Meta-Analysis
Copyright ©The Author(s) 2019. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Meta-Anal. Feb 22, 2019; 7(2): 31-50
Published online Feb 22, 2019. doi: 10.13105/wjma.v7.i2.31
Misclassification of smoking habits: An updated review of the literature
Janette S Hamling, Katharine J Coombs, Peter N Lee
Janette S Hamling, RoeLee Statistics Ltd., 17 Cedar Road, United Kingdom
Katharine J Coombs, Peter N Lee, P.N. Lee Statistics and Computing Ltd., Sutton SM2 5DA, United Kingdom
Author contributions: Hamling JS and Lee PN planned the study; Coombs KJ carried out the literature searches, assisted by the other authors; Hamling JS carried out the data entry, assisted by Lee PN; Hamling JS carried out the statistical analyses along lines discussed and agreed with Lee PN; Lee PN drafted the paper which was critically reviewed by the other authors.
Conflict-of-interest statement: All the authors are long-term consultants to various tobacco companies and organizations.
PRISMA 2009 Checklist statement: The authors have read the PRISMA 2009 Checklist, and the manuscript was prepared and revised according to the PRISMA 2009 Checklist.
Open-Access: This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Corresponding author: Peter N Lee, MA, Director, Senior Statistician, P.N. Lee Statistics and Computing Ltd., 17 Cedar Road, Sutton SM2 5DA, United Kingdom. peterlee@pnlee.co.uk
Telephone: +44-20-6428265 Fax: +44-20-8642135
Received: November 29, 2018
Peer-review started: November 29, 2018
First decision: December 15, 2018
Revised: January 21, 2019
Accepted: January 21, 2019
Article in press: January 21, 2019
Published online: February 22, 2019
Processing time: 85 Days and 15 Hours
Abstract
BACKGROUND

Misclassification of smoking habits leads to underestimation of true relationships between diseases and active smoking, and overestimation of true relationships with passive smoking. Information on misclassification rates can be obtained from studies using cotinine as a marker.

AIM

To estimate overall misclassification rates based on a review and meta-analysis of the available evidence, and to investigate how misclassification rates depend on other factors.

METHODS

We searched for studies using cotinine as a marker which involved at least 200 participants and which provided information on high cotinine levels in self-reported non-, never, or ex-smokers or on low levels in self-reported smokers. We estimated overall misclassification rates weighted on sample size and investigated heterogeneity by various study characteristics. Misclassification rates were calculated for two cotinine cut points to distinguish smokers and non-smokers, the higher cut point intended to distinguish regular smoking.

RESULTS

After avoiding double counting, 226 reports provided 294 results from 205 studies. A total of 115 results were from North America, 128 from Europe, 25 from Asia and 26 from other countries. A study on 6.2 million life insurance applicants was considered separately. Based on the lower cut point, true current smokers represented 4.96% (95% CI 4.32-5.60%) of reported non-smokers, 3.00% (2.45-3.54%) of reported never smokers, and 10.92% (9.23-12.61%) of reported ex-smokers. As percentages of true current smokers, non-, never and ex-smokers formed, respectively, 14.50% (12.36-16.65%), 5.70% (3.20-8.20%), and 8.93% (6.57-11.29%). Reported current smokers represented 3.65% (2.84-4.45%) of true non-smokers. There was considerable heterogeneity between misclassification rates. Rates of claiming never smoking were very high in Asian women smokers, the individual studies reporting rates of 12.5%, 22.4%, 33.3%, 54.2% and 66.3%. False claims of quitting were relatively high in pregnant women, in diseased individuals who may recently have been advised to quit, and in studies considering cigarette smoking rather than any smoking. False claims of smoking were higher in younger populations. Misclassification rates were higher in more recently published studies. There was no clear evidence that rates varied by the body fluid used for the cotinine analysis, the assay method used, or whether the respondent was aware their statements would be validated by cotinine - though here many studies did not provide relevant information. There was only limited evidence that rates were lower in studies classified as being of good quality, based on the extent to which other sources of nicotine were accounted for.

CONCLUSION

It is important for epidemiologists to consider the possibility of bias due to misclassification of smoking habits, especially in circumstances where rates are likely to be high. The evidence of higher rates in more recent studies suggests that the extent of misclassification bias in studies relating passive smoking to smoking-related disease may have been underestimated.

Keywords: Misclassification; Smoking; Cotinine; Cigarettes; Tobacco use; E-cigarettes; Passive smoking; Bias; Systematic review; Meta-analysis

Core tip: We update a meta-analysis of evidence on accuracy of reported smoking, using cotinine as a marker. From 200+ studies, we estimated various misclassification rates. True smokers represented 3.00% (2.45%-3.54%) of reported never smokers and 10.92% (9.23%-12.61%) of reported ex-smokers. Reported never and ex-smokers formed 5.70% (3.20%-8.20%) and 8.93% (6.57%-11.29%) of true smokers. Falsely claiming never smoking was extremely common in Asian women. Rates of falsely claiming quitting were high in pregnant women and diseased individuals advised to quit. Smoking misclassification causes overestimation of true passive smoking relationships, a problem exacerbated by increasing misclassification rates in recently published studies.