Prevalence and risk factors of lung nodules in a non-smoking Chinese population: A prospective study of low-dose computed tomography screening

Prevalence and risk factors of lung nodules in a non-smoking Chinese population: A prospective study of low-dose computed tomography screening | 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 Research Article Prevalence and risk factors of lung nodules in a non-smoking Chinese population: A prospective study of low-dose computed tomography screening Wei Tang, Yanyan Tang, Yi Teng, Jianwei Wang, Lina Zhou, Haohua Zhu, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7282428/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Mar, 2026 Read the published version in BMC Pulmonary Medicine → Version 1 posted 10 You are reading this latest preprint version Abstract Background: Low-dose computed tomography (LDCT) has significantly improved early detection of lung cancer, especially among high-risk populations. However, the risk factors and lung nodule distributions in non-smoking populations remain underexplored, particularly in Asia. Therefore, in this study, we aimed to explore the risk factors and delineate the detection rate, including lung nodule distributions discovered using LDCT, in a non-smoking Chinese population. Methods: This prospective, single-center study included asymptomatic adults who underwent LDCT screening at the National Cancer Center of China between January 2006 and December 2023. Lung nodules were defined as at least one non-calcified nodule, while clinically relevant lung nodules were defined as at least one solid or partially solid nodule, or at least one non-solid nodule. Multivariate logistic regression models were employed to identify risk factors associated with lung nodules. The outcomes included detection rates and distribution of both nodule types. Results: Of 23,271 participants, lung nodules were detected in 40.1% (9,342/23,271); 4.3% (1,012/23,271) had clinically relevant lung nodules. Risk factors for lung nodule development included female sex (odds ratio [OR] 1.12, 95% confidence interval [CI] 1.06–1.19), second-hand smoke (SHS) exposure (OR 1.59, 95% CI 1.49–1.70), and emphysema (OR 1.49, 95% CI 1.24–1.78). The incidence of lung nodules increased with age, peaking at 70–74 years (OR 3.10, 95% CI 2.53–3.79). Risk factors for clinically relevant lung nodules included increasing age, SHS exposure (OR 1.44, 95% CI 1.22–1.69), and emphysema (OR 1.84, 95% CI 1.36–2.49). Detection rates for both nodule types were positively correlated with age (lung nodules: women 33.7–61.4%, men 32.3–57.5%; clinically relevant lung nodules: women 2.4–12.4%, men 2.2–15.1%). Conclusions: This real-world study of a non-smoking Chinese population revealed high lung nodule detection rates, with women exhibiting a higher detection rate than men. SHS has emerged as a significant risk factor for both lung and clinically relevant nodules. These findings highlight the importance of refining LDCT screening strategies and risk models for non-smoking populations in Asia. low-dose computed tomography non-smoking population lung nodule women second-hand smoke Figures Figure 1 Figure 2 Background In 2022, lung cancer exhibited the highest incidence and mortality rates among malignant tumors in men and women in China, surpassing breast cancer as the most prevalent cancer among women.[ 1 ] Randomized clinical trials have demonstrated that low-dose computed tomography (LDCT) screening for lung cancer in high-risk groups can significantly reduce mortality.[ 2 , 3 ] Additionally, opportunistic screening has been associated with lower rates of lung cancer-related deaths and overall mortality.[ 4 ] Consequently, LDCT has gained substantial global support from researchers and clinical studies for lung cancer screening (LCS). The increasing utilization of LDCT has led to a higher detection rate of lung nodules. Over 95% of nodules detected through LDCT are benign, [5] presenting a significant challenge in identifying risk factors for these nodules during LCS. Most guidelines rely on nodule density and size for risk assessment. However, recent LCS studies emphasize the need for a comprehensive evaluation of lung nodules, incorporating individual characteristics and nodule-specific attributes into risk grading. For instance, models developed by the Mayo Clinic and Brock University incorporate multiple predictors, including age, smoking history, and nodule diameter, to estimate lung nodule risk. [6, 7] Moreover, several LCS trials have identified risk factors associated with lung nodules [7] . Conversely, these risk assessments predominantly focus on White individuals from Western countries.[ 8 ] Although risk assessments have been conducted in Asian populations, non-smoking participants were not analyzed separately.[ 9 ] Furthermore, limited data exists on lung nodule risk factors and the effectiveness of LDCT in detecting nodules among non-smokers in Asian countries, particularly China. Globally, the lung cancer rate among non-smokers is increasing and is now the seventh most common cause of cancer-related deaths worldwide. [10] A notably higher proportion of lung cancer cases are detected among non-smokers in Asia than among those in Europe and the United States. While smoking contributes to over 70% of lung cancer diagnoses in Europe and the US, it accounts for less than 40% of lung cancer cases among Asian women. [11] In Asia, lung nodules exhibit unique characteristics, and factors other than tobacco play a significant role in their development. For example, indoor and outdoor air pollution, the high incidence of lung adenocarcinoma in female non-smokers, and the prevalence of sarcoidosis and other infectious diseases that can influence lung nodules must be considered. While nodules in these conditions are predominantly benign, they often exhibit clinical presentations and imaging features that resemble those of malignant nodules. [12, 13] Consequently, it is clinically essential to study the risk factors, detection rates, and distribution of lung nodules in non-smokers in Asia. Despite the progress, existing research on lung nodules has notable limitations. For instance, prior studies have primarily focused on Caucasian populations, including only solid nodules, [8] or involved participants with smoking histories. [9] Additionally, some studies report LDCT-detected nodules without analyzing associated risk factors. [14, 15] In this study, we aimed to evaluate the risk, detection rate, and distribution of lung nodules identified using LDCT in a large, real-world cohort of non-smokers in China. Addressing these gaps will advance the understanding of lung nodule risks in this demographic. Methods Study design and participants This prospective, single-center, hospital-based study was approved by the Ethics Committee of the National Cancer Center of China (NCC) /Cancer Hospital of the Chinese Academy of Medical Sciences (Approval No. 14–115/905) and performed in accordance with the principles of the Declaration of Helsinki. All participants provided informed consent, allowing their respective institutions to offer financial support for LCS. This manuscript was written following the STROBE Statement checklist. This study enrolled asymptomatic individuals who underwent LDCT LCS at the Department of Cancer Prevention, NCC/Cancer Hospital of the Chinese Academy of Medical Sciences, between January 2006 and December 2023. We excluded individuals unable to comprehend the study's objectives, risks, or benefits, or those who could not provide informed consent. Further, individuals ineligible for curative lung cancer surgery owing to severe heart disease, advanced respiratory disease, or other significant comorbidities, as well as those with a prior diagnosis of lung cancer, were also excluded. Participants completed a lung cancer risk assessment questionnaire developed by our organization in conjunction with LDCT screening. The questionnaire collected data on demographics, comorbidities, second-hand smoke (SHS) exposure, history of occupational exposure to hazardous substances, and family history of lung cancer. Demographic data included age, sex, educational level, frequency of physical activity, and intake of fruits and vegetables. Educational level was categorized as low (primary school or lower), medium (middle/high school), or high (college/university or higher). Comorbidities included chronic bronchitis or bronchiectasis, emphysema, chronic obstructive pulmonary disease (COPD), asthma, tuberculosis, angina pectoris, diabetes mellitus, and hypertension. SHS exposure was defined as passive smoking for over 20 years while living with or working with a smoker. Occupational exposure history was defined as exposure to asbestos or soot for over a year. Physical activity levels were categorized as low, medium, or high. Detailed definitions and variable assignments are provided in Additional file 1. LDCT scanning, the imaging assessment, and management of lung nodules Our LDCT scanning protocol and nodule management followed the I-ELCAP program (I-ELCAP Screening Program 2006), [16] which classifies nodule density based on its ability to obscure the lung parenchyma completely. Nodules were categorized as solid nodules (SN), partial solid nodules (PSN), and non-solid nodules (NSN). Non-calcified nodules (NCN) were defined as nodules that did not meet the typical criteria for benign calcified nodules. Detailed information on LDCT parameters, imaging assessments, and nodule management can be found in the Supplementary Appendix of our previously published paper. [17] Definition and description of lung nodules The presence or absence of nodules was defined according to the National Lung Screening Trial (NLST) guidelines for positive nodules.[ 18 ] A nodule was defined as present if LDCT detected at least one NCN with a long diameter of ≥ 4 mm and absent if no nodule was detected or the detected nodule had a long diameter of < 4 mm. Clinically relevant lung nodules were defined according to I-ELCAP guidelines as positive if LDCT screening revealed at least one SN or PSN with a mean diameter ≥ 5 mm, or at least one NSN with a mean diameter ≥ 8 mm. [16] Positive nodules typically required monthly follow-ups as per management recommendations. Each report described up to 10 larger nodules if multiple nodules were detected. Statistical analyses Continuous variables with a normal distribution are expressed as mean ± standard deviation; variables not following a normal distribution are expressed as median with interquartile range (IQR). Data normality was assessed using the Kolmogorov–Smirnov test. For normally distributed data, quantitative variables were analyzed using the independent two-sample t-test; otherwise, the Mann–Whitney U test was applied. Categorical variables are presented as percentages, with frequencies compared using chi-square or Fisher's exact tests. Nodule risk factors were identified using multivariate logistic regression analysis, with proportions and 95% confidence intervals (CIs) reported. A P value of < 0.05 was considered statistically significant. Statistical analyses were performed using SPSS (version 25.0; SPSS Inc.) and R (version 3.6.0; R Foundation for Statistical Computing, Vienna, Austria; https://www.R-project.org/ ). Results Characteristics of the study population As shown in the flowchart in Fig. 1 , 49,099 participants completed the questionnaire between January 2006 and December 2023. After excluding 25,828 participants, the final study population consisted of 23,271 participants. Based on lung nodule distributions, 59% (13,929/23,271) of participants had no nodules, and 40.1% (9,342/23,271) had present nodules. Table 1 presents the participant stratification based on the presence or absence of lung nodules. The median age of participants with lung nodules was 50 years (IQR: 43–58), which was higher than that of participants without nodules (47 years; IQR: 41–54). The median age of participants with clinically relevant nodules was 54 years (IQR: 47–61), compared with the 48 years (IQR: 41–56) of those without clinically relevant nodules. Participants with lung nodules were more likely to be women and exposed to SHS (all P < 0.001). Additionally, hypertension and emphysema were associated with an increased likelihood of lung nodules (all P < 0.05). Conversely, increased exercise frequency and higher educational attainment were linked to a reduced likelihood of pulmonary nodules ( P < 0.05). Among participants with lung nodules, multiple nodules were detected in 42.2% (2,519/5,964) of