Disparities of Depression among Older Adults with Chronic Conditions within Different Age Groups: Evidence from a Longitudinal Analysis

Disparities of Depression among Older Adults with Chronic Conditions within Different Age Groups: Evidence from a Longitudinal Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Disparities of Depression among Older Adults with Chronic Conditions within Different Age Groups: Evidence from a Longitudinal Analysis Zhichao Hao, Tingzhang Yang, Xiaofu Pan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4644190/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Suffering chronic conditions greatly contribute to mental health problems like depression among older adults. Although an array of literature has focused on this field of study, little research has examined how depression among older adults changes over time or differs from each age group from a developmental and comprehensive view. This longitudinal study provides a holistic understanding of risk and protective factors associated with depression to tailor and provide supportive services for older adults according to their changing needs. Methods Utilizing rounds 5–9 of the National Health and Aging Trends Study (NHATS), 3,541 older adults were studied. The Transactional Theory of Stress and Coping (TTSC) model was utilized to select predictors. Descriptive analysis was conducted for all predictors, and a Generalized Estimating Equation (GEE) was applied to explore and identify the risk and protective factors. Results Age, race and ethnicity, self-rated health, number of chronic conditions, cognitive capacity, frequency of negative feelings, self-realization, self-efficacy and resilience, activity participation, and technology use were significantly associated with depression. Furthermore, depression may decrease over time but only happens in a relatively short time, and the extent of decline slows down gradually. Conclusions The findings highlight the need to provide support and link resources to caregivers and call for efficient chronic condition management to provide early screening, assessment, and diagnosis. Recommendations from healthcare providers, proper education of healthy lifestyle and the dissemination of related information, and prompting older adults to engage in more physical activities can also make a difference in helping older adults gain better physical and mental health to prevent them from suffering depression. Depression Older adults Chronic conditions Longitudinal analysis Figures Figure 1 Figure 2 INTRODUCTION Chronic conditions refer to human health conditions or diseases that are persistent or otherwise long-lasting in syndromes, physical impairments, disabilities, and diseases (Bernell & Howard, 2016 ). Chronic conditions can be characterized by chronic physical impairments (CPIs) and chronic diseases (CDs). CPIs stand for functional limitations or disabilities, lasting or being expected to last one year or more (Hwang et al., 2001 ). According to the Centers for Disease Control and Prevention (CDC), CDs are diseases that usually last for one year or more and cannot be cured or may grow worse over time, requiring ongoing medical attention or limiting activities of daily living, or both (CDC, 2019). Due to aging, degeneration of physical function, and other risk factors, older adults aged 65 and older are more likely to suffer from chronic conditions (Rizzuto et al., 2017 ). Except for the congenital diseases resulted from genetic factors, chronic conditions of older adults are usually caused by factors like dietary, lifestyle and metabolic risk factors, and might be prevented by behavioral changes (Neuhouser, 2019 ). Social determinants like socioeconomic status, education level, and race/ethnicity otherwise affect the prevention and treatment of chronic conditions (Clark & Boyd, 2017 ). In the U.S., older adults who are people of color and/or low-income are less likely to get access to medical care services for early detection to stunt severe health outcomes and for treatment to decrease the death rate (Kaholokula, 2016 ). Suffering chronic conditions result in a significant burden on older adults associated with chronic care and chronic care needs (Seeman & Chen, 2002 ). Depression is one of the most common mental disorders among older adults who suffer from chronic conditions (Null & Pennesi, 2017 ). Older adults with depression can have feelings of sadness, hopelessness, loss of interest, fear, apprehension, worry, and restlessness as psychological reflections on stressful life events like suffering chronic conditions (Polku et al., 2015 ). These psychological reflections are usually comorbid and can be short-term or long-term, severe or mild, and vary individually (De Zwart et al., 2019 ). Factors that determine the length of time and the severity of depression rest on the interaction of the different types of chronic conditions, socioeconomic factors, and the personality and mentality of older adults (van der Aa et al., 2015). These factors affect how well older adults manage the chronic conditions they are suffering. The influence these factors exert upon older adults’ management of chronic conditions and the resulting mental conditions change over time (Reid et al., 2015). Older adults have to acclimate themselves to a different lifestyle if factors like the level of chronic conditions, caregiving from family members, and the accessibility of medical treatment do not remain stable but change periodically in their daily lives (Hultman et al., 2008 ). It drastically increases the vulnerability to depression when facing uncertainties (Naughton & Weaver, 2014 ). As older adults age, the change of these factors occurs more commonly and frequently, resulting in the disparities of depression both individually and among different age groups, including young-old (65–74 years), middle-old (75–84 years), and old-old (85 + years) (Byers et al., 2010 ). When older adults suffer from chronic conditions, understanding the risk and protective factors related to depression is essential. It facilitates healthcare practitioners, policymakers, social workers, and even older adults and their caregivers to better acknowledge and manage these factors to help older adults avoid being vulnerable to depression. Previous studies mainly focused on the limited factors to narrowly discuss and present a picture of the risk and protective factors of depression among older adults. Although these studies contributed to the literature, interactions among multiple factors are understudied when using nationwide data. Besides, the vast majority of studies only applied age as a significant demographic factor within a single age group without comparison to other age groups. These studies did not go in-depth to fathom how older adults in different age groups manage their depression. In addition, a multitude of studies conducted one-time cross-sectional studies, which are insufficient to delve into factors associated with depression and how older adults' depression changes with their chronic conditions. Longitudinal studies can allow multiple observations over a long period of time to explore relevant factors associated with depression. Therefore, longitudinal studies must be conducted to address this knowledge gap in the literature. This study aims to understand the disparities of depression of older adults with chronic conditions among different age groups. A longitudinal study will provide a developmental and comprehensive understanding of risk and protective factors associated with depression to analyze how depression changes over time and how it differs from each age group in order for each stakeholder to tailor supportive services for older adults according to their changing needs over time. Theoretical Model Suffering chronic conditions can be a stressful life event for older adults, requiring prompt and efficient approaches to address resultant mental disorders like depression (Fiske et al., 2009 ). The Transactional Theory of Stress and Coping (TTSC) avers that individuals are constantly appraising stimuli within their environment. This appraisal process generates emotions and initiates coping strategies to manage these psychological reactions or attempt to directly address the stressors themselves, and it has two major parts: (1) cognitive appraisal and (2) coping (Lazarus & Folkman, 1984 ). TTSC proposes that the mediating role of cognitive appraisal ascribes meanings to stressful events or stimuli (Boyd et al., 2009 ; Lazarus & Folkman, 1984 ). Cognitive appraisal has two core forms: primary appraisal and secondary appraisal. Primary appraisal facilitates individuals to determine whether the specific individual or environmental transactions to their well-being exert a positive effect, have no effect, or produce harm, threat, or challenge (Lazarus & Folkman, 1984 ). Compared to the first two categories, harm and threat appraisals can especially evoke negative emotions or the need for subsequent coping actions. Challenge appraisals have the potential to bring about positive emotions when sufficient coping resources are available (Lazarus, 1991 ). When a specific transaction is deemed stressful, the secondary appraisal will then be enacted and further determines what can be done to manage the stressor and its resultant mental or physical distress (Dewe & Cooper, 2007 ). The secondary appraisal is an assessment of coping resources to indicate the confidence in one's ability or cognitive and behavioral efforts to master, reduce, or tolerate the internal and/or external demands that are created by the stressful events (Lazarus & Folkman, 1984 ). Coping resources link to the characteristics of the individual and the characteristics of the situation. Moos and Schaefer ( 1993 ) further demonstrated and categorized coping resources into personal and environmental systems. The personal system mainly includes demographic factors, such as age, gender, and income. Elements within the environmental system include physical (e.g., health, cognitive capacity), social (e.g., social support and social network), psychological (e.g., beliefs and self-esteem), and material (e.g., financial and assistive devices; Berjot & Gillet, 2011 ). Both personal and environmental factors influence the presence of a threat to one's health, and appraisals or understandings of the threat (Moos & Schaefer, 1993 ) When a situation is appraised as stressful (primary appraisal) and requiring efforts to manage or resolve the event (secondary appraisal), coping actions are enacted (Folkman & Lazarus, 1988 ). According to the TTSC, coping strategies aim to either directly manage the stressor ( problem-focused coping, PFC ) or regulate emotions as a consequence of the stressful encounter ( emotion-focused coping, EFC ; Lazarus & Folkman, 1984 ). However, unsuccessful coping and its resultant distress may trigger meaning-focused coping (MFC) . Folkman and Moskowitz ( 2004 ) further proposed future-oriented coping (FOC) to advance the TTSC by underlining the experiences of coping with potential future stressors. FOC consists of four coping categories: reactive, anticipatory, preventive, and proactive coping (Schwarzer, 2000 ). Reactive coping claims to situate the harm or loss that has already been experienced or occurred in the past. Anticipatory coping responds to a recognized upcoming event that is likely to happen in the near future to manage known risks and utilize resources to minimize the stressor or maximize anticipated benefits (Schwarzer & Taubert, 2002 ). Preventive coping addresses potential future stressors and aims at accumulating resources to reduce the severity of the stressor. Proactive coping emphasizes the accumulation and utilization of resources to improve one’s potential or seize possible opportunities for personal growth or well-being that is highly likely to occur in the future (Schwarzer, 2000 ). The TTSC has been applied to a slew of studies of depression to understand how older adults cope with stressful life events, such as suffering various chronic conditions (Cheng et al., 2020 ; Eckerblad et al., 2015 ; Naganathan et al., 2016 ). This study also benefits from the TTSC to explore the risk and protective factors of depression among older adults with chronic conditions to understand how to cope with these mental disorders to avoid or mitigate the resultant negative impacts. Figure 1 demonstrates the classical model of the TTSC theory, with some alterations catered for depression research. [Insert Fig. 1 at here] METHODS Study design and Samples This study used the National Health and Aging Trends Study (NHATS) (Kasper & Freedman, 2020 ) which is being sponsored by the National Institute on Aging under a cooperative agreement with the Johns Hopkins University Bloomberg School of Public Health (U01AG032947) to analyze the risk and protective factors associated with depression. NHATS’ study design and content were developed by multidisciplinary cooperation from demography, geriatric medicine, epidemiology, health services research, economics, and gerontology. In this study, the chronic condition was divided into CDs and CPIs. For CDs, NHATS collected data from the following diseases: heart attack, heart disease, diabetes, lung disease, stroke, dementia or Alzheimer’s disease, and cancers (e.g., skin cancer, breast cancer, prostate cancer, bladder cancer, cervical/ovarian/uterine cancer, colon cancer, kidney cancer, and other types of cancer). CPIs included: hearing, vision, chewing or speaking, symptoms of pain and fatigue, breathing, broken or fractured hip, other broken or fractured bones, body strength limit, and balance. The present study applied the latest rounds of NHATS, which are round 5 to round 9. Participants who are eligible for inclusion were those who (1) were sample persons instead of proxies; (2) had at least one chronic condition (1 = Yes, 0 = No); (3) participated in all 5 rounds (round 5 to round 9) from 2015 to 2019. In total, 3541 older adults who were sample persons, had at least one chronic condition, and participated in all 5 rounds were included in the present study. Measures Dependent variables. The dependent variable in the present study was defined as a dichotomous variable, which is “having depression” (1 = Yes, 0 = No). NHATS uses the brief screening instrument that is the Patient Health Questionnaire-2 (PHQ-2) for depression (Kasper & Freedman, 2020 ). If a score ≥ 3, it will be used to indicate either probable major depression (Kroenke et al., 2003 ; Kroenke et al., 2007 ). Independent variables. The independent variables (i.e., factors in the personal system and factors in the environmental system, including physical-related factors, psychological-related factors, social-related factors, and material-related factors) were selected based on the TTSC model. Factors in the personal system include: (1) Age (0 = 65 to 69, 1 = 70 to 74, 2 = 75 to 79, 3 = 80 to 84, 4 = 85 to 89, 5 = 90+); (2) Gender (1 = Male, 0 = Female); (3) Marital status (1 = Partnered, 0 = Not partnered); (4) Household members refer to the total number of family members in the household; (5) Race and ethnicity (1 = White, non-Hispanic, 2 = Black, non-Hispanic, 3 = Other, 4 = Hispanic); (6) Income (1 = less than $ 19,999, 2= $ 20,000 to $ 49,999, 3= $ 50,000 to $ 99,999, and 4= $ 100,000 or more); (7) Veteran’s status (1 = Yes, 0 = No). Factors in the environmental system include physical-related factors, psychological-related factors, social-related factors, and material-related factors. For physical-related factors, they are (1) Self-rated health (1 = Excellent, 2 = Very good, 3 = Good, 4 = Fair, 5 = Poor); (2) Number of Sensory and Physical Impairments and Symptoms (range = 1–19); (3) Cognitive capacity (range = 0–20), higher scores represent better cognitive capacity; (4) Household activities refer to the instrumental activities of daily living (IADL), which measures whether participants can do the household activities independently in the last month (0–6); (5) Rehabilitation (1 = Yes, 0 = No). For psychological-related factors, they are (1) Frequency of various feelings (0–8), higher