Browsing by Subject "Maryland"
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Item Open Access Examining Retail Electricity Practices in Maryland(2022-04-21) Ward, BrandonThe deregulation of electricity markets has allowed third-party suppliers in 15 states and Washington, D.C to compete with the traditional regulated utilities in offering the supply of electricity. The introduction of third-party suppliers to electricity markets has unfortunately come with claims of unethical marketing practices, and questions as to whether commercial and industrial consumer classes enjoy benefits that residential consumers do not because of economies of scale. This Masters Project examines the third-party electric marketplace across customer classes in Maryland. Results from this examination mostly suggests that residential consumers pay more for electric supply from third-party suppliers than they do from their regulated utility. This project also highlights reports of deceitful marketing practices in the third-party retail electric marketplace for Maryland.Item Open Access Mapping the distribution of Lyme disease at a mid-Atlantic site in the United States using electronic health data.(PloS one, 2024-01) Lantos, Paul M; Janko, Mark; Nigrovic, Lise E; Ruffin, Felicia; Kobayashi, Takaaki; Higgins, Yvonne; Auwaerter, Paul GLyme disease is a spatially heterogeneous tick-borne infection, with approximately 85% of US cases concentrated in the mid-Atlantic and northeastern states. Surveillance for Lyme disease and its causative agent, including public health case reporting and entomologic surveillance, is necessary to understand its endemic range, but currently used case detection methods have limitations. To evaluate an alternative approach to Lyme disease surveillance, we have performed a geospatial analysis of Lyme disease cases from the Johns Hopkins Health System in Maryland. We used two sources of cases: a) individuals with both a positive test for Lyme disease and a contemporaneous diagnostic code consistent with a Lyme disease-related syndrome; and b) individuals referred for a Lyme disease evaluation who were adjudicated to have Lyme disease. Controls were individuals from the referral cohort judged not to have Lyme disease. Residential address data were available for all cases and controls. We used a hierarchical Bayesian model with a smoothing function for a coordinate location to evaluate the probability of Lyme disease within 100 km of Johns Hopkins Hospital. We found that the probability of Lyme disease was greatest in the north and west of Baltimore, and the local probability that a subject would have Lyme disease varied by as much as 30-fold. Adjustment for demographic and ecological variables partially attenuated the spatial gradient. Our study supports the suitability of electronic medical record data for the retrospective surveillance of Lyme disease.