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Background:
Since its first detection in 2001, West Nile Virus (WNV) poses a significant health risk for residents of Shelby County in Tennessee. This situation forced public health officials to adopt efficient methods for monitoring disease spread and predicting future outbreaks. Analyses that use environmental variables to find suitable habitats for WNV-infected mosquitoes have the potential to support these efforts. Using the Mahalanobis Distance statistic, we identified areas of Shelby County that are ecologically most suitable for sustaining WNV, based on similarity of environmental characteristics to areas where WNV was found. The environmental characteristics in this study were based on Geographic Information Systems (GIS) data, such as elevation, slope, land use, vegetation density, temperature, and precipitation.
Results:
Our analyses produced maps of likely habitats of WNV-infected mosquitoes for each week of August 2004, revealing the areas that are ecologically most suitable for sustaining WNV within the core of the Memphis urban area. By comparing neighbourhood social characteristics to the environmental factors that contribute to WNV infection, potential social drivers of WNV transmission were revealed in Shelby County. Results show that human population characteristics and housing conditions such as a high percentage of black population, low income, high rental occupation, old structures, and vacant housing are associated with the focal area of WNV identified for each week of the study period.
Conclusions:
We demonstrated that use of the Mahalanobis Distance statistic as a similarity index to assess environmental characteristics is a potential raster-based approach to identify areas ecologically most suitable for sustaining the virus. This approach was also useful to monitor changes over time for likely locations of infected mosquito habitats. This technique is very helpful for authorities when making decisions related to an integrated mosquito management plan and targeted health education outreach.
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Background:
The aim of this study was to determine if remotely sensed data and Digital Elevation Model (DEM) can test relationships between Culex quinquefasciatus and Anopheles gambiae s.l. larval habitats and environmental parameters within Internally Displaced People (IDP) campgrounds in Gulu, Uganda. A total of 65 georeferenced aquatic habitats in various IDP camps were studied to compare the larval abundance of Cx. quinquefasciatus and An. gambiae s.l. The aquatic habitat dataset were overlaid onto Land Use Land Cover (LULC) maps retrieved from Landsat imagery with 150 m × 150 m grid cells stratified by levels of drainage. The LULC change was estimated over a period of 14 years. Poisson regression analyses and Moran's I statistics were used to model relationships between larval abundance and environmental predictors. Individual larval habitat data were further evaluated in terms of their covariations with spatial autocorrelation by regressing them on candidate spatial filter eigenvectors. Multispectral QuickBird imagery classification and DEM-based GIS methods were generated to evaluate stream flow direction and accumulation for identification of immature Cx. quinquefasciatus and An. gambiae s.l. and abundance.
Results:
The main LULC change in urban Gulu IDP camps was non-urban to urban, which included about 71.5 % of the land cover. The regression models indicate that counts of An. gambiae s.l. larvae were associated with shade while Cx. quinquefasciatus were associated with floating vegetation. Moran's I and the General G statistics for mosquito density by species and instars, identified significant clusters of high densities of Anopheles; larvae, however, Culex are not consistently clustered. A stepwise negative binomial regression decomposed the immature An. gambiae s.l. data into empirical orthogonal bases. The data suggest the presence of roughly 11% to 28 % redundant information in the larval count samples. The DEM suggest a positive correlation for Culex (0.24) while for Anopheles there was a negative correlation (-0.23) for a local model distance to stream.
Conclusion:
These data demonstrate that optical remote sensing; geostatistics and DEMs can be used to identify parameters associated with Culex and Anopheles aquatic habitats.
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Background:
To date, few epidemiologic studies have examined the relationship between environmental PCDD/F exposure and breast cancer in human populations. Dioxin emissions from municipal solid waste incinerators (MSWIs) are one of the major sources of environmental dioxins and are therefore an exposure source of public concern. The purpose of this study was to examine the association between dioxins emitted from a polluting MSWI and invasive breast cancer risk among women residing in the area under direct influence of the facility.
Methods:
We compared 434 incident cases of invasive breast cancer diagnosed between 1996 and 2002, and 2170 controls randomly selected from the 1999 population census. A validated dispersion model was used as a proxy for dioxin exposure, yielding four exposure categories. The latter were linked to individual places of residence, using Geographic Information System technology.
Results:
The age distribution at diagnosis for all cases combined showed a bimodal pattern with incidence peaks near 50 and 70 years old. This prompted us to run models separately for women aged 20–59 years, and women aged 60 years or older. Among women younger than 60 years old, no increased or decreased risk was found for any dioxin exposure category. Conversely, women over 60 years old living in the highest exposed zone were 0.31 time less likely (95% confidence interval, 0.08–0.89) to develop invasive breast cancer.
Conclusion:
Before speculating that this decreased risk reflects a dioxin anti-estrogenic activity with greater effect on late-onset acquired breast cancer, some residual confounding must be envisaged.
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