women and 39.9% (1,346/3,378) of men, with an observed significant difference ( P = 0.024). Of the clinically relevant nodules, 69.3% (798/1,012) were solid, 20.1% (232/1,012) were partially solid, and 10.6% (122/1,012) were non-solid. The detection rates of non-solid and partially solid nodules were higher in women than in men (36.9% vs. 20.6%). The median size of clinically relevant nodules was 6.4 mm (IQR: 5.5–8.8) (Table 2 ). Table 1 The characteristics of participants stratified according to the presence or absence of lung nodules Characteristics Nodules P Clinically lung nodules P Total Present (n = 9,342) Absent (n = 13,929) Present (n = 1,023) Absent (n = 22,248) Sex < 0.001 0.600 Male 3,378 (36.2%) 5,493 (39.4%) 382 (37.3%) 8,489 (38.2%) Female 5,964 (63.8%) 8,436 (60.6%) 641 (62.7%) 13,759 (61.8%) Median (Q1–Q3), years 50 (43–58) 47 (41–54) 54 (47–61) 48 (41–56) Age (years) range < 0.001 < 0.001 < 45 2,725 (29.2%) 5,512 (39.6%) 187 (18.3%) 8,050 (36.2%) 45–49 1,660 (17.8%) 2,804 (20.1%) 162 (15.8%) 4,302 (19.3%) 50–54 1,619 (17.3%) 2,204 (15.8%) 1,76 (17.2%) 3,647 (16.4%) 55–59 1,314 (14.1%) 1,596(11.5%) 179 (17.5%) 2,731 (12.3%) 60–64 1,000 (10.7%) 1,007 (7.2%) 129 (12.6%) 1,878 (8.4%) 65–69 622 (6.7%) 515 (3.7%) 104 (10.2%) 1,033 (4.6%) 70–74 259 (2.8%) 174 (1.3%) 54 (5.3%) 379 (1.7%) ≥ 75 143 (1.5%) 117 (0.8%) 32 (3.1%) 228 (1.0%) BMI (kg/m 2 ) 0.812 0.231 < 18.5 4,685 (50.1%) 6,951 (49.9%) 37 (3.62%) 704 (3.16%) 18.5–23.9 286 (3.1%) 455 (3.3%) 484 (47.3%) 11,152 (50.1%) 24–27.9 3,393 (36.3%) 5,043 (36.2%) 380 (37.1%) 8,056 (36.2%) ≥ 28 978 (10.5%) 1,480 (10.6%) 122 (11.9%) 2,336 (10.5%) Educational level <0.001 <0.001 Low 301 (3.2%) 307 (2.2%) 65 (6.4%) 543 (2.4%) Medium 2,650 (28.4%) 3,375 (24.2%) 344 (33.6%) 5,681 (25.5%) High 6,391 (68.4%) 10,247 (73.6%) 614 (60.0%) 16,024 (72.0%) SHS <0.001 0.001 No 1,781 (19.1%) 3,596 (25.8%) 194 (19.0%) 5,183 (23.3%) Yes 7,561 (80.9%) 10,333 (74.2%) 829 (81.0%) 17,065 (76.7%) Family history of lung cancer 0.859 0.507 No 7,858 (84.1%) 11,703 (84.0%) 868 (84.8%) 18,693 (84.0%) Yes 1,484 (15.9%) 2,226 (16.0%) 155 (15.2%) 3,555 (16.0%) Occupational exposure to hazardous substances 0.480 0.590 No 9,046 (96.8%) 13,463 (96.7%) 986 (96.4%) 21,523 (96.7%) Yes 296 (3.2%) 466 (3.3%) 37 (3.6%) 725 (3.3%) History of other cancers 0.155 0.127 No 8,826 (94.5%) 13,220 (94.9%) 958 (93.6%) 21,088 (94.8%) Yes 516 (5.5%) 709 (5.1%) 65 (96.7%) 1,160 (5.2%) COPD 1.000 0.165 No 8,783 (94.0%) 13,096 (94.0%) 951 (93.0%) 20,928 (94.0%) Yes 559 (6.0%) 833 (6.0%) 72 (7.0%) 1,320 (6.0%) Emphysema < 0.001 <0.001 No 9,071 (97.1%) 13,700 (98.4%) 971 (94.9%) 21,800 (98.0%) Yes 271 (2.9%) 229 (1.6%) 52 (5.1%) 448 (2.0%) Angina pectoris 0.562 0.028 No 9,119 (97.6%) 13,614 (97.7%) 989 (96.7%) 21,744 (97.7%) Yes 223 (2.4%) 315 (2.3%) 34 (3.3%) 504 (2.3%) Diabetes 0.176 0.085 No 8,805 (94.3%) 13,187 (94.7%) 954 (93.3%) 21,038 (94.6%) Yes 537 (5.7%) 742 (5.3%) 69 (6.7%) 1,210 (5.4%) Hypertension < 0.001 < 0.001 No 7,509 (80.4%) 11,521 (82.7%) 771 (75.4%) 18,259 (82.1%) Yes 1,83 3(19.6%) 2,408 (17.3%) 252 (24.6%) 3,989 (17.9%) Physical activity 0.002 < 0.001 Low 118 (1.3%) 125 (0.9%) 23 (2.3%) 220 (1.0%) Medium 1,358 (14.5%) 1,885 (13.5%) 168 (16.4%) 3,075 (13.8%) High 7,866 (84.2%) 11,919 (85.6%) 832 (81.3%) 18,953 (85.2%) COPD, chronic obstructive pulmonary disease; SD, standard deviation; SHS, second-hand smoke; BMI, body mass index. Table 2 Nodules were stratified according to sex Nodules Nodule present, n (%) P value Total, n (%) Clinically lung nodules, n (%) P value Total, n (%) Sex Male, 3,378 (36.2%) Female, 5,964 (63.8%) 9,342 (100.0%) Male, 382 (37.3%) Female, 641 (62.7%) 1,023 (100.0%) Number 0.024 0.06 Solitary 2,032 (60.1%) 3,445 (57.8%) 5,477 (58.6%) 338 (88.5%) 58 6(91.4%) 924 (89.4%) Multiple 1,346 (39.9%) 2,519 (42.2%) 3,865 (41.4%) 44 (11.5%) 55 (8.6%) 110 (10.6%) Morphology of nodules 437 715 < 0.001 1,152 (100.0%) Solid - - - 347 (79.4%) 451 (63.1%) 798 (69.3%) Part-solid - - - 55 (12.6%) 177 (24.8%) 232 (20.1%) Non-solid - - - 35 (8.0%) 87 (12.1%) 122 (10.6%) Nodule size, mm - - - 0.095 Median (Q1–Q3) - - - 6.2 (5.5–8.4) 6.5 (5.5–8.9) 6.4 (5.5–8.8) Q, quartile; SD, standard deviation. [Insert Tables 1 and 2 here] Risk factors for the presence of nodules Figure 2 displays a forest plot of the multifactorial analysis of non-smoking participants with at least one lung nodule. Risk factors for different nodule types, as identified by univariate logistic regression, are presented in Additional file 2. Multivariate regression analysis identified female sex (odds ratio [OR] 1.12, 95% CI 1.06–1.19), SHS exposure (OR 1.59, 95% CI 1.49–1.70), and emphysema (OR 1.49, 95% CI 1.24–1.78) as risk factors for the presence of lung nodules. The likelihood of lung nodules also increased with age, peaking at 70–74 years (OR 3.10, 95% CI 2.53–3.79). For clinically relevant lung nodules, risk factors included increasing age, SHS exposure (OR, 1.44; 95% CI, 1.22–1.69), and emphysema (OR, 1.84; 95% CI, 1.36–2.49). Conversely, higher educational attainment was associated with reduced odds of clinically relevant nodules (OR 0.52, 95% CI 0.39–0.69) (Additional file 3). Lung nodule detection rates stratified by sex and age To evaluate the distribution of lung nodules by sex and age, we analyzed detection rates in the "nodule present" category. Female participants had a higher overall detection rate than male participants (41.4% [5,964/14,400] vs. 38.1% [3378/8,871], P < 0.001). Detection rates were higher in female participants than in male participants in the 50–54, 55–59, and 65–69 age groups, with significant differences ( P < 0.05). In the category of clinically relevant lung nodules, detection rates were 4.5% (641/14,400) in female participants and 4.3% (382/8,871) in male participants, with no significant difference between groups (Table 3 ). Table 3 Lung nodule detection rate stratified by sex and age Nodule present, % (n/n) P value Total, % (n/n) Clinically lung nodules, % (n/n) P value Total, % (n/n) Total Male, 38.1 (3,378/8,871) Female, 41.4 (5,964/14,400) < 0.001 40.1, (9,342/23,271) Male, 4.3 (382/8,871) Female, 4.5 (641/14,400) 0.600 4.4, (1,023/23,271) Age (years) range < 45 32.3 (1,154/3,572) 33.7 (1,571/4,665) 0.190 33.1 (2,725/8,237) 2.2 (77/3,572) 2.4 (110/4,665) 0.541 2.3 (187/8,237) 45–49 36.8 (614/1,669) 37.4 (1,046/2,795) 0.671 37.2 (1,660/4,464) 4.0 (66/1,669) 3.4 (96/2,795) 0.369 3.6 (162/4,464) 50–54 39.6 (514/1,298) 43.8 (1,105/2,525) 0.014 42.3 (1,619/3,823) 4.2 (55/1,298) 4.8 (121/2,525) 0.438 4.6 (176/3,823) 55–59 42.4 (430/1,015) 46.6 (884/1,895) 0.027 45.2 (1,314/2,910) 6.0 (61/1,015) 6.2 (118/1,895) 0.816 6.2 (179/2,910) 60–64 48.6 (304/625) 50.4 (696/1,382) 0.475 49.8 (1,000/2,007) 6.2 (39/625) 6.5 (90/1,382) 0.818 6.4 (129/2,007) 65–69 50.5 (208/412) 57.1 (414/725) 0.031 54.7 (622/1,137) 11.2 (46/412) 8.0 (58/725) 0.075 9.1 (104/1,137) 70–74 57.5 (100/174) 61.4 (159/259) 0.415 59.8 (259/433) 12.6 (22/174) 12.4 (32/259) 0.929 12.5 (54/433) ≥ 75 50.9 (54/106) 57.8 (89/154) 0.275 55.0 (143/260) 15.1 (16/106) 10.4 (16/154) 0.256 12.3 (32/260) [Insert Table 3 here] Discussion Key findings This large LCS cohort study, conducted in a hospital in China, examined the risk factors and detection rates of lung nodules, with a focus on nodule detection rates and distribution by sex and age in a non-smoking population. Independent risk factors included female sex, SHS exposure, emphysema, and older age, while higher education levels were associated with lower odds of clinically relevant lung nodules. Among the non-smoking population in China, the detection rates of lung nodules and clinically relevant lung nodules were 40.1% (9,342/23,271) and 4.3% (1,012/23,271), respectively. Women had a significantly higher detection rate of lung nodules (41.4%, 5,964/14,400) than men (38.1%, 3,378/8,871; P < 0.001). The detection rate for clinically relevant lung nodules was also slightly higher in women (4.5%, 641/14,400) than in men (4.3%, 382/8,871). Strengths and limitations This study possesses several notable strengths that enhance the validity and significance of its findings. Its primary strength lies in the inclusion of a large, prospective, real-world cohort of over 23,000 asymptomatic individuals, making it one of the most substantial investigations of its kind. A key novelty is its specific focus on a non-smoking Chinese population, a critical demographic that has been significantly underrepresented in previous lung cancer screening research, which has predominantly centered on Caucasian smokers. By concentrating on non-smokers, this study directly addresses a crucial knowledge gap regarding the etiology and prevalence of lung nodules in a population where risk factors beyond tobacco play a significant role. The use of standardized low-dose computed tomography (LDCT) protocols and established nodule definition criteria (I-ELCAP, NLST) ensures methodological rigor and enhances the comparability of our findings with international studies. Furthermore, the collection of comprehensive data Despite these strengths, we acknowledge some limitations. First, the study was conducted at a single center, the National Cancer Center of China. Although this center recruits participants from diverse geographical regions, the findings may not be fully generalizable to the entire Chinese population, and potential selection biases inherent to a hospital-based cohort may exist. Second, this analysis is cross-sectional, focusing exclusively on baseline LDCT screening data. Consequently, we could not assess the longitudinal evolution of nodules—such as growth, resolution, or the incidence of new nodules—nor could we report on the ultimate rates of malignancy. This precludes any conclusions about the long-term clinical significance of the detected nodules. Finally, the reliance on a self-administered questionnaire for data on comorbidities and lifestyle exposures, such as second-hand smoke, is subject to recall bias, which may have influenced the risk factor analysis. Future longitudinal studies are necessary. Comparison with similar research A strong correlation between lung nodules and increasing age has been consistently reported globally in both smoking and non-smoking populations. [8, 9, 14, 19, 20] For instance, a study in Western Europe showed that individuals aged ≥ 66 years were more than twice as likely to have lung nodules (≥ 100 mm 3 ) compared with those aged 45–55 years.[ 8 ] Similarly, our study found that older age groups were more likely to develop lung nodules, with those aged 70–74 years being more than three times as likely as those aged < 45 years (OR 3.10, 95% CI 2.53–3.79). The risk was even higher for clinically relevant nodules, with individuals aged 70–74 years being approximately five times as likely to have lung nodules as those aged < 45 years (OR 5.12, 95% CI 3.66–7.15). Age-related cumulative exposure to risk factors, including asbestos, radioactive materials, and air pollution, likely contributes to this increased risk, particularly in regions such as China, where air pollution is more severe than in the United States.[ 21 ] Female sex has emerged as a significant risk factor for lung cancer in Asia. A meta-analysis of 141,396 ever-smokers and 109,251 non-smokers revealed that non-smoking women in Asia had a higher lung cancer risk than non-smoking men, comparable to that of high-risk smokers (≥ 30 pack-years; OR = 0.99, 95% CI: 0.65–1.50).[ 22 ] Our study further established that female sex is an independent risk factor for the development of lung nodules. In contrast, Western populations often consider male sex to be at higher risk for pulmonary nodules, likely due to differences in smoking behaviors. Lung nodule prevalence increases with prolonged and intense smoking, showing a higher incidence among male smokers than among female smokers. In the United States, 90.4% of men and 84.3% of women diagnosed with lung cancer are smokers, while in Europe, the figures are 93.3% for men and 68% for women.[ 23 – 25 ] Genetic factors may also play a role, as evidenced by differences in oncogene alterations between Asians and Caucasians, including epidermal growth factor receptor mutations, occurring in 40–55% of Asians compared to 15–25% in Caucasians.[ 26 , 27 ] SHS exposure is another well-established risk factor for lung cancer.[ 28 ] A meta-analysis of 20 randomized controlled trials in Chinese populations, published between 1996 and 2015, revealed that SHS exposure at work increased lung cancer risk by 1.78 (OR 1.78, 95% CI 1.29–2.44), while SHS exposure at home raised the risk by 1.53 times (OR 1.53, 95% CI 1.01–2.33).