The Social Determinants of Mental Health Burden Two Years into the Pandemic: A Brief Report from a Longitudinal Survey

The Social Determinants of Mental Health Burden Two Years into the Pandemic: A Brief Report from a Longitudinal Survey | 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 The Social Determinants of Mental Health Burden Two Years into the Pandemic: A Brief Report from a Longitudinal Survey Katherine Sanchez, Lauren Hall, Briget da Graca, Monica Bennett, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3867798/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 Objective: To examine two waves of longitudinal survey data, at 1- and 2-years into the COVID-19 pandemic, to determine sociodemographic and economic risk factors for prolonged mental health distress. Methods: A longitudinal study of adults (N=1,412) began in April of 2020 in a large health system in Texas. Follow-up surveys were sent at 1, 3, 6, 12, and 24 months. The survey data included demographics, self-report mental health history, and symptoms of depression (PHQ-8). Generalized linear regression models were utilized to determine factors associated with a change in PHQ-8 at 1-year and 2-years from baseline. Results: Significant increases in PHQ-8 scores at both 1- and 2-year follow-up were associated with lower income, lower education, unemployment, history of depression, and being a healthcare worker or essential worker. Conclusions: Lower income individuals, those unemployed at baseline, essential workers, healthcare providers, and people with a history of depression reported worsening depression symptoms from their baseline scores. Policy implications: Establishing mechanisms and pathways of causality in social determinants of health research is critical to inform public health policy and population health interventions. Introduction It has been well documented that the COVID-19 pandemic, like the 1918 influenza and 2009 H1N1 pandemics that preceded it, 1 had a disproportionate impact among low-income, rural, and racial/ethnic minority populations, in terms of both incidence and severity of disease. 2 Furthermore, substantial differences in mental health and psychological distress extended beyond the clinical burden of infection with risk factors for worsening mental health which included lower income, lower educational attainment, younger age, being a member of a racial/ethnic minority, job loss (or reduction in hours or pay), and food insecurity. 3,4 Our previous report from early in the pandemic (June 2020) found income gaps associated with a substantial negative impact on finances and access to basic needs such as food and mental health treatment. 5 We also found low socioeconomic status associated with greater odds of depression and anxiety, a disparity evident prior to the global pandemic, but made more glaring, such that neither the overall prevalence nor the differences between groups could be dismissed. 3 In the current study, we sought to examine two additional waves of survey data, at 1- and 2-years follow up, to determine sociodemographic and economic risk factors for prolonged mental health distress. Methods A longitudinal study investigating the impact of COVID-19 began in April of 2020 in a large health system in Texas. Informed consent was obtained electronically using REDCap™, which was also utilized to capture and manage survey data. Follow up surveys were sent at 1, 3, 6, 12, and 24 months from baseline. Adults (≥ 18 years) were recruited via patient portal, social media, and referral from other health system communications. The survey data included demographic characteristics, self-report mental health history, and symptoms of depression (PHQ-8). We assessed demographic and socioeconomic factors associated with change in mean PHQ-8 scores at 1-year and 2-years from baseline. Continuous variables were summarized with means and standard deviations and categorical variables with counts and percentages. Multivariable generalized linear regression models were utilized to determine factors associated with a change in PHQ-8 at 1-year and 2-years from baseline. To adjust for the imbalance among sociodemographic factors when comparing participants with and without 1- and 2-year follow-up, propensity scores for follow-up were calculated and inverse probability weights were included in the models. All analyses were performed using SAS version 9.4 (SAS Inc., Cary, NC). Results Among the 1,412 participants who completed the baseline survey, 36.2% (n = 511) had complete PHQ-8 score data at 1-year follow-up and 15.4% (n = 217) had complete PHQ-8 score data at 2-year follow-up. Overall, participants were more likely to be female (73.2%), middle-aged (mean, 52.3 years), non-Hispanic White (84.7%), married (73.2%), have an income ≥ $ 75,000 (58.9%), and have a bachelor’s or advanced degree (34.4% and 35.6%) (Table 1 ) . The mean PHQ-8 score at baseline was 8.9 (SD, 6.0) and the majority of participants did not report a history of depression at baseline (68.3%). Table 1 Demographic Summary (at baseline) and Adjusted Change in PHQ8 Score from Baseline to 1 Year and 2 Years Demographics (n = 511) Year 1 (n = 460) 6 Year 2 (n = 199) 6 Characteristic Coefficient (95% CI) P-value Coefficient (95% CI) P-value Age 52.3 ± 14.6 -0.02 (-0.06, 0.02) 0.253 -0.08 (-0.14, -0.03) 0.004 BMI 30.9 ± 8.3 -0.01 (-0.06, 0.04) 0.609 -0.04 (-0.12, 0.04) 0.374 Baseline PHQ8 Score 8.9 ± 6.0 -0.51 (-0.59, -0.44) < 0.0001 -0.46 (-0.58, -0.34) < 0.0001 Sex Male (ref) 137 (26.8%) --- --- --- --- Female 374 (73.2%) -0.61 (-1.65, 0.42) 0.246 -0.75 (-2.36, 0.86) 0.361 Race 1 White (ref) 433 (84.7%) --- --- --- --- Black 15 (2.9%) -3.61(-5.48, -1.74) 0.0002 1.99 (-0.60, 4.58) 0.131 Hispanic 29 (5.7%) -1.53 (-3.16, 0.10) 0.066 0.77 (-1.30, 2.84) 0.466 Other 31 (6.1%) 0.37 (-1.32, 2.06) 0.669 -1.49 (-4.00, 1.02) 0.244 Income Level 2 < $ 45,000 70 (13.7%) 1.72 (0.36, 3.07) 0.013 -0.73 (-2.88, 1.41) 0.503 $ 45,000- $ 74,999 114 (22.3%) 0.69 (-0.43, 1.81) 0.227 0.59 (-1.14, 2.33) 0.503 $ 75,000+ (ref) 301 (58.9%) --- --- --- --- Marital Status Married (ref) 374 (73.2%) --- --- --- --- Not Married 137 (26.8%) -0.33 (-1.53, 0.87) 0.592 0.60 (-1.14, 2.34) 0.501 Highest Education Level 3 High school or less 32 (6.3%) 0.91 (-0.81, 2.62) 0.3 -2.89 (-6.01, 0.24) 0.07 Some college or Vocational /Associates Degree 116 (22.7%) 1.68 (0.51, 2.86) 0.005 -1.67 (-3.41, 0.07) 0.06 Bachelor's degree 176 (34.4%) 0.40 (-0.62, 1.42) 0.443 -1.61 (-3.15, -0.08) 0.039 Advanced degree (ref) 182 (35.6%) --- --- --- --- Baseline Work Status 4 Working (ref) 296 (57.9%) --- --- --- --- Not working/Unemployed due to COVID-19 67 (13.1%) 1.94 (0.66, 3.23) 0.003 2.63 (0.75, 4.51) 0.006 Not working right now for other reasons 145 (28.4%) 1.60 (-0.11, 3.31) 0.067 3.02 (0.49, 5.56) 0.019 Occupation Segment Essential Workers 16 (3.1%) 1.76 (-0.57, 4.08) 0.138 6.32 (3.07, 9.57) 0.0001 Healthcare Providers 108 (21.1%) 1.62 (0.46, 2.78) 0.006 2.74 (1.18, 4.29) 0.0006 General Population (ref) 387 (75.7%) --- --- --- --- Number of people supported by total household income 2.5 ± 1.4 0.21 (-0.14, 0.56) 0.246 -0.02 (-0.49, 0.54) 0.926 Employed before COVID 19 5 380 (74.4%) 1.93 (0.23, 3.64) 0.026 -0.50 (-3.42, 2.40) 0.734 Living Situation Owns home or apartment (ref) 384 (75.1%) --- --- --- --- Rents home or apartment 89 (17.4%) 1.62 (0.40, 2.84) 0.009 0.94 (-0.74, 2.63) 0.271 Lives in family household 30 (5.9%) 2.43 (0.67, 4.18) 0.007 1.97 (-0.81, 4.76) 0.164 Other/unknown 8 (1.6%) 6.86 (3.72, 9.99) < 0.0001 5.05 (0.67, 9.43) 0.024 History of Depression 162 (31.7%) 2.11 (1.16, 3.05) < 0.0001 2.15 (0.73, 3.58) 0.003 Current or past smoker 78 (15.3%) -0.71 (-1.82, 0.39) 0.203 1.74 (-0.26, 3.74) 0.089 Any chronic condition 244 (47.7%) 0.72 (-0.19, 1.64) 0.121 0.33 (-1.11, 1.79) 0.651 Positive COVID-19 Diagnosis (baseline) 347 (67.9%) -1.19 (-2.80, 0.41) 0.145 0.03 (-1.89, 1.94) 0.978 Enrollment Quarter 2Q2020 (ref) 81 (15.8%) --- --- --- --- 3Q2020 145 (28.4%) -0.29 (-2.01, 1.42) 0.738 0.50 (-1.13, 2.12) 0.549 4Q2020 75 (14.7%) -0.19 (-2.36, 1.98) 0.864 -0.52 (-2.69, 1.65) 0.639 1Q2021 128 (25.0%) 0.47 (-1.68, 2.62) 0.666 --- --- 2Q2021 6 (1.2%) 0.21 (-4.22, 4.63) 0.927 --- --- 3Q2021 76 (14.9%) -0.24 (-2.31, 1.82) 0.816 --- --- 1 Race − 3 (0.6%) Unknown/Missing 2 Income − 26 (5.1%) Unknown/Missing 3 Education Level − 5 (1.0%) Unkown/Missing 4 Baseline Work Status − 3 (0.6%) Unknown/Missing 5 Employed Before COVID 19 − 8 (1.5%) Unknown/Missing 6 Sample sizes included in the adjusted models Significant changes in PHQ-8 scores occurred across several sociodemographic factors at both 1- and 2-year follow-up (Table 1 ). At 1-year follow-up, there was a significant decrease in PHQ-8 scores among Black race, and significant increases among participants earning less than $ 45,000, those with some college or associates degree, those who were unemployed or not working due to COVID 19 at baseline, healthcare providers, those who were employed before COVID 19, and participants reporting a history of depression at baseline. At 2-year follow-up, there was an increase in PHQ-8 scores among those unemployed at baseline, those not working due to other reasons, essential, healthcare providers, and participants reporting a history of depression. Bachelor’s degree and age were associated with a decrease in PHQ-8 scores at 2-year follow-up. At both follow-ups there was a decrease in PHQ-8 scores associated with increasing baseline PHQ-8 scores. Discussion Our findings indicate persistent, significant differences in mental health symptoms one and two years into the pandemic across several demographic, occupational, and educational characteristics. Specifically, lower income individuals and those unemployed at baseline reported worsening depression symptoms from their baseline scores. Essential workers, healthcare providers, and people with a history of depression experienced a similar fate. We also found education appears to have acted as a protective factor and led to improved mental health at follow up. Just as the acute COVID-19 illness and disease severity was disproportionately experienced, the long-term social and emotional burden of the pandemic will likely be explained by differences in employment, education, housing, and other critical social determinants of health (SDOH). The characteristics of low-wage work that predispose workers to poor mental health when shelter-in-place orders and other public health measures were implemented included job loss or, in the case of essential workers, a dramatically increased workload and burnout. 6 In fact, changes to work situations related to COVID mitigation strategies have been identified as the single greatest occupational source of anxiety and depression. 7 Low income is a risk factor for poor mental health even outside the pandemic context, and people with previous history of depression are at greater risk for relapse. 3 The economic downturn that has followed has further exacerbated social risks such as homelessness, hunger, and displacement, as many continue to lose employment and associated financial security. 