Impact of cigarette filter ventilation on U.S. smokers’ perceptions and biomarkers of exposure and potential harm

Abstract

Background:

Relationships between cigarette filter ventilation levels, biomarkers of exposure (BOE) and potential harm (BOPH), and harm perceptions were examined.

Methods:

Filter ventilation levels in cigarette brands were merged with Wave 1(2013–2014) Population Assessment of Tobacco Use and Health study. Data was restricted to smokers who reported a usual brand and not regular users of other tobacco products. BOEs included nicotine, tobacco specific nitrosamines (TSNAs), volatile organic compounds (VOCs), and polycyclic aromatic hydrocarbons (PAHs). BOPHs measured inflammation and oxidative stress. Perceived harm was assessed as self-reported risk of one’s usual brand compared with other brands.

Results:

Filter ventilation ranged from 0.2% to 61.1% (n=1,503). Adjusted relationships between filter ventilation and BOE or BOPH were non-significant except for VOC N-Acetyl-S-(phenyl)-L-cysteine (PHMA) and high sensitivity C-reactive protein (hsCRP). In pair-wise comparisons, PHMA was higher in quartile (Q) 4 (4.23 vs. 3.36 pmol/mg;p=0.0103) and Q3 (4.48 vs. 3.36 pmol/mg;p=0.0038) versus Q1 of filter ventilation and hsCRP comparisons were non-significant. Adjusted odds of perceiving one’s own brand as less harmful was 26.87 (95% CI: 4.31, 167.66), 12.55 (3.01, 52.32), and 19.18 (3.87, 95.02) times higher in the Q2, Q3, and Q4 of filter ventilation compared with Q1 (p=0.0037).

Conclusions:

Filter ventilation was not associated with BOE or BOPH, yet smokers of higher ventilated cigarettes perceived their brand as less harmful than other brands compared with smokers of lower ventilated cigarettes.

Impact:

Research to understand the impact of this misperception is needed, and remedial strategies, potentially including a ban on filter ventilation, are recommended.

INTRODUCTION

In the middle of the last century, as the harmful effects of cigarette smoking were being realized, cigarette manufacturers began to make cigarette design changes to reduce smoking-machine tar and nicotine yields, for example by incorporating cigarette filters and then adding ventilation holes to the filters to dilute smoke with air.¹ However, machine-determined yields do not equate to human exposure.² Filter ventilation allows for to the cigarette tobe elastic or variable, meaning that the design characteristics of filter ventilation allow smokers to smoke cigarettes with greater intensity through a greater number of puffs, longer puffs, or by blocking ventilation holes with their fingers or mouths.^2–8 Smokers may also compensate by smoking more cigarettes per day. Despite the growing scientific evidence that low-yield cigarettes did not result in beneficial health effects, manufacturers continued to promote low-yield cigarettes through displaying nicotine/tar yields on packs, misleading advertising campaigns, and descriptors (“lights”, “ultralights”) that implied lower health risks. As a result, lower yield, ventilated cigarettes were widely perceived by smokers to pose lower risks and thus became the cigarette of choice for many smokers, including those who may have otherwise quit or cut back. Emerging evidence over the last decade also indicates that cigarette filter ventilation is one reason why the lung adenocarcinoma rates have been increasing disproportionately to the reduction in cigarette smoking.⁹ This is attributed to changes in how the cigarette burns, affecting carcinogen yields in cigarette smoke, coupled with greater smoke intake as smokers compensate due to lower nicotine yields.

A US court ruling in 2006 found that the major tobacco companies had misled the public about the health effects of ventilated cigarettes.¹⁰ The court later ordered the tobacco companies to make corrective statements to the public.¹¹ The U.S. Federal Trade Commission acted to remove misleading nicotine/tar listings from U.S. cigarette packs in 2008 and the 2009 Tobacco Control Act subsequently banned descriptors or advertising that convey messages of reduced exposure or risk (explicit or implicit) including the terms “light”, “mild”, and “low”, unless authorized by the U.S. Food and Drug Administration (FDA). Since these actions, no recent surveys have been conducted to assess potential health effects of ventilated cigarettes or perceptions. The goal of the present cross-sectional study was to provide a comprehensive and novel picture of the relationships between extent of filter ventilation in U.S. cigarette brands and smokers’ biomarkers of tobacco-related exposure (BOE) and potential harm (BOPH) and perceptions of harm.

