Comorbidities, Rather Than Older Age, Are Strongly Associated With Higher Utilization of Healthcare in Colorectal Cancer Survivors

View More View Less
  • 1 Unit of Cancer Survivorship, Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg;
  • | 2 Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg;
  • | 3 SLK-Clinics, Cancer Center Heilbronn-Franken, Heilbronn;
  • | 4 Unit of Genetic Epidemiology, Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg;
  • | 5 Genetic Tumour Epidemiology Group, University Medical Center Hamburg-Eppendorf, University Cancer Center Hamburg, Hamburg;
  • | 6 Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg; and
  • | 7 German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany.

Background: Colorectal cancer (CRC) survivors generally have a higher healthcare utilization (HCU) than the general population due to cancer burden. However, it is unclear which factors are associated with this increased uptake. Our study aimed to (1) compare CRC-related and non-CRC visits to general practitioners (GPs) and medical specialists (MSs) by comorbidities, and (2) assess whether HCU differs by demographic, clinical, and psychological factors. Methods: We used data from a German population-based cohort of 1,718 survivors of stage I–III CRC diagnosed in 2003 through 2010 who provided information on HCU at 5-year follow-up. Multivariable linear regression was used to calculate least-square means of CRC-related and non-CRC HCU according to the Charlson comorbidity index and comorbidity cluster, adjusting for relevant demographic, clinical, and psychological characteristics. Results: A higher comorbidity level was associated with more CRC-related MS visits and non-CRC GP visits. In addition to being strongly associated with non-CRC GP visits, comorbidity clusters were associated with CRC-related GP and MS visits, but their association varied by specific cardiometabolic comorbidities. HCU was less dependent on prognostic factors for CRC, such as age and tumor stage, but was strongly associated with disease recurrence, depression, and emotional functioning. Conclusions: Comorbidities, rather than age or tumor stage, were related to HCU, suggesting that CRC survivors use healthcare mainly for reasons other than cancer 5 years postdiagnosis. Improved communication between primary and tertiary healthcare providers could enhance the medical care of cancer survivors with complex health needs and thereby also reduce healthcare costs.

Background

Global demographic aging, early diagnosis, and improvement in colorectal cancer (CRC) treatments have increased the number of CRC survivors in many countries. In Germany, the 5-year relative survival rate increased from approximately 60% in 2012 to approximately 62% in 2016.1 CRC is predominantly diagnosed at older age, when chronic conditions such as diabetes and cardiovascular diseases (CVDs) are common.2,3 Comorbidity prevalence in CRC survivors is high, ranging from 46% to 62%,46 when compared with control patients without cancer (11%–50%).7

In addition to being associated with poorer health outcomes, such as higher symptom burden, lower health-related quality of life, and higher mortality,811 comorbidity is associated with higher healthcare utilization (HCU) among cancer survivors than among control patients without cancer.1214 Age could explain this HCU excess. However, 2 Dutch studies that used data from the same national primary care registry reported contradictory results despite having a comparable sampling time frame. The first study, which included all registered cancer survivors, reported that general practitioner (GP) consultations increased with age and number of comorbid conditions, similar for cancer survivors and matched control patients.13 In contrast, the second study, which sampled only survivors of breast cancer, prostate cancer, and CRC, reported that this difference in GP visits was more prominent among younger CRC survivors and those without comorbid disease.15 These studies did not account for the severity of comorbid conditions, but rather reported on either selected individual comorbid conditions or the sum of the selected comorbid conditions. Furthermore, distinct clusters of comorbidities in CRC survivors are differentially associated with treatment and survival.5 Survivors of stage III CRC with preexisting vascular or respiratory conditions are less likely to receive guideline-recommended therapy than survivors with other comorbidities.5 However, we have found no published results on the association between comorbidity clusters and HCU.

The increasing number of older CRC survivors with complex health issues could have a considerable impact on the healthcare system.16 A better understanding of their healthcare needs could help healthcare providers improve efficiency and reduce costs of care delivery. Our study aims were (1) to compare HCU (CRC-related and non-CRC visits) among CRC survivors by comorbidity scores (based on severity of comorbid conditions) and by specific comorbidity clusters (eg, CVD, metabolic diseases),5 and (2) to assess whether and to what extent HCU differs by demographic, clinical, and psychological factors in CRC survivors surviving beyond 5 years.

Methods

Setting and Participants

We used data from the population-based DACHS study, an ongoing case-control study with additional follow-up of patients with CRC. The DACHS study was primarily designed to assess the effect of endoscopic screening on CRC prevention. It was started in 2003 in collaboration with 22 hospitals located in southwestern Germany. Patients are additionally followed up for evaluation of treatment-related and prognostic factors. Eligible patients with a newly diagnosed and histologically confirmed primary CRC (ICD-10 codes C18–C20) are identified by their treating clinicians during their hospital stay for CRC surgery or are contacted by mail shortly after discharge by clinicians or clinical cancer registries. Details of the DACHS study have been reported elsewhere.1719 All respondents to the baseline assessment received a 5-year follow-up. The current study reports on participants diagnosed with stage I–III CRC between 2003 and 2010 who completed a HCU survey at 5-year follow-up between 2009 and 2016 (supplemental eFigure 1, available with this article at JNCCN.org).

The ethics committee of the University of Heidelberg and the state medical boards of Baden-Württemberg and Rhineland-Palatinate approved the DACHS study. All participants provided written informed consent.

Data Collection

At baseline, trained interviewers collected detailed sociodemographic, clinical, and lifestyle history from study participants. Attending physicians provided detailed treatment, recurrence, and incident comorbidity information at the 3-year follow-up, and verified recurrence or new cancers (“disease recurrence”) reported by participants at the 5-year follow-up. At the 5-year follow-up, approximately 86% of participants who were alive completed a mailed questionnaire that included information on HCU, depressive symptoms, fear of recurrence, emotional well-being, and changes in medical or recurrence history. Relevant sociodemographic and clinical data from baseline and the 3-year follow-up and patient-reported outcomes, such as HCU and psychological well-being collected at the 5-year follow-up, were combined for this analysis.

