Characteristics of older Australian community aged care clients who fall: Incidents reported by care staff.
Lindsey BrettMikaela L JorgensenRimma MytonAndrew GeorgiouJohanna I WestbrookPublished in: Health & social care in the community (2020)
Falls are the leading cause of injury and hospitalisation for older adults (aged 65 years or older) worldwide. Data collected by community aged care providers are an underutilised source of information about precipitating risk factors and consequences of falls for older adults living in the community. The objective of this longitudinal, observational study was to describe and compare the characteristics of older Australians who did and did not have falls reported by community aged care staff. We analysed 19 months of routinely collected care management and incident data for 1,596 older clients from a large Australian community care provider. Differences in sociodemographic characteristics, care needs and community care service use were compared between those who had one or more reported falls and those who had none. Fall-related outcomes (injuries, hospitalisations, relocation to residential aged care) were examined. The average age of clients was 82 years and most were women (66%). Seventy-seven (4.8%) clients had one or more reported falls over the study period (total falls = 92). Clients who had falls reported by care staff were more likely to be older adults, male and use more hours of community care services per week. There were 38 falls-related injuries, 5 falls-related hospitalisations and 20 clients relocated to residential aged care after a reported fall. This study demonstrates the potential for using routinely collected community aged care data to understand risk factors and monitor longitudinal outcomes for a population at high risk of falls.
Keyphrases
- healthcare
- palliative care
- quality improvement
- mental health
- community dwelling
- risk factors
- affordable care act
- physical activity
- clinical trial
- cardiovascular disease
- risk assessment
- type diabetes
- metabolic syndrome
- patient safety
- machine learning
- pregnant women
- hiv testing
- study protocol
- hiv infected
- health information