Nicotine Replacement, Topography, and Smoking Phenotypes of E-cigarettes

The potential of new nicotine and tobacco products as tools for people who smoke to quit combustible cigarettes - a systematic review of common practices and guidance towards a robust study protocol to measure cessation efficacy

New types of nicotine and tobacco products like electronic cigarettes (ECs), heated tobacco products or nicotine pouches have been discussed as less harmful alternatives to combustible cigarettes and other toxic forms of tobacco products. Their harm reduction potential lay in the efficient transition away from smoking to those new products. Numerous studies addressing the cessation efficacy of ECs have been published with contradictory outcomes. Yet, a comprehensive Cochrane review concluded with high certainty on the cessation efficacy of ECs. This prompted us to perform a review to identify weaknesses in common study designs and to summarize best practices for the study design on the potential of new nicotine products as cessation aids. 120 articles retrieved from Medline were found to be eligible. Most of the studies in the field were interventional trials while observational studies played a minor role in the evaluation of smoking cessation. Efficacy was predominantly assessed for ECs in 77% of the reports while heated tobacco (17%) and non-combustible products (11%) were less frequently investigated up to now. Measures to determine the efficacy were questionnaire-based assessments as well as use documentation/prevalence and abstinence rates. Studies varied largely in their duration and sample size with medians of 3 months and 156.5 participants, respectively.With the help of this review, we identified several weaknesses in the common study designs. One major limitation in longitudinal trials was the lack of compliance measures suited to verify the use status over longer time periods, relying solely on self-reports. Moreover, the motivation of the participants to quit was rarely defined and a profound familiarization period was not taken into account for the majority of the studies. To what extent such weaknesses influence the outcome of the studies was beyond the scope of this review. We encourage researchers to consider the recommendations which resulted from this review in order to determine the abuse liability and cessation efficacy of the products in a more robust manner. Finally, we like to call attention to the missing data for low- and middle-income countries which would require quitting strategies most urgently to combat the tobacco smoking epidemic.

Electronic cigarettes for smoking cessation

BACKGROUND

Electronic cigarettes (ECs) are handheld electronic vaping devices which produce an aerosol by heating an e-liquid. People who smoke, healthcare providers and regulators want to know if ECs can help people quit smoking, and if they are safe to use for this purpose. This is a review update conducted as part of a living systematic review.

OBJECTIVES

To examine the safety, tolerability and effectiveness of using electronic cigarettes (ECs) to help people who smoke tobacco achieve long-term smoking abstinence, in comparison to non-nicotine EC, other smoking cessation treatments and no treatment.

SEARCH METHODS

We searched the Cochrane Tobacco Addiction Group's Specialized Register to 1 February 2023, and Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, Embase, and PsycINFO to 1 July 2023, and reference-checked and contacted study authors.

SELECTION CRITERIA

We included trials in which people who smoke were randomized to an EC or control condition. We also included uncontrolled intervention studies in which all participants received an EC intervention as these studies have the potential to provide further information on harms and longer-term use. Studies had to report an eligible outcome.

DATA COLLECTION AND ANALYSIS

We followed standard Cochrane methods for screening and data extraction. Critical outcomes were abstinence from smoking after at least six months, adverse events (AEs), and serious adverse events (SAEs). We used a fixed-effect Mantel-Haenszel model to calculate risk ratios (RRs) with a 95% confidence interval (CI) for dichotomous outcomes. For continuous outcomes, we calculated mean differences. Where appropriate, we pooled data in pairwise and network meta-analyses (NMA).

MAIN RESULTS

We included 88 completed studies (10 new to this update), representing 27,235 participants, of which 47 were randomized controlled trials (RCTs). Of the included studies, we rated ten (all but one contributing to our main comparisons) at low risk of bias overall, 58 at high risk overall (including all non-randomized studies), and the remainder at unclear risk. There is high certainty that nicotine EC increases quit rates compared to nicotine replacement therapy (NRT) (RR 1.59, 95% CI 1.29 to 1.93; I2 = 0%; 7 studies, 2544 participants). In absolute terms, this might translate to an additional four quitters per 100 (95% CI 2 to 6 more). There is moderate-certainty evidence (limited by imprecision) that the rate of occurrence of AEs is similar between groups (RR 1.03, 95% CI 0.91 to 1.17; I2 = 0%; 5 studies, 2052 participants). SAEs were rare, and there is insufficient evidence to determine whether rates differ between groups due to very serious imprecision (RR 1.20, 95% CI 0.90 to 1.60; I2 = 32%; 6 studies, 2761 participants; low-certainty evidence). There is moderate-certainty evidence, limited by imprecision, that nicotine EC increases quit rates compared to non-nicotine EC (RR 1.46, 95% CI 1.09 to 1.96; I2 = 4%; 6 studies, 1613 participants). In absolute terms, this might lead to an additional three quitters per 100 (95% CI 1 to 7 more). There is moderate-certainty evidence of no difference in the rate of AEs between these groups (RR 1.01, 95% CI 0.91 to 1.11; I2 = 0%; 5 studies, 1840 participants). There is insufficient evidence to determine whether rates of SAEs differ between groups, due to very serious imprecision (RR 1.00, 95% CI 0.56 to 1.79; I2 = 0%; 9 studies, 1412 participants; low-certainty evidence). Due to issues with risk of bias, there is low-certainty evidence that, compared to behavioural support only/no support, quit rates may be higher for participants randomized to nicotine EC (RR 1.88, 95% CI 1.56 to 2.25; I2 = 0%; 9 studies, 5024 participants). In absolute terms, this represents an additional four quitters per 100 (95% CI 2 to 5 more). There was some evidence that (non-serious) AEs may be more common in people randomized to nicotine EC (RR 1.22, 95% CI 1.12 to 1.32; I2 = 41%, low-certainty evidence; 4 studies, 765 participants) and, again, insufficient evidence to determine whether rates of SAEs differed between groups (RR 0.89, 95% CI 0.59 to 1.34; I2 = 23%; 10 studies, 3263 participants; very low-certainty evidence). Results from the NMA were consistent with those from pairwise meta-analyses for all critical outcomes, and there was no indication of inconsistency within the networks. Data from non-randomized studies were consistent with RCT data. The most commonly reported AEs were throat/mouth irritation, headache, cough, and nausea, which tended to dissipate with continued EC use. Very few studies reported data on other outcomes or comparisons, hence, evidence for these is limited, with CIs often encompassing both clinically significant harm and benefit.

AUTHORS' CONCLUSIONS

There is high-certainty evidence that ECs with nicotine increase quit rates compared to NRT and moderate-certainty evidence that they increase quit rates compared to ECs without nicotine. Evidence comparing nicotine EC with usual care/no treatment also suggests benefit, but is less certain due to risk of bias inherent in the study design. Confidence intervals were for the most part wide for data on AEs, SAEs and other safety markers, with no difference in AEs between nicotine and non-nicotine ECs nor between nicotine ECs and NRT. Overall incidence of SAEs was low across all study arms. We did not detect evidence of serious harm from nicotine EC, but the longest follow-up was two years and the number of studies was small. The main limitation of the evidence base remains imprecision due to the small number of RCTs, often with low event rates. Further RCTs are underway. To ensure the review continues to provide up-to-date information to decision-makers, this review is a living systematic review. We run searches monthly, with the review updated when relevant new evidence becomes available. Please refer to the Cochrane Database of Systematic Reviews for the review's current status.

