Impact of the COVID-19 pandemic on depressive disorder among young adults in the United States: Analysis of the Behavioral Risk Factor Surveillance System data, 2018-2022

Impact of the COVID-19 pandemic on depressive disorder among young adults in the United States: Analysis of the Behavioral Risk Factor Surveillance System data, 2018-2022 | 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 Impact of the COVID-19 pandemic on depressive disorder among young adults in the United States: Analysis of the Behavioral Risk Factor Surveillance System data, 2018-2022 Suman Kanti Chowdhury, Fahad Mansuri, Zailing Xing, Anna Beltrame, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3973430/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 Purpose: Depressive disorder during early adulthood has been a rising public health concern, potentially further compounded by the COVID-19 pandemic. Using nationally representative large survey samples, this study addressed the knowledge gaps in how COVID-19 affected depressive disorder among U.S. young adults. Methods: The analysis included 348,994 U.S. non-institutionalized young adults aged 18-34 years from the Behavioral Risk Factor Surveillance System for 2018-2022. Changes in the prevalence of diagnosed depressive disorder before and during COVID-19 were assessed by weighted bi-variate analysis using Rao-Scott Chi-Square test, with multivariable logistic regression models fitted to assess the magnitude of depressive disorder before and during COVID-19. Results: Overall, the prevalence of depressive disorder increased by 13.7% (p<0.001) from 20.5% before COVID-19 to 23.3% during COVID-19. Adjusted for sociodemographic and lifestyle factors, the odds of depressive disorder during COVID-19 as compared to before COVID-19 were highest for females (OR: 1.35, 95% CI: 1.29-1.40), aged 18-24 years (OR: 1.34, 95% CI: 1.27-1.41), other races (OR: 1.46, 95% CI: 1.31-1.62), attended college or technical school (OR: 1.33, 95% CI: 1.26-1.40), employed (OR: 1.32, 95% CI: 1.27-1.37), and married (OR: 1.32, 95% CI: 1.24-1.40). Conclusion: The study findings revealed the importance of recognizing and understanding the most affected groups of young adults during a pandemic like COVID-19, providing essential insights for developing targeted interventions and policies. Mental health depressive disorder COVID-19 United States young adults BRFSS Background Depressive disorder is characterized by disrupted mood, cognition, and daily functioning that persists for at least two weeks [1,2]. It poses significant public health challenges with adverse effects on the well-being of an individual and society at large. Depressive disorder affects family and personal relationships, performances at work or academic progression at school, sleeping and eating habits, and overall general health [3]. Although it affects people of all ages, increasing depressive disorder in young people is a growing concern because it mostly occurs during the period when they go through a rapid life transition including social, emotional, and cognitive developments [1]. Unmanaged depressive disorder at an early age can have significant negative consequences in later life [4,5] which may result in high medical or life costs as well as high economic burdens to the country [3,4]. Depressive disorder is one of the most common mental health conditions in the United States and around the world. Globally, the twelve-month prevalence of major depressive disorder is about six percent, and almost one in five individuals experience at least one depressive episode at some point in their lifetime [6]. In the United States (U.S.), the prevalence of depressive disorder is even higher with a persistently rising trend over time. In 2022, 8.8 percent of U.S. adults experienced a major depressive episode [7] which was 8.3 percent in 2021 [8] and 7.8 percent in 2019 [9]. Depressive disorder disproportionately affects people of all genders, ages, and racial/ethnic groups. Women are nearly twice as high as men to be affected by depressive disorder across the lifespan [6]. Irrespective of gender identity, the peak age of onset of depressive disorder ranges between the second and third decades of life [6,7,10]. Among different racial/ethnic groups, multiracial adults are more likely to experience depressive disorder in the United States compared to White adults [7,9,11]. As depressive disorder intensifies among U.S. adults, the impact of the coronavirus pandemic known as COVID-19 has further amplified the concerns, including social isolation, losses of family members, financial strain, loss of employment or housing [12–15]. The rise in depression and anxiety among U.S. adults during the coronavirus pandemic was between 30 and 50 percent [13,14,16]. Particularly, during the early period of the COVID-19 pandemic the increase in depression was markedly high among young adults aged 18 to 34 years [16,15]. Understanding the risk factors associated with depression in young adults is critical for providing targeted support and ensuring mental health interventions are directed where most needed. Although there is ample evidence of poor mental health conditions among U.S. adults before and during COVID-19 [16–18,13,14], few studies have focused on young adults [19–21]. Furthermore, most of the available articles studied the early period of the COVID-19 pandemic. As a result, the studies lacked the utilization of large, nationally representative survey samples. In light of the ever-changing mental health landscapes, our study delved into assessing the impact of the COVID-19 pandemic on depressive disorder among young adults in the United States. In this study, we considered the most recent data from the Behavioral Risk Factor Surveillance System (BRFSS) from 2018 to 2022. Methods and materials Data source and study population The BRFSS is an annual computer-assisted telephone survey that is state-based, nationally representative, and aimed at non-institutionalized adult U.S. citizens. It is conducted by the Centers for Disease Control and Prevention (CDC). All participants of the BRFSS survey were 18 years of age or older. The core questionnaire collected data on chronic health issues, health status, risk behaviors connected to health, healthcare access, and demographic and socioeconomic variables. The BRFSS survey data is accessible to the public and does not necessitate any evaluation from a university institutional review body. Two cohorts of BRFSS were examined in this study: before COVID-19 (2018-19) and during COVID-19 (2020-22). The age range of our study participants was 18 to 34 years. Pregnant women and those with missing data for the outcome variable were excluded. As a result, the total sample size for this study was 348,994 (supplementary Fig. 1). Variables and measurements A clinically diagnosed depressive disorder was the study's outcome variable. We assessed the depressive disorders of study populations using the question, "Have you ever been told by medical professionals that you have experienced any of the following conditions? -depressive disorder, encompassing depression, major depression, dysthymia, or minor depression?" The survey includes response options "Yes", "No", "Don't know/Not sure", and "Refused". Participants who responded with "Don't know/Not sure" or "Refused" were not included in the analysis. The predictors consisted of the following variables: age (categorized as 18–24, 24–29, and 30–34), sex (male or female), race/ethnicity (non-Hispanic White, non-Hispanic Black, Hispanic, and Others), marital status (married, divorced/widowed/separated, never married, and a member of an unmarried couple), education level (high school or lower, attended college or technical school, and completed college or technical school), employment status (employed, unemployed, and homemaker). In addition, health insurance, alcohol use, smoking, and exercise were all collected using a binary response format, with participants indicating "yes" or "no". Statistical analysis We utilized survey weights to account for the complex survey design and produce nationally representative results. We used frequency and weighted prevalence to depict the characteristics of the study population. The Rao-Scott chi-square analysis assessed the differences in the weighted prevalences before and after COVID-19. We also computed the relative differences in the weighted prevalences before and after COVID-19 using the formula: Relative Difference = (During COVID weighted percent - Before COVID weighted percent)/Before COVID weighted percent*100. We used logistic regression models to obtain odds ratios (ORs) and 95% confidence intervals (CIs) to estimate the magnitude of changes in depressive disorder before and during COVID-19 for each of the strata of selected determinants. Two distinct models were developed: the crude and adjusted models. The crude model examined the changes in depressive disorder across the categories of determinants before and during COVID-19. The adjusted model was controlled for age, sex, race, education, marital status, employment, health insurance, alcohol consumption, smoking, and exercise. A two-sided p-value of 0.05 was used to determine statistical significance. SAS 9.4 (SAS Institute, Cary, North Carolina, USA) was used for all