Peer-reviewed article
Predictive Model of Stunting in Children 6-59 Months of Age in Kirundo Health District, Burundi
Nzigamasabo Aloys, Ndayikengurukiye Honoré, Nizigama Chris Tresor, Nahimana Paterne ·
Summary
Written by AI, not yet AICS's view · claude-sonnet-5-5This study looked at stunting (low height for age) among children aged 6 to 59 months in Kirundo health district, Burundi. It surveyed 374 randomly selected households at one point in time and measured children's weight and height. About 61.5% of children were stunted. Factors linked to stunting included food insecurity, latrine type, water source, diarrhea, child sex and age, and lower maternal education. Factors linked to lower stunting included complete vaccination, nutritional knowledge, media exposure, birth spacing, and father's occupation. The authors built a prediction model with an AUC of 0.95 and call for urgent action.
Key points
- Stunting prevalence was estimated at 61.5% among children aged 6 to 59 months.
- Food insecurity was strongly associated with stunting (AOR = 10.47; 95% CI: 3.58-30.61).
- Latrine type (AOR = 6.83), water source (AOR = 3.17) and diarrhea (AOR = 2.56) were associated with higher odds of stunting.
- Complete vaccination (AOR = 0.06), nutritional knowledge (AOR = 0.11) and mother's education (AOR = 0.21) were associated with lower odds of stunting.
- The predictive model had an AUC of 0.95, indicating excellent discrimination.
Limitations
This is a cross-sectional study in one district, so it shows associations but cannot show that any factor causes stunting. Some confidence intervals are very wide (for example age, 1.21-88.30), and the abstract does not say how the model was validated, so the very high AUC may be over-optimistic.
Abstract
An analytical cross-sectional study was conducted among a randomly selected sample of 374 households with at least one child aged 6 to 59 months in the Kirundo health district, Burundi. Sociodemographic, socioeconomic, socio-sanitary factors, food insecurity, behavioral, and environmental data were collected using a structured questionnaire. Children's weight was measured using a standard procedure (SECA scale), their height using a standard UNICEF height rod, and their age was obtained from the birth certificate. Anthropometric data were analyzed using Emergency Nutrition Assessment (ENA for Smart) software. Modeling was performed using logistic regression to eliminate confounding factors, and all independent variables with a significance level less than or equal to 20% in the bivariate analysis were included to explore factors associated with stunting in children aged 6 to 59 months. In this study, the prevalence of stunting is estimated at 61.5%. According to multivariate logistic regression, sex (AOR = 2.83; 95% CI:1.40-5.75), age (AOR= 10.40; 95% CI: 1.21-88.30), food insecurity (AOR = 10.47;95% CI: 3.58-30.61), latrine type (AOR = 6.83; 95% CI: 3.12-14.94), diarrhea (AOR = 2.56; 95% CI: 1.19-5.48), water source (AOR = 3.17; 95% CI: 1.54-6.52), media exposure (AOR = 0.24, 95% CI: 0.11-0.51), nutritional knowledge (AOR = 0.11; 95% CI: 0.05-0.25), birth spacing (AOR = 0.39, 95% CI: 0.16-0.93), complete vaccination (AOR = 0.06; 95% CI: 0.02-0.21), father's occupation (AOR = 0.25; 95% CI: 0.09-0.72), and mother's education (AOR = 0.21; 95% CI: 0.07-0.64) were significantly associated with stunting. The predictive model showed an area under the curve (AUC) of 0.95, indicating excellent discrimination ability. The high prevalence of stunting in this study highlights the importance of urgent action to end this problem.