Journal of Cancer Survivorship

, Volume 10, Issue 6, pp 1096–1103 | Cite as

Chronic condition clusters and functional impairment in older cancer survivors: a population-based study

  • Kelly M. Kenzik
  • Erin E. Kent
  • Michelle Y. Martin
  • Smita Bhatia
  • Maria Pisu
Article

Abstract

Purpose

The purpose of the study is to identify chronic condition clusters at pre- and post-cancer diagnosis, evaluate predictors of developing clusters post-cancer, and examine the impact on functional impairment among older cancer survivors.

Methods

We identified 5991 survivors age 65 and older of prostate, breast, colorectal, lung, bladder, kidney, head and neck, and gynecologic cancer and non-Hodgkin lymphoma from the Surveillance, Epidemiology and End Results-Medicare Health Outcomes Survey resource. Survivors completed surveys pre- and post-cancer diagnosis on 13 chronic conditions and functional status. Among those with ≥2 conditions, exploratory factor analysis identified clusters of conditions. Differences in cluster frequency from pre- to post-cancer diagnosis were evaluated across the top five cancer types using chi-square tests. Modified Poisson regression models estimated the relative risk of developing clusters post-diagnosis. Chi-square tests evaluated associations between function and clusters.

Results

Clusters included the following: cardiovascular disease cluster (pre 6.1 % and post 7.7 %), musculoskeletal cluster (28.2 % and 29.3 %), metabolic cluster (14.9 % and 17.6 %), and the major depressive disorder risk (MDDr) + gastrointestinal (GI) + pulmonary condition cluster (5.8 % and 8.7 %). Increases in MDDr + GI + Pulmonary cluster from pre- to post-cancer diagnosis were observed for prostate, lung, and colorectal cancer survivors. Functional impairment was more prevalent in survivors with defined clusters, especially in MDDr + GI + pulmonary, compared to survivors with ≥2 un-clustered conditions.

Conclusions

Distinct condition clusters of two or more chronic conditions are prevalent among older cancer survivors. Cluster prevalence increases from pre- to post-cancer diagnosis and these clusters have a significant impact on functional limitations.

Implications for Cancer Survivors

Tailored management on specific multimorbidity patterns will have implications for functional outcomes among older survivors.

Keywords

Cancer survivor Comorbidity Function Geriatric 

Notes

Acknowledgments

KMK was supported by grants T32HS013852 and K12HS023009 from the Agency for Healthcare Research and Quality. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Cancer Institute or AHRQ.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflicts of interests.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Supplementary material

11764_2016_553_MOESM1_ESM.docx (31 kb)
ESM 1 (DOCX 31 kb)

