The Dynamic Buffering of Social Support on Depressive Symptoms and Cancer Worries in Patients Seeking Cancer Genetic Counseling

The Dynamic Buffering of Social Support on Depressive Symptoms and Cancer Worries in Patients Seeking Cancer Genetic Counseling | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Dynamic Buffering of Social Support on Depressive Symptoms and Cancer Worries in Patients Seeking Cancer Genetic Counseling Sally Ho, Jayme M. Palka, Jacqueline Mersch, W. Blake Martin, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3031154/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Mar, 2024 Read the published version in Journal of Cancer Survivorship → Version 1 posted 3 You are reading this latest preprint version Abstract Purpose: Social support is a crucial protective factor against psychological concerns in patients with cancer. However, there is limited knowledge regarding the differential impacts of social support on cancer worries and depressive symptoms in patients undergoing genetic counseling for hereditary cancer. The current study utilized a high-volume database from a multi-site cancer genetics clinic to assess the impact of perceived social support on depressive symptoms and cancer worries among patients of different age groups (young versus older patients) and diagnosis status (diagnosed survivors versus undiagnosed). Methods : 6,666 patients completed brief assessments of depressive symptoms, cancer worries, social support, and demographic questionnaires as part of routine clinical care between October 2016 and October 2020. Logistics and moderated regression were used to analyze the relationships between social support, depressive symptoms, and cancer worries. Results : Increased social support was associated with fewer depressive symptoms and fewer cancer worries across all patients. Social support mitigated depressive symptoms most significantly for young adult patients with and without cancer. Social support mitigated cancer worries most significantly for young adults with cancer and older adults without cancer. Conclusions While results were mixed, general findings upheld original hypotheses. Social support buffered depressive symptoms and cancer worries differentially for patients of different ages and different disease status. Implications for Cancer Survivors: Social support groups are beneficial for all patients and should be emphasized by cancer clinics. However, increasing patient-tailored and age-appropriate support networks will be crucial for managing depression and cancer worries for high-risk survivors: young adults with cancer. depressive symptoms cancer worries social support young adults genetic counseling Figures Figure 1 Figure 2 Introduction Cancer genetic testing can identify gene mutations and cancer syndromes in individuals undiagnosed with cancer but have a known family history of hereditary cancer. Information received from genetic testing could help patients understand risk levels, aid decisions about reproduction, and in many cases, alleviate feelings of uncertainty [1,2]. In addition, patients with a cancer diagnosis can receive cancer genetic counseling, as the results could assist patients with treatment, surgery decisions, and future cancer risk management. However, despite the known benefits of genetic counseling, patients undergoing this process may experience many negative emotions (e.g., fear, cancer worries, loneliness), with some evidence suggesting that these emotions often arise pre-testing [1,3,4]. Risk factors that could exacerbate these negative emotions include low social support, young age, and having a previous cancer diagnosis [4]. As such, although the clinical populations of both undiagnosed and diagnosed patients seeking genetic counseling might experience similar stressors, young patients with cancer might endure a more significant psychological impact. Indeed, past research consistently demonstrates that young adults with cancer (hereafter referred to as YAs) are prone to much higher frequencies of mood disorders, suicidal ideation, and dysfunctional levels of fear of cancer recurrence when compared to older adult patients with cancer (hereafter referred to OAs) [5-7]. By utilizing natural clinical data, the current study seeks to examine depression and cancer-related worries in various subgroups of patients seeking genetic counseling, including different age groups (young versus older patients) and diagnosis status (diagnosed survivors versus undiagnosed). Depressive Symptoms in Oncology Patients A 2013 large-scale systematic review revealed the global prevalence of depression in the general population to be approximately 5% [8]. Concurrent research by Mitchel and colleagues reported the depression rate among cancer patients as 16.5% using the DSM-5 criteria [9], suggesting that the depression rate in cancer patients is approximately threefold that of the general population. Among patients with cancer, a substantial research body with high-volume samples has demonstrated depression to be more prevalent among those of younger age and those with limited social support [10-11]. Although there is much debate as to whether chronological age is a predictor of depression among oncology patients, there is robust evidence to support that YAs are more prone to elevated depressive symptoms compared to OAs [12]. For instance, among patients with breast cancer, younger patients consistently demonstrated more clinically significant depressive symptoms than their older counterparts [13]. While clinically diagnosed depression in adult cancer survivors was prevalent up to 14.9%, the depression rate in YA survivors of childhood leukemia was two-fold, evident up to 28% [9,14]. More recent research suggests that both YAs and long-term survivors of adolescent and young adult cancers report higher levels of depressive symptoms compared to OA patients [15]. Cancer Worries in Oncology Patients Fear of cancer recurrence (FCR) is one of the most reported worries among cancer survivors [16]. Elevated FCR can adversely affect daily functioning, quality of life, and social engagement among survivors [17]. A 2013 systematic review demonstrated that age is a significant predictor of FCR, with younger cancer survivors endorsing much greater FCR than older survivors [18]. A recent study examining FCR in YAs revealed that over 60% of YAs reported significant FCR, which, in turn, was associated with psychological problems and decreased quality of life [19]. Notably, long-term survivors of young adult cancer also reported substantial levels of FCR, with up to 74% worrying about getting a new diagnosis and up to 20% worrying about disease recurrence [20]. In sum, current literature indicates that YAs and long-term survivors of YA-cancer experience more significant FCR than older cancer survivors, which impairs their functioning across several domains. Social Support as a Protective Factor for Depression and Cancer Worries Previous research has consistently identified social support as a crucial buffer against psychological distress in cancer survivorship [21-22]. Koch-Gallenkamp and colleagues reported that socially isolated survivors endorsed higher levels of FCR than their socially connected counterparts [23]. Social support has also been shown to buffer against depressive symptoms in both YAs and OAs [24-25]. Although current research substantiates the role of social support in buffering against both depressive symptoms and cancer-related worries, it is unclear how social dynamics might differ across age groups of patients. Therefore, this paper seeks to redress the dearth in the literature regarding the differential impact of social support on cancer worries and depression across patient groups seeking genetic counseling for hereditary cancer risks marked by age (young versus older patients) and by cancer diagnosis (diagnosed versus undiagnosed). The Current Study The current study used a high-volume sample from a cancer genetics clinic to evaluate social support, depressive symptoms, and cancer worries outcomes among various patient subgroups. In so doing, our aims were to: 1) examine whether social support buffers against depressive symptoms and cancer-related worry, and 2) examine how these buffering effects differ among the following patient groups: young adult patients with cancer (YAs), older adult patients who were survivors of young-adult cancers (OA/YAs), older adult patients with cancer (OAs), and their undiagnosed comparison groups: young adult patients without cancer (YAWOCs), older adult patients without cancer (OAWOCs). In Aim 1, we hypothesized that social support would be negatively associated with depressive symptoms and cancer worries. In Aim 2, we hypothesized that the buffering effects of social support on depressive symptoms and cancer worries would be the greatest for YAs when compared to OA/YAs, OAs, YAWOCs, and OAWOCs. Methodology Participants and Data Collection Procedure We derived our sample from a pool of 14,802 patients who sought genetic counseling through the Cancer Genetics Program at the University of Texas Southwestern (UTSW) Medical Center between October 2016 and October 2020. Data collection locations included academic medical center clinics, community clinics, and county safety-net hospital clinics. As part of routine clinical care, all patients received secure online questionnaires to complete before their genetic counseling appointments. Per UTSW-approved Institutional Review Board study (STU 062018-060), we identified 6,666 patients who fully completed data on all study variables, including patient sociodemographic, personal and family history of cancer, and several brief psychological and psychosocial assessments. Results were compiled into a singular database, with age at first cancer diagnosis manually entered by genetic counseling assistants. The complete and de-identified database was then transferred to the psychology team for statistical analysis. We established five distinct patient groups based on their age at the genetic counseling appointment and age at the first cancer diagnosis (if applicable). These five groups were: (1) young adult patients diagnosed with cancer (YAs), (2) young adult patients without cancer (YAWOCs), (3) older adult patients diagnosed with cancer (OAs), (4) older adult patients diagnosed with cancer in their young adult years (OA/YAs), and (5) older adult patients without cancer (OAWOCs). In this participant sample, young adults refer to patients between 18 and 39 years of age, an age range consistent with past research [ 26 ]. Older adults refer to patients of 40 years or older. Measures Perceived Social Support Perceived social support was measured using four items derived from the Interpersonal Support Evaluation List (ISEL), a widely reliable and validated instrument often used in health outcomes studies [ 27 – 29 ]. Two of the four items constituted the Appraisal subscale of the ISEL, which measures the perceived availability of someone to discuss personal issues. The remaining items constituted the Belonging subscale, which captured the perception of being part of a social group. Responses were recorded on a 4-point Likert scale (0 = definitely false , 1 = probably false , 2 = probably true , and 3 = definitely true ), with higher scores indicating higher perceived support availability. Depressive Symptom Severity The Center for Epidemiological Studies Depression Scale (CES-D) is a reliable and validated measure for assessing depressive symptoms [ 30 ]. The 10-item version (i.e., CES-D-10) has also demonstrated adequate reliability and validity for screening depressive symptoms in patients with cancer [ 31 – 32 ]. Total scores range from 0 to 30, with higher scores indicating greater symptom severity. Per previous clinical guidelines, a score of 10 was used as the clinical cut-off for indicating significant depressive symptoms. Only composite CES-D-10 scores were available in the de-identified database, preventing subsequent scale reliability analysis. Cancer Worries Previous Cancer Worries Scales have demonstrated adequate internal consistency and convergent validity [ 33 – 35 ]. However, the original items were adapted for use in the current study. The phrasing of items