The Global, Regional, and National Burden of Lower Respiratory Infections Caused by Streptococcus pneumoniae Between 1990 and 2021
Bibliographic record
Abstract
Aims: To investigate the global epidemiological characteristics of lower respiratory infection (LRI) burden caused by Streptococcus pneumoniae (SP) from 1990 to 2021. Methods: Using data from the Global Burden of Disease (GBD) study 2021, we systematically analyzed Streptococcus pneumoniae-related (SP-related) LRI burden, focusing on mortality, disability-adjusted life years (DALYs), and temporal trends by age, gender, geographic region, and socio-demographic index (SDI) quintiles. Decomposition analysis assessed the influence of epidemiological shifts, population growth, and aging on age-standardized mortality rates (ASMRs), while an autoregressive integrated moving average (ARIMA) model projected future trends. Results: Between 1990 and 2021, the global SP-related LRI death number decreased from 1,028,083 (95% uncertainty interval (UI): 923,782–1,146,074) to 505,268 (95% UI: 454,335–552,539), and the ASMR dropped from 19.28 (95% UI: 17.32–21.49) to 6.40 (95% UI: 5.76–7.00) per 100,000. The age distribution consistently exhibited a clear two-tiered pattern, gradually shifting from being predominantly composed of young children to being dominated by older adults. Disparities were stark across SDI quintiles, low-SDI regions exhibited up to 100-times-higher under-five mortality than high-SDI regions. Geographic distribution showed the highest ASMRs in sub-Saharan Africa and the lowest in Canada, the United States, and Australia, with Mongolia and Finland showing the largest reductions in mortality. Epidemiological changes were the most significant factor in ASMR reduction. Conclusions: The SP-related LRI burden has decreased globally but remains a major health concern, especially in low-SDI regions. Targeted public health interventions, particularly for neonates and elderly adults, are essential to address persistent disparities and further reduce mortality.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".