Write a comment

Peer Review 2

Anna Devereaux | April 2026 Peer Review https://doi.org/10.64898/2026.04.04.26350168

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

Summary

This article performs a spatial analysis to understand transmission dynamics and hotspots of Salmonella in the United States, given the recent rise in national cases and the disease burden posed by NTS globally. The study uses a robust longitudinal statistical analysis to measure the level of outbreaks through various spatial elements, including global and local cluster analyses, and addresses the knowledge gap in long-term transmission dynamics that remains after standard short-term studies. Data is taken from a fourteen year period to capture temporal components of outbreaks and uses Bayes analysis to limit the effect of sparsely populated states appearing as outliers when reporting cases. While the study relies on strong statistical methods, concerns arise in the interpretation of findings and dismissal of potential biases that may skew results toward the regions identified as high-risk settings for NTS outbreak. 

Major Concerns

The study relies on the National Outbreak Reporting System to track NTS outbreak, which authors acknowledge as a potential concern– considering that this system is based on voluntary reporting mechanisms. However, they do not acknowledge this bias in their interpretations and conclude that Mountain West, Midwest, and Northeast regions of the US have the highest NTS animal contact-related outbreaks, as confirmed by local cluster detection methods. Because these areas are identified as having high livestock density, it is likely that there are more robust surveillance and reporting systems compared to other regions in the US, perhaps suggesting that a difference in the clusters is due to intra-state differences in surveillance capacity rather than significant differences in outbreaks. Livestock prevalence is noted as a high-risk factor but should also be recognized as a potential confounder in clustered reporting mechanisms, given its relationship to surveillance. This study is also conducted at the state level despite NTS outbreaks being highly localized events, so the study may underestimate the incidence of local outbreaks observed across the United States and in multistate outbreaks. Finally, ecologic fallacy can potentially bias results if researchers conclude that human interaction with animals is a primary transmission pathway. Even if these regions show consistency in the types of animal profiles associated with outbreaks in these regions, more localized testing would need to be completed to confirm if the outbreaks in populations were due to contact with these specific animals. 

Minor Concerns 

Similarly to concerns regarding surveillance capacity, the researchers also use the permanent state population as the denominator in their risk assessment, disregarding the increased reporting that could happen by visitors in areas with national parks and petting zoo attractions (such as those in the identified, high-risk areas). Researchers also note that Covid-19 impacts on surveillance may confound results during this time period, but do not report any statistical findings that this is a reasonable assumption. It could also be helpful to include a disclaimer as to why the study period was broken into split periods, providing more context to the analysis and rationale behind this decision. 

Actionable Recommendations 

To address minor concerns, the researchers can consider performing a sensitivity analysis to compare results with and without the 2020-2021 data to see if there was a significant disruption in spatial trends during the pandemic. Researchers can also consider implementing a multivariable model to account for potential confounders that are not captured in their current model, controlling for factors such as temperature, humidity, and socioeconomic conditions to more effectively isolate the relationship between animal contact and geographic clusters. In-depth analysis that lowers the risk of ecological fallacy may also require more localized geospatial detection to further understand the outbreak dynamics in affected populations. Researchers may also consider addressing the potential biases in their discussion, clarifying the confounding effect between more robust surveillance systems and regions with high animal density and opportunities for contact (petting zoos, high tourism density). 

Competing Interests Statement

Authors declare no competing interests.

Competing interests

The author declares that they have no competing interests.

Use of Artificial Intelligence (AI)

The author declares that they used generative AI to come up with new ideas for their review.