Analisis spasial prevalensi Tuberculosis di Indonesia tahun 2023 menggunakan metode geographically weighted
DOI:
https://doi.org/10.29238/sanitasi.v19i1.3060Keywords:
GWR, Tuberculosis, Spatial, Area, Risk FactorsAbstract
Tuberkulosis (TB) masih menjadi tantangan kesehatan masyarakat utama di Indonesia dengan tingkat prevalensi yang bervariasi antar kabupaten/kota. Penelitian ini menerapkan metode Geographically Weighted Regression (GWR) dengan kernel Adaptive Gaussian untuk mengeksplorasi pola spasial dan mengidentifikasi asosiasi statistik antara prevalensi TB dan faktor sosio-demografis pada tahun 2023. Data yang digunakan merupakan data sekunder resmi dari Badan Pusat Statistik dan Dinas Kesehatan Provinsi yang mencakup 514 kabupaten/kota di seluruh Indonesia. Hasil analisis menunjukkan bahwa model GWR memiliki kinerja sangat baik berdasarkan uji kebaikan model dengan nilai R² sebesar 0,9345, yang berarti mampu menjelaskan 93,45% variasi prevalensi TB secara spasial. Faktor-faktor yang berpengaruh signifikan berbeda di tiap wilayah, meliputi tingkat kemiskinan, kepadatan penduduk, proporsi perokok usia 35–44 tahun, prevalensi HIV/AIDS, dan jumlah tenaga medis. Sesuai desain penelitian observasional, studi ini difokuskan untuk mengkaji hubungan atau asosiasi, bukan kausalitas. Evaluasi menggunakan regresi Kuadrat Terkecil Biasa (OLS) dilakukan hanya untuk menilai performa model dan mendeteksi heterogenitas spasial tanpa dimaksudkan membandingkan efektivitas metode analisis lainnya. Temuan penelitian menegaskan pentingnya pendekekatan berbasis wilayah dalam memahami faktor risiko TB serta memberikan bukti empiris bagi pemerintah dalam merancang strategi pengendalian TB yang lebih tepat sasaran. Hasil penelitian ini diharapkan dapat mendukung penguatan kebijakan kesehatan nasional berbasis data lokal dan menjadi referensi dalam perencanaan intervensi program TB yang lebih terarah, efektif, dan kontekstual di Indonesia.
Abstract: Tuberculosis (TB) remains a major public health challenge in Indonesia, with varying prevalence rates across districts/cities. This study applied the Geographically Weighted Regression (GWR) method with an Adaptive Gaussian kernel to explore spatial patterns and identify statistical associations between TB prevalence and socio-demographic factors in 2023. The data used were official secondary data from the Central Statistics Agency and Provincial Health Offices covering 514 districts/cities throughout Indonesia. The analysis results showed that the GWR model performed very well based on the model goodness-of-fit test with an R² value of 0.9345, which means it was able to explain 93.45% of the spatial variation in TB prevalence. The factors that had a significant effect differed in each region, including poverty levels, population density, the proportion of smokers aged 35–44 years, HIV/AIDS prevalence, and the number of medical personnel. In accordance with the observational research design, this study focused on examining relationships or associations, not causality. The evaluation using Ordinary Least Squares (OLS) regression was conducted only to assess model performance and detect spatial heterogeneity, without the intention of comparing the effectiveness of other analysis methods. The findings of this study emphasise the importance of a region-based approach in understanding TB risk factors and provide empirical evidence for the government in designing more targeted TB control strategies. The results of this study are expected to support the strengthening of national health policies based on local data and serve as a reference for more targeted, effective, and contextual TB programme intervention planning in Indonesia.
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