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Background Dementia is a leading cause of disease burden in Australia, with almost half a million people living with dementia and a steady increase over time in dementia-related deaths. Strengthening the evidence base for dementia risk factors is critical for an effective and efficient public health and policy response. However, the number of Australians with dementia remains unknown, and there are significant gaps in the knowledge on risk factors in the Australian context. In this study we aim to develop reliable data on dementia incidence in Australia and investigate the associated risk factors, using a large population cohort. Specifically, we will assess the relative contribution of risk factors to dementia incidence as a basis for strengthening dementia prevention efforts. Methods We will use data from the 45 and Up Study that includes 267,358 residents of New South Wales, Australia, aged over 45 years, recruited between 2006-2009. To identify dementia cases we will link data from the 45 and Up Study with multiple health datasets containing information relevant to dementia case identification. We will estimate age- and sex-specific dementia incidence and model the association between dementia and risk factors related to socio-demographic characteristics, health conditions and health behaviours. We will also estimate the impact of various modifiable exposures on dementia incidence. Based on the results, we will produce a series of knowledge translation products providing advice on the contribution of identified risk factors to dementia incidence in Australia. Discussion Linking the 45 and Up Study data to multiple health datasets provides a unique opportunity to explore the role of risk factors on dementia incidence, including modelling the effect of modifiable risk factors on dementia incidence in the Australian population. We anticipate the results from this study to guide targeted and gradated strategies for population-level dementia prevention.

Population ageing and urbanisation are global phenomena that call for an understanding of the impacts of features of the urban environment on older adults’ cognitive function. Because neighbourhood characteristics that can potentially have opposite effects on cognitive function are interdependent, they need to be considered in conjunction. Using data from an Australian national sample of 4141 adult urban dwellers, we examined the extent to which the associations of interrelated built and natural environment features and ambient air pollution with cognitive function are explained by cardiometabolic risk factors relevant to cognitive health. All examined environmental features were directly and/or indirectly related to cognitive function via other environmental features and/or cardiometabolic risk factors. Findings suggest that dense, interconnected urban environments with access to parks, blue spaces and low levels of air pollution may benefit cognitive health through cardiometabolic risk factors and other mechanisms not captured in this study. This study also highlights the need for a particularly fine-grained characterisation of the built environment in research on cognitive function, which would enable the differentiation of the positive effects of destination-rich neighbourhoods on cognition via participation in cognition-enhancing activities from the negative effects of air pollutants typically present in dense, destination-rich urban areas.

Traffic-related air pollution (TRAP) is associated with lower cognitive function and diabetes in older adults, but little is known about whether diabetes status moderates the impact of TRAP on older adult cognitive function. We analysed cross-sectional data from 4141 adults who participated in the Australian Diabetes, Obesity and Lifestyle (AusDiab) study in 2011–2012. TRAP exposure was estimated using major and minor road density within multiple residential buffers. Cognitive function was assessed with validated psychometric scales, including: California Verbal Learning Test (memory) and Symbol–Digit Modalities Test (processing speed). Diabetes status was measured using oral glucose tolerance tests. We observed positive associations of some total road density measures with memory but not processing speed. Minor road density was not associated with cognitive function, while major road density showed positive associations with memory and processing speed among larger buffers. Within a 300 m buffer, the relationship between TRAP and memory tended to be positive in controls (β = 0.005; p = 0.062), but negative in people with diabetes (β = −0.013; p = 0.026) and negatively associated with processing speed in people with diabetes only (β = −0.047; p = 0.059). Increased TRAP exposure may be positively associated with cognitive function among urban-dwelling people, but this benefit may not extend to those with diabetes.

