CDC Open Data Product

About the Dataset

The CDC Open Data Product is a comprehensive data solution that transforms and delivers over 1,298 high-quality, up-to-date public health datasets from the Centers for Disease Control and Prevention (CDC). This product encompasses more than 558 GB of data and growing, with over 31,140 attributes, offering researchers, analysts, and data scientists unparalleled access to a vast array of public health information.

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Quick Access

Schemas: DWV* (Data Warehouse Views) Total Datasets: 1,298 views across 50+ categories Update Frequency: Daily to annual, depending on CDC source

Overview

The CDC Open Data Product provides standardized access to diverse health datasets including:

• NCHS Statistics (213 views) - National Center for Health Statistics data

• NNDSS Data (295 views) - National Notifiable Diseases Surveillance System

• CDC Cities (57 views) - City-level health indicator data

• Motor Vehicles (45 views) - Vehicle safety and injury data

• Vaccination Data (82 views) - Immunization coverage and safety

• Environmental Health (25 views) - Environmental exposures and health impacts

• Heart & Stroke Prevention (26 views) - Cardiovascular health data

• Legislative Data (33 views) - Health policy and legislation tracking

Ready to access CDC Open Data?

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Questions? Contact our team for a walkthrough.

Getting Started

Basic Query Examples

Snowflake

-- List all available datasets
SELECT table_schema, table_name,
       ROW_NUMBER() OVER (ORDER BY table_schema, table_name) as dataset_number
FROM information_schema.views
WHERE table_schema LIKE 'DWV%'
AND table_name NOT IN ('DATASETS', 'DATASETS_BATCHES')
LIMIT 10;

-- Search for COVID-related datasets
SELECT v.table_schema, v.table_name, d.dataset_name, d.description
FROM information_schema.views v
JOIN dwv.datasets d ON v.table_name = d.view_name
WHERE v.table_schema LIKE 'DWV%'
AND (UPPER(d.dataset_name) LIKE '%COVID%'
     OR UPPER(d.description) LIKE '%COVID%')
ORDER BY v.table_schema, v.table_name;

-- Get sample data from a specific dataset
SELECT *
FROM dwv_nchs_stats.weekly_deaths_count_state_causes_2014_2__3yf8_kanr
LIMIT 100;

AI-Powered Dataset Discovery

Snowflake

Use Snowflake's CORTEX AI to discover relevant datasets using natural language:

-- Use CORTEX AI to find datasets about invasive bacterial diseases
WITH find_dataset_prompt AS (
      SELECT
          -- =====================================================================
          -- STEP 1: UPDATE YOUR SEARCH PROMPT HERE
          -- =====================================================================
          -- Write a question that can be answered with YES or NO
          -- The AI will evaluate each dataset against this question
          -- TIP: Be specific but include synonyms for better matches
          -- =====================================================================
          'Is this dataset related to flu, influenza, or respiratory illness?' AS prompt
  ),

  dataset_analysis AS (
      SELECT
          source_dataset_id,
          dataset_name,
          description,
          schema_name || '.' || view_name AS view_location,

          -- =====================================================================
          -- CORTEX AI EVALUATION
          -- =====================================================================
          -- This sends each dataset to Snowflake's AI model for evaluation
          -- The AI reads the dataset name and description to determine if it
          -- matches your search criteria, understanding context beyond keywords
          -- =====================================================================
          SNOWFLAKE.CORTEX.COMPLETE(
              'snowflake-arctic',  -- AI model to use
              find_dataset_prompt.prompt ||
              ' Dataset name: ' || dataset_name ||
              ' Description: ' || COALESCE(LEFT(description, 500), 'No description') ||
              ' Answer with just YES or NO.'  -- Forces concise response
          ) AS matches_criteria
      FROM dwv.datasets
      JOIN find_dataset_prompt
  )

  SELECT
      source_dataset_id,
      dataset_name,
      description,
      view_location,
      matches_criteria AS ai_response  -- Optional: include to see actual AI response
  FROM dataset_analysis
  WHERE UPPER(TRIM(matches_criteria)) = 'YES'  -- Matches AI "YES" responses
  ORDER BY dataset_name;

Example Results:

