Spatial variation and factors associated with dengue fever ...€¦ · environmental conditions...
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Motivation Background Objectives Methodology Secondary data (SES and environmental) Data Results Dengue fever is a vector-borne disease that gradually re-emerged across the global South. Rapidly growing urban areas are most vulnerable due to poorly planned urbanization and migration. The dynamics of dengue fever transmission are sensitive to changes in environmental conditions such as temperature and humidity, as well as demographic and socio-economic factors. Projected changes in climate conditions along with other factors, such as population growth, urbanization, lack of sanitation, and ineffective mosquito control are expected to result in the further spread and distribution of dengue fever in the coming decades. Urban environments in tropical areas are particularly fragile due to rapid population movement (e.g. massive influx of migrants, causing unorganized urbanization) and the abundance of potential breeding sites. Eric Delmelle 1,* , Michael Hagenlocher 2 , Stefan Kienberger 2 and Irene Casas 3 1 Department of Geography and Earth Sciences and Center for Applied GIS, The University of North Carolina at Charlotte, USA, 2 Interfaculty Department of Geoinformatics – Z_GIS, University of Salzburg, Austria , 3 Department of Social Sciences, Louisiana Tech University, USA, (*) [email protected] Spatial variation and factors associated with dengue fever outbreaks in an urban environment of Colombia In Colombia, South America, dengue fever reemerged in the 1970s after being eradicated in the 1950s and 1960s. Ever since the disease has become endemic, presenting periodic outbreaks in 1991, 1994, 1998, 2001, 2006, and more recently in 2010. Most outbreaks have been of serotype 1 (DENV-1) and 2 (DENV-2), however, in the last decade type 3 (DENV-3) and type 4 (DENV-4) have also been present. The circulation of new types increases the population at risk of contracting the diseases, since they have not previously been exposed. In Colombia, about 26 million individuals are at risk of contracting dengue fever. Figure 1 . Cali, Colombia Dengue fever in the city of Santiago de Cali (referred to as Cali from here on) has followed a similar temporal pattern to that of Colombia in general]. Based on the City’s Health Municipality, the worst dengue outbreaks occurred in 1995 with 6,433 cases reported, in 2002 (n = 4,358), 2005 (n = 2,338) and 2010 (n = 9,287). In 2010, the total number of cases was the highest in the last 25 years. • Geocode dengue fever cases for the year 2010, and map associated rates • Identify the extent of dengue fever outbreaks and associated spatial pattern • Develop a regression model to estimate the impact of social, economic and environmental factors on dengue fever outbreaks • Compare results from spatial and aspatial regression • Dengue cases for the year 2010 were extracted from SIVIGILA (Colombia’s Public Health Surveillance System) and geocoded in a commercial Geographical Information System (GIS). • Rates of dengue fever cases computed for each neighborhood (n=323) using population estimates. Figure 2 . The methodological framework articulated around four distinct modules (1) data pre-processing, (2) cluster analysis, (3) statistical analysis and (4) visualization. Figure 3: Spatial distribution of dengue fever rates in (a) and kernel density estimation in (b). Aggregated counts per neighbourhood in (c), and rates (counts divided by the population) in (d). Table 1. SES, demographic and environmental variables. Table 2. Coefficient, significance levels and overall fit. Figure 5. Local GWR coefficients and associated local r 2 . Figure 4. Comparison of the three regression methods. References Hagenlocher, M., E. Delmelle, I. Casas, and S. Kienberger. 2013. Assessing socioeconomic vulnerability to dengue fever in Cali, Colombia: statistical vs expert-based modeling. International Journal of Health Geographics 12 (1):36. Khormi, H. M., and L. Kumar. 2011. Modeling dengue fever risk based on socioeconomic parameters, nationality and age groups: GIS and remote sensing based case study. Science of the Total Environment 409 (22):4713-4719. Kienberger, S., M. Hagenlocher, E. Delmelle, and I. Casas. 2013. A WebGIS tool for visualizing and exploring socioeconomic vulnerability to dengue fever in Cali, Colombia. Geospatial health 8 (1):313-316.