Ascertainment of Comorbidities

Comorbidities that preexisted or were diagnosed at the time of CRC diagnosis were ascertained with ICD-10 codes abstracted from medical records.4 We used a modified Charlson comorbidity index (CCI) to calculate an overall comorbidity score (CCIS).20 The definition of “other cancers” in the CCI considered all primary cancers other than CRC and basal cell carcinoma. Survivors were grouped into 3 categories according to the overall comorbidity score: 0 (no comorbidity), 1 (mild comorbidity), or ≥2 (moderate/severe comorbidity). We used the CCI to derive comorbidity clusters: CVD (myocardial infarction, chronic heart failure, peripheral vascular disease, stroke ± hemiplegia), metabolic (diabetes mellitus, chronic renal disease, liver disease), and other (dementia, chronic obstructive pulmonary disease, other cancers, peptic ulcer, rheumatoid disease).

Healthcare Utilization

Participants provided the number of CRC-related and non-CRC visits to the GP or medical specialist (MS) in the past 12 months. Visits to the MS for CRC-related matters included visits to oncologists, internists, or gastroenterologists. For non-CRC matters, visits could include those to oncologists, internists, gastroenterologists, or psychologists.

Psychological Well-Being

Geriatric Depression Scale

The validated and reliable 15-item Geriatric Depression Scale (GDS) was answered with either a yes or a no.21 Of a maximum score of 15, a score of 5 to 10 suggested “mild depression” and a score of ≥11 suggested “severe depression.”22

Fear of Recurrence

We used the item “fear of disease progression” from the validated Questionnaire on Distress in Cancer Survivors.23 This item was scored from 0 (“not applicable”) to 5 (“a very serious problem”).23 We defined a priori that a score of 4 or 5 indicated a moderate/high level of fear of recurrence.24

Emotional Well-Being

The emotional functioning subscale from the validated EORTC Quality of Life Questionnaire-Core 30 (EORTC QLQ-C30) assessed emotional well-being.25 The subscale comprises 4 items, and answers range from 1 (“not at all”) to 4 (“very much”). Raw scores were linearly transformed to a scale of 0 to 100 using standard procedures.26 A higher score indicated better functioning.

Statistical Analyses

Differences in the distributions of demographic and clinical characteristics by CCIS were tested for statistical significance using either an analysis of variance for continuous variables or a chi-square test for categorical variables. We used multivariable linear regression to calculate the least-square means, separately, of CRC-related and non-CRC HCU for overall CCIS and comorbidity cluster. Covariates, selected a priori for adjustment, were age at diagnosis, sex, marital status, health insurance, cancer stage, disease recurrence, GDS score, fear of recurrence, and emotional functioning. We tested for interactions between comorbidity (CCIS and cluster) and sex, disease recurrence, or depression (GDS score) on HCU.

Multiple linear regression was used to explore whether and to what extent demographic, clinical, and psychological characteristics were associated with HCU (separate for CRC-related and non-CRC visits). We conducted sensitivity analyses on the linear regression models, excluding survivors with disease recurrence.

All analyses were conducted using SAS 9.4 (SAS Institute Inc). Statistical significance was determined at P<.05 (2-sided). The P values were not adjusted for multiple testing and referred to the individual tests rather than a global test for differences.

Results

Survivors’ Characteristics

This study included 1,718 survivors of stage I–III CRC (supplemental eFigure 1). Most of the respondents (70%) had no comorbid conditions reported in the medical records (Table 1). Survivors with no preexisting comorbidities were more likely to be younger (no comorbidity: mean age, 64.9 years; mild comorbidity: mean age, 68.6 years; moderate/severe comorbidity: mean age, 70.6 years), female, and employed; have a higher level of education; have a body mass index <25 kg/m2; be a nonsmoker; be less likely to be depressed according to the GDS; have received chemotherapy; and have private insurance compared with survivors with comorbid conditions (Table 1).

Table 1.

Demographic and Clinical Characteristics by Severity of Comorbid Condition Severity at Baseline

Table 1.

Mean HCU

Charlson Comorbidity Index Score

Numbers of both CRC-related and non-CRC GP and MS visits increased with increasing CCIS, even though the differences between groups were statistically significant only for CRC-related MS visits and non-CRC GP visits (Figure 1).

Figure 1.
Figure 1.

Least-square mean number of CRC-related and non-CRC visits made to healthcare providers in the past 12 months by severity of comorbidity. Results were adjusted for age at survey, sex, marital status, health insurance, cancer stage, disease recurrence, GDS score, fear of recurrence, and EORTC QLQ-C30 emotional functioning subscale score.

Abbreviations: CCIS, Charlson comorbidity index score; CRC, colorectal cancer; EORTC QLQ-C30, EORTC Quality of Life Questionnaire-Core 30; GDS, Geriatric Depression Scale; GP, general practitioner; MS, medical specialist.

*The line span between the columns indicates a significant difference in healthcare visits in pairwise comparison (P<.05).

Citation: Journal of the National Comprehensive Cancer Network 20, 5; 10.6004/jnccn.2021.7030

Comorbidity Clusters

There were no significant differences between the comorbidity clusters in CRC-related GP or MS visits (Figure 2). For non-CRC GP visits, survivors with no comorbidities reported the lowest number of visits when compared with survivors with specific clusters of comorbidities, all significantly different except for multiple-comorbidity clusters. Survivors in the CVD cluster reported the highest number of non-CRC GP visits. No significant differences in MS visits were noted between the clusters.

Figure 2.
Figure 2.

