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      Multimorbidity patterns in relation to polypharmacy and dosage frequency: a nationwide, cross-sectional study in a Japanese population

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          Abstract

          In the present study, we aimed to identify multimorbidity patterns in a Japanese population and investigate whether these patterns have differing effects on polypharmacy and dosage frequency. Data was collected on 17 chronic health conditions via nationwide cross-sectional survey of 3,256 adult Japanese residents. Factor analysis was performed to identify multimorbidity patterns, and associations were determined with excessive polypharmacy [concurrent use of ≥ 10 prescription or over-the-counter (OTC) medications] and higher dosage frequency ( ≥ 3 doses per day). Secondary outcomes were the number of concurrent prescription medications and the number of concurrent OTC medications. We used a generalized linear model to adjust for individual sociodemographic characteristics. Five multimorbidity patterns were identified: cardiovascular/renal/metabolic, neuropsychiatric, skeletal/articular/digestive, respiratory/dermal, and malignant/digestive/urologic. Among these patterns, malignant/digestive/urologic and cardiovascular/renal/metabolic patterns showed the strongest associations with excessive polypharmacy and the number of concurrent OTC medications. Malignant/digestive/urologic, respiratory/dermal, and skeletal/articular/digestive patterns were also associated with higher dosage frequency. Multimorbidity patterns have differing effects on excessive polypharmacy and dosage frequency. Malignant/digestive/urologic pattern may be at higher risk of impaired medication safety and increased treatment burden, than other patterns. Continued study is warranted to determine how to incorporate multimorbidity patterns into risk assessments of polypharmacy and overall treatment burden.

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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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            From atopic dermatitis to asthma: the atopic march.

            To examine the mechanisms whereby allergen exposure through the epidermis could initiate systemic allergy and predispose individuals to the development of 1 or more atopic diseases via the so-called atopic march. PubMed databases from 1950 to the present were searched for relevant articles pertaining to epidemiologic and genetic evidence of the progression of the atopic march. Articles concerning pathophysiologic conditions that link atopic dermatitis, allergic rhinitis, and asthma were examined. The data suggest that a sequence of atopic manifestations occurs, typically atopic dermatitis in infancy followed by allergic rhinitis and/or asthma in later stages. Reduced filaggrin expression is implicated as a major predisposing factor for atopy in multiple lines of evidence, including genome-wide analysis and microarray investigations. Other gene products have an important role. Cross-sectional and longitudinal studies provide preliminary epidemiologic support for the sequential development of allergic diseases. The mechanisms by which allergen exposure through the epidermis can initiate systemic allergy and predispose individuals to atopic dermatitis, allergic rhinitis, and asthma have become clearer in recent years. Longitudinal studies of individuals carrying loss-of-function filaggrin gene mutations are needed to further define the risks associated with epidermal barrier dysfunction and potentially identify specific targets for barrier repair and prevention of atopic dermatitis and other atopic disease. The effects of preventive and treatment strategies have been inconsistent across studies, and further research is warranted before any definitive recommendations can be made. Copyright 2010 American College of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.
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              The influence of age, gender and socio-economic status on multimorbidity patterns in primary care. first results from the multicare cohort study

              Background Multimorbidity is a phenomenon with high burden and high prevalence in the elderly. Our previous research has shown that multimorbidity can be divided into the multimorbidity patterns of 1) anxiety, depression, somatoform disorders (ADS) and pain, and 2) cardiovascular and metabolic disorders. However, it is not yet known, how these patterns are influenced by patient characteristics. The objective of this paper is to analyze the association of socio-demographic variables, and especially socio-economic status with multimorbidity in general and with each multimorbidity pattern. Methods The MultiCare Cohort Study is a multicentre, prospective, observational cohort study of 3.189 multimorbid patients aged 65+ randomly selected from 158 GP practices. Data were collected in GP interviews and comprehensive patient interviews. Missing values have been imputed by hot deck imputation based on Gower distance in morbidity and other variables. The association of patient characteristics with the number of chronic conditions is analysed by multilevel mixed-effects linear regression analyses. Results Multimorbidity in general is associated with age (+0.07 chronic conditions per year), gender (-0.27 conditions for female), education (-0.26 conditions for medium and -0.29 conditions for high level vs. low level) and income (-0.27 conditions per logarithmic unit). The pattern of cardiovascular and metabolic disorders shows comparable associations with a higher coefficient for gender (-1.29 conditions for female), while multimorbidity within the pattern of ADS and pain correlates with gender (+0.79 conditions for female), but not with age or socioeconomic status. Conclusions Our study confirms that the morbidity load of multimorbid patients is associated with age, gender and the socioeconomic status of the patients, but there were no effects of living arrangements and marital status. We could also show that the influence of patient characteristics is dependent on the multimorbidity pattern concerned, i.e. there seem to be at least two types of elderly multimorbid patients. First, there are patients with mainly cardiovascular and metabolic disorders, who are more often male, have an older age and a lower socio-economic status. Second, there are patients mainly with ADS and pain-related morbidity, who are more often female and equally distributed across age and socio-economic groups. Trial registration ISRCTN89818205
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                Author and article information

                Contributors
                fukuhara.shunichi.6m@kyoto-u.ac.jp
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                28 February 2018
                28 February 2018
                2018
                : 8
                : 3806
                Affiliations
                [1 ]ISNI 0000 0004 0372 2033, GRID grid.258799.8, Department of Healthcare Epidemiology, , School of Public Health in the Graduate School of Medicine, Kyoto University, ; Kyoto, Japan
                [2 ]Institute for Health Outcomes and Process Evaluation Research (iHope International), Kyoto, Japan
                [3 ]ISNI 0000 0001 1017 9540, GRID grid.411582.b, Department of General Medicine, , Shirakawa Satellite for Teaching And Research (STAR), Fukushima Medical University, ; Fukushima, Japan
                [4 ]ISNI 0000 0001 1017 9540, GRID grid.411582.b, Center for Innovative Research for Communities and Clinical Excellence (CIRC2LE), , Fukushima Medical University, ; Fukushima, Japan
                Author information
                http://orcid.org/0000-0002-8232-2155
                Article
                21917
                10.1038/s41598-018-21917-6
                5830504
                29491441
                aee9cbbe-26f9-48c0-bf5d-705574c62241
                © The Author(s) 2018

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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 images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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                : 6 September 2017
                : 9 February 2018
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