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Provision of safe drinking water in the United States is a great public health achievement. However, new waterborne disease challenges have emerged (e.g., aging infrastructure, chlorine-tolerant and biofilm-related pathogens, increased recreational water use). Comprehensive estimates of the health burden for all water exposure routes (ingestion, contact, inhalation) and sources (drinking, recreational, environmental) are needed. We estimated total illnesses, emergency department (ED) visits, hospitalizations, deaths, and direct healthcare costs for 17 waterborne infectious diseases. About 7.15 million waterborne illnesses occur annually (95% credible interval [CrI] 3.88 million-12.0 million), results in 601,000 ED visits (95% CrI 364,000-866,000), 118,000 hospitalizations (95% CrI 86,800-150,000), and 6,630 deaths (95% CrI 4,520-8,870) and incurring US $3.33 billion (95% CrI 1.37 billion-8.77 billion) in direct healthcare costs. Otitis externa and norovirus infection were the most common illnesses. Most hospitalizations and deaths were caused by biofilm-associated pathogens (nontuberculous mycobacteria, Pseudomonas, Legionella), costing US $2.39 billion annually.

Estimating the number of illnesses caused by foodborne pathogens is critical for allocating resources and prioritizing interventions. We estimated the number of illnesses, hospitalizations, and deaths in the United States caused by 7 major foodborne pathogens by using surveillance data and other sources, adjusted for underreporting and underdiagnosis. Campylobacter spp., Clostridium perfringens, invasive Listeria monocytogenes, norovirus, nontyphoidal Salmonella serotypes, and Shiga toxin-producing Escherichia coli caused ≈9.9 million (90% credible interval [CrI] 5.9-15.4 million) domestically acquired foodborne illnesses in 2019. Together with Toxoplasma gondii, those pathogens caused 53,300 (90% CrI 35,700-74,500) hospitalizations and 931 (90% CrI 530‒1,460) deaths. Norovirus caused most illnesses (≈5.5 million illnesses, 22,400 hospitalizations), followed by Campylobacter spp. (1.9 million illnesses, 13,000 hospitalizations) and nontyphoidal Salmonella serotypes (1.3 million illnesses, 12,500 hospitalizations). Salmonella infection was the leading cause of death (n = 238). Foodborne illness estimates can inform policy and direct food safety interventions that reduce those illnesses.

Few precise estimates of hospitalization and fatality rates from COVID-19 exist for naive populations, especially within demographic subgroups. We estimated rates among persons with SARS-CoV-2 infection in the United States during May 1-December 1, 2020, before vaccines became available. Both rates generally increased with age; fatality rates were highest for persons >85 years of age (24%) and lowest for children 1-14 years of age (0.01%). Age-adjusted case hospitalization rates were highest for African American or Black, not Hispanic persons (14%), and case-fatality rates were highest for Asian or Pacific Islander, not Hispanic persons (4.4%). Eighteen percent of hospitalized patients and 44.2% of those admitted to an intensive care unit died. Male patients had higher hospitalization (6.2% vs. 5.2%) and fatality rates (1.9% vs. 1.5%) than female patients. These findings highlight the importance of collecting surveillance data to devise appropriate control measures for persons in underserved racial/ethnic groups and older adults.

Salmonella enterica bacteria are a leading cause of foodborne illness in the United States; however, most Salmonella illnesses are not associated with known outbreaks, and predicting the source of sporadic illnesses remains a challenge. We used a supervised random forest model to determine the most likely sources responsible for human salmonellosis cases in the United States. We trained the model by using whole-genome multilocus sequence typing data from 18,661 Salmonella isolates from collected single food sources and used feature selection to determine the subset of loci most influential for prediction. The overall out-of-bag accuracy of the trained model was 91%; the highest prediction accuracy was for chicken (97%). We applied the trained model to 6,470 isolates from humans with unknown exposure to predict the source of infection. Our model predicted that >33% of the human-derived Salmonella isolates originated from chicken and 27% were from vegetables.

Illnesses transmitted by food and water cause a major disease burden in the United States despite advancements in food safety, water treatment, and sanitation. We report estimates from a structured expert judgment study using 48 experts who applied Cooke's classical model of the proportion of disease attributable to 5 major transmission pathways (foodborne, waterborne, person-to-person, animal contact, and environmental) and 6 subpathways (food handler-related, under foodborne; recreational, drinking, and nonrecreational/nondrinking, under waterborne; and presumed person-to-person-associated and presumed animal contact-associated, under environmental). Estimates for 33 pathogens were elicited, including bacteria such as Salmonella enterica, Campylobacter spp., Legionella spp., and Pseudomonas spp.; protozoa such as Acanthamoeba spp., Cyclospora cayetanensis, and Naegleria fowleri; and viruses such as norovirus, rotavirus, and hepatitis A virus. The results highlight the importance of multiple pathways in the transmission of the included pathogens and can be used to guide prioritization of public health interventions.

We fit a power law distribution to US foodborne disease outbreaks to assess underdetection and underreporting. We predicted that 788 fewer than expected small outbreaks were identified annually during 1998-2017 and 365 fewer during 2018-2019, after whole-genome sequencing was implemented. Power law can help assess effectiveness of public health interventions.

We sought to determine which Salmonella serotypes cause illness related to the Thanksgiving holiday in the United States and to foods disproportionately eaten then (e.g., turkey). Using routine surveillance for 1998-2018 and a case-crossover design, we found serotype Reading to be most strongly associated with Thanksgiving.

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.

Foodborne outbreaks reported to national surveillance systems represent a subset of all outbreaks in the United States; not all outbreaks are detected, investigated, and reported. We described the structural factors and outbreak characteristics of outbreaks reported during 2009-2018. We categorized states (plus DC) as high (highest quintile), middle (middle 3 quintiles), or low (lowest quintile) reporters on the basis of the number of reported outbreaks per 10 million population. Analysis revealed considerable variation across states in the number and types of foodborne outbreaks reported. High-reporting states reported 4 times more outbreaks than low reporters. Low reporters were more likely than high reporters to report larger outbreaks and less likely to implicate a setting or food vehicle; however, we did not observe a significant difference in the types of food vehicles identified. Per capita funding was strongly associated with increased reporting. Investments in public health programming have a measurable effect on outbreak reporting.