Spatial Analysis of Risk Factors for Tuberculosis Incidence in South Jakarta City in 2022
Abstract
Introduction: South Jakarta City, within DKI Jakarta, bears a substantial burden of TB cases, yet case detection rates and treatment success remain low. Factors such as population density, gender disparity, socio-economic conditions, and healthcare accessibility influence TB transmission. The city's high population density presents challenges in controlling TB spread. Additionally, males and low-income families face higher TB risks.
Objective: This study aims to analyze TB case distribution across 10 districts in South Jakarta, assess TB case clustering, and understand the spatial regression model of TB risk factors.
Method: The methodology of this research utilizes a quantitative approach with an ecological study design and spatial methodology, secondary data from various sources, including the national TB control reporting application. Data processing involves GeoDa v1.22, QGIS v.3.32.3, SPSS v.22, and GWR v4.0 applications for spatial analysis.
Result: Findings reveal that spatial autocorrelation tests using Moran's Index on TB cases in South Jakarta City are not statistically significant, indicating no spatial autocorrelation. The LISA test identifies Mampang Prapatan District as a cold spot in Quadrant III (Low-Low). GWR regression analysis highlights three spatially influential risk factor variables affecting TB cases: gender ratio, number of health centers, and population density. The first two variables affect all districts in South Jakarta City, whereas population density only impacts the Setiabudi District.
Conclusion: In conclusion, Mampang Prapatan district in South Jakarta City exhibits low TB transmission risk, considering population density and other factors. Notably, three spatial risk factors affect TB transmission in South Jakarta City, warranting attention from the health department in TB elimination efforts.
References
Achmadi, U. F. Manajemen Penyakit Berbasis Wilayah. Jurnal Kesehatan Masyarakat Nasional. 2009; 3(4), 147–153.
Ardiyanti, M., Sulistyawati, S., & Puratmaja, Y. Spatial Analysis of Tuberculosis, Population and Housing Density in Yogyakarta City 2017-2018. Epidemiology and Society Health Review. 2021; 3(1), 28–35. https://doi.org/10.26555/ESHR.V3I1.3629
BPS Provinsi DKI Jakarta. Kota Jakarta Selatan Dalam Angka 2023 (BPS Provinsi DKI Jakarta, Ed.). BPS Provinsi DKI Jakarta; 2023.
World Health Organization. Global Tuberculosis Report 2023. Geneva: World Health Organization; 2023.
Kementerian Kesehatan RI. Aplikasi Mobile Dashboard TB Indonesia. Diunduh melalui Dashboard TB Indonesia – Apps on Google Play; 2023.
Kementerian Kesehatan RI. Laporan Program Penanggulangan Tuberkulosis Tahun 2022. Laporan Program Penanggulangan Tuberkulosis Tahun 2022 - TBC Indonesia (tbindonesia.or.id); 2023.
Kementerian Kesehatan RI. Strategi Nasional Penanggulangan Tuberkulosis di Indonesia 2020-2024; 2020.
Lutfiani N, Sugiman S, Mariani S. Pemodelan Geographically Weighted Regression (GWR) dengan Fungsi Pembobot Kernel Gaussian dan Bi-Square. UNNES Journal of Mathematics. 2017;5(1). http://journal.unnes.ac.id/sju/index.php/ujmUJM8
Pratiwi D. Epidemiologi Spasial Kasus Tuberkulosis (TB) Paru Anak Di Kota Medan Tahun 2016-2020. UIN Sumatera Utara; 2021.
Riznawati A. Model Spasial Faktor Risiko Tuberkulosis di Provinsi Jawa Barat. Universitas Indonesia. 2021. https://doi.org/10.52022/jikm.v16i1.640
Saputra FF, Wahjuni CU, Isfandiari MA. Spatial Modeling of Environmental-Based Risk Factors of Tuberculosis in Bali Province: An Ecological Study. Jurnal Berkala Epidemiologi. 2020;8(1):26. https://doi.org/10.20473/jbe.v8i12020.26-34
Sasmita H, Junaid, Ainurafiq. Pola Spasial Kejadian TB Paru BTA Positif di Wilayah Kerja Puskesmas Puuwatu Tahun 2013-2015. JIMKESMAS. 2017;2(6).
Sipahutar T, Eryando T, Budiharsana MP. Fenomena Stunting di Indonesia: Pemanfaatan Data Sekunder untuk Pemetaan Daerah Rawan Stunting Menggunakan Analisis Spasial. Deepublish; 2022.
Sihaloho ED, Kamilah FZ, Rahma GR, Kusumawardani S, Hardiawan D, Siregar AY. Pengaruh Angka Tuberkulosis Terhadap Angka Kemiskinan di Indonesia. Jurnal Ilmu Ekonomi Dan Pembangunan. 2020;20(2). https://doi.org/10.20961/jiep.v20i2.42853
Srisantyorini T, et al. Analisis Spasial Spasial Kejadian Tuberkulosis di Wilayah DKI Jakarta Tahun 2017-2019. Universitas Muhammadiyah Jakarta; 2022. https://doi.org/10.24853/jkk.18.2.131-138
Yasir NA, Fariqa RMN, Ramadhan F, Eka SP, MN P, Bekti RD. Model Regresi Spasial Untuk Analisis Persentase Penduduk Miskin di Propinsi Nanggroe Aceh Darussalam. Jurnal Statistika Industri dan Komputasi. 2016;1(1):53–61.
Nisa F, et al. Pemodelan Faktor-faktor yang Mempengaruhi Jumlah Kasus Tuberkulosis di Jawa Timur Menggunakan Regresi Nonparametrik Spline. Jurnal Sains dan Seni ITS. 2016; 5(2): 2337-3520 (2301-928X Print).
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