References

  1. 1.
    Wolff JL, Starfield B, Anderson G. Prevalence, expenditures, and complications of multiple chronic conditions in the elderly. Arch Intern Med. 2002;162:2269–76.CrossRefPubMedGoogle Scholar
  2. 2.
    Thompson K, Dale W How do I best manage the care of older cancer patients with multimorbidity? J Geriatr Oncol 2015.Google Scholar
  3. 3.
    Sogaard M, Thomsen RW, Bossen KS, Sorensen HT, Norgaard M. The impact of comorbidity on cancer survival: a review. J Clin Epidemiol. 2013;5:3–29.CrossRefGoogle Scholar
  4. 4.
    Geraci JM, Escalante CP, Freeman JL, Goodwin JS. Comorbid disease and cancer: the need for more relevant conceptual models in health services research. J Clin Oncol. 2005;23:7399–404.CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Ritchie CS, Kvale E, Fisch MJ. Multimorbidity: an issue of growing importance for oncologists. J Oncol Pract. 2011;7:371–4.CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Hays RD, Reeve BB, Smith AW, Clauser SB. Associations of cancer and other chronic medical conditions with SF-6D preference-based scores in Medicare beneficiaries. Qual Life Res. 2014;23:385–91.CrossRefPubMedGoogle Scholar
  7. 7.
    Smith AW, Reeve BB, Bellizzi KM, Harlan LC, Klabunde CN, Amsellem M, et al. Cancer, comorbidities, and health-related quality of life of older adults. Health Care Financ Rev. 2008;29:41–56.PubMedPubMedCentralGoogle Scholar
  8. 8.
    Levitzky YS, Pencina MJ, D’Agostino RB, Meigs JB, Murabito JM, Vasan RS, et al. Impact of impaired fasting glucose on cardiovascular disease: the Framingham Heart Study. J Am Coll Cardiol. 2008;51:264–70.CrossRefPubMedGoogle Scholar
  9. 9.
    Vigneri P, Frasca F, Sciacca L, Pandini G, Vigneri R. Diabetes and cancer. Endocr-Relat Cancer. 2009;16:1103–23.CrossRefPubMedGoogle Scholar
  10. 10.
    Bekelman DB, Havranek EP, Becker DM, Kutner JS, Peterson PN, Wittstein IS, et al. Symptoms, depression, and quality of life in patients with heart failure. J Card Fail. 2007;13:643–8.CrossRefPubMedGoogle Scholar
  11. 11.
    Phillips SM, Padgett LS, Leisenring WM, Stratton KK, Bishop K, Krull KR, et al. Survivors of childhood cancer in the United States: prevalence and burden of morbidity. Cancer Epidemiol Biomarkers Prev. 2015;24:653–63.CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Bhatia S. Long-term complications of therapeutic exposures in childhood: lessons learned from childhood cancer survivors. Pediatrics. 2012;130:1141–3.CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Bhatia S. Long-term health impacts of hematopoietic stem cell transplantation inform recommendations for follow-up. Expert Rev Hematol. 2011;4:437–52. quiz 453–4.CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Boyd CM, Darer J, Boult C, Fried LP, Boult L, Wu AW. Clinical practice guidelines and quality of care for older patients with multiple comorbid diseases: implications for pay for performance. JAMA. 2005;294:716–24.CrossRefPubMedGoogle Scholar
  15. 15.
    Institute of Medicine Delivering high-quality cancer care. Charting a new course for a system in crisis. Washington, DC: National Academies Press; 2013.Google Scholar
  16. 16.
    Ambs A, Warren JL, Bellizzi KM, Topor M, Haffer SC, Clauser SB. Overview of the SEER—Medicare Health Outcomes Survey linked dataset. Health Care Financ Rev. 2008;29:5–21.PubMedPubMedCentralGoogle Scholar
  17. 17.
    Haffer SC, Bowen SE. Measuring and improving health outcomes in Medicare: the Medicare HOS program. Health Care Financ Rev. 2004;25:1–3.PubMedPubMedCentralGoogle Scholar
  18. 18.
    Rost K, Burnam MA, Smith GR. Development of screeners for depressive disorders and substance disorder history. Med Care. 1993;31:189–200.CrossRefPubMedGoogle Scholar
  19. 19.
    White AJ, Reeve BB, Chen RC, Stover AM, Irwin DE. Coexistence of urinary incontinence and major depressive disorder with health-related quality of life in older Americans with and without cancer. J Cancer Surviv. 2014;8:497–507.CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Katz S, Down TD, Cash HR, Grotz RC. Progress in the development of the index of ADL. The Gerontologist. 1970;10:20–30.CrossRefPubMedGoogle Scholar
  21. 21.
    Kubinger K. On artificial results due to using factor analysis for dichotomous variables. Psychol Sci. 2003;45:106–10.Google Scholar
  22. 22.
    Fayers PM, Machin D Chapter 6: Factor analysis and structural equation modeling. In: Quality of Life: the assessment, analysis, and interpretation of patient-reported outcomes. Chichester; Hoboken, NJ: John Wiley & Sons, 2007, p. 131–160.Google Scholar
  23. 23.
    Muthen B, Muthen LK Mplus Version 7.1. 2012; 7.Google Scholar
  24. 24.
    Prados-Torres A, Calderon-Larranaga A, Hancco-Saavedra J, Poblador-Plou B, van den Akker M. Multimorbidity patterns: a systematic review. J Clin Epidemiol. 2014;67:254–66.CrossRefPubMedGoogle Scholar
  25. 25.
    van den Bussche H, Koller D, Kolonko T, Hansen H, Wegscheider K, Glaeske G, et al. Which chronic diseases and disease combinations are specific to multimorbidity in the elderly? Results of a claims data based cross-sectional study in Germany. BMC Public Health 2011; 11:101-2458-11-101.Google Scholar
  26. 26.
    Kirchberger I, Meisinger C, Heier M, Zimmermann AK, Thorand B, Autenrieth CS, et al. Patterns of multimorbidity in the aged population results from the KORA-Age study. PLoS One. 2012;7, e30556.CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Schafer I. Does multimorbidity influence the occurrence rates of chronic conditions? A claims data based comparison of expected and observed prevalence rates. PLoS One. 2012;7:e45390.CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Prados-Torres A, Poblador-Plou B, Calderon-Larranaga A, Gimeno-Feliu LA, Gonzalez-Rubio F, Poncel-Falco A, et al. Multimorbidity patterns in primary care: interactions among chronic diseases using factor analysis. PLoS One. 2012;7, e32190.CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Buttros Dde A, Nahas EA, Vespoli Hde L, Uemura G, de Almeida BR, Nahas-Neto J. Risk of metabolic syndrome in postmenopausal breast cancer survivors. Menopause. 2013;20:448–54.PubMedGoogle Scholar
  30. 30.
    Fried TR, Tinetti ME, Iannone L, O’Leary JR, Towle V, Van Ness PH HEalth outcome prioritization as a tool for decision making among older persons with multiple chronic conditions. Arch Intern Med 2011; 171:1856–1858.Google Scholar
  31. 31.
    Smith SM, Soubhi H, Fortin M, Hudon C, O’Dowd T. Interventions for improving outcomes in patients with multimorbidity in primary care and community settings. Cochrane Database Syst Rev. 2012;4, CD006560.Google Scholar
  32. 32.
    Riley G. Two-year changes in health and functional status among elderly Medicare beneficiaries in HMOs and Fee-for-Service. Health Serv Res. 2000;35:44–59.PubMedPubMedCentralGoogle Scholar
  33. 33.
    Watson LC, Amick HR, Gaynes BN, Brownley KA, Thaker S, Viswanathan M, et al. Practice-based interventions addressing concomitant depression and chronic medical conditions in the primary care setting: a systematic review and meta-analysis. J Prim Care Commun Health. 2013;4:294–306.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Kelly M. Kenzik
    • 1
  • Erin E. Kent
    • 2
  • Michelle Y. Martin
    • 3
  • Smita Bhatia
    • 1
  • Maria Pisu
    • 4
  1. 1.Institute for Cancer Outcomes and SurvivorshipUniversity of Alabama at BirminghamBirminghamUSA
  2. 2.Outcomes Research Branch, Division of Cancer Control and Population SciencesNational Cancer InstituteRockvilleUSA
  3. 3.University of Tennessee Health Science CenterMemphisUSA
  4. 4.Division of Preventive MedicineUniversity of Alabama at BirminghamBirminghamUSA

Personalised recommendations