The socioeconomic gradient of secondhand smoke exposure in children: evidence from 26 low-income and middle-income countries
Bibliographic record
Abstract
OBJECTIVE: To provide the first analysis of socioeconomic inequalities in children's daily exposure to indoor smoking in households in 26 low-income and middle-income countries (LMICs). METHODS: We used nationally representative household samples (n=369 654) collected through the Demographic Health Surveys between 2010 and 2014 to calculate daily exposure to secondhand smoke (ESHS) among children aged 0-5 years. The relative and absolute concentration (RC and AC) indices were used to quantify wealth-based inequalities in daily ESHS in each country and in urban and rural areas in each country. We decomposed total socioeconomic inequalities in ESHS into within-group and between-group (rural-urban) inequalities to identify the sources of wealth-based inequality in ESHS in LMICs. FINDINGS: We observed substantial variation across countries in the prevalence of daily ESHS among children. Children's ESHS was higher in rural areas compared to urban areas in the majority of the countries. The RC and AC demonstrated that daily ESHS was concentrated among poorer children in almost all countries (RC, median=-0.179, IQR=0.186 and AC, median=-0.040, IQR=0.055). The concentration of ESHS among poorer children was greater in urban relative to rural areas. The decomposition of the overall socioeconomic inequality in daily ESHS revealed that wealth-based differences in ESHS within urban and rural areas were the main contributor to socioeconomic inequalities in most countries (median=46%, IQR=32%). CONCLUSIONS: Special attention should be given to reduce ESHS among children from rural and socioeconomically disadvantaged households as social inequalities in ESHS might contribute to social inequalities in health over the life course.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".