Mediating effect of smoking on the relationship between educational status and hospitalization in China with COVID-19: a cross-sectional study

Mediating effect of smoking on the relationship between educational status and hospitalization in China with COVID-19: a cross-sectional study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Mediating effect of smoking on the relationship between educational status and hospitalization in China with COVID-19: a cross-sectional study Zhenxiao Huang, Yinghua Li, Zheng Su, Ying Xie, Zhao Liu, Rui Qin, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4863541/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Educational disparities in COVID-19 outcomes are well documented in Western countries, but evidence from China is limited. This study explored the role of smoking in these disparities. Methods: We conducted a cross-sectional study in China between January 28 and February 21, 2023. Participants who reported positive SARS-CoV-2 results via RT‒PCR and/or IgM‒IgG antigen tests provided self-reported data on COVID‒19 hospitalization, educational status, and smoking status through an online questionnaire. Logistic regressions with and without inverse probability weighting estimated odds ratios (ORs) for COVID-19 hospitalization by educational status, adjusting for potential confounders. The Karon‒Holm‒Breen (KHB) method was used to estimate the proportion of mediation attributable to smoking status. Results: Of the 25,827 participants (56.4% male; mean age 40.9 years), 1652 (6.83%) were hospitalized, and higher ORs for COVID-19 hospitalization were found in those with primary school education or below (OR: 1.84), junior high school (OR: 1.48), high school/technical secondary school (OR: 1.44), and smoking history (OR: 1.52). Smoking mediated the relationship between educational status and hospitalization, with mediation proportion ranging from 7.62% to 13.34%, varying by sex. Conclusion: This study highlights an educational gradient in COVID-19 hospitalization in China, with smoking as a partial mediator . COVID-19 hospitalization educational status smoking behavior mediation effect health inequity Figures Figure 1 Figure 2 Introduction Educational inequalities are evident in many disease outcomes[ 1 ], with individuals with elementary or lower education levels experiencing nearly double the mortality rate from infectious diseases compared with those with higher education levels[ 2 ]. This trend extends to COVID-19, where studies from Europe and the USA have shown that lower educational attainment is correlated with higher rates of SARS-CoV-2 infection[ 3 – 5 ], COVID-19 hospitalization[ 4 – 6 ], mortality[ 7 ], and excess mortality[ 8 , 9 ]. Despite these findings, few studies have explored these disparities in Asian countries. One study in Japan produced inconsistent results compared with Western studies[ 10 ], and only one Chinese study has examined education level differences in COVID-19 prevention behavior[ 11 ], leaving a gap in understanding how education influences COVID-19 outcomes in China. Tobacco use is a known risk factor that exacerbates the severity of COVID-19 outcomes. Smoking increases the risk of contracting SARS-CoV-2[ 12 ], requiring hospitalization[ 13 ], and dying from COVID-19[ 14 , 15 ].A large cohort study in China revealed that smoking significantly increases the risk of numerous diseases[ 16 ], which could worsen the prognosis following a COVID-19 infection. According to the Fundamental Cause Theory[ 8 , 17 ], education impacts exposure to COVID-19 and disease severity through factors such as health behaviors and literacy. There are well-documented educational disparities in smoking behavior across different countries[ 18 – 20 ], indicating that smoking could be a critical factor linking educational status and COVID-19 outcomes. Therefore, controlling tobacco use is a vital public health intervention to address these disparities. Understanding the role of smoking in the relationship between education and COVID-19 outcomes can inform strategies to reduce the incidence and severity of COVID-19 and other respiratory infections. The "New 10 Epidemic Prevention Policy" in China and the subsequent surge in infections provide a unique opportunity to examine the social determinants of COVID-19 vulnerability. We hypothesize that educational status is related to COVID-19 hospitalization risk, with smoking behavior serving as a significant factor. By targeting smoking in public health interventions, we can promote health equity and mitigate the impact of COVID-19. Methods Study population An online survey was conducted between January 28 and February 21, 2023, one month after the peak number of COVID-19 infections. A recruitment poster was widely distributed through research group members and the social platform WeChat via a web link. The participants were informed of a chance to receive a modest financial incentive upon completion. The inclusion criteria were Chinese, normal cognitive function, positive SARS-CoV-2 results via RT‒PCR and/or IgM‒IgG antigen tests, age 20–69 years, and informed consent. The exclusion criteria were self-assessed COVID-19 infection on the basis of key symptoms and duplicate questionnaire submissions. A total of 70,879 completed questionnaires were received online, but 45,052 were excluded because of duplication issues (4,342) or symptom-based diagnosis (40,710). Finally, 25,827 participants were included in the analysis. The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board (or Ethics Committee) of China-Japan Friendship Hospital (number: 2022-KY-183-1, FEB. 2023). The questionnaire The questionnaire was independently reviewed by three experts in tobacco medicine and was accepted by all coresearchers after four revisions. The participants were required to answer each question to proceed, and the system automatically redirected inapplicable questions, ensuring comprehensive responses. The participants could revise their answers before submission. This study adhered to the Checklist for Reporting Results of internet E-Surveys (CHERRIES) guidelines. The online questionnaire, developed via the “Questionnaire Star” platform, included the following questions: sex (male, female), age (≤ 29, 30–49, 50–64, ≥ 65), education (primary school or below, junior high, high school/technical secondary, junior college, college or above), average personal monthly income over the past year (≤ 2999 RMB, 3000–5999 RMB, 6000–9999 RMB, ≥ 10000 RMB), residency (urban, rural), health insurance coverage (yes, no), COVID-19 vaccination status (yes, no), COVID-19 hospitalization (yes, no), and comorbidities such as cardiovascular diseases, diabetes (types I/II), chronic respiratory diseases, digestive system diseases, chronic liver diseases, chronic kidney diseases, cancer, hematological diseases, allergic diseases, and depression/anxiety (Appendix 1). Statistical analysis The age distributions of the overall sample and subgroups were generally normal and are presented as the means ± standard deviations. Categorical variables are presented as numbers (percentages). Student's t test was used for continuous variables, and the χ2 test was used for categorical variables. Logistic regression was used to assess the association between educational status and COVID-19 hospitalization. Model 1 was adjusted for educational status alone, Model 2 included all the covariables except the potential mediating variable (smoking history), and Model 3 added smoking history to Model 2. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated. Baron and Kenny’s method[ 21 ]was used to examine the associations among educational status, smoking behavior (smoking history/status), and COVID-19 hospitalization. Here, the independent variable (educational status) was denoted X, the dependent variable (COVID-19 hospitalization) was Y, and the mediating variable (smoking history/status) was M. The Karson‒Holm‒Breen (KHB) method(22) was used to calculate the percentage of mediation contributed by smoking history/status. Considering sex differences in smoking patterns and health impacts(23), mediation analyses were performed separately for each sex group and adjusted for all covariates. To reduce selection bias from the online survey, an unstable inverse probability weighting (IPW) method was applied as a sensitivity analysis to control for potential confounders. In this approach, the inverse probability of different educational statuses was calculated via multivariable logistic regression, with educational status as the dependent variable and all covariates, including smoking history, as independent variables. Each participant was weighted by the inverse probability of their educational status, giving higher weight to underrepresented observations, thus mitigating selection bias. This weighted regression model then estimated the association between educational status and COVID-19 hospitalization. Missing age data were imputed via multiple imputation methods (R package "MASS", random seeds: 1234). Data analyses were conducted via STATA (version 17) and R (version 4.2.1). All p values were two-sided, with statistical significance set at P < 0.05. Results Characteristics of the sample Among the 25,827 respondents, 56.4% (14,577) were male, with a mean age of 40.9 (SD 11.7) years. A total of 1,652 (6.83%) patients were hospitalized due to COVID-19. Among the 7,042 (27.3%) participants with a smoking history, 5,917 (23.0%) were current smokers, and 1,125 (4.3%) were former smokers. The respondents were distributed regionally as follows: 12,719 (49.2%) from East China, 6,569 (25.4%) from Central China, 3,433 (13.3%) from West China, 3,082 (12.0%) from Northeast China, and 24 (0.1%) from other regions. Compared with those not hospitalized for COVID-19, hospitalized individuals were more likely to be male, have a lower educational status, live in rural areas, be unvaccinated, have at least one comorbidity, and smoke (Table 1 ). Association of COVID-19 hospitalization with educational status and smoking history/smoking status In Model 1, a significant increase in the risk of COVID-19 hospitalization was observed with decreasing educational status. This trend remained consistent in Model 2, which included additional covariables. After we adjusted for smoking history in Model 3, participants with high school/technical secondary education (OR, 1.44; 95% CI, 1.24–1.66), junior high school education (OR, 1.48; 95% CI, 1.25–1.76), and primary school education or below (OR, 1.84; 95% CI, 1.46–2.31) still had a significantly greater risk of hospitalization than did those with a college education or above, although all ORs slightly decreased compared with those in Model 