Double Burden, Double Risk: Depression–Frailty Synergy and All‑Cause Mortality in U.S. Cancer Survivors

Double Burden, Double Risk: Depression–Frailty Synergy and All‑Cause Mortality in U.S. Cancer Survivors | 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 Article Double Burden, Double Risk: Depression–Frailty Synergy and All‑Cause Mortality in U.S. Cancer Survivors Yaxin Huang, Yonggang Hu, Qin Li, Haoyu He, Hongyin Zhou This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6656702/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background Depression and frailty each predict excess mortality in cancer survivors, but their combined effect is undefined. We quantified the independent and joint associations of depression and frailty with allcause mortality in a nationally representative cohort of U.S. cancer survivors. Methods We pooled 22 waves (1997–2018) of the National Health Interview Survey and linked records to the National Death Index (followup through December 31 2019). Depression was defined by selfreport of a clinician diagnosis; frailty was assessed with the fiveitem FRAIL scale (frail = score 3–5). Surveyweighted Cox models estimated hazard ratios (HRs) for allcause mortality after adjustment for demographic, socioeconomic, and clinical covariates. Effect modification by age and sex was examined. Results Among 55,751 cancer survivors (mean age, 62.8 ± 15.2 year; 54.4% women), 11,084 (19.9%) had depression, 12,437 (22.3%) were frail, and 1,592 (2.9%) had both conditions. Over 471,838 personyears, 17,603 deaths occurred. Depression was associated with higher mortality (multivariable HR, 1.34; 9 % CI, 1.18–1.53), as was frailty (HR, 1.18; 9 % CI, 1.08–1.30). Survivors with coexisting depression + frailty had the greatest risk (HR, 1.38; 9 % CI, 1.26–1.51) compared with all other survivors; the relative excess was largest in those < 60 years of age (HR, 2.60; 9 % CI, 2.10–3.23; P for interaction < 0.001) and similar in women and men. Absolute 10year survival was 12 percentage points lower in the combinedphenotype group than in controls. Conclusions Depression and frailty independently—and synergistically—elevate allcause mortality among U.S. cancer survivors, with the strongest relative effect in younger adults. Concurrent screening for both conditions and deployment of integrated exercise, nutritional, and psychosocial interventions may improve longterm survival in this growing population. Biological sciences/Cancer/Cancer epidemiology Biological sciences/Psychology/Human behaviour Health sciences/Diseases/Cancer Health sciences/Health care/Geriatrics Health sciences/Health care/Public health cancer survivorship depression frailty allcause mortality National Health Interview Survey epidemiology Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Cancer survivorship has become a distinct phase of the oncologic continuum: more than 18 million Americans—5.4% of the population—are now living with a history of cancer, and this number is projected to exceed 26 million by 2040 1 .​ As this population ages, attention has shifted from acute treatment to the chronic sequelae that compromise longevity and quality of life. Depression is one of the most common and debilitating sequelae. Pooled estimates indicate that roughly one third of cancer survivors meet clinical criteria for depression 2 ,​ a prevalence severalfold higher than that in the general population and one that persists long after completion of therapy 3,4 .​ Metaanalyses show that depression increases allcause mortality in survivors by 2030 %, independent of tumor stage or treatment modality 5 ,​ yet routine screening is inconsistently implemented despite longstanding guideline recommendations 6 .​ Frailty, a multidimensional syndrome of diminished physiologic reserve, is increasingly recognized in oncology. Operationalized either as the Fried physical phenotype​ 7 or as a deficitaccumulation index 8 ,​ frailty affects 40–5 % of older survivors and up to one quarter of middleaged adults with cancer 9 ,​ conferring 20–0 % excess mortality risk and complicating treatment decisionmaking 10 .​ Nonetheless, frailty screening is not yet standard in survivorship clinics, and evidencebased interventions remain nascent. Depression and frailty frequently cooccur and may share pathobiologic pathways—chronic inflammation, sarcopenia, endocrine dysregulation—that accelerate biological aging 1 1 . In geriatric cohorts, their coexistence amplifies the risk of disability and death beyond the sum of their individual effects 1 2 .​ However, data in cancer survivors are scarce, limited to singlecenter studies, short followup, or restricted age ranges; no prior investigation has quantified the longterm mortality consequences of the joint phenotype in a nationally representative survivorship sample. The present study addresses this critical gap. Leveraging 22 waves of the National Health Interview Survey linked to the National Death Index, we examined the independent and combined associations of physiciandiagnosed depression and FRAILdefined frailty with allcause mortality among U.S. cancer survivors. By integrating validated mentalhealth and geriatric constructs within a populationbased framework, our work aims to (1) characterize the prevalence of the joint phenotype across demographic strata, (2) evaluate its impact on longterm survival, and (3) inform riskstratified, multimodal interventions—such as exercise prehabilitation 13,14 ,​ nutritional support, and digital psychosocial programs—that are rapidly emerging but remain undertested in survivorship care. Clarifying the prognostic synergy between depression and frailty has the potential to reshape screening algorithms, sharpen clinical guideline recommendations, and ultimately extend healthy life expectancy for the growing survivor population. Methods Study design and population selection We conducted a secondary analysis of the National Health Interview Survey (NHIS), an annual multistage probability survey administered by the National Center for Health Statistics to assess the health of the noninstitutionalized U.S. population. Each yearly sample is selected with stratification and clustering and oversamples racial and ethnic minority groups. All respondents provide written informed consent, and the protocol is approved annually by the NCHS institutional review board. We pooled data from 22 waves (1997–2018). Adults who completed the Sample Adult questionnaire, which captures physiciandiagnosed cancer, depression, frailty, and sociodemographic factors, were eligible. NHIS records were probabilistically linked to the National Death Index to ascertain vital status through December 31, 2020. Of 671,696 adults surveyed, we excluded 28,661 participants missing depression or frailty data, 47,238 missing covariates, and 5,542 not linked to mortality files, leaving 590,255 participants for analysis (Fig. 1 ). Among them, 55,751 (9.4%) reported a history of cancer. Survey weights, strata, and primary sampling units were applied to generate nationally representative estimates. Depression Assessment Depressive status was determined with the dedicated mentalhealth item included in every NHIS Sample Adult questionnaire from 1997 through 2018: “Has a doctor or other health professional ever told you that you had depression?” Respondents answering “yes” were classified as having physiciandiagnosed depression. This selfreported diagnosis shows good agreement with the Structured Clinical Interview for DSMIV (κ ≈ 0.66; sensitivity 74 %, specificity 95 %) 15 .​ Although the full nineitem Patient Health Questionnaire (PHQ-9) was fielded only in selected, later NHIS waves, its wellestablished psychometric properties (Cronbach’s α ≈ 0.89; optimal screening threshold ≥ 10 yielding88 % sensitivity and85 % specificity for major depressive disorder)​ 6 informed our a priori definition of clinically meaningful depression. To ensure uniform ascertainment across all 22 survey years—and to avoid differential misclassification—our primary exposure relied on the physiciandiagnosed depression variable, which captures professionally confirmed, lifetime disease and has been widely used in NHISbased epidemiologic research. All NHIS interviews are conducted by centrally trained personnel under a rigorous qualityassurance program, minimizing information bias. Frailty Assessment Using the FRAIL Scale Frailty status was determined using a modified version of the FRAIL scale, a well-established screening tool originally proposed by the Geriatric Advisory Panel of the International Society for Nutrition and Aging 16 , 17 . In this analysis, we operationalized frailty using self-reported data collected from the NHIS between 1997 and 2018. The FRAIL scale includes five domains: fatigue, resistance, ambulation, comorbid illness burden, and low body mass index (BMI) 18 . Fatigue was identified based on participants’ responses to NHIS survey items assessing how often they felt unusually tired or lacking in energy over a specified timeframe. Frequent or sustained fatigue was scored as 1, whereas minimal or no fatigue was scored as 0. Resistance was evaluated through questions on the ability to climb 12 stairs without help or the use of mobility aids. Ambulation was assessed by inquiring whether participants had difficulty walking 100 yards on a flat surface (equivalent to the length of a football field or city block) without assistance. For both domains, the presence of any reported difficulty was scored as 1; absence of difficulty was scored as 0. The illness component reflected multimorbidity. Participants were assigned a score of 1 if they reported three or more chronic health conditions from a list of 11 physician-diagnosed diseases: angina, arthritis, asthma, chronic obstructive pulmonary disease, coronary heart disease, dementia, diabetes, myocardial infarction, hyperlipidemia, hypertension, and stroke. Reporting fewer than five conditions resulted in a score of 0. Low BMI was defined as a body mass index below 18.5 kg/m². Participants meeting this criterion received a score of 1; all others were scored as 0. Scores across the five components were summed to yield a total frailty score ranging from 0 to 5. Based on established cutoffs, participants were categorized as frail (score 3–5), pre-frail (score 1–2), or robust (score 0) 17 . Ethical Considerations The NHIS is conducted by the NCHS and has been approved by the NCHS Research Ethics Review Board. All participants provided informed consent at the time of the interview. The current study used publicly available, de-identified NHIS data linked with the National Death Index. As such, this secondary analysis was deemed exempt from institutional review board (IRB) oversight. Covariates The following sociodemographic covariates were selected a priori because of their documented associations with mortality and their potential to confound the relation between psychosocial exposures and survival. All information was obtained during the NHIS household interview by trained personnel using standardized, computerassisted instruments. Age and Sex. Age at interview was recorded in single years and was modeled as a continuous variable; for descriptive purposes, distributions are shown in three strata (18–39, 40–64, and ≥ 65 year). Sex was selfreported as male or female; no respondent recorded another option during the study period. Race and Ethnicity. Participants selfidentified one or more racial groups and indicated whether they were of Hispanic or Latino origin. To preserve statistical power while reflecting U.S. demographic patterns, responses were collapsed into four mutually exclusive categories: nonHispanic White, nonHispanic Black, nonHispanic Asian, and Other (which combined multiracial respondents and those reporting Native Hawaiian, Pacific Islander, American Indian, or Alaska Native ancestry). When weighted, this scheme reproduces census population estimates for each survey year. Educational Attainment. The highest diploma or degree completed was grouped as less than high school, highschool graduate (including GED), and more than high school (some college, associate’s degree, or college graduate). Educational level functions as a stable indicator of socioeconomic position across the life course. HealthInsurance Status. Current coverage was ascertained with a structured series of items on private insurance and public programs (Medicare, Medicaid, Children’s Health Insurance Program, military, and other government plans). Coverage was dichotomized as insured (any plan) versus uninsured—an approach validated against administrative enrollment files. Marital Status. Respondents classified themselves as married, widowed, divorced, separated, never married, or living with a partner. Consistent with prior NHIS mortality analyses, categories were collapsed as married (married or living with a partner) versus unmarried (all other responses). Geographic Region. Residence