Dual use of combustible and heated tobacco products exacerbates long COVID prevalence: a JASTIS 2023 cross‑sectional study

Dual use of combustible and heated tobacco products exacerbates long COVID prevalence: a JASTIS 2023 cross‑sectional study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Dual use of combustible and heated tobacco products exacerbates long COVID prevalence: a JASTIS 2023 cross‑sectional study Erika Toyokura, Kazuhiro Yamada, Kazuhisa Asai, Masaya Tsutsumi, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6669862/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted 4 You are reading this latest preprint version Abstract Multiple reports have identified smoking as a risk factor for long COVID; however, few have distinguished among tobacco product types. We conducted a cross-sectional study using data from an internet-based survey administered in February 2023 to examine the association between 12 persistent symptoms and smoking status in participants with a history of COVID-19. A total of 28,250 participants were included, of whom 5,067 had a history of COVID-19. Among current tobacco users with a history of COVID-19, the odds ratios for persistent symptoms were significantly elevated for four symptoms—arthralgia, chest pain, dyspnea, and dysosmia—compared to never smokers. For subgroup analysis, current tobacco users were categorized into three groups: combustible cigarette (CC), heated tobacco product (HTP), and dual users. Among dual users, the odds ratios were significantly elevated for five symptoms: arthralgia, chest pain, dyspnea, dysgeusia, and dysosmia. CC users showed significantly higher odds for chest pain, dyspnea, and fatigue, while HTP users for dyspnea and sexual dysfunction. Smoking in individuals with a history of COVID-19 exacerbates the prevalence of persistent symptoms, and its impact may vary by smoking type. Separately analyzing smoking subgroups allows for a more accurate understanding of the relationship between long COVID and smoking behavior. Health sciences/Medical research/Epidemiology Health sciences/Diseases/Infectious diseases/Viral infection Introduction Since the onset of COVID-19, individuals adopted preventive measures to avoid infection, and the government launched a vaccination campaign to limit case numbers in Japan 1 , 2 . However, following the pandemic, personal behaviors shifted, and infections became more common, resulting in a rise in cases 3 , 4 , 5 , 6 . This increase has introduced new challenges: the long-term effects of SARS-CoV-2 infection, known as long COVID. Long COVID is defined as a chronic condition occurring after SARS-CoV-2 infection, persisting for ≥ 3 months as a continuous, relapsing and remitting, or progressive disease affecting one or more organ systems 7 . Reported symptoms include fatigue, dyspnea, cough, chest pain, palpitations, fever, memory impairment, difficulty concentrating, dysgeusia, anosmia, hair loss, and arthralgia 7 , 8 . These prolonged symptoms have significantly affected the quality of life (QOL) of individuals recovering from COVID-19, often continuing for extended periods 9 , 10 , 11 , 12 . In contrast, although behavioral restrictions during the pandemic were thought to reduce QOL, they did not result in a significant decline 13 . Survey data suggest that people in Japan and over 60 million globally have experienced long COVID 11 , 14 , 15 , and investigations into associated risk factors have continued since the start of the pandemic 16 , 17 , 18 , 19 . Combustible cigarette (CC) use is a well-established risk factor for numerous respiratory and cardiovascular diseases and exerts complex effects on the central nervous system. In COVID-19 research, CC use has been identified as a potential risk factor, with studies indicating that smokers may be more likely to develop long COVID 20 , 21 , 22 . Recently, alternative tobacco products such as heated tobacco products (HTPs) and electronic cigarettes have gained popularity worldwide. In Japan, HTP use has rapidly expanded, accounting for approximately one-third of the tobacco market as of 2021, according to the Tobacco Institute of Japan 23 . Given this market share, it is essential to investigate the relationship between COVID-19 and tobacco exposure, including both CCs and HTPs, particularly in countries like Japan. However, few comprehensive studies have examined both products. HTP aerosols contain harmful and potentially harmful constituents, such as nicotine and carcinogens, though generally in lower quantities than CCs 24 . Nonetheless, HTPs release some toxic substances in higher concentrations and introduce unique compounds not found in CCs 25 , 26 . Therefore, associations between long COVID, CC users, and HTP users may differ, especially among dual users. Our previous study found that HTP and dual use were associated with a higher likelihood of SARS-CoV-2 infection compared with never use, whereas no significant association was identified for CC use alone 27 . This finding suggests that tobacco-related analyses should distinguish between product types. To date, evidence regarding their respective impacts on long COVID remains limited. In this study, we examined the association between long COVID and smoking status in Japan’s general population, grouping all CC, HTP, and dual users together. We also explored how this association varies among CC, HTP, and dual users. Methods Study design and Participants We conducted a cross-sectional study using data from the Japan “Society and New Tobacco” Internet Survey (JASTIS), an internet-based, self-reported survey. JASTIS aimed to assess the impact of new tobacco products in Japan [Study profile is shown in a previous report 28 ]. Participants were recruited from a large survey panel managed by a major nationwide internet research agency, Rakuten Insight, which maintains a pool of 2.3 million panelists covering all census-defined social categories, such as education, housing tenure, and marital status in Japan. This study used data from the JASTIS 2023 study (conducted in February 2023). A follow-up survey (February 6–27, 2023), for all 44,681 participants of the 2015–2023 survey who could be contacted, and 28,437 responses were received (response rate, 63.6%). An additional 5,563 participants completed a new survey (February 25–27, 2023) using the same questionnaire. These were combined with the follow-up responses for a total of 34,000 participants in the JASTIS 2023 dataset. For this analysis, 2,919 participants were excluded due to inconsistent responses. Of the remaining 31,081 participants, 1,823 and 1,181 participants with a history of COVID-19 within the past 2 months and > 1 year prior to the survey date, respectively, were excluded. We also excluded 130 participants who used only tobacco products other than CCs and HTPs, and 140 participants who gave contradictory responses regarding their smoking status. The final analysis included 28,250 participants. This study was conducted in accordance with the Ethical Guidelines for Life Science and Medical Research Involving Human Subjects. All study protocols were reviewed and approved by the Research Ethics Committee of the Osaka International Cancer Institute (No. 20084-8). Informed consent was obtained from all participants. Definition of Variables Tabacco use Smoking status was determined based on participants’ responses to questions regarding the use of various tobacco products. CCs included both factory-made cigarettes and roll-your-own tobacco. HTPs comprised IQOS, Ploom Tech, Ploom S, Ploom X, glo, and lil HYBRID—brands available in Japan as of February 2023. Participants were classified into three categories according to their smoking status over the past year: current users, defined as individuals who used tobacco within the past year; former users, who had ceased tobacco use for one year or more; and never users, who had never used any tobacco products. Current users were further subdivided into three groups: dual users (those using both CCs and HTPs), CC users (exclusive users of combustible cigarettes), and HTP users (exclusive users of heated tobacco products). COVID-19 history COVID-19 history was categorized based on the time of infection relative to the survey date: within 2 months, 2–6 months, 6 months to 1 year, and > 1 year prior. Participants with infections within the previous 2 months were excluded because such cases may not adequately reflect long COVID. Similarly, those with infections > 1 year prior were excluded, as post-infection smoking cessation could alter smoking status, potentially leading to misclassification. To assess the association between smoking status and long COVID accurately, the main analysis focused on participants with COVID-19 infections occurring 2–6 months or 6 months to 1 year before the survey. Persistent symptoms Long COVID is commonly defined as symptoms persisting for three months or more following acute infection 7 . Although the questionnaire used in this study was not specifically designed to evaluate long COVID, it asked whether participants experienced symptoms lasting more than two months, regardless of their COVID-19 history. For this analysis, long COVID was defined as the presence of persistent symptoms lasting more than two months among individuals with a history of COVID-19. To identify long COVID symptoms, we examined symptoms reported to persist for over two months and evaluated their associations with prior COVID-19 infection using multivariate logistic regression. Based on prior research 14 , 29 , 30 , 12 symptoms were selected for analysis: arthralgia, headache, chest pain, dyspnea, fatigue, dysgeusia, dysosmia, memory impairment, loss of concentration, hair loss, sexual dysfunction, and cough. Statistical analysis We conducted multivariate logistic regression analysis to examine the association between symptoms persisting for > 2 months (outcome variable) and explanatory variables including COVID-19 history and smoking status. The models included an interaction term between COVID-19 history and smoking status, along with covariates such as age, sex, BMI, vaccine status (≥ 2 doses vs. <2 doses), and number of comorbidities (none, one, or ≥ 2). Comorbidities included 20 conditions: diabetes, dyslipidemia, pneumonia, asthma, atopic dermatitis, allergic rhinitis, periodontal disease, dental caries, angina pectoris, stroke, chronic obstructive pulmonary disease, chronic kidney disease, chronic liver disease, immunodeficiency, malignant tumors, chronic pain syndrome, depression, alcoholism, and other mental disorders. Adjusted odds ratios (aORs) and 95% confidence intervals (CIs) were calculated, with p-values < 0.05 considered statistically significant. To assess the interaction between smoking and COVID-19 history, an interaction term was incorporated into the model. Variance inflation factors (VIFs) were calculated for each covariate to evaluate multicollinearity; VIF < 5 was considered to indicate non-linearity. Population bias from the internet-based design was addressed using inverse probability weighting (IPW) 31 , based on a nationally representative sample from the Comprehensive Survey of Living Conditions of People on Health and Welfare (CSLCPHW) 32 . JASTIS and CSLCPHW data were combined, and logistic regression adjusted for background factors (residence, education, housing tenure, and health status) was used to estimate the probability of “being a respondent in an internet survey,” i.e., propensity score. Details on IPW have been reported previously reported 28 , 33 . Participant numbers in the text and tables reflect weighted values; unweighted numbers are shown in Supplementary Tables. All statistical analyses were conducted using EZR Version 1.54 (Jichi Medical University, Saitama, Japan), a graphical interface for R (The R Foundation for Statistical Computing) 34 . Results Participant characteristics Table 1 presents the characteristics of all participants. A total of 28,250 participants were included in the study, of whom 5,067 (17.9%) had a history of COVID-19. Participants with a history of COVID-19 tended to be younger, with a median age of 38 years. Among those infected, 27.5% were current users, compared with 22.5% of those not infected. The prevalence of CC use was 9.6% among those infected and 11.7% among those not infected; for HTP use, 4.4% and 3.4%, respectively; and for dual use, 13.5% and 7.3%, respectively. Participants with a history of COVID-19 were more likely to use dual tobacco products. The proportion of former users was lower among those infected (14.6%) than among those not infected (18.8%). Never users accounted for 57.9% of those infected and 58.7% of those not infected. Participants without a history of COVID-19 were more likely to have received the SARS-CoV-2 vaccine, and increased vaccine doses were associated with a lower likelihood of infection. Among all participants, 24.7% had one comorbidity and 35.7% had two or more, with no significant differences based on COVID-19 