E-cigarette puffing topography: The importance of assessing user behaviour to inform emissions testing

Analysis of the chemical composition of e-cigarette emissions is an important step in determining whether e-cigarettes offer both individual and population-level harm reduction potential. Commonly, e-cigarette emissions for chemical analysis are collected when using e-cigarettes according to standardised puffing regimens, such as those recommended by the International Organization for Standardization (ISO) or the Cooperation Centre for Scientific Research Relative to Tobacco (CORESTA). While the use of such standard puffing regimens affords a degree of uniformity between studies and are also recommended by regulatory authorities who require the submission of e-cigarette emissions data to make decisions regarding allowing a product to be commercially marketed, the standardised regimens do not necessarily reflect human puffing behaviour. This can lead to under- or over-estimating real-world emissions from e-cigarettes and inaccuracy in determining their harm reduction potential. In this review, we describe how human puffing behaviour (topography) information can be collected both in the clinical laboratory and in the real world using a variety of different methodologies. We further discuss how this information can be used to dictate e-cigarette puffing regimens for collecting emissions for chemical analyses and how this may lead to better predictions both of human yields of e-cigarette emissions constituents and of risk assessments to predict e-cigarette tobacco harm reduction potential.

Electronic cigarettes for smoking cessation

BACKGROUND

Electronic cigarettes (ECs) are handheld electronic vaping devices which produce an aerosol by heating an e-liquid. Some people who smoke use ECs to stop or reduce smoking, although some organizations, advocacy groups and policymakers have discouraged this, citing lack of evidence of efficacy and safety. People who smoke, healthcare providers and regulators want to know if ECs can help people quit smoking, and if they are safe to use for this purpose. This is a review update conducted as part of a living systematic review.

OBJECTIVES

To examine the effectiveness, tolerability, and safety of using electronic cigarettes (ECs) to help people who smoke tobacco achieve long-term smoking abstinence.

SEARCH METHODS

We searched the Cochrane Tobacco Addiction Group's Specialized Register, the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, Embase, and PsycINFO to 1 July 2022, and reference-checked and contacted study authors.  SELECTION CRITERIA: We included randomized controlled trials (RCTs) and randomized cross-over trials, in which people who smoke were randomized to an EC or control condition. We also included uncontrolled intervention studies in which all participants received an EC intervention. Studies had to report abstinence from cigarettes at six months or longer or data on safety markers at one week or longer, or both.

DATA COLLECTION AND ANALYSIS

We followed standard Cochrane methods for screening and data extraction. Our primary outcome measures were abstinence from smoking after at least six months follow-up, adverse events (AEs), and serious adverse events (SAEs). Secondary outcomes included the proportion of people still using study product (EC or pharmacotherapy) at six or more months after randomization or starting EC use, changes in carbon monoxide (CO), blood pressure (BP), heart rate, arterial oxygen saturation, lung function, and levels of carcinogens or toxicants, or both. We used a fixed-effect Mantel-Haenszel model to calculate risk ratios (RRs) with a 95% confidence interval (CI) for dichotomous outcomes. For continuous outcomes, we calculated mean differences. Where appropriate, we pooled data in meta-analyses.

MAIN RESULTS

We included 78 completed studies, representing 22,052 participants, of which 40 were RCTs. Seventeen of the 78 included studies were new to this review update. Of the included studies, we rated ten (all but one contributing to our main comparisons) at low risk of bias overall, 50 at high risk overall (including all non-randomized studies), and the remainder at unclear risk. There was high certainty that quit rates were higher in people randomized to nicotine EC than in those randomized to nicotine replacement therapy (NRT) (RR 1.63, 95% CI 1.30 to 2.04; I2 = 10%; 6 studies, 2378 participants). In absolute terms, this might translate to an additional four quitters per 100 (95% CI 2 to 6). There was moderate-certainty evidence (limited by imprecision) that the rate of occurrence of AEs was similar between groups (RR 1.02, 95% CI 0.88 to 1.19; I2 = 0%; 4 studies, 1702 participants). SAEs were rare, but there was insufficient evidence to determine whether rates differed between groups due to very serious imprecision (RR 1.12, 95% CI 0.82 to 1.52; I2 = 34%; 5 studies, 2411 participants). There was moderate-certainty evidence, limited by imprecision, that quit rates were higher in people randomized to nicotine EC than to non-nicotine EC (RR 1.94, 95% CI 1.21 to 3.13; I2 = 0%; 5 studies, 1447 participants). In absolute terms, this might lead to an additional seven quitters per 100 (95% CI 2 to 16). There was moderate-certainty evidence of no difference in the rate of AEs between these groups (RR 1.01, 95% CI 0.91 to 1.11; I2 = 0%; 5 studies, 1840 participants). There was insufficient evidence to determine whether rates of SAEs differed between groups, due to very serious imprecision (RR 1.00, 95% CI 0.56 to 1.79; I2 = 0%; 8 studies, 1272 participants). Compared to behavioural support only/no support, quit rates were higher for participants randomized to nicotine EC (RR 2.66, 95% CI 1.52 to 4.65; I2 = 0%; 7 studies, 3126 participants). In absolute terms, this represents an additional two quitters per 100 (95% CI 1 to 3). However, this finding was of very low certainty, due to issues with imprecision and risk of bias. There was some evidence that (non-serious) AEs were more common in people randomized to nicotine EC (RR 1.22, 95% CI 1.12 to 1.32; I2 = 41%, low certainty; 4 studies, 765 participants) and, again, insufficient evidence to determine whether rates of SAEs differed between groups (RR 1.03, 95% CI 0.54 to 1.97; I2 = 38%; 9 studies, 1993 participants).  Data from non-randomized studies were consistent with RCT data. The most commonly reported AEs were throat/mouth irritation, headache, cough, and nausea, which tended to dissipate with continued EC use. Very few studies reported data on other outcomes or comparisons, hence evidence for these is limited, with CIs often encompassing clinically significant harm and benefit.

AUTHORS' CONCLUSIONS

There is high-certainty evidence that ECs with nicotine increase quit rates compared to NRT and moderate-certainty evidence that they increase quit rates compared to ECs without nicotine. Evidence comparing nicotine EC with usual care/no treatment also suggests benefit, but is less certain. More studies are needed to confirm the effect size. Confidence intervals were for the most part wide for data on AEs, SAEs and other safety markers, with no difference in AEs between nicotine and non-nicotine ECs nor between nicotine ECs and NRT. Overall incidence of SAEs was low across all study arms. We did not detect evidence of serious harm from nicotine EC, but longest follow-up was two years and the number of studies was small. The main limitation of the evidence base remains imprecision due to the small number of RCTs, often with low event rates, but further RCTs are underway. To ensure the review continues to provide up-to-date information to decision-makers, this review is a living systematic review. We run searches monthly, with the review updated when relevant new evidence becomes available. Please refer to the Cochrane Database of Systematic Reviews for the review's current status.

