Skip to main content
SpringerLink
Log in

Symptom cluster among cancer survivors from a nationally representative survey: a network analysis

  • Research
  • Published:
Supportive Care in Cancer Aims and scope Submit manuscript

Abstract

Purpose

To identify the symptom cluster among cancer survivors and examine their subgroup differences via network analysis based on nationally representative data.

Methods

This cross-sectional study included 2966 survivors participating in the 2020 National Health Interview Survey (NHIS). Participants self-reported the presence of 14 symptoms capturing four clusters (physical, somatic, sleep, and psychologic problems). Network analysis models were used to reveal the relationships between symptoms and those interactions. Network comparison tests were applied to compare subgroups.

Results

The core symptoms of the symptom cluster were fatigue (Bet = 33, Clo = 0.0067, Str = 0.9397), pain (Bet = 11, Clo = 0.0060, Str = 0.9226), wake up well rested (Bet = 25, Clo = 0.0057, Str = 0.8491), and anxiety (Bet = 5, Clo = 0.0043, Str = 0.9697) among cancer survivors. The core symptoms, network structure, and global strength were invariant between time since diagnoses (< 2 years vs. ≥ 2 years) or between numbers of cancers (1 vs. ≥ 2), yet varied between the comorbidity group and non-comorbidity group (≥ 1 vs. 0).

Conclusions

Fatigue would be a potential target for alleviating other symptoms through a negative feedback loop of other related symptoms of cancer survivors. In particular, cancer survivors with other chronic diseases should be the focus of attention and strengthen targeted intervention.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Data availability

No datasets were generated or analysed during the current study.

References

  1. Siegel RL, Miller KD, Fuchs HE, Jemal A (2022) Cancer statistics, 2022. CA Cancer J Clin 72(1):7–33. https://doi.org/10.3322/caac.21708

    Article  PubMed  Google Scholar 

  2. Cleeland CS (2007) Symptom burden: multiple symptoms and their impact as patient-reported outcomes. J Natl Cancer Inst Monogr 37:16–21. https://doi.org/10.1093/jncimonographs/lgm005

    Article  Google Scholar 

  3. Wu HS, Harden JK (2015) Symptom burden and quality of life in survivorship: a review of the literature. Cancer Nurs 38(1):E29-54. https://doi.org/10.1097/NCC.0000000000000135

    Article  PubMed  Google Scholar 

  4. Miaskowski C, Aouizerat BE, Dodd M, Cooper B (2007) Conceptual issues in symptom clusters research and their implications for quality-of-life assessment in patients with cancer. JNCI Monographs 2007(37):39–46. https://doi.org/10.1093/jncimonographs/lgm003

    Article  Google Scholar 

  5. Miaskowski C, Barsevick A, Berger A et al (2017) Advancing symptom science through symptom cluster research: expert panel proceedings and recommendations. JNCI J Natl Cancer Inst 109(4):djw253. https://doi.org/10.1093/jnci/djw253

    Article  PubMed  Google Scholar 

  6. Harrington CB, Hansen JA, Moskowitz M, Todd BL, Feuerstein M (2010) It’s not over when it’s over: long-term symptoms in cancer survivors—a systematic review. Int J Psychiatry Med 40(2):163–181. https://doi.org/10.2190/PM.40.2.c

    Article  PubMed  Google Scholar 

  7. Sanford SD, Beaumont JL, Butt Z, Sweet JJ, Cella D, Wagner LI (2014) Prospective longitudinal evaluation of a symptom cluster in breast cancer. J Pain Symptom Manage 47(4):721–730. https://doi.org/10.1016/j.jpainsymman.2013.05.010

    Article  PubMed  Google Scholar 

  8. Nho JH, Reul Kim S, Nam JH (2017) Symptom clustering and quality of life in patients with ovarian cancer undergoing chemotherapy. Eur J Oncol Nurs 30:8–14. https://doi.org/10.1016/j.ejon.2017.07.007

    Article  PubMed  Google Scholar 

  9. Harris CS, Kober KM, Cooper B et al (2022) Symptom clusters in outpatients with cancer using different dimensions of the symptom experience. Support Care Cancer 30(8):6889–6899. https://doi.org/10.1007/s00520-022-07125-z

    Article  PubMed  PubMed Central  Google Scholar 

  10. Choi S, Ryu E (2018) Effects of symptom clusters and depression on the quality of life in patients with advanced lung cancer. Eur J Cancer Care 27(1):e12508. https://doi.org/10.1111/ecc.12508

    Article  Google Scholar 

  11. Dong ST, Butow PN, Costa DSJ, Lovell MR, Agar M (2014) Symptom clusters in patients with advanced cancer: a systematic review of observational studies. J Pain Symptom Manage 48(3):411–450. https://doi.org/10.1016/j.jpainsymman.2013.10.027

