0
No votes yet
Article

Distress and Psychosocial Needs: Demographic Predictors of Clinical Distress After a Diagnosis of Cancer

Jessica Hamilton
Heather Kruse
Lauren Holcomb
Ronald Freche
CJON 2018, 22(4), 390-397 DOI: 10.1188/18.CJON.390-397

Background: Patients with cancer have increased rates of clinical distress compared to healthy individuals. Programs are needed to screen patients for distress and make appropriate psychosocial referrals. 

Objectives: The purpose of this study was to describe the distress levels and psychosocial needs of a large, diverse sample of patients with cancer.

Methods: More than 1,200 adult patients, attending their second appointment with a medical or radiation oncologist, were screened for distress and psychosocial needs. Electronic health records were reviewed to collect demographic data.

Findings: Almost half of the sample reported a clinically meaningful level of distress. Younger age, single status, and female gender were significant predictors of a greater distress score and/or more psychosocial needs. Results suggest that demographic variables may be useful in identifying patients with cancer who are more likely to report higher levels of distress or greater psychosocial needs; these patients should be offered interventions and support services earlier in the treatment process, which may improve outcomes.

About 20%–40% of patients with cancer will experience clinically significant levels of distress (Baken & Woolley, 2011; Zabora, BrintzenhofeSzoc, Curbow, Hooker, & Piantadosi, 2001). In recognition of the appropriate management of the needs of this population, the American College of Surgeons’ (2012) Commission on Cancer mandated, via standard 3.2, that all accredited programs screen patients with cancer at a pivotal time point for distress and make appropriate psychosocial referrals to support services, including social work, psychology, dietetics, and chaplaincy. All cancer centers were required to incorporate distress screening by January 1, 2015.

Several factors may influence an individual’s level of distress related to a cancer diagnosis, including uncertainty about prognosis or side effects of treatment. Zabora et al. (2001) was one of the first to suggest that preexisting demographic characteristics, such as gender, age, and race, may also influence how an individual copes with a new cancer diagnosis. Studies using the National Comprehensive Cancer Network’s Distress Thermometer (DT) (Roth et al., 1998) among patients with cancer have indicated that women (Jacobsen et al., 2005) and younger individuals (Carlson & Bultz, 2004) were more likely than men and older individuals to report clinically significant levels of distress. However, more research is needed to validate these findings across large samples of patients with cancer because the existing research has limitations (e.g., it has focused only specific types of cancer) and because little has been published on this topic that looks comprehensively across cancer types.

Because cancer incidence increases with age (Howlader et al., 2016), it is possible that younger or middle-aged individuals experience more distress related to an unexpected cancer diagnosis. Younger individuals are also more likely to have additional responsibilities related to parenting and employment, the stress of which could be augmented after a health crisis. Giese-Davis et al. (2012) found that younger patients with cancer reported more practical (including issues related to accommodation, transportation, parking, drug coverage, work/school, income/finances, and groceries) and psychosocial areas of distress. In regard to race, Penner et al. (2016) determined that healthcare providers’ implicit biases against African American patients have negative effects on patient–provider communication and patient perceptions of treatment recommendations, which suggests that negative interactions could increase distress during an already stressful time.

The impact of other demographic variables on distress is also worth considering. Previous studies have investigated the effect of marital status on distress in patients with cancer. Zabora et al. (2001) and Kamen et al. (2015) found that married individuals with cancer reported less distress than those who were single. Kamen et al. (2015) also determined that married individuals with low partner support reported significantly greater distress than married individuals with high partner support; no difference in level of distress was noted between unmarried individuals and married individuals with low partner support. Giese-Davis et al. (2012) described marriage as a buffer for practical and psychosocial distress, particularly for younger women with cancer. Research has also shown that married individuals, particularly men, have better oncologic outcomes than unmarried individuals for several cancer types (Aizer et al., 2013; Inverso et al., 2015).

