Relationship between Air Pollution and Lung Cancer in Fujian Province: A Case-control study

Relationship between Air Pollution and Lung Cancer in Fujian Province: A Case-control study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Relationship between Air Pollution and Lung Cancer in Fujian Province: A Case-control study Guangmin Chen, Fei He, Jiasheng Wu, Huimin Yang, Jin Su, Jianbo Lin, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.2.17362/v3 This work is licensed under a CC BY 4.0 License Status: Posted Version 3 posted You are reading this latest preprint version Show more versions Abstract Background: Outdoor air pollutants, especially particulate matters, are defined as a type of carcinogen by the International Agency for Research on Cancer. Studies have shown that air pollutionis associated with lung cancer morbidity or mortality. This study is aimed at exploring the relationship between air pollutants and primary lung cancer in Fujian Province, China. Methods: We conducted a hospital-based, retrospective, case–control epidemiological study on three different populations to assess the occurrence of lung cancer caused by exposure to various levels of air pollution. Statistical analysiswas performed using the SPSS 25.0. Unconditional logistic regression modeling and identification of possible confounding factors were performed by calculating odds ratios (ORs) and 95% confidence intervals (CIs) for air pollution indexes and lung cancer risk. Results: The total study population comprised 885 lung cancer patients and 1,220 healthy controls. The following parameters were identified as risk factors for lung cancer among the total population: smoking; exposure to cooking oil fumes; passive smoking; medical history of lung disease; family history of lung cancer; and exposure to PM 10 , PM 2.5 , and O 3 . For smokers, medical history of lung disease, family history of lung cancer, and exposure to PM 10 , and PM 2.5 were risk factors for lung cancer. Among non-smokers, exposure to cooking oil fumes; medical history of lung disease; family history of lung cancer; and exposure to PM 10 , PM 2.5 , and O 3 were factors increasing the risk of lung cancer. Conclusions: Long-term exposure to PM 10, PM 2.5 , and O 3 was found to be significantly associated with increased risk of lung cancer, with the risk being greater for non-smokers and persons exposed to cooking oil fumes. Lung cancer Air pollutants PM10 PM2.5 Ozone Figures Figure 1 Figure 2 Background As per GLOBOCAN 2018, approximately 18.1 million new cancer cases are diagnosed worldwide each year. Lung cancer ranks first among the causes of new cancer cases, with about 2.09 million cases diagnosed annually, accounting for 11.6% of new cases of malignant tumors. The incidence of lung cancer in East Asia, particularly in China, is far higher than that worldwide (more than 40/100 000)[ 1 ]. Lung cancer was the leading cause of cancer incidence and mortality in China for many years[ 2 ]. According to the 2017 Malignant Tumors report from Fujian Province, lung cancer ranked first among all types of cancers in males (62.61/100 000) and second in females (24.8/100 000). Clearly, lung cancer has become a serious threat to public health and places a heavy disease burden in Fujian Province and China as a whole. Smoking is a known and confirmed risk factor for lung cancer. The increase in the number of female smokers in developed countries is one of the reasons for the increase in the incidence of lung cancer among women and decrease in the comparative incidence among men[ 3 ]. However, some studies have found that about 15% of men and 53% of women with lung cancer do not smoke. Moreover, about 25% of lung cancer cases worldwide cannot be attributed to tobacco usage[ 4 ]. This has led to an increased interest in the exploration of the causes of lung cancer other than smoking, particularly air pollution[ 5 ], diet[ 6 ], infection[ 7 , 8 ], cooking fumes[ 9 ], medical history of respiratory diseases[ 10 ], and occupational exposure[ 11 ]. For nearly 50 years, research has been underway to confirm the relationship between outdoor air pollution and lung cancer[ 12 ]. The International Agency for Research on Cancer (IARC) defines outdoor air pollutants, especially particulate matter, as a type of carcinogen. This classification is primarily based on evidence that long-term persistence of high average concentrations of PM 2.5 in outdoor air is associated with lung cancer morbidity or mortality[ 13 ]. However, the IARC report is a qualitative assessment of hazard identification, and it does not provide the relative risk or odds ratio (RR or OR) of lung cancer associated with outdoor air pollution. The current research results still leave several key points unclear. First, the population groups that are most vulnerable to air pollution are yet to be identified[ 14 – 17 ]. Second, few studies have focused on the dose–response relationship between long-term exposure to outdoor air pollution and lung cancer [ 16 , 18 , 19 ]. In particular, very few studies from China have investigated the risk of lung cancer in relation to air pollution; among these studies, case–control studies and cohort studies are very few, and most of the existing studies focus on lung cancer mortality. Given the limitations of epidemiological research methods, cohort studies on the causal relationship between outdoor air pollution and lung cancer would require a long time to yield conclusive results. In this study, we sought to conduct a hospital-based, case-control study to investigate the relationship between air pollutants and lung cancer in Fujian Province, with special focus on the exposure levels of different population groups to air pollutants. We expect that the results of this study would provide a scientific basis for targeted improvement of the atmospheric environment for protection of public health. Methods Overview of the study area Fujian Province is located on the southeast coast of China. The land area is 124 000 square kilometers in total. From north to south, the province measures about 530 kilometers and occupies about 480 kilometers from east to west. Fujian is located at a latitude of 23°30' and 28°22' north and at longitude of 115°50' and 120°40' east. The terrain is higher in the northwest and lower in the southeast. About 90% of the total land area of the province is covered by mountains and hills. In addition, the coastal landform pattern is dominated by a tortuous coastline that includes several bays and peninsulas. The climate in Fujian Province is subtropical monsoon climate, which makes the area warm and humid. The annual average temperature is 15°C–22°C, which increases from the northwest to the southeast. The annual average precipitation is 800–1,900 mm, with the precipitation being greatest between May and June every year, with several typhoons occurring at the turn of summer and autumn. Air pollution data collection For this study, we aimed to obtain a reasonable estimate of each subject’s exposure to atmospheric pollutants by using a longer average annual concentration. The levels of sulfur dioxide (SO 2 ), nitrogen dioxide (NO 2 ) and suspended particulates smaller than 10 µm in aerodynamic diameter (PM 10 ) in Fujian Province were obtained from the complete monitoring data of national control points, which has been maintained since 2005. Since 2013, China has implemented new monitoring standards, which include the current maximum of eight hours average for ozone (O 3 ). Accordingly, the maximum value of continuous 8-hour ozone concentration in a day is taken as the standard for evaluating the ozone pollution level of the same day and the six indicators of air quality are monitored: SO 2 , NO 2 , PM 10 , carbon monoxide (CO), and suspended particulates smaller than 2.5µm in aerodynamic diameter (PM 2.5 ). The ambient air quality data collected in this study was collected for the period between January 1, 2005 and December 31, 2015. The daily average concentration values and monthly average values of SO 2 , NO 2 , PM 10 , CO, O 3 , and PM 2.5 were collected for 39 monitoring points (national control points) in Fujian Province; the data were collected from the Fujian Provincial Environmental Monitoring Center Station (Fig. 1 ). The strategic value of the location of each monitoring point has been demonstrated previously, and the points have been shown to be geographically representative. The air pollution monitoring data for SO 2 , NO 2 , PM 10 , CO, O 3, and PM 2.5 were evaluated according to the Chinese Ambient Air Quality Standard (GB3095-2012). Since it would be impractical to measure the individual exposure level to concentrations of atmospheric pollutants by having each subject wear a monitoring device, we used an individual atmospheric pollutant exposure estimation method, according to the approach proposed by Dadvand et al[ 20 ]. Basically, we constructed an algorithm to predict atmospheric pollutant exposure for each location within Fujian province by integrating air pollution monitoring data and spatial coordinates of 39 monitoring points. By using the spatial prediction function of ArcGIS 10.3, the spatial distribution map of air pollution indexes in Fujian Province was generated by applying inverse distance to a power (IDW) interpolation in areas that lacked derived data. The annual average level of cumulative concentration served as the matched measurement data for each index. Further, the levels of SO 2 , NO 2 , and PM 10 were the annual average values from 2005 to the time that subjects enrolled. In addition, the concentrations of O 3 , CO, and PM 2.5 were measured from 2013 to the time of enrollment of the subjects. According to the detailed home address (specific to the district, street, and house number), the spatial coordinates (latitude and longitude) of each subject was accurately determined by GPSspgxGeo coding software and Tencent map software, which can be further mapped to the predicting model and used to generate the estimating pollution data for each subject. Hospital Data and Population Data Between January 2010 and December 2015, we recruited from three hospitals: Department of Thoracic Surgery and Respiratory Medicine of the First Affiliated Hospital of Fujian Medical University, Union Hospital Affiliated to Fujian Medical University, and Fuzhou General Hospital of Nanjing Military Region. Patients were included if they had been newly diagnosed with primary lung cancer, as confirmed by fiberopticbronchoscopy or surgical histopathology, and had resided in Fujian province of China for more than 10 years. The population-based approach was applied to the recruit the control group, which comprised healthy community dwellers randomly selected from the resident records of Fujian province. The control group was frequency-matched to the cases by ethnicity, gender, and age. Every control subject was of ages ± 2 years as compared to the matching cases. All the selected control individuals successfully met the inclusion criteria and completed the study, without any drop-out during the study. In total, the case group comprised 885 patients with lung cancer, while the control group comprised 1,220 healthy individuals. All participants provided written, informed consent before undergoing the examination. The study protocol was approved by the Ethics Committee of Fujian Medical University. Questionnaire and variables All participants were surveyed using a standardized questionnaire, which was administered during a scheduled phone interview conducted via a study team member. The 18-page questionnaire had questions pertaining to the patients’ demographic characteristics information, tumor characteristics, and data regarding the subject’s living environment, dietary habits, smoking history, alcohol consumption, intensity of physical activity, occupational exposure to air pollutants, and exposure to domestic pollutants. The questionnaire also included items regarding medical history, family history, and lifestyle-related parameters. Data regarding reproductive health were collected for female participants. Body mass index (BMI) was determined as the ratio of body weight (kg) and height[m] 2 . A positive smoking history was defined as a history of having smoked more than 100 cigarettes during his/her lifetime. Passive smoking history was defined by non-smoking history of inhaling cigarette smoke generated by others or exposure to exhaled smoke more than 15 minutes per day. A history of alcohol consumption was defined as drinking at least one alcoholic beverage per week for more than six months, irrespective of the type of alcoholic drink. Drinking tea was defined as consuming at least one cup of any kind of tea per week for more than six months. A family history of cancer was defined as the occurrence of a malignant tumor in first-degree or second-degree relatives. Occupational physical activity was rated as low, moderate, or high intensity, as defined by the Reference Standard of Labor Intensity recommended by the Chinese Nutrition Society in 2000. To check for exposure to cooking oil fumes, participants were enquired about whether their degree of exposure to fumes in the kitchen was none (no exposure), light, moderate, or heavy[ 21 ]. Statistical analysis The Chi-square test was used to compare the general characteristics of cases and controls. Stratified analysis for different populations was performed using a t test for the exposure levels of air pollution indexes. Unconditional logistic regression modeling and identification of possible confounding factors were performed by calculating odds ratios (ORs) and 95% confidence intervals (CIs) for air pollution indexes and lung cancer risk. All analysis was performed using the SPSS 25.0 software package (IBM Corporation, Armonk, New York, USA) and ArcGIS 10.3 (ESRI Inc, USA). All p-values were based on a two-sided test with an α of 0.05. Results Demographic characteristics of the study subjects The current study comprised 2,105 subjects, including 885 cases and 1,220 controls. Subjects included 1,354 males and 751 females, with ages between 23 and 90 years of age and average age of 58.94 ± 10.73 years. The case and control groups did not show any significant difference in the distribution of gender, age, ethnicity, and marital status (P > 0.05). However, significant intergroup differences were noted in the educational level, occupation, and BMI (P 0.05). However, statistically significant intergroup differences were noted in the distribution of education, occupation, and BMI (P 0.05), but showed significant differences in gender, education level, and occupational exposure (P < 0.05). Among the 885 cases included, 551 (62.3%) were of adenocarcinoma, 208 (23.5%) were of squamous cell carcinoma, and 126 (14.2%) were of other pathological types of cancers. Among the 519 smokers, 253 (48.7%) had adenocarcinoma, 177 (34.1%) had squamous cell carcinoma, and 89 (17.4%) had tumors of other pathological types. Of 366 non-smoking lung cancer patients, 298 (81.4%) had adenocarcinoma, 31 (8.5%) had squamous cell carcinoma, and 37 (10.1%) had lesions of other pathological types (Table 1). The risk factors of lung cancer In this study, we explored the potential factors for lung cancer in the three populations: the total population, the subgroups–smokers, and non-smokers (Table 2 ). Of the total population, after adjusting for BMI, education, and occupation, we found that subjects with a family history of lung cancer or medical history of lung disease, drinking alcohol, smoking, passive smoking, or exposure to cooking oil fumes were susceptible to lung cancer. We found that the risk of lung cancer was low for subjects who performed physical exercise and had a regular intake of fruit (more than 3 times per week). Among the smokers, after adjustment for age, BMI, education, and occupation, the risk factors for lung cancer were family history of lung cancer, medical history of lung disease, and exposure to cooking oil fumes, while the protective factors were physical exercise and fruit intake. Similarly, for non-smokers, the risk factors for lung cancer were family history of lung cancer, history of lung diseases, passive smoking, and exposure to cooking oil fumes, after adjustment for gender, education, and occupation, whereas the protective factors were drinking tea, physical exercise, and fruit intake. Table 1 Comparison of demographic characteristics between two groups in different populations Variables Total Smoker Non-smoker Cases n(%) Control n(%) \({\chi }^{2}\) P Cases n(%) Control n(%) \({\chi }^{2}\) P Cases n(%) Control n(%) \({\chi }^{2}\) P Age (years, \(\stackrel{-}{x}\pm s\) ) 59.34 ± 9.94 58.64 ± 11.26 -1.473 0.141 60.82 ± 9.27 58.90 ± 10.19 -3.030 0.003 57.24 ± 10.47 58.50 ± 11.81 1.746 0.081 Gender 1.379 0.240 0.004 0.948 67.678 < 0.001 Male 582(65.8) 772(63.3) 512(98.7) 423(98.6) 70(19.1) 349(44.1) Female 303(34.2) 448(36.7) 7(1.3) 6(1.4) 296(80.9) 442(55.9) Nationality 0.026 0.873 2.807 0.094 0.670 0.413 Han 863(97.5) 1191(97.6) 505(97.3) 424(98.8) 358(97.8) 767(97.0) Others 22(2.5) 29(2.4) 14(2.7) 5(1.2) 8(2.2) 24(3.0) Marital status 1.547 0.214 1.025 0.311 0.001 0.974 Married 824(93.1) 1118(91.6) 492(94.8) 400(93.2) 332(90.7) 718(90.8) Single 61(6.9) 102(8.4) 27(5.2) 29(6.8) 34(9.3) 73(9.2) Educational status 169.749 < 0.001 46.084 < 0.001 116.641 < 0.001 Primary school and below 477(53.9) 360(29.5) 255(49.1) 133(31.0) 222(60.7) 227(28.7) Middle school 320(36.2) 505(41.4) 221(42.6) 208(48.5) 99(27.0) 297(37.5) College and higher 88(9.9) 355(29.1) 43(8.3) 88(20.5) 45(12.3) 267(33.8) Occupation 114.919 < 0.001 23.277 < 0.001 102.522 < 0.001 Agriculture, forestry, animal husbandry and fishery personnel 263(29.7) 180(14.8) 157(30.3) 76(17.7) 106(29.0) 104(13.1) Production of transport workers 219(24.7) 268(22.0) 134(25.8) 113(26.3) 85(23.2) 155(19.6) Enterprises and institutions personnel 241(27.2) 573(47.0) 159(30.6) 175(40.8) 82(22.4) 398(50.3) Business service staff 74(8.4) 116(9.5) 45(8.7) 47(11.0) 29(7.9) 69(8.7) Other and unemployed 88(9.9) 83(6.8) 24(4.6) 18(4.2) 64(17.5) 65(8.2) BMI 22.331 < 0.001 29.042 < 0.001 1.926 0.382 18.5–24 547(61.8) 682(55.9) 333(64.2) 231(53.8) 214(58.5) 451(57.0) < 18.5 68(7.7) 57(4.7) 42(8.1) 14(3.3) 26(7.1) 43(5.4) ≥ 24 270(30.5) 481(39.4) 144(27.7) 184(42.9) 126(34.4) 297(37.5) Pathological type Adenocarcinoma 551(62.3) 253(48.7) 298(81.4) Squamous cell carcinoma 208(23.5) 177(34.1) 31(8.5) Others 126(14.2) 89(17.1) 37(10.1) Table 2 Analysis of risk factors of lung cancer in different populations a Variables Total Smoker Non-smoker case/control aOR (95% CI ) b case/control aOR (95% CI ) c case/control aOR (95% CI ) d Family history of lung cancer No 825/1185 1 490/421 1 335/764 1 Yes 60/35 3.193(2.029–5.025) 29/8 3.601(1.584–8.187) 31/27 3.448(1.901–6.253) Personal history of lung disease No 761/1135 1 431/381 1 330/754 1 Yes 124/85 2.547(1.874–3.462) 88/48 1.647(1.105–2.457) 36/37 3.486(2.042–5.951) Drinking alcohol No 608/942 1 276/244 1 332/698 1 Yes 277/278 1.568(1.277–1.925) 243/185 1.208(0.924–1.5580) 34/93 1.415(0.879–2.278) Drinking tea No 464/596 1 179/147 1 285/449 1 Yes 421/624 1.088(0.905–1.309) 340/282 1.166(0.878–1.548) 81/342 0.588(0.432–0.799) Smoking No 366/791 1 - - - - Yes 519/429 2.489(2.065-3.000) - - - - Passive smoking No 288/598 1 - - 157/466 1 Yes 597/622 1.933(1.601–2.332) - - 209/325 1.632(1.247–2.135) Stimulating smell after renovation No 789/1080 1 464/369 1 325/711 1 Yes 96/140 1.007(0.754–1.345) 55/60 0.797(0.530–1.198) 41/80 1.153(0.750–1.772) Cooking oil fume exposure No 148/324 1 87/110 1 61/214 1 Yes 737/896 1.615(1.287–2.026) 432/319 1.487(1.070–2.067) 305/577 1.521(1.086–2.130) Physical activity No 683/683 1 403/259 1 280/424 1 Yes 202/537 0.428(0.350–0.524) 116/170 0.475(0.354–0.638) 86/367 0.437(0.325–0.588) Fruit intake ≥ 3 times/week 357/751 1 208/219 1 149/532 1 < 3 times/week 528/469 2.034(1.690–2.447) 311/210 1.447(1.106–1.893) 217/259 2.574(1.953–3.392) a Each variable was run independently b Adjusted for educational status, occupation, BMI c Adjusted for age, educational status, occupation, BMI d Adjusted for gender, educational status, occupation Univariate analysis of levels of exposure to atmospheric pollutants in different populations The annual average of SO 2 , NO 2 , and PM 10 concentrations for the period between 2005 and 2015 and those of CO, PM 2.5 , and O 3 for the period 2013–2015 are shown in Fig. 2 . Notably, the distribution of different pollutants differed from one another. Thus, we noted that NO 2 , PM 2.5 , and O 3 had similar distribution patterns, with the pollutants being highly aggregated in cities along east coast; this may be associated with the dense population and severe traffic pollution. The remaining three common pollutants showed completely different distribution patterns, with CO mainly concentrating in northeast area, SO 2 aggregating in the mid–west region, and PM 10 mainly affecting the southwest. The discrepancy in the distribution patterns may be attributed to the complex effect of factory contamination and factors such as climatic and geographic conditions. Univariate analysis was performed to levels of exposure to evaluate the impact of atmospheric pollutants on different populations. The values for concentrations of atmospheric pollutants were classified into four levels based on ± 1 standard deviation (Table 3 ). For the total population, after adjustment for education, occupation, BMI, family history of lung cancer, and medical history of lung disease, alcohol consumption, smoking, passive smoking, exposure to cooking oil fumes, physical exercise, and fruit intake, the results showed that the risk of developing lung cancer with exposure to NO 2 concentrations of 19–29 (µg/m3) was 1.356 times greater than at concentrations of less than 19 (µg/m3) (95% CI: 1.028–1.788). As compared to exposure to PM 10 concentrations of less than 51 (µg/m3), exposure to concentrations of 51–57 (µg/m3) increased the risk of developing lung cancer by 2.450 times greater (95% CI: 1.728–3.474), while exposure to concentration of 57–64 (µg/m3)and increased the risk by 1.637 times (95% CI: 1.178–2.276). Table 3 Comparison of air pollutant exposure in two groups in different populationsa Variables (mg/m 3 ) Total Smoker Non-smoker case/control aOR (95% CI ) b case/control aOR (95% CI ) c case/control aOR (95% CI ) d SO 2 23 78/106 1.347(0.438–4.141) 46/42 5.288(0.572–48.855) 32/64 0.601(0.131–2.751) Continuous < 0.001(< 0.001–3.315) - - NO 2 < 19 310/229 1 127/108 1 102/202 1 19–29 236/250 1.356(1.028–1.788) 156/88 1.447(0.975–2.147) 97/148 1.241(0.831–1.853) 29–38 257/443 0.862(0.666–1.115) 143/146 0.804(0.555–1.165) 114/297 0.893(0.617–1.293) > 38 139/215 0.889(0.653–1.209) 90/81 0.854(0.559–1.304) 49/134 0.961(0.605–1.529) Continuous < 0.001(< 0.001-1.000) < 0.001(< 0.001–0.977) - PM 10 < 51 79/166 1 49/64 1 30/102 1 51–57 316/287 2.450(1.728–3.474) 181/97 2.571(1.591–4.153) 135/190 2.366(1.395–4.014) 57–64 438/667 1.637(1.178–2.276) 259/231 1.517(0.974–2.363) 179/436 1.795(1.083–2.975) > 64 36/83 0.975(0.575–1.653) 22/31 1.042(0.513–2.116) 14/52 0.946(0.415–2.158) Continuous - - - PM 2.5 < 20 8/132 1 5/19 1 3/113 1 20–27 397/101 33.658(15.450-73.325) 260/41 24.545(8.112–74.265) 137/60 61.431(18.041-209.181) 27–35 497/987 5.059(2.390-10.712) 253/369 2.630(0.910–7.595) 226/618 11.814(3.622–38.540) > 35 0/0 - 0/0 - 0/0 - Continuous 0.001(< 0.001-877.108) - - CO 0.871 0/0 - 0/0 - 0/0 - Continuous 0.839(0.481–1.466) 0.042(0.006–0.311) 2.882(1.306–6.360) O 3 83 36/21 50.896(11.069-234.032) 5/20 - 16/16 37.313(7.301–190.700) Continuous - < 0.001(< 0.001–0.001) - a Each variable was run independently b Adjusted for educational status, occupation, BMI, family history of lung cancer, medical history of lung disease, drinking alcohol, smoking, passive smoking, exposure to cooking oil fumes, physical exercise, and fruit consumption c Adjusted for age, educational status, occupation, BMI, family history of lung cancer, and medical history of lung disease, exposure to cooking oil fumes, physical exercise, and fruit intake d Adjusted for gender, educational status, occupation, family history of lung cancer, medical history of lung disease, passive smoking, exposure to cooking oil fumes, drinking tea, physical exercise, and fruit intake Exposure to PM 2.5 is also a risk factor for lung cancer. For exposure to concentrations of 20–27 (µg/m3), the OR was 33.658 (95% CI: 15.450–73.325), and for exposure to concentrations of 27–35 (µg/m3), the OR was 5.059 (95% CI: 2.390–10.712). Furthermore, exposure to O 3 is also a risk factor for lung cancer. For exposure to O 3 concentrations of 47–65 (µg/m3), the OR was 125.056 (95% CI: 29.902–523.012); 17.746 (95% CI: 4.322–72.862), for O 3 concentrations of 65–83 (µg/m3); and 50.896 (95% CI: 11.069–234.032) for concentrations of more than 83 (µg/m3) . Next, we compared the results for smokers, after adjusting for age, education, occupation, BMI, family history of lung cancer, and medical history of lung disease, exposure to cooking oil fumes, physical exercise, and fruit intake. Further, for non-smokers, the results were compared after adjusting for gender, education, occupation, family history of lung cancer, medical history of lung disease, passive smoking, exposure to cooking oil fumes, drinking tea, physical exercise, and fruit intake. The analysis indicated that for both groups, exposure to PM 10 and PM 2.5 was a risk factors for lung cancer. Moreover, exposure to PM 2.5 had a greater impact on non-smokers than on smokers, as shown by the following results for both groups: OR was 61.431 (95% CI: 18.041–209.181) and 24.545 (95% CI: 8.12–74.265) (in non-smokers and smokers, respectively, for exposure to PM 2.5 concentrations of 20–27 (µg/m3)) and OR was 11.814 (95% CI: 3.622–38.540) and 2.630 (95% CI: 0.910–7.595) (for non-smokers and smokers, respectively, for exposure to PM 2.5 concentrations of 27–35 (µg/m3). Since none of the participants were exposed to O 3 concentrations of less than 47 (µg/m3)in the control group, the effect of O 3 exposure on the smokers could not be assessed. Among the non-smokers, exposure to O 3 was identified as a risk factor for lung cancer. However, there was no association between SO 2 and lung cancer in the total population or among the subgroup of smokers or nonsmokers. Similarly, none of the participants in the control group were exposed to a low concentration of CO, and therefore, the association of CO with lung cancer could not be evaluated (Table 3 ). We also analyzed the relationship between air pollution and lung cancer stratified by passive smoking and exposure to cooking oil fumes, and the results were consistent with those of smoking. The detailed results are shown in Additional Table 1 to Additional 3 (for passive smoking) and Additional Table 4 to Additional 6 (for cooking oil fume exposure). Table 4 Multivariate logistic regression analysis of lung cancer in total population Variables β S.E Wald χ 2 P OR ༊ 95%CI ༊ Family history of lung cancer 1.399 0.290 23.222 < 0.001 4.051 2.293–7.155 Personal history of lung disease 0.878 0.182 23.305 < 0.001 2.405 1.684–3.435 Smoking 0.502 0.115 19.099 < 0.001 1.653 1.319–2.070 Physical activity -0.894 0.122 53.364 < 0.001 0.409 0.322–0.520 Cooking oil fume exposure 0.396 0.137 8.330 0.004 1.486 1.136–1.945 Passive smoking 0.239 0.117 4.171 0.041 1.270 1.01–1.598 Fruit intake 0.523 0.112 21.913 < 0.001 1.686 1.355–2.099 O 3 (mg/m 3 ) 25.979 < 0.001 47–65 3.701 0.884 17.516 83 3.083 0.943 10.687 0.001 21.818 3.437-138.515 PM 2.5 (mg/m 3 ) 19.510 < 0.001 20–27 1.575 0.612 6.628 0.010 4.829 1.456–16.015 27–35 0.398 0.684 0.339 0.560 1.489 0.39–5.688 PM 10 (mg/m 3 ) 34.179 < 0.001 51–57 1.064 0.218 23.827 < 0.001 2.898 1.891–4.444 57–64 1.252 0.273 21.111 64 0.193 0.300 0.415 0.519 1.213 0.674–2.185 Constant -4.018 0.785 26.194 < 0.001 0.018 ༊Unconditional logistic regression with the backward stepwise method Multivariate analysis of levels of exposure to atmospheric pollutants in different populations All the variables that were found to have a significant impact on the development of lung cancer in the previous analysis were further subjected to multi-factor unconditional logistic regression analysis using the backward stepwise method. We used P 0.10 as the exclusion criterion. The results of the analyses indicated that the following factors increased the risk of lung cancer among the general population: smoking;exposure to cooking oil fumes; passive smoking;medicalhistory of lung disease; family history of lung cancer;and exposure to O 3 , PM 10 , and PM 2.5 . On the other hand, fruit intake and physical exercise were found to be protective factors against risk of lung cancer (Table 4 ). For smokers, medical history of lung disease; family history of lung cancer; and exposure to PM 10 , and PM 2.5 were factors that increased the risk of lung cancer, whereas fruit intake and physical exercise were factors that reduce the risk of lung cancer (Table 5 ). Similarly, among the non-smokers, cooking oil fumes; medical history of lung disease; family history of lung cancer; exposure to PM 10 , PM 2.5 , and O 3 may increase the risk of lung cancer, while fruit intake, physical exercise, and drinking tea were found to protect against the risk of lung cancer (Table 6 ). Table 5 Multivariate logistic regression analysis of lung cancer in smokers Variables β S.E Waldχ 2 P OR ༊ 95%CI ༊ Family history of lung cancer 1.599 0.504 10.063 0.002 4.947 1.842–13.285 Personal history of lung disease 0.585 0.234 6.270 0.012 1.796 1.136–2.84 Physical activity -0.890 0.178 24.998 < 0.001 0.411 0.29–0.582 Fruit intake 0.280 0.161 3.026 0.082 1.323 0.965–1.813 PM 2.5 (mg/m 3 ) 137.807 < 0.001 20–27 3.399 0.580 34.317 < 0.001 29.947 9.603–93.392 27–35 1.016 0.554 3.370 0.066 2.763 0.934–8.175 PM 10 (mg/m 3 ) 24.844 < 0.001 51–57 1.285 0.280 21.030 64 0.200 0.412 0.235 0.628 1.221 0.545–2.738 Constant -2.321 0.832 7.782 0.005 0.098 ༊Unconditional logistic regression with the backward stepwise method Table 6 Multivariate logistic regression analysis of lung cancer in non-smokers Variables β S.E Waldχ 2 P OR ༊ 95%CI ༊ Family history of lung cancer 1.357 0.364 13.878 < 0.001 3.885 1.902–7.933 Personal history of lung disease 1.239 0.313 15.667 < 0.001 3.451 1.869–6.373 Physical activity -0.841 0.176 22.935 < 0.001 0.431 0.306–0.608 Cooking oil fume exposure 0.506 0.201 6.331 0.012 1.658 1.118–2.458 Fruit intake 0.749 0.161 21.705 < 0.001 2.115 1.544–2.899 Drinking tea -0.512 0.183 7.849 0.005 0.599 0.419–0.857 PM 2.5 (mg/m 3 ) 10.692 0.005 20–27 2.499 0.891 7.869 0.005 12.173 2.123–69.788 27–35 1.681 1.002 2.813 0.093 5.372 0.753–38.311 PM 10 (mg/m 3 ) 14.721 0.002 51–57 0.874 0.287 9.262 0.002 2.397 1.365–4.208 57–64 0.814 0.272 8.976 0.003 2.258 1.325–3.847 > 64 -0.038 0.438 0.008 0.931 0.963 0.408–2.27 O 3 (mg/m 3 ) 12.585 0.006 47–65 2.619 1.032 6.443 0.011 13.721 1.816-103.662 65–83 1.493 1.135 1.731 0.188 4.450 0.481–41.15 > 83 1.697 1.123 2.283 0.131 5.458 0.604–49.339 Constant -6.482 0.929 48.680 < 0.001 0.002 ༊Unconditional logistic regression with the backward stepwise method The results of the analysis in different populations are shown in Additional Table 7- Additional 9 for passive smoking and in Additional Table 10- Additional 12 for exposure to cooking oil fumes. Discussion This hospital-based case–control study was designed to evaluate the relationship between atmospheric concentrations of air pollutants and the occurrence of lung cancer in Fujian Province. We found that the overall risk factors for lung cancer were smoking; exposure to cooking oil fumes; passive smoking; medical history of lung disease; family history of lung cancer; and exposure to PM 10 , PM 2.5, and O 3 . Fruit intake and physical exercise were identified as protective factors. Our results on the risk factors are consistent with those of several previous reports. We found that long-term exposure to PM 10 , PM 2.5 , and O 3 was significantly associated with an increased risk of lung cancer, and the risk appears to be greater in non-smokers than in smokers or the total population. Further, this difference appears to be even greater for people exposed to cooking oil fumes than among those without such an exposure. In the recent past, extensive research has been conducted in China on the short-term health effects of air pollution [ 22 , 23 ]. However, studies exploring the long-term effects of air pollution on health, particularly those employing more reliable methods such as cohort studies, have been scarce. Most of the studies conducted thus far are related to mortality[ 24 – 27 ]. In recent years, China has been making continuous improvement in air pollution monitoring system. However, China is a vast country. Therefore, further investigation is necessary to evaluate the exposure characteristics, dose–risk models, and long-term health risks of single or multiple pollutants in different regions. At present, there is a strong research focus on the relationship between air pollution and lung cancer. Studies[ 28 , 29 ] have reported that long-term exposure to PM 2.5 , NO 2 , NO x, and SO 2 is significantly associated with an increased risk of lung cancer. Such associations have been shown to hold true for both smokers and non-smokers as well as men and women, with the impact being the same for all subgroups. For non-smokers, exposure to outdoor PM 2.5 is greater for patients with lung cancer and exhibits a linear dose–response relationship[ 30 ]. The results of the ACS CPS-II study[ 31 ] and the European Air Pollution Impact Cohort Study (ESCAPE)[ 32 , 33 ] have also demonstrated a positive correlation between exposure to outdoor PM 2.5 and the occurrence of lung cancer. Our results had a wide 95% CI range, which might be attributed to the small sample size in our study, but our results also suggested that PM 10 and PM 2.5 were risk factors for lung cancer. Similar to our study, the study by Yang et al.[ 28 ] also revealed that the association of PM 2.5 with lung cancer (RR = 1.18) was more pronounced in non-smokers than in the total population (RR = 1.07). However, because of the limited number of studies included (n = 3) and a wide overlap between the total population and people who never were smokers, the controversy still remains regarding whether there is a connection between lung cancer and air pollution. Currently, there is still no report on the effect of exposure to cooking oil fumes on individuals. Gerard Hoek et al.[ 33 ] investigated the risk of lung cancer in the total population associated with exposure to ambient air pollution. They used the population attributable risk fraction (PAF) indicator, which describes the fraction of lung cancer incidence in the total population that can be prevented by eliminating PM 2.5 exposure. The proportion of the population with lung cancer was found to be between 28.6% and 86.7% when RR = 1.5 and the concentration of PM 2.5 was reduced by 10–60 µg/m 3 . The RR value of ambient air pollution was found to be much smaller than that for active smoking; however, air pollution affects the entire population. Thus, a reduction in the concentration of PM 2.5 will result in a substantial reduction in the incidence of lung cancer in the overall population. Study results on the association between long-term exposure to O 3 and lung cancer have been contradictory, with some studies showing a positive[ 34 , 35 ], negative[ 36 , 37 ], or invalid association[ 38 ]. In our study, the OR value of O 3 was 17.746–125.056 (P < 0.05). Since the resulting 95% CI was wide, this could suggest that O 3 may be a risk factor for lung cancer. Recently, N. Rocks et al.[ 39 ] measured the migration of tumor cells using the Boyden chamber test. They also established a mouse model- and employed an automated tumor recognition software with supervised classification to quantify the spread of lung tumor cells. Their results showed that exposure to O 3 enhanced lung cancer cell proliferation and migration. This was strong evidence for the causative relationship between O 3 exposure and lung cancer. In our study, we examined only the relationship between lung cancer and air pollution, but not the specific underlying biological mechanisms. Currently, two main explanations have been proposed to explain this relationship. First, exposure to atmospheric pollution can cause oxidative stress, and which may cause macrophages to release reactive oxygen species (ROS) that can damage DNA, protein, and lipid cells. Additionally, ROS production may be triggered by metals present on the surface of particulate matter through the Fenton reaction or anthraquinones in the redox cycle[ 40 , 41 ]. Second, exposure to air pollutants may directly or indirectly induce inflammatory effects, which may lead to the generation of chemokines and cytokines, thereby inducing angiogenesis and transforming epithelial cells into malignant and invasive cells, which ultimately invade distal organs[ 42 – 44 ]. The current study has a few limitations. First, the study was conducted with a hospital case–control design and, therefore, it has flaws that are inherent in the method itself. To overcome this, various control measures, such as selecting cases from multiple hospitals and choosing objective indicators, were employed in this study. However, most of the controls were from Fuzhou, and the difference in the regional distribution of cases and controls may have influenced the results. Second, although the locations of the atmospheric sampling points were established scientifically and were reasonable, the air quality in Fujian Province that was analyzed in this study may not completely reflect the air quality of the entire Fujian Province because of the limited number of sampling points. Third, the levels of SO 2 , NO 2 , and PM 10 were measured since 2005, while those of O 3 , CO, and PM 2.5 were obtained since 2013. The difference in the data collection period for the two set of parameters may have influenced the results. Fourth, the concentrations of the air pollutants measured in the indices of this study were not obtained by individual portable monitoring; therefore, there is a possibility of an estimate bias that resulted in a wider CI. Future research that is based on a greater sample size and forward-looking research design is necessary to provide in-depth insight into these points and strengthen environmental and public health monitoring. Conclusions This hospital-based case–control study revealed a few factors that influence the onset of lung cancer, including smoking, passive smoking, exposure to cooking oil fumes, family history of lung cancer, and medical history of lung disease. Increasing fruit intake, physical exercise, and drinking tea were found to be protective against lung cancer. More importantly, long-term exposure to PM 10, PM 2.5 , and O 3 is significantly associated with increased risk of lung cancer, and the risk appears to be greater in non-smokers than in smokers or the total population; moreover, this difference appears to be greater in people exposed to cooking oil fumes than those without. In view of the high smoking rate and psychological and economic burden, the Chinese government has launched a national strategy, "Healthy China 2030", which aims to reduce the smoking rate to less than 20 percent[ 45 ]. At the same time, the early lung cancer screening in high-risk patients with lung cancer mortality reduction plays a big role, people at risk for lung cancer in Fujian province, we suggest to between aged 55 to 80 years old, more than 20 years of smoking history of low-dose computed tomography (LDCT) screening, early treatment, thereby to improve the patients quality of life. Abbreviations PM10 Particulate matter with particle size below 10μmmicrons PM2.5 Particulate matter with particle size below 2.5μmmicrons IARC International Agency for Research on Cancer RR risk ratio OR odds ratio O 3 Ozone BMI body mass index SO 2 sulfur dioxide NO 2 nitrogen dioxide CO carbon monoxide IDW inverse distance to a power CI confidence interval NO x nitrous oxides P p-value PAF population attributable risk fraction ACS CPS-II The American Cancer Society’s Cancer Prevention Study II ESCAPE European Air Pollution Impact Cohort Study ROS reactive oxygen species DNA Deoxyribonucleic Acid Declarations Ethics approval and consent to participate This study was approved by the Institutional Review Board of Fujian Medical University (Fuzhou, China) and all participants signed informed consent forms. All methods were carried out in accordance with relevant guidelines and regulations. Consent for publication Not applicable Availability of data and material The datasets generated and/or analysed during the current study are not publicly available due to government data but are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This study was supported by grants from the National Natural Science Foundation of China (Nos 81402738), Fujian Provincial Health Research Talents Training Programme Medical Innovation Project (Nos 2019-CX-33), Fujian Program for Outstanding Young Researchers in University awarded by Education Department of Fujian (Nos 2017B019) and National Major Science and Technology Program of China (Nos 2017ZX10103008) . Authors’ contributions CGM, HF and WJS conceived of the study, and carried out the experiments, participated in the drafted the manuscript. HF,LYH,YHM,SJ, and LJB collected samples. 