Abstract
Background
Tobacco use leads to multiple illnesses. Yet, the effects of different categories of tobacco use on multimorbidity remain understudied. We investigated the associations between tobacco use categories and multimorbidity and the potential moderating effects of age, sex, or race/ethnicity among adults in the United States.
Methods
We conducted a cross-sectional analysis using pooled data from the Behavioral Risk Factor Surveillance System for the years 2020–2022. Multimorbidity was ascertained through self-reported ≥2 chronic health conditions. We categorized tobacco use into nine derived from nonuse (did not use e-cigarettes or cigarettes), former cigarette or e-cigarette use, current (used on some days/everyday) cigarette use or e-cigarette use, or both (dual use). We used multinomial logistic regression to investigate the associations while accounting for potential confounding factors.
Results
Within the sample (N=1,080,257), 28.2% reported multimorbidity. For the categories examined (former exclusive e-cigarette, exclusive e-cigarette, former exclusive cigarette, former dual, former cigarette/current e-cigarette, exclusive cigarette, current cigarette/former e-cigarette and dual use), all reported higher odds of having multimorbidity compared to those who reported nonuse of both e-cigarettes and cigarettes. We found significant interactions for age, sex and race/ethnicity with the tobacco use categories for multimorbidity (p<0.01), where stronger associations were observed among younger adults, females and non-Hispanic Multiracial for current dual use (p<0.05).
Conclusions
The use of cigarettes, e-cigarettes, or both was associated with multimorbidity among adults, which was more pronounced among younger adults, females and non-Hispanic Multiracial. These findings underscore the importance of implementing targeted public health interventions to mitigate the health risks associated with using both products, particularly among specific demographics, to reduce the prevalence of multimorbidity.
Keywords: Cigarette, Dual Use, E-cigarette, Morbidity; Multimorbidity; Tobacco
Highlights
-
•
This study explored associations between tobacco use categories and multimorbidity.
-
•
About a quarter of US adults reported multimorbidity.
-
•
Those who reported cigarettes, e-cigarettes or both had higher odds of multimorbidity.
-
•
Stronger associations were observed among younger adults, females and non-Hispanic Multiracial for current dual use.
-
•
Further research is needed to explore if a causal relationship exists.
1. Background
Multimorbidity, the coexistence of two or more chronic health conditions, poses a significant and complex challenge to public health worldwide.(Moffat & Mercer, 2015) In the United States (US), its prevalence rose from 15.2% in 1999 to 22.7% in 2018 (Caraballo et al., 2022). This multifaceted health condition is associated with higher mortality, lower quality of life, increased healthcare utilization, (Marengoni et al., 2011) and healthcare costs (Marengoni et al., 2011, Salisbury et al., 2011). Multimorbidity is often associated with older age groups (≥65 years),(King et al., 2018) and is more prevalent among females,(King et al., 2018, Marengoni et al., 2011, Salisbury et al., 2011) though some other studies differ (Rizza et al., 2012). Additionally, studies have indicated racial/ethnic disparities in the prevalence of multimorbidity (Caraballo et al., 2022). For instance, prior research found that multimorbidity was more prevalent among Black individuals and less prevalent among Asian and Hispanic individuals compared to White individuals (Caraballo et al., 2022).
Several lifestyle behaviors (Tazzeo et al., 2023) are associated with multimorbidity, including tobacco use, (Alvarez-Galvez et al., 2023) a well-established risk factor for a range of chronic diseases. In recent years, the landscape of tobacco use has transformed with the emergence of electronic nicotine delivery systems (ENDS), commonly known as e-cigarettes (Mirbolouk et al., 2018, Osei et al., 2019). These devices introduced a dynamic element to the landscape, with potential implications for the prevalence and complexity of multimorbidity. E-cigarettes have increased in popularity as an alternative to traditional combustible tobacco products and are often considered less harmful (Fong et al., 2019, Popova and Ling, 2013). However, studies have provided evidence for exposure to toxic volatile chemicals in e-cigarettes (Rubinstein et al., 2018). Those who use e-cigarettes may transition to dual use, i.e., using both cigarettes and e-cigarettes, thereby increasing their risks of developing adverse effects (Osei et al., 2019). However, the long-term health effects of e-cigarettes are still evolving (Osei et al., 2019).
As e-cigarettes gain traction and dual use becomes common (Osei et al., 2019), understanding their relationship with multimorbidity is crucial. We aim to investigate the association between tobacco use categories, including traditional cigarettes and e-cigarettes, and multimorbidity. Sociodemographic characteristics like age, sex, and race/ethnicity can also influence how individuals respond to the health risks associated with tobacco use. (Arrazola et al., 2023, Higgins et al., 2019, Kingsbury et al., 2020) We will therefore explore potential interactions between tobacco use categories and these sociodemographic factors to provide a more comprehensive understanding of how cigarette-, e-cigarette-, and dual use relate to multimorbidity. Such insights may inform evidence-based policies and interventions to reduce the prevalence of chronic diseases and improve overall population health.
2. Methods
We used the STATA statistical software version 18 (StataCorp LLC, College Station, TX) for this cross-sectional analysis. We utilized pooled data from three years of the Behavioral Risk Factor Surveillance System (BRFSS) (2020–2022) (Centers for Disease Control and Prevention, 2020). The BRFSS is a nationally representative survey investigating health behaviors and risks, chronic conditions, and personal behaviors such as smoking and drinking among the noninstitutionalized US population. A detailed description of the BRFSS study's methodology and procedures can be found elsewhere (Centers for Disease Control and Prevention, 2020). This study uses de-identified publicly available data deemed exempt from review by the Florida International University Institutional Review Board.
2.1. Measures
2.1.1. Multimorbidity
We obtained multimorbidity, the dependent variable, from questions about chronic health conditions. This study's assessment of chronic health conditions included participants' self-reported history of receiving a medical diagnosis from a doctor, nurse, or other health professionals. Specifically, participants were asked whether a healthcare provider had ever informed them about the presence of any of the following chronic health conditions: angina or coronary heart disease, arthritis, asthma, cancer, chronic obstructive pulmonary disease or emphysema or chronic bronchitis, depression, diabetes, heart attack (myocardial infarction), kidney disease and stroke. The response options were "Yes" or "No" for each condition, and a new variable was generated by summing the responses and categorizing them into three groups: 0 (reference), 1, and ≥2 (indicating the presence of multimorbidity) in line with prior research (Boyd & Fortin, 2010).
2.1.2. Tobacco use categories
The independent variable in this study was the categories of tobacco use, which was ascertained by the use of cigarettes and e-cigarettes. Current cigarette smokers were participants who reported smoking at least 100 cigarettes in their lifetime and smoking every day or some days at the time of the survey. Former cigarette users were participants who had smoked at least 100 cigarettes in their lifetime but not smoking now at the time of the survey. Never cigarette users were those who responded "No" to the question, "Have you smoked at least 100 cigarettes your entire lifetime?" Current e-cigarette users were participants who responded "Yes" to the question: "Do you now use e-cigarettes or other electronic "Vaping" product, every day or some days?" Former e-cigarette users responded “not at all” to the question. Never e-cigarette users were participants who responded "No" to the question, "Have you ever used an e-cigarette or electronic vaping product even just one time in your entire lifetime?" To conduct our analysis, we combined and coded the responses to the items, resulting in nine distinct categories: 1. nonuse (participants who have never used e-cigarettes and cigarettes), 2. former exclusive e-cigarette use, 3. current exclusive e-cigarette use, 4. former exclusive cigarette use, 5. former dual use, 6. former cigarette/current e-cigarette use, 7. current exclusive cigarette use, 8. current cigarette/former e-cigarette use, and 9. current dual use.
