Estimating the Quantitative Relationship of Disease Control and Spouse Control for Lung Cancer in Case-control Study by Cross-walk Model

Estimating the Quantitative Relationship of Disease Control and Spouse Control for Lung Cancer in Case-control Study by Cross-walk Model | 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 Estimating the Quantitative Relationship of Disease Control and Spouse Control for Lung Cancer in Case-control Study by Cross-walk Model Zemin Cai, Xiao Zhang, Xiaonong Zou, Xia Wan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7772697/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 15 You are reading this latest preprint version Abstract Background Disease control (DC) and spouse control (SC) are commonly used in case-control study. The exposure risk of these two kinds of control may be different, especially in tobacco exposure status which attribute to diseases. However, few study concentrates on the association of DC and SC. Based on this situation, this study aims to explore the quantitative relationship of DC and SC for lung cancer. Methods In this study, the data of cases, DC and SC were collected from the National Retrospective Survey of Deaths, including one million deaths during 1986 to 1988. Referring to cross-walk model, four forms of model, stratified by urban/rural areas and genders, were established to explore the suitable one, for adjusting the Odds Ratio (OR) of DC OR dc and SC OR sc ). Results A total of 51217 cases, 29871 SCs, and 215018 DCs were collected, among which the male accounted for 68.00%, 24.60% and 54.86%, respectively. Accordingly, the smoking prevalence were 66.83%, 24.39%, 40.21%. The case had the highest smoking rate, following by DC and SC in both genders of urban and rural area. In both urban and rural areas, the OR sc of males and females were higher than OR dc . The OR sc and OR dc value gradually increases with the age group, reaching its maximum at 60–64 in urban area and 55–59 in rural area. After establishing the cross-walk model, in urban area, logit ( OR sc )=1.33 logit ( OR dc ) and logit ( OR sc )=-0.34+1.35 logit ( OR dc ) were used for males and females, respectively. In rural area, logit ( OR sc ) =1.09 logit ( OR dc ) and logit ( OR sc ) =1.15 logit ( OR dc ) were used for males and females, respectively. Conclusions OR sc are commonly larger than OR dc in the same urban/rural, gender and age group. Thus, when different types of control are used in the design or analysis of case-control study, the distinction of controls should be considered, especially the quantitative relationship. This study provided an adjustable method for two definitions, which will provide methodological reference for further research. Smoking Lung cancer Disease control Spouse control Cross-walk model Figures Figure 1 Figure 2 1. Introduction In systematic reviews, the definition and methods of data synthesis may vary depending on the study design, data type, or analytical objectives. Typically, data synthesis refers to the process of integrating, summarizing, and analyzing data from multiple independent studies, which aims to derive broader and more robust conclusions by combining the results of different studies[ 1 ]. To ensure the reliability and comparability of the results, it is necessary to standardize the data and make comparisons on a unified basis. Some researches set up adjustive methods to estimate the different data source, and cross-walk model is one of the explorations. Cross-walk is performed by establishing models to adjust the different categories and definitions or consumption frequencies of research factors, so that they are unitized and comparable[ 2 ]. In Ng et al. study[ 2 ], researchers established regression model (cross-walk model) and adjusted different definition or non-standardized age-gender and multi-source data of smoking product and consumption, and calculated the smoking prevalence of particular age-sex-country-year. In Global Burden of Disease (GBD) study, this model was used in definitions or consumption frequencies of smoking, ambient particulate matter pollution, family size of household air pollution, alcohol consumption and physical activity[ 3 , 4 ]. The above studies illustrate us to refer the adjusting method under certain circumstance. In epidemiology, case-control study is a type of observational study commonly used to look at factors associated with diseases or outcome. Selection of cases and controls is an important part of this design. The basic principle of selecting them is that they should be have the same ‘study base’. In general, there are several sources for constructing controls, including: general population; relatives or friends; or hospital patients[ 5 ]. The latter two types are commonly used in some case-control study of China[ 6 , 7 ]. For example, in the study of estimating risk of smoking on cancer deaths in China[ 8 , 9 ], the deceased who died from causes other than those related to smoking and surviving spouses of cancer deceased were invited as disease controls (DC) and spouse controls (SC), respectively. It is worth noting that different controls may bring certain differences in the research. Jiang et al.[ 10 ] compared these two types of control by calculating the ratio of \(\:{OR}_{dc}\) and \(\:{OR}_{sc}\) , and did not detect quantified difference between them. Therefore, based on the previous studies, our study aims to further explore the association of DC and SC, and estimate the quantitative relationship of \(\:{OR}_{dc}\) and \(\:{OR}_{sc}\) in the context of lung cancer attributed to smoking, utilizing the cross-walk model of GBD study as an adjustment approach. The findings may provide methodological insights for systematic reviews and meta-analyses in epidemiological studies. Besides, our data remains the only national resource on this topic that includes DC and SC information, and though the data is old, this did not affect the exploration of the association. Furthermore, the previous meta-analysis[ 11 ] showed that the Relative Risk (RR) of smoking-attributed lung cancer has remained stable (ranged from 2 to 6) in the past four decades in China, and the RR, OR and Hazard ratio (HR) were equally collected in the study. Thus, because the data was unique, and the OR of smoking-attributed lung cancer was stable, our study used the deaths data during 1986 to 1988, which contained both DC and SC, to explore the quantitative relationship between \(\:{OR}_{dc}\) and \(\:{OR}_{sc}\) . 2. Method 2.1 Data Source The data source derived from the National Retrospective Survey of Deaths, including one million deaths during 1986 to 1988. For containing a wide geographical distribution, this survey covered 24 cities, which were chosen non-randomly, and 74 rural countries were chosen by stratified random sampling from over 2000 counties in China. The survey had interviewed surviving family members of one million people who was died, and collected the information of the deaths and surviving spouses, including smoking status[ 12 ]. Therefore, we could obtain data of case, DC and SC. This study restricted the analyses to age 35 and above, and the ages of participants were split into 5-year age categories, and finally eight age groups were divided. The main outcome of this study is lung cancer attributed by smoking. This study was conducted in accordance with the Declaration of Helsinki and followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for case-control studies. 2.2 Definition and cross of cases and controls Cases means the deaths with lung cancer as underlying cause of death (UCOD), DC means the deaths with UCOD irrelative to smoking. SC means the surviving spouses of the cases, excluding the people with smoking-related diseases. The diseases which irrelated with smoking were shown in Table 1 S, coded according to the International Statistical Classification of Diseases and Related Health Problems (ICD). The cross matching of case, DC and SC in male and female was showed in Fig. 1 . Note As for male cases, we chose the husbands of the female cases as male SCs, and other deaths who were not died for smoking related diseases in the same hospital as male DCs, and so as the selection principle of female SCs and DCs. 2.3 Statistical Analysis Data cleaning and statistical analyses were implemented using SAS 9.4 (SAS Institute Inc., Cary, North Carolina, USA) and R 4.3.2 (R Foundation for Statistical Computing; https://www.r-project.org ). The data were cleaned by duplicate checking, processing extreme values, abnormal values and missing values, logical verification and so on. Descriptive analyses were shown as number and percentage. Smoking and smoking rate stratified by urban/rural area and gender were calculated. The multiple comparisons of chi-squared test were used to analyze the categorical variables. Cochran-Mantel-Haenszel test was used to calculate the ORs with 95% Confidence Interval (CI) among gender and age (5-year age categories). Referring to the cross-walk method[ 2 , 13 ] on smoking prevalence proposed by GBD, four forms of models among different urban/rural, genders and age groups were established for exploring the fit one to adjust \(\:{OR}_{sc}\) and \(\:{OR}_{dc}\) . The models were established as follows: Model 1: normal scale with an intercept: \(\:{OR}_{sc}={}_{0}+{}_{1}{OR}_{dc}+\) ; Model 2: normal scale without an intercept: \(\:{OR}_{sc}={}_{1}{OR}_{dc}+\) ; Model 3: logit scale with an intercept: \(\:\:{logit(OR}_{sc})={}_{0}+{}_{1}logit({OR}_{dc})+\) ; Model 4: logit scale without an intercept: \(\:{logit(OR}_{sc})={}_{1}logit({OR}_{dc})+\) ; where \(\:{\beta\:}_{0}\) is the intercept of the model, \(\:{\beta\:}_{1}\) is the coefficient of \(\:{OR}_{dc}\) or \(\:logit\left({OR}_{dc}\right)\) , and \(\:\) is the residual of the model. Several indices were used to evaluate model fit, including Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Akaike's Information Criteria (AIC) and Bayesian information criteria (BIC). The lower the above four indexes, the better the model fit. All tests were two-sided and P- value < 0.05 was considered to be statistically significant. 