Analysis of the Determinants of Stunting among Children Aged below Five Years in Stunting Locus Villages in Indonesia

**Background:** Stunting remains a critical global health problem, with Indonesia having the third highest prevalence in Southeast Asia. East Nusa Tenggara (NTT) province has the highest stunting rate in Indonesia (42.6% per 2018 Riskesdas). The Indonesian government designated 'stunting locus villages' as priority targets for integrated interventions. This study aimed to analyze determinants of stunting specifically within these locus villages in Alor Regency, NTT, an area not previously studied in this context.

**Methods:** A cross-sectional design was conducted from January to March 2022 in four stunting locus villages served by the Apui health center. The study population comprised mothers of children aged 24–59 months. Using the Slovin formula and proportional random sampling, 166 mother-child pairs were recruited. Inclusion criteria: mothers who had lived in the locus village for ≥1 year, had a full-term child aged 24–59 months, and provided informed consent. Exclusion criteria: children with physical disabilities or genetic disorders affecting height measurement. Stunting was defined as height-for-age Z-score < −2 SD per WHO growth standards, based on anthropometric data from the health center (February 2022). Independent variables included gender, birth spacing (normal ≥3 years vs. close <3 years), history of infectious diseases (often sick vs. rarely sick), maternal knowledge (good/sufficient/deficient based on a questionnaire), maternal parenting (positive vs. negative), parental income (above vs. below district minimum wage), utilization of health services (maximum/sufficient/less), and household sanitation (healthy vs. unhealthy). Chi-square tests and binary logistic regression were used, with significance at p < 0.05.

**Key Results:** The prevalence of stunting was 22.3%. Most children were male (52.4%), had normal birth spacing (80.8%), and were often sick (68.7%). The majority of mothers had less knowledge about stunting (72.3%), applied negative parenting (67.5%), and did not utilize health services (65.1%). Household sanitation was unhealthy for 62.7% of children. Multivariate analysis identified the following adjusted odds ratios (AOR): - Less maternal knowledge: AOR 5.310 (95% CI: 0.671–41.997) - Negative parenting: AOR 3.026 (95% CI: 1.191–7.871) - History of infectious disease (often sick): AOR 2.863 (95% CI: 1.112–7.371) - Parental income under minimum wage: AOR 2.712 (95% CI: 1.108–6.643) - Unhealthy household sanitation: AOR 2.610 (95% CI: 1.107–6.151) - Less utilization of health services: AOR 2.416 (95% CI: 0.664–8.788) - Male gender: AOR 2.251 (95% CI: 1.042–4.863) - Close birth spacing: AOR 0.304 (95% CI: 0.087–1.063) — protective effect

**Clinical Implications:** Maternal knowledge and parenting practices emerged as the strongest modifiable risk factors, with less knowledge conferring a 5.3-fold increased odds of stunting and negative parenting a 3-fold increase. The high proportion of mothers with less knowledge (72.3%) is consistent with the low educational attainment in the region (65.18% of married women had primary school education or below). Negative parenting was linked to the predominant occupation (84.01% worked as garden farmers), leading to permissive or neglectful care. The protective effect of close birth spacing (AOR 0.304) is counterintuitive and may reflect confounding or small sample size (CI crosses 1.0). The study underscores that despite government designation of locus villages for focused intervention, stunting prevalence remains high (22.3%), far above the 2024 national target of 14%. Interventions should prioritize: (1) health education to improve maternal knowledge through formal and non-formal channels, (2) promotion of positive parenting practices, (3) improving household sanitation and WASH, (4) enhancing health service utilization, and (5) addressing economic barriers. Limitations include the single-district scope (not generalizable to other locus areas) and reliance on secondary anthropometric data.