analyses. Results The final analytical sample included 348,998 young adults, ages 18 through 34 years old, who were not currently pregnant, and had no missing data for the outcome variable (diagnosed depressive disorder status). Overall, the distribution of participants’ characteristics before and during the COVID-19 pandemic remained similar, with little or no variations. Most young adults in both study periods were in the age group between 18 and 24 years (41.5% before COVID-19 vs 41.0% during COVID-19), male (52.2% vs 52.3%), non-Hispanic White (52.9% vs 51.0%), had high school education or below (43.0% vs 43.3%), employed (67.3% vs 66.8%), and never married (57.9% vs 58.9%). While there was a minor increase in the percentage of having health insurance (81% vs 84.4%) and doing exercise (80.4% vs 81.8%) during COVID-19, alcohol consumption (58.4–57.2%) and smoking tobacco (15.9–12%) decreased slightly (Table 1 ). Table 1 Sociodemographic and lifestyle characteristics of U.S. young adults aged 18–34 years before and during COVID-19, BRFSS 2018–2022 (N = 348994) Sociodemographic and lifestyle characteristics Before COVID-19 (2018–2019) During COVID-19 (2020–2022) Unweighted N Weighted % Unweighted N Weighted % Age (in years) 18–24 49778 41.5 76578 41.0 25–29 41637 27.1 62454 26.7 30–34 45912 31.4 72635 32.3 Sex Male 72603 52.2 112998 52.3 Female 64581 47.8 98669 47.7 Race/ethnicity Non-Hispanic White 84061 52.9 126831 51.0 Non-Hispanic Black 12293 12.6 17362 11.7 Hispanic 23080 23.7 36852 24.7 Others a 15923 10.8 26610 12.6 Education High school or less 50482 43.0 76139 43.3 Attended college or technical school 42306 33.6 60796 31.7 Graduated from college or technical school 44167 23.4 73956 25.0 Employment Employed 95756 67.3 147117 66.8 Unemployed 32560 27.5 51228 28.8 Homemaker 6578 5.2 7918 4.4 Family structure Married 39856 27.3 57740 26.1 Divorced/ Widowed/ Separated 8149 5.6 10971 5.2 Never married 75347 57.9 118910 58.9 A member of an unmarried couple 12992 9.3 22011 9.8 Health insurance Yes 112925 81.0 174069 84.4 No 22877 19.0 26754 15.6 Alcohol consumption Yes 78148 58.4 116854 57.2 No 50975 41.6 77117 42.8 Smoking Yes 21997 15.9 24515 12.0 No 108863 84.1 173643 88.0 Exercise Yes 108042 80.4 175615 81.8 No 25045 19.6 35769 18.2 Note : a Other races included multi-race, American Indian, Alaskan Native, Asian, Pacific Islander, and Hawaiian Native Overall, the prevalence of depressive disorder among U.S. young adults significantly increased by 13.7% in relative terms from 20.5–23.3% during COVID-19 (compared to before COVID-19). In both study periods, the prevalence was higher for the age group between 18 and 24 years (20.8% before COVID-19 and 24.0% during COVID-19), females (26.4% and 30.5%), non-Hispanic White (25.7% and 28.9%), who attended college or technical school (23.2% and 26.8%), unemployed (25.1% and 27.2%), divorced/widowed/separated (30.7% and 32.2%), had health insurance (21% and 24.6%), consumed alcohol (22.0% and 25.2%), smoked tobacco (35.4% and 37.5%), and did not exercise (21.6% and 23.8%) (Table 2 ). Table 2 Weighted prevalence of depressive disorder among young adults (aged 18–34 years) and relative changes in the prevalence before and during COVID-19 by socio-demographic and lifestyle factors, BRFSS 2018–2022 (N = 348994) Sociodemographic and lifestyle factors Before COVID-19 (2018–2019) During COVID-19 (2020–2022) Relative difference a P-value d Weighted % P-value c Weighted % P-value c % Overall depressive disorder 20.5 23.3 13.7 < 0.001 Age (in years) 18–24 20.8 0.370 24.0 < 0.001 15.4 < 0.001 25–29 20.3 23.5 15.8 < 0.001 30–34 20.2 22.4 10.9 < 0.001 Sex Male 15.0 < 0.001 16.8 < 0.001 12.0 < 0.001 Female 26.4 30.5 15.5 < 0.001 Race/ethnicity Non-Hispanic White 25.7 < 0.001 28.9 < 0.001 12.5 < 0.001 Non-Hispanic Black 15.7 18.2 15.9 < 0.001 Others b 14.0 18.2 30.0 < 0.001 Hispanic 14.4 17.1 18.8 < 0.001 Education High school or less 20.4 < 0.001 23.0 < 0.001 12.7 < 0.001 Attended college or technical school 23.2 26.8 15.5 < 0.001 Graduated college or technical school 16.8 19.8 17.9 < 0.001 Employment Employed 18.6 < 0.001 21.9 < 0.001 17.7 < 0.001 Unemployed 25.1 27.2 8.4 < 0.001 Homemaker 22.3 24.9 11.7 0.032 Family structure Married 16.2 < 0.001 19.5 < 0.001 20.4 < 0.001 Divorced/ Widowed/ Separated 30.7 32.2 4.9 0.156 Never married 20.7 23.5 13.5 < 0.001 A member of an unmarried couple 25.4 28.6 12.6 < 0.001 Health insurance Yes 21.0 < 0.001 24.6 < 0.001 17.1 < 0.001 No 18.2 18.2 0.0 0.892 Alcohol consumption Yes 22.0 < 0.001 25.2 < 0.001 14.5 < 0.001 No 19.2 21.9 14.1 < 0.001 Smoking Yes 35.4 < 0.001 37.5 < 0.001 5.9 0.006 No 18.0 21.8 21.1 < 0.001 Exercise Yes 20.4 0.007 23.2 0.209 13.7 < 0.001 No 21.6 23.8 10.2 < 0.001 Note : a Relative difference in the weighted prevalence of depressive disorder among young adults before and during COVID-19, presented in percentage terms; b Other race included multi-race, American Indian, Alaskan Native, Asian, Pacific Islander, and Hawaiian Native; c P-values in the third and fifth columns from left are from Rao-Scott Chi-Square test of association between depressive disorder and each of the independent variables; d Italic P-values are from Rao-Scott Chi-Square test of association between depressive disorder and study periods (before and during COVID-19) for each category of an independent variable separately. The relative changes (in percentage terms) in the prevalence of depressive disorder before and during COVID-19 showed an increase across all categories of sociodemographic and lifestyle factors. The highest relative increase was observed among those aged between 25 and 29 years (15.8%), females (15.5%), other races including multi-races (30.0%), graduated from college or technical school (17.9%), employed (17.7%), married (20.4%), and had health insurance (17.1%). Among lifestyle factors, relative changes in depressive disorder during COVID-19 were higher among those who consumed alcohol (14.5%), did not smoke (21.1%), and did exercise (13.7%) (Table 2 ). Compared with the study period before COVID-19, the levels of depressive disorder remained high among females, non-Hispanic races other than White and Black, employed, and married young adults during COVID-19 after adjusting for sociodemographic and lifestyle factors (Table 3 ). Female young adults had 1.35 times higher odds (OR 1.35, 95% CI: 1.29–1.40) of depressive disorder during COVID-19, whereas it was 1.25 times higher for males (OR 1.21, 95% CI: 1.16–1.27). While Hispanic (OR 1.25, 95% CI: 1.14–1.37) and Non-Hispanic White (OR 1.27, 95% CI: 1.23–1.32) and non-Hispanic Black (OR 1.27, 95% CI: 1.14–1.41) young adults experienced 25 to 27 percent higher odds of depressive disorder during COVID-19, other races including multi-race, American Indian, Alaskan Native, Asian, Pacific Islander, and Hawaiian Native experienced 46 percent higher odds (OR 1.46, 95% CI: 1.31–1.62). During COVID-19 the odds of experiencing depressive disorder were highest among employed young adults (OR 1.32, 95% CI: 1.27–1.37) followed by homemakers (OR 1.26, 95% CI: 1.08–1.47), and unemployed (OR 1.20, 95% CI: 1.14–1.28). Young adults whether married (OR 1.32, 95% CI: 1.24–1.40) or a member of an unmarried couple (OR 1.30, 95% CI: 1.18–1.42) or never married (OR 1.29, 95% CI: 1.23–1.34), all groups experienced 29 to 32 percent higher odds of depressive disorder during COVID-19 except divorced/widowed/separated young adults (OR 1.13, 95%: 1.01–1.27). Although the relative percent increase and crude odds of depressive disorder were higher among those aged 25–29 years and graduated from college or technical school, adjusting for other sociodemographic and lifestyle factors showed higher odds for the age group 18–24 years (OR 1.34, 95% CI: 1.27–1.41) and who attended college or technical school (OR 1.33, 95% CI: 1.26–1.40). Table 3 Depressive disorder among young adults aged 18–34 years during COVID-19 compared to before COVID-19 for each category of sociodemographic factors, presented with (weighted) odds ratios and 95% confidence intervals, BRFSS 2018–2022 Unadjusted Model Adjusted Model b Socio-demographic variables cOR (95% CI) P-value aOR (95% CI) P-value Age (in years) 18–24 1.20 (1.15–1.26) < .001 1.34 (1.27–1.41) < .001 25–29 1.21 (1.15–1.27) < .001 1.30 (1.22–1.37) < .001 30–34 1.14 (1.09–1.20) < .001 1.19 (1.12–1.25) < .001 Sex Male 1.14 (1.09–1.19) < .001 1.21 (1.16–1.27) < .001 Female 1.23 (1.18–1.28) < .001 1.35 (1.29–1.40) < .001 Race Non-Hispanic Black 1.20 (1.09–1.33) < .001 1.27 (1.14–1.41) < .001 Non-Hispanic White 1.18 (1.14–1.22) < .001 1.27 (1.23–1.32) < .001 Others a 1.36 (1.24–1.49) < .001 1.46 (1.31–1.62) < .001 Hispanic 1.23 (1.14–1.34) < .001 1.25 (1.14–1.37) < .001 Education High school or less 1.17 (1.12–1.22) < .001 1.25 (1.19–1.32) < .001 Attended college or technical school 1.21 (1.15–1.27) < .001 1.33 (1.26–1.40) < .001 Graduated from college or technical school 1.22 (1.16–1.28) < .001 1.28 (1.22–1.35) < .001 Employment Employed 1.23 (1.19–1.28) < .001 1.32 (1.27–1.37) < .001 Unemployed 1.12 (1.06–1.18) < .001 1.20 (1.14–1.28) < .001 Homemaker 1.16 (1.01–1.33) 0.041 1.26 (1.08–1.47) 0.003 Marital status Married 1.25 (1.18–1.33) < .001 1.32 (1.24–1.40) < .001 Divorced/ Widowed/ Separated 1.08 (0.97–1.20) 0.164 1.13 (1.01–1.27) 0.040 Never married 1.17 (1.13–1.22) < .001 1.29 (1.23–1.34) < .001 A member of an unmarried couple 1.18 (1.08–1.28) < .001 1.30 (1.18–1.42) < .001 Note : cOR - Crude Odds Ratio; aOR - Adjusted Odds Ratio; 95% CI − 95 percent confidence interval; a Other races included multi-race, American Indian, Alaskan Native, Asian, Pacific Islander, and Hawaiian Native; bAdjusted model for each variable categories were controlled for other socio-demographic variables in the table, health insurance, and lifestyle variables (alcohol consumption, cigarette smoking, and exercise). Discussion Using a large, nationally representative survey sample, this study found an increase in depressive disorder among young adults in the United States between 2018–2019 and 2020–2022. The prevalence of depressive disorder rose by 14 percent, from 20.5 percent before COVID-19 to 23.3 percent during COVID-19. Early adulthood has been a consistently reported period with a higher prevalence of poor mental health conditions, including depressive disorder in the United States. On top of that COVID-19 pandemic overwhelmed already deteriorating mental health conditions in this population. Literature suggests that COVID-19 pervasively disrupted daily life which negatively affected mental health [22]. Young adults were disproportionately affected as this population segment is more likely to be involved in job sectors that were shut down during the pandemic. The financial insecurity due to job loss may have contributed to a persistent increase in depressive disorder among young adults [16]. Furthermore, young adults are most likely to be students at an early age. The disruptions in their education, with shifts to online learning and uncertainties about academic progress might have contributed to increased depressive disorders. Several previous studies identified an increased prevalence of depressive disorder among U.S. adults during the pandemic, although all of them studied the early period of the pandemic and used different data sources with limited national representation [16,23,15,14]. Despite differences in study methods, our study findings were close to the results of previous studies that reported 27.8% of adults had depression [23] and 24% of young adults aged 18–29 years had psychological distress during the first two months of the pandemic [15] with a persistent increase (30% higher odds) in the third year of the pandemic [14]. Stratified analysis in our study found different levels of depressive disorder across sociodemographic factors before and during COVID-19. At both time points, females, aged 18–24 years, non-Hispanic White, unemployed, and divorced/widowed/separated young adults had the highest prevalence of depressive disorder compared to their counterparts. However, the most affected groups with the highest relative increase in the prevalence of depressive disorder were different except for females and the age group 18–24 years during the pandemic. Consistent with previous studies, our findings showed a persistently increased prevalence of depressive disorder among female young adults than males even after controlling for potential confounders [23,14]. Females had a prevalence of depressive disorder twice as high as males during the pandemic. The gender difference in depressive disorder is a complex phenomenon influenced by a combination of biological, psychological, and social factors. Sex differences in susceptibility due to hormonal differences and differences in coping strategy and emotional expression might affect females disproportionately [6,24,12]. In addition to that socio-environmental factors such as cultural expectations, discrimination and inequality, and socialization practices interact with biological and psychological differences which lead to increased vulnerabilities. The higher increase in depressive disorder among females during the pandemic might be linked to the factors like economic strain from the shut-down of female-dominated job sectors, increased caregiving responsibility, and increased household gender-based violence. Like females, young adults aged 18–29 years experienced the highest relative increase in depressive disorder during COVID-19 even after adjusting for the potential confounding factors. Similar findings were reported from a longitudinal study on mental health conditions during COVID-19 in the United Kingdom [12] and a study on U.S. adults [16]. Young adults experience many life transitions that make them vulnerable to poor mental health. Additionally, the COVID-19 pandemic, an unprecedented event, might have overwhelmed their daily life. As this age group is most likely to be student-dominated, the disruption caused by COVID-19 in their academic activities with uncertainties of progress might have contributed to increased depressive disorder. This study found that young adults of other races/ethnicities, who attended college or technical school, who were married and employed had the highest relative increase in depressive disorder during COVID-19. Even after adjusting for potential confounding factors these groups of young adults remained the most affected groups. Other races/ethnicities include multiple races, American Indian, Alaskan Native, Asian, Pacific Islander, and Hawaiian Native who had the highest relative increase in depressive disorder during the pandemic although non-Hispanic Whites had the highest prevalence at both time points. Findings from the previous studies on racial disparities in depressive disorder are inconclusive. While one study reported that non-Hispanic Blacks had a slightly higher likelihood of trauma and stress-related disorder during the pandemic compared to non-Hispanic Whites [25], another study found a protective likelihood of depression among non-Hispanic Blacks [16]. However, our findings of increased depressive disorder among other races/ethnicities (including multi-races/ethnicities) were similar to a study done on U.S. adults during the early period of the pandemic [18]. Although unemployment was found to be an important factor in increased depression [18], employed young adults showed the highest increase in depressive disorder during the pandemic. Similarly, married young adults during the pandemic instead of divorced/separated/widowed, the commonly reported prevalent group had the highest increase in depressive disorder. Plausible explanations for why employed young adults experienced the highest level of depressive disorder during COVID-19 is well described in previous studies [16,23,26]. Early adulthood is when young adults enter the job sector, and/or have family. Due to the pandemic, industries such as retail, hospitality, and entertainment, which often employ young adults, were hit by lockdowns and other restrictions. Many young adults faced job losses or reduced work hours due to business closures and economic downturns leading to financial strain [27]. The situation was perhaps more complex for married young adults. Financial burden, along with the increased caregiving responsibilities, might have contributed to the increased depressive disorder. It might also be possible that young adult groups who were less prone to depressive disorder during the COVID-19 pandemic might have been affected more than the groups who already had a high prevalence of depressive disorder. Further studies are warranted to explore and/or confirm the mechanism of how some groups are disproportionately affected during a pandemic like COVID-19. Strengths and limitations Our study focused on the critical window period of adulthood when depressive disorder was reported to be prevalent at most. One of the main strengths of our study is that we used nationally representative probability-based large samples that included the same measure of depressive disorder at both time points making them meaningfully comparable. Furthermore, our study considered the entire longer pandemic period from the beginning of 2020 through 2022 with the highest level of restrictions, disruptions, and many uncertainties about COVID-19. Most of the restrictions were lifted by the start of 2023 and pandemic status was also rescinded. As we analyzed secondary data from a national survey, our analysis was limited to available data and how data were collected in BRFSS. We had self-reported data on diagnosed depressive disorder, thus prone to recall bias. Furthermore, BRFSS collects data on lifetime depressive disorder that might have influenced the comparison between before and during COVID-19. However, as the study included young adults, lifetime prevalence was a reasonable proxy for measuring the effect of the pandemic on depressive disorder across different sociodemographic strata. It was unlikely that an individual who was ever told to have depressive disorder would not experience any symptom during a pandemic like COVID-19. Even if that happened the percentage was expected to be negligible. Nevertheless, to validate our assumption, we conducted an additional analysis using a different variable in BRFSS that asked about self-reported mental health conditions in the past 30 days. As per the definition of depressive disorder, we dichotomized the number of days with poor mental health into ‘Yes’ if symptoms persisted for 14 days or more, otherwise coded as ‘No’. As expected, the results were consistent with our study