[ 29 ] The "2020 China Smoking and Health Hazards Report" published by the National Health Commission of China revealed that up to 68.1% of non-smokers in China are exposed to SHS in public places. [30] Similarly, our study identified an association between SHS exposure and an increased risk of lung nodules. Higher education levels were associated with a reduced risk of developing clinically relevant lung nodules. The findings align with the results of a European study that reported an increased risk of lung nodules among smokers with lower education levels.[ 8 ] Higher education levels have been shown to protect against lung cancer.[ 31 ] Education, a proxy for socioeconomic status, may influence exposure to environmental carcinogens and high-risk occupations, affecting lung nodule prevalence. Emphysema, a known independent risk factor for lung cancer,[ 32 , 33 ] was associated with a three-fold higher incidence density of lung cancer in patients with emphysema than in those without emphysema. Multifactorial analysis showed that the presence of emphysema on LDCT was an independent risk factor for lung cancer, even in the absence of airway obstruction (RR 2.10, 95% CI 0.79–5.58).[ 32 ] Our study also indicates that pulmonary emphysema is an independent risk factor for the development of lung nodules. Reduced lung function in individuals with emphysema may impair the clearance of abnormal cells, thereby increasing the likelihood of lung nodule formation. Moreover, emphysema patients are often more prone to lung infections and other comorbidities, which can occasionally present as lung nodules. Using the NLST diameter threshold of 4 mm, this study reported a lung nodule detection rate of 40.1% (9,342/23,271), consistent with the findings of a population-based study in Northern Europe (42.0%, 4,377/10,431), which used a comparable threshold.[ 34 ] Our study reported a higher lung nodule detection rate using a 4-mm threshold than that reported by other studies employing the same threshold, including a multicenter study in Shanghai (29.9%, 4,336/14,506) and a single-center cross-sectional study in Korea (16.2%, 6,066/37,436).[ 15 , 35 ] Although the above studies were conducted on both non-smokers and smokers. However, the detection rate in our study was lower than that in a previous Japanese study (42.6%), which included both smoking and non-smoking individuals and used a 5-mm threshold for positivity.[ 14 ] Using the I-ELCAP (2006) definition of a clinically relevant nodule —solid or partially solid NCN ≥ 5 mm or non-solid non-calcified lung nodules ≥ 8 mm—the detection rate in our cohort was 4.3% (1,012/23,271). Additionally, our study revealed a higher detection rate of lung nodules in women than in men when applying the 4-mm threshold (42.2% vs. 38.1%, respectively), with multiple nodules also being more common in women than in men (42.2% vs. 39.9%). In contrast, a Nordic population-based study found higher rates of lung nodules and multiple nodules in men across all age groups, except in the 70–74.9-year-old (1,012/23,271) and ≥ 80-year-old subgroups.[ 8 ] Conversely, this study, conducted in a predominantly non-smoking cohort, included only SN.[ 34 ] The observed discrepancies may be attributed to differences in inclusion criteria, population characteristics, imaging protocols, and interpretation methods. To the best of our knowledge, no prior study of this scale has comprehensively evaluated risk factors, detection rates, and distribution patterns of lung nodules in non-smoking Asian populations. Most previous studies have focused on high-risk populations with a history of smoking or on Caucasian cohorts. [ 8 , 9 ] Our study utilized LDCT screening in asymptomatic non-smokers, collecting extensive epidemiological and imaging data. These findings provide a critical basis for understanding lung nodule characteristics in Asian non-smokers and align with current trends in risk stratification and nodule evaluation using LDCT LCS. Explanations of findings The high detection rate of lung nodules (40.1%) and clinically relevant nodules (4.3%) in this large cohort of 23,271 asymptomatic non-smokers underscores the significant burden of pulmonary abnormalities in this population. Our multifactorial analysis provides explanations for this prevalence by identifying several independent risk factors. The strong association with advancing age, which peaked in the 70–74-year group, likely reflects the cumulative effect of long-term environmental exposures combined with age-related declines in pulmonary immune surveillance and cellular repair mechanisms. Furthermore, the findings establish female sex and exposure to second-hand smoke (SHS) as significant risk factors. The increased risk for women aligns with emerging evidence of higher susceptibility to lung carcinogens in non-smoking Asian females, potentially due to genetic or hormonal factors. The notable risk associated with SHS exposure (OR 1.59, 95% CI 1.49–1.70) highlights the profound impact of environmental tobacco smoke as a pulmonary irritant and carcinogen. Similarly, the link between emphysema and an elevated risk for both general and clinically relevant nodules is consistent with its known role in impairing lung clearance mechanisms and creating a chronic inflammatory state conducive to nodule formation. Implications and actions needed The results of this study have significant implications for both public health initiatives and clinical lung cancer screening (LCS) strategies in China. The data compellingly suggest that current screening paradigms, which are heavily weighted toward smoking history, may fail to identify a large segment of the at-risk population. The high nodule prevalence among non-smokers, particularly in women, older adults, and those with SHS exposure or emphysema, indicates that these factors are critical for risk stratification. Based on these findings, several actions are warranted. First, from a public health perspective, targeted educational campaigns should be developed to raise awareness among non-smokers about the risks of SHS and other environmental exposures. These efforts should prioritize high-risk demographics identified in our study. Second, and most critically, clinical guidelines for LDCT screening should be re-evaluated. Our findings provide a strong evidence base for expanding eligibility criteria to include high-risk non-smokers. The development and validation of refined risk-prediction models that incorporate age, sex, SHS exposure, and comorbidities such as emphysema are essential to improve the accuracy and efficiency of LCS and ultimately reduce the lung cancer burden in this substantial population. Conclusions Our study demonstrates that older age, female sex, SHS exposure, and emphysema are associated with pulmonary nodules in an asymptomatic, non-smoking Asian population. These findings enhance understanding of LCS in China and support the refinement of screening eligibility criteria. Additionally, our research reveals a high detection rate of lung nodules among the non-smoking Chinese population, offering detailed distribution patterns that provide valuable insights for managing pulmonary nodules in this demographic. Abbreviations LC, lung cancer; LCS, lung cancer screening; LDCT, low-dose computed tomography; NLST, National Lung Screening Trial; SHS, second-hand smoke; COPD, chronic obstructive pulmonary disease; I-ELCAP, International Early Lung Cancer Action Program; SN, solid nodules; PSN: partial solid nodules, NSN: Non-solid nodules; IQR, interquartile range. Declarations Ethics approval and consent to participate : The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. This study was approved by the Ethics Committee of the National Cancer Center of China (NCC) /Cancer Hospital of the Chinese Academy of Medical Sciences (Application ID:14-115/905). All participants provided informed consent, allowing their respective institutions to offer financial support for lung cancer screening. Consent for publication : Availability of data and materials: The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests : The authors declare that they have no competing interests. Funding : This work was supported by the National Key R&D Program of China (grant number 2017YFC1308700, 2020AAA0109500), Beijing Hope Run Special Fund of Cancer Foundation of China (grant number LC2021A25). Authors' contributions : Conceptualisation, NW, TYY; methodology, YYT, WT, SJZ, JWW, HHZ, and LNZ; verification of the underlying data, NW, YYT, YH, JWW, ZJX, KZ, and WT; original draft, NW, YYT, SJZ, and WT; review and editing, NW, YYT, YH, JWW, YT, and LNZ. All authors read and approved the final manuscript. Acknowledgments: We thank all study participants for their cooperation. We would like to thank Editage (http://www.editage.cn/) for the English language editing. Clinical trial number: not applicable. References Zheng RS, Chen R, Han BF, Wang SM, Li L, Sun KX, et al. [Cancer incidence and mortality in China, 2022]. Zhonghua Zhong Liu Za Zhi. 2024;46:221-31. National Lung Screening Trial Research Team, Aberle DR, Adams AM, Berg CD, Black WC, Clapp JD, et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med. 2011;365:395-409. de Koning HJ, van der Aalst CM, de Jong PA, Scholten ET, Nackaerts K, Heuvelmans MA, et al. 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Low-dose CT lung cancer screening in never-smokers and smokers: Results of an eight-year observational study. Transl Lung Cancer Res. 2020;9:10-22. Kim YW, Kang HR, Kwon BS, Lim SY, Lee YJ, Park JS, et al. Low-dose chest computed tomographic screening and invasive diagnosis of pulmonary nodules for lung cancer in never-smokers. Eur Respir J. 2020;56:2000177. Henschke CI. International Early Lung Cancer Action Program. Screening protocol; 2006. Available from: http://www.ielcap.org/ielcap.pdf,10/20/2006. Tang W, Liu L, Huang Y, Zhao S, Wang J, Liang M, et al. Opportunistic lung cancer screening with low-dose computed tomography in National Cancer Center of China: The first 14 years’ experience. Cancer Med. 2024;13:e6914. National Lung Screening Trial Research Team, Aberle DR, Berg CD, Black WC, Church TR, Fagerstrom RM, et al. The National Lung Screening Trial: Overview and study design. Radiology. 2011;258:243-53. Kang HR, Cho JY, Lee SH, Lee YJ, Park JS, Cho YJ, et al. Role of low-dose computerized tomography in lung cancer screening among never-smokers. J Thorac Oncol. 2019;14:436-44. Fan L, Wang Y, Zhou Y, Li Q, Yang W, Wang S, et al. Lung cancer screening with low-dose CT: Baseline screening results in shanghai. Acad Radiol. 2019;26:1283-91. Yang D, Liu Y, Bai C, Wang X, Powell CA. Epidemiology of lung cancer and lung cancer screening programs in China and the United States. Cancer Lett. 2020;468:82-7. Triphuridet N, Zhang SS, Nagasaka M, Gao Y, Zhao JJ, Syn NL, et al. Low-dose computed tomography (LDCT) lung cancer screening in Asian female never-smokers is as efficacious in detecting lung cancer as in Asian male ever-smokers: A systematic review and meta-analysis. J Thorac Oncol. 2023;18:698-717. Lam DCL, Liam CK, Andarini S, Park S, Tan DSW, Singh N, et al. Lung cancer screening in Asia: An expert consensus report. J Thorac Oncol. 2023;18:1303-22. Agudo A, Ahrens W, Benhamou E, Benhamou S, Boffetta P, Darby SC, et al. Lung cancer and cigarette smoking in women: A multicenter case-control study in Europe. Int J Cancer. 2000;88:820-7. Siegel DA, Fedewa SA, Henley SJ, Pollack LA, Jemal A. Proportion of never smokers among men and women with lung cancer in 7 US states. JAMA Oncol. 2021;7:302-4. Shi Y, Au JSK, Thongprasert S, Srinivasan S, Tsai CM, Khoa MT, et al. A prospective, molecular epidemiology study of EGFR mutations in Asian patients with advanced non-small-cell lung cancer of adenocarcinoma histology (Pioneer). J Thorac Oncol. 2014;9:154-62. Yang CY, Yang JCH, Yang PC. Precision management of advanced non-small cell lung cancer. Annu Rev Med. 2020;71:117-36. Ni X, Xu N, Wang Q. Meta-analysis and systematic review in environmental tobacco smoke risk of female lung cancer by research type. Int J Environ Res Public Health. 2018;15:1348. Sheng L, Tu JW, Tian JH, Chen HJ, Pan CL, Zhou RZ. A meta-analysis of the relationship between environmental tobacco smoke and lung cancer risk of nonsmoker in China. Med (Baltim). 2018;97:e11389. Report on health hazards of smoking in China. 