and response anchors were slightly modified to improve clarity. Items were measured on a five-point Likert scale (0 = Not at all , 1 = Rarely , 2 = Sometimes , 3 = Often, 4 = Almost all the time ) and were as follows: (a) “How worried are you about getting or having a recurrence of cancer someday?”, (b) “How much does your worry affect your mood?”, (c) “How much does your worry affect your ability to perform daily activities?” Total scores range from 0 to 12, with higher scores indicating greater fear of getting cancer (for undiagnosed patients) or fear of cancer recurrence (for diagnosed patients). Only composite scores were available in the de-identified database, preventing subsequent scale reliability analysis. Sociodemographic Sociodemographic data included age at the genetic counseling appointment, age at first cancer diagnosis, type(s) of cancer (if applicable), sex assigned at birth, race, ethnicity, insurance status, and zip codes (used to approximate household income based on the US Census Bureau). Statistical Analysis Before conducting statistical analyses, data were checked regarding assumptions of linearity and normality. Skewness and kurtosis were within acceptable bounds (i.e., ± 2 for skewness; ± 7 for kurtosis) and did not suggest the presence of outliers [ 36 ]. All categorical variables were dummy coded with the following reference categories: no insurance for insurance type, female for sex, and YAs for group membership. Following this, we conducted two linear regression analyses to determine the direct effects of perceived social support and group membership on both depressive symptom severity and cancer worries. To determine the moderating effects of group membership on (1) the relationship between social support and depressive symptoms and (2) the relationship between social support and cancer worries, we conducted two moderation analyses. Regression and moderation models were adjusted for sex assigned at birth, income (in $ 1000s), and insurance (no insurance, private insurance, Tricare, Medicare, Medicaid, and county hospital insurance). Continuous measures – including perceived social support, depressive symptom severity, and cancer worries – were mean-centered for moderation analyses. All statistical analyses were conducted in IBM SPSS Statistics 28.0 [ 37 ], with moderation analyses performed using the PROCESS macro for SPSS (version 4.0) [ 38 ]. Results were deemed statistically significant at p ≤ .05. Results Sample Characteristics The largest group in the sample was OAs (41.3%), followed by OAWOCs (30.0%), YAWOCs (19.2%), YAs (5.5%), and OA/YAs (4.0%). Participants predominantly identified as assigned female at birth (86.5%), non-Hispanic (86.5%), and White (72.3%). The average age of the sample was 50 ± 14.4 years, with an income (in $ 1,000s) of 80.97 ± 32.33. Most participants had private insurance (69.8%), and the most common diagnosis among cancer patients was breast (48.0%). The average level of cancer worries was 3.84 ± 2.62, while that for CES-D-10 scores was 6.75 ± 5.55, and for social support was 10.18 ± 2.60. Complete descriptive statistics for the full sample and by groups are in Table 1 . Table 1 Descriptive Statistics for Full Sample and by Group Membership Full Sample ( n = 6666) OAWOC ( n = 1999) YAWOC ( n = 1279) OA ( n = 2756) OA/YA ( n = 268) YA ( n = 364) M SD M SD M SD M SD M SD M SD Age 50.32 14.40 53.46 9.62 30.95 5.78 59.03 10.67 52.24 9.66 33.75 4.47 Income ( $ 1000s) 80.97 32.33 82.42 33.57 78.70 30.48 81.23 32.43 83.84 33.11 77.04 29.80 Social Support 10.18 2.60 9.84 2.88 10.18 2.48 10.42 2.39 9.85 2.91 10.49 2.39 Cancer Worries 3.84 2.62 3.20 2.38 3.83 2.46 4.09 2.65 4.26 2.88 5.20 3.00 CES-D-10 6.75 5.55 6.20 5.27 6.73 5.26 6.85 5.65 7.79 6.56 8.27 6.03 Freq. % Freq. % Freq. % Freq. % Freq. % Freq. % Sex Female 5403 81.1 1642 82.1 1076 84.1 2139 77.6 232 86.6 314 86.3 Male 1263 18.9 357 17.9 203 15.9 617 22.4 36 13.4 50 13.7 Race White 4819 72.3 1374 68.7 849 66.4 2163 78.5 206 76.9 227 62.4 Black 660 9.9 257 12.9 118 9.2 219 7.9 28 10.4 38 10.4 Asian 272 4.1 66 3.3 65 5.1 112 4.1 7 2.6 22 6.0 American Indian 28 0.4 9 0.5 9 0.7 6 0.2 2 0.7 2 0.5 Native Hawaiian 9 0.1 2 0.1 1 0.1 6 0.2 0 0.0 0 0.0 Ethnicity Non-Hispanic 5769 86.5 1702 85.1 1035 80.9 2510 91.1 244 91.0 278 76.4 Hispanic 897 13.5 297 14.9 244 19.1 246 8.9 24 9.0 86 23.6 Insurance Private 4654 69.8 1433 71.7 1029 80.5 1715 62.2 201 75.0 276 75.8 Medicare 1069 16.0 248 12.4 8 0.6 772 28.0 36 13.4 5 1.4 No insurance 315 4.7 131 6.6 94 7.3 58 2.1 10 3.7 22 6.0 County Hospital 222 3.3 86 4.3 42 3.3 61 2.2 11 4.1 22 6.0 Medicaid 132 2.0 20 1.0 40 3.1 47 1.7 4 1.5 21 5.8 Tricare 101 1.5 22 1.1 22 1.7 45 1.6 2 0.7 10 2.7 OAWOC = Older adults without cancer, YAWOC = Young adults without cancer, OA = Older adults with cancer, OA/YA = Older adults who were survivors of young-adult cancer, YA = Young adults with cancer. Percentages may not sum to 100 due to missing data Social Support and Depressive Symptoms Regression analysis in which depressive symptom severity specified as the outcome was statistically significant ( F [12, 6231] = 104.180, p < .001), suggesting that the model covariates could explain approximately 17% of the variance in CES-D-10 scores ( R 2 = .167). The main effect of perceived social support was significant (B = -0.756, p < .001), such that more social support was associated with lower depressive symptoms, on average. Relative to YAs, all levels of group membership had significantly less depressive symptom severity, on average (OAWOCs: B = -2.326, p < .001; YAWOCs: B = -1.666, p < .001; OAs: B = -1.132, p < .001; and OA/YA: B = -0.831, p = .047). All other covariates, including sex, income, and insurance status, were also significantly related to CES-D-10 scores. Specifically, those identifying as assigned male at birth (B = -1.751, p < .001) and having higher income (B = -0.007, p < .001) were associated with lower CES-D-10 scores. Compared to having no insurance, having private insurance (B = -0.830, p = .006) or Medicare (B = -0.714, p = .036) was associated with lower CES-D-10 scores, while having Medicaid (B = 1.836, p < .001) was significantly associated with higher CES-D-10 scores. However, neither Tricare nor County Hospital insurance was related to depressive symptoms. Full regression results are in Table 2 . Table 2 Linear Regression and Moderation Results for Depressive Symptom and Cancer-Related Worry 95% CI for B a Linear Regression: CES-D-10 B p Lower Upper Social support -0.756 < .001 -0.806 -0.707 OAWOC (vs. YA) -2.326 < .001 -2.910 -1.743 YAWOC (vs. YA) -1.666 < .001 -2.272 -1.060 OA (vs. YA) -1.132 < .001 -1.709 -0.555 OA/YA (vs. YA) -0.831 .047 -1.653 -0.010 Male (vs. female) -1.751 < .001 -2.078 -1.425 Income ( $ 1000s) -0.007 < .001 -0.011 -0.003 Private insurance (vs. no insurance) -0.830 .006 -1.421 -0.239 Medicare (vs. no insurance) -0.714 .036 -1.381 -0.047 County hospital (vs. no insurance) 0.612 .173 -0.268 1.492 Medicaid (vs. no insurance) 1.836 < .001 0.795 2.877 Tricare (vs. no insurance) -0.446 .458 -1.624 0.732 b Moderated Regression: CES-D-10 B p Lower Upper Social support -0.695 < .001 -0.776 -0.614 OAWOC (vs. YA) 0.641 < .001 0.269 1.013 YAWOC (vs. YA) 1.158 < .001 0.848 1.468 OA (vs. YA) 1.369 < .001 0.702 2.036 OA/YA (vs. YA) 2.366 < .001 1.780 2.953 Social support × OAWOC (vs. YA) -0.160 .028 -0.303 -0.017 Social support × YAWOC (vs. YA) -0.005 .928 -0.120 0.109 Social support × OA (vs. YA) -0.356 .002 -0.582 -0.130 Social support × OA/YA (vs. YA) -0.277 .021 -0.511 -0.042 c Linear Regression: Cancer Worries B p Lower Upper Social support -0.149 < .001 -0.174 -0.125 OAWOC (vs. YA) -1.947 < .001 -2.237 -1.658 YAWOC (vs. YA) -1.392 < .001 -1.693 -1.091 OA (vs. YA) -0.869 < .001 -1.155 -0.583 OA/YA (vs. YA) -0.928 < .001 -1.335 -0.521 Male (vs. female) -0.755 < .001 -0.917 -0.593 Income ( $ 1000s) -0.001 .548 -0.003 0.001 Private insurance (vs. no insurance) -0.616 < .001 -0.910 -0.323 Medicare (vs. no insurance) -1.059 < .001 -1.390 -0.728 County hospital (vs. no insurance) .999 -0.436 0.437 Medicaid (vs. no insurance) 0.429 .103 -0.087 0.945 Tricare (vs. no insurance) -0.405 .176 -0.992 0.181 d Moderated Regression: Cancer Worries B p Lower Upper Social support -0.106 < .001 -0.146 -0.065 OAWOC (vs. YA) 0.540 < .001 0.356 0.725 YAWOC (vs. YA) 1.068 < .001 0.914 1.222 OA (vs. YA) 0.970 < .001 0.639 1.301 OA/YA (vs. YA) 1.955 < .001 1.664 2.246 Social support × OAWOC (vs. YA) -0.052 .154 -0.123 0.019 Social support × YAWOC (vs. YA) -0.060 .040 -0.117 -0.003 Social support × OA (vs. YA) -0.141 .014 -0.253 -0.029 Social support × OA/YA (vs. YA) -0.128 .032 -0.244 -0.011 a F (16,6227) = 79.427, p < .001, R 2 = .170 b F (16,6211) = 37.483, p < .001, R 2 = .088 c F (12,6231) = 104.18, p < .001, R 2 = .167 d F (12,6215) = 49.020, p < .001, R 2 = .086 Following this, a moderation analysis was conducted to examine the interaction between group membership and social support. Group membership emerged as a significant moderator of the relationship between social support and depressive symptoms ( F ( 16, 6227) = 79.43, p < .001), adjusting for the same covariates included in the linear regression analysis. Compared to YAs, all other groups, except YAWOCs, negatively moderated the relationship (OAWOC: B = -0.16, p = .028; OA: B = -0.36, p = .002; OA/YA: B = -0.28, p = .021), such that the inverse relationship between social support and depressive symptoms was lower in magnitude for OAWOCs, OAs, and OA/YAs in comparison to YAs, on average. No difference in magnitude existed in the social support-depressive symptoms relationship between YAs and YAWOCs (B = -0.01, p = .928). Full moderation results are in Table 3, with a graphical depiction of the moderating relationship provided in Fig. 1 . Social Support and Cancer-Related Worry Regression analysis in which cancer worries specified as the outcome was statistically significant ( F [ 12, 6215] = 49.020, p < .001). Together, the model covariates explained approximately 9% of the variance in cancer worries ( R 2 = .086). Similar to the first regression analysis, the main effect of social support was statistically significant (B = -0.149, p < .001), such that greater social support was associated with fewer cancer worries. Relative to YAs, all other levels of group membership had significantly less cancer worries, on average (OAWOCs: B = -1.947, p < .001; YAWOCs: B = -1.392, p < .001; OAs: B = -0.869, p < .001; and OA/YA: B = -0.928, p < .001). Additionally, those identifying as male at birth (B = -0.755, p < .001) tended to have fewer cancer worries. Compared to having no insurance, having private insurance (B = -0.6116, p < .001) or Medicare (B = -1.059, p < .001) was associated with lower cancer worries, on average. The results for the second regression analysis are in Table 2 . After adjusting for covariates, the moderating effect of group membership on the relationship between social support and cancer worries was significant ( F [16, 6211] = 37.48, p < .001). Contrary to the moderation findings with depressive symptoms specified as the outcome, there was no significant difference between OAWOCs and YAs (B = -0.05, p = .154). Compared to YAs, significant interactions between social support and group membership emerged for YAWOCs (B = -0.06, p = .040), OAs (B = -0.14, p = .014), and OA/YAs (B = -0.13, p = .032). Together, these results suggest that while the magnitude of the inverse relationship between social support and cancer worries significantly differed between YAs and YAWOCs, OAs, and OA/Yas, no differences existed between OAWOCs and YAs. That is, the effect of social support on cancer worries was less significant for YAWOCs, OAs, and OA/YAs compared to YAs. Results from the second moderation analysis are in Table 3, with a graphical depiction of the effect presented in Fig. 2 . Discussion The present study evaluated the relationship between social support, cancer worries, and depressive symptoms among a large sample of patients seeking genetic counseling for hereditary cancer. Our participants included YAs, OA/YAs, OAs, and their two undiagnosed comparison groups: YAWOCs and OAWOCs. Our primary aims were 1) to examine whether social support buffers against depressive symptoms and cancer worries and 