3. Results 3.1 Demographic Characteristics A total of 296106 participants were collected, including 51217 cases, 29871 SCs, and 215018 DCs. About 68.00% of cases, 24.60% of SCs and 54.86% of DCs were males. The educational degrees of most participants were under senior high school, accounting for 71.12%, 81.97%, 77.86% among case, SC and DC in males, and 86.92%, 69.00% and 91.10% among the above corresponding three groups in females. Among the three groups of males, the largest proportion of age groups was 70 + years old, so as the case (33.16%) and DC (51.87%) group of females. The proportions of urban participants for case, SC and DC groups were similar among males and females, accounted for 84.74%, 82.60 and 62.87% in males and 85.32%, 83.98% and 66.45% in females (Table 1 ). Table 1 Demographic characteristics of participants Variate Male Female Case (%) SC (%) DC (%) Case (%) SC (%) DC (%) Total 34825 7438 117963 16392 22433 97055 Educational degree High 10058(28.88) 1341(18.03) 26117(22.14) 2144(13.08) 6955(31.00) 8634(8.90) Low 24767(71.12) 6097(81.97) 91846(77.86) 14248(86.92) 15478(69.00) 88421(91.10) Age 35–39 437(1.25) 111(1.49) 7938(6.73) 265(1.62) 494(2.20) 4560(4.70) 40–44 633(1.82) 216(2.90) 7019(5.95) 392(2.39) 894(3.99) 3906(4.02) 45–49 1360(3.91) 401(5.39) 7755(6.57) 787(4.80) 1865(8.31) 4635(4.78) 50–54 3007(8.63) 802(10.78) 10325(8.75) 1586(9.68) 3729(16.62) 6550(6.75) 55–59 5071(14.56) 1214(16.32) 11965(10.14) 2148(13.10) 4482(19.98) 7684(7.92) 60–64 6722(19.30) 1449(19.48) 13367(11.33) 2830(17.26) 4791(21.36) 8999(9.27) 65–69 6742(19.36) 1292(17.37) 14471(12.27) 2948(17.98) 3366(15.00) 10376(10.69) 70+ 10853(31.16) 1953(26.26) 45123(38.25) 5436(33.16) 2812(12.54) 50345(51.87) Urban/Rural Urban 29511(84.74) 6144(82.60) 74158(62.87) 13985(85.32) 18839(83.98) 64489(66.45) Rural 5314(15.26) 1294(17.40) 43805(37.13) 2407(14.68) 3594(16.02) 32566(33.55) SC: Spouse Control; DC: Disease Control; High: Senior High School and above; Low: Under Senior High School. 3.2 Smoking among urban/rural area and genders The smoking rate of total case, SC and DC groups were 66.83%, 24.39%, 40.21%, with 80.28%, 54.41% and 61.44% in males, and 38.27%, 14.44%, 14.39% in females, respectively. The smoking rate of males were consistently higher than females in three groups. Cases exhibited the highest smoking rate, followed by DC and SC in both genders of urban and rural area. As for males, the smoking rates of three groups in urban area were higher than those in rural area, while the smoking rates of females were opposite (Table 2 ). Table 2 Smoking stratified by urban/rural area and gender Smoking Male Female Case (%) SC (%) DC (%) Case (%) SC (%) DC (%) Total 27956(80.28) 4047(54.41) 72479(61.44) 6274(38.27) 3240(14.44) 13970(14.39) Urban/Rural Urban 23607 (79.99) 3232 (52.50) 43870 (59.16) 5822 (41.63) 2920 (15.50) 10950 (16.98) Rural 4349 (81.84) 815 (62.98) 28609 (65.31) 452 (18.78) 320 (8.90) 3020 (9.27) 3.2 Odds ratio of two types of control Two types of OR ( \(\:{OR}_{sc}\) and \(\:{OR}_{dc}\) ) were calculated, stratified by urban/rural, gender and 5-year age categories (Table 3 ). In both urban and rural areas, the \(\:{OR}_{sc}\) of males and females were higher than \(\:{OR}_{dc}\) , and the total \(\:{OR}_{sc}\) of males was smaller than that of females, while the total \(\:{OR}_{dc}\) of males was higher than that of females. In the urban area, the highest \(\:{OR}_{sc}\) and \(\:{OR}_{dc}\) of males and females occurred in 60–64 age group, while in rural area, the highest \(\:{OR}_{sc}\) and \(\:{OR}_{dc}\) occurred in 55–59 age group, that is, the \(\:{OR}_{sc}\) and \(\:{OR}_{dc}\) value gradually increases with the age group, reaching its maximum at 60–64 in urban area and 55–59 in rural area. Especially, in the rural area, the group \(\:{OR}_{sc}\) of males and females, and \(\:{OR}_{dc}\) of females in the age group 35–39 had a relatively high OR, for the small sample of the related groups. Table 3 Odds Ratio (OR) and 95% Confidence Interval (CI) of different region, gender and age group Age Urban Rural \(\:{OR}_{sc}\) (95%CI) \(\:{OR}_{dc}\) (95%CI) \(\:{OR}_{sc}\) (95%CI) \(\:{OR}_{dc}\) (95%CI) Male Female Male Female Male Female Male Female Total 3.60(3.40, 3.82) 3.88(3.69, 4.10) 2.64(2.32, 3.02) 2.37(2.03, 2.76) 2.76(2.67, 2.85) 3.48(3.35, 3.63) 2,39(2.22, 2.57) 2.26(2.03, 2.52) 35–39 2.58(1.56, 4.27) 1.60(0.71, 3.59) 2.15(1.67, 2.77) 1.84(0.96, 3.52) 3.15(1.37, 7.26) 4.68(0.91, 24.02) 2.00(1.28, 3.14) 2.71(1.13, 6.49) 40–44 2.14(1.49, 3.09) 3.42(2.13, 5.47) 1.90(1.54, 2.34) 3.07(2.11, 4.43) 1.84(0.91, 3.71) 1.30(0.49, 3.48) 2.22(1.50, 3.30) 1.91(0.85, 4.27) 45–49 3.00(2.30, 3.91) 3.18(2.48, 4.08) 2.01(1.73, 2.34) 3.13(2.52, 3.90) 2.27(1.40, 3.68) 1.46(0.80, 2.64) 2.33(1.76, 3.07) 1.70(1.03, 2.81) 50–54 3.02(2.52, 3.63) 3.71(3.20, 4.30) 2.43(2.18, 2.71) 3.25(2.84, 3.73) 2.20(1.47, 3.31) 2.57(1.60, 4.13) 2.70(2.13, 3.43) 1.68(1.16, 2.43) 55–59 3.43(2.96, 3.97) 3.49(3.08, 3.97) 2.78(2.54, 3.05) 3.45(3.06, 3.89) 2.85(2.01, 4.06) 3.40(2.27, 5.11) 2.91(2.36, 3.58) 2.52(1.86, 3.43) 60–64 4.70(4.11, 5.37) 4.35(3.88, 4.87) 2.88(2.65, 3.13) 3.87(3.49, 4.29) 2.66(1.95, 3.61) 1.76(1.27, 2.44) 2.22(1.87, 2.64) 2.05(1.57, 2.67) 65–69 3.51(3.06, 4.03) 3.71(3.29, 4.19) 2.61(2.41, 2.83) 3.25(2.94, 3.58) 2.42(1.73, 3.39) 2.80(1.92, 4.09) 2.15(1.81, 2.58) 2.33(1.80, 3.02) 70+ 3.82(3.43, 4.25) 3.33(3.14, 4.01) 2.65(2.51, 2.80) 3.29(3.08, 3.50) 3.38(2.59, 4.40) 2.11(1.49, 3.00) 1.99(1.74, 2.27) 2.30(1.91, 2.78) \(\:{OR}_{sc}\) : Odds Ratio of Spouse Control; \(\:{OR}_{dc}\) : Odds Ratio of Disease Control. 3.3 Selection of cross-walk model In this study, the \(\:{OR}_{sc}\) of the same urban/rural, gender and age group were generally larger than those of \(\:{OR}_{dc}\) . Referring to cross-walk model, four forms of model were alternative to adjust the \(\:{OR}_{sc}\) by \(\:{OR}_{dc}\) . According to the selecting criteria of the accuracy evaluation indexes, in urban area, \(\:{logit(OR}_{sc})=1.33logit({OR}_{dc})\) and \(\:{logit(OR}_{sc})=-0.34+1.35logit({OR}_{dc})\) were selected for males and females, respectively. In rural area, \(\:{logit(OR}_{sc})=1.09logit({OR}_{dc})\) and \(\:{logit(OR}_{sc})=1.15logit({OR}_{dc})\) were selected for males and females, respectively (Table 4 ). The regression lines of the four selected relational expressions were shown in the Fig. 2. Table 4 Accuracy evaluation indexes of cross-walking model Region Gender Model Evaluation indexes RMSE MAE \(\:\) AIC BIC P -value Urban Male \(\:{OR}_{sc}=-1.29+1.88{OR}_{dc}\) 0.35 0.30 0.40 11.90 12.10 < 0.001 \(\:{OR}_{sc}=1.36{OR}_{dc}\) 0.39 0.34 0.42 11.80 11.90 < 0.001 \(\:{logit(OR}_{sc})=-0.05+1.38logit({OR}_{dc})\) 0.10 0.09 0.12 -7.78 -7.54 < 0.001 \(\:{logit(OR}_{sc})=1.33logit({OR}_{dc})\) * 0.10 0.09 0.11 -9.73 -9.57 < 0.001 Female \(\:{OR}_{sc}=-0.81+1.33{OR}_{dc}\) 0.16 0.13 0.18 -0.22 0.02 < 0.001 \(\:{OR}_{sc}=1.08{OR}_{dc}\) 0.21 0.19 0.23 2.09 2.25 < 0.001 \(\:{logit(OR}_{sc})=-0.34+1.35logit({OR}_{dc})\) * 0.05 0.04 0.06 -19.50 -19.20 < 0.001 \(\:{logit(OR}_{sc})=1.06logit({OR}_{dc})\) 0.08 0.06 0.08 -14.10 -13.90 < 0.001 Rural Male \(\:{OR}_{sc}=3.65-0.46{OR}_{dc}\) 0.46 0.40 0.53 16.30 16.50 0.49 \(\:{OR}_{sc}=1.09{OR}_{dc}\) 0.66 0.55 0.71 20.2 20.30 < 0.001 \(\:{logit(OR}_{sc})=1.30-0.44logit({OR}_{dc})\) 0.18 0.15 0.21 1.26 1.50 0.48 \(\:{logit(OR}_{sc})=1.09logit({OR}_{dc})\) * 0.27 0.22 0.28 5.50 5.65 < 0.001 Female \(\:{OR}_{sc}=-2.57+2.36{OR}_{dc}\) 0.64 0.51 0.74 21.70 21.90 < 0.05 \(\:{OR}_{sc}=1.20{OR}_{dc}\) 0.77 0.66 0.82 22.50 22.60 < 0.001 \(\:{logit(OR}_{sc})=-0.52+1.81logit({OR}_{dc})\) 0.27 0.22 0.32 7.98 8.22 < 0.05 \(\:{logit(OR}_{sc})=1.15logit({OR}_{dc})\) * 0.30 0.27 0.32 7.24 7.40 < 0.001 *: Models were selected for adjusting. RMSE: Root Mean Squared Error; MAE: Mean Absolute Error; AIC: Akaike's Information Criteria; BIC: Bayesian information criteria. The lower the above four indexes, the better the model fit. 4. Discussion This is a study that referring to the mind of cross-walk model, quantitative relationship of DC and SC was analyzed, which means that \(\:{OR}_{dc}\) and \(\:{OR}_{sc}\) of lung cancer attributed by smoking was adjusted for comparison. Four forms of cross-walk model among urban/rural, genders and 5-year age categories were established to explore the suitable association. Among the two kinds of OR, \(\:{OR}_{sc}\) are commonly larger than \(\:{OR}_{dc}\) in the same urban/rural, gender and age group. Finally, \(\:{logit(OR}_{sc})=1.33logit({OR}_{dc})\) of males and \(\:{logit(OR}_{sc})=-0.34+1.35logit({OR}_{dc})\) of females in urban area, \(\:{logit(OR}_{sc})=1.09logit({OR}_{dc})\) of males and \(\:{logit(OR}_{sc})=1.15logit({OR}_{dc})\) of females in rural area were selected. In this study, the smoking prevalence of case was the highest in the three group, then DC and SC in descending order. However, \(\:{OR}_{sc}\) are commonly larger than \(\:{OR}_{dc}\) in the same group. As for both genders in urban and rural areas, the smoking rates of DC were higher than those of SC. The reason might be that the smokers from SC groups would quit smoking once they noticed their spouses having the disease, which caused the \(\:{OR}_{sc}\) larger than \(\:{OR}_{dc}\) when they matched to the same case group. Another reason was that although the DCs excluded individuals who died from smoking-related diseases, it may still include a large number of individuals with a smoking history who died from other causes, thereby diluting the association between exposure and lung cancer. Then we found that \(\:{OR}_{dc}\) and \(\:{OR}_{sc}\) of males in the same group were higher than those of females. The most reason is that the smoking rate of males is higher than that of females. The possible reasons for females developed lung cancer were high exposure of second-hand smoke and household cooking for females. Previous studies[ 14 – 17 ] conducted that cooking is the highest risk factor of lung cancer in females, and diet, second-hand smoke, history of cancer and lung disease are also related to lung cancer. As for the choice and difference of DCs and SCs, we analyzed the advantages, confounding control and bias of them. On one hand, the advantage of DCs is that selecting controls with UCOD irrelated to smoking can reduce the risk of underestimation, and SCs can naturally control for living environment and habit, thereby reducing confounding bias, which also reducing the confounding. On the other hand, DCs exists selection bias, arising from differences in socioeconomic status, living environments, and habits between lung cancer cases and control subjects. Moreover, there exists information bias, particularly related to recall by spousal informants. SCs also introduce co-exposure bias, as spouses often have similar lifestyles; for instance, if one spouse smokes, the other may be exposed to secondhand smoke, or they may both smoke or not smoke together, which can diminish the smoking exposure difference between cases and controls, potentially underestimating the risk between smoking and lung cancer. In some studies[ 2 , 13 , 18 ], due to different definition or consumption frequency, methods are established to adjust them, and cross-walk model is one of the methods. Referring to the thinking of cross-walk, we illuminated to explore the association of DC and SC by adopting this model. Via establishing regressions