US County Health Prevalence (CDC PLACES)
Model-based health estimates for EVERY U.S. county in one tidy table - obesity, diabetes, smoking, high blood pressure, mental & physical health, cancer screening, insurance access and ~30 more. CDC's PLACES program publishes these as a long-format release; we pull the whole county file and ship one fat panel keyed by (county FIPS × measure × value-type), with readable measure names (not cryptic codes), both crude and age-adjusted prevalence, 95% confidence limits, the county population and a geocode. Drops straight next to any county-keyed dataset (proptech, insurance, policy) on the 5-digit FIPS. Public domain.
- Source
- CDC
- Licence
- Redistributable open data
- Quality
- QC clean · 0 failures
- Format
- Apache Parquet + dictionary
Safe to train on
Built only from a named, redistributable official source under a documented open licence - not scraped web data. No copyright grey zone, no personal data. Ships machine-readable Croissant metadata (ML Commons - loads in Hugging Face / Kaggle / Google), an AI training-licence manifest documenting source, licence and provenance for your model's data governance, and a machine-readable data dictionary (drop it into an agent / RAG prompt so the model knows every column) - all inspectable before you buy.
What's included
- ~40 health measures for every U.S. county - one tidy long panel (~250k rows)
- Obesity, diabetes, smoking, hypertension, mental/physical health, cancer screening, access…
- Readable measure names (not codes); BOTH crude and age-adjusted prevalence + 95% CIs
- County population and a lat/lon geocode on every row; keyed by 5-digit county FIPS
- Model-based aggregate estimates - no individuals, no PII
- Audiences: health-tech, payers/insurers, pharma, proptech, policy research
- Public domain (U.S. CDC) - free commercial reuse & resale
- Source: U.S. CDC - PLACES: Local Data for Better Health
Schema
| Column | Type | Description | Filled | Distinct |
|---|---|---|---|---|
| year | int | Data release year | 100.0% | 2 |
| state | str | U.S. state abbreviation | 100.0% | 52 |
| county | str | County name | 100.0% | 1,837 |
| county_fips | str | 5-digit county FIPS code (join key) | 100.0% | 3,144 |
| category | str | Measure category (Health Outcomes, Prevention, Health Risk Behaviors, …) | 100.0% | 6 |
| measure_id | str | Short measure id (OBESITY, DIABETES, CSMOKING, …) | 100.0% | 40 |
| measure | str | Full human-readable measure name | 100.0% | 40 |
| short_question | str | Short question text for the measure | 100.0% | 40 |
| data_value_type | str | Crude prevalence or Age-adjusted prevalence | 100.0% | 2 |
| prevalence_pct | float | Estimated prevalence (% of adults) | 100.0% | 904 |
| low_ci | float | Lower 95% confidence limit | 100.0% | 893 |
| high_ci | float | Upper 95% confidence limit | 100.0% | 914 |
| total_population | int | County total population | 100.0% | 3,084 |
| lat | float | County centroid latitude | 100.0% | 3,143 |
| lon | float | County centroid longitude | 100.0% | 3,143 |
Sample & preview
Every purchase ships as Apache Parquet with a data dictionary and the full QC report. A free sample (first rows + schema) is downloadable here - confirm fit before you buy. Source: CDC (redistributable open data; attribution passes through - see our Licence).
Don't trust screenshots - drop the sample into your notebook right now.
import pandas as pd df = pd.read_parquet("us-county-health-prevalence_sample.parquet") df.info() # typed columns, gap-aware, ready to join⬇ Download free Parquet sample or CSV for Excel
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