was assigned to the Northeast, Midwest, South, or West on the basis of the state Federal Information Processing Standard code, thereby accounting for regional differences in cancer epidemiology, healthcare access, and survival. Assessment of Cancer History. Information on cancer history was based on self-reported responses in the NHIS. Participants who responded “yes” to the question “Have you ever been told by a doctor or other health professional that you had cancer or a malignancy of any kind?” were classified as having a history of cancer. Time since cancer diagnosis was calculated as the difference between the year of interview and the self-reported year of the first cancer diagnosis, and was categorized as < 2 years or ≥ 2 years. The number of cancer diagnoses was derived from the total number of different cancer types reported by each participant, and was classified as 1 or ≥ 2. Both variables were included as categorical covariates in multivariable analyses to account for the potential confounding effect of cancer history. Missing covariate values were rare (≤ 0.5% for any single variable) and were imputed with multivariate chained equations to retain the full analytic cohort. All covariates were entered simultaneously in multivariable models, and survey design variables—sampling strata, primary sampling units, and weights—were applied to generate nationally representative estimates while accounting for the complex sampling structure. All-cause mortality Mortality follow-up for NHIS participants was achieved through linkage to the NDI, covering deaths through December 31, 2019. This linkage, conducted by the NCHS as part of its Data Linkage Program, employed probabilistic record-matching techniques to connect NHIS survey data with NDI death certificate records. The process enabled accurate determination of vital status and cause-specific mortality. Strict confidentiality procedures were observed throughout the linkage process. In the publicly available Linked Mortality Files (LMF), data perturbation methods were applied to minimize re-identification risk. For a subset of participants, synthetic values were substituted for certain variables, such as follow-up time or cause of death. Importantly, vital status information was preserved without alteration. Using the NHIS Linked Mortality Files, we conducted analyses to evaluate the association between frailty status and all-cause mortality. This approach integrated prospectively collected health and demographic data with long-term mortality follow-up, allowing for a robust examination of mortality risk in relation to frailty. Statistical analysis All analyses were performed with SAS 9.4 (SAS Institute) and R 4.3.1 (R Foundation for Statistical Computing). NHIS sampling weights, strata, and primary sampling units were applied throughout to account for the complex survey design. Vital status was ascertained through linkage to the National Death Index and followed through December 31 2019. Baseline comparisons. Continuous variables were expressed as means ± standard deviations; categorical variables as unweighted counts with weighted percentages. Baseline characteristics were compared separately for (i) frailty (nonfrail vs. frail), (ii) depression (no depression vs. depression), and (iii) the combined phenotype (coexisting depression + frailty vs. all other participants). Independentsample t tests were used for continuous variables and Pearson’s chisquare tests for categorical variables. To control the familywise error rate generated by the three parallel sets of comparisons, a Bonferroniadjusted threshold of P < 0.017 (0.05 ÷ 3) was adopted. Survival estimation. Persontime accrued from the interview date until death or censoring on December 31 2019. Weighted Kaplan–Meier curves were generated for each exposure category, and differences were assessed with the logrank test. Median survival times with 5 % confidence intervals (CIs) were reported when estimable. Cox regression. Surveyweighted Cox proportionalhazards models estimated the association between each exposure and allcause mortality. Model 1 adjusted for age and sex. Model 2 further adjusted for race or ethnic group, educational attainment, healthinsurance status, marital status, and geographic region. A multiplicative interaction term (depression × frailty) was added to Model 2 to test for effect modification. A sensitivity model treated the fourlevel joint exposure (neither condition, depression alone, frailty alone, both conditions) as a categorical predictor, with the “neither” group as reference. Model diagnostics. The proportionalhazards assumption was examined with Schoenfeld residuals and was not violated for any covariate. Robust (sandwich) variance estimators appropriate for complex survey data provided standard errors and CIs. Statistical significance. All tests were twosided. Significance was defined as P < 0.017 for baseline comparisons and P < 0.05 for all survival models. Missing data. Because the analytic cohort was restricted to respondents with complete information on every study variable, no imputation was required. Results Study population characteristics A total of 55,751 cancer survivors were included in the analytic cohort; of these, 1,592 (2.9%) had coexisting depression and frailty, whereas 54,159 (97.1%) constituted the control group (no condition, depression alone, or frailty alone) (Table 1 ). Survivors with the combined phenotype were younger than controls (mean age, 54.6 ± 15.2 year vs. 63.1 ± 15.1 year; P < 0.001) and were more often women (71.6% vs. 44.6%; P < 0.001). Table 1 Baseline Characteristics of Cancer Survivors according to Coexisting Depression and Frailty, NHIS 1997–2018 Characteristics Control group 54,159 (97.1%) Depression + Frailty 1,592 (2.9%) P -value a Age, years 63.1 (15.1) 54.6 (15.2) < 0.001 Sex, % < 0.001 Women 24,190 (44.6) 1,140 (71.6) Men 29,969 (55.4) 452 (28.4) Race/ethnicity, % < 0.001 White 38,157 (70.5) 1,141 (71.7) Black 2,261 (4.2) 101 (6.3) Asian 183 (0.3) 7 (0.5) Other 13,558 (25.0) 343 (21.5) Education level, % < 0.001 High school 18,414 (34.0) 466 (29.3) Health insurance, % < 0.001 Yes 53,307 (98.4) 1,436 (90.2) No 852 (1.6) 156 (9.8) Marital status, % < 0.001 Married 32,499 (60.0) 542 (34.0) Unmarried 21,660 (40.0) 1,050 (66.0) Region, % 0.288 Northeast 19,632 (36.2) 249 (15.6) Midwest 9,322 (17.2) 381 (23.9) South 15,614 (28.8) 577 (36.2) West 9,591 (17.8) 385 (24.3) Time since cancer diagnosis < 0.001 < 2years 731 (13.5) 296 (18.6) ≥ 2years 53,428 (86.5) 1,296 (81.4) Number of cancer diagnoses < 0.001 1 42,406 (78.3) 1,319 (82.9) ≥ 2 11,753 (21.7) 273 (17.1) Values are means (SDs) for continuous variables and percentages for categorical variables. a The control group comprised individuals without depression or frailty, as well as those with depression alone or frailty alone. b Group differences were assessed using the t-test for continuous variables and the chi-square test for categorical variables. The distribution of race or ethnic group differed modestly but significantly between groups ( P < 0.001). Survivors with coexisting depression and frailty had a slightly higher prevalence of Black race (6.3% vs. 4.2%) and a lower prevalence of the “Other” category (21.5% vs. 25.0%) than controls; the proportion who identified as White was similar (71.7% vs. 70.5%). Markers of socioeconomic status consistently favored controls. Participants with the combined phenotype were less likely to have education beyond high school (29.3% vs. 34.0%; P < 0.001), less likely to be insured (90.2% vs. 98.4%; P < 0.001), and less likely to be married (34.0% vs. 60.0%; P < 0.001). Geographic residence did not differ materially between groups (overall P = 0.29), although small absolute differences were observed: survivors with depression + frailty were somewhat more likely to live in the South (36.2% vs. 28.8%) and less likely to live in the Northeast (15.6% vs. 36.2%). Longitudinal Association between Depression and AllCause Mortality Depression was consistently linked to an elevated risk of death among cancer survivors (Fig. 2 and Table 2 ). In the age and sexadjusted model, the hazard of allcause mortality was more than twice as high in survivors with depression as in those without the disorder (hazard ratio [HR], 2.21;95 % confidence interval [CI], 1.94–2.31; P < 0.001). After additional adjustment for race or ethnic group, educational attainment, healthinsurance status, marital status, and geographic region, the association remained significant, although attenuated (multivariableadjusted HR, 1.3; 95 % CI, 1.18–1.53; P < 0.001). Table 2 Longitudinal Association of Depression with All-Cause Mortality among Cancer Survivors, NHIS 1997–2018 Frailty status Age- and sex-adjusted model a Multivariate adjusted model b HR (95%CI) P -value HR (95%CI) P -value Total cancer survivors Non-depression Ref. Ref. Depression 2.21 (1.94–2.31) < 0.001 1.34 (1.18–1.53) < 0.001 ≥ 60 years old Non-depression Ref. Ref. Depression 1.75 (1.48–2.07) < 0.001 1.37 (1.16–1.62) < 0.001 < 60 years old Non-depression Ref. Ref. Depression 2.75 (2.33–3.24) < 0.001 2.37 (2.01–2.81) < 0.001 Women Non-depression Ref. Ref. Depression 2.22 (1.98–2.48) < 0.001 1.80 (1.60–2.02) < 0.001 Men Non-depression Ref. Ref. Depression 1.99 (1.74–2.28) < 0.001 1.68 (1.46–1.92) < 0.001 a Cox regression model adjusted for age and sex. b Cox regression model adjusted for age, sex, race/ethnicity, education level, health insurance, marital status, region, time since cancer diagnosis, and number of cancer diagnoses. Effect sizes differed by age category. Among survivors younger than 60 years, depression was associated with more than a doubling of the mortality risk in the fully adjusted model (HR, 2.37; 95% CI, 2.01–2.81; P < 0.001), whereas the excess risk among those 60 years of age or older was smaller but still significant (HR, 1.37; 95% CI, 1.16–1.62; P < 0.001). A formal test for interaction confirmed stronger relative effects in the younger group ( P for interaction < 0.001). Results were directionally similar in sexstratified analyses. Depression conferred an 8 % higher risk of death in women (HR, 1.80; 9 % CI, 1.60–2.02; P < 0.001) and a 6 % higher risk in men (HR, 1.68; 9 % CI, 1.46–1.92; P < 0.001) after multivariable adjustment, with no significant heterogeneity by sex ( P for interaction = 0.19). These findings demonstrate that depression independently predicts allcause mortality in cancer survivors, and that the relative impact is greatest among those younger than 60 years. Longitudinal Association between Frailty and AllCause Mortality Frailty predicted an elevated risk of death among cancer survivors, although the magnitude of the association was more modest than that observed for depression (Fig. 3 and Table 3 ). In the age and sexadjusted model, frail survivors had a 1 % higher hazard of allcause mortality than their nonfrail counterparts (hazard ratio [HR], 1.71 95 % confidence interval [CI], 1.62–1.80; P < 0.001). After additional adjustment for race or ethnic group, educational attainment, healthinsurance status, marital status, geographic region, and depression, the association attenuated but remained significant (multivariableadjusted HR, 1.8; 95 % CI, 1.08–1.20; P = 0.01). Table 3 Longitudinal Association of Frailty with All-Cause Mortality among Cancer Survivors, NHIS 1997–2018 Frailty status Age- and sex-adjusted model a Multivariate adjusted model b HR (95%CI) P -value HR (95%CI) P -value Total cancer survivors Non-frailty Ref. Ref. Frailty 1.71 (1.62–1.80) < 0.001 1.18 (1.08–1.20) 0.010 ≥ 60 years old Non-frailty Ref. Ref. Frailty 1.74 (1.65–1.84) < 0.001 1.21 (1.13–1.30) < 0.001 < 60 years old Non-frailty Ref. Ref. Frailty 1.18 (1.02–1.38) 0.003 1.11 (0.96–1.30) 0.168 Women Non-frailty Ref. Ref. Frailty 1.94 (1.80–2.09) < 0.001 1.73 (1.61–1.87) < 0.001 Men Non-frailty Ref. Ref. Frailty 1.55 (1.45–1.67) < 0.001 1.51 (1.41–1.62) < 0.001 a Cox regression model adjusted for age and sex. b Cox regression model adjusted for age, sex, race/ethnicity, education level, health insurance, marital status, region, time since cancer diagnosis, and number of cancer diagnoses. Abbreviations: HR, hazards ratio; CI, confidence interval. Age modified the effect of frailty. Among survivors ≥ 60 years of age, frailty was associated with a 21% increase in mortality risk in the fully adjusted model (HR, 1.21; 95% CI, 1.13–1.30; P < 0.001). In contrast, the corresponding estimate in survivors < 60 years did not reach significance (HR, 1.11; 95% CI, 0.96–1.30; P = 0.17), suggesting that the adverse impact of frailty is largely confined to older adults ( P for interaction < 0.001). Sexstratified analyses showed stronger associations in women than in men. After full multivariable adjustment, frailty conferred a 7 % higher risk of death in women (HR, 1.73; 9 % CI, 1.61–1.87; P < 0.001) and a 5 % higher risk in men (HR, 1.51; 9 % CI, 1.41–1.62; P < 0.001). Although