Mu Li, member of the SPARSE team, has been granted access to the full Accessibility/Remoteness Index of Australia (ARIA), curated by the Hugo Centre for Population and Migration Studies at the University of Adelaide. We expect that this data will improve the accuracy and precision of modelling across various parts of the SPARSE project.
The SPARSE team also plan to use National Drug Strategy Household Survey data to support the spatio-temporal modelling. This survey is run by the Australian Institute of Health and Welfare and certain parts of the data can be accessed from the Australian Data Archive.
We’ve noticed that the NIH in the United States is investing resources in constructing a secure data repository with data contributed by more than 329,000 participants. Their Researcher Workbench includes survey data, physical measurements, electronic health record data and even Fitbit data and whole genome sequences.
The Cancer Atlas of Australia is an example of best practice in the mapping of health outcomes at high resolution across the whole of Australia. We look forward to the possibility of integrating SPARSE project outcomes with the Cancer Atlas to bring together risk factors such as smoking and health outcomes such as cancer.
The Centre of Research Excellence on Achieving the Tobacco Endgame includes a number of ANU researchers interested in reducing smoking prevalence to zero.
The Conference on Big Data for Small Area Estimation will be held in Naples and online, 20 – 24 September 2021. The main purpose of the meeting will be to assess the current state of development and usage of small area methodology. This meeting in Naples will also give researchers an opportunity to learn about state-of-the-art small area estimation techniques from the experts in the field.
GATHER promotes best practices in reporting health estimates. A range of health indicators are used to monitor population health and guide resource allocation throughout the world. But the lack of data for some regions and differing measurement methods present challenges that are often addressed by using statistical modeling techniques to generate coherent estimates based on often disparate sources of data.
The general objective of the project is to develop a framework enabling the production of small area estimates for ESS social surveys.