A 10-year Review of TB Notifications and Mortality Trends Using a Joint Point Analysis in Zambia - a High TB burden country
Bibliographic record
Abstract
BACKGROUND: Zambia is one of the TB high-burden countries. It is important to track the progress being made towards enhancing case finding and reducing mortality. We reviewed routine TB notifications and mortality trends, over a decade from all facilities in Zambia. METHODS: A 10-year retrospective study of TB notifications and mortality trends was performed using a Joint Point Analysis version 4.9.0.0, NCI. We extracted the annual national TB program data for the period under review. RESULTS: There was a decline in annual point average for notification between 2010 and 2020 in both males and females, but the females notification rates had a higher rate of decline (AAPC = -6.7, 95%CI:-8.3 to -5.0), p<0.001) compared to the decline in males notification rate (AAPC = -4.1, 95%CI:-4.1 to -5.1, P<0.001). We found a significant growth rate in the proportion of TB patients that were bacteriologically confirmed (AAPC = 6.1, 95% CI: 3.6 to 8.7, p< 0.001), while the proportion of clinically diagnosed patients declined (AAPC= -0.1, 95%CI: -2.3 to 2.1, p<0.001). Notification of drug-resistant TB increased exponentially (AAPC=27.3, 95% CI: 13 to 41), p< 0.001) while mortality rate declined from 21.3 in 2011 to 12.7 in 2019 per 100,000 population (AAP=-5.6, 95%CI: -9.6 to -1.5, p=0.008). CONCLUSIONS: This study has illustrated the importance of reviewing and analyzing routinely collected TB data by national programs. The study revealed areas of improvement in terms of TB control and underscores the need for increased and sustained investment in case detection and diagnostics.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".