2020. https://www.gov.cn/xinwen/2021-05/30/content_5613994.htm. Guo LW, Chen Q, Shen YC, Meng QC, Zheng LY, Wu Y, et al. Evaluation of a low-dose computed tomography lung cancer screening program in Henan, China. JAMA Netw Open. 2020;3:e2019039. de Torres JP, Bastarrika G, Wisnivesky JP, Alcaide AB, Campo A, Seijo LM, et al. Assessing the relationship between lung cancer risk and emphysema detected on low-dose CT of the chest. Chest. 2007;132:1932-8. Durawa A, Dziadziuszko K, Jelitto M, Gąsiorowski M, Kaszubowski M, Szurowska E, et al. Emphysema and lung cancer risk. Transl Lung Cancer Res. 2024;13:1918-28. Cai J, Vonder M, Pelgrim GJ, Rook M, Kramer G, Groen HJM, et al. Distribution of solid lung nodules presence and size by age and sex in a northern European nonsmoking population. Radiology. 2024;312:e231436. Yang W, Qian F, Teng J, Wang H, Manegold C, Pilz LR, et al. Community-based lung cancer screening with low-dose CT in China: Results of the baseline screening. Lung Cancer. 2018;117:20-6. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 04 Mar, 2026 Read the published version in BMC Pulmonary Medicine → Version 1 posted Editorial decision: Revision requested 27 Nov, 2025 Reviews received at journal 19 Oct, 2025 Reviews received at journal 18 Oct, 2025 Reviewers agreed at journal 15 Oct, 2025 Reviewers agreed at journal 09 Oct, 2025 Reviewers invited by journal 08 Oct, 2025 Editor invited by journal 02 Sep, 2025 Editor assigned by journal 19 Aug, 2025 Submission checks completed at journal 19 Aug, 2025 First submitted to journal 03 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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01:39:25","extension":"xml","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":195971,"visible":true,"origin":"","legend":"","description":"","filename":"d4030953b40f49b6ae88575f0c0952561structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7282428/v1/b8d046204d3b1835380ad85b.xml"},{"id":93977831,"identity":"2baa5e83-b1c1-4b1b-987a-54918ab511ea","added_by":"auto","created_at":"2025-10-21 01:31:25","extension":"html","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":209584,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7282428/v1/813da59c8f81ad04b566a23c.html"},{"id":93977823,"identity":"fcf8cee5-6e19-4ce3-880c-4c9730574669","added_by":"auto","created_at":"2025-10-21 01:31:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":232888,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the participant selection and lung nodule presentation based on baseline LDCT screening. CT, computed tomography; LCS, for lung cancer screening; LDCT, low-dose computed tomography; NSN, non-solid nodules; PSN, partial solid nodules; SN, solid nodules\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7282428/v1/cf651c98838655e6e582b7bb.png"},{"id":93977826,"identity":"445d73e3-5a1a-4953-a320-bd5fbe41d18f","added_by":"auto","created_at":"2025-10-21 01:31:25","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":379816,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of the multifactorial analysis of non-smoking participants with at least one lung nodule\u003c/p\u003e\n\u003cp\u003eLow-dose computed tomography scans with at least one lung nodule are compared using odds ratios for each factor. Nodule types are classified into two categories: the first being at least one non-calcified nodule with a long diameter greater than or equal to 4 mm; clinically relevant nodules were at least one non-calcified SN or PSN with a mean diameter of ≥5 mm, or at least one NSN with a mean diameter of ≥8 mm. SN, solid nodule; PSN, partial solid nodule; NSN, non-solid nodule; SHS, second-hand smoke; OR, odds ratio; CI, confidence interval\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7282428/v1/914fd26779f9bc0f16815cc7.png"},{"id":104252286,"identity":"ed8aa4f3-a8fd-4c5a-9134-e1fb97ebf176","added_by":"auto","created_at":"2026-03-09 16:17:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1562292,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7282428/v1/a65f9e30-c3d2-4b29-a6ac-81218b31ba01.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003ePrevalence and risk factors of lung nodules in a non-smoking Chinese population: A prospective study of low-dose computed tomography screening\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eIn 2022, lung cancer exhibited the highest incidence and mortality rates among malignant tumors in men and women in China, surpassing breast cancer as the most prevalent cancer among women.[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1\u003c/span\u003e] Randomized clinical trials have demonstrated that low-dose computed tomography (LDCT) screening for lung cancer in high-risk groups can significantly reduce mortality.[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] Additionally, opportunistic screening has been associated with lower rates of lung cancer-related deaths and overall mortality.[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e4\u003c/span\u003e] Consequently, LDCT has gained substantial global support from researchers and clinical studies for lung cancer screening (LCS).\u003c/p\u003e\u003cp\u003eThe increasing utilization of LDCT has led to a higher detection rate of lung nodules. Over 95% of nodules detected through LDCT are benign,\u003csup\u003e[5]\u003c/sup\u003e presenting a significant challenge in identifying risk factors for these nodules during LCS. Most guidelines rely on nodule density and size for risk assessment. However, recent LCS studies emphasize the need for a comprehensive evaluation of lung nodules, incorporating individual characteristics and nodule-specific attributes into risk grading. For instance, models developed by the Mayo Clinic and Brock University incorporate multiple predictors, including age, smoking history, and nodule diameter, to estimate lung nodule risk.\u003csup\u003e[6, 7]\u003c/sup\u003e Moreover, several LCS trials have identified risk factors associated with lung nodules\u003csup\u003e[7]\u003c/sup\u003e. Conversely, these risk assessments predominantly focus on White individuals from Western countries.[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] Although risk assessments have been conducted in Asian populations, non-smoking participants were not analyzed separately.[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] Furthermore, limited data exists on lung nodule risk factors and the effectiveness of LDCT in detecting nodules among non-smokers in Asian countries, particularly China.\u003c/p\u003e\u003cp\u003eGlobally, the lung cancer rate among non-smokers is increasing and is now the seventh most common cause of cancer-related deaths worldwide.\u003csup\u003e[10]\u003c/sup\u003e A notably higher proportion of lung cancer cases are detected among non-smokers in Asia than among those in Europe and the United States. While smoking contributes to over 70% of lung cancer diagnoses in Europe and the US, it accounts for less than 40% of lung cancer cases among Asian women.\u003csup\u003e[11]\u003c/sup\u003e In Asia, lung nodules exhibit unique characteristics, and factors other than tobacco play a significant role in their development. For example, indoor and outdoor air pollution, the high incidence of lung adenocarcinoma in female non-smokers, and the prevalence of sarcoidosis and other infectious diseases that can influence lung nodules must be considered. While nodules in these conditions are predominantly benign, they often exhibit clinical presentations and imaging features that resemble those of malignant nodules.\u003csup\u003e[12, 13]\u003c/sup\u003e Consequently, it is clinically essential to study the risk factors, detection rates, and distribution of lung nodules in non-smokers in Asia.\u003c/p\u003e\u003cp\u003eDespite the progress, existing research on lung nodules has notable limitations. For instance, prior studies have primarily focused on Caucasian populations, including only solid nodules,\u003csup\u003e[8]\u003c/sup\u003e or involved participants with smoking histories.\u003csup\u003e[9]\u003c/sup\u003e Additionally, some studies report LDCT-detected nodules without analyzing associated risk factors.\u003csup\u003e[14, 15]\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eIn this study, we aimed to evaluate the risk, detection rate, and distribution of lung nodules identified using LDCT in a large, real-world cohort of non-smokers in China. Addressing these gaps will advance the understanding of lung nodule risks in this demographic.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eStudy design and participants\u003c/p\u003e\u003cp\u003e This prospective, single-center, hospital-based study was approved by the Ethics Committee of the National Cancer Center of China (NCC) /Cancer Hospital of the Chinese Academy of Medical Sciences (Approval No. 14\u0026ndash;115/905) and performed in accordance with the principles of the Declaration of Helsinki. All participants provided informed consent, allowing their respective institutions to offer financial support for LCS. This manuscript was written following the STROBE Statement checklist.\u003c/p\u003e\u003cp\u003eThis study enrolled asymptomatic individuals who underwent LDCT LCS at the Department of Cancer Prevention, NCC/Cancer Hospital of the Chinese Academy of Medical Sciences, between January 2006 and December 2023. We excluded individuals unable to comprehend the study's objectives, risks, or benefits, or those who could not provide informed consent. Further, individuals ineligible for curative lung cancer surgery owing to severe heart disease, advanced respiratory disease, or other significant comorbidities, as well as those with a prior diagnosis of lung cancer, were also excluded. Participants completed a lung cancer risk assessment questionnaire developed by our organization in conjunction with LDCT screening.\u003c/p\u003e\u003cp\u003eThe questionnaire collected data on demographics, comorbidities, second-hand smoke (SHS) exposure, history of occupational exposure to hazardous substances, and family history of lung cancer. Demographic data included age, sex, educational level, frequency of physical activity, and intake of fruits and vegetables. Educational level was categorized as low (primary school or lower), medium (middle/high school), or high (college/university or higher). Comorbidities included chronic bronchitis or bronchiectasis, emphysema, chronic obstructive pulmonary disease (COPD), asthma, tuberculosis, angina pectoris, diabetes mellitus, and hypertension. SHS exposure was defined as passive smoking for over 20 years while living with or working with a smoker. Occupational exposure history was defined as exposure to asbestos or soot for over a year. Physical activity levels were categorized as low, medium, or high. Detailed definitions and variable assignments are provided in Additional file 1.\u003c/p\u003e\u003cp\u003eLDCT scanning, the imaging assessment, and management of lung nodules\u003c/p\u003e\u003cp\u003eOur LDCT scanning protocol and nodule management followed the I-ELCAP program (I-ELCAP Screening Program 2006),\u003csup\u003e[16]\u003c/sup\u003e which classifies nodule density based on its ability to obscure the lung parenchyma completely. Nodules were categorized as solid nodules (SN), partial solid nodules (PSN), and non-solid nodules (NSN). Non-calcified nodules (NCN) were defined as nodules that did not meet the typical criteria for benign calcified nodules. Detailed information on LDCT parameters, imaging assessments, and nodule management can be found in the Supplementary Appendix of our previously published paper.\u003csup\u003e[17]\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eDefinition and description of lung nodules\u003c/p\u003e\u003cp\u003e The presence or absence of nodules was defined according to the National Lung Screening Trial (NLST) guidelines for positive nodules.[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e18\u003c/span\u003e] A nodule was defined as present if LDCT detected at least one NCN with a long diameter of \u0026ge;\u0026thinsp;4 mm and absent if no nodule was detected or the detected nodule had a long diameter of \u0026lt;\u0026thinsp;4 mm. Clinically relevant lung nodules were defined according to I-ELCAP guidelines as positive if LDCT screening revealed at least one SN or PSN with a mean diameter\u0026thinsp;\u0026ge;\u0026thinsp;5 mm, or at least one NSN with a mean diameter\u0026thinsp;\u0026ge;\u0026thinsp;8 mm.\u003csup\u003e[16]\u003c/sup\u003e Positive nodules typically required monthly follow-ups as per management recommendations. Each report described up to 10 larger nodules if multiple nodules were detected.