findings. Our study was done right after the pandemic restrictions got eased, thus it was beyond the scope of the study to assess whether the patterns of most affected groups and the magnitude of depressive disorder returned to the pre-pandemic state. Future studies may focus on assessing the excess burden of depressive disorder during the pandemic and whether the pandemic truly changed the patterns of most affected groups of young adults. Conclusion This study revealed the importance of recognizing and understanding the socio-demographic factors that contribute to poor mental health conditions among young adults during a pandemic like COVID-19, providing essential insights for the development of targeted interventions and policies. As we strive to navigate the complexities of mental health, especially in the context of the pandemic, our findings will contribute to the ongoing efforts of enhancing support systems and addressing the evolving mental health needs of young adults in the United States. Declarations Acknowledgments We are grateful to the Centers for Disease Control and Prevention (CDC) for their management of BRFSS surveys and for making data publicly available. However, CDC bears no responsibility for the analysis and interpretation of data. We are also thankful to participants of BRFSS surveys who shared valuable information. Author contribution SKC, FM, ZX, AB, KC, and RSK contributed to conceptualizing and designing the study. SKC, FM, and ZX prepared datasets and analyzed data under the guidance of RSK. SKC, FM, ZX, AB, and KC contributed to preparing the draft manuscript. RSK thoroughly reviewed the draft manuscript. All authors reviewed the consecutive drafts and approved the final manuscript. Competing interest The authors have no competing interests to declare relevant to the content of this article. Funding The authors did not receive support from any organization for the submitted work. Ethics approval Not applicable—analysis of material in the public domain. References Thapar A, Eyre O, Patel V, Brent D (2022) Depression in young people. 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JAMA 329 (23):2057-2067. doi:10.1001/jama.2023.9297 Kessler RC, Bromet EJ (2013) The epidemiology of depression across cultures. Annu Rev Public Health 34:119-138. doi:10.1146/annurev-publhealth-031912-114409 Simpson SM, Krishnan LL, Kunik ME, Ruiz P (2007) Racial disparities in diagnosis and treatment of depression: a literature review. Psychiatr Q 78 (1):3-14. doi:10.1007/s11126-006-9022-y Daly M, Sutin AR, Robinson E (2022) Longitudinal changes in mental health and the COVID-19 pandemic: evidence from the UK Household Longitudinal Study. Psychol Med 52 (13):2549-2558. doi:10.1017/s0033291720004432 Perlis RH, Lunz Trujillo K, Safarpour A, Quintana A, Simonson MD, Perlis J, Santillana M, Ognyanova K, Baum MA, Druckman JN, Lazer D (2023) Community Mobility and Depressive Symptoms During the COVID-19 Pandemic in the United States. JAMA Netw Open 6 (9):e2334945. doi:10.1001/jamanetworkopen.2023.34945 Kim J, Linos E, Rodriguez CI, Chen ML, Dove MS, Keegan TH (2023) Prevalence and associations of poor mental health in the third year of COVID-19: U.S. population-based analysis from 2020 to 2022. Psychiatry Res 330:115622. doi:10.1016/j.psychres.2023.115622 McGinty EE, Presskreischer R, Han H, Barry CL (2020) Psychological Distress and Loneliness Reported by US Adults in 2018 and April 2020. JAMA 324 (1):93-94. doi:10.1001/jama.2020.9740 Daly M, Sutin AR, Robinson E (2021) Depression reported by US adults in 2017-2018 and March and April 2020. J Affect Disord 278:131-135. doi:10.1016/j.jad.2020.09.065 Jia H, Guerin RJ, Barile JP, Okun AH, McKnight-Eily L, Blumberg SJ, Njai R, Thompson WW (2021) National and State Trends in Anxiety and Depression Severity Scores Among Adults During the COVID-19 Pandemic - United States, 2020-2021. MMWR Morb Mortal Wkly Rep 70 (40):1427-1432. doi:10.15585/mmwr.mm7040e3 Villas-Boas S, Kaplan S, White JS, Hsia RY (2023) Patterns of US Mental Health-Related Emergency Department Visits During the COVID-19 Pandemic. JAMA Netw Open 6 (7):e2322720. doi:10.1001/jamanetworkopen.2023.22720 Brunette MF, Erlich MD, Edwards ML, Adler DA, Berlant J, Dixon L, First MB, Oslin DW, Siris SG, Talley RM (2023) Addressing the Increasing Mental Health Distress and Mental Illness Among Young Adults in the United States. J Nerv Ment Dis 211 (12):961-967. doi:10.1097/NMD.0000000000001734 Thomas PB, Mantey DS, Clendennen SL, Harrell MB (2024) Mental Health Status by Race, Ethnicity and Socioeconomic Status among Young Adults in Texas during COVID-19. J Racial Ethn Health Disparities. doi:10.1007/s40615-024-01923-3 Amsalem D, Fisch CT, Wall M, Choi CJ, Lazarov A, Markowitz JC, LeBeau M, Hinds M, Thompson K, Fisher PW, Smith TE, Hankerson SH, Lewis-Fernández R, Dixon LB, Neria Y (2023) Anxiety and Depression Symptoms Among Young U.S. Essential Workers During the COVID-19 Pandemic. Psychiatr Serv 74 (10):1010-1018. doi:10.1176/appi.ps.20220530 Holmes EA, O'Connor RC, Perry VH, Tracey I, Wessely S, Arseneault L, Ballard C, Christensen H, Cohen Silver R, Everall I, Ford T, John A, Kabir T, King K, Madan I, Michie S, Przybylski AK, Shafran R, Sweeney A, Worthman CM, Yardley L, Cowan K, Cope C, Hotopf M, Bullmore E (2020) Multidisciplinary research priorities for the COVID-19 pandemic: a call for action for mental health science. Lancet Psychiatry 7 (6):547-560. doi:10.1016/s2215-0366(20)30168-1 Ettman CK, Abdalla SM, Cohen GH, Sampson L, Vivier PM, Galea S (2020) Prevalence of Depression Symptoms in US Adults Before and During the COVID-19 Pandemic. JAMA Netw Open 3 (9):e2019686. doi:10.1001/jamanetworkopen.2020.19686 Kuehner C (2017) Why is depression more common among women than among men? Lancet Psychiatry 4 (2):146-158. doi:10.1016/s2215-0366(16)30263-2 Czeisler M, Lane RI, Petrosky E, Wiley JF, Christensen A, Njai R, Weaver MD, Robbins R, Facer-Childs ER, Barger LK, Czeisler CA, Howard ME, Rajaratnam SMW (2020) Mental Health, Substance Use, and Suicidal Ideation During the COVID-19 Pandemic - United States, June 24-30, 2020. MMWR Morb Mortal Wkly Rep 69 (32):1049-1057. doi:10.15585/mmwr.mm6932a1 Sheridan Rains L, Johnson S, Barnett P, Steare T, Needle JJ, Carr S, Lever Taylor B, Bentivegna F, Edbrooke-Childs J, Scott HR, Rees J, Shah P, Lomani J, Chipp B, Barber N, Dedat Z, Oram S, Morant N, Simpson A (2021) Early impacts of the COVID-19 pandemic on mental health care and on people with mental health conditions: framework synthesis of international experiences and responses. Soc Psychiatry Psychiatr Epidemiol 56 (1):13-24. doi:10.1007/s00127-020-01924-7 Joyce R, Xu X (2020) Sector shutdowns during the coronavirus crisis: which workers are most exposed. Institute for Fiscal Studies Briefing Note BN278 6 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3973430","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":274285170,"identity":"62fb8717-8a62-45cf-a090-840adc8c3707","order_by":0,"name":"Suman Kanti Chowdhury","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYDACCSDmbQAS/I2NDxgY5ICsBGK1SBw+bMDAYEyKFoa0NAmitPDPbj4m8XbH4XyDA2fMqnn+GDDws+cY4LfkzrE0yblnDltuONxjdpu3zYBBsucNfi0GEjkglYcNQLbc5m34w2Bwg4AtSFpyzIpBDrMnQUtaGjMPmwFIhIBfbqSl/5zblm4geePwYcm5bQY8EmeeFeDVwj8j+bDB2zZrA77zjY0f3vwxkONvT96AVwscKByA0DzEKQcB+Qbi1Y6CUTAKRsEIAwA+Nkn/oTrI6wAAAABJRU5ErkJggg==","orcid":"","institution":"University of South Florida","correspondingAuthor":true,"prefix":"","firstName":"Suman","middleName":"Kanti","lastName":"Chowdhury","suffix":""},{"id":274285171,"identity":"c2a162c5-5ce5-4c52-b846-b53fab533428","order_by":1,"name":"Fahad Mansuri","email":"","orcid":"","institution":"University of South Florida","correspondingAuthor":false,"prefix":"","firstName":"Fahad","middleName":"","lastName":"Mansuri","suffix":""},{"id":274285172,"identity":"7e667e05-42f8-436c-92e1-c23957e38d92","order_by":2,"name":"Zailing Xing","email":"","orcid":"","institution":"University of South Florida","correspondingAuthor":false,"prefix":"","firstName":"Zailing","middleName":"","lastName":"Xing","suffix":""},{"id":274285173,"identity":"8371e07f-5cdf-4d0b-97b7-61a64dfae059","order_by":3,"name":"Anna Beltrame","email":"","orcid":"","institution":"University of South Florida","correspondingAuthor":false,"prefix":"","firstName":"Anna","middleName":"","lastName":"Beltrame","suffix":""},{"id":274285174,"identity":"941dca3e-b9eb-4820-b822-48fc17ff275f","order_by":4,"name":"Kanika Chandra","email":"","orcid":"","institution":"University of South Florida","correspondingAuthor":false,"prefix":"","firstName":"Kanika","middleName":"","lastName":"Chandra","suffix":""},{"id":274285175,"identity":"9fc3b508-dd21-40c5-b890-59df06c015d6","order_by":5,"name":"Russell S. Kirby","email":"","orcid":"","institution":"University of South Florida","correspondingAuthor":false,"prefix":"","firstName":"Russell","middleName":"S.","lastName":"Kirby","suffix":""}],"badges":[],"createdAt":"2024-02-20 17:20:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3973430/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3973430/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60767188,"identity":"471ec362-6ccf-40da-8851-c902b310af7b","added_by":"auto","created_at":"2024-07-21 11:31:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":846465,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3973430/v1/2a0b1df8-d862-4e2c-8db2-c7d00ac27637.pdf"},{"id":51643195,"identity":"ca1b5763-dc38-4c77-af4e-34e6baf056f2","added_by":"auto","created_at":"2024-02-26 13:40:31","extension":"jpg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":418832,"visible":true,"origin":"","legend":"","description":"","filename":"Fig1.jpg.