2 (Supplementary Table 2, Supplementary Fig. 2). Model 3 also revealed a significant association between smoking history and COVID-19 hospitalization (OR, 1.52; 95% CI, 1.35–1.70). Replacing smoking history with smoking status was associated with a significant risk for ex-smokers (OR, 1.63; 95% CI, 1.32–2.00) and current smokers (OR, 1.50; 95% CI, 1.33–1.69), with nonsmokers as the reference group. Mediation analysis As shown in Fig. 1A2 , educational status was positively associated with smoking history ( path a ), with the highest educational status used as the reference and controlling for all covariates. For path b , after adjusting for covariates and educational status, smoking history was linked to increased COVID-19 hospitalization. For path c (Fig. 1A1) , the education group, with the exception of junior college, had a greater hospitalization risk than did the college education group or above (P < 0.001). Introducing smoking history into the model (path c’ in Fig. 1A2) reduced the corresponding ORs between educational status and hospitalization, indicating a partial mediating effect of smoking history on the relationship between educational status and COVID-19 hospitalization. Similar results are shown in Fig. 1A3 when smoking behavior was analysed in terms of smoking status. The mediating effect of smoking history was most significant between the junior high school and college groups (13.34%), followed by the high school/technical secondary school group (11.70%), and it was lowest in the primary group (7.62%) (Table 2) . The share of "current smokers" in the mediation effect ranged from 80.32–89.97% across all educational status comparisons. Among females, the mediating contribution of smoking history ranged from 14.33–21.20% ( Supplementary Table 2 ), whereas it ranged from 4.46–9.08% among males ( Supplementary Table 3 ). Across all educational status groups, smoking history had a more significant mediating effect for females than for males (Fig. 2 ). Sensitivity analysis The characteristics of the participants across different educational statuses are shown in Supplementary Table 4. Significant differences were found in monthly income, residence, health insurance, vaccination status, likelihood of cardiovascular disease or diabetes (I/II), and smoking history among educational levels (SMD > 10%). After IPW adjustment, participants were well matched on all observed characteristics, with an absolute standardized difference of less than 10% (Supplementary Figure F3). Logistic regression after IPW revealed that educational status remained significantly associated with COVID-19 hospitalization, which is consistent with previous findings (Supplementary Table 5). Discussion This study demonstrated the significant role of smoking behavior in COVID-19-related educational inequality among SARS-CoV-2-infected individuals in China. This study revealed a strong association between lower educational attainment and increased odds of COVID-19 hospitalization. Smoking behavior significantly mediated this association, with effects varying by sex. Specifically, individuals with a lower educational status had higher rates of smoking history or current smoking, increasing their risk of hospitalization. Addressing this knowledge gap, our findings suggest that public health initiatives focused on tobacco control could help mitigate educational disparities in COVID-19 outcomes. There is currently little evidence to explain educational differences in COVID-19 outcomes in terms of smoking behavior. In one study, the association between education and hospitalization due to COVID-19 was partially attenuated by lifestyle factors, which included smoking[ 6 ]. Nevertheless, the mediating effect of smoking is still unclear. The differentiation of educational attainment concerning smoking and resulting health inequalities has been identified across multiple countries[ 22 ]. Individuals aged 35–69 years from disadvantaged groups, including those with low levels of education, presented higher mortality rates due to smoking[ 23 ]. Tobacco use accounts for approximately half of the difference in mortality within this age range due to socioeconomic status [ 24 ]. A study indicated that there are educational disparities in the reduction in smoking prevalence among male adults in China across different cohorts[ 25 ]. This divergence in trends has resulted in a significant increase in educational disparities in smoking behavior among recent cohorts and plateau in the overall decline in smoking prevalence[ 25 ]. These findings underscore the importance of examining education disparities in smoking and assessing inequities in the burden of disease attributable to smoking in China. Smoking behavior has been demonstrated to act as a mediator in the relationship between educational attainment and diseases such as coronary heart disease [ 26 , 27 ]. Our findings on COVID-19 hospitalization indicated that smoking had a mediating effect ranging from 7.62–13.34%, which was smaller than the effects reported in the studies on specific chronic diseases or cancers. This difference may be attributed to different disease outcomes, assessment indicators, and measurement methods of education. Importantly, our findings provide critical insights into the role of smoking in the educational stratification of diseases in China, particularly highlighting its detrimental effects on COVID-19 outcomes. A recent review confirmed that current and former smokers infected with SARS-CoV-2 face increased risks of hospitalization, severity, and mortality compared with those who never smoked[ 28 ]. This underscores the significant public health challenge posed by smoking, particularly as a pathway that exacerbates health disparities among different educational groups. Following the World Health Organization (WHO) declaring COVID-19 as an ongoing health issue rather than a public health emergency[ 29 ], it is an opportune time to focus on mitigating factors that increase vulnerability. Our research emphasizes the urgent need for robust tobacco control policies and smoking cessation programs, especially those that target less educated populations. These interventions are essential not only for reducing the burden of COVID-19 but also for addressing broader health disparities exacerbated by smoking. By prioritizing these measures, we can better protect vulnerable groups from current and future public health challenges. Our study also revealed that the mediating effect of smoking was more pronounced in females than in males. Although the smoking prevalence among women in China is much lower than that among men, recent national surveys have shown a higher smoking prevalence ratio between the lowest and highest education levels in females than in males[ 30 , 31 ], suggesting that educational disparities have a more pronounced impact on smoking behavior in women. Compared with 35.1% of males, 59.6% of females with tobacco dependence have a primary school education or below[ 32 ]. These findings underscore the need for targeted public health interventions aimed at low-educated female smokers, who represent a particularly vulnerable group. Effective interventions and tobacco control policies should prioritize education and support specifically tailored for low-educated female smokers, providing them with resources and assistance in quitting smoking. Such targeted efforts are essential for reducing health disparities and promoting equity in health outcomes, particularly in the context of ongoing and future public health challenges such as COVID-19. By addressing the unique needs of this population, tobacco control initiatives can make significant strides in reducing the burden of tobacco-related diseases and improving overall community health. We also found that ex-smokers had a greater risk of COVID-19 hospitalization than did current smokers. This increased risk in ex-smokers may be due to diseases caused by previous smoking, which can persist for years after cessation. Studies have indicated that smokers often quit smoking due to smoking-related diseases, suggesting that ex-smokers might have poorer health than current smokers do [ 33 ]. Our data revealed that ex-smokers had significantly greater proportions of patients with chronic respiratory diseases, chronic liver diseases, and cancers than did current smokers (P < 0.05). Additionally, individuals who quit smoking immediately before or after hospitalization due to severe COVID-19 symptoms might be recorded as ex-smokers, introducing reverse causality. Last, collider bias might explain this finding, where conditioning on a collider (such as testing or hospitalization) could create an artificial link between smoking status and adverse COVID-19 outcomes[ 34 ]. Our findings revealed that ex-smokers had a greater risk of COVID-19 hospitalization than didi current smokers. This increased risk among ex-smokers may be attributed to the long-term effects of diseases caused by previous smoking, which can persist even after cessation. Studies suggest that many smokers quit smoking due to smoking-related diseases, indicating that ex-smokers might generally have poorer health than current smokers[ 33 ]. Our data revealed significantly greater proportions of chronic respiratory diseases, chronic liver diseases, and cancer among ex-smokers than among current smokers (P < 0.05). Additionally, individuals who quit smoking immediately before or after hospitalization for severe COVID-19 symptoms might be classified as ex-smokers, potentially introducing reverse causality. Moreover, collider bias could also explain this finding, where conditioning on factors such as testing or hospitalization could artificially link smoking status with adverse COVID-19 outcomes[ 34 ]. These insights underscore the importance of targeted smoking cessation programs, especially for those with existing health conditions. By addressing the unique health challenges faced by ex-smokers and supporting current smokers in quitting, public health initiatives can enhance overall health outcomes and progress toward tobacco control goals. Several potential limitations of our study should be noted. First, our research sample was obtained through an online survey. Hence, the potential bias of this methodology may be inherent in our study. For example, people with the lowest income and illiteracy may be excluded from the survey[ 35 ]. Although our study subjects were distributed throughout the Chinese mainland, the average education level of the sample may be higher than the national average because of the data collection method. Considering the issue of sample representativeness, caution should be exercised when generalizing the research findings to populations