Age-Specific Determinants of Depression Prevalence: Insights from Pre- and Post-COVID- 19 Pandemic Analysis

Age-Specific Determinants of Depression Prevalence: Insights from Pre- and Post-COVID- 19 Pandemic Analysis | 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 Age-Specific Determinants of Depression Prevalence: Insights from Pre- and Post-COVID- 19 Pandemic Analysis Jinrong Liu, Wenshuang Sun, Qin Zhu, Hanshu Ji, Nianjiao Zhou, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6475145/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 The COVID-19 pandemic has significantly impacted mental health, particularly increasing depression rates due to social isolation, economic stress, and lifestyle disruptions. However, studies examining its specific effects on depression on difference ages remain limited. Methods This study utilized data from the NHANES database, comparing pre-pandemic (2017–2020) and post-pandemic (2021–2023) cycles. Depression was assessed using the Patient Health Questionnaire (PHQ-9). Demographics, lifestyle behaviors, and medical conditions were examined as potential exposure variables. Logistic regression models were applied to determine the factors associated with depression in three groups: 20–44 years, 45–64 years, and 65–80 years. Results The analysis included 3,247 participants, with 871 in the depression group and 2,376 in the non-depression group. Alcohol abuse increased depression risk, while physical activity was protective across all age groups. In the 20–44 years group, COVID-19 experience (OR = 2.542, 95% CI: 1.783–3.623) and liver disease were significant risk factors. In the 65–80 years group, higher BMI (OR = 1.660, 95% CI: 1.139–2.421), thyroid disease (OR = 1.756, 95% CI: 1.001–3.079, p = 0.049), and longer weekend sleep (OR = 1.291, 95% CI: 1.020–1.634, p = 0.033) were significant. Female and heart/brain diseases elevated depression risk in middle-aged and older adults. Conclusion These findings emphasize the need for age-specific mental health interventions targeting pandemic stressors, chronic diseases, and gender disparities, informing future public health strategies. Depression NHANES COVID-19 physical activity alcohol abuse Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction The COVID-19 pandemic is one of the most significant global public health crises in recent history, profoundly impacting mental health, particularly depression [ 1 , 2 ] . However, systematic studies exploring the pandemic's specific effects on depression and its associated factors remain limited. The pandemic provides a unique opportunity to examine how major external stressors influence depression. Evidence suggests that isolation, reduced social interactions, economic instability, and uncertainty contribute to increased depression risk [ 3 ] . Responses to these stressors may vary by age, gender, and socioeconomic status, highlighting the need for population-specific insights [ 4 ] . Understanding these dynamics is crucial for developing targeted mental health interventions. This study utilizes real-world data from the NHANES database to systematically analyze changes in depression prevalence before and after the COVID-19 pandemic, emphasizing the long-term impact of pandemic experiences on depression. By comparing pre- and post-pandemic data, this study investigates the key factors associated with depression that may persist long after the pandemic itself. Through large-scale data analysis, the study aims to provide scientific evidence for targeted mental health interventions and public health policies, enhancing our understanding and management of mental health in future public health emergencies. 2. Methods 2.1 Study Population This study analyzed data obtained from the NHANES database, a comprehensive and nationally representative resource that collects detailed information on health, nutrition, and demographics across the U.S. population. Specifically, data were drawn from the NHANES 2017 to March 2020 pre-pandemic cycles and the August 2021 to August 2023 cycles. These datasets included information on demographics, anthropometric measurements, sleep and physical activity patterns, dietary and substance use behaviors, as well as medical conditions. Participants included in the analysis were aged 20 to 80 years and had complete data for all relevant variables. Individuals with incomplete or missing data on key variables or those outside the specified age range were excluded to ensure the study focused on adult populations within the given timeframe (Fig. 1). 2.2 Exposure Variables and Outcome Variable Depression levels were evaluated using the Patient Health Questionnaire (PHQ-9), with scores ranging from 0 to 27. A score of ≥ 5 was classified as indicative of depression, whereas scores ≤ 4 were considered normal. Participants were categorized into two groups: those without exposure to the COVID-19 pandemic (pre-pandemic group) and those with pandemic experience (post-pandemic group) [5] . Age was stratified into three categories: 20–44 years, 45–64 years, and 65–80 years. Additional variables included sex (male or female), race/ethnicity (Mexican American, Other Hispanic, Non-Hispanic White, Non-Hispanic Black, Non-Hispanic Asian, and Other Race), education level (ranging from less than 9th grade to college graduate or higher), marital status (categorized as married, not married, or uncertain), income (measured as the income-to-poverty ratio), lifestyle behaviors, and medical conditions. Body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared (kg/m²) and categorized into underweight (< 18.5), normal weight (18.5–24.9), overweight (25-29.9), and obese (≥ 30). Blood pressure measurements were taken using a mercury sphygmomanometer after a 5-minute rest, with the mean of three readings used for analysis. Alcohol consumption was evaluated based on drinking frequency over the past 12 months, with alcohol abuse defined as consuming five or more drinks per day for men and four or more for women. Smoking status was determined as having smoked at least 100 cigarettes in one’s lifetime. Respiratory conditions included self-reported diagnoses of asthma, chronic obstructive pulmonary disease (COPD), emphysema, or chronic bronchitis. Cardiovascular and cerebrovascular conditions encompassed coronary heart disease, stroke, and congestive heart failure. Detailed methodologies for these measurements are publicly available on the Centers for Disease Control and Prevention (CDC) website at www.cdc.gov/nchs/nhanes/. 2.3 Statistical Analysis The statistical analysis was conducted using Stata software (version 16.0, StataCorp, College Station, TX) and EmpowerStats (www.empowerstats.com), with a significance level set at P < 0.05. Continuous variables were presented as means with 95% confidence intervals (CIs), while categorical variables were expressed as percentages with 95% CIs. To compare the differences between groups, the weighted chi-square test and the weighted logistic regression model were used for continuous variables and classification, respectively. 3. Results 3.1 Demographic Characteristics Between the Depressed and Non-Depressed Groups Across the pre-COVID-19 and post-COVID-19 NHANES data cycles, a total of 27493 participants aged 20 to 80 years were initially surveyed. Of these, 3247 individuals met the study's inclusion criteria. Table 1 compares the characteristics of the depression group (n = 871) and the non-depression group (n = 2,376). Significant differences were observed between the two groups in terms of age distribution (p < 0.001), sex distribution (p < 0.001), race and ethnicity (p < 0.001), education level (p = 0.002), income level (p < 0.001), BMI (p < 0.001), physical activity levels (p < 0.001), alcohol use (p < 0.001), smoking status (p = 0.002), COVID-19 experience (p < 0.001), respiratory diseases (p < 0.001), heart and brain diseases (p = 0.002), liver disease (p < 0.001), primary food shopper (p = 0.006), and shared food shopping responsibilities (p = 0.005). Additionally, continuous variables such as average diastolic blood pressure (p < 0.001) and average pulse rate (p < 0.001) demonstrated significant differences (Table 2 ). Table 1 Frequency Table of Descriptive Results in the Non-depression and Depression Groups. Characteristics Total (n = 3247) Non-depression (n = 2376) Depression (n = 871) P value Age, mean (95% CI) y < 0.001* 20–44 0.474 (0.450 to 0.497) 0.443 (0.416 to 0.471) 0.56 (0.516 to 0.603) 45–64 0.362 (0.340 to 0.385) 0.377 (0.351 to 0.404) 0.319 (0.279 to 0.362) ≥ 65 0.165 (0.150 to 0.180) 0.180 (0.162 to 0.198) 0.121 (0.099 to 0.147) Sex, No. (%) < 0.001* Male 0.515 (0.491 to 0.538) 0.537 (0.509 to 0.564) 0.452 (0.408 to 0.497) Female 0.485 (0.462 to 0.509) 0.463 (0.436 to 0.491) 0.548 (0.503 to 0.592) Race and ethnicity No. (%) < 0.001* Mexican American 0.073 (0.064 to 0.084) 0.074 (0.063 to 0.086) 0.072 (0.055 to 0.093) Other Hispanic 0.067 (0.058 to 0.077) 0.059 (0.049 to 0.070) 0.091 (0.072 to 0.115) Non-Hispanic White 0.682 (0.662 to 0.701) 0.696 (0.673 to 0.718) 0.642 (0.602 to 0.681) Non-Hispanic Black 0.09 (0.081 to 0.101) 0.085 (0.075 to 0.097) 0.104 (0.084 to 0.129) Non-Hispanic Asian 0.043 (0.036 to 0.052) 0.048 (0.039 to 0.059) 0.03 (0.018 to 0.050) Other Race 0.044 (0.037 to 0.054) 0.039 (0.030 to 0.049) 0.06 (0.044 to 0.082) Education, No. (%) 0.002* Less than 9th grade 0.014 (0.010 to 0.018) 0.013 (0.009 to 0.019) 0.016 (0.01 to 0.026) 9-11th grade 0.047 (0.039 to 0.056) 0.043 (0.035 to 0.054) 0.057 (0.043 to 0.075) High school graduate 0.209 (0.190 to 0.229) 0.208 (0.186 to 0.232) 0.214 (0.179 to 0.253) College or AA degree 0.313 (0.292 to 0.335) 0.299 (0.276 to 0.325) 0.352 (0.312 to 0.394) College graduate or above 0.417 (0.394 to 0.441) 0.437 (0.410 to 0.465) 0.361 (0.318 to 0.407) Marital status, No. (%) 0.698 Yes 0.610 (0.587 to 0.633) 0.607 (0.579 to 0.634) 0.619 (0.575 to 0.661) No 0.188 (0.171 to 0.207) 0.188 (0.167 to 0.210) 0.189 (0.158 to 0.224) Uncertain 0.202 (0.183 to 0.222) 0.206 (0.183 to 0.230) 0.192 (0.159 to 0.230) Family income to poverty ratio, No. (%) < 0.001* ≤ 1 0.076 (0.067 to 0.087) 0.062 (0.053 to 0.074) 0.116 (0.094 to 0.142) 1–3 0.291 (0.271 to 0.310) 0.267 (0.245 to 0.29) 0.358 (0.319 to 0.400) 3–5 0.633 (0.612 to 0.654) 0.671 (0.646 to 0.694) 0.526 (0.482 to 0.569) BMI group, No. (%) < 0.001* < 18.5 0.010 (0.006 to 0.016) 0.009 (0.005 to 0.015) 0.013 (0.006 to 0.032) 18.5–25 0.263 (0.242 to 0.285) 0.265 (0.241 to 0.290) 0.257 (0.217 to 0.300) ≥ 25–30 0.339 (0.317 to 0.362) 0.368 (0.341 to 0.395) 0.257 (0.221 to 0.297) ≥ 30 0.388 (0.366 to 0.411) 0.359 (0.333 to 0.385) 0.473 (0.430 to 0.518) Activity group, No. (%) < 0.001* < 1 times/week 0.268 (0.248 to 0.289) 0.252 (0.229 to 0.276) 0.316 (0.277 to 0.358) 1–2 times/week 0.177 (0.159 to 0.196) 0.162 (0.142 to 0.184) 0.220 (0.184 to 0.261) ≥ 3 times/week 0.555 (0.531 to 0.578) 0.586 (0.559 to 0.613) 0.464 (0.420 to 0.509) Alcohol abuse, No. (%) < 0.001* ≥ 4/5 drinks per day 0.871 (0.856 to 0.885) 0.903 (0.887 to 0.917) 0.781 (0.743 to 0.814) < 4/5 drinks per day 0.129 (0.115 to 0.144) 0.097 (0.083 to 0.113) 0.219 (0.186 to 0.257) Smoking, No. (%) 0.002* Yes 0.398 (0.375 to 0.421) 0.382 (0.355 to 0.409) 0.443 (0.400 to 0.488) No 0.602 (0.579 to 0.625) 0.618 (0.590 to 0.645) 0.557 (0.512 to 0.600) COVID experience 0.497 (0.473 to 0.520) 0.467 (0.440 to 0.494) 0.582 (0.536 to 0.626) < 0.001* Diabetes < 0.001* Yes 0.082 (0.072 to 0.094) 0.084 (0.071 to 0.099) 0.077 (0.061 to 0.097) No 0.897 (0.884 to 0.909) 0.901 (0.886 to 0.915) 0.886 (0.861 to 0.907) Borderline 0.021 (0.015 to 0.027) 0.015 (0.01 to 0.022) 0.037 (0.025 to 0.055) Respiratory diseases 0.336 (0.315 to 0.358) 0.314 (0.290 to 0.340) 0.397 (0.355 to 0.441) < 0.001* Heart and brain disease 0.057 (0.049 to 0.067) 0.049 (0.041 to 0.060) 0.078 (0.059 to 0.103) 0.002* Arthritis 0.252 (0.233 to 0.272) 0.246 (0.224 to 0.269) 0.272 (0.234 to 0.313) 0.129 Thyroid disease 0.102 (0.090 to 0.116) 0.101 (0.087 to 0.118) 0.105 (0.082 to 0.132) 0.795 Liver disease 0.038 (0.030 to 0.047) 0.031 (0.023 to 0.041) 0.057 (0.040 to 0.079) < 0.001* Gallstones 0.089 (0.076 to 0.104) 0.086 (0.071 to 0.103) 0.099 (0.075 to 0.130) 0.242 Cancer 0.106 (0.093 to 0.120) 0.111 (0.096 to 0.128) 0.092 (0.070 to 0.119) 0.118 Shared meal planning 0.584 (0.561 to 0.607) 0.589 (0.562 to 0.616) 0.569 (0.525 to 0.612) 0.314 Main food shopper 0.624 (0.601 to 0.647) 0.610 (0.583 to 0.638) 0.664 (0.620 to 0.706) 0.006* Shared food shopping duty 0.609 (0.586 to 0.631) 0.623 (0.596 to 0.649) 0.568 (0.524 to 0.611) 0.005* Table 2 Descriptive Results of Continuous Data in the Non-depression and Depression Groups. Characteristics Total (n = 3247) Non-depression group (n = 2376) Depression group (n = 871) P value Vigorous activities minutes 37.874(36.187 to 39.560) 37.364 (35.396 to 39.334) 39.329 (36.057 to 42.602) 0.317 Moderate activities minutes 32.013 (30.382 to 33.643) 32.847 (30.917 to 34.776) 29.627 (26.592 to 32.662) 0.090 Sedentary behavior 372.807 (365.881 to 379.734) 370.856 (362.927 to 378.785) 378.389 (364.231 to 392.546) 0.350 Sleep hours in weekdays 7.571 (7.525 to 7.616) 7.572 (7.524 to 7.622) 7.566 (7.461 to 7.670) 0.893 Sleep hours in weekend 8.232 (8.182 to 8.282) 8.220 (8.166 to 8.275) 8.266 (8.152 to 8.380) 0.436 Systolic blood pressure 119.724 (119.182 to 120.269) 119.722 (119.088 to 120.356) 119.736 (118.680 to 120.792) 0.983 Diastolic blood pressure 74.321 (73.956 to 74.686) 73.893 (73.472 to 74.314) 75.546 (74.822 to 76.270) < 0.001* Pulse 69.490 (69.096 to 69.885) 68.901 (68.454 to 69.348) 71.176 (70.359 to 71.992) < 0.001* In contrast, no significant differences were observed between the groups for variables such as arthritis, thyroid disease, cancer, shared meal planning responsibilities, vigorous activity minutes, moderate activity minutes, sedentary behavior, weekday sleep duration, weekend sleep duration, and systolic blood pressure. 