Least-square mean number of CRC-related and non-CRC visits made to healthcare providers in the past 12 months by clusters of comorbidity. Results were adjusted for age at survey, sex, partnership status, health insurance, cancer stage, disease recurrence, GDS score, fear of recurrence, and EORTC QLQ-C30 emotional functioning subscale score.

Abbreviations: CRC, colorectal cancer; CVD, cardiovascular disease; EORTC QLQ-C30, EORTC Quality of Life Questionnaire-Core 30; GDS, Geriatric Depression Scale; GP, general practitioner; MS, medical specialist.

aMyocardial infarction, chronic heart failure, peripheral vascular disease, stroke with/without hemiplegia.

bDiabetes mellitus, chronic renal disease, liver disease.

cDementia, chronic obstructive pulmonary disease, non-CRC cancers, peptic ulcer, rheumatoid disease.

d>1 cluster of comorbid conditions.

*The line span between the columns indicates a significant difference in healthcare visits in pairwise comparison (P<.05).

Citation: Journal of the National Comprehensive Cancer Network 20, 5; 10.6004/jnccn.2021.7030

Factors Associated With HCU

By Comorbidity Definition

The CCIS was not associated with CRC-related GP visits (Table 2). However, a moderate/severe CCIS remained significantly associated with CRC-related MS visits, even after adjusting for demographic, clinical, and psychological factors. In comorbidity cluster analyses, CVD was significantly associated with CRC-related GP visits but not with MS visits. In contrast, the metabolic cluster was significantly associated with CRC-related MS visits but not with GP visits.

Table 2.

Associations of Different Comorbidity Definitions With CRC-Related Visits to Healthcare Providers in the Past 12 Months

Table 2.

For non-CRC visits, mild and moderate/severe CCIS were significantly associated with more GP visits, even after adjustments for demographic, clinical, and psychological factors (Table 3). No significant association was found between CCIS and MS visits. In comorbidity cluster analyses, CVD, metabolic, and other clusters were significantly associated with increased non-CRC GP visits. Comorbidity clusters were not associated with non-CRC MS visits.

Table 3.

Associations of Different Comorbidity Definitions With Non-CRC Visits to Healthcare Providers in the Past 12 Months

Table 3.

Interactions between comorbidity and sex regarding HCU were generally not significant, except for the comorbidity cluster * sex interaction for CRC-related GP visits, where the effect was more prominent among female survivors with CVD (supplemental eTable 1). The CCIS * disease recurrence interaction for CRC-related and non-CRC MS visits was more prominent among survivors with disease recurrence (P<.001 for interaction). The comorbidity cluster * disease recurrence interactions suggested that survivors with recurrence and CVD or metabolic disease had more CRC-related GP visits. Survivors with recurrence and a metabolic disease comorbidity more often visited an MS for CRC-related reasons.

Other Factors

Age was not significantly associated with CRC-related GP or MS visits (supplemental eTable 2). Disease recurrence was significantly associated with CRC-related GP or MS visits, regardless of adjustment that included the CCIS or comorbidity clusters. Depression was associated with more CRC-related MS visits.

For non-CRC HCU, age was not significantly associated with GP or MS visits (supplemental eTable 3). A lower level of education was associated with increased non-CRC GP visits. In contrast, being single was associated with fewer non-CRC GP visits. Chemotherapy, stage III CRC, and disease recurrence were significantly associated with non-CRC MS visits. Disease recurrence was associated with more non-CRC GP visits in analyses that adjusted for comorbidity clusters but not for the CCIS. Mild depression was associated with more non-CRC MS visits, and severe depression was associated with more GP and MS visits. Higher emotional functioning was associated with fewer GP visits.

Sensitivity Analysis

After excluding survivors with disease recurrence, we observed similar patterns of associations between comorbidity (CCIS or clusters) and CRC-related GP and MS visits. However, the magnitude of the association was attenuated slightly, especially for MS visits (supplemental eTable 4). For non-CRC GP visits, results were similar to those from the whole sample. In contrast, disease-free survivors with preexisting comorbidity showed a tendency for fewer non-CRC MS visits, but the difference was not statistically significant. There was no longer a significant interaction between comorbidity (CCIS or clusters) and sex. Contrary to findings from the whole sample, there was a significant interaction between comorbidity clusters and depression for non-CRC GP visits, specifically among survivors with CVD or metabolic conditions and mild depression symptoms.

Regarding other factors associated with CRC-related visits, the association between private health insurance and MS visits almost doubled and reached statistical significance, in contrast to results using the whole sample (supplemental eTable 5). The magnitude of the association between severe depression and CRC-related MS visits was attenuated and lost statistical significance after excluding survivors with disease recurrence. Results that were adjusted for CCIS were similar to those adjusted for comorbidity clusters (data not shown).

Discussion

Due to the aging population and improvements in treatment, the number of CRC survivors is increasing in many countries, including Germany. Like other cancer survivors, CRC survivors use healthcare more frequently than age-matched control patients without cancer. However, it is unclear which factors are associated with HCU among long-term cancer survivors. In a cohort of survivors of stage I–III CRC assessed at 5-year follow-up, we found that comorbidity rather than older age was strongly associated with more frequent healthcare visits. Other factors independently associated with greater HCU were lower education level, receipt of chemotherapy, disease recurrence, and depression. By contrast, stage III disease and higher emotional functioning were associated with fewer healthcare consultations.

Our data suggest that survivors with disease recurrence had significantly more CRC-related GP and MS visits. This increased HCU is clinically plausible because these survivors were likely to have further treatment. In accordance with German clinical guidelines for stage I–III CRC, follow-up visits are recommended if disease recurrence has therapeutic consequences, but a programmed follow-up is not necessary for survivors with good prognosis.27

Comorbidity showed strong associations with non-CRC GP visits. This finding is in line with previous studies in which cancer survivors with comorbidity visited the GP more often than those without comorbidity or control patients without cancer.1214 This association remained strong even after we excluded survivors with disease recurrence. Notably, the magnitude of associations with HCU did not differ significantly between the 2 comorbidity definitions. It is well-known that information gleaned from comorbidity clusters could be more specific than that available from the CCIS, which is a weighted summation of conditions. Significant interactions were more likely to be found when we used comorbidity clusters rather than the CCIS, indicating that survivors with CVD or metabolic conditions were more likely to visit their healthcare providers. The prognostic utility of comorbidity clusters for health outcomes research should be further assessed in future studies.

