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      Patterns of Multimorbidity in Adults: An Association Rules Analysis Using the Korea Health Panel

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          Abstract

          This study aimed to identify the prevalence and patterns of multimorbidity among Korean adults. A descriptive study design was used. Of 11,232 adults aged 18 and older extracted from the 2014 Korean Health Panel Survey, 7118 had one or more chronic conditions. The chronic conditions code uses the Korean Standard Classification of Diseases. Association rule analysis and network analysis were conducted to identify patterns of multimorbidity among 4922 participants with multimorbidity. The prevalence of multimorbidity in the overall population was 34.8%, with a higher prevalence among women (40.8%) than men (28.6%). Hypertension had the highest prevalence in both men and women. In men, diabetes mellitus and hypertension yielded the highest probability of comorbidity (10.04%). In women, polyarthrosis and hypertension yielded the highest probability of comorbidity (12.51%). The results of the network analysis in four groups divided according to gender and age showed different characteristics for each group. Public health practitioners should adopt an integrated approach to manage multimorbidity rather than an individual disease-specific approach, along with different strategies according to age and gender groups.

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          Time Trends in Prevalence of Chronic Diseases and Multimorbidity Not Only due to Aging: Data from General Practices and Health Surveys

          Sandra van Oostrom, Ronald Gijsen, Irina Stirbu-Wagner … (2016)
          Introduction Chronic diseases and multimorbidity are common and expected to rise over the coming years. The objective of this study is to examine the time trend in the prevalence of chronic diseases and multimorbidity over the period 2001 till 2011 in the Netherlands, and the extent to which this can be ascribed to the aging of the population. Methods Monitoring study, using two data sources: 1) medical records of patients listed in a nationally representative network of general practices over the period 2002–2011, and 2) national health interview surveys over the period 2001–2011. Regression models were used to study trends in the prevalence-rates over time, with and without standardization for age. Results An increase from 34.9% to 41.8% (p<0.01) in the prevalence of chronic diseases was observed in the general practice registration over the period 2004–2011 and from 41.0% to 46.6% (p<0.01) based on self-reported diseases over the period 2001–2011. Multimorbidity increased from 12.7% to 16.2% (p<0.01) and from 14.3% to 17.5% (p<0.01), respectively. Aging of the population explained part of these trends: about one-fifth based on general practice data, and one-third for chronic diseases and half of the trend for multimorbidity based on health surveys. Conclusions The prevalence of chronic diseases and multimorbidity increased over the period 2001–2011. Aging of the population only explained part of the increase, implying that other factors such as health care and society-related developments are responsible for a substantial part of this rise.
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            Economic burden of multimorbidity among older adults: impact on healthcare and societal costs

            Louisa Picco, Evanthia Achilla, Edimansyah Abdin … (2016)
            Background Multimorbidity is not uncommon and the associated impact it places on healthcare utilisation and societal costs is of increased concern. The aim of the current study was to estimate the economic burden of multimorbidity among older adults in Singapore by investigating its association with the healthcare and societal resource use and cost. Methods The Well-being of the Singapore Elderly (WiSE) study was a single phase, cross sectional survey among a nationally representative sample of Singapore residents (N = 2565) aged 60 years and above. Multimorbidity was defined in this study as having two or more chronic conditions, from a list of 10 conditions. Care was classified into healthcare which included direct medical care, intermediate and long-term care, indirect care, and social care, provided by paid caregivers and family members or friends. Costs were calculated from the societal perspective, including healthcare and social care costs, by multiplying each service unit with the relevant unit cost. Generalized linear models were used to investigate the relationship between total annual costs and various socio-demographic factors. Results The prevalence of multimorbidity was 51.5 %. Multimorbid respondents utilised more healthcare and social care resources than those with one or no chronic conditions. The total societal cost of multimorbidity equated to SGD$15,148 per person, annually, while for those with one or no chronic conditions the total annual societal costs per person were SGD$5,610 and SGD$2,806, respectively. Each additional chronic condition was associated with increased healthcare (SGD$2,265) and social care costs (SGD$3,177). Older age (i.e. 75–84 years old, and especially over 85 years), Indian ethnicity and being retired were significantly associated with higher total costs from the societal perspective, while older age (75 years and above) and ‘Other’ ethnicity were significantly associated with higher total healthcare costs. Conclusion Multimorbidity was associated with substantially higher healthcare utilisation and social care costs among older adults in Singapore. With the prevalence of multimorbidity increasing, especially as the population ages, we need healthcare systems that are evolving to address the emerging challenges associated with multimorbidity and the respective healthcare and societal costs.
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              Multimorbidity and comorbidity in the Dutch population – data from general practices

