Evaluation of latent tuberculosis infection surveillance in Peel region, Ontario, 2010–2014
Bibliographic record
Abstract
BACKGROUND: In Canada, identification and treatment of individuals with latent tuberculosis infection (LTBI) is a key component in preventing the progression of LTBI to active tuberculosis (TB). In Peel region, a large municipality in Ontario where half of the population is foreign-born, LTBI surveillance data are also critical to understanding the local epidemiology of TB. OBJECTIVE: To evaluate LTBI surveillance data collected through the integrated Public Health Information System (iPHIS) from 2010 to 2014 by assessing data quality and usefulness and to provide recommendations to improve surveillance for Peel region. METHODS: Using the European Centre for Disease Prevention and Control framework for surveillance evaluation, data quality was assessed based on completeness and validity of key variables in the iPHIS database. Usefulness of surveillance data in informing program decisions was assessed through interviews with stakeholders from Peel Public Health. RESULTS: Of 6,576 iPHIS records evaluated, data for gender and date of birth were greater than 99% complete, while more than half of the risk factor fields were blank or 'unknown'. A comparison of 192 paper charts to the corresponding iPHIS record identified coding errors in over 40% of iPHIS risk factor fields. Treatment completion documented in iPHIS (20%) was lower than data obtained from a follow-up telephone survey of cases (50%). Stakeholders found surveillance data to be useful (100%), however, recommendations were made for improvement of data collection and analysis. CONCLUSION: Evaluating LTBI surveillance to improve data quality and usefulness for program planning is essential in an era of TB elimination. This evaluation resulted in standardization of data entry processes and continuation of direct follow-up with LTBI clients to confirm treatment completion. Work to understand barriers to treatment initiation and completion is currently underway.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".