history. The median BMI was 21.8 kg/m 2 , with no significant differences based on COVID-19 history. No significant differences were found in educational background, although infected participants tended to report higher incomes. Table 1 Baseline characteristics of participants (N = 28,250) (weighted) Previous history of COVID-19 Total None Yes 28250 23182 5067 Age, median (IQR) years 47 [34, 63] 50 [35, 66] 38 [28, 48] Sex, n (%) Male 13988 (49.5) 11354 (49.0) 2633 (52.0) Female 14262 (50.5) 11827 (51.0) 2434 (48.0) Smoking status, n (%) Current user 6603 (23.4) 5207 (22.5) 1395 (27.5) Only CCs 3206 (11.4) 2720 (11.7) 486 (9.6) Only HTPs 1008 (3.6) 786 (3.4) 222 (4.4) Dual tobacco products 2387 (8.5) 1701 (7.3) 686 (13.5) Former user 5101 (18.1) 4363 (18.8) 738 (14.6) Never user 16544 (58.6) 13610 (58.7) 2934 (57.9) SARS-CoV-2 vaccination status, n(%) Two times or more 24642 (87.2) 20286 (87.5) 4355 (86.0) Three times or more 21408 (75.8) 18018 (77.7) 3389 (66.9) Four times or more 14129 (50.0) 12605 (54.4) 1524 (30.1) Comorbidities*, n (%) One comorbidity 6977 (24.7) 5743 (24.8) 1233 (24.3) Two or more 10088 (35.7) 8442 (36.4) 1645 (32.5) BMI, median (IQR) kg/m 2 21.80 [19.72, 24.22] 21.80 [19.74, 24.22] 21.72 [19.72, 24.12] Education, n (%) Junior high school/high school 15358 (54.4) 12785 (55.2) 2573 (50.8) University/technical school or more 12891 (45.6) 10397 (44.8) 2494 (49.2) Income over 4 million yen, n (%) 12803 (45.3) 9826 (42.4) 2977 (58.7) Symptom lasting more than two months Arthralgia 4978 (17.6) 4238 (18.3) 739 (14.6) Headache 3065 (10.9) 2424 (10.5) 640 (12.6) Chest pain 732 (2.6) 535 (2.3) 197 (3.9) Cough 1182 (4.2) 956 (4.1) 226 (4.5) Dyspnea 2283 (8.1) 1349 (5.8) 314 (6.2) Dysgeusia 268 (0.9) 176 (0.8) 91 (1.8) Dysosmia 313 (1.1) 229 (1.0) 84 (1.7) Memory impairment 1420 (5.0) 1152 (5.0) 267 (5.3) Loss of concentration 2639 (9.3) 2164 (9.3) 474 (9.4) Hair loss 1586 (5.6) 1303 (5.6) 283 (5.6) Sexual dysfunction 1642 (5.8) 1416 (6.1) 226 (4.5) Fatigue 2664 (9.4) 2195 (9.5) 468 (9.2) * Comorbidities include the following 20 diseases: diabetes, dyslipidemia, pneumonia, asthma, atopic dermatitis, allergic rhinitis, periodontal disease, dental caries, angina pectoris, stroke, chronic obstructive pulmonary disease, chronic kidney disease, chronic liver disease, immunodeficiency, malignant tumors, chronic pain syndrome, depression, alcoholism, and other mental disorders. Association between each symptom of long COVID and smoking Table 2 shows the results of the analysis examining the associations of COVID-19 history, overall smoking status (combining dual, CC, and HTP users), and their interaction with each persistent symptom. In this analysis, never users and those without a history of COVID-19 were used as the reference group. The interaction term between COVID-19 history and smoking status (current or former users) highlights the influence of smoking on persistent symptoms. Persistent symptoms with COVID-19 history was defined as long COVID. Among current users with a history of COVID-19, aORs significantly increased for arthralgia, chest pain, dyspnea, and dysosmia. In contrast, aORs for dysgeusia, memory impairment, and loss of concentration did not show significant increases. Among former users with a history of COVID-19, aOR significantly increased for arthralgia and headache, but decreased for dyspnea and dysosmia. These results suggest stronger associations between long COVID and both current and former users with a history of COVID-19, compared to never users with a history of COVID-19. In Japan, the use of HTPs has rapidly increased since their introduction as products marketed to contain fewer harmful components than CCs. HTP or dual use, as opposed to exclusive CC use, may be associated with different long COVID risks. Therefore, we further analyzed the associations between long COVID and each tobacco use category—dual, CC, and HTP users. Table 2 Association between smoking status and history of COVID-19 in each symptom lasting for more than two months. Non- COVID-19 history COVID-19 history Never user CC user Former user CC user × COVID-19 history (Interaction) former user × COVID-19 history (Interaction) Symptom aOR † (reference) aOR † (95% CI) aOR † (reference) aOR † (95% CI) aOR † (95% CI) aOR † (95% CI) aOR † (95% CI) Arthralgia 1 0.97 (0.85–1.11) 1 1.27 ** (1.15–1.39) 1.24** (1.13–1.37) 1.37 ** (1.12–1.69) 1.44** (1.14–1.82) Headache 1 0.94 (0.83–1.07) 1 1.30** (1.16–1.46) 1.28** (1.13–1.45) 1.02 (0.81–1.28) 1.58 ** (1.20–2.07) Chest pain 1 1.17 (0.90–1.53) 1 1.10 (0.87–1.38) 1.39** (1.10–1.74) 2.34** (1.60–3.44) 1.39 (0.87–2.23) Cough 1 1.59** (1.28–1.98) 1 1.45** (1.23–1.72) 1.16 (0.97–1.38) 0.77 (0.55–1.09) 0.80 (0.53–1.21) Dyspnea 1 1.23 (1.00-1.52) 1 1.42** (1.22–1.66) 2.21** (1.92–2.55) 1.81** (1.35–2.43) 0.66* (0.46–0.95) Fatigue 1 0.97 (0.87–1.08) 1 1.32** (1.21–1.44) 1.11* (1.00-1.22) 1.01 (0.84–1.20) 1.43** (1.15–1.78) Dysgeusia 1 2.14** (1.47–3.11) 1 0.68 (0.45–1.03) 0.82 (0.55–1.21) 1.78 (0.96–3.30) 1.64 (0.84–3.21) Dysosmia 1 1.86 ** (1.24–2.80) 1 1.25 (0.89–1.75) 1.28 (0.92–1.80) 1.88* (1.08–3.29) 0.40* (0.16–0.98) Memory impairment 1 1.31* (1.06–1.60) 1 1.30** (1.10–1.52) 1.54** (1.32–1.80) 1.10 (0.80–1.51) 0.88 (0.61–1.27) Loss of concentration 1 0.99 (0.85–1.14) 1 1.18** (1.05–1.33) 1.45** (1.29–1.64) 0.92 (0.72–1.19) 1.18 (0.89–1.56) Hair loss 1 1.03 (0.84–1.25) 1 1.26** (1.09–1.46) 1.33** (1.14–1.54) 1.21 (0.89–1.63) 1.02 (0.71–1.46) Sexual dysfunction 1 1.18 (0.91–1.52) 1 1.68** (1.45–1.95) 1.51** (1.30–1.76) 0.73 (0.52–1.03) 0.67 (0.46-1.00) †AOR = adjusted odds ratio. Estimated using multivariable logistic regression modeling with adjustment for the following variables: age, sex, BMI, vaccine status (categorized as two doses or more vs. fewer than two doses), the number of comorbidities (none, one, or two or more), smoking status, COVID-19 history, and interaction term. CI = confidence interval. Values of p < 0.05 were considered statistically significant. *p < 0.05. **p < 0.01. Association between each symptom of long COVID and different tobacco products use Table 3 presents a re-analysis examining the effects of COVID-19 history and smoking status on individual symptoms. Current users were classified into three categories: dual users, CC users, and HTP users. The interaction term between COVID-19 history and smoking status (dual, CC, HTP, or former users) indicates the influence of smoking status on persistent symptoms. Persistent symptoms with COVID-19 history was defined as long COVID. Among dual users with a history of COVID-19, aORs for five persistent symptoms—arthralgia, chest pain, dyspnea, dysgeusia, and dysosmia—were significantly elevated, whereas the aOR for one persistent symptom, sexual dysfunction, was significantly decreased. Among CC users with a history of COVID-19, aORs for three persistent symptoms—chest pain, dyspnea, and fatigue—were significantly increased. Among HTP users a history of COVID-19, aORs for two persistent symptoms—dyspnea and sexual dysfunction—were significantly increased. Overall, dual users with a history of COVID-19 exhibited more long COVID symptoms than CC and HTP users with a history of COVID-19. Furthermore, dual users with a history of COVID-19 showed a significant association with the most frequently reported long COVID symptoms: arthralgia, chest pain, dyspnea, dysgeusia, and dysosmia 35 . Table 3 Association between smoking status subdivided into combustible tobacco/ HTPs and history of COVID-19 in each symptom lasting for more than two months. Non-COVID-19 history (reference) COVID-19 history Never user (reference) Dual user CC user HTP user Former user Dual user × COVID-19 history (Interaction) CC user × COVID-19 history (Interaction) HTP user × COVID-19 history (Interaction) Former user × COVID-19 history (Interaction) Symptom aOR † (reference) aOR † (95% CI) aOR † (reference) aOR † (95% CI) aOR † (95% CI) aOR † (95% CI) aOR † (95% CI) aOR † (95% CI) aOR † (95% CI) aOR † (95% CI) aOR † (95% CI) Arthralgia 1 0.97 (0.85–1.11) 1 1.40** (1.21–1.63) 1.24** (1.11–1.39) 1.13 (0.92–1.38) 1.24** (1.13–1.37) 1.33* (1.01–1.75) 1.31 (0.99–1.75) 1.45 (0.94–2.24) 1.44** (1.14–1.81) Headache 1 0.95 (0.83–1.08) 1 1.56** (1.32–1.84) 1.08 (0.92–1.26) 1.48** (1.18–1.87) 1.27** (1.12–1.44) 0.83 (0.62–1.12) 1.40 (1.00-1.96) 0.71 (0.42–1.21) 1.58** (1.20–2.07) Chest pain 1 1.19 (0.92–1.55) 1 1.33 (0.95–1.84) 0.88 (0.65–1.21) 1.41 (0.91–2.18) 1.38** (1.10–1.73) 2.53** (1.57–4.07) 1.84* (1.00-3.38) 1.43 (0.65–3.17) 1.38 (0.86–2.22) Cough 1 1.58** (1.27–1.96) 1 1.30 (0.99–1.70) 1.74** (1.44–2.10) 0.71 (0.44–1.12) 1.17 (0.98–1.39) 0.99 (0.63–1.57) 0.73 (0.46–1.15) 0.46 (0.14–1.51) 0.80 (0.53–1.21) Dyspnea 1 1.23 (1.00-1.52) 1 1.45** (1.14–1.84) 1.62** (1.36–1.94) 0.65 (0.42–1.02) 2.22** (1.93–2.56) 1.89** (1.29–2.78) 1.73** (1.18–2.54) 2.45* (1.19–5.05) 0.66* (0.46–0.95) Fatigue 1 0.94 (0.81–1.09) 1 1.47** (1.24–1.75) 1.14 (0.98–1.33) 1.30* (1.02–1.67) 1.23** (1.08–1.39) 0.77 (0.55–1.07) 1.55* (1.10–2.17) 1.07 (0.63–1.80) 1.06 (0.78–1.44) Dysgeusia 1 2.20** (1.51–3.20) 1 0.80 (0.43–1.51) 0.54* (0.31–0.96) 1.04 (0.48–2.26) 0.81 (0.55–1.21) 2.60* (1.17–5.74) 0.71 (0.20–2.53) 0.22 (0.02–2.10) 1.62 (0.83–3.17) Dysosmia 1 1.90** (1.27–2.87) 1 1.22 (0.71–2.08) 1.27 (0.85–1.91) 1.36 (0.68–2.74) 1.29 (0.92–1.82) 3.19** (1.57–6.52) 0.97 (0.41–2.27) 0.36 (0.06–2.15) 0.39* (0.16–0.97) memory impairment 1 1.31* (1.07–1.60) 1 1.44** (1.13–1.83) 1.33** (1.10–1.61) 0.91 (0.62–1.35) 1.54** (1.32–1.80) 1.09 (0.72–1.65) 1.01 (0.64–1.60) 1.28 (0.62–2.67) 0.88 (0.61–1.27) loss of concentration 1( 0.99 (0.85–1.15) 1 1.37** (1.16–1.64) 1.11 (0.95–1.29) 1.02 (0.78–1.33) 1.45** (1.29–1.64) 0.73 (0.52–1.03) 1.09 (0.76–1.58) 1.07 (0.62–1.87) 1.18 (0.89–1.55) Hair loss 1 1.03 (0.84–1.26) 1 1.52** (1.24–1.88) 1.20* (1.00-1.44) 0.99 (0.71–1.38) 1.33** (1.14–1.54) 1.14 (0.78–1.66) 1.08 (0.69–1.69) 1.37 (0.71–2.65) 1.01 (0.70–1.46) Sexual dysfunction 1 1.20 (0.93–1.54) 1 2.36** (1.94–2.86) 1.48** (1.25–1.77) 1.14 (0.83–1.56) 1.49 (1.28–1.74) 0.50** (0.33–0.76) 0.62 (0.37–1.03) 1.91* (1.05–3.47) 0.67 (0.45-1.00) †AOR = adjusted odds ratio. Estimated using multivariable logistic regression modeling with adjustment for the following variables: age, sex, BMI, vaccine status (categorized as two doses or more vs. fewer than two doses), the number of comorbidities (none, one, or two or more), smoking status, COVID-19 history, and interaction term. CI = confidence interval. Values of p < 0.05 were considered statistically significant. *p < 0.05. **p < 0.01. Each stratified analysis yielded associations distinct from those in the overall smokers' analysis. For dysgeusia, no significant association was observed in the interaction between overall smokers and COVID-19 history; however, a significant increase in the aOR was noted among dual users with a history of COVID-19. Regarding sexual dysfunction, interaction effects differed between overall smoking status and COVID-19 history and between individual smoking categories and COVID-19 history. These findings suggest that combining data from dual, CC, and HTP users may obscure specific association. Discussion Smoking exacerbates the prevalence of long COVID, with effects varying by smoking type: dual users, CC users, and HTP users. Dual users with a history of COVID-19 were significantly associated with the most commonly reported long COVID symptoms: arthralgia, chest pain, dyspnea, dysgeusia, and dysosmia. CC users with a history of COVID-19 were associated with chest pain, dyspnea, and fatigue, while HTP users with a history of COVID-19 were associated with dyspnea and sexual dysfunction. Dual users with a history of COVID-19 exhibited a greater number of persistent symptoms compared to CC or HTP users with a history of COVID-19 . A detailed analysis of tobacco product types provides more precise insight than analyses treating smokers as a homogeneous group. Although the association between smoking and long COVID has been examined in previous studies, results have been inconsistent 36 , 37 , 38 , 39 . These discrepancies may stem from differences in sex, race, study populations (e.g., outpatients vs hospitalized patients), or—critically—the varying prevalence of smoking types across studies. The rapid rise in HTP and e-cigarette use 23 , 40 , coupled with differing regulatory environments, may also contribute to inconsistent findings. Our results revealed differing interaction effects for persistent symptoms depending on smoking type, highlighting the importance of collecting detailed smoking-type data. Based on these findings, future studies should assess smoking status by product type. CCs contain a complex mixture of over 7,000 chemicals, many of which are toxic and carcinogenic 41 . HTPs were developed to reduce exposure to harmful substances by heating, rather than combusting, tobacco. This method is intended to lower the production of toxic compounds typically generated during combustion 42 , 43 . Mainstream smoke from HTPs contains lower