Vector-borne diseases: studies in human West Nile Virus and canine Lyme nephritis

• Main Author: Carney, Patrick Conor
• Language: en_US
• Published: 2017
• Subjects: Epidemiology
• Online Access: https://hdl.handle.net/2144/20859

Vector-borne diseases are a resurgent focus in public health. As concern about climate change mounts, the close relationship between these diseases and the environment has garnered growing attention. This dissertation examines the relationship between environment and vector-borne disease in both human and veterinary medical contexts and on both a local and national scale. The first study investigated using a novel Internet-based surveillance system for risk mapping of West Nile Virus (WNV) in the contiguous United States from 2007-2014, with meteorological, demographic, and land use variables as predictors. The study found that annual average temperature, minimum temperature, precipitation, and human population density were predictive of WNV reports, but that the novel surveillance data appeared to have systematic gaps that impair the utility of the model. However, the results may help to guide improvements in novel surveillance systems. The second study used the logistic regression model developed in the first study to predict the risk of WNV in the contiguous United States in 2050 and 2070 under four projected climate scenarios. The study found that Southern California is likely to remain the area of greatest risk under all scenarios and that risk would be expected to increase across much of the West under the scenario of uncontrolled carbon dioxide emissions. The results of this study may inform development of more sophisticated models and may help to direct public health resources to areas of greatest impact. The third study investigated the relationship between cases of canine Lyme nephritis and precipitation in the months prior to diagnosis. Precipitation three months prior to diagnosis was found to be associated with the development of Lyme nephritis (hazard ratio for 1 inch/month 1.125, 95% confidence interval 1.009 – 1.254). This finding may improve diagnostic accuracy for dogs with protein-losing nephropathies and may guide studies of additional risk factors.