2.1.3. Moderators
We used the age categories provided in the BRFSS data: 18–24 years, 25–34 years, 35–44 years, 45–54 years, 55–64 years and ≥65 years. Biological sex was reported as males and females. Race/ethnicity was classified as non-Hispanic White, non-Hispanic Black, non-Hispanic Other, non-Hispanic Multiracial, and Hispanic.
2.1.4. Covariates
We obtained covariates, such as demographic characteristics of the study participants, from self-report questionnaires. We categorized the measures and included education (below high school, high school graduate, attended college/technical school and graduated college/technical school), income (<$25,000, $25,000 to <$50,000, ≥$50,000, and don't know/not reported), marital status (single, divorced/widowed/separated, and married/partnered). Mental health distress days was assessed from the question related to number of days in the past 30 days that mental health was not good (0, 1–13 days, and 14–30 days) and heavy alcohol consumption was assessed from adult men having more than 14 drinks per week and women having more than 7 drinks per week in the BRFSS and categorized as Yes or No. Current smokeless tobacco was assessed from the use of smokeless tobacco everyday, somedays or not at all, while self-rated health was assessed on a 5-point Likert scale ranging from excellent to poor and regrouped as optimal (excellent/very good/good) and suboptimal (fair/poor).
2.2. Statistical analysis
We investigated the differences in demographic characteristics between the morbidity categories using chi-square tests.(McHugh, 2013) Then, we analyzed the relationship between tobacco use categories and multimorbidity using multinomial logistic regression. We also analyzed the interactions of age, sex, and race/ethnicity with the tobacco use categories while controlling for sociodemographic factors, mental health distress days, heavy alcohol consumption, current smokeless tobacco, and physical activity. The steps in the analysis were as follows: Model 1 included the individual variable (tobacco use categories) and covariates, and Model 2 had the adjusted model with the interaction terms for sex*tobacco use categories, age*tobacco use categories, and race/ethnicity*tobacco use categories inserted separately. We conducted a stratified analysis for sex age and race/ethnicity following the interaction analysis. The regression models estimated the adjusted odds ratios (aOR) and 95% confidence intervals (CI). The statistical significance for this study was set at a p-value of <0.05. We conducted all analyses employing the BRFSS weighting methodology using the 'svy' procedures in the STATA statistical software.
3. Results
3.1. Participant characteristics
Overall, 28.2% of the study participants (N = 1,080,257; Weighted N= 202,129,317) reported multimorbidity. About half of the participants were female (51.3%). About a fifth of the study participants were 65 years and older (22.9%). The majority (61.7%) of the participants identified as non-Hispanic White, 11.8% as non-Hispanic Black, 7.2% as non-Hispanic Other, 2.1% as non-Hispanic Multiracial, and 17.1% as Hispanic. Additionally, 4.2% reported current exclusive cigarette use, 2.2% reported current exclusive e-cigarette use, and 2.1% reported current dual use. The characteristics of the study participants varied by multimorbidity. Females had a higher prevalence of multimorbidity compared to males (32.1% vs. 24.1%). Non-Hispanic Multiracial and non-Hispanic White participants had a higher prevalence of multimorbidity compared to other racial/ethnic groups. Older age groups had a higher prevalence of multimorbidity than younger age groups. Details of the study participants are in Table 1.
Table 1.
Study sample characteristics, BRFSS 2020–2022.
| Characteristics | Total |
Morbidity |
||
|---|---|---|---|---|
| 0 | 1 | ≥2 | ||
| N (%) | n (%) | n (%) | n (%) | |
| Total | 1,080,257 (100.0) | 395,095 (43.3) | 315,455 (28.5) | 369,707 (28.2) |
| Age in yearsa | ||||
| 18–24 | 64,781 (12.0) | 38,839 (60.1) | 19,144 (29.4) | 6798 (10.4) |
| 25–34 | 114,616 (16.9) | 66,791 (59.6) | 32,228 (27.2) | 15,597 (13.2) |
| 35–44 | 141,401 (16.3) | 76,930 (56.1) | 38,926 (26.6) | 25,545 (17.3) |
| 45–54 | 162,689 (15.4) | 73,180 (46.9) | 46,913 (28.2) | 42,596 (25.0) |
| 55–64 | 205,588 (16.5) | 65,754 (32.3) | 62,264 (30.5) | 77,570 (37.2) |
| ≥65 | 391,182 (22.9) | 73,601 (18.9) | 115,980 (29.1) | 201,601 (51.9) |
| Sex | ||||
| Female | 579,011 (51.3) | 186,271 (38.3) | 173,853 (29.6) | 218,887 (32.1) |
| Male | 501,246 (48.7) | 208,824 (48.6) | 141,602 (27.4) | 150,820 (24.1) |
| Race/ethnicity | ||||
| Non-Hispanic White | 800,672 (61.7) | 268,561 (38.0) | 239,685 (29.8) | 292,426 (32.2) |
| Non-Hispanic Black | 80,246 (11.8) | 31,341 (44.8) | 22,753 (28.0) | 26,152 (27.2) |
| Non-Hispanic Other | 57,331 (7.2) | 29,101 (59.6) | 14,669 (24.4) | 13,561 (16.0) |
| Non-Hispanic Multiracial | 23,256 (2.1) | 8363 (38.3) | 6518 (28.3) | 8375 (33.5) |
| Hispanic | 92,896 (17.1) | 47,509 (55.1) | 24,641 (26.2) | 20,746 (18.7) |
| Education | ||||
| Below high school | 64,153 (11.7) | 21,262 (40.7) | 15,789 (24.9) | 27,102 (34.4) |
| High school graduate | 274,107 (27.7) | 95,971 (43.1) | 78,037 (28.3) | 100,099 (28.6) |
| Attended college/technical school | 297,524 (30.6) | 98,945 (39.8) | 86,630 (29.4) | 111,949 (30.9) |
| Graduated college/technical school | 440,407 (30.0) | 176,915 (48.0) | 133,992 (29.3) | 129,500 (22.7) |
| Income | ||||
| <$25,000 | 159,285 (15.4) | 41,002 (33.8) | 39,755 (25.6) | 78,528 (40.6) |
| $25,000- <$50,000 | 224,966 (20.0) | 72,005 (39.9) | 64,796 (28.1) | 88,165 (32.0) |
| ≥$50,000 | 497,239 (45.2) | 209,901 (47.7) | 152,634 (29.8) | 134,704 (22.5) |