Appetite suppression, weight loss, and a change in eating habits are also commonly associated with increased distress in patients with cancer (Hopkinson, Wright, McDonald, & Corner, 2006). In a study by Barajas Galindo et al. (2017), patients with cancer who experienced a combination of anorexia and early satiety reported worse quality of life, specifically related to health perception, role function, and fatigue. A review of 26 studies by Lis, Gupta, Lammersfeld, Markman, and Vashi (2012) examining the role of nutritional status and quality of life in patients with cancer revealed that better nutritional status was associated with better quality of life; this was shown in 24 of the 26 studies reviewed. However, one of the reviewed studies showed this association to be true only for high-risk patients with cancer (Ravasco, Monteiro-Grillo, & Camilo, 2003), whereas another study indicated no difference in quality of life based on nutritional status (Trabal, Leyes, Forga, & Hervás, 2006). Hopkinson et al. (2006) noted that some patients with cancer reported concerns about a change in body mass index (BMI), regardless of their proximity to death.

Hwang and Yun (2015) examined the association between smoking status and distress, looking at whether individuals who used nicotine were less distressed because they had access to cigarettes as a coping mechanism or if smoking was indicative of higher baseline distress. The authors also investigated distress levels among individuals diagnosed with various stages of cancer; for instance, they investigated whether patients with more advanced cancers experienced increased levels of distress and had more psychosocial needs because of advanced illness (Hwang & Yun, 2015).

These studies have identified demographic variables that may influence an individual’s level of distress following a cancer diagnosis. This still-evolving area of research is important because distress is negatively associated with healthcare satisfaction, quality of life, and treatment adherence and outcomes (Carlson & Bultz, 2004; Pirl, Jacobsen, & Deshields, 2013). The DT has become the standard instrument to measure distress in patients with cancer because of its efficiency and validation (Jacobsen et al., 2005). It is a simple and easy-to-use instrument that asks individuals to rate their level of distress on a scale ranging from 0 (no distress) to 10 (extreme distress).

Given the dissonance in the literature on this topic, the purpose of this exploratory study was to confirm studies suggesting that women and younger individuals may experience more distress following a cancer diagnosis and to build on the existing research to determine if other demographic variables (race, marital status, BMI, smoking status, disease stage) contribute to an individual’s distress or other psychosocial needs. The research questions posed for this study were as follows: 

•  What proportion of the sample reported clinically significant levels of distress? How does this compare to previous studies? 

•  What demographic variables are associated with increased distress and areas of need following a cancer diagnosis?

•  How do these findings add to research on demographic predictors of distress?

Methods

Sample

The current study consisted of 1,213 adult patients attending a second appointment with their medical or radiation oncologist. Data were collected from July 2013 to October 2014 as part of standard clinical care, which was provided at the University of Kansas Cancer Center in Kansas City.

Data Collection

The University of Kansas Medical Center’s institutional review board provided approval for retrospective data collection and analysis, as well as permission to extract data from patients’ electronic health records. Variables extracted were gender, race, age, BMI, marital status, smoking status, and cancer stage. Gender was coded as a dichotomous variable. Race consisted of five self-identified categories: African American, Asian Pacific Islander, Caucasian, Hispanic, and Native American. Age was transformed into meaningful subgroups: aged younger than 40 years, aged 40–69 years, and aged 70 years or older (Sellick & Edwardson, 2007). The World Health Organization (1995) system was used to classify BMI into five categories: underweight (less than 17.5 kg/m2), normal (18–23 kg/m2), overweight (24–29 kg/m2), obese (30–39 kg/m2), and morbidly obese (greater than 40 kg/m2). Marital status was coded dichotomously as married or single, which was inclusive of divorced, widowed, and single individuals. Self-reported smoking status was comprised of three subgroups: never, former, or current smoker. Cancer disease stage was extracted from physician visit notes and ranged from 0–IV.

Following the American College of Surgeons’ (2012) Commission on Cancer standard 3.2, the institution identified the second appointment with an oncology provider as a critical time to assess patient distress and psychosocial needs (Bultz & Johansen, 2011).

To do so, the current authors used the DT (Roth et al., 1998), as well as the Needs Assessment, which is a screening tool specific to the University of Kansas Cancer Center that evaluates patients’ concerns and service needs (Hamilton, Dwyer, Maurer, & Gates, 2015). Goals of the Needs Assessment are as follows:

•  Assess level of emotional distress (on a 0–10 scale ranging from no distress to extreme distress, respectively).