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Supplementary Files AdditionalTables1to12.docx Additional Table 1 Comparison of demographic characteristics between two groups in different populationsAdditional Table 2 Analysis of risk factors of lung cancer in different populationsAdditional Table 3 Comparison of air pollutant exposure in two groups in different populationsAdditional Table 4 Comparison of demographic characteristics between two groups in different populationsAdditional Table 5 Analysis of risk factors of lung cancer in different populationsAdditional Table 6 Comparison of air pollutant exposure in two groups in different populationsAdditional Table 7 Multivariate logistic regression analysis of lung cancer in total populationAdditional Table 8 Multivariate logistic regression analysis of lung cancer in passive smokersAdditional Table 9 Multivariate logistic regression analysis of lung cancer in non-passive smokersAdditional Table 10 Multivariate logistic regression analysis of lung cancer in total populationAdditional Table 11 Multivariate logistic regression analysis of lung cancer in subjects exposed to cooking oil fumeAdditional Table 12 Multivariate logistic regression analysis of lung cancer in in subjects without exposed to cooking oil fume Cite Share Download PDF Status: Posted Version 3 posted You are reading this latest preprint version Show more versions 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7996","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":97826119,"identity":"5e85aba9-30da-4e31-9b42-6b4108842536","order_by":0,"name":"Guangmin Chen","email":"","orcid":"","institution":"Fujian Provincial Center for Disease Control and Prevention, Fuzhou, China; Fujian Provincial Key Laboratory of Zoonosis Research，Fuzhou, China; The practice base on the school of public health Fujian","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Guangmin","middleName":"","lastName":"Chen","suffix":""},{"id":97826120,"identity":"9e594b5b-939a-457b-976f-2fa2868d4fdc","order_by":1,"name":"Fei He","email":"","orcid":"","institution":"Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Fei","middleName":"","lastName":"He","suffix":""},{"id":97826121,"identity":"a9401414-5b26-4113-8d06-c073194a1392","order_by":2,"name":"Jiasheng Wu","email":"","orcid":"","institution":"Administration of Fuzhou Area of China (Fujian) Pilot Free Trade Zone","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jiasheng","middleName":"","lastName":"Wu","suffix":""},{"id":97826122,"identity":"3386b9e9-87ea-4e1d-a45d-f00999a8e84e","order_by":3,"name":"Huimin Yang","email":"","orcid":"","institution":"Fujian Digital Institute of Tumor Big Data, Fujian Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Huimin","middleName":"","lastName":"Yang","suffix":""},{"id":97826123,"identity":"e524882e-ba07-4b82-a30a-5b6d61303b1b","order_by":4,"name":"Jin Su","email":"","orcid":"","institution":"Fujian Provincial Environmental Monitoring Center Station","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jin","middleName":"","lastName":"Su","suffix":""},{"id":97826124,"identity":"bb55b852-98cd-4dc2-ad8a-34f7f7001682","order_by":5,"name":"Jianbo Lin","email":"","orcid":"","institution":"The first affiliated hospital of Fujian Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jianbo","middleName":"","lastName":"Lin","suffix":""},{"id":97826125,"identity":"7458f084-6714-4e30-bf6b-29f67bd6e026","order_by":6,"name":"Yuhang Liu","email":"","orcid":"","institution":"Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yuhang","middleName":"","lastName":"Liu","suffix":""},{"id":97826126,"identity":"70a3a7c0-d782-45bf-98fd-694be696233e","order_by":7,"name":"Xinying Xu","email":"","orcid":"","institution":"Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xinying","middleName":"","lastName":"Xu","suffix":""},{"id":97826127,"identity":"d2d57787-7f0d-4177-8164-1e742d794798","order_by":8,"name":"Liang Bai","email":"","orcid":"","institution":"Fujian Provincial Environmental Monitoring Center Station","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Liang","middleName":"","lastName":"Bai","suffix":""},{"id":97826128,"identity":"df8f8f5e-64ad-41f2-99f5-8ffb015722a8","order_by":9,"name":"Lin Cai","email":"","orcid":"","institution":"Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Lin","middleName":"","lastName":"Cai","suffix":""},{"id":97826129,"identity":"a4137f6c-1452-4a85-a49d-a0f21636cc88","order_by":10,"name":"Kuicheng Zheng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7UlEQVRIiWNgGAWjYBACCWYgwcPAzMPP3tj44IOBjRzxWiR7Dh82nFGQZkxYCwNEC4PBjbQ0aZ4PhxMJapFs5z384k2NtYzBjRwzaRsD5gQG9sNHN+DTIs3Ml2Y551g6j+SZN8bWOQZseQw8aWk38GmRY+YxM+ZhO8zDdzzH8HaOAU8xgwSPGRFa/h3mYTiQYyBtYSCR2EBIizQzj/Fj3rbDPAIn0pKkGQwMCGuRbOYxY5zblw4J5B6DBGM2Qn6ROH/G+MObb9b24Kj88ee/HD/74WN4tQABmwQql4ByEGD+QISiUTAKRsEoGMkAAKQnRZwEzCwjAAAAAElFTkSuQmCC","orcid":"","institution":"Fujian Provincial Center for Disease Control and Prevention, Fuzhou, China; Fujian Provincial Key Laboratory of Zoonosis Research，Fuzhou, China; The practice base on the school of public health Fujian","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Kuicheng","middleName":"","lastName":"Zheng","suffix":""}],"badges":[],"createdAt":"2019-11-14 14:12:03","currentVersionCode":3,"declarations":"","doi":"10.21203/rs.2.17362/v3","doiUrl":"https://doi.org/10.21203/rs.2.17362/v3","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":20240987,"identity":"d0c55aa5-bc5a-4c17-b510-872b3742cb84","added_by":"auto","created_at":"2022-04-12 13:21:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":28406,"visible":true,"origin":"","legend":"\u003cp\u003eMap of the monitoring sites in Fujian Province, China (Map made by ArcGIS10.3 software)\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7996/v3/538021f0a266c7252562413f.png"},{"id":20242004,"identity":"ddbf7dc3-3f40-4a2f-a693-03f711225cbd","added_by":"auto","created_at":"2022-04-12 13:31:03","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1065531,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual average of SO2 (A) , NO2(B) and PM10 (C) concentrations in 2005-2015 and CO(D), O3 (E), and PM2.5 (F)concentrations in 2013-2015 (Map made by ArcGIS10.3 software)\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7996/v3/d5bae8bf728f8e274564dcd5.png"},{"id":31404326,"identity":"40b573b0-13c8-4c2d-a157-3e85ee6ef6e6","added_by":"auto","created_at":"2023-01-11 05:59:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1445432,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7996/v3/9420fbeb-e8ae-482f-a630-b5ef691a9056.pdf"},{"id":20241189,"identity":"b078871b-ac76-4ebb-911e-5cbd025541ed","added_by":"auto","created_at":"2022-04-12 13:26:03","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":77764,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional Table 1 Comparison of demographic characteristics between two groups in different populations\u003c/p\u003e\u003cp\u003eAdditional Table 2 Analysis of risk factors of lung cancer in different populations\u003c/p\u003e\u003cp\u003eAdditional Table 3 Comparison of air pollutant exposure in two groups in different populations\u003c/p\u003e\u003cp\u003eAdditional Table 4 Comparison of demographic characteristics between two groups in different populations\u003c/p\u003e\u003cp\u003eAdditional Table 5 Analysis of risk factors of lung cancer in different populations\u003c/p\u003e\u003cp\u003eAdditional Table 6 Comparison of air pollutant exposure in two groups in different populations\u003c/p\u003e\u003cp\u003eAdditional Table 7 Multivariate logistic regression analysis of lung cancer in total population\u003c/p\u003e\u003cp\u003eAdditional Table 8 Multivariate logistic regression analysis of lung cancer in passive smokers\u003c/p\u003e\u003cp\u003eAdditional Table 9 Multivariate logistic regression analysis of lung cancer in non-passive smokers\u003c/p\u003e\u003cp\u003eAdditional Table 10 Multivariate logistic regression analysis of lung cancer in total population\u003c/p\u003e\u003cp\u003eAdditional Table 11 Multivariate logistic regression analysis of lung cancer in subjects exposed to cooking oil fume\u003c/p\u003e\u003cp\u003eAdditional Table 12 Multivariate logistic regression analysis of lung cancer in in subjects without exposed to cooking oil fume\u003c/p\u003e","description":"","filename":"AdditionalTables1to12.docx","url":"https://assets-eu.researchsquare.com/files/rs-7996/v3/bc0a02ee3a7cf6b9cd79f0be.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Relationship between Air Pollution and Lung Cancer in Fujian Province: A Case-control study","fulltext":[{"header":"Background","content":"\u003cp\u003eAs per GLOBOCAN 2018, approximately 18.1\u0026nbsp;million new cancer cases are diagnosed worldwide each year. Lung cancer ranks first among the causes of new cancer cases, with about 2.09\u0026nbsp;million cases diagnosed annually, accounting for 11.6% of new cases of malignant tumors. The incidence of lung cancer in East Asia, particularly in China, is far higher than that worldwide (more than 40/100 000)[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Lung cancer was the leading cause of cancer incidence and mortality in China for many years[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. According to the 2017 Malignant Tumors report from Fujian Province, lung cancer ranked first among all types of cancers in males (62.61/100 000) and second in females (24.8/100 000). Clearly, lung cancer has become a serious threat to public health and places a heavy disease burden in Fujian Province and China as a whole.\u003c/p\u003e \u003cp\u003eSmoking is a known and confirmed risk factor for lung cancer. The increase in the number of female smokers in developed countries is one of the reasons for the increase in the incidence of lung cancer among women and decrease in the comparative incidence among men[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, some studies have found that about 15% of men and 53% of women with lung cancer do not smoke. Moreover, about 25% of lung cancer cases worldwide cannot be attributed to tobacco usage[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This has led to an increased interest in the exploration of the causes of lung cancer other than smoking, particularly air pollution[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], diet[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], infection[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], cooking fumes[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], medical history of respiratory diseases[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], and occupational exposure[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFor nearly 50 years, research has been underway to confirm the relationship between outdoor air pollution and lung cancer[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The International Agency for Research on Cancer (IARC) defines outdoor air pollutants, especially particulate matter, as a type of carcinogen. This classification is primarily based on evidence that long-term persistence of high average concentrations of PM\u003csub\u003e2.5\u003c/sub\u003e in outdoor air is associated with lung cancer morbidity or mortality[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, the IARC report is a qualitative assessment of hazard identification, and it does not provide the relative risk or odds ratio (RR or OR) of lung cancer associated with outdoor air pollution. The current research results still leave several key points unclear. First, the population groups that are most vulnerable to air pollution are yet to be identified[\u003cspan additionalcitationids=\"CR15 CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Second, few studies have focused on the dose\u0026ndash;response relationship between long-term exposure to outdoor air pollution and lung cancer [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In particular, very few studies from China have investigated the risk of lung cancer in relation to air pollution; among these studies, case\u0026ndash;control studies and cohort studies are very few, and most of the existing studies focus on lung cancer mortality.\u003c/p\u003e \u003cp\u003eGiven the limitations of epidemiological research methods, cohort studies on the causal relationship between outdoor air pollution and lung cancer would require a long time to yield conclusive results. In this study, we sought to conduct a hospital-based, case-control study to investigate the relationship between air pollutants and lung cancer in Fujian Province, with special focus on the exposure levels of different population groups to air pollutants. We expect that the results of this study would provide a scientific basis for targeted improvement of the atmospheric environment for protection of public health.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eOverview of the study area\u003c/h2\u003e \u003cp\u003eFujian Province is located on the southeast coast of China. The land area is 124 000 square kilometers in total. From north to south, the province measures about 530 kilometers and occupies about 480 kilometers from east to west. Fujian is located at a latitude of 23\u0026deg;30' and 28\u0026deg;22' north and at longitude of 115\u0026deg;50' and 120\u0026deg;40' east. The terrain is higher in the northwest and lower in the southeast. About 90% of the total land area of the province is covered by mountains and hills. In addition, the coastal landform pattern is dominated by a tortuous coastline that includes several bays and peninsulas. The climate in Fujian Province is subtropical monsoon climate, which makes the area warm and humid. The annual average temperature is 15\u0026deg;C\u0026ndash;22\u0026deg;C, which increases from the northwest to the southeast. The annual average precipitation is 800\u0026ndash;1,900 mm, with the precipitation being greatest between May and June every year, with several typhoons occurring at the turn of summer and autumn.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eAir pollution data collection\u003c/h2\u003e \u003cp\u003eFor this study, we aimed to obtain a reasonable estimate of each subject\u0026rsquo;s exposure to atmospheric pollutants by using a longer average annual concentration. The levels of sulfur dioxide (SO\u003csub\u003e2\u003c/sub\u003e), nitrogen dioxide (NO\u003csub\u003e2\u003c/sub\u003e) and suspended particulates smaller than 10 \u0026micro;m in aerodynamic diameter (PM\u003csub\u003e10\u003c/sub\u003e) in Fujian Province were obtained from the complete monitoring data of national control points, which has been maintained since 2005. Since 2013, China has implemented new monitoring standards, which include the current maximum of eight hours average for ozone (O\u003csub\u003e3\u003c/sub\u003e). Accordingly, the maximum value of continuous 8-hour ozone concentration in a day is taken as the standard for evaluating the ozone pollution level of the same day and the six indicators of air quality are monitored: SO\u003csub\u003e2\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, carbon monoxide (CO), and suspended particulates smaller than 2.5\u0026micro;m in aerodynamic diameter (PM\u003csub\u003e2.5\u003c/sub\u003e). The ambient air quality data collected in this study was collected for the period between January 1, 2005 and December 31, 2015.\u003c/p\u003e \u003cp\u003eThe daily average concentration values and monthly average values of SO\u003csub\u003e2\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, CO, O\u003csub\u003e3\u003c/sub\u003e, and PM\u003csub\u003e2.5\u003c/sub\u003e were collected for 39 monitoring points (national control points) in Fujian Province; the data were collected from the Fujian Provincial Environmental Monitoring Center Station (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The strategic value of the location of each monitoring point has been demonstrated previously, and the points have been shown to be geographically representative. The air pollution monitoring data for SO\u003csub\u003e2\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, CO, O\u003csub\u003e3,\u003c/sub\u003e and PM\u003csub\u003e2.5\u003c/sub\u003e were evaluated according to the Chinese Ambient Air Quality Standard (GB3095-2012).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSince it would be impractical to measure the individual exposure level to concentrations of atmospheric pollutants by having each subject wear a monitoring device, we used an individual atmospheric pollutant exposure estimation method, according to the approach proposed by Dadvand et al[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Basically, we constructed an algorithm to predict atmospheric pollutant exposure for each location within Fujian province by integrating air pollution monitoring data and spatial coordinates of 39 monitoring points. By using the spatial prediction function of ArcGIS 10.3, the spatial distribution map of air pollution indexes in Fujian Province was generated by applying inverse distance to a power (IDW) interpolation in areas that lacked derived data. The annual average level of cumulative concentration served as the matched measurement data for each index. Further, the levels of SO\u003csub\u003e2\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, and PM\u003csub\u003e10\u003c/sub\u003e were the annual average values from 2005 to the time that subjects enrolled. In addition, the concentrations of O\u003csub\u003e3\u003c/sub\u003e, CO, and PM\u003csub\u003e2.5\u003c/sub\u003e were measured from 2013 to the time of enrollment of the subjects. According to the detailed home address (specific to the district, street, and house number), the spatial coordinates (latitude and longitude) of each subject was accurately determined by GPSspgxGeo coding software and Tencent map software, which can be further mapped to the predicting model and used to generate the estimating pollution data for each subject.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eHospital Data and Population Data\u003c/h2\u003e \u003cp\u003eBetween January 2010 and December 2015, we recruited from three hospitals: Department of Thoracic Surgery and Respiratory Medicine of the First Affiliated Hospital of Fujian Medical University, Union Hospital Affiliated to Fujian Medical University, and Fuzhou General Hospital of Nanjing Military Region. Patients were included if they had been newly diagnosed with primary lung cancer, as confirmed by fiberopticbronchoscopy or surgical histopathology, and had resided in Fujian province of China for more than 10 years.\u003c/p\u003e \u003cp\u003eThe population-based approach was applied to the recruit the control group, which comprised healthy community dwellers randomly selected from the resident records of Fujian province. The control group was frequency-matched to the cases by ethnicity, gender, and age. Every control subject was of ages\u0026thinsp;\u0026plusmn;\u0026thinsp;2 years as compared to the matching cases. All the selected control individuals successfully met the inclusion criteria and completed the study, without any drop-out during the study.\u003c/p\u003e \u003cp\u003eIn total, the case group comprised 885 patients with lung cancer, while the control group comprised 1,220 healthy individuals. All participants provided written, informed consent before undergoing the examination. The study protocol was approved by the Ethics Committee of Fujian Medical University.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eQuestionnaire and variables\u003c/h2\u003e \u003cp\u003eAll participants were surveyed using a standardized questionnaire, which was administered during a scheduled phone interview conducted via a study team member. The 18-page questionnaire had questions pertaining to the patients\u0026rsquo; demographic characteristics information, tumor characteristics, and data regarding the subject\u0026rsquo;s living environment, dietary habits, smoking history, alcohol consumption, intensity of physical activity, occupational exposure to air pollutants, and exposure to domestic pollutants. The questionnaire also included items regarding medical history, family history, and lifestyle-related parameters. Data regarding reproductive health were collected for female participants.\u003c/p\u003e \u003cp\u003eBody mass index (BMI) was determined as the ratio of body weight (kg) and height[m]\u003csup\u003e2\u003c/sup\u003e. A positive smoking history was defined as a history of having smoked more than 100 cigarettes during his/her lifetime. Passive smoking history was defined by non-smoking history of inhaling cigarette smoke generated by others or exposure to exhaled smoke more than 15 minutes per day. A history of alcohol consumption was defined as drinking at least one alcoholic beverage per week for more than six months, irrespective of the type of alcoholic drink. Drinking tea was defined as consuming at least one cup of any kind of tea per week for more than six months. A family history of cancer was defined as the occurrence of a malignant tumor in first-degree or second-degree relatives. Occupational physical activity was rated as low, moderate, or high intensity, as defined by the Reference Standard of Labor Intensity recommended by the Chinese Nutrition Society in 2000. To check for exposure to cooking oil fumes, participants were enquired about whether their degree of exposure to fumes in the kitchen was none (no exposure), light, moderate, or heavy[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe Chi-square test was used to compare the general characteristics of cases and controls. Stratified analysis for different populations was performed using a \u003cem\u003et\u003c/em\u003e test for the exposure levels of air pollution indexes. Unconditional logistic regression modeling and identification of possible confounding factors were performed by calculating odds ratios (ORs) and 95% confidence intervals (CIs) for air pollution indexes and lung cancer risk. All analysis was performed using the SPSS 25.0 software package (IBM Corporation, Armonk, New York, USA) and ArcGIS 10.3 (ESRI Inc, USA). All p-values were based on a two-sided test with an α of 0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n\u003ch2\u003eDemographic characteristics of the study subjects\u003c/h2\u003e\n\u003cp\u003eThe current study comprised 2,105 subjects, including 885 cases and 1,220 controls. Subjects included 1,354 males and 751 females, with ages between 23 and 90 years of age and average age of 58.94\u0026thinsp;\u0026plusmn;\u0026thinsp;10.73 years. The case and control groups did not show any significant difference in the distribution of gender, age, ethnicity, and marital status (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, significant intergroup differences were noted in the educational level, occupation, and BMI (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\n\u003cp\u003eAmong the smokers, no significant difference was noted between the case group and the control group in terms of the distribution of gender, ethnicity, and marital status (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, statistically significant intergroup differences were noted in the distribution of education, occupation, and BMI (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Among the non-smokers, the case group and control groups did not differ significantly in the distribution of age, ethnicity, and marital status (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05), but showed significant differences in gender, education level, and occupational exposure (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\n\u003cp\u003eAmong the 885 cases included, 551 (62.3%) were of adenocarcinoma, 208 (23.5%) were of squamous cell carcinoma, and 126 (14.2%) were of other pathological types of cancers. Among the 519 smokers, 253 (48.7%) had adenocarcinoma, 177 (34.1%) had squamous cell carcinoma, and 89 (17.4%) had tumors of other pathological types. Of 366 non-smoking lung cancer patients, 298 (81.4%) had adenocarcinoma, 31 (8.5%) had squamous cell carcinoma, and 37 (10.1%) had lesions of other pathological types (Table\u0026nbsp;1).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n\u003ch2\u003eThe risk factors of lung cancer\u003c/h2\u003e\n\u003cp\u003eIn this study, we explored the potential factors for lung cancer in the three populations: the total population, the subgroups\u0026ndash;smokers, and non-smokers (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Of the total population, after adjusting for BMI, education, and occupation, we found that subjects with a family history of lung cancer or medical history of lung disease, drinking alcohol, smoking, passive smoking, or exposure to cooking oil fumes were susceptible to lung cancer. We found that the risk of lung cancer was low for subjects who performed physical exercise and had a regular intake of fruit (more than 3 times per week). Among the smokers, after adjustment for age, BMI, education, and occupation, the risk factors for lung cancer were family history of lung cancer, medical history of lung disease, and exposure to cooking oil fumes, while the protective factors were physical exercise and fruit intake. Similarly, for non-smokers, the risk factors for lung cancer were family history of lung cancer, history of lung diseases, passive smoking, and exposure to cooking oil fumes, after adjustment for gender, education, and occupation, whereas the protective factors were drinking tea, physical exercise, and fruit intake.