Measuring the prevalence of 60 health conditions in older Australians in residential aged care with electronic health records: a retrospective dynamic cohort study
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Lind, Kimberly ERaban, Magdalena Z
Brett, Lindsey
Jorgensen, Mikaela L
Georgiou, Andrew
Westbrook, Johanna I
Affiliation
Univ Arizona, Mel & Enid Zuckerman Coll Publ Hlth, Dept Hlth Promot SciIssue Date
2020-10-08Keywords
Health statusMultimorbidity
Multiple chronic conditions
Aged care
Long-term care
Nursing homes
Electronic health record
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Lind, K. E., Raban, M. Z., Brett, L., Jorgensen, M. L., Georgiou, A., & Westbrook, J. I. (2020). Measuring the prevalence of 60 health conditions in older Australians in residential aged care with electronic health records: a retrospective dynamic cohort study. Population Health Metrics, 18, 25.Journal
POPULATION HEALTH METRICSRights
© The Author(s) 2020. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.Collection Information
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.Abstract
Background The number of older Australians using aged care services is increasing, yet there is an absence of reliable data on their health. Multimorbidity in this population has not been well described. A clear picture of the health status of people using aged care is essential for informing health practice and policy to support evidence-based, equitable, high-quality care. Our objective was to describe the health status of older Australians living in residential aged care facilities (RACFs) and develop a model for monitoring health conditions using data from electronic health record systems. Methods Using a dynamic retrospective cohort of 9436 RACF residents living in 68 RACFs in New South Wales and the Australian Capital Territory from 2014 to 2017, we developed an algorithm to identify residents' conditions using aged care funding assessments, medications administered, and clinical notes from their facility electronic health record (EHR). We generated age- and sex-specific prevalence estimates for 60 health conditions. Agreement between conditions recorded in aged care funding assessments and those documented in residents' EHRs was evaluated using Cohen's kappa. Cluster analysis was used to describe combinations of health conditions (multimorbidity) occurring among residents. Results Using all data sources, 93% of residents had some form of circulatory disease, with hypertension the most common (62%). Most residents (93%) had a mental or behavioural disorder, including dementia (58%) or depression (54%). For most conditions, EHR data identified approximately twice the number of people with the condition compared to aged care funding assessments. Agreement between data sources was highest for multiple sclerosis, Huntington's disease, and dementia. The cluster analysis identified seven groups with distinct combinations of health conditions and demographic characteristics and found that the most complex cluster represented a group of residents that had on average the longest lengths of stay in residential care. Conclusions The prevalence of many health conditions among RACF residents in Australia is underestimated in previous reports. Aged care EHR data have the potential to be used to better understand the complex health needs of this vulnerable population and can help fill the information gaps needed for population health surveillance and quality monitoring.Note
Open access journalISSN
1478-7954PubMed ID
33032628Version
Final published versionae974a485f413a2113503eed53cd6c53
10.1186/s12963-020-00234-z
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Except where otherwise noted, this item's license is described as © The Author(s) 2020. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
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