The association between the incidence of Lyme disease in the USA and indicators of greenness and land cover

Abstract

Lyme disease (LD) is the most common vector-borne illness in the USA. Incidence is related to specific environmental conditions such as temperature, metrics of land cover, and vertebrate species diversity. To determine whether greenness, as measured by the Normalized Difference Vegetation Index (NDVI), and other selected indices of land cover were associated with the incidence of LD in the northeastern USA for the years 2000–2018, we conducted an ecological analysis of incidence rates of LD in counties of 15 “high” incidence states and the District of Columbia for 2000–2018. Annual counts of LD by county were obtained from the US Centers for Disease Control and values of NDVI were acquired from the Moderate Resolution Imaging Spectroradiometer instrument aboard Terra and Aqua Satellites. County-specific values of human population density, area of land and water were obtained from the US Census. Using quasi-Poisson regression, multivariable associations were estimated between the incidence of LD, NDVI, land cover variables, human population density, and calendar year. We found that LD incidence increased by 7.1% per year (95% confidence interval: 6.8–8.2%). Land cover variables showed complex non-linear associations with incidence: average county-specific NDVI showed a “u-shaped” association, the standard deviation of NDVI showed a monotonic upward relationship, population density showed a decreasing trend, areas of land and water showed “n-shaped” relationships. We found an interaction between average and standard deviation of NDVI, with the highest average NDVI category; increased standard deviation of NDVI showed the greatest increase in rates. These associations cannot be interpreted as causal but indicate that certain patterns of land cover may have the potential to increase exposure to infected ticks and thereby may contribute indirectly to increased rates of LD. Public health interventions could make use of these results in informing people where risks may be high.