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab1\" style=\"width: 1025px;\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable\u0026nbsp;1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eComparison of demographic characteristics between two groups in different populations\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 210px;\" rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eVariables\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 239px;\" colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eTotal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 234px;\" colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eSmoker\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 64px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 1926.17px;\" colspan=\"6\" align=\"left\"\u003e\n\u003cp\u003eNon-smoker\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003eCases n(%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 69px;\" align=\"left\"\u003e\n\u003cp\u003eControl n(%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 68px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\chi }^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 40px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003eCases n(%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003eControl n(%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\chi }^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 40px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 879.172px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eCases n(%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 76px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eControl n(%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 20px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\chi }^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 1025px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 210px;\" align=\"left\"\u003e\n\u003cp\u003eAge (years,\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\stackrel{-}{x}\\pm s\\)\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e59.34\u0026thinsp;\u0026plusmn;\u0026thinsp;9.94\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 69px;\" align=\"left\"\u003e\n\u003cp\u003e58.64\u0026thinsp;\u0026plusmn;\u0026thinsp;11.26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 68px;\" align=\"left\"\u003e\n\u003cp\u003e-1.473\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 40px;\" align=\"left\"\u003e\n\u003cp\u003e0.141\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e60.82\u0026thinsp;\u0026plusmn;\u0026thinsp;9.27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003e58.90\u0026thinsp;\u0026plusmn;\u0026thinsp;10.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e-3.030\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 40px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 879.172px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e57.24\u0026thinsp;\u0026plusmn;\u0026thinsp;10.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003e58.50\u0026thinsp;\u0026plusmn;\u0026thinsp;11.81\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 20px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1.746\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 20px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.081\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 210px;\" align=\"left\"\u003e\n\u003cp\u003eGender\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 69px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 68px;\" align=\"left\"\u003e\n\u003cp\u003e1.379\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 40px;\" align=\"left\"\u003e\n\u003cp\u003e0.240\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003e0.004\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 40px;\" align=\"left\"\u003e\n\u003cp\u003e0.948\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 879.172px;\" colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 20px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e67.678\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 20px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 210px;\" align=\"left\"\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e582(65.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 69px;\" align=\"left\"\u003e\n\u003cp\u003e772(63.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 68px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 40px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e512(98.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003e423(98.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 40px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 879.172px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e70(19.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003e349(44.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 20px;\" colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 20px;\" colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 210px;\" align=\"left\"\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e303(34.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 69px;\" align=\"left\"\u003e\n\u003cp\u003e448(36.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 68px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 40px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e7(1.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003e6(1.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 40px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 879.172px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e296(80.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003e442(55.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 20px;\" colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 20px;\" colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 210px;\" align=\"left\"\u003e\n\u003cp\u003eNationality\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 69px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 68px;\" align=\"left\"\u003e\n\u003cp\u003e0.026\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 40px;\" align=\"left\"\u003e\n\u003cp\u003e0.873\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003e2.807\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 40px;\" align=\"left\"\u003e\n\u003cp\u003e0.094\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 879.172px;\" colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 20px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.670\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 20px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.413\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 210px;\" align=\"left\"\u003e\n\u003cp\u003eHan\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e863(97.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 69px;\" align=\"left\"\u003e\n\u003cp\u003e1191(97.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 68px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 40px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e505(97.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003e424(98.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 40px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 879.172px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e358(97.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003e767(97.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 20px;\" colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 20px;\" colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 210px;\" align=\"left\"\u003e\n\u003cp\u003eOthers\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e22(2.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 69px;\" align=\"left\"\u003e\n\u003cp\u003e29(2.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 68px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 40px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e14(2.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003e5(1.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 40px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 879.172px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e8(2.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003e24(3.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 20px;\" colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 20px;\" colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 210px;\" align=\"left\"\u003e\n\u003cp\u003eMarital status\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 69px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 68px;\" align=\"left\"\u003e\n\u003cp\u003e1.547\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 40px;\" align=\"left\"\u003e\n\u003cp\u003e0.214\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003e1.025\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 40px;\" align=\"left\"\u003e\n\u003cp\u003e0.311\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 879.172px;\" colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 20px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 20px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.974\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 210px;\" align=\"left\"\u003e\n\u003cp\u003eMarried\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e824(93.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 69px;\" align=\"left\"\u003e\n\u003cp\u003e1118(91.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 68px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 40px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e492(94.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003e400(93.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 40px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 879.172px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e332(90.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003e718(90.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 20px;\" colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 20px;\" colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 210px;\" align=\"left\"\u003e\n\u003cp\u003eSingle\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e61(6.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 69px;\" align=\"left\"\u003e\n\u003cp\u003e102(8.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 68px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 40px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e27(5.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003e29(6.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 40px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 879.172px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e34(9.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003e73(9.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 20px;\" colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 20px;\" colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 210px;\" align=\"left\"\u003e\n\u003cp\u003eEducational status\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 69px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 68px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e169.749\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 40px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e46.084\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 40px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 879.172px;\" colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 20px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e116.641\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 20px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 210px;\" align=\"left\"\u003e\n\u003cp\u003ePrimary school and below\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e477(53.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 69px;\" align=\"left\"\u003e\n\u003cp\u003e360(29.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 68px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 40px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e255(49.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003e133(31.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 40px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 879.172px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e222(60.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003e227(28.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 20px;\" colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 20px;\" colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 210px;\" align=\"left\"\u003e\n\u003cp\u003eMiddle school\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e320(36.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 69px;\" align=\"left\"\u003e\n\u003cp\u003e505(41.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 68px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 40px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e221(42.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003e208(48.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 40px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 879.172px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e99(27.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003e297(37.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 20px;\" colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 20px;\" colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 210px;\" align=\"left\"\u003e\n\u003cp\u003eCollege and higher\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e88(9.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 69px;\" align=\"left\"\u003e\n\u003cp\u003e355(29.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 68px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 40px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e43(8.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003e88(20.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 40px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 879.172px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e45(12.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003e267(33.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 20px;\" colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 20px;\" colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 210px;\" align=\"left\"\u003e\n\u003cp\u003eOccupation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 69px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 68px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e114.919\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 40px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e23.277\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 40px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 879.172px;\" colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 20px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e102.522\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 20px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 210px;\" align=\"left\"\u003e\n\u003cp\u003eAgriculture, forestry, animal husbandry and fishery personnel\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e263(29.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 69px;\" align=\"left\"\u003e\n\u003cp\u003e180(14.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 68px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 40px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e157(30.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003e76(17.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 40px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 879.172px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e106(29.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003e104(13.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 20px;\" colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 20px;\" colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 210px;\" align=\"left\"\u003e\n\u003cp\u003eProduction of transport workers\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e219(24.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 69px;\" align=\"left\"\u003e\n\u003cp\u003e268(22.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 68px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 40px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e134(25.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003e113(26.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 40px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 879.172px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e85(23.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003e155(19.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 20px;\" colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 20px;\" colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 210px;\" align=\"left\"\u003e\n\u003cp\u003eEnterprises and institutions personnel\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e241(27.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 69px;\" align=\"left\"\u003e\n\u003cp\u003e573(47.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 68px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 40px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e159(30.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003e175(40.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 40px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 879.172px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e82(22.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003e398(50.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 20px;\" colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 20px;\" colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 210px;\" align=\"left\"\u003e\n\u003cp\u003eBusiness service staff\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e74(8.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 69px;\" align=\"left\"\u003e\n\u003cp\u003e116(9.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 68px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 40px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e45(8.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003e47(11.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 40px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 879.172px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e29(7.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003e69(8.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 20px;\" colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 20px;\" colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 210px;\" align=\"left\"\u003e\n\u003cp\u003eOther and unemployed\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e88(9.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 69px;\" align=\"left\"\u003e\n\u003cp\u003e83(6.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 68px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 40px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e24(4.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003e18(4.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 40px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 879.172px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e64(17.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003e65(8.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 20px;\" colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 20px;\" colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 210px;\" align=\"left\"\u003e\n\u003cp\u003eBMI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 69px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 68px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e22.331\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 40px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e29.042\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 40px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 879.172px;\" colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 20px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1.926\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 20px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.382\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 210px;\" align=\"left\"\u003e\n\u003cp\u003e18.5\u0026ndash;24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e547(61.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 69px;\" align=\"left\"\u003e\n\u003cp\u003e682(55.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 68px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 40px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e333(64.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003e231(53.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 40px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 879.172px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e214(58.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003e451(57.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 20px;\" colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 20px;\" colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 210px;\" align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;18.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e68(7.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 69px;\" align=\"left\"\u003e\n\u003cp\u003e57(4.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 68px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 40px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e42(8.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003e14(3.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 40px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 879.172px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e26(7.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003e43(5.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 20px;\" colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 20px;\" colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 210px;\" align=\"left\"\u003e\n\u003cp\u003e\u0026ge;\u0026thinsp;24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e270(30.