Risk Factors for Non-O157 Shiga Toxin-Producing Escherichia coli Infections, United States.

Shiga toxin-producing Escherichia coli (STEC) causes acute diarrheal illness. To determine risk factors for non-O157 STEC infection, we enrolled 939 patients and 2,464 healthy controls in a case-control study conducted in 10 US sites. The highest population-attributable fractions for domestically acquired infections were for eating lettuce (39%), tomatoes (21%), or at a fast-food restaurant (23%). Exposures with 10%-19% population attributable fractions included eating at a table service restaurant, eating watermelon, eating chicken, pork, beef, or iceberg lettuce prepared in a restaurant, eating exotic fruit, taking acid-reducing medication, and living or working on or visiting a farm. Significant exposures with high individual-level risk (odds ratio >10) among those >1 year of age who did not travel internationally were all from farm animal environments. To markedly decrease the number of STEC-related illnesses, prevention measures should focus on decreasing contamination of produce and improving the safety of foods prepared in restaurants.

Deaths in Children and Adolescents Associated With COVID-19 and MIS-C in the United States.

OBJECTIVES: To describe the demographics, clinical characteristics, and hospital course among persons <21 years of age with a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-associated death. METHODS: We conducted a retrospective case series of suspected SARS-CoV-2-associated deaths in the United States in persons <21 years of age during February 12 to July 31, 2020. All states and territories were invited to participate. We abstracted demographic and clinical data, including laboratory and treatment details, from medical records. RESULTS: We included 112 SARS-CoV-2-associated deaths from 25 participating jurisdictions. The median age was 17 years (IQR 8.5-19 years). Most decedents were male (71, 63%), 31 (28%) were Black (non-Hispanic) persons, and 52 (46%) were Hispanic persons. Ninety-six decedents (86%) had at least 1 underlying condition; obesity (42%), asthma (29%), and developmental disorders (22%) were most commonly documented. Among 69 hospitalized decedents, common complications included mechanical ventilation (75%) and acute respiratory failure (82%). The sixteen (14%) decedents who met multisystem inflammatory syndrome in children (MIS-C) criteria were similar in age, sex, and race and/or ethnicity to decedents without MIS-C; 11 of 16 (69%) had at least 1 underlying condition. CONCLUSIONS: SARS-CoV-2-associated deaths among persons <21 years of age occurred predominantly among Black (non-Hispanic) and Hispanic persons, male patients, and older adolescents. The most commonly reported underlying conditions were obesity, asthma, and developmental disorders. Decedents with coronavirus disease 2019 were more likely than those with MIS-C to have underlying medical conditions.

An Updated Scheme for Categorizing Foods Implicated in Foodborne Disease Outbreaks: A Tri-Agency Collaboration.

BACKGROUND: Foodborne disease data collected during outbreak investigations are used to estimate the percentage of foodborne illnesses attributable to specific food categories. Current food categories do not reflect whether or how the food has been processed and exclude many multiple-ingredient foods. MATERIALS AND METHODS: Representatives from three federal agencies worked collaboratively in the Interagency Food Safety Analytics Collaboration (IFSAC) to develop a hierarchical scheme for categorizing foods implicated in outbreaks, which accounts for the type of processing and provides more specific food categories for regulatory purposes. IFSAC also developed standard assumptions for assigning foods to specific food categories, including some multiple-ingredient foods. The number and percentage of outbreaks assignable to each level of the hierarchy were summarized. RESULTS: The IFSAC scheme is a five-level hierarchy for categorizing implicated foods with increasingly specific subcategories at each level, resulting in a total of 234 food categories. Subcategories allow distinguishing features of implicated foods to be reported, such as pasteurized versus unpasteurized fluid milk, shell eggs versus liquid egg products, ready-to-eat versus raw meats, and five different varieties of fruit categories. Twenty-four aggregate food categories contained a sufficient number of outbreaks for source attribution analyses. Among 9791 outbreaks reported from 1998 to 2014 with an identified food vehicle, 4607 (47%) were assignable to food categories using this scheme. Among these, 4218 (92%) were assigned to one of the 24 aggregate food categories, and 840 (18%) were assigned to the most specific category possible. CONCLUSIONS: Updates to the food categorization scheme and new methods for assigning implicated foods to specific food categories can help increase the number of outbreaks attributed to a single food category. The increased specificity of food categories in this scheme may help improve source attribution analyses, eventually leading to improved foodborne illness source attribution estimates and enhanced food safety and regulatory efforts.