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      Prevalence of Multiple Chronic Conditions Among US Adults: Estimates From the National Health Interview Survey, 2010

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      , PhD , , MPH
      Preventing Chronic Disease
      Centers for Disease Control and Prevention

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          Abstract

          Preventing and ameliorating chronic conditions has long been a priority in the United States; however, the increasing recognition that people often have multiple chronic conditions (MCC) has added a layer of complexity with which to contend. The objective of this study was to present the prevalence of MCC and the most common MCC dyads/triads by selected demographic characteristics. We used respondent-reported data from the 2010 National Health Interview Survey (NHIS) to study the US adult civilian noninstitutionalized population aged 18 years or older (n = 27,157). We categorized adults as having 0 to 1, 2 to 3, or 4 or more of the following chronic conditions: hypertension, coronary heart disease, stroke, diabetes, cancer, arthritis, hepatitis, weak or failing kidneys, chronic obstructive pulmonary disease, or current asthma. We then generated descriptive estimates and tested for significant differences. Twenty-six percent of adults have MCC; the prevalence of MCC has increased from 21.8% in 2001 to 26.0% in 2010. The prevalence of MCC significantly increased with age, was significantly higher among women than men and among non-Hispanic white and non-Hispanic black adults than Hispanic adults. The most common dyad identified was arthritis and hypertension, and the combination of arthritis, hypertension, and diabetes was the most common triad. The findings of this study contribute information to the field of MCC research. The NHIS can be used to identify population subgroups most likely to have MCC and potentially lead to clinical guidelines for people with more common MCC combinations.

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          Permutation tests for joinpoint regression with applications to cancer rates.

          The identification of changes in the recent trend is an important issue in the analysis of cancer mortality and incidence data. We apply a joinpoint regression model to describe such continuous changes and use the grid-search method to fit the regression function with unknown joinpoints assuming constant variance and uncorrelated errors. We find the number of significant joinpoints by performing several permutation tests, each of which has a correct significance level asymptotically. Each p-value is found using Monte Carlo methods, and the overall asymptotic significance level is maintained through a Bonferroni correction. These tests are extended to the situation with non-constant variance to handle rates with Poisson variation and possibly autocorrelated errors. The performance of these tests are studied via simulations and the tests are applied to U.S. prostate cancer incidence and mortality rates. Copyright 2000 John Wiley & Sons, Ltd.
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            Multimorbidity Patterns in the Elderly: A New Approach of Disease Clustering Identifies Complex Interrelations between Chronic Conditions

            Objective Multimorbidity is a common problem in the elderly that is significantly associated with higher mortality, increased disability and functional decline. Information about interactions of chronic diseases can help to facilitate diagnosis, amend prevention and enhance the patients' quality of life. The aim of this study was to increase the knowledge of specific processes of multimorbidity in an unselected elderly population by identifying patterns of statistically significantly associated comorbidity. Methods Multimorbidity patterns were identified by exploratory tetrachoric factor analysis based on claims data of 63,104 males and 86,176 females in the age group 65+. Analyses were based on 46 diagnosis groups incorporating all ICD-10 diagnoses of chronic diseases with a prevalence ≥ 1%. Both genders were analyzed separately. Persons were assigned to multimorbidity patterns if they had at least three diagnosis groups with a factor loading of 0.25 on the corresponding pattern. Results Three multimorbidity patterns were found: 1) cardiovascular/metabolic disorders [prevalence female: 30%; male: 39%], 2) anxiety/depression/somatoform disorders and pain [34%; 22%], and 3) neuropsychiatric disorders [6%; 0.8%]. The sampling adequacy was meritorious (Kaiser-Meyer-Olkin measure: 0.85 and 0.84, respectively) and the factors explained a large part of the variance (cumulative percent: 78% and 75%, respectively). The patterns were largely age-dependent and overlapped in a sizeable part of the population. Altogether 50% of female and 48% of male persons were assigned to at least one of the three multimorbidity patterns. Conclusion This study shows that statistically significant co-occurrence of chronic diseases can be subsumed in three prevalent multimorbidity patterns if accounting for the fact that different multimorbidity patterns share some diagnosis groups, influence each other and overlap in a large part of the population. In recognizing the full complexity of multimorbidity we might improve our ability to predict needs and achieve possible benefits for elderly patients who suffer from multimorbidity.
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              Which chronic diseases and disease combinations are specific to multimorbidity in the elderly? Results of a claims data based cross-sectional study in Germany

              Background Growing interest in multimorbidity is observable in industrialized countries. For Germany, the increasing attention still goes still hand in hand with a small number of studies on multimorbidity. The authors report the first results of a cross-sectional study on a large sample of policy holders (n = 123,224) of a statutory health insurance company operating nationwide. This is the first comprehensive study addressing multimorbidity on the basis of German claims data. The main research question was to find out which chronic diseases and disease combinations are specific to multimorbidity in the elderly. Methods The study is based on the claims data of all insured policy holders aged 65 and older (n = 123,224). Adjustment for age and gender was performed for the German population in 2004. A person was defined as multimorbid if she/he had at least 3 diagnoses out of a list of 46 chronic conditions in three or more quarters within the one-year observation period. Prevalences and risk-ratios were calculated for the multimorbid and non-multimorbid samples in order to identify diagnoses more specific to multimorbidity and to detect excess prevalences of multimorbidity patterns. Results 62% of the sample was multimorbid. Women in general and patients receiving statutory nursing care due to disability are overrepresented in the multimorbid sample. Out of the possible 15,180 combinations of three chronic conditions, 15,024 (99%) were found in the database. Regardless of this wide variety of combinations, the most prevalent individual chronic conditions do also dominate the combinations: Triads of the six most prevalent individual chronic conditions (hypertension, lipid metabolism disorders, chronic low back pain, diabetes mellitus, osteoarthritis and chronic ischemic heart disease) span the disease spectrum of 42% of the multimorbid sample. Gender differences were minor. Observed-to-expected ratios were highest when purine/pyrimidine metabolism disorders/gout and osteoarthritis were part of the multimorbidity patterns. Conclusions The above list of dominating chronic conditions and their combinations could present a pragmatic start for the development of needed guidelines related to multimorbidity.
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                Author and article information

                Journal
                Prev Chronic Dis
                Prev Chronic Dis
                PCD
                Preventing Chronic Disease
                Centers for Disease Control and Prevention
                1545-1151
                2013
                25 April 2013
                : 10
                : E65
                Affiliations
                [1]Author Affiliations: Jeannine S. Schiller, National Center for Health Statistics, Centers for Disease Control and Prevention, Hyattsville, Maryland.
                Author notes
                Corresponding Author: Brian W. Ward, Division of Health Interview Statistics, National Center for Health Statistics, Centers for Disease Control and Prevention, 3311 Toledo Rd, Room 2330, Hyattsville, MD 20782. Telephone: 301-458-4568. E-mail: bwward@ 123456cdc.gov .
                Article
                12_0203
                10.5888/pcd10.120203
                3652717
                23618545
                fb9def2e-6e25-4f20-8d81-08e8339db194
                History
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                Peer Reviewed

                Health & Social care
                Health & Social care

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