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 69px;\" align=\"left\"\u003e\n\u003cp\u003e481(39.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 68px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 40px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e144(27.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003e184(42.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 40px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 879.172px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e126(34.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003e297(37.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 20px;\" colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 20px;\" colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 210px;\" align=\"left\"\u003e\n\u003cp\u003ePathological type\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 69px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 68px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 40px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 40px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 879.172px;\" colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 20px;\" colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 20px;\" colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 210px;\" align=\"left\"\u003e\n\u003cp\u003eAdenocarcinoma\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e551(62.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 69px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 68px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 40px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e253(48.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 40px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 879.172px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e298(81.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 20px;\" colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 20px;\" colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 210px;\" align=\"left\"\u003e\n\u003cp\u003eSquamous cell carcinoma\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e208(23.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 69px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 68px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 40px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e177(34.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 40px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 879.172px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e31(8.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 20px;\" colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 20px;\" colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 210px;\" align=\"left\"\u003e\n\u003cp\u003eOthers\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e126(14.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 69px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 68px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 40px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e89(17.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 40px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 879.172px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e37(10.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 20px;\" colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 20px;\" colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;Table 2 Analysis of risk factors of lung cancer in different populations\u003csup\u003ea\u003c/sup\u003e\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Taba\" style=\"width: 755px;\" border=\"1\"\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth style=\"width: 86px;\" rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eVariables\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"width: 203px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eTotal\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"width: 203px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eSmoker\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"width: 202.234px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eNon-smoker\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth style=\"width: 78px;\" align=\"left\"\u003e\n\u003cp\u003ecase/control\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"width: 125px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eaOR\u003c/em\u003e (95%\u003cem\u003eCI\u003c/em\u003e)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"width: 78px;\" align=\"left\"\u003e\n\u003cp\u003ecase/control\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"width: 125px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eaOR\u003c/em\u003e (95%\u003cem\u003eCI\u003c/em\u003e)\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"width: 77.2344px;\" align=\"left\"\u003e\n\u003cp\u003ecase/control\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"width: 125px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eaOR\u003c/em\u003e (95%\u003cem\u003eCI\u003c/em\u003e)\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 164px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eFamily history of lung cancer\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 78px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 77.2344px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 86px;\" align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 78px;\" align=\"left\"\u003e\n\u003cp\u003e825/1185\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 78px;\" align=\"left\"\u003e\n\u003cp\u003e490/421\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77.2344px;\" align=\"left\"\u003e\n\u003cp\u003e335/764\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 86px;\" align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 78px;\" align=\"left\"\u003e\n\u003cp\u003e60/35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e3.193(2.029\u0026ndash;5.025)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 78px;\" align=\"left\"\u003e\n\u003cp\u003e29/8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e3.601(1.584\u0026ndash;8.187)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77.2344px;\" align=\"left\"\u003e\n\u003cp\u003e31/27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e3.448(1.901\u0026ndash;6.253)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 289px;\" colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003ePersonal history of lung disease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 78px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 77.2344px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 86px;\" align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 78px;\" align=\"left\"\u003e\n\u003cp\u003e761/1135\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 78px;\" align=\"left\"\u003e\n\u003cp\u003e431/381\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77.2344px;\" align=\"left\"\u003e\n\u003cp\u003e330/754\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 86px;\" align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 78px;\" align=\"left\"\u003e\n\u003cp\u003e124/85\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e2.547(1.874\u0026ndash;3.462)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 78px;\" align=\"left\"\u003e\n\u003cp\u003e88/48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e1.647(1.105\u0026ndash;2.457)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77.2344px;\" align=\"left\"\u003e\n\u003cp\u003e36/37\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e3.486(2.042\u0026ndash;5.951)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 164px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eDrinking alcohol\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 78px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 77.2344px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 86px;\" align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 78px;\" align=\"left\"\u003e\n\u003cp\u003e608/942\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 78px;\" align=\"left\"\u003e\n\u003cp\u003e276/244\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77.2344px;\" align=\"left\"\u003e\n\u003cp\u003e332/698\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 86px;\" align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 78px;\" align=\"left\"\u003e\n\u003cp\u003e277/278\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e1.568(1.277\u0026ndash;1.925)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 78px;\" align=\"left\"\u003e\n\u003cp\u003e243/185\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\n\u003cp\u003e1.208(0.924\u0026ndash;1.5580)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77.2344px;\" align=\"left\"\u003e\n\u003cp\u003e34/93\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\n\u003cp\u003e1.415(0.879\u0026ndash;2.278)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 86px;\" align=\"left\"\u003e\n\u003cp\u003eDrinking tea\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 78px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 78px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 77.2344px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 86px;\" align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 78px;\" align=\"left\"\u003e\n\u003cp\u003e464/596\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 78px;\" align=\"left\"\u003e\n\u003cp\u003e179/147\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77.2344px;\" align=\"left\"\u003e\n\u003cp\u003e285/449\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 86px;\" align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 78px;\" align=\"left\"\u003e\n\u003cp\u003e421/624\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\n\u003cp\u003e1.088(0.905\u0026ndash;1.309)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 78px;\" align=\"left\"\u003e\n\u003cp\u003e340/282\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\n\u003cp\u003e1.166(0.878\u0026ndash;1.548)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77.2344px;\" align=\"left\"\u003e\n\u003cp\u003e81/342\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.588(0.432\u0026ndash;0.799)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 86px;\" align=\"left\"\u003e\n\u003cp\u003eSmoking\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 78px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 78px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 77.2344px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 86px;\" align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 78px;\" align=\"left\"\u003e\n\u003cp\u003e366/791\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 78px;\" align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77.2344px;\" align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 86px;\" align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 78px;\" align=\"left\"\u003e\n\u003cp\u003e519/429\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e2.489(2.065-3.000)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 78px;\" align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77.2344px;\" align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 164px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ePassive smoking\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 78px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 77.2344px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 86px;\" align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 78px;\" align=\"left\"\u003e\n\u003cp\u003e288/598\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 78px;\" align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77.2344px;\" align=\"left\"\u003e\n\u003cp\u003e157/466\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 86px;\" align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 78px;\" align=\"left\"\u003e\n\u003cp\u003e597/622\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e1.933(1.601\u0026ndash;2.332)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 78px;\" align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77.2344px;\" align=\"left\"\u003e\n\u003cp\u003e209/325\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e1.632(1.247\u0026ndash;2.135)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 289px;\" colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eStimulating smell after renovation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 78px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 77.2344px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 86px;\" align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 78px;\" align=\"left\"\u003e\n\u003cp\u003e789/1080\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 78px;\" align=\"left\"\u003e\n\u003cp\u003e464/369\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77.2344px;\" align=\"left\"\u003e\n\u003cp\u003e325/711\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 86px;\" align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 78px;\" align=\"left\"\u003e\n\u003cp\u003e96/140\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\n\u003cp\u003e1.007(0.754\u0026ndash;1.345)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 78px;\" align=\"left\"\u003e\n\u003cp\u003e55/60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\n\u003cp\u003e0.797(0.530\u0026ndash;1.198)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77.2344px;\" align=\"left\"\u003e\n\u003cp\u003e41/80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\n\u003cp\u003e1.153(0.750\u0026ndash;1.772)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 164px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eCooking oil fume exposure\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 78px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 77.2344px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 86px;\" align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 78px;\" align=\"left\"\u003e\n\u003cp\u003e148/324\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 78px;\" align=\"left\"\u003e\n\u003cp\u003e87/110\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77.2344px;\" align=\"left\"\u003e\n\u003cp\u003e61/214\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 86px;\" align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 78px;\" align=\"left\"\u003e\n\u003cp\u003e737/896\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e1.615(1.287\u0026ndash;2.026)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 78px;\" align=\"left\"\u003e\n\u003cp\u003e432/319\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e1.487(1.070\u0026ndash;2.067)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77.2344px;\" align=\"left\"\u003e\n\u003cp\u003e305/577\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e1.521(1.086\u0026ndash;2.130)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 164px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ePhysical activity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 78px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 77.2344px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 86px;\" align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 78px;\" align=\"left\"\u003e\n\u003cp\u003e683/683\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 78px;\" align=\"left\"\u003e\n\u003cp\u003e403/259\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77.2344px;\" align=\"left\"\u003e\n\u003cp\u003e280/424\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 86px;\" align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 78px;\" align=\"left\"\u003e\n\u003cp\u003e202/537\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.428(0.350\u0026ndash;0.524)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 78px;\" align=\"left\"\u003e\n\u003cp\u003e116/170\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.475(0.354\u0026ndash;0.638)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77.2344px;\" align=\"left\"\u003e\n\u003cp\u003e86/367\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.437(0.325\u0026ndash;0.588)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 86px;\" align=\"left\"\u003e\n\u003cp\u003eFruit intake\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 78px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 78px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 77.2344px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 86px;\" align=\"left\"\u003e\n\u003cp\u003e\u0026ge;\u0026thinsp;3 times/week\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 78px;\" align=\"left\"\u003e\n\u003cp\u003e357/751\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 78px;\" align=\"left\"\u003e\n\u003cp\u003e208/219\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77.2344px;\" align=\"left\"\u003e\n\u003cp\u003e149/532\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 86px;\" align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;3 times/week\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 78px;\" align=\"left\"\u003e\n\u003cp\u003e528/469\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e2.034(1.690\u0026ndash;2.447)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 78px;\" align=\"left\"\u003e\n\u003cp\u003e311/210\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e1.447(1.106\u0026ndash;1.893)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77.2344px;\" align=\"left\"\u003e\n\u003cp\u003e217/259\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 125px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e2.574(1.953\u0026ndash;3.392)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 698px;\" colspan=\"7\"\u003ea Each variable was run independently\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 698px;\" colspan=\"7\"\u003eb Adjusted for educational status, occupation, BMI\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 698px;\" colspan=\"7\"\u003ec Adjusted for age, educational status, occupation, BMI\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 698px;\" colspan=\"7\"\u003ed Adjusted for gender, educational status, occupation\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003eUnivariate analysis of levels of exposure to atmospheric pollutants in different populations\u003c/h2\u003e\n\u003cp\u003eThe annual average of SO\u003csub\u003e2\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, and PM\u003csub\u003e10\u003c/sub\u003e concentrations for the period between 2005 and 2015 and those of CO, PM\u003csub\u003e2.5\u003c/sub\u003e, and O\u003csub\u003e3\u003c/sub\u003e for the period 2013\u0026ndash;2015 are shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. Notably, the distribution of different pollutants differed from one another. Thus, we noted that NO\u003csub\u003e2\u003c/sub\u003e, PM\u003csub\u003e2.5\u003c/sub\u003e, and O\u003csub\u003e3\u003c/sub\u003e had similar distribution patterns, with the pollutants being highly aggregated in cities along east coast; this may be associated with the dense population and severe traffic pollution. The remaining three common pollutants showed completely different distribution patterns, with CO mainly concentrating in northeast area, SO\u003csub\u003e2\u003c/sub\u003e aggregating in the mid\u0026ndash;west region, and PM\u003csub\u003e10\u003c/sub\u003e mainly affecting the southwest. The discrepancy in the distribution patterns may be attributed to the complex effect of factory contamination and factors such as climatic and geographic conditions.\u003c/p\u003e\n\u003cp\u003eUnivariate analysis was performed to levels of exposure to evaluate the impact of atmospheric pollutants on different populations. The values for concentrations of atmospheric pollutants were classified into four levels based on \u0026plusmn;\u0026thinsp;1 standard deviation (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). For the total population, after adjustment for education, occupation, BMI, family history of lung cancer, and medical history of lung disease, alcohol consumption, smoking, passive smoking, exposure to cooking oil fumes, physical exercise, and fruit intake, the results showed that the risk of developing lung cancer with exposure to NO\u003csub\u003e2\u003c/sub\u003e concentrations of 19\u0026ndash;29 (\u0026micro;g/m3) was 1.356 times greater than at concentrations of less than 19 (\u0026micro;g/m3) (95% CI: 1.028\u0026ndash;1.788). As compared to exposure to PM\u003csub\u003e10\u003c/sub\u003e concentrations of less than 51 (\u0026micro;g/m3), exposure to concentrations of 51\u0026ndash;57 (\u0026micro;g/m3) increased the risk of developing lung cancer by 2.450 times greater (95% CI: 1.728\u0026ndash;3.474), while exposure to concentration of 57\u0026ndash;64 (\u0026micro;g/m3)and increased the risk by 1.637 times (95% CI: 1.178\u0026ndash;2.276).