Gera County Health Risk Index — Methodology
A transparent, reproducible formula. Every value in this dataset traces directly to real CDC PLACES June 2026 open data. No estimates, no extrapolations.
The formula
GCHRI = 25·norm(OBESITY) + 25·norm(DIABETES) + 30·norm(CHD) + 20·norm(CSMOKING)
where norm(x) = (x − min_x) / (max_x − min_x) across the 480 included counties
All values are CDC PLACES crude prevalence (%) among adults. Coronary heart disease (CHD) is weighted highest at 30% because it is the leading cause of death in the US and the downstream outcome most directly predicted by obesity, diabetes, and smoking.
Normalisation ranges (June 2026)
| Measure | Weight | Min (lowest risk) | Max (highest risk) |
|---|---|---|---|
| Obesity (OBESITY) | 25% | 16.7% | 46.1% |
| Diagnosed diabetes (DIABETES) | 25% | 6.6% | 18.3% |
| Coronary heart disease (CHD) | 30% | 3.7% | 12.1% |
| Current smoking (CSMOKING) | 20% | 6.4% | 19.2% |
Reproduce it yourself — step by step
- 1
Download the CDC PLACES county data
Fetch the CDC PLACES: Local Data for Better Health, County Data release from https://data.cdc.gov/api/views/swc5-untb/rows.csv (no API key required). Filter to rows where DataValueTypeID = "CrdPrv" (crude prevalence) and MeasureId is one of: OBESITY, DIABETES, CHD, CSMOKING.
https://data.cdc.gov/500-Cities-Places/PLACES-Local-Data-for-Better-Health-County-Data-20/swc5-untb - 2
Filter to counties with all four measures
Exclude any county (LocationID / FIPS code) that lacks a valid crude-prevalence value for any of the four measures. Do NOT impute or estimate. Counties with a missing or blank Data_Value for any measure are set aside entirely.
- 3
Select the top 480 counties by population
Sort remaining counties by TotalPopulation descending and take the top 480. This ensures the index covers the US counties where the most people live, while keeping the dataset computationally manageable. The June 2026 threshold is approximately 129,000 residents.
- 4
Min-max normalise each measure across the 480 counties
For each measure m in {OBESITY, DIABETES, CHD, CSMOKING}, compute: norm_m = (value_m − min_m) / (max_m − min_m). This rescales each measure to [0, 1], where 0 is the safest county for that measure and 1 is the highest-risk. The normalisation ranges used in the June 2026 release are shown in the table below.
- 5
Compute the weighted composite GCHRI
GCHRI = (25 × norm_OBESITY + 25 × norm_DIABETES + 30 × norm_CHD + 20 × norm_CSMOKING). Coronary heart disease carries the highest weight (30%) because it is the leading cause of death in the US and is the downstream outcome most directly predicted by the other three measures. The result is in [0, 100]. Round to one decimal place.
- 6
Assign national ranks
Sort all 480 counties by GCHRI descending. Rank 1 = highest chronic-disease burden; rank 480 = lowest. Ties are broken by FIPS code (ascending).
Validation examples (June 2026)
| County | State | Obesity | Diabetes | CHD | Smoking | GCHRI |
|---|---|---|---|---|---|---|
| Caddo | LA | 42.2% | 17.2% | 8.4% | 18.2% | 79.6 / 100 |
| Kanawha | WV | 40.3% | 15.6% | 8.7% | 18.9% | 76.7 / 100 |
| Trumbull | OH | 39.7% | 15.5% | 8.9% | 18.4% | 75.9 / 100 |
| Boulder | CO | 16.7% | 7.1% | 4.8% | 8.8% | 8.7 / 100 |
| King | WA | 22.6% | 7.3% | 4.0% | 7.2% | 8.8 / 100 |
| Arlington | VA | 25.3% | 8.0% | 3.8% | 7.0% | 11.6 / 100 |
Data source and licence
All underlying data is published by the U.S. Centers for Disease Control and Prevention (CDC) as part of the CDC PLACES: Local Data for Better Health program (June 2026 release). This is a U.S. federal government work and is in the public domain under 17 U.S.C. § 105. No API key is required to download the county CSV from data.cdc.gov. Gera does not claim any copyright over the derived index values; the formula and data are published here so any reader can reproduce them.
Frequently asked questions
- Why does GCHRI use crude prevalence rather than age-adjusted prevalence?
- Crude prevalence reflects the actual health burden in the population living in a county — the number that matters for planning health services and understanding real community risk. Age-adjusted rates are useful for comparing across populations with different age structures, but for a county-level risk index aimed at residents and care planners, crude prevalence is the more direct and honest measure.
- Are all US counties included?
- No. The June 2026 index includes the 480 most-populous US counties that have complete crude-prevalence data for all four measures. Counties with any missing CDC value are excluded entirely — never estimated. Smaller counties and those with insufficient CDC model data are omitted rather than carrying imputed numbers.
- How often is the GCHRI updated?
- The CDC PLACES program publishes new county-level estimates annually. Gera re-computes and re-dates the GCHRI each time a new CDC PLACES release becomes available. The current version is based on the June 2026 release and was last updated on 2026-06-20.
- Can I reproduce the GCHRI myself?
- Yes. Download the CDC PLACES county CSV from data.cdc.gov (no key required), filter to crude-prevalence rows for OBESITY, DIABETES, CHD, and CSMOKING, keep counties with all four values, take the top 480 by population, apply min-max normalisation per measure, and compute the weighted sum (25/25/30/20). The formula is deterministic — you will arrive at the same GCHRI values published here.
← Browse all 480 US county health risk scores
Contains public sector information published by U.S. Centers for Disease Control and Prevention (CDC) and licensed under the U.S. Public Domain (federal government work, 17 U.S.C. § 105). Source: CDC PLACES: Local Data for Better Health, County Data 2023 release (June 2026, published 2023 (released 2024)).
Informational/educational only — not a substitute for professional medical advice; a clinician interprets results.