PopHIVE Data Source Documentation

Abcs

CDC monitors invasive bacterial infections that cause bloodstream infections, sepsis, and meningitis in persons living in the community through Active Bacterial Core surveillance (ABCs). ABCs conducts laboratory- and population-based surveillance for invasive pneumococcal disease (IPD). ABCs serotype data are used to measure the impact of vaccine use in the United States on vaccine-type IPD. This table reports IPD case counts in the ABCs catchment area by serotype for years 1998 through 2022. Cases are grouped into the following mutually exclusive age groups: age <2 years old, age 2-4 years old, age 5-17 years old, age 18-49 years old, age 50-64 years old, and age >=65 years old. ABCs methods and surveillance areas reporting IPD cases has changed over time. Given these changes, trends in serotype distribution by year and age group should be interpreted with caution. The all-site summary presented here is calculated based on the 8 sites that consistently report to ABCs and differs from the All-site measure provided by the source. Additional information on ABCs methods and surveillance population is available at https://www.cdc.gov/abcs/methodology/index.html. Analyze and visualize data using the ABCs Bact Facts Interactive Data Dashboard at https://www.cdc.gov/abcs/bact-facts-interactive-dashboard. ABCs IPD Isolates were serotyped by Quellung, PCR, or whole genome sequencing (WGS). Cases without an isolate available or with mixed serotypes reported are listed on the table as MISS. Additionally, non-typeable IPD cases are shown as NT. Zero cell rows were not included in this dataset. Minor changes to previous years serotype data can occur as additional isolates and serotype data become available. Cases were excluded from this dataset if the ABCs site did not perform surveillance in the catchment area for a full calendar year. As a result, cases were excluded from the following sites: TN, 11 counties, Jul-Dec 1999; CO, 5 counties, Jul-Dec 2000; CA, 2 counties (aged <5 years), Oct-Dec 2000.

Sources

• Data Source | Centers for Disease Control and Prevention | API/Data Location
• Data Source | University of Louisville / Pfizer | API/Data Location

Variables

data.csv.gz

uad.csv.gz

Atlas AMR

No standard data files found.

BRFSS

The Behavioral Risk Factor Surveillance System (BRFSS) is the nation's premier system of health-related telephone surveys that collect state data about U.S. residents regarding their health-related risk behaviors, chronic health conditions, and use of preventive services. Established in 1984 with 15 states, BRFSS now collects data in all 50 states, the District of Columbia, and three U.S. territories, completing more than 400,000 adult interviews each year. BRFSS provides state-specific data on health conditions including obesity, diabetes, depression, and health behaviors such as heavy drinking, physical activity, and tobacco use. Data are available by age, sex, race/ethnicity, and education level. BRFSS is a critical resource for public health surveillance and policy-making at both state and national levels.

Sources

• Data Source | Centers for Disease Control and Prevention | API/Data Location

Variables

data_survey.csv.gz

data.csv.gz

CMS Mmd

The Mapping Medicare Disparities (MMD) by Population Tool is an interactive map that displays chronic disease prevalence, costs, hospitalization, and preventive care utilization data for Medicare Fee-for-Service beneficiaries. Data are available at the national, state, and county levels, stratified by age, race/ethnicity, and sex. Condition prevalence rates are calculated using ICD-10 diagnosis codes in Medicare claims data, following the Chronic Conditions Warehouse (CCW) definitions. The tool covers over 30 chronic conditions including diabetes, hypertension, COPD, heart failure, and mental health conditions, as well as preventive service utilization metrics. Data are updated annually.

Sources

• Data Source | Centers for Medicare and Medicaid Services (CMS)

Variables

data_state_county_age_by_race.csv.gz

data_state_county_age_by_sex.csv.gz

data_state_county_age.csv.gz

Delphi Doctors Claims

The Delphi Doctor Visits signal estimates the percentage of outpatient doctor visits with COVID-related diagnoses based on claims data from health system partners. CMU Delphi receives de-identified medical insurance claims data covering a significant fraction of United States healthcare visits. The signal is calculated as the percentage of outpatient visits with COVID-related ICD-10 diagnosis codes (U071, U072, B9729, J1281, Z03818, B342, J1289). Data are smoothed using a Gaussian linear smoother to reduce day-to-day noise. This signal provides near-real-time insight into COVID-19 activity at the community level based on actual healthcare encounters. Data are available at state and county levels with approximately 3-4 day lag from the date of service.