Validation of a High Flow Rate Puff Topography System Designed for Measurement of Sub-Ohm, Third Generation Electronic Nicotine Delivery Systems

There are few known puff topography devices designed solely for gathering electronic cigarette puff topography information, and none made for high-powered sub-ohm devices. Ten replicate Bernoulli flow cells were designed and 3D printed. The relationship between square root of pressure difference and flow rate was determined across 0−70 L/min. One representative flow cell was used to estimate puff volume and flow rate under six simulated puffing regimes (0.710 L, 2.000 L and 3.000 L, at low and high flow rates) to determine the system’s accuracy and utility of using dual pressure sensors for flow measurement. The relationship between flow rate and square root of pressure differential for the ten replicate cells was best fit with a quadratic model (R2 = 0.9991, p < 0.0001). The higher-pressure sensor was accurate at both low and high flow rates for 0.71 L (102% and 111% respectively), 2.00 L (96% and 103% respectively), and 3.00 L (100.1% and 107% respectively) but the lower-pressure sensor provided no utility, underpredicting volume and flow. This puff topography system generates very little resistance to flow, easily fits between user’s atomizer and mouthpiece, and is calibrated to measure flows up to 70 L/min.

Electronic cigarettes for smoking cessation

BACKGROUND

Electronic cigarettes (ECs) are handheld electronic vaping devices which produce an aerosol formed by heating an e-liquid. Some people who smoke use ECs to stop or reduce smoking, but some organizations, advocacy groups and policymakers have discouraged this, citing lack of evidence of efficacy and safety. People who smoke, healthcare providers and regulators want to know if ECs can help people quit and if they are safe to use for this purpose. This is an update conducted as part of a living systematic review.

OBJECTIVES

To examine the effectiveness, tolerability, and safety of using electronic cigarettes (ECs) to help people who smoke tobacco achieve long-term smoking abstinence.

SEARCH METHODS

We searched the Cochrane Tobacco Addiction Group's Specialized Register, the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, Embase, and PsycINFO to 1 May 2021, and reference-checked and contacted study authors. We screened abstracts from the Society for Research on Nicotine and Tobacco (SRNT) 2021 Annual Meeting.   SELECTION CRITERIA: We included randomized controlled trials (RCTs) and randomized cross-over trials, in which people who smoke were randomized to an EC or control condition. We also included uncontrolled intervention studies in which all participants received an EC intervention. Studies had to report abstinence from cigarettes at six months or longer or data on safety markers at one week or longer, or both.

DATA COLLECTION AND ANALYSIS

We followed standard Cochrane methods for screening and data extraction. Our primary outcome measures were abstinence from smoking after at least six months follow-up, adverse events (AEs), and serious adverse events (SAEs). Secondary outcomes included the proportion of people still using study product (EC or pharmacotherapy) at six or more months after randomization or starting EC use, changes in carbon monoxide (CO), blood pressure (BP), heart rate, arterial oxygen saturation, lung function, and levels of carcinogens or toxicants or both. We used a fixed-effect Mantel-Haenszel model to calculate risk ratios (RRs) with a 95% confidence interval (CI) for dichotomous outcomes. For continuous outcomes, we calculated mean differences. Where appropriate, we pooled data in meta-analyses.

MAIN RESULTS

We included 61 completed studies, representing 16,759 participants, of which 34 were RCTs. Five of the 61 included studies were new to this review update. Of the included studies, we rated seven (all contributing to our main comparisons) at low risk of bias overall, 42 at high risk overall (including all non-randomized studies), and the remainder at unclear risk. There was moderate-certainty evidence, limited by imprecision, that quit rates were higher in people randomized to nicotine EC than in those randomized to nicotine replacement therapy (NRT) (risk ratio (RR) 1.53, 95% confidence interval (CI) 1.21 to 1.93; I2 = 0%; 4 studies, 1924 participants). In absolute terms, this might translate to an additional three quitters per 100 (95% CI 1 to 6). There was low-certainty evidence (limited by very serious imprecision) that the rate of occurrence of AEs was similar (RR 0.98, 95% CI 0.80 to 1.19; I2 = 0%; 2 studies, 485 participants). SAEs were rare, but there was insufficient evidence to determine whether rates differed between groups due to very serious imprecision (RR 1.30, 95% CI 0.89 to 1.90: I2 = 0; 4 studies, 1424 participants). There was moderate-certainty evidence, again limited by imprecision, that quit rates were higher in people randomized to nicotine EC than to non-nicotine EC (RR 1.94, 95% CI 1.21 to 3.13; I2 = 0%; 5 studies, 1447 participants). In absolute terms, this might lead to an additional seven quitters per 100 (95% CI 2 to 16). There was moderate-certainty evidence of no difference in the rate of AEs between these groups (RR 1.01, 95% CI 0.91 to 1.11; I2 = 0%; 3 studies, 601 participants). There was insufficient evidence to determine whether rates of SAEs differed between groups, due to very serious imprecision (RR 1.06, 95% CI 0.47 to 2.38; I2 = 0; 5 studies, 792 participants). Compared to behavioural support only/no support, quit rates were higher for participants randomized to nicotine EC (RR 2.61, 95% CI 1.44 to 4.74; I2 = 0%; 6 studies, 2886 participants). In absolute terms this represents an additional six quitters per 100 (95% CI 2 to 15). However, this finding was of very low certainty, due to issues with imprecision and risk of bias. There was some evidence that non-serious AEs were more common in people randomized to nicotine EC (RR 1.22, 95% CI 1.12 to 1.32; I2 = 41%, low certainty; 4 studies, 765 participants), and again, insufficient evidence to determine whether rates of SAEs differed between groups (RR 1.51, 95% CI 0.70 to 3.24; I2 = 0%; 7 studies, 1303 participants).  Data from non-randomized studies were consistent with RCT data. The most commonly reported AEs were throat/mouth irritation, headache, cough, and nausea, which tended to dissipate with continued use. Very few studies reported data on other outcomes or comparisons, hence evidence for these is limited, with CIs often encompassing clinically significant harm and benefit.

AUTHORS' CONCLUSIONS

There is moderate-certainty evidence that ECs with nicotine increase quit rates compared to NRT and compared to ECs without nicotine. Evidence comparing nicotine EC with usual care/no treatment also suggests benefit, but is less certain. More studies are needed to confirm the effect size. Confidence intervals were for the most part wide for data on AEs, SAEs and other safety markers, with no difference in AEs between nicotine and non-nicotine ECs. Overall incidence of SAEs was low across all study arms. We did not detect  evidence of harm from nicotine EC, but longest follow-up was two years and the  number of studies was small. The main limitation of the evidence base remains imprecision due to the small number of RCTs, often with low event rates, but further RCTs are underway. To ensure the review continues to provide up-to-date information to decision-makers, this review is now a living systematic review. We run searches monthly, with the review updated when relevant new evidence becomes available. Please refer to the Cochrane Database of Systematic Reviews for the review's current status.

Exposure to Nicotine and Toxicants Among Dual Users of Tobacco Cigarettes and E-Cigarettes: Population Assessment of Tobacco and Health (PATH) Study, 2013-2014

INTRODUCTION

Concurrent use of tobacco cigarettes and e-cigarettes ("dual use") is common among tobacco users. Little is known about differences in demographics and toxicant exposure among subsets of dual users.

