Monte Carlo Simulation Model for Predicting Salmonella Contamination of Chicken Liver as a Function of Serving Size for Use in Quantitative Microbial Risk Assessment

Colonization, spread and persistence of Salmonella (Typhimurium, Infantis and Reading) in internal organs of broilers

Transfer of Salmonella to internal organs of broilers over a 35 d grow-out period was evaluated. A total of 360 one-day old chicks were placed in 18 floor pens of 3 groups with 6 replicate pens each. On d 0, broilers were orally challenged with a cocktail of Salmonella (equal population of marked serovars; nalidixic acid-resistant S. Typhimurium, rifampicin-resistant S. Infantis, and kanamycin-resistant S. Reading) to have 3 groups: L (low; ∼2 log CFU/bird); M (medium; ∼5 log CFU/bird); and H (High; ∼8 log CFU/bird). On d 2, 7 and 35, 4 birds/pen were euthanized and ceca, liver, and spleen samples were collected aseptically. Gizzard samples (4/pen) were collected on d 35. The concentration of Salmonella in liver and spleen were transformed to binary outcomes (positive and negative) and fitted in glm function of R using cecal Salmonella concentrations (log CFU/g) and inoculation doses (L, M, and H) as inputs. On d 2, H group showed greater (P ≤ 0.05) cecal colonization of all 3 serovars compared to L and M groups. However, M group showed greater (P ≤ 0.05) colonization of all 3 serovars in the liver and spleen compared to L group. Salmonella colonization increased linearly in the ceca and quadratically in the liver and spleen with increasing challenge dose (P ≤ 0.05). On d 35, L group had greater (P ≤ 0.05) S. Infantis colonization in the ceca and liver compared to M and H groups (P ≤ 0.05). Moreover, within each group on d 35, the concentration of S. Reading was greater than those of S. Typhimurium and S. Infantis for all 3 doses in the ceca and high dose in the liver and gizzard (P ≤ 0.05). Salmonella colonization diminished in the ceca, liver, and spleen during grow-out from d 0 to d 35 (P ≤ 0.05). On d 35, birds challenged with different doses of Salmonella cocktail showed a similar total Salmonella spp. population in the ceca (ca. 3.14 log CFU/g), liver (ca. 0.54 log CFU/g), spleen (ca. 0.31 log CFU/g), and gizzard (ca. 0.42 log CFU/g). Estimates from the fitted logistic model showed that one log CFU/g increase in cecal Salmonella concentration will result in an increase in relative risk of liver and spleen being Salmonella-positive by 4.02 and 3.40 times (P ≤ 0.01), respectively. Broilers from H or M group had a lower risk (28 and 23%) of being Salmonella-positive in the liver compared to the L group when the cecal Salmonella concentration is the same (P ≤ 0.05). Oral challenge of broilers with Salmonella spp. with various doses resulted in linear or quadratic increases in Salmonella colonization in the internal organs during early age and these populations decreased during grow-out (d 35). This research can provide guidance on practices to effectively mitigate the risk of Salmonella from chicken parts and enhance public health.

Poultry Food Assess Risk Model for Salmonella and Chicken Gizzards: III. Dose Consumed Step

The Dose Consumed step of the Poultry Food Assess Risk Model (PFARM) for Salmonella and chicken gizzards was presented and compared to the Exposure Assessment step of Quantitative Microbial Risk Assessment (QMRA). The specific objectives were 1) to demonstrate the dose consumed step of PFARM for Salmonella and chicken gizzards; 2) to compare Salmonella dose consumed from cooked chicken gizzards to that from cross-contaminated and temperature-abused lettuce; 3) to determine if Salmonella dose consumed changed over time in a production chain; and 4) to compare PFARM and QMRA predictions of Salmonella dose consumed. The PFARM and QMRA were developed in an Excel notebook and simulated with @Risk. Salmonella prevalence and number data (P = 100) for chicken gizzards (56 g) and scenario analysis were used to address objectives 1, 2, and 4, whereas running windows of 60 consecutive chicken gizzard samples and scenario analysis were used to address objective 3. A lot size of 1,000 kg of chicken gizzards was simulated. Mean portion size was 168 g resulting in the simulation of 5,952 meals per lot. Of these, 3.69 ± 0.32% and 0.49 ± 0.07% (mean ± SD) resulted in Salmonella dose consumed of ≥1 per meal from cooked chicken gizzards and lettuce, respectively. However, the total Salmonella dose consumed per lot from cooked chicken gizzards (272 ± 27) was less (P ≤ 0.05) than from lettuce (6,050 ± 4,929) because of a few highly contaminated (>310 Salmonella) lettuce portions at consumption. Over time in the production chain, Salmonella prevalence and total dose consumed per lot changed (P ≤ 0.05) but the patterns differed. The QMRA predicted higher (P ≤ 0.05) Salmonella dose consumed per meal than PFARM. In part, this was because QMRA only simulated contaminated grams, whereas PFARM simulated contaminated and non-contaminated meals. However, other factors, which are discussed, also contributed to the overestimation of Salmonella dose consumed by QMRA.

