Carole L Birrell

Cohort profile: The Dynamic Analyses to Optimize Ageing (DYNOPTA) project

National Health and Medical Research Council (410215); NHMRC Fellowships (#366756 to K.J.A. and #316970 to P.B.)

Estimates of probable dementia prevalence from population-based surveys compared with dementia prevalence estimates based on meta-analyses

BackgroundNational data on dementia prevalence are not always available, yet it may be possible to obtain estimates from large surveys that include dementia screening instruments. In Australia, many of the dementia prevalence estimates are based on European data collected between 15 and 50 years ago. We derived population-based estimates of probable dementia and possible cognitive impairment in Australian studies using the Mini-Mental State Examination (MMSE), and compared these to estimates of dementia prevalence from meta-analyses of European studies.MethodsData sources included a pooled dataset of Australian longitudinal studies (DYNOPTA), and two Australian Bureau of Statistics National Surveys of Mental Health and Wellbeing. National rates of probable dementia (MMSE < 24) and possible cognitive impairment (24-26) were estimated using combined sample weights.ResultsEstimates of probable dementia were higher in surveys than in meta-analyses for ages 65-84, but were similar at ages 85 and older. Surveys used weights to account for sample bias, but no adjustments were made in meta-analyses. Results from DYNOPTA and meta-analyses had a very similar pattern of increase with age. Contrary to trends from some meta-analyses, rates of probable dementia were not higher among women in the Australian surveys. Lower education was associated with higher prevalence of probable dementia. Data from investigator-led longitudinal studies designed to assess cognitive decline appeared more reliable than government health surveys.ConclusionsThis study shows that estimates of probable dementia based on MMSE in studies where cognitive decline and dementia are a focus, are a useful adjunct to clinical studies of dementia prevalence. Such information and may be used to inform projections of dementia prevalence and the concomitant burden of disease.

Understanding ageing in older Australians: the contribution of the Dynamic Analyses to Optimise Ageing (DYNOPTA) project to the evidence base and policy

Aim:  To describe the Dynamic Analyses to Optimise Ageing (DYNOPTA) project and illustrate its contributions to understanding ageing through innovative methodology, and investigations on outcomes based on the project themes. DYNOPTA provides a platform and technical expertise that may be used to combine other national and international datasets.

Seasonal Adjustment of an Aggregate Series using Univariate and Multivariate Basic Structural Models

Time series resulting from aggregation of several sub-series can be seasonally adjusted directly or indirectly. With model-based seasonal adjustment, the sub-series may also be considered as a multivariate system of series and the analysis may be done jointly. This approach has considerable advantage over the indirect method, as it utilises the covariance structure between the sub-series.This paper compares a model-based univariate and multivariate approach to seasonal adjustment. Firstly, the univariate basic structural model (BSM) is applied directly to the aggregate series. Secondly, the multivariate BSM is applied to a transformed system of sub-series. The prediction mean squared errors of the seasonally adjusted aggregate series resulting from each method are compared by calculating their relative efficiency. Results indicate that gains are achievable using the multivariate approach according to the relative values of the parameters of the sub-series.

Indigenous Australians are under-represented in longitudinal ageing studies.

Objective: Evidence‐based policy depends on the availability of high‐quality research that is relevant to the population. This study aimed to identify the available data on the health of older Indigenous Australians in population‐based longitudinal studies of ageing.

Prevalence of physical activity behaviour in older people: Findings from the Dynamic Analyses to Optimise Ageing (DYNOPTA) project and Australian national survey data

Many older people lead sedentary lives. National Health Survey physical activity prevalence data provide limited coverage of the ‘old old’ (≥75 years).

Univariate and multivariate approaches to seasonal adjustment of aggregate series of different lengths

An aggregate series is a time series resulting from the aggregation of two or more sub-series. Two model-based approaches to seasonal adjustment of the aggregate series include a univariate and multivariate basic structural model. In a previous study (2), the variance of the seasonally adjusted series for the two approaches were compared using a range of true parameter values for a fixed length series. This paper compares the model-based univariate and multivariate approaches for different series lengths using the estimated parameters. A simulation study compares two outcomes: the accuracy of the estimated parameters of the aggregate series, and the naive bias in the prediction error variance. The results show that for the two cases studied, the use of the multivariate approach in the estimation of parameters improves the accuracy of the parameter estimates of the aggregated series. This was especially true for short to medium length time series. The relative efficiencies of the seasonally adjusted aggregated series also showed good gains for the multivariate model. For one of the cases, there was a substantial decrease in the naive bias with the use of the multivariate model. Bias correction is also discussed for the two approaches.