METHODS USED IN THE SPATIAL ANALYSIS OF TB EPIDEMIOLOGY

Tuberculosis transmission often happens inside a household or community, leading to heterogeneous spatial patterns. Therefore, apparent spatial clustering of Tuberculosis could reflect ongoing transmission or co-location of risk factors and can vary considerably depending on the type of data available, the analysis methods employed and the dynamics of the underlying population. Thus, we aimed to review methodological approaches used in the spatial analysis of tuberculosis burden. We conducted a systematic literature search of spatial studies of tuberculosis published in English using Medline, Embase, PsycInfo, Scopus and Web of Science databases with no date restriction from inception to 15 February 2017.The protocol for this systematic review was prospectively registered with PROSPERO. We identified 168 eligible studies with spatial methods used to describe the spatial distribution (n = 154), spatial clusters (n = 73), predictors of spatial patterns (n = 64), the role of congregate settings (n = 3) and the household (n = 2) on tuberculosis transmission. Molecular techniques combined with geospatial methods were used by 25 studies to compare the role of transmission to reactivation as a driver of tuberculosis spatial distribution, finding that geospatial hotspots are not necessarily areas of recent transmission. Almost all studies used notification data for spatial analysis (161 of 168), although none accounted for undetected cases. The most common data visualization technique was notification rate mapping, and the use of smoothing techniques was uncommon. The clusters of spatial were analyzed utilizing the range of methods, with the most commonly employed being Kulldorff’s spatial scan statistic. In the 11 papers that compared two such methods using a single data set the clustering patterns identified were often inconsistent. Classical regression models that did not account for spatial dependence were commonly used to predict spatial tuberculosis risk. Keywords: Spatial analysis, Tuberculosis, Genotypic cluster