Henry Brodaty

Description:Objective: Despite the high prevalence of depression, anxiety, and other mental health conditions in long-term care settings, there are no mental health-related quality indicators mandated for use in Australia. This study aimed to gain national consensus on indicators for inclusion in a mental health benchmarking industry tool for residential aged care. Methods: A modified Delphi study incorporating 2 rounds of online surveys. Methods: We invited a panel of clinical, academic, industry, and consumer experts from across Australia. Methods: Experts were asked to rate 35 potential indicators on a 5-point Likert scale for importance and feasibility. Round 2 included new potential indicators based on qualitative feedback, and merged or reworded indicators that did not previously achieve consensus. Indicators with a median rating ≥4 and an interquartile range ≤1 for importance were deemed acceptable. Additional steering group meetings were held between rounds, for decision-making purposes. Results: Rounds 1 and 2 were completed by 49 and 34 experts, respectively. Twenty-seven indicators achieved consensus of agreement for inclusion on importance, with good to excellent item content validity. These included 6 items relating to assessment, 7 items relating to management, 4 items relating to resources, 5 items relating to staff training, and 5 items relating to resident outcomes. Although these indicators also rated highly on feasibility, there was mixed consensus as measured by an interquartile range >1. Qualitative feedback suggests that the indicators are comprehensive, important, and valuable. Conclusions: Findings provide consensus on a mix of structure (staff training and resources), process (assessment and management), and resident outcome quality indicators. Future research will focus on pilot testing the indicators in residential aged care homes, to ensure and optimize feasibility, reliability, acceptability, and case-mix adjustment considerations. The mental health benchmarking tool has the potential to drive mental health care improvements at both a care home and industry level, in Australia and globally.

Description:Objective: To estimate point prevalence of apathy in older adults, examine its overlap with depression and fatigue, and explore its associations with multimorbidity and objective markers of health. Methods: Sydney Memory and Ageing Study, an Australian population-based cohort. Methods: Community dwellings between 2005-2007. Methods: 1,030 older adults, without dementia, aged 70-90. Methods: Apathy was classified using strict (=3) and standard (≥2) cutoff scores on the self-report Geriatric Depression Scale (GDS)-3A, and a validated cutoff score (>0) on the informant-report Neuropsychiatric Inventory. Depression was assessed with strict and standard cutoffs on the GDS-12D, and fatigue with the Assessment of Quality of Life-6D. Multimorbidity (≥2 chronic conditions; computed with and without cardiovascular conditions), physical performance (walking speed, sit-to-stand, lateral stability, grip strength), adiposity (BMI, waist circumference), blood pressure, cholesterol and glucose were assessed. Results: Prevalence of apathy on the self-reported measure was 15.8 % (strict cutoff) or 48.9 % (standard). Informant-reported apathy was lower (2.9 %). Prevalence of self-reported depression was 5.9 % (strict cutoff) or 15.8 % (standard), and fatigue 9.8 %. Apathy overlapped very little with depression or fatigue (κ = .18, 95 % CI .14-.21). Apathy was associated with multimorbidity (even when excluding cardiovascular conditions), adiposity, fasting blood glucose level and physical performance, but not blood pressure or cholesterol. Conclusions: Apathy is more common than depression or fatigue in dementia-free older adults. It does not typically co-occur with these symptoms, but is accompanied by poorer physical health, including multimorbidity and metabolic dysregulation. Apathy may be relevant for public health and an important consideration in clinical care.

Description:Background: Features of the neighborhood environment and ambient air pollution have been associated with onset and progression of neurocognitive disorders, but data from longitudinal population-based studies are limited. Methods: One thousand thirty-six participants (78.3 ± 4.8 years) of the Sydney Memory and Ageing Study were followed for up to 13.7 years with biennial cognitive assessments. Neighborhood environmental features were assessed around the participants' homes. Associations between environmental features and transitions to cognitive states were estimated. Results: Population density, street connectivity, access to commercial services, public transport, water bodies, and tree canopy were associated with a lower likelihood of worsening cognitive state. The opposite was observed for annual average concentrations of PM2.5. Access to parkland, blue spaces, and public transport were associated with a higher likelihood of reversal from mild cognitive impairment to normal cognition. Conclusions: Healthy neighborhood environments may delay cognitive decline and the onset of dementia in older individuals. Conclusions: This is the first published study on neighborhood built and natural environmental correlates of transition to dementia. This study was conducted in socially advantaged areas with relatively low ambient air pollution. Walkable neighborhoods are associated with a lower likelihood of worsening cognitive state. Neighborhood tree canopy is consistently predictive of better cognitive outcomes. Access to public transport, and blue and green spaces is associated with higher probability of improved cognitive state.