              Sandra van Oostrom, H. Susan J. Picavet, Boukje M van Gelder … (2012)
              Background Multimorbidity is increasingly recognized as a major public health challenge of modern societies. However, knowledge about the size of the population suffering from multimorbidity and the type of multimorbidity is scarce. The objective of this study was to present an overview of the prevalence of multimorbidity and comorbidity of chronic diseases in the Dutch population and to explore disease clustering and common comorbidities. Methods We used 7 years data (2002–2008) of a large Dutch representative network of general practices (212,902 patients). Multimorbidity was defined as having two or more out of 29 chronic diseases. The prevalence of multimorbidity was calculated for the total population and by sex and age group. For 10 prevalent diseases among patients of 55 years and older (N = 52,014) logistic regressions analyses were used to study disease clustering and descriptive analyses to explore common comorbid diseases. Results Multimorbidity of chronic diseases was found among 13% of the Dutch population and in 37% of those older than 55 years. Among patients over 55 years with a specific chronic disease more than two-thirds also had one or more other chronic diseases. Most disease pairs occurred more frequently than would be expected if diseases had been independent. Comorbidity was not limited to specific combinations of diseases; about 70% of those with a disease had one or more extra chronic diseases recorded which were not included in the top five of most common diseases. Conclusion Multimorbidity is common at all ages though increasing with age, with over two-thirds of those with chronic diseases and aged 55 years and older being recorded with multimorbidity. Comorbidity encompassed many different combinations of chronic diseases. Given the ageing population, multimorbidity and its consequences should be taken into account in the organization of care in order to avoid fragmented care, in medical research and healthcare policy.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Int J Environ Res Public Health
                Journal ID (iso-abbrev): Int J Environ Res Public Health
                Journal ID (publisher-id): ijerph
                Title: International Journal of Environmental Research and Public Health
                Publisher: MDPI
                ISSN (Print): 1661-7827
                ISSN (Electronic): 1660-4601
                Publication date (Electronic): 11 April 2020
                Publication date (Print): April 2020
                Volume: 17
                Issue: 8
                Electronic Location Identifier: 2618
                Affiliations
                [1 ]College of Nursing, Pusan National University, Yangsan 50612, Korea; lyj@ 123456pusan.ac.kr
                [2 ]Department of Nursing, The Graduate School, Pusan National University, Yangsan 50612, Korea; pointsun@ 123456naver.com
                [3 ]Department of Statistics, The Graduate School, Pusan National University, Busan 46241, Korea; shepd8516@ 123456naver.com
                Author notes
                [* ]Correspondence: h1001004@ 123456pusan.ac.kr ; Tel.: +82-51-510-8367
                Author information
                https://orcid.org/0000-0001-9378-7022
                https://orcid.org/0000-0003-3769-9591
                Article
                Publisher ID: ijerph-17-02618
                DOI: 10.3390/ijerph17082618
                PMC ID: 7215522
                PubMed ID: 32290367
                SO-VID: 5318517e-7962-445f-b733-cf7013621e41
                Copyright © © 2020 by the authors.
                License:

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                Date received : 02 March 2020
                Date accepted : 08 April 2020
                Categories
                Subject: Article

                ScienceOpen disciplines: Public health
                Keywords: multimorbidity,pattern,association rules analysis,network analysis
                Data availability:
                ScienceOpen disciplines: Public health
                Keywords: multimorbidity, pattern, association rules analysis, network analysis

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