Economic costs of tobacco-related diseases in Hong Kong in 2021
Bibliographic record
Abstract
BACKGROUND: Hong Kong has one of the lowest smoking prevalence both within China and among high-income economies. As tobacco use consistently declined over the past decades, we examine whether there are corresponding cost reductions. METHODS: Data were sourced from diverse population-wide datasets including government reports and public hospital records. Costs were calculated using Global Burden of Disease (GBD) 2019 risk estimates for active smoking. Direct costs encompassed public and private healthcare expenses, while indirect costs included the value of years of productive life lost, days off work due to active smoking or secondhand smoke exposure, and long-term care costs. FINDINGS: Active smoking accounted for 26.3% of deaths in people aged 35 or over. Healthcare costs amounted to US$342 million and US$45 million for active and secondhand smoking. Annual tobacco-related diseases reached US$1.27 billion (0.3% of 2021 Hong Kong GDP). Compared with previous methods, the analysis using GBD 2019 risk estimates showed a twofold increase in lives lost due to active smoking but a 31.0% decrease for secondhand smoking. INTERPRETATION: Past studies greatly underestimate the health burden of tobacco compared with more recent data on the wider risks. Despite a decline in smoking prevalence, the total costs associated with smoking have risen as utilisation shifts from primary care towards more expensive specialist care. The long-term health and economic impacts of tobacco use remain substantial even in regions of China that have now achieved low smoking prevalence.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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 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".