8 Our findings support reports of the how the global pandemic has amplified inequalities. Differences in education and income are better predictors of poor health than any other factors. Adults without a high school diploma are three times as likely as those with a college education to die before age 65. 9 Education plays a significant role in health literacy skills, and is critical for understanding basic health education communications and messaging. Health related behaviors are almost entirely driven by education and income, with low education status accounting for half of all avoidable deaths among working age adults. 10 This study has several limitations that should be considered. As with any survey, self-selection bias is a potential concern, and, since this was an online survey administered only in English, people with limited internet access and/or computer skills, and non-English speakers are likely under-represented. Our data on current or past mental health conditions are based on self-report and measures of depression employed symptom screening tools rather than clinical diagnoses. Lastly, household income information was collected in ranges, and so we could not calculate relative to benchmarks such as the federal poverty level, or median national household income. Public Health Implications Low education level, racial segregation, low social support, and poverty are all social determinants which contribute to increased death rates. 11 Financial and educational assets have been identified as particularly important for reducing symptoms of persistent depression in the absence of stressors, and people with the lowest risk of depression are those with high assets. 4 Since virtually all of the data in SDOH research has been observational, establishing mechanisms and pathways of causality, while accounting for bias in the limitations of the variables, is critical. 12 Methodological advances are needed to elucidate the protective factors and mechanisms of resilience for withstanding adverse events and to inform public health policy and population health interventions. 12 Ultimately, existing public health policy, even that created with emergency legislation, is likely inadequate to address the long term disproportionate hardship associated with low income and low wage occupations. Abbreviations COVID-19 Coronavirus disease 2019 PHQ-8 Personal Health Questionnaire Depression Scale – 8 items H1N1 Influenza Type A virus (swine flu) SDOH social determinants of health Declarations Ethics approval and consent to participate This was a cross-sectional observational nationwide survey-based study. The study was approved by the Institutional Review Board (IRB) at Baylor Scott and White Research Institute (#020-139) with a waiver of the requirement for written informed consent. Consent for publication Not applicable. Availability of data and materials The data that support the findings of this study are available from [Ann Marie Warren, PhD] but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of [Ann Marie Warren, PhD]. Competing interests The authors declare that they have no competing interests. Funding We gratefully acknowledge support for this work from the Baylor Scott & White Dallas Foundation and the W.W. Caruth, Jr. Fund at Communities Foundation of Texas. Authors' contributions All authors contributed to the study design and development of survey questions. KS conceptualized the manuscript. LH and MB carried out analyses. KS, LH, MB and BdG drafted the manuscript, with review from AMW and MP. All authors read and approved the final manuscript. Acknowledgements None. References Sydenstricker E. The incidence of influenza among persons of different economic status during the epidemic of 1918. 1931. Public health reports. 2006;121 Suppl 1:191-204; discussion 190. Garg S, Kim L, Whitaker M, et al. Hospitalization Rates and Characteristics of Patients Hospitalized with Laboratory-Confirmed Coronavirus Disease 2019 - COVID-NET, 14 States, March 1-30, 2020. MMWR Morbidity and mortality weekly report. 2020;69(15):458-464. Swaziek Z, Wozniak A. Disparities Old and New in US Mental Health during the COVID-19 Pandemic. Fisc Stud. 2020;41(3):709-732. Ettman CK, Abdalla SM, Cohen GH, Sampson L, Vivier PM, Galea S. Prevalence of Depression Symptoms in US Adults Before and During the COVID-19 Pandemic. JAMA Netw Open. 2020;3(9):e2019686. Hall LR, Sanchez K, da Graca B, Bennett MM, Powers M, Warren AW. Income Differences and COVID-19: Impact on Daily Life and Mental Health. Population Health Management. 2022;25(3):384-391. Cubrich M. On the frontlines: Protecting low-wage workers during COVID-19. Psychol Trauma. 2020;12(S1):S186-S187. Burstyn I, Huynh T. Symptoms of Anxiety and Depression in Relation to Work Patterns During the First Wave of the COVID-19 Epidemic in Philadelphia PA: A Cross-Sectional Survey. J Occup Environ Med. 2021;63(5):e283-e293. Tipirneni R. A Data-Informed Approach to Targeting Social Determinants of Health as the Root Causes of COVID-19 Disparities. American journal of public health. 2021;111(4):620-622. National Academies of Sciences E, Medicine. Communities in Action: Pathways to Health Equity. Washington, DC: The National Academies Press; 2017. Braveman P, Egerter S, Barclay C. What shapes health-related behaviors? The role of social factors. Exploring the social determinants of health: issue brief no. 1. Princeton, NJ: Robert Wood Johnson Foundation;2011. Galea S, Tracy M, Hoggatt KJ, DiMaggio C, Karpati A. Estimated Deaths Attributable to Social Factors in the United States. American journal of public health. 