3.2 Determinants of Depression from Regression Analysis Logistic regression analysis was conducted to identify factors associated with depression (Table 3 ). Significant predictors included the experience of COVID-19, which substantially increased the likelihood of depression (OR = 1.820, 95% CI: 1.439–2.302, p < 0.001). Age was inversely associated with depression, with older individuals showing lower odds (OR = 0.592, 95% CI: 0.502–0.698, p < 0.001). Female was associated with a higher risk compared to males (OR = 1.491, 95% CI: 1.168–1.903, p = 0.001). Protective factors included higher physical activity levels (OR = 0.752, 95% CI: 0.659–0.858, p < 0.001) and greater income (OR = 0.754, 95% CI: 0.636–0.894, p = 0.001). In contrast, alcohol abuse significantly increased the odds of depression (OR = 2.277, 95% CI: 1.680–3.085, p < 0.001). Several comorbidities were also linked to higher odds of depression, including respiratory diseases (OR = 1.366, 95% CI: 1.078–1.731, p = 0.009), heart and brain diseases (OR = 1.896, 95% CI: 1.227–2.929, p = 0.004), and liver disease (OR = 1.735, 95% CI: 1.083–2.780, p = 0.025). Other variables, such as race and ethnicity, education, BMI, diastolic blood pressure, pulse rate, smoking, and food shopping responsibilities, did not show significant associations with depression. Table 3 Results of Binary Logistic Regression Analysis for Factors Associated with Depression DPQRESULT OR (95%CI) P Value COVID experience 1.820 (1.439 to 2.302) < 0.001* Age group 0.592 (0.502 to 0.698) < 0.001* Sex 1.491 (1.168 to 1.903) 0.001* Race and ethnicity 0.991 (0.914 to 1.074) 0.825 Education 1.062 (0.929 to 1.213) 0.365 Activity group 0.752 (0.659 to 0.858) < 0.001* Family income to poverty ratio 0.754 (0.636 to 0.894) 0.001* Alcohol abuse 2.277 (1.680 to 3.085) < 0.001* BMI Group 1.070 (0.915 to 1.251) 0.355 Diastolic blood pressure 1.009 (0.998 to 1.020) 0.443 Average Pulse 1.002 (0.991 to 1.012) 0.699 Diabetes 0.985 (0.836 to 1.161) 0.886 Smoking 0.891 (0.695 to 1.143) 0.377 Respiratory diseases 1.366 (1.078 to 1.731) 0.009* Heart and brain disease 1.896 (1.227 to 2.929) 0.004* Liver disease 1.735 (1.083 to 2.780) 0.025* Main food shopper 1.120 (0.861 to 1.457) 0.393 Shared food shopping duty 0.852 (0.678 to 1.072) 0.177 3.3 Demographic Characteristics Between the Depressed and Non-Depressed Groups We further divided the participants into three age groups: 20–44 years (n = 1310), 45–64 years (n = 1201), and 65–80 years (n = 731). Detailed demographic information for each group is presented in table S1 and Table 4 . Subsequently, we conducted statistical analyses comparing the depressed and non-depressed individuals within each age group. Table 4 Basic characteristics of patients Between Depression and Non-Depression Groups Across Age Categories Non-depression group Depression group P Value 20-44y Vigorous activities minutes 41.982 (37.31 to 46.655) 44.089 (38.368 to 49.81) 0.490 Moderate activities minutes 37.132 (32.806 to 41.459) 34.79 (28.557 to 41.024) 0.422 Sedentary behavior 370.486 (352.831 to 388.141) 382.416 (355.681 to 409.152) 0.341 Sleep hours in weekdays 7.582 (7.474 to 7.689) 7.679 (7.508 to 7.851) 0.213 Sleep hours in weekend 8.384 (8.277 to 8.492) 8.459 (8.26 to 8.659) 0.382 Systolic blood pressure 114.427 (113.385 to 115.47) 115.343 (113.794 to 116.892) 0.230 Diastolic blood pressure 72.89 (72.042 to 73.738) 74.932 (73.694 to 76.169) 0.001* Pulse 70.385 (69.473 to 71.297) 72.656 (71.151 to 74.162) < 0.001* 45-64y Vigorous activities minutes 33.714 (29.637 to 37.792) 33.862 (26.287 to 41.437) 0.962 Moderate activities minutes 28.273 (25.066 to 31.48) 24.729 (19.004 to 30.453) 0.207 Sedentary behavior 379.909 (361.956 to 397.862) 375.35 (343.509 to 407.19) 0.740 Sleep hours in weekdays 7.489 (7.388 to 7.59) 7.278 (7.042 to 7.515) 0.018* Sleep hours in weekend 8.209 (8.095 to 8.323) 8.011 (7.754 to 8.267) 0.048* Systolic blood pressure 121.966 (120.511 to 123.421) 122.797 (120.107 to 125.487) 0.452 Diastolic blood pressure 76.163 (75.248 to 77.079) 77.518 (75.766 to 79.27) 0.065 Pulse 68.28 (67.318 to 69.241) 69.307 (67.405 to 71.208) 0.181 65-80y Vigorous activities minutes 33.632 (28.554 to 38.711) 31.735 (22.943 to 40.527) 0.693 Moderate activities minutes 31.872 (24.676 to 39.069) 18.67 (9.677 to 27.663) 0.011* Sedentary behavior 352.762 (333.289 to 372.235) 367.782 (329.098 to 406.466) 0.375 Sleep hours in weekdays 7.725 (7.589 to 7.861) 7.796 (7.45 to 8.142) 0.578 Sleep hours in weekend 7.84 (7.7 to 7.981) 8.044 (7.671 to 8.417) 0.124 Systolic blood pressure 128.077 (126.344 to 129.81) 131.967 (128.475 to 135.458) 0.019* Diastolic blood pressure 71.601 (70.592 to 72.61) 73.191 (70.988 to 75.395) 0.096 Average Pulse 66.543 (65.333 to 67.753) 69.257 (67 to 71.514) 0.014* 3.3.1 Demographic Differences and Depression Significant demographic differences were observed across age groups. In the 20–44 years group, non-Hispanic Whites were predominantly in the non-depression group (p = 0.049), while Mexican Americans and non-Hispanic Blacks showed higher proportions in the depression group. This trend was also evident in the 45–64 years group (p = 0.017) but did not reach statistical significance in the 65–80 years group (p = 0.364) (Fig. 2 A). Educational attainment was significantly associated with depression in the 20–44 years group, with individuals holding a college degree or higher more likely to belong to the non-depression group (p = 0.024). However, no significant differences were noted in the 45–64 years or 65–80 years groups (p > 0.05) (Fig. 2 B). Income level consistently showed associations with depression status across all age groups, with higher income levels overrepresented in the non-depression group in the 20–44 years (p < 0.001), 45–64 years (p < 0.001), and 65–80 years groups (p = 0.009) (Fig. 2 C). Marital status exhibited significant associations with depression in the 65–80 years group (p = 0.028) (Fig. 2 D). The proportion of females in the depression group was higher than in the non-depression group across all age groups, though the difference in the 20–44 years group was not significance (p = 0.055), the statistically significant differences were observed in the 45–64 years (p < 0.001) and 65–80 years groups (p = 0.010) (Fig. 2 E). 3.3.2 Lifestyle Factors and Depression Heavy alcohol consumption (≥ 4/5 drinks per day) was more frequent in the depression group across all age groups (20–44 years: p < 0.001; 45–64 years: p < 0.001; 65–80 years: p < 0.001). Conversely, smoking was less prevalent in the depression group compared to the non-depression group in both the 20–44 years (p = 0.044) and 45–64 years (p = 0.004) groups, though the difference was not significant in the 65–80 years group (p = 0.173) (Fig. 2 F). COVID-19 exposure was significantly associated with depression in the 20–44 years group (p < 0.001) but not in the 45–64 years (p = 0.195) or 65–80 years groups (p = 0.895). Shared responsibilities in meal planning and food shopping showed limited associations. In the 20–44 years group, individuals who shared food shopping duties were more likely to belong to the non-depression group (p = 0.006), whereas in the 65–80 years group, the primary responsibility for food shopping was more common in the non-depression group (p = 0.039) (Fig. 2 G). Physical activity showed a consistent relationship with depression across all age groups, with individuals engaging in less than one activity session per week being significantly more likely to belong to the depression group, while those with three or more sessions per week were predominantly in the non-depression group (20–44 years: p = 0.012; 45–64 years: p < 0.001; 65–80 years: p < 0.001) (Fig. 3 A). 3.3.3 Chronic Diseases and Depression Chronic diseases demonstrated strong associations with depression across all age groups. Obesity (BMI ≥ 30) was significantly more prevalent in the depression group, while normal BMI (18.5–25) was more common in the non-depression group (20–44 years: p = 0.003; 45–64 years: p < 0.001; 65–80 years: p < 0.001) (Fig. 3 B). Diabetes status was also associated with depression. In the 20–44 years group, individuals with diabetes or borderline diabetes were more likely to belong to the depression group (p < 0.001). This association persisted in the 45–64 years group (p = 0.015) but was not statistically significant in the 65–80 years group (p = 0.201) (Fig. 3 C). Respiratory diseases, arthritis, and liver disease were more common in the depression group in the 20–44 years (p < 0.001, p = 0.027, and p = 0.002, respectively) and 45–64 years group p < 0.001, p < 0.001, and p = 0.023, respectively). Heart and brain diseases were significantly associated with depression in both the 45–64 years (p < 0.001) and 65–80 years groups (p < 0.001). Thyroid disorders were more prevalent in the depression group in the 65–80 years group (p < 0.001). Gallstones were more prevalent in the depression group in the 45–65 years group (p = 0.038) (Fig. 3 D) 3.4 Age-Stratified Factors Associated with Depression Risk Stratified binary logistic regression analysis revealed distinct age-specific factors influencing depression (Fig. 4 ). Alcohol abuse and higher physical activity consistently influenced depression risk across all age groups. Alcohol abuse significantly increased depression risk (20–44 years: OR = 2.279, 95% CI: 1.393–3.728, p < 0.001; 45–64 years: OR = 2.450, 95% CI: 1.506–3.987, p < 0.001; 65–80 years OR = 2.547, 95% CI: 1.368–4.743, p = 0.003). Conversely, higher physical activity consistently reduced depression risk (20–44 years: OR = 0.746, 95% CI: 0.599–0.928, p = 0.009; 45–64 years: OR = 0.721, 95% CI: 0.564–0.923, p = 0.009; 65–80 years: OR = 0.664, 95% CI: 0.491–0.897, p = 0.008). Other factors varied by age. Higher income (OR = 0.680, 95% CI: 0.531–0.872, p = 0.002) and COVID-19 experience (OR = 2.546, 95% CI: 1.783–3.623, p < 0.001) were both significant and unique influence factors, which only have significance difference in younger adults (20–44 years). Higher BMI (OR = 1.660, 95% CI: 1.139–2.421, p = 0.008), thyroid disease (OR = 1.756, 95% CI: 1.001–3.079, p = 0.049) and longer weekend sleep (OR = 1.291, 95% CI: 1.020–1.634, p = 0.033) were only significant in 65–80 years group. Female and heart/brain diseases were risk factors both in 45–64 years group and 65–80 years group. 4. Discussions We observed that age acts as a protective factor against depression in our analysis [ 6 ] . In contrast, younger individuals face higher risks due to higher risks due to mechanisms like circadian rhythm disturbances, neurological and hormonal changes, and chronic social stress [ 7 – 9 ] . To further understand these age-specific effects, particularly in the context of post-pandemic depression, we conducted age-stratified analyses to identify distinct risk and protective factors, providing a nuanced perspective on the pandemic's long-term influence on mental health. 4.1 Physical Activity and Alcohol Use in Depression Promoting physical activity has been increasingly recognized as a potential strategy for preventing depression [ 10 ] . Studies have shown that low levels of physical activity are a significant risk factor for depression, while regular physical activity is associated with a protective effect against depressive episodes [ 11 ] . Depression is often accompanied by reduced levels of serotonin and norepinephrine in the hippocampus, prefrontal cortex, and striatum [ 12 , 13 ] . Exercise has been shown to elevate these neurotransmitter levels, reshape brain structures, preserve hippocampal integrity, and maintain white matter volume, thereby delaying cognitive decline [ 14 – 16 ] . Additionally, exercise may alleviate depressive symptoms by reducing inflammatory markers and enhancing kynurenine metabolism in skeletal muscle, preventing its accumulation in the brain [ 17 , 18 ] . However, the causality and directionality of this association remain unclear, as physical activity may help prevent depression, but depressive states may also lead to reduced physical activity [ 10 ] . Alcohol abuse remains strongly linked to depression, with a bidirectional relationship where each condition exacerbates the other. A study found that 42% of individuals with alcohol use disorder exhibited depressive symptoms, which dropped to 6% after abstinence, suggesting alcohol may mask underlying depression [ 19 ] . Heavy drinkers have been reported to be at a higher risk of developing depression compared to moderate drinkers or abstainers [ 20 ] . Alcohol disrupts the serotonin and dopamine systems, which are critical for mood regulation, leading to neurochemical imbalances that worsen depressive symptoms [ 21 , 22 ] . Moreover, alcohol abuse has been associated with heightened stress, increased anxiety, and social challenges, such as isolation, interpersonal conflicts, and financial difficulties, which collectively intensify feelings of loneliness and hopelessness, further deepening depression [ 21 ] . These findings suggest contrasting roles for physical activity and alcohol use in mental health, emphasizing the importance of encouraging healthy behaviors while addressing harmful habits to reduce the risk of depression. 4.2 Unique Depression influence Factors in the 20–44-Year Age Group In our study, we found that income was a significant protective factor in younger adults but not in older age groups and the proportion of individuals with low-income levels was significantly higher in the younger group compared to the middle-aged and older groups. Higher income levels may alleviate financial stress, improve access to mental health resources, and facilitate healthier lifestyles, thereby reducing the risk of depression [ 23 – 25 ] . Adolescents attending schools with lower socioeconomic status exhibit higher levels of depressive and anxiety symptoms compared to those in affluent schools [ 24 , 26 ] . Low income can also exacerbate family dynamics, leading to increased parent-child conflict and negatively impacting emotional well-being. Economic pressures within families often heighten tensions, disproportionately affecting younger females who are more sensitive to stressors [ 27 ] . Conversely, COVID-19-related stress stands out as a prominent risk factor uniquely impacting the 20–44-year demographic. The dramatic increase of depression in pandemic has been attributed to factors such as loss of peer interaction, social isolation, and educational disruptions, which disproportionately affect younger individuals still reliant on social and institutional structures for emotional and psychological support [ 27 ] . Additionally, the challenges of adapting to disrupted daily routines, intensified family conflicts, and heightened pandemic-related concerns further compounded the mental health burden in this group [ 28 ] . Unlike older adults, who may have established coping mechanisms and stable social roles, younger individuals often lack such resilience, making them particularly susceptible to the psychological consequences of unprecedented disruptions like the pandemic. Liver disease is another significant risk factor uniquely affecting depression in this age group. Studies have demonstrated that younger patients with chronic liver disease face a markedly higher risk of depression, with the incidence increasing significantly during a five-year follow-up [ 29 ] . In contrast, the impact of liver disease on depression diminishes in middle-aged and older adults, where the prevalence of comorbidities such as cardiovascular disease and diabetes may overshadow the psychological effects of liver conditions [ 30 , 31 ] . Additionally, older adults may possess more effective coping strategies, developed through life experiences, that mitigate the emotional impact of chronic diseases [ 32 ] . 