Mapping high-rate clusters of animal contact-related human Salmonella enterica single-state outbreaks in the United States, 2009–2022: A spatial epidemiological approach to inform public health surveillance

Abstract

Introduction

Nontyphoidal Salmonella enterica (NTS) is a major zoonotic enteric pathogen. Animal contact-related NTS outbreaks have increased in the United States of America (U.S.) over the last decade. Geospatial analysis can identify locations with elevated risk of NTS outbreaks where public health authorities can focus their NTS prevention and intervention efforts.

Methods

We analyzed NTS outbreak data reported from individual states to the Centers for Disease Control via the National Outbreak Reporting System between 2009 and 2022 across the continental contiguous U.S. A geospatial analytical framework that included disease mapping, spatial interpolation, and global and local clustering methods was applied to identify regions with high NTS outbreak rates.

Results

A total of 104 NTS single-state outbreaks were reported to the National Outbreak Reporting System (NORS) during the study period. The mean annual incidence rate was 0.02 NTS outbreaks per million person-years. The primary animal contact categories associated with these outbreaks were mammals (cattle, pigs, sheep, and horses), birds (backyard chickens, ducklings, and turkeys), and reptiles (turtles and lizards). Exposure settings included farms, fairgrounds, agricultural feed stores, veterinary clinics, dairy/agricultural settings, and residential settings. The local cluster detection methods consistently identified areas with significantly high NTS animal contact-related outbreak rates in the Mountain West, Midwest, and Northeast of the US.

Conclusion

NTS animal contact-related single-state outbreaks revealed distinct spatial clustering across the United States, with potentially higher risks in the Mountain West, Midwest, and Northeast. Diversity of animal-contact sources and exposure settings depicted complex transmission dynamics of NTS. Focused prevention and control programs in these areas are needed to mitigate the burden of NTS outbreaks.

Impacts

The most common animals linked to outbreaks were mammals, birds, and reptiles
Main exposure settings included farms, veterinary clinics, feed stores, shelters, schools, and homes.
Outbreaks clustered in the Mountain West, Midwest, and Northeast regions.
Region-specific prevention and interventions are needed to reduce the burden of illnesses.

This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/19863189.

This preprint examines the spatial and temporal distribution of animal contact–associated nontyphoidal Salmonella (NTS) outbreaks in the United States from 2009 to 2022 using data from the CDC's National Outbreak Reporting System (NORS). A total of 104 single-state outbreaks were included in the analysis. The authors apply a very comprehensive geospatial framework, including disease mapping, empirical Bayes smoothing, global and local spatial autocorrelation measures, and spatial and space-time scan statistics, to identify regions with elevated outbreak rates and statistically significant clusters.

Global spatial autocorrelation analysis identified significant clustering, with a peak at 671.38 km during the 2016–2022 period (z = 3.46, p = 0.001), suggesting that outbreaks cluster at a neighboring-state scale. In addition, space-time scan statistics identified statistically significant clusters with elevated relative risk, reinforcing that outbreaks are not randomly distributed across either space or time. The study further identifies persistent geographic clustering, particularly in the Mountain West, Mid-west, and Northeast, as well as recurring space-time clusters, indicating that outbreak risk is both geographically concentrated and temporally dynamic. The findings also highlight the role of animal contact exposures, including poultry, cattle, and reptiles, across a range of settings such as homes, farms, and vet offices. Overall, the study provides a national, longitudinal perspective on the spatial epidemiology of animal-associated NTS outbreaks.

Strengths

The use of multiple spatial methods (global Moran's I, Local Moran's I, Getis-Ord Gi, and SaTScan) strengthens the evidence for the findings. This approach allows the authors to detect overall clustering, identify localized hotspots, and confirm statistically significant space-time clusters.
This study has a very long study period of 14 years and includes a national-level scope across the contiguous United States. This longitudinal approach allows the authors to assess the persistence of spatial patterns over time, which is not commonly addressed in prior studies.
By isolating animal contact-associated outbreaks and focusing only on these transmission cases rather than cases of food-borne illness, the study addresses a distinct and often underexamined transmission pathway within the existing NTS literature. This focus improves the relevance of findings for targeted public health interventions.
Identifying consistent high-risk regions and time periods has clear implications for surveillance, education, and prevention strategies. The findings support the use of place-based approaches to reduce zoonotic transmission.
The paper includes a large number of well-designed figures, including maps and cluster visualizations. These visualizations make complex spatial and space-time patterns more accessible and allow readers to easily identify geographic trends and hotspots, even if they are not overly familiar with the methods.

Potential Limitations Not Addressed by the Authors

The study relies on NORS, a passive surveillance system, which is subject to variability in outbreak detection, investigation capacity, and reporting practices across states and over time. While this limitation is acknowledged, the analysis does not attempt to adjust for or quantify these differences. As a result, observed spatial clusters may reflect variation in surveillance intensity rather than true differences in outbreak risk.
Outbreak data in NORS rely on epidemiologic investigation and reporting, which may be subject to misclassification of exposure sources. Inaccurate or incomplete attribution of animal contact could bias the characterization of outbreak sources and weaken conclusions about source-specific patterns.
All analyses are conducted at the state level, which may introduce ecological fallacy. Associations observed at the state level may not reflect individual-level risk.
Although the study focuses on animal contact-associated outbreaks, the primary spatial analyses group together diverse animal sources, including poultry, cattle, and reptiles. These sources have distinct transmission pathways and risk profiles, and aggregating them may obscure the underlying drivers of observed clusters. As an example, a cluster driven by backyard poultry exposure would require different public health interventions than one associated with reptiles or livestock.
The study uses human population as the denominator for rate calculations but does not incorporate data on animal populations (e.g., livestock density or prevalence of backyard poultry ownership). Without these exposure-relevant measures, it is difficult to determine whether observed clusters reflect higher levels of animal contact or differences in behavior, hygiene, or surveillance.
The analysis is limited to single-state outbreaks, excluding multi-state outbreaks that may represent a substantial and important subset of NTS transmission. This exclusion may bias the spatial patterns observed and limit the generalizability of the findings, particularly for outbreaks linked to widely distributed animal sources or products.
The analysis is conducted at the state level, which may not capture within-state variation. High-risk areas at the county or community level may be obscured, and identified clusters may not accurately represent localized transmission dynamics.

Competing interests

The author declares that they have no competing interests.

Use of Artificial Intelligence (AI)

The author declares that they did not use generative AI to come up with new ideas for their review.

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Mapping high-rate clusters of animal contact-related human Salmonella enterica single-state outbreaks in the United States, 2009–2022: A spatial epidemiological approach to inform public health surveillance

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