2) whether these buffering effects would be the strongest for YAs. Our findings for Aim 1 supported our hypothesis, suggesting that regardless of a patient’s age, diagnosis status, sex, income, and insurance status, social support ameliorates the severity of both depressive symptoms and cancer-related worries. To our knowledge, our study is the first to account for several demographic variables while assessing the clinical significance of how social support mitigates depressive symptoms and cancer worries in patients undergoing genetic counseling. With carefully controlled confounds, our results underscore the importance of social support in managing the psychological burden associated with cancer survivorship, in the context of seeking genetic counseling for hereditary cancer [ 21 – 25 , 39 – 40 ]. For Aim 2, our findings on depressive symptoms partially supported our hypothesis, indicating that although social support mitigates depressive symptoms significantly more for YAs than OA/YAs, OAs, and OAWOCS, these effects do not differ between YAs and YAWOCs. As such, YAs and YAWOCs struggling with depressive symptoms would particularly benefit from high levels of social support compared to the rest of the age and cancer groups. To interpret this finding, we considered previous research revealing that depression was more prevalent among young cancer patients with limited social support, suggesting that the experience of surviving cancer and/or hereditary cancer testing for YAs is often tremendously isolating [ 41 – 42 ]. Combining such experiences with young age (a predisposing factor to loneliness [ 43 ]), it is unsurprising that strong support networks are crucial for relieving depressive symptoms in these two groups. As part of Aim 2, the findings on cancer-related worries partially supported our hypothesis, suggesting that social support would be most effective at mitigating fears of cancer recurrence for YAs and fears of incurring cancer for OAWOCs compared to the rest of the groups. Although an unexpected outcome, our sample characteristics might offer a potential explanation. Despite reporting the lowest CES-D-10 and cancer worries scores, OAWOCs also demonstrated the lowest level of social support relative to the rest of the group. Wang and colleagues (2014) discovered that if high-stress individuals also reported high support, the impact of stress on their depression was much smaller than those of low-stress and low-support [ 44 ]. This research provides some evidence to suggest that an inadequate baseline social network for OAWOCs means receiving more social support may bring significant improvements in their cancer worries levels. Such findings also imply that for OAWOCs, cancer worries might be a particularly burdensome psychological concern as they undergo genetic counseling for hereditary cancer risks. In sum, our findings underscore the differential buffering effects of social support on depressive symptoms and cancer-related worries among various groups. By demonstrating the specific dynamic of these effects among patient groups across different ages (young adult versus older adult patients) and by cancer diagnosis status (diagnosed versus undiagnosed), we found that social support buffers depressive symptoms most significantly for YAs and YAWOCs, and respectively, cancer worries for YAs and OAWOCs. Finally, in addition to the above findings, our results indicated that patients with elevated depressive symptoms and cancer-related worries included those who were YAs, those identified as female, those with low household incomes, and those with no insurance. These findings are consistent with previous research on the socio-demographic factors predisposing patients to increased risks for depression and cancer worries, including age, sex assigned at birth, and socioeconomic status [ 45 – 46 ]. Clinical Recommendations and Future Directions First, although cancer clinics should emphasize social support groups for all patients, a more tailored and age-appropriate support network may be most important for managing depression and fear of cancer recurrence for YAs. Second, cancer genetics clinics consider collaborating closely with mental health providers to implement a specialized support network for managing depression for YAWOCs and cancer worries for OAWOCs. Third, oncology and cancer genetic clinics should continue incorporating screeners for depression, cancer worries, and social support into routine medical care. This research would benefit from future studies that attempt to replicate the above buffering dynamics of social support on depressive symptoms and cancer worries in patients with other cancer types, as many of our patient participants were diagnosed with breast cancer. Studies with analyses by the duration of time living with cancer (for diagnosed patients) and by risk levels (for undiagnosed patients) would also further our understanding of the differential impacts of social support. Lastly, it may be clinically useful to assess the actual impact of increasing social support activities (e.g., through attendance in group therapy or peer support activities) on depressive symptoms and cancer worries severity in high-risk patients (e.g., YAs). Limitations While findings from the present study provided a deeper understanding of social support as a protective factor, there are limitations to note. We categorized diagnosis status as a binary variable (yes/no), which omitted the inherent heterogeneity in cancer diagnoses. We generated the income variable based on zip code data – a proxy sometimes unrepresentative of actual income. Additionally, patients referred to genetic counseling might already be at increased risk for cancer regardless of diagnosis status, combined with the fact that our participant samples were predominantly female-identifying patients with breast cancers, could pose limits to the external validity of our results. Our outcomes might be affected by selection bias to some degree; and thus, might not be generalizable to cancer patients who are not seeking genetic counseling or those without a family history of cancer. Moreover, cross-sectional data analyses were available for patients who fully completed the pre-appointment questionnaires only, introducing nonresponse bias and limiting our ability to infer strong causality. Finally, our total scores on CES-D-10, cancer worries, and social support were pre-computed by the cancer genetics system before statistical analyses, preventing scale reliability analyses. Nevertheless, the high-volume sample size (> 6000) and naturalistic clinical data collected at various locations bolster the ecological validity of our findings. Declarations Funding and Competing Interests: We have no funding source and competing interest to disclose. Author Contributions: All authors contributed to the study conception and design. Material preparation and analysis were performed by Sally Ho and Jayme M. Palka. The first draft of the manuscript was written by Sally Ho. All authors commented on previous versions of the manuscript, read, and approved the final manuscript. Data Availability: The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. References Hamilton JG, Lobel M, Moyer A. Emotional distress following genetic testing for hereditary breast and ovarian cancer: a meta-analytic review. Health Psychol. 2009;28(4):510. 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Psycho‐Oncology. 2018;27(5):1404–141. Thewes B, Kaal SE, Custers JA, Manten-Horst E, Jansen R, Servaes P, …, Husson O. Prevalence and correlates of high fear of cancer recurrence in late adolescents and young adults consulting a specialist adolescent and young adult (AYA) cancer service. Support Care Cancer. 2018;26(5):1479–87. Ferrari AJ, Somerville AJ, Baxter AJ, Norman R, Patten SB, Vos T, Whiteford H. Global variation in the prevalence and incidence of major depressive disorder: a systematic review of the epidemiological literature. Psychol Med. 2013;43(3):471–81. Mitchell AJ, Chan M, Bhatti H, Halton M, Grassi L, Johansen C, Meader N. Prevalence of depression, anxiety, and adjustment disorder in oncological, haematological, and palliative-care settings: a meta-analysis of 94 interview-based studies. Lancet Oncol. 2011;12(2):160–74. Caruso R, Nanni MG, Riba M, Sabato S, Mitchell AJ, Croce E, Grassi L. Depressive spectrum disorders in cancer: prevalence, risk factors and screening for depression: a critical review. Acta Oncol. 2017;56(2):146–55. Walker J, Hansen CH, Martin P, Symeonides S, Ramessur R, Murray G, Sharpe M. Prevalence, associations, and adequacy of treatment of major depression in patients with cancer: a cross-sectional analysis of routinely collected clinical data. The Lancet Psychiatry. 2014;1(5):343–50. Lang MJ, David V, Giese-Davis J. The age conundrum: a scoping review of younger age or adolescent and young adult as a risk factor for clinical distress, depression, or anxiety in cancer. J Adolesc young adult Oncol. 2015;4(4):157–73. Avis NE, Levine BJ, Case LD, Naftalis EZ, Van Zee KJ. Trajectories of depressive symptoms following breast cancer diagnosis. Cancer Epidemiol Biomarkers Prev. 2015;24(11):1789–95. Muffly LS, Hlubocky FJ, Khan N, Wroblewski K, Breitenbach K, Gomez J, …, Daugherty CK. Psychological morbidities in adolescent and young adult blood cancer patients during curative-intent therapy and early survivorship. Cancer. 2016;122(6):954–61. Ho S, Mersch J, Martin WB, Howe-Martin L. (2022). Exploring Depressive Symptoms and Cancer Worries in a High-Volume Cancer Genetics Clinic: What Are the Roles of Age and Cancer Diagnosis?. J Adolesc Young Adult Oncol. Armes J, Crowe M, Colbourne L, Morgan H, Murrells T, Oakley C, …, Richardson A. Patients' supportive care needs beyond the end of cancer treatment: a prospective, longitudinal survey. J Clin Oncol. 2009;27(36):6172–9. Custers JA, Gielissen MF, Janssen SH, de Wilt JH, Prins JB. Fear of cancer recurrence in colorectal cancer survivors. Support Care Cancer. 2016;24(2):555–62. Simard S, Thewes B, Humphris G, Dixon M, Hayden C, Mireskandari S, Ozakinci G. Fear of cancer recurrence in adult cancer survivors: a systematic review of quantitative studies. J Cancer Surviv. 2013;7(3):300–22. Thewes B, Kaal SE, Custers JA, Manten-Horst E, Jansen R, Servaes P, …, Husson O. Prevalence and correlates of high fear of cancer recurrence in late adolescents and young adults consulting a specialist adolescent and young adult (AYA) cancer service. Support Care Cancer. 2018;26:1479–87. Vandraas KF, Reinertsen KV, Kiserud CE, Lie HC. Fear of cancer recurrence among young adult cancer survivors—exploring long-term contributing factors in a large, population-based cohort. J Cancer Surviv. 2021;15(4):497–508. Applebaum AJ, Stein EM, Lord-Bessen J, Pessin H, Rosenfeld B, Breitbart W. Optimism, social support, and mental health outcomes in patients with advanced cancer. Psycho‐oncology. 2014;23(3):299–306. Kornblith, A. B., Herndon, J. E., Zuckerman, E., Viscoli, C. M., Horwitz, R. I., Cooper,M. R., … Cancer and Leukemia Group B. (2001). Social support as a buffer to the psychological impact of stressful life events in women with breast cancer. Cancer, 91 (2), 443–454. Koch-Gallenkamp, L., Bertram, H., Eberle, A., Holleczek, B., Schmid-Höpfner, S., Waldmann,A., … Arndt, V. (2016). Fear of recurrence in long-term cancer survivors—Do cancer type, sex, time since diagnosis, and social support matter? Health Psychology, 35 (12), 1329. Fong AJ, Scarapicchia TM, McDonough MH, Wrosch C, Sabiston CM. Changes in social support predict emotional well-being in breast cancer survivors. Psycho‐oncology. 2017;26(5):664–71. Rodin, G., Walsh, A., Zimmermann, C., Gagliese, L., Jones, J., Shepherd, F. A., …Mikulincer, M. (2007). The contribution of attachment security and social support to depressive symptoms in patients with metastatic cancer. Psycho-Oncology: Journal of the Psychological, Social and Behavioral Dimensions of Cancer, 16 (12), 1080–1091. Salsman JM, Garcia SF, Yanez B, Sanford SD, Snyder MA, Victorson D. Physical, emotional, and social health differences between posttreatment young adults with cancer and matched healthy controls. Cancer. 2014;120(15):2247–54. Cohen S, Hoberman H. Interpersonal support evaluation list (ISEL). J Appl Soc Psychol. 