METHODS

Data Source

Wave 1 (2013–2014) of the Population Assessment of Tobacco and Health (PATH) Study was used. PATH is a nationally representative, longitudinal cohort study of adults and youth in the US.¹² In brief, field interviewers visited respondents’ homes to conduct interviews using audio computer-assisted self-interviews to collect self-report information including socio-demographics and smoking use behavior.¹² For smokers who reported having a usual brand/sub-brand of cigarettes, cigarette pack brand/sub-brand images were provided and participants were asked to select the pack image that represented their usual brand/sub-brand. Urine and blood samples were collected among participants. Additional information on the PATH study is available elsewhere.¹²

Analytic Sample

Adult daily smokers (≥ 18 years of age; n=32,320) who provided urine were included for analysis. Users of other tobacco products (i.e., persons using regularly and using daily or some days the following: electronic cigarettes, cigars, cigarillos, little cigars, pipe tobacco, hookah, smokeless tobacco, snus, dissolvable tobacco) were excluded in efforts to maximize specificity of the biomarker data to cigarettes. The sample was then restricted to those who reported a usual cigarette brand/sub-brand and reported that the last cigarette smoked at the time of the urine sample was their usual brand/sub-brand (n=2,103).

The next step was to identify the cigarette pack images used to aid participants in identifying their usual brand/sub-brand (i.e., Marlboro Black non-menthol 100s, Newport menthol 100s, Newport full flavor menthol 100s). From the Roswell Park Comprehensive Cancer Center’s Tobacco Product Image Library, we obtained images of the self-reported usual brand/sub-brand of cigarettes for the 2,103 PATH participants. Next, we matched the image and name of the usual brand/sub-brand of cigarettes for the 2,103 PATH participants with the corresponding image and brand/sub-brand of cigarettes used in our team’s filter ventilation database. We were unable to complete the match for 600 participants due to not having filter ventilation values for their usual brand/sub-brand of cigarettes. Our final sample size was 1,503 participants, which serves as the sample for all analyses except for the BOPH. For the analyses of BOPH, the 1,503 participants were restricted to participants who provided a blood sample (n=948).

Biomarkers of exposure (BOE) and potential harm (BOPH)

Table 1 provides a summary of the BOE and BOPH measured in PATH participants’ spot urine or blood samples. Assays of the BOE TNE (molar sum of total nicotine, total COT, total 3-HCOT, total cotinine N-oxide, total nicotine N-oxide, total norcotinine, and total nornicotine; where “total” refers to the sum of unconjugated and glucuronide conjugated compounds), total NNAL, total NNN, 3-HPMA, 2-HPMA, PHMA, HMPMA, 2CYEMA, and 1-HOP have been previously described by PATH¹³. Assays of the BOPH 8-iso-PGF_2α, ICAM-1, IL6, hsCRP, and Fibro have also been described by PATH.¹³

Table 1.

PATH Wave 1 biomarkers of exposure and potential harm examined in present study

Biomarkers of exposure (BOE)	Constituent	Matrix
Total nicotine equivalents (TNE)	Nicotinea	Spot urine
4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol and its glucuronides (total NNAL)	4-(Methylnitrosamino)-1-(3-pyridyl)-1-butanonea	Spot urine
N′-nitrosonornicotine and its glucuronides (total NNN)	N′-Nitrosonornicotinea	Spot urine
N-Acetyl-S-(3-hydroxypropyl)-L-cysteine (3-HPMA)	Acroleina	Spot urine
N-Acetyl-S-(2-hydroxypropyl)-L-cysteine (2-HPMA)	Propylene oxidea	Spot urine
N-Acetyl-S-(phenyl)-L-cysteine (PHMA)	Benzenea	Spot urine
N-Acetyl-S-(3-hydroxypropyl-1-methyl)-l-cysteine (HMPMA)	Crotonaldehydea	Spot urine
N-Acetyl-S-(2-cyanoethyl)-L-cysteine (2CYEMA)	Acrylonitrilea	Spot urine
1-Hydroxypyrene (1-HOP)	Pyrene	Spot urine
Biomarkers of potential harm (BOPH)	Indication	Matrix
8-iso-prostaglandin F2α (8-iso-PGF2α)	Oxidative stress	Spot urine
Soluble Intercellular Adhesion Molecular (ICAM-1)	Inflammation	Serum
Interleukin 6 (IL6)	Inflammation	Serum
High sensitivity C-reactive protein (hsCRP)	Inflammation	Serum
Fibrinogen Activity (Fibro)	Inflammation	Serum