Poultry Food Assess Risk Model for Salmonella and Chicken Gizzards: II. Illness Dose Step

The Illness Dose (ID) step of a Poultry Food Assess Risk Model (PFARM) for Salmonella and chicken gizzards (CGs) was shown in the present study. The illness dose is the minimum dose of Salmonella consumed that causes an illness. It depends on zoonotic potential (ZP) of Salmonella, food consumption behavior (FCB), and consumer health and immunity (CHI) or the disease triangle (DT). Zoonotic potential is the ability of Salmonella to survive, grow, and spread in the production chain or food and then cause illness in humans. Illness dose is predicted in PFARM using a DT, dose-response model (DRM) that was developed with human feeding trial (HFT) data and was validated with human outbreak investigation (HOI) data for Salmonella. The ability of the DT, DRM to predict DR data from HOI and HFT for Salmonella was quantified using the Acceptable Prediction Zones (APZ) method where acceptable performance occurred when the proportion of residuals in the APZ (pAPZ) was ≥ 0.7. United States, Centers for Disease Control and Prevention (CDC) data for human salmonellosis from 2007 to 2016 were used to simulate ZP and only minor changes in ZP of 11 Salmonella serotypes were observed during this time. The performance of the DT, DRM for predicting Salmonella DR data from HFT and HOI was acceptable with pAPZ that ranged from 0.87 to 1 for individual serotypes of Salmonella. Simulation results from the DT, DRM in PFARM indicated that ID decreased (P ≤ 0.05) and ZP increased (P ≤ 0.05) over time in the simulated production chain because the main serotype of Salmonella changed from Kentucky (low ZP) to Infantis (high ZP) while FCB and CHI were held constant. These results indicated that the DT, DRM in PFARM can be used with confidence to predict ID as a function of ZP, FCB, and CHI. In other words, the DT, DRM in PFARM can be used with confidence to predict dose-response for Salmonella and CGs.

Integrating life cycle assessment with quantitative microbial risk assessment for a holistic evaluation of sewage treatment plant

An integrated approach was employed in the present study to combine life cycle assessment (LCA) with quantitative microbial risk assessment (QMRA) to assess an existing sewage treatment plant (STP) at Roorkee, India. The midpoint LCA modeling revealed that high electricity consumption (≈ 576 kWh.day^-1) contributed to the maximum environmental burdens. The LCA endpoint result of 0.01 disability-adjusted life years per person per year (DALYs pppy) was obtained in terms of the impacts on human health. Further, a QMRA model was developed based on representative sewage pathogens, including E. coli O157:H7, Giardia sp., adenovirus, norovirus, and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The public health risk associated with intake of pathogen-laden aerosols during treated water reuse in sprinkler irrigation was determined. A cumulative health risk of 0.07 DALYs pppy was obtained, where QMRA risks contributed 86 % of the total health impacts. The annual probability of illness per person was highest for adenovirus and norovirus, followed by SARS-CoV-2, E. coli O157:H7 and Giardia sp. Overall, the study provides a methodological framework for an integrated LCA-QMRA assessment which can be applied across any treatment process to identify the hotspots contributing maximum environmental burdens and microbial health risks. Furthermore, the integrated LCA-QMRA approach could support stakeholders in the water industry to select the most suitable wastewater treatment system and establish regulations regarding the safe reuse of treated water.

