To the Editor The impact of electronic cigarette (e-cigarette) use on health is frequently cited as an urgent research need.1 Population-based, longitudinal cohort studies can provide necessary evidence to inform researchers and the general public about potential health risks that e-cigarettes may pose. However, such studies frequently rely on self-reported data to infer e-cigarette user status, and such reports may not be completely accurate.2 Furthermore, there are populations that frequently misreport tobacco use, many of which constitute vulnerable groups, such as pregnant women and youth.3,4 Currently, data are lacking with respect to whether e-cigarette users may misreport occasional smoking. Recently, a study by Rubinstein et al.5 raised serious concerns regarding adolescents who vape e-cigarettes that also reported no tobacco cigarette use. In their study, self-reported e-cigarette users were found to have detectable levels of tobacco-specific biomarkers, suggesting that there may be potential issues in relying exclusively on self-reported data to establish tobacco use. We investigated this problem among adults by estimating the proportion of e-cigarette users that may be misreporting their smoking status. Methods We analyzed data from Wave 1 of the Population Assessment of Tobacco and Health (PATH) Restricted Use Files via the Inter-University Consortium for Political and Social Research (ICPSR) secure Virtual Data Enclave (VDE).6 Details on the design, sampling, data collection, biospecimen collection, and weighting procedures are described elsewhere.6,7 For this analysis, data from the adult questionnaire were merged with biomarker data for those individuals who provided a urine sample for analysis and were included in the PATH biospecimen subsample at Wave 1. Our analysis focused on four groups: (1) exclusive e-cigarette users, who reported currently using e-cigarettes every day or some days and reported no current use of other tobacco products; (2) former cigarette smokers, who had ever smoked 100 cigarettes in their lifetime and reported no current cigarette or any other tobacco use; (3) current exclusive cigarette users who reported smoking at least 100 cigarettes in their lifetime and reported current every day or some days use of cigarettes, and no other current tobacco product use; and (4) never users, defined as those with no lifetime history of any tobacco use. To be included, all participants had to report no past 3-day use of nicotine replacement therapies, and, among users, reported smoking cessation ≥6 months prior to their interview. Biomarkers were measured using analytical methods described elsewhere.6 This analysis focused on urinary NNAL, a biomarker of exposure to tobacco-specific nitrosamine NNK, which has been demonstrated to distinguish between active and passive smoke exposure.8 Although trace levels of NNK have been detected in some e-cigarette products, levels are significantly lower than tobacco cigarettes, supporting the selection of NNAL as a specific biomarker to distinguish between smokers and exclusive e-cigarette users.9 Levels of urinary NNAL were corrected for creatinine, and measurements below the analytic limit of detection (LOD) were imputed using LOD/√2.10 Participants with urinary creatinine levels less than 10 mg/dL or more than 370 mg/dL were excluded from the analysis to adjust for confounding resulting from overly dilute or overly concentrated urine specimens, along with data points subject to interference in chromatographic peaks in lab assays. In the first step of these analyses, we performed a nonparametric Receiver Operating Curve (ROC) analysis on never tobacco users and current exclusive cigarette smokers using unweighted data to estimate the optimal cutoff value for urinary concentrations of NNAL (n = 4062).8 This cutoff would allow for a highly sensitive and specific classification of study participants into groups of smokers and nonsmokers. The participants with NNAL levels more than this determined cutoff value would be classified as smokers, whereas those with NNAL levels below the cutoff would be classified as nonsmokers. Following this, the determined cutoff value was applied to estimate the proportion of self-reported exclusive e-cigarette users and former cigarette smokers who had urinary NNAL levels that exceeded the cutoff. Those subjects were classified as having false negative results, meaning that although they reported being nonsmokers, their urinary NNAL level suggested that they recently smoked tobacco. In order to determine whether e-cigarette users are more likely to misreport smoking, we compared proportion of exclusive e-cigarette users above the cutoff value with the proportion of ex-smokers above the cutoff value using a χ2 test. All proportions were weighted using urine weights for the biospecimen subsample.6 Results Results indicated that the optimum cutoff value for NNAL in urine that distinguished between current smokers and never tobacco users