AIMS AND METHODS

We analyzed data from adult dual users (current every/some day users of tobacco cigarettes and e-cigarettes, n = 792) included in the PATH Study Wave 1 (2013-2014) and provided urine samples. Samples were analyzed for biomarkers of exposure to nicotine and selected toxicants (tobacco-specific nitrosamine NNK [NNAL], lead, cadmium, naphthalene [2-naphthol], pyrene [1-hydroxypyrene], acrylonitrile [CYMA], acrolein [CEMA], and acrylamide [AAMA]). Subsets of dual users were compared on demographic, behavioral, and biomarker measures to exclusive cigarette smokers (n = 2411) and exclusive e-cigarette users (n = 247).

RESULTS

Most dual users were predominant cigarette smokers (70%), followed by daily dual users (13%), non-daily concurrent dual users (10%), and predominant vapers (7%). Dual users who smoked daily showed significantly higher biomarker concentrations compared with those who did not smoke daily. Patterns of e-cigarette use had little effect on toxicant exposure. Dual users with high toxicant exposure were generally older, female, and smoked more cigarettes per day. Dual users who had low levels of biomarkers of exposure were generally younger, male, and smoked non-daily.

CONCLUSIONS

In 2013-2014, most dual users smoked cigarettes daily and used e-cigarettes occasionally. Cigarette smoking appears to be the primary driver of toxicant exposure among dual users, with little-to-no effect of e-cigarette use on biomarker levels. Results reinforce the need for dual users to stop smoking tobacco cigarettes to reduce toxicant exposure.

IMPLICATIONS

With considerable dual use of tobacco cigarettes and e-cigarettes in the United States, it is important to understand differences in toxicant exposure among subsets of dual users, and how these differences align with user demographics. Findings suggest most dual users smoke daily and use e-cigarettes intermittently. Low exposure to toxicants was most common among younger users, males, and intermittent smokers; high exposure to toxicants was most common among older users, females, and heavier cigarette smokers. Results underscore the heterogeneity occurring within dual users, and the need to quit smoking cigarettes completely in order to reduce toxicant exposure.

Electronic cigarettes for smoking cessation

BACKGROUND

Electronic cigarettes (ECs) are handheld electronic vaping devices which produce an aerosol formed by heating an e-liquid. Some people who smoke use ECs to stop or reduce smoking, but some organizations, advocacy groups and policymakers have discouraged this, citing lack of evidence of efficacy and safety. People who smoke, healthcare providers and regulators want to know if ECs can help people quit and if they are safe to use for this purpose. This is an update of a review first published in 2014.

OBJECTIVES

To examine the effectiveness, tolerability, and safety of using electronic cigarettes (ECs) to help people who smoke achieve long-term smoking abstinence.

SEARCH METHODS

We searched the Cochrane Tobacco Addiction Group's Specialized Register, the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, Embase, and PsycINFO to 1 February 2021, together with reference-checking and contact with study authors.

SELECTION CRITERIA

We included randomized controlled trials (RCTs) and randomized cross-over trials in which people who smoke were randomized to an EC or control condition. We also included uncontrolled intervention studies in which all participants received an EC intervention. To be included, studies had to report abstinence from cigarettes at six months or longer and/or data on adverse events (AEs) or other markers of safety at one week or longer.

DATA COLLECTION AND ANALYSIS

We followed standard Cochrane methods for screening and data extraction. Our primary outcome measures were abstinence from smoking after at least six months follow-up, adverse events (AEs), and serious adverse events (SAEs). Secondary outcomes included changes in carbon monoxide, blood pressure, heart rate, blood oxygen saturation, lung function, and levels of known carcinogens/toxicants. We used a fixed-effect Mantel-Haenszel model to calculate the risk ratio (RR) with a 95% confidence interval (CI) for dichotomous outcomes. For continuous outcomes, we calculated mean differences. Where appropriate, we pooled data from these studies in meta-analyses.

MAIN RESULTS

We included 56 completed studies, representing 12,804 participants, of which 29 were RCTs. Six of the 56 included studies were new to this review update. Of the included studies, we rated five (all contributing to our main comparisons) at low risk of bias overall, 41 at high risk overall (including the 25 non-randomized studies), and the remainder at unclear risk. There was moderate-certainty evidence, limited by imprecision, that quit rates were higher in people randomized to nicotine EC than in those randomized to nicotine replacement therapy (NRT) (risk ratio (RR) 1.69, 95% confidence interval (CI) 1.25 to 2.27; I2 = 0%; 3 studies, 1498 participants). In absolute terms, this might translate to an additional four successful quitters per 100 (95% CI 2 to 8). There was low-certainty evidence (limited by very serious imprecision) that the rate of occurrence of AEs was similar) (RR 0.98, 95% CI 0.80 to 1.19; I2 = 0%; 2 studies, 485 participants). SAEs occurred rarely, with no evidence that their frequency differed between nicotine EC and NRT, but very serious imprecision led to low certainty in this finding (RR 1.37, 95% CI 0.77 to 2.41: I2 = n/a; 2 studies, 727 participants). There was moderate-certainty evidence, again limited by imprecision, that quit rates were higher in people randomized to nicotine EC than to non-nicotine EC (RR 1.70, 95% CI 1.03 to 2.81; I2 = 0%; 4 studies, 1057 participants). In absolute terms, this might again lead to an additional four successful quitters per 100 (95% CI 0 to 11). These trials mainly used older EC with relatively low nicotine delivery. There was moderate-certainty evidence of no difference in the rate of AEs between these groups (RR 1.01, 95% CI 0.91 to 1.11; I2 = 0%; 3 studies, 601 participants). There was insufficient evidence to determine whether rates of SAEs differed between groups, due to very serious imprecision (RR 0.60, 95% CI 0.15 to 2.44; I2 = n/a; 4 studies, 494 participants). Compared to behavioral support only/no support, quit rates were higher for participants randomized to nicotine EC (RR 2.70, 95% CI 1.39 to 5.26; I2 = 0%; 5 studies, 2561 participants). In absolute terms this represents an increase of seven per 100 (95% CI 2 to 17). However, this finding was of very low certainty, due to issues with imprecision and risk of bias. There was no evidence that the rate of SAEs differed, but some evidence that non-serious AEs were more common in people randomized to nicotine EC (AEs: RR 1.22, 95% CI 1.12 to 1.32; I2 = 41%, low certainty; 4 studies, 765 participants; SAEs: RR 1.17, 95% CI 0.33 to 4.09; I2 = 5%; 6 studies, 1011 participants, very low certainty). Data from non-randomized studies were consistent with RCT data. The most commonly reported AEs were throat/mouth irritation, headache, cough, and nausea, which tended to dissipate with continued use. Very few studies reported data on other outcomes or comparisons and hence evidence for these is limited, with confidence intervals often encompassing clinically significant harm and benefit.

AUTHORS' CONCLUSIONS

There is moderate-certainty evidence that ECs with nicotine increase quit rates compared to ECs without nicotine and compared to NRT. Evidence comparing nicotine EC with usual care/no treatment also suggests benefit, but is less certain. More studies are needed to confirm the size of effect, particularly when using modern EC products. Confidence intervals were for the most part wide for data on AEs, SAEs and other safety markers, though evidence indicated no difference in AEs between nicotine and non-nicotine ECs. Overall incidence of SAEs was low across all study arms. We did not detect any clear evidence of harm from nicotine EC, but longest follow-up was two years and the overall number of studies was small. The evidence is limited mainly by imprecision due to the small number of RCTs, often with low event rates. Further RCTs are underway. To ensure the review continues to provide up-to-date information, this review is now a living systematic review. We run searches monthly, with the review updated when relevant new evidence becomes available. Please refer to the Cochrane Database of Systematic Reviews for the review's current status.