4.3 Unique Depression Influence Factors in the 65–80-Year Age Group In the 65–80 years group, the risk profile largely mirrored that of the 45–64-year group, with additional risk factors identified: obesity and thyroid disorders. Obesity emerged as a significant risk factor for depression exclusively in the 65–80 years group. This association may be attributed to the compounded effects of physical limitations, social isolation, and negative self-perception, which are particularly pronounced in older populations [ 33 ] . Unlike younger individuals, older adults with obesity often experience a higher burden of comorbidities, such as cardiovascular disease and diabetes, which exacerbate depressive symptoms [ 34 , 35 ] . Beyond the direct physical health impacts of obesity, the stigma associated with being overweight may intensify feelings of inadequacy and depression. Internalization of negative societal attitudes can result in low self-esteem and a poor body image, further contributing to the onset or exacerbation of depressive symptoms [ 36 ] . These age-specific challenges underscore the need for targeted interventions that address both weight management and the mental health needs of this population. [ 33 ] . Thyroid disorders, particularly hypothyroidism, were another significant risk factor uniquely affecting older adults. Thyroid hormones play a crucial role in regulating mood and cognitive function, and their dysregulation can lead to symptoms such as fatigue, cognitive decline, and emotional instability, which are strongly associated with depression [ 36 , 37 ] . The prevalence of thyroid dysfunction increases with age, making this risk factor particularly relevant in the 65–80-year group [ 38 ] . In addition, the impact of aging on the HPT axis may result in decreased responsiveness of the pituitary gland to thyrotropin-releasing hormone (TRH), which could further exacerbate thyroid dysfunction and contribute to depressive symptoms [ 39 ] . These physiological differences may explain why the impact of thyroid disorders on depression is more statistically significant in this age group compared to younger cohorts. 4.4 Other Depression Risk Factors Female and cardiovascular or cerebrovascular diseases were significant risk factors for depression in both 45–64 years group and 65–80 years group. Women's depression is often closely linked to hormonal fluctuations associated with reproductive cycles, such as premenstrual syndrome, postpartum depression, and perimenopausal depression [ 40 , 41 ] . Additionally, factors like social roles, attachment styles, and gender divisions of labor further amplify these differences [ 42 ] . During the COVID-19 pandemic, depressive symptoms among women increased significantly, primarily due to heightened feelings of loneliness, a tendency to ruminate, and increased social media use. In contrast, men were more likely to exhibit externalized aggression [ 43 , 44 ] . The psychological effects of the pandemic on women may have long-lasting repercussions, deepening the gender disparity in depression even after the pandemic's resolution. Heart and brain diseases have been recognized as significant risk factors for depression. This association may be mediated through multiple mechanisms. First, Heart and brain diseases can trigger chronic inflammatory responses, with elevated levels of inflammatory cytokines closely linked to the development of depression [ 45 , 46 ] . Second, these diseases are often accompanied by abnormalities in the neuroendocrine system, such as hyperactivation of the hypothalamic-pituitary-adrenal axis, which may exacerbate depressive symptoms [ 47 ] . Additionally, patients with Heart and brain diseases frequently face disease-related psychological stress, reduced quality of life, and a lack of social support, all of which are psychosocial factors that further increase the risk of depression [ 48 ] . 5. Conclusions The findings of this study underscore the importance of age-specific approaches to addressing depression. Interventions should account for the unique risk and protective factors present in each age group. For younger adults, strategies to mitigate pandemic-related stressors and manage chronic conditions such as liver disease are critical. For middle-aged adults, addressing gender-specific vulnerabilities and managing comorbidities such as cardiovascular diseases should be prioritized. In older adults, targeted interventions to address obesity, thyroid dysfunction, and other age-related health conditions are essential. Abbreviations COVID-19 Corona Virus Disease 2019 NHANES the National Health and Nutrition Examination Survey OR odds ratio PHQ-9 the Patient Health Questionnaire BMI Body mass index COPD chronic obstructive pulmonary disease CDC the Centers for Disease Control and Prevention CIs confidence intervals P valve Probability value DPQ A nine-item depression screening questionnaire NCHS the National Center for Health Statistics TRH thyrotropin-releasing hormone Declarations Ethics approval and consent to participate The NHANES dataset is publicly accessible and contains de-identified information, ensuring participant confidentiality. The survey follows protocols approved by the Research Ethics Review Board of the National Center for Health Statistics (NCHS). Since this study utilized secondary data, no additional ethical approval was necessary. Consent for publication All authors have read and approved the final version of the manuscript and consent to its submission for publication. Consent for publication was obtained from the participants. Availability of data and materials The data used in this study are publicly available from the National Health and Nutrition Examination Survey (NHANES) website: https://www.cdc.gov/nchs/nhanes/index.htm. Competing Interests The authors declare that there is no conflict of interest regarding the publication of this article. Funding This work was supported by the the Lin He’s New Medicine and Clinical Translation Academician Workstation Research Fund (18331204) and the Zhejiang Provincial Natural Science Foundation of China (LQN25H090019). Authors' contributions Jinrong Liu, Wenshuang Sun: methodology, validation, writing–review and editing. Qin Zhu, Hanshu Ji: data curation. Nianjiao Zhou, Qiao Wang, Boqi Liu: software and formal analysis. Lu Teng, Yiheng Peng: software and validation. Peiye Chen, Lili Chen: project administration. Pinguo Fu, Ruixue Cao: project administration, conceptualization, writing–original draft, writing–review and editing. Acknowledgements We would like to express our gratitude to the NHANES program, conducted by the Centers for Disease Control and Prevention, for providing access to the publicly available dataset used in this study. We also acknowledge the contributions of the NHANES participants and staff who made this research possible. References Rossell SL, Neill E, Phillipou A, et al. An overview of current mental health in the general population of Australia during the COVID-19 pandemic: Results from the COLLATE project. Psychiatry Res. 2021,296:113660. Bueno-Notivol J, Gracia-García P, Olaya B, et al. Prevalence of depression during the COVID-19 outbreak: A meta-analysis of community-based studies. Int J Clin Health Psychol. 2021,21(1):100196. Li S, Guo B, Yang Q, et al. 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Zhao JL, Jiang WT, Wang X, et al. Exercise, brain plasticity, and depression. CNS Neurosci Ther. 2020,26(9):885-95. Kondo M, Nakamura Y, Ishida Y, et al. The 5-HT3 receptor is essential for exercise-induced hippocampal neurogenesis and antidepressant effects. Mol Psychiatry. 2015,20(11):1428-37. Mattson MP. Energy intake and exercise as determinants of brain health and vulnerability to injury and disease. Cell Metab. 2012,16(6):706-22. Allison DJ, Nederveen JP, Snijders T, et al. Exercise training impacts skeletal muscle gene expression related to the kynurenine pathway. Am J Physiol Cell Physiol. 2019,316(3):C444-c8. Agudelo LZ, Femenía T, Orhan F, et al. Skeletal muscle PGC-1α1 modulates kynurenine metabolism and mediates resilience to stress-induced depression. Cell. 2014,159(1):33-45. Kurihara K, Shinzato H, Takaesu Y, et al. Drinking behavior patterns may be associated with persistent depressive symptoms after alcohol abstinence in alcohol use disorder. 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Lee CT, Chiang YC, Huang JY, et al. Incidence of Major Depressive Disorder: Variation by Age and Sex in Low-Income Individuals: A Population-Based 10-Year Follow-Up Study. Medicine (Baltimore). 2016,95(15):e3110. Coley RL, Sims J, Dearing E, et al. Locating Economic Risks for Adolescent Mental and Behavioral Health: Poverty and Affluence in Families, Neighborhoods, and Schools. Child Dev. 2018,89(2):360-9. Wong LP, Farid NDN, Alias H, et al. COVID-19 responses and coping in young Malaysians from low-income families. Front Psychiatry. 2023,14:1165023. Jha A, Kiragasur RM, Manohar H, et al. Lived experiences of adolescents with major depressive disorder during the COVID pandemic: A qualitative study from a tertiary care center. J Neurosci Rural Pract. 2024,15(2):334-40. Chang WH, Foster GR, Kelly DA, et al. Depression, anxiety, substance misuse and self-harm in children and young people with rare chronic liver disease. BJPsych Open. 2022,8(5):e146. Choi JM, Chung GE, Kang SJ, et al. Association Between Anxiety and Depression and Nonalcoholic Fatty Liver Disease. Front Med (Lausanne). 2020,7:585618. Ng CH, Xiao J, Chew NWS, et al. Depression in non-alcoholic fatty liver disease is associated with an increased risk of complications and mortality. Front Med (Lausanne). 2022,9:985803. Seo GH, Yoo JJ. Incidence of major depressive disorder over time in patients with liver cirrhosis: A nationwide population-based study in Korea. PLoS One. 2022,17(12):e0278924. Zaninotto P, Steptoe A, Shim EJ. CVD incidence and mortality among people with diabetes and/or hypertension: Results from the English longitudinal study of ageing. PLoS One. 2024,19(5):e0303306. Csige I, Ujvárosy D, Szabó Z, et al. The Impact of Obesity on the Cardiovascular System. J Diabetes Res. 2018,2018:3407306. Yamada T, Kimura-Koyanagi M, Sakaguchi K, et al. Obesity and risk for its comorbidities diabetes, hypertension, and dyslipidemia in Japanese individuals aged 65 years. Sci Rep. 2023,13(1):2346. Gauntlett-Gilbert J, Bhat C, Clinch J. Body mass in adolescents with chronic pain: observational study. Arch Dis Child. 2020,105(5):476-80. Bortolotto VC, Pinheiro FC, Araujo SM, et al. Chrysin reverses the depressive-like behavior induced by hypothyroidism in female mice by regulating hippocampal serotonin and dopamine. Eur J Pharmacol. 2018,822:78-84. Mirahmad M, Mansour A, Moodi M, et al. Prevalence of thyroid dysfunction among Iranian older adults: a cross-sectional study. Sci Rep. 2023,13(1):21651. Lin IC, Chen HH, Yeh SY, et al. Risk of Depression, Chronic Morbidities, and l-Thyroxine Treatment in Hashimoto Thyroiditis in Taiwan: A Nationwide Cohort Study. Medicine (Baltimore). 2016,95(6):e2842. Platt JM, Bates L, Jager J, et al. Is the US Gender Gap in Depression Changing Over Time? A Meta-Regression. Am J Epidemiol. 2021,190(7):1190-206. Stickel S, Wagels L, Wudarczyk O, et al. Neural correlates of depression in women across the reproductive lifespan - An fMRI review. J Affect Disord. 2019,246:556-70. Maji S. Society and 'good woman': A critical review of gender difference in depression. Int J Soc Psychiatry. 2018,64(4):396-405. Madigan S, Racine N, Vaillancourt T, et al. Changes in Depression and Anxiety Among Children and Adolescents From Before to During the COVID-19 Pandemic: A Systematic Review and Meta-analysis. JAMA Pediatr. 2023,177(6):567-81. Guazzini A, Pesce A, Gino F, et al. How the COVID-19 Pandemic Changed Adolescents' Use of Technologies, Sense of Community, and Loneliness: A Retrospective Perception Analysis. Behav Sci (Basel). 2022,12(7). Gou Y, Cheng S, Kang M, et al. Association of Allostatic Load With Depression, Anxiety, and Suicide: A Prospective Cohort Study. Biol Psychiatry. 2024. Han XX, Zhang HY, Kong JW, et al. Inflammatory index is a promising biomarker for maintenance hemodialysis patients with cardiovascular disease. Eur J Med Res. 2024,29(1):544. Park DH, Cho JJ, Yoon JL, et al. The Impact of Depression on Cardiovascular Disease: A Nationwide Population-Based Cohort Study in Korean Elderly. Korean J Fam Med. 2020,41(5):299-305. T AM, Manolis AA, Manolis AS. Emotional Stress in Cardiac and Vascular Diseases. Curr Vasc Pharmacol. 2025. Additional Declarations No competing interests reported. Supplementary Files TableS1.xlsx Supplementary material Table S1 showed more detail data about demographic characteristics of patients between depression and non-depression groups across age categories. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-6475145","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":463167252,"identity":"6c375603-cf2a-470b-b814-c3a469f57156","order_by":0,"name":"Jinrong Liu","email":"","orcid":"","institution":"Yuying Children’s Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jinrong","middleName":"","lastName":"Liu","suffix":""},{"id":463167253,"identity":"bdd28d17-623e-41ad-b05a-a71a104f68cc","order_by":1,"name":"Wenshuang Sun","email":"","orcid":"","institution":"Wenzhou Medical 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study.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6475145/v1/aabf92558717f335c38b674a.png"},{"id":83772736,"identity":"d99c8350-ae24-4ccb-bea0-b66ebdce141a","added_by":"auto","created_at":"2025-06-02 12:58:26","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":171127,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociations between demographic and lifestyle factors with depression across different age groups. \u003c/strong\u003e(A) Ethnic distributions showed significant differences in the 20–44 and 45–64 years groups but not in the 65–80 years group. (B) Higher educational attainment was associated with a lower prevalence of depression in the 20–44 years group. (C) Income levels were consistently higher in the non-depression group across all age groups. (D) Marital status was significantly associated with depression in the 65–80 years group. (E) Female proportions in the depression group were significantly higher in the 45–64 and 65–80 years groups. (F) Heavy alcohol consumption was more prevalent in the depression group across all age groups, while smoking prevalence was lower in the depression group for the younger and middle-aged groups. (G) COVID-19 exposure was significantly associated with depression in the 20–44 years group. Food shopping responsibilities showed varying associations across age groups. Statistical significance is indicated as *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6475145/v1/2e248985d739c0f0cb42283d.png"},{"id":83772734,"identity":"0927fcdb-7634-4618-a556-50decf741df8","added_by":"auto","created_at":"2025-06-02 12:58:26","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":122911,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociations of physical activity and chronic diseases with depression across different age groups.