    Article  PubMed  Google Scholar 

  12. Nipp RD, El-Jawahri A, Moran SM et al (2017) The relationship between physical and psychological symptoms and health care utilization in hospitalized patients with advanced cancer: Symptoms and Health Care Utilization. Cancer 123(23):4720–4727. https://doi.org/10.1002/cncr.30912

    Article  PubMed  Google Scholar 

  13. Kirkova J, Aktas A, Walsh D, Davis MP (2011) Cancer symptom clusters: clinical and research methodology. J Palliat Med 14(10):1149–1166. https://doi.org/10.1089/jpm.2010.0507

    Article  PubMed  Google Scholar 

  14. Shim E, Ha H, Suh Y et al (2021) Network analyses of associations between cancer-related physical and psychological symptoms and quality of life in gastric cancer patients. Psychooncology 30(6):946–953. https://doi.org/10.1002/pon.5681

    Article  PubMed  Google Scholar 

  15. Mkhitaryan S, Crutzen R, Steenaart E, De Vries NK (2019) Network approach in health behavior research: how can we explore new questions? Health Psychol Behav Med 7(1):362–384. https://doi.org/10.1080/21642850.2019.1682587

    Article  PubMed  PubMed Central  Google Scholar 

  16. Papachristou N, Barnaghi P, Cooper B et al (2019) Network analysis of the multidimensional symptom experience of oncology. Sci Rep 9(1):2258. https://doi.org/10.1038/s41598-018-36973-1

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Beard C, Millner AJ, Forgeard MJC et al (2016) Network analysis of depression and anxiety symptom relationships in a psychiatric sample. Psychol Med 46(16):3359–3369. https://doi.org/10.1017/S0033291716002300

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Borsboom D, Cramer AOJ (2013) Network analysis: an integrative approach to the structure of psychopathology. Annu Rev Clin Psychol 9(1):91–121. https://doi.org/10.1146/annurev-clinpsy-050212-185608

    Article  PubMed  Google Scholar 

  19. De Rooij BH, Oerlemans S, Van Deun K et al (2021) Symptom clusters in 1330 survivors of 7 cancer types from the PROFILES registry: a network analysis. Cancer 127(24):4665–4674. https://doi.org/10.1002/cncr.33852

    Article  PubMed  Google Scholar 

  20. Huang IC, Brinkman TM, Kenzik K et al (2013) Association between the prevalence of symptoms and health-related quality of life in adult survivors of childhood cancer: a report from the St Jude Lifetime Cohort Study. JCO 31(33):4242–4251. https://doi.org/10.1200/JCO.2012.47.8867

    Article  Google Scholar 

  21. Shin H, Dudley WN, Bhakta N et al (2023) Associations of symptom clusters and health outcomes in adult survivors of childhood cancer: a report from the St Jude Lifetime Cohort Study. JCO 41(3):497–507. https://doi.org/10.1200/JCO.22.00361

    Article  CAS  Google Scholar 

  22. Epskamp S, Cramer AO, Waldorp LJ, Schmittmann VD, Borsboom D (2012) qgraph: network visualizations of relationships in psychometric data. J Stat Softw 48(4):1–18. https://doi.org/10.18637/jss.v048.i04

  23. Epskamp S, Fried EI (2018) A tutorial on regularized partial correlation networks. Psychol Methods 23(4):617–634. https://doi.org/10.1037/met0000167

    Article  PubMed  Google Scholar 

  24. Fang J, Wong CL, Liu CQ et al (2023) Identifying central symptom clusters and correlates in children with acute leukemia undergoing chemotherapy: a network analysis. Front Oncol 13:1236129. https://doi.org/10.3389/fonc.2023.1236129

    Article  PubMed  PubMed Central  Google Scholar 

  25. Fernández-de-las-Peñas C, Palacios-Ceña M, Valera-Calero JA et al (2022) Understanding the interaction between clinical, emotional and psychophysical outcomes underlying tension-type headache: a network analysis approach. J Neurol 269(8):4525–4534. https://doi.org/10.1007/s00415-022-11039-5

    Article  PubMed  Google Scholar 

  26. Schlegl S, Smith KE, Vierl L et al (2021) Using network analysis to compare diagnosis-specific and age-specific symptom networks in eating disorders. Intl J Eating Disorders 54(8):1463–1476. https://doi.org/10.1002/eat.23523

    Article  Google Scholar 

  27. Bloch F, Jackson MO, Tebaldi P (2019) Centrality measures in networks. Available at SSRN: https://doi.org/10.2139/ssrn.2749124

  28. Opsahl T, Agneessens F, Skvoretz J (2010) Node centrality in weighted networks: generalizing degree and shortest paths. Soc Networks 32(3):245–251. https://doi.org/10.1016/j.socnet.2010.03.006

    Article  Google Scholar 

  29. Neijenhuijs KI, Peeters CFW, Van Weert H, Cuijpers P, de Leeuw IV (2021) Symptom clusters among cancer survivors: what can machine learning techniques tell us? BMC Med Res Methodol 21(1):166. https://doi.org/10.1186/s12874-021-01352-4

