Pre-frailty and frailty as predictors of mortality in colorectal cancer survivors: a nationally representative study using NHIS 1997–2018

Pre-frailty and frailty as predictors of mortality in colorectal cancer survivors: a nationally representative study using NHIS 1997–2018 | 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 Pre-frailty and frailty as predictors of mortality in colorectal cancer survivors: a nationally representative study using NHIS 1997–2018 Hongyin Zhou, Wen Li, Siqi Liu, Hui Zhang, Yaxin Huang, Yonggang Hu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6922909/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Frailty is a multidimensional syndrome associated with increased vulnerability to adverse health outcomes, particularly among older adults. Its relevance in cancer survivorship is increasingly recognized, yet the prognostic implications of frailty and pre-frailty among colorectal cancer survivors remain poorly defined. Methods We analyzed data from the 1997–2018 National Health Interview Survey (NHIS), linked to mortality outcomes through December 31, 2019, via the National Death Index. Frailty status was determined using a modified FRAIL scale and categorized as robust (score = 0), pre-frail (score = 1–2), or frail (score = 3–5). Cox proportional hazards models were used to estimate hazard ratios (HRs) for all-cause mortality by frailty status, adjusting for demographic, socioeconomic, and clinical variables. Subgroup analyses were conducted by age and sex. Results Among 4,052 colorectal cancer survivors, 70.2% were robust, 12.4% pre-frail, and 17.4% frail. Frailty and pre-frailty were more prevalent among survivors than among cancer-free participants (6.5% frail; 5.0% pre-frail). In fully adjusted models, pre-frail and frail survivors had significantly higher risks of all-cause mortality compared to robust individuals (HR for pre-frail, 1.44; 95% CI, 1.21–1.71; P < 0.001; HR for frail, 2.19; 95% CI, 1.89–2.56; P < 0.001). These associations persisted across age and sex subgroups, although they were attenuated in younger adults and in men for pre-frailty. Kaplan–Meier curves demonstrated significantly reduced survival across increasing frailty categories. Conclusions Frailty and pre-frailty are common among colorectal cancer survivors and are independently associated with increased risk of all-cause mortality. Routine frailty assessment using simple screening tools may aid in identifying vulnerable individuals and informing survivorship care strategies aimed at improving long-term outcomes. Biological sciences/Cancer/Gastrointestinal cancer/Colorectal cancer Health sciences/Health care Frailty Colorectal Cancer Survivors Mortality Survival Analysis FRAIL Scale Figures Figure 1 Figure 2 Figure 3 Introduction Frailty, a multidimensional syndrome characterized by decreased physiologic reserve and diminished capacity to cope with stressors, has emerged as a key determinant of adverse health outcomes in aging populations ( 1 – 4 ). Defined by impairments across multiple domains—including energy, mobility, strength, and comorbidity burden—frailty is associated with functional decline, hospitalization, institutionalization, and premature death ( 5 – 8 ). In recent years, there has been growing recognition of the importance of frailty in the context of oncology, particularly in survivorship care. Cancer survivors, especially those diagnosed at older ages, are at elevated risk of developing or exacerbating frailty due to the combined effects of aging, cancer biology, and treatment-related toxicities ( 9 – 12 ). Colorectal cancer is the third most commonly diagnosed malignancy in the United States and a leading cause of cancer-related mortality.8 Advances in early detection and treatment have substantially improved survival, resulting in a rapidly expanding population of long-term colorectal cancer survivors ( 12 – 14 ). However, survivorship is often accompanied by late treatment effects, multimorbidity, and functional limitations—factors that may predispose individuals to frailty and compromise long-term outcomes ( 15 – 17 ). Despite its clinical relevance, the prognostic role of frailty in colorectal cancer survivors remains inadequately characterized. Previous studies assessing frailty in oncology populations have largely relied on clinical trial cohorts, hospital-based samples, or frailty measures not validated for population-level use ( 18 – 20 ). Furthermore, few investigations have examined the full frailty spectrum—including robust, pre-frail, and frail states—or compared frailty patterns between cancer survivors and individuals without cancer. The distinction between pre-frailty and frailty is particularly important, as pre-frailty represents a potentially reversible stage in the frailty trajectory ( 21 – 25 ). Clarifying the prognostic implications of these intermediate states could offer critical opportunities for early intervention and risk stratification in survivorship care. To address these knowledge gaps, we utilized data from the National Health Interview Survey (NHIS), a nationally representative survey of U.S. adults, linked to long-term mortality outcomes from the National Death Index. We applied a modified version of the FRAIL scale, a simple and validated screening tool suitable for large-scale population studies, to classify frailty status among colorectal cancer survivors. Our objectives were threefold: ( 1 ) to characterize the distribution of frailty among colorectal cancer survivors compared with cancer-free individuals; ( 2 ) to evaluate the association between frailty status and all-cause mortality; and ( 3 ) to examine whether these associations vary by age and sex. This study provides novel, population-based evidence on the prognostic significance of frailty in colorectal cancer survivorship and has potential implications for personalized survivorship care and long-term health planning. Methods Study design and population selection This study was based on data from the NHIS collected between 1997 and 2018. The NHIS is an ongoing, nationally representative, cross-sectional survey administered by the National Center for Health Statistics (NCHS) that gathers information on health status, health-related behaviors, and sociodemographic characteristics of the non-institutionalized U.S. civilian population. From 1997 to 2018, a total of 671,696 adult participants were identified from the National Health Interview Survey (NHIS). We excluded individuals with missing data on frailty status (N=28,461), covariates (N=47,238), or mortality outcomes (N=5,742), as well as those who reported a history of cancer other than colorectal cancer (N=673). After these exclusions, 589,582 participants were included in the final analytic sample. Among these, 4,052 were colorectal cancer survivors, and 585,530 participants had no history of cancer. A detailed flow diagram of participant selection is shown in Figure 1 . 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 (26, 27). 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) (28). 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 five or more chronic health conditions from a list of 12 physician-diagnosed diseases: angina, arthritis, asthma, anxiety disorder, 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) (27). Ethical Considerations 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 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 We assessed a range of covariates using data from the NHIS, including age, sex, race and ethnicity, educational attainment, health insurance status, marital status, geographic region, history of depression, time since cancer diagnosis, and number of cancer diagnoses. The NHIS is an annual, nationally representative, cross-sectional survey conducted by the NCHS to monitor the health status of the non-institutionalized civilian population in the United States. Participants reported their age in years at the time of the household interview and identified their sex as either male or female. Race and Hispanic ethnicity were determined through self-report, with participants selecting one or more racial groups and indicating whether they were of Hispanic or Latino origin. For analytical purposes, race/ethnicity was grouped into four categories: White, Black, Asian, and Other. Educational level was based on the highest degree or level of schooling completed and was classified as less than high school, high school graduate, or more than high school. Health insurance coverage was assessed at the time of interview and included private insurance, Medicare, Medicaid, or other government-sponsored plans; responses were dichotomized as insured or uninsured. Marital status was categorized as married or unmarried based on self-reported current relationship status. Participants’ geographic location was classified into one of four regions—Northeast, Midwest, South, and West—according to the U.S. Census Bureau definitions used by the NHIS. Depression was determined by self-report of a physician or health professional diagnosis of depression, consistent with the approach used in prior NHIS-based epidemiologic research. 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. These covariates were selected due to their established relevance in health outcomes research, and the use of standardized NHIS measures enhances the comparability and validity of our findings. 