Tuberculosis contact investigations by a small public health organization in Canada, 2012 to 2016
Bibliographic record
Abstract
Background Treatment and management of tuberculosis (TB) as well as the identification, investigation and preventive treatment of infected contacts are a complex and resource-intensive duty of public health authorities such as the Eastern Townships Public Health Department (ETPHD) (Quebec, Canada). This study aims to: 1) analyze the sociodemographic and clinical characteristics of active TB cases reported to ETPHD from 01-2012 to 09-2016; 2) describe subsequent contact investigation interventions; and 3) estimate the prevalence of latent TB infection (LTBI) and prevented secondary active TB cases. Methods A retrospective study of ETPHD files was conducted, compiling demographic and clinical data on index cases, as well as the number of contacts screened, diagnosed and treated for LTBI. The number of prevented secondary active TB cases was estimated by factoring treated LTBI among contacts by treatment effectiveness and 5-year or lifetime reactivation risk, as reported in the literature. Results Of the 18 identified TB cases, most were female (56%) and foreign-born (56%), with a median age of 28.5 [3-91] years. Most patients had respiratory TB (89%), with 39% showing cavitations on chest radiography and 41% being sputum-smear positive. In all, 444 contacts were identified (median: 11 [0-184] contacts/index case), of which 79% (CI95%:75-83) underwent complete screening. Diagnosis of LTBI occurred in 16% (CI95%:12-20) of screened cases, with 80% (CI95%:69-91) initiating preventive therapy. The 5-year and lifetime estimates of prevented secondary active TB cases were 1 and 4 cases, respectively. Conclusions Though infrequent, active TB cases lead to a large number of preventative contact investigation interventions. Further studies are required to define the costs and cost savings associated with these preventative interventions. Key messages: TB contact investigation is a resource-intensive task for small public health organizations. These interventions are worthwhile when put in perspective to the cases they can prevent.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".