BEAM Dashboard FAQs: Bacteria, Enterics, Amoeba, and Mycotics Data
- What is the BEAM Dashboard?
- How can I use this information?
- What are the data sources?
- How frequently does CDC update the dashboard?
- Can I filter data in the dashboard?
- What is an HHS region?
- What is an outbreak?
- What is an isolate?
- What is a serotype?
- What is the quarterly report?
- How can I use the outbreak serotypes of concern analyses?
- What methods are used for the outbreak serotypes of concern analyses?
- What is meant by “the possible approaches to reduce illness”?
- How can I view data behind the visualizations?
- Can I download BEAM Dashboard data?
- What is the proper citation to reference BEAM Dashboard?
- What antimicrobial resistance information is included in the BEAM dashboard?
The BEAM (Bacteria, Enterics, Amoeba, and Mycotics) Dashboard is an interactive tool to access and visualize data from the System for Enteric Disease Response, Investigation, and Coordination (SEDRIC). CDC uses SEDRIC to coordinate surveillance and response to disease outbreaks linked to food or animal contact, including data integration. The current version of the dashboard focuses on data for Salmonella, Shiga toxin-producing E. coli (STEC), Shigella and Campylobacter bacteria isolated from human specimens, such as stool or blood. This does include some antimicrobial resistance data. It will eventually include additional pathogens and epidemiologic data from outbreak investigations.
This information provides the public, academia, industry, public health partners and regulatory agencies with timely data on pathogen trends and serotype details to inform work to prevent illnesses from food, water, the environment, and animal contact.
The dashboard also includes automated analytics describing Salmonella outbreak data for chicken, beef, pork, and turkey, and it will include other food categories soon. Markedly reducing infections caused by Salmonella has been a goal of the U.S. Department of Health and Human Services for decades. However, little progress has been made toward this goal during the past 25 years. Many Salmonella serotypes (types) make people sick. CDC has identified some concerning serotypes causing single-state and multistate outbreaks and is sharing data to inform partners and guide efforts on preventing infection by these types of Salmonella. Although most foodborne illnesses are not part of a recognized outbreak, outbreaks provide important information on the sources of illness, including the foods responsible.
SEDRIC integrates data from multiple sources, including PulseNet, the National Antimicrobial Resistance Monitoring System for Enteric Bacteria (NARMS), the National Outbreak Reporting System (NORS), and Epi Info, as well as investigation data from epidemiologists, environmental health partners, and state and federal regulatory agencies. The current dashboard uses PulseNet and NORSdata, and we will integrate other data sources in upcoming versions. NORS data are used to populate the analytic tabs describing serotypes that caused Salmonella outbreaks by food category. These data include single- and multistate outbreaks.
For tabs 1 and 2, we update the dashboard monthly. We plan to increase the frequency to near real-time. For tabs 3 and 4, we update the dashboard annually when NORS data are available.
You can filter data using the filter pane in the dashboard. To select a year or Department of Health and Human Services (HHS) region, click on the relevant filter. For tabs 1 and 2, to select multiple years or regions, press and hold the Ctrl (PC) or Cmd (Mac) key on your keyboard while clicking on the filter. Additionally, you can select a variable in one of the other interactive visuals to filter data based on that variable. For example, if you select “April” in “Number of Isolates by Month,” the other visuals show data for April only. Hovering over any state in the map (New Outbreaks by State) will provide pop-up isolate data for that state.
The Office of Intergovernmental and External Affairs hosts 10 regional offices that directly serve state and local organizations. The regional directors ensure that HHS maintains close contact with state, local, and tribal partners and addresses the needs of communities and individuals served through HHS programs and policies.
When two or more people get the same illness from the same contaminated food or drink, the event is called a foodborne disease outbreak. Similarly, when two or more people get the same illness from contact with the same animal or animal environment, the event is called a zoonotic outbreak.
A bacterial isolate is a group of the same type of bacteria. In public health surveillance systems, an isolate can come from a patient’s clinical sample or from the environment, a food, or animal. Scientists use a standardized laboratory and data analysis method called whole genome sequencing to get detailed information about the bacterium, including whether it is closely related genetically to other bacteria.
Serotypes are groups within a single species of microorganisms, such as bacteria or viruses, which share distinctive surface structures. Salmonella has many serotypes. Some can cause especially severe illnesses when they infect people, while others cause milder illnesses.
The quarterly report compares isolate data for the top 15 most common serotypes during the selected quarter with the average during the same quarter in the previous two years. You can filter data by selecting the quarter from the quarter filter.
The interactive charts and graphs can help guide prevention efforts to reduce Salmonella infections.
- Tab 3 has data on the burden and trajectory of outbreak illnesses caused by specific Salmonella serotypes, by food category
- Tab 4 has data on the number of outbreak illnesses and outbreaks caused by specific Salmonella serotypes, by food category and year
Other data sources should also be considered in assessing serotypes of concern because serotypes that caused outbreaks may differ from those responsible for most illnesses.