levels of harmful constituents than CCs, including tar, nicotine, ammonia, reactive oxygen species, acrolein, and carbon monoxide 25 . Given these chemical differences, our finding that HTP users with a history of COVID-19 exhibited fewer persistent symptoms than dual or CC users with a history of COVID-19 appears reasonable. Conversely, dual users with a history of COVID-19 exhibited a greater number of persistent symptoms than exclusive CC or HTP users with a history of COVID-19. In Japan, HTP adoption among dual users is often motivated by health concerns, protecting bystander health, or avoiding issues related to smell 44 . Based on these motivations, one might expect fewer persistent symptoms among dual users. However, our findings showed that dual users with a history of COVID-19 had more persistent symptoms, suggesting that dual use may negate any risk reduction expected from HTP use alone. In general, HTPs are considered less harmful than CCs. However, emissions from HTPs such as IQOS contain higher levels of several substances compared with combustible cigarettes 45 . An in vitro study demonstrated that dual exposure significantly increased oxidative stress and disrupted mitochondrial homeostasis compared with exposure to either CCs or HTPs alone 46 . These findings suggest that concurrent use of combustible CCs and HTPs may exert a synergistic effect, potentially exacerbating long COVID symptoms. Another possible explanation for the greater number of persistent symptoms among dual users with a history of COVID-19 is that their total tobacco consumption may exceed that of individuals using only CCs or HTPs. Dual users often adopt HTPs to reduce tobacco consumption due to health concerns or other factors, such as issues with the smell of CCs, workplace restrictions, or fewer limitations on home use. In Japan, CC sales have notably declined since the introduction of HTPs 47 , 48 , whereas HTP sales have markedly increased following their launch 23 , 28 . This trend raises concerns that dual users may increase their overall tobacco consumption. Two studies reported that initiating HTP use among CC users can lead to increased overall tobacco use 49 , 50 . In a systematic review of dual users of e-cigarettes and CCs, most continued cigarette use through sustained dual or exclusive CC use 51 . However, the increase in total consumption was modest, with an average rise of fewer than five units per day. Given that exclusive HTP users with a history of COVID-19 exhibited fewer persistent symptoms than CC users with a history of COVID-19, dual users, even if they consume slightly more tobacco overall, would be expected to show symptom prevalence between CC and HTP users. However, in our study, dual users with a history of COVID-19 exhibited a greater number of persistent symptoms than both CC and HTP users with a history of COVID-19, indicating a synergistic effect rather than merely increased total consumption. Former users with a history of COVID-19 demonstrated a different pattern of symptom associations. Specifically, they had a higher likelihood of experiencing headache and joint pain. However, unlike dual users, CC, and HTP, former users with a history of COVID-19 did not show increased rates of symptoms commonly reported in long COVID, such as chest pain, dysgeusia, or dysosmia. Additionally, former users with a history of COVID-19 exhibited a lower likelihood of dyspnea and dysosmia. Taken together, these results suggest that smoking cessation is associated with a lower risk of characteristic long COVID symptoms including dysgeusia, dysosmia, and chest pain. Headache and joint pain—commonly reported as nicotine withdrawal symptoms—may reflect increased pain sensitivity following smoking cessation 52 . Thus, smoking cessation among individuals with a history of COVID-19 may elevate headache and joint pain, although it confers overall protective effects against long COVID and is strongly recommended 53 , 54 , 55 , 56 . Our findings demonstrate that, unlike current smokers, former users did not exhibit typical long COVID symptoms, such as chest pain, dysgeusia, or dysosmia, and had a lower likelihood of dyspnea and dysosmia. Based on these findings, we recommend smoking cessation to reduce the risk of long COVID. This study has several limitations. First, this is a cross-sectional study, and the questionnaires were not specifically developed to identify long COVID symptoms. Nonetheless, the analysis of interaction effects between smoking status and COVID-19 infection enabled the detection of long COVID. Second, because the quantity and duration of tobacco use were not assessed, dual users may have been heavy smokers whose persistent symptoms increased in a dose-dependent manner. However, as previously noted, some studies suggest that overall cigarette consumption by dual users does not increase significantly 51 , and that levels of harmful and potentially harmful constituents are significantly higher in HTP aerosols than in CC smoke 25 , 26 , 45 , implying a potential synergistic effect of CCs and HTPs. Future studies should collect detailed data on the consumption of each tobacco product. Third, we defined long COVID as symptoms lasting > 2 months following COVID-19 infection. However, long COVID is now characterized as a chronic condition that emerges after SARS-CoV-2 infection and persists for at least three months 7 . This discrepancy may slightly overestimate the observed prevalence of symptoms. Nevertheless, the overall finding—that smoking status significantly influences persistent symptoms—is unlikely to change. Prospective studies are needed to assess the joint effects of smoking status and COVID-19 infection on long COVID under the updated definition and with detailed information on tobacco product consumption. Conclusion Smoking in individuals with a history of COVID-19 increases the prevalence of persistent symptoms, with effects varying by smoking type—dual, CC, or HTP use. HTPs should not be assumed to be less harmful, as dual use may heighten the risk of long COVID. Disaggregating smoking subgroups offers more accurate insights into the association between smoking behaviors and long COVID. Smoking cessation should be promoted to reduce the risks associated with long COVID. Declarations Acknowledgements The authors would like to thank all researchers involved in the data accumulation and all participants of JASTIS 2023, and Editage (www.editage.jp) for English language editing. This work was supported by JSPS KAKENHI Grant Number JP23K07631. Funding No funding was received for conducting this study. Author contributions Erika Toyokura: statistical analysis and interpretation of data and drafting of the manuscript and critical revision of the manuscript intellectual content Kazuhiro Yamada: statistical analysis, interpretation of data and study supervision and critical revision of the manuscript intellectual content Kazuhisa Asai: study concept and design, Study concept and design, study supervision and critical revision of the manuscript intellectual content Masaya Tsutsumi, Takahiro Ueda, Kaho Hirai, Yuichiro Furukawa, Atsushi Miyamoto, Misako Nishimura, Kanako Sato and Tetsuya Watanabe: critical revision of the manuscript intellectual content Takahiro Tabuchi: full access to all of the data in the study and takes responsibility for the integrity of the data, acquisition of data, and critical revision of the manuscript intellectual content Tomoya Kawaguchi: study supervision and critical revision of the manuscript intellectual content Data availability statement The data are not publicly available due to privacy restrictions, but available from Takahiro Tabuchi (E-mail: [email protected] ) on reasonable request. Competing interests Erika Toyokura, Kazuhiro Yamada, Kazuhisa Asai, Masaya Tsutsumi, Takahiro Ueda, Kaho Hirai, Yuichiro Furukawa, Atsushi Miyamoto, Misako Nishimura, Kanako Sato, Tetsuya Watanabe, Takahiro Tabuchi, Tomoya Kawaguchi Kazuhisa Asai reports receiving lecture fees from AstraZeneca, GlaxoSmithKline and Sanofi; Tetsuya Watanabe, receiving lecture fees from AstraZeneca, GlaxoSmithKline and Sanofi; Takahiro Tabuchi receiving financial support for research, (consulting fees and lecture fees) from Daiichi Sankyo Healthcare Co., Ltd., Johnson and Johnson K. K., Data Seed Inc. and Workout-Plus LLC, and grants from the Ministry of Health Labour and Welfare, and Japan Society for the Promotion of Science. References Odani, S., Honda, H. ＆ Tabuchi, T. Association of COVID-19 Vaccine Intake with Diagnosis, Hospitalization, and Oxygenation/Ventilation: A Longitudinal Analysis, 2021-2022, Japan. Vaccines (Basel) . 12(11), 1264; 10.3390/vaccines12111264 (2024). Kobayashi, K. et al. 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Awareness and use of electronic cigarettes and heat-not-burn tobacco products in Japan. Addiction . 111(4), 706-713 (2016). Kanda, Y. Investigation of the freely available easy-to-use software 'EZR' for medical statistics. Bone Marrow Transpl . 48(3), 452-458; 10.1038/bmt.2012.2442013 (2013). Aiyegbusi, O. L. et al. Symptoms, complications and management of long COVID: a review. J R Soc Med . 114(9), 428-442 (2021). Subramanian, A. et al. Symptoms and risk factors for long COVID in non-hospitalized adults. Nat Med . 28(8), 1706-1714 (2022). Pinato, D. J. et al. Prevalence and impact of COVID-19 sequelae on treatment and survival of patients with cancer who recovered from SARS-CoV-2 infection: evidence from the OnCovid retrospective, multicentre registry study. Lancet Oncol . 22(12), 1669-1680 (2021). Jacobs, E. T. et al. Pre-existing conditions associated with post-acute sequelae of COVID-19. J Autoimmun . 135, 102991; 10.1016/j.jaut.2022.102991 (2023). Tan, H. Q. M., Pendolino, A. L., Andrews, P. J. ＆ Choi, D. et al. Prevalence of olfactory dysfunction and quality of life in hospitalised patients 1 year after SARS-CoV-2 infection: a cohort study. BMJ Open . 12, e054598; 10.1136/bmjopen-2021-054598 (2022). Hori, A., Tabuchi, T. ＆ Kunugita, N. Rapid increase in heated tobacco product (HTP) use from 2015 to 2019: from the Japan 'Society and New Tobacco' Internet Survey (JASTIS). Tob Control . 30(4), 474-475 (2020). National Center for Chronic Disease Prevention and Health Promotion (US) Office on Smoking and Health. The Health Consequences of Smoking—50 Years of Progress: A Report of the Surgeon General. Atlanta (GA): Centers for Disease Control and Prevention (US). Available from: https://www.ncbi.nlm.nih.gov/books/NBK179276/ (2014). Bekki, K., Inaba, Y., Uchiyama. S. ＆ Kunugita, N. Comparison of Chemicals in Mainstream Smoke in Heat-not-burn Tobacco and Combustion Cigarettes. J UOEH . 39(3), 201-207 (2017). Auer, R et al. Heat-Not-Burn Tobacco Cigarettes: Smoke by Any Other Name. JAMA Intern Med . 177(7), 1050-1052 (2017). Ayaka Igarashi et al. [Why people use heated tobacco products in Japan.] J Dent Hlth . 71(1), 11-18; https://doi.org/10.5834/jdh.71.1_11 (2021). (in Japanese) Helen, G. St., Lii, P. J., Nardone, N. ＆ Benowitz, N. L. IQOS: examination of Philip Morris International's claim of reduced exposure. Tob Control . 27(Suppl 1), s30-s36; 10.1136/tobaccocontrol-2018-054321 (2018). Saha, P. et al. The effects of dual IQOS and cigarette smoke exposure on airway epithelial cells: implications for lung health and respiratory disease pathogenesis. ERJ open Res . 9, 00558-2022; 10.1183/23120541.00558-2022 (2023). The National Health and Nutrition Survey in Japan, 2019. Available from; https://www.mhlw.go.jp/stf/seisakunitsuite/bunya/kenkou_iryou/kenkou/eiyou/r1-houkoku_00002.html (2019). Cummings, K. M., Nahhas, G. J. ＆ Sweanor, D. T. What Is Accounting for the Rapid Decline in Cigarette Sales in Japan? Int J Environ Res Public Health . 17(10), 3570; 10.3390/ijerph17103570 (2020). Chu, S. et al. Impact of Heating Conventional Cigarettes with a Novel Device on Health-related Biomarkers and Cigarette Use Patterns among Chinese Adult Smokers Unwilling to Quit: A Pilot Randomized Controlled Trial. Nicotine Tob Res . XX, 1-11; https://doi.org/10.1093/ntr/ntae177 (2024). Zhang, X. et al. Cigarettes, heated tobacco products and dual use: exhaled carbon monoxide, saliva cotinine and total tobacco consumed by Hong Kong tobacco users. Tob Control . 33(4), 457-463 (2024). Hamoud, J. et al. A Systematic Review Investigating the Impact of Dual Use of E-Cigarettes and Conventional Cigarettes on Smoking Cessation. ERJ Open Research . Preprint at https://doi.org/10.1183/23120541.00902-2024 (2024). Nakajima, M. ＆ al’Absi, M. Nicotine Withdrawal and Stress-Induced Changes in Pain Sensitivity: A Cross-sectional Investigation between Abstinent Smokers and Nonsmokers. Psychophysiology . 51(10), 1015–1022 (2014). Barthélémy, H. et al. Smoking increases the risk of post-acute COVID-19 syndrome: Results from a French community-based survey. Tob Induc Dis . 20, 59; 10.18332/tid/1502952022 (2022). Takakura, K., Suka, M., Kajihara, M. ＆ Koido, S. Clinical features, therapeutic outcomes, and recovery period of long COVID. J Med Virol . 