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# Dietary macro-nutrient and micro-nutrient intake estimation in children

This question was posted the Assessment and Surveillance forum area and has 9 replies.

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### Mark Myatt

Frequent user

10 Jun 2015, 15:19

One approach is to use adult male equivalents (AMEs). The AME is the age and gender specific energy requirement divided by the energy requirement for an adult male aged 18-30 years. Data tables for calculating AMEs for different activity levels can be found in "Human energy requirements (Report of a Joint FAO/WHO/UNU Expert Consultation), Rome, 17–24 October 2001" which was, I think, published in 2004. Here is a table of AMEs based on moderate activity: ``` Age (low) Age (high) Males AME Female AME --------- ---------- --------- ---------- 6 months < 1 0.22 0.22 1 2 0.31 0.28 2 3 0.37 0.34 3 4 0.41 0.38 4 5 0.44 0.41 5 6 0.48 0.43 6 7 0.52 0.47 7 8 0.56 0.51 8 9 0.60 0.56 9 10 0.65 0.61 10 11 0.70 0.66 11 12 0.77 0.70 12 13 0.84 0.75 13 14 0.91 0.78 14 15 0.98 0.80 15 16 1.04 0.82 16 17 1.09 0.82 17 18 1.11 0.82 18 30 1.00 0.79 30 60 0.97 0.77 60 > 60 0.80 0.69 --------- ---------- --------- ---------- ``` Each person in the household is assigned an energy requirement based on a table of AMEs (as above). This individual AME is constructed by taking the ratio of their requirement to that of an adult male age 18-30 years. This is done for all members of the household. The individual AMEs are summed to give the total household AME. Each person’s share is calculated as the ratio of their individual AME to the total household AME. For example, if you have 200g of wheat flour to divided over all HH members and your total HH AMEs sum (e.g.) to 6.5 then an adult male aged 18-30 years would get (1.0 / 6.5) * 200 or 30.8g while a boy aged four years would get (0.44 / 6.5) * 200 or 13.5g. You can then use food tables to estimate individual energy intake from a given food. The idea can be extended to other nutrients / micronutrients. I hope this is of some use.

### Merry Fitzpatrick

Assistant Research Professor

Normal user

10 Jun 2015, 15:58

This is a common difficulty and there is no easy solution for actual intake as opposed to requirements. I'm also struggling with this same problem for a study in the DRCongo and I haven't found anything in the literature that helps very much. Each household might be a bit different in how they divide out the food within the common plate. The mother may help the younger children, selecting bites for them and feeding them. So how each mother chooses those bites might vary by household. In some households the men and older boys may eat separately from the women and other children. Knowing how women divide the food and prioritize certain favored foods might be a start. What I plan to do, though there may be more robust methods out there that I haven't found, is to start with some discussions with mothers, but more importantly, attempting to observe meal-times (and snacking), perhaps through female staff that might have a similar status to the women of the household so we can see what is normal. In this way, I can at least get an idea for how much difference there is between households and what the usual trends are. From that, I'll try to extrapolate from a 7-day household dietary measurement how much the child received, as well as an attempt at a 24-hour dietary recall based on the amounts that the mother thinks the child ate. This isn't optimal, but I'm not sure there is an optimal way that doesn't change the eating habits.

### Anonymous 81

Public Health Nutritionist

Normal user

10 Jun 2015, 17:09

Dear Tigabu, For various reasons, it is difficult to measure the amount of food intake by individual. that is why, as a variable, it is not included in the WHO 15 IYCF indicators. so, you better collect information to measure, dietary diversity, meal frequency and minimum acceptable diet as they are a proxy for positive outcomes. Research has shown that Dietary diversity is a proxy for adequate micronutrient-density of foods.

### Rosemary Otiende

MOH

Normal user

15 Jun 2015, 14:12

Dear Tigabu I would also agree that proxy indicators of nutritional status especially of the vulnerable that is women and children would be a good start up for you in the households where sharing is practiced to determine if sharing is positively or negatively affecting nutritional status and thereby have it as a talking point to the community

### Mark Myatt

Frequent user

16 Jun 2015, 08:29

Just to say that I agree with all those posting here that urge you to collect IYCF indicators and to use meal frequency and dietary diversity.