•  Triage and coordinate care based on level of distress.

•  Identify needs in each of the following domains: practical, emotional, nutritional, spiritual, and physical (3 items per domain, with 15 items total).

•  Give patients the opportunity to self-select for services.

The following dependent variables were collected from this tool:

•  Self-reported distress in the past 24 hours on a 0–10 scale

•  Total number of self-reported psychosocial needs, ranging from 0–15 items

During the critical second appointment, trained medical assistants provided scripted instruction to patients, patients completed the DT and the Needs Assessment, and medical assistants recorded responses in the electronic health record. Based on patients’ responses, nurses triaged patients to appropriate psychosocial services. Trained research assistants extracted all study variables from the electronic health record.

Results

Sample Characteristics

Participant ages ranged from 19–94 years, with a mean age of 62 years (SD = 13.73). BMI ranged from 13.96–68.59, with a mean BMI of 28.45 (SD = 7.23). Most participants were women (n = 480) and married (n = 541). Consistent with regional demographics, 719 participants identified as Caucasian, 94 as African American, 13 as Hispanic, 16 as Asian, and 35 as other. Cancer diagnoses included breast (n = 214), lung (n = 139), head and neck (n = 128), and colon (n = 109). Disease stage consisted of stage 0 (n = 11), stage I (n = 115), stage II (n = 123), stage III (n = 155), and stage IV (n = 249). For smoking status, 355 of participants identified as never smoking, 102 reported currently smoking, and 397 indicated formerly smoking.

Distress

Descriptive statistics were used to assess the average distress score among participants in the current study and to compare it to scores in other studies. A score of 4 or greater on the DT is the established cutoff for clinically significant distress (Zabora et al., 2001). In the current study, 351 participants had a score of 4 or higher on the DT. The mean distress score was 3.25 (SD = 2.89).

Demographic Predictors of Distress

Demographic variables were included in a multivariate analysis of variance (MANOVA) to predict distress score and total number of psychosocial needs. The overall model was statistically significant (F[1, 106] = 164.37, p < 0.0005). Follow-up ANOVA tests were conducted for each independent variable to predict distress score and total number of psychosocial needs (see Tables 1 and 2). Post hoc analyses were completed for significant ANOVA results. When the total number of psychosocial needs was significant, differences for each domain were also explored through post hoc tests (see Table 3).

Age: ANOVAs indicated significant differences among age groups for distress score (F[2, 820] = 5.603, p = 0.004) and for total number of needs (F[2, 851] = 4.548, p = 0.011). Post hoc tests revealed that participants aged 70 years or older were significantly less distressed than their younger counterparts. Middle-aged individuals had significantly more needs, specifically practical needs (such as information on advance directives and living wills, details about travel or lodging, and discussion of Social Security or work issues), than older adults. Younger individuals had significantly more emotional needs than individuals in the other age groups and significantly more physical needs than older adult individuals.

Gender: ANOVA indicated that women had higher distress scores than men (F[2, 821] = 6.2, p = 0.002). Men and women had a comparable number of needs.

Marital status: ANOVA for marital status yielded significant results for total distress score (F[1, 801] = 4.668, p = 0.031). Married individuals had lower distress scores than single individuals. Results were also significant for total number of needs reported (F[1, 831] = 8.418, p = 0.004). Married individuals reported fewer psychosocial needs. Post hoc analyses revealed that single individuals noted significantly more practical and nutritional needs than married individuals.

Body mass index: No significant differences were identified for distress score by BMI (F[3, 817] = 1.093, p = 0.385) or for number of needs by BMI category (F[3, 817] = 1.612, p = 0.185).

Disease stage: Distress score did not vary significantly by disease stage. However, ANOVA for cancer stage yielded significant differences for total number of needs endorsed (F[4, 625] = 4.315, p = 0.002). Post hoc tests showed significant differences between individuals with stage I disease and individuals with stage IV disease; the latter group had more needs. In terms of domain, individuals with stage I disease reported significantly fewer nutritional needs than those with stage IV disease. Individuals with stage II disease had significantly fewer nutritional needs than those with stage III or IV disease. Individuals with stage I disease had fewer physical needs than those with stage IV disease.