2011;101(8):1456-1465. Palmer RC, Ismond D, Rodriquez EJ, Kaufman JS. Social Determinants of Health: Future Directions for Health Disparities Research. American journal of public health. 2019;109(S1):S70-S71. 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-3867798","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":270197768,"identity":"1f596e41-035c-4fe5-b3e9-0e105195d1c5","order_by":0,"name":"Katherine 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distress extended beyond the clinical burden of infection with risk factors for worsening mental health which included lower income, lower educational attainment, younger age, being a member of a racial/ethnic minority, job loss (or reduction in hours or pay), and food insecurity.\u003csup\u003e3,4\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur previous report from early in the pandemic (June 2020) found income gaps associated with a substantial negative impact on finances and access to basic needs such as food and mental health treatment.\u003csup\u003e5\u003c/sup\u003e We also found low socioeconomic status associated with greater odds of depression and anxiety, a disparity evident prior to the global pandemic, but made more glaring, such that neither the overall prevalence nor the differences between groups could be dismissed.\u003csup\u003e3\u003c/sup\u003e In the current study, we sought to examine two additional waves of survey data, at 1- and 2-years follow up, to determine sociodemographic and economic risk factors for prolonged mental health distress.\u0026nbsp;\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eA longitudinal study investigating the impact of COVID-19 began in April of 2020 in a large health system in Texas. Informed consent was obtained electronically using REDCap\u0026trade;, which was also utilized to capture and manage survey data. Follow up surveys were sent at 1, 3, 6, 12, and 24 months from baseline. Adults (\u0026ge;\u0026thinsp;18 years) were recruited via patient portal, social media, and referral from other health system communications. The survey data included demographic characteristics, self-report mental health history, and symptoms of depression (PHQ-8). We assessed demographic and socioeconomic factors associated with change in mean PHQ-8 scores at 1-year and 2-years from baseline.\u003c/p\u003e \u003cp\u003eContinuous variables were summarized with means and standard deviations and categorical variables with counts and percentages. Multivariable generalized linear regression models were utilized to determine factors associated with a change in PHQ-8 at 1-year and 2-years from baseline. To adjust for the imbalance among sociodemographic factors when comparing participants with and without 1- and 2-year follow-up, propensity scores for follow-up were calculated and inverse probability weights were included in the models. All analyses were performed using SAS version 9.4 (SAS Inc., Cary, NC).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eAmong the 1,412 participants who completed the baseline survey, 36.2% (n\u0026thinsp;=\u0026thinsp;511) had complete PHQ-8 score data at 1-year follow-up and 15.4% (n\u0026thinsp;=\u0026thinsp;217) had complete PHQ-8 score data at 2-year follow-up. Overall, participants were more likely to be female (73.2%), middle-aged (mean, 52.3 years), non-Hispanic White (84.7%), married (73.2%), have an income \u0026ge; \u003cspan\u003e$\u003c/span\u003e75,000 (58.9%), and have a bachelor\u0026rsquo;s or advanced degree (34.4% and 35.6%) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cb\u003e)\u003c/b\u003e. The mean PHQ-8 score at baseline was 8.9 (SD, 6.0) and the majority of participants did not report a history of depression at baseline (68.3%).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic Summary (at baseline) and Adjusted Change in PHQ8 Score from Baseline to 1 Year and 2 Years\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDemographics (n\u0026thinsp;=\u0026thinsp;511)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eYear 1 (n\u0026thinsp;=\u0026thinsp;460) \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eYear 2 (n\u0026thinsp;=\u0026thinsp;199) \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoefficient (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCoefficient (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52.3\u0026thinsp;\u0026plusmn;\u0026thinsp;14.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.02 (-0.06, 0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.08 (-0.14, -0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.9\u0026thinsp;\u0026plusmn;\u0026thinsp;8.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.01 (-0.06, 0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.609\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.04 (-0.12, 0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.374\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBaseline PHQ8 Score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.9\u0026thinsp;\u0026plusmn;\u0026thinsp;6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.51 (-0.59, -0.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.46 (-0.58, -0.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e137 (26.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e374 (73.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.61 (-1.65, 0.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.75 (-2.36, 0.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.361\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e433 (84.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (2.