We found associations between psychological factors and HCU. Higher levels of emotional functioning were associated with fewer consultations, but depression was associated with increased visits to healthcare professionals, similar to previous evidence.28 Medical expenditures for cancer survivors with psychological distress were significantly higher than those for survivors without psychological distress or control patients without cancer who had psychological distress.29 Fear of recurrence could be associated with more frequent medical consultations.30 Cancer survivors with an elevated fear of disease recurrence have been shown to consult medical and psychosocial personnel more often within 18 months after cancer surgery.31 However, we did not find an association between HCU and fear of recurrence. Possible reasons for this disparity could be that previous studies included survivors of heterogeneous cancers up to 10 years postdiagnosis30 or those who were younger, female, and predominantly with breast cancer.31 Our sample consisted of survivors with at least a 5-year survival. It is possible that within these 5 years, access to regular follow-ups with a cancer specialist in accordance with clinical guidelines27 could have attenuated the association between fear of recurrence and HCU.

GP consultations generally increase with age.12,13,15,32 Although we noted a similar trend in our study, this association was not significant. This difference in results could be because previous studies compared HCU in patients with cancer with HCU in control patients without cancer and did not correct extensively for other potential demographic, clinical, and psychological factors as in our study. Instead, we noted that a lower level of education was associated with more frequent GP visits. For these survivors, visits to their GP could serve as a coping mechanism for illness-related psychological distress.28 We found that survivors with a lower level of education were more likely to have depressive symptoms (P<.001; data not shown). It is also possible that survivors feel more comfortable communicating with their GP than an MS even for CRC-related matters. Cancer survivors spoke of not feeling known or heard by their oncology healthcare team.33 The provision of clear information (eg, using less-technical language) could reduce psychological distress and improve functional health literacy in cancer survivors,34,35 especially among those with a lower level of education who also have multiple morbidities.36

Clinical Implications

Cancer specialists may consider the management of comorbid conditions beyond their role or expertise37,38 and may prefer that GPs manage preexisting cardiometabolic or psychiatric conditions of cancer survivors.39 In contrast, GPs have been shown to be less comfortable with managing potential cancer-related symptoms.37 In our study, we observed that survivors would visit a GP for CRC-related matters. Previous studies suggest that cancer survivors with multiple morbidities may have fragmented care across medical specialties and an increased risk of potentially inappropriate medications due to polypharmacy, both of which may increase HCU and costs.40,41 Therefore, the provision of care to cancer survivors with comorbidities needs to be better integrated and coordinated between cancer specialists and GPs.40,42

Chemotherapy was associated with increased non–CRC-related MS visits in our study, probably because of long-term adverse treatment effects, such as neuropathy or fatigue. These symptoms can persist and have a negative impact on functioning.43,44 Our results suggest that improved communication about the risk of potential chronic treatment-related effects could be a point of focus for healthcare providers. The provision of clear information may also assist CRC survivors better adapt to and self-manage these persistent adverse effects.4547

We found that depressive symptoms were associated with HCU, even after excluding survivors with disease recurrence. However, we used the GDS, which is a screening instrument. Hence, depression needs to be confirmed through a clinical interview. Detailed evaluation not only assists with treatment planning but also rules out possible fatigue, which is associated with depression.47 Healthcare providers should therefore monitor cancer survivors for possible psychological distress and refer them to psychological care, if necessary. Cancer survivors who received treatment for depression had lower total annual healthcare expenditures than those with untreated depression.48

Strengths and Limitations

Our study collected detailed baseline comorbidity information from a large population-based sample of patients who completed uniform follow-ups. Nevertheless, our study has limitations. We explored the association between baseline comorbidity and HCU at 5 years postdiagnosis, even though comorbidity prevalence may have increased postcancer.49 Visits to healthcare providers were self-reported, thereby increasing the possibility of recall bias. We do not have information on HCU from healthcare providers for corroboration. In addition, we only assessed the frequency of HCU, and we lacked information on the specifics of the visits. Furthermore, we did not have a standardized criterion to determine excessive HCU. Given that a number of psychological factors were assessed at the time of evaluating HCU (cross-sectional), we were not able to infer any causal relationship between psychological factors and uptake of healthcare.

Conclusions

Among CRC survivors, HCU at 5-year follow-up was less dependent on age and tumor stage but was largely related to comorbidities, suggesting that the uptake of healthcare is based mainly on reasons other than cancer. Improved communication between primary and tertiary healthcare providers and with cancer survivors could benefit the care of cancer survivors with complex health needs and thereby also reduce healthcare costs.

Acknowledgments

The authors thank Ute Handte-Daub, Ansgar Brandhorst, and Petra Bächer for their excellent technical assistance. The authors thank the study participants and the interviewers who collected the data. The authors also thank the following hospitals and cooperating institutions that recruited patients for this study: Chirurgische Universitätsklinik Heidelberg, Klinik am Gesundbrunnen Heilbronn, St. Vincentiuskrankenhaus Speyer, St. Josefskrankenhaus Heidelberg, Chirurgische Universitätsklinik Mannheim, Diakonissenkrankenhaus Speyer, Krankenhaus Salem Heidelberg, Kreiskrankenhaus Schwetzingen, St. Marienkrankenhaus Ludwigshafen, Klinikum Ludwigshafen, Stadtklinik Frankenthal, Diakoniekrankenhaus Mannheim, Kreiskrankenhaus Sinsheim, Klinikum am Plattenwald Bad Friedrichshall, Kreiskrankenhaus Weinheim, Kreiskrankenhaus Eberbach, Kreiskrankenhaus Buchen, Kreiskrankenhaus Mosbach, Enddarmzentrum Mannheim, Kreiskrankenhaus Brackenheim, and the Cancer Registry of Rhineland-Palatinate, Mainz.

