Past and projected trends of body mass index and weight status in South Australia: 2003 to 2019
OBJECTIVE: Functional data analysis (FDA) is a forecasting approach that, to date, has not been applied to obesity, and that may provide more accurate forecasting analysis to manage uncertainty in public health. This paper uses FDA to provide projections of Body Mass Index (BMI), overweight and obes...
| Main Authors: | , , , , , , , , |
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| Format: | Journal Article |
| Published: |
2015
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| Online Access: | http://hdl.handle.net/20.500.11937/21934 |
| _version_ | 1848750729660989440 |
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| author | Hendrie, G. Ullah, S. Scott, Jane Gray, J. Berry, N. Booth, S. Carter, P. Cobiac, L. Coveney, J. |
| author_facet | Hendrie, G. Ullah, S. Scott, Jane Gray, J. Berry, N. Booth, S. Carter, P. Cobiac, L. Coveney, J. |
| author_sort | Hendrie, G. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | OBJECTIVE: Functional data analysis (FDA) is a forecasting approach that, to date, has not been applied to obesity, and that may provide more accurate forecasting analysis to manage uncertainty in public health. This paper uses FDA to provide projections of Body Mass Index (BMI), overweight and obesity in an Australian population through to 2019. METHODS: Data from the South Australian Monitoring and Surveillance System (January 2003 to December 2012, n=51,618 adults) were collected via telephone interview survey. FDA was conducted in four steps: 1) age-gender specific BMIs for each year were smoothed using a weighted regression; 2) the functional principal components decomposition was applied to estimate the basis functions; 3) an exponential smoothing state space model was used for forecasting the coefficient series; and 4) forecast coefficients were combined with the basis function. RESULTS: The forecast models suggest that between 2012 and 2019 average BMI will increase from 27.2 kg/m(2) to 28.0 kg/m(2) in males and 26.4 kg/m(2) to 27.6 kg/m(2) in females. The prevalence of obesity is forecast to increase by 6-7 percentage points by 2019 (to 28.7% in males and 29.2% in females). CONCLUSIONS: Projections identify age-gender groups at greatest risk of obesity over time. The novel approach will be useful to facilitate more accurate planning and policy development. |
| first_indexed | 2025-11-14T07:41:28Z |
| format | Journal Article |
| id | curtin-20.500.11937-21934 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:41:28Z |
| publishDate | 2015 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-219342017-09-13T13:54:25Z Past and projected trends of body mass index and weight status in South Australia: 2003 to 2019 Hendrie, G. Ullah, S. Scott, Jane Gray, J. Berry, N. Booth, S. Carter, P. Cobiac, L. Coveney, J. OBJECTIVE: Functional data analysis (FDA) is a forecasting approach that, to date, has not been applied to obesity, and that may provide more accurate forecasting analysis to manage uncertainty in public health. This paper uses FDA to provide projections of Body Mass Index (BMI), overweight and obesity in an Australian population through to 2019. METHODS: Data from the South Australian Monitoring and Surveillance System (January 2003 to December 2012, n=51,618 adults) were collected via telephone interview survey. FDA was conducted in four steps: 1) age-gender specific BMIs for each year were smoothed using a weighted regression; 2) the functional principal components decomposition was applied to estimate the basis functions; 3) an exponential smoothing state space model was used for forecasting the coefficient series; and 4) forecast coefficients were combined with the basis function. RESULTS: The forecast models suggest that between 2012 and 2019 average BMI will increase from 27.2 kg/m(2) to 28.0 kg/m(2) in males and 26.4 kg/m(2) to 27.6 kg/m(2) in females. The prevalence of obesity is forecast to increase by 6-7 percentage points by 2019 (to 28.7% in males and 29.2% in females). CONCLUSIONS: Projections identify age-gender groups at greatest risk of obesity over time. The novel approach will be useful to facilitate more accurate planning and policy development. 2015 Journal Article http://hdl.handle.net/20.500.11937/21934 10.1111/1753-6405.12442 unknown |
| spellingShingle | Hendrie, G. Ullah, S. Scott, Jane Gray, J. Berry, N. Booth, S. Carter, P. Cobiac, L. Coveney, J. Past and projected trends of body mass index and weight status in South Australia: 2003 to 2019 |
| title | Past and projected trends of body mass index and weight status in South Australia: 2003 to 2019 |
| title_full | Past and projected trends of body mass index and weight status in South Australia: 2003 to 2019 |
| title_fullStr | Past and projected trends of body mass index and weight status in South Australia: 2003 to 2019 |
| title_full_unstemmed | Past and projected trends of body mass index and weight status in South Australia: 2003 to 2019 |
| title_short | Past and projected trends of body mass index and weight status in South Australia: 2003 to 2019 |
| title_sort | past and projected trends of body mass index and weight status in south australia: 2003 to 2019 |
| url | http://hdl.handle.net/20.500.11937/21934 |