model, cross-walk used to adjust and \(\:{OR}_{sc}\) by \(\:{OR}_{dc}\) , which quantize the relationship of two control. It is worth mentioning that we should choose the cross-walk model according to different scenarios, which is not necessarily linear regression model. Besides, in the majority systematic review study, ORs might be pooled without considering their distinction or quantitative adjustment, which may cause some bias, and cross-walk model is a good attempt to quantify data relationship. The important condition of cross-walk model is that two or more definitions or consumption frequencies must collected, which allows them to unitize and adjust. This study is an exploration for adjusting the ORs of different kinds of control, which may provide a reference for case-control study. Despite of the data source was deaths data during 1986 to 1988, it remains the only national resource on this topic that includes DC and SC information, and the OR of smoking attributed to diseases was stable, as the previous study showed[ 11 ]. Therefore, we cannot neglect the distinguishment, especially the quantitative relationship of DC and SC in case-control study, which may affect risk estimates. In the meantime, this study provided an adjustable method for two definitions, which will provide methodological reference for further research. In the future, more adjusted models can be used to explore the relationships between different controls. Limitation and strength This study has several limitations. Firstly, due to the limitations of the data, the selected models were lack of further verification. Secondly, the information of smoking cessation status was not collected in the survey. Fortunately, the data perfectly included two types of control, and the OR was relatively stable in the past decades. Unlike previous applications of cross-walk models in GBD research, our study innovatively utilizes this approach to quantify the relationship between different types of controls in case-control studies. This methodological exploration provides a new perspective for data harmonization in systematic reviews and meta-analyses. 5. Conclusion \(\:{OR}_{sc}\) are commonly larger than \(\:{OR}_{dc}\) in the same group. Thus, when selecting control groups in case-control studies, differences between control types should be carefully considered, particularly in terms of their quantitative relationships. This study provided an adjustable method for two definitions, which will provide methodological reference for further research. Abbreviations DC Disease control SC Spouse control GBD Global Burden of Disease OR Odds Ratio RR Relative Risk HR Hazard ratio UCOD Underlying cause of death ICD International Statistical Classification of Diseases and Related Health Problems CI Confidence Interval RMSE Root Mean Squared Error MAE Mean Absolute Error AIC Akaike's Information Criteria BIC Bayesian information criteria Declarations Ethic Approval Retrospective information collected was limited to smoking exposure only, and oral informed consent for this study was obtained. Ethical approval from the INSTITUTIONAL REVIEW BOARD of Chinese Academy of Medical Sciences was obtained[ 19 ]. Clinical trial number Not Applicable. Consent for publication Not Applicable. Competing interests The authors declare that they have no competing interests. Funding (1) State Key Laboratory Special Fund (2060204); (2) Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences (2023-I2M-2-001); (3) Strengthen Capacity of Study and Application on the Burden of Disease in Health Care Systems in China: Establishment and Development of Chinese Burden of Disease Research and Dissemination Center (15–208) supported by the China Medical Board (CMB). Author Contribution ZMC organized and analyzed the data, interpreted the results and drafted the manuscript. XNZ and XW supported the data acquisition process. XW designed the study, guided its implementation. XZ and XW helped with the interpretation of the results and revised the manuscript. All authors read and approved the final manuscript. Acknowledgement We thank Prof. Gonghuan Yang for her valuable advice during the study design and the data analysis. Data Availability The data is not publicly available, but is available from the corresponding author on reasonable request. References Higgins JPT, Green S. Cochrane Handbook for Systematic Reviews of Interventions (Version 5.1.0). The Cochrane Collaboration; 2011. Ng M, Freeman MK, Fleming TD, Robinson M, Dwyer-Lindgren L, Thomson B, et al. Smoking prevalence and cigarette consumption in 187 countries, 1980–2012. JAMA. 2014;311(2):183–92. Forouzanfar MH, Alexander L, Anderson HR, Bachman VF, Biryukov S, Brauer M, et al. Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks in 188 countries, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet. 2015;386(10010):2287–323. GBD 2019 Risk Factors Collaborators. Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396(10258):1223–49. Setia MS. Methodology Series Module 2: Case-control Studies. Indian J Dermatol. 2016;61(2):146–51. Jiang JM, Zeng XJ, Chen JS, Li JY, Zhang KL, Wu YP, et al. Smoking and mortality from esophageal cancer in China: a large case-control study of 19,734 male esophageal cancer deaths and 104,846 living spouse controls. Int J Cancer. 2006;119(6):1427–32. Chang ET, Liu Z, Hildesheim A, Liu Q, Cai Y, Zhang Z, et al. Active and Passive Smoking and Risk of Nasopharyngeal Carcinoma: A Population-Based Case-Control Study in Southern China. Am J Epidemiol. 2017;185(12):1272–80. Jiang J, Liu B, Sitas F, Zeng X, Chen J, Han W, et al. Case-spouse control design in practice: an experience in estimating smoking and chronic obstructive pulmonary disease deaths in Chinese adults. J Formos Med Assoc. 2010;109(5):369–77. Lewandowska A, Lewandowski T, Zych B, Papp K, Zrubcová D, Ejder Apay S, et al. Risk Factors for the Diagnosis of Lung Cancer in Poland: A Large-Scale, Population-Based Case-Control Study. Asian Pac J Cancer Prev. 2022;23(10):3299–307. Jiang J, Liu B, Nasca PC, Han W, Zou X, Zeng X, et al. Comparative study of control selection in a national population-based case-control study: Estimating risk of smoking on cancer deaths in Chinese men. Int J Med Sci. 2009;6(6):329–37. Zhao J, Shi YL, Wang YT, Ai FL, Wang XW, Yang WY, et al. Lung Cancer Risk Attributable to Active Smoking in China: A Systematic Review and Meta-Analysis. Biomed Environ Sci. 2023;36(9):850–61. Liu BQ, Peto R, Chen ZM, Boreham J, Wu YP, Li JY, et al. Emerging tobacco hazards in China: 1. Retrospective proportional mortality study of one million deaths. BMJ. 1998;317(7170):1411–22. GBD 2015 Tobacco Collaborators. Smoking prevalence and attributable disease burden in 195 countries and territories, 1990–2015: a systematic analysis from the Global Burden of Disease Study 2015. Lancet. 2017;389(10082):1885–906. Huang J, Yue N, Shi N, Wang Q, Cui T, Ying H, et al. Influencing factors of lung cancer in nonsmoking women: systematic review and meta-analysis. J Public Health (Oxf). 2022;44(2):259–68. Hackshaw AK. Lung cancer and passive smoking. Stat Methods Med Res. 1998;7(2):119–36. Taylor R, Najafi F, Dobson A. Meta-analysis of studies of passive smoking and lung cancer: effects of study type and continent. Int J Epidemiol. 2007;36(5):1048–59. Sandler DP, Everson RB, Wilcox AJ. Passive smoking in adulthood and cancer risk. Am J Epidemiol. 1985;121(1):37–48. Jürgens V, Ess S, Schwenkglenks M, Cerny T, Vounatsou P. Using lung cancer mortality to indirectly approximate smoking patterns in space. Spat Spatiotemporal Epidemiol. 2015;14–15:23–31. Hou L, Han W, Jiang J, Liu B, Wu Y, Zou X, et al. Passive smoking and stroke in men and women: a national population-based case-control study in China. Sci Rep. 2017;7:45542. Additional Declarations No competing interests reported. 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08:41:00","extension":"xml","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":106589,"visible":true,"origin":"","legend":"","description":"","filename":"07658e92bf58417d98fe4f7a69e34a5f1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7772697/v1/d6e9fe83bdc6e9548598ad4e.xml"},{"id":96252037,"identity":"bec10775-e43f-4b7d-b48d-500165aee99a","added_by":"auto","created_at":"2025-11-19 07:40:22","extension":"html","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":120644,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7772697/v1/21080d6916e7314befaf38b1.html"},{"id":96159286,"identity":"bab3e208-a62b-4906-9fc9-02481c83acaa","added_by":"auto","created_at":"2025-11-18 08:40:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":131268,"visible":true,"origin":"","legend":"\u003cp\u003eThe frame of cross matching\u003c/p\u003e\n\u003cp\u003eNote: As for male cases, we chose the husbands of the female cases as male SCs, and other deaths who were not died for smoking related diseases in the same hospital as male DCs, and so as the selection principle of female SCs and DCs.\u003c/p\u003e","description":"","filename":"Figure1Theframeofcrossmatching.png","url":"https://assets-eu.researchsquare.com/files/rs-7772697/v1/c190c940f3e08356f1e22502.png"},{"id":96159287,"identity":"043ba49a-741e-4da5-a6ca-3cd23d31c397","added_by":"auto","created_at":"2025-11-18 08:40:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":176097,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"Figure2FittedregressionlinesofcrosswalkmodelamongtwokindsofOddsRatios.png","url":"https://assets-eu.researchsquare.com/files/rs-7772697/v1/03ad0ef9a192cff4ad538f4a.png"},{"id":96453421,"identity":"1f33c8fc-248a-429a-acf1-66c6afe84392","added_by":"auto","created_at":"2025-11-21 09:59:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1219981,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7772697/v1/97d1cd5a-2307-4a3f-bbf2-4e40092201b5.pdf"},{"id":96250085,"identity":"7d60d46e-60c2-46a4-8b66-070c169bbc8c","added_by":"auto","created_at":"2025-11-19 07:37:25","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":32900,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-7772697/v1/61e3c74b5a1b5af07dd69c1f.docx"},{"id":96159290,"identity":"a70024db-f153-42ad-9f45-362390d7ce7a","added_by":"auto","created_at":"2025-11-18 08:40:59","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17582,"visible":true,"origin":"","legend":"","description":"","filename":"Supplymentalmaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-7772697/v1/f3c8ed851ce092449fbc5002.