Global tuberculosis trends: a reflection of changes in tuberculosis control or in population health?
Bibliographic record
Abstract
BACKGROUND: Many international organizations are advocating for new funds for tuberculosis (TB) specific interventions. Although this approach should help reduce TB incidence, improvements in population health may also be important. We have analyzed the association between changes in population health and health service indicators, and concomitant changes in TB incidence between 1990 and 2005. METHODS: Country level data on population health and health services, economic and epidemiologic indicators were obtained for 165 countries. Regression methods were used to estimate the association of changes in potential predictors with changes in TB incidence. RESULTS: Improvements in population health and health services are associated with improvements in TB outcomes. In adjusted analyses, each 1 year increase in life expectancy was associated with a 7.8/100,000 decline in TB incidence. A 1/1000 decrease in mortality rate in children aged <5 years and a 1% increase in measles vaccination coverage (serving as a general health services indicator) was associated with approximately a 1/100,000 decrease in TB incidence. In countries with a lower prevalence of human immunodeficiency virus (HIV) infection, a 1% increase in TB treatment success rate was also associated with a 1/100,000 decrease in incidence. CONCLUSION: Investment in improving population health and health services may be as important as targeted strategies for controlling TB.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".