Current challenges and opportunities of an ageing longitudinal study
Issues associated with the ageing population are important for the future of the Australian society and economy. Good quality data is required that permit reliable analysis of a range of issues to be used in planning, policy development and evaluation. Obtaining reliable and useful data and analysing it appropriately raises many issues. Ideal data sources should provide large data sets that include a large number of people selected using scientific sampling methods and collect rich data about them over time, so that the experience of ageing is properly understood.
The 45 and Up Study is a prospective study on over a quarter of a million people aged 45 year and over in NSW. The 45 and Up Study was established as a collaborative research infrastructure to facilitate research into ageing and health. Between 2006 and 2009 a broadly random sample of the NSW general population aged 45 and over were sampled from the Medicare Australia enrolment database and invited to participate. Participants have provided information on their health, lifestyle and demographic characteristics, as well as consent to link this data with other administrative data collections and to be contacted for sub-studies. This large scale longitudinal study has provided and will continue to provide information on a wide range of exposures and outcomes of public health importance for the ageing population.
However, the 45 and Up Study is subject to many of the issues that affect large-scale surveys, including a relatively low response rate, missing data items, self-reported data, adjustments to account for different selection probabilities, effective cohort management, use of mixed mode data collection, sample attrition over time, and data linkage errors. This presentation will cover these issues and some of the solutions being considered and/or being considered including: the recruitment sampling and weighting strategy; follow-up response rates by collection mode (online and mail out); validation studies of the self-reported data; studies on the effect of non-response on estimates and analysis; assessment of data linkage results using multiple sources; innovative uses of the resource; and replenishment design options to maintain sample size and minimize bias.