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Optimisation Modelling to Assess Cost of Dietary Improvement in Remote Aboriginal Australia

BACKGROUND: The cost and dietary choices required to fulfil nutrient recommendations defined nationally, need investigation, particularly for disadvantaged populations. OBJECTIVE: We used optimisation modelling to examine the dietary change required to achieve nutrient requirements at minimum cost f...

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Autores principales: Brimblecombe, Julie, Ferguson, Megan, Liberato, Selma C., O'Dea, Kerin, Riley, Malcolm
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3877064/
https://www.ncbi.nlm.nih.gov/pubmed/24391790
http://dx.doi.org/10.1371/journal.pone.0083587
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author Brimblecombe, Julie
Ferguson, Megan
Liberato, Selma C.
O'Dea, Kerin
Riley, Malcolm
author_facet Brimblecombe, Julie
Ferguson, Megan
Liberato, Selma C.
O'Dea, Kerin
Riley, Malcolm
author_sort Brimblecombe, Julie
collection PubMed
description BACKGROUND: The cost and dietary choices required to fulfil nutrient recommendations defined nationally, need investigation, particularly for disadvantaged populations. OBJECTIVE: We used optimisation modelling to examine the dietary change required to achieve nutrient requirements at minimum cost for an Aboriginal population in remote Australia, using where possible minimally-processed whole foods. DESIGN: A twelve month cross-section of population-level purchased food, food price and nutrient content data was used as the baseline. Relative amounts from 34 food group categories were varied to achieve specific energy and nutrient density goals at minimum cost while meeting model constraints intended to minimise deviation from the purchased diet. RESULTS: Simultaneous achievement of all nutrient goals was not feasible. The two most successful models (A & B) met all nutrient targets except sodium (146.2% and 148.9% of the respective target) and saturated fat (12.0% and 11.7% of energy). Model A was achieved with 3.2% lower cost than the baseline diet (which cost approximately AUD$13.01/person/day) and Model B at 7.8% lower cost but with a reduction in energy of 4.4%. Both models required very large reductions in sugar sweetened beverages (−90%) and refined cereals (−90%) and an approximate four-fold increase in vegetables, fruit, dairy foods, eggs, fish and seafood, and wholegrain cereals. CONCLUSION: This modelling approach suggested population level dietary recommendations at minimal cost based on the baseline purchased diet. Large shifts in diet in remote Aboriginal Australian populations are needed to achieve national nutrient targets. The modeling approach used was not able to meet all nutrient targets at less than current food expenditure.
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spelling pubmed-38770642014-01-03 Optimisation Modelling to Assess Cost of Dietary Improvement in Remote Aboriginal Australia Brimblecombe, Julie Ferguson, Megan Liberato, Selma C. O'Dea, Kerin Riley, Malcolm PLoS One Research Article BACKGROUND: The cost and dietary choices required to fulfil nutrient recommendations defined nationally, need investigation, particularly for disadvantaged populations. OBJECTIVE: We used optimisation modelling to examine the dietary change required to achieve nutrient requirements at minimum cost for an Aboriginal population in remote Australia, using where possible minimally-processed whole foods. DESIGN: A twelve month cross-section of population-level purchased food, food price and nutrient content data was used as the baseline. Relative amounts from 34 food group categories were varied to achieve specific energy and nutrient density goals at minimum cost while meeting model constraints intended to minimise deviation from the purchased diet. RESULTS: Simultaneous achievement of all nutrient goals was not feasible. The two most successful models (A & B) met all nutrient targets except sodium (146.2% and 148.9% of the respective target) and saturated fat (12.0% and 11.7% of energy). Model A was achieved with 3.2% lower cost than the baseline diet (which cost approximately AUD$13.01/person/day) and Model B at 7.8% lower cost but with a reduction in energy of 4.4%. Both models required very large reductions in sugar sweetened beverages (−90%) and refined cereals (−90%) and an approximate four-fold increase in vegetables, fruit, dairy foods, eggs, fish and seafood, and wholegrain cereals. CONCLUSION: This modelling approach suggested population level dietary recommendations at minimal cost based on the baseline purchased diet. Large shifts in diet in remote Aboriginal Australian populations are needed to achieve national nutrient targets. The modeling approach used was not able to meet all nutrient targets at less than current food expenditure. Public Library of Science 2013-12-31 /pmc/articles/PMC3877064/ /pubmed/24391790 http://dx.doi.org/10.1371/journal.pone.0083587 Text en © 2013 Brimblecombe et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Brimblecombe, Julie
Ferguson, Megan
Liberato, Selma C.
O'Dea, Kerin
Riley, Malcolm
Optimisation Modelling to Assess Cost of Dietary Improvement in Remote Aboriginal Australia
title Optimisation Modelling to Assess Cost of Dietary Improvement in Remote Aboriginal Australia
title_full Optimisation Modelling to Assess Cost of Dietary Improvement in Remote Aboriginal Australia
title_fullStr Optimisation Modelling to Assess Cost of Dietary Improvement in Remote Aboriginal Australia
title_full_unstemmed Optimisation Modelling to Assess Cost of Dietary Improvement in Remote Aboriginal Australia
title_short Optimisation Modelling to Assess Cost of Dietary Improvement in Remote Aboriginal Australia
title_sort optimisation modelling to assess cost of dietary improvement in remote aboriginal australia
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3877064/
https://www.ncbi.nlm.nih.gov/pubmed/24391790
http://dx.doi.org/10.1371/journal.pone.0083587
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