Sensitization to common aeroallergens in a population of young adults in a sub-Saharan Africa setting: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Sensitization to aeroallergens increases the risk of developing asthma or allergic rhinitis. Data on sensitization to airborne allergens in the general population in sub-Saharan Africa are lacking. The aim of this study was to determine the prevalence and determinants of sensitization to common aeroallergens in a population of young adults. METHODS: A cross-sectional study was conducted among students of the Faculty of Medicine and Pharmaceutical Sciences of the University of Douala between 1st February and 30th April 2014. We consecutively recruited all the students present in class or in hospital during our visit. They filled an anonymous questionnaire and underwent skin prick tests with common aeroallergens. A logistic regression model of the SPSS.20 software was used to investigate factors associated with sensitization to common aeroallergens. RESULTS: Of the 600 students included in the study, 305 (50.8 %) were female. The mean age of participants was 22.6 ± 2.7 years. The prevalence of sensitization to aeroallergens was 42.8 % (95 % CI 38.8-46.8). Dermatophagoides pteronyssimus (24.2 %), Dermatophagoides farinae (22.8 %), Blomia tropicalis (23.3 %) and Blatella germanica (15.2 %) were the most common allergens found. Allergic rhinitis, asthma symptoms and family atopy were independently associated to sensitization to common aeroallergens. CONCLUSION: A significant proportion of young adults are sensitized to common aeroallergens. Dust mites and cockroach should be included in the panel of aeroallergens in Cameroon.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".