Assessing the Respiratory and Eye Effects of Biomass for Healthy Adult Populations in Mogadishu, Somalia
Bibliographic record
Abstract
Biomass burning, primarily involving wood and charcoal, is a prevalent source of energy in Mogadishu, Somalia.This study assesses the impact of exposure biomass fuel smoke on Respiratory and Eye symptoms for Healthy Adult Populations in Mogadishu, Somalia.A hospital-based cross-sectional study was conducted from January to March 2024 with 220 participants.Data were gathered using a structured, pretested questionnaire.Logistic regression analysis, including multivariate logistic regression, was performed to assess the association between independent variables and respiratory symptoms.Key findings include that 37.3% of respondents have respiratory symptoms, while 62.7% did not experience any respiratory symptoms.Additionally, 78 participants (35.5%) experienced eye irritation, whereas 142 participants (64.5%) did not.Wood biomass use significantly increased the risk of respiratory symptoms with an adjusted odds ratio (AOR) of 4.635 (95% CI: 1.663-12.919,p=0.012).Prolonged exposure of 1-2 hours (AOR=0.246,95% CI: 0.101-0.598,p=0.001) and 3-4 hours (AOR=0.114,95% CI: 0.044-0.294)significantly lowered the risk of ARI compared to 5-6 hours.Indoor biomass exposure showed higher odds of respiratory symptoms (AOR=2.201,95% CI: 0.762-6.355),although not statistically significant (p=0.145).The study concludes that biomass burning significantly contributes to health outcomes, underscoring the need for sustainable energy alternatives and health awareness campaigns tailored to local needs.
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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 | 0.001 | 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".