Date of Award

1-1-2019

Document Type

Open Access Thesis

Degree Name

Master of Public Health (MPH)

Department

School of Public Health

First Advisor

Robert Dubrow

Abstract

Foodborne Disease (FBD) impacts individuals through the ingestion of foods contaminated with microbes and can lead to an array of adverse health consequences ranging from mild symptoms such as nausea to those that evolve to become life-threatening (World Health Organization, 2015a). The incidence of FBD is expected to increase in the presence of climate change due to an increase in ambient temperature creating an environment where microbes can rapidly multiply and thrive (Gregory, Johnson, Newton, & Ingram, 2009; Kovats et al., 2004). Foodborne cases of Salmonella make up the second largest cause of gastrointestinal infection in the United States (Scallan et al., 2011). In the United States, the Centers for Disease Control and Prevention’s National Outbreak Response System database documents nation-wide occurrences of FBD outbreaks. In state-level analyses for Florida, Illinois, Maryland, Michigan, Minnesota, New York, Ohio, and Washington, negative binomial regression models were used to examine the association between monthly average maximum daily temperature and monthly incidence of foodborne Salmonella between 1998 and 2017, using models with the same month’s average maximum daily temperature as the predictor (zero-month lag) and models with the previous month’s average maximum daily temperature as the predictor (one-month lag). The zero-month lag analysis yielded significant results for Illinois, Maryland, Minnesota, New York, Ohio, and Washington, and the one-month lag analysis yielded significant results for Maryland, Minnesota, New York, and Washington. A single model with state as an additional covariate, was also run and found summary statistics with the middle and highest temperature categories having 1.52 (95% CI: 1.05, 2.21; p= 0.03) and 3.46 (95% CI: 2.40, 5.01; p

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This is an Open Access Thesis.

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