Evidence map›Paper›PMID 39375543›Full record

SynthesisNature human behaviour2024

A systematic review and network meta-analysis of population-level interventions to tackle smoking behaviour.

Shamima Akter, Md Mizanur Rahman, Thomas Rouyard, Sarmin Aktar, Raïssa Shiyghan Nsashiyi, Ryota Nakamura

Abstract readSystematic ReviewNetwork Meta-Analysis
In one paragraph

Synthesis in Nature human behaviour, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 3 pooled it
–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

24 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Article
  5. Article
  6. Article
  7. Identifying Key Predictors of Smoking Cessation Success: Text-Based Feature Selection Using a Large Language Model.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2026
    Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. 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

6 authors.

Shamima AkterHitotsubashi Institute for Advanced Study, Hitotsubashi University, Tokyo, Japan.ORCID http://orcid.org/0000-0001-9304-9660
Md Mizanur RahmanHitotsubashi Institute for Advanced Study, Hitotsubashi University, Tokyo, Japan.ORCID http://orcid.org/0000-0001-9190-4707
Thomas RouyardHitotsubashi Institute for Advanced Study, Hitotsubashi University, Tokyo, Japan.ORCID http://orcid.org/0000-0001-6412-1360
Sarmin AktarGlobal Public Health Research Foundation, Dhaka, Bangladesh.ORCID http://orcid.org/0009-0001-0077-7499
Raïssa Shiyghan NsashiyiInstitute for Nature, Health, and Agricultural Research, Yaounde, Cameroon.ORCID http://orcid.org/0000-0002-7282-8136
Ryota NakamuraHitotsubashi Institute for Advanced Study, Hitotsubashi University, Tokyo, Japan. ryota.nakamura@r.hit-u.ac.jp.ORCID http://orcid.org/0000-0002-3217-6452

Funding

Ministry of Health, Labour and Welfare (Ministry of Health, Labour and Welfare, Japan) Health and Labour Sciences Research Grant 20FA1022
6 · The paper itself

Abstract

This preregistered systematic review and meta-analysis (PROSPERO: CRD 42022311392) aimed to synthesize the effectiveness of all available population-level tobacco policies on smoking behaviour. Our search across 5 databases and leading organizational websites resulted in 9,925 records, with 476 studies meeting our inclusion criteria. In our narrative summary and both pairwise and network meta-analyses, we identified anti-smoking campaigns, health warnings and tax increases as the most effective tobacco policies for promoting smoking cessation. Flavour bans and free/discounted nicotine replacement therapy also showed statistically significant positive effects on quit rates. The network meta-analysis results further indicated that smoking bans, anti-tobacco campaigns and tax increases effectively reduced smoking prevalence. In addition, flavour bans significantly reduced e-cigarette consumption. Both the narrative summary and the meta-analyses revealed that smoking bans, tax increases and anti-tobacco campaigns were associated with reductions in tobacco consumption and sales. On the basis of the available evidence, anti-tobacco campaigns, smoking bans, health warnings and tax increases are probably the most effective policies for curbing smoking behaviour.

Indexed as

Smoking CessationHealth PromotionHumansSmokingSmoking PreventionTaxes

Identifiers

PMID39375543
PMCPMC11659173

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.