References

  • 1.

    Robert Koch Institute. Cancer in Germany 2015-2016. Berlin, Germany: Robert Koch-Institut; 2019.

  • 2.

    Cohen HJ. Keynote comment: cancer survivorship and ageing—a double whammy. Lancet Oncol 2006;7:882883.

  • 3.

    Fowler H, Belot A, Ellis L, et al. Comorbidity prevalence among cancer patients: a population-based cohort study of four cancers. BMC Cancer 2020;20:2.

  • 4.

    Boakye D, Jansen L, Schneider M, et al. Personalizing the prediction of colorectal cancer prognosis by incorporating comorbidities and functional status into prognostic nomograms. Cancers (Basel) 2019;11:1435.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 5.

    Hahn EE, Gould MK, Munoz-Plaza CE, et al. Understanding comorbidity profiles and their effect on treatment and survival in patients with colorectal cancer. J Natl Compr Canc Netw 2018;16:2334.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 6.

    van Leersum NJ, Janssen-Heijnen ML, Wouters MW, et al. Increasing prevalence of comorbidity in patients with colorectal cancer in the South of the Netherlands 1995-2010. Int J Cancer 2013;132:21572163.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 7.

    Berry NM, Miller MD, Woodman RJ, et al. Differences in chronic conditions and lifestyle behaviour between people with a history of cancer and matched controls. Med J Aust 2014;201:96100.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 8.

    Morishima T, Matsumoto Y, Koeda N, et al. Impact of comorbidities on survival in gastric, colorectal, and lung cancer patients. J Epidemiol 2019;29:110115.

  • 9.

    Cummings A, Grimmett C, Calman L, et al. Comorbidities are associated with poorer quality of life and functioning and worse symptoms in the 5 years following colorectal cancer surgery: results from the ColoREctal Well-being (CREW) cohort study. Psychooncology 2018;27:24272435.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 10.

    Fosså SD, Hess SL, Dahl AA, et al. Stability of health-related quality of life in the Norwegian general population and impact of chronic morbidity in individuals with and without a cancer diagnosis. Acta Oncol 2007;46:452461.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 11.

    Heins MJ, Korevaar JC, Hopman PEPC, et al. Health-related quality of life and health care use in cancer survivors compared with patients with chronic diseases. Cancer 2016;122:962970.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 12.

    Heins MJ, Korevaar JC, Donker GA, et al. The combined effect of cancer and chronic diseases on general practitioner consultation rates. Cancer Epidemiol 2015;39:109114.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 13.

    Jabaaij L, van den Akker M, Schellevis FG. Excess of health care use in general practice and of comorbid chronic conditions in cancer patients compared to controls. BMC Fam Pract 2012;13:60.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 14.

    Ng HS, Koczwara B, Roder D, et al. Patterns of health service utilisation among the Australian population with cancer compared with the general population. Aust Health Rev 2020;44:470479.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 15.

    Heins M, Schellevis F, Rijken M, et al. Determinants of increased primary health care use in cancer survivors. J Clin Oncol 2012;30:41554160.

  • 16.

    Wodchis WP, Arthurs E, Khan AI, et al. Cost trajectories for cancer patients. Curr Oncol 2016;23(Suppl 1):S6475.

  • 17.

    Boakye D, Walter V, Jansen L, et al. Magnitude of the age-advancement effect of comorbidities in colorectal cancer prognosis. J Natl Compr Canc Netw 2020;18:5968.

  • 18.

    Brenner H, Chang-Claude J, Jansen L, et al. Reduced risk of colorectal cancer up to 10 years after screening, surveillance, or diagnostic colonoscopy. Gastroenterology 2014;146:709717.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 19.

    Eyl RE, Thong MSY, Carr PR, et al. Physical activity and long-term fatigue among colorectal cancer survivors—a population-based prospective study. BMC Cancer 2020;20:438.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 20.

    Charlson ME, Pompei P, Ales KL, et al. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 1987;40:373383.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 21.

    Yesavage JA, Sheikh JI. Geriatric Depression Scale (GDS): recent evidence and development of a shorter version. Clin Gerontol 1986;5:165173.

  • 22.

    Clegg A, Barber S, Young J, et al. The Home-Based Older People’s Exercise (HOPE) trial: a pilot randomised controlled trial of a home-based exercise intervention for older people with frailty. Age Ageing 2014;43:687695.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 23.

    Book K, Marten-Mittag B, Henrich G, et al. Distress screening in oncology—evaluation of the Questionnaire on Distress in Cancer Patients-short form (QSC-R10) in a German sample. Psychooncology 2011;20:287293.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 24.

    Thong MSY, Jansen L, Chang-Claude J, et al. Association of laparoscopic colectomy versus open colectomy on the long-term health-related quality of life of colon cancer survivors. Surg Endosc 2020;34:55935603.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 25.

    Aaronson NK, Ahmedzai S, Bergman B, et al. The European Organization for Research and Treatment of Cancer QLQ-C30: a quality-of-life instrument for use in international clinical trials in oncology. J Natl Cancer Inst 1993;85:365376.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 26.

    Fayers PM, Aaronson NK, Bjordal K, et al. EORTC QLQ-C30 Scoring Manual. Brussels, Belgium: EORTC; 1995.

  • 27.

    German Guideline Program in Oncology. Colorectal cancer, version 2.1. Accessed February 23, 2021. Available at: https://www.leitlinienprogramm-onkologie.de/leitlinien/kolorektales-karzinom/

    • Search Google Scholar
    • Export Citation
  • 28.

