Question #1:

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

 Child Population Prevalence Underweight (%) # Underweight Children* District A 200,000 20 40,000 District B 50,000 40 20,000 Region 24 60,000

* estimate that 20% of child population is underweight.

 Prevalence Underweight (%) Total Population (million) District A 15 2.468 District B 30 1.234 Region 20

If you have any questions about how these numbers were calculated, see page 2, Chapter 3

Question #2:

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.

 Table 1: Rift Valley Area Profile Descriptive % Female Child 49.8 (1276) % Underweight (<-2SDs) 20.5 (806) Socioeconomic % Without Toilet/Latrine Access 21.2 (1746) % With Poor Housing (thatched or other type of roof) 41.6 (1739) % Illiterate Respondents 44.9 (1750) % Respondents with Low education (incomplete 1o or none) 64.0 (1754) Health Services % Received Measles Vaccine (children >= 9 months) 81.0 (699) % Respondents With Health Card 94.9 (953) % Home Delivery Location 62.2 (953) Environmental Characteristics % Bad Drinking Water Source (surface or other) 50.1 (1737) % Bad Household Water Source (surface or other) 50.0 (1749) % Time to Water Source >30 min 16.9 (1327)

Question #3:

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.

 Table 2: Targeting Districts by Prevalence of Malnutrition District Population # Children <5 years % Underweight (<-2 SDs) % of All Malnourished Children % Received Measles Vaccine % Without Access to Toilet/Latrine % Low Education of Mother Kericho (rural) 322 152 26.3 24.2 88.1 28.3 69.3 Other rural 300 132 25.0 20.0 74.6 36.4 75.0 Uasin Gishu (rural) 315 142 19.7 17.0 81.3 17.6 66.7 Nakuru (rural) 252 125 19.2 14.5 88.2 1.6 56.3 Nandi (rural) 403 204 16.7 20.6 73.8 28.4 67.7 Other urban 162 51 11.8 3.6 85.2 2.0 30.2 Total 1754 806 20.5 99.9 81.0 22.0 64.0

Question #4:

Current Program Coverage and Targeting

The current program coverage for several health access variables and education is summarized in Table 5. Table 3 gives the coverage, coverage of the malnourished and focusing over population prevalence for each of these associations. From these results we can see that none of the current programs are particularly well targeted to the malnourished. This is evidenced by the ratio of prevalence in the program to population prevalence being < 1.00. In an ideal situation, where a program is well targeted to the malnourished this ratio would be > 1.00.

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.

 Table 3: Current Program Coverage and Targeting Weight for Age Prevalence (<-2SDs of ref. Median) Health Card Coverage 0.97 % Malnourished in program 0.98 F/PP 1.00 Tetanus Toxoid Coverage 0.89 % Malnourished in program 0.85 F/PP 0.96 Measles Vaccination Coverage 0.81 % Malnourished in program 0.82 F/PP 1.01 Delivery Location Coverage 0.38 % Malnourished in program 0.27 F/PP 0.70

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.

 Table 4: Targeting and Coverage of Current Programs and Interventions Weight for age Prevalence (<-2 SDs) Weight for age Prevalence (<-2 SDs) Health Card Mean N # maln # not maln Delivery Location Mean N # maln # not maln Has health card (pgm) 0.2006 783 157 626 Public or private sector (pgm) 0.1433 307 44 263 No health card (no pgm) 0.1739 23 4 19 Homes or other (no pgm) 0.243 498 121 377 Total 0.2047 806 165 641 Total 0.205 805 165 640 Coverage 0.97 Coverage 0.38 % of malnourished rec. pgm 0.98 % of malnourished rec. pgm 0.27 Focusing 0.20 Focusing 0.14 Population prevalence 0.20 Population prevalence 0.20 F/PP 1.00 F/PP 0.70 Weight for age Prevalence (<-2 SDs) Weight for age Prevalence (<-2 SDs) Tetanus Toxoid Mean N # maln # not maln Measles Vaccine Mean N # maln # not maln Rec'd injection (pgm) 0.1972 710 140 570 Rec'd Vaccine (pgm) 0.2485 495 123 372 No injection (no pgm) 0.2697 89 24 65 No Vaccine (no pgm) 0.2348 115 27 88 Total 0.2053 799 164 635 Total 0.2459 610 150 460 Coverage 0.89 Coverage 0.81 % of malnourished rec. pgm 0.85 % of malnourished rec. pgm 0.82 Focusing 0.20 Focusing 0.25 Population prevalence 0.21 Population prevalence 0.25 F/PP 0.96 F/PP 1.01

Question #5:

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.

 Table 4: One-Way Analysis Mean WAZ Underweight Prevalence (%) n Toilet Access p = 0.035 p = 0.025 No access to toilet -1.11 26.6 177 Access to toilet -0.87 18.9 626 Difference (b/n worse and best off) -0.2 7.7 Total -0.93 20.6 803 Roof p = 0.000 p = 0.001 Thatched or other roof -1.12 25.9 359 Corrugated iron or tiles -0.76 16.3 443 Difference (b/n worse and best off) -0.4 9.7 Total -0.92 20.6 802 Drinking water source p = 0.064 p = 0.292 Surface -1.00 22.0 396 pub. Tap and well w/o pump -0.91 21.4 262 piped to res. And well w/ pump -0.68 15.4 117 Difference (b/n worse and best off) -0.3 6.6 Total -0.95 20.8 775 Educational attainment p = 0.006 p = 0.005 none or incomplete 1o -1.02 23.5 511 complete 1o or more -0.76 15.3 295 Difference (b/n worse and best off) -0.3 8.2 Total 0.92 20.5 806 Delivery location p = 0.000 p = 0.001 Homes or other -1.09 24.3 498 Public or private sector -0.66 14.3 307 Difference (b/n worse and best off) -0.4 10.0 Total -0.93 20.5 805