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.61(-5.48, -1.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.99 (-0.60, 4.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.131\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\u003e29 (5.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.53 (-3.16, 0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.77 (-1.30, 2.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.466\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 (6.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.37 (-1.32, 2.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.49 (-4.00, 1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.244\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIncome Level\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u003cspan\u003e$\u003c/span\u003e45,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70 (13.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.72 (0.36, 3.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.73 (-2.88, 1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.503\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e45,000-\u003cspan\u003e$\u003c/span\u003e74,999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e114 (22.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.69 (-0.43, 1.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.59 (-1.14, 2.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.503\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e75,000+ (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e301 (58.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital Status\u003c/b\u003e\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e374 (73.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot Married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e137 (26.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.33 (-1.53, 0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.60 (-1.14, 2.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHighest Education Level\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e\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 \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\u003e32 (6.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.91 (-0.81, 2.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.89 (-6.01, 0.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSome college or Vocational /Associates Degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e116 (22.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.68 (0.51, 2.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.67 (-3.41, 0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBachelor's degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e176 (34.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.40 (-0.62, 1.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.61 (-3.15, -0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdvanced degree (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e182 (35.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBaseline Work Status\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorking (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e296 (57.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot working/Unemployed due to COVID-19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67 (13.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.94 (0.66, 3.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.63 (0.75, 4.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot working right now for other reasons\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e145 (28.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.60 (-0.11, 3.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.02 (0.49, 5.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOccupation Segment\u003c/b\u003e\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEssential Workers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (3.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.76 (-0.57, 4.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.32 (3.07, 9.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealthcare Providers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e108 (21.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.62 (0.46, 2.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.74 (1.18, 4.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneral Population (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e387 (75.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of people supported by total household income\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.21 (-0.14, 0.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.02 (-0.49, 0.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.926\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEmployed before COVID 19\u003c/b\u003e\u003csup\u003e\u003cb\u003e5\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e380 (74.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.93 (0.23, 3.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.50 (-3.42, 2.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.734\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLiving Situation\u003c/b\u003e\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOwns home or apartment (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e384 (75.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRents home or apartment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89 (17.