with lower education levels. However, we performed a sensitivity analysis of an inverse-probability weighted method to eliminate the selection bias. Second, in our retrospective survey, self-reported information regarding COVID-19 hospitalizations may pose a greater risk of misreporting than medical records do. Nevertheless, we implemented rigorous measures for data quality control. Third, certain confounding factors, such as COVID-19 medications, treatment, and proximity to healthcare facilities, could not be collected in the questionnaire. Finally, our results do not establish causality and, more importantly, do not guarantee that controlling smoking among individuals with lower educational status will necessarily reduce their risk of contracting COVID-19 and other pandemics. However, our findings suggest that this scenario is plausible. Therefore, extending alternative approaches, such as Mendelian randomization, could be employed to further clarify the causality. Conclusion By conducting an online survey during the COVID-19 surge in mainland China, we identified an educational gradient in COVID-19 hospitalization among SARS-CoV-2-infected individuals. We also demonstrated that smoking behavior mediated the relationship between educational status and COVID-19 hospitalization, with notable sex differences. Individuals with lower education levels face an elevated risk of smoking, increasing their likelihood of COVID-19 hospitalization. These findings highlight the importance of targeted health promotion and smoking cessation interventions, especially for women with low education levels. Strengthening smoking cessation efforts can increase population resilience against respiratory pandemics such as COVID-19 and reduce health disparities. Table 1 Baseline characteristics of participants by COVID-19 hospitalization under the analytic sample Characteristics Non COVID-19 Hospitalization (N = 24175) COVID-19 Hospitalization (N = 1652) P value Total (N = 25827) Age, No. (%), y 20–29 4239 (17.5) 296 (17.9) .35 4535 (17.6) 30–49 14346 (59.3) 998 (60.3) 15344 (59.4) 50–64 4758 (19.7) 296 (18.0) 5054 (19.6) ≥ 65 832 (3.5) 62 (3.8) 894 (3.4) Age, Mean (SD),y 40.9 (11.7) 40.6 (11.7) .30 40.9 (11.7) Sex, No. (%) Male 13506 (55.9) 1071 (64.8) < .001 14577 (56.4) Female 10669 (44.1) 581 (35.2) 11250 (43.6) Educational status, No. (%) College or above 8943 (37.0) 438 (26.6) < .001 9381 (36.3) Junior college 5937 (24.6) 364 (22.0) 6301 (24.4) High school/ Technical secondary school 5482 (22.6) 456 (27.6) 5938 (23.0) Junior high school 2855 (11.8) 268 (16.2) 3123 (12.1) Primary school or below 958 (4.0) 126 (7.6) 1084 (4.2) Salary, No. (%) (RMB/M) a ≥ 10000 6985 (28.9) 500 (30.3) .186 7485 (29.0) 6000–9999 9288 (38.4) 653 (39.5) 9941 (38.5) 3000–5999 5587 (23.1) 346 (21.0) 5933 (23.0) ≤ 2999 2315 (9.6) 153 (9.2) 2468 (9.5) Residency, No. (%) Urban area 17451 (72.2) 1132 (68.5) < .001 18583 (72.0) Rural area 6724 (27.8) 520 (31.5) 7244 (28.0) Insurance, No. (%) Yes 23289 (96.3) 1592 (96.4) .95 24881 (96.3) No 886 (3.7) 60 (3.6) 946 (3.7) Vaccine, No. (%) Yes 21006 (86.9) 1298 (78.6) < .001 22304 (86.4) No 3169 (13.1) 354 (21.4) 3532 (13.6) Cardiovascular diseases, No. (%) Yes 3226 (13.3) 436 (26.4) < .001 3662 (14.2) No 20949 (86.7) 1216 (73.6) 22165 (85.8) Diabetes (Type I/II), No. (%) Yes 21556 (89.2) 1272 (77.0) < .001 22828 (88.4) No 2619 (10.8) 380 (23.0) 2999 (11.6) Table 1 Baseline characteristics of participants by COVID-19 hospitalization under the analytic sample(continued) Characteristics Non COVID-19 Hospitalization (N = 24175) COVID-19 Hospitalization (N = 1652) P value Total (N = 25827) Chronic respiratory diseases, No. (%) Yes 21094 (87.3) 1283 (77.7) < .001 22377 (86.6) No 3081 (12.7) 369 (22.3) 3450 (13.4) Digestive system diseases, No. (%) Yes 2182 (9.1) 236 (14.3) < .001 2418 (9.4) No 21993 (90.9) 1416 (85.7) 23409 (90.6) Chronic liver diseases, No. (%) Yes 1276 (5.3) 147 (8.9) < .001 1423 (5.5) No 22899 (94.7) 1505 (91.1) 24404 (94.5) Chronic kidney diseases, No. (%) Yes 730 (3.0) 117 (7.1) < .001 847 (3.3) No 23445 (97.0) 1535 (92.9) 24980 (96.7) Cancer, No. (%) Yes 612 (2.5) 76 (4.6) < .001 688 (2.7) No 23563 (97.5) 1576 (95.4) 25139 (97.3) Hematological diseases, No. (%) Yes 478 (2.0) 59 (3.6) < .001 537 (2.0) No 23697 (98.0) 1593 (96.4) 25290 (98.0) Allergic diseases, No. (%) Yes 1419 (5.9) 128 (7.8) .002 1547 (6.0) No 22756 (94.1) 1524 (92.3) 24280 (94.0) Depression/anxiety, No. (%) Yes 1322 (5.4) 140 (8.5) < .001 1462 (5.7) No 22853 (94.6) 1512 (91.5) 24365 (94.3) Smoking history, No. (%) No 17829 (73.8) 956 (57.9) < .001 18785 (72.7) Yes 6346 (26.2) 696 (42.1) 7042 (27.3) Smoking status, No. (%) Non-smoker 17829 (73.8) 956 (57.9) < .001 18785 (72.7) Ex-smoker 1001 (4.1) 124 (7.5) 1125 (4.3) Current smoker 5345 (22.1) 572 (34.6) 5917 (23.0) a average personal monthly income over the past year Table 2. Total, mediation, and direct effects of educational status through smoking history on COVID-19 hospitalization, and the proportion of total effect mediated (%) Total effects a Mediation effects through smoking history b Direct effects c (OR, 95% CI) P value (OR, 95% CI) P value Mediation (%) d (OR, 95% CI) P value Educational status College or above 1[reference] NA 1[reference] NA 1[reference] NA Junior college 1.15 (1.00,1.34) .05 NA NA 1.12 (0.97,1.30) .10 High school/ Technical secondary school 1.50 (1.30,1.74) <.001 1.05 (1.03,1.07) <.001 11.70 1.43 (1.24,1.66) <.001 Junior high school 1.58 (1.33,1.87) <.001 1.06 (1.04,1.09) <.001 13.34 1.48 (1.25,1.76) <.001 Primary school or below 1.95 (1.55,2.44) <.001 1.05 (1.03,1.07) <.001 7.62 1.85 (1.47,2.32) <.001 Abbreviation: CI, confidence interval; OR, odds ratio; NA, not applicable. a Total effect was the effect of educational status on COVID-19 hospitalization adjusted for all the covariates except for smoking history. b Mediation effect was the effect of educational status on COVID-19 hospitalization through smoking history. c Direct effect was the effect of educational status on COVID-19 hospitalization adjusted for all the covariates including smoking history. d Mediation (%) was calculated by mediation effect/total effect ´100%. Declarations Supplementary material Supplementary material is available at Tobacco control online. Authors’ contributions Conceptualization, CW, and DX; Methodology, ZXH.; Software, ZXH; Validation, ZXH, ZS, and XX; Formal Analysis, ZXH; Investigation, YHL, ZXH, ZS, XX, XMZ, YL, XX, QQS, JXL, RQ, ZL, AQC, LZ, and XWW; Resources, CW and DX; Data Curation, AQC and LZ; Writing – Original Draft Preparation, ZXH; Writing – Review & Editing, ZL, KFC, DX, and CW; Visualization, ZXH; Supervision, DX and CW; Project Administration, LZ and DX; Funding Acquisition, DX and CW. Funding This work was supported by the China Zhongguancun Precision Medicine Science and Technology Foundation (2020-HX-3) and the National High-Level Hospital Clinical Research Funding of China (2022-NHLHCRF-LX-01). Acknowledgment We would like to thank the countless participants of this COVID-19 study. Conflict of interest statement All the authors declare no conflict of interest. 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David A, Esson K, Perucic AM, Fitzpatrick C, Blas E, Kurup AS: Tobacco use: equity and social determinants . 2010. Jha P, Peto R, Zatonski W, Boreham J, Jarvis MJ, Lopez AD: Social inequalities in male mortality, and in male mortality from smoking: indirect estimation from national death rates in England and Wales, Poland, and North America . Lancet 2006, 368 (9533):367-370. Jin L, Tao L, Lao X: Diverging Trends and Expanding Educational Gaps in Smoking in China . Int J Environ Res Public Health 2022, 19 (8). Zhang YB, Chen C, Pan XF, Guo J, Li Y, Franco OH, Liu G, Pan A: Associations of healthy lifestyle and socioeconomic status with mortality and incident cardiovascular disease: two prospective cohort studies . BMJ 2021, 373 :n604. Tillmann T, Vaucher J, Okbay A, Pikhart H, Peasey A, Kubinova R, Pajak A, Tamosiunas A, Malyutina S, Hartwig FP et al : Education and coronary heart disease: mendelian randomisation study . BMJ 2017, 358 :j3542. Gallus S, Scala M, Possenti I, Jarach CM, Clancy L, Fernandez E, Gorini G, Carreras G, Malevolti MC, Commar A et al : The role of smoking in COVID-19 progression: a comprehensive meta-analysis . Eur Respir Rev 2023, 32 (167). Statement on the fifteenth meeting of the IHR (2005) Emergency Committee on the COVID-19 pandemic [https://www.who.int/news/item/05-05-2023-statement-on-the-fifteenth-meeting-of-the-international-health-regulations-(2005)-emergency-committee-regarding-the-coronavirus-disease-(covid-19)-pandemic] Zhang M, Liu S, Yang L, Jiang Y, Huang Z, Zhao Z, Deng Q, Li Y, Zhou M, Wang L et al : Prevalence of Smoking and Knowledge About the Hazards of Smoking Among 170 000 Chinese Adults, 2013-2014 . Nicotine Tob Res 2019, 21 (12):1644-1651. Liu S, Zhang M, Yang L, Li Y, Wang L, Huang Z, Wang L, Chen Z, Zhou M: Prevalence and patterns of tobacco smoking among Chinese adult men and women: findings of the 2010 national smoking survey . J Epidemiol Community Health 2017, 71 (2):154-161. Liu Z, Li YH, Cui ZY, Li L, Nie XQ, Yu CD, Shan GL, Zhou XM, Qin R, Cheng AQ et al : Prevalence of tobacco dependence and associated factors in China: Findings from nationwide China Health Literacy Survey during 2018-19 . Lancet Reg Health West Pac 2022, 24 :100464. Twardella D, Loew M, Rothenbacher D, Stegmaier C, Ziegler H, Brenner H: The diagnosis of a smoking-related disease is a prominent trigger for smoking cessation in a retrospective cohort study . J Clin Epidemiol 2006, 59 (1):82-89. Griffith GJ, Morris TT, Tudball MJ, Herbert A, Mancano G, Pike L, Sharp GC, Sterne J, Palmer TM, Davey Smith G et al : Collider bias undermines our understanding of COVID-19 disease risk and severity . Nat Commun 2020, 11 (1):5749. Ball HL: Conducting Online Surveys . J Hum Lact 2019, 35 (3):413-417. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4863541","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":336586015,"identity":"37c74b0d-b668-4f7e-880c-716b95905127","order_by":0,"name":"Zhenxiao Huang","email":"","orcid":"","institution":"Chinese Academy of Medical Sciences \u0026 Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Zhenxiao","middleName":"","lastName":"Huang","suffix":""},{"id":336586016,"identity":"de2f24a2-516f-42f4-8690-d795fa44bd62","order_by":1,"name":"Yinghua Li","email":"","orcid":"","institution":"China Health Education Center","correspondingAuthor":false,"prefix":"","firstName":"Yinghua","middleName":"","lastName":"Li","suffix":""},{"id":336586017,"identity":"5aea2c04-9804-42fa-86bd-0d57c4f7756a","order_by":2,"name":"Zheng Su","email":"","orcid":"","institution":"China-Japan Friendship Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zheng","middleName":"","lastName":"Su","suffix":""},{"id":336586018,"identity":"bc843f4b-fd8e-41b7-93eb-15fe63e30163","order_by":3,"name":"Ying Xie","email":"","orcid":"","institution":"Chinese Academy of Medical Sciences \u0026 Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Xie","suffix":""},{"id":336586019,"identity":"7066166a-efcb-45e5-b2e9-9ef89b841b2a","order_by":4,"name":"Zhao Liu","email":"","orcid":"","institution":"China-Japan Friendship 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Xiao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwUlEQVRIiWNgGAWjYDCCA0CcwMAgx8/efoA0LcaSPWeAFIMBkVqAIHHDDQcD4rTwHT97TOJBjU16ww2GBOaCij+EtUieyUs2SDiWlts4u/EA84wzRNhicCDH8EEC2+HcZpkDCcy8bcRoOf/G4EDCv8PpbBIJBsy8/4jRcgNoS2Lb4QQesJYGIrRI3nhjbJDYl2Y4g+dMwmGeY8aEtfCdzzGT/PHNRt7+ePvBxzw1coS1oIADJKofBaNgFIyCUYALAACXjzzaD979JQAAAABJRU5ErkJggg==","orcid":"","institution":"China-Japan Friendship Hospital","correspondingAuthor":true,"prefix":"","firstName":"Dan","middleName":"","lastName":"Xiao","suffix":""},{"id":336586039,"identity":"34c0cd8a-a071-4608-9513-959bf3d8c013","order_by":16,"name":"Chen Wang","email":"","orcid":"","institution":"Chinese Academy of Medical Sciences \u0026 Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Chen","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-08-05 16:54:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4863541/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4863541/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":64249040,"identity":"22cda21b-f6f8-44ca-9fe9-896030e7e7ec","added_by":"auto","created_at":"2024-09-10 20:58:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":80809,"visible":true,"origin":"","legend":"\u003cp\u003eMediation of the association between educational status and COVID-19 hospitalization by smoking history/status\u003c/p\u003e\n\u003cp\u003eNotes: (A1) Model without mediation factors. (A2) Model with mediation factors (smoking history). (A3) Model with mediation factors (smoking status).\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-4863541/v1/06927d9fc358d1171e8c0946.png"},{"id":64249043,"identity":"62914725-6d18-4d04-85e5-dd3423fdf597","added_by":"auto","created_at":"2024-09-10 20:58:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":178936,"visible":true,"origin":"","legend":"\u003cp\u003eAdjusted association between educational status and COVID-19 hospitalization in the absence and presence of smoking history as the mediation for subgroups by sex, respectively.\u003c/p\u003e\n\u003cp\u003eAbbreviation: CI, confidence interval.\u003c/p\u003e\n\u003cp\u003eNotes: The models were also adjusted by other covariates.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-4863541/v1/327efad0f4d613d5df6e6d56.png"},{"id":101383671,"identity":"bb243a0c-e995-406b-a195-790cda55796c","added_by":"auto","created_at":"2026-01-29 06:43:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2905375,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4863541/v1/ec02e919-1aaa-40f3-a7e3-04d206469cfb.pdf"},{"id":64249042,"identity":"4b98dbba-c596-46db-891e-72e61e2cec19","added_by":"auto","created_at":"2024-09-10 20:58:40","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":106003,"visible":true,"origin":"","legend":"","description":"","filename":"STROBEchecklistcrosssectional.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4863541/v1/44c9c74663c85051e7364f26.pdf"},{"id":64249041,"identity":"446f76f1-9f37-472c-bd71-613a38662443","added_by":"auto","created_at":"2024-09-10 20:58:40","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":305646,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalOnlineContent.docx","url":"https://assets-eu.researchsquare.com/files/rs-4863541/v1/f87abe89947def4f41955dbe.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Mediating effect of smoking on the relationship between educational status and hospitalization in China with COVID-19: a cross-sectional study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEducational inequalities are evident in many disease outcomes[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], with individuals with elementary or lower education levels experiencing nearly double the mortality rate from infectious diseases compared with those with higher education levels[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This trend extends to COVID-19, where studies from Europe and the USA have shown that lower educational attainment is correlated with higher rates of SARS-CoV-2 infection[\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], COVID-19 hospitalization[\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], mortality[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], and excess mortality[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Despite these findings, few studies have explored these disparities in Asian countries. One study in Japan produced inconsistent results compared with Western studies[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], and only one Chinese study has examined education level differences in COVID-19 prevention behavior[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], leaving a gap in understanding how education influences COVID-19 outcomes in China.\u003c/p\u003e \u003cp\u003eTobacco use is a known risk factor that exacerbates the severity of COVID-19 outcomes. Smoking increases the risk of contracting SARS-CoV-2[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], requiring hospitalization[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], and dying from COVID-19[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].A large cohort study in China revealed that smoking significantly increases the risk of numerous diseases[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], which could worsen the prognosis following a COVID-19 infection. According to the Fundamental Cause Theory[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], education impacts exposure to COVID-19 and disease severity through factors such as health behaviors and literacy. There are well-documented educational disparities in smoking behavior across different countries[\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], indicating that smoking could be a critical factor linking educational status and COVID-19 outcomes.\u003c/p\u003e \u003cp\u003eTherefore, controlling tobacco use is a vital public health intervention to address these disparities. Understanding the role of smoking in the relationship between education and COVID-19 outcomes can inform strategies to reduce the incidence and severity of COVID-19 and other respiratory infections. The \"New 10 Epidemic Prevention Policy\" in China and the subsequent surge in infections provide a unique opportunity to examine the social determinants of COVID-19 vulnerability. We hypothesize that educational status is related to COVID-19 hospitalization risk, with smoking behavior serving as a significant factor. By targeting smoking in public health interventions, we can promote health equity and mitigate the impact of COVID-19.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eAn online survey was conducted between January 28 and February 21, 2023, one month after the peak number of COVID-19 infections. A recruitment poster was widely distributed through research group members and the social platform WeChat via a web link. The participants were informed of a chance to receive a modest financial incentive upon completion. The inclusion criteria were Chinese, normal cognitive function, positive SARS-CoV-2 results via RT‒PCR and/or IgM‒IgG antigen tests, age 20\u0026ndash;69 years, and informed consent. The exclusion criteria were self-assessed COVID-19 infection on the basis of key symptoms and duplicate questionnaire submissions. A total of 70,879 completed questionnaires were received online, but 45,052 were excluded because of duplication issues (4,342) or symptom-based diagnosis (40,710). Finally, 25,827 participants were included in the analysis.\u003c/p\u003e \u003cp\u003e The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board (or Ethics Committee) of China-Japan Friendship Hospital (number: 2022-KY-183-1, FEB. 2023).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eThe questionnaire\u003c/h2\u003e \u003cp\u003eThe questionnaire was independently reviewed by three experts in tobacco medicine and was accepted by all coresearchers after four revisions. The participants were required to answer each question to proceed, and the system automatically redirected inapplicable questions, ensuring comprehensive responses. The participants could revise their answers before submission. This study adhered to the Checklist for Reporting Results of internet E-Surveys (CHERRIES) guidelines.\u003c/p\u003e \u003cp\u003eThe online questionnaire, developed via the \u0026ldquo;Questionnaire Star\u0026rdquo; platform, included the following questions: sex (male, female), age (\u0026le;\u0026thinsp;29, 30\u0026ndash;49, 50\u0026ndash;64, \u0026ge;\u0026thinsp;65), education (primary school or below, junior high, high school/technical secondary, junior college, college or above), average personal monthly income over the past year (\u0026le;\u0026thinsp;2999 RMB, 3000\u0026ndash;5999 RMB, 6000\u0026ndash;9999 RMB, \u0026ge;\u0026thinsp;10000 RMB), residency (urban, rural), health insurance coverage (yes, no), COVID-19 vaccination status (yes, no), COVID-19 hospitalization (yes, no), and comorbidities such as cardiovascular diseases, diabetes (types I/II), chronic respiratory diseases, digestive system diseases, chronic liver diseases, chronic kidney diseases, cancer, hematological diseases, allergic diseases, and depression/anxiety (Appendix 1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe age distributions of the overall sample and subgroups were generally normal and are presented as the means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations. Categorical variables are presented as numbers (percentages). Student's t test was used for continuous variables, and the χ2 test was used for categorical variables.\u003c/p\u003e \u003cp\u003eLogistic regression was used to assess the association between educational status and COVID-19 hospitalization. Model 1 was adjusted for educational status alone, Model 2 included all the covariables except the potential mediating variable (smoking history), and Model 3 added smoking history to Model 2. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated.\u003c/p\u003e \u003cp\u003eBaron and Kenny\u0026rsquo;s method[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]was used to examine the associations among educational status, smoking behavior (smoking history/status), and COVID-19 hospitalization. Here, the independent variable (educational status) was denoted X, the dependent variable (COVID-19 hospitalization) was Y, and the mediating variable (smoking history/status) was M. The Karson‒Holm‒Breen (KHB) method(22) was used to calculate the percentage of mediation contributed by smoking history/status. Considering sex differences in smoking patterns and health impacts(23), mediation analyses were performed separately for each sex group and adjusted for all covariates.\u003c/p\u003e \u003cp\u003eTo reduce selection bias from the online survey, an unstable inverse probability weighting (IPW) method was applied as a sensitivity analysis to control for potential confounders. In this approach, the inverse probability of different educational statuses was calculated via multivariable logistic regression, with educational status as the dependent variable and all covariates, including smoking history, as independent variables. Each participant was weighted by the inverse probability of their educational status, giving higher weight to underrepresented observations, thus mitigating selection bias. This weighted regression model then estimated the association between educational status and COVID-19 hospitalization.\u003c/p\u003e \u003cp\u003eMissing age data were imputed via multiple imputation methods (R package \"MASS\", random seeds: 1234). Data analyses were conducted via STATA (version 17) and R (version 4.2.1). All p values were two-sided, with statistical significance set at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of the sample\u003c/h2\u003e \u003cp\u003eAmong the 25,827 respondents, 56.4% (14,577) were male, with a mean age of 40.9 (SD 11.7) years. A total of 1,652 (6.83%) patients were hospitalized due to COVID-19. Among the 7,042 (27.3%) participants with a smoking history, 5,917 (23.0%) were current smokers, and 1,125 (4.3%) were former smokers. The respondents were distributed regionally as follows: 12,719 (49.2%) from East China, 6,569 (25.4%) from Central China, 3,433 (13.3%) from West China, 3,082 (12.0%) from Northeast China, and 24 (0.1%) from other regions. Compared with those not hospitalized for COVID-19, hospitalized individuals were more likely to be male, have a lower educational status, live in rural areas, be unvaccinated, have at least one comorbidity, and smoke (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAssociation of COVID-19 hospitalization with educational status and smoking history/smoking status\u003c/h2\u003e \u003cp\u003eIn Model 1, a significant increase in the risk of COVID-19 hospitalization was observed with decreasing educational status. This trend remained consistent in Model 2, which included additional covariables. After we adjusted for smoking history in Model 3, participants with high school/technical secondary education (OR, 1.44; 95% CI, 1.24\u0026ndash;1.66), junior high school education (OR, 1.48; 95% CI, 1.25\u0026ndash;1.76), and primary school education or below (OR, 1.84; 95% CI, 1.46\u0026ndash;2.31) still had a significantly greater risk of hospitalization than did those with a college education or above, although all ORs slightly decreased compared with those in Model 2 (Supplementary Table\u0026nbsp;2, Supplementary Fig.\u0026nbsp;2). Model 3 also revealed a significant association between smoking history and COVID-19 hospitalization (OR, 1.52; 95% CI, 1.35\u0026ndash;1.70). Replacing smoking history with smoking status was associated with a significant risk for ex-smokers (OR, 1.63; 95% CI, 1.32\u0026ndash;2.00) and current smokers (OR, 1.50; 95% CI, 1.33\u0026ndash;1.69), with nonsmokers as the reference group.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eMediation analysis\u003c/h2\u003e \u003cp\u003eAs shown in \u003cb\u003eFig.\u0026nbsp;1A2\u003c/b\u003e, educational status was positively associated with smoking history (\u003cb\u003epath a\u003c/b\u003e), with the highest educational status used as the reference and controlling for all covariates. For \u003cb\u003epath b\u003c/b\u003e, after adjusting for covariates and educational status, smoking history was linked to increased COVID-19 hospitalization. For path c \u003cb\u003e(Fig.\u0026nbsp;1A1)\u003c/b\u003e, the education group, with the exception of junior college, had a greater hospitalization risk than did the college education group or above (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Introducing smoking history into the model \u003cb\u003e(path c\u0026rsquo; in Fig.\u0026nbsp;1A2)\u003c/b\u003e reduced the corresponding ORs between educational status and hospitalization, indicating a partial mediating effect of smoking history on the relationship between educational status and COVID-19 hospitalization. Similar results are shown in \u003cb\u003eFig.\u0026nbsp;1A3\u003c/b\u003e when smoking behavior was analysed in terms of smoking status. The mediating effect of smoking history was most significant between the junior high school and college groups (13.34%), followed by the high school/technical secondary school group (11.70%), and it was lowest in the primary group (7.62%) \u003cb\u003e(Table\u0026nbsp;2)\u003c/b\u003e. The share of \"current smokers\" in the mediation effect ranged from 80.32\u0026ndash;89.97% across all educational status comparisons.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAmong females, the mediating contribution of smoking history ranged from 14.33\u0026ndash;21.20% (\u003cb\u003eSupplementary Table\u0026nbsp;2\u003c/b\u003e), whereas it ranged from 4.46\u0026ndash;9.08% among males (\u003cb\u003eSupplementary Table\u0026nbsp;3\u003c/b\u003e). Across all educational status groups, smoking history had a more significant mediating effect for females than for males (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity analysis\u003c/h2\u003e \u003cp\u003eThe characteristics of the participants across different educational statuses are shown in Supplementary Table\u0026nbsp;4. Significant differences were found in monthly income, residence, health insurance, vaccination status, likelihood of cardiovascular disease or diabetes (I/II), and smoking history among educational levels (SMD\u0026thinsp;\u0026gt;\u0026thinsp;10%). After IPW adjustment, participants were well matched on all observed characteristics, with an absolute standardized difference of less than 10% (Supplementary Figure F3). Logistic regression after IPW revealed that educational status remained significantly associated with COVID-19 hospitalization, which is consistent with previous findings (Supplementary Table\u0026nbsp;5).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study demonstrated the significant role of smoking behavior in COVID-19-related educational inequality among SARS-CoV-2-infected individuals in China. This study revealed a strong association between lower educational attainment and increased odds of COVID-19 hospitalization. Smoking behavior significantly mediated this association, with effects varying by sex. Specifically, individuals with a lower educational status had higher rates of smoking history or current smoking, increasing their risk of hospitalization. Addressing this knowledge gap, our findings suggest that public health initiatives focused on tobacco control could help mitigate educational disparities in COVID-19 outcomes.\u003c/p\u003e \u003cp\u003eThere is currently little evidence to explain educational differences in COVID-19 outcomes in terms of smoking behavior. In one study, the association between education and hospitalization due to COVID-19 was partially attenuated by lifestyle factors, which included smoking[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Nevertheless, the mediating effect of smoking is still unclear. The differentiation of educational attainment concerning smoking and resulting health inequalities has been identified across multiple countries[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Individuals aged 35\u0026ndash;69 years from disadvantaged groups, including those with low levels of education, presented higher mortality rates due to smoking[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Tobacco use accounts for approximately half of the difference in mortality within this age range due to socioeconomic status [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. A study indicated that there are educational disparities in the reduction in smoking prevalence among male adults in China across different cohorts[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. This divergence in trends has resulted in a significant increase in educational disparities in smoking behavior among recent cohorts and plateau in the overall decline in smoking prevalence[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. These findings underscore the importance of examining education disparities in smoking and assessing inequities in the burden of disease attributable to smoking in China.\u003c/p\u003e \u003cp\u003eSmoking behavior has been demonstrated to act as a mediator in the relationship between educational attainment and diseases such as coronary heart disease [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Our findings on COVID-19 hospitalization indicated that smoking had a mediating effect ranging from 7.62\u0026ndash;13.34%, which was smaller than the effects reported in the studies on specific chronic diseases or cancers. This difference may be attributed to different disease outcomes, assessment indicators, and measurement methods of education.\u003c/p\u003e \u003cp\u003eImportantly, our findings provide critical insights into the role of smoking in the educational stratification of diseases in China, particularly highlighting its detrimental effects on COVID-19 outcomes. A recent review confirmed that current and former smokers infected with SARS-CoV-2 face increased risks of hospitalization, severity, and mortality compared with those who never smoked[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. This underscores the significant public health challenge posed by smoking, particularly as a pathway that exacerbates health disparities among different educational groups.\u003c/p\u003e \u003cp\u003eFollowing the World Health Organization (WHO) declaring COVID-19 as an ongoing health issue rather than a public health emergency[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], it is an opportune time to focus on mitigating factors that increase vulnerability. Our research emphasizes the urgent need for robust tobacco control policies and smoking cessation programs, especially those that target less educated populations. These interventions are essential not only for reducing the burden of COVID-19 but also for addressing broader health disparities exacerbated by smoking. By prioritizing these measures, we can better protect vulnerable groups from current and future public health challenges.