Measuring the prevalence of 60 health conditions in older Australians in residential aged care with electronic health records: a retrospective dynamic cohort study

Kimberly E Lind, Magdalena Z Raban, Lindsey Brett, Mikaela L Jorgensen, Andrew Georgiou, Johanna I Westbrook, Kimberly E Lind, Magdalena Z Raban, Lindsey Brett, Mikaela L Jorgensen, Andrew Georgiou, Johanna I Westbrook

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.

Keywords: Aged care; Electronic health record; Health status; Long-term care; Multimorbidity; Multiple chronic conditions; Nursing homes.

Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Comorbidity cluster summary. Comorbidity clusters were identified using latent class analysis. More detail on cluster characters is presented in Supplemental Tables 7–9 and Supplemental Figure 3. MND, motor neurone disease; MS, multiple sclerosis; Y, year; MI, myocardial infarction; UTI, urinary tract infection

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