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3973430/v1/17e4a11097acbf661eeb1811.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impact of the COVID-19 pandemic on depressive disorder among young adults in the United States: Analysis of the Behavioral Risk Factor Surveillance System data, 2018-2022","fulltext":[{"header":"Background","content":"\u003cp\u003eDepressive disorder is characterized by disrupted mood, cognition, and daily functioning that persists for at least two weeks [1,2]. It poses significant public health challenges with adverse effects on the well-being of an individual and society at large. Depressive disorder affects family and personal relationships, performances at work or academic progression at school, sleeping and eating habits, and overall general health [3]. Although it affects people of all ages, increasing depressive disorder in young people is a growing concern because it mostly occurs during the period when they go through a rapid life transition including social, emotional, and cognitive developments [1]. Unmanaged depressive disorder at an early age can have significant negative consequences in later life [4,5] which may result in high medical or life costs as well as high economic burdens to the country [3,4].\u003c/p\u003e \u003cp\u003eDepressive disorder is one of the most common mental health conditions in the United States and around the world. Globally, the twelve-month prevalence of major depressive disorder is about six percent, and almost one in five individuals experience at least one depressive episode at some point in their lifetime [6]. In the United States (U.S.), the prevalence of depressive disorder is even higher with a persistently rising trend over time. In 2022, 8.8 percent of U.S. adults experienced a major depressive episode [7] which was 8.3 percent in 2021 [8] and 7.8 percent in 2019 [9]. Depressive disorder disproportionately affects people of all genders, ages, and racial/ethnic groups. Women are nearly twice as high as men to be affected by depressive disorder across the lifespan [6]. Irrespective of gender identity, the peak age of onset of depressive disorder ranges between the second and third decades of life [6,7,10]. Among different racial/ethnic groups, multiracial adults are more likely to experience depressive disorder in the United States compared to White adults [7,9,11].\u003c/p\u003e \u003cp\u003eAs depressive disorder intensifies among U.S. adults, the impact of the coronavirus pandemic known as COVID-19 has further amplified the concerns, including social isolation, losses of family members, financial strain, loss of employment or housing [12\u0026ndash;15]. The rise in depression and anxiety among U.S. adults during the coronavirus pandemic was between 30 and 50 percent [13,14,16]. Particularly, during the early period of the COVID-19 pandemic the increase in depression was markedly high among young adults aged 18 to 34 years [16,15].\u003c/p\u003e \u003cp\u003eUnderstanding the risk factors associated with depression in young adults is critical for providing targeted support and ensuring mental health interventions are directed where most needed. Although there is ample evidence of poor mental health conditions among U.S. adults before and during COVID-19 [16\u0026ndash;18,13,14], few studies have focused on young adults [19\u0026ndash;21]. Furthermore, most of the available articles studied the early period of the COVID-19 pandemic. As a result, the studies lacked the utilization of large, nationally representative survey samples. In light of the ever-changing mental health landscapes, our study delved into assessing the impact of the COVID-19 pandemic on depressive disorder among young adults in the United States. In this study, we considered the most recent data from the Behavioral Risk Factor Surveillance System (BRFSS) from 2018 to 2022.\u003c/p\u003e"},{"header":"Methods and materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData source and study population\u003c/h2\u003e \u003cp\u003eThe BRFSS is an annual computer-assisted telephone survey that is state-based, nationally representative, and aimed at non-institutionalized adult U.S. citizens. It is conducted by the Centers for Disease Control and Prevention (CDC). All participants of the BRFSS survey were 18 years of age or older. The core questionnaire collected data on chronic health issues, health status, risk behaviors connected to health, healthcare access, and demographic and socioeconomic variables. The BRFSS survey data is accessible to the public and does not necessitate any evaluation from a university institutional review body. Two cohorts of BRFSS were examined in this study: before COVID-19 (2018-19) and during COVID-19 (2020-22). The age range of our study participants was 18 to 34 years. Pregnant women and those with missing data for the outcome variable were excluded. As a result, the total sample size for this study was 348,994 (supplementary Fig.\u0026nbsp;1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eVariables and measurements\u003c/h2\u003e \u003cp\u003eA clinically diagnosed depressive disorder was the study's outcome variable. We assessed the depressive disorders of study populations using the question, \"Have you ever been told by medical professionals that you have experienced any of the following conditions? -depressive disorder, encompassing depression, major depression, dysthymia, or minor depression?\" The survey includes response options \"Yes\", \"No\", \"Don't know/Not sure\", and \"Refused\". Participants who responded with \"Don't know/Not sure\" or \"Refused\" were not included in the analysis.\u003c/p\u003e \u003cp\u003eThe predictors consisted of the following variables: age (categorized as 18\u0026ndash;24, 24\u0026ndash;29, and 30\u0026ndash;34), sex (male or female), race/ethnicity (non-Hispanic White, non-Hispanic Black, Hispanic, and Others), marital status (married, divorced/widowed/separated, never married, and a member of an unmarried couple), education level (high school or lower, attended college or technical school, and completed college or technical school), employment status (employed, unemployed, and homemaker). In addition, health insurance, alcohol use, smoking, and exercise were all collected using a binary response format, with participants indicating \"yes\" or \"no\".\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eWe utilized survey weights to account for the complex survey design and produce nationally representative results. We used frequency and weighted prevalence to depict the characteristics of the study population. The Rao-Scott chi-square analysis assessed the differences in the weighted prevalences before and after COVID-19. We also computed the relative differences in the weighted prevalences before and after COVID-19 using the formula: Relative Difference = (During COVID weighted percent - Before COVID weighted percent)/Before COVID weighted percent*100. We used logistic regression models to obtain odds ratios (ORs) and 95% confidence intervals (CIs) to estimate the magnitude of changes in depressive disorder before and during COVID-19 for each of the strata of selected determinants. Two distinct models were developed: the crude and adjusted models. The crude model examined the changes in depressive disorder across the categories of determinants before and during COVID-19. The adjusted model was controlled for age, sex, race, education, marital status, employment, health insurance, alcohol consumption, smoking, and exercise. A two-sided p-value of 0.05 was used to determine statistical significance. SAS 9.4 (SAS Institute, Cary, North Carolina, USA) was used for all analyses.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe final analytical sample included 348,998 young adults, ages 18 through 34 years old, who were not currently pregnant, and had no missing data for the outcome variable (diagnosed depressive disorder status). Overall, the distribution of participants\u0026rsquo; characteristics before and during the COVID-19 pandemic remained similar, with little or no variations. Most young adults in both study periods were in the age group between 18 and 24 years (41.5% before COVID-19 vs 41.0% during COVID-19), male (52.2% vs 52.3%), non-Hispanic White (52.9% vs 51.0%), had high school education or below (43.0% vs 43.3%), employed (67.3% vs 66.8%), and never married (57.9% vs 58.9%). While there was a minor increase in the percentage of having health insurance (81% vs 84.4%) and doing exercise (80.4% vs 81.8%) during COVID-19, alcohol consumption (58.4\u0026ndash;57.2%) and smoking tobacco (15.9\u0026ndash;12%) decreased slightly (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSociodemographic and lifestyle characteristics of U.S. young adults aged 18\u0026ndash;34 years before and during COVID-19, BRFSS 2018\u0026ndash;2022 (N\u0026thinsp;=\u0026thinsp;348994)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSociodemographic and lifestyle characteristics\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003eBefore COVID-19\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003e(2018\u0026ndash;2019)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cem\u003eDuring COVID-19\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003e(2020\u0026ndash;2022)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnweighted N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWeighted %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnweighted N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWeighted %\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (in years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e112998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e52.