Department of Population Medicine, University of Guelph, Guelph, ON, Canada.
Int J Health Geogr. 2012 May 29;11:17. doi: 10.1186/1476-072X-11-17.
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.
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.
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.
这是首次对加拿大西北地区(NWT)的法定胃肠道疾病(NGI)的地理和时间分布进行描述的研究。了解 NGI 在空间和时间上的分布情况对于确定高风险社区非常重要。利用源自西北地区传染病报告系统(NWT CDR)的数据,采用了多种空间和时间技术,对 1991 年至 2008 年 NGI 的发病率进行了探索和分析。相对风险绘图用于研究疾病风险的变化。扫描测试统计用于进行空间、时间和时空的聚类识别。时间序列的季节性分解用于评估数据中的季节性变化和趋势。
NGI 的发病率存在地理差异,南部的发病率高于北部。NGI 的发病率具有季节性,大多数年份的秋季发病率较高。1995 年和 2001 年秋季检测到两次可能的暴发,其中一次与之前确认的暴发相符。总体而言,NGI 的发病率从 1991 年到 2001 年波动,随后在 2002 年至 2008 年期间发病率呈下降趋势。
NGI 通知的分布根据地理位置、季节和年份有很大差异。虽然分析突出了监测数据可能存在偏差,但这些信息有助于提出感染风险因素的假设。