Evaluating trends in cigarette and HTP use in Japan and measurement issues in the National Health and Nutrition Survey
Bibliographic record
Abstract
INTRODUTION: Studies have reported that the rapid rise in heated tobacco product (HTP) sales in Japan accompanied an accelerated decline in cigarette sales. However, these studies do not distinguish whether those who previously smoked cigarettes became dual users with HTPs (smoking fewer cigarettes) or instead switched completely to HTPs. If HTPs present lower health risks than cigarettes, replacing cigarettes with HTPs is more likely to improve public health than cigarette users continuing as dual users. METHODS: To evaluate the role of HTP introduction relative to smoking prevalence, we examine trends in cigarette prevalence as related to trends in HTP use using Japan's National Health and Nutrition Survey (NHNS) from 2011 to 2019. We develop measures of relative changes in smoking prevalence use by age and gender in the pre-HTP and post-HTP periods. We then analyse prevalence data by year using joinpoint regression to statistically distinguish changes in trend. RESULTS: Compared with the pre-HTP 2011-2014 period, cigarette prevalence decreased more rapidly during the post-HTP 2014-2017 period, particularly among younger age groups. However, the changing format of NHNS questions limits our ability to determine the impact on smoking prevalence, particularly after 2017. CONCLUSIONS: While suggesting that HTPs helped some people who smoke to quit smoking, this study also shows the difficulties in eliciting accurate survey responses about product use and distinguishing the impact of a potentially harm-reducing product in an environment subject to rapidly evolving patterns of use.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".