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64581\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace/ethnicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e126831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school or less\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAttended college or technical school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGraduated from college or technical school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e147117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHomemaker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily structure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced/ Widowed/ Separated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e118910\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA member of an unmarried couple\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e112925\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e174069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol consumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e116854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e108863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e173643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e88.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExercise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e108042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e175615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eNote\u003c/em\u003e: \u003csup\u003ea\u003c/sup\u003eOther races included multi-race, American Indian, Alaskan Native, Asian, Pacific Islander, and Hawaiian Native\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOverall, the prevalence of depressive disorder among U.S. young adults significantly increased by 13.7% in relative terms from 20.5\u0026ndash;23.3% during COVID-19 (compared to before COVID-19). In both study periods, the prevalence was higher for the age group between 18 and 24 years (20.8% before COVID-19 and 24.0% during COVID-19), females (26.4% and 30.5%), non-Hispanic White (25.7% and 28.9%), who attended college or technical school (23.2% and 26.8%), unemployed (25.1% and 27.2%), divorced/widowed/separated (30.7% and 32.2%), had health insurance (21% and 24.6%), consumed alcohol (22.0% and 25.2%), smoked tobacco (35.4% and 37.5%), and did not exercise (21.6% and 23.8%) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eWeighted prevalence of depressive disorder among young adults (aged 18\u0026ndash;34 years) and relative changes in the prevalence before and during COVID-19 by socio-demographic and lifestyle factors, BRFSS 2018\u0026ndash;2022 (N\u0026thinsp;=\u0026thinsp;348994)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSociodemographic and lifestyle factors\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003eBefore COVID-19 (2018\u0026ndash;2019)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cem\u003eDuring COVID-19 (2020\u0026ndash;2022)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eRelative difference\u003c/em\u003e\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP-value\u003c/em\u003e\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWeighted %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall depressive disorder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (in years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace/ethnicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e30.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school or less\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAttended college or technical school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGraduated college or technical school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHomemaker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e0.032\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily structure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e20.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced/ Widowed/ Separated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e0.156\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA member of an unmarried couple\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e0.892\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol consumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e0.006\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e21.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExercise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cem\u003eNote\u003c/em\u003e: \u003csup\u003ea\u003c/sup\u003eRelative difference in the weighted prevalence of depressive disorder among young adults before and during COVID-19, presented in percentage terms; \u003csup\u003eb\u003c/sup\u003eOther race included multi-race, American Indian, Alaskan Native, Asian, Pacific Islander, and Hawaiian Native; \u003csup\u003ec\u003c/sup\u003eP-values in the third and fifth columns from left are from Rao-Scott Chi-Square test of association between depressive disorder and each of the independent variables; \u003csup\u003ed\u003c/sup\u003e\u003cem\u003eItalic P-values\u003c/em\u003e are from Rao-Scott Chi-Square test of association between depressive disorder and study periods (before and during COVID-19) for each category of an independent variable separately.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe relative changes (in percentage terms) in the prevalence of depressive disorder before and during COVID-19 showed an increase across all categories of sociodemographic and lifestyle factors. The highest relative increase was observed among those aged between 25 and 29 years (15.8%), females (15.5%), other races including multi-races (30.0%), graduated from college or technical school (17.9%), employed (17.7%), married (20.4%), and had health insurance (17.1%). Among lifestyle factors, relative changes in depressive disorder during COVID-19 were higher among those who consumed alcohol (14.5%), did not smoke (21.1%), and did exercise (13.7%) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCompared with the study period before COVID-19, the levels of depressive disorder remained high among females, non-Hispanic races other than White and Black, employed, and married young adults during COVID-19 after adjusting for sociodemographic and lifestyle factors (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Female young adults had 1.35 times higher odds (OR 1.35, 95% CI: 1.29\u0026ndash;1.40) of depressive disorder during COVID-19, whereas it was 1.25 times higher for males (OR 1.21, 95% CI: 1.16\u0026ndash;1.27). While Hispanic (OR 1.25, 95% CI: 1.14\u0026ndash;1.37) and Non-Hispanic White (OR 1.27, 95% CI: 1.23\u0026ndash;1.32) and non-Hispanic Black (OR 1.27, 95% CI: 1.14\u0026ndash;1.41) young adults experienced 25 to 27 percent higher odds of depressive disorder during COVID-19, other races including multi-race, American Indian, Alaskan Native, Asian, Pacific Islander, and Hawaiian Native experienced 46 percent higher odds (OR 1.46, 95% CI: 1.31\u0026ndash;1.62). During COVID-19 the odds of experiencing depressive disorder were highest among employed young adults (OR 1.32, 95% CI: 1.27\u0026ndash;1.37) followed by homemakers (OR 1.26, 95% CI: 1.08\u0026ndash;1.47), and unemployed (OR 1.20, 95% CI: 1.14\u0026ndash;1.28). Young adults whether married (OR 1.32, 95% CI: 1.24\u0026ndash;1.40) or a member of an unmarried couple (OR 1.30, 95% CI: 1.18\u0026ndash;1.42) or never married (OR 1.29, 95% CI: 1.23\u0026ndash;1.34), all groups experienced 29 to 32 percent higher odds of depressive disorder during COVID-19 except divorced/widowed/separated young adults (OR 1.13, 95%: 1.01\u0026ndash;1.27). Although the relative percent increase and crude odds of depressive disorder were higher among those aged 25\u0026ndash;29 years and graduated from college or technical school, adjusting for other sociodemographic and lifestyle factors showed higher odds for the age group 18\u0026ndash;24 years (OR 1.34, 95% CI: 1.27\u0026ndash;1.41) and who attended college or technical school (OR 1.33, 95% CI: 1.26\u0026ndash;1.40).