Smoking status: Cigarette smoking status did not affect an individual’s distress score. However, ANOVA for smoking status yielded significant differences for total number of needs reported (F[3, 818] = 4.058, p = 0.007). Post hoc tests showed that individuals who had never smoked had significantly fewer psychosocial needs (mean = 3.01) than current smokers (mean = 4.07, p = 0.003). In addition, current smokers had significantly more practical and emotional needs than those who had never smoked and those who had formerly smoked. Current and former smokers had more nutritional needs than those who had never smoked.

Race: Distress score did not vary significantly by race. ANOVA for race yielded significant differences for total number of needs reported (F[5, 834] = 2.942, p = 0.013). Post hoc tests revealed that Caucasian participants had fewer needs than did African American participants. Specifically, African American participants reported significantly more practical and spiritual needs than did Caucasian participants.

Discussion

The broad aim of this study was to describe the distress experienced by and the psychosocial needs of patients with cancer, based on demographic characteristics. Forty-three percent of the sample reported a clinically meaningful level of distress, as measured by the DT. This is slightly higher than previous reports estimating that 20%–40% of patients with cancer experience significant distress (Baken & Woolley, 2011; Zabora et al., 2001). Distress was measured at patients’ second appointments with a medical or radiation oncologist. This is important because high levels of distress early on may be predictive of greater distress later in treatment, as well as other negative health outcomes (Sales, Carvalho, McIntyre, Pavlidis, & Hyphantis, 2014; Stafford et al., 2016). Emotional distress is associated with poorer treatment adherence, relapse, and survival (Chen, Hsu, Felix, Garst, & Yoshizaki, 2018), along with higher medical costs and greater burden on the medical system (Carlson & Bultz, 2004).

The current study also aimed to evaluate demographic variables as predictors of distress and the number of psychosocial needs reported by patients. Understanding the impact of demographic variables on distress may aid in improving biopsychosocial screening and triaging of patients with cancer. Results suggested that age, gender, and marital status were significant predictors of distress. Age, marital status, disease stage, smoking status, and race were also significant predictors of total number of needs as measured by the Needs Assessment. BMI was not a predictor of distress or number of psychosocial needs. In addition, age and marital status were significant predictors of distress and number of psychosocial needs; they may be considered as significant factors when nurses are screening and triaging patients with cancer.

In regard to number of psychosocial needs, patients who were middle-aged (aged 40–69 years), were single, had stage IV disease (compared to stage I), were current smokers (compared to never smokers), and were African American (compared to Caucasian) reported more needs. In regard to total number of needs, follow-up analyses were completed to identify associations between demographic variables and specific domains of needs (practical, emotional, nutritional, spiritual, and physical).

Younger adults (those aged younger than 40 years) had significantly more emotional and physical needs. This finding may be attributable to the impact of an unexpected cancer diagnosis at a young age, which is consistent with Weinstein’s (1980) research regarding “unrealistic optimism” about the likelihood of positive events and the unlikelihood of negative events happening to an individual. Participants aged 40–69 years reported more practical needs, which is consistent with research suggesting that this group may have more responsibilities, including a career, child-rearing, and the care of older adult parents (Jacobsen et al., 2005; Sellick & Edwardson, 2007).

African American participants in this study reported significantly more spiritual needs than did Caucasian participants. Multicultural awareness is critical for medical providers to establish trust, as well as improve communication and treatment adherence (Betancourt, Green, Carrillo, & Ananeh-Firempong, 2003). For example, acknowledging the role of spirituality in the African American community and referring patients to chaplain services may have a positive impact on patient care.

Marital status also affected needs reported; single patients noted more practical and nutritional needs than did married patients. Practical needs, like transportation and housing, and nutritional needs, like regular meals that follow any dietary restrictions, may be less stable without a partner serving as a caregiver. Practical and nutritional needs require social support beyond the medical environment; results from this study suggest that marriage may help to protect against unmet needs.

