Early detection of a new outbreak or new information about a public health issue could prevent morbidity and mortality and reduce healthcare expenditures for the economy. Syndromic surveillance is a subset of public health surveillance practice that uses pre-diagnostic data to monitor public health threats. The syndromic surveillance approach posits that patients first interface with the healthcare system in non-traditional ways (e.g., buying over-the-counter medications, calling healthcare hotlines) before seeking traditional healthcare avenues such as emergency rooms and outpatient clinics. Thus detection of public health issues may be more timely using syndromic surveillance data sources compared to diagnosis-based surveillance systems.
One source of information not yet fully integrated in syndromic surveillance is calls to poison centers. United States poison centers offer free, confidential medical advice through a national help line to assist in poison exposures. Call data are transmitted and stored in an electronic database within minutes to the National Poison Data System (NPDS), which can be used for near-real-time surveillance for disease conditions or exposures.
The studies presented in the dissertation explore new ways for poison center records to be used for early identification of public health threats and for evaluating policy and program impact by identifying changing trends in poison center records. The approach and findings from these three studies expand upon current knowledge of how poison center records can be used for syndromic surveillance and provide evidence that justifies expansion of poison center surveillance into avenues not yet explored by local, state, and federal public health.
Identifer | oai:union.ndltd.org:GEORGIA/oai:scholarworks.gsu.edu:sph_diss-1013 |
Date | 09 August 2016 |
Creators | Law, Kai Yee |
Publisher | ScholarWorks @ Georgia State University |
Source Sets | Georgia State University |
Detected Language | English |
Type | text |
Format | application/pdf |
Source | Public Health Dissertations |
Page generated in 0.0019 seconds