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 statistical analyses were conducted using SAS software, version 9.4 (SAS Institute Inc., Cary, NC) and R software, version 4.3.1 (R Foundation for Statistical Computing, Vienna, Austria; https://www.R-project.org). Descriptive statistics were used to summarize the baseline characteristics of participants. Means and standard deviations (SDs) were reported for continuous variables, while categorical variables were presented as frequencies and percentages. To assess differences in baseline characteristics across frailty categories (robust, pre-frail, and frail), we used one-way analysis of variance (ANOVA) for continuous variables and Pearson’s chi-square (χ²) test for categorical variables. Post hoc pairwise comparisons were performed as needed to identify specific group differences. Survival probabilities were estimated using the Kaplan–Meier method, and differences in survival across frailty categories were evaluated using the log-rank test. Median survival times and 95% confidence intervals (CIs) were calculated where appropriate. To examine the association between frailty status and all-cause mortality, we employed Cox proportional hazards regression models. Hazard ratios (HRs) and 95% CIs were estimated for pre-frail and frail participants, using the robust group as the reference. Two models were constructed: an age- and sex-adjusted model, and a fully adjusted multivariable model incorporating additional covariates, including race/ethnicity, educational level, health insurance status, marital status, region, self-reported depression, time since cancer diagnosis, and number of cancer diagnoses. The proportional hazards assumption was tested using Schoenfeld residuals and was not violated. All statistical tests were two-sided, and a P value of less than 0.05 was considered to indicate statistical significance. No imputation was performed for missing data, as the analytic sample was restricted to individuals with complete information on all variables of interest. Results Study population characteristics A total of 4,052 adults with self-reported colorectal cancer were included in the analytic sample, comprising 2,847 (70.3%) robust individuals, 501 (12.4%) pre-frail individuals, and 704 (17.3%) frail individuals ( Table 1 ). The mean age increased across frailty categories, from 69.1 years (SD, 12.6) in the robust group to 73.0 years (SD, 10.4) in the pre-frail group and 73.4 years (SD, 11.6) in the frail group ( P <0.001). Differences in sociodemographic characteristics were evident across frailty groups. Frail individuals were more likely to be female (57.4%) than those who were pre-frail (45.9%) or robust (49.8%) ( P <0.001). The distribution of race and ethnicity varied significantly, with a markedly higher proportion of individuals identifying as “Other” among the frail group (76.5%) compared to the robust (35.1%) and pre-frail (32.3%) groups ( P <0.001). Educational attainment was inversely associated with frailty: 56.4% of frail individuals had less than a high school education, compared with 51.1% and 45.8% in the pre-frail and robust groups, respectively ( P <0.001). The proportion of participants without health insurance was higher among frail individuals (2.8%) than among those who were pre-frail (2.2%) or robust (1.2%) ( P =0.004). Marital status differed significantly by frailty status. The proportion of unmarried individuals was highest among those categorized as frail (69.0%), followed by pre-frail (61.3%) and robust (57.5%) participants ( P <0.001). Geographic distribution also varied, with a greater proportion of frail individuals residing in the South and Midwest ( P =0.013). Finally, the prevalence of self-reported depression increased across the frailty spectrum, from 2.6% in the robust group to 6.0% in the pre-frail group and 7.2% in the frail group ( P <0.001). Comparison of frailty status between colorectal cancer survivors and healthy participants Frailty status differed significantly between colorectal cancer survivors and individuals without a history of cancer ( P <0.001 by chi-square test), as illustrated in Figure 2. Among survivors of colorectal cancer, 70.2% were classified as robust, 12.4% as pre-frail, and 17.4% as frail. In contrast, the distribution of frailty among cancer-free participants showed a higher proportion of robust individuals (88.5%) and markedly lower proportions of both pre-frail (5.0%) and frail (6.5%) individuals. These differences were illustrated in Figure 2 . Survival probability across different frailty statuses in colorectal cancer survivors Kaplan–Meier survival curves demonstrated significant differences in all-cause mortality across frailty categories among colorectal cancer survivors ( P <0.001 by log-rank test) ( Figure 3a ). Robust individuals exhibited the highest survival probability over time, followed by those categorized as pre-frail and frail. This pattern remained consistent in subgroup analyses. Among survivors aged 60 years or older, survival curves continued to diverge significantly by frailty status ( P <0.001) and closely mirrored those observed in the overall cohort ( Figure 3b ). Similarly, among female survivors, survival probabilities differed markedly across frailty groups ( P <0.001), showing a trend comparable to that of the overall study population and the older subgroup ( Figure 3d ). Among survivors younger than 60 years, the survival curves also differed significantly by frailty status ( P =0.046); however, wide confidence intervals reflected smaller sample sizes and greater uncertainty in the estimates ( Figure 3c ). In male survivors, the association between frailty status and survival remained significant ( P =0.028), though the separation between frailty groups was less pronounced than in other subgroups ( Figure 3e ). Longitudinal association between frailty and all-cause mortality Frailty status was significantly associated with all-cause mortality among colorectal cancer survivors ( Table 2 ). In the fully adjusted model, individuals classified as pre-frail had a 44% higher risk of death compared with those who were robust (hazard ratio [HR], 1.44; 95% confidence interval [CI], 1.21–1.71; P <0.001), while frail individuals had more than double the mortality risk (HR, 2.19; 95% CI, 1.89–2.56; P <0.001). These associations remained robust after adjustment for age, sex, race/ethnicity, education level, insurance status, marital status, region, depression, time since cancer diagnosis, and number of cancer diagnoses. Subgroup analyses revealed consistent patterns across age and sex strata. Among participants aged 60 years or older, both pre-frailty (HR, 1.45; 95% CI, 1.21–1.74; P <0.001) and frailty (HR, 2.47; 95% CI, 2.14–2.78; P <0.001) were associated with significantly increased mortality. In contrast, among those younger than 60 years, pre-frailty remained significantly associated with higher mortality (HR, 1.81; 95% CI, 1.01–3.25; P =0.046), whereas frailty did not reach statistical significance (HR, 2.25; 95% CI, 0.80–3.98; P =0.261), likely due to limited sample size and wide confidence intervals. Sex-stratified analyses demonstrated a stronger association between frailty and mortality among women. In fully adjusted models, pre-frailty (HR, 1.62; 95% CI, 1.25–2.10; P <0.001) and frailty (HR, 2.55; 95% CI, 2.01–2.94; P <0.001) were both significantly associated with increased mortality in women. Among men, frailty was also significantly associated with mortality (HR, 2.07; 95% CI, 1.80–2.39; P <0.001), but pre-frailty was not (HR, 1.21; 95% CI, 0.95–1.53; P =0.118), suggesting potential sex-based differences in frailty’s prognostic value. Discussion In this nationally representative study of over 4,000 colorectal cancer survivors, we found that both frailty and pre-frailty were significantly more prevalent in survivors than in cancer-free individuals, and both conditions were independently associated with increased all-cause mortality. Among survivors, nearly one in five met criteria for frailty, and an additional 12.4% were classified as pre-frail—more than twice the prevalence observed in the cancer-free population. These findings underscore the high burden of physiological vulnerability in colorectal cancer survivors and suggest that frailty may play a central role in shaping long-term outcomes in this growing population. Our study adds to a small but growing body of literature that highlights the importance of frailty in cancer survivorship. While prior studies have demonstrated that frailty predicts poor short-term outcomes in older cancer patients undergoing treatment, few have examined its long-term prognostic implications among survivors in the general population ( 12 , 25 ). By using data from the NHIS linked to mortality outcomes, we were able to evaluate these associations in a large, community-based sample, thereby enhancing the generalizability and public health relevance of our findings. Furthermore, we leveraged a validated, multidimensional frailty tool (the FRAIL scale) to capture functional, clinical, and physiological domains, allowing for a comprehensive characterization of frailty status. The observed stepwise association between frailty severity and mortality suggests that even early frailty manifestations carry prognostic significance. Pre-frail individuals had a 44% increased risk of death compared with their robust counterparts, while frail individuals had more than a twofold risk. These associations remained robust after adjustment for key demographic, socioeconomic, and health-related confounders ( 29 – 31 ). Importantly, our findings were consistent across subgroups, including women and adults aged 60 years or older, although estimates were less precise among younger survivors due to smaller sample sizes. Several additional factors may contribute to the excess burden of frailty observed among colorectal cancer survivors. Cancer-related fatigue, persistent gastrointestinal symptoms, and chemotherapy-induced neuropathy may collectively impair physical functioning long after treatment completion ( 32 , 33 ). These chronic effects can limit mobility, reduce exercise capacity, and promote sedentary behavior, all of which accelerate frailty progression ( 34 ). Survivors often face complex medication regimens and multimorbidity, which may further compromise physiologic reserve ( 35 ). In addition, disparities in access to rehabilitation, nutritional support, and psychosocial services may exacerbate frailty risk, particularly among socioeconomically disadvantaged