COMPARATIVE INCIDENCE OF TUBERCULOSIS IN CANADA: THE PAST, PRESENT AND FUTURE
Bibliographic record
Abstract
Canada and the United States (US) are both high income, low tuberculosis (TB) incidence countries with similar TB control programs, yet an explicit comparison of TB incidence over time is lacking. Objective one explored the impact of TB and other disease case definition change, and methods to control for the change via a general literature search. Underreporting/increases in reported cases and differences in sensitivity and specificity measures were among others noted to arise from changes in case definition. For appropriate comparisons within and between populations, consistent ascertainment criteria of cases should be adopted. Objective 2 explored and compared TB incidence rates in Canada and the US from 1953-2015. TB rate from 1953-2015 was retrieved for both countries. Joinpoint and direct standardization were performed. Canada’s TB rates/100,000 were higher from 1953-1974. Canada’s average annual percent change in rate from 1975-2015 was -2.9% compared to the US -4.1%. Case definition change, HIV+/TB co-infection, and Foreign-born (FB) TB were the main contributors to the differences. Objective three compared the rate of TB decline in subpopulations. TB cases and population by ethnicity from 2001-2011, and the percent of HIV+/TB co-infection cases from 1997-2012 were retrieved for Canada and the US. Segmented and decomposition analysis was performed. FB and Indigenous TB rate declined by -3.7% and -6.3% in the US and by -1.7% and -4.5% in Canada. Changes in age-specific rates declined overall rates in Canada by 80.1% and the US at 66.7%. Overall, the percentage of HIV+/TB cases declined more rapidly in the US than in Canada. After adjusting for age, FB and Indigenous populations, rates decline more in the US than in Canada. Objective four forecasted and then compared year-over-year TB rates between Canada and US from 2017-2035. TB rate from 1975-2016 and rates by ethnicity from 1993-2016 were retrieved for both countries. Autoregressive integrated moving average and multivariate vector autoregression models were performed. The forecasted models showed a gradually decreasing trend from 2017-2035, reaching a rate of 2.2 for Canada and 1.3 for the US by 2035. The prediction suggests that achieving 2035 WHO set target could be a challenge for both countries.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".