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.62 (0.40, 2.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.94 (-0.74, 2.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.271\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLives in family household\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 (5.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.43 (0.67, 4.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.97 (-0.81, 4.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.164\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther/unknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (1.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.86 (3.72, 9.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.05 (0.67, 9.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHistory of Depression\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e162 (31.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.11 (1.16, 3.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.15 (0.73, 3.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCurrent or past smoker\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78 (15.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.71 (-1.82, 0.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.74 (-0.26, 3.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAny chronic condition\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e244 (47.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.72 (-0.19, 1.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.33 (-1.11, 1.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.651\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePositive COVID-19 Diagnosis (baseline)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e347 (67.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.19 (-2.80, 0.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03 (-1.89, 1.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.978\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEnrollment Quarter\u003c/b\u003e\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2Q2020 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81 (15.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3Q2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e145 (28.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.29 (-2.01, 1.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.50 (-1.13, 2.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.549\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4Q2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75 (14.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.19 (-2.36, 1.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.52 (-2.69, 1.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.639\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1Q2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e128 (25.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.47 (-1.68, 2.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2Q2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (1.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.21 (-4.22, 4.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3Q2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76 (14.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.24 (-2.31, 1.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e1\u003c/sup\u003eRace \u0026minus;\u0026thinsp;3 (0.6%) Unknown/Missing\u003c/p\u003e \u003cp\u003e\u003csup\u003e2\u003c/sup\u003eIncome \u0026minus;\u0026thinsp;26 (5.1%) Unknown/Missing\u003c/p\u003e \u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003eEducation Level \u0026minus;\u0026thinsp;5 (1.0%) Unkown/Missing\u003c/p\u003e \u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003eBaseline Work Status \u0026minus;\u0026thinsp;3 (0.6%) Unknown/Missing\u003c/p\u003e \u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003eEmployed Before COVID 19\u0026thinsp;\u0026minus;\u0026thinsp;8 (1.5%) Unknown/Missing\u003c/p\u003e \u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003eSample sizes included in the adjusted models\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSignificant changes in PHQ-8 scores occurred across several sociodemographic factors at both 1- and 2-year follow-up (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). At 1-year follow-up, there was a significant decrease in PHQ-8 scores among Black race, and significant increases among participants earning less than \u003cspan\u003e$\u003c/span\u003e45,000, those with some college or associates degree, those who were unemployed or not working due to COVID 19 at baseline, healthcare providers, those who were employed before COVID 19, and participants reporting a history of depression at baseline. At 2-year follow-up, there was an increase in PHQ-8 scores among those unemployed at baseline, those not working due to other reasons, essential, healthcare providers, and participants reporting a history of depression. Bachelor\u0026rsquo;s degree and age were associated with a decrease in PHQ-8 scores at 2-year follow-up. At both follow-ups there was a decrease in PHQ-8 scores associated with increasing baseline PHQ-8 scores.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur findings indicate persistent, significant differences in mental health symptoms one and two years into the pandemic across several demographic, occupational, and educational characteristics. Specifically, lower income individuals and those unemployed at baseline reported worsening depression symptoms from their baseline scores. Essential workers, healthcare providers, and people with a history of depression experienced a similar fate. We also found education appears to have acted as a protective factor and led to improved mental health at follow up.