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      Defining Multimorbidity and Its Impact in Older United States Veterans Newly Treated for Multiple Myeloma

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          Traditional count-based measures of comorbidity are unlikely to capture the complexity of multiple chronic conditions (multimorbidity) in older adults with cancer. We aimed to define patterns of multimorbidity and their impact in older United States veterans with multiple myeloma (MM).


          We measured 66 chronic conditions in 5076 veterans aged 65 years and older newly treated for MM in the national Veterans Affairs health-care system from 2004 to 2017. Latent class analysis was used to identify patterns of multimorbidity among these conditions. These patterns were then assessed for their association with overall survival, our primary outcome. Secondary outcomes included emergency department visits and hospitalizations.


          Five patterns of multimorbidity emerged from the latent class analysis, and survival varied across these patterns (log-rank 2-sided P < .001). Older veterans with cardiovascular and metabolic disease (30.9%, hazard ratio [HR] = 1.33, 95% confidence interval [CI] = 1.21 to 1.45), psychiatric and substance use disorders (9.7%, HR = 1.58, 95% CI = 1.39 to 1.79), chronic lung disease (15.9%, HR = 1.69, 95% CI = 1.53 to 1.87), and multisystem impairment (13.8%, HR = 2.25, 95% CI = 2.03 to 2.50) had higher mortality compared with veterans with minimal comorbidity (29.7%, reference). Associations with mortality were maintained after adjustment for sociodemographic variables, measures of disease risk, and the count-based Charlson Comorbidity Index. Multimorbidity patterns were also associated with emergency department visits and hospitalizations.


          Our findings demonstrate the need to move beyond count-based measures of comorbidity and consider cancer in the context of multiple chronic conditions.

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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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            The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies.

            Much biomedical research is observational. The reporting of such research is often inadequate, which hampers the assessment of its strengths and weaknesses and of a study's generalisability. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) initiative developed recommendations on what should be included in an accurate and complete report of an observational study. We defined the scope of the recommendations to cover three main study designs: cohort, case-control, and cross-sectional studies. We convened a 2-day workshop in September, 2004, with methodologists, researchers, and journal editors to draft a checklist of items. This list was subsequently revised during several meetings of the coordinating group and in e-mail discussions with the larger group of STROBE contributors, taking into account empirical evidence and methodological considerations. The workshop and the subsequent iterative process of consultation and revision resulted in a checklist of 22 items (the STROBE statement) that relate to the title, abstract, introduction, methods, results, and discussion sections of articles.18 items are common to all three study designs and four are specific for cohort, case-control, or cross-sectional studies.A detailed explanation and elaboration document is published separately and is freely available on the websites of PLoS Medicine, Annals of Internal Medicine, and Epidemiology. We hope that the STROBE statement will contribute to improving the quality of reporting of observational studies
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              mice: Multivariate Imputation by Chained Equations inR


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                JNCI: Journal of the National Cancer Institute
                Oxford University Press (OUP)
                February 01 2021
                February 01 2021
                [1 ]VA Boston CSP Center, Boston, MA, USA
                [2 ]Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), Boston, MA, USA
                [3 ]VA Boston Healthcare System, Boston, MA, USA
                [4 ]Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
                [5 ]Harvard Medical School, Boston, MA, USA
                [6 ]Division of Aging, Brigham and Women’s Hospital, Boston, MA, USA
                [7 ]New England GRECC (Geriatrics Research, Education and Clinical Center), VA Boston Healthcare System, Boston, MA, USA
                [8 ]The Meyers Primary Care Institute and the Department of Medicine, University of Massachusetts Medical School, Worcester, MA, USA
                [9 ]Massachusetts General Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
                [10 ]Michael E Debakey VA Medical Center and Dan L Duncan Cancer Center, Baylor College of Medicine, Houston, TX, USA
                [11 ]Divisions of Hematologic Malignancy and Population Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
                [12 ]Boston University School of Medicine, Boston, MA, USA
                [13 ]Marcus Institute for Aging Research, Hebrew SeniorLife, Boston, MA, USA; and
                [14 ]Division of Gerontology, Beth Israel Deaconess Medical Center, Boston, MA, USA
                © 2021


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