: From November 2019 to June 2020, we screened 916 people referred from pharmacies or drug outlets with TB symptoms, with median age of 31 years (54% male). Overall, 126 (14%) had bacteriologically confirmed TB. Odds of TB diagnosis were significantly lower with increasing age (adjusted OR (aOR) per additional year=0.98; 95% CI 0.97 to 0.99) and higher among males (aOR=1.57; 95% CI 1.04 to 2.39) and those with symptoms. Those identified at drug outlets had significantly faster time to presentation from symptom onset than pharmacies (adjusted HR=1.73; 95% CI 1.50 to 1.99). The total cost of the intervention per person referred was US$32.66 and per person diagnosed and treated for TB disease of US$237.45. CONCLUSION: Intensified TB case finding through pharmacies and drug outlets is a feasible and effective way to increase TB detection in settings where self-medicating is common, and TB is under-detected.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".