\u003c/p\u003e \u003cp\u003eJust as the acute COVID-19 illness and disease severity was disproportionately experienced, the long-term social and emotional burden of the pandemic will likely be explained by differences in employment, education, housing, and other critical social determinants of health (SDOH). The characteristics of low-wage work that predispose workers to poor mental health when shelter-in-place orders and other public health measures were implemented included job loss or, in the case of essential workers, a dramatically increased workload and burnout.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e In fact, changes to work situations related to COVID mitigation strategies have been identified as the single greatest occupational source of anxiety and depression.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e Low income is a risk factor for poor mental health even outside the pandemic context, and people with previous history of depression are at greater risk for relapse.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e The economic downturn that has followed has further exacerbated social risks such as homelessness, hunger, and displacement, as many continue to lose employment and associated financial security.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eOur findings support reports of the how the global pandemic has amplified inequalities. Differences in education and income are better predictors of poor health than any other factors. Adults without a high school diploma are three times as likely as those with a college education to die before age 65.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e Education plays a significant role in health literacy skills, and is critical for understanding basic health education communications and messaging. Health related behaviors are almost entirely driven by education and income, with low education status accounting for half of all avoidable deaths among working age adults.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThis study has several limitations that should be considered. As with any survey, self-selection bias is a potential concern, and, since this was an online survey administered only in English, people with limited internet access and/or computer skills, and non-English speakers are likely under-represented. Our data on current or past mental health conditions are based on self-report and measures of depression employed symptom screening tools rather than clinical diagnoses. Lastly, household income information was collected in ranges, and so we could not calculate relative to benchmarks such as the federal poverty level, or median national household income.\u003c/p\u003e\n\u003ch3\u003ePublic Health Implications\u003c/h3\u003e\n\u003cp\u003eLow education level, racial segregation, low social support, and poverty are all social determinants which contribute to increased death rates.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e Financial and educational assets have been identified as particularly important for reducing symptoms of persistent depression in the absence of stressors, and people with the lowest risk of depression are those with high assets.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e Since virtually all of the data in SDOH research has been observational, establishing mechanisms and pathways of causality, while accounting for bias in the limitations of the variables, is critical.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e Methodological advances are needed to elucidate the protective factors and mechanisms of resilience for withstanding adverse events and to inform public health policy and population health interventions.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e Ultimately, existing public health policy, even that created with emergency legislation, is likely inadequate to address the long term disproportionate hardship associated with low income and low wage occupations.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCOVID-19\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCoronavirus disease 2019\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePHQ-8\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePersonal Health Questionnaire Depression Scale \u0026ndash; 8 items\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eH1N1\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInfluenza Type A virus (swine flu)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSDOH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esocial determinants of health\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis was a cross-sectional observational nationwide survey-based study. The study was approved by the Institutional Review Board (IRB) at Baylor Scott and White Research Institute\u0026nbsp;(#020-139) with a waiver of the requirement for written informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from [Ann Marie Warren, PhD] but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of [Ann Marie Warren, PhD].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe gratefully acknowledge support for this work from the Baylor Scott \u0026amp; White Dallas Foundation and the W.W. Caruth, Jr. Fund at Communities Foundation of Texas.