We developed a comprehensive risk assessment tool for dementia – Cognitive Health and Dementia Risk Assessment (CogDrisk) and a version specifically for Alzheimer’s disease called CogDrisk-AD that could be applicable in low and high-resource settings. This tool incorporates risk and protective factors identified through systematic synthesis of observational studies that report risk ratios. Risk and protective factors included in the tool were selected on the strength of evidence as well as the availability of measures that are practicable in a range of clinical and research contexts. Seventeen risk/protective factors were identified for inclusion in the dementia algorithm to estimate the risk of dementia while sixteen factors were identified for the AD model, with an overlap in the majority of the factors. CogDrisk and the CogDrisk-AD were predictive of dementia and AD when validated across four high-quality international cohort studies. To enable the CogDrisk tool to be implemented in practice our team has developed an interactive website where individuals 18 years and above can complete the CogDrisk questionnaire, obtain a personalised risk profile, and receive feedback on their risk profile. The website was developed with the capacity to collect and store data. We anticipate that the tool can be used by members of the public, in clinical settings and as a screening or outcome measure for clinical trials.

Background There is a need to understand how early adversity is linked with mental health in older adults. The aim of this study was to 1) explore the optimal way to operationalise a scale of adverse childhood experiences (ACEs) and 2) examine the association between ACEs with depression and anxiety in older adulthood. Methods Data were from Wave 1 of the Personality and Total Health (PATH) Through Life Project (N = 7485, 51% women). Older adults aged 60–65 reported their childhood experiences of domestic adversity on a 17-item scale (e.g., physical abuse, neglect, poverty). Depression and anxiety were assessed using four validated screening instruments (GDS, GAS, MCS-12, PHQ-9). Three approaches to scoring the ACE scale were compared: i) cumulative risk, ii) factor analysis, and iii) latent class analysis (LCA). Confirmatory factor analysis (CFA) of the dimensional model of adversity and psychopathology was examined. Linear regression models estimated associations between ACEs and mental health, adjusting for age, race, education, and gender. Results Childhood adversity was associated with late life depression and anxiety using the cumulative risk approach. CFA examined latent factors of threat and deprivation in our dataset, which were highly correlated, leading to problems with multicollinearity when estimating associations. Finally, LCA revealed six classes of ACEs: high adversity, low adversity, low affection, authoritarian upbringing, high parental dysfunction, and moderate parental dysfunction. High adversity and high parental dysfunction were associated with higher levels of depression and anxiety symptoms compared to other latent classes.

Existing evidence suggests that individuals’ subjective experience of cognitive decline may be a risk state for dementia. However, whether self-awareness of positive changes confer cognitive protection is unknown. We examined the extent to which awareness of positive (AARC gains) and negative (AARC losses) age-related changes explains variability in objective cognitive performance in a sample of 6,231 UK residents (Mean age= 66.1 years, 75.9% women) without cognitive impairment. We tested a structural equation model with AARC gains and losses as predictors of cognitive performance and depressive symptoms as a mediator of the association of AARC losses with cognitive performance. The model fit the data well. The correlation between AARC gains and losses was negligible, yet higher levels of both AARC gains and losses predicted poorer cognitive scores. Hence, higher AARC gains did not confer cognitive protection. This unexpected pattern of results underscores the complexity of mapping individuals’ awareness onto objective outcomes.

This paper describes the design of a dynamic microsimulation model being built as part of the DYNOPTA (Dynamic Analyses to Optimize Ageing) Project. The model aims to establish a demographic modelling infrastructure to simulate the health outcomes of Australia's baby boomer and aged cohorts and to examine the impacts of possible social and medical interventions to compress morbidity and optimise ageing in Australia over the next 30 years. The DYNOPTA microsimulation model is founded on a pooled dataset which combines data from nine Australian Longitudinal Studies of Ageing. The pooled dataset is being used to inform the base file; identify key incidence and prevalence rates; examine risk and protective factors for health outcomes; and estimate disease pathways and transition rates. The modelling focuses on four conditions that contribute to burden of disease and quality of life in the aged - cognitive decline and dementia, sensory impairment, impairment in mobility, and depression. The microsimulation modelling is underpinned by a life course approach to development and ageing that recognises interdependencies among demographic, social, economic and health factors. The base population comprises individuals aged 45 years and over in 1996. The number of individuals in various states of disability, and the length of time spent in these states prior to death, will be modelled over the 30-year simulation period. Once built, the model will provide a new and innovative means of directly informing health promotion policy on the aged in Australia