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Comorbid chronic diseases and cancer diagnosis: disease-specific effects and underlying mechanisms

Nature Reviews Clinical Oncology volume 16, pages 746–761 (2019)Cite this article

Abstract

An earlier diagnosis is a key strategy for improving the outcomes of patients with cancer. However, achieving this goal can be challenging, particularly for the growing number of people with one or more chronic conditions (comorbidity/multimorbidity) at the time of diagnosis. Pre-existing chronic diseases might affect patient participation in cancer screening, help-seeking for new and/or changing symptoms and clinicians’ decision-making on the use of diagnostic investigations. Evidence suggests, for example, that pre-existing pulmonary, cardiovascular, neurological and psychiatric conditions are all associated with a more advanced stage of cancer at diagnosis. By contrast, hypertension and certain gastrointestinal and musculoskeletal conditions might be associated with a more timely diagnosis. In this Review, we propose a comprehensive framework that encompasses the effects of disease-specific, patient-related and health-care-related factors on the diagnosis of cancer in individuals with pre-existing chronic illnesses. Several previously postulated aetiological mechanisms (including alternative explanations, competing demands and surveillance effects) are integrated with newly identified mechanisms, such as false reassurances, or patient concerns about appearing to be a hypochondriac. By considering specific effects of chronic diseases on diagnostic processes and outcomes, tailored early diagnosis initiatives can be developed to improve the outcomes of the large proportion of patients with cancer who have pre-existing chronic conditions.

Key points

  • Many individuals with possible symptoms of cancer also have pre-existing chronic diseases, which can affect both diagnostic timeliness and cancer stage at diagnosis.

  • Evidence suggests that pulmonary, cardiovascular, neurological and psychiatric disorders are associated with longer intervals before cancer diagnosis and more advanced-stage disease at diagnosis.

  • Effects of specific chronic conditions seem to vary in terms of both direction and size according to pre-existing disease type and the nature of the presenting symptoms.

  • Targeted interventions designed to expedite cancer diagnosis and thus improve patient outcomes might be possible by considering the effects of chronic diseases on participation in cancer screening, patient help-seeking for cancer symptoms, and doctors’ decision-making about the use of investigations.

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Fig. 1: Overview of quantitative studies providing evidence on the role of chronic diseases in influencing the diagnosis of cancer.
Fig. 2: Mechanisms through which chronic diseases can influence the timely diagnosis of cancer along the diagnostic pathway.

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Acknowledgements

This research arises from the CanTest Collaborative, which is funded by Cancer Research UK (C8640/A23385). C.R. acknowledges funding from a BMA TP Gunton research grant. H.S. is partly supported by the VA Health Services Research and Development Service Center for Innovations in Quality, Effectiveness and Safety (CIN13-413). G.L. acknowledges funding from Cancer Research UK (Advanced Clinician Scientist Fellowship Award, grant number C18081/A18180). J.E. acknowledges funding from an NHMRC Practitioner Fellowship.

Review criteria

This Review includes original research involving quantitative, qualitative and mixed methods. Qualitative studies are included in order to provide insights into the complex effects of chronic conditions and their underlying mechanisms. The available evidence refers to cohort (n = 31), cross-sectional (n = 25) and case-control (n = 6) studies, as well as case-series (n = 13) and qualitative studies (n = 11). A quality score was assigned to each reference according to the Mixed Methods Appraisal Tool (MMAT) (further details on the review methods are provided in the Supplementary Box and Figure). The MMAT is a validated quality assessment tool, enabling the merits of each study to be evaluated based on various criteria specific for the different study designs (with a highest possible score of 100, if all criteria are met). Most studies received a MMAT score of 75 or 50 (35 and 31 studies, respectively); a score of 100 was assigned to 11 studies; and only 1 study received a score of 25 (Supplementary Table 1).

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Authors and Affiliations

  1. ECHO (Epidemiology of Cancer Healthcare and Outcomes) Research Group, Department of Behavioural Science and Health, Institute of Epidemiology & Health Care, University College London, London, UK

    Cristina Renzi, Aradhna Kaushal & Georgios Lyratzopoulos

  2. Cancer Survival Group, Department of Non-communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK

    Cristina Renzi & Bernard Rachet

  3. Centre for Cancer Research and Department of General Practice, University of Melbourne, Victorian Comprehensive Cancer Centre, Melbourne, Victoria, Australia

    Jon Emery

  4. St Luke’s Campus, University of Exeter Medical School, Exeter, UK

    Willie Hamilton

  5. Leeds Institute of Health Sciences, University of Leeds, Leeds, UK

    Richard D. Neal

  6. Institute of Health and Society, Sir James Spence Institute, Newcastle University, Royal Victoria Infirmary, Newcastle upon Tyne, UK

    Greg Rubin

  7. Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX, USA

    Hardeep Singh

  8. The Primary Care Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK

    Fiona M. Walter & Georgios Lyratzopoulos

  9. Julius Center for Health Sciences and Primary Care, University Medical Center, Utrecht University, Utrecht, Netherlands

    Niek J. de Wit

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  1. Cristina Renzi
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  7. Greg Rubin
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Contributions

C.R. and A.K researched data for the manuscript. All authors made a substantial contribution to discussions of content. C.R. and G.L. wrote the manuscript. All authors reviewed and/or edited the manuscript before submission.

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Correspondence to Cristina Renzi.

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Renzi, C., Kaushal, A., Emery, J. et al. Comorbid chronic diseases and cancer diagnosis: disease-specific effects and underlying mechanisms. Nat Rev Clin Oncol 16, 746–761 (2019). https://doi.org/10.1038/s41571-019-0249-6

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