Understanding disparities in salmonellosis burden is critical for developing effective, equitable prevention programs. Past efforts to characterize disparities were limited in scope and by the analytical methods available when the study was conducted. We aim to address this gap by identifying disparities in salmonellosis incidence between counties with different determinants of health (DOH) profiles. Using national U.S. Laboratory-based Enteric Disease Surveillance (LEDS) data for 1997-2019, age-adjusted county-level salmonellosis incidence/100,000 persons was calculated and linked to publicly available DOH data. We used hurdle counterfactual random forest (CFRF) to quantify, for each DOH, the risk that (i) ≥1 versus no cases were reported by a county, and (ii) when ≥1 case was reported, whether a high (≥16 cases/100,000 persons) or low incidence (≥1 & <4 cases/100,000 persons) was reported. Risk in both models was significantly associated with demographic DOH, suggesting a disparity between counties with different demographic profiles. Risk was also significantly associated with food, healthcare, physical, and socioeconomic environment. The risk was generally greater for counties with more negative food resources, and for under-resourced counties (e.g., fewer healthcare and social services, fewer grocery stores). Risk was also significantly higher if any extreme weather event occurred. The study also found that underreporting and underascertainment appeared to result in underestimation of salmonellosis incidence in economically marginalized and under-resourced communities. Overall, our analyses indicated that, regardless of other county characteristics, extreme weather was associated with increased salmonellosis incidence, and that certain communities were differentially disadvantaged toward a higher incidence. This information can facilitate the development of community-specific prevention efforts.

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.

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.

Laboratory-based surveillance for enteric pathogens causing diarrhea is foundational for monitoring foodborne diseases in the United States. However, diarrheal illnesses are not always confirmed by laboratory testing, so estimates of the true number of illnesses must adjust for underdiagnosis, including underdiagnosis due to ill persons not seeking medical care or submitting a stool sample for laboratory testing. We assessed these factors among persons with an acute diarrheal illness who responded to the most recent Foodborne Diseases Active Surveillance Network (FoodNet) Population Survey (2018-2019). Multiple modes of administration (telephone, web-based) and multiple sampling frames were used to ask survey respondents in English or Spanish about diarrhea and other symptoms experienced in the 30 days before the interview and to ask if they had sought medical care or submitted a stool sample. Of 1018 respondents with an acute diarrheal illness, 22.0% had sought medical care and 4.7% submitted a stool sample. On multivariable analysis, older adults (aged 65 years and over), male respondents, and persons with a household income of ≥$40,000 per annum were significantly more likely to seek medical care, as were respondents reporting cough, fever, vomiting, recent international travel, or duration of diarrhea for ≥3 days. Older adults and persons with five or more loose stools in 24 h who sought medical care were significantly more likely to submit a stool sample. Ill respondents with a concurrent cough were less likely to submit a stool sample. Sociodemographic characteristics, symptoms, and international travel influence whether a patient with an acute diarrheal illness will seek care or submit a stool specimen. Accounting for these factors when analyzing surveillance data will likely produce more precise estimates of the true number of foodborne illnesses.

OBJECTIVES: Integrating pathogen genomic surveillance with bioinformatics can enhance public health responses by identifying risk and guiding interventions. This study focusses on the two predominant Campylobacter species, which are commonly found in the gut of birds and mammals and often infect humans via contaminated food. Rising incidence and antimicrobial resistance (AMR) are a global concern and there is an urgent need to quantify the main routes to human infection. METHODS: During routine US national surveillance (2009-2019), 8,856 Campylobacter genomes from human infections and 16,703 from possible sources were sequenced. Using machine learning and probabilistic models, we target genetic variation associated with host adaptation to attribute the source of human infections and estimate the importance of different disease reservoirs. RESULTS: Poultry was identified as the primary source of human infections, responsible for an estimated 68% of cases, followed by cattle (28%), and only a small contribution from wild birds (3%) and pork sources (1%). There was also evidence of an increase in multidrug resistance, particularly among isolates attributed to chickens. CONCLUSIONS: National surveillance and source attribution can guide policy, and our study suggests that interventions targeting poultry will yield the greatest reductions in campylobacteriosis and spread of AMR in the US. DATA AVAILABILITY: All sequence reads were uploaded and shared on NCBI's Sequence Read Archive (SRA) associated with BioProjects; PRJNA239251 (CDC / PulseNet surveillance), PRJNA287430 (FSIS surveillance), PRJNA292668 & PRJNA292664 (NARMS) and PRJNA258022 (FDA surveillance). Publicly available genomes, including reference genomes and isolates sampled worldwide from wild birds are associated with BioProject accessions: PRJNA176480, PRJNA177352, PRJNA342755, PRJNA345429, PRJNA312235, PRJNA415188, PRJNA524300, PRJNA528879, PRJNA529798, PRJNA575343, PRJNA524315 and PRJNA689604. Contiguous assemblies of all genome sequences compared are available at Mendeley data (assembled C. coli genomes doi: 10.17632/gxswjvxyh3.1; assembled C. jejuni genomes doi: 10.17632/6ngsz3dtbd.1) and individual project and accession numbers can be found in Supplementary tables S1 and S2, which also includes pubMLST identifiers for assembled genomes. Figshare (10.6084/m9.figshare.20279928). Interactive phylogenies are hosted on microreact separately for C. jejuni (https://microreact.org/project/pascoe-us-cjejuni) and C. coli (https://microreact.org/project/pascoe-us-ccoli).