Poultry Food Assess Risk Model for Salmonella and Chicken Gizzards: I. Initial Contamination

The Poultry Food Assess Risk Model (PFARM) project was initiated in 1995 to develop data collection and modeling methods for simulating the risk of salmonellosis from poultry food produced by individual production chains. In the present study, the Initial Contamination (IC) step of PFARM for Salmonella and chicken gizzards (CG) was conducted as a case study. Salmonella prevalence (Pr), number (N), and serotype/zoonotic potential (ZP) data (n = 100) for one sample size (56 g) of CG were collected at meal preparation (MP), and then Monte Carlo simulation (MCS) was used to obtain data for other sample sizes (112, 168, 224, 280 g). The PFARM was developed in Excel and was simulated with @Risk. Data were simulated using a moving window of 60 samples to determine how Salmonella Pr, N, and ZP changed over time in the production chain. The ability of Salmonella to survive, grow, and spread in the production chain and food, and then cause disease in humans was ZP, which was based on U. S. Centers for Disease Control and Prevention data for salmonellosis. Of 100 CG samples tested, 35 were contaminated with Salmonella with N from 0 to 0.809 (median) to 2.788 log per 56 g. Salmonella serotype Pr per 56 g was 16% for Kentucky (ZP_mode = 1.1), 9% for Infantis (ZP_mode = 4.4), 6% for Enteritidis (ZP_mode = 5.0), 3% for Typhimurium (ZP_mode = 4.9), and 1% for Thompson (ZP_mode = 3.7). Results from MCS indicated that Salmonella Pr, N, and ZP among portions of CG at MP changed (P ≤ 0.05) over time in the production chain. Notably, the main serotype changed from Kentucky (low ZP) to Infantis (high ZP). However, the pattern of change for Salmonella Pr, N, and ZP differed over time in the production chain and by the statistic used to characterize it. Thus, a performance standard (PS) based on Salmonella Pr, N, or ZP at testing or MP will likely not be a good indicator of poultry food safety or risk of salmonellosis.

Quantitative risk assessment of salmonellosis in Cambodian consumers through chicken and pork salad consumption

Salmonella is a globally important foodborne bacterial pathogen that poses a high risk to human health. This study aimed to estimate the risk to Cambodian consumers from acquiring salmonellosis after consuming chicken and pork salad, using a quantitative microbial risk assessment (QMRA). Chicken and pork salads are typical Cambodian dishes containing raw vegetables and boiled chicken meat or pork. As previously described, chicken meat and pork samples (n = 204 of each) were collected from traditional markets in 25 Cambodian provinces to generate data on Salmonella contamination. Salad preparation and consumption practices were surveyed in 93 Cambodian households and this information was used to design an experiment to assess Salmonella cross-contamination from raw meat to ready-to-eat salad. In the part of the study reported here, data on consumption, Salmonella in salad, dose-response, and predicted salmonellosis were modeled using Monte Carlo simulations at 10,000 iterations. The prevalence of Salmonella in chicken meat and pork were set to 42.6 and 45.1%, respectively, with average most probable number (MPN) per gram of Salmonella in chicken meat was 10.6 and in pork 11.1 MPN/g, based on an earlier study. Half of the interviewed households cooked meat for the salad directly after purchase. The QMRA model showed that the modeled annual risk of salmonellosis from consuming chicken salad, pork salad and both chicken and pork salad were 11.1% probability of illness per person per year (90% CI 0.0–35.1), 4.0% (90% CI 0.0–21.3), and 14.5% (90% CI 0.0–33.5), respectively. The factors most influencing the estimate were cross-contamination while preparing the salad, followed by the prevalence of Salmonella in chicken meat and pork at the market. The wide confidence interval for the incidence was mainly due to the variability in reducing bacteria concentration by cooking and salad consumption. The predicted risk of salmonellosis due to chicken and pork salad consumption is high, and the study provides evidence supporting control measures of improving the safety of retailed chicken and pork obtained from markets to households and improving food preparation methods in the household.