Only serotypes that caused outbreaks associated with each food category during 10 consecutive years ending with the year selected were considered in this analysis. For example, if 2020 is selected, data from 2011 through 2020 were used.
Outbreak burden was calculated as the sum of illnesses associated with outbreaks linked to the food category considered for that serotype in the most recent 5 years. For example, if 2020 is selected, data from 2016 through 2020 were considered the most recent 5 years analyzed.
- “High” corresponds to values in the 75th–100th percentile.
- “Moderate” corresponds to values in the 51st–74th percentile.
- “Low” corresponds to values in the 0–50th percentile.
- “No outbreak illnesses” corresponds to serotypes that did not cause an outbreak during the most recent 5 years but caused an outbreak during the previous 5 years.
To determine percentile, we ordered serotypes that caused Salmonella outbreaks linked to a particular food during the most recent 5-year period by number of illnesses, with serotypes that caused the most illnesses at the top.
- Serotypes that did not cause an outbreak during the most recent 5 years were not included in the percentile calculation. The 75th percentile corresponds to the value that 75% of outbreak illnesses during a 5-year period would be below.
- The number of serotypes listed will depend on the number that caused outbreaks for each food category during a 10-year period.
- It is important to note that the values at or above the 75th percentile are not likely to represent 75% of all outbreak illnesses for that food category. This proportion will vary based on the distribution of the outbreak illnesses by serotype.
Outbreak illness trajectory
Outbreak illness trajectory was calculated as follows:
- Counts of 0 illnesses during the most recent 5 years or previous 5 years were changed to 0.5.
- The number of illnesses associated with outbreaks linked to the food category in the most recent 5 years was subtracted from the number of illnesses associated with outbreaks linked to the food category in the previous 5 years.
- The relative change was determined by dividing this number by the number of illnesses associated with outbreaks linked to the food category in the previous 5 years.
- “Increased” trajectory is defined as a 50% or greater increase in the most recent 5 years compared with previous 5 years.
- “Stable” trajectory is defined as less than a 50% increase, no change, or less than a 50% decrease.
- “Decreased” trajectory is defined as a 50% or greater decrease.
Measures could be implemented along the farm-to-fork continuum to reduce Salmonella infections transmitted by each food category.
- Intensify prevention measures: This may be considered for 1) serotypes with high burden and increased trajectory and 2) serotypes with moderate burden and increased trajectory, which may indicate an emerging problem with a unique opportunity for focused prevention measures. Industry, academic partners, and regulatory agencies could evaluate current measures and use findings to design and implement new measures or intensify current measures.
- Enhance prevention measures: This may be considered for serotypes with high burden and stable trajectory; these serotypes have continued to cause outbreaks and ongoing illness. Industry, academic partners, and regulators could evaluate current measures to determine how and if they could be improved or whether additional measures should be implemented.
- Continue prevention measures: This may be considered for serotypes with high burden and decreased trajectory, moderate burden and decreased or stable trajectory, and all serotypes with low burden.
- Continue surveillance: Surveillance is needed for all serotypes regardless of burden or trajectory. This includes surveillance of illnesses in people and detection of pathogens in food products, production or processing plants, food-producing animals and their environments, and food growing environments.
To view tabular data, right click on any visualization and select “Show as a table.”
Yes, click on the download links below to download BEAM dashboard datasets.
Suggested Citation for BEAM Dashboard:
Centers for Disease Control and Prevention (CDC). BEAM (Bacteria, Enterics, Amoeba, and Mycotics) Dashboard. Atlanta, Georgia: U.S. Department of Health and Human Services. www.cdc.gov/ncezid/dfwed/BEAM-dashboard.html. Accessed MM/DD/YYYY.
The dashboard shows the percentage of outbreak-associated isolates that have clinically important antimicrobial resistance. This percentage is calculated by dividing the number of isolates with resistance determinants (resistance genes and mutations) for antibiotics recommended for treatment by the total number of sequenced isolates analyzed by the National Antimicrobial Resistance Monitoring System (NARMS).
NARMS uses special software to predict resistance based on the bacterial genomes of whole genome sequenced isolates in PulseNet. This method of predicted resistance correlates well with traditional antimicrobial susceptibility testing for enteric pathogens.
For Salmonella and Shigella, we define clinically important resistance as the presence of at least one resistance determinant for at least one of five antibiotics recommended for treatment (ampicillin, azithromycin, ceftriaxone, ciprofloxacin, or trimethoprim-sulfamethoxazole). For Campylobacter, we define clinically important resistance as the presence of at least one resistance determinant for either ciprofloxacin or azithromycin. For Shiga toxin-producing E. coli (STEC), we do not report resistance information because antibiotics are not generally recommended to treat STEC infection.
For more information about resistance data for enteric pathogens, see NARMS Now: Human Data.