Over 40% of all U.S. Salmonella illnesses are attributed to consumption of contaminated meat and poultry products each year. Determining which serotypes cause the most outbreak illnesses associated with specific meat and poultry types can inform prevention measures. We developed an approach to categorize serotypes using outbreak illness burden (high, moderate, low) and trajectory (increased, stable, decreased). We used data from 192 foodborne Salmonella outbreaks resulting in 7,077 illnesses, 1,330 hospitalizations, and 9 deaths associated with chicken, turkey, beef, or pork during 2012-2021. We linked each meat and poultry type to 1-3 serotypes that we categorized high outbreak illness burden and increased trajectory during 2021. Calculation and public display of outbreak illness burden and trajectory annually could facilitate prioritization of serotypes for prevention by federal and state health and regulatory agencies and by the meat and poultry industry.

BACKGROUND: In the US, yersinosis was understood to predominantly occur in winter and among Black or African American infants and Asian children. Increased use of culture-independent diagnostic tests (CIDTs) has led to marked increases in yersinosis diagnoses. METHODS: We describe differences in the epidemiology of yersiniosis diagnosed by CIDT versus culture in 10 US sites, and identify determinants of health associated with diagnostic method. RESULTS: Annual reported incidence increased from 0.3/100 000 in 2010 to 1.3/100 000 in 2021, particularly among adults ≥18 years, regardless of race and ethnicity, and during summer months. The proportion of CIDT-diagnosed infections increased from 3% in 2012 to 89% in 2021. An ill person's demographic characteristics and location of residence had a significant impact on their odds of being diagnosed by CIDT. CONCLUSIONS: Improved detection due to increased CIDT use has altered our understanding of yersinosis epidemiology, however differential access to CIDTs may still affect our understanding of yersinosis.

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.

Measles is a highly infectious, vaccine-preventable disease that can cause severe illness, hospitalization, and death. A measles outbreak associated with a migrant shelter in Chicago occurred during February-April 2024, in which a total of 57 confirmed cases were identified, including 52 among shelter residents, three among staff members, and two among community members with a known link to the shelter. CDC simulated a measles outbreak among shelter residents using a dynamic disease model, updated in real time as additional cases were identified, to produce outbreak forecasts and assess the impact of public health interventions. As of April 8, the model forecasted a median final outbreak size of 58 cases (IQR = 56-60 cases); model fit and prediction range improved as more case data became available. Counterfactual analysis of different intervention scenarios demonstrated the importance of early deployment of public health interventions in Chicago, with a 69% chance of an outbreak of 100 or more cases had there been no mass vaccination or active case-finding compared with only a 1% chance when those interventions were deployed. This analysis highlights the value of using real-time, dynamic models to aid public health response, set expectations about outbreak size and duration, and quantify the impact of interventions. The model shows that prompt mass vaccination and active case-finding likely substantially reduced the chance of a large (100 or more cases) outbreak in Chicago.

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.