\u003c/p\u003e \u003cp\u003eOur study also revealed that the mediating effect of smoking was more pronounced in females than in males. Although the smoking prevalence among women in China is much lower than that among men, recent national surveys have shown a higher smoking prevalence ratio between the lowest and highest education levels in females than in males[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], suggesting that educational disparities have a more pronounced impact on smoking behavior in women. Compared with 35.1% of males, 59.6% of females with tobacco dependence have a primary school education or below[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. These findings underscore the need for targeted public health interventions aimed at low-educated female smokers, who represent a particularly vulnerable group. Effective interventions and tobacco control policies should prioritize education and support specifically tailored for low-educated female smokers, providing them with resources and assistance in quitting smoking. Such targeted efforts are essential for reducing health disparities and promoting equity in health outcomes, particularly in the context of ongoing and future public health challenges such as COVID-19. By addressing the unique needs of this population, tobacco control initiatives can make significant strides in reducing the burden of tobacco-related diseases and improving overall community health.\u003c/p\u003e \u003cp\u003eWe also found that ex-smokers had a greater risk of COVID-19 hospitalization than did current smokers. This increased risk in ex-smokers may be due to diseases caused by previous smoking, which can persist for years after cessation. Studies have indicated that smokers often quit smoking due to smoking-related diseases, suggesting that ex-smokers might have poorer health than current smokers do [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Our data revealed that ex-smokers had significantly greater proportions of patients with chronic respiratory diseases, chronic liver diseases, and cancers than did current smokers (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Additionally, individuals who quit smoking immediately before or after hospitalization due to severe COVID-19 symptoms might be recorded as ex-smokers, introducing reverse causality. Last, collider bias might explain this finding, where conditioning on a collider (such as testing or hospitalization) could create an artificial link between smoking status and adverse COVID-19 outcomes[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur findings revealed that ex-smokers had a greater risk of COVID-19 hospitalization than didi current smokers. This increased risk among ex-smokers may be attributed to the long-term effects of diseases caused by previous smoking, which can persist even after cessation. Studies suggest that many smokers quit smoking due to smoking-related diseases, indicating that ex-smokers might generally have poorer health than current smokers[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Our data revealed significantly greater proportions of chronic respiratory diseases, chronic liver diseases, and cancer among ex-smokers than among current smokers (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Additionally, individuals who quit smoking immediately before or after hospitalization for severe COVID-19 symptoms might be classified as ex-smokers, potentially introducing reverse causality. Moreover, collider bias could also explain this finding, where conditioning on factors such as testing or hospitalization could artificially link smoking status with adverse COVID-19 outcomes[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. These insights underscore the importance of targeted smoking cessation programs, especially for those with existing health conditions. By addressing the unique health challenges faced by ex-smokers and supporting current smokers in quitting, public health initiatives can enhance overall health outcomes and progress toward tobacco control goals.\u003c/p\u003e \u003cp\u003eSeveral potential limitations of our study should be noted. First, our research sample was obtained through an online survey. Hence, the potential bias of this methodology may be inherent in our study. For example, people with the lowest income and illiteracy may be excluded from the survey[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Although our study subjects were distributed throughout the Chinese mainland, the average education level of the sample may be higher than the national average because of the data collection method. Considering the issue of sample representativeness, caution should be exercised when generalizing the research findings to populations with lower education levels. However, we performed a sensitivity analysis of an inverse-probability weighted method to eliminate the selection bias. Second, in our retrospective survey, self-reported information regarding COVID-19 hospitalizations may pose a greater risk of misreporting than medical records do. Nevertheless, we implemented rigorous measures for data quality control. Third, certain confounding factors, such as COVID-19 medications, treatment, and proximity to healthcare facilities, could not be collected in the questionnaire. Finally, our results do not establish causality and, more importantly, do not guarantee that controlling smoking among individuals with lower educational status will necessarily reduce their risk of contracting COVID-19 and other pandemics. However, our findings suggest that this scenario is plausible. Therefore, extending alternative approaches, such as Mendelian randomization, could be employed to further clarify the causality.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eBy conducting an online survey during the COVID-19 surge in mainland China, we identified an educational gradient in COVID-19 hospitalization among SARS-CoV-2-infected individuals. We also demonstrated that smoking behavior mediated the relationship between educational status and COVID-19 hospitalization, with notable sex differences. Individuals with lower education levels face an elevated risk of smoking, increasing their likelihood of COVID-19 hospitalization. These findings highlight the importance of targeted health promotion and smoking cessation interventions, especially for women with low education levels. Strengthening smoking cessation efforts can increase population resilience against respiratory pandemics such as COVID-19 and reduce health disparities.\u0026nbsp;\u003c/p\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBaseline characteristics of participants by COVID-19 hospitalization under the analytic sample\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNon COVID-19 Hospitalization\u003c/p\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;24175)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCOVID-19 Hospitalization\u003c/p\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;1652)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;25827)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAge, No. (%), y\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u0026ndash;29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4239 (17.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e296 (17.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4535 (17.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u0026ndash;49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14346 (59.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e998 (60.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15344 (59.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u0026ndash;64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4758 (19.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e296 (18.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5054 (19.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e832 (3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e62 (3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e894 (3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge, Mean (SD),y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40.9 (11.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40.6 (11.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40.9 (11.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex, No. (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13506 (55.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1071 (64.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14577 (56.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10669 (44.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e581 (35.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11250 (43.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducational status, No. (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCollege or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8943 (37.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e438 (26.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9381 (36.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJunior college\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5937 (24.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e364 (22.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6301 (24.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh school/ Technical secondary school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5482 (22.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e456 (27.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5938 (23.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJunior high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2855 (11.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e268 (16.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3123 (12.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrimary school or below\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e958 (4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e126 (7.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1084 (4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSalary, No. (%) (RMB/M)\u003c/strong\u003e \u003csup\u003e\u003cstrong\u003ea\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;10000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6985 (28.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e500 (30.