scores represent that participants have more negative feelings; (2) Self-realisation (0–8), higher scores represent that participants have a more negative attitude toward life; (3) Self-efficacy and resilienc e (0–6), higher scores represent that participants have less sense of control. For Social-related factors, they are (1) Social network (range = 0–5), NHATS provided 38 options and allowed participants to choose up to 5 people; (2) Community (range = 0–6), higher scores represent better recognition to the community; (3) Activity participation (physical activities: range = 0–2; social activities: range = 0–4), with higher scores indicating participation in more activities within the respective domain. For Material-related factors, they are (1) Home environment (range = 0–7), higher scores represent more available bathroom facilities; (2) Technology Use (range = 0–4), higher scores represent more utilization of technological devices; (3) Health Insurance plans (range = 0–6), higher scores represent more health insurance coverage that participants had. Research Questions With the identification of major gaps in the current knowledge of the risk and protective factors of depression, this study used a longitudinal data analysis approach depending on the Transactional Theory of Stress and Coping (TTSC): (a) to explore factors associated with depression among older adults with chronic conditions from different age groups who lived in the U.S.; (b) to analyze how depression changes over time and how it differs from each age group under the influence of these factors. To achieve these research goals, the following research questions were studied: 1. What are the most reported chronic conditions among a nationally-representative sample of older adults in the U.S.? 2. How do factors in the personal system and factors in the environmental system, including physical-related factors, psychological-related factors, social-related factors, and material-related factors, affect the occurrence of depression over time? 3. How does depression change over time? 4. How does depression differ from different age groups? Data analysis Descriptive analysis. Descriptive analysis was conducted for all variables. Standard deviation, mean, median, range, minimum, and maximum were calculated for continuous variables. Frequencies were calculated for categorical variables. In order to compare the difference between participants who suffered from depression and those who did not, Chi-square tests were applied to each categorical variable, and Point biserial correlations analyses were applied to each continuous variable. Multivariate analysis. Since the present study used 5 rounds of NHATS (2015–2019), time-variant variables allow for the analyses of the autocorrelations between the outcome variable and explanatory variables and the analyses of the autoregressive correlation. This study applied a generalized estimating equation (GEE) as a statistic model to explore and identify the risk and protective factors. Using an empirical covariance matrix, GEE is an optimal model for repeated measure studies to estimate the within-subject correlations (Hardin & Hilbe, 2014 ). As the outcome variable in this study, having depression is a dichotomous variable, GEE offers an option that is Binomial Family and Logit Link to help conduct the analysis. Besides, GEE has the ability to handle missing at random or missing not at random in the longitudinal dataset (Garcia & Marder, 2017 ; Hardin & Hilbe, 2014 ). Stata 16 was used for the data analysis. An alpha level of .05 was used to determine statistical significance for relationships examined in the present study. RESULTS Descriptive results This section reports the baseline characteristics of factors in the personal system and environmental system (see Table 1 and Fig. 2 ). Table 1 Baseline characteristics of factors in personal system and environmental system by depression, NHATS 2015 ( n = 3541) Total Depression No Depression χ 2 a r pb b P -value Factors Frequency ( n , %) M ± SD Frequency ( n , %) M ± SD Frequency ( n , %) M ± SD 3541 (100%) 375 (10.6%) 3166 (89.4%) Personal System Age 65 to 69 557 (15.7%) 70 (18.7%) 487 (15.4%) 5.276 .383 70 to 74 1004 (28.4%) 94 (25.1%) 910 (28.7%) 75 to 79 834 (23.6%) 96 (25.6%) 738 (23.3%) 80 to 84 645 (18.2%) 67 (17.9%) 578 (18.3%) 85 to 89 357 (10.1%) 35 (9.3%) 322 (10.2%) 90 + 144 (4.1%) 13 (3.5%) 131 (4.1%) Gender Female 2108 (59.5%) 241 (64.3%) 1867 (59.0%) 3.904 .048 Male 1433 (40.5%) 134 (35.7%) 1299 (41.0%) Marital Not partnered Status 1693 (47.8%) 223 (59.5%) 1470 (46.4%) 22.834 .000 Partnered 1848 (52.2%) 152 (40.5%) 1696 (53.6%) Household members 1.94 ± 1.018 2.02 ± 1.120 1.92 ± 1.005 .030 .074 Race White 2632 (74.3%) 207 (55.2%) 2425 (76.6%) 83.328 .000 Black 657 (18.6%) 115 (30.7%) 542 (17.1%) Other (IANP) c 78 (2.2%) 18 (4.8%) 60 (1.9%) Hispanic 174 (4.9%) 35 (9.3%) 139 (4.4%) Income Less than $ 19,999 1912 (54.0%) 249 (66.4%) 1663 (52.5%) 43.667 .000 20,000 to 49,999 762 (21.5%) 85 (22.7%) 677 (21.4%) 50,000 to 99,999 545 (15.4%) 26 (6.9%) 519 (16.4%) 100,000 or more 322 (9.1%) 15 (4.0%) 307 (9.7%) Veteran Status No 2764 (78.1%) 312 (83.2%) 2452 (77.4%) 6.477 .011 Yes 777 (21.9%) 63 (16.8%) 714 (22.6%) Environmental System Physical-related Factors Self-rated Health Excellent 424 (12.0%) 12 (3.2%) 412 (13.0%) 300.836 .000 Very good 1128 (31.9%) 54 (14.4%) 1074 (33.9%) Good 1256 (35.5%) 123 (32.8%) 1133 (35.8%) Fair 622 (17.6%) 134 (35.7%) 488 (15.4%) Poor 111 (3.1%) 52 (13.9%) 59 (1.9%) Chronic Conditions# 3.96 ± 2.332 5.42 ± 2.650 3.79 ± 2.230 .215 .000 Cognitive Capacity 8.99 ± 3.256 7.78 ± 3.148 9.13 ± 3.239 − .128 .000 Household Activities 2.09 ± 1.318 2.43 ± 1.380 2.04 ± 1.304 .091 .000 Rehab No 2862 (80.8%) 290 (77.3%) 2572 (81.2%) 3.299 .069 Yes 679 (19.2%) 85 (22.7%) 594 (18.8%) Phycological-related Factors Various Feelings 2.00 ± 1.289 3.16 ± 1.650 1.87 ± 1.166 .309 .000 Self-realization .70 ± 1.146 1.57 ± 1.645 .59 ± 1.024 .261 .000 Self-efficacy & Resilience .88 ± .981 1.38 ± 1.244 .82 ± .927 .178 .000 Social-related Factors Social Network# 2.31 ± 1.381 2.10 ± 1.303 2.33 ± 1.388 − .053 .002 Community 3.68 ± 1.303 3.54 ± 1.429 3.70 ± 1.286 − .039 .022 Physical Activities 1.05 ± .777 .77 ± .729 1.08 ± .776 − .126 .000 Social Activities 2.76 ± 1.036 2.22 ± 1.052 2.82 ± 1.015 − .180 .000 Material-related Factors Building Amenities .95 ± .614 .97 ± .693 .95 ± .604 .013 .426 Bath Feature 3.10 ± 1.495 3.18 ± 1.539 3.09 ± 1.490 .019 .267 Technology Use 2.75 ± 1.010 2.35 ± 1.025 2.80 ± .997 − .139 .000 Health Insurance Plans 2.59 ± .932 2.60 ± .1.026 2.59 ± .920 .004 .801 a Pearson Chi-square value; b Point Biserial correlation value; c Indian/Asian/Native Hawaiian/Pacific Islander [Insert Fig. 2 at here] The result of the number of chronic conditions was presented in Fig. 2 , responding to the first research question. Among all the 19 chronic conditions, the most reported one was vision impairment ( n = 2407, 68.0%), followed by chronic pain ( n = 2126, 60.0%) and fatigue ( n = 1669, 47.1%) that were all chronic physical impairments (CPIs, from upper fracture to balance, n = 12), far more than reported chronic diseases (CDs, from heart attack to cancer, n = 7). [Insert Table 1 at here] Factors in the personal system. At the baseline survey (NHATS, 2015; n = 3541), 375 (10.6%) participants reported having depression. The age of participants generally distributed equally but convergent to 70 to 74 years old ( n = 1004, 28.4%) and 75 to 79 years old ( n = 834, 23.6%). Participants who were older than 90 years old were the least ( n = 144, 4.1%). Among all the participants, the majority were female ( n = 2108, 59.5%), and most of the participants were married or partnered ( n = 1848, 52.2%). Participants lived with one to three household members on average ( n = 1.94 ± 1.018). Among all the participants, the majority were non-Hispanic White ( n = 2632, 74.3%), followed by Black ( n = 657, 18.6%). More than half of the participants had an annual income of less than $ 19,999 ( n = 1912, 54.0%). Around one-fifth of the participants were veterans ( n = 777, 21.9%). Factors in the environmental system Physical-related Factors. Most participants reported a very good ( n = 1128, 31.9%) or good ( n = 1256, 35.5%) self-rated health. Participants had at least one to six chronic conditions ( n = 3.96 ± 2.332) on average. Participants reported a score of cognitive capacity of 8.99 ± 3.256 (range = 0–20; higher scores represent better cognitive capacity). The score of household activities was 2.09 ± 1.318 (range = 0–6; higher scores represent that participants have more difficulties finishing household activities independently). The majority of the participants did not receive any rehab service ( n = 2826, 80.8%). Phycological-related Factors. The frequency of various feelings was 2.00 ± 1.289 (range = 0–8; higher scores represent that participants have more negative feelings). The score of self-realization was .70 ± 1.146 (range = 0–8; higher scores represent that participants have a worse attitude toward life). The score of self-efficacy and resilience was .88 ± .981 (range = 0–6; higher scores represent that participants have less sense of control). Social-related Factors. The total number of people participants talk to about important things (social network) was 2.31 ± 1.381 (range = 0–5). The score of participants’ perspectives and attitudes toward the community they were living (community) was 3.68 ± 1.303 (range = 0–6; higher scores represent better recognition to the community). Activity participation was divided into physical activities and social activities. The number of physical activities was 1.05 ± .777 (range = 0–2), and the number of social activities was 2.76 ± 1.036 (range = 0–4). Higher scores indicate that older adults participated in more activities within the respective domain. Material-related Factors. The home environment was represented by two parts: building amenities and bathroom features. The total number of building amenities was .95 ± .614 (range = 0–6; higher scores represent more available building amenities). The total number of bathroom facilities was 3.10 ± 1.495 (range = 0–7; higher scores represent more available bathroom facilities). The total number of technological devices participants used was 2.75 ± 1.010 (range = 0–4; higher scores represent more utilization of technological devices). The total number of health insurance plans participants had was 2.59 ± .932 (range = 0–6; higher scores represent more health insurance coverage that participants had). Comparison Based on depression This section reports detailed results of Point biserial correlations analyses and Chi-square tests of the relationship between the explanatory variables and depression (see Table 1 ). In Table 1 , there was a significant difference of gender ( χ 2 = 3.904, P = .048), marital status ( χ 2 = 22.834, P < .001), race ( χ 2 = 83.328, P < .001), income ( χ 2 = 43.667, P < .001), veteran status ( χ 2 = 6.477, P = .011), self-rated health ( χ 2 = 300.836, P < .001), number of chronic conditions ( r pb = .215, P < .001), cognitive capacity ( r pb = − .128, P < .001), household activities ( r pb = .091, P < .001), various feelings ( r pb = .309, P < .001), self-realization ( r pb = .261, P < .001), self-efficacy and resilience ( r pb = .178, P < .001), number of social network ( r pb = − .053, P = .002), community ( r pb = − .039, P = .022), physical activities ( r pb = − .126, P < .001), social activities ( r pb = − .180, P < .001), and technology use ( r pb = − .139, P < .001) between older adults with chronic conditions who had depression and who did not. [Insert Table 2 at here] Table 2 Multivariate Generalized Estimating Equation (GEE) Regression Model Depression Factors β Exp (B) P -value Personal System Age 65 to 69 (ref) 70 to 74 − .205 .815 .109 75 to 79 − .127 .880 .322 80 to 84 − .320 .726 .018 85 to 89 − .426 .653 .006 90 + − .589 .555 .001 Gender Female (ref) Male − .124 .884 .232 Marital Not partnered Status (ref) Partnered − .060 .942 .488 Household members − .016 .984 .586 Race White (ref) Black .557 1.745 .000 Other (IANP) a .459 1.583 .022 Hispanic .766 2.152 .000 Income Less than $ 19,999 (ref) 20,000 to 49,999 − .106 .900 .201 50,000 to 99,999 − .190 .827 .086 100,000 or more − .149 .862 .371 Veteran Status No (ref) Yes .099 1.104 .395 Environmental System Physical Factors Self-rated Health Excellent (ref) Very good .205 1.228 .224 Good .355 1.426 .034 Fair .662 1.939 .000 Poor 1.130 3.096 .000 Chronic Conditions# .141 1.152 .000 Cognitive Capacity − .048 .953 .000 Household Activities .038 1.038 .086 Rehab No (ref) Yes .073 1.076 .305 Phycological Factors Various Feelings .388 1.474 .000 Self-realization .200 1.221 .000 Self-efficacy & Resilience .081 1.084 .002 Social Factors Social Network# − .044 .957 .068 Community .004 1.004 .820 Physical Activities − .116 .891 .011 Social Activities − .138 .871 .000 Material Factors Building Amenities − .012 .988 .813 Bath Feature .037 1.038 .075 Technology Use − .136 .873 .000 Health Insurance Plans .027 1.028 .423 Time indicator Visit 1 (ref) Visit 2 − .114 .892 .169 Visit 3 − .073 .929 .383 Visit 4 − .382 .683 .000 Visit 5 − .227 .797 .011 a Indian/Asian/Native Hawaiian/Pacific Islander Multivariate Generalized Estimating Equation (GEE) Regression Model Among factors in the personal system, compared to participants who were 65 to 69 years old, those who were 80 to 84 ( e b = .726, P = .018), 85 to 90 ( e b = .653, P = .006), and 90+ ( e b = .555, P = .001) years old were less likely to have depression. The results above answered the fourth research question. The following results answered the second research question. Compared to White participants, those who were Black ( e b = 1.745, P < .001), Other (Indian/Asian/Native Hawaiian/Pacific Islander) ( e b = 1.583, P = .022), and Hispanic ( e b = 2.152, P < .001) were more likely to have depression. Among factors in the environmental system, for physical factors, compared to participants who reported excellent self-rated health, those who reported good ( e b = 1.426, P = .034), fair ( e b = 1.939, P < .001), and poor ( e b = 1.130, P < .001) self-rated health were more likely to have depression, and the worse the self-rated health, the more likely to have depression. Participants who had more chronic conditions ( e b = 1.152, P < .001) were more likely to have depression, but those who had better cognitive capacity ( e b = .953, P < .001) were less likely to have depression. For psychological factors, participants who had more negative feelings ( e b = 1.474, P < .001), worse self-realization ( e b = 1.221, P < .001), and worse self-efficacy and resilience ( e b = 1.084, P = .002) were more likely to have depression. For social factors, participants who attended more physical activities ( e b = .891, P = .011) and more social activities ( e b = .871, P < .001) were less likely to have depression. For material factors, participants who used more technological devices ( e b = .873, P < .001) were less likely to have depression. For time indicator, compared to the first visit (i.e., baseline survey, NHATS 2015), depression in visit 4 ( e b = .683, P < .001) and visit 5 ( e b = .797, P = .011) was less likely to occur over time. The results above answered the third research question. DISCUSSION This study explored the disparities of depression among older adults with chronic conditions. The results from a longitudinal analysis provided a developmental and comprehensive understanding of factors associated with depression. The results showed that the most reported chronic conditions were chronic physical impairments (CPIs), far more than chronic diseases (CDs). Specifically, the top five reported CPIs were vision impairment ( n = 2407, 68.0%), followed by chronic pain ( n = 2126, 60.0%), fatigue ( n = 1669, 47.1%), lower body strength limit ( n = 1271, 35.9%), and upper body strength limit and the issue of balance ( n = 1126, 31.8%). All of these CPIs are associated with body movements and mobility, impacting the capacity to perform daily tasks independently (Reid et al., 2015). Besides, the top three