individuals ( 36 ). Our findings that frailty was more common in participants with lower educational attainment, lack of insurance, and depression highlight the convergence of clinical and social vulnerability in shaping long-term survivorship trajectories. These findings have important implications for survivorship care. As life expectancy improves among colorectal cancer patients, survivorship strategies must move beyond surveillance for recurrence and second primaries to encompass functional health and aging-related vulnerability. Frailty screening using simple instruments such as the FRAIL scale may help identify survivors at high risk for functional decline, hospitalization, or premature death. Importantly, pre-frailty—often overlooked in clinical practice—represents a potentially reversible state. Interventions such as tailored exercise programs, nutritional support, and multimorbidity management have shown promise in reversing or attenuating frailty progression ( 37 – 41 ). Embedding frailty screening into survivorship care plans—especially for older adults, women, and those with multiple chronic conditions—could help clinicians personalize surveillance intensity, prioritize supportive services, and coordinate multidisciplinary care. Research into scalable models of frailty mitigation, including community-based programs and geriatric co-management, is warranted to support the translation of frailty-informed care into oncology practice. Several mechanisms may explain the observed associations. Cancer diagnosis and treatment can accelerate biological aging through pathways such as inflammation, hormonal dysregulation, mitochondrial dysfunction, and muscle catabolism ( 4 , 42 – 45 ). Survivors may also experience long-term sequelae of chemotherapy, radiation, or surgical interventions that compromise mobility, nutritional status, and psychological well-being. In our study, frailty was disproportionately observed among women and individuals with depression or limited social support, reinforcing the interplay between biological, psychological, and social determinants of health. Our study has several strengths, including the use of a large, nationally representative dataset, a validated frailty instrument, and long-term mortality follow-up. The analytic approach accounted for a wide range of potential confounders, enhancing internal validity. Nevertheless, several limitations should be acknowledged. Frailty was assessed at a single time point using self-reported data, which may be subject to measurement error or misclassification. Although the FRAIL scale is well-suited for population-based research, it may not capture more nuanced or subclinical manifestations of frailty. Furthermore, cause-specific mortality could not be evaluated due to data constraints in the publicly available NHIS Linked Mortality Files. In conclusion, frailty and pre-frailty are common among colorectal cancer survivors and are independently associated with increased risk of all-cause mortality. These findings highlight the importance of incorporating frailty assessment into survivorship care and suggest that early identification and intervention may improve long-term outcomes in this vulnerable population. As cancer survivorship continues to increase, a broader approach to survivorship—one that integrates aging science, functional assessment, and multidisciplinary care—is essential to optimizing quality of life and longevity. Declarations Ethics approval and consent to participate Not applicable. Conflict of interest The authors declare no competing interest. Consent for publication Not applicable. Funding information This research was financially supported by the Luzhou City People's Hospital of Naxi District. The findings and conclusions presented in this article are solely those of the authors and do not necessarily reflect the official views of the Centers for Disease Control and Prevention (CDC) or the U.S. Department of Health and Human Services. No commercial sponsors were involved in the design of the study, data collection, data analysis, data interpretation, manuscript preparation, or the decision to submit the manuscript for publication. Author Contribution All authors contributed significantly to the development of this manuscript. Hongyin Zhou and Wen Li conceived and designed the study. Yonggang Hu, Siqi Liu and Hui Zhang were responsible for data analysis and interpretation. Yaxin Huang and Yonggang Hu contributed to the literature review and manuscript drafting. All authors reviewed and revised the manuscript critically for intellectual content, and all approved the final version for submission. Each author agrees to be accountable for all aspects of the work, ensuring the accuracy and integrity of the research. Acknowledgement We thank the Director and staff of the Department of Clinical Laboratory, People's Hospital of Naxi District, for their valuable assistance and full support during the conduct of this study. 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. References Cohen CI, Benyaminov R, Rahman M, Ngu D, Reinhardt M. Frailty: A Multidimensional Biopsychosocial Syndrome. Med Clin North Am . 2023 Jan;107(1):183-197. eng. 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Kristjansson SR, Nesbakken A, Jordhøy MS, Skovlund E, Audisio RA, Johannessen HO, Bakka A, Wyller TB. Comprehensive geriatric assessment can predict complications in elderly patients after elective surgery for colorectal cancer: a prospective observational cohort study. Crit Rev Oncol Hematol . 2010 Dec;76(3):208-17. eng. Epub 2009/12/17. doi:10.1016/j.critrevonc.2009.11.002. Cited in: Pubmed; PMID 20005123. Ommundsen N, Wyller TB, Nesbakken A, Jordhøy MS, Bakka A, Skovlund E, Rostoft S. Frailty is an independent predictor of survival in older patients with colorectal cancer. Oncologist . 2014 Dec;19(12):1268-75. eng. Epub 2014/10/31. doi:10.1634/theoncologist.2014-0237. Cited in: Pubmed; PMID 25355846. Tan KY, Kawamura YJ, Tokomitsu A, Tang T. Assessment for frailty is useful for predicting morbidity in elderly patients undergoing colorectal cancer resection whose comorbidities are already optimized. Am J Surg . 2012 Aug;204(2):139-43. eng. Epub 2011/12/20. doi:10.1016/j.amjsurg.2011.08.012. Cited in: Pubmed; PMID 22178483. Molina-Garrido MJ, Guillen-Ponce C. Comparison of two frailty screening tools in older women with early breast cancer. Crit Rev Oncol Hematol . 2011 Jul;79(1):51-64. eng. Epub 2010/07/29. doi:10.1016/j.critrevonc.2010.06.004. Cited in: Pubmed; PMID 20663685. Antonio M, Carmona-Bayonas A, Saldaña J, Navarro V, Tebé C, Salazar R, Borràs JM. Factors Predicting Adherence to a Tailored-Dose Adjuvant Treatment on the Basis of Geriatric Assessment in Elderly People With Colorectal Cancer: A Prospective Study. Clin Colorectal Cancer . 2018 Mar;17(1):e59-e68. eng. Epub 2017/10/22. doi:10.1016/j.clcc.2017.09.003. Cited in: Pubmed; PMID 29054805. Wang Y, Hekimi S. Mitochondrial dysfunction and longevity in animals: Untangling the knot. Science . 2015 Dec 4;350(6265):1204-7. eng. Epub 2016/01/20. doi:10.1126/science.aac4357. Cited in: Pubmed; PMID 26785479. Fried LP, Ferrucci L, Darer J, Williamson JD, Anderson G. Untangling the concepts of disability, frailty, and comorbidity: implications for improved targeting and care. J Gerontol A Biol Sci Med Sci . 2004 Mar;59(3):255-63. eng. Epub 2004/03/20. doi:10.1093/gerona/59.3.m255. Cited in: Pubmed; PMID 15031310. Kim DH, Rockwood K. Frailty in Older Adults. N Engl J Med . 2024 Aug 8;391(6):538-548. eng. Epub 2024/08/08. doi:10.1056/NEJMra2301292. Cited in: Pubmed; PMID 39115063. Kirkland JL, Tchkonia T. Cellular Senescence: A Translational Perspective. EBioMedicine . 2017 Jul;21:21-28. eng. Epub 2017/04/19. doi:10.1016/j.ebiom.2017.04.013. Cited in: Pubmed; PMID 28416161. Tables Table 1. Baseline characteristics of colorectal cancer patients according to frailty status, NHIS 1997–2018 Characteristics Robust 2,847 (70.3%) Pre-frail 501 (12.4%) Frail 704 (17.3%) P -value a Age, years 69.1 (12.6) 73.0 (10.4) 73.4 (11.6) <0.001 Sex, % <0.001 Women 1419 (49.8) 230 (45.9) 404 (57.4) Men 1428 (50.2) 271 (54.1) 300 (42.6) Race/ethnicity, % <0.001 White 1595 (56.0) 295 (58.9) 134 (19.0) Black 243 (8.5) 43 (8.6) 32 (4.5) Asian 10 (0.4) 1 (0.2) 0 (0.0) Other 999 (35.1) 162 (32.3) 538 (76.5) Education level, % <0.001 High school 968 (34.0) 147 (29.3) 151 (21.4) Health insurance, % 0.004 Yes 2813 (98.8) 490 (97.8) 684 (97.2) No 34 (1.2) 11 (2.2) 20 (2.8) Marital status, % <0.001 Married 1209 (42.5) 194 (38.7) 218 (31.0) Unmarried 1638 (57.5) 307 (61.3) 486 (69.0) Region, % 0.013 Northeast 499 (18.3) 99 (20.2) 110 (17.1) Midwest 680 (24.9) 118 (24.2) 187 (29.0) South 979 (35.8) 173 (35.5) 251 (38.9) West 573 (21.0) 98 (20.1) 97 (15.0) Depression, % <0.001 No 2772 (97.4) 471 (94.0) 653 (92.8) Yes 75 (2.6) 30 (6.0) 51 (7.2) Time since cancer diagnosis <0.001 <2years 390 (13.7) 94 (18.7) 177 (25.1) ≥2years 2,457 (86.3) 407 (81.3) 527 (74.9) Number of cancer diagnoses <0.001 1 2,249 (79.0) 418 (83.5) 621 (88.2) ≥2 598 (21.0) 83 (16.5) 83 (11.8) Values are means (SDs) for continuous variables and percentages for categorical variables. a Group differences were assessed using analysis of variance (ANOVA) for continuous variables and the chi-square test for categorical variables. Table 2. Association between frailty status and all-cause mortality among colorectal cancer patients, 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 colorectal cancer patients Robust Ref. Ref. Ref. Ref. Pre-frail 1.51 1.27–1.80 <0.001 1.44 1.21–1.71 <0.001 Frail 2.26 1.98–2.77 <0.001 2.19 1.89–2.56 <0.001 ³ 60 years old Robust Ref. Ref. Ref. Ref. Pre-frail 1.50 1.25–1.79 <0.001 1.45 1.21–1.74 <0.001 Frail 2.54 2.31–2.80 <0.001 2.47 2.14–2.78 <0.001 <60 years old Robust Ref. Ref. Ref. Ref. Pre-frail 2.01 1.13–3.57 0.017 1.81 1.01–3.25 0.046 Frail 2.47 0.88–4.02 0.285 2.25 0.80–3.98 0.261 Women Robust Ref. Ref. Ref. Ref. Pre-frail 1.70 1.32–2.19 <0.001 1.62 1.25–2.10 <0.001 Frail 2.62 2.13–3.05 <0.001 2.55 2.01–2.94 <0.001 Men Robust Ref. Ref. Ref. Ref. Pre-frail 1.26 1.00–1.59 0.053 1.21 0.95–1.53 0.118 Frail 2.13 1.87–2.48 <0.001 2.07 1.80–2.39 <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, depression, time since cancer diagnosis, and number of cancer diagnoses. Abbreviations: HR, hazards ratio; CI, confidence interval. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6922909","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":473683086,"identity":"f3c17d60-f7f3-4b1a-b09a-6ab1afac4c9f","order_by":0,"name":"Hongyin Zhou","email":"","orcid":"","institution":"Zigong First People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hongyin","middleName":"","lastName":"Zhou","suffix":""},{"id":473683087,"identity":"88e879be-c44d-4b77-82a5-7df1e6b4a69e","order_by":1,"name":"Wen Li","email":"","orcid":"","institution":"Panzhihua Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wen","middleName":"","lastName":"Li","suffix":""},{"id":473683088,"identity":"713dae45-9a67-4061-b9de-f7e32885f323","order_by":2,"name":"Siqi Liu","email":"","orcid":"","institution":"Panzhihua Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Siqi","middleName":"","lastName":"Liu","suffix":""},{"id":473683089,"identity":"babe44ca-11b4-4696-95b5-78061d7c6f61","order_by":3,"name":"Hui Zhang","email":"","orcid":"","institution":"Gaoxin Tumor Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Zhang","suffix":""},{"id":473683090,"identity":"32ccf95e-e706-453e-93d3-7cd103e52f2b","order_by":4,"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":473683091,"identity":"a1535f88-e3cd-45fb-b9f1-6a637e367b3d","order_by":5,"name":"Yonggang Hu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYBAC+/mPj//+UfFfjrG9gUgtBgxpCdIMZ5iNmXsOEK0lx0CasY05sX1GApFazBkOGBgXnGEz5p35eOMNhhqbaIJaLBsbEpJnVPDISc5OK7ZgOJaW20BQz2GGAwd4zkgYG87OMZNgbDhMhJZjjI0NvG0GiftvniFSi8EZZmZm3raExMYZPERqkZzBxsY448wBY8YeoF8SiPELvwT/N4YPFQeAUXl4440PNTZE+AXZkRIJpCiHaCFVxygYBaNgFIwMAADWQUG1M01lzgAAAABJRU5ErkJggg==","orcid":"","institution":"People's Hospital of Naxi District","correspondingAuthor":true,"prefix":"","firstName":"Yonggang","middleName":"","lastName":"Hu","suffix":""}],"badges":[],"createdAt":"2025-06-18 12:08:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6922909/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6922909/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85184680,"identity":"26a94c93-e0a4-4f43-94ce-abbe035abc55","added_by":"auto","created_at":"2025-06-23 07:57:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":59781,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of study participants selection.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6922909/v1/afa9a214b2b147546969812f.png"},{"id":85183528,"identity":"f5d3c63d-c9c9-49ee-8381-57c427bebbc4","added_by":"auto","created_at":"2025-06-23 07:49:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":67526,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of frailty status among colorectal cancer survivors and cancer-free participants\u003c/p\u003e\n\u003cp\u003eThis figure illustrates the distribution of frailty status among colorectal cancer survivors (N = 4,052) and cancer-free participants (N = 585,530) in the National Health Interview Survey (NHIS), 1997–2018. Frailty was assessed using a modified FRAIL scale and categorized as robust (score = 0), pre-frail (score = 1–2), and frail (score = 3–5). Among colorectal cancer survivors, 70.2% were classified as robust, 12.4% as pre-frail, and 17.4% as frail. In contrast, among participants without a history of cancer, 88.5% were robust, 5.0% were pre-frail, and 6.5% were frail. The difference in frailty distribution between the two groups was statistically significant (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001 by chi-square test).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6922909/v1/59bd44dc04087b6e0460c80a.png"},{"id":85185164,"identity":"51dda56c-1ac3-4d81-aec3-02386fa909fe","added_by":"auto","created_at":"2025-06-23 08:05:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":104433,"visible":true,"origin":"","legend":"\u003cp\u003eSurvival probability by frailty status among colorectal cancer survivors\u003c/p\u003e\n\u003cp\u003eKaplan–Meier survival curves illustrate differences in survival probabilities across frailty categories (robust, pre-frail, and frail) among colorectal cancer survivors in NHIS 1997–2018. \u003cstrong\u003eFigure 3a\u003c/strong\u003e: Overall colorectal cancer survivors show a significant decline in survival with increasing frailty (\u003cem\u003elog-rank\u003c/em\u003e \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.001). \u003cstrong\u003eFigure 3b\u003c/strong\u003e: Among survivors aged ≥60 years, survival curves for pre-frail and frail groups are closely aligned, indicating comparable mortality risks. \u003cstrong\u003eFigure 3c\u003c/strong\u003e: In survivors aged \u0026lt;60 years, frailty remains strongly associated with mortality, but limited sample size results in wide confidence intervals. \u003cstrong\u003eFigure 3d\u003c/strong\u003e: Among women, pre-frail and frail individuals have similar survival patterns, resembling the older age group. \u003cstrong\u003eFigure 3e\u003c/strong\u003e: Among men, survival curves mirror those of the overall cohort, with frailty significantly impacting survival (\u003cem\u003elog-rank\u003c/em\u003e \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6922909/v1/12a1fa73b951eb852aa67031.png"},{"id":95524197,"identity":"3900338e-a048-4d2d-a8b8-aeec855a2612","added_by":"auto","created_at":"2025-11-10 10:02:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1174912,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6922909/v1/818333a6-896c-4011-b08c-1e3ea4ba63f3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Pre-frailty and frailty as predictors of mortality in colorectal cancer survivors: a nationally representative study using NHIS 1997–2018","fulltext":[{"header":"Introduction","content":"\u003cp\u003eFrailty, a multidimensional syndrome characterized by decreased physiologic reserve and diminished capacity to cope with stressors, has emerged as a key determinant of adverse health outcomes in aging populations (\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Defined by impairments across multiple domains\u0026mdash;including energy, mobility, strength, and comorbidity burden\u0026mdash;frailty is associated with functional decline, hospitalization, institutionalization, and premature death (\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). In recent years, there has been growing recognition of the importance of frailty in the context of oncology, particularly in survivorship care. Cancer survivors, especially those diagnosed at older ages, are at elevated risk of developing or exacerbating frailty due to the combined effects of aging, cancer biology, and treatment-related toxicities (\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eColorectal cancer is the third most commonly diagnosed malignancy in the United States and a leading cause of cancer-related mortality.8 Advances in early detection and treatment have substantially improved survival, resulting in a rapidly expanding population of long-term colorectal cancer survivors (\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). However, survivorship is often accompanied by late treatment effects, multimorbidity, and functional limitations\u0026mdash;factors that may predispose individuals to frailty and compromise long-term outcomes (\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Despite its clinical relevance, the prognostic role of frailty in colorectal cancer survivors remains inadequately characterized.\u003c/p\u003e \u003cp\u003ePrevious studies assessing frailty in oncology populations have largely relied on clinical trial cohorts, hospital-based samples, or frailty measures not validated for population-level use (\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Furthermore, few investigations have examined the full frailty spectrum\u0026mdash;including robust, pre-frail, and frail states\u0026mdash;or compared frailty patterns between cancer survivors and individuals without cancer. The distinction between pre-frailty and frailty is particularly important, as pre-frailty represents a potentially reversible stage in the frailty trajectory (\u003cspan additionalcitationids=\"CR22 CR23 CR24\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Clarifying the prognostic implications of these intermediate states could offer critical opportunities for early intervention and risk stratification in survivorship care.\u003c/p\u003e \u003cp\u003eTo address these knowledge gaps, we utilized data from the National Health Interview Survey (NHIS), a nationally representative survey of U.S. adults, linked to long-term mortality outcomes from the National Death Index. We applied a modified version of the FRAIL scale, a simple and validated screening tool suitable for large-scale population studies, to classify frailty status among colorectal cancer survivors. Our objectives were threefold: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) to characterize the distribution of frailty among colorectal cancer survivors compared with cancer-free individuals; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) to evaluate the association between frailty status and all-cause mortality; and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) to examine whether these associations vary by age and sex. This study provides novel, population-based evidence on the prognostic significance of frailty in colorectal cancer survivorship and has potential implications for personalized survivorship care and long-term health planning.