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Estimating the Quantitative Relationship of Disease Control and Spouse Control for Lung Cancer in Case-control Study by Cross-walk Model","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIn systematic reviews, the definition and methods of data synthesis may vary depending on the study design, data type, or analytical objectives. Typically, data synthesis refers to the process of integrating, summarizing, and analyzing data from multiple independent studies, which aims to derive broader and more robust conclusions by combining the results of different studies[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. To ensure the reliability and comparability of the results, it is necessary to standardize the data and make comparisons on a unified basis. Some researches set up adjustive methods to estimate the different data source, and cross-walk model is one of the explorations. Cross-walk is performed by establishing models to adjust the different categories and definitions or consumption frequencies of research factors, so that they are unitized and comparable[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In Ng et al. study[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], researchers established regression model (cross-walk model) and adjusted different definition or non-standardized age-gender and multi-source data of smoking product and consumption, and calculated the smoking prevalence of particular age-sex-country-year. In Global Burden of Disease (GBD) study, this model was used in definitions or consumption frequencies of smoking, ambient particulate matter pollution, family size of household air pollution, alcohol consumption and physical activity[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The above studies illustrate us to refer the adjusting method under certain circumstance.\u003c/p\u003e\u003cp\u003eIn epidemiology, case-control study is a type of observational study commonly used to look at factors associated with diseases or outcome. Selection of cases and controls is an important part of this design. The basic principle of selecting them is that they should be have the same \u0026lsquo;study base\u0026rsquo;. In general, there are several sources for constructing controls, including: general population; relatives or friends; or hospital patients[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The latter two types are commonly used in some case-control study of China[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. For example, in the study of estimating risk of smoking on cancer deaths in China[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], the deceased who died from causes other than those related to smoking and surviving spouses of cancer deceased were invited as disease controls (DC) and spouse controls (SC), respectively. It is worth noting that different controls may bring certain differences in the research. Jiang et al.[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] compared these two types of control by calculating the ratio of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{OR}_{dc}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{OR}_{sc}\\)\u003c/span\u003e\u003c/span\u003e, and did not detect quantified difference between them.\u003c/p\u003e\u003cp\u003eTherefore, based on the previous studies, our study aims to further explore the association of DC and SC, and estimate the quantitative relationship of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{OR}_{dc}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{OR}_{sc}\\)\u003c/span\u003e\u003c/span\u003e in the context of lung cancer attributed to smoking, utilizing the cross-walk model of GBD study as an adjustment approach. The findings may provide methodological insights for systematic reviews and meta-analyses in epidemiological studies.\u003c/p\u003e\u003cp\u003eBesides, our data remains the only national resource on this topic that includes DC and SC information, and though the data is old, this did not affect the exploration of the association. Furthermore, the previous meta-analysis[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] showed that the Relative Risk (RR) of smoking-attributed lung cancer has remained stable (ranged from 2 to 6) in the past four decades in China, and the RR, OR and Hazard ratio (HR) were equally collected in the study. Thus, because the data was unique, and the OR of smoking-attributed lung cancer was stable, our study used the deaths data during 1986 to 1988, which contained both DC and SC, to explore the quantitative relationship between \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{OR}_{dc}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{OR}_{sc}\\)\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e"},{"header":"2. Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Data Source\u003c/h2\u003e\u003cp\u003eThe data source derived from the National Retrospective Survey of Deaths, including one million deaths during 1986 to 1988. For containing a wide geographical distribution, this survey covered 24 cities, which were chosen non-randomly, and 74 rural countries were chosen by stratified random sampling from over 2000 counties in China. The survey had interviewed surviving family members of one million people who was died, and collected the information of the deaths and surviving spouses, including smoking status[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Therefore, we could obtain data of case, DC and SC. This study restricted the analyses to age 35 and above, and the ages of participants were split into 5-year age categories, and finally eight age groups were divided. The main outcome of this study is lung cancer attributed by smoking. This study was conducted in accordance with the Declaration of Helsinki and followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for case-control studies.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Definition and cross of cases and controls\u003c/h2\u003e\u003cp\u003eCases means the deaths with lung cancer as underlying cause of death (UCOD), DC means the deaths with UCOD irrelative to smoking. SC means the surviving spouses of the cases, excluding the people with smoking-related diseases. The diseases which irrelated with smoking were shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003eS, coded according to the International Statistical Classification of Diseases and Related Health Problems (ICD). The cross matching of case, DC and SC in male and female was showed in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e\u003cp\u003eAs for male cases, we chose the husbands of the female cases as male SCs, and other deaths who were not died for smoking related diseases in the same hospital as male DCs, and so as the selection principle of female SCs and DCs.\u003c/p\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Statistical Analysis\u003c/h2\u003e\u003cp\u003eData cleaning and statistical analyses were implemented using SAS 9.4 (SAS Institute Inc., Cary, North Carolina, USA) and R 4.3.2 (R Foundation for Statistical Computing; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-project.org\u003c/span\u003e\u003cspan address=\"https://www.r-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The data were cleaned by duplicate checking, processing extreme values, abnormal values and missing values, logical verification and so on. Descriptive analyses were shown as number and percentage. Smoking and smoking rate stratified by urban/rural area and gender were calculated. The multiple comparisons of chi-squared test were used to analyze the categorical variables. Cochran-Mantel-Haenszel test was used to calculate the ORs with 95% Confidence Interval (CI) among gender and age (5-year age categories).\u003c/p\u003e\u003cp\u003eReferring to the cross-walk method[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] on smoking prevalence proposed by GBD, four forms of models among different urban/rural, genders and age groups were established for exploring the fit one to adjust \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{OR}_{sc}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{OR}_{dc}\\)\u003c/span\u003e\u003c/span\u003e. The models were established as follows:\u003c/p\u003e\u003cp\u003eModel 1: normal scale with an intercept: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{OR}_{sc}={}_{0}+{}_{1}{OR}_{dc}+\\)\u003c/span\u003e\u003c/span\u003e ;\u003c/p\u003e\u003cp\u003eModel 2: normal scale without an intercept: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{OR}_{sc}={}_{1}{OR}_{dc}+\\)\u003c/span\u003e\u003c/span\u003e ;\u003c/p\u003e\u003cp\u003eModel 3: logit scale with an intercept:\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:{logit(OR}_{sc})={}_{0}+{}_{1}logit({OR}_{dc})+\\)\u003c/span\u003e\u003c/span\u003e ;\u003c/p\u003e\u003cp\u003eModel 4: logit scale without an intercept: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{logit(OR}_{sc})={}_{1}logit({OR}_{dc})+\\)\u003c/span\u003e\u003c/span\u003e ;\u003c/p\u003e\u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{0}\\)\u003c/span\u003e\u003c/span\u003e is the intercept of the model, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{1}\\)\u003c/span\u003e\u003c/span\u003e is the coefficient of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{OR}_{dc}\\)\u003c/span\u003e\u003c/span\u003e or \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:logit\\left({OR}_{dc}\\right)\\)\u003c/span\u003e\u003c/span\u003e, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\)\u003c/span\u003e\u003c/span\u003e is the residual of the model. Several indices were used to evaluate model fit, including Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Akaike's Information Criteria (AIC) and Bayesian information criteria (BIC). The lower the above four indexes, the better the model fit. All tests were two-sided and \u003cem\u003eP-\u003c/em\u003evalue\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered to be statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Demographic Characteristics\u003c/h2\u003e\u003cp\u003eA total of 296106 participants were collected, including 51217 cases, 29871 SCs, and 215018 DCs. About 68.00% of cases, 24.60% of SCs and 54.86% of DCs were males. The educational degrees of most participants were under senior high school, accounting for 71.12%, 81.97%, 77.86% among case, SC and DC in males, and 86.92%, 69.00% and 91.10% among the above corresponding three groups in females. Among the three groups of males, the largest proportion of age groups was 70\u0026thinsp;+\u0026thinsp;years old, so as the case (33.16%) and DC (51.87%) group of females. The proportions of urban participants for case, SC and DC groups were similar among males and females, accounted for 84.74%, 82.60 and 62.87% in males and 85.32%, 83.98% and 66.45% in females (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDemographic characteristics of participants\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCase (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSC (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDC (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCase (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSC (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eDC (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34825\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7438\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e117963\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16392\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e22433\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e97055\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducational degree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10058(28.