Higher Line Speed in Young Chicken Slaughter Establishments Does Not Predict Increased Salmonella Contamination Risks

INTRODUCTION

In August 2014, the United States Department of Agriculture (USDA), Food Safety and Inspection Service (FSIS) established the New Poultry Inspection System (NPIS) as an additional inspection system for young chicken and all turkey slaughter establishments (U.S. Department of Agriculture, 2014). In poultry inspection systems other than the NPIS, online inspectors are positioned along the slaughter line and are responsible for identifying unacceptable carcasses and parts, examining carcasses for visual defects and directing establishment employees to take appropriate corrective actions if carcass defects can be corrected through trimming and processing. The maximum line speed authorized under these inspection systems reflects the time it takes for an inspector to effectively perform the online inspection procedures required for the system. The fastest line speed authorized for a non-NPIS young chicken inspection system is 140 bpm, which requires four online inspectors with each inspecting 35 bpm. The NPIS system, described next, enables more efficient inspection and faster line speeds in many establishments, raising the question of whether NPIS young chicken establishments can maintain process control at higher line speeds, keeping microbial hazards such as contamination of carcasses with Salmonella no greater than in establishments with lower line speeds. This paper presents data and analyses to help answer that question.

Under the NPIS, establishment employees sort carcasses and remove unacceptable carcasses and parts before the birds are presented to a single online carcass inspector located at the end of the line just before the chiller (U.S. Department of Agriculture, 2014). Because the online inspector under the NPIS is presented with carcasses that have been sorted, washed, and trimmed by establishment employees, and are thus much more likely to pass inspection, the inspector is able to conduct a more efficient and effective online inspection of each bird processed. The NPIS was informed by FSIS’s experience under the Hazard Analysis and Critical Control Point (HACCP)-Based Inspection Models Project (HIMP) pilot study (U.S. Department of Agriculture, 2011a). FSIS’s experience under the HIMP pilot showed that when presented with young chicken carcasses that have been sorted, washed, and trimmed by establishment employees, a single online inspector positioned at the end of the line is able to conduct an effective online inspection of each carcass at line speeds of up to 175 bpm, and that establishments participating in the HIMP pilot were able to maintain process control when operating at the line speeds authorized under HIMP (U.S. Department of Agriculture, 2014). Based on its experience under HIMP, FSIS initially proposed 175 bpm as the maximum line speed for young chicken establishments operating under the NPIS (U.S. Department of Agriculture, 2012). However, after considering public comments on the proposed NPIS, FSIS determined that it was important to assess young chicken establishments’ ability to maintain process control as they implement changes to operate under the NPIS (U.S. Department of Agriculture, 2014). Therefore, FSIS established 140 bpm as the maximum line speed for young chicken establishments operating under the NPIS, instead of 175 bpm as was proposed, with an exception for the 20 young chicken establishments that had participated in the HIMP pilot (U.S. Department of Agriculture, 2014). FSIS granted regulatory waivers under the Agency’s Salmonella Initiative Program (SIP)to allow the 20 former HIMP establishments to continue to operate at line speeds up to 175 bpm because data from the HIMP pilot demonstrated that they were capable of consistently producing safe, wholesome, and unadulterated product and meeting Salmonella reduction performance standards at line speeds up to 175 bpm (U.S. Department of Agriculture, 2014).

Under FSIS’s SIP, meat and poultry slaughter establishments receive waivers of regulatory requirements on condition that they will conduct regular microbial testing and share the resulting data with FSIS (U.S. Department of Agriculture, 2011b). The SIP benefits public health in that establishments operating under SIP waivers are required to conduct testing for microbial pathogens, which is a key feature of effective process control, and to respond to testing results by taking steps, when necessary, to regain process control. SIP establishments test for Salmonella, Campylobacter (if applicable), and generic E. coli or other indicator organism, and share all sample results with FSIS (U.S. Department of Agriculture, 2011b).

When FSIS established 140 bpm as the maximum line speed for young chicken establishments under the NPIS, the Agency made clear that it would continue to consider line speeds at which establishments operating under NPIS are capable of producing safe, wholesome, and unadulterated product and are meeting pathogen reduction and other performance standards (U.S. Department of Agriculture, 2014). Therefore, in February 2018, after FSIS had at least a year’s worth of demonstrated process control history for many young chicken establishments operating under the NPIS, the Agency announced that it had decided to consider requests for waivers from other young chicken establishments in addition to the 20 former HIMP establishments, to operate at line speeds up to 175 bpm (U.S. Department of Agriculture, 2018a). FSIS also determined that, in addition to participating in the SIP, young chicken establishments would need to meet additional criteria in order to be eligible for a line speed waiver (U.S. Department of Agriculture, 2018a, 2018b).FSIS required these additional criteria to ensure that establishments operating under line speed waivers incorporate measures into their food safety systems to maintain process control when operating at faster line speeds (U.S. Department of Agriculture, 2018b).