Sources

• Data Source | Carnegie Mellon University Delphi Research Group | API/Data Location

Variables

data.csv.gz

Delphi Hospital Claims

The Delphi Hospital Admissions signal estimates the percentage of new hospital admissions with COVID-19 or influenza diagnoses based on electronic medical records and claims data from health system partners. CMU Delphi receives de-identified hospital admission data covering a significant fraction of United States hospitals. The signals track inpatient admissions with relevant ICD-10 diagnosis codes for COVID-19 and influenza. Data are smoothed using a Gaussian linear smoother to reduce day-to-day variation. This signal provides near-real-time insight into severe respiratory illness at the community level based on actual hospitalizations. Data are available at state and county levels with approximately 3-4 day lag from the date of admission.

Sources

• Data Source | Carnegie Mellon University Delphi Research Group | API/Data Location

Variables

data.csv.gz

Delphi ILI Fluview

Influenza-Like Illness (ILI) surveillance data from the CDC's ILINet network, accessed via the CMU Delphi Epidata API. ILINet is a collaborative effort between the CDC, state and local health departments, and approximately 3,000 outpatient healthcare providers across all 50 states, Puerto Rico, the District of Columbia, and the U.S. Virgin Islands. Providers report the total number of patients seen and the number presenting with ILI (defined as fever >=100F plus cough and/or sore throat without a known cause other than influenza). Data are available from 1997 week 40 onward at national, HHS regional, and state levels. Both weighted (population-adjusted) and unweighted percentages are provided. This long-running surveillance system is the primary source for tracking seasonal influenza activity in the United States.

Sources

• Data Source | Carnegie Mellon University Delphi Research Group | API/Data Location

Variables

data.csv.gz

Delphi NHSN

Weekly hospital respiratory data reported to CDC's National Healthcare Safety Network (NHSN), accessed via the CMU Delphi Epidata API. NHSN is the nation's most widely used healthcare-associated infection tracking system, collecting data from hospitals across the United States. The data includes COVID-19, influenza, and RSV associated hospital admissions aggregated to national and state levels. Data collection became mandatory for hospitals in November 2024; prior to this, reporting was voluntary with variable participation rates.

Sources

• Data Source | Carnegie Mellon University Delphi Research Group | API/Data Location

Variables

data.csv.gz

Epic

Epic Cosmos is a collaborative research platform containing de-identified patient data from over 300 million patients across more than 1,600 hospitals and health systems using Epic electronic health record systems. Data is accessed via SlicerDicer, a self-service analytics tool. The dataset includes emergency department visits, diagnoses, immunizations, laboratory results, and other clinical data. Due to privacy protections, counts fewer than 10 are suppressed and imputed. Coverage extends across all U.S. states and territories.

Sources

• Data Source | Epic Systems

Variables

county_no_time.csv.gz

county_year.csv.gz

heat_year_county.csv.gz

monthly_injury.csv.gz

monthly_tests.csv.gz

monthly.csv.gz

no_geo.csv.gz

state_no_time.csv.gz

state_year.csv.gz

weekly.csv.gz

yearly_injury.csv.gz

Gtrends

Google Health Trends data accessed via the Google Health Trends API, processed and collected using Yale DISSC's gtrends_collection framework. The data represents the probability of a short search session including a specific health-related term within a geography and timeframe, multiplied by 10 million for readability. Search volumes are provided at the DMA (Designated Market Area) and state level on a weekly basis. This data source enables tracking of public interest in health topics such as RSV, overdose, and naloxone as potential early indicators of disease activity or public health concerns.

Sources

• Data Source | Yale Data-Intensive Social Science Center (DISSC)

Variables

data_dma_year.csv.gz

data_dma.csv.gz

data_year.csv.gz

data.csv.gz

Measles Age CDC

Cumulative measles case counts stratified by age group, reported on the CDC Measles Cases and Outbreaks surveillance page. Age groups include 0-4 years, 5-19 years, 20+ years, and Unknown. Data is updated weekly as part of CDC's enhanced measles surveillance program. This age-stratified data helps public health officials understand which populations are most affected by measles outbreaks and identify gaps in vaccination coverage. Cases are classified as confirmed or probable following Council of State and Territorial Epidemiologists (CSTE) case definitions.