\u003c/p\u003e\u003cp\u003eStatistical analyses\u003c/p\u003e\u003cp\u003eContinuous variables with a normal distribution are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation; variables not following a normal distribution are expressed as median with interquartile range (IQR). Data normality was assessed using the Kolmogorov\u0026ndash;Smirnov test. For normally distributed data, quantitative variables were analyzed using the independent two-sample t-test; otherwise, the Mann\u0026ndash;Whitney U test was applied. Categorical variables are presented as percentages, with frequencies compared using chi-square or Fisher's exact tests. Nodule risk factors were identified using multivariate logistic regression analysis, with proportions and 95% confidence intervals (CIs) reported. A \u003cem\u003eP\u003c/em\u003e value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant. Statistical analyses were performed using SPSS (version 25.0; SPSS Inc.) and R (version 3.6.0; R Foundation for Statistical Computing, Vienna, Austria; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.R-project.org/\u003c/span\u003e\u003cspan address=\"https://www.R-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eCharacteristics of the study population\u003c/p\u003e\u003cp\u003eAs shown in the flowchart in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, 49,099 participants completed the questionnaire between January 2006 and December 2023. After excluding 25,828 participants, the final study population consisted of 23,271 participants. Based on lung nodule distributions, 59% (13,929/23,271) of participants had no nodules, and 40.1% (9,342/23,271) had present nodules.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the participant stratification based on the presence or absence of lung nodules. The median age of participants with lung nodules was 50 years (IQR: 43\u0026ndash;58), which was higher than that of participants without nodules (47 years; IQR: 41\u0026ndash;54). The median age of participants with clinically relevant nodules was 54 years (IQR: 47\u0026ndash;61), compared with the 48 years (IQR: 41\u0026ndash;56) of those without clinically relevant nodules. Participants with lung nodules were more likely to be women and exposed to SHS (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Additionally, hypertension and emphysema were associated with an increased likelihood of lung nodules (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Conversely, increased exercise frequency and higher educational attainment were linked to a reduced likelihood of pulmonary nodules (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Among participants with lung nodules, multiple nodules were detected in 42.2% (2,519/5,964) of women and 39.9% (1,346/3,378) of men, with an observed significant difference (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.024). Of the clinically relevant nodules, 69.3% (798/1,012) were solid, 20.1% (232/1,012) were partially solid, and 10.6% (122/1,012) were non-solid. The detection rates of non-solid and partially solid nodules were higher in women than in men (36.9% vs. 20.6%). The median size of clinically relevant nodules was 6.4 mm (IQR: 5.5\u0026ndash;8.8) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\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\u003eThe characteristics of participants stratified according to the presence or absence of lung nodules\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eNodules\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eClinically lung nodules\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePresent\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;9,342)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAbsent\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;13,929)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePresent\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1,023)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAbsent\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;22,248)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.600\u003c/p\u003e\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\u003e3,378 (36.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5,493 (39.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e382 (37.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8,489 (38.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u003e5,964 (63.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8,436 (60.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e641 (62.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e13,759 (61.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian (Q1\u0026ndash;Q3), years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50 (43\u0026ndash;58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e47 (41\u0026ndash;54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e54 (47\u0026ndash;61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e48 (41\u0026ndash;56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years) range\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;45\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2,725 (29.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5,512 (39.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e187 (18.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8,050 (36.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e45\u0026ndash;49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,660 (17.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,804 (20.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e162 (15.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4,302 (19.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e50\u0026ndash;54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,619 (17.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,204 (15.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1,76 (17.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3,647 (16.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e55\u0026ndash;59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,314 (14.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,596(11.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e179 (17.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2,731 (12.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e60\u0026ndash;64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,000 (10.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,007 (7.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e129 (12.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1,878 (8.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e65\u0026ndash;69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e622 (6.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e515 (3.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e104 (10.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1,033 (4.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e70\u0026ndash;74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e259 (2.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e174 (1.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e54 (5.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e379 (1.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e143 (1.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e117 (0.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e32 (3.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e228 (1.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.812\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.231\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;18.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4,685 (50.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6,951 (49.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e37 (3.62%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e704 (3.16%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e18.5\u0026ndash;23.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e286 (3.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e455 (3.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e484 (47.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11,152 (50.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e24\u0026ndash;27.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3,393 (36.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5,043 (36.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e380 (37.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8,056 (36.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e978 (10.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,480 (10.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e122 (11.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2,336 (10.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducational level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e301 (3.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e307 (2.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e65 (6.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e543 (2.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2,650 (28.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3,375 (24.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e344 (33.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5,681 (25.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6,391 (68.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10,247 (73.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e614 (60.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e16,024 (72.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSHS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,781 (19.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3,596 (25.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e194 (19.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5,183 (23.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7,561 (80.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10,333 (74.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e829 (81.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e17,065 (76.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFamily history of lung cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.859\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.507\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7,858 (84.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11,703 (84.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e868 (84.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e18,693 (84.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,484 (15.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,226 (16.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e155 (15.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3,555 (16.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOccupational exposure to hazardous substances\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.480\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.590\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9,046 (96.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13,463 (96.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e986 (96.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e21,523 (96.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e296 (3.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e466 (3.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e37 (3.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e725 (3.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHistory of other cancers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.155\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.127\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8,826 (94.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13,220 (94.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e958 (93.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e21,088 (94.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e516 (5.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e709 (5.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e65 (96.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1,160 (5.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCOPD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.165\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8,783 (94.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13,096 (94.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e951 (93.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e20,928 (94.