The first step when analyzing data for Iron deficiency after cleaning and characterizing the data, is to compile a situation profile that describes the population using the cleaned data and data from previous surveys and compilations. The first exploration of this question is through the geographical knowledge of the country to determine the known "hot spots" for IDD. Usually there will be a written history of the situation so that if areas in the country are flood plains or mountainous regions and are at high risk for IDD, then it is known. Once you break the data into known geographic regions based on knowledge of high-risk areas, take a look at the usual surveillance groups for signs of IDD in these areas. School age children are typically used as the survey group because they are easy to find and measure at schools, but it should be noted that the entire population is vulnerable to IDD.

When we obtain an appropriate dataset the following examples will be added:

Now using dataset name.sav, try compiling a situation analysis for IDD to summarize the situation in country name. This should help in beginning a formulation of ideas about further exploratory analysis on the causes and of IDD and intervention strategies that might be needed. Because IDD is now typically combated using salt fortification, this method is promoted globally. In cases of severe deficiency calling for immediate intervention, then urgency might be given to supplementation to those areas that show signs of IDD and no access to iodized salt. The situation analysis should start showing some of the patterns of IDD, which will help proceed to more probing analysis. If data is available in one data set for many different deficiencies (e.g. IDD, VAD, IDA, etc.) then make one overall situation analysis to show overlaps in the groups with deficiencies and regions where they exist. Also include information on general malnutrition (stunting, wasting, and underweight) and socio-demographic data.