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

Census

Extraction and formatting provided by Metopio, The American Community Survey (ACS) is an ongoing survey conducted by the U.S. Census Bureau that provides detailed demographic, social, economic, and housing data for communities across the United States. The 5-year estimates pool data over a 60-month period, providing greater statistical reliability for small geographic areas (including ZIP Code Tabulation Areas) at the cost of timeliness. Variables cover a broad range of social determinants of health including poverty, housing, education, employment, insurance coverage, and racial/ethnic composition. Data are retrieved via the Census Bureau's public API using the censusapi R package.

Sources

• Data Source | U.S. Census Bureau | API/Data Location

Variables

data_county.csv.gz

data_state.csv.gz

data_zcta_2019_2020.csv.gz

data_zcta_2021_2022.csv.gz

data_zcta_2023_2024.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 Chronic

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. Note that county-level and city-level stratifications could differ markedly in total sample size due to high levels of missingness of county data in some states.

Sources

• Data Source | Epic Systems

Variables

county_no_time.csv.gz

county_year.csv.gz

state_no_time.csv.gz

state_year.csv.gz

Epic Hepb Vax

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. Note that county-level and city-level stratifications could differ markedly in total sample size due to high levels of missingness of county data in some states.

Sources

• Data Source | Epic Systems

Variables

data.csv.gz

Epic Injury

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. Note that county-level and city-level stratifications could differ markedly in total sample size due to high levels of missingness of county data in some states.

Sources

• Data Source | Epic Systems

Variables

heat_year_county.csv.gz

monthly_injury.csv.gz

yearly_injury.csv.gz

Epic Resp Infections

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. Note that county-level and city-level stratifications could differ markedly in total sample size due to high levels of missingness of county data in some states.

Sources

• Data Source | Epic Systems

Variables

monthly_tests.csv.gz

no_geo.csv.gz

weekly.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 CDC2

Weekly new and cumulative case and hospitalization counts for measles in the United States, stratified by age group (<5, 5-19, 20+, and Total) and vaccination status (Total, Unvaccinated/Unknown, One dose MMR, Two doses MMR). Data is updated weekly on the CDC Measles Cases and Outbreaks surveillance page. Negative values in case counts reflect retroactive corrections by the CDC. Cases are classified as confirmed or probable following CSTE case definitions. The source presents cumulative counts or percentages. The number of new counts is derived from the difference in cumulative cases in 2 successive weeks. For the stratification by vaccine status, the source provided the percent in each vaccinated group, and this was multiplied by the cumulative cases to approximate the number in each group

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

Annual state-level performance data on the Medicaid and CHIP Adult Core Set and Child Core Set quality measures, as voluntarily reported by states to CMS and analyzed by Mathematica via the Quality Measure Reporting (QMR) system. Measures span behavioral health (e.g., follow-up after ED visits for mental illness, substance use disorder treatment), primary and preventive care (e.g., well-child visits, immunizations), maternal and perinatal health (e.g., prenatal and postpartum care, low birthweight), dental health, care of chronic conditions (e.g., diabetes, asthma, hypertension), and long-term services and supports. Each annual dataset includes state rates and national 25th and 75th percentile benchmarks. Data cover federal fiscal years 2014–2023. Reporting is voluntary; not all states report all measures each year. The wide-format standard file contains one row per state-year-payer-domain combination, with each measure-statistic combination as a separate column following the naming convention medicaid_{measure_abbr}_{population}_{sub_metric}_{stat} where population is 'ch' (child) or 'ad' (adult), sub_metric captures time windows or sub-components, and stat is 'rate', 'pct_25', or 'pct_75'.

Sources

• Data Source | Centers for Medicare & Medicaid Services (CMS) | API/Data Location

Variables

data.csv.gz

MMR Epic

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