the formal test for interaction by sex did not achieve statistical significance ( P = 0.08), the point estimates indicate a potentially greater vulnerability among female survivors. Collectively, these findings indicate that frailty is an independent, albeit moderate, predictor of allcause mortality in cancer survivors, with the association most pronounced in women and in survivors aged 60 years or older. Joint Effect of Coexisting Depression and Frailty on Survival Figure 4 and Table 4 jointly illustrate the adverse prognostic influence of having both depression and frailty. Survival curves separated within the first few years after interview (Fig. 1 , Panel A) and remained widely apart throughout two decades of followup (logrank P < 0.001). At 10 years, absolute survival was ≈ 12 percentage points lower in survivors with the combined phenotype than in the control group; by 20 years, the gap exceeded 15 percentage points. Table 4 Longitudinal Joint Association of Depression and Frailty with All-Cause Mortality among Cancer Survivors, NHIS 1997–2018 Frailty status Age- and sex-adjusted model a Multivariate adjusted model b HR (95%CI) P -value HR (95%CI) P -value Total cancer survivors Control group c Ref. Ref. Depression + Frailty 2.33 (2.05–2.66) < 0.001 1.38 (1.26–1.51) < 0.001 ≥ 60 years old Control group Ref. Ref. Depression + Frailty 1.78 (1.61–1.98) < 0.001 1.45 (1.31–1.62) < 0.001 < 60 years old Control group Ref. Ref. Depression + Frailty 3.35 (2.71–4.14) < 0.001 2.60 (2.10–3.23) < 0.001 Women Control group Ref. Ref. Depression + Frailty 2.36 (2.00–2.79) < 0.001 1.81 (1.53–2.14) < 0.001 Men Control group Ref. Ref. Depression + Frailty 2.31 (1.87–2.84) < 0.001 1.87 (1.51–2.30) < 0.001 a Cox regression model adjusted for age and sex. b Cox regression model adjusted for age, sex, race/ethnicity, education level, health insurance, marital status, region, time since cancer diagnosis, and number of cancer diagnoses. c Group differences were assessed using the t-test for continuous variables and the chi-square test for categorical variables. Abbreviations: HR, hazards ratio; CI, confidence interval. In agespecific analyses the disparity was evident across strata but was particularly pronounced in younger survivors (Panels B and C). Multivariable Cox models confirmed this gradient: coexisting depression and frailty more than doubled the risk of death in survivors < 60 years of age (hazard ratio [HR], 2.60; 9 % confidence interval [CI], 2.10–3.23; P < 0.001), whereas the excess risk in those ≥ 60 years was 4 % (HR, 1.45; 9 % CI, 1.31–1.62; P < 0.001; P for interaction < 0.001). Sexstratified survival curves (Panels D and E) showed parallel patterns in women and men, and the multivariable HRs were virtually identical (women, 1.81; 9 % CI, 1.53–2.14; men, 1.87; 9 % CI, 1.51–2.30), with no significant interaction by sex ( P = 0.48). Overall, the coexistence of depression and frailty was associated with a 38% increase in allcause mortality after adjustment for demographic and socioeconomic covariates (HR, 1.38; 9 % CI, 1.26–1.51; P < 0.001). The persistently lower survival probabilities seen in Kaplan–Meier analyses, together with the elevated adjusted hazard ratios, underscore a robust and clinically meaningful joint effect that is most pronounced in survivors younger than 60 years. Discussion In this nationally representative cohort of 55 751 U.S. cancer survivors, depression and frailty each foretold excess allcause mortality, and their coexistence portended the poorest prognosis. After rigorous multivariable adjustment, the joint phenotype conferred a 3 % increase in the hazard of death and produced absolute survival deficits that widened steadily for two decades. The relative effect was most pronounced in survivors younger than 60 years, suggesting that psychosocial and functional vulnerability accelerates premature mortality even when lifeexpectancy is otherwise favorable. Our effect estimates for depression (multivariable HR, 1.34) align with two recent metaanalyses that reported 19–2 % excess mortality across solid and hematologic malignancies​ 1 9, 2 0 . Likewise, the 1 % relative increase associated with frailty is concordant with pooled estimates from contemporary cohort studies​ 2 1, 2 2 . Few investigations have evaluated the concurrent presence of both conditions. In geriatric populations, Soysal and colleagues described a bidirectional, amplifying relation between depression and frailty​ 2 3 , but ours is the first study to demonstrate a compounded effect on longterm survival in cancer survivors across the adult life span. Depression and frailty share pathophysiologic pathways—chronic inflammation, hypothalamic–pituitary–adrenal axis activation, and metabolic dysregulation—that accelerate catabolism and immunosenescence. Elevated interleukin-6 and Creactive protein concentrations have been independently linked to both conditions in oncologic settings​ 2 0, 2 4 , and sarcopenia, a hallmark of frailty, mediates inflammationdriven physical decline as well as depressive symptomatology​ 2 5 , 26 . The resulting reduction in physiologic reserve may impair tolerance to subsequent cancer therapy, heighten susceptibility to infection and cardiovascular disease, and limit engagement in healthpromoting behaviors. Additionally, depression undermines adherence to followup and reduces motivation to exercise or maintain adequate nutrition, thereby exacerbating frailty—a vicious cycle that reflects accelerated biological aging. Screening. The Patient Health Questionnaire-9 is validated in oncology populations​ 27 and can be combined with rapid frailty screens such as the Fried phenotype or deficitaccumulation index, both included in recent survivorship toolkits​ 2 2, 2 8 . The U.S. Preventive Services Task Force recommends routine depression screening in adults​, and our data argue for incorporating frailty assessment into the same clinical encounter. Neither the current National Comprehensive Cancer Network nor American Society of Clinical Oncology survivorship guidelines explicitly address this joint screening need​ 2 9 ; policy updates are warranted. Intervention. Exercise, whether aerobic, resistance, or qigongbased, consistently alleviates depressive symptoms and reverses frailty deficits in survivors​ 1 9, 3 0 . Recent metaanalyses demonstrate that multicomponent training reduces depression and anxiety by ≈ 0 % and improves quality of life​ 14 . Nutrition counseling and protein supplementation further augment gains in lean mass and functional status. Prehabilitation programs that integrate exercise, dietary optimization, and psychosocial support have reduced postoperative complications and length of stay in frail surgical candidates​ 31 ; analogous multimodal “rehabilitation” strategies are now being tested in longitudinal survivorship trials​ 32 . Digital health platforms—including appbased cognitive behavioral therapy and remote strengthtraining modules—extend reach to rural or mobilitylimited survivors and show promise in randomized controlled trials​ 33,34 . Risk Stratification and Resource Allocation. Our data indicate that survivors < 60 years with coexisting depression and frailty represent a highpriority subgroup. Younger adults typically engage with the workforce and may face financial toxicity that compounds psychosocial distress​ 3 5 . Embedding socialwork and financialnavigation services in survivorship clinics may mitigate this hidden burden and improve adherence to lifestyle interventions. First, mechanistic studies should dissect the temporal sequencing of depression, frailty, and inflammation by incorporating serial biomarker assessments and epigenetic clocks. Second, adaptive, factorial trials are needed to determine the optimal combination and timing of exercise, nutritional, and psychotherapeutic components for reversing the joint phenotype. Third, implementation science must evaluate pragmatic screening pathways and digital platforms across diverse health systems, with attention to rural equity and the digital divide​ 34 , 36 . Finally, costeffectiveness analyses will be essential for informing payers and policy makers about the value of integrated interventions. Strengths include the use of 22 NHIS waves linked to the National Death Index, yielding a large, nationally representative sample with adjudicated mortality; validated instruments for depression and frailty; and surveyweighted Cox models that preserve external validity. Limitations comprise singletimepoint exposure assessment, potential residual confounding by cancer stage or treatment, and misclassification inherent to selfreport. Nevertheless, nondifferential misclassification would bias results toward the null, and the observation of strong associations despite this bias underscores the robustness of our findings. Conclusion Coexisting depression and frailty identify a subgroup of cancer survivors at markedly elevated risk for premature death, with the greatest relative hazard observed in survivors younger than 60 years. Immediate clinical translation involves dual screening during routine survivorship visits and deployment of multimodal, behaviorally anchored interventions that target both conditions simultaneously. Aligning guideline recommendations with this evidence and investing in scalable programs—particularly those leveraging digital health—could appreciably extend healthy life expectancy for the rapidly growing population of cancer survivors. Declarations Data availability The data, codebook, and analytic code will not be made available as the data used in this study are from the publicly accessible NHANES database, available to researchers worldwide. The database can be accessed at https://www.cdc.gov/nchs/nhis.htm. Acknowledgments We thank the leaders of the Zigong First People’s Hospital for their full support during the implementation of the project. Author contributions The authors’ responsibilities were as follows—Ya-Xin Huang, and Yong-Gang Hu contributed to conceptualization, data curation, formal analysis, writing – original draft, and project administration; Qin Li was responsible for conceptualization, visualization and methodology. Hong-Yin Zhou was responsible for funding acquisition and investigation. Hong-Yin Zhou was responsible for supervision, validation and writing – review & editing. All authors declare that they have read and approved the final version of the manuscript. Funding The research received financial support from the Zigong First People’s Hospital. The findings and conclusions expressed in this article are those of the authors and do not necessarily represent the official position of the CDC or the U.S. Department of Health and Human Services. No private sponsors were involved in the decision to design the study, collect data, analyze or interpret data, write reports, or submit manuscripts. Competing interests The authors declare no competing interest. Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Ethics approval and consent to participate The National Health Interview Survey (NHIS) is conducted by the National Center for Health Statistics (NCHS) and has been approved by the NCHS Research Ethics Review Board. All participants provided informed consent at the time of the interview. The present study used publicly available, de-identified NHIS data linked with the National Death Index and was conducted in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments. As a secondary analysis of anonymized data, this study was deemed exempt from institutional review board (IRB) oversight. References Abdullah, M. et al. Cancer Incidence in Kabul, Afghanistan: The First Report From the Population-Based Cancer Registry. Cancer Med. 14 , e70844. 10.1002/cam4.70844 (2025). Getie, A., Ayalneh, M. & Bimerew, M. 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Response to Comment on Prevalence and Association of Sarcopenia with Mortality in Patients with Head and Neck Cancer: A Systematic Review and Meta-analysis. Ann. Surg. Oncol. 10.1245/s10434-025-17201-3 (2025). Han, J., Zhang, Q., Lan, J., Yu, F. & Liu, J. Frailty worsens long-term survival in patients with colorectal cancer: a systematic review and meta-analysis. Front. Oncol. 14 , 1326292. 10.3389/fonc.2024.1326292 (2024). Soysal, P. et al. Relationship between depression and frailty in older adults: A systematic review and meta-analysis. Ageing Res. Rev. 36 , 78–87. 10.1016/j.arr.2017.03.005 (2017). Di Meglio, A. & Vaz-Luis, I. Systemic inflammation and cancer-related frailty: shifting the paradigm toward precision survivorship medicine. ESMO Open. 9 , 102205. 10.1016/j.esmoop.2023.102205 (2024). Koh, J. H. et al. ASO Author Reflections: Prevalence and Association of Sarcopenia with Mortality in Patients with Head and Neck Cancer-A Systematic Review and Meta-analysis. Ann. Surg. Oncol. 31 , 6077–6078. 