Spatial mapping of temporal risk characteristics to improve environmental health risk identification: A case study of a dengue epidemic in Taiwan

https://doi.org/10.1016/j.scitotenv.2006.02.009Get rights and content

Abstract

Mapping uneven events, such as disease cases or pollutants, is a basic but important procedure for analyzing regional relationships and variation in public health and environmental agencies. The purpose of mapping is to find out the spatial clustering of uneven events and identify spatial risk areas, which could lead to potential environmental hazards or epidemics. Meanwhile, more hypotheses could be generated through mapping process for further investigations. This paper proposed a novel spatial–temporal approach to focusing on: (1) how often these uneven cases occur, (2) how long these cases persist and (3) how significant cases occur in consecutive periods across the study area. The proposed model was applied to the dengue fever epidemic in Taiwan in 2002 as a case study, which was the worst epidemic in the last 60 years. This approach provides procedures to identify spatial health risk levels with temporal characteristics and assists in generating hypothesis that will be investigated in further detail.

Introduction

Mapping uneven events, such as disease cases or pollutants, makes it possible for public administrators to discover the origin of pollution or source of epidemic outbreaks and generate more hypotheses for further investigations. More importantly, it may spatially identify the high-risk areas, which can be targeted for environmental hazards and public health prevention. Mapping has been made easier and become more wisely used with the development of geographical information systems (GIS). However, mapping provides only a visual display of the uneven cases, but cannot definitively confirm clustering of cases or spatial correlations. Spatial statistics is extensively used to find relationship of case rates and their geographical location (Croner et al., 2000). Point pattern analysis is a method often employed in the detection of clustering patterns. Since points may represent actual locations of uneven events, the method involves in the issue of privacy (Maheswan and Haining, 2004). Risk surface estimation, including kernel estimation and geostatistical methods, produces continuous surfaces of risk across the whole study areas and potentially offer more insight into the nature of the clusters (Bithell, 1990, Bithell, 1999, Diggle, 2000). However, mapping the spatial clusters of uneven events is a static snapshot, which ignores the temporal kinetics of these uneven events, and it is difficult to evaluate whether the hazards or epidemics have been broken out or kept under control by policymakers. Time-series statistical techniques, such as Box-Jenkins (seasonal) autoregressive integrated moving average (ARIMA) models, are used to forecast the outbreak of uneven cases or to estimate the expected incidence values (Zeger et al., 2004, Earnest et al., 2005). But these kinds of approaches could not provide clues for identification of spatial risk areas. This paper intends to propose a spatial–temporal risk model for mapping geographic distribution of uneven events with temporally defined indices to improve health risks identifications. The model focuses on three temporal risk characteristics across geographic space: (1) how often these uneven cases occur, (2) how long these cases persist and (3) how significant cases occurring in consecutive periods. This model will be applied to the dengue fever epidemic in Taiwan in 2002 as a case study, which is the worst epidemic in the last 60 years.

Dengue is a member of flavivirus with four known antigenically distinct serotypes and is vectored mainly by domestically adapted mosquito species, specifically Aedes aegypti. Environmental risk factors, including water storage containers, housing patterns and frequency of waste disposal, etc., reflect the condition of environmental health in certain area (Bohra and Andrianasolo, 2001). These risk factors could have potential to cause more mosquito breeding sites, which might lead to an outbreak of dengue epidemic. However, routinely monitoring environmental risk factors is not feasible for public health and environment agencies. Therefore, this study only used dengue case-incidence data, including onset date of a confirmed dengue case and its geographic location, collected by official surveillance systems to investigate whether the approach will improve the detection of environmental health risk areas and the evaluation of vector or mosquito control measures.

Section snippets

Study area and data

Kaohsiung City, located in southern Taiwan, is the second largest metropolitan city, the largest commercial harbor and an important land, marine and air transportation hub. Fig. 1 shows the location of Kaohsiung City and one of its satellite cities, Fengshan City. This two-city area was used as our study area because it was the major dengue epidemic foci in southern Taiwan. The study focused on dengue cases based on surveillance data, which reported 4790 confirmed dengue cases (DEN), including

Spatial pattern analysis of temporal risk indices

Since any epidemic is a dynamic process, it would be important to identify spatial risk areas, if any, through the use of different temporal characteristics. For example, some area might have longer epidemic duration and the others might have stronger intensity even though the duration is short. Once the risk areas at different epidemic periods are identified, the comparisons of case-incidence with its temporal–spatial dynamics can be helpful in identifying the possible causes or effective

Discussion

We have described a procedure for identifying spatial risk with different temporal characteristics of disease epidemics. The spatial risk maps with the three proposed temporal characteristics improve spatial clustering analysis, which focused mainly on case-incidence data obtained through passive surveillance. We are able to identify other case clusters when we factored in the temporal properties, such as case-incidence frequency and the number of cases that occurred within a certain time, or

Concluding remarks

Space and time are two important dimensions in describing epidemic dynamics and risk distribution. Methods based on either complicated statistical analysis or sophisticated surveillance systems are difficult to carry out in developing counties. Therefore, this paper attempted to use minimum data requirement and more straightforward statistical methods to capture major temporal characteristics of epidemic dynamic process, including frequency, duration and intensity. Different epidemic patterns

Acknowledgements

The authors sincerely thank the staff of Kaohsiung City and County Health Bureau and the Center for Disease Control in Taiwan (Taiwan-CDC) for their efforts in surveillance and vector control during the hard time of the dengue epidemic. The authors also thank anonymous referee's comments for clarifying the original manuscript. Neal Lin would like to express appreciation for the support from the Fulbright grant, sponsored by the Foundation for Scholarly Exchange in Taiwan.

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