Article

Using Reference Nutrient Density Goals with Food Balance Sheet Data to Identify Likely Micronutrient Deficits for Fortification Planning in Countries in the Western Pacific Region

Department of Human Nutrition, University of Otago, Union St., PO Box 56, Dunedin 9015, New Zealand.
Food and nutrition bulletin (Impact Factor: 1.15). 09/2012; 33(3 Suppl):S214-20. DOI: 10.1177/15648265120333S210
Source: PubMed

ABSTRACT

Collection of nationwide food consumption data at the individual level is the preferred option for planning fortification programs. However, such data are seldom collected in low-income countries. In contrast, Food Balance Sheets (FBS), published annually for approximately 180 countries, may provide a source of national data for program planning.
To explore the use of micronutrient densities from FBS data to identify likely deficits for eight micronutrients in national diets.
Micronutrient densities in the daily available food supply per capita were calculated from the micronutrient contents of 95 food commodities in 17 Western Pacific Region countries. Densities were compared with reference nutrient density goals developed to ensure that at least 95% of individuals, irrespective of life-stage group, are likely to have adequate intakes.
Of the eight micronutrients, Cambodia and Korea D.P.R. had likely deficits for six; China, Fiji, Kiribati, Korea Republic, Lao P.D.R., Philippines, Solomon Islands, Vanuatu, and Viet Nam had likely deficits for five; Brunei Darussalam, Malaysia, Mongolia, New Zealand, and Papua New Guinea had likely deficits for four; and New Caledonia had likely deficits for three. The most frequent deficits were for iron, zinc, and calcium (all countries), followed by vitamin B2 and vitamin A (n = 13), vitamin B1 (n = 2), and vitamin B12 (n = 1).
The nutrient density approach could be applied to FBS data for ranking countries according to likely micronutrient deficits, but it provides no information on distribution of nutrient supply for fortification program planning. The approach described here could be applied to data from Household Consumption and Expenditures Surveys (HCES) to characterize households at greatest risk.

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