reported CDs were cancer ( n = 518, 14.6%), heart disease and heart attack ( n = 505, 14.3%), and diabetes ( n = 468, 13.2%). All of these CDs require older adults to utilize healthcare services and prescription drugs regularly and periodically. The results of chronic conditions in this study are consistent with the latest report of Centers for Disease Control and Prevention (CDC) that 2 in 5 adults age 65 and older are affected by dysfunction and other mobility-related limitations, and cancer, heart disease and heart attack, and diabetes are among most reported chronic diseases (Villarroel et al., 2019). The results also represented that among 3541 participants, 375 (10.6%) participants reported having depression. This result is consistent with the CDC’s report that the depression rate among older adults living in the community ranges from less than 1% to about 5% but rises to 13.5% in those who require home healthcare and 11.5% in older hospitalized patients (CDC, 2017). Risk and protective factors of depression among U.S. older adults with chronic conditions are discussed below. Findings from factors in the personal system showed that age is significantly associated with depression over time. Specifically, compared to participants who were 65 to 69 years old, those aged 80 and older were less likely to have depression, and the older the participants, the lesser likely to have depression. As the population ages, especially for those who were 80 + years old or even 90 + years old, survivorship plays a pivotal role in the low level of depression. For example, the oldest-old view their late-life and health condition more positively than younger older adults since recognizing that they have lived well beyond their expected lifespan or they are survivors of their chronic conditions (DeSantis et al., 2019 ). Besides, people who live into their 80s and 90s will more likely have fewer health problems than those who died at earlier ages, which may contribute to lower levels of depression (Cho, 2011 ). In addition, according to the Transactional Theory of Stress and Coping (TTSC) model, suffering chronic conditions can be a stressful life event to older adults, triggering appraisal and then prompting older adults to cope with this stressor by invoking their coping resources. When the applied coping strategies fail to address the influence resulting from chronic conditions, this failure initiates further coping strategies, and its resultant distress may trigger meaning-focused coping (MFC), particularly when older adults perceive that stressors like suffering chronic conditions are overwhelmingly uncontrollable. Older adults may ascribe positive meaning to suffering chronic conditions or find and remind themselves of the benefits of this stressor. For example, although they are suffering chronic conditions and have to endure its negative influences over time, it provides more opportunities for them to spend time with their caregivers who are usually their family members. Older adults can benefit from this advantage to reorder their life priorities from focusing on chronic conditions to the relationships, especially when they are at an older age when medical treatment is futile to offer additional benefits (Moorman, 2011 ). Meanwhile, this advantage can also alleviate their depression from suffering chronic conditions to further sustain extant coping efforts over time (Folkman, 2008 ). The results further presented that compared to White participants, Black, Indian/Asian/Native Hawaiian/Pacific Islander, and Hispanic were more likely to have depression. This result is consistent with a multitude of studies showing that older adults of color are more likely to experience mental disorders, like depression, due to the disparities in socioeconomic status (Bui et al., 2020 ). Moreover, older adults with higher socioeconomic status, like higher annual income, can greatly reduce the occurrence or severity of mental disorders since they can access more healthcare and life resources (Xue et al., 2021). Findings from factors in the environmental system showed that compared to participants who reported excellent self-rated health, those who reported good, fair, and poor self-rated health were more likely to have depression. Besides, the worse the self-rated health, the more likely to be depressed over time. According to the TTSC model, coping with stressful life events, like suffering chronic conditions, is an appraisal-coping-reappraisal process that appraises whether the stressor produces harm, threat, or challenge. Or this process reappraises whether the coping efforts have been successful or determines if the nature of the situation has changed from stressful to irrelevant, benign, or positive (Lazarus & Folkman, 1984 ). Regardless of whether the participants first encountered the stressor (i.e., suffering chronic conditions) or already applied coping strategies to cope with this stressor when the survey was administered, fair or poor self-rated health as unfavorable appraisal may exacerbate individual’s mental or physical distress, leading to depression. This result is consistent with a slew of studies suggesting that older adults who poorly rate their health are more likely to be depressed (Campos et al., 2015 ). Findings further presented that older adults who had more chronic conditions were more likely to have depression over time, but those who had better cognitive capacity were less likely to have depression over time. According to the TTSC model, older adults may appraise the severity of their chronic conditions and then invoke coping resources to address this stressor. Negative emotions and feelings can result from unfavorable appraisal or unsuccessful coping over time when their chronic conditions do not become better or even grow worse and more. Just as the results of psychological factors represented, participants who had more negative feelings, worse self-realization, and worse self-efficacy and resilience were more likely to have depression over time. These results are consistent with a wealth of studies asserting that the more comorbidities older adults have, the more likely to stress themselves and their caregivers, bringing physical or mental distress (e.g., negative emotions or bad self-realization and self-efficacy) and eventually result in mental disorders like depression (Mindlis et al., 2021 ). Besides, having better cognitive capacity as one of the powerful coping resources can facilitate older adults better manage their chronic conditions when synergistically applying other coping resources during their coping processes over time. This result is consistent with multiple studies presenting that older adults with better cognitive capacity are more likely to be able to address mental distress (e.g., negative emotions or bad self-realization and self-efficacy) resulted from suffering chronic conditions, and to make proper plans for their healthcare services and life so that increase the possibility to bring about positive outcomes for their physical and mental health (Dragioti et al., 2021 ). The results of social factors showed that participants who actively utilized coping resource “activity participation” by attending more physical activities and social activities were less likely to have depression over time. According to the TTSC model, older adults not only take actions to cope with the impacts of extant stressor that is suffering chronic conditions, but also apply future-oriented coping to avoid negative influence or outcomes from possible stressors. Attending physical activities regularly can benefit older adults with chronic conditions with health improvements and protecting them from getting additional chronic conditions (Taylor, 2014). Besides, attending social activities offers peer support, encouragement, or emotional relief to older adults from friends and other people outside the family. Using technology devices, like cellphones, computers, and tablets, further facilitate older adults to build connections with people and access supportive information to mitigate the negative emotions or mental disorders from suffering chronic conditions (Stacy et al., 2020 ). In addition, the findings of this longitudinal study revealed a pattern of how depression changed over time in that depression was less likely to occur over time. However, this pattern existed in two consecutive visits only, suggesting that although depression tends to decrease over time, it happens in a relatively short time range that is one year. Besides, the odds ratio of visit 5 ( e b = .797, P = .011) is higher than visit 4 ( e b = .683, P < .001), meaning the occurrence of depression at visit 5 is higher than visit 4, or the likelihood of the decrease of depression is lower at visit 5 compared to visit 4. It further suggests that although the occurrence of depression declines over time, the extent of decline slows down gradually. This finding further calls for joint efforts involving policymakers, healthcare practitioners, researchers, and social workers to develop new and creative mental health programs and interventions to help minimize the impacts of depression by extending the decline of depression over time. LIMITATIONS AND FUTURE DIRECTIONS The findings of this study presented a relatively comprehensive understanding of the risk and protective factors of depression among U.S. older adults who were suffering chronic conditions. However, some limitations in this study should be noted to advance the knowledge and understanding in future studies. The primary limitations of this study are related to the research methodology. For the measurement of dependent variables (i.e., depression), the NHATS survey applied a brief screening instruments: The Patient Health Questionnaire-2 (PHQ-2) for depression (Kasper & Freedman, 2020 ). Since brief measurements may sacrifice the precision or reliability of the results (Kohout et al., 1993 ), future studies can use detailed measurements to assess depression. For example, the Patient Health Questionnaire-9 (PHQ-9; Kroenke et al., 2001 ) may be used in future studies. Despite this limitation, a multitude of studies have used PHQ-2 to explore relationships between depression and various predictors (Hamedani et al., 2020 ; Jones et al., 2016 ; Levine et al., 2018 ). Besides, the NHATS survey measured age by categorizing continuous data into six categories (0 = 65 to 69, 1 = 70 to 74, 2 = 75 to 79, 3 = 80 to 84, 4 = 85 to 89, 5 = 90+). Since continuous variables applying a simple linear or polynomial function can adequately describe the relationship between the response and the predictor (in this study, the relationship between depression and the age; Royston et al., 2006 ), future studies can apply continuous data for measuring age. Moreover, the NHATS survey does not collect certain data, like geographic region, so that this study was limited to use this predictor to comprehensively explore more about whether the urban or rural area has a significant association with the disparities of depression. Future studies can involve this factor to present a more holistic picture of depression. In addition, the cohorts in the analysis were unweighted, prompting further analyses to apply analytic weights to adjust for differential nonresponse and produce national prevalence estimates to show the relationship between depression and selected predictors. Finally, this study is limited by its inability to generate conclusions related to the comparison among chronic conditions to show which specific chronic conditions were significantly associated with depression among U.S. older adults over time. Future studies can focus on the comparison to contribute to the knowledge of depression across different chronic conditions by using data collected from clinical trials. Future studies can also explore this relationship more in-depth using qualitative methods, such as case studies. CONCLUSIONS This study explored and identified risk and protective factors of depression among U.S. older adults who were suffering chronic conditions from a longitudinal perspective. The findings of this study highlight the need to provide support and link resources to caregivers to facilitate them to deliver quality care to older adults. Besides, necessary training provided by the community and the online free education are vital for caregivers to get knowledge and skills related to taking care of themselves and delivering caregiving. The findings of this study call for efficient chronic condition management to provide early screening, assessment, and diagnosis to prevent or find diseases or physical impairments early. Recommendations from healthcare providers and proper education of healthy lifestyles and the dissemination of related information can also make a difference in helping older adults gain better physical and mental health to prevent them from suffering depression. The findings of the study further underscored the indispensable role of physical and social activities in protecting older adults from mental disorders like depression. Moreover, the findings of this study emphasized that utilizing technology devices greatly reduces the likelihood of having depression. Lastly, joint efforts involving policymakers, healthcare practitioners, researchers, and social workers are needed to develop new and creative mental health programs and interventions to help minimize the impacts of depression by extending the decline of depression over time. Declarations Ethics approval and consent to participate This study obtained ethical exemption from the research committee of Southwest University and the Institutional Review Board since the data this study used is secondary data. Consent for publication Not applicable. Availability of data and material The data this study used was obtained from the National Health and Aging Trends Study (NHATS) website, which can be found at: https://www.nhats.org/researcher/nhats/methods-documentation?id=data_collection Competing interests The authors declare no competing interests. Funding Funds for this study were provided by the Chongqing Educational Science Planning Project (K22YE202095). Authors' contributions HZC, YTZ and PXF were all involved in the design of the study and initiated the study. HZC was responsible for conceptualization, methodology, data analyses, and drafted the manuscript. YTZ reviewed, edited, and controlled the quality of the study. YTZ and PXF both worked on data curation and commented the manuscript. All authors read and approve the final version of the manuscript. Acknowledgments We sincerely thank the research cohorts at Southwest University and the University of Alabama for the support in data analysis, and the feedback from our colleagues. References Berjot, S., & Gillet, N. (2011). Stress and coping with discrimination and stigmatisation. Frontiers in Psychology , 2 , 33. https://doi.org/10.3389/fpsyg.2011.00033 Bernell, S., & Howard, S. W. (2016). Use your words carefully: what is a chronic disease? 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The Journals of Gerontology Series B: Psychological Sciences and Social Sciences , 57 (3), S135–S144. Stacy, M., Lindsey, H., & Tsai, J. (2020). Association between technology use and social integration among veterans with disabilities. The Journal of Nervous and Mental Disease , 208 (4), 306–311. