Literature DB >> 21079620

'Traffic-light' nutrition labelling and 'junk-food' tax: a modelled comparison of cost-effectiveness for obesity prevention.

G Sacks1, J L Veerman, M Moodie, B Swinburn.   

Abstract

INTRODUCTION: Cost-effectiveness analyses are important tools in efforts to prioritise interventions for obesity prevention. Modelling facilitates evaluation of multiple scenarios with varying assumptions. This study compares the cost-effectiveness of conservative scenarios for two commonly proposed policy-based interventions: front-of-pack 'traffic-light' nutrition labelling (traffic-light labelling) and a tax on unhealthy foods ('junk-food' tax).
METHODS: For traffic-light labelling, estimates of changes in energy intake were based on an assumed 10% shift in consumption towards healthier options in four food categories (breakfast cereals, pastries, sausages and preprepared meals) in 10% of adults. For the 'junk-food' tax, price elasticities were used to estimate a change in energy intake in response to a 10% price increase in seven food categories (including soft drinks, confectionery and snack foods). Changes in population weight and body mass index by sex were then estimated based on these changes in population energy intake, along with subsequent impacts on disability-adjusted life years (DALYs). Associated resource use was measured and costed using pathway analysis, based on a health sector perspective (with some industry costs included). Costs and health outcomes were discounted at 3%. The cost-effectiveness of each intervention was modelled for the 2003 Australian adult population.
RESULTS: Both interventions resulted in reduced mean weight (traffic-light labelling: 1.3 kg (95% uncertainty interval (UI): 1.2; 1.4); 'junk-food' tax: 1.6 kg (95% UI: 1.5; 1.7)); and DALYs averted (traffic-light labelling: 45,100 (95% UI: 37,700; 60,100); 'junk-food' tax: 559,000 (95% UI: 459,500; 676,000)). Cost outlays were AUD81 million (95% UI: 44.7; 108.0) for traffic-light labelling and AUD18 million (95% UI: 14.4; 21.6) for 'junk-food' tax. Cost-effectiveness analysis showed both interventions were 'dominant' (effective and cost-saving).
CONCLUSION: Policy-based population-wide interventions such as traffic-light nutrition labelling and taxes on unhealthy foods are likely to offer excellent 'value for money' as obesity prevention measures.

Entities:  

Mesh:

Year:  2010        PMID: 21079620     DOI: 10.1038/ijo.2010.228

Source DB:  PubMed          Journal:  Int J Obes (Lond)        ISSN: 0307-0565            Impact factor:   5.095


  62 in total

Review 1.  The Influence of Nutrition Labeling and Point-of-Purchase Information on Food Behaviours.

Authors:  Ekaterina Volkova; Cliona Ni Mhurchu
Journal:  Curr Obes Rep       Date:  2015-03

2.  Understanding price elasticities to inform public health research and intervention studies: key issues.

Authors:  Nhung Nghiem; Nick Wilson; Murat Genç; Tony Blakely
Journal:  Am J Public Health       Date:  2013-09-12       Impact factor: 9.308

3.  Values at stake: autonomy, responsibility, and trustworthiness in relation to genetic testing and personalized nutrition advice.

Authors:  Karin Nordström; Niklas Juth; Sofia Kjellström; Franck L B Meijboom; Ulf Görman
Journal:  Genes Nutr       Date:  2013-03-16       Impact factor: 5.523

4.  Quantification of the effect of energy imbalance on bodyweight.

Authors:  Kevin D Hall; Gary Sacks; Dhruva Chandramohan; Carson C Chow; Y Claire Wang; Steven L Gortmaker; Boyd A Swinburn
Journal:  Lancet       Date:  2011-08-27       Impact factor: 79.321

5.  Habitual street food intake and subclinical carotid atherosclerosis.

Authors:  Silvio Buscemi; Alessandro Mattina; Giuseppe Rosafio; Fatima M Massenti; Fabio Galvano; Giuseppe Grosso; Emanuele Amodio; Anna M Barile; Vincenza Maniaci; Alice Bonura; Delia Sprini; Giovam B Rini
Journal:  Eat Weight Disord       Date:  2013-10-23       Impact factor: 4.652

6.  Changing the future of obesity: science, policy, and action.

Authors:  Steven L Gortmaker; Boyd A Swinburn; David Levy; Rob Carter; Patricia L Mabry; Diane T Finegood; Terry Huang; Tim Marsh; Marjory L Moodie
Journal:  Lancet       Date:  2011-08-27       Impact factor: 79.321

7.  Cost-Effectiveness of the US Food and Drug Administration Added Sugar Labeling Policy for Improving Diet and Health.

Authors:  Yue Huang; Chris Kypridemos; Junxiu Liu; Yujin Lee; Jonathan Pearson-Stuttard; Brendan Collins; Piotr Bandosz; Simon Capewell; Laurie Whitsel; Parke Wilde; Dariush Mozaffarian; Martin O'Flaherty; Renata Micha
Journal:  Circulation       Date:  2019-04-15       Impact factor: 29.690

8.  Traffic-light labels and choice architecture: promoting healthy food choices.

Authors:  Anne N Thorndike; Jason Riis; Lillian M Sonnenberg; Douglas E Levy
Journal:  Am J Prev Med       Date:  2014-02       Impact factor: 5.043

9.  Designing a food tax to impact food-related non-communicable diseases: the case of Chile.

Authors:  Juan Carlos Caro; Lindsey Smith-Taillie; Shu Wen Ng; Barry Popkin
Journal:  Food Policy       Date:  2017-08-08       Impact factor: 4.552

10.  Modelling obesity trends in Australia: unravelling the past and predicting the future.

Authors:  A J Hayes; T W C Lung; A Bauman; K Howard
Journal:  Int J Obes (Lond)       Date:  2016-09-27       Impact factor: 5.095

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