Risk factor analysis for stunting incidence using sparse categorical principal component logistic regression - Anna Islamiyati; Muhammad Nur; Abdul Salam; Wan Zuki Azman Wan Muhamad; Dwi Auliyah (MethodsX)
The risk factors for stunting incidence involve categorical data in both the response and predictor variables. Therefore, we developed a sparse categorical principal component logistic regression model capable of handling data with multicollinearity. The parameters of the sparse categorical principal component logistic regression model were estimated using the maximum likelihood method and the Newton-Raphson iterative approach. The analysis yielded a likelihood ratio value of 144.81 and a chi-square statistic value of 11.07, indicating that all factors included in the model are statistically significant. The results highlight that medical history, inadequate complementary feeding, formula feeding, lack of complementary feeding programs, and lack of iron supplementation for mothers are highly associated with the risk of stunting in toddlers. This emphasizes the need for attention to maternal nutrition from pregnancy through breastfeeding, as well as the nutrition of the toddler. Some important points proposed in this method are: • Stunting data consists of categorical variables containing multicollinearity. • The method applied is sparse logistic regression combined with categorical principal component analysis. • Analysis of risk factors for stunting in toddlers is based on the child's own condition, as well as parental factors, namely age, education, and intake of additional food and supplementary tablets during pregnancy.