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Given the following data for two districts the prevalence of underweight for the region overall is given in each table for each example.

Area Profile

Table 1 gives an overview of the Rift Valley region of Kenya by summarizing some important descriptive statistics. The ratio of female to male children is roughly equal, and the overall prevalence of malnutrition in this region is 20.5%. Compared to average levels of malnutrition of 27.2% (Average 1985 to 1995) for Sub-Saharan Africa, this level of underweight prevalence is below average. The percent of people without access to toilet is not too bad, while the percent with poor roofing (a proxy indicator for SES) is higher. The level respondents with low education is greater than half and literacy rates are not much better. The coverage rate of measles vaccination for children ³ 9 months is not far from the ideal coverage rate of 90%. A very high percentage of people in this region have a health card, whether this means that they take advantage of health services or not is unclear. The percent of women who deliver in a home is fairly high at 62.2%. Only half of the residents of this region have access to a good water source.

By compiling district level area profile data we can rank districts by malnutrition prevalence and then add other indicators to see if they are in line with the malnutrition ranking. Table 2 ranks districts based on malnutrition and compares this with the contribution each area makes to the total number of malnourished. This ranking shows that Kericho and Other rural districts have the highest prevalence of malnutrition in kids under five. It is also important to note that in Nandi, although the prevalence of malnutrition is not among the highest, as this region is the largest (population), it contributes the second highest number of malnourished children and should also be targeted.

It would be smart to look at all current interventions and those that are not targeted to the malnourished should be re-targeted to the worse-off.

These results are taken from Table 4 which shows the prevalence of underweight receiving or not receiving the program. From that we can calculate the ratio of prevalence among program participants to population prevalence to see how well current programs are targeted.

Associations with Nutritional Status

The one-way analysis looks at the associations between important variables and mean weight for age z score as well as underweight prevalence. This is summarized in Table 4, showing that toilet access, roof (proxy for SES), educational attainment and delivery location are all significantly associated with both mean WAZ and underweight prevalence.