10
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Burden of multimorbidity in relation to age, gender and immigrant status: a cross-sectional study based on administrative data

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Objectives

          Many studies have investigated multimorbidity, whose prevalence varies according to settings and data sources. However, few studies on this topic have been conducted in Italy, a country with universal healthcare and one of the most aged populations in the world. The aim of this study was to estimate the prevalence of multimorbidity in a Northern Italian region, to investigate its distribution by age, gender and citizenship and to analyse the correlations of diseases.

          Design

          Cross-sectional study based on administrative data.

          Setting

          Emilia-Romagna, an Italian region with ∼4.4 million inhabitants, of which almost one-fourth are aged ≥65 years.

          Participants

          All adults residing in Emilia-Romagna on 31 December 2012. Hospitalisations, drug prescriptions and contacts with community mental health services from 2003 to 2012 were traced to identify the presence of 17 physical and 9 mental health disorders.

          Primary and secondary outcome measures

          Descriptive analysis of differences in the prevalence of multimorbidity in relation to age, gender and citizenship. The correlations of diseases were analysed using exploratory factor analysis.

          Results

          The study population included 622 026 men and 751 011women, with a mean age of 66.4 years. Patients with multimorbidity were 33.5% in 75 years and >60% among patients aged ≥90 years; among patients aged ≥65 years, the proportion of multimorbidity was 39.9%. After standardisation by age and gender, multimorbidity was significantly more frequent among Italian citizens than among immigrants. Factor analysis identified 5 multimorbidity patterns: (1) psychiatric disorders, (2) cardiovascular, renal, pulmonary and cerebrovascular diseases, (3) neurological diseases, (4) liver diseases, AIDS/HIV and substance abuse and (5) tumours.

          Conclusions

          Multimorbidity was highly prevalent in Emilia-Romagna and strongly associated with age. This finding highlights the need for healthcare providers to adopt individualised care plans and ensure continuity of care.

          Related collections

          Most cited references 21

          • Record: found
          • Abstract: found
          • Article: not found

          Comorbidity measures for use with administrative data.

          This study attempts to develop a comprehensive set of comorbidity measures for use with large administrative inpatient datasets. The study involved clinical and empirical review of comorbidity measures, development of a framework that attempts to segregate comorbidities from other aspects of the patient's condition, development of a comorbidity algorithm, and testing on heterogeneous and homogeneous patient groups. Data were drawn from all adult, nonmaternal inpatients from 438 acute care hospitals in California in 1992 (n = 1,779,167). Outcome measures were those commonly available in administrative data: length of stay, hospital charges, and in-hospital death. A comprehensive set of 30 comorbidity measures was developed. The comorbidities were associated with substantial increases in length of stay, hospital charges, and mortality both for heterogeneous and homogeneous disease groups. Several comorbidities are described that are important predictors of outcomes, yet commonly are not measured. These include mental disorders, drug and alcohol abuse, obesity, coagulopathy, weight loss, and fluid and electrolyte disorders. The comorbidities had independent effects on outcomes and probably should not be simplified as an index because they affect outcomes differently among different patient groups. The present method addresses some of the limitations of previous measures. It is based on a comprehensive approach to identifying comorbidities and separates them from the primary reason for hospitalization, resulting in an expanded set of comorbidities that easily is applied without further refinement to administrative data for a wide range of diseases.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Multimorbidity in older adults.

             M Salive (2013)
            Multimorbidity, the coexistence of 2 or more chronic conditions, has become prevalent among older adults as mortality rates have declined and the population has aged. We examined population-based administrative claims data indicating specific health service delivery to nearly 31 million Medicare fee-for-service beneficiaries for 15 prevalent chronic conditions. A total of 67% had multimorbidity, which increased with age, from 50% for persons under age 65 years to 62% for those aged 65-74 years and 81.5% for those aged ≥85 years. A systematic review identified 16 other prevalence studies conducted in community samples that included older adults, with median prevalence of 63% and a mode of 67%. Prevalence differences between studies are probably due to methodological biases; no studies were comparable. Key methodological issues arise from elements of the case definition, including type and number of chronic conditions included, ascertainment methods, and source population. Standardized methods for measuring multimorbidity are needed to enable public health surveillance and prevention. Multimorbidity is associated with elevated risk of death, disability, poor functional status, poor quality of life, and adverse drug events. Additional research is needed to develop an understanding of causal pathways and to further develop and test potential clinical and population interventions targeting multimorbidity. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health 2013.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              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.
                Bookmark

                Author and article information

                Journal
                BMJ Open
                BMJ Open
                bmjopen
                bmjopen
                BMJ Open
                BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                2044-6055
                2016
                21 December 2016
                : 6
                : 12
                Affiliations
                Department of Biomedical and Neuromotor Sciences, Alma Mater Studiorum—University of Bologna , Bologna, Italy
                Author notes
                [Correspondence to ] Professor Maria Pia Fantini; mariapia.fantini@ 123456unibo.it
                Article
                bmjopen-2016-012812
                10.1136/bmjopen-2016-012812
                5223687
                28003289
                803729f3-d229-4fae-8a9f-f8d4531c532d
                Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

                This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/

                Product
                Categories
                Epidemiology
                Research
                1506
                1692
                1724

                Medicine

                epidemiology, primary care, public health

                Comments

                Comment on this article