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study design and development of survey questions. KS conceptualized the manuscript. LH and MB carried out analyses. KS, LH, MB and BdG drafted the manuscript, with review from AMW and MP. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eSydenstricker E. The incidence of influenza among persons of different economic status during the epidemic of 1918. 1931. \u003cem\u003ePublic health reports.\u0026nbsp;\u003c/em\u003e2006;121 Suppl 1:191-204; discussion 190.\u003c/li\u003e\n \u003cli\u003eGarg S, Kim L, Whitaker M, et al. Hospitalization Rates and Characteristics of Patients Hospitalized with Laboratory-Confirmed Coronavirus Disease 2019 - COVID-NET, 14 States, March 1-30, 2020. \u003cem\u003eMMWR Morbidity and mortality weekly report.\u0026nbsp;\u003c/em\u003e2020;69(15):458-464.\u003c/li\u003e\n \u003cli\u003eSwaziek Z, Wozniak A. Disparities Old and New in US Mental Health during the COVID-19 Pandemic. \u003cem\u003eFisc Stud.\u0026nbsp;\u003c/em\u003e2020;41(3):709-732.\u003c/li\u003e\n \u003cli\u003eEttman CK, Abdalla SM, Cohen GH, Sampson L, Vivier PM, Galea S. Prevalence of Depression Symptoms in US Adults Before and During the COVID-19 Pandemic. \u003cem\u003eJAMA Netw Open.\u0026nbsp;\u003c/em\u003e2020;3(9):e2019686.\u003c/li\u003e\n \u003cli\u003eHall LR, Sanchez K, da Graca B, Bennett MM, Powers M, Warren AW. Income Differences and COVID-19: Impact on Daily Life and Mental Health. \u003cem\u003ePopulation Health Management.\u0026nbsp;\u003c/em\u003e2022;25(3):384-391.\u003c/li\u003e\n \u003cli\u003eCubrich M. On the frontlines: Protecting low-wage workers during COVID-19. \u003cem\u003ePsychol Trauma.\u0026nbsp;\u003c/em\u003e2020;12(S1):S186-S187.\u003c/li\u003e\n \u003cli\u003eBurstyn I, Huynh T. Symptoms of Anxiety and Depression in Relation to Work Patterns During the First Wave of the COVID-19 Epidemic in Philadelphia PA: A Cross-Sectional Survey. \u003cem\u003eJ Occup Environ Med.\u0026nbsp;\u003c/em\u003e2021;63(5):e283-e293.\u003c/li\u003e\n \u003cli\u003eTipirneni R. A Data-Informed Approach to Targeting Social Determinants of Health as the Root Causes of COVID-19 Disparities. \u003cem\u003eAmerican journal of public health.\u0026nbsp;\u003c/em\u003e2021;111(4):620-622.\u003c/li\u003e\n \u003cli\u003eNational Academies of Sciences E, Medicine. \u003cem\u003eCommunities in Action: Pathways to Health Equity.\u003c/em\u003e Washington, DC: The National Academies Press; 2017.\u003c/li\u003e\n \u003cli\u003eBraveman P, Egerter S, Barclay C. \u003cem\u003eWhat shapes health-related behaviors? The role of social factors. Exploring the social determinants of health: issue brief no. 1.\u0026nbsp;\u003c/em\u003ePrinceton, NJ: Robert Wood Johnson Foundation;2011.\u003c/li\u003e\n \u003cli\u003eGalea S, Tracy M, Hoggatt KJ, DiMaggio C, Karpati A. Estimated Deaths Attributable to Social Factors in the United States. \u003cem\u003eAmerican journal of public health.\u0026nbsp;\u003c/em\u003e2011;101(8):1456-1465.\u003c/li\u003e\n \u003cli\u003ePalmer RC, Ismond D, Rodriquez EJ, Kaufman JS. Social Determinants of Health: Future Directions for Health Disparities Research. \u003cem\u003eAmerican journal of public health.\u0026nbsp;\u003c/em\u003e2019;109(S1):S70-S71.\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":"","lastPublishedDoi":"10.21203/rs.3.rs-3867798/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3867798/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e To examine two waves of longitudinal survey data, at 1- and 2-years into the COVID-19 pandemic, to determine sociodemographic and economic risk factors for prolonged mental health distress.\u003cbr\u003e\n \u003cstrong\u003eMethods:\u003c/strong\u003e A longitudinal study of adults (N=1,412) began in April of 2020 in a large health system in Texas. Follow-up surveys were sent at 1, 3, 6, 12, and 24 months. \u0026nbsp;The survey data included demographics, self-report mental health history, and symptoms of depression (PHQ-8). Generalized linear regression models were utilized to determine factors associated with a change in PHQ-8 at 1-year and 2-years from baseline.\u003cbr\u003e\n \u003cstrong\u003eResults:\u003c/strong\u003e Significant increases in PHQ-8 scores at both 1- and 2-year follow-up were associated with lower income, lower education, unemployment, history of depression, and being a healthcare worker or essential worker.\u003cbr\u003e\n \u003cstrong\u003eConclusions:\u003c/strong\u003e Lower income individuals, those unemployed at baseline, essential workers, healthcare providers, and people with a history of depression reported worsening depression symptoms from their baseline scores.\u003cbr\u003e\n \u003cstrong\u003ePolicy implications:\u003c/strong\u003e Establishing mechanisms and pathways of causality in social determinants of health research is critical to inform public health policy and population health interventions.\u003c/p\u003e","manuscriptTitle":"The Social Determinants of Mental Health Burden Two Years into the Pandemic: A Brief Report from a Longitudinal Survey","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-01 05:17:47","doi":"10.21203/rs.3.rs-3867798/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":"016b4fca-1135-4dec-a984-fbe8576ac245","owner":[],"postedDate":"February 1st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-01T09:47:03+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-01 05:17:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3867798","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3867798","identity":"rs-3867798","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}