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e.186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7485 (29.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6000\u0026ndash;9999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9288 (38.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e653 (39.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9941 (38.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3000\u0026ndash;5999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5587 (23.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e346 (21.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5933 (23.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;2999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2315 (9.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e153 (9.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2468 (9.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eResidency, No. (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17451 (72.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1132 (68.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18583 (72.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRural area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6724 (27.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e520 (31.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7244 (28.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eInsurance, No. (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23289 (96.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1592 (96.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24881 (96.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e886 (3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60 (3.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e946 (3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eVaccine, No. (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21006 (86.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1298 (78.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22304 (86.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3169 (13.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e354 (21.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3532 (13.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCardiovascular diseases, No. (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3226 (13.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e436 (26.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3662 (14.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20949 (86.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1216 (73.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22165 (85.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes (Type I/II), No. (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21556 (89.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1272 (77.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22828 (88.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2619 (10.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e380 (23.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2999 (11.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBaseline characteristics of participants by COVID-19 hospitalization under the analytic sample(continued)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNon COVID-19 Hospitalization\u003c/p\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;24175)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCOVID-19 Hospitalization\u003c/p\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;1652)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;25827)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eChronic respiratory diseases, No. (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21094 (87.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1283 (77.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22377 (86.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3081 (12.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e369 (22.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3450 (13.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDigestive system diseases, No. (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2182 (9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e236 (14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2418 (9.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21993 (90.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1416 (85.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23409 (90.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eChronic liver diseases, No. (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1276 (5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e147 (8.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1423 (5.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22899 (94.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1505 (91.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24404 (94.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eChronic kidney diseases, No. (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e730 (3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e117 (7.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e847 (3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23445 (97.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1535 (92.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24980 (96.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCancer, No. (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e612 (2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e76 (4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e688 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23563 (97.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1576 (95.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25139 (97.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHematological diseases, No. (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e478 (2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e59 (3.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e537 (2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23697 (98.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1593 (96.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25290 (98.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAllergic diseases, No. (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1419 (5.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e128 (7.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1547 (6.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22756 (94.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1524 (92.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24280 (94.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDepression/anxiety, No. (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1322 (5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e140 (8.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1462 (5.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22853 (94.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1512 (91.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24365 (94.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking history, No. (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17829 (73.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e956 (57.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18785 (72.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6346 (26.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e696 (42.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7042 (27.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking status, No. (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17829 (73.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e956 (57.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18785 (72.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEx-smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1001 (4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e124 (7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1125 (4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5345 (22.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e572 (34.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5917 (23.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\u003csup\u003ea\u003c/sup\u003e average personal monthly income over the past year\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;2. Total, mediation, and direct effects of educational status through smoking history on COVID-19 hospitalization, and the proportion of total effect mediated (%)\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"784\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.50955414012739%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.656050955414013%\" colspan=\"2\"\u003e\n \u003cp\u003eTotal effects \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.4203821656050954%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.656050955414013%\" colspan=\"2\"\u003e\n \u003cp\u003eMediation effects through smoking history \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.681528662420382%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.4203821656050954%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.656050955414013%\" colspan=\"2\"\u003e\n \u003cp\u003eDirect effects\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.108974358974358%\"\u003e\n \u003cp\u003e(OR, 95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.134615384615385%\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.0448717948717947%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.108974358974358%\"\u003e\n \u003cp\u003e(OR, 95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.134615384615385%\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.179487179487179%\"\u003e\n \u003cp\u003eMediation (%)\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.0448717948717947%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.108974358974358%\"\u003e\n \u003cp\u003e(OR, 95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.134615384615385%\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.50955414012739%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducational status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.394904458598726%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.261146496815287%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.4203821656050954%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.394904458598726%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.261146496815287%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.681528662420382%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.4203821656050954%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.394904458598726%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.261146496815287%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.50955414012739%\"\u003e\n \u003cp\u003eCollege or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.394904458598726%\"\u003e\n \u003cp\u003e1[reference]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.261146496815287%\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.4203821656050954%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.394904458598726%\"\u003e\n \u003cp\u003e1[reference]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.261146496815287%\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.681528662420382%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.4203821656050954%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.394904458598726%\"\u003e\n \u003cp\u003e1[reference]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.261146496815287%\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.50955414012739%\"\u003e\n \u003cp\u003eJunior college\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.394904458598726%\"\u003e\n \u003cp\u003e1.15 (1.00,1.