1983;13(1):99–125. Brookings JB, Bolton B. Confirmatory factor analysis of the interpersonal support evaluation list. Am J Community Psychol. 1988;16(1):137–47. Payne TJ, Andrew M, Butler KR, Wyatt SB, Dubbert PM, Mosley TH. Psychometric evaluation of the interpersonal support evaluation list–short form in the ARIC study cohort. Sage Open. 2012;2(3):2158244012461923. Hann D, Winter K, Jacobsen P. Measurement of depressive symptoms in cancer patients: evaluation of the Center for Epidemiological Studies Depression Scale (CES-D). J Psychosom Res. 1999;46(5):437–43. Mohebbi, M., Nguyen, V., McNeil, J. J., Woods, R. L., Nelson, M. R., Shah, R. C.,… ASPREE Investigator Group. (2018). Psychometric properties of a short form of the Center for Epidemiologic Studies Depression (CES-D-10) scale for screening depressive symptoms in healthy community dwelling older adults. General hospital psychiatry, 51 , 118–125. Vodermaier A, Linden W, Siu C. Screening for emotional distress in cancer patients: a systematic review of assessment instruments. J Natl Cancer Inst. 2009;101(21):1464–88. Easterling DV, Leventhal H. Contribution of concrete cognition to emotion: neutral symptoms as elicitors of worry about cancer. J Appl Psychol. 1989;74(5):787. Lerman C, Trock B, Rimer BK, Jepson C, Brody D, Boyce A. Psychological side effects of breast cancer screening. Health Psychol. 1991;10(4):259. Thewes B, Butow P, Zachariae R, Christensen S, Simard S, Gotay C. Fear of cancer recurrence: a systematic literature review of self-report measures. Psycho‐oncology. 2012;21(6):571–87. West SG, Finch JF, Curran PJ. Structural equation models with nonnormal variables. Problems and remedies; 1995. IBM Corp. Released 2021. IBM SPSS Statistics for Windows, Version 28.0. Armonk, NY: IBM Corp. Hayes AF. Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. Guilford publications; 2017. Bjorvatn C, Eide GE, Hanestad BR, Havik OE. Anxiety and depression among subjects attending genetic counseling for hereditary cancer. Patient Educ Couns. 2008;71(2):234–43. Gonzalez-Saenz de Tejada, M., Bilbao, A., Baré, M., Briones, E., Sarasqueta, C., Quintana,J. M., … CARESS‐CCR Group. (2017). Association between social support, functional status, and change in health‐related quality of life and changes in anxiety and depression in colorectal cancer patients. Psycho‐oncology , 26 (9), 1263–1269. Hann, D., Baker, F., Denniston, M., Gesme, D., Reding, D., Flynn, T., … Kieltyka,R. L. (2002). The influence of social support on depressive symptoms in cancer patients:age and gender differences. Journal of psychosomatic research , 52 (5), 279–283. Oostrom, I. V., Meijers-Heijboer, H., Duivenvoorden, H. J., Bröcker‐Vriends, A. H.,van Asperen, C. J., Sijmons, R. H., … Tibben, A. (2007). A prospective study of the impact of genetic susceptibility testing for BRCA1/2 or HNPCC on family relationships. Psycho‐Oncology: Journal of the Psychological, Social and Behavioral Dimensions of Cancer , 16 (4), 320–328. Beam CR, Kim AJ. Psychological sequelae of social isolation and loneliness might be a larger problem in young adults than older adults. Psychol Trauma: Theory Res Pract Policy. 2020;12(S1):58. Wang X, Cai L, Qian J, Peng J. Social support moderates stress effects on depression. Int J mental health Syst. 2014;8(1):1–5. Riedl D, Schüßler G. Factors associated with and risk factors for depression in cancer patients–A systematic literature review. Translational Oncol. 2022;16:101328. Simonelli LE, Siegel SD, Duffy NM. Fear of cancer recurrence: a theoretical review and its relevance for clinical presentation and management. Psycho-oncology. 2017;26(10):1444–54. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 21 Mar, 2024 Read the published version in Journal of Cancer Survivorship → Version 1 posted Editorial decision: Accepted 02 Oct, 2023 Submission checks completed at journal 27 Sep, 2023 First submitted to journal 26 Sep, 2023 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-3031154","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":237446588,"identity":"a4412bdc-4026-4b44-8f09-8c6204076ff6","order_by":0,"name":"Sally Ho","email":"","orcid":"","institution":"The University of Texas Southwestern Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Sally","middleName":"","lastName":"Ho","suffix":""},{"id":237446589,"identity":"2a3d9f6a-65aa-4eb9-ba36-0344f6c9111f","order_by":1,"name":"Jayme M. Palka","email":"","orcid":"","institution":"The University of Texas Southwestern Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Jayme","middleName":"M.","lastName":"Palka","suffix":""},{"id":237446590,"identity":"162884b5-b716-4be6-94c0-8603f525069e","order_by":2,"name":"Jacqueline Mersch","email":"","orcid":"","institution":"The University of Texas Southwestern Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Jacqueline","middleName":"","lastName":"Mersch","suffix":""},{"id":237446591,"identity":"ffa4611f-79de-4704-9926-934141c7aef6","order_by":3,"name":"W. Blake Martin","email":"","orcid":"","institution":"The University of Texas Southwestern Medical Center","correspondingAuthor":false,"prefix":"","firstName":"W.","middleName":"Blake","lastName":"Martin","suffix":""},{"id":237446592,"identity":"f7c5f996-ceff-4fcd-929d-1e242775ad4b","order_by":4,"name":"Laura Howe-Martin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABUUlEQVRIie2PMUvDQBTHLxST5WnWK5T6FS4cpBaHfpWEQlyCRQKSQSRSqEvBVemXqAidHK4ctMt9gJSKVYRMGSIFaaGISQOaFqmrw/2W93jv/fjzEJJI/iEHeWHfA0BakHekcFbs1S2FAAKmZBImhcVOJb21/lA0YSYL9FQl42Cvv3xcVWrluPa68PllDTf5PPGfT5HWHuCCAi6966KIEsHUSTciUO+5RhsEx/Vbx8FMeB6C0XlRQS5NX+b2IGx9hMAIkGmqKB2O08ZEw45lB9g1i4oeU2WVKbMXdbLKlIkwrpZrpTVPhp+pchhvKNilpTwFqdN1SghGsJ+nIDwMshTYVCKvVCGcNoSlTiuMAhHOWQ/ESbk/i0wsRpbdAcc7+lF0vfmgxD6vlq/T92NWbZAx778v/GOdCP6W+BeWfaPx+xBtQbYHG6g7txKJRCL5hS96M4Si4yJ+TgAAAABJRU5ErkJggg==","orcid":"","institution":"The University of Texas Southwestern Medical Center","correspondingAuthor":true,"prefix":"","firstName":"Laura","middleName":"","lastName":"Howe-Martin","suffix":""}],"badges":[],"createdAt":"2023-06-06 20:59:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3031154/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3031154/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11764-023-01479-x","type":"published","date":"2024-03-21T15:01:05+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":50041898,"identity":"f14a8c66-a63c-466f-b1c0-69b18d58f8c1","added_by":"auto","created_at":"2024-01-23 15:44:49","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":12163,"visible":true,"origin":"","legend":"\u003cp\u003eThe moderating role of group on the relationship between perceived social support and depressive symptom severity. \u003cstrong\u003eOAWOC\u003c/strong\u003e = Older adults without cancer, \u003cstrong\u003eYAWOC\u003c/strong\u003e = Young adults without cancer, \u003cstrong\u003eOA\u003c/strong\u003e = Older adults with cancer, \u003cstrong\u003eOA/YA \u003c/strong\u003e= Older adults who were survivors of young-adult cancer, \u003cstrong\u003eYA \u003c/strong\u003e= Young adults with cancer.\u003c/p\u003e","description":"","filename":"Onlinedrawingimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3031154/v1/55b45c6b40e890910a186271.png"},{"id":50041899,"identity":"41dc25bb-7d6d-40d1-a34f-f5ddd79e439a","added_by":"auto","created_at":"2024-01-23 15:44:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":11800,"visible":true,"origin":"","legend":"\u003cp\u003eThe moderating role of group on the relationship between perceived social support and cancer worries.\u003cstrong\u003e OAWOC\u003c/strong\u003e = Older adults without cancer, \u003cstrong\u003eYAWOC\u003c/strong\u003e = Young adults without cancer, \u003cstrong\u003eOA\u003c/strong\u003e = Older adults with cancer, \u003cstrong\u003eOA/YA \u003c/strong\u003e= Older adults who were survivors of young-adult cancer, \u003cstrong\u003eYA \u003c/strong\u003e= Young adults with cancer.\u003c/p\u003e","description":"","filename":"Onlinedrawingimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3031154/v1/8fe18089899b2845793ddaea.png"},{"id":53403605,"identity":"023bb7c8-c387-4e96-bc8d-2aa8340cb226","added_by":"auto","created_at":"2024-03-25 15:13:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":530815,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3031154/v1/da581029-f338-47c0-8bfb-bad320402197.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Dynamic Buffering of Social Support on Depressive Symptoms and Cancer Worries in Patients Seeking Cancer Genetic Counseling","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCancer genetic testing can identify gene mutations and cancer syndromes in individuals undiagnosed with cancer but have a known family history of hereditary cancer. Information received from genetic testing could help patients understand risk levels, aid decisions about reproduction, and in many cases, alleviate feelings of uncertainty [1,2]. In addition, patients with a cancer diagnosis can receive cancer genetic counseling, as the results could assist patients with treatment, surgery decisions, and future cancer risk management. However, despite the known benefits of genetic counseling, patients undergoing this process may experience many negative emotions (e.g., fear, cancer worries, loneliness), with some evidence suggesting that these emotions often arise pre-testing [1,3,4]. Risk factors that could exacerbate these negative emotions include low social support, young age, and having a previous cancer diagnosis [4]. As such, although the clinical populations of both undiagnosed and diagnosed patients seeking genetic counseling might experience similar stressors, young patients with cancer might endure a more significant psychological impact. Indeed, past research consistently demonstrates that young adults with cancer (hereafter referred to as YAs) are prone to much higher frequencies of mood disorders, suicidal ideation, and dysfunctional levels of fear of cancer recurrence when compared to older adult patients with cancer (hereafter referred to OAs)\u003csup\u003e\u0026nbsp;\u003c/sup\u003e[5-7]. By utilizing natural clinical data, the current study seeks to examine depression and cancer-related worries in various subgroups of patients seeking genetic counseling, including different age groups (young versus older patients) and diagnosis status (diagnosed survivors versus undiagnosed).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDepressive Symptoms in Oncology Patients\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA 2013 large-scale systematic review revealed the global prevalence of depression in the general population to be approximately 5%\u003csup\u003e\u0026nbsp;\u003c/sup\u003e[8]. Concurrent research by Mitchel and colleagues reported the depression rate among cancer patients as 16.5% using the DSM-5 criteria [9], suggesting that the depression rate in cancer patients is approximately threefold that of the general population. Among patients with cancer, a substantial research body with high-volume samples has demonstrated depression to be more prevalent among those of younger age and those with limited social support\u003csup\u003e\u0026nbsp;\u003c/sup\u003e[10-11]. Although there is much debate as to whether chronological age is a predictor of depression among oncology patients, there is robust evidence to support that YAs are more prone to elevated depressive symptoms compared to OAs\u003csup\u003e\u0026nbsp;\u003c/sup\u003e[12]. For instance, among patients with breast cancer, younger patients consistently demonstrated more clinically significant depressive symptoms than their older counterparts\u003csup\u003e\u0026nbsp;\u003c/sup\u003e[13]. While clinically diagnosed depression in adult cancer survivors was prevalent up to 14.9%, the depression rate in YA survivors of childhood leukemia was two-fold, evident up to 28% [9,14]. More recent research suggests that both YAs and long-term survivors of adolescent and young adult cancers report higher levels of depressive symptoms compared to OA patients\u003csup\u003e\u0026nbsp;\u003c/sup\u003e[15].