Spatial Determinants of Childhood Stunting in Lambitu, Bima: A GIS-based Multilevel Analysis

Darmin Darmin; M Noris; Nur Husnul Khatimah; Sudirman Sudirman; Hairil Akbar

doi:10.70716/mohr.v4i2.434

Authors

Darmin Prodi Gizi, Fakultas Kesehatan, Universitas Muhammadiyah Bima, Kota Bima, Indonesia.
M Noris Prodi Pedagogi, Fakultas Pascasarjana, Universitas Muhammadiyah Bima, Kota Bima, Indonesia.
Nur Husnul Khatimah Prodi Gizi, Fakultas Kesehatan, Universitas Muhammadiyah Bima, Kota Bima, Indonesia.
Sudirman Prodi Kesehatan Masyarakat, Sekolah Tinggi Ilmu Kesehatan Indonesia Jaya Palu, Sulawesi Tengah, Indonesia.
Hairil Akbar Prodi Kesehatan Masyarakat, Institut Kesehatan Teknologi Graha Medika, Kota Mobagu, Indonesia.

DOI:

https://doi.org/10.70716/mohr.v4i2.434

Keywords:

distribution of stunting, geographic information system (GIS), stunting, spatial context & service access, stunting outcomes

Abstract

Stunting is a public health issue that requires special attention, especially in relation to risk factors such as low maternal education, low birth weight babies, maternal age, premature birth, and inadequate antenatal care. The objective of this study was to map the distribution of stunting in Lambitu Subdistrict, Bima Regency, using a Geographic Information System (GIS) to assist policymakers in designing more targeted and effective intervention programmes. The method used was quantitative descriptive research with a GIS approach, including the collection of secondary data from the Lambitu Community Health Centre on the number of toddlers and stunting cases, as well as spatial data in the form of village boundary maps analysed using GIS software to produce thematic maps of stunting distribution. The results showed a fluctuating trend and uneven distribution of stunting cases in the six villages during the period 2023 to 2025. In 2023, Kuta village had the highest stunting prevalence at 16% (orange), followed by Teta at 15% and Londu at 14% (bright orange). In 2024, Ka'owa had the highest prevalence at 22% (bright red), followed by Teta at 19% (orange) and Kuta at 10% (yellow). In 2025, Ka'owa remained the highest at 22.78% (bright red), followed by Sambori at 17.91% and Kaboro at 16.92% (orange), while Teta and Londu decreased to 10% and 9.69% (yellow). The conclusion of this study is that the use of GIS is highly effective in mapping and analysing the distribution of stunting, as well as assisting in decision-making and planning more targeted spatial data-based interventions.

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