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy design and population selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was based on data from the NHIS collected between 1997 and 2018. The NHIS is an ongoing, nationally representative, cross-sectional survey administered by the National Center for Health Statistics (NCHS) that gathers information on health status, health-related behaviors, and sociodemographic characteristics of the non-institutionalized U.S. civilian population.\u003c/p\u003e\n\u003cp\u003eFrom 1997 to 2018, a total of 671,696 adult participants were identified from the National Health Interview Survey (NHIS). We excluded individuals with missing data on frailty status (N=28,461), covariates (N=47,238), or mortality outcomes (N=5,742), as well as those who reported a history of cancer other than colorectal cancer (N=673). After these exclusions, 589,582 participants were included in the final analytic sample.\u003c/p\u003e\n\u003cp\u003eAmong these, 4,052 were colorectal cancer survivors, and 585,530 participants had no history of cancer. A detailed flow diagram of participant selection is shown in \u003cstrong\u003eFigure 1\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFrailty Assessment Using the FRAIL Scale\u003c/strong\u003e\u003c/p\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 (26, 27). 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) (28).\u003c/p\u003e\n\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\n\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\n\u003cp\u003eThe illness component reflected multimorbidity. Participants were assigned a score of 1 if they reported five or more chronic health conditions from a list of 12 physician-diagnosed diseases: angina, arthritis, asthma, anxiety disorder, 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\n\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\n\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) (27).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Considerations\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 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\u003cp\u003e\u003cstrong\u003eCovariates\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe assessed a range of covariates using data from the NHIS, including age, sex, race and ethnicity, educational attainment, health insurance status, marital status, geographic region, history of depression, time since cancer diagnosis, and number of cancer diagnoses. The NHIS is an annual, nationally representative, cross-sectional survey conducted by the NCHS to monitor the health status of the non-institutionalized civilian population in the United States.\u003c/p\u003e\n\u003cp\u003eParticipants reported their age in years at the time of the household interview and identified their sex as either male or female. Race and Hispanic ethnicity were determined through self-report, with participants selecting one or more racial groups and indicating whether they were of Hispanic or Latino origin. For analytical purposes, race/ethnicity was grouped into four categories: White, Black, Asian, and Other.\u003c/p\u003e\n\u003cp\u003eEducational level was based on the highest degree or level of schooling completed and was classified as less than high school, high school graduate, or more than high school. Health insurance coverage was assessed at the time of interview and included private insurance, Medicare, Medicaid, or other government-sponsored plans; responses were dichotomized as insured or uninsured. Marital status was categorized as married or unmarried based on self-reported current relationship status.\u003c/p\u003e\n\u003cp\u003eParticipants\u0026rsquo; geographic location was classified into one of four regions\u0026mdash;Northeast, Midwest, South, and West\u0026mdash;according to the U.S. Census Bureau definitions used by the NHIS. Depression was determined by self-report of a physician or health professional diagnosis of depression, consistent with the approach used in prior NHIS-based epidemiologic research.\u003c/p\u003e\n\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;2 years or \u0026ge;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;2. Both variables were included as categorical covariates in multivariable analyses to account for the potential confounding effect of cancer history.\u003c/p\u003e\n\u003cp\u003eThese covariates were selected due to their established relevance in health outcomes research, and the use of standardized NHIS measures enhances the comparability and validity of our findings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAll-cause mortality\u003c/strong\u003e\u003c/p\u003e\n\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\n\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\n\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\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were conducted using SAS software, version 9.4 (SAS Institute Inc., Cary, NC) and R software, version 4.3.1 (R Foundation for Statistical Computing, Vienna, Austria; https://www.R-project.org). Descriptive statistics were used to summarize the baseline characteristics of participants. Means and standard deviations (SDs) were reported for continuous variables, while categorical variables were presented as frequencies and percentages.\u003c/p\u003e\n\u003cp\u003eTo assess differences in baseline characteristics across frailty categories (robust, pre-frail, and frail), we used one-way analysis of variance (ANOVA) for continuous variables and Pearson\u0026rsquo;s chi-square (\u0026chi;\u0026sup2;) test for categorical variables. Post hoc pairwise comparisons were performed as needed to identify specific group differences.\u003c/p\u003e\n\u003cp\u003eSurvival probabilities were estimated using the Kaplan\u0026ndash;Meier method, and differences in survival across frailty categories were evaluated using the log-rank test. Median survival times and 95% confidence intervals (CIs) were calculated where appropriate.\u003c/p\u003e\n\u003cp\u003eTo examine the association between frailty status and all-cause mortality, we employed Cox proportional hazards regression models. Hazard ratios (HRs) and 95% CIs were estimated for pre-frail and frail participants, using the robust group as the reference. Two models were constructed: an age- and sex-adjusted model, and a fully adjusted multivariable model incorporating additional covariates, including race/ethnicity, educational level, health insurance status, marital status, region, self-reported depression, time since cancer diagnosis, and number of cancer diagnoses. The proportional hazards assumption was tested using Schoenfeld residuals and was not violated.\u003c/p\u003e\n\u003cp\u003eAll statistical tests were two-sided, and a \u003cem\u003eP\u003c/em\u003e value of less than 0.05 was considered to indicate statistical significance. No imputation was performed for missing data, as the analytic sample was restricted to individuals with complete information on all variables of interest.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eStudy population characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 4,052 adults with self-reported colorectal cancer were included in the analytic sample, comprising 2,847 (70.3%) robust individuals, 501 (12.4%) pre-frail individuals, and 704 (17.3%) frail individuals (\u003cstrong\u003eTable 1\u003c/strong\u003e). The mean age increased across frailty categories, from 69.1 years (SD, 12.6) in the robust group to 73.0 years (SD, 10.4) in the pre-frail group and 73.4 years (SD, 11.6) in the frail group (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001).\u003c/p\u003e\n\u003cp\u003eDifferences in sociodemographic characteristics were evident across frailty groups. Frail individuals were more likely to be female (57.4%) than those who were pre-frail (45.9%) or robust (49.8%) (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001). The distribution of race and ethnicity varied significantly, with a markedly higher proportion of individuals identifying as \u0026ldquo;Other\u0026rdquo; among the frail group (76.5%) compared to the robust (35.1%) and pre-frail (32.3%) groups (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001).\u003c/p\u003e\n\u003cp\u003eEducational attainment was inversely associated with frailty: 56.4% of frail individuals had less than a high school education, compared with 51.1% and 45.8% in the pre-frail and robust groups, respectively (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001). The proportion of participants without health insurance was higher among frail individuals (2.8%) than among those who were pre-frail (2.2%) or robust (1.2%) (\u003cem\u003eP\u003c/em\u003e=0.004).\u003c/p\u003e\n\u003cp\u003eMarital status differed significantly by frailty status. The proportion of unmarried individuals was highest among those categorized as frail (69.0%), followed by pre-frail (61.3%) and robust (57.5%) participants (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001). Geographic distribution also varied, with a greater proportion of frail individuals residing in the South and Midwest (\u003cem\u003eP\u003c/em\u003e=0.013).\u003c/p\u003e\n\u003cp\u003eFinally, the prevalence of self-reported depression increased across the frailty spectrum, from 2.6% in the robust group to 6.0% in the pre-frail group and 7.2% in the frail group (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComparison of frailty status between colorectal cancer survivors and healthy participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrailty status differed significantly between colorectal cancer survivors and individuals without a history of cancer (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001 by chi-square test), as illustrated in Figure 2. Among survivors of colorectal cancer, 70.2% were classified as robust, 12.4% as pre-frail, and 17.4% as frail. In contrast, the distribution of frailty among cancer-free participants showed a higher proportion of robust individuals (88.5%) and markedly lower proportions of both pre-frail (5.0%) and frail (6.5%) individuals. These differences were illustrated in \u003cstrong\u003eFigure 2\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSurvival probability across different frailty statuses in colorectal cancer survivors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKaplan\u0026ndash;Meier survival curves demonstrated significant differences in all-cause mortality across frailty categories among colorectal cancer survivors (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001 by log-rank test) (\u003cstrong\u003eFigure 3a\u003c/strong\u003e). Robust individuals exhibited the highest survival probability over time, followed by those categorized as pre-frail and frail.