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1341(18.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26117(22.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2144(13.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6955(31.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8634(8.90)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24767(71.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6097(81.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e91846(77.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14248(86.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e15478(69.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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colname=\"c4\"\u003e\u003cp\u003e7938(6.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e265(1.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e494(2.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4560(4.70)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e40\u0026ndash;44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e633(1.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e216(2.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7019(5.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e392(2.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e894(3.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3906(4.02)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e45\u0026ndash;49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1360(3.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e401(5.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7755(6.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e787(4.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1865(8.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4635(4.78)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e50\u0026ndash;54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3007(8.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e802(10.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10325(8.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1586(9.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3729(16.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6550(6.75)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e55\u0026ndash;59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5071(14.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1214(16.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11965(10.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2148(13.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4482(19.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7684(7.92)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e60\u0026ndash;64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6722(19.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1449(19.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13367(11.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2830(17.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4791(21.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8999(9.27)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e65\u0026ndash;69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6742(19.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1292(17.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14471(12.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2948(17.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3366(15.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e10376(10.69)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e70+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10853(31.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1953(26.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e45123(38.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5436(33.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2812(12.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e50345(51.87)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban/Rural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29511(84.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6144(82.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e74158(62.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13985(85.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e18839(83.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e64489(66.45)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5314(15.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1294(17.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e43805(37.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2407(14.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3594(16.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e32566(33.55)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eSC: Spouse Control; DC: Disease Control; High: Senior High School and above; Low: Under Senior High School.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Smoking among urban/rural area and genders\u003c/h2\u003e\u003cp\u003eThe smoking rate of total case, SC and DC groups were 66.83%, 24.39%, 40.21%, with 80.28%, 54.41% and 61.44% in males, and 38.27%, 14.44%, 14.39% in females, respectively. The smoking rate of males were consistently higher than females in three groups. Cases exhibited the highest smoking rate, followed by DC and SC in both genders of urban and rural area. As for males, the smoking rates of three groups in urban area were higher than those in rural area, while the smoking rates of females were opposite (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSmoking stratified by urban/rural area and gender\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSmoking\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCase (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSC (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDC (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCase (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSC (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eDC (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e27956(80.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4047(54.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e72479(61.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6274(38.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3240(14.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e13970(14.39)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban/Rural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e23607 (79.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3232 (52.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e43870 (59.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5822 (41.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2920 (15.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e10950 (16.98)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4349 (81.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e815 (62.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e28609 (65.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e452 (18.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e320 (8.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3020 (9.27)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Odds ratio of two types of control\u003c/h2\u003e\u003cp\u003eTwo types of OR (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{OR}_{sc}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{OR}_{dc}\\)\u003c/span\u003e\u003c/span\u003e) were calculated, stratified by urban/rural, gender and 5-year age categories (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In both urban and rural areas, the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{OR}_{sc}\\)\u003c/span\u003e\u003c/span\u003e of males and females were higher than \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{OR}_{dc}\\)\u003c/span\u003e\u003c/span\u003e, and the total \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{OR}_{sc}\\)\u003c/span\u003e\u003c/span\u003e of males was smaller than that of females, while the total \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{OR}_{dc}\\)\u003c/span\u003e\u003c/span\u003e of males was higher than that of females. In the urban area, the highest \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{OR}_{sc}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{OR}_{dc}\\)\u003c/span\u003e\u003c/span\u003e of males and females occurred in 60\u0026ndash;64 age group, while in rural area, the highest \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{OR}_{sc}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{OR}_{dc}\\)\u003c/span\u003e\u003c/span\u003e occurred in 55\u0026ndash;59 age group, that is, the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{OR}_{sc}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{OR}_{dc}\\)\u003c/span\u003e\u003c/span\u003e value gradually increases with the age group, reaching its maximum at 60\u0026ndash;64 in urban area and 55\u0026ndash;59 in rural area. Especially, in the rural area, the group \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{OR}_{sc}\\)\u003c/span\u003e\u003c/span\u003e of males and females, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{OR}_{dc}\\)\u003c/span\u003e\u003c/span\u003e of females in the age group 35\u0026ndash;39 had a relatively high OR, for the small sample of the related groups.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eOdds Ratio (OR) and 95% Confidence Interval (CI) of different region, gender and age group\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{OR}_{sc}\\)\u003c/span\u003e\u003c/span\u003e (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{OR}_{dc}\\)\u003c/span\u003e\u003c/span\u003e (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{OR}_{sc}\\)\u003c/span\u003e\u003c/span\u003e (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{OR}_{dc}\\)\u003c/span\u003e\u003c/span\u003e (95%CI)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.60(3.40, 3.