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e559 (6.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e833 (6.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e72 (7.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1,320 (6.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmphysema\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9,071 (97.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13,700 (98.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e971 (94.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e21,800 (98.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e271 (2.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e229 (1.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e52 (5.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e448 (2.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAngina pectoris\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.562\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9,119 (97.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13,614 (97.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e989 (96.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e21,744 (97.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e223 (2.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e315 (2.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e34 (3.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e504 (2.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.176\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.085\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8,805 (94.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13,187 (94.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e954 (93.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e21,038 (94.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e537 (5.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e742 (5.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e69 (6.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1,210 (5.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7,509 (80.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11,521 (82.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e771 (75.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e18,259 (82.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,83 3(19.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,408 (17.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e252 (24.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3,989 (17.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhysical activity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e118 (1.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e125 (0.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e23 (2.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e220 (1.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,358 (14.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,885 (13.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e168 (16.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3,075 (13.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7,866 (84.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11,919 (85.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e832 (81.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e18,953 (85.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eCOPD, chronic obstructive pulmonary disease; SD, standard deviation; SHS, second-hand smoke; BMI, body mass index.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\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\u003eNodules were stratified according to sex\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNodules\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eNodule present, n (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTotal, n (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eClinically lung nodules, n (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eTotal, n (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale,\u003c/p\u003e\u003cp\u003e3,378 (36.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFemale,\u003c/p\u003e\u003cp\u003e5,964 (63.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9,342\u003c/p\u003e\u003cp\u003e(100.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMale,\u003c/p\u003e\u003cp\u003e382 (37.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eFemale,\u003c/p\u003e\u003cp\u003e641 (62.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1,023 (100.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSolitary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2,032 (60.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3,445 (57.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5,477 (58.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e338 (88.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e58 6(91.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e924 (89.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMultiple\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,346 (39.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,519 (42.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3,865 (41.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e44 (11.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e55 (8.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e110 (10.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMorphology of nodules\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e437\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e715\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1,152 (100.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSolid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e347 (79.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e451 (63.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e798 (69.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePart-solid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e55 (12.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e177 (24.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e232 (20.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-solid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e35 (8.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e87 (12.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e122 (10.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNodule size, mm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.095\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian (Q1\u0026ndash;Q3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.2 (5.5\u0026ndash;8.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6.5 (5.5\u0026ndash;8.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e6.4 (5.5\u0026ndash;8.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eQ, quartile; SD, standard deviation.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e[Insert Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e here]\u003c/p\u003e\u003cp\u003eRisk factors for the presence of nodules\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e displays a forest plot of the multifactorial analysis of non-smoking participants with at least one lung nodule. Risk factors for different nodule types, as identified by univariate logistic regression, are presented in Additional file 2. Multivariate regression analysis identified female sex (odds ratio [OR] 1.12, 95% CI 1.06\u0026ndash;1.19), SHS exposure (OR 1.59, 95% CI 1.49\u0026ndash;1.70), and emphysema (OR 1.49, 95% CI 1.24\u0026ndash;1.78) as risk factors for the presence of lung nodules. The likelihood of lung nodules also increased with age, peaking at 70\u0026ndash;74 years (OR 3.10, 95% CI 2.53\u0026ndash;3.79). For clinically relevant lung nodules, risk factors included increasing age, SHS exposure (OR, 1.44; 95% CI, 1.22\u0026ndash;1.69), and emphysema (OR, 1.84; 95% CI, 1.36\u0026ndash;2.49). Conversely, higher educational attainment was associated with reduced odds of clinically relevant nodules (OR 0.52, 95% CI 0.39\u0026ndash;0.69) (Additional file 3).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eLung nodule detection rates stratified by sex and age\u003c/p\u003e\u003cp\u003eTo evaluate the distribution of lung nodules by sex and age, we analyzed detection rates in the \"nodule present\" category. Female participants had a higher overall detection rate than male participants (41.4% [5,964/14,400] vs. 38.1% [3378/8,871], \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Detection rates were higher in female participants than in male participants in the 50\u0026ndash;54, 55\u0026ndash;59, and 65\u0026ndash;69 age groups, with significant differences (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In the category of clinically relevant lung nodules, detection rates were 4.5% (641/14,400) in female participants and 4.3% (382/8,871) in male participants, with no significant difference between groups (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\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\u003eLung nodule detection rate stratified by sex and age\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eNodule present, % (n/n)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTotal, % (n/n)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eClinically lung nodules, % (n/n)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eTotal, % (n/n)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale, 38.1\u003c/p\u003e\u003cp\u003e(3,378/8,871)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFemale, 41.4\u003c/p\u003e\u003cp\u003e(5,964/14,400)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e40.1,\u003c/p\u003e\u003cp\u003e(9,342/23,271)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMale, 4.3\u003c/p\u003e\u003cp\u003e(382/8,871)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eFemale, 4.5\u003c/p\u003e\u003cp\u003e(641/14,400)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e4.4,\u003c/p\u003e\u003cp\u003e(1,023/23,271)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years) range\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;45\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32.3 (1,154/3,572)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33.7 (1,571/4,665)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.190\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e33.1 (2,725/8,237)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.2 (77/3,572)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.4 (110/4,665)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.541\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2.3 (187/8,237)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e45\u0026ndash;49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36.8 (614/1,669)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37.4 (1,046/2,795)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.671\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e37.2 (1,660/4,464)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.0 (66/1,669)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.4 (96/2,795)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.369\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3.6 (162/4,464)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e50\u0026ndash;54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e39.6 (514/1,298)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43.8 (1,105/2,525)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e42.3 (1,619/3,823)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.2 (55/1,298)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.8 (121/2,525)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.438\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e4.6 (176/3,823)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e55\u0026ndash;59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42.4 (430/1,015)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e46.6 (884/1,895)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e45.2 (1,314/2,910)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.0 (61/1,015)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6.2 (118/1,895)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.816\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e6.2 (179/2,910)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e60\u0026ndash;64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e48.6 (304/625)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50.4 (696/1,382)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.475\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e49.8 (1,000/2,007)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.2 (39/625)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6.5 (90/1,382)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.818\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e6.4 (129/2,007)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e65\u0026ndash;69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50.5 (208/412)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e57.1 (414/725)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.031\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e54.7 (622/1,137)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11.2 (46/412)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8.0 (58/725)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e9.1 (104/1,137)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e70\u0026ndash;74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e57.5 (100/174)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e61.4 (159/259)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.415\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e59.8 (259/433)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12.6 (22/174)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e12.4 (32/259)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.929\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e12.5 (54/433)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50.9 (54/106)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e57.8 (89/154)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.275\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e55.0 (143/260)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e15.1 (16/106)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e10.4 (16/154)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.256\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e12.3 (32/260)\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[Insert Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e here]\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eKey findings\u003c/p\u003e\u003cp\u003eThis large LCS cohort study, conducted in a hospital in China, examined the risk factors and detection rates of lung nodules, with a focus on nodule detection rates and distribution by sex and age in a non-smoking population. Independent risk factors included female sex, SHS exposure, emphysema, and older age, while higher education levels were associated with lower odds of clinically relevant lung nodules. Among the non-smoking population in China, the detection rates of lung nodules and clinically relevant lung nodules were 40.1% (9,342/23,271) and 4.3% (1,012/23,271), respectively. Women had a significantly higher detection rate of lung nodules (41.4%, 5,964/14,400) than men (38.1%, 3,378/8,871; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The detection rate for clinically relevant lung nodules was also slightly higher in women (4.5%, 641/14,400) than in men (4.3%, 382/8,871).\u003c/p\u003e\u003cp\u003eStrengths and limitations\u003c/p\u003e\u003cp\u003eThis study possesses several notable strengths that enhance the validity and significance of its findings. Its primary strength lies in the inclusion of a large, prospective, real-world cohort of over 23,000 asymptomatic individuals, making it one of the most substantial investigations of its kind. A key novelty is its specific focus on a non-smoking Chinese population, a critical demographic that has been significantly underrepresented in previous lung cancer screening research, which has predominantly centered on Caucasian smokers. By concentrating on non-smokers, this study directly addresses a crucial knowledge gap regarding the etiology and prevalence of lung nodules in a population where risk factors beyond tobacco play a significant role. The use of standardized low-dose computed tomography (LDCT) protocols and established nodule definition criteria (I-ELCAP, NLST) ensures methodological rigor and enhances the comparability of our findings with international studies. Furthermore, the collection of comprehensive data\u003c/p\u003e\u003cp\u003eDespite these strengths, we acknowledge some limitations. First, the study was conducted at a single center, the National Cancer Center of China. Although this center recruits participants from diverse geographical regions, the findings may not be fully generalizable to the entire Chinese population, and potential selection biases inherent to a hospital-based cohort may exist. Second, this analysis is cross-sectional, focusing exclusively on baseline LDCT screening data. Consequently, we could not assess the longitudinal evolution of nodules\u0026mdash;such as growth, resolution, or the incidence of new nodules\u0026mdash;nor could we report on the ultimate rates of malignancy. This precludes any conclusions about the long-term clinical significance of the detected nodules. Finally, the reliance on a self-administered questionnaire for data on comorbidities and lifestyle exposures, such as second-hand smoke, is subject to recall bias, which may have influenced the risk factor analysis. Future longitudinal studies are necessary.\u003c/p\u003e\u003cp\u003eComparison with similar research\u003c/p\u003e\u003cp\u003eA strong correlation between lung nodules and increasing age has been consistently reported globally in both smoking and non-smoking populations.\u003csup\u003e[8, 9, 14, 19, 20]\u003c/sup\u003e For instance, a study in Western Europe showed that individuals aged\u0026thinsp;\u0026ge;\u0026thinsp;66 years were more than twice as likely to have lung nodules (\u0026ge;\u0026thinsp;100 mm\u003csup\u003e3\u003c/sup\u003e) compared with those aged 45\u0026ndash;55 years.[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] Similarly, our study found that older age groups were more likely to develop lung nodules, with those aged 70\u0026ndash;74 years being more than three times as likely as those aged\u0026thinsp;\u0026lt;\u0026thinsp;45 years (OR 3.10, 95% CI 2.53\u0026ndash;3.79). The risk was even higher for clinically relevant nodules, with individuals aged 70\u0026ndash;74 years being approximately five times as likely to have lung nodules as those aged\u0026thinsp;\u0026lt;\u0026thinsp;45 years (OR 5.12, 95% CI 3.66\u0026ndash;7.15). Age-related cumulative exposure to risk factors, including asbestos, radioactive materials, and air pollution, likely contributes to this increased risk, particularly in regions such as China, where air pollution is more severe than in the United States.[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eFemale sex has emerged as a significant risk factor for lung cancer in Asia. A meta-analysis of 141,396 ever-smokers and 109,251 non-smokers revealed that non-smoking women in Asia had a higher lung cancer risk than non-smoking men, comparable to that of high-risk smokers (\u0026ge;\u0026thinsp;30 pack-years; OR\u0026thinsp;=\u0026thinsp;0.99, 95% CI: 0.65\u0026ndash;1.50).[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] Our study further established that female sex is an independent risk factor for the development of lung nodules. In contrast, Western populations often consider male sex to be at higher risk for pulmonary nodules, likely due to differences in smoking behaviors. Lung nodule prevalence increases with prolonged and intense smoking, showing a higher incidence among male smokers than among female smokers. In the United States, 90.4% of men and 84.3% of women diagnosed with lung cancer are smokers, while in Europe, the figures are 93.3% for men and 68% for women.[\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] Genetic factors may also play a role, as evidenced by differences in oncogene alterations between Asians and Caucasians, including epidermal growth factor receptor mutations, occurring in 40\u0026ndash;55% of Asians compared to 15\u0026ndash;25% in Caucasians.[\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eSHS exposure is another well-established risk factor for lung cancer.[\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e28\u003c/span\u003e] A meta-analysis of 20 randomized controlled trials in Chinese populations, published between 1996 and 2015, revealed that SHS exposure at work increased lung cancer risk by 1.78 (OR 1.78, 95% CI 1.29\u0026ndash;2.44), while SHS exposure at home raised the risk by 1.53 times (OR 1.53, 95% CI 1.01\u0026ndash;2.33).[\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e29\u003c/span\u003e] The \"2020 China Smoking and Health Hazards Report\" published by the National Health Commission of China revealed that up to 68.1% of non-smokers in China are exposed to SHS in public places.\u003csup\u003e[30]\u003c/sup\u003e Similarly, our study identified an association between SHS exposure and an increased risk of lung nodules.\u003c/p\u003e\u003cp\u003eHigher education levels were associated with a reduced risk of developing clinically relevant lung nodules. The findings align with the results of a European study that reported an increased risk of lung nodules among smokers with lower education levels.[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] Higher education levels have been shown to protect against lung cancer.[\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e31\u003c/span\u003e] Education, a proxy for socioeconomic status, may influence exposure to environmental carcinogens and high-risk occupations, affecting lung nodule prevalence.\u003c/p\u003e\u003cp\u003eEmphysema, a known independent risk factor for lung cancer,[\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e33\u003c/span\u003e] was associated with a three-fold higher incidence density of lung cancer in patients with emphysema than in those without emphysema. Multifactorial analysis showed that the presence of emphysema on LDCT was an independent risk factor for lung cancer, even in the absence of airway obstruction (RR 2.10, 95% CI 0.79\u0026ndash;5.58).[\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e32\u003c/span\u003e] Our study also indicates that pulmonary emphysema is an independent risk factor for the development of lung nodules. Reduced lung function in individuals with emphysema may impair the clearance of abnormal cells, thereby increasing the likelihood of lung nodule formation. Moreover, emphysema patients are often more prone to lung infections and other comorbidities, which can occasionally present as lung nodules.\u003c/p\u003e\u003cp\u003eUsing the NLST diameter threshold of 4 mm, this study reported a lung nodule detection rate of 40.1% (9,342/23,271), consistent with the findings of a population-based study in Northern Europe (42.0%, 4,377/10,431), which used a comparable threshold.[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] Our study reported a higher lung nodule detection rate using a 4-mm threshold than that reported by other studies employing the same threshold, including a multicenter study in Shanghai (29.9%, 4,336/14,506) and a single-center cross-sectional study in Korea (16.2%, 6,066/37,436).[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e35\u003c/span\u003e] Although the above studies were conducted on both non-smokers and smokers. However, the detection rate in our study was lower than that in a previous Japanese study (42.6%), which included both smoking and non-smoking individuals and used a 5-mm threshold for positivity.[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] Using the I-ELCAP (2006) definition of a clinically relevant nodule \u0026mdash;solid or partially solid NCN\u0026thinsp;\u0026ge;\u0026thinsp;5 mm or non-solid non-calcified lung nodules\u0026thinsp;\u0026ge;\u0026thinsp;8 mm\u0026mdash;the detection rate in our cohort was 4.3% (1,012/23,271). Additionally, our study revealed a higher detection rate of lung nodules in women than in men when applying the 4-mm threshold (42.2% vs. 38.1%, respectively), with multiple nodules also being more common in women than in men (42.2% vs. 39.9%). In contrast, a Nordic population-based study found higher rates of lung nodules and multiple nodules in men across all age groups, except in the 70\u0026ndash;74.9-year-old (1,012/23,271) and \u0026ge;\u0026thinsp;80-year-old subgroups.[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] Conversely, this study, conducted in a predominantly non-smoking cohort, included only SN.[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] The observed discrepancies may be attributed to differences in inclusion criteria, population characteristics, imaging protocols, and interpretation methods.