Private sector tuberculosis care quality during the COVID-19 pandemic: a repeated cross-sectional standardised patients study of adherence to national TB guidelines in urban Nigeria
Bibliographic record
Abstract
Only a third of tuberculosis (TB) cases in Nigeria in 2020 were diagnosed and notified, in part due to low detection and under-reporting from the private health sector. Using a standardised patient (SP) survey approach, we assessed how management of presumptive TB in the private sector aligns with national guidelines and whether this differed from a study conducted before the start of the COVID-19 pandemic. 13 SPs presented a presumptive TB case to 511 private providers in urban areas of Lagos and Kano states in May and June 2021. Private provider case management was compared with national guidelines divided into three main steps: SP questioned about cough duration; sputum collection attempted for TB testing; and non-prescription of anti-TB medications, antibiotics and steroids. SP visits conducted in May-June 2021 were directly compared to SP visits conducted in the same areas in June-July 2019. Overall, 28% of interactions (145 of 511, 95% CI 24.5% to 32.5%) were correctly managed according to Nigerian guidelines, as few providers completed all three necessary steps. Providers in 71% of visits asked about cough duration (362 of 511, 95% CI 66.7% to 74.7%), 35% tested or recommended a sputum test (181 of 511, 95% CI 31.3% to 39.8%) and 79% avoided prescribing or dispensing unnecessary medications (406 of 511, 95% CI 75.6% to 82.8%). COVID-19 related questions were asked in only 2.4% (12 of 511, 95% CI 1.3% to 4.2%) of visits. During the COVID-19 pandemic, few providers completed all steps of the national guidelines. Providers performed better on individual steps, particularly asking about symptoms and avoiding prescription of harmful medications. Comparing visits conducted before and during the COVID-19 pandemic showed that COVID-19 did not significantly change the quality of TB care.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.001 | 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".