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.88(3.69, 4.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.64(2.32, 3.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.37(2.03, 2.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.76(2.67, 2.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e3.48(3.35, 3.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e2,39(2.22, 2.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e2.26(2.03, 2.52)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e35\u0026ndash;39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.58(1.56, 4.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.60(0.71, 3.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.15(1.67, 2.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.84(0.96, 3.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3.15(1.37, 7.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e4.68(0.91, 24.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e2.00(1.28, 3.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e2.71(1.13, 6.49)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e40\u0026ndash;44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.14(1.49, 3.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.42(2.13, 5.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.90(1.54, 2.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.07(2.11, 4.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.84(0.91, 3.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.30(0.49, 3.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e2.22(1.50, 3.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e1.91(0.85, 4.27)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e45\u0026ndash;49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.00(2.30, 3.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.18(2.48, 4.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.01(1.73, 2.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.13(2.52, 3.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.27(1.40, 3.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.46(0.80, 2.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e2.33(1.76, 3.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e1.70(1.03, 2.81)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e50\u0026ndash;54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.02(2.52, 3.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.71(3.20, 4.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.43(2.18, 2.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.25(2.84, 3.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.20(1.47, 3.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.57(1.60, 4.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e2.70(2.13, 3.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e1.68(1.16, 2.43)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e55\u0026ndash;59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.43(2.96, 3.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.49(3.08, 3.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.78(2.54, 3.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.45(3.06, 3.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.85(2.01, 4.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e3.40(2.27, 5.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e2.91(2.36, 3.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e2.52(1.86, 3.43)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e60\u0026ndash;64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.70(4.11, 5.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.35(3.88, 4.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.88(2.65, 3.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.87(3.49, 4.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.66(1.95, 3.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.76(1.27, 2.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e2.22(1.87, 2.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e2.05(1.57, 2.67)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e65\u0026ndash;69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.51(3.06, 4.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.71(3.29, 4.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.61(2.41, 2.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.25(2.94, 3.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.42(1.73, 3.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.80(1.92, 4.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e2.15(1.81, 2.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e2.33(1.80, 3.02)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e70+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.82(3.43, 4.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.33(3.14, 4.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.65(2.51, 2.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.29(3.08, 3.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3.38(2.59, 4.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.11(1.49, 3.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1.99(1.74, 2.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e2.30(1.91, 2.78)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{OR}_{sc}\\)\u003c/span\u003e\u003c/span\u003e: Odds Ratio of Spouse Control; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{OR}_{dc}\\)\u003c/span\u003e\u003c/span\u003e: Odds Ratio of Disease Control.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Selection of cross-walk model\u003c/h2\u003e\u003cp\u003eIn this study, the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{OR}_{sc}\\)\u003c/span\u003e\u003c/span\u003e of the same urban/rural, gender and age group were generally larger than those of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{OR}_{dc}\\)\u003c/span\u003e\u003c/span\u003e. Referring to cross-walk model, four forms of model were alternative to adjust the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{OR}_{sc}\\)\u003c/span\u003e\u003c/span\u003e by \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{OR}_{dc}\\)\u003c/span\u003e\u003c/span\u003e. According to the selecting criteria of the accuracy evaluation indexes, in urban area, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{logit(OR}_{sc})=1.33logit({OR}_{dc})\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{logit(OR}_{sc})=-0.34+1.35logit({OR}_{dc})\\)\u003c/span\u003e\u003c/span\u003e were selected for males and females, respectively. In rural area, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{logit(OR}_{sc})=1.09logit({OR}_{dc})\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{logit(OR}_{sc})=1.15logit({OR}_{dc})\\)\u003c/span\u003e\u003c/span\u003e were selected for males and females, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The regression lines of the four selected relational expressions were shown in the Fig.\u0026nbsp;2.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAccuracy evaluation indexes of cross-walking model\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eRegion\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eModel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c9\" namest=\"c4\"\u003e\u003cp\u003eEvaluation indexes\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRMSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMAE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAIC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eBIC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{OR}_{sc}=-1.29+1.88{OR}_{dc}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e11.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e12.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{OR}_{sc}=1.36{OR}_{dc}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e11.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e11.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{logit(OR}_{sc})=-0.05+1.38logit({OR}_{dc})\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-7.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-7.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{logit(OR}_{sc})=1.33logit({OR}_{dc})\\)\u003c/span\u003e\u003c/span\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-9.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-9.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{OR}_{sc}=-0.81+1.33{OR}_{dc}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{OR}_{sc}=1.08{OR}_{dc}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{logit(OR}_{sc})=-0.34+1.35logit({OR}_{dc})\\)\u003c/span\u003e\u003c/span\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-19.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-19.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{logit(OR}_{sc})=1.06logit({OR}_{dc})\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-14.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-13.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{OR}_{sc}=3.65-0.46{OR}_{dc}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e16.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e16.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{OR}_{sc}=1.09{OR}_{dc}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e20.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e20.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{logit(OR}_{sc})=1.30-0.44logit({OR}_{dc})\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{logit(OR}_{sc})=1.09logit({OR}_{dc})\\)\u003c/span\u003e\u003c/span\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e5.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e5.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{OR}_{sc}=-2.57+2.36{OR}_{dc}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e21.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e21.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{OR}_{sc}=1.20{OR}_{dc}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e22.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e22.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{logit(OR}_{sc})=-0.52+1.81logit({OR}_{dc})\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e7.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e8.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{logit(OR}_{sc})=1.15logit({OR}_{dc})\\)\u003c/span\u003e\u003c/span\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e7.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e7.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003e*: Models were selected for adjusting. RMSE: Root Mean Squared Error; MAE: Mean Absolute Error; AIC: Akaike's Information Criteria; BIC: Bayesian information criteria. The lower the above four indexes, the better the model fit.