\u003c/strong\u003e (A) Physical activity frequency was inversely associated with depression, with fewer activity sessions linked to higher depression prevalence across all age groups. (B) Obesity (BMI ≥ 30) was significantly more common in the depression group, while normal BMI was predominant in the non-depression group in all age groups. (C) Diabetes and borderline diabetes were associated with depression in the younger and middle-aged groups but not in the older age group. (D) Respiratory diseases, arthritis, liver diseases, heart and brain diseases, thyroid disorders, and gallstones showed varying associations with depression across age groups, with significant differences highlighted. Statistical significance is indicated as *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6475145/v1/d8b0aa20e9cfc5d0a26035b3.png"},{"id":83772738,"identity":"9ea88ef3-25b6-4e46-b638-71febd15523d","added_by":"auto","created_at":"2025-06-02 12:58:26","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":272995,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAge-stratified binary logistic regression analysis highlighting key factors associated with depression in three age groups. \u003c/strong\u003eOR with 95% CI are presented, with significance levels indicated as *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6475145/v1/9aa04f25e9597cf43dcbc923.png"},{"id":96920454,"identity":"0e7f5671-fa34-4cff-9610-96da902b9edd","added_by":"auto","created_at":"2025-11-27 14:15:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2526767,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6475145/v1/3bd2acc4-fc5a-4a89-b047-bbae55ffa5bb.pdf"},{"id":83772740,"identity":"f7bf7299-a4be-4b85-a610-a9ae76b11319","added_by":"auto","created_at":"2025-06-02 12:58:26","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16846,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable S1 showed more detail data about demographic characteristics of patients between depression and non-depression groups across age categories.\u003c/p\u003e","description":"","filename":"TableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6475145/v1/4ecba710a206d6231396af36.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Age-Specific Determinants of Depression Prevalence: Insights from Pre- and Post-COVID- 19 Pandemic Analysis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe COVID-19 pandemic is one of the most significant global public health crises in recent history, profoundly impacting mental health, particularly depression\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. However, systematic studies exploring the pandemic's specific effects on depression and its associated factors remain limited. The pandemic provides a unique opportunity to examine how major external stressors influence depression. Evidence suggests that isolation, reduced social interactions, economic instability, and uncertainty contribute to increased depression risk\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Responses to these stressors may vary by age, gender, and socioeconomic status, highlighting the need for population-specific insights\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Understanding these dynamics is crucial for developing targeted mental health interventions.\u003c/p\u003e \u003cp\u003eThis study utilizes real-world data from the NHANES database to systematically analyze changes in depression prevalence before and after the COVID-19 pandemic, emphasizing the long-term impact of pandemic experiences on depression. By comparing pre- and post-pandemic data, this study investigates the key factors associated with depression that may persist long after the pandemic itself. Through large-scale data analysis, the study aims to provide scientific evidence for targeted mental health interventions and public health policies, enhancing our understanding and management of mental health in future public health emergencies.\u003c/p\u003e"},{"header":"2. Methods","content":"\n \u003ch2\u003e2.1 Study Population\u003c/h2\u003e\n \u003cp\u003eThis study analyzed data obtained from the NHANES database, a comprehensive and nationally representative resource that collects detailed information on health, nutrition, and demographics across the U.S. population. Specifically, data were drawn from the NHANES 2017 to March 2020 pre-pandemic cycles and the August 2021 to August 2023 cycles. These datasets included information on demographics, anthropometric measurements, sleep and physical activity patterns, dietary and substance use behaviors, as well as medical conditions.\u003c/p\u003e\n \u003cp\u003eParticipants included in the analysis were aged 20 to 80 years and had complete data for all relevant variables. Individuals with incomplete or missing data on key variables or those outside the specified age range were excluded to ensure the study focused on adult populations within the given timeframe (Fig. 1).\u003c/p\u003e\n\n\u003ch3\u003e2.2 Exposure Variables and Outcome Variable\u003c/h3\u003e\n\u003cp\u003eDepression levels were evaluated using the Patient Health Questionnaire (PHQ-9), with scores ranging from 0 to 27. A score of ≥ 5 was classified as indicative of depression, whereas scores ≤ 4 were considered normal. Participants were categorized into two groups: those without exposure to the COVID-19 pandemic (pre-pandemic group) and those with pandemic experience (post-pandemic group)\u003csup\u003e[5]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eAge was stratified into three categories: 20–44 years, 45–64 years, and 65–80 years. Additional variables included sex (male or female), race/ethnicity (Mexican American, Other Hispanic, Non-Hispanic White, Non-Hispanic Black, Non-Hispanic Asian, and Other Race), education level (ranging from less than 9th grade to college graduate or higher), marital status (categorized as married, not married, or uncertain), income (measured as the income-to-poverty ratio), lifestyle behaviors, and medical conditions.\u003c/p\u003e\n\u003cp\u003eBody mass index (BMI) was calculated as weight in kilograms divided by height in meters squared (kg/m²) and categorized into underweight (\u0026lt; 18.5), normal weight (18.5–24.9), overweight (25-29.9), and obese (≥ 30). Blood pressure measurements were taken using a mercury sphygmomanometer after a 5-minute rest, with the mean of three readings used for analysis. Alcohol consumption was evaluated based on drinking frequency over the past 12 months, with alcohol abuse defined as consuming five or more drinks per day for men and four or more for women. Smoking status was determined as having smoked at least 100 cigarettes in one’s lifetime.\u003c/p\u003e\n\u003cp\u003eRespiratory conditions included self-reported diagnoses of asthma, chronic obstructive pulmonary disease (COPD), emphysema, or chronic bronchitis. Cardiovascular and cerebrovascular conditions encompassed coronary heart disease, stroke, and congestive heart failure. Detailed methodologies for these measurements are publicly available on the Centers for Disease Control and Prevention (CDC) website at www.cdc.gov/nchs/nhanes/.\u003c/p\u003e\n\u003ch3\u003e2.3 Statistical Analysis\u003c/h3\u003e\n\u003cp\u003eThe statistical analysis was conducted using Stata software (version 16.0, StataCorp, College Station, TX) and EmpowerStats (www.empowerstats.com), with a significance level set at P \u0026lt; 0.05. Continuous variables were presented as means with 95% confidence intervals (CIs), while categorical variables were expressed as percentages with 95% CIs. To compare the differences between groups, the weighted chi-square test and the weighted logistic regression model were used for continuous variables and classification, respectively.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Demographic Characteristics Between the Depressed and Non-Depressed Groups\u003c/h2\u003e\n \u003cp\u003eAcross the pre-COVID-19 and post-COVID-19 NHANES data cycles, a total of 27493 participants aged 20 to 80 years were initially surveyed. Of these, 3247 individuals met the study\u0026apos;s inclusion criteria. Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e compares the characteristics of the depression group (n\u0026thinsp;=\u0026thinsp;871) and the non-depression group (n\u0026thinsp;=\u0026thinsp;2,376). Significant differences were observed between the two groups in terms of age distribution (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), sex distribution (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), race and ethnicity (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), education level (p\u0026thinsp;=\u0026thinsp;0.002), income level (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), BMI (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), physical activity levels (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), alcohol use (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), smoking status (p\u0026thinsp;=\u0026thinsp;0.002), COVID-19 experience (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), respiratory diseases (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), heart and brain diseases (p\u0026thinsp;=\u0026thinsp;0.002), liver disease (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), primary food shopper (p\u0026thinsp;=\u0026thinsp;0.006), and shared food shopping responsibilities (p\u0026thinsp;=\u0026thinsp;0.005). Additionally, continuous variables such as average diastolic blood pressure (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and average pulse rate (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) demonstrated significant differences (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\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\u003eFrequency Table of Descriptive Results in the Non-depression and Depression Groups.\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\u003eTotal\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;3247)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNon-depression\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;2376)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDepression\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;871)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge, mean (95% CI) y\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=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u0026ndash;44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.474 (0.450 to 0.497)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.443 (0.416 to 0.471)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.56 (0.516 to 0.603)\u003c/p\u003e\n \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\u003e45\u0026ndash;64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.362 (0.340 to 0.385)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.377 (0.351 to 0.404)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.319 (0.279 to 0.362)\u003c/p\u003e\n \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;65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.165 (0.150 to 0.180)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.180 (0.162 to 0.198)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.121 (0.099 to 0.147)\u003c/p\u003e\n \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\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=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\n \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=\"left\"\u003e\n \u003cp\u003e0.515 (0.491 to 0.538)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.537 (0.509 to 0.564)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.452 (0.408 to 0.497)\u003c/p\u003e\n \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\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.485 (0.462 to 0.509)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.463 (0.436 to 0.491)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.548 (0.503 to 0.592)\u003c/p\u003e\n \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\u003cstrong\u003eRace and ethnicity 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=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMexican American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.073 (0.064 to 0.084)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.074 (0.063 to 0.086)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.072 (0.055 to 0.093)\u003c/p\u003e\n \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\u003eOther Hispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.067 (0.058 to 0.077)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.059 (0.049 to 0.070)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.091 (0.072 to 0.115)\u003c/p\u003e\n \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-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.682 (0.662 to 0.701)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.696 (0.673 to 0.718)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.642 (0.602 to 0.681)\u003c/p\u003e\n \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-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09 (0.081 to 0.101)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.085 (0.075 to 0.097)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.104 (0.084 to 0.129)\u003c/p\u003e\n \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-Hispanic Asian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.043 (0.036 to 0.052)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.048 (0.039 to 0.059)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03 (0.018 to 0.050)\u003c/p\u003e\n \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\u003eOther Race\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.044 (0.037 to 0.054)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.039 (0.030 to 0.049)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06 (0.044 to 0.082)\u003c/p\u003e\n \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\u003cstrong\u003eEducation, 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=\"char\"\u003e\n \u003cp\u003e0.002*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLess than 9th grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.014 (0.010 to 0.018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.013 (0.009 to 0.019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.016 (0.01 to 0.026)\u003c/p\u003e\n \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\u003e9-11th grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.047 (0.039 to 0.056)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.043 (0.035 to 0.054)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.057 (0.043 to 0.075)\u003c/p\u003e\n \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\u003eHigh school graduate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.209 (0.190 to 0.229)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.208 (0.186 to 0.232)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.214 (0.179 to 0.253)\u003c/p\u003e\n \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 AA degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.313 (0.292 to 0.335)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.299 (0.276 to 0.325)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.352 (0.312 to 0.394)\u003c/p\u003e\n \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 graduate or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.417 (0.394 to 0.441)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.437 (0.410 to 0.465)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.361 (0.318 to 0.407)\u003c/p\u003e\n \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\u003cstrong\u003eMarital 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=\"char\"\u003e\n \u003cp\u003e0.698\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=\"left\"\u003e\n \u003cp\u003e0.610 (0.587 to 0.633)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.607 (0.579 to 0.634)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.619 (0.575 to 0.661)\u003c/p\u003e\n \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=\"left\"\u003e\n \u003cp\u003e0.188 (0.171 to 0.207)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.188 (0.167 to 0.210)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.189 (0.158 to 0.224)\u003c/p\u003e\n \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\u003eUncertain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.202 (0.183 to 0.222)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.206 (0.183 to 0.230)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.192 (0.159 to 0.230)\u003c/p\u003e\n \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\u003cstrong\u003eFamily income to poverty ratio, 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=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.076 (0.067 to 0.087)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.062 (0.053 to 0.074)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.116 (0.094 to 0.142)\u003c/p\u003e\n \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\u003e1\u0026ndash;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.291 (0.271 to 0.310)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.267 (0.245 to 0.