Specifically, in addition to participating in the SIP, to be eligible for a new line speed waiver, an establishment:

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Must have been operating under the NPIS for at least one year;

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Must be in FSIS’s Salmonella performance standard category 1 or 2 for young chicken carcasses. FSIS continuously samples (up to five times per month) poultry establishments producing young chicken and turkey carcasses, and raw chicken parts, so that it can more closely monitor an establishment’s process control over time. FSIS also continuously samples not-ready-to-eat (NRTE) comminuted chicken and turkey products for Salmonella. FSIS uses these Salmonella sampling results to assess establishment performance during a reference period of one completed 52-week moving window based on a 3-category system. Establishments at or below half of the performance standard over the previous moving window are placed in Category 1, establishments that have a higher Salmonella positive rate required for Category 1 but that are at or below the standard in that period are placed in Category 2, and those that fail the standard in the previous moving window are placed in Category 3. To be categorized, establishments have to meet a minimum number of samples in the 52-week window (11 for broiler carcasses) not including follow-up sampling for positives. Additionally, FSIS sets the allowable positives for small sample numbers, so that smaller establishments are not at a disadvantage for having fewer collected samples;

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Must have a demonstrated history of regulatory compliance. For line speed waiver purposes, a demonstrated history of regulatory compliance means that the establishment has not received a public health regulation alert issued from FSIS’s Public Health Information System (PHIS) for non-compliance with public health regulations in the last 120 days; has not had an enforcement action as a result of a Food Safety Assessment (FSA) conducted in the last 120 days; has not been the subject of a public health related enforcement action in the last 120 days; and must not have had a non-compliance record for violation of FSIS’s good commercial practices (GCPs) regulations (9 CFR 381.65(b)) in the last 120 days; and

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Must be able to demonstrate that the new equipment, technologies, or procedures that allow the establishment to operate at faster line speeds will maintain or improve food safety (U.S. Department of Agriculture, 2018a, 2018b).

In addition, instead of conducting weekly testing for indicator organisms and daily test for Salmonella, which had typically been required under the SIP, NPIS young chicken establishments that are granted new line speed waivers must conduct daily aerobic plate count (APC) testing and at least weekly testing for Salmonella and share the results with Sisals a condition of their waiver (U.S. Department of Agriculture, 2018b). All establishments granted line speed waivers are required to routinely operate at least one line above 140 bpm on average, but not higher than 175 bpm (U.S. Department of Agriculture, 2018b).

FSIS decided to provide for additional line speed waivers so that the Agency could use data collected from establishments that were granted new waivers, along with data collected from the 20 former HIMP establishments currently operating under line speed waivers, to assess the ability of NPIS young chicken establishments to maintain process control at higher line speeds and to inform future rulemaking, if supported (U.S. Department of Agriculture, 2018b). To ensure that the data collected from all NPIS establishments with line speed waivers were comparable, the former HIMP young chicken establishments were given 120 days to meet the additional criteria that the other NPIS establishments were required to meet to be eligible for a waiver (U.S. Department of Agriculture, 2018b). All but two of the 20 former HIMP establishments met these criteria and maintain their line speed waivers. (One establishment was in Salmonella category 3 and the other was not using its waiver to operate faster than 140 bpm) (U.S. Department of Agriculture, 2018b).

As discussed next, data were collected from all young chicken slaughter establishments operating under the NPIS from July 2, 2018 to July 12, 2019, including those with line speed waivers. The data were used to determine how higher line speeds under the NPIS affect microbial safety outcomes, as measured by positive Salmonella samples and non-compliances related to food safety and process control. Because establishments must meet certain criteria to be eligible for a line speed waiver, the data collected from establishments operating at line speeds faster than 140 bpm reflect the joint effects of both line speed and any responses to the criteria specified in their waiver. The former is the primary focus of this analysis.

As discussed below, this study has compared the frequencies of Salmonella positives across NPIS young chicken establishments with varying line speeds. Although they were permitted to do so, not all establishments operating under line speed waivers chose to operate at the maximum line speed of 175 bpm. Establishments consider a number of factors to determine their line speed, including their equipment and facilities, bird size and flock condition, and their ability to maintain process control (i.e. adhere to the conditions of their waiver) when operating at a given line speed. In addition, establishments operating under the NPIS consider the number of employees that have been trained and are available to conduct carcass sorting when determining line speed.

MATERIALS AND METHODS

Data Analysis

We applied several multivariate statistical and machine learning methods (classification and regression (CART) trees, random forest ensembles, partial dependence plots, and Bayesian network learning algorithms) to seek statistically significant dependence relationships among variables and to study how the frequencies of positive Salmonella samples vary with line speed, given the values of other variables. We used the free in-browser Causal Analytics Toolkit (CAT) analytics software (available at http://cox-associates.com:8899/), which includes on-line documentation for all statistical and machine learning packages used; all of the component packages and detailed documentation are provided in the CRAN repository (https://cran.r-project.org/).These methods are described further in (Cox, 2018) and the references therein. The scatter plot and regression lines in Figure 3 were produced with the Statistica statistical computing software (www.statsoft.de/en/statistica/statistica-software); any standard statistics software would produce similar results.