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis is a study that referring to the mind of cross-walk model, quantitative relationship of DC and SC was analyzed, which means that \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{OR}_{dc}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{OR}_{sc}\\)\u003c/span\u003e\u003c/span\u003e of lung cancer attributed by smoking was adjusted for comparison. Four forms of cross-walk model among urban/rural, genders and 5-year age categories were established to explore the suitable association. Among the two kinds of OR, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{OR}_{sc}\\)\u003c/span\u003e\u003c/span\u003e are commonly larger than \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{OR}_{dc}\\)\u003c/span\u003e\u003c/span\u003e in the same urban/rural, gender and age group. Finally, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{logit(OR}_{sc})=1.33logit({OR}_{dc})\\)\u003c/span\u003e\u003c/span\u003e of males and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{logit(OR}_{sc})=-0.34+1.35logit({OR}_{dc})\\)\u003c/span\u003e\u003c/span\u003e of females in urban area, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{logit(OR}_{sc})=1.09logit({OR}_{dc})\\)\u003c/span\u003e\u003c/span\u003e of males and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{logit(OR}_{sc})=1.15logit({OR}_{dc})\\)\u003c/span\u003e\u003c/span\u003e of females in rural area were selected.\u003c/p\u003e\u003cp\u003eIn this study, the smoking prevalence of case was the highest in the three group, then DC and SC in descending order. However, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{OR}_{sc}\\)\u003c/span\u003e\u003c/span\u003e are commonly larger than \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{OR}_{dc}\\)\u003c/span\u003e\u003c/span\u003e in the same group. As for both genders in urban and rural areas, the smoking rates of DC were higher than those of SC. The reason might be that the smokers from SC groups would quit smoking once they noticed their spouses having the disease, which caused the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{OR}_{sc}\\)\u003c/span\u003e\u003c/span\u003e larger than \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{OR}_{dc}\\)\u003c/span\u003e\u003c/span\u003e when they matched to the same case group. Another reason was that although the DCs excluded individuals who died from smoking-related diseases, it may still include a large number of individuals with a smoking history who died from other causes, thereby diluting the association between exposure and lung cancer. Then we found that \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{OR}_{dc}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{OR}_{sc}\\)\u003c/span\u003e\u003c/span\u003e of males in the same group were higher than those of females. The most reason is that the smoking rate of males is higher than that of females. The possible reasons for females developed lung cancer were high exposure of second-hand smoke and household cooking for females. Previous studies[\u003cspan additionalcitationids=\"CR15 CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] conducted that cooking is the highest risk factor of lung cancer in females, and diet, second-hand smoke, history of cancer and lung disease are also related to lung cancer.\u003c/p\u003e\u003cp\u003eAs for the choice and difference of DCs and SCs, we analyzed the advantages, confounding control and bias of them. On one hand, the advantage of DCs is that selecting controls with UCOD irrelated to smoking can reduce the risk of underestimation, and SCs can naturally control for living environment and habit, thereby reducing confounding bias, which also reducing the confounding. On the other hand, DCs exists selection bias, arising from differences in socioeconomic status, living environments, and habits between lung cancer cases and control subjects. Moreover, there exists information bias, particularly related to recall by spousal informants. SCs also introduce co-exposure bias, as spouses often have similar lifestyles; for instance, if one spouse smokes, the other may be exposed to secondhand smoke, or they may both smoke or not smoke together, which can diminish the smoking exposure difference between cases and controls, potentially underestimating the risk between smoking and lung cancer.\u003c/p\u003e\u003cp\u003eIn some studies[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], due to different definition or consumption frequency, methods are established to adjust them, and cross-walk model is one of the methods. Referring to the thinking of cross-walk, we illuminated to explore the association of DC and SC by adopting this model. Via establishing regressions model, cross-walk used to adjust and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{OR}_{sc}\\)\u003c/span\u003e\u003c/span\u003e by \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{OR}_{dc}\\)\u003c/span\u003e\u003c/span\u003e, which quantize the relationship of two control. It is worth mentioning that we should choose the cross-walk model according to different scenarios, which is not necessarily linear regression model. Besides, in the majority systematic review study, ORs might be pooled without considering their distinction or quantitative adjustment, which may cause some bias, and cross-walk model is a good attempt to quantify data relationship. The important condition of cross-walk model is that two or more definitions or consumption frequencies must collected, which allows them to unitize and adjust. This study is an exploration for adjusting the ORs of different kinds of control, which may provide a reference for case-control study.\u003c/p\u003e\u003cp\u003eDespite of the data source was deaths data during 1986 to 1988, it remains the only national resource on this topic that includes DC and SC information, and the OR of smoking attributed to diseases was stable, as the previous study showed[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Therefore, we cannot neglect the distinguishment, especially the quantitative relationship of DC and SC in case-control study, which may affect risk estimates. In the meantime, this study provided an adjustable method for two definitions, which will provide methodological reference for further research. In the future, more adjusted models can be used to explore the relationships between different controls.\u003c/p\u003e\u003cp\u003e\u003cem\u003eLimitation and strength\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThis study has several limitations. Firstly, due to the limitations of the data, the selected models were lack of further verification. Secondly, the information of smoking cessation status was not collected in the survey. Fortunately, the data perfectly included two types of control, and the OR was relatively stable in the past decades. Unlike previous applications of cross-walk models in GBD research, our study innovatively utilizes this approach to quantify the relationship between different types of controls in case-control studies. This methodological exploration provides a new perspective for data harmonization in systematic reviews and meta-analyses.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{OR}_{sc}\\)\u003c/span\u003e\u003c/span\u003e are commonly larger than \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{OR}_{dc}\\)\u003c/span\u003e\u003c/span\u003e in the same group. Thus, when selecting control groups in case-control studies, differences between control types should be carefully considered, particularly in terms of their quantitative relationships. This study provided an adjustable method for two definitions, which will provide methodological reference for further research.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDisease control\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSpouse control\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGBD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGlobal Burden of Disease\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eOdds Ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eRR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eRelative Risk\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHazard ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eUCOD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eUnderlying cause of death\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eICD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eInternational Statistical Classification of Diseases and Related Health Problems\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eConfidence Interval\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eRMSE\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eRoot Mean Squared Error\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMAE\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMean Absolute Error\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAIC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAkaike's Information Criteria\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBIC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBayesian information criteria\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cb\u003eEthic Approval\u003c/b\u003e\u003c/p\u003e\u003cp\u003eRetrospective information collected was limited to smoking exposure only, and oral informed consent for this study was obtained. Ethical approval from the INSTITUTIONAL REVIEW BOARD of Chinese Academy of Medical Sciences was obtained[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eClinical trial number\u003c/b\u003e\u003c/p\u003e\u003cp\u003eNot Applicable.\u003c/p\u003e\n\u003cp\u003e\u003ch2\u003eConsent for publication\u003c/h2\u003e\u003cp\u003eNot Applicable.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003e(1) State Key Laboratory Special Fund (2060204);\u003c/p\u003e\u003cp\u003e(2) Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences (2023-I2M-2-001);\u003c/p\u003e\u003cp\u003e(3) Strengthen Capacity of Study and Application on the Burden of Disease in Health Care Systems in China: Establishment and Development of Chinese Burden of Disease Research and Dissemination Center (15\u0026ndash;208) supported by the China Medical Board (CMB).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eZMC organized and analyzed the data, interpreted the results and drafted the manuscript. XNZ and XW supported the data acquisition process. XW designed the study, guided its implementation. XZ and XW helped with the interpretation of the results and revised the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe thank Prof. Gonghuan Yang for her valuable advice during the study design and the data analysis.