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCancer Worries in Oncology Patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFear of cancer recurrence (FCR) is one of the most reported worries among cancer survivors\u003csup\u003e\u0026nbsp;\u003c/sup\u003e[16]. Elevated FCR can adversely affect daily functioning, quality of life, and social engagement among survivors [17]. A 2013 systematic review demonstrated that age is a significant predictor of FCR, with younger cancer survivors endorsing much greater FCR than older survivors [18]. A recent study examining FCR in YAs revealed that over 60% of YAs reported significant FCR, which, in turn, was associated with psychological problems and decreased quality of life\u003csup\u003e\u0026nbsp;\u003c/sup\u003e[19]. Notably, long-term survivors of young adult cancer also reported substantial levels of FCR, with up to 74% worrying about getting a new diagnosis and up to 20% worrying about disease recurrence\u003csup\u003e\u0026nbsp;\u003c/sup\u003e[20]. In sum, current literature indicates that YAs and long-term survivors of YA-cancer experience more significant FCR than older cancer survivors, which impairs their functioning across several domains.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSocial Support as a Protective Factor for Depression and Cancer Worries\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Previous research has consistently identified social support as a crucial buffer against psychological distress in cancer survivorship\u003csup\u003e\u0026nbsp;\u003c/sup\u003e[21-22]. Koch-Gallenkamp and colleagues reported that socially isolated survivors endorsed higher levels of FCR than their socially connected counterparts\u003csup\u003e\u0026nbsp;\u003c/sup\u003e[23]. Social support has also been shown to buffer against depressive symptoms in both YAs and OAs [24-25]. Although current research substantiates the role of social support in buffering against both depressive symptoms and cancer-related worries, it is unclear how social dynamics might differ across age groups of patients. Therefore, this paper seeks to redress the dearth in the literature regarding the differential impact of social support on cancer worries and depression across patient groups seeking genetic counseling for hereditary cancer risks marked by age (young versus older patients) and by cancer diagnosis (diagnosed versus undiagnosed).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe Current Study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe current study used a high-volume sample from a cancer genetics clinic to evaluate social support, depressive symptoms, and cancer worries outcomes among various patient subgroups. In so doing, our aims were to: 1) examine whether social support buffers against depressive symptoms and cancer-related worry, and 2) examine how these buffering effects differ among the following patient groups: young adult patients with cancer (YAs), older adult patients who were survivors of young-adult cancers (OA/YAs), older adult patients with cancer (OAs), and their undiagnosed comparison groups: young adult patients without cancer (YAWOCs), older adult patients without cancer (OAWOCs). In Aim 1, we hypothesized that social support would be negatively associated with depressive symptoms and cancer worries. In Aim 2, we hypothesized that the buffering effects of social support on depressive symptoms and cancer worries would be the greatest for YAs when compared to OA/YAs, OAs, YAWOCs, and OAWOCs.\u0026nbsp;\u003c/p\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eParticipants and Data Collection Procedure\u003c/h2\u003e \u003cp\u003eWe derived our sample from a pool of 14,802 patients who sought genetic counseling through the Cancer Genetics Program at the University of Texas Southwestern (UTSW) Medical Center between October 2016 and October 2020. Data collection locations included academic medical center clinics, community clinics, and county safety-net hospital clinics. As part of routine clinical care, all patients received secure online questionnaires to complete before their genetic counseling appointments. Per UTSW-approved Institutional Review Board study (STU 062018-060), we identified 6,666 patients who fully completed data on all study variables, including patient sociodemographic, personal and family history of cancer, and several brief psychological and psychosocial assessments. Results were compiled into a singular database, with age at first cancer diagnosis manually entered by genetic counseling assistants. The complete and de-identified database was then transferred to the psychology team for statistical analysis.\u003c/p\u003e \u003cp\u003e We established five distinct patient groups based on their age at the genetic counseling appointment and age at the first cancer diagnosis (if applicable). These five groups were: (1) young adult patients diagnosed with cancer (YAs), (2) young adult patients without cancer (YAWOCs), (3) older adult patients diagnosed with cancer (OAs), (4) older adult patients diagnosed with cancer in their young adult years (OA/YAs), and (5) older adult patients without cancer (OAWOCs). In this participant sample, young adults refer to patients between 18 and 39 years of age, an age range consistent with past research [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Older adults refer to patients of 40 years or older.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003eMeasures\u003c/h2\u003e \u003cdiv id=\"Sec8\" class=\"Section4\"\u003e \u003ch2\u003ePerceived Social Support\u003c/h2\u003e \u003cp\u003ePerceived social support was measured using four items derived from the Interpersonal Support Evaluation List (ISEL), a widely reliable and validated instrument often used in health outcomes studies [\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Two of the four items constituted the Appraisal subscale of the ISEL, which measures the perceived availability of someone to discuss personal issues. The remaining items constituted the Belonging subscale, which captured the perception of being part of a social group. Responses were recorded on a 4-point Likert scale (0\u0026thinsp;=\u0026thinsp;\u003cem\u003edefinitely false\u003c/em\u003e, 1\u0026thinsp;=\u0026thinsp;\u003cem\u003eprobably false\u003c/em\u003e, 2\u0026thinsp;=\u0026thinsp;\u003cem\u003eprobably true\u003c/em\u003e, and 3\u0026thinsp;=\u0026thinsp;\u003cem\u003edefinitely true\u003c/em\u003e), with higher scores indicating higher perceived support availability.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eDepressive Symptom Severity\u003c/h2\u003e \u003cp\u003eThe Center for Epidemiological Studies Depression Scale (CES-D) is a reliable and validated measure for assessing depressive symptoms [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The 10-item version (i.e., CES-D-10) has also demonstrated adequate reliability and validity for screening depressive symptoms in patients with cancer [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Total scores range from 0 to 30, with higher scores indicating greater symptom severity. Per previous clinical guidelines, a score of 10 was used as the clinical cut-off for indicating significant depressive symptoms. Only composite CES-D-10 scores were available in the de-identified database, preventing subsequent scale reliability analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003eCancer Worries\u003c/h2\u003e \u003cp\u003ePrevious Cancer Worries Scales have demonstrated adequate internal consistency and convergent validity [\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. However, the original items were adapted for use in the current study. The phrasing of items and response anchors were slightly modified to improve clarity. Items were measured on a five-point Likert scale (0\u0026thinsp;=\u0026thinsp;\u003cem\u003eNot at all\u003c/em\u003e, \u003cem\u003e1\u0026thinsp;=\u0026thinsp;Rarely\u003c/em\u003e, 2\u0026thinsp;=\u0026thinsp;\u003cem\u003eSometimes\u003c/em\u003e, \u003cem\u003e3\u0026thinsp;=\u0026thinsp;Often, 4\u0026thinsp;=\u0026thinsp;Almost all the time\u003c/em\u003e) and were as follows: (a) \u0026ldquo;How worried are you about getting or having a recurrence of cancer someday?\u0026rdquo;, (b) \u0026ldquo;How much does your worry affect your mood?\u0026rdquo;, (c) \u0026ldquo;How much does your worry affect your ability to perform daily activities?\u0026rdquo; Total scores range from 0 to 12, with higher scores indicating greater fear of getting cancer (for undiagnosed patients) or fear of cancer recurrence (for diagnosed patients). Only composite scores were available in the de-identified database, preventing subsequent scale reliability analysis.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSociodemographic\u003c/h2\u003e \u003cp\u003eSociodemographic data included age at the genetic counseling appointment, age at first cancer diagnosis, type(s) of cancer (if applicable), sex assigned at birth, race, ethnicity, insurance status, and zip codes (used to approximate household income based on the US Census Bureau).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eBefore conducting statistical analyses, data were checked regarding assumptions of linearity and normality. Skewness and kurtosis were within acceptable bounds (i.e., \u0026plusmn; 2 for skewness; \u0026plusmn; 7 for kurtosis) and did not suggest the presence of outliers [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. All categorical variables were dummy coded with the following reference categories: \u003cem\u003eno insurance\u003c/em\u003e for insurance type, \u003cem\u003efemale\u003c/em\u003e for sex, and \u003cem\u003eYAs\u003c/em\u003e for group membership. Following this, we conducted two linear regression analyses to determine the direct effects of perceived social support and group membership on both depressive symptom severity and cancer worries. To determine the moderating effects of group membership on (1) the relationship between social support and depressive symptoms and (2) the relationship between social support and cancer worries, we conducted two moderation analyses. Regression and moderation models were adjusted for sex assigned at birth, income (in \u003cspan\u003e$\u003c/span\u003e1000s), and insurance (no insurance, private insurance, Tricare, Medicare, Medicaid, and county hospital insurance). Continuous measures \u0026ndash; including perceived social support, depressive symptom severity, and cancer worries \u0026ndash; were mean-centered for moderation analyses. All statistical analyses were conducted in IBM SPSS Statistics 28.0 [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], with moderation analyses performed using the PROCESS macro for SPSS (version 4.0) [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Results were deemed statistically significant at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSample Characteristics\u003c/h2\u003e \u003cp\u003eThe largest group in the sample was OAs (41.3%), followed by OAWOCs (30.0%), YAWOCs (19.2%), YAs (5.5%), and OA/YAs (4.0%). Participants predominantly identified as assigned female at birth (86.5%), non-Hispanic (86.5%), and White (72.3%). The average age of the sample was 50\u0026thinsp;\u0026plusmn;\u0026thinsp;14.4 years, with an income (in \u003cspan\u003e$\u003c/span\u003e1,000s) of 80.97\u0026thinsp;\u0026plusmn;\u0026thinsp;32.33. Most participants had private insurance (69.8%), and the most common diagnosis among cancer patients was breast (48.0%). The average level of cancer worries was 3.84\u0026thinsp;\u0026plusmn;\u0026thinsp;2.62, while that for CES-D-10 scores was 6.75\u0026thinsp;\u0026plusmn;\u0026thinsp;5.55, and for social support was 10.18\u0026thinsp;\u0026plusmn;\u0026thinsp;2.60. Complete descriptive statistics for the full sample and by groups are in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive Statistics for Full Sample and by Group Membership\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"14\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\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\u003eFull Sample\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6666)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eOAWOC\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1999)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eYAWOC\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1279)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eOA\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2756)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eOA/YA\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;268)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c14\" namest=\"c12\"\u003e \u003cp\u003eYA\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;364)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c14\" namest=\"c14\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e59.