Global burden of lower respiratory infections during the last three decades
Bibliographic record
Abstract
Background: Lower respiratory infections (LRIs) cause a substantial mortality, morbidity and economic burden. The present study reported the global, regional and national burden of LRIs and their attributable risk factors in 204 countries and territories, between 1990 and 2019, by age, sex, etiology, and Socio-demographic Index (SDI). Methods: Using publicly available data from the Global Burden of Disease (GBD) study 2019, we reported the incidence, deaths and disability-adjusted life-years (DALYs), due to LRIs. Estimates were presented as counts and age-standardized rates per 100,000 population with their associated uncertainty intervals (UIs). Results: Globally, in 2019 there were 488.9 million (95% UI: 457.6 to 522.6) incident cases and 2.4 million (2.3-2.7) deaths due to LRIs. The global age-standardized incidence and death rates for LRIs were 6,295 (5,887.4-6,737.3) and 34.3 (31.1-37.9) per 100,000 in 2019, which represents a 23.9% (22.5-25.4) and 48.5% (42.9-54.0) decrease, respectively since 1990. In 2019, Guinea [12,390.4 (11,495.5-13,332.8)], Chad [12,208.1 (11,289.3-13,202.5)] and India [11,862.1 (11,087.0-12,749.0)] had the three highest age-standardized incidence rates of LRI. Equatorial Guinea [-52.7% (95% UI: -55.8 to -49.3)], Chile [-50.2% (95% UI: -53.4 to -47.0)] and Albania [-48.6% (95% UI: -51.7 to -45.3)] showed the largest decreases from 1990 to 2019. In 2019, a decrease in the incidence rate of LRI was observed at the global level up to the 25-29 age group, then the incidence rates increased with age. The burden of LRIs decreased with increasing SDI at both the regional and national levels. Globally, child wasting (33.1%), household air pollution from solid fuels (24.9%) and a lack of access to handwashing facilities (14.4%) made the largest contributions to the LRI burden in 2019. Conclusions: Although the burden of LRIs decreased over the period 1990-2019, LRIs still contribute to a large number of incident cases, deaths and DALYs. Preventative programs with a focus on reducing exposure to attributable risk factors should be implemented, especially in less developed 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.004 | 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".