Nutritional characterisation of low-income households of Nairobi: Socioeconomic, livestock and gender considerations and predictors of malnutrition from a cross-sectional survey

Background In sub-Saharan Africa, urban informal settlements are rapidly expanding, leading to overcrowding and constituting challenging environments for food and water supplies, health and nutrition. The study objectives were to characterise and compare two low-income areas of Nairobi according to socioeconomic (including livestock and gender) indicators and the nutritional status of non-pregnant women of reproductive age and 1 to 3 year-old children; and to investigate socioeconomic predictors of malnutrition in these areas. Methods In this cross-sectional survey 205 low-income households in deprived areas of Dagoretti and Korogocho (Nairobi) were randomly selected. Socioeconomic data were collected via an interviewer-administered questionnaire. Maternal and child dietary data were collected by a 24-h dietary recall. Maternal and child anthropometric and haemoglobin measurements were taken. Chi-square, t-test and Wilcoxon-Mann–Whitney test were used to compare groups and multivariable linear regression to assess predictors of malnutrition. Results Dagoretti consistently showed better socioeconomic indicators including: income, education and occupation of household head, land ownership, housing quality and domestic asset ownership. Animal ownership was more than twice as high in Dagoretti as in Korogocho (53.0 % vs 22.9 % of households; p-value < 0.0001). A double burden of malnutrition existed: 41.5 % of children were stunted, and 29.0 % of women were overweight. In addition, 74.0 % of the children and 25.9 % of the women were anaemic, and were at risk of inadequate intakes for a number of micronutrients. Nutritional status and nutrient intakes were consistently better in Dagoretti than Korogocho; height-for-age (0.47 Z-scores higher; p-value = 0.004), the minimum dietary diversity (80.0 % vs 57.7 % in children, p-value = 0.001) and intakes of several nutrients were significantly higher. Positive predictors of maternal nutritional status were income, age and not having a premature delivery. Positive predictors of child nutritional status were area, household head education, mother not being married, female animal ownership and child’s sex (female). Conclusions Malnutrition is prevalent in these settings, which could be partly due to low nutrient intakes, and to socioeconomic factors (including poverty), thus requiring comprehensive approaches that include increased accessibility and affordability of nutrient-dense foods. This study indicates that differences among low-income areas may need consideration for prioritisation and design of interventions.