| Don't know/Missing/Not reported | 198,767 (19.5) | 72,187 (44.1) | 58,270 (28.3) | 68,310 (27.6) |
| Marital status | ||||
| Single | 189,875 (24.6) | 90,937 (53.8) | 55,226 (28.7) | 43,712 (17.4) |
| Divorced/widowed/separated | 279,273 (20.0) | 65,615 (28.3) | 77,456 (27.1) | 136,202 (44.6) |
| Married/partnered | 602,024 (55.5) | 234,166 (43.9) | 180,332 (29.0) | 187,526 (27.2) |
| Self-rated health | ||||
| Optimal | 894,802 (83.1) | 374,147 (48.9) | 276,745 (29.5) | 243,910 (21.6) |
| Suboptimal | 182,968 (16.9) | 20,360 (16.1) | 38,049 (23.6) | 124,559 (60.4) |
| Mental health distress days | ||||
| 0 | 661,821 (58.9) | 272,303 (49.5) | 192,596 (27.1) | 196,922 (23.4) |
| 1–13 | 259,690 (26.0) | 91,812 (41.9) | 78,490 (30.5) | 89,388 (27.6) |
| ≥14 | 139,122 (15.0) | 25,233 (22.5) | 38,983 (30.8) | 74,906 (46.8) |
| Heavy alcohol consumption | ||||
| Yes | 65,792 (6.3) | 25,424 (43.2) | 21,091 (31.6) | 19,277 (25.2) |
| No | 982,040 (93.7) | 355,784 (43.1) | 285,255 (28.4) | 341,001 (28.6) |
| Current smokeless tobacco | ||||
| Yes | 34,732 (3.3) | 14,745 (45.9) | 9819 (28.0) | 10,168 (26.2) |
| No | 1,044,641 (96.7) | 379,987 (43.2) | 305,399 (28.5) | 359,255 (28.3) |
| Tobacco use categories | ||||
| Nonuse | 404,067 (37.2) | 167,867 (48.5) | 119,856 (28.3) | 116,344 (23.2) |
| Former exclusive e-cigarette use | 224,389 (23.0) | 98,586 (51.6) | 67,041 (28.5) | 58,762 (20.0) |
| Current exclusive e-cigarette use | 14,090 (2.2) | 6853 (50.2) | 4658 (33.6) | 2579 (16.3) |
| Former exclusive cigarette use | 154,881 (11.5) | 38,374 (28.6) | 44,158 (28.2) | 72,349 (43.2) |
| Former dual use | 122,973 (10.2) | 33,954 (33.3) | 35,460 (29.4) | 53,559 (37.3) |
| Former cigarette/current e-cigarette use | 19,990 (2.4) | 7058 (38.7) | 6302 (32.5) | 6630 (28.8) |
| Current exclusive cigarette use | 45,966 (4.2) | 14,153 (35.6) | 12,383 (25.9) | 19,430 (38.5) |
| Current cigarette/former e-cigarette use | 77,101 (7.3) | 22,962 (34.3) | 20,711 (27.4) | 33,428 (38.4) |
| Current dual use | 16,800 (2.1) | 5288 (34.6) | 4886 (30.1) | 6626 (35.4) |
Abbreviations: BRFSS: Behavioral Risk Factor Surveillance System. Analytic ns (overall column numbers) do not always add to total in columns due to missing data. Ns are unweighted; percentages are weighted. Nonuse: no use of e-cigarettes or cigarettes; e-cigarette use: individuals reporting current/former use of e-cigarettes; cigarette use: individuals who report current/former use of traditional cigarettes; dual use: individuals who report both current e-cigarette and cigarette use. The characteristics of the study participants varied by multimorbidity (p <0.001). aDistribution across morbidity are row percentages.
3.2. Regression analysis
In the adjusted regression analysis, the results showed that individuals who reported current exclusive cigarettes (adjusted odds ratio [aOR]: 1.49 [95% CI: 1.40, 1.59]), e-cigarettes (1.55 [1.40, 1.72]), or dual use (2.58 [2.36, 2.82]) had higher odds of having multimorbidity compared to those who reported nonuse (Table 2). Although we observed these associations in individuals with single morbidity, the magnitude of associations was larger among those with multimorbidity.
Table 2.
Association between multimorbidity and tobacco use categories, overall and by sex.
| Morbidity | ||||
|---|---|---|---|---|
| 1 | ≥2 | |||
| Overall | Adjusted OR (95% CI) | |||
| Nonuse | Ref | Ref | ||
| Former exclusive e-cigarette use | 1.03 (1.00,1.06) | 1.01 (0.97, 1.04) | ||
| Current exclusive e-cigarette use | 1.41 (1.31, 1.53) | 1.55 (1.40, 1.72) | ||
| Former exclusive cigarette use | 1.28 (1.24, 1.33) | 1.77 (1.70, 1.84) | ||
| Former dual use | 1.36 (1.31, 1.42) | 1.82 (1.74, 1.89) | ||
| Former cigarette/current e-cigarette use | 1.56 (1.45, 1.68) | 2.17 (2.01, 2.35) | ||
| Current exclusive cigarette use | 1.11 (1.04, 1.18) | 1.49 (1.40, 1.59) | ||
| Current cigarette/former e-cigarette use | 1.33 (1.27, 1.40) | 1.95 (1.85, 2.05) | ||
| Current dual use | 1.56 (1.43, 1.69) | 2.58 (2.36, 2.82) | ||
|
Female | ||||
| Nonuse | Ref | Ref | ||
| Former exclusive e-cigarette use | 0.99 (0.95, 1.03) | 0.99 (0.94, 1.04) | ||
| Current exclusive e-cigarette use | 1.67 (1.48, 1.88) | 1.79 (1.54, 2.08) | ||
| Former exclusive cigarette use | 1.29 (1.22, 1.36) | 1.73 (1.64, 1.83) | ||
| Former dual use | 1.44 (1.36, 1.53) | 1.96 (1.84, 2.09) | ||
| Former cigarette/current e-cigarette use | 1.84 (1.60, 2.10) | 2.55 (2.25, 2.88) | ||
| Current exclusive cigarette use | 1.19 (1.08, 1.30) | 1.55 (1.42, 1.68) | ||
| Current cigarette/former e-cigarette use | 1.47 (1.37, 1.58) | 2.29 (2.13, 2.47) | ||
| Current dual use | 1.70 (1.49, 1.94) | 2.90 (2.54, 3.31) | ||
|
Male | ||||
| Nonuse | Ref | Ref | ||
| Former exclusive e-cigarette use | 1.07 (1.02, 1.11) | 1.03 (0.97, 1.09) | ||
| Current exclusive e-cigarette use | 1.27 (1.15, 1.41) | 1.40 (1.20, 1.63) | ||
| Former exclusive cigarette use | 1.26 (1.19, 1.32) | 1.72 (1.63, 1.82) | ||
| Former dual use | 1.31 (1.24, 1.38) | 1.66 (1.57, 1.76) | ||
| Former cigarette/current e-cigarette use | 1.44 (1.32, 1.57) | 2.00 (1.79, 2.24) | ||
| Current exclusive cigarette use | 1.05 (0.96, 1.14) | 1.42 (1.29, 1.56) | ||
| Current cigarette/former e-cigarette use | 1.26 (1.18, 1.34) | 1.69 (1.57, 1.81) | ||
| Current dual use | 1.51 (1.36, 1.69) | 2.41 (2.11, 2.74) | ||
Adjusted model; adjusted for age, sex, race/ethnicity, education, income, marital status, general health status, mental health distress days, heavy alcohol consumption, and current smokeless tobacco use. Interaction by age and sex with morbidity: p<0.001 for 1 and ≥2. Interaction for race: 1:0.179; ≥2: 0.003. The models for the sex variable adjusted for similar variables as the adjusted model without sex. Numbers in italics are not statistically significant (p>0.05).
