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.
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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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.
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).
The authors declare no competing interests.
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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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