CDC Open Data Product

About the Dataset

The CDC Open Data Product is a comprehensive data solution that transforms and delivers over 1,200 high-quality, up-to-date public health datasets from the Centers for Disease Control and Prevention (CDC). This product encompasses more than 500GB of data and growing, with over 27,000 attributes, offering researchers, analysts, and data scientists unparalleled access to a vast array of public health information.

🔗 Find the CDC Open Data Product on the Snowflake Marketplace.

Quick Access

Schemas: DWV* (Data Warehouse Views) Total Datasets: 1,200+ 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

Browse by Category

Category	Datasets	Documentation
Administrative	4	View datasets →
500 Cities & Places	57	View datasets →
Assisted Reproductive Technology (ART)	11	View datasets →
Behavioral Risk Factors	28	View datasets →
Cancer Research Citation Search	1	View datasets →
Case Surveillance	71	View datasets →
Cessation Coverage	6	View datasets →
Child Vaccinations	15	View datasets →
Chronic Disease Indicators	44	View datasets →
Coronavirus and Other Respiratory Viruses	18	View datasets →
Disability and Health	11	View datasets →
Environmental Health and Toxicology	25	View datasets →
Flu Vaccinations	12	View datasets →
Foodborne, Waterborne, and Related Diseases	9	View datasets →
Funding	1	View datasets →
General	23	View datasets →
Global Health	2	View datasets →
Global Survey Data	1	View datasets →
Health Consequences and Costs	10	View datasets →
Health Statistics	18	View datasets →
Healthy Aging	1	View datasets →
Healthy People 2020	1	View datasets →
Heart Disease and Stroke Prevention	26	View datasets →
Injury and Violence	25	View datasets →
Laboratory Surveillance	8	View datasets →
Legislation	33	View datasets →
Maternal and Child Health	21	View datasets →
Mental Health	11	View datasets →
Models	5	View datasets →
Motor Vehicle	45	View datasets →
National Center for Environmental Health	3	View datasets →
National Center for Health Statistics	213	View datasets →
National Center for Immunization and Respiratory Diseases	1	View datasets →
National Center for State, Tribal, Local, and Territorial Public Health Infrastructure and Workforce	1	View datasets →
NNDSS	295	View datasets →
Nutrition, Physical Activity, and Obesity	16	View datasets →
Oral Health	4	View datasets →
Policy	2	View datasets →
Policy Surveillance	5	View datasets →
Pregnancy and Vaccination	1	View datasets →
Public Health Surveillance	66	View datasets →
Quitline	2	View datasets →
Smoking and Tobacco Use	3	View datasets →
Survey Data	30	View datasets →
Survey Questions Tobacco Use	1	View datasets →
Teen Vaccinations	4	View datasets →
Traumatic Brain Injury	3	View datasets →
Vaccinations	82	View datasets →
Vision and Eye Health	4	View datasets →
Web Metrics	3	View datasets →
Youth Risk Behaviors	28	View datasets →
Uncategorized	55	View datasets →

Total Categories: 47 | Total Datasets: 1,200+

Getting Started

Basic Query Examples

-- 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

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

Core Health Statistics

Schema	Description	View Count
DWV_NCHS_STATS	National Center for Health Statistics - mortality, births, health trends	213
DWV_NNDSS_DATA	National Notifiable Diseases Surveillance System	295
DWV_PUB_HEALTH_SURV	Public Health Surveillance data	66
DWV_CDC_DATA_CAT	CDC Data Catalog entries	55

Disease-Specific Data

Schema	Description	View Count
DWV_VACC_DATA	Vaccination coverage, adverse events, hesitancy	82
DWV_FLU_VACCINATIONS	Influenza vaccination data	12
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	26
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

Complete category listing →

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

-- 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 datasets d
WHERE d.view_name = 'YOUR_VIEW_NAME';

Support & Documentation

Source Data Access

Access original CDC data sources and documentation:

-- 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;

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.

-- 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;

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

 -- 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;

Example 2: Latest Vaccination Coverage with Metadata

-- 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.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;

Example 3: Latest Environmental Health Data with Batch Tracking

-- 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;

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

---

Docs Last Updated: July 2025 &#xNAN;Total Views: 1,200+ &#xNAN;Data Source: Centers for Disease Control and Prevention (CDC)