^aIncluded on the Food and Drug Administration list of harmful or potentially harmful constituents in tobacco or tobacco smoke

Harm perceptions

We examined the following PATH measure: ‘Do you think the brand of cigarettes you usually smoke might be less harmful, no different, or more harmful compared to other cigarette brands?’. Responses were dichotomized to less harmful versus no different/more harmful for analyses. This approach is similar to that used in prior studies.¹⁴

Filter ventilation

Convenience sample of 114 cigarette varieties representing sub-brands of 11 popular cigarette brands were purchased from retail stores in Minneapolis, MN in 2015–2016. To generate representative average filter ventilation values, 3 packs of each cigarette variety were purchased from different retailers (1 pack per retailer). One cigarette was randomly taken out of each pack and analyzed separately to obtain triplicate data per cigarette variety. Level of ventilation (%) was measured using a PV-10 or KC-3 apparatus (Borgwaldt, Richmond, VA) at Roswell Park Comprehensive Cancer Center using previously described methods.^15–19

Statistical Analyses

Filter ventilation values for each cigarette brand/sub-brand were merged with PATH data based on the smoker’s usual cigarette brand/sub-brand. Because filter ventilation was not normally distributed (Supplementary Figure 1), filter ventilation was modeled as a categorical variable based on quartiles. Socio-demographics and smoking characteristics were summarized across filter ventilation quartiles by means or proportions and 95% confidence intervals (CI). Urinary biomarkers were standardized by urinary creatinine (biomarker/creatinine and expressed per mg creatinine) to account for differences in hydration between participants. Biomarkers had skewed distributions and were transformed using the natural logarithm to approximate normality and summarized using geometric mean (GM) and 95% CI.

Linear regression was used to assess significant relationships, at p<0.05, between filter ventilation quartiles and BOE and BOPH. If significant, pairwise testing of filter ventilation quartiles was performed with adjustment for multiple comparisons using the Tukey method. Logistic regression modeling was used to assess statistically significant differences, at p<0.05, between extent of filter ventilation in perceptions of harm of one’s own usual brand. Socio-demographic and smoking characteristics of the participants were considered potential confounders if associated with filter ventilation at a p<0.2 (Table 2). To isolate the impact of filter ventilation on BOE, BOPH, and harm perceptions we controlled for all potential confounders in modeling. Due to the correlation between lower filter ventilation and menthol use in cigarettes (menthol cigarettes are more likely to have lower ventilation levels), we conducted sensitivity analyses removing menthol from the adjusted models (Supplementary Tables 1, 2). Since the results from the sensitivity analysis were similar to the results including menthol, menthol was included as a covariate in the final models. We did not control for cigarettes per day (CPD) since its considered a potential mediator (e.g., relationship between filter ventilation and BOE is impacted, at least partly, to differences in compensatory behavior) and therefore controlling for CPD may bias the estimates in favor of finding lower BOE and BOPH among higher filter ventilated smokers.

Table 2.

Characteristics of smokers by quartile of filter ventilation, Wave 1 Population Assessment of Tobacco Use and Health (PATH) Study (N=1,503)