\u003c/p\u003e\u003cp\u003eTo the best of our knowledge, no prior study of this scale has comprehensively evaluated risk factors, detection rates, and distribution patterns of lung nodules in non-smoking Asian populations. Most previous studies have focused on high-risk populations with a history of smoking or on Caucasian cohorts. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] Our study utilized LDCT screening in asymptomatic non-smokers, collecting extensive epidemiological and imaging data. These findings provide a critical basis for understanding lung nodule characteristics in Asian non-smokers and align with current trends in risk stratification and nodule evaluation using LDCT LCS.\u003c/p\u003e\u003cp\u003eExplanations of findings\u003c/p\u003e\u003cp\u003eThe high detection rate of lung nodules (40.1%) and clinically relevant nodules (4.3%) in this large cohort of 23,271 asymptomatic non-smokers underscores the significant burden of pulmonary abnormalities in this population. Our multifactorial analysis provides explanations for this prevalence by identifying several independent risk factors. The strong association with advancing age, which peaked in the 70\u0026ndash;74-year group, likely reflects the cumulative effect of long-term environmental exposures combined with age-related declines in pulmonary immune surveillance and cellular repair mechanisms.\u003c/p\u003e\u003cp\u003eFurthermore, the findings establish female sex and exposure to second-hand smoke (SHS) as significant risk factors. The increased risk for women aligns with emerging evidence of higher susceptibility to lung carcinogens in non-smoking Asian females, potentially due to genetic or hormonal factors. The notable risk associated with SHS exposure (OR 1.59, 95% CI 1.49\u0026ndash;1.70) highlights the profound impact of environmental tobacco smoke as a pulmonary irritant and carcinogen. Similarly, the link between emphysema and an elevated risk for both general and clinically relevant nodules is consistent with its known role in impairing lung clearance mechanisms and creating a chronic inflammatory state conducive to nodule formation.\u003c/p\u003e\u003cp\u003eImplications and actions needed\u003c/p\u003e\u003cp\u003eThe results of this study have significant implications for both public health initiatives and clinical lung cancer screening (LCS) strategies in China. The data compellingly suggest that current screening paradigms, which are heavily weighted toward smoking history, may fail to identify a large segment of the at-risk population. The high nodule prevalence among non-smokers, particularly in women, older adults, and those with SHS exposure or emphysema, indicates that these factors are critical for risk stratification.\u003c/p\u003e\u003cp\u003eBased on these findings, several actions are warranted. First, from a public health perspective, targeted educational campaigns should be developed to raise awareness among non-smokers about the risks of SHS and other environmental exposures. These efforts should prioritize high-risk demographics identified in our study. Second, and most critically, clinical guidelines for LDCT screening should be re-evaluated. Our findings provide a strong evidence base for expanding eligibility criteria to include high-risk non-smokers. The development and validation of refined risk-prediction models that incorporate age, sex, SHS exposure, and comorbidities such as emphysema are essential to improve the accuracy and efficiency of LCS and ultimately reduce the lung cancer burden in this substantial population.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur study demonstrates that older age, female sex, SHS exposure, and emphysema are associated with pulmonary nodules in an asymptomatic, non-smoking Asian population. These findings enhance understanding of LCS in China and support the refinement of screening eligibility criteria. Additionally, our research reveals a high detection rate of lung nodules among the non-smoking Chinese population, offering detailed distribution patterns that provide valuable insights for managing pulmonary nodules in this demographic.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eLC, lung cancer; LCS, lung cancer screening; LDCT, low-dose computed tomography; NLST, National Lung Screening Trial; SHS, second-hand smoke; COPD, chronic obstructive pulmonary disease; I-ELCAP, International Early Lung Cancer Action Program; SN, solid nodules; PSN: partial solid nodules, NSN: Non-solid nodules; IQR, interquartile range.\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e: The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. This study was approved by the Ethics Committee of the National Cancer Center of China (NCC) /Cancer Hospital of the Chinese Academy of Medical Sciences (Application ID:14-115/905). All participants provided informed consent, allowing their respective institutions to offer financial support for lung cancer screening.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u003c/strong\u003e The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e: The authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: This work was supported by the National Key R\u0026amp;D Program of China (grant number 2017YFC1308700, 2020AAA0109500), Beijing Hope Run Special Fund of Cancer Foundation of China (grant number LC2021A25).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e: Conceptualisation, NW, TYY; methodology, YYT, WT, SJZ, JWW, HHZ, and LNZ; verification of the underlying data, NW, YYT, YH, JWW, ZJX, KZ, and WT; original draft, NW, YYT, SJZ, and WT; review and editing, NW, YYT, YH, JWW, YT, and LNZ. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u0026nbsp;\u003c/strong\u003eWe thank all study participants for their cooperation. We would like to thank Editage (http://www.editage.cn/) for the English language editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u003c/strong\u003e not applicable.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eZheng RS, Chen R, Han BF, Wang SM, Li L, Sun KX, et al. [Cancer incidence and mortality in China, 2022]. Zhonghua Zhong Liu Za Zhi. 2024;46:221-31.\u003c/li\u003e\n\u003cli\u003eNational Lung Screening Trial Research Team, Aberle DR, Adams AM, Berg CD, Black WC, Clapp JD, et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. 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A meta-analysis of the relationship between environmental tobacco smoke and lung cancer risk of nonsmoker in China. Med (Baltim). 2018;97:e11389.\u003c/li\u003e\n\u003cli\u003eReport on health hazards of smoking in China. 2020. https://www.gov.cn/xinwen/2021-05/30/content_5613994.htm.\u003c/li\u003e\n\u003cli\u003eGuo LW, Chen Q, Shen YC, Meng QC, Zheng LY, Wu Y, et al. Evaluation of a low-dose computed tomography lung cancer screening program in Henan, China. JAMA Netw Open. 2020;3:e2019039.\u003c/li\u003e\n\u003cli\u003ede Torres JP, Bastarrika G, Wisnivesky JP, Alcaide AB, Campo A, Seijo LM, et al. Assessing the relationship between lung cancer risk and emphysema detected on low-dose CT of the chest. Chest. 2007;132:1932-8.\u003c/li\u003e\n\u003cli\u003eDurawa A, Dziadziuszko K, Jelitto M, Gąsiorowski M, Kaszubowski M, Szurowska E, et al. Emphysema and lung cancer risk. Transl Lung Cancer Res. 2024;13:1918-28.\u003c/li\u003e\n\u003cli\u003eCai J, Vonder M, Pelgrim GJ, Rook M, Kramer G, Groen HJM, et al. Distribution of solid lung nodules presence and size by age and sex in a northern European nonsmoking population. Radiology. 2024;312:e231436.\u003c/li\u003e\n\u003cli\u003eYang W, Qian F, Teng J, Wang H, Manegold C, Pilz LR, et al. Community-based lung cancer screening with low-dose CT in China: Results of the baseline screening. Lung Cancer. 2018;117:20-6.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"low-dose computed tomography, non-smoking population, lung nodule, women, second-hand smoke","lastPublishedDoi":"10.21203/rs.3.rs-7282428/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7282428/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e\u003cp\u003eLow-dose computed tomography (LDCT) has significantly improved early detection of lung cancer, especially among high-risk populations. However, the risk factors and lung nodule distributions in non-smoking populations remain underexplored, particularly in Asia. Therefore, in this study, we aimed to explore the risk factors and delineate the detection rate, including lung nodule distributions discovered using LDCT, in a non-smoking Chinese population.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e\u003cp\u003eThis prospective, single-center study included asymptomatic adults who underwent LDCT screening at the National Cancer Center of China between January 2006 and December 2023. Lung nodules were defined as at least one non-calcified nodule, while clinically relevant lung nodules were defined as at least one solid or partially solid nodule, or at least one non-solid nodule. Multivariate logistic regression models were employed to identify risk factors associated with lung nodules. The outcomes included detection rates and distribution of both nodule types.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e\u003cp\u003e Of 23,271 participants, lung nodules were detected in 40.1% (9,342/23,271); 4.3% (1,012/23,271) had clinically relevant lung nodules. Risk factors for lung nodule development included female sex (odds ratio [OR] 1.12, 95% confidence interval [CI] 1.06\u0026ndash;1.19), second-hand smoke (SHS) exposure (OR 1.59, 95% CI 1.49\u0026ndash;1.70), and emphysema (OR 1.49, 95% CI 1.24\u0026ndash;1.78). The incidence of lung nodules increased with age, peaking at 70\u0026ndash;74 years (OR 3.10, 95% CI 2.53\u0026ndash;3.79). Risk factors for clinically relevant lung nodules included increasing age, SHS exposure (OR 1.44, 95% CI 1.22\u0026ndash;1.69), and emphysema (OR 1.84, 95% CI 1.36\u0026ndash;2.49). Detection rates for both nodule types were positively correlated with age (lung nodules: women 33.7\u0026ndash;61.4%, men 32.3\u0026ndash;57.5%; clinically relevant lung nodules: women 2.4\u0026ndash;12.4%, men 2.2\u0026ndash;15.1%).\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e\u003cp\u003eThis real-world study of a non-smoking Chinese population revealed high lung nodule detection rates, with women exhibiting a higher detection rate than men. SHS has emerged as a significant risk factor for both lung and clinically relevant nodules. These findings highlight the importance of refining LDCT screening strategies and risk models for non-smoking populations in Asia.\u003c/p\u003e","manuscriptTitle":"Prevalence and risk factors of lung nodules in a non-smoking Chinese population: A prospective study of low-dose computed tomography screening","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-21 01:31:20","doi":"10.21203/rs.3.rs-7282428/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-27T09:40:01+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-19T12:54:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-18T18:32:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"258567652001530477735563906700268674011","date":"2025-10-16T02:37:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"84689824921473895589747244439116545418","date":"2025-10-09T06:41:06+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-08T07:53:12+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-02T05:29:46+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-19T11:18:10+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-19T11:15:51+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pulmonary Medicine","date":"2025-08-03T09:20:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a5b1efbd-7c8d-4415-9f47-fb6fdfd87f95","owner":[],"postedDate":"October 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-09T16:15:41+00:00","versionOfRecord":{"articleIdentity":"rs-7282428","link":"https://doi.org/10.1186/s12890-026-04108-2","journal":{"identity":"bmc-pulmonary-medicine","isVorOnly":false,"title":"BMC Pulmonary Medicine"},"publishedOn":"2026-03-04 15:57:35","publishedOnDateReadable":"March 4th, 2026"},"versionCreatedAt":"2025-10-21 01:31:20","video":"","vorDoi":"10.1186/s12890-026-04108-2","vorDoiUrl":"https://doi.org/10.1186/s12890-026-04108-2","workflowStages":[]},"version":"v1","identity":"rs-7282428","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7282428","identity":"rs-7282428","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}