SOURCE_DATASET_ID	DATASET_NAME	DESCRIPTION_PREVIEW	FULL_TABLE_NAME	AVAILABLE
qvzb-qs6p	1998-2023 Serotype Data for Invasive Pneumococcal...	CDC monitors invasive bacterial infections that cause bloodstream infections, sepsis, and meningitis in persons living in the community...	dwv_pub_health_surv.abcs_pneumococcal_serotype_data_1998_20__qvzb_qs6p	Yes
7mra-9cq9	2023 Respiratory Virus Response - NSSP Emergency...	2023 Respiratory Viruses Response – National Syndromic Surveillance Program Emergency Department Visit Trajectories - COVID-19, Flu, RSV...	dwv_pub_health_surv.nssp_ed_visit_trajectories_by_state_202__7mra_9cq9	Yes

Entity Relationship Diagram

The CDC Open Data Product follows a standardized data architecture where metadata tables track datasets and their processing batches, while actual health data is stored in categorized schemas:

Color Legend:

• 🔵 Blue (Metadata): Core system tables (datasets) - Dataset catalog and metadata

• 🟠 Orange (Processing): Batch management (datasets_batches) - Processing workflow and versioning

• 🔴 Red (Mortality): Death and mortality data - NCHS vital statistics

• 🟢 Green (Vaccination): Immunization and vaccine data - Coverage and safety monitoring

• 🟣 Purple (Surveillance): Disease surveillance - Infectious disease tracking

• 🟢 Teal (Environmental): Environmental health - Air quality and toxicology

Key Relationships:

• Each dataset can have multiple datasets_batches over time

• Each data table record links to both its parent dataset and specific batch_id

• Use datasets_batches.is_latest_batch = TRUE to get current data

• All data tables follow the same schema pattern with id, dataset_id, batch_id, and created_at

Data Categories

Snowflake

Core Health Statistics

Schema	Description	View Count
DWV_NCHS_STATS	National Center for Health Statistics - mortality, births, health trends	235
DWV_NNDSS_DATA	National Notifiable Diseases Surveillance System	295
DWV_PUB_HEALTH_SURV	Public Health Surveillance data	73
DWV_CDC_DATA_CAT	CDC Data Catalog entries	60

Disease-Specific Data

Schema	Description	View Count
DWV_VACC_DATA	Vaccination coverage, adverse events, hesitancy	87
DWV_FLU_VACCINATIONS	Influenza vaccination data	13
DWV_CHILD_VAX	Childhood immunization coverage	15
DWV_FOOD_WATER_DISEASES	Foodborne and waterborne illness tracking	9

Chronic Disease & Prevention

Schema	Description	View Count
DWV_HEART_STROKE_PREV	Cardiovascular disease prevention data	28
DWV_B_RISK_FACTORS	Behavioral Risk Factor Surveillance System (BRFSS)	28
DWV_TOBACCO_USE	Tobacco use surveillance and cessation	1
DWV_SMOKING_TOBACCO_USE	Smoking and tobacco use patterns	3

Environmental & Occupational Health

Schema	Description	View Count
DWV_ENV_HEALTH_TOX	Environmental health and toxicology	25
DWV_MOTOR_VEHICLES	Vehicle safety, crashes, and injuries	45
DWV_TBI_DATA	Traumatic brain injury surveillance	3