    Article  PubMed  PubMed Central  Google Scholar 

  30. Ma Y, He B, Jiang M et al (2020) Prevalence and risk factors of cancer-related fatigue: a systematic review and meta-analysis. Int J Nurs Stud 111:103707. https://doi.org/10.1016/j.ijnurstu.2020.103707

    Article  PubMed  Google Scholar 

  31. Trudel-Fitzgerald C, Savard J, Ivers H (2013) Which symptoms come first? Exploration of temporal relationships between cancer-related symptoms over an 18-month period. Ann Behav Med 45(3):329–337. https://doi.org/10.1007/s12160-012-9459-1

    Article  PubMed  Google Scholar 

  32. Sotelo JL, Musselman D, Nemeroff C (2014) The biology of depression in cancer and the relationship between depression and cancer progression. Int Rev Psychiatry 26(1):16–30. https://doi.org/10.3109/09540261.2013.875891

    Article  PubMed  Google Scholar 

  33. Götze H, Friedrich M, Taubenheim S, Dietz A, Lordick F, Mehnert A (2020) Depression and anxiety in long-term survivors 5 and 10 years after cancer diagnosis. Support Care Cancer 28(1):211–220. https://doi.org/10.1007/s00520-019-04805-1

    Article  PubMed  Google Scholar 

  34. Bai W, Zhao YJ, Cai H et al (2022) Network analysis of depression, anxiety, insomnia and quality of life among Macau residents during the COVID-19 pandemic. J Affect Disord 311:181–188. https://doi.org/10.1016/j.jad.2022.05.061

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Cox RC, Olatunji BO (2021) Sleep in a pandemic: implications of COVID-19 for sleep through the lens of the 3P model of insomnia. Am Psychol 76(7):1159–1171. https://doi.org/10.1037/amp0000850

    Article  PubMed  PubMed Central  Google Scholar 

  36. Mullarkey MC, Marchetti I, Beevers CG (2019) Using network analysis to identify central symptoms of adolescent depression. J Clin Child Adolesc Psychol 48(4):656–668. https://doi.org/10.1080/15374416.2018.1437735

    Article  PubMed  Google Scholar 

  37. Rutten RJT, Broekman TG, Schippers GM, Schellekens AFA (2021) Symptom networks in patients with substance use disorders. Drug Alcohol Depend 229:109080. https://doi.org/10.1016/j.drugalcdep.2021.109080

    Article  PubMed  Google Scholar 

  38. Nguyen LT, Alexander K, Yates P (2018) Psychoeducational intervention for symptom management of fatigue, pain, and sleep disturbance cluster among cancer patients: a pilot quasi-experimental study. J Pain Symptom Manage 55(6):1459–1472. https://doi.org/10.1016/j.jpainsymman.2018.02.019

    Article  PubMed  Google Scholar 

  39. Cillessen L, Johannsen M, Speckens AEM, Zachariae R (2019) Mindfulness-based interventions for psychological and physical health outcomes in cancer patients and survivors: a systematic review and meta-analysis of randomized controlled trials. Psychooncology 28(12):2257–2269. https://doi.org/10.1002/pon.5214

    Article  PubMed  PubMed Central  Google Scholar 

  40. Zhao C, Lai L, Zhang L et al (2021) The effects of acceptance and commitment therapy on the psychological and physical outcomes among cancer patients: a meta-analysis with trial sequential analysis. J Psychosom Res 140:110304. https://doi.org/10.1016/j.jpsychores.2020.110304

    Article  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Nursing and Rehabilitation, Cheeloo College of Medicine, Shandong University, Jinan, China

    Zhen Zhang

  2. Department of Emergency, Qilu Hospital, Shandong University, Jinan, Shandong, China

    Zhen Zhang, Mingming Liu, Shenglong Zheng & Ming Zhao

  3. The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Jinan, Shandong, China

    Jiahui Lao

  4. Department of Nursing, Qilu Hospital, Shandong University, Jinan, China

    Min Zhang

Authors
  1. Zhen Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jiahui Lao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mingming Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shenglong Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ming Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Min Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Zhen Zhang and Jiahui Lao. The first draft of the manuscript was written by Zhen Zhang. Supervision and writing—review and editing were performed by Min Zhang, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Min Zhang.

Ethics declarations

Ethics approval

The National Health Interview Survey (NHIS) is publicly available. The data we used were collected by the National Center for Health Statistics (NCHS). The NHIS protocols were authorized by the NCHS ethics review board. So, the ethical approval was not required.

Consent to participate

All survey participants provided informed consent to participate in NHIS.

Competing interests

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 70 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, Z., Lao, J., Liu, M. et al. Symptom cluster among cancer survivors from a nationally representative survey: a network analysis. Support Care Cancer 32, 333 (2024). https://doi.org/10.1007/s00520-024-08531-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00520-024-08531-1

Keywords

Search

Navigation