	Filter ventilation quartile 1	Filter ventilation quartile 2	Filter ventilation quartile 3	Filter ventilation quartile 4	p-value
Age (years), mean	39.25 (36.30, 42.21)	40.32 (38.34, 42.29)	41.88 (39.87, 43.88)	46.87 (44.67, 49.09)	<.0001
Male sex, %	59.19 (49.33, 69.06)	50.73 (43.47, 57.98)	50.70 (41.58, 59.82)	38.46 (32.29, 44.64)	0.0052
Race, %					<.0001
White, Non-Hispanic	33.42 (24.89, 41.96)	79.73 (72.67, 86.78)	68.62 (60.78, 76.46)	83.33 (75.98, 90.68)
Black, Non-Hispanic	54.19 (43.95, 64.43)	5.56 (0.82, 10.30)	14.07 (12.18, 21.95)	1.61 (0.56, 2.67)
Other	12.39 (7.23, 17.54)	14.71 (9.05, 20.38)	14.31 (9.60, 19.02)	15.06 (7.90, 22.22)
> High school education, %	29.74 (21.51, 37.98)	40.66 (33.30, 48.02)	44.27 (36.44, 52.10)	46.17 (38.87, 53.48)	0.0269
Body mass index, mean	28.71 (27.22, 30.20)	27.35 (25.98, 28.73)	28.22 (27.20, 29.25)	28.11 (27.17, 29.06)	0.6044
HSI, mean	2.85 (2.64, 3.06)	2.93 (2.71, 3.16)	3.06 (2.75, 3.37)	3.02 (2.85, 3.19)	0.5678
pWISDM	3.14 (2.92, 3.35)	3.17 (3.00, 3.34)	3.29 (3.16, 3.43)	3.29 (3.16, 3.43)	0.5131
Duration regular smoking, mean	18.43 (15.77, 21.09)	21.58 (19.55, 23.59)	22.94 (21.15, 24.74)	27.52 (25.66, 29.38)	<.0001
Menthol, %	81.03 (74.73, 87.34)	21.92 (15.20, 28.63)	37.08 (27.74, 46.43)	26.56 (16.70, 36.39)	<.0001
Quit effort in past 12 months, %	73.00 (66.24, 79.75)	55.12 (47.32, 62.92)	57.78 (49.42, 66.14)	58.36 (47.36, 69.36)	0.0436
Cigarettes per day, mean	13.28 (11.60, 14.96)	15.42 (13.91, 16.93)	15.61 (14.34, 16.87)	16.02 (15.04, 17.00)	0.0377

Parentheses include 95% confidence intervals

All analyses were performed in SAS version 9.4 using survey procedures and weights that account for the complex sampling design. Per instructions provided by PATH, urine biomarker weights were used in all analyses of biomarkers of exposure with the exception of 8-iso which used blood biomarker weights.¹³ Blood biomarker weights were used in all analyses of biomarkers of biological effect.¹³ PATH data variances were estimated by the balanced repeated replication method²⁰ with Fay’s adjustment set to 0.3 to increase estimate stability.²¹

RESULTS

Filter Ventilation and Cigarette Brands

Cigarette filter ventilation ranged from 0.2% to 61.1% among the thirty-eight different cigarette brands/sub-brands used among smokers in the sample Supplementary Figure 1. First quartile (Q1), second quartile (Q2), third quartile (Q3), and fourth quartile’s ranges of filter ventilation were 0.2–10.04%, 10.05–23.40%, 23.41–28.12%, and 28.13–61.10%, respectively (n=1,503). Median filter ventilation values of Q1, Q2, Q3, and the fourth quartile (Q4) were 0.94%, 14.45%, 26.38%, and 35.71%. Cigarette brands/sub-brand by filter ventilation quartile are displayed in Supplementary Table 3.

Participant Characteristics

Table 2 provides characteristics of U.S. smokers by filter ventilation quartile. Increasing quartiles of ventilation were significantly associated with older age (p<.0001), female sex (p=0.0052), White, non-Hispanic race (p<.0001), and having greater than a high school education (p=0.0269). Increasing quartiles of ventilation were also associated with a longer duration of regular smoking (p<.0001), smoking quit effort in the past 12 months (p=0.0436), and non-menthol use (p<.0001). Greater cigarettes per day were also observed among higher quartiles of ventilation (p=0.0384), suggesting the presence of compensation to obtain desired levels of nicotine.

