Spatial and Temporal Variations in Incidence of Tuberculosis in Africa, 1991 to 2005
Bibliographic record
Abstract
OBJECTIVE: To investigate the geographical and temporal distribution of tuberculosis in Africa in order to identify possible high-risk areas. DESIGN: Time-trend and spatial analyses. DATA SOURCES: World Health Organization Statistical Information System and U.S. Census Bureau International Data Base. METHODS: Time trends in the 15-year study period from 1991 to 2005 were analyzed by Poisson regression models. Global Moran's I and Moran Local Indicators of Spatial Associations were used to test for evidence of global and local spatial clustering, respectively. RESULTS: Southern, Eastern and Middle Africa experienced an upward trend in the number of reported cases of tuberculosis (TB). The number of Northern African TB cases declined steadily over the 15-year study period. The spatial distribution of TB cases was nonrandom and clustered, with a Moran's I = 0.492 (p = .001). Spatial clustering suggested that 25 countries were at increased risk of tuberculosis, and ten countries could be grouped as "hot spots." CONCLUSIONS: The study identified spatial and temporal patterns in tuberculosis distribution, providing a means to quantify explicit tuberculosis risks and laying a foundation to pursue further investigation into the environmental factors responsible for increased disease risk. This information is important in guiding decisions on tuberculosis control strategies.
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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 | 0.001 | 0.001 |
| 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".