Background: In 2012 mobile phone numbers were included into the ongoing New South Wales Population Health Survey (NSWPHS) using an overlapping dual-frame design. Previously in the NSWPHS the sample was selected using random digit dialing (RDD) of landline phone numbers. The survey was undertaken using computer assisted telephone interviewing (CATI). The weighting strategy needed to be significantly expanded to manage the differing probabilities of selection by frame, including that of children of mobile-only phone users, and to adjust for the increased chance of selection of dual-phone users. This paper describes the development of the final weighting strategy to properly combine the data from two overlapping sample frames accounting for the fact that population benchmarks for the different sampling frames were not available at the state or regional level.
Methods: Estimates of the number of phone numbers for the landline and mobile phone frames used to calculate the differing probabilities of selection by frame, for New South Wales (NSW) and by stratum, were obtained by apportioning Australian estimates as none were available for NSW. The weighting strategy was then developed by calculating person selection probabilities, selection weights, applying a constant composite factor to the dual-phone users sample weights, and benchmarking to the latest NSW population by age group, sex and stratum.
Results: Data from the NSWPHS for the first quarter of 2012 was used to test the weighting strategy. This consisted of data on 3395 respondents with 2171 (64%) from the landline frame and 1224 (36%) from the mobile frame. However, in order to calculate the weights, data needed to be available for all core weighting variables and so 3378 respondents, 2933 adults and 445 children, had sufficient data to be included. Average person weights were 3.3 times higher for the mobile-only respondents, 1.3 times higher for the landline-only respondents and 1.7 times higher for dual-phone users in the mobile frame compared to the dual-phone users in the landline frame. The overall weight effect for the first quarter of 2012 was 1.93 and the coefficient of variation of the weights was 0.96. The weight effects for 2012 were similar to, and in many cases less than, the effects found in the corresponding quarter of the 2011 NSWPHS when only a landline based sample was used.
Conclusions: The inclusion of mobile phone numbers, through an overlapping dual-frame design, improved the coverage of the survey and an appropriate weighing procedure is feasible, although it added substantially to the complexity of the weighting strategy. Access to accurate Australian, State and Territory estimates of the number of landline and mobile phone numbers and type of phone use by at least age group and sex would greatly assist in the weighting of dual-frame surveys in Australia.
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http://dx.doi.org/10.1186/1471-2288-14-102 | DOI Listing |
J Surg Res
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School of Medicine, Tongji University, Shanghai, China; Department of Health Statistics, Navy Medical University, Shanghai, China. Electronic address:
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Ultrasound Obstet Gynecol
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Robinson Research Institute, University of Adelaide, Adelaide, Australia.
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PLoS One
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Faculty of Environmental Engineering, The University of Kitakyushu, Kitakyushu, Japan.
Petroleum hydrocarbon pollution causes significant damage to soil, so accurate prediction and early intervention are crucial for sustainable soil management. However, traditional soil analysis methods often rely on statistical methods, which means they always rely on specific assumptions and are sensitive to outliers. Existing machine learning based methods convert features containing spatial information into one-dimensional vectors, resulting in the loss of some spatial features of the data.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
January 2025
Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, People's Republic of China.
Lignin degradation by biocatalysts is a key strategy to develop a plant-based sustainable carbon economy and thus alleviate global climate change. This process involves synergy between ligninases and auxiliary enzymes. However, auxiliary enzymes within secretomes, which are composed of thousands of enzymes, remain enigmatic, although several ligninolytic enzymes have been well characterized.
View Article and Find Full Text PDFJAMA Netw Open
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Interdisciplinary Orthopaedics, Department of Orthopaedic Surgery, Aalborg University Hospital, Aalborg, Denmark.
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