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Salmonella and the changing environment: systematic review using New York State as a model
Author(s) -
Kevin D. Welch,
Asante Shipp-Hilts,
Millicent Eidson,
Shubhayu Saha,
Shelley M. Zansky
Publication year - 2018
Publication title -
journal of water and health
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.482
H-Index - 59
eISSN - 1996-7829
pISSN - 1477-8920
DOI - 10.2166/wh.2018.224
Subject(s) - vulnerability (computing) , salmonella , climate change , environmental health , environmental resource management , environmental planning , geography , biology , computer science , ecology , medicine , computer security , environmental science , genetics , bacteria
Salmonella is a public health concern, for which a complex interplay between host, agent, and environment exists. An improved understanding of causal processes can be used to better gauge the causes and trajectory of Salmonella in a changing environment. This would be useful in determining the impact of climate change on the New York State (NYS) environment, the effect of climate change on Salmonella in NYS, factors contributing to Salmonella vulnerability in humans, and aspects of climate change and Salmonella which necessitate further research. A systematic review was conducted to study associations between Salmonella and the environment. Using the search criteria, a total of 91 relevant articles were identified from four electronic databases. Key information was abstracted, organized, and synthesized to identify causal processes and linkages between climate change, the environment of NYS, and Salmonella-related outcomes, as well as risk factors to characterize Salmonella vulnerabilities. Three inter-related domains were identified for consideration and application to epidemiological research to confirm and extrapolate disease patterns using climate change scenarios: improved quantification of causal relationships, inclusion of factors linked to sectors not immediately associated with the exposure and outcome, and increased capacity to validate models in diverse settings.

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