Transcript of Spatial variation and factors associated with dengue fever ...€¦ · environmental conditions...
Motivation
Background
Objectives
Methodology Secondary data (SES and environmental)
Data
Results
Dengue fever is a vector-borne disease that gradually re-emerged across the global South. Rapidly growing urban areas are most vulnerable due to poorly planned urbanization and migration. The dynamics of dengue fever transmission are sensitive to changes in environmental conditions such as temperature and humidity, as well as demographic and socio-economic factors. Projected changes in climate conditions along with other factors, such as population growth, urbanization, lack of sanitation, and ineffective mosquito control are expected to result in the further spread and distribution of dengue fever in the coming decades. Urban environments in tropical areas are particularly fragile due to rapid population movement (e.g. massive influx of migrants, causing unorganized urbanization) and the abundance of potential breeding sites.
Eric Delmelle1,*
, Michael Hagenlocher2, Stefan Kienberger
2 and Irene Casas
3
1Department of Geography and Earth Sciences and Center for Applied GIS, The University of North Carolina at Charlotte, USA, 2Interfaculty Department of Geoinformatics – Z_GIS, University of Salzburg, Austria , 3Department of Social Sciences, Louisiana Tech University, USA, (*) [email protected]
Spatial variation and factors associated with dengue fever
outbreaks in an urban environment of Colombia
In Colombia, South America, dengue fever reemerged in the 1970s after being eradicated in the 1950s and 1960s. Ever since the disease has become endemic, presenting periodic outbreaks in 1991, 1994, 1998, 2001, 2006, and more recently in 2010. Most outbreaks have been of serotype 1 (DENV-1) and 2 (DENV-2), however, in the last decade type 3 (DENV-3) and type 4 (DENV-4) have also been present. The circulation of new types increases the population at risk of contracting the diseases, since they have not previously been exposed. In Colombia, about 26 million individuals are at risk of contracting dengue fever.
Figure 1 . Cali, Colombia Dengue fever in the city of Santiago de Cali (referred to as Cali from here on) has followed a similar temporal pattern to that of Colombia in general]. Based on the City’s Health Municipality, the worst dengue outbreaks occurred in 1995 with 6,433 cases reported, in 2002 (n = 4,358), 2005 (n = 2,338) and 2010 (n = 9,287). In 2010, the total number of cases was the highest in the last 25 years.
• Geocode dengue fever cases for the year 2010, and map associated rates
• Identify the extent of dengue fever outbreaks and associated spatial pattern
• Develop a regression model to estimate the impact of social, economic and environmental factors on dengue fever outbreaks
• Compare results from spatial and aspatial regression
• Dengue cases for the year 2010 were extracted from SIVIGILA (Colombia’s Public Health Surveillance System) and geocoded in a commercial Geographical Information System (GIS).
• Rates of dengue fever cases computed for each neighborhood (n=323) using population estimates.
Figure 2 . The methodological framework articulated around four distinct modules (1) data pre-processing, (2) cluster analysis, (3) statistical analysis and (4) visualization.
Figure 3: Spatial distribution of dengue fever rates in (a) and kernel density estimation in (b). Aggregated counts per neighbourhood in (c), and rates (counts divided by the population) in (d).
Table 1. SES, demographic and environmental variables.
Table 2. Coefficient, significance levels and overall fit.
Figure 5. Local GWR coefficients and associated local r2.
Figure 4. Comparison of the three regression methods.
References
Hagenlocher, M., E. Delmelle, I. Casas, and S. Kienberger. 2013. Assessing socioeconomic vulnerability to dengue fever in Cali, Colombia: statistical vs expert-based modeling. International Journal of Health Geographics 12 (1):36. Khormi, H. M., and L. Kumar. 2011. Modeling dengue fever risk based on socioeconomic parameters, nationality and age groups: GIS and remote sensing based case study. Science of the Total Environment 409 (22):4713-4719. Kienberger, S., M. Hagenlocher, E. Delmelle, and I. Casas. 2013. A WebGIS tool for visualizing and exploring socioeconomic vulnerability to dengue fever in Cali, Colombia. Geospatial health 8 (1):313-316.