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDepressive disorder among young adults aged 18\u0026ndash;34 years during COVID-19 compared to before COVID-19 for each category of sociodemographic factors, presented with (weighted) odds ratios and 95% confidence intervals, BRFSS 2018\u0026ndash;2022\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003eUnadjusted Model\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cem\u003eAdjusted Model\u003c/em\u003e\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSocio-demographic variables\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecOR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eaOR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (in years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.20 (1.15\u0026ndash;1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.34 (1.27\u0026ndash;1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.21 (1.15\u0026ndash;1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.30 (1.22\u0026ndash;1.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.14 (1.09\u0026ndash;1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.19 (1.12\u0026ndash;1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.14 (1.09\u0026ndash;1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.21 (1.16\u0026ndash;1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.23 (1.18\u0026ndash;1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.35 (1.29\u0026ndash;1.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.20 (1.09\u0026ndash;1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.27 (1.14\u0026ndash;1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.18 (1.14\u0026ndash;1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.27 (1.23\u0026ndash;1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.36 (1.24\u0026ndash;1.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.46 (1.31\u0026ndash;1.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.23 (1.14\u0026ndash;1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.25 (1.14\u0026ndash;1.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school or less\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.17 (1.12\u0026ndash;1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.25 (1.19\u0026ndash;1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAttended college or technical school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.21 (1.15\u0026ndash;1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.33 (1.26\u0026ndash;1.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGraduated from college or technical school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.22 (1.16\u0026ndash;1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.28 (1.22\u0026ndash;1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.23 (1.19\u0026ndash;1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.32 (1.27\u0026ndash;1.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.12 (1.06\u0026ndash;1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.20 (1.14\u0026ndash;1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHomemaker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.16 (1.01\u0026ndash;1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.26 (1.08\u0026ndash;1.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.25 (1.18\u0026ndash;1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.32 (1.24\u0026ndash;1.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced/ Widowed/ Separated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.08 (0.97\u0026ndash;1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.13 (1.01\u0026ndash;1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.17 (1.13\u0026ndash;1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.29 (1.23\u0026ndash;1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA member of an unmarried couple\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.18 (1.08\u0026ndash;1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.30 (1.18\u0026ndash;1.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eNote\u003c/em\u003e: cOR - Crude Odds Ratio; aOR - Adjusted Odds Ratio; 95% CI \u0026minus;\u0026thinsp;95 percent confidence interval; \u003csup\u003ea\u003c/sup\u003eOther races included multi-race, American Indian, Alaskan Native, Asian, Pacific Islander, and Hawaiian Native; bAdjusted model for each variable categories were controlled for other socio-demographic variables in the table, health insurance, and lifestyle variables (alcohol consumption, cigarette smoking, and exercise).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eUsing a large, nationally representative survey sample, this study found an increase in depressive disorder among young adults in the United States between 2018\u0026ndash;2019 and 2020\u0026ndash;2022. The prevalence of depressive disorder rose by 14 percent, from 20.5 percent before COVID-19 to 23.3 percent during COVID-19. Early adulthood has been a consistently reported period with a higher prevalence of poor mental health conditions, including depressive disorder in the United States. On top of that COVID-19 pandemic overwhelmed already deteriorating mental health conditions in this population. Literature suggests that COVID-19 pervasively disrupted daily life which negatively affected mental health [22]. Young adults were disproportionately affected as this population segment is more likely to be involved in job sectors that were shut down during the pandemic. The financial insecurity due to job loss may have contributed to a persistent increase in depressive disorder among young adults [16]. Furthermore, young adults are most likely to be students at an early age. The disruptions in their education, with shifts to online learning and uncertainties about academic progress might have contributed to increased depressive disorders. Several previous studies identified an increased prevalence of depressive disorder among U.S. adults during the pandemic, although all of them studied the early period of the pandemic and used different data sources with limited national representation [16,23,15,14]. Despite differences in study methods, our study findings were close to the results of previous studies that reported 27.8% of adults had depression [23] and 24% of young adults aged 18\u0026ndash;29 years had psychological distress during the first two months of the pandemic [15] with a persistent increase (30% higher odds) in the third year of the pandemic [14].\u003c/p\u003e \u003cp\u003eStratified analysis in our study found different levels of depressive disorder across sociodemographic factors before and during COVID-19. At both time points, females, aged 18\u0026ndash;24 years, non-Hispanic White, unemployed, and divorced/widowed/separated young adults had the highest prevalence of depressive disorder compared to their counterparts. However, the most affected groups with the highest relative increase in the prevalence of depressive disorder were different except for females and the age group 18\u0026ndash;24 years during the pandemic. Consistent with previous studies, our findings showed a persistently increased prevalence of depressive disorder among female young adults than males even after controlling for potential confounders [23,14]. Females had a prevalence of depressive disorder twice as high as males during the pandemic. The gender difference in depressive disorder is a complex phenomenon influenced by a combination of biological, psychological, and social factors. Sex differences in susceptibility due to hormonal differences and differences in coping strategy and emotional expression might affect females disproportionately [6,24,12]. In addition to that socio-environmental factors such as cultural expectations, discrimination and inequality, and socialization practices interact with biological and psychological differences which lead to increased vulnerabilities. The higher increase in depressive disorder among females during the pandemic might be linked to the factors like economic strain from the shut-down of female-dominated job sectors, increased caregiving responsibility, and increased household gender-based violence. Like females, young adults aged 18\u0026ndash;29 years experienced the highest relative increase in depressive disorder during COVID-19 even after adjusting for the potential confounding factors. Similar findings were reported from a longitudinal study on mental health conditions during COVID-19 in the United Kingdom [12] and a study on U.S. adults [16]. Young adults experience many life transitions that make them vulnerable to poor mental health. Additionally, the COVID-19 pandemic, an unprecedented event, might have overwhelmed their daily life. As this age group is most likely to be student-dominated, the disruption caused by COVID-19 in their academic activities with uncertainties of progress might have contributed to increased depressive disorder.