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-4644190","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":327795823,"identity":"fa4a9b97-9069-4986-8aeb-42848af8606b","order_by":0,"name":"Zhichao Hao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYDACCSD+UMHAwIYkZkBQC+OMM6RqYeZsQxXDr0V+dvPDx4zz7PL5xI4/fFzAcFiegb15mwQ+LYxzjhkbF25LtmyTzjE2nsFw2LCB51gZXi3MEglm0jO3HTBgk85hk+ZhOMzYIJFjhlcLm0T6N2neOSAt6c9AWuwb5N/g18IDNFOatwGkBWgdUEtigwQPfi0SEjnFhjOOJYMcZmzMY5Ce3MaTVmyBT4v8jPSNDz7U2BnIz05/+Jinwtq2n/3wxhv4tKABA9Q4HQWjYBSMglFAJgAAQ387cBQuc2IAAAAASUVORK5CYII=","orcid":"","institution":"Southwest University","correspondingAuthor":true,"prefix":"","firstName":"Zhichao","middleName":"","lastName":"Hao","suffix":""},{"id":327795824,"identity":"5fd96cfe-8342-4c47-9579-990dcef3ba6b","order_by":1,"name":"Tingzhang Yang","email":"","orcid":"","institution":"Guizhou Modern Research Institute of Urban and Rural Economic Development","correspondingAuthor":false,"prefix":"","firstName":"Tingzhang","middleName":"","lastName":"Yang","suffix":""},{"id":327795825,"identity":"050b4127-d52e-407b-942d-8754cd218875","order_by":2,"name":"Xiaofu Pan","email":"","orcid":"","institution":"Southwest University","correspondingAuthor":false,"prefix":"","firstName":"Xiaofu","middleName":"","lastName":"Pan","suffix":""}],"badges":[],"createdAt":"2024-06-26 16:56:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4644190/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4644190/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":61335855,"identity":"5eef34f8-f30a-4d20-be02-08c17138f741","added_by":"auto","created_at":"2024-07-29 15:35:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":45619,"visible":true,"origin":"","legend":"\u003cp\u003eThe Transactional Theory of Stress and Coping (TTSC)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4644190/v1/a3105bd26c82db07f66e4dce.png"},{"id":61335854,"identity":"c48bceab-261b-4e80-b142-4e8f30a03bd8","added_by":"auto","created_at":"2024-07-29 15:35:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":41645,"visible":true,"origin":"","legend":"\u003cp\u003eTotal number of chronic conditions reported at the baseline survey (NHATS 2015)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4644190/v1/a69fd814ed24aad9f35b769f.png"},{"id":84196499,"identity":"c6063143-c9b5-4d53-b47c-4f73ded54e94","added_by":"auto","created_at":"2025-06-09 07:53:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1684231,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4644190/v1/16166df2-cf59-4b77-97a3-51fb709c5f60.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Disparities of Depression among Older Adults with Chronic Conditions within Different Age Groups: Evidence from a Longitudinal Analysis","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eChronic conditions refer to human health conditions or diseases that are persistent or otherwise long-lasting in syndromes, physical impairments, disabilities, and diseases (Bernell \u0026amp; Howard, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Chronic conditions can be characterized by chronic physical impairments (CPIs) and chronic diseases (CDs). CPIs stand for functional limitations or disabilities, lasting or being expected to last one year or more (Hwang et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). According to the Centers for Disease Control and Prevention (CDC), CDs are diseases that usually last for one year or more and cannot be cured or may grow worse over time, requiring ongoing medical attention or limiting activities of daily living, or both (CDC, 2019).\u003c/p\u003e \u003cp\u003eDue to aging, degeneration of physical function, and other risk factors, older adults aged 65 and older are more likely to suffer from chronic conditions (Rizzuto et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Except for the congenital diseases resulted from genetic factors, chronic conditions of older adults are usually caused by factors like dietary, lifestyle and metabolic risk factors, and might be prevented by behavioral changes (Neuhouser, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Social determinants like socioeconomic status, education level, and race/ethnicity otherwise affect the prevention and treatment of chronic conditions (Clark \u0026amp; Boyd, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In the U.S., older adults who are people of color and/or low-income are less likely to get access to medical care services for early detection to stunt severe health outcomes and for treatment to decrease the death rate (Kaholokula, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSuffering chronic conditions result in a significant burden on older adults associated with chronic care and chronic care needs (Seeman \u0026amp; Chen, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Depression is one of the most common mental disorders among older adults who suffer from chronic conditions (Null \u0026amp; Pennesi, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Older adults with depression can have feelings of sadness, hopelessness, loss of interest, fear, apprehension, worry, and restlessness as psychological reflections on stressful life events like suffering chronic conditions (Polku et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). These psychological reflections are usually comorbid and can be short-term or long-term, severe or mild, and vary individually (De Zwart et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFactors that determine the length of time and the severity of depression rest on the interaction of the different types of chronic conditions, socioeconomic factors, and the personality and mentality of older adults (van der Aa et al., 2015). These factors affect how well older adults manage the chronic conditions they are suffering. The influence these factors exert upon older adults\u0026rsquo; management of chronic conditions and the resulting mental conditions change over time (Reid et al., 2015). Older adults have to acclimate themselves to a different lifestyle if factors like the level of chronic conditions, caregiving from family members, and the accessibility of medical treatment do not remain stable but change periodically in their daily lives (Hultman et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). It drastically increases the vulnerability to depression when facing uncertainties (Naughton \u0026amp; Weaver, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). As older adults age, the change of these factors occurs more commonly and frequently, resulting in the disparities of depression both individually and among different age groups, including young-old (65\u0026ndash;74 years), middle-old (75\u0026ndash;84 years), and old-old (85\u0026thinsp;+\u0026thinsp;years) (Byers et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhen older adults suffer from chronic conditions, understanding the risk and protective factors related to depression is essential. It facilitates healthcare practitioners, policymakers, social workers, and even older adults and their caregivers to better acknowledge and manage these factors to help older adults avoid being vulnerable to depression. Previous studies mainly focused on the limited factors to narrowly discuss and present a picture of the risk and protective factors of depression among older adults. Although these studies contributed to the literature, interactions among multiple factors are understudied when using nationwide data. Besides, the vast majority of studies only applied age as a significant demographic factor within a single age group without comparison to other age groups. These studies did not go in-depth to fathom how older adults in different age groups manage their depression. In addition, a multitude of studies conducted one-time cross-sectional studies, which are insufficient to delve into factors associated with depression and how older adults' depression changes with their chronic conditions. Longitudinal studies can allow multiple observations over a long period of time to explore relevant factors associated with depression. Therefore, longitudinal studies must be conducted to address this knowledge gap in the literature.\u003c/p\u003e \u003cp\u003eThis study aims to understand the disparities of depression of older adults with chronic conditions among different age groups. A longitudinal study will provide a developmental and comprehensive understanding of risk and protective factors associated with depression to analyze how depression changes over time and how it differs from each age group in order for each stakeholder to tailor supportive services for older adults according to their changing needs over time.\u003c/p\u003e\n\u003ch3\u003eTheoretical Model\u003c/h3\u003e\n\u003cp\u003eSuffering chronic conditions can be a stressful life event for older adults, requiring prompt and efficient approaches to address resultant mental disorders like depression (Fiske et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The Transactional Theory of Stress and Coping (TTSC) avers that individuals are constantly appraising stimuli within their environment. This appraisal process generates emotions and initiates coping strategies to manage these psychological reactions or attempt to directly address the stressors themselves, and it has two major parts: (1) cognitive appraisal and (2) coping (Lazarus \u0026amp; Folkman, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1984\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTTSC proposes that the mediating role of cognitive appraisal ascribes meanings to stressful events or stimuli (Boyd et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Lazarus \u0026amp; Folkman, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1984\u003c/span\u003e). Cognitive appraisal has two core forms: primary appraisal and secondary appraisal. Primary appraisal facilitates individuals to determine whether the specific individual or environmental transactions to their well-being exert a positive effect, have no effect, or produce harm, threat, or challenge (Lazarus \u0026amp; Folkman, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1984\u003c/span\u003e). Compared to the first two categories, harm and threat appraisals can especially evoke negative emotions or the need for subsequent coping actions. Challenge appraisals have the potential to bring about positive emotions when sufficient coping resources are available (Lazarus, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1991\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhen a specific transaction is deemed stressful, the secondary appraisal will then be enacted and further determines what can be done to manage the stressor and its resultant mental or physical distress (Dewe \u0026amp; Cooper, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The secondary appraisal is an assessment of coping resources to indicate the confidence in one's ability or cognitive and behavioral efforts to master, reduce, or tolerate the internal and/or external demands that are created by the stressful events (Lazarus \u0026amp; Folkman, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1984\u003c/span\u003e). Coping resources link to the characteristics of the individual and the characteristics of the situation. Moos and Schaefer (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1993\u003c/span\u003e) further demonstrated and categorized coping resources into personal and environmental systems. The personal system mainly includes demographic factors, such as age, gender, and income. Elements within the environmental system include physical (e.g., health, cognitive capacity), social (e.g., social support and social network), psychological (e.g., beliefs and self-esteem), and material (e.g., financial and assistive devices; Berjot \u0026amp; Gillet, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Both personal and environmental factors influence the presence of a threat to one's health, and appraisals or understandings of the threat (Moos \u0026amp; Schaefer, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1993\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eWhen a situation is appraised as stressful (primary appraisal) and requiring efforts to manage or resolve the event (secondary appraisal), coping actions are enacted (Folkman \u0026amp; Lazarus, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). According to the TTSC, coping strategies aim to either directly manage the stressor (\u003cem\u003eproblem-focused coping, PFC\u003c/em\u003e) or regulate emotions as a consequence of the stressful encounter (\u003cem\u003eemotion-focused coping, EFC\u003c/em\u003e; Lazarus \u0026amp; Folkman, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1984\u003c/span\u003e). However, unsuccessful coping and its resultant distress may trigger \u003cem\u003emeaning-focused coping (MFC)\u003c/em\u003e. Folkman and Moskowitz (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) further proposed \u003cem\u003efuture-oriented coping (FOC)\u003c/em\u003e to advance the TTSC by underlining the experiences of coping with potential future stressors. FOC consists of four coping categories: reactive, anticipatory, preventive, and proactive coping (Schwarzer, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Reactive coping claims to situate the harm or loss that has already been experienced or occurred in the past. Anticipatory coping responds to a recognized upcoming event that is likely to happen in the near future to manage known risks and utilize resources to minimize the stressor or maximize anticipated benefits (Schwarzer \u0026amp; Taubert, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Preventive coping addresses potential future stressors and aims at accumulating resources to reduce the severity of the stressor. Proactive coping emphasizes the accumulation and utilization of resources to improve one\u0026rsquo;s potential or seize possible opportunities for personal growth or well-being that is highly likely to occur in the future (Schwarzer, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2000\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe TTSC has been applied to a slew of studies of depression to understand how older adults cope with stressful life events, such as suffering various chronic conditions (Cheng et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Eckerblad et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Naganathan et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This study also benefits from the TTSC to explore the risk and protective factors of depression among older adults with chronic conditions to understand how to cope with these mental disorders to avoid or mitigate the resultant negative impacts. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e demonstrates the classical model of the TTSC theory, with some alterations catered for depression research.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e[Insert Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e at here]\u003c/h2\u003e "},{"header":"METHODS","content":"\u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003cdiv id=\"Sec5\" class=\"Section4\"\u003e \u003ch2\u003eStudy design and Samples\u003c/h2\u003e \u003cp\u003eThis study used the National Health and Aging Trends Study (NHATS) (Kasper \u0026amp; Freedman, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) which is being sponsored by the National Institute on Aging under a cooperative agreement with the Johns Hopkins University Bloomberg School of Public Health (U01AG032947) to analyze the risk and protective factors associated with depression. NHATS\u0026rsquo; study design and content were developed by multidisciplinary cooperation from demography, geriatric medicine, epidemiology, health services research, economics, and gerontology. In this study, the chronic condition was divided into CDs and CPIs. For CDs, NHATS collected data from the following diseases: heart attack, heart disease, diabetes, lung disease, stroke, dementia or Alzheimer\u0026rsquo;s disease, and cancers (e.g., skin cancer, breast cancer, prostate cancer, bladder cancer, cervical/ovarian/uterine cancer, colon cancer, kidney cancer, and other types of cancer). CPIs included: hearing, vision, chewing or speaking, symptoms of pain and fatigue, breathing, broken or fractured hip, other broken or fractured bones, body strength limit, and balance.\u003c/p\u003e \u003cp\u003eThe present study applied the latest rounds of NHATS, which are round 5 to round 9. Participants who are eligible for inclusion were those who (1) were sample persons instead of proxies; (2) had at least one chronic condition (1\u0026thinsp;=\u0026thinsp;Yes, 0\u0026thinsp;=\u0026thinsp;No); (3) participated in all 5 rounds (round 5 to round 9) from 2015 to 2019. In total, 3541 older adults who were sample persons, had at least one chronic condition, and participated in all 5 rounds were included in the present study.