Cigarette smokers reported more needs in the practical, emotional, and nutritional domains. Former smokers had more nutritional needs than did patients who never smoked. From a practical standpoint, the cost of tobacco products may be financially burdensome (Ward et al., 2004). Some people use cigarettes to feel calm (Hwang & Yun, 2015), which implies a higher level of baseline distress. Some smokers may experience self-blame for their cancer diagnosis (Else-Quest, LoConte, Schiller, & Hyde, 2009). Negative dietary effects of tobacco, including appetite suppression, may result in difficulty for current smokers to maintain their weight. Weight and nutritional needs were significantly higher for patients with stage III or IV disease than for those with stage I or II disease. Patients with stage IV disease also reported more physical needs. These results are expected, given that the disease itself is more advanced and more intensive treatment is required.

Strengths and Limitations

Understanding the demographic variables that contribute to distress and psychosocial needs at an earlier stage of cancer treatment may encourage earlier intervention, particularly for high-risk patients. Studies have demonstrated negative health outcomes related to distress levels early in treatment in patients with cancer (Chen et al., 2018). Research to better understand these variables and other possible predictors of distress may lead to improved standard-of-care models for early psychosocial interventions and to protocols for triaging high-risk patients.

This study boasts a number of methodologic strengths, beginning with a large, diverse sample comprised of multiple cancer types. Distress was measured with the DT, a clinically validated tool (Bultz & Johansen, 2011). Inclusion of the Needs Assessment allowed for examination of practical, nutritional, emotional, spiritual, and physical needs in the context of distress score from the DT and demographic variables. Several demographic variables were assessed in this sample, providing a comprehensive picture of the relationship among these variables and distress. The variables of smoking status and BMI have not been previously studied as risk factors for increased distress in a sample of patients with cancer.

Despite the strengths of this study, several considerations affect the application of its findings. Most patient responses were self-reported at a single point in time when the cancer experience may have required greater adjustment and processing of information compared to the day-to-day experiences of treatment and recovery. As such, the distress a patient experienced closer to the time of his or her diagnosis and treatment initiation may not reflect the average distress experienced throughout treatment. The patient’s needs experienced at the beginning of treatment may have increased or decreased in relation to the severity of treatment and as his or her support network adapted to the cancer diagnosis. In addition, the DT is a relatively new instrument, and the body of literature used to inform this research (e.g., examinations of distress and the needs of patients with cancer) is still growing.

Conclusion

These data provide a more refined clinical picture of patient needs compared to previous studies about distress among patients with cancer; this is because the current study had a large, relatively diverse sample of patients with cancer with data on general distress (via the DT) and different types of needs (practical, emotional, nutritional, spiritual, and physical). Future studies could examine interactions among these demographic variables to identify patients with cancer at highest risk for experiencing distress. Another study could measure distress at multiple time points to identify patterns of change over time, or examine the number of referrals to supportive services and patient engagement in those services following completion of a tool like the Needs Assessment. This study identified several demographic variables that are associated with higher distress and more psychosocial needs among patients with cancer. Such findings may be useful to oncology nurses to identify patients who are at risk for increased or significant distress and to, consequently, offer additional support and appropriate resources.

About the Author(s)

Jessica Hamilton, LP, PhD, and Heather Kruse, PhD, are assistant professors, Lauren Holcomb, PhD, is a postdoctoral fellow, and Ronald Freche, MA, is a graduate student, all in the Department of Psychiatry and Behavioral Sciences at the University of Kansas Medical Center in Kansas City. The authors take full responsibility for this content and did not receive honoraria or disclose any relevant financial relationships. The article has been reviewed by independent peer reviewers to ensure that it is objective and free from bias. Hamilton can be reached at jhamilton2@kumc.edu, with copy to CJONEditor@ons.org. (Submitted October 2017. Accepted December 10, 2017.)