Biomarkers of Exposure (BOE)

Table 3 provides estimates from the unadjusted and adjusted relationships between filter ventilation quartiles and BOE. Unadjusted relationships were all significant, where higher quartiles of filter ventilation had lower levels of BOE than lower quartiles of filter ventilation. When adjusting for covariates (i.e., age, sex, race, education, smoking duration, menthol, and quit effort), the relationships between filter ventilation and BOE were non-significant with the exception of PHMA (p=0.0014). In pairwise testing, PHMA was significantly higher in Q4 versus Q1 (4.23 vs. 3.36 pmol/mg; p=0.0103) and in Q3 versus Q1 (4.48 vs. 3.36 pmol/mg; p=0.0038). The covariates that acted as the main confounders (i.e., contributed to the change from significant to non-significant relationships) in the adjusted modeling between filter ventilation quartiles and BOE were sex, race, quit effort, and smoking duration (Supplementary Table 3). In additional analyses not presented in the paper, these covariates were significantly associated with TNE. Specifically, geometric mean value of TNE was greater for females than males, Whites than Blacks, those of older age, and those who indicated quit effort in the past 12 months. Thus, differences in smoking intensity (i.e., TNE) within subgroups of these covariates contributed to their confounding effects.

Table 3.

Geometric means and 95% confidence intervals of biomarkers of exposure and potential harm by cigarette filter ventilation quartile among daily cigarette smokers in Wave 1 Population Assessment of Tobacco Use and Health (PATH) Study

	Filter ventilation Q1	Filter ventilation Q2	Filter ventilation Q3	Filter ventilation Q4		p-value
Biomarkers of Exposure (BOE) (N=1,503)
TNE (nmol/mg)	52.85 (43.21, 64.64)	68.63 (59.91, 78.63)	66.29 (58.33, 75.35)	79.11 (73.42, 85.24)		0.0004
TNE (nmol/mg)a	58.45 (51.85, 65.88)	57.31 (50.60, 64.91)	57.04 (50.49, 64.43)	58.74 (53.77, 64.17)		0.9730
Total NNAL (pmol/mg)	1.12 (0.93, 1.36)	1.52 (1.33, 1.73)	1.39 (1.20, 1.61)	1.59 (1.42, 1.79)		0.0080
Total NNAL (pmol/mg)a	1.24 (1.04, 1.40)	1.25 (1.09, 1.42)	1.19 (1.02, 1.35)	1.13 (1.01, 1.31)		0.6783
Total NNN (pmolmg)	0.064 (0.050, 0.083)	0.078 (0.070, 0.086)	0.093 (0.077, 0.113)	0.094 (0.084, 0.106)		0.0055
Total NNN (pmol/mg)a	0.074 (0.62, 0.088)	0.060 (0.052, 0.070)	0.074 (0.064, 0.086)	0.063 (0.054, 0.074)		0.1406
3-HPMA (nmol/mg)	5.14 (4.43, 5.97)	5.90 (5.18, 6.73)	5.99 (5.43, 6.62)	7.52 (6.91, 8.17)		<.0001
3-HPMA (nmol/mg)a	5.48 (4.95, 6.06)	4.96 (4.46, 5.52)	5.18 (4.74, 5.66)	5.69 (5.20, 6.22)		0.2153
2-HPMA (nmol/mg)	0.30 (0.26, 0.35)	0.34 (0.30, 0.39)	0.33 (0.30, 0.36)	0.41 (0.38, 0.46)		0.0021
2-HPMA (nmol/mg)a	0.33 (0.29, 0.37)	0.31 (0.27, 0.36)	0.31 (0.28, 0.34)	0.35 (0.30, 0.41)		0.4182
HMPMA (nmol/mg)	9.18 (7.82, 10.80)	12.33 (10.91, 13.93)	12.12 (10.84, 13.54)	15.00 (13.80, 16.32)		<.0001
HMPMA (nmol/mg)a	10.11 (9.14, 11.20)	9.88 (9.01, 10.84)	10.11 (9.20, 11.11)	10.71 (9.81, 11.68)		0.6033
PHMA (pmol/mg)	3.00 (2.60, 3.46)	4.32 (3.82, 4.87)	4.94 (4.29, 5.70)	5.24 (4.29, 5.70)		<.0001
PHMA (pmol/mg)a	3.43 (3.06, 3.85)	3.73 (3.25, 4.30)	4.51 (3.98, 5.10)*	4.18 (3.80, 4.61)*		0.0024
2CYEMA (nmol/mg)	0.77 (0.67, 0.88)	0.81 (0.71, 0.92)	0.79 (0.69, 0.90)	0.98 (0.89, 1.08)		0.0043
2CYEMA (nmol/mg)a	0.81(0.71, 0.92)	0.72 (0.65, 0.81)	0.72 (0.63, 0.82)	0.82 (0.73, 0.92)		0.2379
1-HOP (pmol/mg)	1.30 (1.14, 1.48)	1.62 (1.43, 1.84)	1.59 (1.45, 1.75)	1.83 (1.67, 1.99)		0.0007
1-HOP (pmol/mg)a	1.44 (1.28, 1.61)	1.44 (1.29, 1.60)	1.48 (1.35, 1.62)	1.53 (1.37, 1.72)		0.8791
Biomarkers of Potential Harm (BOPH) (N=948)
8-iso-PGF2α (pg/mg)	576.34 (509.64, 651.84)	586.57 (538.18, 639.32)	611.98 (554.96, 674.86)	637.91 (586.75, 693.46)		0.4265
8-iso-PGF2α (pg/mg)a	584.23 (528.90, 645.35)	545.94 (494.87, 602.27)	567.31 (525.74, 612.10)	573.52 (525.00, 626.53)		0.7625
ICAM-1 (ng/ml)	229.73 (175.30, 301.03)	300.82 (276.64, 327.11)	298.45 (265.18, 335.93)	300.82 (283.64, 319.03)		0.2895
ICAM-1 (ng/ml)a	244.40 (193.31, 309.02)	255.29 (230.49, 282.73)	267.44 (241.77, 295.83)	242.74 (223.48, 263.70)		0.5028
IL6 (pg/ml)	1.50 (1.33, 1.69)	1.75 (1.52, 2.01)	1.71 (1.46, 2.00)	1.78 (1.62, 1.97)		0.1723
IL6 (pg/ml)a	1.55 (1.36, 1.76)	1.72 (1.49, 1.99)	1.66 (1.44, 1.91)	1.58 (1.40, 1.78)		0.6583
hsCRP (mg/L)	2.13 (1.69, 2.68)	1.89 (1.43, 2.51)	1.89 (1.48, 2.40)	1.67 (1.41, 1.97)		0.4328
hsCRP (mg/L)a	1.99 (1.58, 2.52)	1.83 (1.45, 2.31)	1.81 (1.44, 2.29)	1.36 (1.11, 1.67)		0.0337
Fibro (mg/L)	337.98 (322.89, 353.82)	341.11 (305.36, 381.04)	319.39 (297.29, 343.13)	329.15 (313.75, 345.30)		0.5792
Fibro (mg/L)a	349.71 (329.94, 370.67)	342.65 (318.17, 369.00)	322.05 (304.30, 340.80)	315.32 (296.69, 335.09)		0.1333

