Socioeconomic status and COPD among low- and middle-income countries
Bibliographic record
Abstract
BACKGROUND: Socioeconomic status (SES) is a strong social determinant of health. There remains a limited understanding of the association between SES and COPD prevalence among low- and middle-income countries where the majority of COPD-related morbidity and mortality occurs. We examined the association between SES and COPD prevalence using data collected in Argentina, Bangladesh, Chile, Peru, and Uruguay. METHODS: We compiled lung function, demographic, and SES data from three population-based studies for 11,042 participants aged 35-95 years. We used multivariable alternating logistic regressions to study the association between COPD prevalence and SES indicators adjusted for age, sex, self-reported daily smoking, and biomass fuel smoke exposure. Principal component analysis was performed on monthly household income, household size, and education to create a composite SES index. RESULTS: Overall COPD prevalence was 9.2%, ranging from 1.7% to 15.4% across sites. The adjusted odds ratio of having COPD was lower for people who completed secondary school (odds ratio [OR] =0.73, 95% CI 0.55-0.98) and lower with higher monthly household income (OR =0.96 per category, 95% CI 0.93-0.99). When combining SES factors into a composite index, we found that the odds of having COPD was greater with lower SES (interquartile OR =1.23, 95% CI 1.05-1.43) even after controlling for subject-specific factors and environmental exposures. CONCLUSION: In this analysis of multiple population-based studies, lower education, lower household income, and lower composite SES index were associated with COPD. Since household income may be underestimated in population studies, adding household size and education into a composite index may provide a better surrogate for SES.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".