95(1), e28316; 10.1002/jmv.28316 (2022). Buonsenso, D. et al. Post-Acute COVID-19 Sequelae in a Working Population at One Year Follow-Up: A Wide Range of Impacts from an Italian Sample. Int J Environ Res Public Health . 19(17), 11093; 10.3390/ijerph191711093 (2022). Paul, E. ＆ Fancourt, D. Health behaviours the month prior to COVID-19 infection and the development of self-reported long COVID and specific long COVID symptoms: a longitudinal analysis of 1581 UK adults. BMC Public Health . 22(1), 1716; 10.1186/s12889-022-14123-7 (2022). Additional Declarations Competing interest reported. Kazuhisa Asai reports receiving lecture fees from AstraZeneca, GlaxoSmithKline and Sanofi; Tetsuya Watanabe, receiving lecture fees from AstraZeneca, GlaxoSmithKline and Sanofi; Takahiro Tabuchi receiving financial support for research, (consulting fees and lecture fees) from Daiichi Sankyo Healthcare Co., Ltd., Johnson and Johnson K. K., Data Seed Inc. and Workout-Plus LLC, and grants from the Ministry of Health Labour and Welfare, and Japan Society for the Promotion of Science. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6669862","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":460442951,"identity":"38c19903-28fc-4c02-9298-e5948a200fed","order_by":0,"name":"Erika Toyokura","email":"","orcid":"","institution":"Osaka City University","correspondingAuthor":false,"prefix":"","firstName":"Erika","middleName":"","lastName":"Toyokura","suffix":""},{"id":460442955,"identity":"119d6b97-4057-40b9-9f09-ca2cf294bd81","order_by":1,"name":"Kazuhiro Yamada","email":"","orcid":"","institution":"Osaka Metropolitan 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Kazuhisa Asai reports receiving lecture fees from AstraZeneca, GlaxoSmithKline and Sanofi; Tetsuya Watanabe, receiving lecture fees from AstraZeneca, GlaxoSmithKline and Sanofi; Takahiro Tabuchi receiving financial support for research, (consulting fees and lecture fees) from Daiichi Sankyo Healthcare Co., Ltd., Johnson and Johnson K. K., Data Seed Inc. and Workout-Plus LLC, and grants from the Ministry of Health Labour and Welfare, and Japan Society for the Promotion of Science.","formattedTitle":"Dual use of combustible and heated tobacco products exacerbates long COVID prevalence: a JASTIS 2023 cross‑sectional study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSince the onset of COVID-19, individuals adopted preventive measures to avoid infection, and the government launched a vaccination campaign to limit case numbers in Japan \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. However, following the pandemic, personal behaviors shifted, and infections became more common, resulting in a rise in cases \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. This increase has introduced new challenges: the long-term effects of SARS-CoV-2 infection, known as long COVID. Long COVID is defined as a chronic condition occurring after SARS-CoV-2 infection, persisting for \u0026ge;\u0026thinsp;3 months as a continuous, relapsing and remitting, or progressive disease affecting one or more organ systems \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Reported symptoms include fatigue, dyspnea, cough, chest pain, palpitations, fever, memory impairment, difficulty concentrating, dysgeusia, anosmia, hair loss, and arthralgia \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. These prolonged symptoms have significantly affected the quality of life (QOL) of individuals recovering from COVID-19, often continuing for extended periods \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. In contrast, although behavioral restrictions during the pandemic were thought to reduce QOL, they did not result in a significant decline \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Survey data suggest that people in Japan and over 60\u0026nbsp;million globally have experienced long COVID \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, and investigations into associated risk factors have continued since the start of the pandemic \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCombustible cigarette (CC) use is a well-established risk factor for numerous respiratory and cardiovascular diseases and exerts complex effects on the central nervous system. In COVID-19 research, CC use has been identified as a potential risk factor, with studies indicating that smokers may be more likely to develop long COVID \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Recently, alternative tobacco products such as heated tobacco products (HTPs) and electronic cigarettes have gained popularity worldwide. In Japan, HTP use has rapidly expanded, accounting for approximately one-third of the tobacco market as of 2021, according to the Tobacco Institute of Japan \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Given this market share, it is essential to investigate the relationship between COVID-19 and tobacco exposure, including both CCs and HTPs, particularly in countries like Japan. However, few comprehensive studies have examined both products.\u003c/p\u003e \u003cp\u003eHTP aerosols contain harmful and potentially harmful constituents, such as nicotine and carcinogens, though generally in lower quantities than CCs \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Nonetheless, HTPs release some toxic substances in higher concentrations and introduce unique compounds not found in CCs \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Therefore, associations between long COVID, CC users, and HTP users may differ, especially among dual users. Our previous study found that HTP and dual use were associated with a higher likelihood of SARS-CoV-2 infection compared with never use, whereas no significant association was identified for CC use alone \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. This finding suggests that tobacco-related analyses should distinguish between product types. To date, evidence regarding their respective impacts on long COVID remains limited. In this study, we examined the association between long COVID and smoking status in Japan\u0026rsquo;s general population, grouping all CC, HTP, and dual users together. We also explored how this association varies among CC, HTP, and dual users.\u003c/p\u003e"},{"header":"Methods","content":" \u003cp\u003eStudy design and Participants\u003c/p\u003e \u003cp\u003eWe conducted a cross-sectional study using data from the Japan \u0026ldquo;Society and New Tobacco\u0026rdquo; Internet Survey (JASTIS), an internet-based, self-reported survey. JASTIS aimed to assess the impact of new tobacco products in Japan [Study profile is shown in a previous report \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e]. Participants were recruited from a large survey panel managed by a major nationwide internet research agency, Rakuten Insight, which maintains a pool of 2.3\u0026nbsp;million panelists covering all census-defined social categories, such as education, housing tenure, and marital status in Japan. This study used data from the JASTIS 2023 study (conducted in February 2023). A follow-up survey (February 6\u0026ndash;27, 2023), for all 44,681 participants of the 2015\u0026ndash;2023 survey who could be contacted, and 28,437 responses were received (response rate, 63.6%). An additional 5,563 participants completed a new survey (February 25\u0026ndash;27, 2023) using the same questionnaire. These were combined with the follow-up responses for a total of 34,000 participants in the JASTIS 2023 dataset. For this analysis, 2,919 participants were excluded due to inconsistent responses. Of the remaining 31,081 participants, 1,823 and 1,181 participants with a history of COVID-19 within the past 2 months and \u0026gt;\u0026thinsp;1 year prior to the survey date, respectively, were excluded. We also excluded 130 participants who used only tobacco products other than CCs and HTPs, and 140 participants who gave contradictory responses regarding their smoking status. The final analysis included 28,250 participants. This study was conducted in accordance with the Ethical Guidelines for Life Science and Medical Research Involving Human Subjects. All study protocols were reviewed and approved by the Research Ethics Committee of the Osaka International Cancer Institute (No. 20084-8). Informed consent was obtained from all participants.\u003c/p\u003e \u003cp\u003eDefinition of Variables\u003c/p\u003e \u003cp\u003eTabacco use\u003c/p\u003e \u003cp\u003e Smoking status was determined based on participants\u0026rsquo; responses to questions regarding the use of various tobacco products. CCs included both factory-made cigarettes and roll-your-own tobacco. HTPs comprised IQOS, Ploom Tech, Ploom S, Ploom X, glo, and lil HYBRID\u0026mdash;brands available in Japan as of February 2023. Participants were classified into three categories according to their smoking status over the past year: current users, defined as individuals who used tobacco within the past year; former users, who had ceased tobacco use for one year or more; and never users, who had never used any tobacco products. Current users were further subdivided into three groups: dual users (those using both CCs and HTPs), CC users (exclusive users of combustible cigarettes), and HTP users (exclusive users of heated tobacco products).\u003c/p\u003e \u003cp\u003eCOVID-19 history\u003c/p\u003e \u003cp\u003eCOVID-19 history was categorized based on the time of infection relative to the survey date: within 2 months, 2\u0026ndash;6 months, 6 months to 1 year, and \u0026gt;\u0026thinsp;1 year prior. Participants with infections within the previous 2 months were excluded because such cases may not adequately reflect long COVID. Similarly, those with infections\u0026thinsp;\u0026gt;\u0026thinsp;1 year prior were excluded, as post-infection smoking cessation could alter smoking status, potentially leading to misclassification. To assess the association between smoking status and long COVID accurately, the main analysis focused on participants with COVID-19 infections occurring 2\u0026ndash;6 months or 6 months to 1 year before the survey.\u003c/p\u003e \u003cp\u003ePersistent symptoms\u003c/p\u003e \u003cp\u003eLong COVID is commonly defined as symptoms persisting for three months or more following acute infection \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Although the questionnaire used in this study was not specifically designed to evaluate long COVID, it asked whether participants experienced symptoms lasting more than two months, regardless of their COVID-19 history. For this analysis, long COVID was defined as the presence of persistent symptoms lasting more than two months among individuals with a history of COVID-19. To identify long COVID symptoms, we examined symptoms reported to persist for over two months and evaluated their associations with prior COVID-19 infection using multivariate logistic regression. Based on prior research \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e, 12 symptoms were selected for analysis: arthralgia, headache, chest pain, dyspnea, fatigue, dysgeusia, dysosmia, memory impairment, loss of concentration, hair loss, sexual dysfunction, and cough.