10.1245/s10434-024-15654-6 (2024). Dai, Y., Lan, J., Li, S. & Xu, G. Exploring the Impact of Sarcopenia on Mortality in Breast Cancer Patients: A Comprehensive Systematic Review and Meta-Analysis. Breast Care (Basel) . 19 , 316–328. 10.1159/000541421 (2024). Hinz, A. et al. Assessment of depression severity with the PHQ-9 in cancer patients and in the general population. BMC Psychiatry . 16 10.1186/s12888-016-0728-6 (2016). Lin, Y. C. & Yan, H. T. Frailty phenotypes and their association with health consequences: a comparison of different measures. Aging Clin. Exp. Res. 36 , 233. 10.1007/s40520-024-02887-4 (2024). Phelan, R. & Quality of Life Working Committee of the Center for International Blood and Marrow Transplant Research and Transplant Complications Working Party. Male-Specific Late Effects in Adult Hematopoietic Cell Transplantation Recipients: A Systematic Review from the Late Effects and of the European Society of Blood and Marrow Transplantation. Transplant Cell Ther 28, 335.e331- (2022). 335.e317 , doi:10.1016/j.jtct.2021.10.013. Cheung, D. S. T. et al. A pilot randomized controlled trial using Baduanjin qigong to reverse frailty status among post-treatment older cancer survivors. J. Geriatr. Oncol. 13 , 682–690. 10.1016/j.jgo.2022.02.014 (2022). Guo, Y. et al. Effects of prehabilitation on postoperative outcomes in frail cancer patients undergoing elective surgery: a systematic review and meta-analysis. Support Care Cancer . 31 , 57. 10.1007/s00520-022-07541-1 (2022). von Grundherr, J. et al. A Multimodal Lifestyle Psychosocial Survivorship Program in Young Cancer Survivors: The CARE for CAYA Program-A Randomized Clinical Trial Embedded in a Longitudinal Cohort Study. JAMA Netw. Open. 7 , e242375. 10.1001/jamanetworkopen.2024.2375 (2024). Lee, K. et al. Digital Health Interventions for Adult Patients With Cancer Evaluated in Randomized Controlled Trials: Scoping Review. J. Med. Internet Res. 25 , e38333. 10.2196/38333 (2023). Morris, B. B., Rossi, B. & Fuemmeler, B. The role of digital health technology in rural cancer care delivery: A systematic review. J. Rural Health . 38 , 493–511. 10.1111/jrh.12619 (2022). Lao, J. et al. Frailty and medical financial hardship among older adults with cancer in the United States. Front. Oncol. 13 , 1202575. 10.3389/fonc.2023.1202575 (2023). Sampieri, G. et al. Interventions for Concerning Patient-Reported Outcomes in Routine Cancer Care: A Systematic Review. Ann. Surg. Oncol. 31 , 1148–1170. 10.1245/s10434-023-14576-z (2024). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 03 Apr, 2026 Reviews received at journal 06 Feb, 2026 Reviewers agreed at journal 22 Jan, 2026 Reviews received at journal 16 Nov, 2025 Reviewers agreed at journal 01 Nov, 2025 Reviewers invited by journal 26 Sep, 2025 Editor invited by journal 31 May, 2025 Editor assigned by journal 19 May, 2025 Submission checks completed at journal 16 May, 2025 First submitted to journal 13 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6656702","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":456044632,"identity":"55e0e95c-a8aa-44ed-958c-d53f1dd36676","order_by":0,"name":"Yaxin Huang","email":"","orcid":"","institution":"Beijing Anzhen Nanchong Hospital of Capital Medical University \u0026 Nanchong Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yaxin","middleName":"","lastName":"Huang","suffix":""},{"id":456044633,"identity":"208ce668-5e5f-4b0d-be4e-98960280a43f","order_by":1,"name":"Yonggang Hu","email":"","orcid":"","institution":"People's Hospital of Naxi District","correspondingAuthor":false,"prefix":"","firstName":"Yonggang","middleName":"","lastName":"Hu","suffix":""},{"id":456044634,"identity":"7c814e50-d9d2-488c-b6fa-a3da474fbb53","order_by":2,"name":"Qin Li","email":"","orcid":"","institution":"Beijing Anzhen Nanchong Hospital of Capital Medical University \u0026 Nanchong Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Qin","middleName":"","lastName":"Li","suffix":""},{"id":456044635,"identity":"07e6a90d-863b-46bb-842a-5a6b56a00eaf","order_by":3,"name":"Haoyu He","email":"","orcid":"","institution":"Beijing Anzhen Nanchong Hospital of Capital Medical University \u0026 Nanchong Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Haoyu","middleName":"","lastName":"He","suffix":""},{"id":456044636,"identity":"30add48f-ba9a-47a6-91e6-ab176ae13663","order_by":4,"name":"Hongyin Zhou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxElEQVRIiWNgGAWjYBAC++PNBww/VEjw2Lc3EKvnzLGEYokzNjIGPAeI1XIjR+EDb1uajYFEApE6GHvOMG6QOHOYx1zy8cYbDDU20QS1MLP3HjYoqDjMYzk7rdiC4VhabgMhLWw859IMQLYw3M4xk2BsOExYC49EjvkP3jaglptniNQiIZFjYAD0Po/BDR4itRjwHEswBgYyj2QP0C8JxPjFgB0Slfb87Ic33vhQY0NYC4p2oqMGSQupOkbBKBgFo2BkAADCZUDv3dC6SgAAAABJRU5ErkJggg==","orcid":"","institution":"Zigong First People’s Hospital","correspondingAuthor":true,"prefix":"","firstName":"Hongyin","middleName":"","lastName":"Zhou","suffix":""}],"badges":[],"createdAt":"2025-05-13 14:53:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6656702/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6656702/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82680717,"identity":"3fec473f-87d1-4df8-a154-a7862d30c48a","added_by":"auto","created_at":"2025-05-14 05:40:36","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":75899,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow chart of study participants selection.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6656702/v1/053484a09fc61a4aca3d880f.jpg"},{"id":82679655,"identity":"560156f3-7f93-42d5-b6d9-038373db198e","added_by":"auto","created_at":"2025-05-14 05:32:37","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":67264,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSurvey‑Weighted Kaplan–Meier Survival According to Depression Status among U.S. Cancer Survivors (NHIS 1997–2018)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe orange curve represents survivors who reported a clinician diagnosis of depression; the green curve represents those without depression. a): Entire survivor cohort. b): Survivors ≥ 60 years of age. c): Survivors \u0026lt; 60 years of age. d): Female survivors. e): Male survivors.\u003c/p\u003e\n\u003cp\u003eFor each stratum, survival probabilities diverged early and remained lower in the depression group throughout 20 years of follow‑up (weighted log‑rank P \u0026lt; 0.001 for all comparisons).\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6656702/v1/cbd43b2adf49a69c91ad6968.jpg"},{"id":82680718,"identity":"94357d8c-75ce-4d79-92a0-54ebc847a609","added_by":"auto","created_at":"2025-05-14 05:40:36","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":66287,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSurvey‑Weighted Kaplan–Meier Survival According to Frailty Status among U.S. Cancer Survivors (NHIS 1997–2018)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrailty was defined with the five‑item FRAIL scale. Green denotes non-frail survivors (score 0–2), and orange denotes frail survivors (score 3–5). a): Entire survivor cohort. b): Survivors ≥ 60 years of age. c): Survivors \u0026lt; 60 years of age. d): Female survivors. e): Male survivors.\u003c/p\u003e\n\u003cp\u003eSurvival declined in a stepwise fashion from robust to pre‑frail to frail groups in every stratum (trend P\u0026lt;0.001 by weighted log‑rank test).\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6656702/v1/8ef843006d3bf60441383456.jpg"},{"id":82679646,"identity":"065a55a5-f2ff-4384-a484-e15a34676f87","added_by":"auto","created_at":"2025-05-14 05:32:36","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":76748,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSurvey‑Weighted Kaplan–Meier Estimates of All‑Cause Survival According to Coexisting Depression and Frailty among U.S. Cancer Survivors (NHIS 1997–2018).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe orange curve depicts survivors who reported both physician‑diagnosed depression and FRAIL‑defined frailty; the green curve depicts all other survivors (no condition, depression alone, or frailty alone). a): Entire survivor cohort. b): Survivors ≥ 60 years of age. c): Survivors \u0026lt; 60 years of age. d): Female survivors. e): Male survivors.\u003c/p\u003e\n\u003cp\u003eSurvival probabilities diverged within the first few years of follow‑up and remained separated throughout 20 years. Weighted log‑rank tests were significant for the overall cohort and for each stratum (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001 for all comparisons).\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6656702/v1/fe80ae59df53143b19800e54.jpg"},{"id":82681739,"identity":"0900aeb7-8dc4-4098-a9d3-ffdbbf6d9026","added_by":"auto","created_at":"2025-05-14 06:04:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1700547,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6656702/v1/f8d3d00a-2971-43e1-936a-a9c1b4ccd8fd.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Double Burden, Double Risk: Depression–Frailty Synergy and All‑Cause Mortality in U.S. Cancer Survivors","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCancer survivorship has become a distinct phase of the oncologic continuum: more than 18\u0026nbsp;million Americans\u0026mdash;5.4% of the population\u0026mdash;are now living with a history of cancer, and this number is projected to exceed 26\u0026nbsp;million by 2040 \u003csup\u003e1\u003c/sup\u003e.​ As this population ages, attention has shifted from acute treatment to the chronic sequelae that compromise longevity and quality of life.\u003c/p\u003e \u003cp\u003eDepression is one of the most common and debilitating sequelae. Pooled estimates indicate that roughly one third of cancer survivors meet clinical criteria for depression \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e,​ a prevalence severalfold higher than that in the general population and one that persists long after completion of therapy \u003csup\u003e3,4\u003c/sup\u003e.​ Metaanalyses show that depression increases allcause mortality in survivors by 2030 %, independent of tumor stage or treatment modality \u003csup\u003e5\u003c/sup\u003e,​ yet routine screening is inconsistently implemented despite longstanding guideline recommendations \u003csup\u003e6\u003c/sup\u003e.​\u003c/p\u003e \u003cp\u003eFrailty, a multidimensional syndrome of diminished physiologic reserve, is increasingly recognized in oncology. Operationalized either as the Fried physical phenotype​ \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e or as a deficitaccumulation index \u003csup\u003e8\u003c/sup\u003e,​ frailty affects 40\u0026ndash;5 % of older survivors and up to one quarter of middleaged adults with cancer \u003csup\u003e9\u003c/sup\u003e,​ conferring 20\u0026ndash;0 % excess mortality risk and complicating treatment decisionmaking \u003csup\u003e10\u003c/sup\u003e.​ Nonetheless, frailty screening is not yet standard in survivorship clinics, and evidencebased interventions remain nascent.\u003c/p\u003e \u003cp\u003eDepression and frailty frequently cooccur and may share pathobiologic pathways\u0026mdash;chronic inflammation, sarcopenia, endocrine dysregulation\u0026mdash;that accelerate biological aging \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e1\u003c/sup\u003e. In geriatric cohorts, their coexistence amplifies the risk of disability and death beyond the sum of their individual effects \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e2\u003c/sup\u003e.​ However, data in cancer survivors are scarce, limited to singlecenter studies, short followup, or restricted age ranges; no prior investigation has quantified the longterm mortality consequences of the joint phenotype in a nationally representative survivorship sample.\u003c/p\u003e \u003cp\u003eThe present study addresses this critical gap. Leveraging 22 waves of the National Health Interview Survey linked to the National Death Index, we examined the independent and combined associations of physiciandiagnosed depression and FRAILdefined frailty with allcause mortality among U.S. cancer survivors. By integrating validated mentalhealth and geriatric constructs within a populationbased framework, our work aims to (1) characterize the prevalence of the joint phenotype across demographic strata, (2) evaluate its impact on longterm survival, and (3) inform riskstratified, multimodal interventions\u0026mdash;such as exercise prehabilitation \u003csup\u003e13,14\u003c/sup\u003e,​ nutritional support, and digital psychosocial programs\u0026mdash;that are rapidly emerging but remain undertested in survivorship care. Clarifying the prognostic synergy between depression and frailty has the potential to reshape screening algorithms, sharpen clinical guideline recommendations, and ultimately extend healthy life expectancy for the growing survivor population.