Sources

• Data Source | Centers for Disease Control and Prevention

Variables

data.csv.gz

Measles CDC

The CDC Measles Cases and Outbreaks surveillance system tracks confirmed and probable measles cases reported to CDC by state and local health departments. Data includes weekly national case counts and outbreak information. Measles became a nationally notifiable disease in 1912, and since 2000 (when measles was declared eliminated in the U.S.), CDC has continued enhanced surveillance to detect and respond to imported cases and outbreaks. Case definitions follow the Council of State and Territorial Epidemiologists (CSTE) criteria requiring clinical symptoms and either laboratory confirmation or epidemiological linkage to a confirmed case.

Sources

• Data Source | Centers for Disease Control and Prevention
• Data Source | The Washington Post

Variables

data.csv.gz

Measles JHU

The Johns Hopkins University Measles Tracking Team compiles laboratory-confirmed measles case data from official state and county health department reports across the United States. The team aggregates data from 37+ jurisdictions, providing both state-level weekly summaries (using rash onset dates when available) and county-level daily case counts (based on official reporting dates). Data is updated on Tuesdays and Fridays at approximately 5:00 PM Eastern Time. The tracking effort was established in response to the 2025 measles outbreak to provide timely, granular geographic data on measles transmission. All data is released under CC BY 4.0 license with attribution required.

Sources

• Data Source | Johns Hopkins Bloomberg School of Public Health

Variables

data_county.csv.gz

data_state.csv.gz

data.csv.gz

Medicaid Quality

No standard data files found.

MMR Healthmap

County, ZIP code, and state-level estimates of MMR (measles, mumps, and rubella) vaccine coverage among US children, developed using small area estimation with multilevel regression and post-stratification (MRP). The methodology integrates participatory surveillance data from digital health platforms with demographic and contextual covariates to produce granular geographic estimates of vaccination coverage gaps. Estimates include posterior mean coverage percentages, risk classifications for under-vaccination, and spatial autocorrelation measures (Local Moran's I) to identify geographic clustering of under-vaccinated areas. This research was conducted in response to the 2025 measles outbreak to support targeted public health interventions.

Sources

• Data Source | HealthMap / Boston Children's Hospital

Variables

data_county.csv.gz

data_state.csv.gz

data_zcta.csv.gz

Narms

What is NARMS? The National Antimicrobial Resistance Monitoring System (NARMS) is a U.S. public health monitoring system that tracks antimicrobial resistance (AMR) in foodborne and other intestinal bacteria using a One Health approach. As outlined in the NARMS Strategic Plan: 2021-2025, the overall purpose of NARMS is to: Monitor trends in antimicrobial resistance among enteric bacteria from humans, retail meats, and animals at the time of slaughter; Disseminate timely information on antimicrobial resistance in pathogenic and commensal microorganisms to stakeholders in the U.S. and abroad to promote interventions that reduce resistance among foodborne bacteria; Conduct research to better understand the emergence, persistence, and spread of antimicrobial resistance; Provide timely antimicrobial resistance data for outbreak investigations; and Provide data that assist the FDA in making decisions related to the approval of safe and effective antimicrobial drugs for animals. What are the different components of NARMS? NARMS was established in 1996 as a partnership between the Food and Drug Administration (FDA), the Centers for Disease Control and Prevention (CDC), and the U.S. Department of Agriculture (USDA) to track antibiotic resistance in foodborne bacteria from retail meats, human illnesses, and food producing animals. In partnership with FDA’s Veterinary Laboratory Investigation and Response Network (Vet-LIRN) and USDA’s National Animal Health Laboratory Network (NAHLN), NARMS was expanded to encompass select animal pathogens. In partnership with the Environmental Protection Agency (EPA), NARMS is also working to understand antimicrobial resistance in environmental waters following the One Health paradigm to understand AMR in the environment. NARMS works closely with other Agencies in the USDA including the Animal and Plant Health Inspection Service (APHIS) and the Agricultural Research Service (ARS) to collect animal data and develop microbiological methods, the National Center for Biotechnology Information (NCBI) to publish genomic data, and state public health and agriculture agencies and universities to collect retail meat samples. Guidance to viewers: Antimicrobial resistance is extremely complex and driven by many factors. In general, it is difficult to draw meaningful conclusions by comparing just one year to another. Instead, it is best to look for patterns that emerge over several years. Genotypic resistance data available in NARMS Now will be updated on a rolling basis. NARMS human isolate data are available only from states that have given CDC permission to share it; however aggregate data from all states are included in the total counts in tables, graphs, and maps in the interactive displays. Note: Persons who use these data should cite the National Antimicrobial Resistance Monitoring System (NARMS) as the source of the original data. The data in these tables and displays are not confidential. Additional information on sampling and testing methodologies can be found here. Suggested citation: Food and Drug Administration (FDA). NARMS Now. Rockville, MD: U.S. Department of Health and Human Services. Available from URL: https://www.fda.gov/animal-veterinary/national-antimicrobial-resistance-monitoring-system/narms-now-integrated-data. Accessed MM/DD/YYYY