\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eWe conducted multivariate logistic regression analysis to examine the association between symptoms persisting for \u0026gt;\u0026thinsp;2 months (outcome variable) and explanatory variables including COVID-19 history and smoking status. The models included an interaction term between COVID-19 history and smoking status, along with covariates such as age, sex, BMI, vaccine status (\u0026ge;\u0026thinsp;2 doses vs. \u0026lt;2 doses), and number of comorbidities (none, one, or \u0026ge;\u0026thinsp;2). Comorbidities included 20 conditions: diabetes, dyslipidemia, pneumonia, asthma, atopic dermatitis, allergic rhinitis, periodontal disease, dental caries, angina pectoris, stroke, chronic obstructive pulmonary disease, chronic kidney disease, chronic liver disease, immunodeficiency, malignant tumors, chronic pain syndrome, depression, alcoholism, and other mental disorders. Adjusted odds ratios (aORs) and 95% confidence intervals (CIs) were calculated, with p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant. To assess the interaction between smoking and COVID-19 history, an interaction term was incorporated into the model. Variance inflation factors (VIFs) were calculated for each covariate to evaluate multicollinearity; VIF\u0026thinsp;\u0026lt;\u0026thinsp;5 was considered to indicate non-linearity. Population bias from the internet-based design was addressed using inverse probability weighting (IPW) \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, based on a nationally representative sample from the Comprehensive Survey of Living Conditions of People on Health and Welfare (CSLCPHW) \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. JASTIS and CSLCPHW data were combined, and logistic regression adjusted for background factors (residence, education, housing tenure, and health status) was used to estimate the probability of \u0026ldquo;being a respondent in an internet survey,\u0026rdquo; i.e., propensity score. Details on IPW have been reported previously reported \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Participant numbers in the text and tables reflect weighted values; unweighted numbers are shown in Supplementary Tables. All statistical analyses were conducted using EZR Version 1.54 (Jichi Medical University, Saitama, Japan), a graphical interface for R (The R Foundation for Statistical Computing) \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eParticipant characteristics\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the characteristics of all participants. A total of 28,250 participants were included in the study, of whom 5,067 (17.9%) had a history of COVID-19. Participants with a history of COVID-19 tended to be younger, with a median age of 38 years. Among those infected, 27.5% were current users, compared with 22.5% of those not infected. The prevalence of CC use was 9.6% among those infected and 11.7% among those not infected; for HTP use, 4.4% and 3.4%, respectively; and for dual use, 13.5% and 7.3%, respectively. Participants with a history of COVID-19 were more likely to use dual tobacco products. The proportion of former users was lower among those infected (14.6%) than among those not infected (18.8%). Never users accounted for 57.9% of those infected and 58.7% of those not infected. Participants without a history of COVID-19 were more likely to have received the SARS-CoV-2 vaccine, and increased vaccine doses were associated with a lower likelihood of infection. Among all participants, 24.7% had one comorbidity and 35.7% had two or more, with no significant differences based on COVID-19 history. The median BMI was 21.8 kg/m\u003csup\u003e2\u003c/sup\u003e, with no significant differences based on COVID-19 history. No significant differences were found in educational background, although infected participants tended to report higher incomes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of participants (N\u0026thinsp;=\u0026thinsp;28,250) (weighted)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003ePrevious history of COVID-19\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5067\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, median (IQR) years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47 [34, 63]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50 [35, 66]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38 [28, 48]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSex, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13988 (49.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11354 (49.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2633 (52.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14262 (50.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11827 (51.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2434 (48.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSmoking status, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent user\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6603 (23.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5207 (22.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1395 (27.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOnly CCs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3206 (11.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2720 (11.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e486 (9.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOnly HTPs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1008 (3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e786 (3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e222 (4.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDual tobacco products\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2387 (8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1701 (7.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e686 (13.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormer user\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5101 (18.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4363 (18.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e738 (14.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever user\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16544 (58.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13610 (58.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2934 (57.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eSARS-CoV-2 vaccination status, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTwo times or more\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24642 (87.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20286 (87.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4355 (86.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThree times or more\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21408 (75.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18018 (77.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3389 (66.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFour times or more\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14129 (50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12605 (54.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1524 (30.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eComorbidities*, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOne comorbidity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6977 (24.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5743 (24.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1233 (24.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTwo or more\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10088 (35.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8442 (36.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1645 (32.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, median (IQR) kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.80 [19.72, 24.22]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.80 [19.74, 24.22]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.72 [19.72, 24.12]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEducation, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJunior high school/high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15358 (54.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12785 (55.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2573 (50.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUniversity/technical school or more\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12891 (45.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10397 (44.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2494 (49.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome over 4\u0026nbsp;million yen, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12803 (45.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9826 (42.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2977 (58.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eSymptom lasting more than two months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArthralgia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4978 (17.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4238 (18.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e739 (14.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeadache\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3065 (10.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2424 (10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e640 (12.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChest pain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e732 (2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e535 (2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e197 (3.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCough\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1182 (4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e956 (4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e226 (4.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDyspnea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2283 (8.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1349 (5.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e314 (6.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDysgeusia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e268 (0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e176 (0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91 (1.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDysosmia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e313 (1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e229 (1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84 (1.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMemory impairment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1420 (5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1152 (5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e267 (5.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLoss of concentration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2639 (9.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2164 (9.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e474 (9.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHair loss\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1586 (5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1303 (5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e283 (5.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSexual dysfunction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1642 (5.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1416 (6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e226 (4.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFatigue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2664 (9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2195 (9.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e468 (9.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e* Comorbidities include the following 20 diseases: diabetes, dyslipidemia, pneumonia, asthma, atopic dermatitis, allergic rhinitis, periodontal disease, dental caries, angina pectoris, stroke, chronic obstructive pulmonary disease, chronic kidney disease, chronic liver disease, immunodeficiency, malignant tumors, chronic pain syndrome, depression, alcoholism, and other mental disorders.\u003c/p\u003e \u003cp\u003eAssociation between each symptom of long COVID and smoking\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the results of the analysis examining the associations of COVID-19 history, overall smoking status (combining dual, CC, and HTP users), and their interaction with each persistent symptom. In this analysis, never users and those without a history of COVID-19 were used as the reference group. The interaction term between COVID-19 history and smoking status (current or former users) highlights the influence of smoking on persistent symptoms. Persistent symptoms with COVID-19 history was defined as long COVID. Among current users with a history of COVID-19, aORs significantly increased for arthralgia, chest pain, dyspnea, and dysosmia. In contrast, aORs for dysgeusia, memory impairment, and loss of concentration did not show significant increases. Among former users with a history of COVID-19, aOR significantly increased for arthralgia and headache, but decreased for dyspnea and dysosmia. These results suggest stronger associations between long COVID and both current and former users with a history of COVID-19, compared to never users with a history of COVID-19. In Japan, the use of HTPs has rapidly increased since their introduction as products marketed to contain fewer harmful components than CCs. HTP or dual use, as opposed to exclusive CC use, may be associated with different long COVID risks. Therefore, we further analyzed the associations between long COVID and each tobacco use category\u0026mdash;dual, CC, and HTP users.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between smoking status and history of COVID-19 in each symptom lasting for more than two months.