    Arts LPJ, Oerlemans S, Tick L, et al. More frequent use of health care services among distressed compared with nondistressed survivors of lymphoma and chronic lymphocytic leukemia: results from the population-based PROFILES registry. Cancer 2018;124:30163024.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 29.

    Han X, Lin CC, Li C, et al. Association between serious psychological distress and health care use and expenditures by cancer history. Cancer 2015;121:614622.

  • 30.

    Lebel S, Tomei C, Feldstain A, et al. Does fear of cancer recurrence predict cancer survivors’ health care use? Support Care Cancer 2013;21:901906.

  • 31.

    Champagne A, Ivers H, Savard J. Utilization of health care services in cancer patients with elevated fear of cancer recurrence. Psychooncology 2018;27:19581964.

  • 32.

    Rijken M, Valderas JM, Heins M, et al. Identifying high-need patients with multimorbidity from their illness perceptions and personal resources to manage their health and care: a longitudinal study. BMC Fam Pract 2020;21:75.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 33.

    Duthie K, Strohschein FJ, Loiselle CG. Living with cancer and other chronic conditions: patients’ perceptions of their healthcare experience. Can Oncol Nurs J 2017;27:4348.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 34.

    Cox N, Bowmer C, Ring A. Health literacy and the provision of information to women with breast cancer. Clin Oncol (R Coll Radiol) 2011;23:223227.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 35.

    Husson O, Mols F, van de Poll-Franse LV. The relation between information provision and health-related quality of life, anxiety and depression among cancer survivors: a systematic review. Ann Oncol 2011;22:761772.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 36.

    Friis K, Lasgaard M, Osborne RH, et al. Gaps in understanding health and engagement with healthcare providers across common long-term conditions: a population survey of health literacy in 29,473 Danish citizens. BMJ Open 2016;6:e009627.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 37.

    Cavers D, Habets L, Cunningham-Burley S, et al. Living with and beyond cancer with comorbid illness: a qualitative systematic review and evidence synthesis. J Cancer Surviv 2019;13:148159.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 38.

    Ritchie CS, Zhao F, Patel K, et al. Association between patients’ perception of the comorbidity burden and symptoms in outpatients with common solid tumors. Cancer 2017;123:38353842.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 39.

    Chou C, Hohmann NS, Hastings TJ, et al. How comfortable are primary care physicians and oncologists prescribing medications for comorbidities in patients with cancer? Res Social Adm Pharm 2020;16:10871094.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 40.

    Blane DN, Lewandowska M. Living with cancer and multimorbidity: the role of primary care. Curr Opin Support Palliat Care 2019;13:213219.

  • 41.

    Feng X, Higa GM, Safarudin F, et al. Potentially inappropriate medication use and associated healthcare utilization and costs among older adults with colorectal, breast, and prostate cancers. J Geriatr Oncol 2019;10:698704.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 42.

    Sarfati D, Koczwara B, Jackson C. The impact of comorbidity on cancer and its treatment. CA Cancer J Clin 2016;66:337350.

  • 43.

    Mols F, Beijers T, Lemmens V, et al. Chemotherapy-induced neuropathy and its association with quality of life among 2- to 11-year colorectal cancer survivors: results from the population-based PROFILES registry. J Clin Oncol 2013;31:26992707.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 44.

    Husson O, Mols F, van de Poll-Franse LV, et al. The course of fatigue and its correlates in colorectal cancer survivors: a prospective cohort study of the PROFILES registry. Support Care Cancer 2015;23:33613371.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 45.

    Tanay MAL, Armes J, Ream E. The experience of chemotherapy-induced peripheral neuropathy in adult cancer patients: a qualitative thematic synthesis. Eur J Cancer Care (Engl) 2017;26:e12443.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 46.

    Knoerl R, Smith EML, Han A, et al. Characterizing patient-clinician chemotherapy-induced peripheral neuropathy assessment and management communication approaches. Patient Educ Couns 2019;102:16361643.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 47.

    Thong MSY, van Noorden CJF, Steindorf K, et al. Cancer-related fatigue: causes and current treatment options. Curr Treat Options Oncol 2020;21:17.

  • 48.

    Mausbach BT, Bos T, Irwin SA. Mental health treatment dose and annual healthcare costs in patients with cancer and major depressive disorder. Health Psychol 2018;37:10351040.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 49.

    Kenzik KM, Kent EE, Martin MY, et al. Chronic condition clusters and functional impairment in older cancer survivors: a population-based study. J Cancer Surviv 2016;10:10961103.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation

Submitted July 17, 2020; final revision received February 1, 2021; accepted for publication February 17, 2021. Published online December 7, 2021.

Author contributions: Study concept: Thong, Boakye, Arndt. Conceptualization of DACHS and IMPACT studies: Chang-Claude, Hoffmeister, Brenner. Data curation: Jansen. Formal analysis: Thong, Boakye. Funding acquisition: Chang-Claude, Hoffmeister, Brenner, Arndt. Writing—original draft: Thong. Writing—review and editing: All authors.

Disclosures: The authors have disclosed that they have not received any financial consideration from any person or organization to support the preparation, analysis, results, or discussion of this article.

Funding: This work was supported by the German Research Council (BR 1704/6-1, BR 1704/6-3, BR 1704/6-4, BR 1704/6-6, CH 117/1-1) and the German Federal Ministry of Education and Research (01KH0404, 01ER0814, 01ER0815).

Disclaimer: The funding source was neither involved in the collection, interpretation, and analysis of the data nor in the decision for the writing and submission of this report for publication.

Correspondence: Melissa S.Y. Thong, PhD, Unit of Cancer Survivorship (C071), Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), P.O. Box 101949, 69009 Heidelberg, Germany. Email: m.thong@dkfz.de

Supplementary Materials

  • View in gallery

    Least-square mean number of CRC-related and non-CRC visits made to healthcare providers in the past 12 months by severity of comorbidity. Results were adjusted for age at survey, sex, marital status, health insurance, cancer stage, disease recurrence, GDS score, fear of recurrence, and EORTC QLQ-C30 emotional functioning subscale score.

