Analisis Spasial Kejadian Stunting Berbasis Rekam Medis Elektronik dan Data Geospasial di Kecamatan Piyungan, Bantul Spatial Analysis of Stunting Incidence Based on Electronic Medical Records and Geospatial Data in Piyungan District, Bantul
Article Sidebar
Page Numbers:
1054-1066
Digital Object Identifier:
10.58578/masaliq.v6i3.9570
Viewed: 72 times
Downloaded: 62 times
LYAS Publisher cordially invites qualified professionals to serve as Editors or Reviewers. Your expertise will make an important contribution to maintaining and strengthening the academic quality of our publications. Interested applicants are kindly requested to complete the application form at the following link: Editors & Reviewers
Please feel free to contact us if you need any further information about the submission process or if you have any additional questions.
Authors:Andhy Sulistyo1*, Resmiaini Resmiaini2
Copyright :Authors retain copyright and grant the journal right of first publication.
Main Article Content
Abstract
Stunting remains a complex public health problem, thus requiring a spatial approach to identify patterns of case distribution more precisely. This study aims to analyze the spatial pattern of stunting cases in Piyungan Subdistrict through a local hotspot approach, global spatial autocorrelation, and individual point-based micro-clustering. This study used spatial data and electronic medical records analyzed using ArcGIS software. Local hotspot identification was conducted using the Getis-Ord Gi* method, global spatial autocorrelation was analyzed using Moran’s I, while micro-clustering was analyzed using Average Nearest Neighbor (ANN). The results of the Getis-Ord Gi* analysis of 132 spatial units showed that 131 units (99.2%) were not classified as statistically significant hotspots or coldspots, while 1 unit (0.8%) was identified as a local hotspot at the 95% confidence level with a z-score of 2.21 and a p-value of 0.0267. Moran’s I analysis produced a value of -0.0365 with a z-score of -0.6623 and a p-value of 0.5078, indicating the absence of significant global spatial autocorrelation so that the distribution pattern of stunting cases at the aggregate level tended to be random. However, the ANN analysis showed an observed mean distance of 83.88 meters, an expected mean distance of 236.57 meters, a nearest neighbor ratio of 0.3545, a z-score of -20.214, and a p-value of <0.001, indicating the presence of very strong spatial clustering at the micro level. These findings indicate that the spatial pattern of stunting in Piyungan Subdistrict depends on the scale of analysis; at the administrative level, no strong cluster was found, whereas at the individual level, there was significant case clustering. Thus, the results of this study confirm the importance of more targeted nutritional interventions in micro-clusters to improve the effectiveness of stunting prevention and management.
Downloads
Article Details
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
References
Amalo, R., et al. (2023). Electronic health records and GIS integration for stunting surveillance in East Nusa Tenggara. Journal of Health Informatics in Developing Countries, 17(2).
Aswi, A., Rahardiantoro, S., Kurnia, A., Sartono, B., Handayani, D., Nurwan, N., & Cramb, S. (2024). Childhood stunting in Indonesia: Assessing the performance of Bayesian spatial conditional autoregressive models. Geospatial Health, 19(2). https://doi.org/10.4081/gh.2024.1321
Beal, T., Tumilowicz, A., Sutrisna, A., Izwardy, D., & Neufeld, L. M. (2018). A review of child stunting determinants in Indonesia. Maternal & Child Nutrition, 14(4), e12617. https://doi.org/10.1111/mcn.12617
Çalışkan, M., & Anbaroğlu, B. (2023). Space time cube analytics in QGIS and Python for hot spot detection. SoftwareX, 24, 101498. https://doi.org/10.1016/j.softx.2023.101498
Girma, B., Sasahu, L. D., & Rahman, A. (2025). Spatial distribution of stunting among breast feeding children in Sub-Sahara Africa. PLOS ONE, 20(6), e0325812. https://doi.org/10.1371/journal.pone.0325812
Khasanah, N. N., Rustina, Y., Sari, D. W. P., & Wuriningsih, A. Y. (2022). Information system records of nutritional status of stunted children aged under five: A literature review of stunting management in pandemic era. Amerta Nutrition, 6(4), 432–436. https://doi.org/10.20473/amnt.v6i4.2022.432-436
Lan, Y., & Delmelle, E. (2023). Space-time cluster detection techniques for infectious diseases: A systematic review. Spatial and Spatio-Temporal Epidemiology, 44, 100563. https://doi.org/10.1016/j.sste.2022.100563
Lee, J., & Wong, D. W. S. (2024). Advanced nearest neighbor methods for micro-scale disease clustering. International Journal of Geographical Information Science, 38(3).