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and population selection\u003c/h2\u003e \u003cp\u003eWe conducted a secondary analysis of the National Health Interview Survey (NHIS), an annual multistage probability survey administered by the National Center for Health Statistics to assess the health of the noninstitutionalized U.S. population. Each yearly sample is selected with stratification and clustering and oversamples racial and ethnic minority groups. All respondents provide written informed consent, and the protocol is approved annually by the NCHS institutional review board.\u003c/p\u003e \u003cp\u003eWe pooled data from 22 waves (1997\u0026ndash;2018). Adults who completed the Sample Adult questionnaire, which captures physiciandiagnosed cancer, depression, frailty, and sociodemographic factors, were eligible. NHIS records were probabilistically linked to the National Death Index to ascertain vital status through December 31, 2020.\u003c/p\u003e \u003cp\u003eOf 671,696 adults surveyed, we excluded 28,661 participants missing depression or frailty data, 47,238 missing covariates, and 5,542 not linked to mortality files, leaving 590,255 participants for analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Among them, 55,751 (9.4%) reported a history of cancer. Survey weights, strata, and primary sampling units were applied to generate nationally representative estimates.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDepression Assessment\u003c/h3\u003e\n\u003cp\u003eDepressive status was determined with the dedicated mentalhealth item included in every NHIS Sample Adult questionnaire from 1997 through 2018: \u003cem\u003e\u0026ldquo;Has a doctor or other health professional ever told you that you had depression?\u0026rdquo;\u003c/em\u003e Respondents answering \u0026ldquo;yes\u0026rdquo; were classified as having physiciandiagnosed depression. This selfreported diagnosis shows good agreement with the Structured Clinical Interview for DSMIV (κ\u0026thinsp;\u0026asymp;\u0026thinsp;0.66; sensitivity 74 %, specificity 95 %) \u003csup\u003e15\u003c/sup\u003e.​\u003c/p\u003e \u003cp\u003eAlthough the full nineitem Patient Health Questionnaire (PHQ-9) was fielded only in selected, later NHIS waves, its wellestablished psychometric properties (Cronbach\u0026rsquo;s α\u0026thinsp;\u0026asymp;\u0026thinsp;0.89; optimal screening threshold\u0026thinsp;\u0026ge;\u0026thinsp;10 yielding88 % sensitivity and85 % specificity for major depressive disorder)​\u003csup\u003e6\u003c/sup\u003e informed our a priori definition of clinically meaningful depression. To ensure uniform ascertainment across all 22 survey years\u0026mdash;and to avoid differential misclassification\u0026mdash;our primary exposure relied on the physiciandiagnosed depression variable, which captures professionally confirmed, lifetime disease and has been widely used in NHISbased epidemiologic research. All NHIS interviews are conducted by centrally trained personnel under a rigorous qualityassurance program, minimizing information bias.\u003c/p\u003e\n\u003ch3\u003eFrailty Assessment Using the FRAIL Scale\u003c/h3\u003e\n\u003cp\u003eFrailty status was determined using a modified version of the FRAIL scale, a well-established screening tool originally proposed by the Geriatric Advisory Panel of the International Society for Nutrition and Aging \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. In this analysis, we operationalized frailty using self-reported data collected from the NHIS between 1997 and 2018. The FRAIL scale includes five domains: fatigue, resistance, ambulation, comorbid illness burden, and low body mass index (BMI) \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFatigue was identified based on participants\u0026rsquo; responses to NHIS survey items assessing how often they felt unusually tired or lacking in energy over a specified timeframe. Frequent or sustained fatigue was scored as 1, whereas minimal or no fatigue was scored as 0.\u003c/p\u003e \u003cp\u003eResistance was evaluated through questions on the ability to climb 12 stairs without help or the use of mobility aids. Ambulation was assessed by inquiring whether participants had difficulty walking 100 yards on a flat surface (equivalent to the length of a football field or city block) without assistance. For both domains, the presence of any reported difficulty was scored as 1; absence of difficulty was scored as 0.\u003c/p\u003e \u003cp\u003eThe illness component reflected multimorbidity. Participants were assigned a score of 1 if they reported three or more chronic health conditions from a list of 11 physician-diagnosed diseases: angina, arthritis, asthma, chronic obstructive pulmonary disease, coronary heart disease, dementia, diabetes, myocardial infarction, hyperlipidemia, hypertension, and stroke. Reporting fewer than five conditions resulted in a score of 0.\u003c/p\u003e \u003cp\u003eLow BMI was defined as a body mass index below 18.5 kg/m\u0026sup2;. Participants meeting this criterion received a score of 1; all others were scored as 0.\u003c/p\u003e \u003cp\u003eScores across the five components were summed to yield a total frailty score ranging from 0 to 5. Based on established cutoffs, participants were categorized as frail (score 3\u0026ndash;5), pre-frail (score 1\u0026ndash;2), or robust (score 0) \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eEthical Considerations\u003c/h3\u003e\n\u003cp\u003eThe NHIS is conducted by the NCHS and has been approved by the NCHS Research Ethics Review Board. All participants provided informed consent at the time of the interview. The current study used publicly available, de-identified NHIS data linked with the National Death Index. As such, this secondary analysis was deemed exempt from institutional review board (IRB) oversight.\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eThe following sociodemographic covariates were selected a priori because of their documented associations with mortality and their potential to confound the relation between psychosocial exposures and survival. All information was obtained during the NHIS household interview by trained personnel using standardized, computerassisted instruments.\u003c/p\u003e \u003cp\u003eAge and Sex. Age at interview was recorded in single years and was modeled as a continuous variable; for descriptive purposes, distributions are shown in three strata (18\u0026ndash;39, 40\u0026ndash;64, and \u0026ge;\u0026thinsp;65\u0026nbsp;year). Sex was selfreported as male or female; no respondent recorded another option during the study period.\u003c/p\u003e \u003cp\u003eRace and Ethnicity. Participants selfidentified one or more racial groups and indicated whether they were of Hispanic or Latino origin. To preserve statistical power while reflecting U.S. demographic patterns, responses were collapsed into four mutually exclusive categories: nonHispanic White, nonHispanic Black, nonHispanic Asian, and Other (which combined multiracial respondents and those reporting Native Hawaiian, Pacific Islander, American Indian, or Alaska Native ancestry). When weighted, this scheme reproduces census population estimates for each survey year.\u003c/p\u003e \u003cp\u003eEducational Attainment. The highest diploma or degree completed was grouped as less than high school, highschool graduate (including GED), and more than high school (some college, associate\u0026rsquo;s degree, or college graduate). Educational level functions as a stable indicator of socioeconomic position across the life course.\u003c/p\u003e \u003cp\u003eHealthInsurance Status. Current coverage was ascertained with a structured series of items on private insurance and public programs (Medicare, Medicaid, Children\u0026rsquo;s Health Insurance Program, military, and other government plans). Coverage was dichotomized as insured (any plan) versus uninsured\u0026mdash;an approach validated against administrative enrollment files.\u003c/p\u003e \u003cp\u003eMarital Status. Respondents classified themselves as married, widowed, divorced, separated, never married, or living with a partner. Consistent with prior NHIS mortality analyses, categories were collapsed as married (married or living with a partner) versus unmarried (all other responses).\u003c/p\u003e \u003cp\u003eGeographic Region. Residence was assigned to the Northeast, Midwest, South, or West on the basis of the state Federal Information Processing Standard code, thereby accounting for regional differences in cancer epidemiology, healthcare access, and survival.\u003c/p\u003e \u003cp\u003eAssessment of Cancer History. Information on cancer history was based on self-reported responses in the NHIS. Participants who responded \u0026ldquo;yes\u0026rdquo; to the question \u0026ldquo;Have you ever been told by a doctor or other health professional that you had cancer or a malignancy of any kind?\u0026rdquo; were classified as having a history of cancer. \u003cem\u003eTime since cancer diagnosis\u003c/em\u003e was calculated as the difference between the year of interview and the self-reported year of the first cancer diagnosis, and was categorized as \u0026lt;\u0026thinsp;2 years or \u0026ge;\u0026thinsp;2 years. The \u003cem\u003enumber of cancer diagnoses\u003c/em\u003e was derived from the total number of different cancer types reported by each participant, and was classified as 1 or \u0026ge;\u0026thinsp;2. Both variables were included as categorical covariates in multivariable analyses to account for the potential confounding effect of cancer history.\u003c/p\u003e \u003cp\u003eMissing covariate values were rare (\u0026le;\u0026thinsp;0.5% for any single variable) and were imputed with multivariate chained equations to retain the full analytic cohort. All covariates were entered simultaneously in multivariable models, and survey design variables\u0026mdash;sampling strata, primary sampling units, and weights\u0026mdash;were applied to generate nationally representative estimates while accounting for the complex sampling structure.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAll-cause mortality\u003c/h2\u003e \u003cp\u003eMortality follow-up for NHIS participants was achieved through linkage to the NDI, covering deaths through December 31, 2019. This linkage, conducted by the NCHS as part of its Data Linkage Program, employed probabilistic record-matching techniques to connect NHIS survey data with NDI death certificate records. The process enabled accurate determination of vital status and cause-specific mortality.\u003c/p\u003e \u003cp\u003eStrict confidentiality procedures were observed throughout the linkage process. In the publicly available Linked Mortality Files (LMF), data perturbation methods were applied to minimize re-identification risk. For a subset of participants, synthetic values were substituted for certain variables, such as follow-up time or cause of death. Importantly, vital status information was preserved without alteration.\u003c/p\u003e \u003cp\u003eUsing the NHIS Linked Mortality Files, we conducted analyses to evaluate the association between frailty status and all-cause mortality. This approach integrated prospectively collected health and demographic data with long-term mortality follow-up, allowing for a robust examination of mortality risk in relation to frailty.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll analyses were performed with SAS 9.4 (SAS Institute) and R 4.3.1 (R Foundation for Statistical Computing). NHIS sampling weights, strata, and primary sampling units were applied throughout to account for the complex survey design. Vital status was ascertained through linkage to the National Death Index and followed through December 31 2019.\u003c/p\u003e \u003cp\u003eBaseline comparisons. Continuous variables were expressed as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations; categorical variables as unweighted counts with weighted percentages. Baseline characteristics were compared separately for (i) frailty (nonfrail vs. frail), (ii) depression (no depression vs. depression), and (iii) the combined phenotype (coexisting depression\u0026thinsp;+\u0026thinsp;frailty vs. all other participants). Independentsample \u003cem\u003et\u003c/em\u003e tests were used for continuous variables and Pearson\u0026rsquo;s chisquare tests for categorical variables. To control the familywise error rate generated by the three parallel sets of comparisons, a Bonferroniadjusted threshold of \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.017 (0.05\u0026thinsp;\u0026divide;\u0026thinsp;3) was adopted.\u003c/p\u003e \u003cp\u003eSurvival estimation. Persontime accrued from the interview date until death or censoring on December 31 2019. Weighted Kaplan\u0026ndash;Meier curves were generated for each exposure category, and differences were assessed with the logrank test. Median survival times with 5 % confidence intervals (CIs) were reported when estimable.