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Objectives: Dementia guidelines recommend antipsychotics are only used for behavioral and psychological symptoms when non‐drug interventions fail, and to regularly review use. Population‐level clinical quality indicators (CQIs) for dementia care in permanent residential aged care (PRAC) typically monitor prevalence of antipsychotic use but not prolonged use. This study aimed to develop a CQI for antipsychotic use >90 days and examine trends, associated factors, and variation in CQI incidence; and examine duration of the first episode of use among individuals with dementia accessing home care packages (HCPs) or PRAC. Methods: Retrospective cohort study, including older individuals with dementia who accessed HCPs (n = 50,257) or PRAC (n = 250,196). Trends in annual CQI incidence (2011–12 to 2015–16) and associated factors were determined using Poisson regression. Funnel plots examined geographical and facility variation. Time to antipsychotic discontinuation was estimated among new antipsychotic users accessing HCP (n = 2367) and PRAC (n = 15,597) using the cumulative incidence function. Results: Between 2011–12 and 2015–16, antipsychotic use for >90 days decreased in HCP recipients from 10.7% (95% CI 10.2–11.1) to 10.1% (95% CI 9.6–10.5, adjusted incidence rate ratio (aIRR) 0.97 (95% CI 0.95–0.98)), and in PRAC residents from 24.5% (95% CI 24.2–24.7) to 21.8% (95% CI 21.5–22.0, aIRR 0.97 (95% CI 0.96–0.98)). Prior antipsychotic use (both cohorts) and being male and greater socioeconomic disadvantage (PRAC cohort) were associated with higher CQI incidence. Little geographical/facility variation was observed. Median treatment duration in HCP and PRAC was 334 (interquartile range [IQR] 108–958) and 555 (IQR 197–1239) days, respectively. Conclusions: While small decreases in antipsychotic use >90 days were observed between 2011–12 and 2015–16, findings suggest antipsychotic use among aged care recipients with dementia can be further minimized. Key points: We developed a quality indicator for monitoring prolonged antipsychotic use (>90 days) and examined trends, associated factors and facility and geographical variation among aged care recipients with dementia.We also examined time to antipsychotic discontinuation among new antipsychotic users who had diagnosed dementia.Trends in antipsychotic use for >90 days decreased among individuals receiving home care packages (HCPs) and residents of permanent residential aged care (PRAC) facilities during 2011–12 and 2015–16.The median duration of treatment among individuals commencing an antipsychotic was 0.9 and 1.5 years among older people accessing HCPs and PRAC, respectively. [ABSTRACT FROM AUTHOR]

The article presents a study which examined diabetes prevalence in Australian long-term care (LTC) facilities using a linked national health and aged care data platform and the level of agreement of prevalence estimates between datasets that capture diabetes cases. Topics include diabetes case ascertainment using linked aged care and health administrative data sets, prevalence of diabetes in National Death Index (NDI) data, and the high burden of diabetes in Australian LTC facilities.

• Two-thirds of people living with diabetes in LTCFs used GLMs annually. • Metformin was the most commonly used GLM, and its use increased slightly. • Insulin, sulfonylureas, and DPP-4 inhibitors were other frequently used GLMs. • Sulfonylurea use declined during the study period while insulin use increased. • The use of newer GLMs increased noticeably, particularly use of DPP-4 inhibitors. To examine national trends in glucose lowering medicine (GLM) use among older people with diabetes in long-term care facilities (LTCFs) during 2009–2019. A repeated cross-sectional study of individuals ≥65 years with diabetes in Australian LTCFs (n = 140,322) was conducted. Annual age-sex standardised prevalence of GLM use and number of defined daily doses (DDDs)/1000 resident-days were estimated. Multivariable Poisson or Negative binomial regression models were used to estimate adjusted rate ratios (aRRs) and 95 % confidence intervals (CIs). Prevalence of GLM use remained steady between 2009 (63.9%, 95 %CI 63.3–64.4) and 2019 (64.3%, 95 %CI 63.9–64.8) (aRR 1.00, 95 %CI 1.00–1.00). The percentage of residents receiving metformin increased from 36.0% (95 %CI 35.3–36.7) to 43.5% (95 %CI 42.9–44.1) (aRR 1.01, 95 %CI 1.01–1.01). Insulin use also increased from 21.5% (95 %CI 21.0–22.0) to 27.0% (95 %CI 26.5–27.5) (aRR 1.02, 95 %CI 1.02–1.02). Dipeptidyl peptidase-4 inhibitor use increased from 1.0% (95 %CI 0.9–1.1) to 21.1% (95 %CI 20.7–21.5) (aRR 1.24, 95 %CI 1.24–1.25), while sulfonylurea use decreased from 34.4% (95 %CI 33.8–35.1) to 19.3% (95 %CI 18.9–19.7) (aRR 0.93, 95 %CI 0.93–0.94). Similar trends were observed in DDDs/1000 resident days. The increasing use of insulin and ongoing use of sulfonylureas suggests a need to implement evidence-based strategies to optimise diabetes care in LTCFs. [ABSTRACT FROM AUTHOR]