Muhammad, F. S., Shahabudin, S. M., & Talib, M. B. A. (2024). Measuring spatial inequalities in maternal and child mortalities in Pakistan: Evidence from geographically weighted regression. BMC Public Health, 24, 2229. https://doi.org/10.1186/s12889-024-19682-5
Eryando, T., Sipahutar, T., Budiharsana, M. P., Siregar, K. N., Aidi, M. N., Minarto, Utari, D. M., Rahmaniati, M., & Hendarwan, H. (2022). Spatial analysis of stunting determinants in 514 Indonesian districts/cities: Implications for intervention and setting of priority. Geospatial Health, 17(1). https://doi.org/10.4081/gh.2022.1055
Ndagijimana, A., Nduwayezu, G., Kagoyire, C., Elfving, K., Umubyeyi, A., Mansourian, A., & Lind, T. (2024). Childhood stunting is highly clustered in Northern Province of Rwanda: A spatial analysis of a population-based study. Heliyon, 10(2), e24922. https://doi.org/10.1016/j.heliyon.2024.e24922
Ntawuyirushintege, S., Ahmed, A., Bucyibaruta, G., Siddig, E. E., Remera, E., Tediosi, F., & Wyss, K. (2025). Spatiotemporal trends in stunting prevalence among children aged two years old in Rwanda (2020–2024): A retrospective analysis. Nutrients, 17(17), 2808. https://doi.org/10.3390/nu17172808
Pangestu, H. G., Sinaga, R. Y., Ulya, F. Z., Athiyah, U., Muhammad, A. W., & Alameka, F. (2023). Analisis Efisiensi Metode K-Nearest Neighbor dan Forward Chaining Untuk Prediksi Stunting Pada Balita. Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer, 18(2), 78–85. https://doi.org/10.30872/jim.v18i2.10169
Rahardiantoro, S., Juhanda, A. R. N., Kurnia, A., Aswi, Sartono, B., Handayani, D., Soleh, A. M., Yanti, Y., & Cramb, S. (2024). Spatio-temporal modeling to identify factors associated with stunting in Indonesia using a modified generalized lasso. Spatial and Spatio-Temporal Epidemiology, 51, 100694. https://doi.org/10.1016/j.sste.2024.100694
Sanggelorang, Y., Sebayang, F. A. A., Malonda, N. S. H., & Rumayar, A. A. (2024). Insights into childhood malnutrition: An analysis on food vulnerability and stunting using 2021 Indonesian Nutritional Status Survey data. Media Gizi Indonesia, 19(3), 282–290. https://doi.org/10.20473/mgi.v19i3.282-290
Seifu, B. L., Tesema, G. A., Fentie, B. M., Yehuala, T. Z., Moloro, A. H., & Mare, K. U. (2024). Geographical variation in hotspots of stunting among under-five children in Ethiopia: A geographically weighted regression and multilevel robust Poisson regression analysis. PLOS ONE, 19(5), e0303071. https://doi.org/10.1371/journal.pone.0303071
Şener, R., & Türk, T. (2021). Spatiotemporal analysis of cardiovascular disease mortality with geographical information systems. Applied Spatial Analysis and Policy, 14(4), 929–945. https://doi.org/10.1007/s12061-021-09382-7
Simanungkalit, M. A., Hidayat, A., Nugroho, R. A., & Depari, A. S. (2024). Analisis Hotspot (Getis Ord Gi*) Pola Spasial Frekuensi Kecelakaan Lalu Lintas di Kota Balikpapan. COMPACT: Spatial Development Journal, 3(1), 158–167. https://doi.org/10.35718/compact.v3i1.1156
Sipahutar, T., Eryando, T., & Budiharsana, M. P. (2022). Spatial analysis of seven islands in Indonesia to determine stunting hotspots. Kesmas: Jurnal Kesehatan Masyarakat Nasional (National Public Health Journal), 17(3), 228–234. https://doi.org/10.21109/kesmas.v17i3.6201
Siramaneerat, I., Astutik, E., Agushybana, F., Bhumkittipich, P., & Lamprom, W. (2024). Examining determinants of stunting in urban and rural Indonesian: A multilevel analysis using the population-based Indonesian family life survey (IFLS). BMC Public Health, 24, 1371. https://doi.org/10.1186/s12889-024-18824-z
Suryanto, A., et al. (2024). Optimizing hotspot analysis for low-density health data in East Java. Geospatial Health, 19(1).
Tanjung, R., Lestrina, D., & Sinaga, J. (2024). Spatial analysis of environmental sanitation and stunting incidents. International Journal of Public Health Science (IJPHS), 13(4), 1968–1977. https://doi.org/10.11591/ijphs.v13i4.23442