The burden of lower respiratory infections and their underlying etiologies in the Middle East and North Africa region, 1990–2019: results from the Global Burden of Disease Study 2019
Bibliographic record
Abstract
BACKGROUND: Lower respiratory infections (LRIs) cause substantial mortality and morbidity. The present study reported and analysed the burden of LRIs in the Middle East and North Africa (MENA) region between 1990 and 2019, by age, sex, etiology, and socio-demographic index (SDI). METHODS: The data used in this study were sourced from the Global Burden of Disease (GBD) study 2019. The annual incidence, deaths, and disability-adjusted life-years (DALYs) due to LRIs were presented as counts and age-standardised rates per 100,000 population, along with their 95% uncertainty intervals (UIs). The average annual percent changes (AAPC) in the age-standardised incidence, death and DALYs rates were calculated using Joinpoint software and correlations (Pearson's correlation coefficient) between the AAPCs and SDIs were calculated using Stata software. RESULTS: In 2019, there were 34.1 million (95% UI 31.7-36.8) incident cases of LRIs in MENA, with an age-standardised rate of 6510.2 (95% UI 6063.6-6997.8) per 100,000 population. The number of regional DALYs was 4.7 million (95% UI 3.9-5.4), with an age-standardised rate of 888.5 (95% UI 761.1-1019.9) per 100,000 population, which has decreased since 1990. Furthermore, Egypt [8150.8 (95% UI 7535.8-8783.5)] and Afghanistan [61.9 (95% UI 52.1-72.6)] had the highest age-standardised incidence and death rates, respectively. In 2019, the regional incidence and DALY rates were highest in the 1-4 age group, in both females and males. In terms of deaths, pneumococcus and H. influenza type B were the most and least common types of LRIs, respectively. From 1990 to 2019, the burden of LRIs generally decreased with increasing SDI. There were significant positive correlations between SDI and the AAPCs for the age-standardised incidence, death and DALY rates (p < 0.05). Over the 1990-2019 period, the regional incidence, deaths and DALYs attributable to LRIs decreased with AAPCs of - 1.19% (- 1.25 to - 1.13), - 2.47% (- 2.65 to - 2.28) and - 4.21% (- 4.43 to - 3.99), respectively. CONCLUSIONS: The LRI-associated burden in the MENA region decreased between 1990 and 2019. SDI had a significant positive correlation with the AAPC and pneumococcus was the most common underlying cause of LRIs. Afghanistan, Yemen and Egypt had the largest burdens in 2019. Further studies are needed to investigate the effectiveness of healthcare interventions and programs to control LRIs and their risk factors.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".