title: Spatial analysis of tuberculosis in an urban west African setting: is there evidence of clustering?
abstract: OBJECTIVE
  To describe the pattern of tuberculosis (TB) occurrence in Greater Banjul, The Gambia with Geographical Information Systems (GIS) and Spatial Scan Statistics (SaTScan) and to determine whether there is significant TB case clustering.
  
  
  METHODS
  In Greater Banjul, where 80% of all Gambian TB cases arise, all patients with TB registered at chest clinics between March 2007 and February 2008 were asked to participate. Demographic, clinical characteristics and GPS co-ordinates for the residence of each consenting TB case were recorded. A spatial scan statistic was used to identify purely spatial and space-time clusters of tuberculosis among permanent residents.
  
  
  RESULTS
  Of 1145 recruited patients with TB, 84% were permanent residents with 88% living in 37 settlements that had complete maps available down to settlement level. Significant high- and low-rate spatial and space-time clusters were identified in two districts. The most likely cluster of high rate from both the purely spatial analysis and the retrospective space-time analysis were from the same geographical area. A significant secondary cluster was also identified in one of the densely populated areas of the study region.
  
  
  CONCLUSIONS
  There is evidence of significant clustering of TB cases in Greater Banjul, The Gambia. Systematic use of cluster detection techniques for regular TB surveillance in The Gambia may aid effective deployment of resources. However, passive case detection dictates that community-based active case detection and risk factor surveys would help confirm the presence of true clusters and their causes.
authors.affiliation: Bacterial Diseases Programme, MRC Laboratories, Banjul, The Gambia. ktouray@mrc.gm
authors.forename: K
authors.initials: K
authors.lastname: Touray
authors.forename: I M
authors.initials: IM
authors.lastname: Adetifa
authors.forename: A
authors.initials: A
authors.lastname: Jallow
authors.forename: J
authors.initials: J
authors.lastname: Rigby
authors.forename: D
authors.initials: D
authors.lastname: Jeffries
authors.forename: Y B
authors.initials: YB
authors.lastname: Cheung
authors.forename: S
authors.initials: S
authors.lastname: Donkor
authors.forename: R A
authors.initials: RA
authors.lastname: Adegbola
authors.forename: P C
authors.initials: PC
authors.lastname: Hill
mesh_terms: D000293:Adolescent; D000328:Adult; D002648:Child; D002675:Child, Preschool; D016000:Cluster Analysis; D005260:Female; D005714:Gambia / Q000453:epidemiology; D040362:Geographic Information Systems; D006801:Humans; D015994:Incidence; D007223:Infant; D007231:Infant, Newborn; D008297:Male; D011446:Prospective Studies; D012306:Risk; D013029:Space-Time Clustering; D014376:Tuberculosis / Q000453:epidemiology*; D055815:Young Adult