| Literature DB >> 23339933 |
I Lenoir-Wijnkoop1, P J Jones, R Uauy, L Segal, J Milner.
Abstract
Non-communicable diseases (NCD) are a major and increasing contributor to morbidity and mortality in developed and developing countries. Much of the chronic disease burden is preventable through modification of lifestyle behaviours, and increased attention is being focused on identifying and implementing effective preventative health strategies. Nutrition has been identified as a major modifiable determinant of NCD. The recent merging of health economics and nutritional sciences to form the nascent discipline of nutrition economics aims to assess the impact of diet on health and disease prevention, and to evaluate options for changing dietary choices, while incorporating an understanding of the immediate impacts and downstream consequences. In short, nutrition economics allows for generation of policy-relevant evidence, and as such the discipline is a crucial partner in achieving better population nutritional status and improvements in public health and wellness. The objective of the present paper is to summarise presentations made at a satellite symposium held during the 11th European Nutrition Conference, 28 October 2011, where the role of nutrition and its potential to reduce the public health burden through alleviating undernutrition and nutrition deficiencies, promoting better-quality diets and incorporating a role for functional foods were discussed.Entities:
Mesh:
Year: 2013 PMID: 23339933 PMCID: PMC3583164 DOI: 10.1017/S0007114512005107
Source DB: PubMed Journal: Br J Nutr ISSN: 0007-1145 Impact factor: 3.718
Fig. 1Maternal and child undernutrition and its short-term and long-term consequences().
Direct healthcare expenditure and burden of disease attributable to low consumption of dairy products in Australia, 2010–11()*
| Costs of illness attributable to low consumption of dairy products | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Base case analysis | Sensitivity analysis S1 | Sensitivity analysis S2 | |||||||||
| $million | DALY | $million | DALY | $million | DALY | ||||||
| Disease or risk factor | PAR (%) | Sep | Σ | Sep | Σ | PAR (%) | Σ | Σ | PAR (%) | Σ | Σ |
| Obesity | 18·4 | 1468 | 1076 | 54 754 | 8365 | 10·1 | 588 | 4574 | 29·8 | 1741 | 13 536 |
| T2DM | 10·2 | 503 | 237 | 46 208 | 18 342 | 5·1 | 119 | 9233 | 13·0 | 304 | 23 465 |
| IHD | 5·0 | 122 | 122 | 13 638 | 13 638 | 2·5 | 61 | 6862 | 14·3 | 347 | 38 867 |
| Stroke | 16·2 | 238 | 238 | 21 873 | 21 873 | 8·2 | 120 | 11 015 | 26·4 | 388 | 35 641 |
| Hypertension | 8·3 | 173 | 112 | 17 148 | 10 794 | 4·3 | 58 | 5608 | 25·6 | 345 | 33 130 |
| Osteoporosis | 6·2 | 223 | 223 | 2000 | 2000 | 3·1 | 112 | 1006 | 19·9 | 716 | 6423 |
| Total | 2007 | 75 012 | 1059 | 38 299 | 3839 | 151 061 |
DALY, disability-adjusted life year; PAR, population attributable risk; Sep, separately; T2DM, type 2 diabetes mellitus.
Values are point estimates.
Application of the PAR to the corresponding estimate of individual direct healthcare expenditure or burden of disease.
Application of the PAR to the corresponding estimate of combined direct healthcare expenditure or burden of disease.
Based on combination of data for Australian population and data reported in Pereira et al.().
Based on data reported in Choi et al.().
Based on data reported in van der Pols et al.().
Based on data reported in Alonso et al.().
Based on data reported in Jaglal et al.().
Fig. 2Box plot: incremental cost-effectiveness ratios for published Australian cost-effectiveness studies of 245 health interventions().
Fig. 3The three types of biomarkers needed to determine response to foods/components (source: J Milner, unpublished results).