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Figure 3. Scatter plot of mean values of Salmonella against line speed, with quadratic regression curve and 95% confidence bands. Data points represent establishments. The data do not reject the null hypothesis that Salmonella risk has the same value (e.g., 0.04) for all line speeds. Units are bpm for line speed and relative frequency for Salmonella. (This plot was generated using the Statistica statistics software package.)

RESULTS

Regression Models

Figure 2 shows relative frequency histograms of speed (upper left, with the tall spike near the middle at 140 bpm) and average frequency of positive Salmonella samples for the establishments (lower right, horizontal scale at top for legibility). The lower left panel shows a scatterplot of these two variables, with each point representing one establishment. A non-parametric regression curve (kernel density smoothing regression) is superimposed on it to indicate visually how average frequency of positive Salmonella samples vary with observed line speeds at each establishment(allowing other variables to change, unlike the PDP in Figure 1). This curve is approximately a flat horizontal line, although it is difficult to discern whether there might be a significant trend that is obscured by random variations. The upper right corner shows that the linear (Pearson’s) correlation coefficient between line speed and Salmonella is 0.00 to two significant digits (0.0047 to four significant digits), and not statistically significantly different from 0.

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Figure 2. Histograms (with kernel density overlays), scatterplot, and correlation for line speed and Salmonella (mean positive Salmonella count) for 97 establishments. The histograms show relative proportions of the establishments. Units for the axes (at the top right and bottom left) are bpm for line speed and relative frequency for Salmonella. Horizontal axes: Line speeds in birds-per-minute (bpm) for the two left panels (upper panel shows a histogram of relative number of establishments with different line speeds; lower left panel shows Salmonella contamination rates (y axis) vs. line spped in bpm. The lower right panel shows a histogram for relative number of establishments (vertical axis) with different contamination levels (horozontal axis, scale at top right). Upper right panel has no figure, but shows the linear correlation (0.0047, not significantly different from 0) between line speed and Salmonella contamination rates in carcasses from different establishments.

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Figure 1. A partial dependence plot (PDP) for the dependence of Salmonella on speed. (This PDP was generated by the Causal Analytics Toolkit (CAT) software at http://cox-associates.com:8899/ using the randomForest package in R.) Units are bpm for line speed and relative frequency for Salmonella.

Figure 3shows an expanded view of this scatterplot and fits three parametric regression models – linear, quadratic, and cubic – and their 95% confidence bands to it. The best-fitting (ordinary least squares) regression line is approximately a horizontal line at height 0.04 positive Salmonella samples per establishment per day, i.e., Salmonella appears to be independent of line speed. (This is the red regression line in Figure 3.) The best-fitting quadratic and cubic models (and also quartic and quintic polynomial regression models, not shown) lie nearly on top of each other and hence are visually indistinguishable in Figure 3. They all correspond to the solid curve, with wide, flat, peak at about 120 bpm. The null hypothesis of no relation (i.e., a flat horizontal line, slope = 0) cannot be rejected by these data and models, since a horizontal line at height of 0.04 positive counts per establishment-day fits between the upper and lower confidence bands for all models. (The narrowest pair of confidence bands, indicated by the pair of dashed curves that extends beyond the data points on the left and right, is for the linear model. The pair of confidence bands with the wide bulge at 100 bpm is for the cubic model. The remaining pair is for the quadratic model.) There is no significant departure from a zero slope (no statistical dependence) between Salmonella and line speed in these parametric regression models. The same is found in multivariate linear regression modeling and also in non-parametric classification and regression tree (CART analyses (not shown): none of the other variables in Table 1 is identified as a significant predictor of Salmonella (or Campylobacter).

DISCUSSION

More than three decades ago, the National Research Council (NRC) wrote that “Before it can be stated with assurance that line speed is irrelevant from a public health perspective, more studies on this subject need to be conducted. Current evidence seems to indicate, however, that the mix of changing conditions in production and slaughter—including accelerated line speeds—results in a product that is not contaminated more often than it was before line speeds were increased” (National Research Council, 1987). The analyses presented here indicate that today’s establishments running at higher line speeds do not cause increased Salmonella risk under the conditions present during this study. Although there has been useful clarification and much discussion of various potential sources of risk at higher line speed in regulatory, industry, and food safety circles in the decades since the NRC report, including discussions of occupational safety (Ronholm, 2018), to our knowledge this is the first study to provide recent survey data on today’s establishments addressing the issue of line speed and Salmonella contamination rates on chicken carcasses.

Due to the type and availability of data, this study has several limitations. No data were available to identify potential causal drivers of Salmonella such as changes in temperatures, flock health, inspection practices, or other unmeasured variables. Therefore this analysis focused on whether differences in line speed predicted significant differences in Salmonella and other microbial outcomes under the conditions of the study.(There were insufficient data on Campylobacter to conduct a useful analysis, or to perform detailed intervention (interrupted time series) analyses.)The very large differences in average Salmonella-positive rates across establishments (Figure 3) invite further analysis into causes and effective controls, but such analysis is outside the intended scope of this study.

Uncited reference

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