Methods used in the spatial analysis of tuberculosis epidemiology: a systematic review

Abstract

BackgroundTuberculosis (TB) transmission often occurs within a household or community, leading to heterogeneous spatial patterns. However, apparent spatial clustering of TB 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 TB burden.MethodsWe conducted a systematic literature search of spatial studies of TB 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 (CRD42016036655).ResultsWe 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 TB transmission. Molecular techniques combined with geospatial methods were used by 25 studies to compare the role of transmission to reactivation as a driver of TB 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 visualisation technique was notification rate mapping, and the use of smoothing techniques was uncommon. Spatial clusters were identified using a range of methods, with the most commonly employed being Kulldorff’s spatial scan statistic followed by local Moran’s I and Getis and Ord’s local Gi(d) tests. In the 11 papers that compared two such methods using a single dataset, the clustering patterns identified were often inconsistent. Classical regression models that did not account for spatial dependence were commonly used to predict spatial TB risk. In all included studies, TB showed a heterogeneous spatial pattern at each geographic resolution level examined.ConclusionsA range of spatial analysis methodologies has been employed in divergent contexts, with all studies demonstrating significant heterogeneity in spatial TB distribution. Future studies are needed to define the optimal method for each context and should account for unreported cases when using notification data where possible. Future studies combining genotypic and geospatial techniques with epidemiologically linked cases have the potential to provide further insights and improve TB control.Electronic supplementary materialThe online version of this article (10.1186/s12916-018-1178-4) contains supplementary material, which is available to authorized users.

Determinant and Spatial Analysis of Factor that Influencend multi Drug Resistant Tuberculosis Cases Inindramayu District, Indonesia at 2017-2018

“…This is in line with research by Lili Maria, Bresaw Demile, and Girum [6,4,13]. Spatial factor already researched in Debebe [14].…”

Spatiotemporal analysis of drug-resistant TB patients registered in selected districts of Karnataka, South India: a cross-sectional study

“…The heat maps depicting spatial heterogeneity [19] were constructed for the year 2015 to 2018. The quadratic weight was used as a density measure [20], and the heat maps [5,20] were constructed with a buffer radius appropriate to the layer units [21]. The heat map constructed depicts the geographical aggregation of the DR-TB patients but not the relatedness of the cases.…”

Spatio-temporal co-occurrence of hotspots of tuberculosis, poverty and air pollution in Lima, Peru

“…The use of geographical surveillance in public health allows for the detection of areas with a high prevalence or incidence of a particular disease in order to identify socio-economic factors associated with the phenomenon [4]. These methods have been applied to TB transmission [5]. Spatial information contributes to appropriate decision-making with a more efficient budget and human resources allocation and has been used previously in infectious diseases to detect hotspots and epidemics [6].…”