Additional Categories

Schema	Description	View Count
DWV_ADM_DATA	Hospital admission data	8
DWV_ART_CDC	Assisted Reproductive Technology data	12
DWV_CANCER_RESEARCH_CITA	Cancer research citations	1
DWV_CDC_CASE_SURV	Case surveillance data	6
DWV_CDC_CHRONIC_DISEASE	Chronic disease surveillance	2
DWV_CDC_CITIES	City-level health indicators	64
DWV_CDC_MODELS	Disease modeling data	2
DWV_CESSATION_COV	Smoking cessation coverage	7
DWV_CORONA_RESPIRATORY	Coronavirus respiratory data	2
DWV_DISABILITY_HEALTH	Disability health data	3
DWV_FUNDING_DATA	CDC funding information	9
DWV_GLOBAL_HEALTH_DATA	Global health indicators	8
DWV_GLOBAL_SURVEY_DATA	Global survey data	4
DWV_HEALTHY_AGING	Healthy aging data	3
DWV_HLTH_COSTS	Healthcare costs data	2
DWV_HLTH_PEOPLE2020	Healthy People 2020 indicators	1
DWV_HLTH_STATS	Health statistics	11
DWV_LAB_SURVEILLANCE	Laboratory surveillance	9
DWV_LEGIS_DATA	Legislative data	33
DWV_MCH_HEALTH	Maternal and Child Health	12
DWV_MH_DATA	Mental health data	4
DWV_NCEH_DATA	Environmental health (NCEH)	1
DWV_NCIRD_IMMUNIZATION	Immunization data	3
DWV_NUTRI_PHYS_OBES	Nutrition and obesity data	9
DWV_ORAL_HEALTH	Oral health data	11
DWV_POLICY_DATA	Healthcare policy data	5
DWV_POL_SURV	Policy surveillance	17
DWV_PREG_VACC	Pregnancy vaccination data	13
DWV_PUB_HEALTH_INFRA	Public health infrastructure	2
DWV_QUITLINE_DATA	Quitline data	8
DWV_SURVEY_DATA	Survey data	18
DWV_TEEN_VAX	Teen vaccination data	1
DWV_UNSAFERESPONSEFILTER	Response filtering data	4
DWV_V_EYE_HEALTH	Vision and eye health	17
DWV_WEB_METRICS	Web metrics data	6
DWV_YRB_BEHAVIORS	Youth risk behaviors	18
DWV_NCEZ_ID	National Center for Environmental Health - NCEZ ID tracking and management data.	6
DWV_NCH_PREV	National Center for Health Statistics - prevalence of chronic diseases and risk factors.	1
DWV_OTHER_DATA	Other health-related data not covered by specific categories within the CDC healthcare data warehouse.	35

Databricks

Dataset Features

• Comprehensive Coverage: Over 50 health topic areas from infectious diseases to health behaviors

• Continuous Monitoring: New CDC datasets and updates are automatically incorporated

• Rigorous Quality Assurance: Each dataset batch undergoes automated QA checks

• Schema Evolution: Automatic adaptation to changes in source data structures

• Standardized Format: Consistent column naming and data types across all datasets

Data Quality and Maintenance

Quality Assurance Process

• Automated Checks: Each dataset batch is subject to automated QA checks before being made available

• Data Validation: Checks for data integrity, including row count validation and data type consistency

• Schema Evolution: System automatically adapts to changes in source data schemas

• Freshness Tracking: last_refresh_timestamp in DW.DATASETS indicates most recent updates

Update Schedule

• Daily: NNDSS surveillance data

• Weekly: NCHS mortality data, vaccination monitoring

• Monthly: BRFSS, chronic disease indicators

• Quarterly: Survey data, policy tracking

• Annual: Comprehensive health surveys

Business Applications

The CDC Open Data Product can be utilized in various business applications:

• Public Health Research: Epidemiological studies and population health analysis

• Healthcare Policy Development: Evidence-based policy creation and evaluation

• Risk Assessment: Health risk assessment and management across populations

• Resource Allocation: Data-driven healthcare resource planning

• Disease Surveillance: Outbreak monitoring and prediction modeling

• Population Health Management: Community health assessment and intervention planning

Data Dictionary

Common Columns

Most CDC datasets include these standard columns:

Column	Description	Type
ID	Unique record identifier	VARCHAR(36)
DATASET_ID	Links to dataset metadata	VARCHAR(36)
BATCH_ID	Processing batch identifier	VARCHAR(36)
CREATED_AT	Record creation timestamp	TIMESTAMP_NTZ

Metadata Access

Snowflake

-- Get detailed dataset information
SELECT
    d.source_dataset_id,
    d.dataset_name,
    d.description,
    d.category,
    d.url as source_url,
    d.last_refresh_timestamp
FROM dwv.datasets d
WHERE d.view_name = 'YOUR_VIEW_NAME';

Databricks

Support & Documentation

Source Data Access

Access original CDC data sources and documentation:

Snowflake

-- Find datasets with original source URLs
SELECT
    view_name,
    dataset_name,
    url as source_url
FROM dwv.datasets
WHERE url IS NOT NULL
ORDER BY dataset_name;

Databricks

Working with Latest Data Batches

Important: CDC datasets are processed in batches over time. To avoid duplicate records and ensure you're working with the most recent data, always filter for the latest batch using datasets_batches.is_latest_batch = TRUE.

