A site in a mid-sized metro might find that 60% of its eligible patients for a Type 2 diabetes trial are concentrated in 6 ZIP codes — while 30% of their advertising budget is reaching ZIP codes with minimal eligible population. Fixing this single issue typically reduces cost-per-inquiry by 25–40%. Here is the 3-step process to build your geographic patient map using free public data sources.
Step 1: Pull CDC PLACES Data for Your County
CDC PLACES (cdc.gov/places) provides condition prevalence estimates at the census tract and ZIP code level for 40+ health conditions including diabetes, hypertension, obesity, asthma, depression, and COPD. Access it at no cost. Navigate to: CDC PLACES → Local Data for Better Health → select your state → select your county → download the data table. The columns you need: GEOLOC (ZIP or census tract), Measure (your condition), Data_Value (prevalence percentage), Population (denominator). Multiply prevalence by population to get estimated patient count per ZIP code. Sort descending. Your top 10 ZIP codes by estimated patient count are your primary geographic targets.
Step 2: Apply Your Trial’s Eligibility Filters
CDC PLACES gives you condition prevalence. Your trial has additional criteria that narrow the eligible pool further. Apply two adjustments: Age filter — look up the age distribution for your top ZIP codes using Census ACS data at data.census.gov. If your trial requires ages 40–70 and a ZIP code is predominantly under 35, it moves down your priority list. Travel distance filter — calculate driving time, not straight-line distance, from each ZIP code to your site using Google Maps. For a 30-minute acceptable travel time, remove ZIP codes with 35+ minutes drive time even if their prevalence is high. After these two filters, you have your actual priority ZIP list.
Step 3: Configure Geo-Targeted Campaigns
In Google Ads: Navigate to your campaign → Locations → select “Enter another location” → type each target ZIP code individually → add as targets. Then go to Bid adjustments → Locations → select your top 3 ZIP codes → add +20–30% bid adjustment. This concentrates spend in high-density areas while maintaining coverage across the broader radius.
In Facebook Ads Manager: In your ad set location targeting, click “Add locations” → select “Zip codes” → enter each code. Facebook allows bulk import of ZIP codes — paste your list directly. If your top ZIPs have populations under 10,000, group them to keep your total audience above 80,000.
How to Validate and Update the Map
After 60 days of active enrollment, pull the ZIP codes of your enrolled patients from your pipeline tracker. Compare to your predicted top ZIP codes. The match rate tells you how accurate your map is. ZIP codes that outperform predictions become permanent priority targets. ZIP codes that consistently underperform despite high predicted prevalence suggest a travel barrier, a competing site in that area, or a physician referral network gap worth investigating. Update your geographic map at the start of each new trial using the most recent PLACES data (updated annually).
Your 3-Day Implementation Plan
- Day 1: Download CDC PLACES data for your county. Calculate estimated patient count per ZIP code for your primary indication. Identify top 10 ZIP codes.
- Day 2: Apply age filter using Census ACS data. Apply travel time filter using Google Maps. Finalize priority ZIP list.
- Day 3: Add ZIP code targeting to your Google Ads campaigns. Add bid adjustments for top 3 ZIPs. Update Facebook ad set location targeting.