S B Trooskin1, J Hadler, T St Louis, V J Navarro. 1. The Connecticut Emerging Infections Program, The Connecticut Department of Public Health, Yale University School of Medicine, New Haven, CT, USA.
Abstract
OBJECTIVES: This study aimed to use geographic information systems (GIS) that facilitate analysis of associations between location, environment and disease to document the non-random distribution of hepatitis C, identify infection cluster areas, and describe the demographic characteristics of those areas. METHODS: Spatial analysis was conducted of newly reported positive hepatitis C test results using the Connecticut Hepatitis C Reporting Database. A complete database of unduplicated hepatitis C laboratory reports that were sent to the Connecticut Department of Public Health during 1999 was created. Spatial filtering was used to eliminate random noise generated by sparsely populated towns or small number of cases per town. Cluster analysis was used to determine whether cases of hepatitis C virus (HCV) infection tend to occur closer in space to other cases than would be expected by chance alone. The demographic attributes of identified clusters of HCV-positive reports were examined. RESULTS: Areas with the highest concentration of HCV reports roughly correspond to the major metropolitan areas of Connecticut. Six significant clusters of HCV reports were identified in Connecticut. Four of the six clusters identified were located in the most densely populated and most urban areas of the state. All but one identified cluster had been described previously as areas of substantial injection drug use, as indicated by their designation as five of the sites of syringe exchange programmes in Connecticut. This finding suggests that geospatial analysis may assist in the identification of clusters that would not otherwise be suspected based on local demographics or other characteristics. CONCLUSIONS: These findings contribute significantly to the understanding of the state-specific epidemiology of HCV infection. This methodology can be applied wherever a similar database exists to enable the implementation of targeted prevention and educational campaigns to raise awareness of HCV risk factors, the importance of being tested, and treatment options.
OBJECTIVES: This study aimed to use geographic information systems (GIS) that facilitate analysis of associations between location, environment and disease to document the non-random distribution of hepatitis C, identify infection cluster areas, and describe the demographic characteristics of those areas. METHODS: Spatial analysis was conducted of newly reported positive hepatitis C test results using the Connecticut Hepatitis C Reporting Database. A complete database of unduplicated hepatitis C laboratory reports that were sent to the Connecticut Department of Public Health during 1999 was created. Spatial filtering was used to eliminate random noise generated by sparsely populated towns or small number of cases per town. Cluster analysis was used to determine whether cases of hepatitis C virus (HCV) infection tend to occur closer in space to other cases than would be expected by chance alone. The demographic attributes of identified clusters of HCV-positive reports were examined. RESULTS: Areas with the highest concentration of HCV reports roughly correspond to the major metropolitan areas of Connecticut. Six significant clusters of HCV reports were identified in Connecticut. Four of the six clusters identified were located in the most densely populated and most urban areas of the state. All but one identified cluster had been described previously as areas of substantial injection drug use, as indicated by their designation as five of the sites of syringe exchange programmes in Connecticut. This finding suggests that geospatial analysis may assist in the identification of clusters that would not otherwise be suspected based on local demographics or other characteristics. CONCLUSIONS: These findings contribute significantly to the understanding of the state-specific epidemiology of HCV infection. This methodology can be applied wherever a similar database exists to enable the implementation of targeted prevention and educational campaigns to raise awareness of HCV risk factors, the importance of being tested, and treatment options.
Authors: Stacey B Trooskin; Joanna Poceta; Caitlin M Towey; Annajane Yolken; Jennifer S Rose; Najia L Luqman; Ta-Wanda L Preston; Philip A Chan; Curt Beckwith; Sophie C Feller; Hwajin Lee; Amy S Nunn Journal: J Gen Intern Med Date: 2015-02-14 Impact factor: 5.128
Authors: Amit Saha; Andrew Hayen; Mohammad Ali; Alexander Rosewell; C Raina MacIntyre; John D Clemens; Firdausi Qadri Journal: Vaccine Date: 2018-05-08 Impact factor: 3.641