Parentheses include 95% confidence intervals

^aAdjusted for age, sex, race, education, smoking duration, menthol status, quit effort;

^*indicates statistically significance difference from Q1 when accounting for multiple comparisons

Biomarkers of Potential Harm (BOPH)

Table 3 provides estimates of the unadjusted and adjusted relationships between filter ventilation quartiles and BOPH. Unadjusted relationships were all non-significant. When adjusting for covariates (i.e., age, sex, race, education, smoking duration, menthol, and quit effort), the relationships between filter ventilation quartiles and biomarkers of effect remained non-significant with the exception of hsCRP (p=0.0337). However, pair-wise comparisons of hsCRP between filter ventilation quartiles, which controls for multiple comparisons, were non-significant (all pairwise p-values > 0.05).

Harm Perceptions

Proportion perceiving one’s own brand as less harmful than other brands was 1.3% in quartile 1, 9.7% in quartile 2, 6.4% in quartile 3, and 8.0% in quartile 4 of filter ventilation (p=0.0178). Table 4 provides unadjusted and adjusted odds ratios from modeling the relationship between filter ventilation quartiles and perception of harm of one’s own brand. All models were significant. In analyses adjusting for covariates, the odds of perceiving one’s own brand as less harmful than other brands was 26.87 (95% CI: 4.31, 167.66), 12.55 (3.01, 52.32), and 19.18 (3.87, 95.02) times higher in the Q2, Q3, and Q4, respectively, than Q1 (p=0.0037). Because the relationships were significant but the odds ratios did not increase in a linear fashion with increasing filter ventilation quartile, filter ventilation was re-categorized to a dichotomous variable comparing Q1 versus Q2-Q4. Using the dichotomous variable, the odds of perceiving one’s own brand as less harmful than other brands was 16.71 (95% CI: 3.83, 72.99) times higher in smokers in Q2–4 versus Q1.

