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GIS-Based Spatial, Temporal, and Space–Time Analysis of Haemorrhagic Fever with Renal Syndrome

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GIS-Based Spatial, Temporal, and Space–Time Analysis of Haemorrhagic Fever with Renal Syndrome Epidemiol. Infect. (2009), 137, 1766–1775. f Cambridge University Press 2009 doi:10.1017/S0950268809002659 Printed in the United Kingdom GIS-based spatial, temporal, and space–time analysis of haemorrhagic fever with renal syndrome W. WU 1, J.-Q. GUO 2, Z.-H. YIN 1,P.WANG3 AND B.-S. ZHOU 1* 1 Department of Epidemiology, School of Public Health, China Medical University, Shenyang, PR China 2 Liaoning Provincial Centre for Disease Control and Prevention, Shenyang, PR China 3 Shenyang Municipal Centre for Disease Control and Prevention, Shenyang, PR China (Accepted 23 March 2009; first published online 27 April 2009) SUMMARY We obtained a list of all reported cases of haemorrhagic fever with renal syndrome (HFRS) in Shenyang, China, during 1990–2003, and used GIS-based scan statistics to determine the distribution of HFRS cases and to identify key areas and periods for future risk-factor research. Spatial cluster analysis suggested three areas were at increased risk for HFRS. Temporal cluster analysis suggested one period was at increased risk for HFRS. Space–time cluster analysis suggested six areas from 1995 to 1996 and four areas from 1998 to 2003 were at increased risk for HFRS. We also discussed the likely reasons for these clusters. We conclude that GIS-based scan statistics may provide an opportunity to classify the epidemic situation of HFRS, and we can pursue future investigations to study the likely factors responsible for the increased disease risk based on the classification. Key words: Cluster analysis, geographical information system, HFRS. INTRODUCTION understanding of the spatial, temporal and space– time distribution patterns of HFRS would help in Haemorrhagic fever with renal syndrome (HFRS) is a identifying areas and periods at high risk, and might zoonosis caused by Hantaviruses from the family be very useful in surveillance of HFRS, discovering Bunyaviridae. It was first recognized in northeastern the risk factors behind its spread, and help better China in 1931 and has been prevalent in many other prevent and control HFRS. parts of China since 1955. At present, HFRS is en- Spatial analysis with spatial smoothing and cluster demic in 28 of the 31 provinces of China, autonomous analysis are commonly used to characterize spatial regions, and metropolitan areas and accounts for 90% patterns of diseases [2–6]. A spatial-rate smoother is a of HFRS cases reported globally [1]. HFRS is also special case of a non-parametric rate estimator based endemic in Shenyang, China. HFRS incidence, with on the principle of locally weighted estimation. A map an increasing trend, was seen from 1990 to 2003, and of spatially smoothed rates tends to emphasize broad reached a historically high level in 2003. A better trends and is useful for identifying general features of the data. The spatial-rate smoother is easy to im- * Author for correspondence: Professor B.-S. Zhou, Department plement in software systems for exploratory spatial of Epidemiology, School of Public Health, China Medical Uni- data analysis [7, 8]. versity, Shenyang, PR China. (Email: [email protected]) Scan statistics are used to detect and evaluate (Email: [email protected]) [J.-Q. Guo] clusters in temporal, spatial or space–time settings. Downloaded from https://www.cambridge.org/core. IP address: 170.106.35.93, on 27 Sep 2021 at 09:46:07, subject to the Cambridge Core terms of use, available at https://www.cambridge.org/core/terms. https://doi.org/10.1017/S0950268809002659 Clusters of HFRS in Shenyang, China 1767 N W E S Shenyang Liaoning Province Liaoning Province China Fig. 1. Location of Shenyang in Liaoning Province, China Temporal, spatial and space–time scan statistics are an opportunity to classify the epidemic situation of now commonly used for disease cluster detection and HFRS. evaluation, for many diseases including infectious diseases [9–13], cancer [14–17], cardiology [18], auto- immune diseases [19], liver diseases [20], and veterin- METHODS ary medicine [21–24]. In SaTScan software [25], this is Data collection and management done by gradually scanning a window across time and/or space and noting the number of observed and The study site is located in Shenyang (122x 25k–123x expected observations inside the window at each lo- 48k E, 41x 11k–43x 02k N), in the southern part of cation. The scanning window is an interval (in time), northeast China and in the central part of Liaoning a circle or an ellipse (in space) or a cylinder with a Province (Fig. 1). The area of Shenyang is 13008 km2, circular or elliptic base (in space–time). Multiple dif- and has a population of 7 200 000. Most of Shenyang ferent window sizes are used. The window with the region rests on a flat plain. Mountains and highlands