Household biomass fuel use, asthma symptoms severity, and asthma underdiagnosis in rural schoolchildren in Nigeria: a cross-sectional observational study
Bibliographic record
Abstract
BACKGROUND: In 2014, the International Study of Asthma and Allergies in Childhood (ISAAC) reported that the highest prevalence of symptoms of severe asthma was found in the low- and middle-income countries (LMICs), including Nigeria. While exposure to biomass fuel use may be an important risk factor in the development of asthma, its association with asthma symptoms severity has not been well-established. The aim of this study is to extend the spectrum of environmental risk factors that may be contributing towards increasing asthma morbidity, especially asthma symptoms severity in rural schoolchildren in Nigeria and to examine possible asthma underdiagnosis among this population. METHODS: Authors conducted a cross-sectional survey in three rural communities in Nigeria. Asthma symptoms were defined according to the ISAAC criteria. Information on the types of household fuel used for cooking was used to determine household cooking fuel status. Asthma symptoms severity was defined based on frequencies of wheeze, day- and night-time symptoms, and speech limitations. Logistic regression analyses were used to explore associations. RESULTS: A total of 1,690 Nigerian schoolchildren participated in the study. Overall, 37 (2.2%) had diagnosed asthma and 413 (24.4%) had possible asthma (asthma-related symptoms but not diagnosed asthma). Children from biomass fuel households had higher proportion of possible asthma (27.7 vs. 22.2%; p < 0.05) and symptoms of severe asthma (18.2 vs. 7.6%; p = 0.048). In adjusted analyses, biomass fuel use was associated with increased odds of severe symptoms of asthma [odds ratios (OR) = 2.37; 95% CI: 1.16-4.84], but not with possible asthma (OR = 1.22; 95% CI: 0.95-1.56). CONCLUSION: In rural Nigerian children with asthma symptoms, the use of biomass fuel for cooking is associated with an increased risk of severe asthma symptoms. There is additional evidence that rural children might be underdiagnosed for asthma.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".