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab2\" style=\"width: 1025px;\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eComparison of air pollutant exposure in two groups in different populationsa\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth style=\"width: 92px;\" rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eVariables\u003c/p\u003e\n\u003cp\u003e(mg/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"width: 300px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eTotal\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"width: 276px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eSmoker\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"width: 292.875px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eNon-smoker\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth style=\"width: 106px;\" align=\"left\"\u003e\n\u003cp\u003ecase/control\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"width: 194px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eaOR\u003c/em\u003e (95%\u003cem\u003eCI\u003c/em\u003e)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"width: 106px;\" align=\"left\"\u003e\n\u003cp\u003ecase/control\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"width: 170px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eaOR\u003c/em\u003e (95%\u003cem\u003eCI\u003c/em\u003e)\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"width: 105.875px;\" align=\"left\"\u003e\n\u003cp\u003ecase/control\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"width: 187px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eaOR\u003c/em\u003e (95%\u003cem\u003eCI\u003c/em\u003e)\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 92px;\" align=\"left\"\u003e\n\u003cp\u003eSO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 194px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 170px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 105.875px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 187px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 92px;\" align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\n\u003cp\u003e6/12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 194px;\" align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\n\u003cp\u003e1/6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 170px;\" align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 105.875px;\" align=\"left\"\u003e\n\u003cp\u003e5/6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 187px;\" align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 92px;\" align=\"left\"\u003e\n\u003cp\u003e11\u0026ndash;17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\n\u003cp\u003e617/840\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 194px;\" align=\"left\"\u003e\n\u003cp\u003e1.466(0.499\u0026ndash;4.309)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\n\u003cp\u003e361/285\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 170px;\" align=\"left\"\u003e\n\u003cp\u003e5.674(0.640-50.333)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 105.875px;\" align=\"left\"\u003e\n\u003cp\u003e256/555\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 187px;\" align=\"left\"\u003e\n\u003cp\u003e0.637(0.150\u0026ndash;2.704)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 92px;\" align=\"left\"\u003e\n\u003cp\u003e17\u0026ndash;23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\n\u003cp\u003e170/246\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 194px;\" align=\"left\"\u003e\n\u003cp\u003e1.186(0.397\u0026ndash;3.541)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\n\u003cp\u003e101/90\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 170px;\" align=\"left\"\u003e\n\u003cp\u003e4.953(0.550-44.602)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 105.875px;\" align=\"left\"\u003e\n\u003cp\u003e69/156\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 187px;\" align=\"left\"\u003e\n\u003cp\u003e0.503(0.115\u0026ndash;2.191)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 92px;\" align=\"left\"\u003e\n\u003cp\u003e\u0026gt;\u0026thinsp;23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\n\u003cp\u003e78/106\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 194px;\" align=\"left\"\u003e\n\u003cp\u003e1.347(0.438\u0026ndash;4.141)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\n\u003cp\u003e46/42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 170px;\" align=\"left\"\u003e\n\u003cp\u003e5.288(0.572\u0026ndash;48.855)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 105.875px;\" align=\"left\"\u003e\n\u003cp\u003e32/64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 187px;\" align=\"left\"\u003e\n\u003cp\u003e0.601(0.131\u0026ndash;2.751)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 92px;\" align=\"left\"\u003e\n\u003cp\u003eContinuous\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 194px;\" align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001(\u0026lt;\u0026thinsp;0.001\u0026ndash;3.315)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 170px;\" align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 105.875px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 187px;\" align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 92px;\" align=\"left\"\u003e\n\u003cp\u003eNO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 194px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 170px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 105.875px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 187px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 92px;\" align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\n\u003cp\u003e310/229\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 194px;\" align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\n\u003cp\u003e127/108\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 170px;\" align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 105.875px;\" align=\"left\"\u003e\n\u003cp\u003e102/202\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 187px;\" align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 92px;\" align=\"left\"\u003e\n\u003cp\u003e19\u0026ndash;29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\n\u003cp\u003e236/250\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 194px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e1.356(1.028\u0026ndash;1.788)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\n\u003cp\u003e156/88\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 170px;\" align=\"left\"\u003e\n\u003cp\u003e1.447(0.975\u0026ndash;2.147)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 105.875px;\" align=\"left\"\u003e\n\u003cp\u003e97/148\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 187px;\" align=\"left\"\u003e\n\u003cp\u003e1.241(0.831\u0026ndash;1.853)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 92px;\" align=\"left\"\u003e\n\u003cp\u003e29\u0026ndash;38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\n\u003cp\u003e257/443\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 194px;\" align=\"left\"\u003e\n\u003cp\u003e0.862(0.666\u0026ndash;1.115)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\n\u003cp\u003e143/146\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 170px;\" align=\"left\"\u003e\n\u003cp\u003e0.804(0.555\u0026ndash;1.165)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 105.875px;\" align=\"left\"\u003e\n\u003cp\u003e114/297\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 187px;\" align=\"left\"\u003e\n\u003cp\u003e0.893(0.617\u0026ndash;1.293)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 92px;\" align=\"left\"\u003e\n\u003cp\u003e\u0026gt;\u0026thinsp;38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\n\u003cp\u003e139/215\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 194px;\" align=\"left\"\u003e\n\u003cp\u003e0.889(0.653\u0026ndash;1.209)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\n\u003cp\u003e90/81\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 170px;\" align=\"left\"\u003e\n\u003cp\u003e0.854(0.559\u0026ndash;1.304)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 105.875px;\" align=\"left\"\u003e\n\u003cp\u003e49/134\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 187px;\" align=\"left\"\u003e\n\u003cp\u003e0.961(0.605\u0026ndash;1.529)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 92px;\" align=\"left\"\u003e\n\u003cp\u003eContinuous\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 194px;\" align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001(\u0026lt;\u0026thinsp;0.001-1.000)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 170px;\" align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001(\u0026lt;\u0026thinsp;0.001\u0026ndash;0.977)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 105.875px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 187px;\" align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 92px;\" align=\"left\"\u003e\n\u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 194px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 170px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 105.875px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 187px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 92px;\" align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\n\u003cp\u003e79/166\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 194px;\" align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\n\u003cp\u003e49/64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 170px;\" align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 105.875px;\" align=\"left\"\u003e\n\u003cp\u003e30/102\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 187px;\" align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 92px;\" align=\"left\"\u003e\n\u003cp\u003e51\u0026ndash;57\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\n\u003cp\u003e316/287\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 194px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e2.450(1.728\u0026ndash;3.474)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\n\u003cp\u003e181/97\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 170px;\" 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align=\"left\"\u003e\n\u003cp\u003e1.517(0.974\u0026ndash;2.363)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 105.875px;\" align=\"left\"\u003e\n\u003cp\u003e179/436\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 187px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e1.795(1.083\u0026ndash;2.975)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 92px;\" align=\"left\"\u003e\n\u003cp\u003e\u0026gt;\u0026thinsp;64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\n\u003cp\u003e36/83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 194px;\" align=\"left\"\u003e\n\u003cp\u003e0.975(0.575\u0026ndash;1.653)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\n\u003cp\u003e22/31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 170px;\" align=\"left\"\u003e\n\u003cp\u003e1.042(0.513\u0026ndash;2.116)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 105.875px;\" align=\"left\"\u003e\n\u003cp\u003e14/52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 187px;\" align=\"left\"\u003e\n\u003cp\u003e0.946(0.415\u0026ndash;2.158)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 92px;\" align=\"left\"\u003e\n\u003cp\u003eContinuous\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 194px;\" align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 170px;\" align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 105.875px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 187px;\" align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 92px;\" align=\"left\"\u003e\n\u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 194px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 170px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 105.875px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 187px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 92px;\" align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\n\u003cp\u003e8/132\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 194px;\" align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\n\u003cp\u003e5/19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 170px;\" align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 105.875px;\" align=\"left\"\u003e\n\u003cp\u003e3/113\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 187px;\" align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 92px;\" align=\"left\"\u003e\n\u003cp\u003e20\u0026ndash;27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\n\u003cp\u003e397/101\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 194px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e33.658(15.450-73.325)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\n\u003cp\u003e260/41\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 170px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e24.545(8.112\u0026ndash;74.265)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 105.875px;\" align=\"left\"\u003e\n\u003cp\u003e137/60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 187px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e61.431(18.041-209.181)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 92px;\" align=\"left\"\u003e\n\u003cp\u003e27\u0026ndash;35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\n\u003cp\u003e497/987\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 194px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e5.059(2.390-10.712)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\n\u003cp\u003e253/369\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 170px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e2.630(0.910\u0026ndash;7.595)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 105.875px;\" align=\"left\"\u003e\n\u003cp\u003e226/618\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 187px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e11.814(3.622\u0026ndash;38.540)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 92px;\" align=\"left\"\u003e\n\u003cp\u003e\u0026gt;\u0026thinsp;35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\n\u003cp\u003e0/0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 194px;\" align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\n\u003cp\u003e0/0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 170px;\" align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 105.875px;\" align=\"left\"\u003e\n\u003cp\u003e0/0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 187px;\" align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 92px;\" align=\"left\"\u003e\n\u003cp\u003eContinuous\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 194px;\" align=\"left\"\u003e\n\u003cp\u003e0.001(\u0026lt;\u0026thinsp;0.001-877.108)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 170px;\" align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 105.875px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 187px;\" align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 92px;\" align=\"left\"\u003e\n\u003cp\u003eCO\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 194px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 170px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 105.875px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 187px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 92px;\" align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.491\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\n\u003cp\u003e0/128\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 194px;\" align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\n\u003cp\u003e0/18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 170px;\" align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 105.875px;\" align=\"left\"\u003e\n\u003cp\u003e0/110\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 187px;\" align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 92px;\" align=\"left\"\u003e\n\u003cp\u003e0.491\u0026ndash;0.681\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\n\u003cp\u003e342/74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 194px;\" align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\n\u003cp\u003e221/36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 170px;\" align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 105.875px;\" align=\"left\"\u003e\n\u003cp\u003e121/38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 187px;\" align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 92px;\" align=\"left\"\u003e\n\u003cp\u003e0.681\u0026ndash;0.871\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\n\u003cp\u003e542/1018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 194px;\" align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\n\u003cp\u003e297/375\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 170px;\" align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 105.875px;\" align=\"left\"\u003e\n\u003cp\u003e245/643\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 187px;\" align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 92px;\" align=\"left\"\u003e\n\u003cp\u003e\u0026gt;\u0026thinsp;0.871\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\n\u003cp\u003e0/0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 194px;\" align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\n\u003cp\u003e0/0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 170px;\" align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 105.875px;\" align=\"left\"\u003e\n\u003cp\u003e0/0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 187px;\" align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 92px;\" align=\"left\"\u003e\n\u003cp\u003eContinuous\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 194px;\" align=\"left\"\u003e\n\u003cp\u003e0.839(0.481\u0026ndash;1.466)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 170px;\" align=\"left\"\u003e\n\u003cp\u003e0.042(0.006\u0026ndash;0.311)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 105.875px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 187px;\" align=\"left\"\u003e\n\u003cp\u003e2.882(1.306\u0026ndash;6.360)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 92px;\" align=\"left\"\u003e\n\u003cp\u003eO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 194px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 170px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 105.875px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 187px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 92px;\" align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\n\u003cp\u003e2/129\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 194px;\" align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\n\u003cp\u003e0/18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 170px;\" align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 105.875px;\" align=\"left\"\u003e\n\u003cp\u003e2/111\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 187px;\" align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 92px;\" align=\"left\"\u003e\n\u003cp\u003e47\u0026ndash;65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\n\u003cp\u003e367/86\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 194px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e125.056(29.902-523.012)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\n\u003cp\u003e243/38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 170px;\" align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 105.875px;\" align=\"left\"\u003e\n\u003cp\u003e124/48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 187px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e95.041(21.772\u0026ndash;414.879)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 92px;\" align=\"left\"\u003e\n\u003cp\u003e65\u0026ndash;83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\n\u003cp\u003e479/984\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 194px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e17.746(4.322\u0026ndash;72.862)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\n\u003cp\u003e255/368\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 170px;\" align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 105.875px;\" align=\"left\"\u003e\n\u003cp\u003e224/616\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 187px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e17.068(4.084\u0026ndash;71.342)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 92px;\" align=\"left\"\u003e\n\u003cp\u003e\u0026gt;\u0026thinsp;83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\n\u003cp\u003e36/21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 194px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e50.896(11.069-234.032)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\n\u003cp\u003e5/20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 170px;\" align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 105.875px;\" align=\"left\"\u003e\n\u003cp\u003e16/16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 187px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e37.313(7.301\u0026ndash;190.700)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 92px;\" align=\"left\"\u003e\n\u003cp\u003eContinuous\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 194px;\" align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 170px;\" align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001(\u0026lt;\u0026thinsp;0.001\u0026ndash;0.001)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 105.875px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 187px;\" align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 967px;\" colspan=\"7\"\u003ea Each variable was run independently\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 967px;\" colspan=\"7\"\u003eb Adjusted for educational status, occupation, BMI, family history of lung cancer, medical history of lung disease, drinking alcohol, smoking, passive smoking, exposure to cooking oil fumes, physical exercise, and fruit consumption\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 967px;\" colspan=\"7\"\u003ec Adjusted for age, educational status, occupation, BMI, family history of lung cancer, and medical history of lung disease, exposure to cooking oil fumes, physical exercise, and fruit intake\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 967px;\" colspan=\"7\"\u003ed Adjusted for gender, educational status, occupation, family history of lung cancer, medical history of lung disease, passive smoking, exposure to cooking oil fumes, drinking tea, physical exercise, and fruit intake\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eExposure to PM\u003csub\u003e2.5\u003c/sub\u003e is also a risk factor for lung cancer. For exposure to concentrations of 20\u0026ndash;27 (\u0026micro;g/m3), the OR was 33.658 (95% CI: 15.450\u0026ndash;73.325), and for exposure to concentrations of 27\u0026ndash;35 (\u0026micro;g/m3), the OR was 5.059 (95% CI: 2.390\u0026ndash;10.712). Furthermore, exposure to O\u003csub\u003e3\u003c/sub\u003e is also a risk factor for lung cancer. For exposure to O\u003csub\u003e3\u003c/sub\u003e concentrations of 47\u0026ndash;65 (\u0026micro;g/m3), the OR was 125.056 (95% CI: 29.902\u0026ndash;523.012); 17.746 (95% CI: 4.322\u0026ndash;72.862), for O\u003csub\u003e3\u003c/sub\u003e concentrations of 65\u0026ndash;83 (\u0026micro;g/m3); and 50.896 (95% CI: 11.069\u0026ndash;234.032) for concentrations of more than 83 (\u0026micro;g/m3) .