\u003c/p\u003e\n\u003cp\u003eThis pattern remained consistent in subgroup analyses. Among survivors aged 60 years or older, survival curves continued to diverge significantly by frailty status (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001) and closely mirrored those observed in the overall cohort (\u003cstrong\u003eFigure 3b\u003c/strong\u003e). Similarly, among female survivors, survival probabilities differed markedly across frailty groups (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001), showing a trend comparable to that of the overall study population and the older subgroup (\u003cstrong\u003eFigure 3d\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eAmong survivors younger than 60 years, the survival curves also differed significantly by frailty status (\u003cem\u003eP\u003c/em\u003e=0.046); however, wide confidence intervals reflected smaller sample sizes and greater uncertainty in the estimates (\u003cstrong\u003eFigure 3c\u003c/strong\u003e). In male survivors, the association between frailty status and survival remained significant (\u003cem\u003eP\u003c/em\u003e=0.028), though the separation between frailty groups was less pronounced than in other subgroups (\u003cstrong\u003eFigure 3e\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLongitudinal association between frailty and all-cause mortality\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrailty status was significantly associated with all-cause mortality among colorectal cancer survivors (\u003cstrong\u003eTable 2\u003c/strong\u003e). In the fully adjusted model, individuals classified as pre-frail had a 44% higher risk of death compared with those who were robust (hazard ratio [HR], 1.44; 95% confidence interval [CI], 1.21\u0026ndash;1.71; \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001), while frail individuals had more than double the mortality risk (HR, 2.19; 95% CI, 1.89\u0026ndash;2.56; \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001). These associations remained robust after adjustment for age, sex, race/ethnicity, education level, insurance status, marital status, region, depression, time since cancer diagnosis, and number of cancer diagnoses.\u003c/p\u003e\n\u003cp\u003eSubgroup analyses revealed consistent patterns across age and sex strata. Among participants aged 60 years or older, both pre-frailty (HR, 1.45; 95% CI, 1.21\u0026ndash;1.74; \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001) and frailty (HR, 2.47; 95% CI, 2.14\u0026ndash;2.78; \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001) were associated with significantly increased mortality. In contrast, among those younger than 60 years, pre-frailty remained significantly associated with higher mortality (HR, 1.81; 95% CI, 1.01\u0026ndash;3.25; \u003cem\u003eP\u003c/em\u003e=0.046), whereas frailty did not reach statistical significance (HR, 2.25; 95% CI, 0.80\u0026ndash;3.98; \u003cem\u003eP\u003c/em\u003e=0.261), likely due to limited sample size and wide confidence intervals.\u003c/p\u003e\n\u003cp\u003eSex-stratified analyses demonstrated a stronger association between frailty and mortality among women. In fully adjusted models, pre-frailty (HR, 1.62; 95% CI, 1.25\u0026ndash;2.10; \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001) and frailty (HR, 2.55; 95% CI, 2.01\u0026ndash;2.94; \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001) were both significantly associated with increased mortality in women. Among men, frailty was also significantly associated with mortality (HR, 2.07; 95% CI, 1.80\u0026ndash;2.39; \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001), but pre-frailty was not (HR, 1.21; 95% CI, 0.95\u0026ndash;1.53; \u003cem\u003eP\u003c/em\u003e=0.118), suggesting potential sex-based differences in frailty\u0026rsquo;s prognostic value.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this nationally representative study of over 4,000 colorectal cancer survivors, we found that both frailty and pre-frailty were significantly more prevalent in survivors than in cancer-free individuals, and both conditions were independently associated with increased all-cause mortality. Among survivors, nearly one in five met criteria for frailty, and an additional 12.4% were classified as pre-frail\u0026mdash;more than twice the prevalence observed in the cancer-free population. These findings underscore the high burden of physiological vulnerability in colorectal cancer survivors and suggest that frailty may play a central role in shaping long-term outcomes in this growing population.\u003c/p\u003e \u003cp\u003eOur study adds to a small but growing body of literature that highlights the importance of frailty in cancer survivorship. While prior studies have demonstrated that frailty predicts poor short-term outcomes in older cancer patients undergoing treatment, few have examined its long-term prognostic implications among survivors in the general population (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). By using data from the NHIS linked to mortality outcomes, we were able to evaluate these associations in a large, community-based sample, thereby enhancing the generalizability and public health relevance of our findings. Furthermore, we leveraged a validated, multidimensional frailty tool (the FRAIL scale) to capture functional, clinical, and physiological domains, allowing for a comprehensive characterization of frailty status.\u003c/p\u003e \u003cp\u003eThe observed stepwise association between frailty severity and mortality suggests that even early frailty manifestations carry prognostic significance. Pre-frail individuals had a 44% increased risk of death compared with their robust counterparts, while frail individuals had more than a twofold risk. These associations remained robust after adjustment for key demographic, socioeconomic, and health-related confounders (\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Importantly, our findings were consistent across subgroups, including women and adults aged 60 years or older, although estimates were less precise among younger survivors due to smaller sample sizes.\u003c/p\u003e \u003cp\u003eSeveral additional factors may contribute to the excess burden of frailty observed among colorectal cancer survivors. Cancer-related fatigue, persistent gastrointestinal symptoms, and chemotherapy-induced neuropathy may collectively impair physical functioning long after treatment completion (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). These chronic effects can limit mobility, reduce exercise capacity, and promote sedentary behavior, all of which accelerate frailty progression (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Survivors often face complex medication regimens and multimorbidity, which may further compromise physiologic reserve (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). In addition, disparities in access to rehabilitation, nutritional support, and psychosocial services may exacerbate frailty risk, particularly among socioeconomically disadvantaged individuals (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Our findings that frailty was more common in participants with lower educational attainment, lack of insurance, and depression highlight the convergence of clinical and social vulnerability in shaping long-term survivorship trajectories.\u003c/p\u003e \u003cp\u003eThese findings have important implications for survivorship care. As life expectancy improves among colorectal cancer patients, survivorship strategies must move beyond surveillance for recurrence and second primaries to encompass functional health and aging-related vulnerability. Frailty screening using simple instruments such as the FRAIL scale may help identify survivors at high risk for functional decline, hospitalization, or premature death. Importantly, pre-frailty\u0026mdash;often overlooked in clinical practice\u0026mdash;represents a potentially reversible state. Interventions such as tailored exercise programs, nutritional support, and multimorbidity management have shown promise in reversing or attenuating frailty progression (\u003cspan additionalcitationids=\"CR38 CR39 CR40\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). Embedding frailty screening into survivorship care plans\u0026mdash;especially for older adults, women, and those with multiple chronic conditions\u0026mdash;could help clinicians personalize surveillance intensity, prioritize supportive services, and coordinate multidisciplinary care. Research into scalable models of frailty mitigation, including community-based programs and geriatric co-management, is warranted to support the translation of frailty-informed care into oncology practice.\u003c/p\u003e \u003cp\u003eSeveral mechanisms may explain the observed associations. Cancer diagnosis and treatment can accelerate biological aging through pathways such as inflammation, hormonal dysregulation, mitochondrial dysfunction, and muscle catabolism (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan additionalcitationids=\"CR43 CR44\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). Survivors may also experience long-term sequelae of chemotherapy, radiation, or surgical interventions that compromise mobility, nutritional status, and psychological well-being. In our study, frailty was disproportionately observed among women and individuals with depression or limited social support, reinforcing the interplay between biological, psychological, and social determinants of health.