A spatial and temporal analysis of notifiable gastrointestinal illness in the Northwest Territories, Canada, 1991-2008

• Main Authors: Pardhan-Ali, Aliya, Berke, Olaf, Wilson, Jeff, Edge, Victoria L, Furgal, Chris, Reid-Smith, Richard, Santos, Maria, McEwen, Scott A
• Format: Other/Unknown Material
• Language: English
• Published: BioMed Central Ltd. 2012
• Subjects: Gastrointestinal illness; Foodborne diseases; Waterborne diseases; Minority health; Population surveillance; Spatial epidemiology; Temporal epidemiology; Spatio-temporal epidemiology; Canada; Northwest Territories
• Online Access: http://www.ij-healthgeographics.com/content/11/1/17

Abstract Background This is the first study to describe the geographical and temporal distribution of notifiable gastrointestinal illness (NGI) in the Northwest Territories (NWT), Canada. Understanding the distribution of NGI in space and time is important for identifying communities at high risk. Using data derived from the Northwest Territories Communicable Disease Registry (NWT CDR), a number of spatial and temporal techniques were used to explore and analyze NGI incidence from the years 1991 to 2008. Relative risk mapping was used to investigate the variation of disease risk. Scan test statistics were applied to conduct cluster identification in space, time and space-time. Seasonal decomposition of the time series was used to assess seasonal variation and trends in the data. Results There was geographic variability in the rates of NGI with higher notifications in the south compared to the north. Incidence of NGI exhibited seasonality with peaks in the fall months for most years. Two possible outbreaks were detected in the fall of 1995 and 2001, of which one coincided with a previously recognized outbreak. Overall, incidence of NGI fluctuated from 1991 to 2001 followed by a tendency for rates to decrease from 2002 to 2008. Conclusions The distribution of NGI notifications varied widely according to geographic region, season and year. While the analyses highlighted a possible bias in the surveillance data, this information is beneficial for generating hypotheses about risk factors for infection.