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e52.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e9.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e33.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e4.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c14\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome (\u003cspan\u003e$\u003c/span\u003e1000s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e78.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e30.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e81.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e32.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e83.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e33.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e77.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e29.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c14\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial Support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e9.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e10.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c14\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCancer Worries\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e5.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e3.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c14\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCES-D-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e6.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e8.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e6.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c14\" namest=\"c14\"\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\u003e\u003cb\u003eFreq.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eFreq.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eFreq.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eFreq.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eFreq.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003eFreq.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c14\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \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 \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c14\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e82.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e84.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e77.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e86.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e86.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c14\" namest=\"c14\"\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\u003e1263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e617\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e22.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e13.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e13.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c14\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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 \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c14\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e68.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e66.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e78.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e76.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e62.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c14\" namest=\"c14\"\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\u003e660\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e10.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e10.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c14\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c14\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmerican Indian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c14\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNative Hawaiian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c14\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthnicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \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 \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c14\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e85.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e80.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e91.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e91.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e76.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c14\" namest=\"c14\"\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\u003e897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e9.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e23.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c14\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \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 \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c14\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrivate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e71.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e80.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e62.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e75.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e75.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c14\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedicare\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e28.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e13.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c14\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c14\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCounty Hospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c14\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedicaid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c14\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTricare\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c14\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"14\"\u003e\u003cb\u003eOAWOC\u003c/b\u003e\u0026thinsp;=\u0026thinsp;Older adults without cancer, \u003cb\u003eYAWOC\u003c/b\u003e\u0026thinsp;=\u0026thinsp;Young adults without cancer, \u003cb\u003eOA\u003c/b\u003e\u0026thinsp;=\u0026thinsp;Older adults with cancer, \u003cb\u003eOA/YA\u003c/b\u003e\u0026thinsp;=\u0026thinsp;Older adults who were survivors of young-adult cancer, \u003cb\u003eYA\u003c/b\u003e\u0026thinsp;=\u0026thinsp;Young adults with cancer. Percentages may not sum to 100 due to missing data\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eSocial Support and Depressive Symptoms\u003c/h2\u003e \u003cp\u003eRegression analysis in which depressive symptom severity specified as the outcome was statistically significant (\u003cem\u003eF\u003c/em\u003e[12, 6231]\u0026thinsp;=\u0026thinsp;104.180, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), suggesting that the model covariates could explain approximately 17% of the variance in CES-D-10 scores (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.167). The main effect of perceived social support was significant (B = -0.756, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), such that more social support was associated with lower depressive symptoms, on average. Relative to YAs, all levels of group membership had significantly less depressive symptom severity, on average (OAWOCs: B = -2.326, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001; YAWOCs: B = -1.666, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001; OAs: B = -1.132, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001; and OA/YA: B = -0.831, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.047). All other covariates, including sex, income, and insurance status, were also significantly related to CES-D-10 scores. Specifically, those identifying as assigned male at birth (B = -1.751, \u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;.001) and having higher income (B = -0.007, \u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;.001) were associated with lower CES-D-10 scores. Compared to having no insurance, having private insurance (B = -0.830, \u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;.006) or Medicare (B = -0.714, \u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;.036) was associated with lower CES-D-10 scores, while having Medicaid (B\u0026thinsp;=\u0026thinsp;1.836, \u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;.001) was significantly associated with higher CES-D-10 scores. However, neither Tricare nor County Hospital insurance was related to depressive symptoms. Full regression results are in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLinear Regression and Moderation Results for Depressive Symptom and Cancer-Related Worry\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e95% CI for B\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csup\u003ea\u003c/sup\u003eLinear Regression: CES-D-10\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e-0.806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.707\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOAWOC (vs. YA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-2.326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e-2.910\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.743\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYAWOC (vs. YA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-1.666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e-2.272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOA (vs. YA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-1.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e-1.709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.555\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOA/YA (vs. YA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e-1.653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale (vs. female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-1.751\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e-2.