Stratifying residents at increased risk for fractures in long-term care facilities (LTCFs) can potentially improve awareness and facilitate the delivery of targeted interventions to reduce risk. Although several fracture risk assessment tools exist, most are not suitable for individuals entering LTCF. Moreover, existing tools do not examine risk profiles of individuals at key periods in their aged care journey, specifically at entry into LTCFs. Our objectives were to identify fracture predictors, develop a fracture risk prognostic model for new LTCF residents and compare its performance to the Fracture Risk Assessment in Long term care (FRAiL) model using the Registry of Senior Australians (ROSA) Historical National Cohort, which contains integrated health and aged care information for individuals receiving long term care services. Individuals aged ≥65 years old who entered 2079 facilities in three Australian states between 01/01/2009 and 31/12/2016 were examined. Fractures (any) within 365 days of LTCF entry were the outcome of interest. Individual, medication, health care, facility and system-related factors were examined as predictors. A fracture prognostic model was developed using elastic nets penalised regression and Fine-Gray models. Model discrimination was examined using area under the receiver operating characteristics curve (AUC) from the 20 % testing dataset. Model performance was compared to an existing risk model (i.e., FRAiL model). Of the 238,782 individuals studied, 62.3 % (N = 148,838) were women, 49.7 % (N = 118,598) had dementia and the median age was 84 (interquartile range 79–89). Within 365 days of LTCF entry, 7.2 % (N = 17,110) of individuals experienced a fracture. The strongest fracture predictors included: complex health care rating (no vs high care needs, sub-distribution hazard ratio (sHR) = 1.52, 95 % confidence interval (CI) 1.39–1.67), nutrition rating (moderate vs worst, sHR = 1.48, 95%CI 1.38–1.59), prior fractures (sHR ranging from 1.24 to 1.41 depending on fracture site/type), one year history of general practitioner attendances (≥16 attendances vs none, sHR = 1.35, 95%CI 1.18–1.54), use of dopa and dopa derivative antiparkinsonian medications (sHR = 1.28, 95%CI 1.19–1.38), history of osteoporosis (sHR = 1.22, 95%CI 1.16–1.27), dementia (sHR = 1.22, 95%CI 1.17–1.28) and falls (sHR = 1.21, 95%CI 1.17–1.25). The model AUC in the testing cohort was 0.62 (95%CI 0.61–0.63) and performed similar to the FRAiL model (AUC = 0.61, 95%CI 0.60–0.62). Critical information captured during transition into LTCF can be effectively leveraged to inform fracture risk profiling. New fracture predictors including complex health care needs, recent emergency department encounters, general practitioner and consultant physician attendances, were identified. • At least 7.2% of older Australians had a fracture within a year after entry into long-term care facilities (LTCFs). • We developed a tailored and moderately well performing prognostic model to identify new LTCF residents most at risk for fractures using routinely collected data and identified 31 predictors of fracture risk upon entry. • The discriminative power of our prognostic model in the testing cohort was AUC=0.62 (95% confidence interval (CI) 0.61-0.63). • Our model's performance was similar to the recreated Fracture Risk Assessment in Long term care (FRAiL) model using our datasets (AUC=0.61, 95%CI 0.60-0.62). • Critical information captured during transition into LTCF can be effectively leveraged to inform fracture risk profiling. • These tools can assist health and aged care providers to identify those at greatest risk and inform risk-based prevention strategies for fracture at entry into LTCFs. [ABSTRACT FROM AUTHOR]