\u003c/p\u003e \u003cp\u003eOur study has several strengths, including the use of a large, nationally representative dataset, a validated frailty instrument, and long-term mortality follow-up. The analytic approach accounted for a wide range of potential confounders, enhancing internal validity. Nevertheless, several limitations should be acknowledged. Frailty was assessed at a single time point using self-reported data, which may be subject to measurement error or misclassification. Although the FRAIL scale is well-suited for population-based research, it may not capture more nuanced or subclinical manifestations of frailty. Furthermore, cause-specific mortality could not be evaluated due to data constraints in the publicly available NHIS Linked Mortality Files.\u003c/p\u003e \u003cp\u003eIn conclusion, frailty and pre-frailty are common among colorectal cancer survivors and are independently associated with increased risk of all-cause mortality. These findings highlight the importance of incorporating frailty assessment into survivorship care and suggest that early identification and intervention may improve long-term outcomes in this vulnerable population. As cancer survivorship continues to increase, a broader approach to survivorship\u0026mdash;one that integrates aging science, functional assessment, and multidisciplinary care\u0026mdash;is essential to optimizing quality of life and longevity.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eConflict of interest\u003c/h2\u003e\n\u003cp\u003eThe authors declare no competing interest.\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eFunding information\u003c/h2\u003e\n\u003cp\u003eThis research was financially supported by the Luzhou City People\u0026apos;s Hospital of Naxi District. The findings and conclusions presented in this article are solely those of the authors and do not necessarily reflect the official views of the Centers for Disease Control and Prevention (CDC) or the U.S. Department of Health and Human Services. No commercial sponsors were involved in the design of the study, data collection, data analysis, data interpretation, manuscript preparation, or the decision to submit the manuscript for publication.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eAll authors contributed significantly to the development of this manuscript. Hongyin Zhou and Wen Li conceived and designed the study. Yonggang Hu, Siqi Liu and Hui Zhang were responsible for data analysis and interpretation. Yaxin Huang and Yonggang Hu contributed to the literature review and manuscript drafting. All authors reviewed and revised the manuscript critically for intellectual content, and all approved the final version for submission. Each author agrees to be accountable for all aspects of the work, ensuring the accuracy and integrity of the research.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eWe thank the Director and staff of the Department of Clinical Laboratory, People\u0026apos;s Hospital of Naxi District, for their valuable assistance and full support during the conduct of this study.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\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"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCohen CI, Benyaminov R, Rahman M, Ngu D, Reinhardt M. Frailty: A Multidimensional Biopsychosocial Syndrome. \u003cem\u003eMed Clin North Am\u003c/em\u003e. 2023 Jan;107(1):183-197. eng. 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Cited in: Pubmed; PMID 15031310.\u003c/li\u003e\n\u003cli\u003eKim DH, Rockwood K. Frailty in Older Adults. \u003cem\u003eN Engl J Med\u003c/em\u003e. 2024 Aug 8;391(6):538-548. eng. Epub 2024/08/08. doi:10.1056/NEJMra2301292. Cited in: Pubmed; PMID 39115063.\u003c/li\u003e\n\u003cli\u003eKirkland JL, Tchkonia T. Cellular Senescence: A Translational Perspective.\u003cem\u003e EBioMedicine\u003c/em\u003e. 2017 Jul;21:21-28. eng. Epub 2017/04/19. doi:10.1016/j.ebiom.2017.04.013. Cited in: Pubmed; PMID 28416161.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eBaseline characteristics of colorectal cancer patients according to frailty status, NHIS 1997\u0026ndash;2018\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRobust\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e2,847 (70.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePre-frail\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e501 (12.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrail\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e704 (17.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003eAge, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e69.1 (12.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e73.0 (10.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e73.4 (11.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003eSex, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e\u0026nbsp;Women\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e1419 (49.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e230 (45.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e404 (57.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e\u0026nbsp;Men\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e1428 (50.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e271 (54.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e300 (42.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003eRace/ethnicity, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e\u0026nbsp;White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e1595 (56.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e295 (58.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e134 (19.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e\u0026nbsp;Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e243 (8.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e43 (8.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e32 (4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e\u0026nbsp;Asian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e10 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e1 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e\u0026nbsp;Other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e999 (35.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e162 (32.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e538 (76.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003eEducation level, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u0026lt;High school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e1,304 (45.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e256 (51.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e397 (56.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e\u0026nbsp;High school graduate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e575 (20.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e98 (19.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e156 (22.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u0026gt;High school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e968 (34.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e147 (29.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e151 (21.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003eHealth insurance, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e\u0026nbsp;Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e2813 (98.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e490 (97.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e684 (97.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e\u0026nbsp;No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e34 (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e11 (2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e20 (2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003eMarital status, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e\u0026nbsp;Married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e1209 (42.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e194 (38.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e218 (31.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e\u0026nbsp;Unmarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e1638 (57.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e307 (61.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e486 (69.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003eRegion, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e\u0026nbsp;Northeast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e499 (18.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e99 (20.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e110 (17.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e\u0026nbsp;Midwest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e680 (24.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e118 (24.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e187 (29.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e\u0026nbsp;South\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e979 (35.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e173 (35.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e251 (38.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e\u0026nbsp;West\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e573 (21.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e98 (20.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e97 (15.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003eDepression, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e\u0026nbsp;No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e2772 (97.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e471 (94.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e653 (92.