Why the tuberculosis incidence rate is not falling in New Zealand.
Bibliographic record
Abstract
AIMS: To assess the role of migration from high-incidence countries, HIV/AIDS infection, and prevalence of multi-drug resistant organisms as contributors to tuberculosis (TB) incidence in New Zealand (NZ) relative to ongoing local transmission and reactivation of disease. METHODS: TB notification data and laboratory data for the period 1995 to 2004 and population data from the 1996 and 2001 Census were used to calculate incidence rates of TB by age and ethnicity, country of birth (distinguishing high and low -incidence countries), and interval between migration and onset of disease. Published reports of multi-drug-resistant TB for the period 1995 to 2004 were reviewed. Anonymous HIV surveillance data held by AIDS Epidemiology Group were matched with coded and anonymised TB surveillance data to measure the extent of HIV/AIDS coinfection in notified TB cases. RESULTS: Migration of people from high-TB incidence countries is the main source of TB in NZ. Of those who develop TB, a quarter does so within a year of migration, and a quarter of this group (mainly refugees) probably enter the country with pre-existing disease. Rates of local TB transmission and reactivation of old disease are declining steadily for NZ-born populations, except for NZ-born Maori and Pacific people under 40. HIV/AIDS and multi-drug-resistant organisms are not significant contributors to TB incidence in NZ and there is no indication that their role is increasing. CONCLUSION: TB incidence is not decreasing in NZ mainly due to migration of TB infected people from high-incidence countries and subsequent development of active disease in some of them in NZ. This finding emphasises the importance of regional and global TB control initiatives. Refugees and migrants are not acting as an important source of TB for most NZ-born populations. Those caring for them should have a high level of clinical suspicion for TB.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".