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.425\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome (\u003cspan\u003e$\u003c/span\u003e1000s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e-0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrivate insurance (vs. no insurance)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.830\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e-1.421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.239\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedicare (vs. no insurance)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e-1.381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCounty hospital (vs. no insurance)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e-0.268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.492\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedicaid (vs. no insurance)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt; .001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.795\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.877\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTricare (vs. no insurance)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e-1.624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.732\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e\u003cb\u003eModerated Regression: CES-D-10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eB\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eLower\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003eUpper\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSocial support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.776\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-0.614\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eOAWOC (vs. YA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eYAWOC (vs. YA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.468\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eOA (vs. YA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2.036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eOA/YA (vs. YA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2.953\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSocial support \u0026times; OAWOC (vs. YA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSocial support \u0026times; YAWOC (vs. YA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSocial support \u0026times; OA (vs. YA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-0.130\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSocial support \u0026times; OA/YA (vs. YA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-0.042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csup\u003e\u003cb\u003ec\u003c/b\u003e\u003c/sup\u003e\u003cb\u003eLinear Regression: Cancer Worries\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eB\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003eLower\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eUpper\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e-0.174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.125\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOAWOC (vs. YA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-1.947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e-2.237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.658\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYAWOC (vs. YA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-1.392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e-1.693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.091\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOA (vs. YA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e-1.155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.583\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOA/YA (vs. YA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e-1.335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.521\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale (vs. female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e-0.917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.593\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome (\u003cspan\u003e$\u003c/span\u003e1000s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e-0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrivate insurance (vs. no insurance)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.616\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e-0.910\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.323\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedicare (vs. no insurance)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-1.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e-1.390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.728\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCounty hospital (vs. no insurance)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e-0.436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.437\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedicaid (vs. no insurance)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e-0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.945\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTricare (vs. no insurance)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e-0.992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.181\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csup\u003e\u003cb\u003ed\u003c/b\u003e\u003c/sup\u003e\u003cb\u003eModerated Regression: Cancer Worries\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eB\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003eLower\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eUpper\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e-0.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.065\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOAWOC (vs. YA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.725\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYAWOC (vs. YA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.222\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOA (vs. YA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.301\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOA/YA (vs. YA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.955\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1.664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.246\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial support \u0026times; OAWOC (vs. YA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e-0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial support \u0026times; YAWOC (vs. YA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e-0.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial support \u0026times; OA (vs. YA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e-0.253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial support \u0026times; OA/YA (vs. YA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e-0.244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003ea\u003c/sup\u003e\u003cem\u003eF\u003c/em\u003e(16,6227)\u0026thinsp;=\u0026thinsp;79.427, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.170\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003eb\u003c/sup\u003e\u003cem\u003eF\u003c/em\u003e(16,6211)\u0026thinsp;=\u0026thinsp;37.483, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.088\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003e\u003cem\u003ec\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eF\u003c/em\u003e(12,6231)\u0026thinsp;=\u0026thinsp;104.18, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.167\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003ed\u003c/sup\u003e\u003cem\u003eF\u003c/em\u003e(12,6215)\u0026thinsp;=\u0026thinsp;49.020, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.086\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFollowing this, a moderation analysis was conducted to examine the interaction between group membership and social support. Group membership emerged as a significant moderator of the relationship between social support and depressive symptoms (\u003cem\u003eF (\u003c/em\u003e16, 6227)\u0026thinsp;=\u0026thinsp;79.43, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), adjusting for the same covariates included in the linear regression analysis. Compared to YAs, all other groups, except YAWOCs, negatively moderated the relationship (OAWOC: B = -0.16, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.028; OA: B = -0.36, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.002; OA/YA: B = -0.28, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.021), such that the inverse relationship between social support and depressive symptoms was lower in magnitude for OAWOCs, OAs, and OA/YAs in comparison to YAs, on average. No difference in magnitude existed in the social support-depressive symptoms relationship between YAs and YAWOCs (B = -0.01, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.928). Full moderation results are in Table\u0026nbsp;3, with a graphical depiction of the moderating relationship provided in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eSocial Support and Cancer-Related Worry\u003c/h2\u003e \u003cp\u003eRegression analysis in which cancer worries specified as the outcome was statistically significant (\u003cem\u003eF [\u003c/em\u003e12, 6215]\u0026thinsp;=\u0026thinsp;49.020, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). Together, the model covariates explained approximately 9% of the variance in cancer worries (\u003cem\u003eR\u003c/em\u003e2\u0026thinsp;=\u0026thinsp;.086). Similar to the first regression analysis, the main effect of social support was statistically significant (B = -0.149, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), such that greater social support was associated with fewer cancer worries. Relative to YAs, all other levels of group membership had significantly less cancer worries, on average (OAWOCs: B = -1.947, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001; YAWOCs: B = -1.392, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001; OAs: B = -0.869, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001; and OA/YA: B = -0.928, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). Additionally, those identifying as male at birth (B = -0.755, \u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;.001) tended to have fewer cancer worries. Compared to having no insurance, having private insurance (B = -0.6116, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) or Medicare (B = -1.059, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) was associated with lower cancer worries, on average. The results for the second regression analysis are in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eAfter adjusting for covariates, the moderating effect of group membership on the relationship between social support and cancer worries was significant (\u003cem\u003eF\u003c/em\u003e [16, 6211]\u0026thinsp;=\u0026thinsp;37.48, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). Contrary to the moderation findings with depressive symptoms specified as the outcome, there was no significant difference between OAWOCs and YAs (B = -0.05, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.154). Compared to YAs, significant interactions between social support and group membership emerged for YAWOCs (B = -0.06, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.040), OAs (B = -0.14, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.014), and OA/YAs (B = -0.13, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.032). Together, these results suggest that while the magnitude of the inverse relationship between social support and cancer worries significantly differed between YAs and YAWOCs, OAs, and OA/Yas, no differences existed between OAWOCs and YAs. That is, the effect of social support on cancer worries was less significant for YAWOCs, OAs, and OA/YAs compared to YAs. Results from the second moderation analysis are in Table\u0026nbsp;3, with a graphical depiction of the effect presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study evaluated the relationship between social support, cancer worries, and depressive symptoms among a large sample of patients seeking genetic counseling for hereditary cancer. Our participants included YAs, OA/YAs, OAs, and their two undiagnosed comparison groups: YAWOCs and OAWOCs. Our primary aims were 1) to examine whether social support buffers against depressive symptoms and cancer worries and 2) whether these buffering effects would be the strongest for YAs. Our findings for Aim 1 supported our hypothesis, suggesting that regardless of a patient\u0026rsquo;s age, diagnosis status, sex, income, and insurance status, social support ameliorates the severity of both depressive symptoms and cancer-related worries. To our knowledge, our study is the first to account for several demographic variables while assessing the clinical significance of how social support mitigates depressive symptoms and cancer worries in patients undergoing genetic counseling. With carefully controlled confounds, our results underscore the importance of social support in managing the psychological burden associated with cancer survivorship, in the context of seeking genetic counseling for hereditary cancer [\u003cspan additionalcitationids=\"CR22 CR23 CR24\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFor Aim 2, our findings on depressive symptoms partially supported our hypothesis, indicating that although social support mitigates depressive symptoms significantly more for YAs than OA/YAs, OAs, and OAWOCS, these effects do not differ between YAs and YAWOCs. As such, YAs and YAWOCs struggling with depressive symptoms would particularly benefit from high levels of social support compared to the rest of the age and cancer groups. To interpret this finding, we considered previous research revealing that depression was more prevalent among young cancer patients with limited social support, suggesting that the experience of surviving cancer and/or hereditary cancer testing for YAs is often tremendously isolating [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Combining such experiences with young age (a predisposing factor to loneliness [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]), it is unsurprising that strong support networks are crucial for relieving depressive symptoms in these two groups. As part of Aim 2, the findings on cancer-related worries partially supported our hypothesis, suggesting that social support would be most effective at mitigating fears of cancer recurrence for YAs and fears of incurring cancer for OAWOCs compared to the rest of the groups. Although an unexpected outcome, our sample characteristics might offer a potential explanation. Despite reporting the lowest CES-D-10 and cancer worries scores, OAWOCs also demonstrated the lowest level of social support relative to the rest of the group. Wang and colleagues (2014) discovered that if high-stress individuals also reported high support, the impact of stress on their depression was much smaller than those of low-stress and low-support [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. This research provides some evidence to suggest that an inadequate baseline social network for OAWOCs means receiving more social support may bring significant improvements in their cancer worries levels. Such findings also imply that for OAWOCs, cancer worries might be a particularly burdensome psychological concern as they undergo genetic counseling for hereditary cancer risks.