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eMeasures\u003c/h2\u003e \u003cp\u003e \u003cb\u003eDependent variables.\u003c/b\u003e The dependent variable in the present study was defined as a dichotomous variable, which is \u0026ldquo;having depression\u0026rdquo; (1\u0026thinsp;=\u0026thinsp;Yes, 0\u0026thinsp;=\u0026thinsp;No).\u003c/p\u003e \u003cp\u003eNHATS uses the brief screening instrument that is the Patient Health Questionnaire-2 (PHQ-2) for depression (Kasper \u0026amp; Freedman, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). If a score\u0026thinsp;\u0026ge;\u0026thinsp;3, it will be used to indicate either probable major depression (Kroenke et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Kroenke et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eIndependent variables.\u003c/b\u003e The independent variables (i.e., factors in the personal system and factors in the environmental system, including physical-related factors, psychological-related factors, social-related factors, and material-related factors) were selected based on the TTSC model.\u003c/p\u003e \u003cp\u003eFactors in the personal system include: (1) Age (0\u0026thinsp;=\u0026thinsp;65 to 69, 1\u0026thinsp;=\u0026thinsp;70 to 74, 2\u0026thinsp;=\u0026thinsp;75 to 79, 3\u0026thinsp;=\u0026thinsp;80 to 84, 4\u0026thinsp;=\u0026thinsp;85 to 89, 5\u0026thinsp;=\u0026thinsp;90+); (2) Gender (1\u0026thinsp;=\u0026thinsp;Male, 0\u0026thinsp;=\u0026thinsp;Female); (3) Marital status (1\u0026thinsp;=\u0026thinsp;Partnered, 0\u0026thinsp;=\u0026thinsp;Not partnered); (4) Household members refer to the total number of family members in the household; (5) Race and ethnicity (1\u0026thinsp;=\u0026thinsp;White, non-Hispanic, 2\u0026thinsp;=\u0026thinsp;Black, non-Hispanic, 3\u0026thinsp;=\u0026thinsp;Other, 4\u0026thinsp;=\u0026thinsp;Hispanic); (6) Income (1\u0026thinsp;=\u0026thinsp;less than \u003cspan\u003e$\u003c/span\u003e19,999, 2= \u003cspan\u003e$\u003c/span\u003e20,000 to \u003cspan\u003e$\u003c/span\u003e49,999, 3= \u003cspan\u003e$\u003c/span\u003e50,000 to \u003cspan\u003e$\u003c/span\u003e99,999, and 4= \u003cspan\u003e$\u003c/span\u003e100,000 or more); (7) Veteran\u0026rsquo;s status (1\u0026thinsp;=\u0026thinsp;Yes, 0\u0026thinsp;=\u0026thinsp;No).\u003c/p\u003e \u003cp\u003eFactors in the environmental system include physical-related factors, psychological-related factors, social-related factors, and material-related factors. For physical-related factors, they are (1) Self-rated health (1\u0026thinsp;=\u0026thinsp;Excellent, 2\u0026thinsp;=\u0026thinsp;Very good, 3\u0026thinsp;=\u0026thinsp;Good, 4\u0026thinsp;=\u0026thinsp;Fair, 5\u0026thinsp;=\u0026thinsp;Poor); (2) Number of Sensory and Physical Impairments and Symptoms (range\u0026thinsp;=\u0026thinsp;1\u0026ndash;19); (3) Cognitive capacity (range\u0026thinsp;=\u0026thinsp;0\u0026ndash;20), higher scores represent better cognitive capacity; (4) Household activities refer to the instrumental activities of daily living (IADL), which measures whether participants can do the household activities independently in the last month (0\u0026ndash;6); (5) Rehabilitation (1\u0026thinsp;=\u0026thinsp;Yes, 0\u0026thinsp;=\u0026thinsp;No).\u003c/p\u003e \u003cp\u003eFor psychological-related factors, they are (1) Frequency of various feelings (0\u0026ndash;8), higher scores represent that participants have more negative feelings; (2) Self-realisation (0\u0026ndash;8), higher scores represent that participants have a more negative attitude toward life; (3) Self-efficacy and resilienc\u003cem\u003ee\u003c/em\u003e (0\u0026ndash;6), higher scores represent that participants have less sense of control.\u003c/p\u003e \u003cp\u003eFor Social-related factors, they are (1) Social network (range\u0026thinsp;=\u0026thinsp;0\u0026ndash;5), NHATS provided 38 options and allowed participants to choose up to 5 people; (2) Community (range\u0026thinsp;=\u0026thinsp;0\u0026ndash;6), higher scores represent better recognition to the community; (3) Activity participation (physical activities: range\u0026thinsp;=\u0026thinsp;0\u0026ndash;2; social activities: range\u0026thinsp;=\u0026thinsp;0\u0026ndash;4), with higher scores indicating participation in more activities within the respective domain.\u003c/p\u003e \u003cp\u003eFor Material-related factors, they are (1) Home environment (range\u0026thinsp;=\u0026thinsp;0\u0026ndash;7), higher scores represent more available bathroom facilities; (2) Technology Use (range\u0026thinsp;=\u0026thinsp;0\u0026ndash;4), higher scores represent more utilization of technological devices; (3) Health Insurance plans (range\u0026thinsp;=\u0026thinsp;0\u0026ndash;6), higher scores represent more health insurance coverage that participants had.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eResearch Questions\u003c/h2\u003e \u003cp\u003eWith the identification of major gaps in the current knowledge of the risk and protective factors of depression, this study used a longitudinal data analysis approach depending on the Transactional Theory of Stress and Coping (TTSC): (a) to explore factors associated with depression among older adults with chronic conditions from different age groups who lived in the U.S.; (b) to analyze how depression changes over time and how it differs from each age group under the influence of these factors.\u003c/p\u003e \u003cp\u003eTo achieve these research goals, the following research questions were studied:\u003c/p\u003e \u003cp\u003e1. What are the most reported chronic conditions among a nationally-representative sample of older adults in the U.S.?\u003c/p\u003e\u003cp\u003e2. How do factors in the personal system and factors in the environmental system, including physical-related factors, psychological-related factors, social-related factors, and material-related factors, affect the occurrence of depression over time?\u003c/p\u003e\u003cp\u003e3. How does depression change over time?\u003c/p\u003e \u003cp\u003e4. How does depression differ from different age groups?\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003e \u003cb\u003eDescriptive analysis.\u003c/b\u003e Descriptive analysis was conducted for all variables. Standard deviation, mean, median, range, minimum, and maximum were calculated for continuous variables. Frequencies were calculated for categorical variables. In order to compare the difference between participants who suffered from depression and those who did not, Chi-square tests were applied to each categorical variable, and Point biserial correlations analyses were applied to each continuous variable.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMultivariate analysis.\u003c/b\u003e Since the present study used 5 rounds of NHATS (2015\u0026ndash;2019), time-variant variables allow for the analyses of the autocorrelations between the outcome variable and explanatory variables and the analyses of the autoregressive correlation. This study applied a generalized estimating equation (GEE) as a statistic model to explore and identify the risk and protective factors. Using an empirical covariance matrix, GEE is an optimal model for repeated measure studies to estimate the within-subject correlations (Hardin \u0026amp; Hilbe, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). As the outcome variable in this study, having depression is a dichotomous variable, GEE offers an option that is Binomial Family and Logit Link to help conduct the analysis. Besides, GEE has the ability to handle missing at random or missing not at random in the longitudinal dataset (Garcia \u0026amp; Marder, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Hardin \u0026amp; Hilbe, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStata 16 was used for the data analysis. An alpha level of .05 was used to determine statistical significance for relationships examined in the present study.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive results\u003c/h2\u003e \u003cp\u003eThis section reports the baseline characteristics of factors in the personal system and environmental system (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\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\u003eBaseline characteristics of factors in personal system and environmental system by depression, NHATS 2015 (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3541)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\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\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eNo Depression\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2 a\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003csub\u003epb\u003c/sub\u003e \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eM\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eM\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3541 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e375 (10.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e3166 (89.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePersonal System\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge 65 to 69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e557 (15.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70 (18.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e487 (15.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.383\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e70 to 74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1004 (28.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e94 (25.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e910 (28.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e75 to 79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e834 (23.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96 (25.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e738 (23.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e80 to 84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e645 (18.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67 (17.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e578 (18.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e85 to 89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e357 (10.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35 (9.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e322 (10.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e90 +\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e144 (4.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (3.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e131 (4.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender Female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2108 (59.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e241 (64.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1867 (59.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e3.904\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e.048\u003c/b\u003e\u003c/p\u003e \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\u003e1433 (40.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e134 (35.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1299 (41.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital Not partnered\u003c/p\u003e \u003cp\u003eStatus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1693 (47.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e223 (59.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1470 (46.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e22.834\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePartnered\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1848 (52.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e152 (40.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1696 (53.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold members\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.94\u0026thinsp;\u0026plusmn;\u0026thinsp;1.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.02\u0026thinsp;\u0026plusmn;\u0026thinsp;1.120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e1.92\u0026thinsp;\u0026plusmn;\u0026thinsp;1.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.074\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2632 (74.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e207 (55.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2425 (76.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e83.328\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e.000\u003c/b\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\u003e657 (18.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e115 (30.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e542 (17.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther (IANP) \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78 (2.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (4.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e60 (1.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\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\u003e174 (4.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35 (9.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e139 (4.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome Less than \u003cspan\u003e$\u003c/span\u003e19,999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1912 (54.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e249 (66.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1663 (52.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e43.667\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20,000 to 49,999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e762 (21.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85 (22.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e677 (21.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50,000 to 99,999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e545 (15.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26 (6.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e519 (16.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e100,000 or more\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e322 (9.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (4.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e307 (9.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVeteran Status No\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2764 (78.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e312 (83.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2452 (77.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e6.477\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e.011\u003c/b\u003e\u003c/p\u003e \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\u003e777 (21.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63 (16.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e714 (22.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEnvironmental System\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 \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePhysical-related Factors\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 \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-rated Health Excellent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e424 (12.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (3.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e412 (13.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e300.836\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery good\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1128 (31.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54 (14.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1074 (33.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1256 (35.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e123 (32.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1133 (35.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFair\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e622 (17.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e134 (35.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e488 (15.