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.358 (0.319 to 0.400)\u003c/p\u003e\n \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\u003e3\u0026ndash;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.633 (0.612 to 0.654)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.671 (0.646 to 0.694)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.526 (0.482 to 0.569)\u003c/p\u003e\n \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\u003cstrong\u003eBMI group, 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=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;18.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.010 (0.006 to 0.016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.009 (0.005 to 0.015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.013 (0.006 to 0.032)\u003c/p\u003e\n \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\u003e18.5\u0026ndash;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.263 (0.242 to 0.285)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.265 (0.241 to 0.290)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.257 (0.217 to 0.300)\u003c/p\u003e\n \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;25\u0026ndash;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.339 (0.317 to 0.362)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.368 (0.341 to 0.395)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.257 (0.221 to 0.297)\u003c/p\u003e\n \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;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.388 (0.366 to 0.411)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.359 (0.333 to 0.385)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.473 (0.430 to 0.518)\u003c/p\u003e\n \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\u003cstrong\u003eActivity group, 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=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;1 times/week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.268 (0.248 to 0.289)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.252 (0.229 to 0.276)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.316 (0.277 to 0.358)\u003c/p\u003e\n \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\u003e1\u0026ndash;2 times/week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.177 (0.159 to 0.196)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.162 (0.142 to 0.184)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.220 (0.184 to 0.261)\u003c/p\u003e\n \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;3 times/week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.555 (0.531 to 0.578)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.586 (0.559 to 0.613)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.464 (0.420 to 0.509)\u003c/p\u003e\n \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\u003cstrong\u003eAlcohol abuse, 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=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;4/5 drinks per day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.871 (0.856 to 0.885)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.903 (0.887 to 0.917)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.781 (0.743 to 0.814)\u003c/p\u003e\n \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\u0026lt;\u0026thinsp;4/5 drinks per day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.129 (0.115 to 0.144)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.097 (0.083 to 0.113)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.219 (0.186 to 0.257)\u003c/p\u003e\n \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\u003cstrong\u003eSmoking, 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=\"char\"\u003e\n \u003cp\u003e0.002*\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=\"left\"\u003e\n \u003cp\u003e0.398 (0.375 to 0.421)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.382 (0.355 to 0.409)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.443 (0.400 to 0.488)\u003c/p\u003e\n \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=\"left\"\u003e\n \u003cp\u003e0.602 (0.579 to 0.625)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.618 (0.590 to 0.645)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.557 (0.512 to 0.600)\u003c/p\u003e\n \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\u003cstrong\u003eCOVID experience\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.497 (0.473 to 0.520)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.467 (0.440 to 0.494)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.582 (0.536 to 0.626)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes\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=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\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=\"left\"\u003e\n \u003cp\u003e0.082 (0.072 to 0.094)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.084 (0.071 to 0.099)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.077 (0.061 to 0.097)\u003c/p\u003e\n \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=\"left\"\u003e\n \u003cp\u003e0.897 (0.884 to 0.909)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.901 (0.886 to 0.915)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.886 (0.861 to 0.907)\u003c/p\u003e\n \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\u003eBorderline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.021 (0.015 to 0.027)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.015 (0.01 to 0.022)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.037 (0.025 to 0.055)\u003c/p\u003e\n \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\u003cstrong\u003eRespiratory diseases\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.336 (0.315 to 0.358)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.314 (0.290 to 0.340)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.397 (0.355 to 0.441)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHeart and brain disease\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.057 (0.049 to 0.067)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.049 (0.041 to 0.060)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.078 (0.059 to 0.103)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eArthritis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.252 (0.233 to 0.272)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.246 (0.224 to 0.269)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.272 (0.234 to 0.313)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.129\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eThyroid disease\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.102 (0.090 to 0.116)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.101 (0.087 to 0.118)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.105 (0.082 to 0.132)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.795\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLiver disease\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.038 (0.030 to 0.047)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.031 (0.023 to 0.041)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.057 (0.040 to 0.079)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGallstones\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.089 (0.076 to 0.104)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.086 (0.071 to 0.103)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.099 (0.075 to 0.130)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.242\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCancer\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.106 (0.093 to 0.120)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.111 (0.096 to 0.128)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.092 (0.070 to 0.119)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.118\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eShared meal planning\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.584 (0.561 to 0.607)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.589 (0.562 to 0.616)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.569 (0.525 to 0.612)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.314\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMain food shopper\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.624 (0.601 to 0.647)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.610 (0.583 to 0.638)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.664 (0.620 to 0.706)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.006*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eShared food shopping duty\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.609 (0.586 to 0.631)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.623 (0.596 to 0.649)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.568 (0.524 to 0.611)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.005*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDescriptive Results of Continuous Data in the Non-depression and Depression Groups.\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\u003eTotal\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;3247)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNon-depression group\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;2376)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDepression group\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;871)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eVigorous activities minutes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.874(36.187 to 39.560)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.364 (35.396 to 39.334)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.329 (36.057 to 42.602)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.317\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eModerate activities minutes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.013 (30.382 to 33.643)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.847 (30.917 to 34.776)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.627 (26.592 to 32.662)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSedentary behavior\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e372.807 (365.881 to 379.734)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e370.856 (362.927 to 378.785)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e378.389 (364.231 to 392.546)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.350\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSleep hours in weekdays\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.571 (7.525 to 7.616)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.572 (7.524 to 7.622)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.566 (7.461 to 7.670)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.893\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSleep hours in weekend\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.232 (8.182 to 8.282)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.220 (8.166 to 8.275)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.266 (8.152 to 8.380)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.436\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSystolic blood pressure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e119.724 (119.182 to 120.269)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e119.722 (119.088 to 120.356)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e119.736 (118.680 to 120.792)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.983\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiastolic blood pressure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74.321 (73.956 to 74.686)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73.893 (73.472 to 74.314)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75.546 (74.822 to 76.270)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePulse\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69.490 (69.096 to 69.885)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68.901 (68.454 to 69.348)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71.176 (70.359 to 71.992)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\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\u003eIn contrast, no significant differences were observed between the groups for variables such as arthritis, thyroid disease, cancer, shared meal planning responsibilities, vigorous activity minutes, moderate activity minutes, sedentary behavior, weekday sleep duration, weekend sleep duration, and systolic blood pressure.