Background Enteroaggregative Escherichia coli (EAEC) is increasingly recognized as an enteric pathogen as clinical laboratories transition to culture-independent diagnostic tests that detect EAEC. To date, epidemiological studies have focused on children aged <5 years, and information on EAEC incidence, illness outcomes, and transmission avenues is limited. Methods Enteric disease surveillance data in Minnesota were used to describe EAEC illnesses reported to the Minnesota Department of Health from September 2016 through August 2017. We determined laboratory characteristics of EAEC using pulsed-field gel electrophoresis and next-generation sequencing. Frequency of EAEC illness, demographic profile of cases, clinical characteristics of illness, and plausible food or environmental exposures leading to EAEC transmission were assessed. Results During the study period, 329 EAEC cases were reported. Among a subset of health systems able to detect EAEC over the entire study, EAEC was the second most common reportable enteric pathogen detected after Campylobacter and the most detected diarrheagenic E. coli pathotype. No other reportable enteric pathogens were detected among 75.3% of EAEC cases, and 68% of cases reported no international travel before onset. Several virulence genes were associated with clinical characteristics. Conclusions We provide evidence that EAEC is a likely causative agent of diarrheal illness in the United States. Our study contributes to criteria development for identification of pathogenic EAEC and proposes potential exposure avenues.

Salmonella is a common cause of foodborne illness in the U.S. and often is attributed to chicken products. Previous studies have associated Salmonella contamination with meat processing facility characteristics such as the number of establishment employees (i.e., HACCP size). An evaluation of risk factors for Salmonella contamination in U.S. poultry has not been performed since implementation of the New Poultry Inspection System (NPIS) in 2014. The goal of this study was to determine if risk factors for Salmonella contamination changed following implementation of NPIS. Presence/absence of Salmonella in whole chicken carcasses was modeled using microbiological testing data collected from 203 poultry processing establishments by the U.S. Department of Agriculture’s Food Safety and Inspection Service (USDA-FSIS) between May 2015 and December 2019. A model was fit using generalized estimating equations for weekly presence/absence of Salmonella with production volume, geographic location, and season included as potential covariates, among other establishment demographics. Odds ratios (OR) and 95% confidence intervals (CI) were calculated from the marginal model. Of the 40,497 analyzable samples, 1,725 (4.26%) were positive for Salmonella. Odds of contamination was lower among establishments slaughtering ≥ 10,000,000 birds per year (OR = 0.466; 95% CI: [0.307,0.710]) and establishments producing ready-to-eat (RTE) finished products (OR = 0.498; 95% CI: [0.298,0.833]) while higher among establishments historically (previous 84-days) noncompliant with HACCP (OR = 1.249; 95% CI: [1.071,1.456]). Contamination also significantly varied by season and geographic region, with higher odds of contamination during summer and outside the Mid-East Central region. These results support continuation of targeted food safety policies and initiatives promoting pathogen reduction by smaller-volume establishments and those noncompliant with HACCP regulations.

Individual burden and cost of hemolytic uremic syndrome (HUS)—a medical condition characterized by acute kidney failure—can be substantial when accounting for long-term health outcomes (LTHOs). Because of the low incidence of HUS, evaluation of associated LTHOs is often restricted to physician and outbreak cohorts, both of which may not be representative of all HUS cases. This exploratory study recruited participants from private social media support groups for families of HUS cases to identify potential LTHOs and costs of HUS that are not currently measured. Additionally, this study sought to identify case characteristics that may confound or modify these LTHOs and costs of HUS. Respondents self-selected to complete an online cross-sectional survey on acute and chronic illness history, treatments, and public health follow-up for HUS cases. Posttraumatic stress among respondents (typically case parents) was also evaluated. Responses were received for 74 HUS cases from 71 families representing all geographic regions, and levels of urbanicity within the US self-reported symptoms were typical for HUS, while 35.1% of cases reported antibiotic treatment at any point during the acute illness. Hospital transfers were reported by 71.6% of cases introducing possible delays to care. More than 70% of cases reported experiencing at least one LTHO, with 45% of cases reporting renal sequelae. Posttraumatic stress symptoms were frequently reported by respondents indirectly affected by HUS. Potentially large economic costs that are not addressed in existing analyses were identified including both financial and more general welfare losses (lost utility). While biases in the study design limit the generalizability of results to all HUS cases, this study provides new insights into unmeasured LTHOs and costs associated with HUS. These results suggest that robustly designed cohort studies on HUS should include measures of psychosocial impacts on both the affected individual and their family members.

The complexity of the food system makes analyzing microbiological data from food studies challenging because many of the assumptions (e.g., linear relationship between independent and dependent variable and independence of observations) associated with common analytical approaches (e.g., analysis of variance) are violated. Repeated sampling within an establishment introduces longitudinal correlation that must be accounted for during analyses. In this study, statistical methods for clustered or correlated data were used to determine how correlation impacts conclusions and to compare how assumptions associated with statistical methods impact the appropriateness of these methods within the context of food safety. Risk factor analyses for Salmonella contamination of whole chicken carcasses were conducted as a case study with regulatory data collected by the U.S. Department of Agriculture Food Safety and Inspection Service between May 2015 and December 2019 from 203 regulated establishments. Three models, generalized estimating equation, random effects, and logistic, were fit to Salmonella presence or absence data with establishment demographics and inspection history included as potential covariates. Beta parameter estimates and their standard errors and odds ratios and their 95% confidence intervals were compared across models. Conclusions drawn from the three models differed with respect to geographic region, whether the chicken establishment also slaughters turkeys, and establishment noncompliance with 9 CFR §417.4 (hazard analysis critical control point system validation, verification, and reassessment) in the 84 days leading up to sample collection. The results of this study reveal the need to consider clustering and correlation when analyzing food microbiological data, provide context for selecting a statistical method, and suggest that generalized estimating equation and random effects models are preferrable over logistic regression when analyzing correlated food data. These results support a renewed focus on statistical methodology in food safety. HIGHLIGHTS