\u003c/p\u003e \u003cp\u003eCox regression. Surveyweighted Cox proportionalhazards models estimated the association between each exposure and allcause mortality. Model 1 adjusted for age and sex. Model 2 further adjusted for race or ethnic group, educational attainment, healthinsurance status, marital status, and geographic region. A multiplicative interaction term (depression \u0026times; frailty) was added to Model 2 to test for effect modification. A sensitivity model treated the fourlevel joint exposure (neither condition, depression alone, frailty alone, both conditions) as a categorical predictor, with the \u0026ldquo;neither\u0026rdquo; group as reference.\u003c/p\u003e \u003cp\u003eModel diagnostics. The proportionalhazards assumption was examined with Schoenfeld residuals and was not violated for any covariate. Robust (sandwich) variance estimators appropriate for complex survey data provided standard errors and CIs.\u003c/p\u003e \u003cp\u003eStatistical significance. All tests were twosided. Significance was defined as \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.017 for baseline comparisons and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for all survival models.\u003c/p\u003e \u003cp\u003eMissing data. Because the analytic cohort was restricted to respondents with complete information on every study variable, no imputation was required.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStudy population characteristics\u003c/h2\u003e \u003cp\u003eA total of 55,751 cancer survivors were included in the analytic cohort; of these, 1,592 (2.9%) had coexisting depression and frailty, whereas 54,159 (97.1%) constituted the control group (no condition, depression alone, or frailty alone) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Survivors with the combined phenotype were younger than controls (mean age, 54.6\u0026thinsp;\u0026plusmn;\u0026thinsp;15.2\u0026nbsp;year vs. 63.1\u0026thinsp;\u0026plusmn;\u0026thinsp;15.1\u0026nbsp;year; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and were more often women (71.6% vs. 44.6%; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline Characteristics of Cancer Survivors according to Coexisting Depression and Frailty, NHIS 1997\u0026ndash;2018\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl group\u003c/p\u003e \u003cp\u003e54,159 (97.1%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDepression\u0026thinsp;+\u0026thinsp;Frailty\u003c/p\u003e \u003cp\u003e1,592 (2.9%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e63.1 (15.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54.6 (15.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24,190 (44.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,140 (71.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29,969 (55.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e452 (28.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace/ethnicity, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38,157 (70.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,141 (71.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,261 (4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e101 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e183 (0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7 (0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13,558 (25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e343 (21.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level, %\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;High school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24,805 (45.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e814 (51.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school graduate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10,940 (20.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e312 (19.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;High school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18,414 (34.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e466 (29.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth insurance, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e53,307 (98.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,436 (90.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e852 (1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e156 (9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32,499 (60.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e542 (34.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnmarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21,660 (40.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,050 (66.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion, %\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.288\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNortheast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19,632 (36.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e249 (15.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMidwest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9,322 (17.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e381 (23.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15,614 (28.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e577 (36.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9,591 (17.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e385 (24.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime since cancer diagnosis\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e731 (13.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e296 (18.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e53,428 (86.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,296 (81.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of cancer diagnoses\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42,406 (78.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,319 (82.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11,753 (21.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e273 (17.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eValues are means (SDs) for continuous variables and percentages for categorical variables. \u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e The control group comprised individuals without depression or frailty, as well as those with depression alone or frailty alone. \u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e Group differences were assessed using the t-test for continuous variables and the chi-square test for categorical variables.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe distribution of race or ethnic group differed modestly but significantly between groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Survivors with coexisting depression and frailty had a slightly higher prevalence of Black race (6.3% vs. 4.2%) and a lower prevalence of the \u0026ldquo;Other\u0026rdquo; category (21.5% vs. 25.0%) than controls; the proportion who identified as White was similar (71.7% vs. 70.5%).\u003c/p\u003e \u003cp\u003eMarkers of socioeconomic status consistently favored controls. Participants with the combined phenotype were less likely to have education beyond high school (29.3% vs. 34.0%; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), less likely to be insured (90.2% vs. 98.4%; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and less likely to be married (34.0% vs. 60.0%; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eGeographic residence did not differ materially between groups (overall \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.29), although small absolute differences were observed: survivors with depression\u0026thinsp;+\u0026thinsp;frailty were somewhat more likely to live in the South (36.2% vs. 28.8%) and less likely to live in the Northeast (15.6% vs. 36.2%).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eLongitudinal Association between Depression and AllCause Mortality\u003c/h2\u003e \u003cp\u003eDepression was consistently linked to an elevated risk of death among cancer survivors (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In the age and sexadjusted model, the hazard of allcause mortality was more than twice as high in survivors with depression as in those without the disorder (hazard ratio [HR], 2.21;95 % confidence interval [CI], 1.94\u0026ndash;2.31; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). After additional adjustment for race or ethnic group, educational attainment, healthinsurance status, marital status, and geographic region, the association remained significant, although attenuated (multivariableadjusted HR, 1.3; 95 % CI, 1.18\u0026ndash;1.53; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\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\u003eLongitudinal Association of Depression with All-Cause Mortality among Cancer Survivors, NHIS 1997\u0026ndash;2018\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFrailty status\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eAge- and sex-adjusted model \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eMultivariate adjusted model \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eTotal cancer survivors\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-depression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \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 \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.21 (1.94\u0026ndash;2.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.34 (1.18\u0026ndash;1.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e\u0026ge; 60 years old\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-depression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \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 \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.75 (1.48\u0026ndash;2.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.37 (1.16\u0026ndash;1.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;60 years old\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-depression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \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 \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.75 (2.33\u0026ndash;3.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.37 (2.01\u0026ndash;2.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWomen\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-depression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \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 \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.22 (1.98\u0026ndash;2.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.80 (1.60\u0026ndash;2.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMen\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-depression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \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 \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.99 (1.74\u0026ndash;2.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.68 (1.46\u0026ndash;1.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e Cox regression model adjusted for age and sex.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e Cox regression model adjusted for age, sex, race/ethnicity, education level, health insurance, marital status, region, time since cancer diagnosis, and number of cancer diagnoses.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eEffect sizes differed by age category. Among survivors younger than 60 years, depression was associated with more than a doubling of the mortality risk in the fully adjusted model (HR, 2.37; 95% CI, 2.01\u0026ndash;2.81; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas the excess risk among those 60 years of age or older was smaller but still significant (HR, 1.37; 95% CI, 1.16\u0026ndash;1.62; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). A formal test for interaction confirmed stronger relative effects in the younger group (\u003cem\u003eP\u003c/em\u003e for interaction\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eResults were directionally similar in sexstratified analyses. Depression conferred an 8 % higher risk of death in women (HR, 1.80; 9 % CI, 1.60\u0026ndash;2.02; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and a 6 % higher risk in men (HR, 1.68; 9 % CI, 1.46\u0026ndash;1.92; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) after multivariable adjustment, with no significant heterogeneity by sex (\u003cem\u003eP\u003c/em\u003e for interaction\u0026thinsp;=\u0026thinsp;0.19).\u003c/p\u003e \u003cp\u003eThese findings demonstrate that depression independently predicts allcause mortality in cancer survivors, and that the relative impact is greatest among those younger than 60 years.