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e111 (3.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52 (13.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e59 (1.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic Conditions#\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.96\u0026thinsp;\u0026plusmn;\u0026thinsp;2.332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.42\u0026thinsp;\u0026plusmn;\u0026thinsp;2.650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e3.79\u0026thinsp;\u0026plusmn;\u0026thinsp;2.230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e.215\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCognitive Capacity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.99\u0026thinsp;\u0026plusmn;\u0026thinsp;3.256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.78\u0026thinsp;\u0026plusmn;\u0026thinsp;3.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e9.13\u0026thinsp;\u0026plusmn;\u0026thinsp;3.239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;\u0026thinsp;.128\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold Activities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.09\u0026thinsp;\u0026plusmn;\u0026thinsp;1.318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.43\u0026thinsp;\u0026plusmn;\u0026thinsp;1.380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e2.04\u0026thinsp;\u0026plusmn;\u0026thinsp;1.304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e.091\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRehab No\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2862 (80.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e290 (77.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2572 (81.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.069\u003c/p\u003e \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\u003e679 (19.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85 (22.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e594 (18.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePhycological-related Factors\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 \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVarious Feelings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.00\u0026thinsp;\u0026plusmn;\u0026thinsp;1.289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.16\u0026thinsp;\u0026plusmn;\u0026thinsp;1.650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e1.87\u0026thinsp;\u0026plusmn;\u0026thinsp;1.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e.309\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-realization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.70\u0026thinsp;\u0026plusmn;\u0026thinsp;1.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.57\u0026thinsp;\u0026plusmn;\u0026thinsp;1.645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e.59\u0026thinsp;\u0026plusmn;\u0026thinsp;1.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e.261\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-efficacy \u0026amp; Resilience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.88\u0026thinsp;\u0026plusmn;\u0026thinsp;.981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.38\u0026thinsp;\u0026plusmn;\u0026thinsp;1.244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e.82\u0026thinsp;\u0026plusmn;\u0026thinsp;.927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e.178\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSocial-related Factors\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 \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial Network#\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.31\u0026thinsp;\u0026plusmn;\u0026thinsp;1.381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.10\u0026thinsp;\u0026plusmn;\u0026thinsp;1.303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e2.33\u0026thinsp;\u0026plusmn;\u0026thinsp;1.388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;\u0026thinsp;.053\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommunity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.68\u0026thinsp;\u0026plusmn;\u0026thinsp;1.303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.54\u0026thinsp;\u0026plusmn;\u0026thinsp;1.429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e3.70\u0026thinsp;\u0026plusmn;\u0026thinsp;1.286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;\u0026thinsp;.039\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e.022\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical Activities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.05\u0026thinsp;\u0026plusmn;\u0026thinsp;.777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.77\u0026thinsp;\u0026plusmn;\u0026thinsp;.729\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e1.08\u0026thinsp;\u0026plusmn;\u0026thinsp;.776\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;\u0026thinsp;.126\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial Activities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.76\u0026thinsp;\u0026plusmn;\u0026thinsp;1.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.22\u0026thinsp;\u0026plusmn;\u0026thinsp;1.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e2.82\u0026thinsp;\u0026plusmn;\u0026thinsp;1.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;\u0026thinsp;.180\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMaterial-related Factors\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 \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuilding Amenities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.95\u0026thinsp;\u0026plusmn;\u0026thinsp;.614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.97\u0026thinsp;\u0026plusmn;\u0026thinsp;.693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e.95\u0026thinsp;\u0026plusmn;\u0026thinsp;.604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.426\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBath Feature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.10\u0026thinsp;\u0026plusmn;\u0026thinsp;1.495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.18\u0026thinsp;\u0026plusmn;\u0026thinsp;1.539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e3.09\u0026thinsp;\u0026plusmn;\u0026thinsp;1.490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.267\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTechnology Use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.75\u0026thinsp;\u0026plusmn;\u0026thinsp;1.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.35\u0026thinsp;\u0026plusmn;\u0026thinsp;1.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e2.80\u0026thinsp;\u0026plusmn;\u0026thinsp;.997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;\u0026thinsp;.139\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth Insurance Plans\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.59\u0026thinsp;\u0026plusmn;\u0026thinsp;.932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.60\u0026thinsp;\u0026plusmn;\u0026thinsp;.1.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e2.59\u0026thinsp;\u0026plusmn;\u0026thinsp;.920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.801\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e\u003csup\u003ea\u003c/sup\u003e Pearson Chi-square value; \u003csup\u003eb\u003c/sup\u003e Point Biserial correlation value; \u003csup\u003ec\u003c/sup\u003e Indian/Asian/Native Hawaiian/Pacific Islander\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e[Insert Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e at here]\u003c/h2\u003e \u003cp\u003eThe result of the number of chronic conditions was presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, responding to the first research question. Among all the 19 chronic conditions, the most reported one was vision impairment (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2407, 68.0%), followed by chronic pain (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2126, 60.0%) and fatigue (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1669, 47.1%) that were all chronic physical impairments (CPIs, from upper fracture to balance, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;12), far more than reported chronic diseases (CDs, from heart attack to cancer, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e[Insert Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e at here]\u003c/h2\u003e \u003cp\u003e \u003cb\u003eFactors in the personal system.\u003c/b\u003e At the baseline survey (NHATS, 2015; \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3541), 375 (10.6%) participants reported having depression. The age of participants generally distributed equally but convergent to 70 to 74 years old (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1004, 28.4%) and 75 to 79 years old (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;834, 23.6%). Participants who were older than 90 years old were the least (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;144, 4.1%). Among all the participants, the majority were female (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2108, 59.5%), and most of the participants were married or partnered (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1848, 52.2%). Participants lived with one to three household members on average (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.94\u0026thinsp;\u0026plusmn;\u0026thinsp;1.018). Among all the participants, the majority were non-Hispanic White (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2632, 74.3%), followed by Black (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;657, 18.6%). More than half of the participants had an annual income of less than \u003cspan\u003e$\u003c/span\u003e19,999 (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1912, 54.0%). Around one-fifth of the participants were veterans (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;777, 21.9%).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eFactors in the environmental system\u003c/h2\u003e \u003cp\u003e \u003cb\u003ePhysical-related Factors.\u003c/b\u003e Most participants reported a very good (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1128, 31.9%) or good (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1256, 35.5%) self-rated health. Participants had at least one to six chronic conditions (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.96\u0026thinsp;\u0026plusmn;\u0026thinsp;2.332) on average. Participants reported a score of cognitive capacity of 8.99\u0026thinsp;\u0026plusmn;\u0026thinsp;3.256 (range\u0026thinsp;=\u0026thinsp;0\u0026ndash;20; higher scores represent better cognitive capacity). The score of household activities was 2.09\u0026thinsp;\u0026plusmn;\u0026thinsp;1.318 (range\u0026thinsp;=\u0026thinsp;0\u0026ndash;6; higher scores represent that participants have more difficulties finishing household activities independently). The majority of the participants did not receive any rehab service (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2826, 80.8%).\u003c/p\u003e \u003cp\u003e\u003cb\u003ePhycological-related Factors.\u003c/b\u003e The frequency of various feelings was 2.00\u0026thinsp;\u0026plusmn;\u0026thinsp;1.289 (range\u0026thinsp;=\u0026thinsp;0\u0026ndash;8; higher scores represent that participants have more negative feelings). The score of self-realization was .70\u0026thinsp;\u0026plusmn;\u0026thinsp;1.146 (range\u0026thinsp;=\u0026thinsp;0\u0026ndash;8; higher scores represent that participants have a worse attitude toward life). The score of self-efficacy and resilience was .88\u0026thinsp;\u0026plusmn;\u0026thinsp;.981 (range\u0026thinsp;=\u0026thinsp;0\u0026ndash;6; higher scores represent that participants have less sense of control).\u003c/p\u003e \u003cp\u003e\u003cb\u003eSocial-related Factors.\u003c/b\u003e The total number of people participants talk to about important things (social network) was 2.31\u0026thinsp;\u0026plusmn;\u0026thinsp;1.381 (range\u0026thinsp;=\u0026thinsp;0\u0026ndash;5). The score of participants\u0026rsquo; perspectives and attitudes toward the community they were living (community) was 3.68\u0026thinsp;\u0026plusmn;\u0026thinsp;1.303 (range\u0026thinsp;=\u0026thinsp;0\u0026ndash;6; higher scores represent better recognition to the community). Activity participation was divided into physical activities and social activities. The number of physical activities was 1.05\u0026thinsp;\u0026plusmn;\u0026thinsp;.777 (range\u0026thinsp;=\u0026thinsp;0\u0026ndash;2), and the number of social activities was 2.76\u0026thinsp;\u0026plusmn;\u0026thinsp;1.036 (range\u0026thinsp;=\u0026thinsp;0\u0026ndash;4). Higher scores indicate that older adults participated in more activities within the respective domain.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMaterial-related Factors.\u003c/b\u003e The home environment was represented by two parts: building amenities and bathroom features. The total number of building amenities was .95\u0026thinsp;\u0026plusmn;\u0026thinsp;.614 (range\u0026thinsp;=\u0026thinsp;0\u0026ndash;6; higher scores represent more available building amenities). The total number of bathroom facilities was 3.10\u0026thinsp;\u0026plusmn;\u0026thinsp;1.495 (range\u0026thinsp;=\u0026thinsp;0\u0026ndash;7; higher scores represent more available bathroom facilities). The total number of technological devices participants used was 2.75\u0026thinsp;\u0026plusmn;\u0026thinsp;1.010 (range\u0026thinsp;=\u0026thinsp;0\u0026ndash;4; higher scores represent more utilization of technological devices). The total number of health insurance plans participants had was 2.59\u0026thinsp;\u0026plusmn;\u0026thinsp;.932 (range\u0026thinsp;=\u0026thinsp;0\u0026ndash;6; higher scores represent more health insurance coverage that participants had).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eComparison Based on depression\u003c/h2\u003e \u003cp\u003eThis section reports detailed results of Point biserial correlations analyses and Chi-square tests of the relationship between the explanatory variables and depression (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, there was a significant difference of gender (\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;3.904, \u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;.048), marital status (\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;22.834, \u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;.001), race (\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;83.328, \u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;.001), income (\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;43.667, \u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;.001), veteran status (\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;6.477, \u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;.011), self-rated health (\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;300.836, \u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;.001), number of chronic conditions (\u003cem\u003er\u003c/em\u003e\u003csub\u003epb\u003c/sub\u003e = .215, \u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;.001), cognitive capacity (\u003cem\u003er\u003c/em\u003e\u003csub\u003epb\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.128, \u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;.001), household activities (\u003cem\u003er\u003c/em\u003e\u003csub\u003epb\u003c/sub\u003e = .091, \u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;.001), various feelings (\u003cem\u003er\u003c/em\u003e\u003csub\u003epb\u003c/sub\u003e = .309, \u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;.001), self-realization (\u003cem\u003er\u003c/em\u003e\u003csub\u003epb\u003c/sub\u003e = .261, \u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;.001), self-efficacy and resilience (\u003cem\u003er\u003c/em\u003e\u003csub\u003epb\u003c/sub\u003e = .178, \u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;.001), number of social network (\u003cem\u003er\u003c/em\u003e\u003csub\u003epb\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.053, \u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;.002), community (\u003cem\u003er\u003c/em\u003e\u003csub\u003epb\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.039, \u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;.022), physical activities (\u003cem\u003er\u003c/em\u003e\u003csub\u003epb\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.126, \u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;.001), social activities (\u003cem\u003er\u003c/em\u003e\u003csub\u003epb\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.180, \u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;.001), and technology use (\u003cem\u003er\u003c/em\u003e\u003csub\u003epb\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.139, \u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;.001) between older adults with chronic conditions who had depression and who did not.