\u003c/p\u003e \u003cp\u003eThis study found that young adults of other races/ethnicities, who attended college or technical school, who were married and employed had the highest relative increase in depressive disorder during COVID-19. Even after adjusting for potential confounding factors these groups of young adults remained the most affected groups. Other races/ethnicities include multiple races, American Indian, Alaskan Native, Asian, Pacific Islander, and Hawaiian Native who had the highest relative increase in depressive disorder during the pandemic although non-Hispanic Whites had the highest prevalence at both time points. Findings from the previous studies on racial disparities in depressive disorder are inconclusive. While one study reported that non-Hispanic Blacks had a slightly higher likelihood of trauma and stress-related disorder during the pandemic compared to non-Hispanic Whites [25], another study found a protective likelihood of depression among non-Hispanic Blacks [16]. However, our findings of increased depressive disorder among other races/ethnicities (including multi-races/ethnicities) were similar to a study done on U.S. adults during the early period of the pandemic [18]. Although unemployment was found to be an important factor in increased depression [18], employed young adults showed the highest increase in depressive disorder during the pandemic. Similarly, married young adults during the pandemic instead of divorced/separated/widowed, the commonly reported prevalent group had the highest increase in depressive disorder. Plausible explanations for why employed young adults experienced the highest level of depressive disorder during COVID-19 is well described in previous studies [16,23,26]. Early adulthood is when young adults enter the job sector, and/or have family. Due to the pandemic, industries such as retail, hospitality, and entertainment, which often employ young adults, were hit by lockdowns and other restrictions. Many young adults faced job losses or reduced work hours due to business closures and economic downturns leading to financial strain [27]. The situation was perhaps more complex for married young adults. Financial burden, along with the increased caregiving responsibilities, might have contributed to the increased depressive disorder. It might also be possible that young adult groups who were less prone to depressive disorder during the COVID-19 pandemic might have been affected more than the groups who already had a high prevalence of depressive disorder. Further studies are warranted to explore and/or confirm the mechanism of how some groups are disproportionately affected during a pandemic like COVID-19.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eOur study focused on the critical window period of adulthood when depressive disorder was reported to be prevalent at most. One of the main strengths of our study is that we used nationally representative probability-based large samples that included the same measure of depressive disorder at both time points making them meaningfully comparable. Furthermore, our study considered the entire longer pandemic period from the beginning of 2020 through 2022 with the highest level of restrictions, disruptions, and many uncertainties about COVID-19. Most of the restrictions were lifted by the start of 2023 and pandemic status was also rescinded. As we analyzed secondary data from a national survey, our analysis was limited to available data and how data were collected in BRFSS. We had self-reported data on diagnosed depressive disorder, thus prone to recall bias. Furthermore, BRFSS collects data on lifetime depressive disorder that might have influenced the comparison between before and during COVID-19. However, as the study included young adults, lifetime prevalence was a reasonable proxy for measuring the effect of the pandemic on depressive disorder across different sociodemographic strata. It was unlikely that an individual who was ever told to have depressive disorder would not experience any symptom during a pandemic like COVID-19. Even if that happened the percentage was expected to be negligible. Nevertheless, to validate our assumption, we conducted an additional analysis using a different variable in BRFSS that asked about self-reported mental health conditions in the past 30 days. As per the definition of depressive disorder, we dichotomized the number of days with poor mental health into \u0026lsquo;Yes\u0026rsquo; if symptoms persisted for 14 days or more, otherwise coded as \u0026lsquo;No\u0026rsquo;. As expected, the results were consistent with our study findings. Our study was done right after the pandemic restrictions got eased, thus it was beyond the scope of the study to assess whether the patterns of most affected groups and the magnitude of depressive disorder returned to the pre-pandemic state. Future studies may focus on assessing the excess burden of depressive disorder during the pandemic and whether the pandemic truly changed the patterns of most affected groups of young adults.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study revealed the importance of recognizing and understanding the socio-demographic factors that contribute to poor mental health conditions among young adults during a pandemic like COVID-19, providing essential insights for the development of targeted interventions and policies. As we strive to navigate the complexities of mental health, especially in the context of the pandemic, our findings will contribute to the ongoing efforts of enhancing support systems and addressing the evolving mental health needs of young adults in the United States.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eAcknowledgments\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to the Centers for Disease Control and Prevention (CDC) for their management of BRFSS surveys and for making data publicly available. However, CDC bears no responsibility for the analysis and interpretation of data. We are also thankful to participants of BRFSS surveys who shared valuable information.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthor contribution\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eSKC, FM, ZX, AB, KC, and RSK contributed to conceptualizing and designing the study. SKC, FM, and ZX prepared datasets and analyzed data under the guidance of RSK. SKC, FM, ZX, AB, and KC contributed to preparing the draft manuscript. RSK thoroughly reviewed the draft manuscript. All authors reviewed the consecutive drafts and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting interest\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors have no competing interests to declare relevant to the content of this article.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors did not receive support from any organization for the submitted work.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEthics approval\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable—analysis of material in the public domain.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eThapar A, Eyre O, Patel V, Brent D (2022) Depression in young people. 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Institute for Fiscal Studies Briefing Note BN278 6\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":"Mental health, depressive disorder, COVID-19, United States, young adults, BRFSS","lastPublishedDoi":"10.21203/rs.3.rs-3973430/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3973430/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose: \u003c/strong\u003eDepressive disorder during early adulthood has been a rising public health concern, potentially further compounded by the COVID-19 pandemic. Using nationally representative large survey samples, this study addressed the knowledge gaps in how COVID-19 affected depressive disorder among U.S. young adults.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThe analysis included 348,994 U.S. non-institutionalized young adults aged 18-34 years from the Behavioral Risk Factor Surveillance System for 2018-2022. Changes in the prevalence of diagnosed depressive disorder before and during COVID-19 were assessed by weighted bi-variate analysis using Rao-Scott Chi-Square test, with multivariable logistic regression models fitted to assess the magnitude of depressive disorder before and during COVID-19.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eOverall, the prevalence of depressive disorder increased by 13.7% (p\u0026lt;0.001) from 20.5% before COVID-19 to 23.3% during COVID-19. Adjusted for sociodemographic and lifestyle factors, the odds of depressive disorder during COVID-19 as compared to before COVID-19 were highest for females (OR: 1.35, 95% CI: 1.29-1.40), aged 18-24 years (OR: 1.34, 95% CI: 1.27-1.41), other races (OR: 1.46, 95% CI: 1.31-1.62), attended college or technical school (OR: 1.33, 95% CI: 1.26-1.40), employed (OR: 1.32, 95% CI: 1.27-1.37), and married (OR: 1.32, 95% CI: 1.24-1.40).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThe study findings revealed the importance of recognizing and understanding the most affected groups of young adults during a pandemic like COVID-19, providing essential insights for developing targeted interventions and policies.\u003c/p\u003e","manuscriptTitle":"Impact of the COVID-19 pandemic on depressive disorder among young adults in the United States: Analysis of the Behavioral Risk Factor Surveillance System data, 2018-2022","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-26 13:40:26","doi":"10.21203/rs.3.rs-3973430/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":"01c77500-59c4-4c30-a94f-4996f99d3c20","owner":[],"postedDate":"February 26th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-21T11:23:30+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-26 13:40:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3973430","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3973430","identity":"rs-3973430","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}