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eLongitudinal Association between Frailty and AllCause Mortality\u003c/h2\u003e \u003cp\u003eFrailty predicted an elevated risk of death among cancer survivors, although the magnitude of the association was more modest than that observed for depression (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In the age and sexadjusted model, frail survivors had a 1 % higher hazard of allcause mortality than their nonfrail counterparts (hazard ratio [HR], 1.71 95 % confidence interval [CI], 1.62\u0026ndash;1.80; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). After additional adjustment for race or ethnic group, educational attainment, healthinsurance status, marital status, geographic region, and depression, the association attenuated but remained significant (multivariableadjusted HR, 1.8; 95 % CI, 1.08\u0026ndash;1.20; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLongitudinal Association of Frailty with All-Cause Mortality among Cancer Survivors, NHIS 1997\u0026ndash;2018\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFrailty status\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eAge- and sex-adjusted model \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eMultivariate adjusted model \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eTotal cancer survivors\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-frailty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \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 \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrailty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.71 (1.62\u0026ndash;1.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.18 (1.08\u0026ndash;1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e\u0026ge; 60 years old\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-frailty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \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 \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrailty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.74 (1.65\u0026ndash;1.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.21 (1.13\u0026ndash;1.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;60 years old\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-frailty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \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 \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrailty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.18 (1.02\u0026ndash;1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.11 (0.96\u0026ndash;1.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.168\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWomen\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-frailty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \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 \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrailty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.94 (1.80\u0026ndash;2.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.73 (1.61\u0026ndash;1.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMen\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-frailty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \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 \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrailty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.55 (1.45\u0026ndash;1.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.51 (1.41\u0026ndash;1.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e Cox regression model adjusted for age and sex.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e Cox regression model adjusted for age, sex, race/ethnicity, education level, health insurance, marital status, region, time since cancer diagnosis, and number of cancer diagnoses.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eAbbreviations: HR, hazards ratio; CI, confidence interval.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAge modified the effect of frailty. Among survivors\u0026thinsp;\u0026ge;\u0026thinsp;60 years of age, frailty was associated with a 21% increase in mortality risk in the fully adjusted model (HR, 1.21; 95% CI, 1.13\u0026ndash;1.30; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In contrast, the corresponding estimate in survivors\u0026thinsp;\u0026lt;\u0026thinsp;60 years did not reach significance (HR, 1.11; 95% CI, 0.96\u0026ndash;1.30; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.17), suggesting that the adverse impact of frailty is largely confined to older adults (\u003cem\u003eP\u003c/em\u003e for interaction\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eSexstratified analyses showed stronger associations in women than in men. After full multivariable adjustment, frailty conferred a 7 % higher risk of death in women (HR, 1.73; 9 % CI, 1.61\u0026ndash;1.87; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and a 5 % higher risk in men (HR, 1.51; 9 % CI, 1.41\u0026ndash;1.62; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Although the formal test for interaction by sex did not achieve statistical significance (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.08), the point estimates indicate a potentially greater vulnerability among female survivors.\u003c/p\u003e \u003cp\u003eCollectively, these findings indicate that frailty is an independent, albeit moderate, predictor of allcause mortality in cancer survivors, with the association most pronounced in women and in survivors aged 60 years or older.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eJoint Effect of Coexisting Depression and Frailty on Survival\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e jointly illustrate the adverse prognostic influence of having both depression and frailty. Survival curves separated within the first few years after interview (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Panel A) and remained widely apart throughout two decades of followup (logrank \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). At 10 years, absolute survival was \u0026asymp;\u0026thinsp;12 percentage points lower in survivors with the combined phenotype than in the control group; by 20 years, the gap exceeded 15 percentage points.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLongitudinal Joint Association of Depression and Frailty with All-Cause Mortality among Cancer Survivors, NHIS 1997\u0026ndash;2018\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFrailty status\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eAge- and sex-adjusted model \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eMultivariate adjusted model \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eTotal cancer survivors\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl group \u003csup\u003e\u003cb\u003ec\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \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 \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression\u0026thinsp;+\u0026thinsp;Frailty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.33 (2.05\u0026ndash;2.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.38 (1.26\u0026ndash;1.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e\u0026ge; 60 years old\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \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 \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression\u0026thinsp;+\u0026thinsp;Frailty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.78 (1.61\u0026ndash;1.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.45 (1.31\u0026ndash;1.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;60 years old\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \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 \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression\u0026thinsp;+\u0026thinsp;Frailty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.35 (2.71\u0026ndash;4.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.60 (2.10\u0026ndash;3.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWomen\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \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 \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression\u0026thinsp;+\u0026thinsp;Frailty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.36 (2.00\u0026ndash;2.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.81 (1.53\u0026ndash;2.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMen\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \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 \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression\u0026thinsp;+\u0026thinsp;Frailty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.31 (1.87\u0026ndash;2.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.87 (1.51\u0026ndash;2.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e Cox regression model adjusted for age and sex.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e Cox regression model adjusted for age, sex, race/ethnicity, education level, health insurance, marital status, region, time since cancer diagnosis, and number of cancer diagnoses.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003e\u003cb\u003ec\u003c/b\u003e\u003c/sup\u003e Group differences were assessed using the t-test for continuous variables and the chi-square test for categorical variables.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eAbbreviations: HR, hazards ratio; CI, confidence interval.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn agespecific analyses the disparity was evident across strata but was particularly pronounced in younger survivors (Panels B and C). Multivariable Cox models confirmed this gradient: coexisting depression and frailty more than doubled the risk of death in survivors\u0026thinsp;\u0026lt;\u0026thinsp;60 years of age (hazard ratio [HR], 2.60; 9 % confidence interval [CI], 2.10\u0026ndash;3.23; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas the excess risk in those\u0026thinsp;\u0026ge;\u0026thinsp;60 years was 4 % (HR, 1.45; 9 % CI, 1.31\u0026ndash;1.62; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; \u003cem\u003eP\u003c/em\u003e for interaction\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eSexstratified survival curves (Panels D and E) showed parallel patterns in women and men, and the multivariable HRs were virtually identical (women, 1.81; 9 % CI, 1.53\u0026ndash;2.14; men, 1.87; 9 % CI, 1.51\u0026ndash;2.30), with no significant interaction by sex (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.48).\u003c/p\u003e \u003cp\u003eOverall, the coexistence of depression and frailty was associated with a 38% increase in allcause mortality after adjustment for demographic and socioeconomic covariates (HR, 1.38; 9 % CI, 1.26\u0026ndash;1.51; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The persistently lower survival probabilities seen in Kaplan\u0026ndash;Meier analyses, together with the elevated adjusted hazard ratios, underscore a robust and clinically meaningful joint effect that is most pronounced in survivors younger than 60 years.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this nationally representative cohort of 55 751 U.S. cancer survivors, depression and frailty each foretold excess allcause mortality, and their coexistence portended the poorest prognosis. After rigorous multivariable adjustment, the joint phenotype conferred a 3 % increase in the hazard of death and produced absolute survival deficits that widened steadily for two decades. The relative effect was most pronounced in survivors younger than 60 years, suggesting that psychosocial and functional vulnerability accelerates premature mortality even when lifeexpectancy is otherwise favorable.\u003c/p\u003e \u003cp\u003eOur effect estimates for depression (multivariable HR, 1.34) align with two recent metaanalyses that reported 19\u0026ndash;2 % excess mortality across solid and hematologic malignancies​ \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e9, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e0\u003c/sup\u003e. Likewise, the 1 % relative increase associated with frailty is concordant with pooled estimates from contemporary cohort studies​ \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e1, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e2\u003c/sup\u003e. Few investigations have evaluated the \u003cem\u003econcurrent\u003c/em\u003e presence of both conditions. In geriatric populations, Soysal and colleagues described a bidirectional, amplifying relation between depression and frailty​ \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e3\u003c/sup\u003e, but ours is the first study to demonstrate a compounded effect on longterm survival in cancer survivors across the adult life span.