Table 4.

Odds ratios and 95% confidence intervals of perceiving own brand as less harmful than other brands among daily cigarette smokers in Wave 1 Population Assessment of Tobacco Use and Health (PATH) Study (N=1,503)

	Filter ventilation Q1	Filter ventilation Q2	Filter ventilation Q3	Filter ventilation Q4	p-value
Less harmful than other brands	Reference	8.07 (2.19, 29.76)	5.13 (1.14, 18.35)	6.54 (1.94, 22.06)	0.0110
Less harmful than other brandsa	Reference	26.87 (4.31, 167.66)	12.55 (3.01, 52.32)	19.18 (3.87, 95.02)	0.0037
	Filter ventilation Q1	Filter ventilation Q2-Q4
Less harmful than other brands	Reference	6.66 (2.06, 21.49)			0.0018
Less harmful than other brandsa	Reference	16.71 (3.83, 72.99)			0.0003

^aAdjusted for age, sex, race, education, smoking duration, menthol status, quit effort

DISCUSSION

We created a novel dataset by integrating filter ventilation levels in U.S. cigarette brands with biomarker and harm perception measures from the PATH study. Our results indicate that smokers of higher versus lower filter ventilated cigarettes perceived their cigarette brand to be less harmful than other brands. Yet, greater cigarette filter ventilation was not found to be associated with a reduction in circulating BOE or BOPH measured in the blood and urine. Our results can be explained by prior studies that have shown filter ventilation allows for cigarettes to be elastic whereby smokers can smoke with greater intensity to maintain a desired level of nicotine despite lower machine yields.^2–8 Similar levels of exposure may also be achieved through users of higher ventilated cigarettes consuming more cigarettes per day,^2–8 which was observed in our study.

A novel contribution of this study was the comparison of BOPH indicative of inflammation and oxidative stress across levels of filter ventilation. Inflammation and oxidative stress are integral players in the development of smoking-related diseases and prior studies have shown that levels of these biomarkers increase with pack-years and smoking dose.^22–24 In the present study, filter ventilation level was not associated with BOPH or BOE, including those on the FDA list of harmful or potentially harmful constituents in tobacco products or tobacco smoke.²⁵ The sole exception was PHMA, a biomarker for benzene that is not tobacco-specific²⁵, which was higher in the top quartiles of filter ventilation. Whether this is a chance finding or reflects a non-tobacco exposure that differs by smokers of different cigarettes is unclear.

Our BOPH and BOE results are consistent with prior research summarized in a review of the literature⁹ that showed little to no evidence of a relation between extent of filter ventilation in cigarettes and BOE and are in line with the conclusion of NCI Monograph 13 (2001): ‘There is no convincing evidence that changes in cigarette design between 1950 and the mid-1980s have resulted in an important decrease in the disease burden caused by cigarette use either for smokers as a group or for the whole population.”¹ That analysis and more recent data has identified ventilation as a likely contributor to an increase in risk for disease, specifically lung adenocarcinoma.^9,26 The increased risk is believed to be due to increased depth of inhalation from smoking more ventilated cigarettes which results in carcinogens reaching the outer parts of the lung in greater quantities where there is less air flow and a greater presence of cells vulnerable to the development of adenocarcinomas.⁹ Today, levels of exposure for tobacco carcinogens in the lung are unknown, precluding an analysis for how urine or blood levels might be surrogates for lung levels, or how lung toxicity might differ across the lungs by filter ventilation levels. Thus, the data herein provides no explanation for why lung adenocarcinoma rates have not decreased with less smoking, in contrast to other lung cancer histological types.