    Abbreviations: CCIS, Charlson comorbidity index score; CRC, colorectal cancer; EORTC QLQ-C30, EORTC Quality of Life Questionnaire-Core 30; GDS, Geriatric Depression Scale; GP, general practitioner; MS, medical specialist.

    *The line span between the columns indicates a significant difference in healthcare visits in pairwise comparison (P<.05).

  • View in gallery

    Least-square mean number of CRC-related and non-CRC visits made to healthcare providers in the past 12 months by clusters of comorbidity. Results were adjusted for age at survey, sex, partnership status, health insurance, cancer stage, disease recurrence, GDS score, fear of recurrence, and EORTC QLQ-C30 emotional functioning subscale score.

    Abbreviations: CRC, colorectal cancer; CVD, cardiovascular disease; EORTC QLQ-C30, EORTC Quality of Life Questionnaire-Core 30; GDS, Geriatric Depression Scale; GP, general practitioner; MS, medical specialist.

    aMyocardial infarction, chronic heart failure, peripheral vascular disease, stroke with/without hemiplegia.

    bDiabetes mellitus, chronic renal disease, liver disease.

    cDementia, chronic obstructive pulmonary disease, non-CRC cancers, peptic ulcer, rheumatoid disease.

    d>1 cluster of comorbid conditions.

    *The line span between the columns indicates a significant difference in healthcare visits in pairwise comparison (P<.05).

  • 1.

    Robert Koch Institute. Cancer in Germany 2015-2016. Berlin, Germany: Robert Koch-Institut; 2019.

  • 2.

    Cohen HJ. Keynote comment: cancer survivorship and ageing—a double whammy. Lancet Oncol 2006;7:882883.

  • 3.

    Fowler H, Belot A, Ellis L, et al. Comorbidity prevalence among cancer patients: a population-based cohort study of four cancers. BMC Cancer 2020;20:2.

  • 4.

    Boakye D, Jansen L, Schneider M, et al. Personalizing the prediction of colorectal cancer prognosis by incorporating comorbidities and functional status into prognostic nomograms. Cancers (Basel) 2019;11:1435.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 5.

    Hahn EE, Gould MK, Munoz-Plaza CE, et al. Understanding comorbidity profiles and their effect on treatment and survival in patients with colorectal cancer. J Natl Compr Canc Netw 2018;16:2334.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 6.

    van Leersum NJ, Janssen-Heijnen ML, Wouters MW, et al. Increasing prevalence of comorbidity in patients with colorectal cancer in the South of the Netherlands 1995-2010. Int J Cancer 2013;132:21572163.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 7.

    Berry NM, Miller MD, Woodman RJ, et al. Differences in chronic conditions and lifestyle behaviour between people with a history of cancer and matched controls. Med J Aust 2014;201:96100.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 8.

    Morishima T, Matsumoto Y, Koeda N, et al. Impact of comorbidities on survival in gastric, colorectal, and lung cancer patients. J Epidemiol 2019;29:110115.

  • 9.

    Cummings A, Grimmett C, Calman L, et al. Comorbidities are associated with poorer quality of life and functioning and worse symptoms in the 5 years following colorectal cancer surgery: results from the ColoREctal Well-being (CREW) cohort study. Psychooncology 2018;27:24272435.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 10.

    Fosså SD, Hess SL, Dahl AA, et al. Stability of health-related quality of life in the Norwegian general population and impact of chronic morbidity in individuals with and without a cancer diagnosis. Acta Oncol 2007;46:452461.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 11.

    Heins MJ, Korevaar JC, Hopman PEPC, et al. Health-related quality of life and health care use in cancer survivors compared with patients with chronic diseases. Cancer 2016;122:962970.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 12.

    Heins MJ, Korevaar JC, Donker GA, et al. The combined effect of cancer and chronic diseases on general practitioner consultation rates. Cancer Epidemiol 2015;39:109114.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 13.

    Jabaaij L, van den Akker M, Schellevis FG. Excess of health care use in general practice and of comorbid chronic conditions in cancer patients compared to controls. BMC Fam Pract 2012;13:60.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 14.

    Ng HS, Koczwara B, Roder D, et al. Patterns of health service utilisation among the Australian population with cancer compared with the general population. Aust Health Rev 2020;44:470479.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 15.

    Heins M, Schellevis F, Rijken M, et al. Determinants of increased primary health care use in cancer survivors. J Clin Oncol 2012;30:41554160.

  • 16.

    Wodchis WP, Arthurs E, Khan AI, et al. Cost trajectories for cancer patients. Curr Oncol 2016;23(Suppl 1):S6475.

  • 17.

    Boakye D, Walter V, Jansen L, et al. Magnitude of the age-advancement effect of comorbidities in colorectal cancer prognosis. J Natl Compr Canc Netw 2020;18:5968.

  • 18.

    Brenner H, Chang-Claude J, Jansen L, et al. Reduced risk of colorectal cancer up to 10 years after screening, surveillance, or diagnostic colonoscopy. Gastroenterology 2014;146:709717.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 19.

    Eyl RE, Thong MSY, Carr PR, et al. Physical activity and long-term fatigue among colorectal cancer survivors—a population-based prospective study. BMC Cancer 2020;20:438.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 20.

    Charlson ME, Pompei P, Ales KL, et al. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 1987;40:373383.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 21.

    Yesavage JA, Sheikh JI. Geriatric Depression Scale (GDS): recent evidence and development of a shorter version. Clin Gerontol 1986;5:165173.

  • 22.

    Clegg A, Barber S, Young J, et al. The Home-Based Older People’s Exercise (HOPE) trial: a pilot randomised controlled trial of a home-based exercise intervention for older people with frailty. Age Ageing 2014;43:687695.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 23.

