Fifty-year forecasts of daily smoking prevalence: can Australia reach 5% by 2030?
Bibliographic record
Abstract
OBJECTIVE: To compare 50-year forecasts of Australian tobacco smoking rates in relation to trends in smoking initiation and cessation and in relation to a national target of ≤5% adult daily prevalence by 2030. METHODS: A compartmental model of Australian population daily smoking, calibrated to the observed smoking status of 229 523 participants aged 20-99 years in 26 surveys (1962-2016) by age, sex and birth year (1910-1996), estimated smoking prevalence to 2066 using Australian Bureau of Statistics 50-year population predictions. Prevalence forecasts were compared across scenarios in which smoking initiation and cessation trends from 2017 were continued, kept constant or reversed. RESULTS: At the end of the observation period in 2016, model-estimated daily smoking prevalence was 13.7% (90% equal-tailed interval (EI) 13.4%-14.0%). When smoking initiation and cessation rates were held constant, daily smoking prevalence reached 5.2% (90% EI 4.9%-5.5%) after 50 years, in 2066. When initiation and cessation rates continued their trajectory downwards and upwards, respectively, daily smoking prevalence reached 5% by 2039 (90% EI 2037-2041). The greatest progress towards the 5% goal came from eliminating initiation among younger cohorts, with the target met by 2037 (90% EI 2036-2038) in the most optimistic scenario. Conversely, if initiation and cessation rates reversed to 2007 levels, estimated prevalence was 9.1% (90% EI 8.8%-9.4%) in 2066. CONCLUSION: A 5% adult daily smoking prevalence target cannot be achieved by the year 2030 based on current trends. Urgent investment in concerted strategies that prevent smoking initiation and facilitate cessation is necessary to achieve 5% prevalence by 2030.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| 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".