A Novel Sampling Method to Measure Socioeconomic Drivers of Aedes Albopictus Distribution in Mecklenburg County, North Carolina

Abstract

Climate change, urbanization, and globalization have facilitated the spread of Aedes mosquitoes into regions that were previously unsuitable, causing an increased threat of arbovirus transmission on a global scale. While numerous studies have addressed the urban ecology of Ae. albopictus, few have accounted for socioeconomic factors that affect their range in urban regions. Here we introduce an original sampling design for Ae. albopictus, that uses a spatial optimization process to identify urban collection sites based on both geographic parameters as well as the gradient of socioeconomic variables present in Mecklenburg County, North Carolina, encompassing the city of Charlotte, a rapidly growing urban environment. We collected 3,645 specimens of Ae. albopictus (87% of total samples) across 12 weeks at the 90 optimized site locations and modelled the relationships between the abundance of gravid Ae. albopictus and a variety of neighborhood socioeconomic attributes as well as land cover characteristics. Our results demonstrate that the abundance of gravid Ae. albopictus is inversely related to the socioeconomic status of the neighborhood and directly related to both landscape heterogeneity as well as proportions of particular resident races/ethnicities. We present our results alongside a description of our novel sampling scheme and its usefulness as an approach to urban vector epidemiology. Additionally, we supply recommendations for future investigations into the socioeconomic determinants of vector-borne disease risk.

Keywords: health disparities; optimization; social determinants of health; vector-borne disease.

Figure 1

Location of Mecklenburg County and Charlotte city limits in North Carolina (NPA = Neighborhood Planning Area).

Figure 2

These nine variables, broken into quintiles, were used in the optimization process to identify NPAs suitable for surveying.

Figure 3

Histogram for each of nine variables used in the optimization process, using all NPAs. The red lines indicate the limits of each quintile.

Figure 4

Histogram for each of nine variables using the 90 sites selected in the optimization process (x-axis: standardized variable value; y-axis: count).

Figure 5

Illustration of Phase 2 mechanism in the optimization procedure. If a sampling unit at i = 12 is selected, it will “cover” neighborhoods j = 7, 8, 9, 12 and 13.

Figure 6

Location of selected NPAs.

Figure 7

Average number of gravid Ae. albopictus caught each week from 26 May 2017 to 21 August 2017.

Figure 8

Sum of gravid A. albopictus caught over 12 weeks in each selected NPA (small circle: fewer total samples; large circle: more total samples).