Sources

• Data Source | Centers for Disease Control and Prevention | API/Data Location

No standard data files found.

NCHS Mortality

Provisional counts for drug overdose deaths from the National Vital Statistics System. Provisional counts may be incomplete and causes may be pending investigation; methods exist to adjust for reporting delays. Data updated monthly.

Sources

• Data Source | Centers for Disease Control and Prevention (NCHS)
• National Center for Health Statistics (NCHS)

Variables

data_county.csv.gz

data_state_21_causes.csv.gz

data.csv.gz

NIS

The National Immunization Surveys (NIS) are a group of telephone surveys used to monitor vaccination coverage among children 19-35 months, teens 13-17 years, flu vaccinations for children 6 months-17 years, and COVID-19 vaccination for children, teens, and adults. The surveys are sponsored and conducted by the National Center for Immunization and Respiratory Diseases (NCIRD) of the CDC and authorized by the Public Health Service Act. NIS provides population-based, state and local area estimates of vaccination coverage using a standard survey methodology. Surveys collect data through telephone interviews with parents or guardians in all 50 states, the District of Columbia, and some U.S. territories. Cell phone numbers are randomly selected and called to enroll age-eligible children. With parental permission, vaccination providers are contacted to verify immunization records. Children and teens are classified as up to date based on ACIP-recommended vaccine doses.

Sources

• Data Source | Centers for Disease Control and Prevention

Variables

data_insurance.csv.gz

data_urban.csv.gz

data.csv.gz

NNDS

The National Notifiable Diseases Surveillance System (NNDSS) is a nationwide collaboration that enables all levels of public health to share notifiable disease related health information. Public health uses this information to monitor, control, and prevent the occurrence and spread of state-reportable and nationally notifiable infectious and some non-infectious diseases and conditions.

Sources

• Data Source | Centers for Disease Control and Prevention

Variables

data.csv.gz

NREVSS

The National Respiratory and Enteric Virus Surveillance System (NREVSS) is a voluntary, laboratory-based surveillance system that monitors temporal and geographic trends for respiratory syncytial virus (RSV), human parainfluenza viruses, respiratory adenoviruses, human metapneumovirus, human coronaviruses, and rotavirus circulation in the United States. Participating laboratories report weekly to CDC on the number of tests performed and the number positive for each virus. NREVSS data are used to characterize seasonal patterns of these viruses and to help public health officials anticipate and prepare for outbreaks. Data are aggregated at the HHS regional and national levels. The system has been operational since 1987 and includes approximately 300 participating laboratories across the United States.

Sources

• Data Source | Centers for Disease Control and Prevention

Variables

data.csv.gz

NSSP

This dataset provides the percentage of emergency department patient visits for the specified pathogen of all ED patient visits for the specified geographic part of the country that were observed for the given week from data submitted to the National Syndromic Surveillance Program (NSSP). In addition, the trend over time is characterized as increasing, decreasing or no change, with exceptions for when there are no data available, the data are too sparse, or there are not enough data to compute a trend. These data are to provide awareness of how the weekly trend is changing for the given geographic region. Note that the reported sub-state trends are from Health Service Areas (HSA) and the data reported from the health care facilities located within the given HSA. Health Service Areas are regions of one or more counties that align to patterns of care seeking. The HSA level data are reported for each county in the HSA.. These data are made available by the CDC.

