Trends in US Adult Smoking Prevalence, 2011 to 2022
Bibliographic record
Abstract
Importance: President Biden recently prioritized the fight against smoking as key to reducing cancer mortality. Objective: To assess trends in smoking and illuminate the association between smoking and reducing deaths due to cancer. Design, Setting, and Participants: This cross-sectional study used responses to National Health Interview Surveys from January 1, 2011, to December 31, 2022, to characterize trends in current smoking for key sociodemographic groups among US adults. Exposures: Age (18-24, 25-39, 40-64, and ≥65 years), family income (<200%, 200%-399%, and ≥400% of the federal poverty level [FPL]), educational level (less than high school, high school degree or General Educational Development, some college, and college degree or above), and race and ethnicity (Black, Hispanic, White, and other). Main Outcomes and Measures: Weighted current smoking prevalence with 95% CIs by analysis group from 2011 to 2022. Average annual percentage change (AAPC) in smoking prevalence by analysis group is calculated using Joinpoint regression. Results: Data from 353 555 adults surveyed by the National Health Interview Surveys from 2011 to 2022 were included (12.6% Black, 15.0% Hispanic, 65.2% White, and 7.3% other race or ethnicity). Overall, smoking prevalence decreased among adults aged 18 to 24 years from 19.2% (95% CI, 17.5%-20.9%) in 2011 to 4.9% (95% CI, 3.7%-6.0%) in 2022 at an AAPC of -11.3% (95% CI, -13.2% to -9.4%), while it remained roughly constant among adults 65 years or older at 8.7% (95% CI, 7.9%-9.5%) in 2011 and 9.4% (95% CI, 8.7%-10.2%) in 2022 (AAPC, -0.1% [95% CI, -0.8% to 0.7%]). Among adults 65 years or older, smoking prevalence increased from 13.0% (95% CI, 11.2%-14.7%) in 2011 to 15.8% (95% CI, 14.1%-17.6%) for those with income less than 200% FPL (AAPC, 1.1% [95% CI, 0.1%-2.1%]) and remained roughly constant with no significant change for those of higher income. Similar age patterns are seen across educational level and racial and ethnic groups. Conclusions and Relevance: This cross-sectional study found that smoking prevalence decreased from 2011 to 2022 in all age groups except adults 65 years or older, with faster decreases among younger than older adults. These findings suggest that the greatest gains in terms of reducing smoking-attributable morbidity and mortality could be achieved by focusing on individuals with low socioeconomic status, as this population has the highest smoking rates and the worst health prospects.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".