Socioeconomic disparities in child malnutrition: trends, determinants, and policy implications from the Kenya demographic and health survey (2014 - 2022).

Background: Child malnutrition remains a critical public health problem, with socioeconomic factors playing a significant role. Socioeconomic factors include household income, parental education, and access to healthcare, which influence a child's nutritional status. Despite overall progress in reducing under-five child malnutrition in Kenya, disparities persist. This paper analyzes changes, and determinants of child malnutrition, contributions of these determinants to health inequality, and their potential use in the screening for chronic malnutrition in children under five years.

Methods: We use data from the Kenyan Demographic and Health Survey (KDHS 2014 and 2022) and analyze malnutrition using three indicators: Stunting, underweight, and wasting. The determinants of malnutrition are analyzed using multivariate logistic regression. Trends in socioeconomic inequality are analyzed using concentration indices and visualized using concentration curves. Wagstaff decomposition is used to explore the contributions of determinants to inequality in child malnutrition. We investigate diagnostic utility using sensitivity, specificity, predictive values, and area under the ROC curve.

Results: Socioeconomic inequality in under-five child malnutrition increased between 2014 and 2022, with children from the poorest socioeconomic quintiles being disproportionately affected. A child's age (in months) (Adjusted Odds Ratio [AOR] = 1.01; 95% Confidence Interval [CI]: 1.01 - 1.02), being born to a household in the poorest socioeconomic quintile (AOR = 2.67; 95%CI: 1.92 - 3.72), and sex (male) (AOR = 1.50; 95%CI: 1.35 - 1.67) were associated with an increased risk of stunting. The mother's age, sex of the child (male), and household socioeconomic status (poorest) was associated with an increased risk of being underweight and wasted, whereas residence was associated with an increased risk of wasting alone after adjusting for potential confounders. A household's socioeconomic status was the largest contributing factor to health inequality. Sensitivity, specificity, and AUC values were 67.4% (95% CI: 66.4% - 68.4%), 50.6% (95%CI: 50.0% - 51.1%), and 0.59 (95%CI: 0.58 - 0.60), respectively, when using socioeconomic status as a screening tool for stunting.

Conclusion: Socioeconomic disparities are a major barrier to reducing child malnutrition in Kenya, with children from lower socioeconomic quintiles at a greater risk of stunting, underweight, or wasting. This study identifies a child's sex, age, and household socioeconomic status as key predictors of malnutrition, highlighting the need to include these factors in public health interventions. Addressing these disparities with targeted strategies considering immediate health risks and underlying socioeconomic challenges is essential for equitably improving child health outcomes.