\u003c/p\u003e\n\u003cp\u003eNext, we compared the results for smokers, after adjusting for age, education, occupation, BMI, family history of lung cancer, and medical history of lung disease, exposure to cooking oil fumes, physical exercise, and fruit intake. Further, for non-smokers, the results were compared after adjusting for gender, education, occupation, family history of lung cancer, medical history of lung disease, passive smoking, exposure to cooking oil fumes, drinking tea, physical exercise, and fruit intake. The analysis indicated that for both groups, exposure to PM\u003csub\u003e10\u003c/sub\u003e and PM\u003csub\u003e2.5\u003c/sub\u003e was a risk factors for lung cancer. Moreover, exposure to PM\u003csub\u003e2.5\u003c/sub\u003e had a greater impact on non-smokers than on smokers, as shown by the following results for both groups: OR was 61.431 (95% CI: 18.041\u0026ndash;209.181) and 24.545 (95% CI: 8.12\u0026ndash;74.265) (in non-smokers and smokers, respectively, for exposure to PM\u003csub\u003e2.5\u003c/sub\u003e concentrations of 20\u0026ndash;27 (\u0026micro;g/m3)) and OR was 11.814 (95% CI: 3.622\u0026ndash;38.540) and 2.630 (95% CI: 0.910\u0026ndash;7.595) (for non-smokers and smokers, respectively, for exposure to PM\u003csub\u003e2.5\u003c/sub\u003e concentrations of 27\u0026ndash;35 (\u0026micro;g/m3). Since none of the participants were exposed to O\u003csub\u003e3\u003c/sub\u003e concentrations of less than 47 (\u0026micro;g/m3)in the control group, the effect of O\u003csub\u003e3\u003c/sub\u003e exposure on the smokers could not be assessed. Among the non-smokers, exposure to O\u003csub\u003e3\u003c/sub\u003e was identified as a risk factor for lung cancer. However, there was no association between SO\u003csub\u003e2\u003c/sub\u003e and lung cancer in the total population or among the subgroup of smokers or nonsmokers. Similarly, none of the participants in the control group were exposed to a low concentration of CO, and therefore, the association of CO with lung cancer could not be evaluated (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eWe also analyzed the relationship between air pollution and lung cancer stratified by passive smoking and exposure to cooking oil fumes, and the results were consistent with those of smoking. The detailed results are shown in Additional Table\u0026nbsp;1 to Additional 3 (for passive smoking) and Additional Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e to Additional 6 (for cooking oil fume exposure).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eMultivariate logistic regression analysis of lung cancer in total population\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariables\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u0026beta;\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eS.E\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eWald\u003cem\u003e\u0026chi;\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eOR\u003csup\u003e༊\u003c/sup\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e95%CI\u003csup\u003e༊\u003c/sup\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFamily history of lung cancer\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.399\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.290\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23.222\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.051\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.293\u0026ndash;7.155\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePersonal history of lung disease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.878\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.182\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23.305\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.405\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.684\u0026ndash;3.435\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSmoking\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.502\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.115\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19.099\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.653\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.319\u0026ndash;2.070\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePhysical activity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.894\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.122\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e53.364\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.409\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.322\u0026ndash;0.520\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCooking oil fume exposure\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.396\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.137\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.330\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.004\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.486\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.136\u0026ndash;1.945\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePassive smoking\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.239\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.117\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.171\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.041\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.270\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.01\u0026ndash;1.598\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFruit intake\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.523\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.112\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21.913\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.686\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.355\u0026ndash;2.099\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eO\u003csub\u003e3\u003c/sub\u003e(mg/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25.979\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e47\u0026ndash;65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.701\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.884\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17.516\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e40.493\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.155-229.155\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e65\u0026ndash;83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.749\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.942\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.525\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.004\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15.635\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.469\u0026ndash;99.007\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026gt;\u0026thinsp;83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.083\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.943\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10.687\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21.818\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.437-138.515\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e(mg/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19.510\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20\u0026ndash;27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.575\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.612\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.628\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.010\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.829\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.456\u0026ndash;16.015\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27\u0026ndash;35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.398\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.684\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.339\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.560\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.489\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.39\u0026ndash;5.688\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e(mg/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e34.179\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e51\u0026ndash;57\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.064\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.218\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23.827\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.898\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.891\u0026ndash;4.444\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e57\u0026ndash;64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.252\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.273\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21.111\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.499\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.051\u0026ndash;5.969\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026gt;\u0026thinsp;64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.193\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.300\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.415\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.519\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.213\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.674\u0026ndash;2.185\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eConstant\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-4.018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.785\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26.194\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\"\u003e༊Unconditional logistic regression with the backward stepwise method\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n\u003ch2\u003eMultivariate analysis of levels of exposure to atmospheric pollutants in different populations\u003c/h2\u003e\n\u003cp\u003eAll the variables that were found to have a significant impact on the development of lung cancer in the previous analysis were further subjected to multi-factor unconditional logistic regression analysis using the backward stepwise method. We used P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 as the inclusion criterion and P\u0026thinsp;\u0026gt;\u0026thinsp;0.10 as the exclusion criterion. The results of the analyses indicated that the following factors increased the risk of lung cancer among the general population: smoking;exposure to cooking oil fumes; passive smoking;medicalhistory of lung disease; family history of lung cancer;and exposure to O\u003csub\u003e3\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, and PM\u003csub\u003e2.5\u003c/sub\u003e. On the other hand, fruit intake and physical exercise were found to be protective factors against risk of lung cancer (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). For smokers, medical history of lung disease; family history of lung cancer; and exposure to PM\u003csub\u003e10\u003c/sub\u003e, and PM\u003csub\u003e2.5\u003c/sub\u003e were factors that increased the risk of lung cancer, whereas fruit intake and physical exercise were factors that reduce the risk of lung cancer (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). Similarly, among the non-smokers, cooking oil fumes; medical history of lung disease; family history of lung cancer; exposure to PM\u003csub\u003e10\u003c/sub\u003e, PM\u003csub\u003e2.5\u003c/sub\u003e, and O\u003csub\u003e3\u003c/sub\u003e may increase the risk of lung cancer, while fruit intake, physical exercise, and drinking tea were found to protect against the risk of lung cancer (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eMultivariate logistic regression analysis of lung cancer in smokers\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariables\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u0026beta;\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eS.E\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eWald\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eOR\u003csup\u003e༊\u003c/sup\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e95%CI\u003csup\u003e༊\u003c/sup\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFamily history of lung cancer\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.599\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.504\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10.063\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.947\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.842\u0026ndash;13.285\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePersonal history of lung disease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.585\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.234\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.270\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.012\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.796\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.136\u0026ndash;2.84\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePhysical activity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.890\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.178\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24.998\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.411\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.29\u0026ndash;0.582\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFruit intake\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.280\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.161\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.026\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.082\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.323\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.965\u0026ndash;1.813\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e(mg/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e137.807\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20\u0026ndash;27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.399\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.580\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e34.317\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29.947\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.603\u0026ndash;93.392\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27\u0026ndash;35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.016\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.554\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.370\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.066\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.763\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.934\u0026ndash;8.175\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e(mg/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24.844\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e51\u0026ndash;57\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.285\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.280\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21.030\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.614\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.087\u0026ndash;6.258\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e57\u0026ndash;64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.781\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.260\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.003\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.184\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.312\u0026ndash;3.636\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026gt;\u0026thinsp;64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.200\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.412\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.235\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.628\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.221\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.545\u0026ndash;2.738\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eConstant\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-2.321\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.832\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.782\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.098\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\"\u003e༊Unconditional logistic regression with the backward stepwise method\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab5\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eMultivariate logistic regression analysis of lung cancer in non-smokers\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariables\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u0026beta;\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eS.E\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eWald\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eOR\u003csup\u003e༊\u003c/sup\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e95%CI\u003csup\u003e༊\u003c/sup\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFamily history of lung cancer\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.357\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.364\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13.878\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.885\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.902\u0026ndash;7.933\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePersonal history of lung disease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.239\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.313\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15.667\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.451\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.869\u0026ndash;6.373\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePhysical activity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.841\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.176\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22.935\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.431\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.306\u0026ndash;0.608\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCooking oil fume exposure\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.506\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.201\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.331\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.012\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.658\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.118\u0026ndash;2.458\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFruit intake\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.749\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.161\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21.705\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.115\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.544\u0026ndash;2.899\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDrinking tea\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.512\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.183\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.849\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.599\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.419\u0026ndash;0.857\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e(mg/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10.692\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20\u0026ndash;27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.499\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.891\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.869\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12.173\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.123\u0026ndash;69.788\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27\u0026ndash;35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.681\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.813\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.093\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.372\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.753\u0026ndash;38.311\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e(mg/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14.721\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e51\u0026ndash;57\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.874\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.287\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.262\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.397\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.365\u0026ndash;4.208\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e57\u0026ndash;64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.814\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.272\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.976\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.003\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.258\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.325\u0026ndash;3.847\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026gt;\u0026thinsp;64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.038\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.438\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.008\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.931\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.963\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.408\u0026ndash;2.27\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eO\u003csub\u003e3\u003c/sub\u003e(mg/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12.585\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.006\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e47\u0026ndash;65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.619\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.032\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.443\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.011\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13.721\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.816-103.662\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e65\u0026ndash;83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.493\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.135\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.731\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.188\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.450\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.481\u0026ndash;41.15\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026gt;\u0026thinsp;83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.697\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.123\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.283\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.131\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.458\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.604\u0026ndash;49.339\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eConstant\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-6.482\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.929\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e48.680\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\"\u003e༊Unconditional logistic regression with the backward stepwise method\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe results of the analysis in different populations are shown in Additional Table\u0026nbsp;7- Additional 9 for passive smoking and in Additional Table\u0026nbsp;10- Additional 12 for exposure to cooking oil fumes.