Impact on Smoking Behavior of the New Zealand Annual Increase in Tobacco Tax: Data for the Fifth and Sixth Year of Increases
Bibliographic record
Abstract
INTRODUCTION: New Zealand has implemented a series of seven annual increases in tobacco tax since 2010. All tax increases, except for the first in the series, were preannounced. It is unusual for governments to introduce small, persistent, and predictable increases in tobacco tax, and little is known about the impact of such a strategy. This paper evaluates the impact of the fifth and sixth annual increases. METHODS: Smokers' behaviors were self-reported during the 3-month period before, and the 3-month period after, the two annual increases. Responses to the two increases were analyzed separately, and generalized estimating equations models were used to control for sociodemographic variables, recent quit attempts, and the research design. RESULTS: Findings were consistent across years. The proportion of participants who made a smoking-related (54%-56% before and after each tax increase) or product-related change (fifth tax increase: 17%-19%; sixth tax increase: 21%-22%) did not significantly alter from before to after each tax increase. However, it should be noted that the proportion of participants making smoking-related changes was generally high, even prior to each increase. For example, before the 2015 tax increase, 1% reported quitting completely, 21% trying to quit, and 53% cutting down. CONCLUSIONS: In New Zealand, with its series of annual tobacco tax increases since 2010, there were no significant changes in smoking- or product-related behavior associated with the fifth and sixth increases. Nevertheless, overall cessation-related activity was high, with a majority of participants reporting either quitting and/or cutting down recently. IMPLICATIONS: Little is known about the impact of small, persistent, predictable tobacco tax increases on smoking behavior. This study evaluated the impact of the fifth (in 2014) and sixth (2015) tax increases in an annual series implemented in New Zealand. Although there were no detectable changes in smoking behaviors from before to after each tax increase, self-reported cessation-related activity was high overall (i.e., even prior to each increase). Given that there are multiple possible interpretations for these findings, more in-depth time-series analyses are needed to understand how such a tax strategy influences smoking behavior.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".