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-\u003c/p\u003e \u003cp\u003eCOVID-19 history\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCOVID-19 history\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNever user\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCC user\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFormer user\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCC user \u0026times;\u003c/p\u003e \u003cp\u003eCOVID-19\u003c/p\u003e \u003cp\u003ehistory\u003c/p\u003e \u003cp\u003e(Interaction)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eformer user \u0026times; COVID-19 history\u003c/p\u003e \u003cp\u003e(Interaction)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSymptom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eaOR\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eaOR\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eaOR\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eaOR\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eaOR\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eaOR\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eaOR\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArthralgia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003cp\u003e(0.85\u0026ndash;1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.27 **\u003c/p\u003e \u003cp\u003e(1.15\u0026ndash;1.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.24**\u003c/p\u003e \u003cp\u003e(1.13\u0026ndash;1.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.37 **\u003c/p\u003e \u003cp\u003e(1.12\u0026ndash;1.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.44**\u003c/p\u003e \u003cp\u003e(1.14\u0026ndash;1.82)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeadache\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003cp\u003e(0.83\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.30**\u003c/p\u003e \u003cp\u003e(1.16\u0026ndash;1.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.28**\u003c/p\u003e \u003cp\u003e(1.13\u0026ndash;1.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003cp\u003e(0.81\u0026ndash;1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.58 **\u003c/p\u003e \u003cp\u003e(1.20\u0026ndash;2.07)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChest pain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003cp\u003e(0.90\u0026ndash;1.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003cp\u003e(0.87\u0026ndash;1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.39**\u003c/p\u003e \u003cp\u003e(1.10\u0026ndash;1.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.34**\u003c/p\u003e \u003cp\u003e(1.60\u0026ndash;3.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.39\u003c/p\u003e \u003cp\u003e(0.87\u0026ndash;2.23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCough\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.59**\u003c/p\u003e \u003cp\u003e(1.28\u0026ndash;1.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.45**\u003c/p\u003e \u003cp\u003e(1.23\u0026ndash;1.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003cp\u003e(0.97\u0026ndash;1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003cp\u003e(0.55\u0026ndash;1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003cp\u003e(0.53\u0026ndash;1.21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDyspnea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003cp\u003e(1.00-1.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.42**\u003c/p\u003e \u003cp\u003e(1.22\u0026ndash;1.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.21**\u003c/p\u003e \u003cp\u003e(1.92\u0026ndash;2.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.81**\u003c/p\u003e \u003cp\u003e(1.35\u0026ndash;2.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.66*\u003c/p\u003e \u003cp\u003e(0.46\u0026ndash;0.95)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFatigue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003cp\u003e(0.87\u0026ndash;1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.32**\u003c/p\u003e \u003cp\u003e(1.21\u0026ndash;1.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.11*\u003c/p\u003e \u003cp\u003e(1.00-1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003cp\u003e(0.84\u0026ndash;1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.43**\u003c/p\u003e \u003cp\u003e(1.15\u0026ndash;1.78)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDysgeusia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.14**\u003c/p\u003e \u003cp\u003e(1.47\u0026ndash;3.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003cp\u003e(0.45\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003cp\u003e(0.55\u0026ndash;1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.78\u003c/p\u003e \u003cp\u003e(0.96\u0026ndash;3.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.64\u003c/p\u003e \u003cp\u003e(0.84\u0026ndash;3.21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDysosmia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.86 **\u003c/p\u003e \u003cp\u003e(1.24\u0026ndash;2.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003cp\u003e(0.89\u0026ndash;1.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003cp\u003e(0.92\u0026ndash;1.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.88*\u003c/p\u003e \u003cp\u003e(1.08\u0026ndash;3.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.40*\u003c/p\u003e \u003cp\u003e(0.16\u0026ndash;0.98)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMemory impairment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.31*\u003c/p\u003e \u003cp\u003e(1.06\u0026ndash;1.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.30**\u003c/p\u003e \u003cp\u003e(1.10\u0026ndash;1.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.54**\u003c/p\u003e \u003cp\u003e(1.32\u0026ndash;1.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003cp\u003e(0.80\u0026ndash;1.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003cp\u003e(0.61\u0026ndash;1.27)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLoss of concentration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003cp\u003e(0.85\u0026ndash;1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.18**\u003c/p\u003e \u003cp\u003e(1.05\u0026ndash;1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.45**\u003c/p\u003e \u003cp\u003e(1.29\u0026ndash;1.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003cp\u003e(0.72\u0026ndash;1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003cp\u003e(0.89\u0026ndash;1.56)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHair loss\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003cp\u003e(0.84\u0026ndash;1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.26**\u003c/p\u003e \u003cp\u003e(1.09\u0026ndash;1.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.33**\u003c/p\u003e \u003cp\u003e(1.14\u0026ndash;1.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003cp\u003e(0.89\u0026ndash;1.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003cp\u003e(0.71\u0026ndash;1.46)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSexual dysfunction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003cp\u003e(0.91\u0026ndash;1.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.68**\u003c/p\u003e \u003cp\u003e(1.45\u0026ndash;1.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.51**\u003c/p\u003e \u003cp\u003e(1.30\u0026ndash;1.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003cp\u003e(0.52\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003cp\u003e(0.46-1.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e\u0026dagger;AOR\u0026thinsp;=\u0026thinsp;adjusted odds ratio. Estimated using multivariable logistic regression modeling with adjustment for the following variables: age, sex, BMI, vaccine status (categorized as two doses or more vs. fewer than two doses), the number of comorbidities (none, one, or two or more), smoking status, COVID-19 history, and interaction term. CI\u0026thinsp;=\u0026thinsp;confidence interval. Values of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant. *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01.\u003c/p\u003e \u003cp\u003eAssociation between each symptom of long COVID and different tobacco products use\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents a re-analysis examining the effects of COVID-19 history and smoking status on individual symptoms. Current users were classified into three categories: dual users, CC users, and HTP users. The interaction term between COVID-19 history and smoking status (dual, CC, HTP, or former users) indicates the influence of smoking status on persistent symptoms. Persistent symptoms with COVID-19 history was defined as long COVID. Among dual users with a history of COVID-19, aORs for five persistent symptoms\u0026mdash;arthralgia, chest pain, dyspnea, dysgeusia, and dysosmia\u0026mdash;were significantly elevated, whereas the aOR for one persistent symptom, sexual dysfunction, was significantly decreased. Among CC users with a history of COVID-19, aORs for three persistent symptoms\u0026mdash;chest pain, dyspnea, and fatigue\u0026mdash;were significantly increased. Among HTP users a history of COVID-19, aORs for two persistent symptoms\u0026mdash;dyspnea and sexual dysfunction\u0026mdash;were significantly increased. Overall, dual users with a history of COVID-19 exhibited more long COVID symptoms than CC and HTP users with a history of COVID-19. Furthermore, dual users with a history of COVID-19 showed a significant association with the most frequently reported long COVID symptoms: arthralgia, chest pain, dyspnea, dysgeusia, and dysosmia \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between smoking status subdivided into combustible tobacco/ HTPs and history of COVID-19 in each symptom lasting for more than two months.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-COVID-19\u003c/p\u003e \u003cp\u003ehistory \u003c/p\u003e \u003cp\u003e(reference)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCOVID-19\u003c/p\u003e \u003cp\u003ehistory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNever user\u003c/p\u003e \u003cp\u003e(reference)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDual user\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCC user\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHTP user\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFormer user\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eDual user \u0026times;\u003c/p\u003e \u003cp\u003eCOVID-19\u003c/p\u003e \u003cp\u003ehistory\u003c/p\u003e \u003cp\u003e(Interaction)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCC user \u0026times;\u003c/p\u003e \u003cp\u003eCOVID-19\u003c/p\u003e \u003cp\u003ehistory\u003c/p\u003e \u003cp\u003e(Interaction)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eHTP user \u0026times;\u003c/p\u003e \u003cp\u003eCOVID-19\u003c/p\u003e \u003cp\u003ehistory\u003c/p\u003e \u003cp\u003e(Interaction)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eFormer user \u0026times;\u003c/p\u003e \u003cp\u003eCOVID-19\u003c/p\u003e \u003cp\u003ehistory\u003c/p\u003e \u003cp\u003e(Interaction)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSymptom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eaOR\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eaOR\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eaOR\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eaOR\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eaOR\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eaOR\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eaOR\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eaOR\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eaOR\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eaOR\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eaOR\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArthralgia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003cp\u003e(0.85\u0026ndash;1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.40**\u003c/p\u003e \u003cp\u003e(1.21\u0026ndash;1.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.24**\u003c/p\u003e \u003cp\u003e(1.11\u0026ndash;1.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003cp\u003e(0.92\u0026ndash;1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.24**\u003c/p\u003e \u003cp\u003e(1.13\u0026ndash;1.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.33*\u003c/p\u003e \u003cp\u003e(1.01\u0026ndash;1.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.31\u003c/p\u003e \u003cp\u003e(0.99\u0026ndash;1.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003cp\u003e(0.94\u0026ndash;2.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.44**\u003c/p\u003e \u003cp\u003e(1.14\u0026ndash;1.81)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeadache\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003cp\u003e(0.83\u0026ndash;1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.56**\u003c/p\u003e \u003cp\u003e(1.32\u0026ndash;1.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003cp\u003e(0.92\u0026ndash;1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.48**\u003c/p\u003e \u003cp\u003e(1.18\u0026ndash;1.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.27**\u003c/p\u003e \u003cp\u003e(1.12\u0026ndash;1.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003cp\u003e(0.62\u0026ndash;1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.40\u003c/p\u003e \u003cp\u003e(1.00-1.