Comparing video observation to electronic topography device as a method for measuring cigarette puffing behavior

BACKGROUND

Smoking topography, or puffing behavior, is an important measure of how consumers may use tobacco products. However, numerous issues may prevent collection of this data via in-person, electronic topography device (e.g., CReSS). This study compared cigarette topography measures collected by video observation and electronic device.

METHODS

Laboratory smoking sessions were video recorded and scored for 96 cigarettes collected from 34 daily, adult non-treatment-seeking smokers (73.5 % male, 82.4 % White). Participants smoked three of their preferred brand cigarettes using an electronic topography device, providing carbon monoxide (CO) samples before and after each cigarette. Analyses compared measures from both assessment methods and examined associations with device-obtained total puff volume and CO boost.

RESULTS

Agreement analyses indicated robust similarity between methods for measures of puff count and total interpuff interval (Intraclass Correlation Coefficient [ICC]'s > 0.96,p's < 0.001; Bland-Altman [B-A] plotted differences within a priori limit of clinical significance) but diverged on total duration (ICC's > .93, p's < .001, yet B-A plots outside a priori limits). Regardless of assessment method, total duration and puff count (but not total interpuff interval) predicted total puff volume (p's < .001). None predicted CO boost (p's = .07-.90)."

CONCLUSIONS

Although some topography outcomes (e.g., total puff volume) cannot be assessed via video observation, video-observed measures of puff count, total duration, and total interpuff interval are generally interchangeable with their topography device-obtained counterparts. Thus, video observation is likely a sufficient substitute method for assessing cigarette topography when using an electronic device is not possible.

Exclusive E-Cigarette Users Report Lower Levels of Respiratory Symptoms Relative to Dual E-Cigarette and Cigarette Users

INTRODUCTION

Exclusive e-cigarette use has been shown to be associated with reduced levels of respiratory symptoms relative to smoking combustible cigarettes; this association has been less frequently studied in smokers using advanced-generation e-cigarette devices. Advanced-generation devices generate denser vapor than either early generation or pod-style devices, and engender longer inhalations; these vaping topography patterns may contribute to respiratory symptoms.

METHODS

In a single-session, cross-sectional study of exclusive e-cigarette users (N = 59) and dual users of e-cigarettes and cigarettes (N = 54), participants completed questionnaires, including the American Thoracic Society Questionnaire (ATSQ) and were videotaped vaping their own device in the lab for 1 hour. Using a hierarchical regression method, we examined whether topography variables, level of nicotine concentration used in their e-cigarette device in the past month, e-cigarette dependence, amount of e-cigarette use in the past month, and smoking status (any smoking in the last month vs. none) predicted ATSQ score severity.

RESULTS

There was a significant mean difference in ATSQ score across smoking status, with greater ATSQ scores for vapers who also smoked cigarettes (19.0, SD = 6.7) than for exclusive vapers (13.4, SD = 5.3). In the final model, of the predictors of interest, only cigarette smoking status predicted significantly greater ATSQ scores (overall F = 2.51, p = .006; R2 = .26; smoking status β = 0.39, p < .0001).

CONCLUSIONS

Findings suggest that differences in respiratory symptoms between dual and exclusive e-cigarette users appear to be attributable to combustible cigarette smoking, rather than more intense or frequent e-cigarette use across groups.

IMPLICATIONS

In this comparison of exclusive advanced-generation vape device users (N = 59) versus dual users of these devices and combustible cigarettes (N = 54), we set out to determine the extent to which smoking status and e-cigarette use variables predicted self-reported respiratory symptom severity. We found that dual users showed greater respiratory symptom severity (ATSQ scores) than exclusive vapers. Despite examining vaping topography and other variables, smoking status and race were the only significant predictor of respiratory symptoms. We conclude that combustible cigarette use, not individual vaping topography, likely accounts for differences in respiratory symptoms between dual users and exclusive vapers.

Nicotine and Toxicant Exposure Among Concurrent Users (Co-Users) of Tobacco and Cannabis

BACKGROUND

Smoking cannabis may potentially increase exposure to numerous toxic chemicals that are commonly associated with tobacco use. There is a paucity of data related to toxicant exposures among concurrent users of tobacco and cannabis (co-users).

METHODS

Data are from the Population Assessment of Tobacco and Health Study Wave 1 Biomarker Restricted-Use Files. Analyses focused on adults who provided urine samples (N = 5859). Urine samples were analyzed for biomarkers of exposure to nicotine, tobacco-specific nitrosamines, polycyclic aromatic hydrocarbons, and volatile organic compounds. Using weighted linear regression, we compared adjusted geometric mean concentrations of 15 biomarkers between user groups of various tobacco product types according to their self-reported past 30-day cannabis use.

RESULTS

Past 30-day cannabis use was similar across various types of tobacco product use subgroups (range: 13%-23%) and significantly more common compared to non-tobacco users (1.0%; p < .001). Across all groups of tobacco users, those who co-used cannabis exhibited significantly higher concentrations of the biomarker of exposure to acrylonitrile (CYMA) compared to non-cannabis users (by 39%-464%). Tobacco-cannabis co-users also showed significantly elevated levels of the biomarker of exposure to acrylamide (AAMA) compared to exclusive tobacco users, and significantly higher exposure to many polycyclic aromatic hydrocarbons (including fluorene and pyrene).

CONCLUSIONS

Co-users exhibited higher concentrations for biomarkers of exposure to many combustion byproducts, compared to exclusive tobacco users. More robust measurements of cannabis use can address potential confounding in assessments of exposures to tobacco-related constituents, and potential health effects resulting from co-use.

IMPLICATIONS

With disproportionately greater rates of cannabis use occurring among tobacco users, it is critical to consider how concurrent cannabis use may influence health-related outcomes among smokers. Our findings suggest potential additive toxicant exposures among co-users of tobacco and cannabis. Lack of consideration and measurement of cannabis use in assessing tobacco-related exposures may confound estimates thought to be attributable to tobacco, particularly for non-specific biomarkers. Assessing tobacco and cannabis use in tandem will allow for more precise measurement of outcomes related to one or both substances, and can provide additional information on potential health effects related to co-use.

Effects of Nicotine Metabolic Rate on Cigarette Reinforcement

INTRODUCTION

The rate of nicotine metabolism, estimated by the nicotine metabolite ratio (NMR), is an important determinant of tobacco dependence. This study investigated the effect of NMR on smoking behavior due to nicotine reinforcement during ad libitum smoking.

AIMS AND METHODS

As part of a larger study, participants were stratified based on saliva NMR as fast and slow metabolizers. After smoking a cigarette and measuring nicotine blood concentrations, participants smoked as desired over a 90-minute period. Analysis included time to first cigarette, total number of cigarettes, total number of puffs, and weight of tobacco consumed.