Information on the causative agent in an enteric disease outbreak can be used to generate hypotheses about the route of transmission and possible vehicles, to guide environmental assessments, and to target outbreak control measures. However, only about 40% of outbreaks reported in the United States include a confirmed etiology. The goal of this project was to identify clinical and demographic characteristics that can be used to predict the causative agent in an enteric disease outbreak and to use these data to develop an online tool for investigators to use during an outbreak when hypothesizing about the causative agent. Using data on enteric disease outbreaks from all transmission routes (animal contact, environmental contamination, foodborne, person-to-person, waterborne, unknown) reported to the U.S. Centers for Disease Control and Prevention, we developed random forest models to predict the etiology of an outbreak based on aggregated clinical and demographic characteristics at both the etiology category (i.e., bacteria, parasites, toxins, viruses) and individual etiology (Clostridium perfringens, Campylobacter, Cryptosporidium, norovirus, Salmonella, Shiga toxin-producing Escherichia coli, and Shigella) levels. The etiology category model had a kappa of 0.85 and an accuracy of 0.92, whereas the etiology-specific model had a kappa of 0.75 and an accuracy of 0.86. The highest sensitivities in the etiology category model were for bacteria and viruses; all categories had high specificities (>0.90). For the etiology-specific model, norovirus and Salmonella had the highest sensitivity and all etiologies had high specificities. When laboratory confirmation is unavailable, information on the clinical signs and symptoms reported by people associated with the outbreak, with other characteristics including case demographics and illness severity, can be used to predict the etiology or etiology category. An online publicly available tool was developed to assist investigators in their enteric disease outbreak investigations.

BACKGROUND: Nontyphoidal Salmonella causes an estimated 1.35 million U.S. infections annually. Antimicrobial-resistant strains are a serious public health threat. We examined the association between resistance and the clinical outcomes of hospitalization, length-of-stay ≥3 days, and death. METHODS: We linked epidemiologic data from the Foodborne Diseases Active Surveillance Network with antimicrobial resistance data from the National Antimicrobial Resistance Monitoring System (NARMS) for nontyphoidal Salmonella infections from 2004-2018. We defined any resistance as resistance to ≥1 antimicrobial and clinical resistance as resistance to ampicillin, azithromycin, ceftriaxone, ciprofloxacin, or trimethoprim-sulfamethoxazole (for the subset of isolates tested for all five agents). We compared outcomes before and after adjusting for age, state, race/ethnicity, international travel, outbreak association, and isolate serotype and source. RESULTS: Twenty percent of isolates (1,105/5,549) had any resistance and 16% (469/2,969) had clinical resistance. Persons whose isolates had any resistance were more likely to be hospitalized (31% vs. 28%, P=0.01) or have length-of-stay ≥3 days (20% vs. 16%, P=0.01). Deaths were rare, but more common among those with any than no resistance (1.0% vs. 0.4%, P=0.01). Outcomes for patients whose isolates had clinical resistance did not differ significantly from those with no resistance. After adjustment, any resistance (adjusted odds ratio 1.23, 95% confidence interval 1.04-1.46) remained significantly associated with hospitalization. CONCLUSIONS: We observed a significant association between nontyphoidal Salmonella infections caused by resistant pathogens and likelihood of hospitalization. Clinical resistance was not associated with poorer outcomes, suggesting that factors other than treatment failure (e.g., strain virulence, strain source, host factors) may be important.

BACKGROUND: Culture-independent diagnostic testing (CIDT) provides rapid results to clinicians and is quickly displacing traditional detection methods. Increased CIDT use and sensitivity likely result in higher case detection but might also obscure infection trends. Severe illness outcomes, such as hospitalization and death, are likely less affected by changes in testing practices and can be used as indicators of the expected case incidence trend had testing methods not changed. METHODS: Using US Foodborne Diseases Active Surveillance Network data during 1996-2019 and mixed effects quasi-Poisson regression, we estimated the expected yearly incidence for nine enteric pathogens. RESULTS: Removing the effect of CIDT use, CIDT panel testing and culture-confirmation of CIDT testing, the modelled incidence in all but three pathogens (Salmonella, Shigella, STEC O157) was significantly lower than the observed and the upward trend in Campylobacter was reversed from an observed 2.8% yearly increase to a modelled -2.8% yearly decrease (95% credible interval: -4.0, -1.4). CONCLUSIONS: Severe outcomes may be useful indicators in evaluating trends in surveillance systems that have undergone a marked change.