Furthermore, the study found significant interactions between age*tobacco use categories and sex*tobacco use categories (p<0.001), where we observed stronger associations among younger adults and females (p<0.05) (Table 2, Table 3). However, we observed no significant interaction based on race/ethnicity for single morbidity (1 morbidity: p=0.179; ≥2: 0.003; Table 4).
Table 3.
Association between multimorbidity and tobacco use categories by age group.
| Morbidity | |||
|---|---|---|---|
| 1 | ≥2 | ||
| Adjusted OR (95% CI) |
|||
|
18–24 years | |||
| Nonuse | Ref | Ref | |
| Former exclusive e-cigarette use | 1.08 (0.99, 1.17) | 1.15 (1.01, 1.31) | |
| Current exclusive e-cigarette use | 1.53 (1.37, 1.72) | 1.80 (1.52, 2.14) | |
| Former exclusive cigarette use | 1.85 (1.29, 2.66) | 1.52 (0.82, 2.82) | |
| Former dual use | 1.52 (1.23, 1.88) | 2.25 (1.68, 3.01) | |
| Former cigarette/current e-cigarette use | 1.94 (1.58, 2.39) | 2.72 (2.15, 3.43) | |
| Current exclusive cigarette use | 1.44 (0.92, 2.25) | 4.21 (2.02, 8.79) | |
| Current cigarette/former e-cigarette use | 1.68 (1.39, 2.03) | 2.86 (2.23, 3.67) | |
| Current dual use | 2.05 (1.68, 2.50) | 3.93 (3.15, 4.90) | |
|
25–34 years | |||
| Nonuse | Ref | Ref | |
| Former exclusive e-cigarette use | 1.08 (1.01, 1.15) | 1.06 (0.96, 1.18) | |
| Current exclusive e-cigarette use | 1.42 (1.21, 1.65) | 1.35 (1.10, 1.66) | |
| Former exclusive cigarette use | 1.52 (1.30, 1.78) | 1.70 (1.41, 2.05) | |
| Former dual use | 1.57 (1.42, 1.74) | 1.74 (1.52, 1.98) | |
| Former cigarette/current e-cigarette use | 1.66 (1.47, 1.88) | 2.07 (1.76, 2.43) | |
| Current exclusive cigarette use | 1.27 (1.08, 1.50) | 1.80 (1.48, 2.20) | |
| Current cigarette/former e-cigarette use | 1.72 (1.55, 1.91) | 2.31 (2.02, 2.64) | |
| Current dual use | 1.71 (1.46, 2.00) | 2.53 (2.09, 3.06) | |
|
35–44 years | |||
| Nonuse | Ref | Ref | |
| Former exclusive e-cigarette use | 1.05 (0.98, 1.13) | 1.13 (1.01, 1.25) | |
| Current exclusive e-cigarette use | 1.48 (1.10, 1.98) | 1.89 (1.36, 2.64) | |
| Former exclusive cigarette use | 1.23 (1.12, 1.35) | 1.66 (1.43, 1.92) | |
| Former dual use | 1.32 (1.21, 1.44) | 1.91 (1.70, 2.14) | |
| Former cigarette/current e-cigarette use | 1.56 (1.36, 1.79) | 2.27 (1.94, 2.65) | |
| Current exclusive cigarette use | 1.23 (1.05, 1.45) | 1.66 (1.42, 1.93) | |
| Current cigarette/former e-cigarette use | 1.39 (1.26, 1.53) | 2.29 (2.03, 2.58) | |
| Current dual use | 1.57 (1.34, 1.84) | 2.70 (2.27, 3.21) | |
|
45–54 years | |||
| Nonuse | Ref | Ref | |
| Former exclusive e-cigarette use | 0.99 (0.92, 1.06) | 1.03 (0.94, 1.12) | |
| Current exclusive e-cigarette use | 1.21 (0.87, 1.69) | 1.35 (0.88, 2.07) | |
| Former exclusive cigarette use | 1.27 (1.16, 1.38) | 1.65 (1.50, 1.82) | |
| Former dual use | 1.35 (1.22, 1.48) | 1.65 (1.48, 1.85) | |
| Former cigarette/current e-cigarette use | 1.38 (1.09, 1.75) | 1.91 (1.57, 2.32) | |
| Current exclusive cigarette use | 1.06 (0.93, 1.20) | 1.31 (1.15, 1.50) | |
| Current cigarette/former e-cigarette use | 1.25 (1.11, 1.40) | 1.73 (1.54, 1.94) | |
| Current dual use | 1.29 (1.03, 1.62) | 2.05 (1.61, 2.61) | |
|
55–64 years | |||
| Nonuse | Ref | Ref | |
| Former exclusive e-cigarette use | 0.97 (0.90, 1.05) | 0.91 (0.84, 0.99) | |
| Current exclusive e-cigarette use | 0.98 (0.62, 1.55) | 1.70 (1.15, 2.51) | |
| Former exclusive cigarette use | 1.22 (1.13, 1.32) | 1.62 (1.50, 1.76) | |
| Former dual use | 1.27 (1.16, 1.38) | 1.84 (1.68, 2.01) | |
| Former cigarette/current e-cigarette use | 1.34 (1.06, 1.69) | 2.39 (1.89 3.02) | |
| Current exclusive cigarette use | 1.02 (0.90, 1.15) | 1.39 (1.24, 1.56) | |
| Current cigarette/former e-cigarette use | 1.08 (0.97, 1.20) | 1.73 (1.55, 1.94) | |
| Current dual use | 1.02 (0.75, 1.40) | 1.73 (1.26, 2.37) | |
|
≥65 years | |||
| Nonuse | Ref | Ref | |
| Former exclusive e-cigarette use | 0.97 (0.90, 1.03) | 0.92 (0.86, 0.98) | |
| Current exclusive e-cigarette use | 1.01 (0.63, 1.60) | 1.52 (0.97, 2.37) | |
| Former exclusive cigarette use | 1.22 (1.15, 1.30) | 1.72 (1.62, 1.82) | |
| Former dual use | 1.29 (1.19, 1.40) | 1.68 (1.56, 1.81) | |
| Former cigarette/current e-cigarette use | 0.91 (0.62, 1.34) | 1.54 (1.06, 2.24) | |
| Current exclusive cigarette use | 0.91 (0.80, 1.04) | 1.15 (1.02, 1.30) | |
| Current cigarette/former e-cigarette use | 0.90 (0.78, 1.03) | 1.14 (1.02, 1.29) | |
| Current dual use | 0.78 (0.54, 1.14) | 1.64 (1.18, 2.27) | |
Model adjusted for sex, race/ethnicity, education, income, marital status, self-rated health, mental health distress days, heavy alcohol consumption, and current smokeless tobacco use. Numbers in italics are not statistically significant (p>0.05).