was 14.5 pg/mg creatinine. This concentration allowed for 93.3% sensitivity and 95.9% specificity to differentiate between current exclusive cigarette smokers and never tobacco users, respectively. Put differently, 93.3% of those who reported smoking had urinary NNAL levels above 14.5 pg/mg (true positive) and 4.1% of those who reported never tobacco use had urinary levels above 14.5 pg/mg (100%–95.9%; false positive). Overall, applying this cutoff value allowed for correct classification of 94.3% cases (ROC Area: 0.9797, SE = 0.0021, asymptomatic normal CI = 0.975 to 0.984). In applying this cutoff to respondents who reported exclusive use of e-cigarette (no smoking or use of other tobacco products within the past 6 months) (n = 144), we found that 15% of this population had urinary NNAL levels above 14.5 pg/mg creatinine. The proportion of exclusive e-cigarette users above the established cutoff value was similar to the proportion of former smokers reporting no past 6-month tobacco product use (n = 156) who also had urinary NNAL levels that exceeded this threshold (15%; p = .85). Discussion Our analyses suggest that approximately one in six respondents who reported only using e-cigarettes may have smoked tobacco cigarettes. Our analyses also suggest that caution is needed when relying on self-reported tobacco use status among ex-smokers, as one in six respondents who reported quitting within the past 6 months may still smoke, at least occasionally. Studies that utilize the PATH and other survey data need to account for potential reporting biases due to misreported smoking status. Given the robust design of the PATH project in conjunct with its available biomarker data, we recommend using urinary NNAL as confirmation to objectively verify nonsmoking among e-cigarette users and former smokers. Funding This work was supported by NCI P30 CA16056. Ethics Approval Approval for this study was granted by the Institutional Review Board at Roswell Park Comprehensive Cancer Center. Declaration of Interests Maciej L. Goniewicz reports receiving fees for serving on an advisory board from Johnson & Johnson, and grant support from Pfizer. Danielle M. Smith has no conflicts of interest to declare. References 1. National Academies of Sciences, Engineering, and Medicine. Public Health Consequences of E-Cigarettes . Washington, DC: The National Academies Press; 2018. 2. Patrick DL, Cheadle A, Thompson DC, Diehr P, Koepsell T, Kinne S. The validity of self-reported smoking: A review and meta-analysis. Am J Public Health . 1994; 84( 7): 1086– 1093. Google Scholar CrossRef Search ADS PubMed 3. Aurrekoetxea JJ, Murcia M, Rebagliato Met al. Determinants of self-reported smoking and misclassification during pregnancy, and analysis of optimal cut-off points for urinary cotinine: A cross-sectional study. BMJ Open . 2013; 3:e002034. 4. Brener ND, Billy JO, Grady WR. Assessment of factors affecting the validity of self-reported health-risk behavior among adolescents: Evidence from the scientific literature. J Adolesc Health . 2003; 33( 6): 436– 457. Google Scholar CrossRef Search ADS PubMed 5. Rubinstein ML, Delucchi K, Benowitz NL, Ramo DE. Adolescent exposure to toxic volatile organic chemicals from e-cigarettes. Pediatrics . 2018; 141( 4):e2017355. 6. United States Department of Health and Human Services, National Institutes of Health, National Institute on Drug Abuse, United States Departmen of Health and Human Services, Food and Drug Administration, Center for Tobacco Products. Population Assessment of Tobacco and Health (PATH) Study [United States] Restricted-Use Files . 2017. https://www.icpsr.umich.edu/icpsrweb/NAHDAP/studies/36231. 7. Hyland A, Ambrose BK, Conway KPet al. Design and methods of the Population Assessment of Tobacco and Health (PATH) Study. Tob Control . 2017; 26( 4): 371– 378. Google Scholar CrossRef Search ADS PubMed 8. Goniewicz ML, Eisner MD, Lazcano-Ponce Eet al. Comparison of urine cotinine and the tobacco-specific nitrosamine metabolite 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL) and their ratio to discriminate active from passive smoking. Nicotine Tob Res . 2011; 13( 3): 202– 208. Google Scholar CrossRef Search ADS PubMed 9. Goniewicz ML, Knysak J, Gawron Met al. Levels of selected carcinogens and toxicants in vapour from electronic cigarettes. Tob Control . 2014; 23( 2): 133– 139. Google Scholar CrossRef Search ADS PubMed 10. Hornung RW, Reed LD. Estimation of average concentration in the presence of nondetectable values. Applied Occupational and Environmental Hygiene . 1990; 5( 1): 46– 51. Google Scholar CrossRef Search ADS © The Author(s) 2018. Published by Oxford University Press on behalf of the Society for Research on Nicotine and Tobacco. All rights reserved. For permissions, please e-mail: firstname.lastname@example.org. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices)
Nicotine and Tobacco Research – Oxford University Press
Published: May 2, 2018
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