Sources

• Data Source | Centers for Disease Control and Prevention | API/Data Location

Variables

data.csv.gz

Respnet

The Respiratory Virus Hospitalization Surveillance Network (RESP-NET) monitors laboratory-confirmed hospitalizations associated with influenza, COVID-19, and respiratory syncytial virus (RSV) among children and adults. The CDC's Respiratory Virus Hospitalization Surveillance Network (RESP-NET) monitors laboratory-confirmed hospitalizations associated with influenza, COVID-19, and respiratory syncytial virus (RSV) among children and adults. The data are collected from hospitals in selected counties and county equivalents. This dataset has several important advantages: the area around the hospitals is well described, so rates of disease adjusted for population size can be accurately reported. The selected counties include ~10% of the US population and are demographically representative of the country. Detailed patient demographic information is available, and officials actively search for cases to ensure they capture all cases in the data. A limitation is that the network relies on the clinicians to perform viral tests as part of their routine clinical practice, so they likely miss cases that are not tested.

Sources

• Data Source | Centers for Disease Control and Prevention

Variables

data.csv.gz

Schoolvax Washpost

School and county-level vaccination rate data compiled by the Washington Post from state health department records.

Sources

• Data Source | The Washington Post

Variables

data_counties.csv.gz

data_schools.csv.gz

Schoolvaxview

SchoolVaxView monitors vaccination coverage among U.S. school-aged children. Data are collected annually by states, territories, and select local jurisdictions through school vaccination assessments, which review student vaccination records at kindergarten entry. These data are made available by the CDC.

Sources

• Data Source | Centers for Disease Control and Prevention | API/Data Location

Variables

data_exemptions.csv.gz

data.csv.gz

Vaccine Exemptions Kiang

A comprehensive study of medical vaccine exemption rates among U.S. kindergartners from 2009-2024, published in JAMA by Kiang et al. The study compiled exemption data from all 50 U.S. states and Washington, DC, providing state- and county-level medical exemption rates for MMR vaccination. The dataset spans prepandemic (2009-2019) and postpandemic (2020-2024) periods, enabling analysis of how exemption patterns changed over time and following the COVID-19 pandemic. Medical exemptions are granted when a physician determines that vaccination poses a health risk to a specific child, distinct from religious or philosophical exemptions. Values are rounded for privacy protection. The research was conducted in collaboration with NBC News and methodology is documented in an accompanying article. Full data and code are available on GitHub.

