Does intensified case finding increase tuberculosis case notification among children in resource-poor settings? A report from Nigeria
Bibliographic record
Abstract
OBJECTIVE/BACKGROUND: Tuberculosis (TB) is a major cause of morbidity and mortality in developing countries. Passive case detection in national TB programmes is associated with low case notification, especially in children. This study was undertaken to improve detection of childhood TB in resource-poor settings through intensified case-finding strategies. METHODS: A community-based intervention was carried out in six states in Nigeria. The creation of TB awareness was undertaken, and work aids, guidelines, and diagnostic charts were produced, distributed, and used. Various cadres of health workers and ad hoc project staff were trained. Child contacts with TB patients were screened in their homes, and children presenting at various hospital units were screened for TB. Baseline and intervention data were collected for evaluation populations and control populations. RESULTS: Detection of childhood TB increased in the evaluation population during the intervention, with a mean quarterly increase of 4.0% [new smear positive (NSP), although the increasing trend was not statistically significant (χ(2)=1.8; p<.179)]. Additionally, there was a mean quarterly increase of 3% for all forms of TB, although the trend was not statistically significant (χ(2)=1.48; p<.224). Conversely, there was a decrease in case notification in the control population, with a mean decline of 3% (all forms). Compared to the baseline, there was an increase of 31% (all forms) and 22% (NSP) in the evaluation population. CONCLUSION: Intensified case finding combined with capacity building, provision of work aids/guidelines, and TB health education can improve childhood-TB notification.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".