Evidence map›Paper›PMID 33863348›Full record

ArticleHarm reduction journal2021

Shock and awe or incentive-compatible harm reduction? Graphic health warnings on tobacco packages.

Ian Irvine, Hai V Nguyen

Abstract read
In one paragraph

Article in Harm reduction journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Ian IrvineDepartment of Economics, Concordia University, S-H 1155-11, Henry F. Hall Building, 1455 De Maisonneuve Blvd. W, Montreal, Canada. ian.irvine@concordia.ca.ORCID 0000-0002-0308-0646
Hai V NguyenSchool of Pharmacy, Memorial University of Newfoundland, St. John's, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGraphic Health Warnings (GHWs) on cigarette packages were first introduced in Canada in 2001 and will become mandatory in the US as of January 2022. While previous studies have evaluated the impacts of GHWs, the data used in these studies have several shortcomings. The objective of this paper was to investigate the likely impact of such warnings in the US based upon the experience of Canada using hitherto unexplored monthly cigarette sales data, and to explore if alternative approaches involving risk-reduced products might be more successful in reducing smoking.

methodsWe used quasi-experimental segmented regression and difference-in-differences analyses. Data on monthly sales (i.e., shipments) of cigarettes from Canadian manufacturers to Canadian retailers during 1995-2005 were obtained from Statistics Canada.

resultsWe found that GHWs did not have a significant impact on the sales of cigarettes in Canada. We propose an alternative type of graphical health messaging that actively combines information on how to quit with the legally required messaging. The novelty of the proposal is that it is incentive compatible for the supply side of the market and if adopted in several states, the measure could be tested by using a suitable treatment-control design.

conclusionsOur findings imply that we should not expect any notable decline in sales or consumption as a result of implementation of GHWs in the US. The main impact of GHWs will be to add to the anti-smoking culture that has grown steadily over several decades, and this may impact smoking in the longer term.

Indexed as

Tobacco ProductsCanadaHarm ReductionHumansMotivationProduct LabelingSmoking PreventionCanadaFood and drug administrationGraphic health warningsSmokingTobaccoUS

Identifiers

PMID33863348
PMCPMC8051545

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.