RESULTS

Sixty-one (48%) participants were fast metabolizers and 66 (52%) slow metabolizers by NMR. No significant differences were found regarding the smoking topography variables by NMR. Normal metabolizers by genotype (n = 79) had a shorter time to first cigarette than reduced metabolizers (n = 39; p = .032). Blacks smoked fewer cigarettes (p = .008) and took fewer total puffs (p = .002) compared with Whites. Among Whites, fast metabolizers by NMR had a shorter time to first cigarette compared with slow metabolizers (p = .014). Among fast metabolizers, Whites had, compared with Blacks, shorter latency to first cigarette (p = .003) and higher number of total puffs (p = .014) and cigarettes smoked (p = .014). Baseline cigarettes per day and nicotine elimination half-life significantly predicted topography outcomes.

CONCLUSIONS

Saliva NMR did not predict cigarette reinforcement during a relatively brief period of ad libitum smoking. Differences were seen by race, with White fast metabolizers by NMR having shorter time to first cigarettes compared with slow metabolizers.

IMPLICATIONS

After a 90-minute period of nicotine abstinence, NMR was not significantly associated with smoking reinforcement. Slow and fast metabolizers had similar time to first cigarette, number of cigarettes smoked, total number of puffs, and tobacco consumed; however, within-race differences show that within Whites, fast metabolizers had a faster time to first cigarette than slow metabolizers.

Electronic cigarettes for smoking cessation

BACKGROUND

Electronic cigarettes (ECs) are handheld electronic vaping devices which produce an aerosol formed by heating an e-liquid. People who smoke report using ECs to stop or reduce smoking, but some organisations, advocacy groups and policymakers have discouraged this, citing lack of evidence of efficacy and safety. People who smoke, healthcare providers and regulators want to know if ECs can help people quit and if they are safe to use for this purpose. This review is an update of a review first published in 2014.

OBJECTIVES

To evaluate the effect and safety of using electronic cigarettes (ECs) to help people who smoke achieve long-term smoking abstinence.

SEARCH METHODS

We searched the Cochrane Tobacco Addiction Group's Specialized Register, the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, Embase, and PsycINFO for relevant records to January 2020, together with reference-checking and contact with study authors.

SELECTION CRITERIA

We included randomized controlled trials (RCTs) and randomized cross-over trials in which people who smoke were randomized to an EC or control condition. We also included uncontrolled intervention studies in which all participants received an EC intervention. To be included, studies had to report abstinence from cigarettes at six months or longer and/or data on adverse events (AEs) or other markers of safety at one week or longer.

DATA COLLECTION AND ANALYSIS

We followed standard Cochrane methods for screening and data extraction. Our primary outcome measures were abstinence from smoking after at least six months follow-up, AEs, and serious adverse events (SAEs). Secondary outcomes included changes in carbon monoxide, blood pressure, heart rate, blood oxygen saturation, lung function, and levels of known carcinogens/toxicants. We used a fixed-effect Mantel-Haenszel model to calculate the risk ratio (RR) with a 95% confidence interval (CI) for dichotomous outcomes. For continuous outcomes, we calculated mean differences. Where appropriate, we pooled data from these studies in meta-analyses.

MAIN RESULTS

We include 50 completed studies, representing 12,430 participants, of which 26 are RCTs. Thirty-five of the 50 included studies are new to this review update. Of the included studies, we rated four (all which contribute to our main comparisons) at low risk of bias overall, 37 at high risk overall (including the 24 non-randomized studies), and the remainder at unclear risk. There was moderate-certainty evidence, limited by imprecision, that quit rates were higher in people randomized to nicotine EC than in those randomized to nicotine replacement therapy (NRT) (risk ratio (RR) 1.69, 95% confidence interval (CI) 1.25 to 2.27; I2 = 0%; 3 studies, 1498 participants). In absolute terms, this might translate to an additional four successful quitters per 100 (95% CI 2 to 8). There was low-certainty evidence (limited by very serious imprecision) of no difference in the rate of adverse events (AEs) (RR 0.98, 95% CI 0.80 to 1.19; I2 = 0%; 2 studies, 485 participants). SAEs occurred rarely, with no evidence that their frequency differed between nicotine EC and NRT, but very serious imprecision led to low certainty in this finding (RR 1.37, 95% CI 0.77 to 2.41: I2 = n/a; 2 studies, 727 participants). There was moderate-certainty evidence, again limited by imprecision, that quit rates were higher in people randomized to nicotine EC than to non-nicotine EC (RR 1.71, 95% CI 1.00 to 2.92; I2 = 0%; 3 studies, 802 participants). In absolute terms, this might again lead to an additional four successful quitters per 100 (95% CI 0 to 12). These trials used EC with relatively low nicotine delivery. There was low-certainty evidence, limited by very serious imprecision, that there was no difference in the rate of AEs between these groups (RR 1.00, 95% CI 0.73 to 1.36; I2 = 0%; 2 studies, 346 participants). There was insufficient evidence to determine whether rates of SAEs differed between groups, due to very serious imprecision (RR 0.25, 95% CI 0.03 to 2.19; I2 = n/a; 4 studies, 494 participants). Compared to behavioural support only/no support, quit rates were higher for participants randomized to nicotine EC (RR 2.50, 95% CI 1.24 to 5.04; I2 = 0%; 4 studies, 2312 participants). In absolute terms this represents an increase of six per 100 (95% CI 1 to 14). However, this finding was very low-certainty, due to issues with imprecision and risk of bias. There was no evidence that the rate of SAEs varied, but some evidence that non-serious AEs were more common in people randomized to nicotine EC (AEs: RR 1.17, 95% CI 1.04 to 1.31; I2 = 28%; 3 studies, 516 participants; SAEs: RR 1.33, 95% CI 0.25 to 6.96; I2 = 17%; 5 studies, 842 participants). Data from non-randomized studies were consistent with RCT data. The most commonly reported AEs were throat/mouth irritation, headache, cough, and nausea, which tended to dissipate over time with continued use. Very few studies reported data on other outcomes or comparisons and hence evidence for these is limited, with confidence intervals often encompassing clinically significant harm and benefit.

AUTHORS' CONCLUSIONS

There is moderate-certainty evidence that ECs with nicotine increase quit rates compared to ECs without nicotine and compared to NRT. Evidence comparing nicotine EC with usual care/no treatment also suggests benefit, but is less certain. More studies are needed to confirm the degree of effect, particularly when using modern EC products. Confidence intervals were wide for data on AEs, SAEs and other safety markers. Overall incidence of SAEs was low across all study arms. We did not detect any clear evidence of harm from nicotine EC, but longest follow-up was two years and the overall number of studies was small. The main limitation of the evidence base remains imprecision due to the small number of RCTs, often with low event rates. Further RCTs are underway. To ensure the review continues to provide up-to-date information for decision-makers, this review is now a living systematic review. We will run searches monthly from December 2020, with the review updated as relevant new evidence becomes available. Please refer to the Cochrane Database of Systematic Reviews for the review's current status.

Differences in nicotine intake and effects from electronic and combustible cigarettes among dual users

AIM

To describe systemic nicotine exposure and subjective effects of electronic cigarettes (e-cigarettes) in people who use both e-cigarettes and cigarettes (dual users), including within-subject comparisons of e-cigarette and cigarette use.

DESIGN

Two-arm, counterbalanced cross-over study. Participants used their usual brand of e-cigarette or cigarette during a standardized session in a 2-week study.