Table 4.
Association between multimorbidity and tobacco use categories by race/ethnicity.
| Morbidity | ||
|---|---|---|
| 1 | ≥2 | |
| Adjusted OR (95% CI) |
||
|
Non-Hispanic White | ||
| Nonuse | Ref | Ref |
| Former exclusive e-cigarette use | 1.03 (1.00, 1.07) | 1.01 (0.97, 1.04) |
| Current exclusive e-cigarette use | 1.30 (1.19, 1.42) | 1.48 (1.30, 1.67) |
| Former exclusive cigarette use | 1.27 (1.22, 1.32) | 1.71 (1.64, 1.77) |
| Former dual use | 1.34 (1.28, 1.39) | 1.73 (1.66, 1.81) |
| Former cigarette/current e-cigarette use | 1.45 (1.35, 1.56) | 2.08 (1.91, 2.26) |
| Current exclusive cigarette use | 1.06 (1.00, 1.14) | 1.36 (1.27, 1.46) |
| Current cigarette/former e-cigarette use | 1.27 (1.20, 1.34) | 1.83 (1.73, 1.94) |
| Current dual use | 1.51 (1.38, 1.65) | 2.45 (2.21, 2.71) |
|
Non-Hispanic Black | ||
| Nonuse | Ref | Ref |
| Former exclusive e-cigarette use | 1.07 (0.98, 1.16) | 1.05 (0.95, 1.16) |
| Current exclusive e-cigarette use | 1.61 (1.28, 2.04) | 1.73 (1.29, 2.33) |
| Former exclusive cigarette use | 1.39 (1.21, 1.60) | 2.03 (1.76, 2.34) |
| Former dual use | 1.33 (1.15, 1.54) | 1.88 (1.61, 2.18) |
| Former cigarette/current e-cigarette use | 1.99 (1.40, 2.84) | 1.90 (1.31, 2.75) |
| Current exclusive cigarette use | 1.10 (0.93, 1.31) | 1.46 (1.23, 1.74) |
| Current cigarette/former e-cigarette use | 1.31 (1.13, 1.50) | 1.46 (1.26, 1.69) |
| Current dual use | 1.42 (1.07, 1.89) | 2.34 (1.70, 3.23) |
|
Non-Hispanic Other | ||
| Nonuse | Ref | Ref |
| Former exclusive e-cigarette use | 0.97 (0.85, 1.11) | 0.95 (0.78, 1.17) |
| Current exclusive e-cigarette use | 1.50 (1.09, 2.06) | 1.99 (1.16, 3.42) |
| Former exclusive cigarette use | 1.41 (1.15, 1.73) | 2.20 (1.74, 2.77) |
| Former dual use | 1.65 (1.32, 2.06) | 2.56 (2.05, 3.21) |
| Former cigarette/current e-cigarette use | 1.74 (1.24, 2.44) | 2.99 (2.06, 4.34) |
| Current exclusive cigarette use | 1.19 (0.88, 1.61) | 1.89 (1.37, 2.61) |
| Current cigarette/former e-cigarette use | 1.30 (0.99, 1.71) | 1.60 (1.23, 2.08) |
| Current dual use | 1.87 (1.28, 2.75) | 2.53 (1.62, 3.94) |
|
Non-Hispanic Multiracial | ||
| Nonuse | Ref | Ref |
| Former exclusive e-cigarette use | 0.88 (0.71, 1.08) | 0.82 (0.63, 1.07) |
| Current exclusive e-cigarette use | 1.33 (0.86, 2.06) | 1.48 (0.84, 2.61) |
| Former exclusive cigarette use | 1.20 (0.89, 1.63) | 1.74 (1.26, 2.39) |
| Former dual use | 1.40 (1.07, 1.82) | 1.72 (1.30, 2.27) |
| Former cigarette/current e-cigarette use | 1.60 (1.10, 2.34) | 1.74 (1.15, 2.65) |
| Current exclusive cigarette use | 1.11 (0.76, 1.63) | 1.68 (1.18, 2.41) |
| Current cigarette/former e-cigarette use | 1.23 (0.94, 1.62) | 2.21 (1.64, 2.96) |
| Current dual use | 1.42 (0.91, 2.20) | 2.80 (1.82, 4.33) |
|
Hispanic | ||
| Nonuse | Ref | Ref |
| Former exclusive e-cigarette use | 1.00 (0.91, 1.09) | 1.03 (0.91, 1.16) |
| Current exclusive e-cigarette use | 1.57 (1.29, 1.90) | 1.37 (1.03, 1.82) |
| Former exclusive cigarette use | 1.24 (1.08, 1.44) | 1.73 (1.46, 2.05) |
| Former dual use | 1.31 (1.13, 1.52) | 1.78 (1.50, 2.12) |
| Former cigarette/current e-cigarette use | 1.66 (1.20, 2.29) | 2.29 (1.66, 3.16) |
| Current exclusive cigarette use | 1.14 (0.92, 1.41) | 1.54 (1.25, 1.90) |
| Current cigarette/former e-cigarette use | 1.49 (1.26, 1.75) | 2.69 (2.21, 3.26) |
| Current dual use | 1.53 (1.15, 2.02) | 2.56 (1.87, 3.49) |
Model adjusted for age, sex, education, income, marital status, self-rated health, mental health distress days, heavy alcohol consumption, and current smokeless tobacco use. Numbers in italics arenot statistically significant (p>0.05).
4. Discussion
This nationally representative study in the US showed that about 1 in 4 adults reported multimorbidity in 2020–2022. Individuals who reported former or current exclusive cigarette or e-cigarette use and former or current dual use had higher odds of multimorbidity compared to those who reported nonuse. Notably, we observed stronger associations among younger adults, females and non-Hispanic Multiracial for current dual use. While these associations were evident in US adults with single morbidity, the strengths of the associations were more pronounced among those with multimorbidity, particularly among those who reported cigarette and dual use compared to those who reported nonuse. These findings highlight the urgent need for prospective studies that explore the impact of different cigarette/e-cigarette use on multimorbidity risk to decrease the prevalence of cigarette and e-cigarette use behaviors. Specifically concerning is the notably higher odds of dual use among younger adults, females and non-Hispanic Multiracial, emphasizing the necessity for focused efforts to address these vulnerable populations and mitigate the potential public health impact.
