Household and dietary assessment of women within the reproductive age of 18 to 45 years and adolescents in Vihiga County, Kenya (July 2023)

Maina, E.W.; Aluso, L.O.; Wanyama, R.N.; Termote, C.; Akingbemisilu, T.H. (2024) Household and dietary assessment of women within the reproductive age of 18 to 45 years and adolescents in Vihiga County, Kenya (July 2023). https://doi.org/10.7910/DVN/HQ0UXS

The primary objective of this dataset is to evaluate the socio-economic characteristics, behaviour and dietary intake patterns of women within the reproductive age of 18 to 45 years and adolescents in Vihiga County, Kenya. This data serves as a foundation for understanding nutritional behaviors and informing targeted interventions to improve dietary outcomes in the study population.

Methodology: The survey used a multi-stage sampling technique to facilitate sequential sampling across two or more hierarchical levels. In the first stage, Vihiga County was purposively selected and all households constituted in the study population. The second stage three sub-locations that is Ebunangwe, Emanda and Mwitubwi sub-locations in Emuhaya, Vihiga and Luanda Sub-Counties respectively were purposively selected. A listing of the households with woman within the reproductive age of 18 to 45 years was obtained from local authorities with the assistance of community health workers. In households with available adolescents, consent was obtained from the mothers to interview the adolescents. Households from thirty-two (32) randomly selected villages within each of three selected sub-locations were interviewed attaining a sample size of 96 women and 19 adolescents. The survey tool encompassed socio-economic and household characteristics, as well as a single 24-hour qualitative dietary intake questionnaire for both women and adolescents. Data collection was carried out using the KOBO Toolbox platform. Prior to deployment, all tools were pre-tested and refined as necessary. Local enumerators were recruited and trained to administer the surveys effectively. The collected data was exported in Excel format for subsequent cleaning and analysis. The primary tools utilized for data cleaning and analysis were Python and R.