PREVALENCE OF ACUTE RESPIRATORY INFECTIONS IN SAUDI ARABIAN CHILDREN: AN ANALYTICAL STUDY OF CAUSES AND RISK FACTORS
Bibliographic record
Abstract
Background: Acute respiratory infections (ARIs) are a leading cause of morbidity and mortality in children globally, with significant prevalence in Saudi Arabia due to environmental, socioeconomic, and healthcarerelated factors. This study aimed to assess the prevalence of ARIs among Saudi children and identify key risk factors contributing to their occurrence. Methods: A cross-sectional analytical study was conducted, involving 300 children aged 6 months to 12 years. Data were collected via structured interviews with parents/guardians and clinical record reviews. Variables included socio-demographics, environmental exposures, immunization status, and nutritional indicators. Statistical analyses, including chi-square tests and logistic regression, were performed using SPSS version 26. Results: The prevalence of ARIs was 33%, with higher rates observed in children exposed to parental smoking (48.8%), poor indoor ventilation (50.9%), and indoor charcoal heating (51.4%). Incomplete immunization (60.3%) and underweight status (55.2%) were significant predictors of ARIs. Logistic regression confirmed strong associations between ARIs and incomplete immunization (OR = 3.2), underweight status (OR = 2.6), parental smoking (OR = 2.1), and poor ventilation (OR = 1.8). Conclusion: ARIs remain a major health concern among Saudi children, driven by modifiable risk factors such as environmental exposures, inadequate immunization, and malnutrition. Targeted public health interventions, including parental education, improved vaccination coverage, and better indoor air quality, are essential to reduce the burden of ARIs in this population.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".