In our study, we observed that using cigarettes or e-cigarettes, including former use, when compared to those reporting nonuse, was associated with morbidity, regardless of whether individuals had single morbidity or multimorbidity. Extensive literature shows that cigarette use is associated with a myriad of adverse health outcomes, (USDHHS, 2014) while emerging evidence suggests that e-cigarette use is associated with several adverse health effects, including cardiovascular disease, (Lee et al., 2019) cancer, (Canistro et al., 2017) mental health issues,(Kaplan et al., 2021) neurological diseases, (Faulcon et al., 2020) and oro-dental diseases (Jeong et al., 2020). The prevalence of e-cigarette use in the US is substantial, with many of those who report use also using cigarettes (Owusu et al., 2019). According to the US Centers for Disease Control and Prevention, in 2021, over 11 million US adults reported current use of e-cigarettes, (Romeh et al., 2023) and 28.3 million reported current cigarette use (Cornelius et al., 2023). In 2019, among adults who currently used e-cigarettes overall, 36.9% also reported current use of cigarettes (Cornelius et al., 2020). As previously mentioned, while e-cigarettes are not harmless and the long-term effects associated with e-cigarette use are limited, e-cigarettes have been promoted as a tool for smoking cessation (Abrams et al., 2018). Although randomized clinical trials suggest that e-cigarettes may show promise as a smoking cessation tool, real-world cohort studies, whether used as a cessation aid or as a cigarette substitute, have not consistently demonstrated their effectiveness in achieving smoking cessation (Wang et al., 2021). It is likely that adults diagnosed with multimorbidity may turn to e-cigarettes as a less harmful alternative to traditional cigarettes. Concerns about their health may motivate them to adopt such alternatives, as switching to e-cigarettes can be perceived as a step towards less harmful behavior than combustible cigarettes (Kosterman et al., 2022). This decision aligns with the understanding that chronic illnesses are not curable, and individuals may seek ways to mitigate their risks by reducing exposure to harmful substances like those found in cigarettes.
The dual use of both traditional cigarettes and e-cigarettes, as demonstrated in the current study, presents a significant concern due to its strong association with multimorbidity. This pattern of dual use remains prevalent among US adults (Mirbolouk et al., 2018). It is plausible that individuals who smoke cigarettes may initiate e-cigarette use as a potential pathway toward harm reduction or complete cessation (Glantz & Bareham, 2018). However, existing evidence remains conflicting. While some studies suggest benefits from e-cigarette use in reducing harm or achieving smoking cessation (Kalkhoran & Glantz, 2016), others indicate that those who report dual use may transition back to cigarette use or continue with dual use (Osibogun et al., 2022), potentially impeding cessation efforts (El Dib et al., 2017). This is particularly concerning because dual use of tobacco products may result in greater exposure to toxins and worse respiratory outcomes than using either product alone (Reddy et al., 2021). Conversely, a Cochrane review found high-certainty evidence that e-cigarettes with nicotine increase quit rates compared to nicotine replacement therapy. However, a limitation identified in the review is that the included studies primarily comprised randomized clinical trials, and the estimates were imprecise (Hartmann-Boyce et al., 2022). Similarly, another study found that switching from cigarette use to e-cigarette or dual use appears to reduce biomarkers of potential harm (Hartmann-Boyce et al., 2023). This current study’s finding raises significant concerns about the use of these tobacco products and underscores the importance of reducing their use.
Our study also reveals that multimorbidity is not confined to older age groups, although its prevalence tends to be higher among older individuals, consistent with prior studies (King et al., 2018). Notably, our findings highlight stronger associations between tobacco use categories and multimorbidity among younger adults as well. Previous research on multimorbidity has primarily focused on older populations, leaving multimorbidity categories in younger individuals underrepresented and under-studied (Carrilero et al., 2020). Additionally, our study findings showed a higher prevalence of multimorbidity among females compared to males, which aligns with other study findings (Sharma et al., 2023). This sex difference is often attributed to higher healthcare utilization and longer life expectancy among females (Sharma et al., 2023).
The relationship between tobacco use and multimorbidity is undoubtedly complex. The well-documented adverse health consequences of tobacco have led to extensive public health efforts to curb its prevalence and mitigate its effects. To effectively reduce the prevalence of multimorbidity, a comprehensive approach is needed, addressing multiple risk factors, with tobacco use being a crucial target for prevention and intervention through various concerted public health policies. Quitting tobacco use is pivotal in improving overall health and reducing the risk of single or multimorbidity.
The primary strength of this study is the use of a three-year nationally representative dataset, which allows us to provide estimates applicable to the noninstitutionalized US civilian population. Another strength is the ability to categorize tobacco use into nine different groups that are mutually exclusive. However, some limitations should be taken into consideration. First, it is essential to acknowledge that this was a cross-sectional analysis, which restricts our ability to establish causal inferences or determine the temporal sequence of the observed associations. Reverse causation remains possible and cannot be excluded. It is also plausible that e-cigarettes are used as part of quit attempts among those who reported dual use. Therefore, while our findings have potential public health and regulatory implications, they should be considered exploratory rather than definitive and seen as a starting point for future research. Second, our analysis relied on a one-time assessment, and the impact of the tobacco use categories on multimorbidity likely evolved over time. Longitudinal research is needed to provide a more comprehensive understanding of these dynamics. Third, our measure of multimorbidity was based on a simple count of diseases, which may not fully capture the complexity of the relationship between tobacco use categories and specific combinations of conditions or the broader impact of multimorbidity on healthcare systems (Rizza et al., 2012). Next, our analysis did not explore the duration or intensity of cigarette/e-cigarette product use and its potential association with multimorbidity, as this information was unavailable in the BRFSS dataset. Consequently, we cannot rule out the possibility that dual use may represent a transitional phase in tobacco product use. Fourth, there is also the potential for the healthy survivor effect as a potential bias of the observed association; those who report dual use may likely have worse health due to pre-existing conditions that could increase their odds of additional morbidities or that those with more severe health consequences due to their tobacco use may be more likely to die prematurely preventing them from participating in the survey, thereby leading to an underestimation of the true association between the tobacco use categories and multimorbidity. Last, data for 2020 and 2021 BRFSS were collected during the COVID-19 pandemic, and results should be interpreted in light of the association between COVID-19 and tobacco use categories, which was explored in a previous study using the 2021 US National Health Interview Survey (Kalan et al., 2023).
The high prevalence of multimorbidity among adults who reported current use of cigarettes, e-cigarettes, or both in the US underscores the critical need for enhanced regulatory measures and targeted clinical interventions to address the substantial health risks associated with these products and to curb the prevalence of chronic diseases in this population, especially among certain demographics such as females. However, it's crucial to note that prospective studies are needed to establish a causal link between these products (e.g., exclusive e-cigarette/dual use) and their impact on health through long-term longitudinal studies. Additionally, more research is warranted to explore potential causal relationships between exclusive cigarette use, e-cigarette use, and the dual use of cigarettes and e-cigarettes in relation to multimorbidity. This type of research can inform the development of tailored interventions to reduce the prevalence of multimorbidity, particularly among specific demographic groups exhibiting distinct tobacco use behaviors.
Disclosures
The authors have nothing to disclose
Funding
Olatokunbo Osibogun is supported by the National Institute on Drug Abuse of the National Institutes of Health under award number K01DA055127. Wei Li was supported by the US Food and Drug Administration (FDA) Center for Tobacco Products (CTP) (U54DA036151). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. All other authors have no financial disclosures to declare.