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e[Insert Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e at here]\u003c/h2\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\u003eMultivariate Generalized Estimating Equation (GEE) Regression Model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExp (B)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePersonal System\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge 65 to 69 (ref)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e70 to 74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.109\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e75 to 79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.322\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e80 to 84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e.018\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e85 to 89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e90 +\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender Female (ref)\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 \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\u003e\u0026minus;\u0026thinsp;.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.232\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital Not partnered\u003c/p\u003e \u003cp\u003eStatus (ref)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePartnered\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.488\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold members\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.586\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace White (ref)\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 \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\u003e.557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther (IANP) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e.022\u003c/b\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e.766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome Less than \u003cspan\u003e$\u003c/span\u003e19,999 (ref)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20,000 to 49,999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.201\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50,000 to 99,999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.827\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.086\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e100,000 or more\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.862\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.371\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVeteran Status No (ref)\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 \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\u003e.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.395\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEnvironmental System\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePhysical Factors\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-rated Health Excellent (ref)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery good\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.224\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e.034\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFair\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic Conditions#\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCognitive Capacity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold Activities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.086\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRehab No (ref)\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 \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\u003e.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.305\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePhycological Factors\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVarious Feelings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-realization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-efficacy \u0026amp; Resilience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSocial Factors\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial Network#\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.068\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommunity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.820\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical Activities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e.011\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial Activities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMaterial Factors\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuilding Amenities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.813\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBath Feature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.075\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTechnology Use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth Insurance Plans\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.423\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTime indicator\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVisit 1 (ref)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVisit 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.169\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVisit 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.383\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVisit 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVisit 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e.011\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003ea\u003c/sup\u003e Indian/Asian/Native Hawaiian/Pacific Islander\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003eMultivariate Generalized Estimating Equation (GEE) Regression Model\u003c/h2\u003e \u003cp\u003eAmong factors in the personal system, compared to participants who were 65 to 69 years old, those who were 80 to 84 (\u003cem\u003ee\u003c/em\u003e\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e= .726, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.018), 85 to 90 (\u003cem\u003ee\u003c/em\u003e\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e= .653, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.006), and 90+ (\u003cem\u003ee\u003c/em\u003e\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e= .555, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.001) years old were less likely to have depression. The results above answered the fourth research question.\u003c/p\u003e \u003cp\u003eThe following results answered the second research question. Compared to White participants, those who were Black (\u003cem\u003ee\u003c/em\u003e\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e= 1.745, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), Other (Indian/Asian/Native Hawaiian/Pacific Islander) (\u003cem\u003ee\u003c/em\u003e\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e= 1.583, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.022), and Hispanic (\u003cem\u003ee\u003c/em\u003e\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e= 2.152, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) were more likely to have depression. Among factors in the environmental system, for physical factors, compared to participants who reported excellent self-rated health, those who reported good (\u003cem\u003ee\u003c/em\u003e\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e= 1.426, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.034), fair (\u003cem\u003ee\u003c/em\u003e\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e= 1.939, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), and poor (\u003cem\u003ee\u003c/em\u003e\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e= 1.130, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) self-rated health were more likely to have depression, and the worse the self-rated health, the more likely to have depression. Participants who had more chronic conditions (\u003cem\u003ee\u003c/em\u003e\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e= 1.152, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) were more likely to have depression, but those who had better cognitive capacity (\u003cem\u003ee\u003c/em\u003e\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e= .953, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) were less likely to have depression.\u003c/p\u003e \u003cp\u003eFor psychological factors, participants who had more negative feelings (\u003cem\u003ee\u003c/em\u003e\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e= 1.474, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), worse self-realization (\u003cem\u003ee\u003c/em\u003e\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e= 1.221, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), and worse self-efficacy and resilience (\u003cem\u003ee\u003c/em\u003e\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e= 1.084, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.002) were more likely to have depression. For social factors, participants who attended more physical activities (\u003cem\u003ee\u003c/em\u003e\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e= .891, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.011) and more social activities (\u003cem\u003ee\u003c/em\u003e\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e= .871, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) were less likely to have depression. For material factors, participants who used more technological devices (\u003cem\u003ee\u003c/em\u003e\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e= .873, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) were less likely to have depression. For time indicator, compared to the first visit (i.e., baseline survey, NHATS 2015), depression in visit 4 (\u003cem\u003ee\u003c/em\u003e\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e= .683, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) and visit 5 (\u003cem\u003ee\u003c/em\u003e\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e= .797, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.011) was less likely to occur over time. The results above answered the third research question.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study explored the disparities of depression among older adults with chronic conditions. The results from a longitudinal analysis provided a developmental and comprehensive understanding of factors associated with depression.\u003c/p\u003e \u003cp\u003eThe results showed that the most reported chronic conditions were chronic physical impairments (CPIs), far more than chronic diseases (CDs). Specifically, the top five reported CPIs were vision impairment (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2407, 68.0%), followed by chronic pain (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2126, 60.0%), fatigue (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1669, 47.1%), lower body strength limit (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1271, 35.9%), and upper body strength limit and the issue of balance (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1126, 31.8%). All of these CPIs are associated with body movements and mobility, impacting the capacity to perform daily tasks independently (Reid et al., 2015). Besides, the top three reported CDs were cancer (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;518, 14.6%), heart disease and heart attack (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;505, 14.3%), and diabetes (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;468, 13.2%). All of these CDs require older adults to utilize healthcare services and prescription drugs regularly and periodically. The results of chronic conditions in this study are consistent with the latest report of Centers for Disease Control and Prevention (CDC) that 2 in 5 adults age 65 and older are affected by dysfunction and other mobility-related limitations, and cancer, heart disease and heart attack, and diabetes are among most reported chronic diseases (Villarroel et al., 2019). The results also represented that among 3541 participants, 375 (10.6%) participants reported having depression. This result is consistent with the CDC\u0026rsquo;s report that the depression rate among older adults living in the community ranges from less than 1% to about 5% but rises to 13.5% in those who require home healthcare and 11.5% in older hospitalized patients (CDC, 2017). Risk and protective factors of depression among U.S. older adults with chronic conditions are discussed below.\u003c/p\u003e \u003cp\u003eFindings from factors in the personal system showed that age is significantly associated with depression over time. Specifically, compared to participants who were 65 to 69 years old, those aged 80 and older were less likely to have depression, and the older the participants, the lesser likely to have depression. As the population ages, especially for those who were 80\u0026thinsp;+\u0026thinsp;years old or even 90\u0026thinsp;+\u0026thinsp;years old, survivorship plays a pivotal role in the low level of depression. For example, the oldest-old view their late-life and health condition more positively than younger older adults since recognizing that they have lived well beyond their expected lifespan or they are survivors of their chronic conditions (DeSantis et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Besides, people who live into their 80s and 90s will more likely have fewer health problems than those who died at earlier ages, which may contribute to lower levels of depression (Cho, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn addition, according to the Transactional Theory of Stress and Coping (TTSC) model, suffering chronic conditions can be a stressful life event to older adults, triggering appraisal and then prompting older adults to cope with this stressor by invoking their coping resources. When the applied coping strategies fail to address the influence resulting from chronic conditions, this failure initiates further coping strategies, and its resultant distress may trigger meaning-focused coping (MFC), particularly when older adults perceive that stressors like suffering chronic conditions are overwhelmingly uncontrollable. Older adults may ascribe positive meaning to suffering chronic conditions or find and remind themselves of the benefits of this stressor. For example, although they are suffering chronic conditions and have to endure its negative influences over time, it provides more opportunities for them to spend time with their caregivers who are usually their family members. Older adults can benefit from this advantage to reorder their life priorities from focusing on chronic conditions to the relationships, especially when they are at an older age when medical treatment is futile to offer additional benefits (Moorman, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Meanwhile, this advantage can also alleviate their depression from suffering chronic conditions to further sustain extant coping efforts over time (Folkman, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe results further presented that compared to White participants, Black, Indian/Asian/Native Hawaiian/Pacific Islander, and Hispanic were more likely to have depression. This result is consistent with a multitude of studies showing that older adults of color are more likely to experience mental disorders, like depression, due to the disparities in socioeconomic status (Bui et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Moreover, older adults with higher socioeconomic status, like higher annual income, can greatly reduce the occurrence or severity of mental disorders since they can access more healthcare and life resources (Xue et al., 2021).