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data is not publicly available, but is available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHiggins JPT, Green S. Cochrane Handbook for Systematic Reviews of Interventions (Version 5.1.0). The Cochrane Collaboration; 2011.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNg M, Freeman MK, Fleming TD, Robinson M, Dwyer-Lindgren L, Thomson B, et al. Smoking prevalence and cigarette consumption in 187 countries, 1980\u0026ndash;2012. JAMA. 2014;311(2):183\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eForouzanfar MH, Alexander L, Anderson HR, Bachman VF, Biryukov S, Brauer M, et al. Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks in 188 countries, 1990\u0026ndash;2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet. 2015;386(10010):2287\u0026ndash;323.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGBD 2019 Risk Factors Collaborators. Global burden of 87 risk factors in 204 countries and territories, 1990\u0026ndash;2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396(10258):1223\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSetia MS. Methodology Series Module 2: Case-control Studies. Indian J Dermatol. 2016;61(2):146\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJiang JM, Zeng XJ, Chen JS, Li JY, Zhang KL, Wu YP, et al. Smoking and mortality from esophageal cancer in China: a large case-control study of 19,734 male esophageal cancer deaths and 104,846 living spouse controls. Int J Cancer. 2006;119(6):1427\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChang ET, Liu Z, Hildesheim A, Liu Q, Cai Y, Zhang Z, et al. Active and Passive Smoking and Risk of Nasopharyngeal Carcinoma: A Population-Based Case-Control Study in Southern China. Am J Epidemiol. 2017;185(12):1272\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJiang J, Liu B, Sitas F, Zeng X, Chen J, Han W, et al. Case-spouse control design in practice: an experience in estimating smoking and chronic obstructive pulmonary disease deaths in Chinese adults. J Formos Med Assoc. 2010;109(5):369\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLewandowska A, Lewandowski T, Zych B, Papp K, Zrubcov\u0026aacute; D, Ejder Apay S, et al. Risk Factors for the Diagnosis of Lung Cancer in Poland: A Large-Scale, Population-Based Case-Control Study. Asian Pac J Cancer Prev. 2022;23(10):3299\u0026ndash;307.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJiang J, Liu B, Nasca PC, Han W, Zou X, Zeng X, et al. Comparative study of control selection in a national population-based case-control study: Estimating risk of smoking on cancer deaths in Chinese men. Int J Med Sci. 2009;6(6):329\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhao J, Shi YL, Wang YT, Ai FL, Wang XW, Yang WY, et al. Lung Cancer Risk Attributable to Active Smoking in China: A Systematic Review and Meta-Analysis. Biomed Environ Sci. 2023;36(9):850\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu BQ, Peto R, Chen ZM, Boreham J, Wu YP, Li JY, et al. Emerging tobacco hazards in China: 1. Retrospective proportional mortality study of one million deaths. BMJ. 1998;317(7170):1411\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGBD 2015 Tobacco Collaborators. Smoking prevalence and attributable disease burden in 195 countries and territories, 1990\u0026ndash;2015: a systematic analysis from the Global Burden of Disease Study 2015. Lancet. 2017;389(10082):1885\u0026ndash;906.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuang J, Yue N, Shi N, Wang Q, Cui T, Ying H, et al. Influencing factors of lung cancer in nonsmoking women: systematic review and meta-analysis. J Public Health (Oxf). 2022;44(2):259\u0026ndash;68.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHackshaw AK. Lung cancer and passive smoking. Stat Methods Med Res. 1998;7(2):119\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTaylor R, Najafi F, Dobson A. Meta-analysis of studies of passive smoking and lung cancer: effects of study type and continent. Int J Epidemiol. 2007;36(5):1048\u0026ndash;59.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSandler DP, Everson RB, Wilcox AJ. Passive smoking in adulthood and cancer risk. Am J Epidemiol. 1985;121(1):37\u0026ndash;48.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJ\u0026uuml;rgens V, Ess S, Schwenkglenks M, Cerny T, Vounatsou P. Using lung cancer mortality to indirectly approximate smoking patterns in space. Spat Spatiotemporal Epidemiol. 2015;14\u0026ndash;15:23\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHou L, Han W, Jiang J, Liu B, Wu Y, Zou X, et al. Passive smoking and stroke in men and women: a national population-based case-control study in China. Sci Rep. 2017;7:45542.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Smoking, Lung cancer, Disease control, Spouse control, Cross-walk model","lastPublishedDoi":"10.21203/rs.3.rs-7772697/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7772697/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDisease control (DC) and spouse control (SC) are commonly used in case-control study. The exposure risk of these two kinds of control may be different, especially in tobacco exposure status which attribute to diseases. However, few study concentrates on the association of DC and SC. Based on this situation, this study aims to explore the quantitative relationship of DC and SC for lung cancer.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, the data of cases, DC and SC were collected from the National Retrospective Survey of Deaths, including one million deaths during 1986 to 1988. Referring to cross-walk model, four forms of model, stratified by urban/rural areas and genders, were established to explore the suitable one, for adjusting the Odds Ratio (OR) of DC \u003cem\u003eOR\u003c/em\u003e\u003csub\u003e\u003cem\u003e dc\u003c/em\u003e\u003c/sub\u003e and SC \u003cem\u003eOR\u003c/em\u003e\u003csub\u003e\u003cem\u003esc\u003c/em\u003e\u003c/sub\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 51217 cases, 29871 SCs, and 215018 DCs were collected, among which the male accounted for 68.00%, 24.60% and 54.86%, respectively. Accordingly, the smoking prevalence were 66.83%, 24.39%, 40.21%. The case had the highest smoking rate, following by DC and SC in both genders of urban and rural area. In both urban and rural areas, the \u003cem\u003eOR\u003c/em\u003e\u003csub\u003e\u003cem\u003esc\u003c/em\u003e\u003c/sub\u003e of males and females were higher than \u003cem\u003eOR\u003c/em\u003e\u003csub\u003e\u003cem\u003edc\u003c/em\u003e\u003c/sub\u003e. The \u003cem\u003eOR\u003c/em\u003e\u003csub\u003e\u003cem\u003esc\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eOR\u003c/em\u003e\u003csub\u003e\u003cem\u003edc\u003c/em\u003e\u003c/sub\u003e value gradually increases with the age group, reaching its maximum at 60–64 in urban area and 55–59 in rural area. After establishing the cross-walk model, in urban area, \u003cem\u003elogit\u003c/em\u003e(\u003cem\u003eOR\u003c/em\u003e\u003csub\u003e\u003cem\u003esc\u003c/em\u003e\u003c/sub\u003e)=1.33 \u003cem\u003elogit\u003c/em\u003e(\u003cem\u003eOR\u003c/em\u003e\u003csub\u003e\u003cem\u003edc\u003c/em\u003e\u003c/sub\u003e) and \u003cem\u003elogit\u003c/em\u003e(\u003cem\u003eOR\u003c/em\u003e\u003csub\u003e\u003cem\u003esc\u003c/em\u003e\u003c/sub\u003e)=-0.34+1.35 \u003cem\u003elogit\u003c/em\u003e(\u003cem\u003eOR\u003c/em\u003e\u003csub\u003e\u003cem\u003edc\u003c/em\u003e\u003c/sub\u003e) were used for males and females, respectively. In rural area, \u003cem\u003elogit\u003c/em\u003e(\u003cem\u003eOR\u003c/em\u003e\u003csub\u003e\u003cem\u003esc\u003c/em\u003e\u003c/sub\u003e) =1.09 \u003cem\u003elogit\u003c/em\u003e(\u003cem\u003eOR\u003c/em\u003e\u003csub\u003e\u003cem\u003edc\u003c/em\u003e\u003c/sub\u003e) and \u003cem\u003elogit\u003c/em\u003e(\u003cem\u003eOR\u003c/em\u003e\u003csub\u003e\u003cem\u003esc\u003c/em\u003e\u003c/sub\u003e) =1.15 \u003cem\u003elogit\u003c/em\u003e(\u003cem\u003eOR\u003c/em\u003e\u003csub\u003e\u003cem\u003edc\u003c/em\u003e\u003c/sub\u003e) were used for males and females, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eOR\u003c/em\u003e\u003csub\u003e\u003cem\u003esc\u003c/em\u003e\u003c/sub\u003e are commonly larger than \u003cem\u003eOR\u003c/em\u003e\u003csub\u003e\u003cem\u003edc\u003c/em\u003e\u003c/sub\u003e in the same urban/rural, gender and age group. Thus, when different types of control are used in the design or analysis of case-control study, the distinction of controls should be considered, especially the quantitative relationship. This study provided an adjustable method for two definitions, which will provide methodological reference for further research.\u003c/p\u003e","manuscriptTitle":"Estimating the Quantitative Relationship of Disease Control and Spouse Control for Lung Cancer in Case-control Study by Cross-walk Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-18 08:40:55","doi":"10.21203/rs.3.rs-7772697/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-10T07:46:37+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-09T03:42:36+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-09T02:38:18+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-07T10:42:59+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-07T08:15:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"18554155046355526319721873753743031765","date":"2025-11-07T08:11:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"330215620431378278977019031982424221282","date":"2025-11-06T10:25:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"216001808651637417694622850719804450285","date":"2025-11-06T10:11:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"50557228665532251830025898058406522977","date":"2025-11-06T10:07:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"304022546186461353776123074974956818075","date":"2025-11-06T09:04:22+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-06T08:21:43+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-04T07:39:13+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-30T03:37:04+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-22T15:16:12+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Oncology","date":"2025-10-22T14:29:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3343af85-bfec-4ca4-ab8e-2d65a5bd9b55","owner":[],"postedDate":"November 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-13T09:24:30+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-18 08:40:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7772697","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7772697","identity":"rs-7772697","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}