Corrigendum to: Public sector residential aged care: identifying novel associations between quality indicators and other demographic and health-related factors.

To explore associations among quality indicators (QI; e.g. pressure ulcers, falls and/or fractures, physical restraint, use of multiple medications, unplanned weight loss) of the Victorian Public Sector Residential Aged Care Services (VPSRACS) with other demographic and health-related factors. Data for 380 residents over a 3-month period were extracted retrospectively from client databases at four VPSRAC facilities. Four significant logistic regression models were developed. The strongest models related to falls and polypharmacy. Significant associations for these models included the following: (1) residents with a higher body mass index were 6% less likely (95% confidence interval (CI) 1%-11%) to fall, whereas high levels of cognitive impairment increased the risk of falling by 8% (95% CI 2%-14%); (2) being ambulant with a gait aid more than doubled the risk of falling compared with non-ambulant residents (95% CI 19%-546%); and (3) higher cognitive impairment was associated with a 6% (95% CI 1%-11%) reduction in the likelihood of polypharmacy. Identification of significant relationships between the VPSRACS QI and other demographic and health-related factors is a preliminary step towards a more in-depth understanding of the factors that influence the QI and predict adverse events. Currently, the VPSRACS report on five QI. Previous research has shown associations between several of these indicators, but not all. This paper examines associations between the five VPSRAC QI as well as other key demographic and health-related factors. Novel findings from regression analyses included an increased risk of falls associated with recommended body mass index and using gait aids, but no association between pressure ulcers and the Norton score. Regression models for other QI were limited by the small occurrences of the QI. However, significant associations were identified indicating that residents using a gait aid had a lower level of unplanned weight loss and residents with polypharmacy had higher unplanned weight loss. This paper reinforces the value of collecting VPSRAC QI data to enable facilities to consider how these variables could impact on care quality and to proactively plan to reduce the risk of adverse events. Although QI data can be used to benchmark with other settings, this paper shows how QI data can be used to inform practice within a facility and help identify patient-related factors that may warrant further investigation.

Public sector residential aged care: identifying novel associations between quality indicators and other demographic and health-related factors.

OBJECTIVE: To explore associations among quality indicators (QI; e.g. pressure ulcers, falls and/or fractures, physical restraint, use of multiple medications, unplanned weight loss) of the Victorian Public Sector Residential Aged Care Services (VPSRACS) with other demographic and health-related factors. METHODS: Data for 380 residents over a 3-month period were extracted retrospectively from client databases at four VPSRAC facilities. RESULTS: Four significant logistic regression models were developed. The strongest models related to falls and polypharmacy. Significant associations for these models included the following: (1) residents with a higher body mass index were 6% less likely (95% confidence interval (CI) 1%-11%) to fall, whereas high levels of cognitive impairment increased the risk of falling by 8% (95% CI 2%-14%); (2) being ambulant with a gait aid more than doubled the risk of falling compared with non-ambulant residents (95% CI 19%-546%); and (3) higher cognitive impairment was associated with a 6% (95% CI 1%-11%) reduction in the likelihood of polypharmacy. CONCLUSIONS: Identification of significant relationships between the VPSRACS QI and other demographic and health-related factors is a preliminary step towards a more in-depth understanding of the factors that influence the QI and predict adverse events.