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Determinants of Depression from Regression Analysis\u003c/h2\u003e\n \u003cp\u003eLogistic regression analysis was conducted to identify factors associated with depression (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Significant predictors included the experience of COVID-19, which substantially increased the likelihood of depression (OR\u0026thinsp;=\u0026thinsp;1.820, 95% CI: 1.439\u0026ndash;2.302, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Age was inversely associated with depression, with older individuals showing lower odds (OR\u0026thinsp;=\u0026thinsp;0.592, 95% CI: 0.502\u0026ndash;0.698, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Female was associated with a higher risk compared to males (OR\u0026thinsp;=\u0026thinsp;1.491, 95% CI: 1.168\u0026ndash;1.903, p\u0026thinsp;=\u0026thinsp;0.001). Protective factors included higher physical activity levels (OR\u0026thinsp;=\u0026thinsp;0.752, 95% CI: 0.659\u0026ndash;0.858, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and greater income (OR\u0026thinsp;=\u0026thinsp;0.754, 95% CI: 0.636\u0026ndash;0.894, p\u0026thinsp;=\u0026thinsp;0.001). In contrast, alcohol abuse significantly increased the odds of depression (OR\u0026thinsp;=\u0026thinsp;2.277, 95% CI: 1.680\u0026ndash;3.085, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Several comorbidities were also linked to higher odds of depression, including respiratory diseases (OR\u0026thinsp;=\u0026thinsp;1.366, 95% CI: 1.078\u0026ndash;1.731, p\u0026thinsp;=\u0026thinsp;0.009), heart and brain diseases (OR\u0026thinsp;=\u0026thinsp;1.896, 95% CI: 1.227\u0026ndash;2.929, p\u0026thinsp;=\u0026thinsp;0.004), and liver disease (OR\u0026thinsp;=\u0026thinsp;1.735, 95% CI: 1.083\u0026ndash;2.780, p\u0026thinsp;=\u0026thinsp;0.025). Other variables, such as race and ethnicity, education, BMI, diastolic blood pressure, pulse rate, smoking, and food shopping responsibilities, did not show significant associations with depression.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eResults of Binary Logistic Regression Analysis for Factors Associated with Depression\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDPQRESULT\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP Value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCOVID experience\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.820 (1.439 to 2.302)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.592 (0.502 to 0.698)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.491 (1.168 to 1.903)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace and ethnicity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.991 (0.914 to 1.074)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.825\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.062 (0.929 to 1.213)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.365\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eActivity group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.752 (0.659 to 0.858)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFamily income to poverty ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.754 (0.636 to 0.894)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlcohol abuse\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.277 (1.680 to 3.085)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI Group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.070 (0.915 to 1.251)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.355\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiastolic blood pressure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.009 (0.998 to 1.020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.443\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage Pulse\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.002 (0.991 to 1.012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.699\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.985 (0.836 to 1.161)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.886\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.891 (0.695 to 1.143)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.377\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRespiratory diseases\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.366 (1.078 to 1.731)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.009*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHeart and brain disease\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.896 (1.227 to 2.929)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.004*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLiver disease\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.735 (1.083 to 2.780)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.025*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMain food shopper\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.120 (0.861 to 1.457)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.393\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eShared food shopping duty\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.852 (0.678 to 1.072)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.177\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003ch3\u003e3.3 Demographic Characteristics Between the Depressed and Non-Depressed Groups\u003c/h3\u003e\n\u003cp\u003eWe further divided the participants into three age groups: 20\u0026ndash;44 years (n\u0026thinsp;=\u0026thinsp;1310), 45\u0026ndash;64 years (n\u0026thinsp;=\u0026thinsp;1201), and 65\u0026ndash;80 years (n\u0026thinsp;=\u0026thinsp;731). Detailed demographic information for each group is presented in table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e and Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. Subsequently, we conducted statistical analyses comparing the depressed and non-depressed individuals within each age group.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBasic characteristics of patients Between Depression and Non-Depression Groups Across Age Categories\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNon-depression group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDepression group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP Value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e20-44y\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 \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVigorous activities minutes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.982 (37.31 to 46.655)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.089 (38.368 to 49.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.490\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate activities minutes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.132 (32.806 to 41.459)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.79 (28.557 to 41.024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.422\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSedentary behavior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e370.486 (352.831 to 388.141)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e382.416 (355.681 to 409.152)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.341\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSleep hours in weekdays\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.582 (7.474 to 7.689)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.679 (7.508 to 7.851)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.213\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSleep hours in weekend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.384 (8.277 to 8.492)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.459 (8.26 to 8.659)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.382\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSystolic blood pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e114.427 (113.385 to 115.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e115.343 (113.794 to 116.892)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.230\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiastolic blood pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72.89 (72.042 to 73.738)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74.932 (73.694 to 76.169)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePulse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70.385 (69.473 to 71.297)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72.656 (71.151 to 74.162)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e45-64y\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 \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVigorous activities minutes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.714 (29.637 to 37.792)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.862 (26.287 to 41.437)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.962\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate activities minutes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.273 (25.066 to 31.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.729 (19.004 to 30.453)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.207\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSedentary behavior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e379.909 (361.956 to 397.862)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e375.35 (343.509 to 407.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.740\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSleep hours in weekdays\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.489 (7.388 to 7.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.278 (7.042 to 7.515)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.018*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSleep hours in weekend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.209 (8.095 to 8.323)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.011 (7.754 to 8.267)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.048*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSystolic blood pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e121.966 (120.511 to 123.421)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e122.797 (120.107 to 125.487)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.452\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiastolic blood pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76.163 (75.248 to 77.079)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77.518 (75.766 to 79.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePulse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68.28 (67.318 to 69.241)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69.307 (67.405 to 71.208)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.181\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e65-80y\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 \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVigorous activities minutes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.632 (28.554 to 38.711)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.735 (22.943 to 40.527)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.693\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate activities minutes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.872 (24.676 to 39.069)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.67 (9.677 to 27.663)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.011*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSedentary behavior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e352.762 (333.289 to 372.235)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e367.782 (329.098 to 406.466)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.375\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSleep hours in weekdays\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.725 (7.589 to 7.861)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.796 (7.45 to 8.142)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.578\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSleep hours in weekend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.84 (7.7 to 7.981)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.044 (7.671 to 8.417)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.124\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSystolic blood pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e128.077 (126.344 to 129.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e131.967 (128.475 to 135.458)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.019*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiastolic blood pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71.601 (70.592 to 72.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73.191 (70.988 to 75.395)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAverage Pulse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.543 (65.333 to 67.753)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69.257 (67 to 71.514)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.014*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch3\u003e3.3.1 Demographic Differences and Depression\u003c/h3\u003e\n\u003cp\u003eSignificant demographic differences were observed across age groups. In the 20\u0026ndash;44 years group, non-Hispanic Whites were predominantly in the non-depression group (p\u0026thinsp;=\u0026thinsp;0.049), while Mexican Americans and non-Hispanic Blacks showed higher proportions in the depression group. This trend was also evident in the 45\u0026ndash;64 years group (p\u0026thinsp;=\u0026thinsp;0.017) but did not reach statistical significance in the 65\u0026ndash;80 years group (p\u0026thinsp;=\u0026thinsp;0.364) (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA). Educational attainment was significantly associated with depression in the 20\u0026ndash;44 years group, with individuals holding a college degree or higher more likely to belong to the non-depression group (p\u0026thinsp;=\u0026thinsp;0.024). However, no significant differences were noted in the 45\u0026ndash;64 years or 65\u0026ndash;80 years groups (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e\n\u003cp\u003eIncome level consistently showed associations with depression status across all age groups, with higher income levels overrepresented in the non-depression group in the 20\u0026ndash;44 years (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), 45\u0026ndash;64 years (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and 65\u0026ndash;80 years groups (p\u0026thinsp;=\u0026thinsp;0.009) (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC). Marital status exhibited significant associations with depression in the 65\u0026ndash;80 years group (p\u0026thinsp;=\u0026thinsp;0.028) (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eD). The proportion of females in the depression group was higher than in the non-depression group across all age groups, though the difference in the 20\u0026ndash;44 years group was not significance (p\u0026thinsp;=\u0026thinsp;0.055), the statistically significant differences were observed in the 45\u0026ndash;64 years (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and 65\u0026ndash;80 years groups (p\u0026thinsp;=\u0026thinsp;0.010) (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eE).\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3.2 Lifestyle Factors and Depression\u003c/h2\u003e\n \u003cp\u003eHeavy alcohol consumption (\u0026ge;\u0026thinsp;4/5 drinks per day) was more frequent in the depression group across all age groups (20\u0026ndash;44 years: p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; 45\u0026ndash;64 years: p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; 65\u0026ndash;80 years: p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Conversely, smoking was less prevalent in the depression group compared to the non-depression group in both the 20\u0026ndash;44 years (p\u0026thinsp;=\u0026thinsp;0.044) and 45\u0026ndash;64 years (p\u0026thinsp;=\u0026thinsp;0.004) groups, though the difference was not significant in the 65\u0026ndash;80 years group (p\u0026thinsp;=\u0026thinsp;0.173) (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eF).