Identifying High-Risk Populations of Tuberculosis Using Environmental Factors and Gis Based Multi-Criteria Decision Making Method

Abstract

ABSTRACT:Development of an innovative method to enhance the detection of tuberculosis (TB) in Malaysia is the latest agenda of the Ministry of Health. Therefore, a geographical information system (GIS) based index model is proposed as an alternative method for defining potential high-risk areas of local TB cases at Section U19, Shah Alam. It is adopted a spatial multi-criteria decision making (MCDM) method for ranking environmental risk factors of the disease in a standardised five-score scale. Scale 1 and 5 illustrate the lowest and the highest risk of the TB spread respectively, while scale from 3 to 5 is included as a potential risk level. These standardised scale values are then combined with expert normalised weights (0 to 1) to calculate the overall index values and produce a TB ranked map using a GIS overlay analysis and weighted linear combination. It is discovered that 71.43% of the Section is potential as TB high risk areas particularly at urban and densely populated settings. This predictive result is also reliable with the current real cases in 2015 by 76.00% accuracy. A GIS based MCDM method has demonstrated analytical capabilities in targeting high-risk spots and TB surveillance monitoring system of the country, but the result could be strengthened by applying other uncertainty assessment method.

The Invention of Geospatial Decision Support System for Malaysian Tuberculosis Surveillance Data Management

“…The five-score scale is calculated from the combination values from the expert's opinion, existing theories, and previous studies. The scale of 1 to 5 shows the lowest and the highest level of risk area, respectively as studied by Abdul Rasam et al, (2016).…”

“…A GIS can produce maps of the exposure and disease to reveal spatial patterns and predict the complex phenomena. Previous studies have practically proven that the technology can be used in TB epidemiological management, disease mapping, risk assessment and spread prediction (Abdul Rasam et al, 2016;Carroll et al, 2014;Li et al, 2014;Pfeiffer & Stevens, 2010;Zhao et al, 2013, Rasam et al, 2017.…”

“…Jarup (2004) added that these roles utilize disease mapping to reveal spatial patterns, exposure assessment in ecological epidemiology, and risk estimation to explore disease changes and environmental patterns. In Malaysia, Abdul Rasam et al, (2016) indicated a GIS-MCDM approach can identify the potential risk areas of TB using selected environmental risk factors. By bringing the geospatial values in the context of the local MyTB system, this will optimize its current analytical competencies and transform the national health delivery system into a global inspiration as established in this exploratory research of MyGeoTB © (Abdul Rasam et al, 2017a).…”

Disease Mapping and Spatial Landscape Characterization of Tuberculosis Ecology in Malaysia

“…It is evident that human factors are the main factors in Shah Alam instead of ecological factors, especially the number of population (housing or localities), risk group or low income, and TB mobility which require empirical proof. However, the combination of ecological risk factors such as urban area, type of house, healthcare distance may increase the risk factor in an area (Liu et al, 2011;Zhao et al, 2013;Abdul Rasam et al, 2016). This finding is important for the model development process and to enhance the understanding of the local health staff in explaining the TB dynamics.…”

Socio-Environmental Factors and Tuberculosis: an Exploratory Spatial Analysis in Peninsular Malaysia

“…Previous studies have shown the environmental risk factors in high burden countries of TB can be divided into two main indicators, including ecological environment, human or social environment. The ecological environment is another term of the environment, relating the biotic and abiotic surrounding of an organism or population, For example, TB high-risk prevalence areas are co-impacted by spatial proximity or geographical factor [1][2] [12] especially in urban area [1] [12][3] [38], poor housing quality, crowded and small living condition [14]. Low SES also contributes to the global TB occurrences [2][12] such as unemployment, low educational level and poverty.…”