Snowflake

-- Get latest batch information for datasets
SELECT
    d.dataset_name,
    d.view_name,
    b.id batch_id,
    b.total_rows_processed row_count,
    b.created_at as batch_created,
    b.processing_date as batch_completed
FROM dwv.datasets d
JOIN dwv.datasets_batches b ON d.id = b.dataset_id
WHERE b.is_latest_batch = TRUE
ORDER BY b.processing_date DESC
LIMIT 10;

Databricks

Joining to Actual Data Tables

Here are examples showing how to properly join from metadata tables to actual data tables using the latest batch filter:

Example 1: Latest COVID-19 Mortality Data

Snowflake

-- Weekly death counts by state for trend analysis
SELECT
    jurisdiction_of_occurrence,
    mmwryear,
    mmwrweek,
    allcause,
    naturalcause,
    flag_diab
FROM dwv.datasets d
JOIN dwv.datasets_batches db ON d.id = db.dataset_id
JOIN dwv_nchs_stats.weekly_deaths_count_state_causes_2014_2__3yf8_kanr a
    ON db.id = a.batch_id
WHERE mmwryear >= 2018
    AND is_latest_batch
ORDER BY jurisdiction_of_occurrence, mmwryear, mmwrweek;

Databricks

Example 2: Latest Vaccination Coverage with Metadata

Snowflake

-- Get latest childhood vaccination data with full context
SELECT
    d.dataset_name,
    d.category,
    d.url as source_url,
    vax.geography state,
    vax.year_season,
    vax.vaccine,
    vax.dimension_type,
    vax.coverage_estimate
FROM dwv.datasets d
JOIN dwv.datasets_batches b ON d.id = b.dataset_id
JOIN dwv_child_vax.vaccination_coverage_young_children_0_3__fhky_rtsk vax
    ON vax.batch_id = b.id
WHERE b.is_latest_batch = TRUE
ORDER BY vax.geography, vax.vaccine;

Databricks

Example 3: Latest Environmental Health Data with Batch Tracking

Snowflake

-- Get latest air quality measures with processing information
SELECT
    d.dataset_name,
    air.statename,
    air.countyname,
    air.reportyear,
    air.measuretype,
    air.value,
    air.unit,
    b.total_rows_processed as total_records_in_batch,
    b.processing_date as data_refresh_date
FROM dwv.datasets d
JOIN dwv.datasets_batches b ON d.id = b.dataset_id
JOIN dwv_env_health_tox.air_quality_measures_neht_network__cjae_szjv air
    ON air.batch_id = b.id
WHERE b.is_latest_batch = TRUE
    AND air.measurename LIKE '%PM2.5%'
ORDER BY air.statename, air.countyname, air.reportyear DESC;

Databricks

Why Use Latest Batch Filtering?

Without latest batch filtering, you may encounter:

• Duplicate records from historical processing runs

• Inconsistent row counts across queries

• Outdated data mixed with current data

With is_latest_batch = TRUE, you ensure:

• Only the most recent version of each dataset

• Consistent results across different query runs

• Accurate row counts and data freshness

• Optimal query performance

Example Use Cases

1. COVID-19 Impact Analysis: Analyze trends in COVID-19 deaths across different age groups and regions

2. Tobacco Consumption Trends: Track changes in tobacco consumption patterns over time and across states

3. Bacterial Surveillance: Monitor the prevalence of invasive bacterial infections across demographics

4. Cardiovascular Disease Risk Assessment: Analyze risk factors and trends in cardiovascular diseases

5. Immunization Coverage Evaluation: Assess vaccination rates and their impact on disease outbreaks

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Get Started

CDC Public Health Data Access

Includes	All 1,294+ datasets, daily to annual updates, full documentation
Support	Email support included
Cancellation	Cancel anytime, no long-term commitment

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Docs Last Updated: 1/27/2026