\u003c/p\u003e\n \u003cp\u003eCOVID-19 exposure was significantly associated with depression in the 20\u0026ndash;44 years group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) but not in the 45\u0026ndash;64 years (p\u0026thinsp;=\u0026thinsp;0.195) or 65\u0026ndash;80 years groups (p\u0026thinsp;=\u0026thinsp;0.895). Shared responsibilities in meal planning and food shopping showed limited associations. In the 20\u0026ndash;44 years group, individuals who shared food shopping duties were more likely to belong to the non-depression group (p\u0026thinsp;=\u0026thinsp;0.006), whereas in the 65\u0026ndash;80 years group, the primary responsibility for food shopping was more common in the non-depression group (p\u0026thinsp;=\u0026thinsp;0.039) (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eG).\u003c/p\u003e\n \u003cp\u003ePhysical activity showed a consistent relationship with depression across all age groups, with individuals engaging in less than one activity session per week being significantly more likely to belong to the depression group, while those with three or more sessions per week were predominantly in the non-depression group (20\u0026ndash;44 years: p\u0026thinsp;=\u0026thinsp;0.012; 45\u0026ndash;64 years: p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; 65\u0026ndash;80 years: p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3.3 Chronic Diseases and Depression\u003c/h2\u003e\n \u003cp\u003eChronic diseases demonstrated strong associations with depression across all age groups. Obesity (BMI\u0026thinsp;\u0026ge;\u0026thinsp;30) was significantly more prevalent in the depression group, while normal BMI (18.5\u0026ndash;25) was more common in the non-depression group (20\u0026ndash;44 years: p\u0026thinsp;=\u0026thinsp;0.003; 45\u0026ndash;64 years: p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; 65\u0026ndash;80 years: p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB). Diabetes status was also associated with depression. In the 20\u0026ndash;44 years group, individuals with diabetes or borderline diabetes were more likely to belong to the depression group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This association persisted in the 45\u0026ndash;64 years group (p\u0026thinsp;=\u0026thinsp;0.015) but was not statistically significant in the 65\u0026ndash;80 years group (p\u0026thinsp;=\u0026thinsp;0.201) (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC).\u003c/p\u003e\n \u003cp\u003eRespiratory diseases, arthritis, and liver disease were more common in the depression group in the 20\u0026ndash;44 years (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, p\u0026thinsp;=\u0026thinsp;0.027, and p\u0026thinsp;=\u0026thinsp;0.002, respectively) and 45\u0026ndash;64 years group p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, and p\u0026thinsp;=\u0026thinsp;0.023, respectively). Heart and brain diseases were significantly associated with depression in both the 45\u0026ndash;64 years (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and 65\u0026ndash;80 years groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Thyroid disorders were more prevalent in the depression group in the 65\u0026ndash;80 years group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Gallstones were more prevalent in the depression group in the 45\u0026ndash;65 years group (p\u0026thinsp;=\u0026thinsp;0.038) (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eD)\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Age-Stratified Factors Associated with Depression Risk\u003c/h2\u003e\n \u003cp\u003eStratified binary logistic regression analysis revealed distinct age-specific factors influencing depression (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Alcohol abuse and higher physical activity consistently influenced depression risk across all age groups. Alcohol abuse significantly increased depression risk (20\u0026ndash;44 years: OR\u0026thinsp;=\u0026thinsp;2.279, 95% CI: 1.393\u0026ndash;3.728, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; 45\u0026ndash;64 years: OR\u0026thinsp;=\u0026thinsp;2.450, 95% CI: 1.506\u0026ndash;3.987, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; 65\u0026ndash;80 years OR\u0026thinsp;=\u0026thinsp;2.547, 95% CI: 1.368\u0026ndash;4.743, p\u0026thinsp;=\u0026thinsp;0.003). Conversely, higher physical activity consistently reduced depression risk (20\u0026ndash;44 years: OR\u0026thinsp;=\u0026thinsp;0.746, 95% CI: 0.599\u0026ndash;0.928, p\u0026thinsp;=\u0026thinsp;0.009; 45\u0026ndash;64 years: OR\u0026thinsp;=\u0026thinsp;0.721, 95% CI: 0.564\u0026ndash;0.923, p\u0026thinsp;=\u0026thinsp;0.009; 65\u0026ndash;80 years: OR\u0026thinsp;=\u0026thinsp;0.664, 95% CI: 0.491\u0026ndash;0.897, p\u0026thinsp;=\u0026thinsp;0.008).\u003c/p\u003e\n \u003cp\u003eOther factors varied by age. Higher income (OR\u0026thinsp;=\u0026thinsp;0.680, 95% CI: 0.531\u0026ndash;0.872, p\u0026thinsp;=\u0026thinsp;0.002) and COVID-19 experience (OR\u0026thinsp;=\u0026thinsp;2.546, 95% CI: 1.783\u0026ndash;3.623, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were both significant and unique influence factors, which only have significance difference in younger adults (20\u0026ndash;44 years). Higher BMI (OR\u0026thinsp;=\u0026thinsp;1.660, 95% CI: 1.139\u0026ndash;2.421, p\u0026thinsp;=\u0026thinsp;0.008), thyroid disease (OR\u0026thinsp;=\u0026thinsp;1.756, 95% CI: 1.001\u0026ndash;3.079, p\u0026thinsp;=\u0026thinsp;0.049) and longer weekend sleep (OR\u0026thinsp;=\u0026thinsp;1.291, 95% CI: 1.020\u0026ndash;1.634, p\u0026thinsp;=\u0026thinsp;0.033) were only significant in 65\u0026ndash;80 years group. Female and heart/brain diseases were risk factors both in 45\u0026ndash;64 years group and 65\u0026ndash;80 years group.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussions","content":"\u003cp\u003eWe observed that age acts as a protective factor against depression in our analysis\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. In contrast, younger individuals face higher risks due to higher risks due to mechanisms like circadian rhythm disturbances, neurological and hormonal changes, and chronic social stress\u003csup\u003e[\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e–\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. To further understand these age-specific effects, particularly in the context of post-pandemic depression, we conducted age-stratified analyses to identify distinct risk and protective factors, providing a nuanced perspective on the pandemic's long-term influence on mental health.\u003c/p\u003e\u003ch2\u003e4.1 Physical Activity and Alcohol Use in Depression\u003c/h2\u003e\u003cp\u003ePromoting physical activity has been increasingly recognized as a potential strategy for preventing depression\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Studies have shown that low levels of physical activity are a significant risk factor for depression, while regular physical activity is associated with a protective effect against depressive episodes\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Depression is often accompanied by reduced levels of serotonin and norepinephrine in the hippocampus, prefrontal cortex, and striatum\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Exercise has been shown to elevate these neurotransmitter levels, reshape brain structures, preserve hippocampal integrity, and maintain white matter volume, thereby delaying cognitive decline\u003csup\u003e[\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e–\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Additionally, exercise may alleviate depressive symptoms by reducing inflammatory markers and enhancing kynurenine metabolism in skeletal muscle, preventing its accumulation in the brain\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. However, the causality and directionality of this association remain unclear, as physical activity may help prevent depression, but depressive states may also lead to reduced physical activity\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAlcohol abuse remains strongly linked to depression, with a bidirectional relationship where each condition exacerbates the other. A study found that 42% of individuals with alcohol use disorder exhibited depressive symptoms, which dropped to 6% after abstinence, suggesting alcohol may mask underlying depression\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Heavy drinkers have been reported to be at a higher risk of developing depression compared to moderate drinkers or abstainers\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. Alcohol disrupts the serotonin and dopamine systems, which are critical for mood regulation, leading to neurochemical imbalances that worsen depressive symptoms \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Moreover, alcohol abuse has been associated with heightened stress, increased anxiety, and social challenges, such as isolation, interpersonal conflicts, and financial difficulties, which collectively intensify feelings of loneliness and hopelessness, further deepening depression \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThese findings suggest contrasting roles for physical activity and alcohol use in mental health, emphasizing the importance of encouraging healthy behaviors while addressing harmful habits to reduce the risk of depression.\u003c/p\u003e\u003ch2\u003e4.2 Unique Depression influence Factors in the 20–44-Year Age Group\u003c/h2\u003e\u003cp\u003eIn our study, we found that income was a significant protective factor in younger adults but not in older age groups and the proportion of individuals with low-income levels was significantly higher in the younger group compared to the middle-aged and older groups. Higher income levels may alleviate financial stress, improve access to mental health resources, and facilitate healthier lifestyles, thereby reducing the risk of depression\u003csup\u003e[\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e–\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. Adolescents attending schools with lower socioeconomic status exhibit higher levels of depressive and anxiety symptoms compared to those in affluent schools\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. Low income can also exacerbate family dynamics, leading to increased parent-child conflict and negatively impacting emotional well-being. Economic pressures within families often heighten tensions, disproportionately affecting younger females who are more sensitive to stressors\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eConversely, COVID-19-related stress stands out as a prominent risk factor uniquely impacting the 20–44-year demographic. The dramatic increase of depression in pandemic has been attributed to factors such as loss of peer interaction, social isolation, and educational disruptions, which disproportionately affect younger individuals still reliant on social and institutional structures for emotional and psychological support\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. Additionally, the challenges of adapting to disrupted daily routines, intensified family conflicts, and heightened pandemic-related concerns further compounded the mental health burden in this group \u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. Unlike older adults, who may have established coping mechanisms and stable social roles, younger individuals often lack such resilience, making them particularly susceptible to the psychological consequences of unprecedented disruptions like the pandemic.\u003c/p\u003e\u003cp\u003eLiver disease is another significant risk factor uniquely affecting depression in this age group. Studies have demonstrated that younger patients with chronic liver disease face a markedly higher risk of depression, with the incidence increasing significantly during a five-year follow-up\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. In contrast, the impact of liver disease on depression diminishes in middle-aged and older adults, where the prevalence of comorbidities such as cardiovascular disease and diabetes may overshadow the psychological effects of liver conditions\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. Additionally, older adults may possess more effective coping strategies, developed through life experiences, that mitigate the emotional impact of chronic diseases \u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003ch2\u003e4.3 Unique Depression Influence Factors in the 65–80-Year Age Group\u003c/h2\u003e\u003cp\u003eIn the 65–80 years group, the risk profile largely mirrored that of the 45–64-year group, with additional risk factors identified: obesity and thyroid disorders. Obesity emerged as a significant risk factor for depression exclusively in the 65–80 years group. This association may be attributed to the compounded effects of physical limitations, social isolation, and negative self-perception, which are particularly pronounced in older populations\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. Unlike younger individuals, older adults with obesity often experience a higher burden of comorbidities, such as cardiovascular disease and diabetes, which exacerbate depressive symptoms\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. Beyond the direct physical health impacts of obesity, the stigma associated with being overweight may intensify feelings of inadequacy and depression. Internalization of negative societal attitudes can result in low self-esteem and a poor body image, further contributing to the onset or exacerbation of depressive symptoms\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. These age-specific challenges underscore the need for targeted interventions that address both weight management and the mental health needs of this population.\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThyroid disorders, particularly hypothyroidism, were another significant risk factor uniquely affecting older adults. Thyroid hormones play a crucial role in regulating mood and cognitive function, and their dysregulation can lead to symptoms such as fatigue, cognitive decline, and emotional instability, which are strongly associated with depression\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. The prevalence of thyroid dysfunction increases with age, making this risk factor particularly relevant in the 65–80-year group\u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e. In addition, the impact of aging on the HPT axis may result in decreased responsiveness of the pituitary gland to thyrotropin-releasing hormone (TRH), which could further exacerbate thyroid dysfunction and contribute to depressive symptoms\u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. These physiological differences may explain why the impact of thyroid disorders on depression is more statistically significant in this age group compared to younger cohorts.