Applied Veterinary Informatics: Development of a Semantic and Domain-Specific Method to Construct a Canine Data Repository

Harnessing electronic health records to study emerging environmental disasters: a proof of concept with perfluoroalkyl substances (PFAS)

Environmental disasters are anthropogenic catastrophic events that affect health. Famous disasters include the Seveso disaster and the Fukushima-Daiichi nuclear meltdown, which had disastrous health consequences. Traditional methods for studying environmental disasters are costly and time-intensive. We propose the use of electronic health records (EHR) and informatics methods to study the health effects of emergent environmental disasters in a cost-effective manner. An emergent environmental disaster is exposure to perfluoroalkyl substances (PFAS) in the Philadelphia area. Penn Medicine (PennMed) comprises multiple hospitals and facilities within the Philadelphia Metropolitan area, including over three thousand PFAS-exposed women living in one of the highest PFAS exposure areas nationwide. We developed a high-throughput method that utilizes only EHR data to evaluate the disease risk in this heavily exposed population. We replicated all five disease/conditions implicated by PFAS exposure, including hypercholesterolemia, thyroid disease, proteinuria, kidney disease and colitis, either directly or via closely related diagnoses. Using EHRs coupled with informatics enables the health impacts of environmental disasters to be more easily studied in large cohorts versus traditional methods that rely on interviews and expensive serum-based testing. By reducing cost and increasing the diversity of individuals included in studies, we can overcome many of the hurdles faced by previous studies, including a lack of racial and ethnic diversity. This proof-of-concept study confirms that EHRs can be used to study human health and disease impacts of environmental disasters and produces equivalent disease-exposure knowledge to prospective epidemiology studies while remaining cost-effective.

Development of an Informatics Algorithm to Link Seasonal Infectious Diseases to Birth-Dependent Diseases Across Species: A Case Study with Osteosarcoma

Many diseases have been linked with birth seasonality, and these fall into four main categories: mental, cardiovascular, respiratory and women's reproductive health conditions. Informatics methods are needed to uncover seasonally varying infectious diseases that may be responsible for the increased birth month-dependent disease risk observed. We have developed a method to link seasonal infectious disease data from the USA to birth month dependent disease data from humans and canines. We also include seasonal air pollution and climate data to determine the seasonal factors most likely involved in the response. We test our method with osteosarcoma, a rare bone cancer. We found the Lyme disease incidence was the most strongly correlated significant factor in explaining the birth month-osteosarcoma disease pattern (R=0.418, p=2.80X10-23), and this was true across all populations observed: canines, pediatric, and adult populations.