Listeria monocytogenes can cause severe foodborne illness, including miscarriage during pregnancy or death in newborn infants. When outbreaks of L. monocytogenes illness occur, it may be possible to determine the food source of the outbreak. However, most reported L. monocytogenes illnesses do not occur as part of a recognized outbreak and most of the time the food source of sporadic L. monocytogenes illness in people cannot be determined. In the United States, L. monocytogenes isolates from patients, foods, and environments are routinely sequenced and analyzed by whole genome multilocus sequence typing (wgMLST) for outbreak detection by PulseNet, the national molecular surveillance system for foodborne illnesses. We investigated whether machine learning approaches applied to wgMLST allele call data could assist in attribution analysis of food source of L. monocytogenes isolates. We compiled isolates with a known source from five food categories (dairy, fruit, meat, seafood, and vegetable) using the metadata of L. monocytogenes isolates in PulseNet, deduplicated closely genetically related isolates, and developed random forest models to predict the food sources of isolates. Prediction accuracy of the final model varied across the food categories; it was highest for meat (65%), followed by fruit (45%), vegetable (45%), dairy (44%), and seafood (37%); overall accuracy was 49%, compared with the naive prediction accuracy of 28%. Our results show that random forest can be used to capture genetically complex features of high-resolution wgMLST for attribution of isolates to their sources.

Abstract CDC and health departments investigate foodborne disease outbreaks to identify a source. To generate and test hypotheses about vehicles, investigators typically compare exposure prevalence among case-patients with the general population using a one-sample binomial test. We propose a Bayesian alternative that also accounts for uncertainty in the estimate of exposure prevalence in the reference population. We compared exposure prevalence in a 2020 outbreak of Escherichia coli O157:H7 illnesses linked to leafy greens with 2018-2019 FoodNet Population Survey estimates. We ran prospective simulations using our Bayesian approach at three time points during the investigation. The posterior probability that leafy green consumption prevalence was higher than the general population prevalence increased as additional case-patients were interviewed. Probabilities were >0.70 for multiple leafy green items 2 weeks before the exact binomial p-value was statistically significant. A Bayesian approach to assessing exposure prevalence among cases could be superior to the one-sample binomial test typically used during foodborne outbreak investigations.

BACKGROUND: Campylobacter is the most common cause of bacterial diarrhea in the United States; resistance to macrolides and fluoroquinolones limits treatment options. We examined the epidemiology of US Campylobacter infections and changes in resistance over time. METHODS: The Foodborne Diseases Active Surveillance Network receives information on laboratory-confirmed Campylobacter cases from 10 US sites, and the National Antimicrobial Resistance Monitoring System receives a subset of isolates from these cases for antimicrobial susceptibility testing. We estimated trends in incidence of Campylobacter infection, adjusting for sex, age, and surveillance changes attributable to culture-independent diagnostic tests. We compared percentages of isolates resistant to erythromycin or ciprofloxacin during 2005-2016 with 2017-2018 and used multivariable logistic regression to examine the association of international travel with resistance. RESULTS: Adjusted Campylobacter incidence remained stable or decreased for all groups analyzed since 2012. Among 2449 linked records in 2017-2018, the median patient age was 40.2 years (interquartile range, 21.6-57.8 years), 54.8% of patients were male, 17.2% were hospitalized, and 0.2% died. The percentage of resistant infections increased from 24.5% in 2005-2016 to 29.7% in 2017-2018 for ciprofloxacin (P < .001) and from 2.6% to 3.3% for erythromycin (P = .04). Persons with recent international travel had higher odds than nontravelers of having isolates resistant to ciprofloxacin (adjusted odds ratio [aOR] varied from 1.7 to 10.6 by race/ethnicity) and erythromycin (aOR = 1.7; 95% confidence interval, 1.3-2.1). CONCLUSIONS: Campylobacter incidence has remained stable or decreased, whereas resistance to antimicrobials recommended for treatment has increased. Recent international travel increased the risk of resistance.

This brief methods paper is being published concomitantly with "Preliminary Incidence and Trends of Infections Caused by Pathogens Transmitted Commonly Through Food- Foodborne Diseases Active Surveillance Network, 10 U.S. Sites, 2016-2021" in Morbidity and Mortality Weekly Reports (MMWR). That article describes the application of the new model described here to analyze trends and evaluate progress towards the prevention of infection from enteric pathogens in the United States. Copyright The copyright holder for this preprint is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available for use under a CC0 license.