• Little is known regarding changes in primary health care utilisation following LTC entry. • After hours and urgent after hours GP attendances increased almost 2-fold after LTC entry. • Only a third of residents received a GP health assessments or care planning after LTC entry. • Utilisation of pain, palliative and geriatric specialist medicine attendances were low (<3%). • There is an opportunity to improve the utilisation of primary health care services to ensure that residents' complex care needs are adequately met. To examine utilisation of primary health care services (subsidised by the Australian Government, Medicare Benefits Schedule, MBS) before and after entry into long-term care (LTC) in Australia. A retrospective cohort study of older people (aged ≥65 years) who entered LTC in Australia between 2012 and 2016 using the Historical Cohort of the Registry of Senior Australians. MBS-subsidised general attendances (general practitioner (GP), medical and nurse practitioners), health assessment and management plans, allied health, mental health services and selected specialist attendances accessed in 91-day periods 12 months before and after LTC entry were examined. Adjusted relative changes in utilisation 0–3 months before and after LTC entry were estimated using risk ratios (RR) calculated using Generalised Estimating Equation Poisson models. 235,217 residents were included in the study with a median age of 84 years (interquartile range 79–89) and 61.1% female. In the first 3 months following LTC entry, GP / medical practitioner attendances increased from 86.6% to 95.6% (aRR 1.10 95%CI 1.10–1.11), GP / medical practitioner urgent after hours (from 12.3% to 21.1%; aRR 1.72, 95%CI 1.70–1.74) and after-hours attendances (from 18.5% to 33.8%; aRR 1.83, 95%CI 1.81–1.84) increased almost two-fold. Pain, palliative and geriatric specialist medicine attendances were low in the 3 months prior (<3%) and decreased further following LTC admission. There is an opportunity to improve the utilisation of primary health care services following LTC entry to ensure that residents' increasingly complex care needs are adequately met. [ABSTRACT FROM AUTHOR]

Objective: The study examined emergency department (ED) presentations, unplanned hospitalisations and potentially preventable hospitalisations in older people receiving long-term care by type of care received (i.e. permanent residential aged care or home care packages in the community), in Australia in 2019. Methods: A retrospective cohort study was conducted using the Registry of Senior Australians National Historical Cohort. Individuals were included if they resided in South Australia, Queensland, Victoria or New South Wales, received a home care package or permanent residential aged care in 2019 and were aged ≥65 years. The cumulative incidence of ED presentations, unplanned hospitalisations and potentially preventable hospitalisations in each of the long-term care service types were estimated during the year. Days in hospital per 1000 individuals were also calculated. Results: The study included 203,278 individuals accessing permanent residential aged care (209,639 episodes) and 118,999 accessing home care packages in the community (127,893 episodes). A higher proportion of people accessing home care packages had an ED presentation (43.1% [95% confidence interval, 42.8–43.3], vs 37.8% [37.6–38.0]), unplanned hospitalisation (39.8% [39.6–40.1] vs 33.4% [33.2–33.6]) and potentially preventable hospitalisation (11.8% [11.6–12.0] vs 8.2% [8.1–8.4]) than people accessing permanent residential aged care. Individuals with home care packages had more days in hospital due to unplanned hospitalisations than those in residential care (7745 vs 3049 days/1000 individuals). Conclusions: While a high proportion of older people in long-term care have ED presentations, unplanned hospitalisations and potentially preventable hospitalisations, people in the community with home care packages experience these events at a higher frequency. What is known about the topic? Unplanned hospitalisations and emergency department presentations are frequent and burdensome for older people receiving long-term aged care. What does this paper add? While unplanned hospitalisations and emergency department presentations were high in all people receiving long-term care, these events were higher in people receiving home care packages than those in residential aged care. What are the implications for practitioners? Further efforts to address the high unplanned hospitalisations and emergency department presentations experienced by the increasing number of older adults accessing home care packages are required to allow this population to remain at home successfully. This article belongs to the Special Issue: Value-based Healthcare. [ABSTRACT FROM AUTHOR]

We investigated the association between pre-existing comorbidities and prostate cancer management. High overall comorbidity burden, cardiac and respiratory disorders, thrombosis, diabetes, depression and anxiety, and chronic pain were associated with selecting EBRT, ADT alone or watchful waiting over RP. Introduction: We aimed to assess the association between comorbidities and prostate cancer management. Patients and methods: We studied 12,603 South Australian men diagnosed with prostate cancer between 2003 and 2019. Comorbidity was measured one year prior to prostate cancer diagnosis using a medication-based comorbidity index (Rx-Risk). Binomial logistic regression analyses were used to assess the association between comorbidities and primary treatment selection (active surveillance, radical prostatectomy (RP), external beam radiotherapy (EBRT) with or without androgen deprivation therapy (ADT), brachytherapy, ADT alone, and watchful waiting (WW)). Certain common comorbidities within Rx-Risk (cardiac disorders, diabetes, chronic airway diseases, depression and anxiety, thrombosis, and chronic pain) were also assessed. All models were adjusted for sociodemographic and tumor characteristics. Results: Likelihood of receiving RP was lower among men with Rx-Risk score ≥3 (odds ratio (OR) 0.62, 95%CI:0.56-0.69) and Rx-Risk 2 (OR 0.80, 95%CI:0.70-0.92) compared with no comorbidity (Rx-Risk ≤0). Men with high comorbidity (Rx-Risk ≥3) were more likely to have received ADT alone (OR 1.76, 95%CI:1.40-2.21), EBRT (OR 1.30, 95%CI:1.17-1.45) or WW (OR 1.49, 95%CI:1.19-1.88) compared with Rx-Risk ≤0. Pre-existing cardiac and respiratory disorders, thrombosis, diabetes, depression and anxiety, and chronic pain were associated with lower likelihood of selecting RP and higher likelihood of EBRT (except chronic airway disease) or WW (except diabetes and depression and anxiety). Cardiac disorders and thrombosis were associated with higher likelihood of selecting ADT alone. Furthermore, age had greater effect on treatment choice than the level of comorbidit y. Conclusion: High comorbidit y burden was associated with primary treatment choice, with significantly less RP and more EBRT, WW and ADT alone among men with higher levels of comorbidity. Each of the individual comorbid conditions also influenced treatment selection. [ABSTRACT FROM AUTHOR]