\u003c/p\u003e \u003cp\u003eIn sum, our findings underscore the differential buffering effects of social support on depressive symptoms and cancer-related worries among various groups. By demonstrating the specific dynamic of these effects among patient groups across different ages (young adult versus older adult patients) and by cancer diagnosis status (diagnosed versus undiagnosed), we found that social support buffers depressive symptoms most significantly for YAs and YAWOCs, and respectively, cancer worries for YAs and OAWOCs.\u003c/p\u003e \u003cp\u003eFinally, in addition to the above findings, our results indicated that patients with elevated depressive symptoms and cancer-related worries included those who were YAs, those identified as female, those with low household incomes, and those with no insurance. These findings are consistent with previous research on the socio-demographic factors predisposing patients to increased risks for depression and cancer worries, including age, sex assigned at birth, and socioeconomic status [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eClinical Recommendations and Future Directions\u003c/h2\u003e \u003cp\u003eFirst, although cancer clinics should emphasize social support groups for all patients, a more tailored and age-appropriate support network may be most important for managing depression and fear of cancer recurrence for YAs. Second, cancer genetics clinics consider collaborating closely with mental health providers to implement a specialized support network for managing depression for YAWOCs and cancer worries for OAWOCs. Third, oncology and cancer genetic clinics should continue incorporating screeners for depression, cancer worries, and social support into routine medical care.\u003c/p\u003e \u003cp\u003eThis research would benefit from future studies that attempt to replicate the above buffering dynamics of social support on depressive symptoms and cancer worries in patients with other cancer types, as many of our patient participants were diagnosed with breast cancer. Studies with analyses by the duration of time living with cancer (for diagnosed patients) and by risk levels (for undiagnosed patients) would also further our understanding of the differential impacts of social support. Lastly, it may be clinically useful to assess the actual impact of increasing social support activities (e.g., through attendance in group therapy or peer support activities) on depressive symptoms and cancer worries severity in high-risk patients (e.g., YAs).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eWhile findings from the present study provided a deeper understanding of social support as a protective factor, there are limitations to note. We categorized diagnosis status as a binary variable (yes/no), which omitted the inherent heterogeneity in cancer diagnoses. We generated the income variable based on zip code data \u0026ndash; a proxy sometimes unrepresentative of actual income. Additionally, patients referred to genetic counseling might already be at increased risk for cancer regardless of diagnosis status, combined with the fact that our participant samples were predominantly female-identifying patients with breast cancers, could pose limits to the external validity of our results. Our outcomes might be affected by selection bias to some degree; and thus, might not be generalizable to cancer patients who are not seeking genetic counseling or those without a family history of cancer. Moreover, cross-sectional data analyses were available for patients who fully completed the pre-appointment questionnaires only, introducing nonresponse bias and limiting our ability to infer strong causality. Finally, our total scores on CES-D-10, cancer worries, and social support were pre-computed by the cancer genetics system before statistical analyses, preventing scale reliability analyses. Nevertheless, the high-volume sample size (\u0026gt;\u0026thinsp;6000) and naturalistic clinical data collected at various locations bolster the ecological validity of our findings.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding and Competing Interests:\u0026nbsp;\u003c/strong\u003eWe have no funding source and competing interest to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u0026nbsp;\u003c/strong\u003eAll authors contributed to the study conception and design. Material preparation and analysis were performed by Sally Ho and Jayme M. Palka. The first draft of the manuscript was written by Sally Ho. All authors commented on previous versions of the manuscript, read, and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability:\u0026nbsp;\u003c/strong\u003eThe datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHamilton JG, Lobel M, Moyer A. Emotional distress following genetic testing for hereditary breast and ovarian cancer: a meta-analytic review. Health Psychol. 2009;28(4):510.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchwartz MD, Lerman C, Brogan B, Peshkin BN, Halbert H, DeMarco C, Isaacs T, C. Impact of BRCA1/BRCA2 counseling and testing on newly diagnosed breast cancer patients. J Clin Oncol. 2004;22(10):1823\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchlich-Bakker KJ, ten Kroode HF, Ausems MG. 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Psychol Trauma: Theory Res Pract Policy. 2020;12(S1):58.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang X, Cai L, Qian J, Peng J. Social support moderates stress effects on depression. Int J mental health Syst. 2014;8(1):1\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRiedl D, Sch\u0026uuml;\u0026szlig;ler G. Factors associated with and risk factors for depression in cancer patients\u0026ndash;A systematic literature review. Translational Oncol. 2022;16:101328.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSimonelli LE, Siegel SD, Duffy NM. Fear of cancer recurrence: a theoretical review and its relevance for clinical presentation and management. Psycho-oncology. 2017;26(10):1444\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-cancer-survivorship","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jcsu","sideBox":"Learn more about [Journal of Cancer Survivorship](https://www.springer.com/journal/11764)","snPcode":"11764","submissionUrl":"https://submission.nature.com/new-submission/11764/3","title":"Journal of Cancer Survivorship","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"depressive symptoms, cancer worries, social support, young adults, genetic counseling","lastPublishedDoi":"10.21203/rs.3.rs-3031154/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3031154/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose:\u003c/strong\u003e Social support is a crucial protective factor against psychological concerns in patients with cancer. However, there is limited knowledge regarding the differential impacts of social support on cancer worries and depressive symptoms in patients undergoing genetic counseling for hereditary cancer. The current study utilized a high-volume database from a multi-site cancer genetics clinic to assess the impact of perceived social support on depressive symptoms and cancer worries among patients of different age groups (young versus older patients) and diagnosis status (diagnosed survivors versus undiagnosed).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: 6,666 patients completed brief assessments of depressive symptoms, cancer worries, social support, and demographic questionnaires as part of routine clinical care between October 2016 and October 2020. Logistics and moderated regression were used to analyze the relationships between social support, depressive symptoms, and cancer worries.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Increased social support was associated with fewer depressive symptoms and fewer cancer worries across all patients. Social support mitigated depressive symptoms most significantly for young adult patients with and without cancer. Social support mitigated cancer worries most significantly for young adults with cancer and older adults without cancer.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e While results were mixed, general findings upheld original hypotheses. Social support buffered depressive symptoms and cancer worries differentially for patients of different ages and different disease status.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImplications for Cancer Survivors: \u003c/strong\u003eSocial support groups are beneficial for all patients and should be emphasized by cancer clinics. However, increasing patient-tailored and age-appropriate support networks will be crucial for managing depression and cancer worries for high-risk survivors: young adults with cancer.\u003c/p\u003e","manuscriptTitle":"The Dynamic Buffering of Social Support on Depressive Symptoms and Cancer Worries in Patients Seeking Cancer Genetic Counseling","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-23 15:44:45","doi":"10.21203/rs.3.rs-3031154/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Accepted","date":"2023-10-02T20:13:03+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-09-27T17:00:44+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Cancer Survivorship","date":"2023-09-26T21:19:59+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-cancer-survivorship","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jcsu","sideBox":"Learn more about [Journal of Cancer Survivorship](https://www.springer.com/journal/11764)","snPcode":"11764","submissionUrl":"https://submission.nature.com/new-submission/11764/3","title":"Journal of Cancer Survivorship","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"12ed0211-fb3d-4e20-a804-6deb1cf82e3f","owner":[],"postedDate":"January 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-03-25T15:05:23+00:00","versionOfRecord":{"articleIdentity":"rs-3031154","link":"https://doi.org/10.1007/s11764-023-01479-x","journal":{"identity":"journal-of-cancer-survivorship","isVorOnly":false,"title":"Journal of Cancer Survivorship"},"publishedOn":"2024-03-21 15:01:05","publishedOnDateReadable":"March 21st, 2024"},"versionCreatedAt":"2024-01-23 15:44:45","video":"","vorDoi":"10.1007/s11764-023-01479-x","vorDoiUrl":"https://doi.org/10.1007/s11764-023-01479-x","workflowStages":[]},"version":"v1","identity":"rs-3031154","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3031154","identity":"rs-3031154","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}