Burden of Respiratory Infection and Tuberculosis Among US States from 1990 to 2019
Bibliographic record
Abstract
PURPOSE: To estimate the incidence, death, disability-adjusted life years (DALYs) and attributable risk factors for respiratory infection and tuberculosis (RIT) in the US from 1990 to 2019. METHODS: Following the methodology framework and analytical strategies used in the Global Burden of Disease Study 2019, the incidence, death, DALYs and risk factors of RIT were examined by age, gender and states from 1990 to 2019 in the US. All estimates were calculated as counts, age-standardized rates per 100,000 people and percentage change, with 95% confidence intervals (CIs). RESULTS: In 2019, the age-standardized incidence, death and DALY rates per 100,000 people of RIT were 339,703 (95% CI 303,184 to 382,354), 13.6 (95% CI 12.2 to 14.4) and 384.9 (95% CI 330.6 to 458.6), respectively. Among RIT causes, upper respiratory infection accounted for the large majority of RIT age-standardized incidence rate, while lower respiratory infection constituted the highest proportion of RIT age-standardized death and DALY rates. The age-standardized incidence, death and DALY rates of RIT in 2019 and their temporal trends since 1990 varied widely across states and socio-demographic index. Among all attributable risk factors, smoking was the leading one for age-standardized RIT deaths in 2019, followed by low temperature and alcohol use (the attributable fractions were 17.7%, 15.3% and 6.9%, respectively). CONCLUSION: Our results suggest that RIT remained a major cause of health burden in the US, with large disparities persisting between US states. Intervention efforts for RIT hotspots, high-risk populations and modifiable risk factors are necessary.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".