Description:Background: The structure of psychopathology can be organized hierarchically into a set of transdiagnostic dimensional phenotypes. No studies have examined whether these phenotypes are associated with brain structure or dementia in older adults. Methods: Data were drawn from a longitudinal study of older adults aged 70-90 years at baseline (N = 1072; 44.8% male). Confirmatory factor models were fit to baseline psychiatric symptoms, with model fit assessed via traditional fit indices, model-based reliability estimates, and evaluation of model parameters. Bayesian plausible values were generated from the best-fitting model for use in subsequent analyses. Linear mixed models examined intraindividual change in global and regional gray matter volume (GMV) and cortical thickness over 6 years. Logistic regression examined whether symptom dimensions predicted incident dementia over 12 years. Results: A higher-order model showed a good fit to the data (BIC = 28,691.85; ssaBIC = 28,396.47; CFI = 0.926; TLI = 0.92; RMSEA = 0.047), including a general factor and lower-order dimensions of internalizing, disinhibited externalizing, and substance use. Baseline symptom dimensions did not predict change over time in total cortical and subcortical GMV or average cortical thickness; regional GMV or cortical thickness in the frontal, parietal, temporal, or occipital lobes; or regional GMV in the hippocampus and cerebellum (all p-values >0.5). Finally, baseline symptom dimensions did not predict incident dementia across follow-ups (all p-values >0.5). Conclusions: We found no evidence that transdiagnostic dimensions are associated with gray matter structure or dementia in older adults. Future research should examine these relationships using psychiatric indicators capturing past history of chronic mental illness rather than current symptoms.

Description:Background: Limited research has examined how older adults' lifestyles intersect with multimorbidity to influence mortality risk. Methods: In this community-dwelling prospective cohort, the Sydney Memory and Ageing Study, principal component analysis was used to identify lifestyle patterns using baseline self-reported data on nutrition, lifestyle factors, and social engagement activities. Multimorbidity was defined by self-reported physician diagnoses. Multivariable logistic regression was used to estimate odds ratios (ORs) for multimorbidity cross-sectionally, and Cox proportional hazards models were used to assess hazard ratios (HRs) for mortality risk longitudinally. Results: Of 895 participants (mean age: 78.2 years; 56.3% female) with complete lifestyle data, 597 had multimorbidity. Two distinct lifestyle patterns emerged: (i) a nutrition pattern characterised by higher intakes of protein, fibre, iron, zinc, magnesium, potassium, and folate, and (ii) an exercise-sleep-social pattern marked by weekly physical activities like bowling, bicycling, sleep quality (low snoring/sleepiness), and high social engagement. Neither pattern was associated with multimorbidity cross-sectionally. Over a median 5.8-year follow-up (n = 869; 140 deaths), participants in the upper tertiles for combined lifestyle pattern scores had a 20% lower mortality risk than those in the lowest tertile [adjusted HR: 0.80 (95% CI: 0.65-0.97); p-trend = 0.02]. This association was stronger in participants with multimorbidity, with a 29% lower risk [0.71 (0.56-0.89); p-trend = 0.01], likely due to multimorbidity modifying the relationship between nutrition and mortality risk (p-interaction < 0.05). While multimorbidity did not modify the relationship between the exercise-sleep-social pattern and risk of mortality, it was consistently associated with a 19-20% lower risk (p-trend < 0.03), regardless of the multimorbidity status. Conclusions: Older adults with multimorbidity may particularly benefit from adopting healthy lifestyles focusing on nutrition, physical activity, sleep quality, and social engagement to reduce their mortality risk.