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# Nutritional Status: Anthropometric Measurements

Anthropometric measurements include height, weight, middle upper arm circumference[1], age, and gender. The measurements are done on children under the age of five years and women of childbearing age or mothers of children within the household being surveyed. The goal of the indicator is to assess prevalence of malnutrition, evaluate the impact of interventions on nutrition, enable the identification of at-risk persons, and monitor the nutritional status of a household or community over time (Jones et al., 2013). The measurements at the household level may be used, depending on sampling design, to calculate metrics at the community level to measure, for instance, rate stunting and wasting, percent of respondents underweight, and average birth weight (if the weight is collected or enumerated).

[1] Proxy for under nutrition

## How to operationalize the metric

#### Method of data collection and data needed to compute the method:

The data for the anthropometric measurements is collected as part of the household survey in most multi-indicator surveys. It is a separate section that requires specific equipment to ensure that the measurements are done correctly and at a time when the children and the mother of the children in the household (or a woman between the ages of 15 and 45) are present. The women are measured for indicators that determine status of wellbeing for women of reproductive age. To collect the data on height, weight, and mid upper arm circumference, data collectors need the following items: an adult digital weight scale, Leicester height measure, length mat to measure children under the age of 2 years, mid-upper arm circumference tape measure, and height stick about 100 centimeters long for children 2 to5years old. The weight should be measured in kilograms, height in centimeters, and age in months for children below five years (max 60 months). This will ensure that the data can be accurately analyzed by the software. The procedure and measurement are laid out in the Living Standards Measurement Study (LSMS) (World Bank, 2017) survey and the Vital Signs Protocol for household survey (VS, 2014)

#### Unit of analysis:

Once the data on height, weight, mid upper arm circumference, the age and gender of the children and mother of children is recorded, the estimation are performed to determine children who are stunted, underweight, wasting, and overweight. The steps below indicate the algorithms and steps needed to construct the outcome indicators; % stunting, % wasting, % underweight, and % over weight. The World Health Organization has developed a software package that enables one to generate Z-scores for; weight-for-age (underweight), height-for-age (stunting), and weight-for-height (wasting) scores using the international growth standards for children under five years. The software can be downloaded from the WHO website (WHO, 2017). The scripts were developed for R, STATA, SAS, SPSS, and S-Plus software and you can choose the package with which you are most familiar with. An example is provided in the Appendix for STATA but for R can be found on this link and WHO site (http://www.who.int/childgrowth/software/en/). An analysis is then done for weight-for-age (zwei), height-for-age (zlen), and weight-for-height (zwfl) z-scores, and a body mass index (BMI) is calculated.

#### Underweight, stunting, overweight, and wasting

Percentage underweight is defined as the weight for age z-score (zwei) less than (-) 2 *sd*. Severe underweight is a z-score for weight for age such that . Extreme (i.e., biologically implausible) z-scores for each indicator are flagged. Stunting is defined as circumstance where a height for age z-score is more than two standard deviations below average. Severe stunting is defined as z-score for height-for-age <-3sd. Extreme (i.e., biologically implausible) z-scores for each indicator are flagged. Wasting is defined as the weight for height z score less than 2 standard deviations. Severe wasting is defined as z-score for weight-for-height <-3sd. Extreme (i.e., biologically implausible) z-scores for each indicator are flagged. Overweight is defined as BMI greater than 25. Obesity is defined as BMI >30. Extreme (i.e., biologically implausible) BMI scores are flagged according to the following system: BMI < 5 or zwfl > 60. Also note that BMI under 18.5 is underweight (Remans et al., 2015).

#### Limitations regarding estimating and interpreting:

Using anthropometric measurements to assess nutritional impact of an intervention are very important but have a few limitations. First, in order to have a robust estimate of these metrics, a large sample size is needed from the population surveyed and households with children under the age of five (60 months) should be sampled. This data collection can be costly to gather. Secondly, changes in nutritional outcomes have to be observed overtime, which means that the data collection must be done at baseline and for subsequent years. The target population, that is children and women, may also fluctuate across the time of study and this should be taken into consideration. For example, children who are four years old at the time of the baseline study, may exit the sample the next year of the survey since they will be over five years (60 months). These issues should be addressed with nutrition experts as sampling is done to collect these indicators.

#### Method of data collection and data needed to compute the method:

Collection of data on nutritional outcomes of the population of focus for some projects may be too costly and hence the need for alternative approaches of assessing the nutritional status of the population in area of study. Additional data on anthropometry, or calculated nutritional indicators, may be obtained from secondary data to provide an indication on the nutritional status of the area at baseline. In addition, health center or national statistical offices may have health records or statistics. The following sources can be explored to provide an indication of the nutritional status of the study population: demographic health surveys (DHS, 2017); living standard measurement study surveys (World Bank, 2017); multiple indicator cluster surveys conducted by UNICEF (UNICEF, 2017); and local and national health center records.

#### Unit of analysis:

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#### Limitations regarding estimating and interpreting:

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