Geospatial-Based Model for Diagnosing Potential High-Risk Areas of Tuberculosis Disease in Malaysia

Abstract

Malaysia has a medium burden of tuberculosis (TB) incidence based on World Health Organization (WHO) indicator, but the current trend of TB cases is generally alarming. The Ministry of Health (MOH), Malaysia has set up several guidelines to control the disease, however, the national TB technical report in 2015 addressed that existing detection methods of TB on the site still need to be integrated with relevant alternatives. A geospatial based model is proposed to identify potential high-risk areas of TB especially for targeting missing cases and undiagnosed people. The model was developed with three core stages; framework construction, data collection, and risk analysis and modelling. Eight risk factors: urbanisation, distance to factory, socio-economic status (SES), risk group, human mobility, house type, distance to healthcare centres, and number of population were utilised to determine risk rate of TB modelling. This innovative model has successfully estimated a 65 % of potential high-risk TB areas and targeted 106 high-risk localities in the 10 risk sections of the study area. These risk localities have general similarities with other endemic areas worldwide, but there are some interesting findings revealed in this local study towards in the TB control programme. Most of these cases did not only occur in high rise housing areas, but they are concentrated at industrial location, mobility pattern and socio-economic status in urban city. Although, urban areas are favoured area for the local TB, the disease could also potentially occur in semi-urban or rural areas.

Analysis of Pulmonary Tuberculosis Incidence in the City of Bukittinggi in 2018-2020

“…Kepadatan penduduk tidak bisa dianggap sebagai penyebab tunggal terjadinya kasus TB paru BTA positif, sehingga ada kemungkinan faktor-faktor lain seperti iklim, ketinggian wilayah, pendidikan dan usia yang ikut andil dalam peningkatan kasus TB paru BTA positif di Indonesia, maka perlu dikembangkan alat deteksi TB yang layak serta model penyebab penyakit yang cermat sebelum suatu penyakit muncul (Puspita at.al 2021). Menggabungkan model berbasis geospasial dengan pendekatan berbasis epidemi untuk mendeteksi TB pada tubuh manusia dan menyelidiki faktor lingkungan diperlukan untuk deteksi holistik atau rencana pencegahan penyebaran TB (Rauf et al 2019). Penelitian (Yeffi Masnarian, Arinil Haq 2020) bahwa kepadatan penduduk merupakan salah variabel yang secara signifikan membedakan antara keempat kluster yang mempengaruhi kejadian TB paru.…”

Susceptibility Risk Index Mapping of Population at Tuberculosis Epidemic Risk

“…urbanization, population, type of housing, built-up area and health care centre, are selected for this study. Local researchers have also used similar factors and models to define the risk areas of TB in Shah Alam (Abdul Rasam et al, 2019;Abdul Rasam et al, 2016). The model is built in three main stages: frame development, data processing Abdul Rasam et al, 2016, and risk analysis and modelling.…”

“…Integrating the GIS and Multi-Criteria Decision Making (MCDM) process can enhance the technology's technical capabilities. Integrating GIS capabilities with the MCDM methodology offers more fantastic performance and decision-making capacity when solving spatial final decision problems (Abdul Rasam et al, 2020;Abdul Rasam et al, 2019;Maris et al, 2008;Rajab et al, 2020). In general, input map layers are prepared, and two different MCDM methods are introduced into GIS: Weighted Linear Combination (WLC) and Analytical Hierarchy Process (AHP).…”

Investigating Spatial Patterns of Pulmonary Tuberculosis and Main Related Factors in Bandar Lampung, Indonesia Using Geographically Weighted Poisson Regression

“…Meanwhile, Li [16] used the Bayesian spatiotemporal model to analyze the correlation of socio-economic, health, demographic, and meteorological factors with the population level of TB. Other studies have utilized a weight-rating and multi-criteria decision-making score model to map TB risk areas [17].…”