Worldwide impact of human development and inequality on the prevalence of asthma, rhinoconjunctivitis and eczema.
Bibliographic record
Abstract
Background Lower income countries have lower asthma prevalence. However, how differences in human development and inequality can explain changes in the prevalence of asthma and allergic diseases is not known. Methods The Global Asthma Network Phase I study reported prevalence of asthma and allergic diseases in children (6-7 years), adolescents (13-14 years), and their parents/guardians in 16, 25 and 17 countries. Gini inequality index (GinI) and human development index (HDI), together with mean annual relative humidity and temperature, and latitude, were used as potential explanatory factors of prevalence differences using meta-regression models, fitted values and heatmaps of prevalence. Results GinI and HDI explained some proportion of asthma prevalence variability, which was highest in children (up to ~70% for disease indicators such as current wheeze or symptoms of severe asthma) and lowest in adolescents (~22% for symptoms of severe asthma or asthma ever). Rhinoconjunctivitis prevalence variability was poorly explained by covariates (from ~53% for current rhinoconjunctivitis among children -an exception- to none). Eczema indicators were explained in a range from ~60% in children (current eczema symptoms and symptoms of severe eczema) to ~12% in adolescents (current eczema symptoms). Overall, heatmaps showed areas of higher prevalence in the intersection of high GinI and high HDI values. Conclusions HDI and Gini explain part of the worldwide variability in the prevalence of asthma, rhinoconjunctivitis, and eczema. This explanatory power is highest for asthma and lowest for rhinoconjunctivitis. Individuals from lower-resourced communities in highly developed countries are at the greatest risk, particularly 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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".