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.261146496815287%\"\u003e\n \u003cp\u003e.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.4203821656050954%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.394904458598726%\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.261146496815287%\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.681528662420382%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.4203821656050954%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.394904458598726%\"\u003e\n \u003cp\u003e1.12 (0.97,1.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.261146496815287%\"\u003e\n \u003cp\u003e.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.50955414012739%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u0026nbsp;\u003c/strong\u003eHigh school/ Technical secondary school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.394904458598726%\"\u003e\n \u003cp\u003e1.50 (1.30,1.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.261146496815287%\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.4203821656050954%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.394904458598726%\"\u003e\n \u003cp\u003e1.05 (1.03,1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.261146496815287%\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.681528662420382%\" valign=\"top\"\u003e\n \u003cp\u003e11.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.4203821656050954%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.394904458598726%\"\u003e\n \u003cp\u003e1.43 (1.24,1.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.261146496815287%\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.50955414012739%\"\u003e\n \u003cp\u003eJunior high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.394904458598726%\"\u003e\n \u003cp\u003e1.58 (1.33,1.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.261146496815287%\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.4203821656050954%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.394904458598726%\"\u003e\n \u003cp\u003e1.06 (1.04,1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.261146496815287%\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.681528662420382%\" valign=\"top\"\u003e\n \u003cp\u003e13.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.4203821656050954%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.394904458598726%\"\u003e\n \u003cp\u003e1.48 (1.25,1.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.261146496815287%\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.50955414012739%\"\u003e\n \u003cp\u003ePrimary school or below\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.394904458598726%\"\u003e\n \u003cp\u003e1.95 (1.55,2.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.261146496815287%\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.4203821656050954%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.394904458598726%\"\u003e\n \u003cp\u003e1.05 (1.03,1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.261146496815287%\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.681528662420382%\" valign=\"top\"\u003e\n \u003cp\u003e7.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.4203821656050954%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.394904458598726%\"\u003e\n \u003cp\u003e1.85 (1.47,2.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.261146496815287%\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAbbreviation: CI, confidence interval; OR, odds ratio; NA, not applicable.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Total effect was the effect of educational status on COVID-19 hospitalization adjusted for all the covariates except for smoking history.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eb\u003c/sup\u003e Mediation effect was the effect of educational status on COVID-19 hospitalization through smoking history.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ec\u003c/sup\u003e Direct effect was the effect of educational status on COVID-19 hospitalization adjusted for all the covariates including smoking history.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ed\u003c/sup\u003e Mediation (%) was calculated by mediation effect/total effect \u0026acute;100%.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eSupplementary material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupplementary material is available at \u003cem\u003eTobacco control\u0026nbsp;\u003c/em\u003eonline.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, CW, and DX; Methodology, ZXH.; Software, ZXH; Validation, ZXH, ZS, and XX; Formal Analysis, ZXH; Investigation, YHL, ZXH, ZS, XX, XMZ, YL, XX, QQS, JXL, RQ, ZL, AQC, LZ, and XWW; Resources, CW and DX; Data Curation, AQC and LZ; Writing \u0026ndash; Original Draft Preparation, ZXH; Writing \u0026ndash; Review \u0026amp; Editing, ZL, KFC, DX, and CW; Visualization, ZXH; Supervision, DX and CW; Project Administration, LZ and DX; Funding Acquisition, DX and CW.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the China Zhongguancun Precision Medicine Science and Technology Foundation (2020-HX-3) and the National High-Level Hospital Clinical Research Funding of China\u0026nbsp;(2022-NHLHCRF-LX-01).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank the countless participants of this COVID-19 study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBefore collecting any data, the study was approved by\u0026nbsp;the Institutional Review Board of China-Japan Friendship Hospital\u0026nbsp;(number: 2022-KY-183-1, FEB. 2023). The survey respondents indicated their willingness to participate by selecting \u0026lsquo;Yes\u0026rsquo; or \u0026lsquo;No\u0026rsquo; on the online survey before being shown the survey questions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data underlying this article will be shared on reasonable request to the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eRoss CE, Wu C-l: \u003cstrong\u003eThe Links Between Education and Health\u003c/strong\u003e. \u003cem\u003eAmerican Sociological Review \u003c/em\u003e1995, \u003cstrong\u003e60\u003c/strong\u003e(5):719-745.\u003c/li\u003e\n\u003cli\u003eRegidor E, Mateo Sd, Calle ME, Dom\u0026iacute;nguez V: \u003cstrong\u003eEducational level and mortality from infectious diseases\u003c/strong\u003e. \u003cem\u003eJournal of Epidemiology and Community Health \u003c/em\u003e2002, 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al\u003c/em\u003e: \u003cstrong\u003eCollider bias undermines our understanding of COVID-19 disease risk and severity\u003c/strong\u003e. \u003cem\u003eNat Commun \u003c/em\u003e2020, \u003cstrong\u003e11\u003c/strong\u003e(1):5749.\u003c/li\u003e\n\u003cli\u003eBall HL: \u003cstrong\u003eConducting Online Surveys\u003c/strong\u003e. \u003cem\u003eJ Hum Lact \u003c/em\u003e2019, \u003cstrong\u003e35\u003c/strong\u003e(3):413-417.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"COVID-19 hospitalization, educational status, smoking behavior, mediation effect, health inequity","lastPublishedDoi":"10.21203/rs.3.rs-4863541/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4863541/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground: Educational disparities in COVID-19 outcomes are well documented in Western countries, but evidence from China is limited. This study explored the role of smoking in these disparities.\u003c/p\u003e\n\u003cp\u003eMethods: We conducted a cross-sectional study in China between January 28 and February 21, 2023. Participants who reported positive SARS-CoV-2 results via RT‒PCR and/or IgM‒IgG antigen tests provided self-reported data on COVID‒19 \u0026nbsp;hospitalization, educational status, and smoking status through an online questionnaire. Logistic regressions with and without inverse probability weighting estimated odds ratios (ORs) for COVID-19 hospitalization by educational status, adjusting for potential confounders. The Karon‒Holm‒Breen (KHB) method was used to estimate the proportion of mediation attributable to smoking status.\u003c/p\u003e\n\u003cp\u003eResults: Of the 25,827 participants (56.4% male; mean age 40.9 years), 1652 (6.83%) were hospitalized, and higher ORs for COVID-19 hospitalization were found in those with primary school education or below (OR: 1.84), junior high school (OR: 1.48), high school/technical secondary school (OR: 1.44), and smoking history (OR: 1.52). Smoking mediated the relationship between educational status and hospitalization, with mediation proportion ranging from 7.62% to 13.34%, varying by sex.\u003c/p\u003e\n\u003cp\u003eConclusion: This study highlights an educational gradient in COVID-19 hospitalization in China, with smoking as a partial mediator\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e","manuscriptTitle":"Mediating effect of smoking on the relationship between educational status and hospitalization in China with COVID-19: a cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-10 20:58:35","doi":"10.21203/rs.3.rs-4863541/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1399ef25-16ce-4d0c-86ec-809f665abfd0","owner":[],"postedDate":"September 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-29T06:42:33+00:00","versionOfRecord":[],"versionCreatedAt":"2024-09-10 20:58:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4863541","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4863541","identity":"rs-4863541","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}