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Research Article

Potentials and limitations of a food group-based algorithm to assess dietary nutrient intake of women in rural areas in Tanzania

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Received 11 Aug 2023, Accepted 21 Mar 2024, Published online: 19 Apr 2024

Figures & data

Table 1. Description of the food groups in CIMI-algorithm.

Table 2. Socio-demographic characteristics of women who participated in Scale-N and Trans-SEC surveys.

Table 3. Comparison of means and medians of energy and nutrient intake calculated by CIMI and NutriSurvey in Chamwino and Kilosa district.

Figure 1. Scatter plots (upper row) and Bland-Altman plots (lower row) of energy (A,B), protein (C,D), iron (E,F) and zinc (G,H) intake calculated by CIMI and NutriSurvey (NS). Scatter plots include predictive equations, vertical lines in the Bland-Altman plots represent the recommended nutrient intake values (RNI).

Figure 1. Scatter plots (upper row) and Bland-Altman plots (lower row) of energy (A,B), protein (C,D), iron (E,F) and zinc (G,H) intake calculated by CIMI and NutriSurvey (NS). Scatter plots include predictive equations, vertical lines in the Bland-Altman plots represent the recommended nutrient intake values (RNI).

Table 4. Haemoglobin, iron status (iron stores, serum ferritin and soluble transferrin receptor) and serum retinol and zinc of the women who participated in the Scale-N survey.

Table 5. Prevalence of anaemia and vitamin A, iron and zinc micronutrient deficiencies in the Scale-N women depending on nutrient intake (vs. < and ≥ RNI) calculated by CIMI-algorithm and NutriSurvey.