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      Comorbidity prevalence among cancer patients: a population-based cohort study of four cancers

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          Abstract

          Background

          The presence of comorbidity affects the care of cancer patients, many of whom are living with multiple comorbidities. The prevalence of cancer comorbidity, beyond summary metrics, is not well known. This study aims to estimate the prevalence of comorbid conditions among cancer patients in England, and describe the association between cancer comorbidity and socio-economic position, using population-based electronic health records.

          Methods

          We linked England cancer registry records of patients diagnosed with cancer of the colon, rectum, lung or Hodgkin lymphoma between 2009 and 2013, with hospital admissions records. A comorbidity was any one of fourteen specific conditions, diagnosed during hospital admission up to 6 years prior to cancer diagnosis. We calculated the crude and age-sex adjusted prevalence of each condition, the frequency of multiple comorbidity combinations, and used logistic regression and multinomial logistic regression to estimate the adjusted odds of having each condition and the probability of having each condition as a single or one of multiple comorbidities, respectively, by cancer type.

          Results

          Comorbidity was most prevalent in patients with lung cancer and least prevalent in Hodgkin lymphoma patients. Up to two-thirds of patients within each of the four cancer patient cohorts we studied had at least one comorbidity, and around half of the comorbid patients had multiple comorbidities. Our study highlighted common comorbid conditions among the cancer patient cohorts. In all four cohorts, the odds of having a comorbidity and the probability of multiple comorbidity were consistently highest in the most deprived cancer patients.

          Conclusions

          Cancer healthcare guidelines may need to consider prominent comorbid conditions, particularly to benefit the prognosis of the most deprived patients who carry the greater burden of comorbidity. Insight into patterns of cancer comorbidity may inform further research into the influence of specific comorbidities on socio-economic inequalities in receipt of cancer treatment and in short-term mortality.

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          Most cited references57

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          A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation

          The objective of this study was to develop a prospectively applicable method for classifying comorbid conditions which might alter the risk of mortality for use in longitudinal studies. A weighted index that takes into account the number and the seriousness of comorbid disease was developed in a cohort of 559 medical patients. The 1-yr mortality rates for the different scores were: "0", 12% (181); "1-2", 26% (225); "3-4", 52% (71); and "greater than or equal to 5", 85% (82). The index was tested for its ability to predict risk of death from comorbid disease in the second cohort of 685 patients during a 10-yr follow-up. The percent of patients who died of comorbid disease for the different scores were: "0", 8% (588); "1", 25% (54); "2", 48% (25); "greater than or equal to 3", 59% (18). With each increased level of the comorbidity index, there were stepwise increases in the cumulative mortality attributable to comorbid disease (log rank chi 2 = 165; p less than 0.0001). In this longer follow-up, age was also a predictor of mortality (p less than 0.001). The new index performed similarly to a previous system devised by Kaplan and Feinstein. The method of classifying comorbidity provides a simple, readily applicable and valid method of estimating risk of death from comorbid disease for use in longitudinal studies. Further work in larger populations is still required to refine the approach because the number of patients with any given condition in this study was relatively small.
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            Epidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study.

            Long-term disorders are the main challenge facing health-care systems worldwide, but health systems are largely configured for individual diseases rather than multimorbidity. We examined the distribution of multimorbidity, and of comorbidity of physical and mental health disorders, in relation to age and socioeconomic deprivation. In a cross-sectional study we extracted data on 40 morbidities from a database of 1,751,841 people registered with 314 medical practices in Scotland as of March, 2007. We analysed the data according to the number of morbidities, disorder type (physical or mental), sex, age, and socioeconomic status. We defined multimorbidity as the presence of two or more disorders. 42·2% (95% CI 42·1-42·3) of all patients had one or more morbidities, and 23·2% (23·08-23·21) were multimorbid. Although the prevalence of multimorbidity increased substantially with age and was present in most people aged 65 years and older, the absolute number of people with multimorbidity was higher in those younger than 65 years (210,500 vs 194,996). Onset of multimorbidity occurred 10-15 years earlier in people living in the most deprived areas compared with the most affluent, with socioeconomic deprivation particularly associated with multimorbidity that included mental health disorders (prevalence of both physical and mental health disorder 11·0%, 95% CI 10·9-11·2% in most deprived area vs 5·9%, 5·8%-6·0% in least deprived). The presence of a mental health disorder increased as the number of physical morbidities increased (adjusted odds ratio 6·74, 95% CI 6·59-6·90 for five or more disorders vs 1·95, 1·93-1·98 for one disorder), and was much greater in more deprived than in less deprived people (2·28, 2·21-2·32 vs 1·08, 1·05-1·11). Our findings challenge the single-disease framework by which most health care, medical research, and medical education is configured. A complementary strategy is needed, supporting generalist clinicians to provide personalised, comprehensive continuity of care, especially in socioeconomically deprived areas. Scottish Government Chief Scientist Office. Copyright © 2012 Elsevier Ltd. All rights reserved.
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              Updating and validating the Charlson comorbidity index and score for risk adjustment in hospital discharge abstracts using data from 6 countries.

