Infectious Disease Epidemiology & Geospatial Health

Geographical Information System (GIS) Application in Tuberculosis Spatial Clustering Studies: A Systematic Review

Abstract and Background

Tuberculosis (TB) remains one of the most significant infectious disease threats globally, responsible for millions of new cases and hundreds of thousands of deaths annually. TB transmission characteristically occurs within households or small communities where prolonged contact facilitates the spread of Mycobacterium tuberculosis, leading to heterogeneous spatial patterns of disease distribution. Understanding these spatial patterns is essential for effective TB surveillance, resource allocation, and targeted public health interventions.

In recent decades, the application of Geographical Information System (GIS) technology in epidemiological research has expanded dramatically, particularly in the study of infectious diseases with spatial clustering tendencies. This systematic review, published in the Malaysian Journal of Public Health Medicine in 2018, aimed to synthesise the evidence on GIS applications in TB spatial clustering studies, determine the types of spatial analysis methods most commonly employed, and evaluate the role of GIS in TB surveillance and control programmes.

Methodology

The researchers conducted a systematic literature search of articles published in English between 2000 and November 2015 using MEDLINE and Science Direct databases. Search terms were developed to capture studies employing spatial analysis in the context of tuberculosis clustering. The search strategy was adapted for each database using appropriate subject headings and keywords related to both GIS technology and TB epidemiology. Studies were included if they applied any form of spatial analysis, GIS mapping, or geospatial clustering technique to TB surveillance, distribution, or risk factor identification.

The review process followed established systematic review methodology, with study selection, data extraction, and quality assessment performed according to predefined criteria. The 71 studies that met the inclusion criteria were analysed to determine patterns in study design, geographic focus, spatial methods employed, and key findings regarding TB clustering.

Key Findings

Geographic Distribution of Studies

The 71 included studies were predominantly conducted in developing countries, reflecting both the higher TB burden in these settings and the growing accessibility of GIS technology in resource-limited environments. Countries in Africa, Asia, and South America were well represented, alongside studies from high-income nations where TB hotspot identification remains relevant for targeted control among at-risk populations.

Spatial Analysis Methods

The review found that spatial scan statistics, particularly Kulldorff’s spatial scan statistic (SaTScan), were the most commonly employed method for detecting TB clusters. Other frequently used approaches included kernel density estimation, Moran’s I statistic for spatial autocorrelation, Local Indicators of Spatial Association (LISA), and various forms of spatial regression modelling. Many studies employed multiple complementary methods to provide a more comprehensive understanding of TB spatial patterns.

Evidence of TB Clustering

The literature reviewed showed strong and consistent evidence that TB clustering occurs in high-risk areas across both developed and developing country settings. TB spatial patterns were heterogeneous at every geographic resolution level examined, from national to neighbourhood scales. Clusters tended to correspond to areas with specific socioeconomic, demographic, and environmental characteristics that facilitate TB transmission.

Challenges Identified

A significant methodological challenge identified across the reviewed studies was the reliance on pre-collected aggregated data, which can affect spatial accuracy. When TB cases are assigned to administrative boundaries rather than precise locations, the ecological fallacy may limit the validity of spatial inferences. The review also noted that in the 11 papers that compared two spatial methods using a single dataset, the clustering patterns identified were often inconsistent, highlighting the sensitivity of results to methodological choices.

Implications for Public Health Practice

The systematic review underscored the value of GIS as a tool for TB surveillance and control planning. Effective TB control requires real-time GIS applications for surveillance and decision-making, moving beyond retrospective mapping to prospective, operational use of geospatial intelligence. In Malaysia, where TB remains a notifiable disease with ongoing transmission, the application of GIS techniques could enhance the targeting of screening programmes, contact tracing efforts, and resource deployment.

The review recommended that future studies account for unreported cases when using notification data, as TB surveillance systems typically capture only a fraction of actual cases, particularly in developing countries where healthcare access and diagnostic capacity may be limited. The integration of genotypic data with geospatial analysis was identified as a promising approach for distinguishing between ongoing transmission and reactivation of latent infection within spatial clusters.

Relevance to the Malaysian Context

Malaysia continues to face a significant TB burden, with the disease remaining endemic despite decades of control efforts. The country’s TB control programme, which operates through a network of public health facilities, generates substantial surveillance data that could be leveraged through GIS analysis to identify transmission hotspots and guide targeted interventions. The systematic review provides a valuable methodological framework for Malaysian researchers and public health practitioners seeking to apply geospatial approaches to TB control.

Limitations

The review was limited to articles published in English and indexed in MEDLINE and Science Direct, potentially excluding relevant studies published in other languages or indexed in other databases. The search period ended in November 2015, meaning that more recent advances in GIS technology and spatial analysis methods for TB were not captured. The heterogeneity of study designs, data sources, and analytical methods across the included studies limited the ability to perform quantitative synthesis or meta-analysis.

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