SETTING

Hospital research ward, San Francisco, CA, USA.

PARTICIPANTS

Thirty-six healthy (eight women, 28 men) participants.

MEASUREMENTS

Plasma nicotine was analyzed by gas chromatography-tandem mass spectrometry; nicotine withdrawal, urge to smoke and vape, affective states, craving, satisfaction and psychological reward were measured by standardized questionnaires.

FINDINGS

Compared with cigarettes, average maximum plasma nicotine concentration (C_max ) was lower with e-cigarettes [6.1 ± 5.5 ng/ml, mean ± standard deviation (SD) versus 20.2 ± 11.1 ng/ml, P < 0.001] and time of maximal concentration (T_max ) was longer (6.5 ± 5.4 versus 2.7 ± 2.4 minutes, P < 0.001). Use of both products resulted in a reduction in the severity of withdrawal symptoms, negative affect and urge to use either product. E-cigarettes were less rewarding and satisfying and reduced craving to a lesser degree than cigarettes. We were not able to detect any differences in withdrawal symptoms, affective states and urge to smoke cigarettes between e-cigarette and cigarette use.

CONCLUSION

Systemic nicotine exposure was, on average, lower with single use of e-cigarettes compared with cigarettes, and e-cigarettes were judged to be less satisfying and rewarding and reduced craving less than cigarettes.

Nicotinic Receptors Underlying Nicotine Dependence: Evidence from Transgenic Mouse Models

Nicotine underlies the reinforcing properties of tobacco cigarettes and e-cigarettes. After inhalation and absorption, nicotine binds to various nicotinic acetylcholine receptor (nAChR) subtypes localized on the pre- and postsynaptic membranes of cells, which subsequently leads to the modulation of cellular function and neurotransmitter signaling. In this chapter, we begin by briefly reviewing the current understanding of nicotine's actions on nAChRs and highlight considerations regarding nAChR subtype localization and pharmacodynamics. Thereafter, we discuss the seminal discoveries derived from genetically modified mouse models, which have greatly contributed to our understanding of nicotine's effects on the reward-related mesolimbic pathway and the aversion-related habenulo-interpeduncular pathway. Thereafter, emerging areas of research focusing on modulation of nAChR expression and/or function are considered. Taken together, these discoveries have provided a foundational understanding of various genetic, neurobiological, and behavioral factors underlying the motivation to use nicotine and related dependence processes, which are thereby advancing drug discovery efforts to promote long-term abstinence.

Use of social media to establish vapers puffing behaviour: Findings and implications for laboratory evaluation of e-cigarette emissions

The recent growth in e-cigarette use has presented many challenges to Public Health research, including understanding the potential for e-cigarettes to generate toxic aerosol constituents during use. Recent research has established that the way e-cigarettes are puffed influences the magnitude of emissions from these devices, with puff duration the dominant driving force. Standardised puffing machine methods are being developed to harmonise testing approaches across laboratories, but critical to their success is the degree with which they accurately reflect vapers real-world puffing behaviours (topography). Relatively limited data is available examining the way vapers puff, with significant inconsistencies between studies. Here we report the creation and analysis of a large database of public-domain vaping videos to establish e-cigarettes puffing behaviour in near natural settings. Over 300 videos containing 1200 puffing events from 252 vapers were obtained from social media sources, divided approximately equally amongst cigalike, Ego and Advanced Personal Vapouriser ("APV", also referred to as "boxmod") types of e-cigarettes. Analysis showed that similar mean puff durations were found for all three categories of vaping devices. This includes direct-to-lung as well as mouth-to-lung puffing behaviours. A 3 s puff duration, as used in the recently published ISO puffing standard ISO 20,768:2018, appears appropriate for average behaviours. However, the wide diversity of puffing durations observed amongst vapers means it may be challenging to identify a simple yet comprehensively representative single machine-puffing regimen for laboratory studies. A puff duration of around 5.6 s appears to represent 95th percentile puffing behaviours amongst vapers and may be an appropriate choice for scientists and regulators seeking an additional more intense puffing regime. A range of new behavioural patterns have been identified whose impact on aerosol exposure need to be considered. Public-domain video records of vapers provides valuable and accessible insights into real-world use behaviours. It is freely available, and constantly updated with new material, and therefore provides a valuable resource for scientists seeking to understand real-world vaping behaviours.

Fourth generation e-cigarette vaping induces transient lung inflammation and gas exchange disturbances: results from two randomized clinical trials

When heated by an electronic cigarette, propylene glycol and glycerol produce a nicotine-carrying-aerosol. This hygroscopic/hyperosmolar aerosol can deposit deep within the lung. Whether these deposits trigger local inflammation and disturb pulmonary gas exchanges is not known. The aim of this study was to assess the acute effects of high-wattage electronic cigarette vaping with or without nicotine on lung inflammation biomarkers, transcutaneous gas tensions, and pulmonary function tests in young and healthy tobacco smokers. Acute effects of vaping without nicotine on arterial blood gas tensions were also assessed in heavy smokers suspected of coronary artery disease. Using a single-blind within-subjects study design, 25 young tobacco smokers underwent three experimental sessions in random order: sham-vaping and vaping with and without nicotine at 60 W. Twenty heavy smokers were also exposed to sham-vaping (n = 10) or vaping without nicotine (n = 10) in an open-label, randomized parallel study. In the young tobacco smokers, compared with sham-vaping: 1) serum club cell protein-16 increased after vaping without nicotine (mean ± SE, −0.5 ± 0.2 vs. +1.1 ± 0.3 µg/l, P = 0.013) and vaping with nicotine (+1.2 ± 0.3 µg/l, P = 0.009); 2) transcutaneous oxygen tension decreased for 60 min after vaping without nicotine (nadir, −0.3 ± 1 vs. −15.3 ± 2.3 mmHg, P < 0.001) and for 80-min after vaping with nicotine (nadir, −19.6 ± 2.8 mmHg, P < 0.001). Compared with sham vaping, vaping without nicotine decreased arterial oxygen tension for 5 min in heavy-smoking patients (+5.4 ± 3.3 vs. −5.4 ± 1.9 mmHg, P = 0.012). Acute vaping of propylene glycol/glycerol aerosol at high wattage with or without nicotine induces airway epithelial injury and sustained decrement in transcutaneous oxygen tension in young tobacco smokers. Intense vaping conditions also transiently impair arterial oxygen tension in heavy smokers.

Recommendations for the Appropriate Structure, Communication, and Investigation of Tobacco Harm Reduction Claims. An Official American Thoracic Society Policy Statement

RATIONALE

The tobacco harm reduction literature is replete with vague language, far-reaching claims, and unwarranted certainty. The American Thoracic Society has increasingly recognized the need for a framework for reliably making such claims. Evidence-based standards improving the scientific value and transparency of harm reduction claims are expected to improve their trustworthiness, clarity, and consistency.

METHODS

Experts from relevant American Thoracic Society committees identified key topic areas for discussion. Literature search strategy included English language articles across Medline, Google Scholar, and the Cochrane Collaborative databases, with expanded search terms including tobacco, addiction, smoking, cigarettes, nicotine, and harm reduction. Workgroup members synthesized their evidentiary summaries into a list of candidate topics suitable for inclusion in the final report. Breakout groups developed detailed content maps of each topic area, including points to be considered for suggested recommendations. Successive draft recommendations were modified using an iterative consensus process until unanimous approval was achieved. Patient representatives ensured the document's relevance to the lay public.