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003cp\u003e(0.42\u0026ndash;1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.58**\u003c/p\u003e \u003cp\u003e(1.20\u0026ndash;2.07)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChest pain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003cp\u003e(0.92\u0026ndash;1.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003cp\u003e(0.95\u0026ndash;1.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003cp\u003e(0.65\u0026ndash;1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003cp\u003e(0.91\u0026ndash;2.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.38**\u003c/p\u003e \u003cp\u003e(1.10\u0026ndash;1.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.53**\u003c/p\u003e \u003cp\u003e(1.57\u0026ndash;4.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.84*\u003c/p\u003e \u003cp\u003e(1.00-3.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003cp\u003e(0.65\u0026ndash;3.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.38\u003c/p\u003e \u003cp\u003e(0.86\u0026ndash;2.22)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCough\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.58**\u003c/p\u003e \u003cp\u003e(1.27\u0026ndash;1.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003cp\u003e(0.99\u0026ndash;1.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.74**\u003c/p\u003e \u003cp\u003e(1.44\u0026ndash;2.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003cp\u003e(0.44\u0026ndash;1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003cp\u003e(0.98\u0026ndash;1.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003cp\u003e(0.63\u0026ndash;1.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003cp\u003e(0.46\u0026ndash;1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003cp\u003e(0.14\u0026ndash;1.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003cp\u003e(0.53\u0026ndash;1.21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDyspnea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003cp\u003e(1.00-1.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.45**\u003c/p\u003e \u003cp\u003e(1.14\u0026ndash;1.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.62**\u003c/p\u003e \u003cp\u003e(1.36\u0026ndash;1.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003cp\u003e(0.42\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.22**\u003c/p\u003e \u003cp\u003e(1.93\u0026ndash;2.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.89**\u003c/p\u003e \u003cp\u003e(1.29\u0026ndash;2.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.73**\u003c/p\u003e \u003cp\u003e(1.18\u0026ndash;2.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.45*\u003c/p\u003e \u003cp\u003e(1.19\u0026ndash;5.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.66*\u003c/p\u003e \u003cp\u003e(0.46\u0026ndash;0.95)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFatigue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003cp\u003e(0.81\u0026ndash;1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.47**\u003c/p\u003e \u003cp\u003e(1.24\u0026ndash;1.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003cp\u003e(0.98\u0026ndash;1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.30*\u003c/p\u003e \u003cp\u003e(1.02\u0026ndash;1.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.23**\u003c/p\u003e \u003cp\u003e(1.08\u0026ndash;1.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003cp\u003e(0.55\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.55*\u003c/p\u003e \u003cp\u003e(1.10\u0026ndash;2.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003cp\u003e(0.63\u0026ndash;1.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003cp\u003e(0.78\u0026ndash;1.44)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDysgeusia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.20**\u003c/p\u003e \u003cp\u003e(1.51\u0026ndash;3.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003cp\u003e(0.43\u0026ndash;1.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.54*\u003c/p\u003e \u003cp\u003e(0.31\u0026ndash;0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003cp\u003e(0.48\u0026ndash;2.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003cp\u003e(0.55\u0026ndash;1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.60*\u003c/p\u003e \u003cp\u003e(1.17\u0026ndash;5.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003cp\u003e(0.20\u0026ndash;2.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003cp\u003e(0.02\u0026ndash;2.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.62\u003c/p\u003e \u003cp\u003e(0.83\u0026ndash;3.17)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDysosmia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.90**\u003c/p\u003e \u003cp\u003e(1.27\u0026ndash;2.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003cp\u003e(0.71\u0026ndash;2.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003cp\u003e(0.85\u0026ndash;1.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003cp\u003e(0.68\u0026ndash;2.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003cp\u003e(0.92\u0026ndash;1.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.19**\u003c/p\u003e \u003cp\u003e(1.57\u0026ndash;6.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003cp\u003e(0.41\u0026ndash;2.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003cp\u003e(0.06\u0026ndash;2.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.39*\u003c/p\u003e \u003cp\u003e(0.16\u0026ndash;0.97)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ememory impairment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.31*\u003c/p\u003e \u003cp\u003e(1.07\u0026ndash;1.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.44**\u003c/p\u003e \u003cp\u003e(1.13\u0026ndash;1.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.33**\u003c/p\u003e \u003cp\u003e(1.10\u0026ndash;1.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003cp\u003e(0.62\u0026ndash;1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.54**\u003c/p\u003e \u003cp\u003e(1.32\u0026ndash;1.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003cp\u003e(0.72\u0026ndash;1.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003cp\u003e(0.64\u0026ndash;1.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003cp\u003e(0.62\u0026ndash;2.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003cp\u003e(0.61\u0026ndash;1.27)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eloss of concentration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003cp\u003e(0.85\u0026ndash;1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.37**\u003c/p\u003e \u003cp\u003e(1.16\u0026ndash;1.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003cp\u003e(0.95\u0026ndash;1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003cp\u003e(0.78\u0026ndash;1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.45**\u003c/p\u003e \u003cp\u003e(1.29\u0026ndash;1.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003cp\u003e(0.52\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003cp\u003e(0.76\u0026ndash;1.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003cp\u003e(0.62\u0026ndash;1.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003cp\u003e(0.89\u0026ndash;1.55)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHair loss\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003cp\u003e(0.84\u0026ndash;1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.52**\u003c/p\u003e \u003cp\u003e(1.24\u0026ndash;1.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.20*\u003c/p\u003e \u003cp\u003e(1.00-1.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003cp\u003e(0.71\u0026ndash;1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.33**\u003c/p\u003e \u003cp\u003e(1.14\u0026ndash;1.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003cp\u003e(0.78\u0026ndash;1.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003cp\u003e(0.69\u0026ndash;1.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003cp\u003e(0.71\u0026ndash;2.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003cp\u003e(0.70\u0026ndash;1.46)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSexual dysfunction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003cp\u003e(0.93\u0026ndash;1.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.36**\u003c/p\u003e \u003cp\u003e(1.94\u0026ndash;2.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.48**\u003c/p\u003e \u003cp\u003e(1.25\u0026ndash;1.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003cp\u003e(0.83\u0026ndash;1.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.49\u003c/p\u003e \u003cp\u003e(1.28\u0026ndash;1.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.50**\u003c/p\u003e \u003cp\u003e(0.33\u0026ndash;0.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003cp\u003e(0.37\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.91*\u003c/p\u003e \u003cp\u003e(1.05\u0026ndash;3.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003cp\u003e(0.45-1.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e\u0026dagger;AOR\u0026thinsp;=\u0026thinsp;adjusted odds ratio. Estimated using multivariable logistic regression modeling with adjustment for the following variables: age, sex, BMI, vaccine status (categorized as two doses or more vs. fewer than two doses), the number of comorbidities (none, one, or two or more), smoking status, COVID-19 history, and interaction term. CI\u0026thinsp;=\u0026thinsp;confidence interval. Values of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant. *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01.\u003c/p\u003e \u003cp\u003eEach stratified analysis yielded associations distinct from those in the overall smokers' analysis. For dysgeusia, no significant association was observed in the interaction between overall smokers and COVID-19 history; however, a significant increase in the aOR was noted among dual users with a history of COVID-19. Regarding sexual dysfunction, interaction effects differed between overall smoking status and COVID-19 history and between individual smoking categories and COVID-19 history. These findings suggest that combining data from dual, CC, and HTP users may obscure specific association.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eSmoking exacerbates the prevalence of long COVID, with effects varying by smoking type: dual users, CC users, and HTP users. Dual users with a history of COVID-19 were significantly associated with the most commonly reported long COVID symptoms: arthralgia, chest pain, dyspnea, dysgeusia, and dysosmia. CC users with a history of COVID-19 were associated with chest pain, dyspnea, and fatigue, while HTP users with a history of COVID-19 were associated with dyspnea and sexual dysfunction. Dual users with a history of COVID-19 exhibited a greater number of persistent symptoms compared to CC or HTP users with a history of COVID-19 .\u003c/p\u003e \u003cp\u003eA detailed analysis of tobacco product types provides more precise insight than analyses treating smokers as a homogeneous group. Although the association between smoking and long COVID has been examined in previous studies, results have been inconsistent \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. These discrepancies may stem from differences in sex, race, study populations (e.g., outpatients vs hospitalized patients), or\u0026mdash;critically\u0026mdash;the varying prevalence of smoking types across studies. The rapid rise in HTP and e-cigarette use \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e, coupled with differing regulatory environments, may also contribute to inconsistent findings. Our results revealed differing interaction effects for persistent symptoms depending on smoking type, highlighting the importance of collecting detailed smoking-type data. Based on these findings, future studies should assess smoking status by product type.\u003c/p\u003e \u003cp\u003eCCs contain a complex mixture of over 7,000 chemicals, many of which are toxic and carcinogenic \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. HTPs were developed to reduce exposure to harmful substances by heating, rather than combusting, tobacco. This method is intended to lower the production of toxic compounds typically generated during combustion \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Mainstream smoke from HTPs contains lower levels of harmful constituents than CCs, including tar, nicotine, ammonia, reactive oxygen species, acrolein, and carbon monoxide \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Given these chemical differences, our finding that HTP users with a history of COVID-19 exhibited fewer persistent symptoms than dual or CC users with a history of COVID-19 appears reasonable. Conversely, dual users with a history of COVID-19 exhibited a greater number of persistent symptoms than exclusive CC or HTP users with a history of COVID-19. In Japan, HTP adoption among dual users is often motivated by health concerns, protecting bystander health, or avoiding issues related to smell \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Based on these motivations, one might expect fewer persistent symptoms among dual users. However, our findings showed that dual users with a history of COVID-19 had more persistent symptoms, suggesting that dual use may negate any risk reduction expected from HTP use alone.