              With advances in the effectiveness of treatment and disease management, the contribution of chronic comorbid diseases (comorbidities) found within the Charlson comorbidity index to mortality is likely to have changed since development of the index in 1984. The authors reevaluated the Charlson index and reassigned weights to each condition by identifying and following patients to observe mortality within 1 year after hospital discharge. They applied the updated index and weights to hospital discharge data from 6 countries and tested for their ability to predict in-hospital mortality. Compared with the original Charlson weights, weights generated from the Calgary, Alberta, Canada, data (2004) were 0 for 5 comorbidities, decreased for 3 comorbidities, increased for 4 comorbidities, and did not change for 5 comorbidities. The C statistics for discriminating in-hospital mortality between the new score generated from the 12 comorbidities and the Charlson score were 0.825 (new) and 0.808 (old), respectively, in Australian data (2008), 0.828 and 0.825 in Canadian data (2008), 0.878 and 0.882 in French data (2004), 0.727 and 0.723 in Japanese data (2008), 0.831 and 0.836 in New Zealand data (2008), and 0.869 and 0.876 in Swiss data (2008). The updated index of 12 comorbidities showed good-to-excellent discrimination in predicting in-hospital mortality in data from 6 countries and may be more appropriate for use with more recent administrative data. © The Author 2011. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved.
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                Author and article information

                Contributors
                Helen.Fowler@lshtm.ac.uk
                Aurelien.Belot@lshtm.ac.uk
                Ellis@lshtm.ac.uk
                Camille.Maringe@lshtm.ac.uk
                miguel.luque.easp@juntadeandalucia.es
                Edmund-Njeru.Njagi@lshtm.ac.uk
                n.navani@ucl.ac.uk
                diana.sarfati@otago.ac.nz
                Bernard.Rachet@lshtm.ac.uk
                Journal
                BMC Cancer
                BMC Cancer
                BMC Cancer
                BioMed Central (London )
                1471-2407
                28 January 2020
                28 January 2020
                2020
                : 20
                : 2
                Affiliations
                [1 ]ISNI 0000 0004 0425 469X, GRID grid.8991.9, Cancer Survival Group, Department of Non-Communicable Disease Epidemiology, , London School of Hygiene & Tropical Medicine, ; Keppel Street, London, WC1E 7HT UK
                [2 ]ISNI 0000000121678994, GRID grid.4489.1, Biomedical Research Institute of Granada, Non-Communicable and Cancer Epidemiology Group, , University of Granada, ; Granada, Spain
                [3 ]ISNI 0000 0004 0425 469X, GRID grid.8991.9, Department of Non-Communicable Disease Epidemiology, , London School of Hygiene & Tropical Medicine, ; London, UK
                [4 ]ISNI 0000000121901201, GRID grid.83440.3b, UCL Respiratory, , University College London, ; London, UK
                [5 ]ISNI 0000 0004 0612 2754, GRID grid.439749.4, Department of Thoracic Medicine, , University College London Hospital, ; London, UK
                [6 ]ISNI 0000 0004 1936 7830, GRID grid.29980.3a, Department of Public Health, , University of Otago, ; Wellington, New Zealand
                Author information
                http://orcid.org/0000-0003-2233-3183
                Article
                6472
                10.1186/s12885-019-6472-9
                6986047
                31987032
                0e4e241c-8eaa-43ed-9bbf-e6bfa22596d2
                © The Author(s). 2020

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 8 May 2019
                : 17 December 2019
                Funding
                Funded by: Cancer Research UK
                Award ID: C7923/A18525
                Award Recipient :
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2020

                Oncology & Radiotherapy
                cancer,comorbidity,multimorbidity,deprivation,prevalence,england,epidemiology
                Oncology & Radiotherapy
                cancer, comorbidity, multimorbidity, deprivation, prevalence, england, epidemiology

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