Assessing socioeconomic vulnerability to dengue fever in Cali, Colombia: statistical vs expert-based modeling

Abstract

Background: As a result of changes in climatic conditions and greater resistance to insecticides, many regions across the globe, including Colombia, have been facing a resurgence of vector-borne diseases, and dengue fever in particular. Timely information on both (1) the spatial distribution of the disease, and (2) prevailing vulnerabilities of the population are needed to adequately plan targeted preventive intervention. We propose a methodology for the spatial assessment of current socioeconomic vulnerabilities to dengue fever in Cali, a tropical urban environment of Colombia.

Methods: Based on a set of socioeconomic and demographic indicators derived from census data and ancillary geospatial datasets, we develop a spatial approach for both expert-based and purely statistical-based modeling of current vulnerability levels across 340 neighborhoods of the city using a Geographic Information System (GIS). The results of both approaches are comparatively evaluated by means of spatial statistics. A web-based approach is proposed to facilitate the visualization and the dissemination of the output vulnerability index to the community.

Results: The statistical and the expert-based modeling approach exhibit a high concordance, globally, and spatially. The expert-based approach indicates a slightly higher vulnerability mean (0.53) and vulnerability median (0.56) across all neighborhoods, compared to the purely statistical approach (mean = 0.48; median = 0.49). Both approaches reveal that high values of vulnerability tend to cluster in the eastern, north-eastern, and western part of the city. These are poor neighborhoods with high percentages of young (i.e., < 15 years) and illiterate residents, as well as a high proportion of individuals being either unemployed or doing housework.

Conclusions: Both modeling approaches reveal similar outputs, indicating that in the absence of local expertise, statistical approaches could be used, with caution. By decomposing identified vulnerability "hotspots" into their underlying factors, our approach provides valuable information on both (1) the location of neighborhoods, and (2) vulnerability factors that should be given priority in the context of targeted intervention strategies. The results support decision makers to allocate resources in a manner that may reduce existing susceptibilities and strengthen resilience, and thus help to reduce the burden of vector-borne diseases.

Figure 1

Base map showing the location of Cali, Colombia.

Figure 2

Study design and workflow.

Figure 3

Final selection of susceptibility (SUS) and lack of resilience (LoR) indicators.

Figure 4

Socioeconomic vulnerability to dengue fever in Cali, Colombia. Figure 4a shows the results based on a statistical modeling approach, while Figure 4b shows the results based on an expert-based modeling approach.

Figure 5

Comparative analysis of statistical and expert-based modeling approaches. Map showing the difference between both vulnerability indices (VU_exp – VU_stat). The outcomes of a cluster analysis (Local Moran’s I) are displayed as an additional layer.

Figure 6

Illustrative visualization of expert-based modeling results in ArcGIS explorer online. Illustrative visualization of the results of the expert-based model (there was no particular reason why the expert-based model was selected; we could also display the results of the statistical model). This allows not only sharing results with a wider public, but also enables an assessment of the share of the underlying vulnerability indicators per neighborhood (see table and bar-charts in the figure).

Figure 7

Comparison of vulnerability at neighborhood level and dengue cases. Figure 7a shows the spatial distribution of dengue cases (January-December, 2010). A density map (i.e., cases per km²) is shown in Figure 7b. The dengue density layer is displayed on top of the two vulnerability maps (Figures 7c, 7d) to enable a visual comparison of vulnerability at the neighborhood level and the spatial distribution of dengue cases.