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis hospital-based case\u0026ndash;control study was designed to evaluate the relationship between atmospheric concentrations of air pollutants and the occurrence of lung cancer in Fujian Province. We found that the overall risk factors for lung cancer were smoking; exposure to cooking oil fumes; passive smoking; medical history of lung disease; family history of lung cancer; and exposure to PM\u003csub\u003e10\u003c/sub\u003e, PM\u003csub\u003e2.5,\u003c/sub\u003e and O\u003csub\u003e3\u003c/sub\u003e. Fruit intake and physical exercise were identified as protective factors. Our results on the risk factors are consistent with those of several previous reports. We found that long-term exposure to PM\u003csub\u003e10\u003c/sub\u003e, PM\u003csub\u003e2.5\u003c/sub\u003e, and O\u003csub\u003e3\u003c/sub\u003e was significantly associated with an increased risk of lung cancer, and the risk appears to be greater in non-smokers than in smokers or the total population. Further, this difference appears to be even greater for people exposed to cooking oil fumes than among those without such an exposure.\u003c/p\u003e \u003cp\u003eIn the recent past, extensive research has been conducted in China on the short-term health effects of air pollution [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. However, studies exploring the long-term effects of air pollution on health, particularly those employing more reliable methods such as cohort studies, have been scarce. Most of the studies conducted thus far are related to mortality[\u003cspan additionalcitationids=\"CR25 CR26\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In recent years, China has been making continuous improvement in air pollution monitoring system. However, China is a vast country. Therefore, further investigation is necessary to evaluate the exposure characteristics, dose\u0026ndash;risk models, and long-term health risks of single or multiple pollutants in different regions.\u003c/p\u003e \u003cp\u003eAt present, there is a strong research focus on the relationship between air pollution and lung cancer. Studies[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] have reported that long-term exposure to PM\u003csub\u003e2.5\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, NO\u003csub\u003ex,\u003c/sub\u003e and SO\u003csub\u003e2\u003c/sub\u003e is significantly associated with an increased risk of lung cancer. Such associations have been shown to hold true for both smokers and non-smokers as well as men and women, with the impact being the same for all subgroups. For non-smokers, exposure to outdoor PM\u003csub\u003e2.5\u003c/sub\u003e is greater for patients with lung cancer and exhibits a linear dose\u0026ndash;response relationship[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The results of the ACS CPS-II study[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] and the European Air Pollution Impact Cohort Study (ESCAPE)[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] have also demonstrated a positive correlation between exposure to outdoor PM\u003csub\u003e2.5\u003c/sub\u003e and the occurrence of lung cancer. Our results had a wide 95% CI range, which might be attributed to the small sample size in our study, but our results also suggested that PM\u003csub\u003e10\u003c/sub\u003e and PM\u003csub\u003e2.5\u003c/sub\u003e were risk factors for lung cancer. Similar to our study, the study by Yang et al.[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] also revealed that the association of PM\u003csub\u003e2.5\u003c/sub\u003e with lung cancer (RR\u0026thinsp;=\u0026thinsp;1.18) was more pronounced in non-smokers than in the total population (RR\u0026thinsp;=\u0026thinsp;1.07). However, because of the limited number of studies included (n\u0026thinsp;=\u0026thinsp;3) and a wide overlap between the total population and people who never were smokers, the controversy still remains regarding whether there is a connection between lung cancer and air pollution. Currently, there is still no report on the effect of exposure to cooking oil fumes on individuals.\u003c/p\u003e \u003cp\u003eGerard Hoek et al.[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] investigated the risk of lung cancer in the total population associated with exposure to ambient air pollution. They used the population attributable risk fraction (PAF) indicator, which describes the fraction of lung cancer incidence in the total population that can be prevented by eliminating PM\u003csub\u003e2.5\u003c/sub\u003e exposure. The proportion of the population with lung cancer was found to be between 28.6% and 86.7% when RR\u0026thinsp;=\u0026thinsp;1.5 and the concentration of PM\u003csub\u003e2.5\u003c/sub\u003e was reduced by 10\u0026ndash;60 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e. The RR value of ambient air pollution was found to be much smaller than that for active smoking; however, air pollution affects the entire population. Thus, a reduction in the concentration of PM\u003csub\u003e2.5\u003c/sub\u003e will result in a substantial reduction in the incidence of lung cancer in the overall population.\u003c/p\u003e \u003cp\u003eStudy results on the association between long-term exposure to O\u003csub\u003e3\u003c/sub\u003e and lung cancer have been contradictory, with some studies showing a positive[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], negative[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], or invalid association[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. In our study, the OR value of O\u003csub\u003e3\u003c/sub\u003e was 17.746\u0026ndash;125.056 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Since the resulting 95% CI was wide, this could suggest that O\u003csub\u003e3\u003c/sub\u003e may be a risk factor for lung cancer. Recently, N. Rocks et al.[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] measured the migration of tumor cells using the Boyden chamber test. They also established a mouse model- and employed an automated tumor recognition software with supervised classification to quantify the spread of lung tumor cells. Their results showed that exposure to O\u003csub\u003e3\u003c/sub\u003e enhanced lung cancer cell proliferation and migration. This was strong evidence for the causative relationship between O\u003csub\u003e3\u003c/sub\u003e exposure and lung cancer.\u003c/p\u003e \u003cp\u003eIn our study, we examined only the relationship between lung cancer and air pollution, but not the specific underlying biological mechanisms. Currently, two main explanations have been proposed to explain this relationship. First, exposure to atmospheric pollution can cause oxidative stress, and which may cause macrophages to release reactive oxygen species (ROS) that can damage DNA, protein, and lipid cells. Additionally, ROS production may be triggered by metals present on the surface of particulate matter through the Fenton reaction or anthraquinones in the redox cycle[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Second, exposure to air pollutants may directly or indirectly induce inflammatory effects, which may lead to the generation of chemokines and cytokines, thereby inducing angiogenesis and transforming epithelial cells into malignant and invasive cells, which ultimately invade distal organs[\u003cspan additionalcitationids=\"CR43\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe current study has a few limitations. First, the study was conducted with a hospital case\u0026ndash;control design and, therefore, it has flaws that are inherent in the method itself. To overcome this, various control measures, such as selecting cases from multiple hospitals and choosing objective indicators, were employed in this study. However, most of the controls were from Fuzhou, and the difference in the regional distribution of cases and controls may have influenced the results. Second, although the locations of the atmospheric sampling points were established scientifically and were reasonable, the air quality in Fujian Province that was analyzed in this study may not completely reflect the air quality of the entire Fujian Province because of the limited number of sampling points. Third, the levels of SO\u003csub\u003e2\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, and PM\u003csub\u003e10\u003c/sub\u003e were measured since 2005, while those of O\u003csub\u003e3\u003c/sub\u003e, CO, and PM\u003csub\u003e2.5\u003c/sub\u003e were obtained since 2013. The difference in the data collection period for the two set of parameters may have influenced the results. Fourth, the concentrations of the air pollutants measured in the indices of this study were not obtained by individual portable monitoring; therefore, there is a possibility of an estimate bias that resulted in a wider CI. Future research that is based on a greater sample size and forward-looking research design is necessary to provide in-depth insight into these points and strengthen environmental and public health monitoring.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis hospital-based case\u0026ndash;control study revealed a few factors that influence the onset of lung cancer, including smoking, passive smoking, exposure to cooking oil fumes, family history of lung cancer, and medical history of lung disease. Increasing fruit intake, physical exercise, and drinking tea were found to be protective against lung cancer. More importantly, long-term exposure to PM\u003csub\u003e10,\u003c/sub\u003e PM\u003csub\u003e2.5\u003c/sub\u003e, and O\u003csub\u003e3\u003c/sub\u003e is significantly associated with increased risk of lung cancer, and the risk appears to be greater in non-smokers than in smokers or the total population; moreover, this difference appears to be greater in people exposed to cooking oil fumes than those without.\u003c/p\u003e \u003cp\u003eIn view of the high smoking rate and psychological and economic burden, the Chinese government has launched a national strategy, \"Healthy China 2030\", which aims to reduce the smoking rate to less than 20 percent[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. At the same time, the early lung cancer screening in high-risk patients with lung cancer mortality reduction plays a big role, people at risk for lung cancer in Fujian province, we suggest to between aged 55 to 80 years old, more than 20 years of smoking history of low-dose computed tomography (LDCT) screening, early treatment, thereby to improve the patients quality of life.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003ePM10 \u0026nbsp; \u0026nbsp; Particulate matter with particle size below 10\u0026mu;mmicrons\u003c/p\u003e\n\u003cp\u003ePM2.5 \u0026nbsp; \u0026nbsp;Particulate matter with particle size below 2.5\u0026mu;mmicrons\u003c/p\u003e\n\u003cp\u003eIARC \u0026nbsp; \u0026nbsp; International Agency for Research on Cancer\u003c/p\u003e\n\u003cp\u003eRR \u0026nbsp; \u0026nbsp; \u0026nbsp; risk ratio\u003c/p\u003e\n\u003cp\u003eOR \u0026nbsp; \u0026nbsp; \u0026nbsp; odds ratio\u003c/p\u003e\n\u003cp\u003eO\u003csub\u003e3\u003c/sub\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Ozone\u003c/p\u003e\n\u003cp\u003eBMI \u0026nbsp; \u0026nbsp; body mass index\u003c/p\u003e\n\u003cp\u003eSO\u003csub\u003e2\u003c/sub\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;sulfur dioxide\u003c/p\u003e\n\u003cp\u003eNO\u003csub\u003e2 \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/sub\u003enitrogen dioxide\u003c/p\u003e\n\u003cp\u003eCO \u0026nbsp; \u0026nbsp; carbon monoxide\u003c/p\u003e\n\u003cp\u003eIDW \u0026nbsp; \u0026nbsp;inverse distance to a power\u003c/p\u003e\n\u003cp\u003eCI \u0026nbsp; \u0026nbsp; \u0026nbsp;confidence interval\u003c/p\u003e\n\u003cp\u003eNO\u003csub\u003ex \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/sub\u003enitrous oxides\u003c/p\u003e\n\u003cp\u003eP \u0026nbsp; \u0026nbsp; \u0026nbsp; p-value\u003c/p\u003e\n\u003cp\u003ePAF \u0026nbsp; \u0026nbsp;population attributable risk fraction\u003c/p\u003e\n\u003cp\u003eACS CPS-II \u0026nbsp;The American Cancer Society\u0026rsquo;s Cancer Prevention Study II\u003c/p\u003e\n\u003cp\u003eESCAPE \u0026nbsp; \u0026nbsp; European Air Pollution Impact Cohort Study\u003c/p\u003e\n\u003cp\u003eROS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;reactive oxygen species\u003c/p\u003e\n\u003cp\u003eDNA \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Deoxyribonucleic Acid\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Institutional Review Board of Fujian Medical University (Fuzhou, China) and all participants signed informed consent forms. All methods were carried out in accordance with relevant guidelines and regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are not publicly available due to government data but are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by grants from the National Natural Science Foundation of China (Nos 81402738), Fujian Provincial Health Research Talents Training Programme Medical Innovation Project (Nos 2019-CX-33), Fujian Program for Outstanding Young Researchers in University awarded by Education Department of Fujian (Nos 2017B019) and National Major Science and Technology Program of China (Nos 2017ZX10103008) .\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCGM, HF and WJS conceived of the study, and carried out the experiments, participated in the drafted the manuscript. HF,LYH,YHM,SJ, and LJB collected samples. BL, ZKC and CL participated in the design of the study and helped to review the manuscript. CGM,HF,LYH, XXY, and YHM performed the statistical analysis. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A: Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a cancer journal for clinicians 2018, 68(6):394\u0026ndash;424.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen W, Zheng R, Zhang S, Zeng H, Zuo T, Xia C, Yang Z, He J: Cancer incidence and mortality in China in 2013: an analysis based on urbanization level. 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American journal of respiratory and critical care medicine 1999, 159(2):373\u0026ndash;382.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJerrett M, Burnett RT, Pope CA, 3rd, Ito K, Thurston G, Krewski D, Shi Y, Calle E, Thun M: Long-term ozone exposure and mortality. The New England journal of medicine 2009, 360(11):1085\u0026ndash;1095.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarey IM, Atkinson RW, Kent AJ, van Staa T, Cook DG, Anderson HR: Mortality associations with long-term exposure to outdoor air pollution in a national English cohort. American journal of respiratory and critical care medicine 2013, 187(11):1226\u0026ndash;1233.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJerrett M, Burnett RT, Beckerman BS, Turner MC, Krewski D, Thurston G, Martin RV, van Donkelaar A, Hughes E, Shi Y \u003cem\u003eet al\u003c/em\u003e: Spatial analysis of air pollution and mortality in California. American journal of respiratory and critical care medicine 2013, 188(5):593\u0026ndash;599.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLipsett MJ, Ostro BD, Reynolds P, Goldberg D, Hertz A, Jerrett M, Smith DF, Garcia C, Chang ET, Bernstein L: Long-term exposure to air pollution and cardiorespiratory disease in the California teachers study cohort. American journal of respiratory and critical care medicine 2011, 184(7):828\u0026ndash;835.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXindong L: Molecular epidemiology of esophageal cancer in China. Chinese Journal of Epidemiology 2003, 24(10):939\u0026ndash;943.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRosanna DP, Salvatore C: Reactive oxygen species, inflammation, and lung diseases. Current pharmaceutical design 2012, 18(26):3889\u0026ndash;3900.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVattanasit U, Navasumrit P, Khadka MB, Kanitwithayanun J, Promvijit J, Autrup H, Ruchirawat M: Oxidative DNA damage and inflammatory responses in cultured human cells and in humans exposed to traffic-related particles. International journal of hygiene and environmental health 2014, 217(1):23\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang L, Hou XY, Wei Y, Thai P, Chai F: Biomarkers of the health outcomes associated with ambient particulate matter exposure. Sci Total Environ 2017, 579:1446\u0026ndash;1459.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi R, Kou X, Xie L, Cheng F, Geng H: Effects of ambient PM2.5 on pathological injury, inflammation, oxidative stress, metabolic enzyme activity, and expression of c-fos and c-jun in lungs of rats. Environmental science and pollution research international 2015, 22(24):20167\u0026ndash;20176.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Oliveira Alves N, Martins Pereira G, Di Domenico M, Costanzo G, Benevenuto S, de Oliveira Fonoff AM, de Souza Xavier Costa N, Ribeiro J\u0026uacute;nior G, Satoru Kajitani G, Cestari Moreno N \u003cem\u003eet al\u003c/em\u003e: Inflammation response, oxidative stress and DNA damage caused by urban air pollution exposure increase in the lack of DNA repair XPC protein. Environ Int 2020, 145:106150.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoodchild M, Zheng R: Tobacco control and Healthy China 2030. Tobacco control 2019, 28(4):409\u0026ndash;413.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Lung cancer, Air pollutants, PM10, PM2.5, Ozone","lastPublishedDoi":"10.21203/rs.2.17362/v3","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.2.17362/v3","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003eOutdoor air pollutants, especially particulate matters, are defined as a type of carcinogen by the International Agency for Research on Cancer. Studies have shown that air pollutionis associated with lung cancer morbidity or mortality. This study is aimed at exploring the relationship between air pollutants and primary lung cancer in Fujian Province, China. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003eWe conducted a hospital-based, retrospective, case–control epidemiological study on three different populations to assess the occurrence of lung cancer caused by exposure to various levels of air pollution. Statistical analysiswas performed using the SPSS 25.0. Unconditional logistic regression modeling and identification of possible confounding factors were performed by calculating odds ratios (ORs) and 95% confidence intervals (CIs) for air pollution indexes and lung cancer risk. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThe total study population comprised 885 lung cancer patients and 1,220 healthy controls. The following parameters were identified as risk factors for lung cancer among the total population: smoking; exposure to cooking oil fumes; passive smoking; medical history of lung disease; family history of lung cancer; and exposure to PM\u003csub\u003e10\u003c/sub\u003e, PM\u003csub\u003e2.5\u003c/sub\u003e, and O\u003csub\u003e3\u003c/sub\u003e. For smokers, medical history of lung disease, family history of lung cancer, and exposure to PM\u003csub\u003e10\u003c/sub\u003e, and PM\u003csub\u003e2.5\u003c/sub\u003e were risk factors for lung cancer. Among non-smokers, exposure to cooking oil fumes; medical history of lung disease; family history of lung cancer; and exposure to PM\u003csub\u003e10\u003c/sub\u003e, PM\u003csub\u003e2.5\u003c/sub\u003e, and O\u003csub\u003e3\u003c/sub\u003e were factors increasing the risk of lung cancer. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eLong-term exposure to PM\u003csub\u003e10,\u003c/sub\u003e PM\u003csub\u003e2.5\u003c/sub\u003e, and O\u003csub\u003e3\u003c/sub\u003e was found to be significantly associated with increased risk of lung cancer, with the risk being greater for non-smokers and persons exposed to cooking oil fumes.\u003c/p\u003e","manuscriptTitle":"Relationship between Air Pollution and Lung Cancer in Fujian Province: A Case-control study","msid":"","msnumber":"","nonDraftVersions":[{"code":3,"date":"2022-04-12 13:21:01","doi":"10.21203/rs.2.17362/v3","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}},{"code":2,"date":"2021-11-05 19:14:13","doi":"10.21203/rs.2.17362/v2","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}},{"code":1,"date":"2019-11-15 23:21:58","doi":"10.21203/rs.2.17362/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1978944a-08fe-4122-80fa-10b311ddf7f2","owner":[],"postedDate":"April 12th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-01-11T05:59:18+00:00","versionOfRecord":[],"versionCreatedAt":"2022-04-12 13:21:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v3","identity":"rs-7996","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"identity":"rs-7996","version":["v3"]},"buildId":"ehx78VzkSd0WSzXnipQa-","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}