Sources

• Data Source | Stanford University / Massachusetts General Hospital

Variables

data_county.csv.gz

data_state.csv.gz

data.csv.gz

VAERS

Established in 1990, the Vaccine Adverse Event Reporting System (VAERS) is a national early warning system to detect possible safety problems in U.S.-licensed vaccines. VAERS is co-managed by the Centers for Disease Control and Prevention (CDC) and the U.S. Food and Drug Administration (FDA). VAERS accepts and analyzes reports of adverse events (possible side effects) after a person has received a vaccination. Anyone can report an adverse event to VAERS. Healthcare professionals are required to report certain adverse events and vaccine manufacturers are required to report all adverse events that come to their attention. VAERS is a passive reporting system, meaning it relies on individuals to send in reports of their experiences to CDC and FDA. VAERS is not designed to determine if a vaccine caused a health problem, but is especially useful for detecting unusual or unexpected patterns of adverse event reporting that might indicate a possible safety problem with a vaccine. This way, VAERS can provide CDC and FDA with valuable information that additional work and evaluation is necessary to further assess a possible safety concern. VAERS accepts reports of adverse events that occur following vaccination. Anyone, including Healthcare providers, vaccine manufacturers, and the public can submit reports to the system. While very important in monitoring vaccine safety, VAERS reports alone cannot be used to determine if a vaccine caused or contributed to an adverse event or illness. Vaccine providers are encouraged to report any clinically significant health problem following vaccination to VAERS even if they are not sure if the vaccine was the cause. In some situations, reporting to VAERS is required of healthcare providers and vaccine manufacturers. VAERS reports may contain information that is incomplete, inaccurate, coincidental, or unverifiable. Reports to VAERS can also be biased. As a result, there are limitations on how the data can be used scientifically. Data from VAERS reports should always be interpreted with these limitations in mind. The strengths of VAERS are that it is national in scope and can often quickly detect an early hint or warning of a safety problem with a vaccine. VAERS is one component of CDC's and FDA's multifaceted approach to monitoring safety after vaccines are licensed or authorized for use. There are multiple, complementary systems that CDC and FDA use to capture and validate data from different sources. VAERS is designed to rapidly detect unusual or unexpected patterns of adverse events, also referred to as "safety signals." If a possible safety signal is found in VAERS, further analysis is performed with other safety systems, such as the CDC’s Vaccine Safety Datalink (VSD) and Clinical Immunization Safety Assessment (CISA) Project, or in the FDA BEST (Biologics Effectiveness and Safety) system. These systems are less impacted by the limitations of spontaneous and voluntary reporting in VAERS and can better assess possible links between vaccination and adverse events. Additionally, CDC and FDA cannot provide individual medical advice regarding any report to VAERS. Key considerations and limitations of VAERS data: The number of reports alone cannot be interpreted as evidence of a causal association between a vaccine and an adverse event, or as evidence about the existence, severity, frequency, or rates of problems associated with vaccines. Reports may include incomplete, inaccurate, coincidental and unverified information. VAERS does not obtain follow up records on every report. If a report is classified as serious, VAERS requests additional information, such as health records, to further evaluate the report. VAERS data are limited to vaccine adverse event reports received between 1990 and the most recent date for which data are available. VAERS data do not represent all known safety information for a vaccine and should be interpreted in the context of other scientific information. VAERS data available to the public include only the initial report data to VAERS. Updated data which contains data from medical records and corrections reported during follow up are used by the government for analysis. However, for numerous reasons including data consistency, these amended data are not available to the public. Additionally, reports to VAERS that appear to be potentially false or fabricated with the intent to mislead CDC and FDA may be reviewed before they are added to the VAERS database. Knowingly filing a false VAERS report is a violation of Federal law (18 U.S. Code § 1001) punishable by fine and imprisonment.

Sources

• Data Source | Centers for Disease Control and Prevention | API/Data Location

No standard data files found.

Wastewater

The National Wastewater Surveillance System (NWSS) is a national surveillance system coordinated by CDC that monitors SARS-CoV-2, influenza A, and RSV levels in wastewater across the United States. Wastewater surveillance provides population-level data on pathogen circulation regardless of whether individuals seek healthcare or testing. The Viral Activity Level (VAL) represents the scaled number of standard deviations above a dynamic baseline, allowing comparison across different geographic areas and time periods. Data is aggregated to state and territory levels.

Sources

• Data Source | Centers for Disease Control and Prevention | API/Data Location

Variables

data.csv.gz

Wastewater Measles

The CDC National Wastewater Surveillance System (NWSS) tracks measles virus RNA in wastewater samples from participating wastewater treatment facilities across the United States. Wastewater surveillance provides a complement to traditional clinical surveillance by detecting viral shedding in a community regardless of healthcare-seeking behavior. The measles wastewater surveillance program was expanded in response to the 2025 measles outbreak to provide early warning of community transmission. Data include detection rates (percentage of samples positive), detection counts, sample counts, and population served by participating sewersheds. Surveillance data are aggregated at state and national levels on a weekly basis. This approach can detect measles circulation before cases are clinically confirmed, supporting rapid public health response.

Sources

• Data Source | Centers for Disease Control and Prevention

Variables

data_county.csv.gz

data_state.csv.gz

data.csv.gz

Wisqars

WISQARS (Web-based Injury Statistics Query and Reporting System) is an interactive, online database that provides fatal and nonfatal injury, violent death, and cost of injury data from a variety of trusted sources. Maintained by CDC's National Center for Injury Prevention and Control, WISQARS enables public health professionals and researchers to access injury-related mortality data from the National Vital Statistics System, nonfatal injury data from the National Electronic Injury Surveillance System (NEISS), and violent death data from the National Violent Death Reporting System (NVDRS). Data are available at national and state levels for various injury mechanisms including motor vehicle crashes, falls, poisonings (including drug overdoses), firearms, and self-harm. WISQARS supports injury surveillance, research, and prevention program planning.

Sources

• Data Source | Centers for Disease Control and Prevention

Variables

data.csv.gz