I have been speaking often about Salmonella and the subject of attribution data always comes up, primarily because of the following statement by the FSIS:

“Despite FSIS sampling data showing reductions in Salmonella contamination in poultry products, the current approach to Salmonella has not led to a demonstrable reduction in Salmonella infections.” – FSIS Leadership, 2022

Technically this statement is true, but as it is with all data, there has to be an element of critical thinking and interpretation in order to fully utilize it properly. While “chicken” is the No. 1 source of Salmonella outbreaks, according to CDC attribution data, there is no information on the type of chicken. A quick google search of “Salmonella and Chicken Livers” will land you on an abundance of information about Salmonella outbreaks associated with consumption of chicken livers. This is a substantial source of illness, which isn’t intuitive from “chicken." Fruits and veggies are a close second on the attribution list, but we don’t seem to hear any concerted efforts at the government level to address this. Pork is also high on the list, but there seems to be radio silence about Pork Performance Standards designed to drive down Salmonella in pork. Currently, FSIS has focused enormous resources in addressing “raw, stuffed, breaded chicken breast” because “chicken” is the No. 1 cause of Salmonellosis…but will this move the needle in terms of attribution data? No…the number of illnesses associated with this product and the small per capita consumption is not enough to change attribution data.

I am a data-driven scientist, so I want to break this down a little bit….

First of all, there are great limitations to the attribution data published by the CDC. These data only apply to outbreaks and not sporadic cases, and thus the majority of foodborne illnesses are not considered in this report. There is also a significant disconnect with the human illness isolates and the food isolates. USDA and FDA isolate, conduct WGS, and AMR analysis on all Salmonella positive samples from foods. However, human illnesses are diagnosed at the doctor’s office or hospital most often using a CIDT (culture independent diagnostic test). There is no isolate as this is a rapid immunological or DNA-based test. The doctor and/or hospital most likely will not culture the samples unless there is an issue with AMR and they need to identify an antibiotic. When trends develop, outbreaks are reported to a local health department, but they do not have the time or resources to obtain human isolates. In an outbreak of 100s of illnesses, it is likely that there are less than a dozen isolates from the human cases. This makes it very difficult for the FSIS, FDA and CDC investigative team to identify the exact source. This leads to a reliance on epidemiological data. When a patient is told that they have Salmonella, the first thing that comes to mind is “chicken” or “eggs” and they will recall which poultry product they have eaten overlooking other items of consumption, thus biasing the data.

Another key fact about attribution data is that attribution lags behind product data. This is likely partially due to the epidemiological bias and it takes time for the shift in perceptions to occur, but there are many elements that drive the lag. It is important to note that E. coli O157:H7 was declared an adulterant in ground beef in 1994 and there was zero impact on human attribution data until 2003, thus illustrating the lag. This information isn’t anything that the FDA, FSIS or CDC is unaware of. Anyone involved with outbreak investigations knows the limitations and how to interpret the information.

On April 23, USDA proposed to declare Salmonella an adulterant in breaded stuffed raw chicken products. The amount of time, energy and very limited FSIS resources (labor, laboratory testing, legal interpretation) that is being poured into this one product will be monumental. There are data-driven initiatives that can make a real difference in impacting the number of illnesses. Focusing on the quantification of Salmonella and highly-pathogenic serotypes/strains can genuinely make a difference in preventing illnesses. This proposal from the USDA is open for comments and I urge you to use your voice and data to comment to ensure that data-driven decisions are made to ensure that a real impact on public health can be realized.

Putting attribution data in perspective

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