    Book K, Marten-Mittag B, Henrich G, et al. Distress screening in oncology—evaluation of the Questionnaire on Distress in Cancer Patients-short form (QSC-R10) in a German sample. Psychooncology 2011;20:287293.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 24.

    Thong MSY, Jansen L, Chang-Claude J, et al. Association of laparoscopic colectomy versus open colectomy on the long-term health-related quality of life of colon cancer survivors. Surg Endosc 2020;34:55935603.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 25.

    Aaronson NK, Ahmedzai S, Bergman B, et al. The European Organization for Research and Treatment of Cancer QLQ-C30: a quality-of-life instrument for use in international clinical trials in oncology. J Natl Cancer Inst 1993;85:365376.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 26.

    Fayers PM, Aaronson NK, Bjordal K, et al. EORTC QLQ-C30 Scoring Manual. Brussels, Belgium: EORTC; 1995.

  • 27.

    German Guideline Program in Oncology. Colorectal cancer, version 2.1. Accessed February 23, 2021. Available at: https://www.leitlinienprogramm-onkologie.de/leitlinien/kolorektales-karzinom/

    • Search Google Scholar
    • Export Citation
  • 28.

    Arts LPJ, Oerlemans S, Tick L, et al. More frequent use of health care services among distressed compared with nondistressed survivors of lymphoma and chronic lymphocytic leukemia: results from the population-based PROFILES registry. Cancer 2018;124:30163024.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 29.

    Han X, Lin CC, Li C, et al. Association between serious psychological distress and health care use and expenditures by cancer history. Cancer 2015;121:614622.

  • 30.

    Lebel S, Tomei C, Feldstain A, et al. Does fear of cancer recurrence predict cancer survivors’ health care use? Support Care Cancer 2013;21:901906.

  • 31.

    Champagne A, Ivers H, Savard J. Utilization of health care services in cancer patients with elevated fear of cancer recurrence. Psychooncology 2018;27:19581964.

  • 32.

    Rijken M, Valderas JM, Heins M, et al. Identifying high-need patients with multimorbidity from their illness perceptions and personal resources to manage their health and care: a longitudinal study. BMC Fam Pract 2020;21:75.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 33.

    Duthie K, Strohschein FJ, Loiselle CG. Living with cancer and other chronic conditions: patients’ perceptions of their healthcare experience. Can Oncol Nurs J 2017;27:4348.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 34.

    Cox N, Bowmer C, Ring A. Health literacy and the provision of information to women with breast cancer. Clin Oncol (R Coll Radiol) 2011;23:223227.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 35.

    Husson O, Mols F, van de Poll-Franse LV. The relation between information provision and health-related quality of life, anxiety and depression among cancer survivors: a systematic review. Ann Oncol 2011;22:761772.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 36.

    Friis K, Lasgaard M, Osborne RH, et al. Gaps in understanding health and engagement with healthcare providers across common long-term conditions: a population survey of health literacy in 29,473 Danish citizens. BMJ Open 2016;6:e009627.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 37.

    Cavers D, Habets L, Cunningham-Burley S, et al. Living with and beyond cancer with comorbid illness: a qualitative systematic review and evidence synthesis. J Cancer Surviv 2019;13:148159.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 38.

    Ritchie CS, Zhao F, Patel K, et al. Association between patients’ perception of the comorbidity burden and symptoms in outpatients with common solid tumors. Cancer 2017;123:38353842.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 39.

    Chou C, Hohmann NS, Hastings TJ, et al. How comfortable are primary care physicians and oncologists prescribing medications for comorbidities in patients with cancer? Res Social Adm Pharm 2020;16:10871094.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 40.

    Blane DN, Lewandowska M. Living with cancer and multimorbidity: the role of primary care. Curr Opin Support Palliat Care 2019;13:213219.

  • 41.

    Feng X, Higa GM, Safarudin F, et al. Potentially inappropriate medication use and associated healthcare utilization and costs among older adults with colorectal, breast, and prostate cancers. J Geriatr Oncol 2019;10:698704.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 42.

    Sarfati D, Koczwara B, Jackson C. The impact of comorbidity on cancer and its treatment. CA Cancer J Clin 2016;66:337350.

  • 43.

    Mols F, Beijers T, Lemmens V, et al. Chemotherapy-induced neuropathy and its association with quality of life among 2- to 11-year colorectal cancer survivors: results from the population-based PROFILES registry. J Clin Oncol 2013;31:26992707.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 44.

    Husson O, Mols F, van de Poll-Franse LV, et al. The course of fatigue and its correlates in colorectal cancer survivors: a prospective cohort study of the PROFILES registry. Support Care Cancer 2015;23:33613371.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 45.

    Tanay MAL, Armes J, Ream E. The experience of chemotherapy-induced peripheral neuropathy in adult cancer patients: a qualitative thematic synthesis. Eur J Cancer Care (Engl) 2017;26:e12443.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 46.

    Knoerl R, Smith EML, Han A, et al. Characterizing patient-clinician chemotherapy-induced peripheral neuropathy assessment and management communication approaches. Patient Educ Couns 2019;102:16361643.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 47.

    Thong MSY, van Noorden CJF, Steindorf K, et al. Cancer-related fatigue: causes and current treatment options. Curr Treat Options Oncol 2020;21:17.

  • 48.

    Mausbach BT, Bos T, Irwin SA. Mental health treatment dose and annual healthcare costs in patients with cancer and major depressive disorder. Health Psychol 2018;37:10351040.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 49.

    Kenzik KM, Kent EE, Martin MY, et al. Chronic condition clusters and functional impairment in older cancer survivors: a population-based study. J Cancer Surviv 2016;10:10961103.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 314 314 314
PDF Downloads 102 102 102
EPUB Downloads 0 0 0