RESULTS

Fifteen recommendations were identified, organized into four framework elements dealing with: estimating harm reduction among individuals, making claims on the basis of population impact, appropriately careful use of language, and ethical considerations in harm reduction.

DISCUSSION

This statement clarifies important principles guiding valid direct and inferential harm reduction claims. Ideals for effective communication with the lay public and attention to unique ethical concerns are also delineated. The authors call for formal systems of grading harm reduction evidence and regulatory assurances of longitudinal surveillance systems to document the impact of harm reduction policies.

Impact of e-liquid flavors on e-cigarette vaping behavior

OBJECTIVE

The primary objective of this pilot study was to describe the impact of e-cigarette liquid flavors on experienced e-cigarette users' vaping behavior.

METHODS

11 males and 3 females participated in a 3-day inpatient crossover study using e-cigarettes with strawberry, tobacco, and their usual brand e-liquid. Nicotine levels were nominally 18 mg/mL in the strawberry and tobacco e-liquids and ranged between 3-18 mg/mL in the usual brands. On each day, participants had access to the study e-cigarette (KangerTech mini ProTank 3, 1.5 Ohms, 3.7 V) and the assigned e-liquid during a 90-minute videotaped ad libitum session.

RESULTS

Average puff duration was significantly longer when using the strawberry e-liquid (3.2 ± 1.3 s, mean ± SD) compared to the tobacco e-liquid (2.8 ± 1.1 s) but the average number of puffs was not significantly different (strawberry, 73 ± 35; tobacco, 69 ± 46). Compared to the strawberry- and tobacco-flavored e-liquids, average puff duration was significantly longer (4.3 ± 1.6 s) and the average number of puffs was significantly higher (106 ± 67 puffs) when participants used their usual brand of e-liquid. Participants generally puffed more frequently in small groups of puffs (1-5 puffs) with the strawberry compared to the tobacco e-liquid and more frequently in larger groups (>10 puffs) with their usual brand. The strength of the relationship between vaping topography and nicotine intake and exposure were not consistent across e-liquids.

CONCLUSION

Vaping behavior changes across e-liquids and influences nicotine intake. Research is needed to understand the mechanisms that underlie these behavioral changes, including e-liquid pH and related sensory effects, subjective liking, and nicotine effects.

Differences in vaping topography in relation to adherence to exclusive electronic cigarette use in veterans

Background

Understanding vaping patterns of electronic cigarette (EC) use is important to understand the real-life exposure to EC vapor. Long term information on vaping topography in relation to tobacco cigarette (TC) smoking cessation success has not been explored.

Methods

Observational non-blinded study where active TC smokers were asked to replace TC with EC over 4 weeks (replacement phase, RP) followed by exclusive EC use for an additional 12 weeks (maintenance phase, MP). TC use and EC compliance was monitored weekly. Subjects were classified as success or failure whether or not they completed the protocol. Vaping information was stored and downloaded directly from the EC device and averaged per calendar day for analysis.

Results

From 25 subjects that followed the protocol, sixteen succeeded in completing the RP and 8 the MP (32%). No significant differences in baseline characteristics were noted between subjects in the success and failure groups including markers of nicotine addiction, plasma cotinine levels or smoking history. Success subjects showed significantly longer puff duration (seconds per vape) and total overall vapor exposure (number of vapes x average vape duration or vape-seconds) in both study phases. Furthermore, subjects in the success group continued to increase the number of vapes, device voltage and wattage significantly as they transitioned into the MP. After an initial drop, subjects in the success group were able to regain plasma cotinine levels comparable to their TC use while subjects in the failure group could not. Cotinine levels significantly correlated with the average number of daily vapes and vapes-seconds, but not with other vaping parameters.

Conclusion

The topography of smokers who adhere to exclusive EC use reflects a progressive and dynamic device adaptation over weeks to maintain baseline cotinine levels. The higher inhaled volume over time should be considered when addressing the potential toxic effects of EC and the variable EC adherence when addressing public health policies regarding their use.

E-cigarettes: Impact of E-Liquid Components and Device Characteristics on Nicotine Exposure

BACKGROUND

Electronic cigarette (e-cigarette) use has increased substantially in recent years. While e-cigarettes have been proposed as a potentially effective smoking cessation tool, dualuse in smokers is common and e-cigarettes are widely used by non-smokers, including youth and young-adult non-smokers. Nicotine, the primary addictive component in cigarettes, is present at varying levels in many e-liquids. E-cigarettes may lead to initiation of nicotine use in adult and youth non-smokers, re-initiation of nicotine dependence in ex-smokers or increased severity of nicotine dependence in dual-users of cigarettes and e-cigarettes. As such, there are important clinical and policy implications to understanding factors impacting nicotine exposure from e-cigarettes. However, the broad and rapidly changing range of e-liquid constituents and e-cigarette hardware which could impact nicotine exposure presents a challenge. Recent changes in regulatory oversight of e-cigarettes underscore the importance of synthesizing current knowledge on common factors which may impact nicotine exposure.

METHODS

This review focuses on factors which may impact nicotine exposure by changing e-cigarette use behavior, puff topography, altering the nicotine yield (amount of nicotine exiting the e-cigarette mouth piece including nicotine exhaled as vapor) or more directly by altering nicotine absorption and bioavailability.

RESULTS

Topics reviewed include e-liquid components or characteristics including flavor additives (e.g., menthol), base e-liquid ingredients (propylene glycol, vegetable glycerin), components commonly used to dissolve flavorants (e.g., ethanol), and resulting properties of the e-liquid (e.g., pH), e-cigarette device characteristics (e.g., wattage, temperature, model) and user behavior (e.g., puff topography) which may impact nicotine exposure.

CONCLUSION

E-liquid characteristics and components, e-cigarette hardware and settings, and user behavior can all contribute substantially to nicotine exposure from e-cigarettes.

Advances in Behavioral Laboratory Methods that Inform Tobacco Regulatory Science: A TCORS Working Group Special Issue

Objective

The 2009 Family Smoking Prevention and Tobacco Control Act (TCA) created unprecented enabling conditions for establishing national regulatory policy that reduces the burden of public health and societal problems associated with tobacco product use. The Center for Tobacco Products (CTP), created by the FDA to implement the TCA, developed a first-of-its-kind FDA/National Institutes of Health (NIH) collaborative program to fund Tobacco Centers of Regulatory Science (TCORS).

Methods

To assist the TCORS with addressing research priorites, working groups (WGs) comprised of FDA-CTP liasions and TCORS investigators were formed. Under the direction of the Center for Evaluation and Coordination of Trainin and Research (CECTR), the TCORS WGs seek to develop tangible work products in their respective areas of focus.

Results

The focus of the behavioral pharmacology WG evolved from publishing a narrow paper on behavioral methods in electronic cigarette research to a collection of papers on advances in behavioral laboratory methods that may inform tobacco regulatory science.

Conclusion

This Special Issue contains articles that address all of the CTP research priorities and demonstrates how advances in behavioral laboratory methods made by TCORS investigators can inform FDA efforst to regulate tobacco products.