Economic loss attributable to cigarette smoking in the USA: an economic modelling study
Bibliographic record
Abstract
BACKGROUND: Despite large geographical disparities in the prevalence of cigarette smoking across the USA, there is a paucity of state-level estimates of economic loss attributable to smoking to inform tobacco control policies at the national and state levels. We aimed to estimate the state-level economic loss attributable to cigarette smoking in the USA. METHODS: In this economic modelling study, we used a dynamic macroeconomic model of personal income per capita at the state level. Based on publicly available data on state-level income, its determinants, and smoking status for 2011-20, we first estimated the elasticity of personal income per capita with respect to the prevalence of non-smoking adults (aged ≥18 years) in the USA using a mixed-effects, generalised linear, dynamic panel data model. We used the estimated elasticity to measure the state-specific, annual, avoidable economic loss attributable to cigarette smoking in 2020 under the counterfactual 5% prevalence of cigarette smoking. We then estimated the state-specific cumulative economic loss attributable to cigarette smoking in 2020 using the coefficient of lagged income in the dynamic model. National estimates on economic loss attributable to cigarette smoking were obtained by summing state-specific estimates. FINDINGS: In the mixed-effects model, the elasticity of personal income per capita with respect to the prevalence of non-smoking adults was 0·143 (p=0·063). The estimated annual income loss per capita in 2020 ranged from US$331 in Utah to $1674 in Kentucky. The state mean population-weighted loss per capita was $1100. The annual combined loss of income and unpaid household production at the national level was $436·7 billion (equivalent to 2·1% of US gross domestic product [GDP] in 2020). The cumulative loss of income and unpaid household production was $864·5 billion (equivalent to 4·3% of US GDP in 2020). INTERPRETATION: Smoking causes substantial economic loss in the USA. Tobacco control efforts that lower the prevalence of smoking equitably can contribute considerably to improved macroeconomic performance in the short and long term by reducing health expenditures and avoiding productivity losses. FUNDING: American Cancer Society.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".