Complementary feeding and nutritional status among children 6-23 months old in Marsabit county, Kenya.
Mutuku, Joyce Nzilani
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Optimal infant and young child feeding practices rank among the most effective interventions to improve child health. In appropriate feeding practices is a major cause of the onset of malnutrition in young children. From the age of 6 months onwards when breast milk alone is no longer sufficient to meet all nutritional requirements, infants enter a particularly vulnerable period of complementary feeding during which they make a gradual transition to eating ordinary foods. Relationship between complementary feeding and nutritional status of children aged 6-23 months has not been adequately studied. This study seeks to establish the influence of complementary feeding on nutritional status of children aged 6-23 months in Laisamis sub-county and document the magnitude of association. The study will target mothers and! primary caregivers of children aged 6-23 months. A cross-sectional study design will be used to collect data that is relevant to objectives and describe characteristics of the situation. Semi-structured questionnaires will be administered to 289 randomly selected mothers/caregivers while focus group guide and key-in-depth interview schedules will be applied to mothers with children aged 6-23 months and key informants respectively. Data will be analyzed using statistical package for social scientists (SPSS) version 16.1. Data entry template will be created in line with the questionnaire. The collected data will be coded then entered into the created data entry. After data is entered, cleaning and validation will be conducted through use of frequencies and cross tabulations to ward off any anomalies in the data. Descriptive statistics will be used to describe the study population characteristics, complementary feeding and nutritional status. Chi-square test shall be used to test for associations between categorical variables and t-test will be used to test for differences between continuous variables. Variables that will be significantly associated in univariate analysis will be subjected to logistic regression to test the magnitude of association. ENA for SMART software (2010) will be used to analyze anthropometry data. The results shall be presented in tables, graphs and charts. The findings of the study will be beneficial to individuals and interest groups. The findings will also inform individuals and agencies dealing with child health in Laisamis sub-county in providing information for programme planning.