Why Do Stunting Interventions Remain Unequal in Urban Areas? A Multivariate Analysis of Subdistrict Risk Profiles in Cirebon City, Indonesia.

PURPOSE: Childhood stunting remains a persistent human development barrier in Indonesia. While national prevalence has declined to 19.8% as of 2024, aggregate data frequently mask profound micro-level disparities that threaten the 14% national target. A critical research gap exists in meso-level diagnostics; traditional monitoring evaluates indicators in silos, failing to capture the systemic convergence of interventions. We selected four principal indicators, namely Health Insurance (PBI-JKN), Food Assistance (BPNT), Iron Supplementation, and Unmet Contraceptive Need, as they represent the essential intersection of social protection and reproductive health required for effective prevention. This study addresses the problem of localized systemic risk, where standardized city-wide policies fail to reach "red zones" within urban villages. Our objective was to quantify subdistrict-level inequality and identify specific performance typologies across 22 subdistricts in Cirebon City.

PATIENTS AND

METHODS

We conducted a retrospective, cross-sectional analysis of 2023 administrative data. ANOVA-Simultaneous Component Analysis (ASCA) with 1000 permutations was used to decompose variance, followed by SVD-based biplot visualization to map the multivariate performance landscape.

RESULTS

The subdistrict effect was statistically significant ([Formula: see text]), with the first two components capturing 92.99% of total variance. Biplot analysis distinguished between "access-lagging" facing structural deficits in social protection and "compliance-lagging" clusters hampered by behavioral and service-outreach gaps.

CONCLUSION

Urban stunting risk is not monolithic; geography remains a decisive determinant of intervention coverage. This study contributes to the precision public health paradigm by providing a diagnostic framework that enables local governments to move beyond one-size-fits-all models. By identifying neighborhood-specific vulnerabilities, including administrative exclusion and behavioral barriers, policymakers can implement targeted, risk-informed governance to ensure more equitable health outcomes.