\u003c/p\u003e\u003ch2\u003e4.4 Other Depression Risk Factors\u003c/h2\u003e\u003cp\u003eFemale and cardiovascular or cerebrovascular diseases were significant risk factors for depression in both 45–64 years group and 65–80 years group. Women's depression is often closely linked to hormonal fluctuations associated with reproductive cycles, such as premenstrual syndrome, postpartum depression, and perimenopausal depression\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e. Additionally, factors like social roles, attachment styles, and gender divisions of labor further amplify these differences\u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e. During the COVID-19 pandemic, depressive symptoms among women increased significantly, primarily due to heightened feelings of loneliness, a tendency to ruminate, and increased social media use. In contrast, men were more likely to exhibit externalized aggression\u003csup\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e. The psychological effects of the pandemic on women may have long-lasting repercussions, deepening the gender disparity in depression even after the pandemic's resolution.\u003c/p\u003e\u003cp\u003eHeart and brain diseases have been recognized as significant risk factors for depression. This association may be mediated through multiple mechanisms. First, Heart and brain diseases can trigger chronic inflammatory responses, with elevated levels of inflammatory cytokines closely linked to the development of depression\u003csup\u003e[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/sup\u003e. Second, these diseases are often accompanied by abnormalities in the neuroendocrine system, such as hyperactivation of the hypothalamic-pituitary-adrenal axis, which may exacerbate depressive symptoms\u003csup\u003e[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/sup\u003e. Additionally, patients with Heart and brain diseases frequently face disease-related psychological stress, reduced quality of life, and a lack of social support, all of which are psychosocial factors that further increase the risk of depression\u003csup\u003e[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThe findings of this study underscore the importance of age-specific approaches to addressing depression. Interventions should account for the unique risk and protective factors present in each age group. For younger adults, strategies to mitigate pandemic-related stressors and manage chronic conditions such as liver disease are critical. For middle-aged adults, addressing gender-specific vulnerabilities and managing comorbidities such as cardiovascular diseases should be prioritized. In older adults, targeted interventions to address obesity, thyroid dysfunction, and other age-related health conditions are essential.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCOVID-19\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCorona Virus Disease 2019\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNHANES\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ethe National Health and Nutrition Examination Survey\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eodds ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePHQ-9\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ethe Patient Health Questionnaire\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBody mass index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCOPD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003echronic obstructive pulmonary disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCDC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ethe Centers for Disease Control and Prevention\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCIs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003econfidence intervals\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eP valve\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProbability value\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDPQ\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eA nine-item depression screening questionnaire\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNCHS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ethe National Center for Health Statistics\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTRH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ethyrotropin-releasing hormone\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe NHANES dataset is publicly accessible and contains de-identified information, ensuring participant confidentiality. The survey follows protocols approved by the Research Ethics Review Board of the National Center for Health Statistics (NCHS). Since this study utilized secondary data, no additional ethical approval was necessary.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have read and approved the final version of the manuscript and consent to its submission for publication. Consent for publication was obtained from the participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Availability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in this study are publicly available from the National Health and Nutrition Examination Survey (NHANES) website: https://www.cdc.gov/nchs/nhanes/index.htm.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Competing Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there is no conflict of interest regarding the publication of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the the Lin He\u0026rsquo;s New Medicine and Clinical Translation Academician Workstation Research Fund (18331204) and the Zhejiang Provincial Natural Science Foundation of China (LQN25H090019).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Authors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJinrong Liu, Wenshuang Sun: methodology, validation, writing\u0026ndash;review and editing. Qin Zhu, Hanshu Ji: data curation. Nianjiao Zhou, Qiao Wang, Boqi Liu: software and formal analysis. Lu Teng, Yiheng Peng: software and validation. Peiye Chen, Lili Chen: project administration. Pinguo Fu, Ruixue Cao: project administration, conceptualization, writing\u0026ndash;original draft, writing\u0026ndash;review and editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Acknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; We would like to express our gratitude to the NHANES program, conducted by the Centers for Disease Control and Prevention, for providing access to the publicly available dataset used in this study. We also acknowledge the contributions of the NHANES participants and staff who made this research possible.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eRossell SL, Neill E, Phillipou A, et al. 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Understanding the relationship between income and mental health among 16- to 24-year-olds: Analysis of 10 waves (2009-2020) of Understanding Society to enable modelling of income interventions. PLoS One. 2023,18(2):e0279845.\u003c/li\u003e\n \u003cli\u003ePabayo R, Dunn EC, Gilman SE, et al. Income inequality within urban settings and depressive symptoms among adolescents. J Epidemiol Community Health. 2016,70(10):997-1003.\u003c/li\u003e\n \u003cli\u003eLee CT, Chiang YC, Huang JY, et al. Incidence of Major Depressive Disorder: Variation by Age and Sex in Low-Income Individuals: A Population-Based 10-Year Follow-Up Study. Medicine (Baltimore). 2016,95(15):e3110.\u003c/li\u003e\n \u003cli\u003eColey RL, Sims J, Dearing E, et al. Locating Economic Risks for Adolescent Mental and Behavioral Health: Poverty and Affluence in Families, Neighborhoods, and Schools. Child Dev. 2018,89(2):360-9.\u003c/li\u003e\n \u003cli\u003eWong LP, Farid NDN, Alias H, et al. COVID-19 responses and coping in young Malaysians from low-income families. Front Psychiatry. 2023,14:1165023.\u003c/li\u003e\n \u003cli\u003eJha A, Kiragasur RM, Manohar H, et al. Lived experiences of adolescents with major depressive disorder during the COVID pandemic: A qualitative study from a tertiary care center. J Neurosci Rural Pract. 2024,15(2):334-40.\u003c/li\u003e\n \u003cli\u003eChang WH, Foster GR, Kelly DA, et al. Depression, anxiety, substance misuse and self-harm in children and young people with rare chronic liver disease. BJPsych Open. 2022,8(5):e146.\u003c/li\u003e\n \u003cli\u003eChoi JM, Chung GE, Kang SJ, et al. Association Between Anxiety and Depression and Nonalcoholic Fatty Liver Disease. Front Med (Lausanne). 2020,7:585618.\u003c/li\u003e\n \u003cli\u003eNg CH, Xiao J, Chew NWS, et al. Depression in non-alcoholic fatty liver disease is associated with an increased risk of complications and mortality. Front Med (Lausanne). 2022,9:985803.\u003c/li\u003e\n \u003cli\u003eSeo GH, Yoo JJ. Incidence of major depressive disorder over time in patients with liver cirrhosis: A nationwide population-based study in Korea. PLoS One. 2022,17(12):e0278924.\u003c/li\u003e\n \u003cli\u003eZaninotto P, Steptoe A, Shim EJ. CVD incidence and mortality among people with diabetes and/or hypertension: Results from the English longitudinal study of ageing. PLoS One. 2024,19(5):e0303306.\u003c/li\u003e\n \u003cli\u003eCsige I, Ujv\u0026aacute;rosy D, Szab\u0026oacute; Z, et al. The Impact of Obesity on the Cardiovascular System. J Diabetes Res. 2018,2018:3407306.\u003c/li\u003e\n \u003cli\u003eYamada T, Kimura-Koyanagi M, Sakaguchi K, et al. Obesity and risk for its comorbidities diabetes, hypertension, and dyslipidemia in Japanese individuals aged 65 years. Sci Rep. 2023,13(1):2346.\u003c/li\u003e\n \u003cli\u003eGauntlett-Gilbert J, Bhat C, Clinch J. Body mass in adolescents with chronic pain: observational study. Arch Dis Child. 2020,105(5):476-80.\u003c/li\u003e\n \u003cli\u003eBortolotto VC, Pinheiro FC, Araujo SM, et al. Chrysin reverses the depressive-like behavior induced by hypothyroidism in female mice by regulating hippocampal serotonin and dopamine. Eur J Pharmacol. 2018,822:78-84.\u003c/li\u003e\n \u003cli\u003eMirahmad M, Mansour A, Moodi M, et al. Prevalence of thyroid dysfunction among Iranian older adults: a cross-sectional study. Sci Rep. 2023,13(1):21651.\u003c/li\u003e\n \u003cli\u003eLin IC, Chen HH, Yeh SY, et al. Risk of Depression, Chronic Morbidities, and l-Thyroxine Treatment in Hashimoto Thyroiditis in Taiwan: A Nationwide Cohort Study. Medicine (Baltimore). 2016,95(6):e2842.\u003c/li\u003e\n \u003cli\u003ePlatt JM, Bates L, Jager J, et al. Is the US Gender Gap in Depression Changing Over Time? A Meta-Regression. Am J Epidemiol. 2021,190(7):1190-206.\u003c/li\u003e\n \u003cli\u003eStickel S, Wagels L, Wudarczyk O, et al. Neural correlates of depression in women across the reproductive lifespan - An fMRI review. J Affect Disord. 2019,246:556-70.\u003c/li\u003e\n \u003cli\u003eMaji S. Society and \u0026apos;good woman\u0026apos;: A critical review of gender difference in depression. Int J Soc Psychiatry. 2018,64(4):396-405.\u003c/li\u003e\n \u003cli\u003eMadigan S, Racine N, Vaillancourt T, et al. Changes in Depression and Anxiety Among Children and Adolescents From Before to During the COVID-19 Pandemic: A Systematic Review and Meta-analysis. JAMA Pediatr. 2023,177(6):567-81.\u003c/li\u003e\n \u003cli\u003eGuazzini A, Pesce A, Gino F, et al. How the COVID-19 Pandemic Changed Adolescents\u0026apos; Use of Technologies, Sense of Community, and Loneliness: A Retrospective Perception Analysis. Behav Sci (Basel). 2022,12(7).\u003c/li\u003e\n \u003cli\u003eGou Y, Cheng S, Kang M, et al. Association of Allostatic Load With Depression, Anxiety, and Suicide: A Prospective Cohort Study. Biol Psychiatry. 2024.\u003c/li\u003e\n \u003cli\u003eHan XX, Zhang HY, Kong JW, et al. Inflammatory index is a promising biomarker for maintenance hemodialysis patients with cardiovascular disease. Eur J Med Res. 2024,29(1):544.\u003c/li\u003e\n \u003cli\u003ePark DH, Cho JJ, Yoon JL, et al. The Impact of Depression on Cardiovascular Disease: A Nationwide Population-Based Cohort Study in Korean Elderly. Korean J Fam Med. 2020,41(5):299-305.\u003c/li\u003e\n \u003cli\u003eT AM, Manolis AA, Manolis AS. Emotional Stress in Cardiac and Vascular Diseases. Curr Vasc Pharmacol. 2025.\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":"Depression, NHANES, COVID-19, physical activity, alcohol abuse","lastPublishedDoi":"10.21203/rs.3.rs-6475145/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6475145/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe COVID-19 pandemic has significantly impacted mental health, particularly increasing depression rates due to social isolation, economic stress, and lifestyle disruptions. However, studies examining its specific effects on depression on difference ages remain limited.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThis study utilized data from the NHANES database, comparing pre-pandemic (2017\u0026ndash;2020) and post-pandemic (2021\u0026ndash;2023) cycles. Depression was assessed using the Patient Health Questionnaire (PHQ-9). Demographics, lifestyle behaviors, and medical conditions were examined as potential exposure variables. Logistic regression models were applied to determine the factors associated with depression in three groups: 20\u0026ndash;44 years, 45\u0026ndash;64 years, and 65\u0026ndash;80 years.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe analysis included 3,247 participants, with 871 in the depression group and 2,376 in the non-depression group. Alcohol abuse increased depression risk, while physical activity was protective across all age groups. In the 20\u0026ndash;44 years group, COVID-19 experience (OR\u0026thinsp;=\u0026thinsp;2.542, 95% CI: 1.783\u0026ndash;3.623) and liver disease were significant risk factors. In the 65\u0026ndash;80 years group, higher BMI (OR\u0026thinsp;=\u0026thinsp;1.660, 95% CI: 1.139\u0026ndash;2.421), thyroid disease (OR\u0026thinsp;=\u0026thinsp;1.756, 95% CI: 1.001\u0026ndash;3.079, p\u0026thinsp;=\u0026thinsp;0.049), and longer weekend sleep (OR\u0026thinsp;=\u0026thinsp;1.291, 95% CI: 1.020\u0026ndash;1.634, p\u0026thinsp;=\u0026thinsp;0.033) were significant. Female and heart/brain diseases elevated depression risk in middle-aged and older adults.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThese findings emphasize the need for age-specific mental health interventions targeting pandemic stressors, chronic diseases, and gender disparities, informing future public health strategies.\u003c/p\u003e","manuscriptTitle":"Age-Specific Determinants of Depression Prevalence: Insights from Pre- and Post-COVID- 19 Pandemic Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-02 12:58:21","doi":"10.21203/rs.3.rs-6475145/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":"43b3dca9-d20d-4e8c-a41d-e1aba11c18e8","owner":[],"postedDate":"June 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-27T03:38:25+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-02 12:58:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6475145","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6475145","identity":"rs-6475145","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}