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Record W4306907909 · doi:10.1093/ntr/ntac244

Concentrated Distribution of Nicotine Patches to a Town With High Smoking Rates—Any Evidence of Large Impacts on Tobacco Cessation?

2022· letter· en· W4306907909 on OpenAlexafffund
John Cunningham, Scott T. Leatherdale, Michael Chaiton, Rachel F. Tyndale, Christina Schell, Alexandra Godinho

Bibliographic record

VenueNicotine & Tobacco Research · 2022
Typeletter
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsPublic Health OntarioUniversity of WaterlooUniversity of TorontoCentre for Addiction and Mental Health
FundersInstitute of Neurosciences, Mental Health and AddictionCanadian Institutes of Health ResearchCanada Research ChairsInstitute of Population and Public HealthCanadian Cancer SocietyPublic Health AgencyPublic Health Agency of Canada
KeywordsNicotineSmoking cessationNicotine dependenceDistribution (mathematics)Environmental healthMedicinePsychiatry

Abstract

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Rates of cigarette smoking can vary greatly between geographic regions.1–3 This pilot study examined whether there was benefit to the concentrated distribution of tobacco cessation resources—in this case the distribution of nicotine patches—to a region with higher-than-average smoking rates. The goal of this research was to contribute towards policy decisions regarding the efficient use of public resources to promote the largest number of people quitting smoking. If the study observed that offering free nicotine patches to all households in a community with higher-than-average smoking rates led to large reductions in smoking in the community, it would provide support for initiatives designed to target mass distributions of nicotine replacement therapy to areas where smoking is most common. The study protocol is published elsewhere.4 Briefly, general population survey data were used to select a region (specifically, a small town in Canada) with an estimated smoking rate of 35% over the two most recent survey periods.1,5 A letter offering five weeks of free-of-charge nicotine patches was mailed to every residence in the community (7257 households; mailed late October, 2020; 1 person per household could participate). The letter provided the choice of an online link or a telephone number to contact to make the request to be mailed nicotine patches. Each letter also had a unique ID number that needed to be entered as part of the nicotine patch ordering process (so each invitation could only be used once). At the same time, an article was published in the local newspaper to inform residents of the town about the ongoing project. In early December of 2020, a final newspaper article was published, offering the nicotine patches to any eligible residents of the town without the need to provide a unique ID number. After consenting to take part in the study, participants were asked their age (needed to be 18 or older) and number of cigarettes smoked per day (10+ for a year or more). Participants provided their name, telephone number, and postal address. The 5-week supply of nicotine patches was couriered to them. Participants also agreed to take part in a 6-month follow-up asking about their smoking status at that point (payment of $20). This research project was approved by the standing ethics review committee of the Centre for Addiction and Mental Health (CAMH—REB Protocol #122/2019). A total of 123 participants registered for the study and received nicotine patches (117 of these provided a unique ID, indicating that they responded to the invitation letter). Mean (SD) age was 48.6 (11.9), 59% were female, and 32% reported a gross family income of CAN$40 000 or less per year. Participants reported smoking an average (SD) of 19.5 (7.6) cigarettes per day at baseline. About three-quarters (72%, n = 88/123) completed the 6-month follow-up. Of those who completed the follow-up, 43.8% reported using all, and 18% reported using most, of the five-week supply of nicotine patches. Of the 123 participants, 18.7% (n = 23) reported 30-day point prevalence abstinence at 6-month follow-up (23.6% reported no smoking in the past 24 h and 8.9% reported no smoking since receiving the nicotine patches). The majority of current smokers say that they would be interested in receiving free-of-charge nicotine patches to help them stop smoking.6 Further, previous research has demonstrated that prospectively contacted smokers participating in random digit dialing telephone surveys willingly have nicotine patches mailed to their household.6 However, the current study found that a postal mailed offer of free-of-charge nicotine patches targeted to a region with high smoking rates yielded only a limited number of participants actually requesting the patches. The potential benefits and feasibility of this targeted approach warrant additional expanded testing in additional jurisdictions. The implementation of this project was limited by the COVID-19 pandemic, with plans to promote the distribution, including an onsite pick-up point, canceled due to the need for social distancing. This, in addition to possible concerns about the offer being a “scam”, might have reduced the overall response rate. However, it is probably reasonable to assume from these results that, even without the ongoing pandemic, it is likely that only a small minority of current smokers will proactively engage with an offer to be sent free-of-charge nicotine patches to help them stop smoking. Along with replication of this research once the pandemic subsides to determine if uptake can be increased, further research is merited on whether even this low level of engagement is cost-effective, and on whether more support (eg, additional patches or combined with other support services) would yield improved results. Professor Cunningham had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Professor Cunningham is currently supported by the Nat and Loretta Rothschild Chair in Addictions Treatment and Recovery Studies. Dr. Leatherdale is a Chair in Applied Public Health funded by the Public Health Agency of Canada (PHAC) in partnership with Canadian Institutes of Health Research (CIHR) Institute of Neurosciences, Mental Health and Addiction (INMHA) and Institute of Population and Public Health (IPPH). Support to CAMH for salary of scientists and infrastructure has been provided by the Ontario Ministry of Health and Long Term Care. The views expressed in this article do not necessarily reflect those of the Ministry of Health and Long Term Care. Clinical Trials registration: NCT04534231 This research is funded by the Canadian Cancer Society (grant #706201). The funder played no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. This research was undertaken in part thanks to funding from the Canada Research Chairs program for support of Dr. Tyndale (Canada Research Chair in Pharmacogenomics) and Professor Cunningham (Canada Research Chair in Addictions). All authors have no conflicts of interest to declare. JAC, SL, MC, AG, and CS have no financial disclosures to declare. RFT declares that, in the past five years, she has consulted for Quinn Emanuel and Ethismos Research Inc. Data is available from the corresponding author on reasonable request.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.037
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.018
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0030.003
Open science0.0020.002
Research integrity0.0370.021
Insufficient payload (model declined to judge)0.0110.003

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.

Opus teacher head0.089
GPT teacher head0.398
Teacher spread0.308 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreCommentary

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".

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Citations1
Published2022
Admission routes2
Has abstractno

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