\u003c/p\u003e \u003cp\u003eFindings from factors in the environmental system showed that compared to participants who reported excellent self-rated health, those who reported good, fair, and poor self-rated health were more likely to have depression. Besides, the worse the self-rated health, the more likely to be depressed over time. According to the TTSC model, coping with stressful life events, like suffering chronic conditions, is an appraisal-coping-reappraisal process that appraises whether the stressor produces harm, threat, or challenge. Or this process reappraises whether the coping efforts have been successful or determines if the nature of the situation has changed from stressful to irrelevant, benign, or positive (Lazarus \u0026amp; Folkman, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1984\u003c/span\u003e). Regardless of whether the participants first encountered the stressor (i.e., suffering chronic conditions) or already applied coping strategies to cope with this stressor when the survey was administered, fair or poor self-rated health as unfavorable appraisal may exacerbate individual\u0026rsquo;s mental or physical distress, leading to depression. This result is consistent with a slew of studies suggesting that older adults who poorly rate their health are more likely to be depressed (Campos et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFindings further presented that older adults who had more chronic conditions were more likely to have depression over time, but those who had better cognitive capacity were less likely to have depression over time. According to the TTSC model, older adults may appraise the severity of their chronic conditions and then invoke coping resources to address this stressor. Negative emotions and feelings can result from unfavorable appraisal or unsuccessful coping over time when their chronic conditions do not become better or even grow worse and more. Just as the results of psychological factors represented, participants who had more negative feelings, worse self-realization, and worse self-efficacy and resilience were more likely to have depression over time. These results are consistent with a wealth of studies asserting that the more comorbidities older adults have, the more likely to stress themselves and their caregivers, bringing physical or mental distress (e.g., negative emotions or bad self-realization and self-efficacy) and eventually result in mental disorders like depression (Mindlis et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Besides, having better cognitive capacity as one of the powerful coping resources can facilitate older adults better manage their chronic conditions when synergistically applying other coping resources during their coping processes over time. This result is consistent with multiple studies presenting that older adults with better cognitive capacity are more likely to be able to address mental distress (e.g., negative emotions or bad self-realization and self-efficacy) resulted from suffering chronic conditions, and to make proper plans for their healthcare services and life so that increase the possibility to bring about positive outcomes for their physical and mental health (Dragioti et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe results of social factors showed that participants who actively utilized coping resource \u0026ldquo;activity participation\u0026rdquo; by attending more physical activities and social activities were less likely to have depression over time. According to the TTSC model, older adults not only take actions to cope with the impacts of extant stressor that is suffering chronic conditions, but also apply future-oriented coping to avoid negative influence or outcomes from possible stressors. Attending physical activities regularly can benefit older adults with chronic conditions with health improvements and protecting them from getting additional chronic conditions (Taylor, 2014). Besides, attending social activities offers peer support, encouragement, or emotional relief to older adults from friends and other people outside the family. Using technology devices, like cellphones, computers, and tablets, further facilitate older adults to build connections with people and access supportive information to mitigate the negative emotions or mental disorders from suffering chronic conditions (Stacy et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn addition, the findings of this longitudinal study revealed a pattern of how depression changed over time in that depression was less likely to occur over time. However, this pattern existed in two consecutive visits only, suggesting that although depression tends to decrease over time, it happens in a relatively short time range that is one year. Besides, the odds ratio of visit 5 (\u003cem\u003ee\u003c/em\u003e\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e= .797, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.011) is higher than visit 4 (\u003cem\u003ee\u003c/em\u003e\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e= .683, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), meaning the occurrence of depression at visit 5 is higher than visit 4, or the likelihood of the decrease of depression is lower at visit 5 compared to visit 4. It further suggests that although the occurrence of depression declines over time, the extent of decline slows down gradually. This finding further calls for joint efforts involving policymakers, healthcare practitioners, researchers, and social workers to develop new and creative mental health programs and interventions to help minimize the impacts of depression by extending the decline of depression over time.\u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eLIMITATIONS AND FUTURE DIRECTIONS\u003c/h2\u003e \u003cp\u003eThe findings of this study presented a relatively comprehensive understanding of the risk and protective factors of depression among U.S. older adults who were suffering chronic conditions. However, some limitations in this study should be noted to advance the knowledge and understanding in future studies.\u003c/p\u003e \u003cp\u003eThe primary limitations of this study are related to the research methodology. For the measurement of dependent variables (i.e., depression), the NHATS survey applied a brief screening instruments: The Patient Health Questionnaire-2 (PHQ-2) for depression (Kasper \u0026amp; Freedman, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Since brief measurements may sacrifice the precision or reliability of the results (Kohout et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1993\u003c/span\u003e), future studies can use detailed measurements to assess depression. For example, the Patient Health Questionnaire-9 (PHQ-9; Kroenke et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) may be used in future studies. Despite this limitation, a multitude of studies have used PHQ-2 to explore relationships between depression and various predictors (Hamedani et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Jones et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Levine et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBesides, the NHATS survey measured age by categorizing continuous data into six categories (0\u0026thinsp;=\u0026thinsp;65 to 69, 1\u0026thinsp;=\u0026thinsp;70 to 74, 2\u0026thinsp;=\u0026thinsp;75 to 79, 3\u0026thinsp;=\u0026thinsp;80 to 84, 4\u0026thinsp;=\u0026thinsp;85 to 89, 5\u0026thinsp;=\u0026thinsp;90+). Since continuous variables applying a simple linear or polynomial function can adequately describe the relationship between the response and the predictor (in this study, the relationship between depression and the age; Royston et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), future studies can apply continuous data for measuring age. Moreover, the NHATS survey does not collect certain data, like geographic region, so that this study was limited to use this predictor to comprehensively explore more about whether the urban or rural area has a significant association with the disparities of depression. Future studies can involve this factor to present a more holistic picture of depression. In addition, the cohorts in the analysis were unweighted, prompting further analyses to apply analytic weights to adjust for differential nonresponse and produce national prevalence estimates to show the relationship between depression and selected predictors.\u003c/p\u003e \u003cp\u003eFinally, this study is limited by its inability to generate conclusions related to the comparison among chronic conditions to show which specific chronic conditions were significantly associated with depression among U.S. older adults over time. Future studies can focus on the comparison to contribute to the knowledge of depression across different chronic conditions by using data collected from clinical trials. Future studies can also explore this relationship more in-depth using qualitative methods, such as case studies.\u003c/p\u003e \u003c/div\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eThis study explored and identified risk and protective factors of depression among U.S. older adults who were suffering chronic conditions from a longitudinal perspective. The findings of this study highlight the need to provide support and link resources to caregivers to facilitate them to deliver quality care to older adults. Besides, necessary training provided by the community and the online free education are vital for caregivers to get knowledge and skills related to taking care of themselves and delivering caregiving. The findings of this study call for efficient chronic condition management to provide early screening, assessment, and diagnosis to prevent or find diseases or physical impairments early. Recommendations from healthcare providers and proper education of healthy lifestyles and the dissemination of related information can also make a difference in helping older adults gain better physical and mental health to prevent them from suffering depression.\u003c/p\u003e \u003cp\u003eThe findings of the study further underscored the indispensable role of physical and social activities in protecting older adults from mental disorders like depression. Moreover, the findings of this study emphasized that utilizing technology devices greatly reduces the likelihood of having depression. Lastly, joint efforts involving policymakers, healthcare practitioners, researchers, and social workers are needed to develop new and creative mental health programs and interventions to help minimize the impacts of depression by extending the decline of depression over time.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study obtained ethical exemption from the research committee of Southwest University and the Institutional Review Board since the data this study used is secondary data.\u0026nbsp;\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 material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data this study used was obtained from the National Health and Aging Trends Study (NHATS) website, which can be found at: https://www.nhats.org/researcher/nhats/methods-documentation?id=data_collection\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFunds for this study were provided by the Chongqing Educational Science Planning Project (K22YE202095).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHZC, YTZ and PXF were all involved in the design of the study and initiated the study. HZC was responsible for conceptualization, methodology, data analyses, and drafted the manuscript. YTZ reviewed, edited, and controlled the quality of the study. YTZ and PXF both worked on data curation and commented the manuscript. All authors read and approve the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe sincerely thank the research cohorts at Southwest University and the University of Alabama for the support in data analysis, and the feedback from our colleagues.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBerjot, S., \u0026amp; Gillet, N. (2011). Stress and coping with discrimination and stigmatisation. \u003cem\u003eFrontiers in Psychology\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e, 33. https://doi.org/10.3389/fpsyg.2011.00033\u003c/li\u003e\n\u003cli\u003eBernell, S., \u0026amp; Howard, S. W. (2016). 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Association between technology use and social integration among veterans with disabilities. \u003cem\u003eThe Journal of Nervous and Mental Disease\u003c/em\u003e, \u003cem\u003e208\u003c/em\u003e(4), 306\u0026ndash;311.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Depression, Older adults, Chronic conditions, Longitudinal analysis","lastPublishedDoi":"10.21203/rs.3.rs-4644190/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4644190/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eSuffering chronic conditions greatly contribute to mental health problems like depression among older adults. Although an array of literature has focused on this field of study, little research has examined how depression among older adults changes over time or differs from each age group from a developmental and comprehensive view. This longitudinal study provides a holistic understanding of risk and protective factors associated with depression to tailor and provide supportive services for older adults according to their changing needs.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eUtilizing rounds 5\u0026ndash;9 of the National Health and Aging Trends Study (NHATS), 3,541 older adults were studied. The Transactional Theory of Stress and Coping (TTSC) model was utilized to select predictors. Descriptive analysis was conducted for all predictors, and a Generalized Estimating Equation (GEE) was applied to explore and identify the risk and protective factors.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAge, race and ethnicity, self-rated health, number of chronic conditions, cognitive capacity, frequency of negative feelings, self-realization, self-efficacy and resilience, activity participation, and technology use were significantly associated with depression. Furthermore, depression may decrease over time but only happens in a relatively short time, and the extent of decline slows down gradually.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe findings highlight the need to provide support and link resources to caregivers and call for efficient chronic condition management to provide early screening, assessment, and diagnosis. Recommendations from healthcare providers, proper education of healthy lifestyle and the dissemination of related information, and prompting older adults to engage in more physical activities can also make a difference in helping older adults gain better physical and mental health to prevent them from suffering depression.\u003c/p\u003e","manuscriptTitle":"Disparities of Depression among Older Adults with Chronic Conditions within Different Age Groups: Evidence from a Longitudinal Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-29 15:35:06","doi":"10.21203/rs.3.rs-4644190/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":"445d7138-9956-4ff2-9167-f046ba9e117f","owner":[],"postedDate":"July 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-09T07:53:09+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-29 15:35:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4644190","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4644190","identity":"rs-4644190","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}