Each year, infections from major foodborne pathogens are responsible for an estimated 9.4 million illnesses, 56,000 hospitalizations, and 1,350 deaths in the United States (1). To evaluate progress toward prevention of enteric infections in the United States, the Foodborne Diseases Active Surveillance Network (FoodNet) conducts surveillance for laboratory-diagnosed infections caused by eight pathogens transmitted commonly through food at 10 U.S. sites. During 2020-2021, FoodNet detected decreases in many infections that were due to behavioral modifications, public health interventions, and changes in health care-seeking and testing practices during the COVID-19 pandemic. This report presents preliminary estimates of pathogen-specific annual incidences during 2022, compared with average annual incidences during 2016-2018, the reference period for the U.S. Department of Health and Human Services' Healthy People 2030 targets (2). Many pandemic interventions ended by 2022, resulting in a resumption of outbreaks, international travel, and other factors leading to enteric infections. During 2022, annual incidences of illnesses caused by the pathogens Campylobacter, Salmonella, Shigella, and Listeria were similar to average annual incidences during 2016-2018; however, incidences of Shiga toxin-producing Escherichia coli (STEC), Yersinia, Vibrio, and Cyclospora illnesses were higher. Increasing culture-independent diagnostic test (CIDT) usage likely contributed to increased detection by identifying infections that would have remained undetected before widespread CIDT usage. Reducing pathogen contamination during poultry slaughter and processing of leafy greens requires collaboration among food growers and processors, retail stores, restaurants, and regulators.

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.

Consumption of unpasteurised milk in the United States has presented a public health challenge for decades because of the increased risk of pathogen transmission causing illness outbreaks. We analysed Foodborne Disease Outbreak Surveillance System data to characterise unpasteurised milk outbreaks. Using Poisson and negative binomial regression, we compared the number of outbreaks and outbreak-associated illnesses between jurisdictions grouped by legal status of unpasteurised milk sale based on a May 2019 survey of state laws. During 2013-2018, 75 outbreaks with 675 illnesses occurred that were linked to unpasteurised milk; of these, 325 illnesses (48%) were among people aged 0-19 years. Of 74 single-state outbreaks, 58 (78%) occurred in states where the sale of unpasteurised milk was expressly allowed. Compared with jurisdictions where retail sales were prohibited (n = 24), those where sales were expressly allowed (n = 27) were estimated to have 3.2 (95% CI 1.4-7.6) times greater number of outbreaks; of these, jurisdictions where sale was allowed in retail stores (n = 14) had 3.6 (95% CI 1.3-9.6) times greater number of outbreaks compared with those where sale was allowed on-farm only (n = 13). This study supports findings of previously published reports indicating that state laws resulting in increased availability of unpasteurised milk are associated with more outbreak-associated illnesses and outbreaks.

To evaluate progress toward prevention of enteric infections in the United States, the Foodborne Diseases Active Surveillance Network (FoodNet) conducts active population-based surveillance for laboratory-diagnosed infections caused by Campylobacter, Cyclospora, Listeria, Salmonella, Shiga toxin-producing Escherichia coli (STEC), Shigella, Vibrio, and Yersinia at 10 U.S. sites. This report summarizes preliminary 2021 data and describes changes in annual incidence compared with the average annual incidence for 2016-2018, the reference period for the U.S. Department of Health and Human Services' (HHS) Healthy People 2030 goals for some pathogens (1). During 2021, the incidence of infections caused by Salmonella decreased, incidence of infections caused by Cyclospora, Yersinia, and Vibrio increased, and incidence of infections caused by other pathogens did not change. As in 2020, behavioral modifications and public health interventions implemented to control the COVID-19 pandemic might have decreased transmission of enteric infections (2). Other factors (e.g., increased use of telemedicine and continued increase in use of culture-independent diagnostic tests [CIDTs]) might have altered their detection or reporting (2). Much work remains to achieve HHS Healthy People 2030 goals, particularly for Salmonella infections, which are frequently attributed to poultry products and produce, and Campylobacter infections, which are frequently attributed to chicken products (3).

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.

