ic data. Epi Info was used to classify the school children into categories of nutritional status. Multiple regression analysis was used to establish the SLP variables which were associated with the effect SLP have on nutritional status of the study children. Food composition tables were used to compute the nutrient intake of index children. Significance was accepted at 5% probability level (P<0.05).
Demographic and socio-economic characteristics of the study children's parents
The response rate was 100%. From the findings a larger percentage of respondents in both groups were females (64.4% and 75% for participants' and non-participants parents) respectively. Mean age was 40 years. Dependency ratio for this study population was 1:2 and the average household size was six people. Mean income was Kshs. 6900 ± 50.50 for participants and Kshs. 6200 ± 75.60 for non-participants.
Nutritional status of the children
Nutritional status was determined using weight-for-age, height-for-age and weight-for-height indices. Based on the weight-for-age index, there was a significant difference (P<0.05) in prevalence of underweight between the two groups. Only 8.1% participants were malnourished as compared to 16.3% non-participants. There was a significant association between school lunch and weight-for-age. The prevalence of underweight was much lower among the participants in the school lunch than the non-participants.
The height-for-age index revealed a significantly higher (P<0.05) level of stunting among the non-participants (53.1%) than the participants (30%). There was an association between height-for-age and the school lunch. Participants had consumed the school lunch for at least two years. Adequate kilocalorie and protein intake at school was associated with reduced levels of stunting. The association was not however statistically significant. The weight-forheight index showed that 5% of the participants and 18.2% of the non-participants were wasted. Regression coefficients with school lunch variables; energy and protein intake were statistically significantly different among the two groups for weight-for-height. The prevalence of wasting was lower among participants and much higher among the nonparticipants.
This study focused only on two major nutrients; calories and proteins. On average, the school lunch which consisted of maize and beans in the selected schools, provided each child with 841 kilocalories, which is 33.6% of RDA and 40g of protein, which is 88.8% of RDA. On the other hand, the home lunch for the non-participants provided each child on average with 720 kilocalories, which is 28.8% of RDA and 24g of protein, which is 53.3% of RDA. The lunch nutrient intake of the participants was significantly higher than that of the nonparticipants.
There was a significant relationship between school lunch and nutritional status (weight-forage and weight-for-height). The regression coefficients with school lunch variables; kilocalorie and protein intake were significant for weight-for-age and weight-for-height. Thus, higher kilocalorie and protein intake at school was associated with reduced prevalence of underweight and wasting.
Table 1 shows the mean caloric and protein intake of index children. The results indicated a significantly higher intake of kilocalories (2089 ± 12.41 Kcal) among participants than the non-participants (1841 ± 15.68 Kcal) for all the three meals. There was no significant difference in protein intake at breakfast and supper between the two groups. At lunchtime however, protein intake was significantly higher among participants (40 ± 7.2) than the nonparticipants (24 ± 1.4g). Thus, the total protein intake per day was significantly higher among participants (58 ± 7.5 g) than the non-participants (40 ± 2.4g). Lunch and supper contributed higher amounts of nutrients to the daily nutrient intake of children in both groups than breakfast.Adequacy of dietary intake
Children in both groups took fewer calories and more proteins than the Recommended Daily Allowance (RDA). For both groups, breakfast and supper co