maximum likelihood is the most likely cluster, i.e. the are mainly concentrated in the southeastern part, cluster least likely to be due to chance; a P value is belonging to the extended part of the Liaodong assigned to this cluster. mountainous highlands. The aim of the present study was to investigate the There are five urban areas, four suburban areas and spatial and temporal distribution of confirmed cases four counties in Shenyang. For our study, we used of HFRS and identify the areas and periods of high these 13 areas as study areas. The information in- risk. In this study we used the geographical infor- cludes the number of HFRS cases per month and mation system and temporal, spatial and space– per year in every county from 1990 to 2003. For the time scan statistics to investigate statistically signifi- 14-year period (1990–2003), the average annual inci- cant clusters of HFRS in Shenyang, China during dence was 3.2 cases/100 000 persons, 3010 cases were 1990–2003. The tools used in this study provide involved in this study. Downloaded from https://www.cambridge.org/core. IP address: 170.106.35.93, on 27 Sep 2021 at 09:46:07, subject to the Cambridge Core terms of use, available at https://www.cambridge.org/core/terms. https://doi.org/10.1017/S0950268809002659 1768 W. Wu and others HFRS cases were first diagnosed using clinical ratio of the total sum of all events over the total sum symptoms, then blood samples were collected in hos- of all populations at risk [26]. pitals, serological identification was performed at the Spatial-rate smoothing consists of computing the laboratory of Liaoning Provincial Center for Disease rate in a moving window centred on each area in turn. Control and Prevention (CDC) to confirm the clinical The moving window includes the county as well as its diagnosis, and the data was collected by case number neighbours; the neighbours are defined by means of according to the sampling results. Records on HFRS a spatial weights file. Therefore, the first step in the cases between 1990 and 2003 were obtained from analysis was to construct a spatial weights file that Liaoning Provincial CDC. contained information on the ‘neighbourhood’ struc- To conduct a GIS-based analysis on the spatial ture of each area. The k-nearest-neighbour criterion distribution of HFRS, a county-level polygon map ensured each observed object had exactly the same of Shenyang at a scale of 1:1 000 000 was obtained number (k) of neighbours. In the analysis, six neigh- on which the point layers containing information bours were chosen for each county by k-nearest- regarding latitude and longitude of central points neighbour criterion. The second step was to load the of each area were created. Demographic information weights file and perform smoothing analysis [26]. based on the fifth National Census (2000) was integrated in terms of an administration code. All HFRS cases were geocoded and matched to the Spatial autocorrelation analysis county-level layers on the polygons and points by Moran’s I spatial autocorrelation statistic and its administration code using ArcGIS9.2 software (ESRI visualization in the form of a Moran scatter plot are Inc., USA). usually used in global spatial autocorrelation analysis. First, a spatial weight was constructed for each area. Thematic maps for the incidence of HFRS Second, Moran’s scatter plot was produced with a spatial lag of incidence on the vertical axis and a To reduce variations of incidence in small populations standardized incidence on the horizontal axis. Third, and areas, we calculated annualized average inci- a significant test was performed through the permu- dences of HFRS/100 000 in each area over the 14-year tation test, and a reference distribution was generated period. under an assumption that the incidence was randomly To describe the upper outliers and lower outliers distributed. For this study, the number of permu- in different areas, a circular cartogram was produced. tation tests was set to 999 and the pseudo-significance A circular cartogram is a map where the original ir- level was set as 0.001. regular polygons are replaced by circles. The place- ment of the circles is such that the original pattern is mimicked as much as possible, both in terms of ab- Spatial, temporal, and space–time cluster analysis solute location and in terms of relative location (neighbours, or topology). The size (area) of the cir- Temporal, spatial, and space–time scan statistics cles is proportional to the value of the selected vari- (SaTScan) [25] were applied to identify clusters of able [26]. In this study the hinge used to identify HFRS. outliers was set as 1.5. The spatial scan statistic imposes a circular window Based on annual average incidence, all counties on the map. The window is in turn centred on each of were grouped into three categories: low endemic areas several possible grid points positioned throughout the with an annual average incidence between 0 and study region.
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