Spatial Epidemiology of Salmonellosis in Florida, 2009-2018

Abstract

Non-typhoidal Salmonella enterica infections cause a high disease burden in the United States with an estimated 1.2 million illnesses annually. The state of Florida consistently has a relatively high incidence compared to other states in the United States. Nevertheless, studies regarding the epidemiology of nontyphoidal salmonellosis and its spatial and temporal patterns in Florida were rarely reported. We examined the spatial and temporal patterns of 62,947 salmonellosis cases reported to FL Health Charts between 2009 and 2018. Dominant serotypes circulating in Florida were also explored using whole genome sequencing (WGS) based serotype-prediction for 2,507 Salmonella isolates sequenced by the Florida Department of Health during 2017 and 2018. The representativeness of laboratory-sequenced isolates for reported cases was determined by regression modeling. The annual incidence rate of salmonellosis decreased from 36.0 per 100,000 population in 2009 to 27.8 per 100,000 in 2016, and gradually increased in 2017 and 2018. Increased use of culture-independent testing did not fully explain this increase. The highest incidence rate was observed in children, contributing 40.9% of total reported cases during this period. A seasonal pattern was observed with the incidence peaking in September and October, later than the national average pattern. Over these 10 years, the Northeast and Northwest regions of the state had higher reported incidence rates, while reported rates in the Southeast and South were gradually increasing over time. Serotypes were predicted based on WGS data in the EnteroBase platform. The top-five most prevalent serotypes in Florida during 2017-2018 were Enteritidis, Newport, Javiana, Sandiego and Braenderup. The highest percentage of isolates was from children under 5 years of age (41.4%), and stool (84.7%) was the major source of samples. A zero-inflated negative binomial regression model showed that the reported case number was a strong predictor for the number of lab-sequenced isolates in individual counties, and the geospatial distribution of sequenced isolates was not biased by other factors such as age group. The spatial and temporal patterns identified in this study along with the prevalence of different serotypes will be helpful for the development of efficient prevention and control strategies for salmonellosis in Florida.

Keywords: Salmonella enterica; epidemiology; seasonality; serotypes; spatial-temporal trends.

Figure 1

Incidence of reported cases of salmonellosis in Florida, 2009–2018 CIDT: Culture-independent diagnostic tests. (A) Incidence rate of salmonellosis in Florida and national average, 2009–2018; (B) Incidence of salmonellosis cases detected with different methods in Florida, 2009–2018.

Figure 2

Age distribution of salmonellosis in Florida, 2009–2018.

Figure 3

Monthly incidence and 3-month moving average of salmonellosis in Florida from January 2009 to December 2018 (A): 3-month moving average smoothed monthly incidence of salmonellosis in Florida from January 2009 to December 2018; (B): Cumulative incidence of salmonellosis for each month in Florida over the years 2009–2018.

Figure 4

Geographical distribution of salmonellosis age-adjusted incidence rate at county level in Florida, 2009–2018.

Figure 5

Residuals of age-adjusted incidence rate of salmonellosis for various regions and years after median polish.

Figure 6

Comparison of the predictor performance in the zero-inflated negative binomial regression model for lab-sequenced isolates per county. Each predictor was standardized by subtracting the mean and dividing by the standard deviation. Model predictions were then calculated for each predictor separately while holding the other predictors at their median values.

Figure 7

Age-adjusted incidence rate of lab-sequenced Salmonella isolates in Florida, 2017–2018.