CRediT authorship contribution statement
Mohammad Ebrahimi Kalan: Conceptualization, Formal analysis, Investigation, Methodology, Writing – review & editing. Rime Jebai: Conceptualization, Formal analysis, Investigation, Methodology, Writing – review & editing. Olatokunbo Osibogun: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Writing – original draft, Writing – review & editing. Wei Li: Conceptualization, Formal analysis, Investigation, Methodology, Writing – review & editing.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
References
- Abrams D.B., Glasser A.M., Pearson J.L., Villanti A.C., Collins L.K., Niaura R.S. Harm minimization and tobacco control: reframing societal views of nicotine use to rapidly save lives. Annu Rev. Public Health. 2018;39:193–213. doi: 10.1146/annurev-publhealth-040617-013849. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Alvarez-Galvez J., Ortega-Martin E., Carretero-Bravo J., Perez-Munoz C., Suarez-Lledo V., Ramos-Fiol B. Social determinants of multimorbidity patterns: a systematic review. Front Public Health. 2023;11 doi: 10.3389/fpubh.2023.1081518. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Arrazola R.A., Griffin T., Lunsford N.B., Kittner D., Bammeke P., Courtney-Long E.A., Armour B.S. US cigarette smoking disparities by race and ethnicity - keep going and going! Prev. Chronic Dis. 2023;20 doi: 10.5888/pcd20.220375. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Boyd C.M., Fortin M. Future of multimorbidity research: how should understanding of multimorbidity inform health system design? Public Health Rev. 2010;32(2):451–474. doi: 10.1007/BF03391611. [DOI] [Google Scholar]
- Canistro D., Vivarelli F., Cirillo S., Babot Marquillas C., Buschini A., Lazzaretti M., Marchi L., Cardenia V., Rodriguez-Estrada M.T., Lodovici M., Cipriani C., Lorenzini A., Croco E., Marchionni S., Franchi P., Lucarini M., Longo V., Della Croce C.M., Vornoli A., Colacci A., Vaccari M., Sapone A., Paolini M. E-cigarettes induce toxicological effects that can raise the cancer risk. Sci. Rep. 2017;7(1):2028. doi: 10.1038/s41598-017-02317-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Caraballo C., Herrin J., Mahajan S., Massey D., Lu Y., Ndumele C.D., Drye E.E., Krumholz H.M. Temporal Trends in Racial and Ethnic Disparities in Multimorbidity Prevalence in the United States, 1999-2018. Am. J. Med. 2022;135(9):1083–1092. doi: 10.1016/j.amjmed.2022.04.010. e1014. [DOI] [PubMed] [Google Scholar]
- Carrilero N., Dalmau-Bueno A., Garcia-Altes A. Comorbidity patterns and socioeconomic inequalities in children under 15 with medical complexity: a population-based study. BMC Pedia. 2020;20(1):358. doi: 10.1186/s12887-020-02253-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Centers for Disease Control and Prevention. (2020). Centers for Disease Control and Prevention. Behavioral Risk Factor Surveillance System 2020 Summary Data Quality Report. Retrieved September 24 from https://www.cdc.gov/brfss/annual_data/2020/pdf/2020-sdqr-508.pdf
- Cornelius M.E., Loretan C.G., Jamal A., Davis Lynn B.C., Mayer M., Alcantara I.C., Neff L. Tobacco Product Use Among Adults - United States, 2021. MMWR Morb. Mortal. Wkly Rep. 2023;72(18):475–483. doi: 10.15585/mmwr.mm7218a1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Cornelius M.E., Wang T.W., Jamal A., Loretan C.G., Neff L.J. Tobacco Product Use Among Adults - United States, 2019. MMWR Morb. Mortal. Wkly Rep. 2020;69(46):1736–1742. doi: 10.15585/mmwr.mm6946a4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- El Dib R., Suzumura E.A., Akl E.A., Gomaa H., Agarwal A., Chang Y., Prasad M., Ashoorion V., Heels-Ansdell D., Maziak W., Guyatt G. Electronic nicotine delivery systems and/or electronic non-nicotine delivery systems for tobacco smoking cessation or reduction: a systematic review and meta-analysis. BMJ Open. 2017;7(2) doi: 10.1136/bmjopen-2016-012680. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Faulcon L.M., Rudy S., Limpert J., Wang B., Murphy I. Adverse Experience Reports of Seizures in Youth and Young Adult Electronic Nicotine Delivery Systems Users. J. Adolesc. Health. 2020;66(1):15–17. doi: 10.1016/j.jadohealth.2019.10.002. [DOI] [PubMed] [Google Scholar]
- Fong G.T., Elton-Marshall T., Driezen P., Kaufman A.R., Cummings K.M., Choi K., Kwan J., Koblitz A., Hyland A., Bansal-Travers M., Carusi C., Thompson M.E. U.S. adult perceptions of the harmfulness of tobacco products: descriptive findings from the 2013-14 baseline wave 1 of the path study. Addict. Behav. 2019;91:180–187. doi: 10.1016/j.addbeh.2018.11.023. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Glantz S.A., Bareham D.W. E-Cigarettes: Use, Effects on Smoking, Risks, and Policy Implications. Annu Rev. Public Health. 2018;39:215–235. doi: 10.1146/annurev-publhealth-040617-013757. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Hartmann-Boyce J., Butler A.R., Theodoulou A., Onakpoya I.J., Hajek P., Bullen C., Rigotti N.A., Lindson N. Biomarkers of potential harm in people switching from smoking tobacco to exclusive e-cigarette use, dual use or abstinence: secondary analysis of Cochrane systematic review of trials of e-cigarettes for smoking cessation. Addiction. 2023;118(3):539–545. doi: 10.1111/add.16063. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Hartmann-Boyce J., Lindson N., Butler A.R., McRobbie H., Bullen C., Begh R., Theodoulou A., Notley C., Rigotti N.A., Turner T., Fanshawe T.R., Hajek P. Electronic cigarettes for smoking cessation. Cochrane Database Syst. Rev. 2022;11(11):CD010216. doi: 10.1002/14651858.CD010216.pub7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Higgins S.T., Kurti A.N., Palmer M., Tidey J.W., Cepeda-Benito A., Cooper M.R., Krebs N.M., Baezconde-Garbanati L., Hart J.L., Stanton C.A. A review of tobacco regulatory science research on vulnerable populations. Prev. Med. 2019;128 doi: 10.1016/j.ypmed.2019.04.024. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Jeong W., Choi D.W., Kim Y.K., Lee H.J., Lee S.A., Park E.C., Jang S.I. Associations of electronic and conventional cigarette use with periodontal disease in South Korean adults. J. Periodo. 2020;91(1):55–64. doi: 10.1002/JPER.19-0060. [DOI] [PubMed] [Google Scholar]