\u003c/p\u003e \u003cp\u003eDepression and frailty share pathophysiologic pathways\u0026mdash;chronic inflammation, hypothalamic\u0026ndash;pituitary\u0026ndash;adrenal axis activation, and metabolic dysregulation\u0026mdash;that accelerate catabolism and immunosenescence. Elevated interleukin-6 and Creactive protein concentrations have been independently linked to both conditions in oncologic settings​ \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e0, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e4\u003c/sup\u003e, and sarcopenia, a hallmark of frailty, mediates inflammationdriven physical decline as well as depressive symptomatology​ \u003csup\u003e2\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, 26\u003c/sup\u003e. The resulting reduction in physiologic reserve may impair tolerance to subsequent cancer therapy, heighten susceptibility to infection and cardiovascular disease, and limit engagement in healthpromoting behaviors. Additionally, depression undermines adherence to followup and reduces motivation to exercise or maintain adequate nutrition, thereby exacerbating frailty\u0026mdash;a vicious cycle that reflects accelerated biological aging.\u003c/p\u003e \u003cp\u003eScreening. The Patient Health Questionnaire-9 is validated in oncology populations​ \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e and can be combined with rapid frailty screens such as the Fried phenotype or deficitaccumulation index, both included in recent survivorship toolkits​ \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e2, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e8\u003c/sup\u003e. The U.S. Preventive Services Task Force recommends routine depression screening in adults​, and our data argue for incorporating frailty assessment into the same clinical encounter. Neither the current National Comprehensive Cancer Network nor American Society of Clinical Oncology survivorship guidelines explicitly address this joint screening need​ \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e9\u003c/sup\u003e; policy updates are warranted.\u003c/p\u003e \u003cp\u003eIntervention. Exercise, whether aerobic, resistance, or qigongbased, consistently alleviates depressive symptoms and reverses frailty deficits in survivors​ \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e9, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e0\u003c/sup\u003e. Recent metaanalyses demonstrate that multicomponent training reduces depression and anxiety by \u0026asymp;\u0026thinsp;0 % and improves quality of life​ \u003csup\u003e14\u003c/sup\u003e. Nutrition counseling and protein supplementation further augment gains in lean mass and functional status. Prehabilitation programs that integrate exercise, dietary optimization, and psychosocial support have reduced postoperative complications and length of stay in frail surgical candidates​ \u003csup\u003e31\u003c/sup\u003e; analogous multimodal \u0026ldquo;rehabilitation\u0026rdquo; strategies are now being tested in longitudinal survivorship trials​ \u003csup\u003e32\u003c/sup\u003e. Digital health platforms\u0026mdash;including appbased cognitive behavioral therapy and remote strengthtraining modules\u0026mdash;extend reach to rural or mobilitylimited survivors and show promise in randomized controlled trials​ \u003csup\u003e33,34\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRisk Stratification and Resource Allocation. Our data indicate that survivors\u0026thinsp;\u0026lt;\u0026thinsp;60 years with coexisting depression and frailty represent a highpriority subgroup. Younger adults typically engage with the workforce and may face financial toxicity that compounds psychosocial distress​ \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e5\u003c/sup\u003e. Embedding socialwork and financialnavigation services in survivorship clinics may mitigate this hidden burden and improve adherence to lifestyle interventions.\u003c/p\u003e \u003cp\u003eFirst, mechanistic studies should dissect the temporal sequencing of depression, frailty, and inflammation by incorporating serial biomarker assessments and epigenetic clocks. Second, adaptive, factorial trials are needed to determine the optimal combination and timing of exercise, nutritional, and psychotherapeutic components for reversing the joint phenotype. Third, implementation science must evaluate pragmatic screening pathways and digital platforms across diverse health systems, with attention to rural equity and the digital divide​ \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Finally, costeffectiveness analyses will be essential for informing payers and policy makers about the value of integrated interventions.\u003c/p\u003e \u003cp\u003eStrengths include the use of 22 NHIS waves linked to the National Death Index, yielding a large, nationally representative sample with adjudicated mortality; validated instruments for depression and frailty; and surveyweighted Cox models that preserve external validity. Limitations comprise singletimepoint exposure assessment, potential residual confounding by cancer stage or treatment, and misclassification inherent to selfreport. Nevertheless, nondifferential misclassification would bias results toward the null, and the observation of strong associations despite this bias underscores the robustness of our findings.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eCoexisting depression and frailty identify a subgroup of cancer survivors at markedly elevated risk for premature death, with the greatest relative hazard observed in survivors younger than 60 years. Immediate clinical translation involves dual screening during routine survivorship visits and deployment of multimodal, behaviorally anchored interventions that target both conditions simultaneously. Aligning guideline recommendations with this evidence and investing in scalable programs\u0026mdash;particularly those leveraging digital health\u0026mdash;could appreciably extend healthy life expectancy for the rapidly growing population of cancer survivors.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data, codebook, and analytic code will not be made available as the data used in this study are from the publicly accessible NHANES database, available to researchers worldwide. The database can be accessed at https://www.cdc.gov/nchs/nhis.htm.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the leaders of the Zigong First People\u0026rsquo;s Hospital for their full support during the implementation of the project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors\u0026rsquo; responsibilities were as follows\u0026mdash;Ya-Xin Huang, and Yong-Gang Hu contributed to conceptualization, data curation, formal analysis, writing \u0026ndash; original draft, and project administration; Qin Li was responsible for conceptualization, visualization and methodology. Hong-Yin Zhou was responsible for funding acquisition and investigation. Hong-Yin Zhou was responsible for supervision, validation and writing \u0026ndash; review \u0026amp; editing. All authors declare that they have read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research received financial support from the Zigong First People\u0026rsquo;s Hospital. The findings and conclusions expressed in this article are those of the authors and do not necessarily represent the official position of the CDC or the U.S. Department of Health and Human Services. No private sponsors were involved in the decision to design the study, collect data, analyze or interpret data, write reports, or submit manuscripts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe National Health Interview Survey (NHIS) is conducted by the National Center for Health Statistics (NCHS) and has been approved by the NCHS Research Ethics Review Board. All participants provided informed consent at the time of the interview. The present study used publicly available, de-identified NHIS data linked with the National Death Index and was conducted in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments. As a secondary analysis of anonymized data, this study was deemed exempt from institutional review board (IRB) oversight.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbdullah, M. et al. Cancer Incidence in Kabul, Afghanistan: The First Report From the Population-Based Cancer Registry. \u003cem\u003eCancer Med.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, e70844. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/cam4.70844\u003c/span\u003e\u003cspan address=\"10.1002/cam4.70844\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGetie, A., Ayalneh, M. \u0026amp; Bimerew, M. 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Oncol.\u003c/em\u003e \u003cb\u003e31\u003c/b\u003e, 1148\u0026ndash;1170. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1245/s10434-023-14576-z\u003c/span\u003e\u003cspan address=\"10.1245/s10434-023-14576-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"cancer survivorship, depression, frailty, allcause mortality, National Health Interview Survey, epidemiology","lastPublishedDoi":"10.21203/rs.3.rs-6656702/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6656702/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eDepression and frailty each predict excess mortality in cancer survivors, but their combined effect is undefined. We quantified the independent and joint associations of depression and frailty with allcause mortality in a nationally representative cohort of U.S. cancer survivors.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe pooled 22 waves (1997\u0026ndash;2018) of the National Health Interview Survey and linked records to the National Death Index (followup through December 31 2019). Depression was defined by selfreport of a clinician diagnosis; frailty was assessed with the fiveitem FRAIL scale (frail\u0026thinsp;=\u0026thinsp;score 3\u0026ndash;5). Surveyweighted Cox models estimated hazard ratios (HRs) for allcause mortality after adjustment for demographic, socioeconomic, and clinical covariates. Effect modification by age and sex was examined.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong 55,751 cancer survivors (mean age, 62.8\u0026thinsp;\u0026plusmn;\u0026thinsp;15.2\u0026nbsp;year; 54.4% women), 11,084 (19.9%) had depression, 12,437 (22.3%) were frail, and 1,592 (2.9%) had both conditions. Over 471,838 personyears, 17,603 deaths occurred. Depression was associated with higher mortality (multivariable HR, 1.34; 9 % CI, 1.18\u0026ndash;1.53), as was frailty (HR, 1.18; 9 % CI, 1.08\u0026ndash;1.30). Survivors with coexisting depression\u0026thinsp;+\u0026thinsp;frailty had the greatest risk (HR, 1.38; 9 % CI, 1.26\u0026ndash;1.51) compared with all other survivors; the relative excess was largest in those\u0026thinsp;\u0026lt;\u0026thinsp;60 years of age (HR, 2.60; 9 % CI, 2.10\u0026ndash;3.23; \u003cem\u003eP\u003c/em\u003e for interaction\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and similar in women and men. Absolute 10year survival was 12 percentage points lower in the combinedphenotype group than in controls.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eDepression and frailty independently\u0026mdash;and synergistically\u0026mdash;elevate allcause mortality among U.S. cancer survivors, with the strongest relative effect in younger adults. Concurrent screening for both conditions and deployment of integrated exercise, nutritional, and psychosocial interventions may improve longterm survival in this growing population.\u003c/p\u003e","manuscriptTitle":"Double Burden, Double Risk: Depression–Frailty Synergy and All‑Cause Mortality in U.S. Cancer Survivors","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-14 05:32:31","doi":"10.21203/rs.3.rs-6656702/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-03T09:34:20+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-07T01:56:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"325247015274157669220326497202679188589","date":"2026-01-22T14:21:31+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-17T00:13:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"19766935382105380508241936306494208208","date":"2025-11-01T16:53:13+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-26T06:16:29+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-05-31T14:23:25+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-19T05:07:09+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-17T02:57:28+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-05-13T14:40:18+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2df5d702-cfe6-48b5-ba48-967b02887ad0","owner":[],"postedDate":"May 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":48475888,"name":"Biological sciences/Cancer/Cancer epidemiology"},{"id":48475889,"name":"Biological sciences/Psychology/Human behaviour"},{"id":48475890,"name":"Health sciences/Diseases/Cancer"},{"id":48475891,"name":"Health sciences/Health care/Geriatrics"},{"id":48475892,"name":"Health sciences/Health care/Public health"}],"tags":[],"updatedAt":"2026-05-27T05:08:35+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-14 05:32:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6656702","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6656702","identity":"rs-6656702","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}