Cigarette taxation and socioeconomic inequalities in under-5 mortality across 94 low-income and middle-income countries: a longitudinal ecological study
Bibliographic record
Abstract
BACKGROUND: Although increasing cigarette taxes is known to improve child survival, there are few data on their effect on socioeconomic inequalities in child mortality. We investigated the association between cigarette taxation and socioeconomic inequalities in mortality in children younger than 5 years (hereafter referred to as under-5 mortality) in low-income and middle-income countries (LMICs). METHODS: This was a longitudinal ecological study. We linked country-level annual data on 94 LMICs, as defined by the World Bank, and annual data on under-5 mortality by wealth quintile from the UN Inter-agency Group for Child Mortality Estimation from 2008 to 2020. We used fixed-effect panel regression models to assess the association of cigarette taxes with absolute and relative inequalities in under-5 mortality by wealth quintile. FINDINGS: Increasing total cigarette tax by 10-percentage-points was associated with reduced under-5 mortality rates in all wealth quintiles. Raising total cigarette tax from 0·0-24·9% to 25·0-74·9% and 75·0% or more of their total retail value was associated with 3·8% (95% CI 0·2 to 7·3) and 7·6% (1·4 to 13·4) decreases in absolute inequality in under-5 mortality, respectively. This finding was mainly attributable to specific tax, which was associated with a 1·4% (0·3 to 2·6) reduction in absolute inequality for each 10-percentage-point increase. We estimated that raising total cigarette taxes to 75·0% or more in all 94 LMICs could have averted 281 017 (196 916 to 362 301) under-5 deaths in 2021. INTERPRETATION: High cigarette taxes are associated with a large decrease in absolute inequality in child mortality in LMICs. These findings support raising cigarette taxes to the WHO-recommended 75% or more of the retail value to protect the poorest children. FUNDING: Swedish Research Council for Health, Working Life, and Welfare; Stiftelsen Riksbankens Jubileumsfond; European Union's Horizon 2020 Research and Innovation; and UK National Institute for Health and Care Research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".