BACKGROUND: Case-control studies are commonly used to explore factors associated with enteric bacterial diseases. Control of confounding is challenging due to the large number of exposures of interest and the low frequencies of many of them. METHODS: We evaluated nearest-neighbors matching in a case-control study (originally 1:1 matched, published in 2004) of sporadic Campylobacter infections that included information on 433 exposures in 2,632 subjects during 1998-1999. We performed multiple imputation of missing data (m=100) and calculated Gower distances between cases and controls using all possible confounders for each exposure in each dataset. We matched each case with ≤20 controls within a data-determined distance. We calculated odds ratios and population attributable fractions (PAFs). RESULTS: Examination of pairwise correlation between exposures found very strong associations for 1,046 pairs of exposures. More than 100 exposures were associated with campylobacteriosis, including nearly all risk factors identified using the previously published approach that included only 16 exposures and some less studied, rare exposures such as consumption of chicken liver and raw clams. Consumption of chicken and non-poultry meat had the highest PAFs (62% and 59%, respectively). CONCLUSIONS: Nearest-neighbors matching appears to provide an improved ability to examine rare exposures and better control for numerous highly associated confounders.

This multi-agency report developed under the Interagency Collaboration for Genomics for Food and Feed Safety (Gen-FS) provides an overview of the use of and transition to Whole-Genome Sequencing (WGS) technology to detect and characterize pathogens transmitted commonly by food and identify their sources. We describe foodborne pathogen analysis, investigation, and harmonization efforts among federal agencies, including the National Institutes of Health (NIH); the Department of Health and Human Services' Centers for Disease Control and Prevention (CDC) and the Food and Drug Administration (FDA); and the U.S. Department of Agriculture's Food Safety and Inspection Service (FSIS), Agricultural Research Service (ARS), and Animal and Plant Health Inspection Service (APHIS). We describe single nucleotide polymorphism (SNP), core-genome (cg) and whole-genome multi-locus sequence typing (wgMLST) data analysis methods as used in CDC's PulseNet and FDA's GenomeTrakr networks, underscoring the complementary nature of the results for linking genetically related foodborne pathogens during outbreak investigations while allowing flexibility to meet the specific needs of Gen-FS agency partners. We highlight how we apply WGS to pathogen characterization (virulence and antimicrobial resistance profiles), source attribution efforts, and increasing transparency by making the sequences and other data publicly available through the National Center for Biotechnology Information (NCBI). Finally, we highlight the impact of current trends in the use of culture-independent diagnostics tests (CIDT) for human diagnostic testing on analytical approaches related to food safety. Lastly, we highlight what is next for WGS in food safety.

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.

Estimates of the overall human health impact of agents transmitted commonly through food complement surveillance and help guide food safety interventions and regulatory initiatives. The purpose of this scoping review was to summarize the methods and reporting practices used in studies that estimate the total number of illnesses caused by these agents. We identified and included 43 studies published from January 1, 1995, to December 31, 2019, by searching PubMed and screening selected articles for other relevant publications. Selected articles presented original estimates of the number of illnesses caused by ≥1 agent transmitted commonly through food. The number of agents (species or subspecies for pathogens) included in each study ranged from 1 to 31 (median: 4.5; mean: 9.2). Of the 40 agents assessed across the 43 studies, the most common agent was Salmonella (36; 84% of studies), followed by Campylobacter (33; 77%), Shiga toxin-producing Escherichia coli (25; 58%), and norovirus (20; 47%). Investigators used a variety of data sources and methods that could be grouped into four distinct estimation approaches-direct, surveillance data scaled-up, syndrome or population scaled-down, and inferred. Based on our review, we propose four recommendations to improve the interpretability, comparability, and reproducibility of studies that estimate the number of illnesses caused by agents transmitted commonly through food. These include providing an assessment of statistical and nonstatistical uncertainty, providing a ranking of estimates by agent, including uncertainties; describing the rationale used to select agents and data sources; and publishing raw data and models, along with clear, detailed methods. These recommendations could lead to better decision-making about food safety policies. Although these recommendations have been made in the context of illness estimation for agents transmitted commonly through food, they also apply to estimates of other health outcomes and conditions.

