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. 2019 Sep;127(9):97005.
doi: 10.1289/EHP4621. Epub 2019 Sep 19.

Precipitation and Salmonellosis Incidence in Georgia, USA: Interactions between Extreme Rainfall Events and Antecedent Rainfall Conditions

Debbie Lee  1 , Howard H Chang  2 , Stefanie Ebelt Sarnat  1 , Karen Levy  1
Affiliations

Precipitation and Salmonellosis Incidence in Georgia, USA: Interactions between Extreme Rainfall Events and Antecedent Rainfall Conditions

Debbie Lee et al. Environ Health Perspect. 2019 Sep.

Abstract

Background: The southeastern United States consistently has high salmonellosis incidence, but disease drivers remain unknown. Salmonella is regularly detected in this region's natural environment, leading to numerous exposure opportunities. Rainfall patterns may impact the survival/transport of environmental Salmonella in ways that can affect disease transmission.

Objectives: This study investigated associations between short-term precipitation (extreme rainfall events) and longer-term precipitation (rainfall conditions antecedent to these extreme events) on salmonellosis counts in the state of Georgia in the United States.

Methods: For the period 1997-2016, negative binomial models estimated associations between weekly county-level extreme rainfall events (≥90th percentile of daily rainfall) and antecedent conditions (8-week precipitation sums, categorized into tertiles) and weekly county-level salmonellosis counts.

Results: In Georgia's Coastal Plain counties, extreme and antecedent rainfall were associated with significant differences in salmonellosis counts. In these counties, extreme rainfall was associated with a 5% increase in salmonellosis risk (95% CI: 1%, 10%) compared with weeks with no extreme rainfall. Antecedent dry periods were associated with a 9% risk decrease (95% CI: 5%, 12%), whereas wet periods were associated with a 5% increase (95% CI: 1%, 9%), compared with periods of moderate rainfall. In models considering the interaction between extreme and antecedent rainfall conditions, wet periods were associated with a 13% risk increase (95% CI: 6%, 19%), whereas wet periods followed by extreme events were associated with an 11% increase (95% CI: 5%, 18%). Associations were substantially magnified when analyses were restricted to cases attributed to serovars commonly isolated from wildlife/environment (e.g., Javiana). For example, wet periods followed by extreme rainfall were associated with a 34% risk increase (95% CI: 20%, 49%) in environmental serovar infection.

Conclusions: Given the associations of short-term extreme rainfall events and longer-term rainfall conditions on salmonellosis incidence, our findings suggest that avoiding contact with environmental reservoirs of Salmonella following heavy rainfall events, especially during the rainy season, may reduce the risk of salmonellosis. https://doi.org/10.1289/EHP4621.

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Figures

Figure 1a is a line graph plotting median precipitation in coastal plain counties (ranging from 0 to 600 millimeters in intervals of 200) (y-axis) across the four seasons (winter, spring, summer, and fall) in the years 1997 to 2016 in unit intervals (x-axis). Figure 1b is a line graph plotting median precipitation in northern counties (ranging from 0 to 600 millimeters in intervals of 200) (y-axis) across the four seasons (winter, spring, summer, and fall) in the years 1997 to 2016 in unit intervals (x-axis).
Figure 1.
Median precipitation (mm) by year and season for (A) Coastal Plain and (B) Northern counties during 1997–2016. Tick marks (x-axis) correspond to the four seasons: winter (W), spring, summer, fall. W marks the first season of each year.
Figure 2a is a map of Georgia representing salmonellosis incidence for all serovars by county, with county colors corresponding to 6 to 24, 24 to 41, 41 to 59, 59 to 76, and 76 to 94 cases per 100,000 population. Figure 2b is a map of Georgia representing salmonellosis incidence for environmental serovars by county, with county corresponding to 0 to 12, 12 to 25, 25 to 37, 37 to 49, and 49 to 61 cases per 100,000 population. The four major cities of Georgia, which are Atlanta, Columbus, Augusta, and Savannah, are indicated on the map.
Figure 2.
Mean yearly salmonellosis incidence (cases per 100,000 population) in Georgia by county for (A) all serovars and (B) environmental serovars during 1997–2016. Borders of Coastal Plain counties are outlined in black.
Figure 3a is a line graph plotting salmonellosis incidence. Figure 3b is a line graph plotting salmonellosis incidence (cases per 100,000 population) (ranging from 0 to 20 in intervals of 5) (y-axis) across the years 2000, 2005, 2010, and 2015 (x-axis) for environmental serovars in coastal plain and northern counties.
Figure 3.
Median yearly salmonellosis incidence (cases per 100,000 population) in Georgia during 1997–2016 attributed to (A) all serovars and (B) environmental serovars.
Figure 4 is a graph plotting incidence rate ratio (ranging from 0.8 to 1.4 in intervals of 0.2) (y-axis) across antecedent rainfall conditions (dry, moderate, and wet) (x-axis) for all salmonella serovars and environmental serovars in northern counties and coastal plain counties.
Figure 4.
Comparison of incidence rate ratios (IRRs) and 95% confidence intervals for the associations between an extreme rainfall event at the 90th percentile (1-week lag) and differing antecedent rainfall conditions (dry, moderate, wet) by county location and serovar type.
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