 

References 

Aizer, A.A., Chen, M.-H., McCarthy, E.P., Mendu, M.L., Koo, S., Wilhite, T.J., . . . Nguyen, P.L. (2013). Marital status and survival in patients with cancer. Journal of Clinical Oncology, 31, 3869–3876. https://doi.org/10.1200/JCO.2013.49.6489

American College of Surgeons. (2012). Cancer program standards 2012: Ensuring patient-centered care [v1.2.1]. Retrieved from https://www.facs.org/~/media/files/quality%20programs/cancer/coc/program...

Baken, D.M., & Woolley, C. (2011). Validation of the Distress Thermometer, Impact Thermometer and combinations of these in screening for distress. Psycho-Oncology, 20, 609–614. https://doi.org/10.1002/pon.1934

Barajas Galindo, D.E., Vidal-Casariego, A., Calleja-Fernández, A., Hernández-Moreno, A., Pintor de la Maza, B., Pedraza-Lorenzo, M., . . . Ballesteros-Pomar, M.D. (2017). Appetite disorders in cancer patients: Impact on nutritional status and quality of life. Appetite, 114, 23–27. https://doi.org/10.1016/j.appet.2017.03.020

Betancourt, J.R., Green, A.R., Carrillo, J.E., & Ananeh-Firempong, O., III. (2003). Defining cultural competence: A practical framework for addressing racial/ethnic disparities in health and health care. Public Health Reports, 118, 293–302. https://doi.org/10.1093/phr/118.4.293

Bultz, B.D., & Johansen, C. (2011). Screening for distress, the 6th vital sign: Where are we, and where are we going? Psycho-Oncology, 20, 569–571. https://doi.org/10.1002/pon.1986

Carlson, L.E., & Bultz, B.D. (2004). Efficacy and medical cost offset of psychosocial interventions in cancer care: Making the case for economic analyses. Psycho-Oncology, 13, 837–849. https://doi.org/10.1002/pon.832

Chen, A.M., Hsu, S., Felix, C., Garst, J., & Yoshizaki, T. (2018). Effect of psychosocial distress on outcome for head and neck cancer patients undergoing radiation. Laryngoscope, 128, 641–645. https://doi.org/10.1002/lary.26751

Else-Quest, N.M., LoConte, N.K., Schiller, J.H., & Hyde, J.S. (2009). Perceived stigma, self-blame, and adjustment among lung, breast and prostate cancer patients. Psychology and Health, 24, 949–964. https://doi.org/10.1080/08870440802074664

Giese-Davis, J., Waller, A., Carlson, L.E., Groff, S., Zhong, L., Neri, E., . . . Bultz, B.D. (2012). Screening for distress, the 6th vital sign: Common problems in cancer outpatients over one year in usual care: Associations with marital status, sex, and age. BMC Cancer, 12, 441. https://doi.org/10.1186/1471-2407-12-441

Hamilton, J., Dwyer, M., Maurer, S., & Gates, M. (2015). Beyond distress: A fully integrated needs-based model for screening, triage, and care of cancer patients [Poster abstract P2–62]. Psycho-Oncology, 24(Suppl. 2), 262.

Hopkinson, J.B., Wright, D.N., McDonald, J.W., & Corner, J.L. (2006). The prevalence of concern about weight loss and change in eating habits in people with advanced cancer. Journal of Pain and Symptom Management, 32, 322–331. https://doi.org/10.1016/j.jpainsymman.2006.05.012

Howlader, N., Noone, A.M., Krapcho, M., Miller, D., Bishop, K., Altekruse, S.F., . . . Cronin, K.A. (Eds.). (2016). SEER cancer statistics review, 1975–2013. Retrieved from https://seer.cancer.gov/csr/1975_2013

Hwang, J., & Yun, Z.-S. (2015). Mechanism of psychological distress-driven smoking addiction behavior. Journal of Business Research, 68, 2189–2197. https://doi.org/10.1016/j.jbusres.2015.03.019

Inverso, G., Mahal, B.A., Aizer, A.A., Donoff, R.B., Chau, N.G., & Haddad, R.I. (2015). Marital status and head and neck cancer outcomes. Cancer, 121, 1273–1278. https://doi.org/10.1002/cncr.29171

