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Cessation classification likelihood increases with higher expired-air carbon monoxide cutoffs: a meta-analysis

Authors :
Caitlin Wolford-Clevenger
Karen L. Cropsey
Erin A. McClure
Joshua L. Karelitz
Lauren R. Pacek
Source :
Drug Alcohol Depend
Publication Year :
2021
Publisher :
Elsevier BV, 2021.

Abstract

Background Expired-air carbon monoxide (CO) is commonly used to biochemically verify smoking status. The CO cutoff and CO monitor brand may affect the probability of classifying smokers as abstinent, thus influencing conclusions about the efficacy of cessation trials. No systematic reviews have tested this hypothesis. Therefore, we performed a meta-analysis examining whether the likelihood of smoking cessation classification varied due to CO cutoff and monitor brand. Methods Eligible studies (k = 122) longitudinally assessed CO-verified cessation in adult smokers in randomized trials. Primary meta-regressions separately assessed differences in quit classification likelihood due to continuous and categorical CO cutoffs (Low, 3−4 parts per million [ppm]; [SRNT] Recommended, 5−6 ppm; Moderate, 7−8 ppm; and High, 9−10 ppm); exploratory analyses compared likelihood outcomes between monitor brands: Bedfont and Vitalograph. Results The likelihood of quit classification increased 18% with each 1 ppm increase above the lowest cutoff (3 ppm). Odds of classification as quit significantly increased between each cutoff category and High: 261% increase from Low; 162% increase from Recommended; and 150% increase from Moderate. There were no differences in cessation classification between monitor brands. Conclusions As expected, higher CO cutoffs were associated with greater likelihood of cessation classification. The lack of CO monitor brand differences may have been due to model-level variance not able to be followed up in the present dataset. Researchers are advised to report outcomes using a range of cutoffs—including the recommended range (5−6 ppm)—and the CO monitor brand/model used. Using higher CO cutoffs significantly increases likelihood of quit classification, possibly artificially elevating treatment strategies.

Details

ISSN :
03768716
Volume :
221
Database :
OpenAIRE
Journal :
Drug and Alcohol Dependence
Accession number :
edsair.doi.dedup.....80a3cf420e6486f2049b82ece24f24a6
Full Text :
https://doi.org/10.1016/j.drugalcdep.2021.108570