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      Effect of socio-demographic factors on the association between multimorbidity and healthcare costs: a population-based, retrospective cohort study


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          To estimate the attributable costs of multimorbidity and assess whether the association between the level of multimorbidity and health system costs varies by socio-demographic factors in young (<65 years) and older (≥65 years) adults living in Ontario, Canada.


          A population-based, retrospective cohort study


          The province of Ontario, Canada


          6 639 089 Ontarians who were diagnosed with at least one of 16 selected medical conditions on 1 April 2009.

          Main outcome measures

          From the perspective of the publicly funded healthcare system, total annual healthcare costs were derived from linked provincial health administrative databases using a person-level costing method. We used generalised linear models to examine the association between the level of multimorbidity and healthcare costs and the extent to which socio-demographic variables modified this association.


          Attributable total costs of multimorbidity ranged from C$377 to C$2073 for young individuals and C$1026 to C$3831 for older adults. The association between the degree of multimorbidity and healthcare costs was significantly modified by age (p<0.001), sex (p<0.001) and neighbourhood income (p<0.001) in both age groups, and the positive association between healthcare costs and levels of multimorbidity was statistically stronger for older than younger adults. For individuals aged 65 years or younger, the increase in healthcare costs was more gradual in women than in their male counterparts, however, for those aged 65 years or older, the increase in healthcare costs was significantly greater among women than men. Lastly, we also observed that the positive association between the level of multimorbidity and healthcare costs was significantly greater at higher levels of marginalisation.


          Socio-demographic factors are important effect modifiers of the relationship between multimorbidity and healthcare costs and should therefore be considered in any discussion of the implementation of healthcare policies and the organisation of healthcare services aimed at controlling healthcare costs associated with multimorbidity.

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          Most cited references30

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          Causes and consequences of comorbidity: a review.

          A literature search was carried out to identify and summarize the existing information on causes and consequences of comorbidity of chronic somatic diseases. A selection of 82 articles met our inclusion criteria. Very little work has been done on the causes of comorbidity. On the other hand, much work has been done on consequences of comorbidity, although comorbidity is seldom the main subject of study. We found comorbidity in general to be associated with mortality, quality of life, and health care. The consequences of specific disease combinations, however, depended on many factors. We recommend more etiological studies on shared risk factors, especially for those comorbidities that occur at a higher rate than expected. New insights in this field can lead to better prevention strategies. Health care workers need to take comorbid diseases into account in monitoring and treating patients. Future studies on consequences of comorbidity should investigate specific disease combinations.
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            Multimorbidity, health care utilization and costs in an elderly community-dwelling population: a claims data based observational study

            Background Chronic conditions and multimorbidity have become one of the main challenges in health care worldwide. However, data on the burden of multimorbidity are still scarce. The purpose of this study is to examine the association between multimorbidity and the health care utilization and costs in the Swiss community-dwelling population, taking into account several sociodemographic factors. Methods The study population consists of 229'493 individuals aged 65 or older who were insured in 2013 by the Helsana Group, the leading health insurer in Switzerland, covering all 26 Swiss cantons. Multimorbidity was defined as the presence of two or more chronic conditions of a list of 22 conditions that were identified using an updated measure of the Pharmacy-based Cost Group model. The number of consultations (total and divided by primary care physicians and specialists), the number of different physicians contacted, the type of physician contact (face-to-face, phone, and home visits), the number of hospitalisations and the length of stay were assessed separately for the multimorbid and non-multimorbid sample. The costs (total and divided by inpatient and outpatient costs) covered by the compulsory health insurance were calculated for both samples. Multiple linear regression modelling was conducted to adjust for influencing factors: age, sex, linguistic region, purchasing power, insurance plan, and nursing dependency. Results Prevalence of multimorbidity was 76.6%. The mean number of consultations per year was 15.7 in the multimorbid compared to 4.4 in the non-multimorbid sample. Total costs were 5.5 times higher in multimorbid patients. Each additional chronic condition was associated with an increase of 3.2 consultations and increased costs of 33%. Strong positive associations with utilization and costs were also found for nursing dependency. Multimorbid patients were 5.6 times more likely to be hospitalised. Furthermore, results revealed a significant age-gender interaction and a socioeconomic gradient. Conclusions Multimorbidity is associated with substantial higher health care utilization and costs in Switzerland. Quantified data on the current burden of multimorbidity are fundamental for the management of patients in health service delivery systems and for health care policy debates about resource allocation. Strategies for a better coordination of multimorbid patients are urgently needed. Electronic supplementary material The online version of this article (doi:10.1186/s12913-015-0698-2) contains supplementary material, which is available to authorized users.
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              Accuracy of administrative databases in identifying patients with hypertension

              Background Traditionally, the determination of the occurrence of hypertension in patients has relied on costly and time-consuming survey methods that do not allow patients to be followed over time. Objectives To determine the accuracy of using administrative claims data to identify rates of hypertension in a large population living in a single-payer health care system. Methods Various definitions for hypertension using administrative claims databases were compared with 2 other reference standards: (1) data obtained from a random sample of primary care physician offices throughout the province, and (2) self-reported survey data from a national census. Results A case-definition algorithm employing 2 outpatient physician billing claims for hypertension over a 3-year period had a sensitivity of 73% (95% confidence interval [CI] 69%–77%), a specificity of 95% (CI 93%–96%), a positive predictive value of 87% (CI 84%–90%), and a negative predictive value of 88% (CI 86%–90%) for detecting hypertensive adults compared with physician-assigned diagnoses. Compared with self-reported survey data, the algorithm had a sensitivity of 64% (CI 63%–66%), a specificity of 94%(CI 93%–94%), a positive predictive value of 77% (76%–78%), and negative predictive value of 89% (CI 88%–89%). When this algorithm was applied to the entire province of Ontario, the age- and sex-standardized prevalence of hypertension in adults older than 35 years increased from 20% in 1994 to 29% in 2002. Conclusions It is possible to use administrative data to accurately identify from a population sample those patients who have been diagnosed with hypertension. Given that administrative data are already routinely collected, their use is likely to be substantially less expensive compared with serial cross-sectional or cohort studies for surveillance of hypertension occurrence and outcomes over time in a large population.

                Author and article information

                BMJ Open
                BMJ Open
                BMJ Open
                BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                6 October 2017
                : 7
                : 10
                [1 ] Ottawa Hospital Research Institute, The Ottawa Hospital , Ottawa, Canada
                [2 ] departmentSchool of Epidemiology, Public Health and Preventive Medicine , University of Ottawa , Ottawa, Canada
                [3 ] Institute for Clinical Evaluative Sciences , Toronto, Canada
                [4 ] Schools of Pharmacy, University of Waterloo , Ontario, Canada
                [5 ] Institute of Health Policy, Management and Evaluation, University of Toronto , Toronto, Ontario, Canada
                [6 ] Women’s College Research Institute, Women’s College Hospital , Toronto, Canada
                [7 ] departmentDepartment of Family Medicine , University of Alberta , Alberta, Canada
                [8 ] departmentDepartment of Health Sciences , Lakehead University , Thunder Bay, Canada
                [9 ] departmentDalla Lana School of Public Health , University of Toronto , Toronto, Canada
                [10 ] Toronto Rehabilitation Institute , Toronto, Canada
                Author notes
                [Correspondence to ] Dr Walter P Wodchis; walter.wodchis@ 123456utoronto.ca
                © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

                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/

                Funded by: Ontario Ministry of Health and Long-Term Care;
                Health Services Research
                Custom metadata

                multimorbidity,health system costs,socio-demographic factors,population-based study,publicly funded healthcare system


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