Jacobsen, P.B., Donovan, K.A., Trask, P.C., Fleishman, S.B., Zabora, J., Baker, F., & Holland, J.C. (2005). Screening for psychologic distress in ambulatory cancer patients: A multicenter evaluation of the Distress Thermometer. Cancer, 103, 1494–1502. https://doi.org/10.1002/cncr.20940

Kamen, C., Mustian, K.M., Heckler, C., Janelsins, M.C., Peppone, L.J., Mohile, S., . . . Morrow, G.R. (2015). The association between partner support and psychological distress among prostate cancer survivors in a nationwide study. Journal of Cancer Survivorship, 9, 492–499. https://doi.org/10.1007/s11764-015-0425-3

Lis, C.G., Gupta, D., Lammersfeld, C.A., Markman, M., & Vashi, P.G. (2012). Role of nutritional status in predicting quality of life outcomes in cancer—A systematic review of the epidemiological literature. Nutrition Journal, 11, 27. https://doi.org/10.1186/1475-2891-11-27

Penner, L.A., Dovidio, J.F., Gonzalez, R., Albrecht, T.L., Chapman, R., Foster, T., . . . Eggly, S. (2016). The effects of oncologist implicit racial bias in racially discordant oncology interactions. Journal of Clinical Oncology, 34, 2874–2880. https://doi.org/10.1200/JCO.2015.66.3658

Pirl, W.F., Jacobsen, P.B., & Deshields, T.L. (2013). Opportunities for improving psychosocial care for cancer survivors. Journal of Clinical Oncology, 31, 1920–1921. https://doi.org/10.1200/JCO.2012.48.0566

Ravasco, P., Monteiro-Grillo, I., & Camilo, M.E. (2003). Does nutrition influence quality of life in cancer patients undergoing radiotherapy? Radiotherapy and Oncology, 67, 213–220. https://doi.org/10.1016/S0167-8140(03)00040-9

Roth, A.J., Kornblith, A.B., Batel-Copel, L., Peabody, E., Scher, H.I., & Holland, J.C. (1998). Rapid screening for psychological distress in men with prostate carcinoma. Cancer, 82, 1904–1908.

Sales, P.M., Carvalho, A.F., McIntyre, R.S., Pavlidis, N., & Hyphantis, T.N. (2014). Psychosocial predictors of health outcomes in colorectal cancer: A comprehensive review. Cancer Treatment Reviews, 40, 800–809. https://doi.org/10.1016/j.ctrv.2014.03.001

Sellick, S.M., & Edwardson, A.D. (2007). Screening new cancer patients for psychological distress using the Hospital Anxiety and Depression Scale. Psycho-Oncology, 16, 534–542. https://doi.org/10.1002/pon.1085

Stafford, L., Komiti, A., Bousman, C., Judd, F., Gibson, P., Mann, G.B., & Quinn, M. (2016). Predictors of depression and anxiety symptom trajectories in the 24 months following diagnosis of breast or gynaecologic cancer. Breast, 26, 100–105. https://doi.org/10.1016/j.breast.2016.01.008

Trabal, J., Leyes, P., Forga, M.T., & Hervás, S. (2006). Quality of life, dietary intake and nutritional status assessment in hospital admitted cancer patients. Nutricion Hospitalaria, 21, 505–510.

Ward, E., Jemal, A., Cokkinides, V., Sing, G.K., Cardinez, C., Ghafoor, A., & Thun, M. (2004). Cancer disparities by race/ethnicity and socioeconomic status. CA: A Cancer Journal for Clinicians, 54, 78–93.

Weinstein, N.D. (1980). Unrealistic optimism about future life events. Journal of Personality and Social Psychology, 39, 806–820. https://doi.org/10.1037/0022-3514.39.5.806

World Health Organization. (1995). Physical status: The use and interpretation of anthropometry. Report of a WHO Expert Committee. Retrieved from http://apps.who.int/iris/bitstream/handle/10665/37003/WHO_TRS_854.pdf;js...

Zabora, J., BrintzenhofeSzoc, K., Curbow, B., Hooker, C., & Piantadosi, S. (2001). The prevalence of psychological distress by cancer site. Psycho-Oncology, 10, 19–28.