\u003c/p\u003e \u003cp\u003eIn general, HTPs are considered less harmful than CCs. However, emissions from HTPs such as IQOS contain higher levels of several substances compared with combustible cigarettes \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. An in vitro study demonstrated that dual exposure significantly increased oxidative stress and disrupted mitochondrial homeostasis compared with exposure to either CCs or HTPs alone \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. These findings suggest that concurrent use of combustible CCs and HTPs may exert a synergistic effect, potentially exacerbating long COVID symptoms.\u003c/p\u003e \u003cp\u003eAnother possible explanation for the greater number of persistent symptoms among dual users with a history of COVID-19 is that their total tobacco consumption may exceed that of individuals using only CCs or HTPs. Dual users often adopt HTPs to reduce tobacco consumption due to health concerns or other factors, such as issues with the smell of CCs, workplace restrictions, or fewer limitations on home use. In Japan, CC sales have notably declined since the introduction of HTPs \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e, whereas HTP sales have markedly increased following their launch \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. This trend raises concerns that dual users may increase their overall tobacco consumption. Two studies reported that initiating HTP use among CC users can lead to increased overall tobacco use \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. In a systematic review of dual users of e-cigarettes and CCs, most continued cigarette use through sustained dual or exclusive CC use \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. However, the increase in total consumption was modest, with an average rise of fewer than five units per day. Given that exclusive HTP users with a history of COVID-19 exhibited fewer persistent symptoms than CC users with a history of COVID-19, dual users, even if they consume slightly more tobacco overall, would be expected to show symptom prevalence between CC and HTP users. However, in our study, dual users with a history of COVID-19 exhibited a greater number of persistent symptoms than both CC and HTP users with a history of COVID-19, indicating a synergistic effect rather than merely increased total consumption.\u003c/p\u003e \u003cp\u003eFormer users with a history of COVID-19 demonstrated a different pattern of symptom associations. Specifically, they had a higher likelihood of experiencing headache and joint pain. However, unlike dual users, CC, and HTP, former users with a history of COVID-19 did not show increased rates of symptoms commonly reported in long COVID, such as chest pain, dysgeusia, or dysosmia. Additionally, former users with a history of COVID-19 exhibited a lower likelihood of dyspnea and dysosmia. Taken together, these results suggest that smoking cessation is associated with a lower risk of characteristic long COVID symptoms including dysgeusia, dysosmia, and chest pain. Headache and joint pain\u0026mdash;commonly reported as nicotine withdrawal symptoms\u0026mdash;may reflect increased pain sensitivity following smoking cessation \u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. Thus, smoking cessation among individuals with a history of COVID-19 may elevate headache and joint pain, although it confers overall protective effects against long COVID and is strongly recommended \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. Our findings demonstrate that, unlike current smokers, former users did not exhibit typical long COVID symptoms, such as chest pain, dysgeusia, or dysosmia, and had a lower likelihood of dyspnea and dysosmia. Based on these findings, we recommend smoking cessation to reduce the risk of long COVID.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, this is a cross-sectional study, and the questionnaires were not specifically developed to identify long COVID symptoms. Nonetheless, the analysis of interaction effects between smoking status and COVID-19 infection enabled the detection of long COVID.\u003c/p\u003e \u003cp\u003eSecond, because the quantity and duration of tobacco use were not assessed, dual users may have been heavy smokers whose persistent symptoms increased in a dose-dependent manner. However, as previously noted, some studies suggest that overall cigarette consumption by dual users does not increase significantly \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e, and that levels of harmful and potentially harmful constituents are significantly higher in HTP aerosols than in CC smoke \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e, implying a potential synergistic effect of CCs and HTPs. Future studies should collect detailed data on the consumption of each tobacco product.\u003c/p\u003e \u003cp\u003eThird, we defined long COVID as symptoms lasting\u0026thinsp;\u0026gt;\u0026thinsp;2 months following COVID-19 infection. However, long COVID is now characterized as a chronic condition that emerges after SARS-CoV-2 infection and persists for at least three months \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. This discrepancy may slightly overestimate the observed prevalence of symptoms. Nevertheless, the overall finding\u0026mdash;that smoking status significantly influences persistent symptoms\u0026mdash;is unlikely to change. Prospective studies are needed to assess the joint effects of smoking status and COVID-19 infection on long COVID under the updated definition and with detailed information on tobacco product consumption.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eSmoking in individuals with a history of COVID-19 increases the prevalence of persistent symptoms, with effects varying by smoking type\u0026mdash;dual, CC, or HTP use. HTPs should not be assumed to be less harmful, as dual use may heighten the risk of long COVID. Disaggregating smoking subgroups offers more accurate insights into the association between smoking behaviors and long COVID. Smoking cessation should be promoted to reduce the risks associated with long COVID.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank all researchers involved in the data accumulation and all participants of JASTIS 2023, and Editage (www.editage.jp) for English language editing.\u003c/p\u003e\n\u003cp\u003eThis work was supported by JSPS KAKENHI Grant Number JP23K07631.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received for conducting this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eErika Toyokura: statistical analysis and interpretation of data and drafting of the manuscript and critical revision of the manuscript intellectual content\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eKazuhiro Yamada: statistical analysis, interpretation of data and study supervision and critical revision of the manuscript intellectual content\u003c/p\u003e\n\u003cp\u003eKazuhisa Asai: study concept and design, Study concept and design, study supervision and critical revision of the manuscript intellectual content\u003c/p\u003e\n\u003cp\u003eMasaya Tsutsumi, Takahiro Ueda, Kaho Hirai, Yuichiro Furukawa, Atsushi Miyamoto, Misako Nishimura, Kanako Sato and Tetsuya Watanabe: critical revision of the manuscript intellectual content\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTakahiro Tabuchi: full access to all of the data in the study and takes responsibility for the integrity of the data, acquisition of data, and critical revision of the manuscript intellectual content\u003c/p\u003e\n\u003cp\u003eTomoya Kawaguchi: study supervision and critical revision of the manuscript intellectual content\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data are not publicly available due to privacy restrictions, but available from Takahiro Tabuchi (E-mail: [email protected]) on reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eErika Toyokura, Kazuhiro Yamada, Kazuhisa Asai, Masaya Tsutsumi, Takahiro Ueda, Kaho Hirai, Yuichiro Furukawa, Atsushi Miyamoto, Misako Nishimura, Kanako Sato, Tetsuya Watanabe, Takahiro Tabuchi, Tomoya Kawaguchi\u003c/p\u003e\n\u003cp\u003eKazuhisa Asai reports receiving lecture fees from AstraZeneca, GlaxoSmithKline and Sanofi; Tetsuya Watanabe, receiving lecture fees from AstraZeneca, GlaxoSmithKline and Sanofi; Takahiro Tabuchi receiving financial support for research, (consulting fees and lecture fees) from Daiichi Sankyo Healthcare Co., Ltd., Johnson and Johnson K. K., Data Seed Inc. and Workout-Plus LLC, and grants from the Ministry of Health Labour and Welfare, and Japan Society for the Promotion of Science.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eOdani, S., Honda, H. ＆ Tabuchi, T. Association of COVID-19 Vaccine Intake with Diagnosis, Hospitalization, and Oxygenation/Ventilation: A Longitudinal Analysis, 2021-2022, \u003cem\u003eJapan. Vaccines (Basel)\u003c/em\u003e. \u003cstrong\u003e12(11), \u003c/strong\u003e1264; 10.3390/vaccines12111264 (2024).\u003c/li\u003e\n\u003cli\u003eKobayashi, K. et al. COVID-19 vaccination benefits in preventing severe disease in mild-to-moderate cases: An analysis in the first specialized hospital for COVID-19 in Japan.\u003cem\u003e Respir Investig\u003c/em\u003e. \u003cstrong\u003e61(2),\u003c/strong\u003e 230\u0026ndash;239 (2023).\u003c/li\u003e\n\u003cli\u003eTakamatsu, A. et al. 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Health behaviours the month prior to COVID-19 infection and the development of self-reported long COVID and specific long COVID symptoms: a longitudinal analysis of 1581 UK adults. \u003cem\u003eBMC Public Health\u003c/em\u003e. \u003cstrong\u003e22(1), \u003c/strong\u003e1716; 10.1186/s12889-022-14123-7 (2022).\u003c/li\u003e\n\u003c/ol\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6669862/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6669862/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMultiple reports have identified smoking as a risk factor for long COVID; however, few have distinguished among tobacco product types. We conducted a cross-sectional study using data from an internet-based survey administered in February 2023 to examine the association between 12 persistent symptoms and smoking status in participants with a history of COVID-19. A total of 28,250 participants were included, of whom 5,067 had a history of COVID-19. Among current tobacco users with a history of COVID-19, the odds ratios for persistent symptoms were significantly elevated for four symptoms\u0026mdash;arthralgia, chest pain, dyspnea, and dysosmia\u0026mdash;compared to never smokers. For subgroup analysis, current tobacco users were categorized into three groups: combustible cigarette (CC), heated tobacco product (HTP), and dual users. Among dual users, the odds ratios were significantly elevated for five symptoms: arthralgia, chest pain, dyspnea, dysgeusia, and dysosmia. CC users showed significantly higher odds for chest pain, dyspnea, and fatigue, while HTP users for dyspnea and sexual dysfunction. Smoking in individuals with a history of COVID-19 exacerbates the prevalence of persistent symptoms, and its impact may vary by smoking type. Separately analyzing smoking subgroups allows for a more accurate understanding of the relationship between long COVID and smoking behavior.\u003c/p\u003e","manuscriptTitle":"Dual use of combustible and heated tobacco products exacerbates long COVID prevalence: a JASTIS 2023 cross‑sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-28 06:44:45","doi":"10.21203/rs.3.rs-6669862/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-22T12:35:54+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-22T12:05:36+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-21T08:52:12+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-05-15T07:12:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"55a316cf-1a84-4392-9e0f-37d039e792f1","owner":[],"postedDate":"May 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":48910043,"name":"Health sciences/Medical research/Epidemiology"},{"id":48910044,"name":"Health sciences/Diseases/Infectious diseases/Viral infection"}],"tags":[],"updatedAt":"2025-11-17T16:03:45+00:00","versionOfRecord":{"articleIdentity":"rs-6669862","link":"https://doi.org/10.1038/s41598-025-22050-x","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-11-14 15:58:30","publishedOnDateReadable":"November 14th, 2025"},"versionCreatedAt":"2025-05-28 06:44:45","video":"","vorDoi":"10.1038/s41598-025-22050-x","vorDoiUrl":"https://doi.org/10.1038/s41598-025-22050-x","workflowStages":[]},"version":"v1","identity":"rs-6669862","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6669862","identity":"rs-6669862","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}