Background To date, the excess mortality experienced by residential aged care facility (RACF) residents related to COVID-19 has not been estimated in Australia. This study examined (i) the historical mortality trends (2008–09 to 2021–22) and (ii) the excess mortality (2019–20 to 2021–22) of Australian RACF residents. Methods A retrospective population-based study was conducted using the Australian Institute of Health and Welfare's GEN website data (publicly available aged care services information). Non-Aboriginal, older (≥65 years old) RACF residents between 2008–09 and 2021–22 were evaluated. The observed mortality rate was estimated from RACF exits compared with the RACF cohort yearly. Direct standardization was employed to estimate age-standardized mortality rates and 95% CIs. Excess mortality and 95% prediction intervals (PIs) for 2019–20 to 2021–22 were estimated using four negative binomial (NB) and NB generalized additive models and compared. Results The age-standardized mortality rate in 2018–19 was 23 061/100 000 residents (95% CI, 22 711–23 412). This rate remained similar in 2019–20 (23 023/100 000; 95% CI, 22 674–23 372), decreased in 2020–21 (22 559/100 000; 95% CI, 22 210–22 909) and increased in 2021–22 (24 885/100 000; 95% CI, 24 543–25 227). The mortality rate increase between 2020–21 and 2021–22 was observed in all age and sex groups. All models yielded excess mortality in 2021–22. Using the best-performing model (NB), the excess mortality for 2019–20 was –160 (95% PI, –418 to 98), –958 (95% PI, –1279 to –637) for 2020–21 and 4896 (95% PI, 4503–5288) for 2021–22. Conclusions In 2021–22, RACF residents, who represented <1% of the population, experienced 21% of the Australian national excess mortality (4896/22 886). As Australia adjusts to COVID-19, RACF residents remain a population vulnerable to COVID-19. [ABSTRACT FROM AUTHOR]

To examine the historical trends and predict the future rates and total volumes of permanent residential aged care (PRAC) service utilization in Australia. A population-based repeated cross-sectional and projection study of non-indigenous older people (≥65 years) accessing PRAC in Australia was conducted. Publicly available aged care admissions from the Australian Institute of Health and Welfare and population estimates from the Australian Bureau of Statistics were used. Historical incidence rates (per 1000 people), incidence rate ratios (IRRs) and 95% CIs of PRAC admission from 2008–2009 to 2020–2021 were estimated using negative binomial regression models. The future incidence and prediction intervals (PIs) of PRAC admission between 2021–2022 and 2051–2052 were projected using a generalized additive model-negative binomial regression. All estimates were adjusted or standardized by sex and age. Between 2008–2009 and 2020–2021, the adjusted admission to PRAC decreased (from 23.6/1000 people to 15.7/1000 people with an IRR = 0.97/year, 95% CI 0.97–0.98). The projected PRAC admission rate will decrease to 12.1/1000 (95%PI 10.8–13.3) by 2037–2038 and 9.0/1000 (95%PI 7.6–10.4) by 2051–2052. The projected volume of PRAC admission will be 73,988 (95%PI 65,960–81,425) at its highest point in 2037–2038 and 64,579 (95%PI 54,258–74,543) in 2051–2052. The utilization of PRAC has decreased in the past decade, and a predicted decrease in PRAC use in future years is estimated. However, the volume of PRAC utilization will still increase for the next 15 years (until 2037–2038) due to our increasingly older population. These findings can inform service planning of PRAC access in Australia. [ABSTRACT FROM AUTHOR]