Effects of programmatic interventions to improve the management of latent tuberculosis: a follow up study up to five months after implementation
Bibliographic record
Abstract
BACKGROUND: Less than 19% of those needing tuberculosis (TB) preventive treatment complete it, due to losses in several steps of the cascade of care for latent TB infection. A cluster randomized trial of a programmatic public health intervention to improve management of latent TB infection in household contacts was conducted in Rio de Janeiro. Interventions included contact registry, initial and in-service training, and a TB booklet. We conducted a follow-up study starting one month after the conclusion of this trial, to measure the effect of interventions implemented, and to identify remaining barriers and facilitators to latent TB infection treatment, from different perspectives. METHODS: In two health clinics in Rio de Janeiro that received the interventions in the trial, data for the latent TB infection cascade of care for household contacts was collected over a five-month period. The number of household contacts initiating treatment per 100 index-TB patients was compared with the cascade of care data obtained before and during the intervention trial. Semi-structured open-ended questionnaires were administered to healthcare workers, household contacts and index-TB patients regarding knowledge and perceptions about TB and study interventions. RESULTS: In this follow-up study, 184 household contacts per 100 index-TB patients were identified. When compared to the intervention period, there were 65 fewer household contacts per 100 index-TB patients, (95% CI -115, - 15) but the number starting latent TB infection treatment was sustained (difference -2, 95% CI -8,5). A total of 31 index-TB patients, 22 household contacts and 19 health care workers were interviewed. Among index-TB patients, 61% said all their household contacts had been tested for latent TB infection. All health care workers said it was very important to test household contacts, and 95% mentioned that possessing correct knowledge on the benefits of latent TB infection treatment was the main facilitator to enable them to recommend this treatment. CONCLUSION: In this follow-up study, we observed a sustained effect of interventions to strengthen the latent TB infection cascade of care on increasing the number of household contacts starting latent TB infection treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| 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 | 0.002 | 0.002 |
| 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".