Examining the association between livestock ownership typologies and child nutrition in the Luangwa Valley, Zambia

<div><div>These data were collected in a cross-sectional survey of 838 children aged 6-36 months living in 40 rural communities in the Luangwa Valley, Zambia. This is a portion of the baseline data collected as part of a larger impact evaluation study, the objective of which was to examine the impact of an intervention promoting village-scale egg production on dietary quality and growth among children 6-36 months of age in participating communities. The full study is registered at ClinicalTrials.gov (ID: NCT02516852).</div><div><br></div><div>Data were collected over four weeks from Dec 2014 to Jan 2015, and the research staff administered in-home questionnaires through face-to-face interviews. </div><div><br></div><div>Sampling: Twenty rural field sites within four Chiefdoms of Zambia's Eastern Province were purposively selected by our implementing partner based on internal program targets to receive a poultry intervention, and 20 additional sites were purposively selected, subjectively matched for community size, density, and proximity to major roads. Each of the 40 field sites was marked with a GPS point representing the approximate center of the site. Inclusion criteria for participation were: 1) the household was located ≤ 1.5 km from the field site GPS point; and 2) a child 6-36 months of age lived in the household. The 20 eligible households nearest to the central GPS point of each field site were recruited and enrolled in the study, for a target of approximately 800 total households. All children 6-36 months of age living within enrolled households were included, and one child from each household was randomly selected during the analysis phase.</div><div><br></div><div>Questionnaires: Data were collected about the participating households, including: household composition (gender, ages, and education of individuals residing in each household), asset ownership, water/electricity/sanitation access, housing materials, agricultural production, livestock ownership, income sources, food sources, and food security. Data were also collected about the eligible child and his/her mother or primary female caretaker, including: ages, education, diets over the past 24 hours, animal source food consumption over the past 7 days, child morbidities over the past 14 days, child breastfeeding history, and subjective maternal wellbeing and physical/emotional health.</div><div> </div><div>Infant and young child feeding indicators (exclusively breastfed to 6mo, continued breastfeeding at 1y and 2y, minimum meal frequency, minimum dietary diversity, minimum adequate diets) were calculated from questionnaire responses using formulas given in the WHO's "Indicators for assessing infant and young child feeding practices, part 2, measurements." Women's dietary diversity was calculated using the older 9-food group score (WDDS) from the FAO's "Guidelines for Measuring Household and Individual Dietary Diversity".</div><div><br></div><div>Three indicators of food security are used: HFIAS, HHS, and coping strategies index (CSI). The CSI items were each weighted using locally adapted weights derived from focus groups. </div><div><br></div><div>Anthropometry: Heights, weights, and mid-upper arm circumferences for children and women were also taken at home after the interview. Height and weight measures were then taken on the mother and child using standardized seca 872 electronic scales with mother/child function and seca 213 portable stadiometers (seca GMbH & Co., Hamburg, Germany). For both height and weight, two measures were taken; a third measure was taken if there was a difference of at least 0.5 kg or 1.0 cm between the first two measures [24]. The mean of the two most similar measures was defined as the child’s height and weight. Height-for-age, weight-for-age, and weight-for-height z-scores were calculated using the WHO Growth Standards in Stata using the zscore06 command. Stunting, severe stunting, wasting, severe wasting, underweight, and severe underweight variables were created using standard cut-offs (< -2 SD from median and <-3 SD from median for severe categorization). Women's BMI was calculated in the standard manner from her height and weight, and overweight, underweight, and obese variables were created using standard cut-offs (< 18.5, >=25, and >=30). </div></div><div><br>