The odds of perceiving one’s own brand as less harmful than other brands was approximately 17 times higher in smokers in quartiles 2 to 3 versus quartile 1 of filter ventilation. Considering the BOE and BOPH findings, this is concerning and raises the question of the public health impact of this misperception that fosters continued smoking or greater uptake of smoking by youth and young adults. This misperception is likely the result of decades of prior marketing by cigarette manufacturers who falsely portrayed low yield cigarettes as lower risk because of the promised delivery of less tar. Even with brand descriptors such as “light” now banned, the marketing of presumably lower yield products continues to be reinforced through package coloring (i.e., lighter colors for presumably lower yield products), as well as the sensory perception of reduced harshness attributable to filter ventilation.^1,16,27–29

Another noteworthy observation is the inverse relationship between menthol and filter ventilation in cigarettes. Specifically, the proportion of menthol smokers in the lowest quartile of filter ventilation was approximately three-fold the proportion of menthol smokers in the highest quartile of filter ventilation. This is consistent with prior research that quantified filter ventilation in leading cigarette brands in 1997 and observed no (0%) filter ventilation among the two leading menthol brands (i.e., Newport and Kools).³⁰ In 2011, the FDA Tobacco Products Scientific Advisory Committee synthesized existing studies on the impact of smoking menthol cigarettes versus nonmenthol cigarettes on disease risk. Eleven studies did not find any differences in disease risk, while one study found a greater disease risk for menthol smokers among some population sub-groups, and two studies, one of which was an industry-sponsored meta-analysis, found evidence that menthol smoking may be associated with lower cancer risk in some population sub-groups. Since that report, two additional epidemiologic studies have been published with one finding no difference³¹ and the other finding a lower lung cancer risk in menthol versus nonmenthol cigarettes.³² Cigarette filter ventilation has been suggested as a potential reason for the lower disease risk among menthol smokers observed in some studies because ventilated cigarettes, which are more likely to be nonmenthol cigarettes, tend to be smoked with greater intensity potentially giving rise to greater carcinogen exposure. The results of the present study on BOE and BOPH in the urine and blood do not support this hypothesis; however, further study examining exposure in the lung is recommended for reasons previously discussed.

Our findings have broad implications, given the vast-majority of cigarettes sold in the US and other Western countries have filter ventilation, and middle and lower income countries are now selling more highly ventilated cigarettes.¹⁵ Tar/nicotine yields on cigarette packages and advertising should be eliminated in countries such as Japan, Korea, and China that have continued to use them.^19,33–35 The elimination of colors and descriptive terms on packages that depict or imply cigarette strength should also be considered due to their conditioned association with ventilated cigarettes and the public perception that they are less harmful,^36,37 with the potential goal of only allowing plain packaging. We also recommend research on the public health impact of restricting or banning filter ventilation so that regulators such as the FDA can make an informed decision on these policies. Such research may include examining the impact of such policies on other tobacco product use and analysis of which populations would be most impacted. For example, based on our univariate analyses, smokers who are Black and use menthol cigarettes are more represented among lower filter ventilation groups and therefore may need additional efforts to curb their smoking prevalence if a filter ventilation ban is implemented.

The results are subject to limitations. First, we did not exclusively assess the impact of filter ventilation because cigarette brand styles differ by other characteristics (e.g., tobacco filler weight, filter length, and ventilation through the rod which occurs at a lesser extent than the filter) that were not examined. Second, the cross-sectional nature of this study limits our ability to know if the observed levels of BOE and BOPH are caused through self-selection by a smoker with characteristics that may influence which brand they choose and which may also influence BOE and BOPH independent of filter ventilation. To combat this concern, we conducted analyses adjusting for known characteristics of the smokers associated with filter ventilation such as gender, race, and quit effort. Third, we used Wave 1 of the PATH Study which took place during 2013–2014 and filter ventilation data from 2015–2016. Our reason for using Wave 1 over more recent waves is that at present Wave 1 is the only wave available for analysis that includes BOPH.

In summary, we found no evidence that filter ventilation level is associated with lower levels of BOE or BOPH. This is concerning since smokers of higher versus lower ventilated cigarettes perceived their cigarettes to be less harmful compared to other brands. Given these findings, research to understand the impact of this misperception is needed, and remedial strategies, potentially including a restriction or ban on filter ventilation, are strongly encouraged.