DOI: https://doi.org/10.14710/jekk.v11i1.30126

Spatial Risk Model for Mapping Tuberculosis (TB) Hotspot Areas in West Lombok Regency

*Mega Sara Yulianti  -  Faculty of Public Health , Nahdlatul Ulama University of West Nusa Tenggara (NTB), Jln. Pendidikan No. 6, Mataram City, West Nusa Tenggara, Indonesia, Indonesia
Muhamad Sadli  -  Faculty of Public Health , Nahdlatul Ulama University of West Nusa Tenggara (NTB), Jln. Pendidikan No. 6, Mataram City, West Nusa Tenggara, Indonesia, Indonesia
Muhammad Syukri  -  Faculty of Public Health , Nahdlatul Ulama University of West Nusa Tenggara (NTB), Jln. Pendidikan No. 6, Mataram City, West Nusa Tenggara, Indonesia, Indonesia
Warni Farida  -  Faculty of Public Health, Jambi University, Jl. Letjend Soeprapto No. 33, Telanaipura, Jambi 36122, Indonesia, Indonesia
Lia Apriani  -  Student, Nahdlatul Ulama University of West Nusa Tenggara (NTB), Jln. Pendidikan No. 6, Mataram City, West Nusa Tenggara 83125, Indonesia, Indonesia
Received: 20 Nov 2025; Revised: 23 Dec 2025; Accepted: 17 Feb 2026; Available online: 20 Feb 2026; Published: 20 Feb 2026.

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Abstract

Background: Tuberculosis (TB) is a global public health problem. Conventional approaches often overlook the spatial components that influence transmission dynamics, highlighting the need for more accurate area-based surveillance. This study aims to identify spatial risk patterns using a geo-targeting approach and map vulnerable areas to support sustainable precision control strategies.

Methods: This study applied a quantitative design integrating spatial ecological analysis with a case–control approach. A total of 1,658 registered TB cases were geocoded and analyzed to detect spatial clustering patterns. Based on hotspot and non-hotspot classifications, a case–control survey involving 226 respondents (113 cases and 113 controls) assessed environmental, socioeconomic, and behavioral determinants. Data were collected through structured interviews, household environmental observations, and secondary health records. Analysis used spatial statistical techniques and multivariable logistic regression.

Result: Significant spatial clustering of TB was identified, with hotspots located in Gerung, Lembar, Kuripan, and Sekotong, West Lombok. Increased TB risk was associated with high household humidity (OR 5.40), low income (OR 5.42), low education level (OR 4.26), and elevated indoor temperature (OR 2.87). Inverse associations were observed for smoking, infrequent health-facility visits, and male sex, likely reflecting information bias rather than protective effects.

Conclusion: Integrating spatial hotspot mapping with epidemiological assessment improves identification of TB transmission risk. In West Lombok, hotspot areas were linked to adverse environmental and socioeconomic conditions, supporting geo-targeted TB interventions focusing on housing improvement, socioeconomic support, and education-based risk communication.

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Spatial Risk Model for Mapping Tuberculosis (TB) Hotspot Areas in West
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Keywords: Hotspot ; Geo-Targeting ; Spatial Analysis ; Tuberculosis ; Risk Factors
Funding: Ministry of Education, Research, and Technology (Kemendiktisaintek) through the Beginner Lecturer Research Grant (PDP) scheme, under grant number 2166/LL8/AL.04/2025.

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