Sri Suwitri
Department of Public Administration, Faculty of Social and Political Sciences, Universitas Diponegoro, Semarang 50275, Central Java, Indonesia

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Development of Predictive Model for Stunting Incidence among At-Risk Families in Rural Areas of Kubu Raya Regency: Unsafe Drinking Water and Inadequate Sanitation as Key Indicators Ria Risti Komala Dewi; Mohammad Zen Rahfiludin; Sri Suwitri; Sulistyani
JURNAL KESEHATAN LINGKUNGAN Vol. 18 No. 3 (2026): JURNAL KESEHATAN LINGKUNGAN
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jkl.v18i3.2026.199-208

Abstract

Introduction: Stunting is the symptom of the chronic undernutrition that disturbs child growth and development and has long-term health and productivity implications. Prevalence of stunting in Indonesia in 2024 was 19.8% which is below the national target of 14%. It is especially the rural households whose access to safe drinking water and sanitation is limited. This study evaluated risk factors in at-risk families in rural Kubu Raya Regency and generated predictive model of stunting. Methods: Matched case-control design was performed, with 100 cases (height-for-age < -2 SD) and 100 controls (height-for-age -2 SD to +3 SD). The sampling was done by total sampling. Data was collected through interviews and observations through standardized means. The predictors and predictive model were determined using logistic regression. Results and Discussion: Four variables emerged as significant predictors; unsafe drinking water source (OR = 4.6; 95% CI: 1.84-11.82), inadequate latrine ownership (OR = 3.4; 95% CI: 1.69-6.87), birth intervals < 24 months (OR = 5.3; 95% CI: 2.16-13.36), and having > 2 children (OR = 5.5; 95% CI: 1.66-18.14). The model had a 80.7% accuracy, 84% sensitivity and 64% specificity. Conclusion: Poor sources of drinking water and latrines ownership were key predictors of stunting in children aged between 6-23 months of age in at-risk families in Kubu Raya Regency. These indicators were incorporated into a predictive model that had a good discriminatory performance, thus it was possible to utilize it as a useful tool to risk early stunting in rural areas that could be effectively used.