BACKGROUND: Late sequelae of COVID-19 have been reported; however, few studies have investigated the time-course or incidence of late new COVID-19-related health conditions (post-COVID conditions) after COVID-19 diagnosis. Studies distinguishing post-COVID conditions from late conditions caused by other etiologies are lacking. Using data from a large administrative all-payer database, we assessed the type, association, and timing of post-COVID conditions following COVID-19 diagnosis. METHODS: Using the Premier Healthcare Database Special COVID-19 Release (PHD-SR) (release date, October 20, 2020) data, during March-June 2020, 27,589 inpatients and 46,857 outpatients diagnosed with COVID-19 (case-patients) were 1:1 matched with patients without COVID-19 through the 4-month follow-up period (control-patients) by using propensity score matching. In this matched-cohort study, adjusted odds ratios were calculated to assess for late conditions that were more common in case-patients compared with control-patients. Incidence proportion was calculated for conditions that were more common in case-patients than control-patients during 31-120 days following a COVID-19 encounter. RESULTS: During 31-120 days after an initial COVID-19 inpatient hospitalization, 7.0% of adults experienced at least one of five post-COVID conditions. Among adult outpatients with COVID-19, 7.7% experienced at least one of ten post-COVID conditions. During 31-60 days after an initial outpatient encounter, adults with COVID-19 were 2.8 times as likely to experience acute pulmonary embolism as outpatient control-patients and were also more likely to experience a range of conditions affecting multiple body systems (e.g. nonspecific chest pain, fatigue, headache, and respiratory, nervous, circulatory, and gastrointestinal system symptoms) than outpatient control-patients. Children with COVID-19 were not more likely to experience late conditions than children without COVID-19. CONCLUSIONS: These findings add to the evidence of late health conditions possibly related to COVID-19 in adults following COVID-19 diagnosis and can inform health care practice and resource planning for follow-up COVID-19 care.

Persons from racial and ethnic minority groups are disproportionately affected by COVID-19, including experiencing increased risk for infection (1), hospitalization (2,3), and death (4,5). Using administrative discharge data, CDC assessed monthly trends in the proportion of hospitalized patients with COVID-19 among racial and ethnic groups in the United States during March-December 2020 by U.S. Census region. Cumulative and monthly age-adjusted COVID-19 proportionate hospitalization ratios (aPHRs) were calculated for racial and ethnic minority patients relative to non-Hispanic White patients. Within each of the four U.S. Census regions, the cumulative aPHR was highest for Hispanic or Latino patients (range = 2.7-3.9). Racial and ethnic disparities in COVID-19 hospitalization were largest during May-July 2020; the peak monthly aPHR among Hispanic or Latino patients was >9.0 in the West and Midwest, >6.0 in the South, and >3.0 in the Northeast. The aPHRs declined for most racial and ethnic groups during July-November 2020 but increased for some racial and ethnic groups in some regions during December. Disparities in COVID-19 hospitalization by race/ethnicity varied by region and became less pronounced over the course of the pandemic, as COVID-19 hospitalizations increased among non-Hispanic White persons. Identification of specific social determinants of health that contribute to geographic and temporal differences in racial and ethnic disparities at the local level can help guide tailored public health prevention strategies and equitable allocation of resources, including COVID-19 vaccination, to address COVID-19-related health disparities and can inform approaches to achieve greater health equity during future public health threats.

We compared the characteristics of hospitalized and nonhospitalized patients who had coronavirus disease in Atlanta, Georgia, USA. We found that risk for hospitalization increased with a patient's age and number of concurrent conditions. We also found a potential association between hospitalization and high hemoglobin A1c levels in persons with diabetes.

BACKGROUND: The epidemiological features and outcomes of hospitalized adults with coronavirus disease 2019 (COVID-19) have been described; however, the temporal progression and medical complications of disease among hospitalized patients require further study. Detailed descriptions of the natural history of COVID-19 among hospitalized patients are paramount to optimize health care resource utilization, and the detection of different clinical phenotypes may allow tailored clinical management strategies. METHODS: This was a retrospective cohort study of 305 adult patients hospitalized with COVID-19 in 8 academic and community hospitals. Patient characteristics included demographics, comorbidities, medication use, medical complications, intensive care utilization, and longitudinal vital sign and laboratory test values. We examined laboratory and vital sign trends by mortality status and length of stay. To identify clinical phenotypes, we calculated Gower's dissimilarity matrix between each patient's clinical characteristics and clustered similar patients using the partitioning around medoids algorithm. RESULTS: One phenotype of 6 identified was characterized by high mortality (49%), older age, male sex, elevated inflammatory markers, high prevalence of cardiovascular disease, and shock. Patients with this severe phenotype had significantly elevated peak C-reactive protein creatinine, D-dimer, and white blood cell count and lower minimum lymphocyte count compared with other phenotypes (P < .01, all comparisons). CONCLUSIONS: Among a cohort of hospitalized adults, we identified a severe phenotype of COVID-19 based on the characteristics of its clinical course and poor prognosis. These findings need to be validated in other cohorts, as improved understanding of clinical phenotypes and risk factors for their development could help inform prognosis and tailored clinical management for COVID-19.