### BEBOU Midassirou

division nutrition

Normal user

4 Nov 2023, 04:25

Many thanks

Could I have with you the macros for estimating macronutrients and microtriments, or the optifood software?

### Kemal J. Tunne

Emergency project coordinator

Normal user

5 Nov 2023, 13:19

Macros for estimating macronutrients and micronutrients

The following macros can be used to estimate macronutrients and micronutrients from food composition data:

Python

`import numpy as np def estimate_macronutrients(food_composition_data): """Estimates macronutrients from food composition data. Args: food_composition_data: A NumPy array containing the food composition data, with the following columns: * food: Name of the food. * calories: Calories per 100 grams. * protein: Protein per 100 grams (grams). * fat: Fat per 100 grams (grams). * carbohydrates: Carbohydrates per 100 grams (grams). Returns: A NumPy array containing the estimated macronutrient intake, with the following columns: * food: Name of the food. * protein_intake: Protein intake (grams). * fat_intake: Fat intake (grams). * carbohydrate_intake: Carbohydrate intake (grams). """ protein_intake = food_composition_data['protein'] * food_composition_data['serving_size'] / 100 fat_intake = food_composition_data['fat'] * food_composition_data['serving_size'] / 100 carbohydrate_intake = food_composition_data['carbohydrates'] * food_composition_data['serving_size'] / 100 estimated_macronutrients = np.c_[food_composition_data['food'], protein_intake, fat_intake, carbohydrate_intake] return estimated_macronutrients def estimate_micronutrients(food_composition_data): """Estimates micronutrients from food composition data. Args: food_composition_data: A NumPy array containing the food composition data, with the following columns: * food: Name of the food. * micronutrients: A dictionary of micronutrient values, with the following keys: * vitamin_a: Vitamin A (IU). * vitamin_c: Vitamin C (mg). * vitamin_d: Vitamin D (IU). * vitamin_e: Vitamin E (mg). * calcium: Calcium (mg). * iron: Iron (mg). * potassium: Potassium (mg). * sodium: Sodium (mg). Returns: A NumPy array containing the estimated micronutrient intake, with the following columns: * food: Name of the food. * micronutrient_intake: A dictionary of micronutrient intake values, with the following keys: * vitamin_a: Vitamin A intake (IU). * vitamin_c: Vitamin C intake (mg). * vitamin_d: Vitamin D intake (IU). * vitamin_e: Vitamin E intake (mg). * calcium: Calcium intake (mg). * iron: Iron intake (mg). * potassium: Potassium intake (mg). * sodium: Sodium intake (mg). """ micronutrient_intake = {} for micronutrient in food_composition_data['micronutrients']: micronutrient_intake[micronutrient] = food_composition_data['micronutrients'][micronutrient] * food_composition_data['serving_size'] / 100 estimated_micronutrients = np.c_[food_composition_data['food'], micronutrient_intake] return estimated_micronutrients `

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To use these macros, you would first need to create a NumPy array containing the food composition data. The food composition data can be obtained from a variety of sources, such as the USDA Food Composition Database. Once you have created the food composition data array, you can call the `estimate_macronutrients()` or `estimate_micronutrients()` macro to estimate the macronutrient or micronutrient intake, respectively.

For example, the following code shows how to use the `estimate_macronutrients()` macro to estimate the macronutrient intake from a simple food composition data array:

Python

`food_composition_data = np.array([ ['Apple', 52, 0.3, 0.25, 13.8], ['Banana', 89, 1.1, 0.3, 22.8], ['Orange', 47, 1, 0.2, 11.4] ]) estimated_macronutrients = estimate_macronutrients(food_composition_data) print( `

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I think this some help.

### Lynette R

web seo zone

Normal user

5 Nov 2023, 20:21

I Really like your this post this post is very helpfull for me beause it gives me great help in my work

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