Integrated Social-Behavioral and Ecological Risk Maps to Prioritize Local Public Health Responses to Lyme Disease

Abstract

Background: The risk of contracting Lyme disease (LD) can vary spatially because of spatial heterogeneity in risk factors such as social-behavior and exposure to ecological risk factors. Integrating these risk factors to inform decision-making should therefore increase the effectiveness of mitigation interventions.

Objectives: The objective of this study was to develop an integrated social-behavioral and ecological risk-mapping approach to identify priority areas for LD interventions.

Methods: The study was conducted in the Montérégie region of Southern Quebec, Canada, where LD is a newly endemic disease. Spatial variation in LD knowledge, risk perceptions, and behaviors in the population were measured using web survey data collected in 2012. These data were used as a proxy for the social-behavioral component of risk. Tick vector population densities were measured in the environment during field surveillance from 2007 to 2012 to provide an index of the ecological component of risk. Social-behavioral and ecological components of risk were combined with human population density to create integrated risk maps. Map predictions were validated by testing the association between high-risk areas and the current spatial distribution of human LD cases.

Results: Social-behavioral and ecological components of LD risk had markedly different distributions within the study region, suggesting that both factors should be considered for locally adapted interventions. The occurrence of human LD cases in a municipality was positively associated with tick density (p<0.01) but was not significantly associated with social-behavioral risk.

Conclusion: This study is an applied demonstration of how integrated social-behavioral and ecological risk maps can be created to assist decision-making. Social survey data are a valuable but underutilized source of information for understanding regional variation in LD exposure, and integrating this information into risk maps provides a novel approach for prioritizing and adapting interventions to the local characteristics of target populations. https://doi.org/10.1289/EHP1943.

Figure 1.

Conceptual structure for the integration of social-behavioral and ecological risk to adapt local responses to Lyme disease risk.

Figure 2.

Point map showing the distribution of locations of residence of survey participants in Montérégie in 2012 ($n=379$).

Figure 3.

Point map showing the distribution of the field surveillance site locations visited in Montérégie, 2007–2012 ($n=378$ site visits).

Figure 4.

Spatial variation in the level of (A) adoption of individual preventive behaviors (i.e., global preventive behavior score; GPB); (B) knowledge about the disease (i.e., global knowledge score; GK); and (C) risk perceptions (i.e., global risk perception score; GRP). Darker areas represent higher index scores for these social-behavioral drivers but a lower risk for Lyme disease risk transmission.

Figure 4.

Spatial variation in the level of (A) adoption of individual preventive behaviors (i.e., global preventive behavior score; GPB); (B) knowledge about the disease (i.e., global knowledge score; GK); and (C) risk perceptions (i.e., global risk perception score; GRP). Darker areas represent higher index scores for these social-behavioral drivers but a lower risk for Lyme disease risk transmission.

Figure 5.

Spatial variation in the level of (A) population density based on the 2011 census, and (B) predicted tick density (PTD) based on ticks collected through active field surveillance from 2007–2012. Darker areas represent a higher Lyme disease risk.

Figure 6.

Social/behavioral-ecological vulnerability index map. Darker areas represent a higher Lyme disease risk based on the global preventive behavior score and the predicted tick density (PTD).

Figure 7.

Prioritization index risk map. Darker areas represent a higher Lyme disease risk based on the social/behavioral-ecological vulnerability index and the human population at risk.