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e\u0026nbsp;Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e75 (2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e30 (6.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e51 (7.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003eTime since cancer diagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e\u0026lt;2years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e390 (13.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e94 (18.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e177 (25.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e\u0026ge;2years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e2,457 (86.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e407 (81.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e527 (74.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003eNumber of cancer diagnoses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e2,249 (79.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e418 (83.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e621 (88.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e\u0026ge;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e598 (21.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e83 (16.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e83 (11.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eValues are means (SDs) for continuous variables and percentages for categorical variables. \u003cstrong\u003e\u003csup\u003ea\u0026nbsp;\u003c/sup\u003e\u003c/strong\u003eGroup differences were assessed using analysis of variance (ANOVA) for continuous variables and the chi-square test for categorical variables.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Association between frailty status and all-cause mortality among colorectal cancer patients, NHIS 1997\u0026ndash;2018\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrailty status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge- and sex-adjusted model\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 244px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultivariate adjusted model\u003csup\u003e\u0026nbsp;b\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95%CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95%CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" valign=\"top\" style=\"width: 633px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal colorectal cancer patients\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eRobust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003ePre-frail\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.27\u0026ndash;1.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.21\u0026ndash;1.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eFrail\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e2.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.98\u0026ndash;2.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e2.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.89\u0026ndash;2.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" valign=\"top\" style=\"width: 633px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026sup3;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;60 years old\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eRobust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003ePre-frail\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.25\u0026ndash;1.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.21\u0026ndash;1.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eFrail\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e2.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e2.31\u0026ndash;2.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e2.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e2.14\u0026ndash;2.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" valign=\"top\" style=\"width: 633px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;60 years old\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eRobust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003ePre-frail\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e2.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.13\u0026ndash;3.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.01\u0026ndash;3.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eFrail\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e2.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e0.88\u0026ndash;4.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e2.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.80\u0026ndash;3.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.261\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" valign=\"top\" style=\"width: 633px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWomen\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eRobust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003ePre-frail\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.32\u0026ndash;2.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.25\u0026ndash;2.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eFrail\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e2.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e2.13\u0026ndash;3.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e2.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e2.01\u0026ndash;2.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" valign=\"top\" style=\"width: 633px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMen\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eRobust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003ePre-frail\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.00\u0026ndash;1.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.95\u0026ndash;1.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.118\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eFrail\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e2.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.87\u0026ndash;2.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e2.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.80\u0026ndash;2.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e Cox regression model adjusted for age and sex.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e Cox regression model adjusted for age, sex, race/ethnicity, education level, health insurance, marital status, region, depression, time since cancer diagnosis, and number of cancer diagnoses.\u003c/p\u003e\n\u003cp\u003eAbbreviations: HR, hazards ratio; CI, confidence interval.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Frailty, Colorectal Cancer Survivors, Mortality, Survival Analysis, FRAIL Scale","lastPublishedDoi":"10.21203/rs.3.rs-6922909/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6922909/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eFrailty is a multidimensional syndrome associated with increased vulnerability to adverse health outcomes, particularly among older adults. Its relevance in cancer survivorship is increasingly recognized, yet the prognostic implications of frailty and pre-frailty among colorectal cancer survivors remain poorly defined.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe analyzed data from the 1997\u0026ndash;2018 National Health Interview Survey (NHIS), linked to mortality outcomes through December 31, 2019, via the National Death Index. Frailty status was determined using a modified FRAIL scale and categorized as robust (score\u0026thinsp;=\u0026thinsp;0), pre-frail (score\u0026thinsp;=\u0026thinsp;1\u0026ndash;2), or frail (score\u0026thinsp;=\u0026thinsp;3\u0026ndash;5). Cox proportional hazards models were used to estimate hazard ratios (HRs) for all-cause mortality by frailty status, adjusting for demographic, socioeconomic, and clinical variables. Subgroup analyses were conducted by age and sex.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong 4,052 colorectal cancer survivors, 70.2% were robust, 12.4% pre-frail, and 17.4% frail. Frailty and pre-frailty were more prevalent among survivors than among cancer-free participants (6.5% frail; 5.0% pre-frail). In fully adjusted models, pre-frail and frail survivors had significantly higher risks of all-cause mortality compared to robust individuals (HR for pre-frail, 1.44; 95% CI, 1.21\u0026ndash;1.71; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; HR for frail, 2.19; 95% CI, 1.89\u0026ndash;2.56; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These associations persisted across age and sex subgroups, although they were attenuated in younger adults and in men for pre-frailty. Kaplan\u0026ndash;Meier curves demonstrated significantly reduced survival across increasing frailty categories.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eFrailty and pre-frailty are common among colorectal cancer survivors and are independently associated with increased risk of all-cause mortality. Routine frailty assessment using simple screening tools may aid in identifying vulnerable individuals and informing survivorship care strategies aimed at improving long-term outcomes.\u003c/p\u003e","manuscriptTitle":"Pre-frailty and frailty as predictors of mortality in colorectal cancer survivors: a nationally representative study using NHIS 1997–2018","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-23 07:49:42","doi":"10.21203/rs.3.rs-6922909/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c5258f0c-0633-4efe-81ce-2786e52ecd79","owner":[],"postedDate":"June 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":50306151,"name":"Biological sciences/Cancer/Gastrointestinal cancer/Colorectal cancer"},{"id":50306152,"name":"Health sciences/Health care"}],"tags":[],"updatedAt":"2025-11-06T17:38:30+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-23 07:49:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6922909","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6922909","identity":"rs-6922909","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}