- Kalan M.E., Jebai R., Li W., Gautam P., Alemohammad S.Y., Mortazavizadeh Z., Kenneth D.W., Chakraborty A., Abbasabad G.D., Behaleh R., Bursac Z., Ben Taleb Z. COVID-19 and tobacco products use among US adults, 2021 National Health Interview Survey. Health Sci. Rep. 2023;6(9) doi: 10.1002/hsr2.1542. https://doi.org/ARTN e154210.1002/hsr2.1542. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Kalkhoran S., Glantz S.A. E-cigarettes and smoking cessation in real-world and clinical settings: a systematic review and meta-analysis. Lancet Respir. Med. 2016;4(2):116–128. doi: 10.1016/S2213-2600(15)00521-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Kaplan B., Thrul J., Cohen J.E. Association of cigarette and electronic nicotine delivery systems use with internalizing and externalizing problems among US adults: Findings from wave 3 (2015-2016) of the PATH study. PLoS One. 2021;16(6) doi: 10.1371/journal.pone.0253061. [DOI] [PMC free article] [PubMed] [Google Scholar]
- King D.E., Xiang J., Pilkerton C.S. Multimorbidity Trends in United States Adults, 1988-2014. J. Am. Board Fam. Med. 2018;31(4):503–513. doi: 10.3122/jabfm.2018.04.180008. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Kingsbury J.H., D'Silva J., O'Gara E., Parks M.J., Boyle R.G. How Much Progress Have We Made? Trends in Disparities in Tobacco Use. Prev. Chronic Dis. 2020;17 doi: 10.5888/pcd17.200090. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Kosterman R., Epstein M., Bailey J.A., Hawkins J.D. Is e-cigarette use associated with better health and functioning among smokers approaching midlife? Drug Alcohol Depend. 2022;234 doi: 10.1016/j.drugalcdep.2022.109395. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Lee W.H., Ong S.G., Zhou Y., Tian L., Bae H.R., Baker N., Whitlatch A., Mohammadi L., Guo H., Nadeau K.C., Springer M.L., Schick S.F., Bhatnagar A., Wu J.C. Modeling Cardiovascular Risks of E-Cigarettes With Human-Induced Pluripotent Stem Cell-Derived Endothelial Cells. J. Am. Coll. Cardiol. 2019;73(21):2722–2737. doi: 10.1016/j.jacc.2019.03.476. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Marengoni A., Angleman S., Melis R., Mangialasche F., Karp A., Garmen A., Meinow B., Fratiglioni L. Aging with multimorbidity: a systematic review of the literature. Ageing Res Rev. 2011;10(4):430–439. doi: 10.1016/j.arr.2011.03.003. [DOI] [PubMed] [Google Scholar]
- McHugh M.L. The chi-square test of independence. Biochem Med (Zagreb) 2013;23(2):143–149. doi: 10.11613/bm.2013.018. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Mirbolouk M., Charkhchi P., Kianoush S., Uddin S.M.I., Orimoloye O.A., Jaber R., Bhatnagar A., Benjamin E.J., Hall M.E., DeFilippis A.P., Maziak W., Nasir K., Blaha M.J. Prevalence and Distribution of E-Cigarette Use Among U.S. Adults: Behavioral Risk Factor Surveillance System, 2016. Ann. Intern Med. 2018;169(7):429–438. doi: 10.7326/M17-3440. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Moffat K., Mercer S.W. Challenges of managing people with multimorbidity in today's healthcare systems. BMC Fam. Pr. 2015;16:129. doi: 10.1186/s12875-015-0344-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Osei A.D., Mirbolouk M., Orimoloye O.A., Dzaye O., Uddin S.M.I., Benjamin E.J., Hall M.E., DeFilippis A.P., Stokes A., Bhatnagar A., Nasir K., Blaha M.J. Association Between E-Cigarette Use and Cardiovascular Disease Among Never and Current Combustible-Cigarette Smokers. Am. J. Med. 2019;132(8):949–954. doi: 10.1016/j.amjmed.2019.02.016. e942. [DOI] [PubMed] [Google Scholar]
- Osibogun O., Bursac Z., Maziak W. Longitudinal transition outcomes among adult dual users of e-cigarettes and cigarettes with the intention to quit in the United States: PATH Study (2013-2018) Prev. Med Rep. 2022;26 doi: 10.1016/j.pmedr.2022.101750. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Owusu D., Huang J., Weaver S.R., Pechacek T.F., Ashley D.L., Nayak P., Eriksen M.P. Patterns and trends of dual use of e-cigarettes and cigarettes among U.S. adults, 2015-2018. Prev. Med Rep. 2019;16 doi: 10.1016/j.pmedr.2019.101009. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Popova L., Ling P.M. Alternative tobacco product use and smoking cessation: a national study. Am. J. Public Health. 2013;103(5):923–930. doi: 10.2105/AJPH.2012.301070. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Reddy K.P., Schwamm E., Kalkhoran S., Noubary F., Walensky R.P., Rigotti N.A. Respiratory Symptom Incidence among People Using Electronic Cigarettes, Combustible Tobacco, or Both. Am. J. Respir. Crit. Care Med. 2021;204(2):231–234. doi: 10.1164/rccm.202012-4441LE. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Rizza A., Kaplan V., Senn O., Rosemann T., Bhend H., Tandjung R., group F. s. Age- and gender-related prevalence of multimorbidity in primary care: the Swiss FIRE project. BMC Fam. Pr. 2012;13:113. doi: 10.1186/1471-2296-13-113. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Romeh F.R.M., Seidenberg A.B., Crane E., Seaman E.L., Tynan M.A., Marynak K. E-cigarette Unit Sales by Product and Flavor Type, and Top-Selling Brands, United States, 2020-2022. Morb. Mortal. Wkly. Rep. 2023;72(25):672–677. doi: 10.15585/mmwr.mm7225a1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Rubinstein M.L., Delucchi K., Benowitz N.L., Ramo D.E. Adolescent Exposure to Toxic Volatile Organic Chemicals From E-Cigarettes. Pediatrics. 2018;141(4) doi: 10.1542/peds.2017-3557. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Salisbury C., Johnson L., Purdy S., Valderas J.M., Montgomery A.A. Epidemiology and impact of multimorbidity in primary care: a retrospective cohort study. Br. J. Gen. Pr. 2011;61(582):e12–21. doi: 10.3399/bjgp11×548929. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Sharma S.K., Nambiar D., Ghosh A. Sex differences in non-communicable disease multimorbidity among adults aged 45 years or older in India. BMJ Open. 2023;13(3) doi: 10.1136/bmjopen-2022-067994. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Tazzeo C., Zucchelli A., Vetrano D.L., Demurtas J., Smith L., Schoene D., Sanchez-Rodriguez D., Onder G., Balci C., Bonetti S., Grande G., Torbahn G., Veronese N., Marengoni A. Risk factors for multimorbidity in adulthood: A systematic review. Ageing Res Rev. 2023;91 doi: 10.1016/j.arr.2023.102039. [DOI] [PubMed] [Google Scholar]
- United States Department of Health and Human services . Office on Smoking and Health. 2014. The Health Consequences of Smoking 50 years of progress: A report of the surgeon general. Atlanta, GA: U.S. Department of Health and Human Services,Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion. [Google Scholar]
- Wang R.J., Bhadriraju S., Glantz S.A. E-Cigarette Use and Adult Cigarette Smoking Cessation: A Meta-Analysis. Am. J. Public Health. 2021;111(2):230–246. doi: 10.2105/AJPH.2020.305999. [DOI] [PMC free article] [PubMed] [Google Scholar]