PopulationSim provides population-level intelligence using public data sources. Use this skill for ANY request involving: (1) population demographics or profiles, (2) geographic health patterns or disparities, (3) social determinants of health (SDOH), (4) SVI or ADI analysis, (5) cohort definition or specification, (6) clinical trial feasibility, site selection, or enrollment projection, (7) service area analysis, (8) health equity assessment, (9) census data or ACS variables, (10) CDC PLACES health indicators.
PopulationSim provides population-level intelligence using public data sources. Use this skill for ANY request involving: (1) population demographics or profiles, (2) geographic health patterns or disparities, (3) social determinants of health (SDOH), (4) SVI or ADI analysis, (5) cohort definition or specification, (6) clinical trial feasibility, site selection, or enrollment projection, (7) service area analysis, (8) health equity assessment, (9) census data or ACS variables, (10) CDC PLACES health indicators.
PopulationSim - Population Intelligence & Cohort Generation
Overview
PopulationSim provides population-level intelligence using public data (Census ACS, CDC PLACES, SVI, ADI) for:
Standalone Analysis: Geographic profiling, health disparities, population comparisons
PopulationSim outputs synthetic, fictional, simulated data — never real patient records. All profiles and cohort specs are derived from aggregated public statistics and must not be treated as real patient data.
Do NOT:
Present synthetic data as actual patient records
Make clinical recommendations (e.g., "you should prescribe," "the patient needs") based on generated data
Pull from or reference real patient databases — use only public reference data
Do:
Remind users that all generated data is synthetic test data
Use real, valid medical code systems for standards: ICD-10 (diagnoses), CPT/HCPCS (procedures), LOINC (labs), SNOMED (clinical terms), RxNorm/NDC (medications), NPI (providers)
Use real public reference data (Census ACS, CDC PLACES, SVI, ADI) for population characteristics
Negative Examples — What PopulationSim Must NOT Do
Scenario
Wrong Response
Correct Response
"What should this patient take?"
"I recommend starting them on metformin"
"This is synthetic test data; PopulationSim does not provide clinical recommendations."
"Generate individual patient records"
Emit named patient rows
Route to PatientSim — PopulationSim produces population-level profiles and cohort specs, not individual records
"Show me real patient data from the database"
Pull from a patient database
"All PopulationSim data is synthetic. Real reference data (Census, PLACES) is population-level only."
Population prevalence as individual risk
"This patient has a 28% chance of obesity"
"The county obesity prevalence is 28.0% (CDC PLACES 2024)" — population rates are not individual probabilities
Edge Cases
Missing FIPS: Validate inputs; return clear error if FIPS not found in crosswalk files
Partial data: Some tracts lack PLACES or SVI coverage — flag gaps rather than imputing zeros
Invalid codes: Only emit ICD-10, CPT, LOINC, RxNorm, NDC codes from recognized systems
Output Types
PopulationProfile
Geographic entity with demographics, health indicators, and SDOH indices:
{"geography":{"type":"county","fips":"06073","name":"San Diego County","state":"CA","region":"Pacific"},"demographics":{"total_population":3286069,"median_age":37.1,"age_distribution":{"0-17":0.21,"18-64":0.62,"65+":0.17},"race_ethnicity":{"white_nh":0.43,"hispanic":0.34,"asian":0.12,"black":0.05,"other":0.06},"median_household_income":102285,"poverty_rate":0.103},"health_indicators":{"source":"CDC_PLACES_2024","diabetes_prevalence":0.095,"obesity_prevalence":0.280,"hypertension_prevalence":0.285,"depression_prevalence":0.195,"smoking_prevalence":0.098},"sdoh_indices":{"svi_overall":0.42,"svi_themes":{"socioeconomic":0.38,"household_composition":0.45,"minority_language":0.52,"housing_transportation":0.35},"adi_national_rank":35},"healthcare_access":{"uninsured_rate":0.071,"pcp_per_100k":82.4,"insurance_mix":{"employer":0.52,"medicare":0.15,"medicaid":0.18,"individual":0.08,"uninsured":0.07}}}
Request: "Profile San Diego County health indicators"
Response:
San Diego County, CA (FIPS 06073) Population Profile:
Demographics (ACS 2022 5-year):
Total Population: 3,286,069
Median Age: 37.1 years
Race/Ethnicity: 43% White NH, 34% Hispanic, 12% Asian, 5% Black
Health Indicators (CDC PLACES 2024):
Diabetes: 9.5% (vs 10.1% national)
Obesity: 28.0% (vs 32.1% national)
Hypertension: 28.5% (vs 32.4% national)
SDOH Indices:
SVI Overall: 0.42 (moderate vulnerability)
ADI National Rank: 35th percentile
Example 2: Disparities by SVI Quartile
Request: "How do health outcomes vary across SVI quartiles?"
Response: Stratification by SVI quartile shows clear outcome variation:
SVI Quartile
Diabetes
Obesity
Uninsured
Q1 (lowest vulnerability)
8.2%
26%
5.1%
Q4 (highest vulnerability)
13.8%
38%
14.2%
Disparity: Q4 tracts show higher prevalence vs Q1 across all indicators; lower-vulnerability areas have better outcomes. See health-patterns/health-outcome-disparities.md.
Example 3: Cohort for PatientSim
Request: "Define a cohort of high-risk diabetics in underserved California"
Response: Creates CohortSpecification with:
Geography: CA census tracts with SVI ≥ 0.70
Demographics: Hispanic 58%, mean age 58.4
Comorbidities: HTN 71%, obesity 62%
SDOH: Rx cost barrier 31%, food insecurity 22%
Example 4: Trial Feasibility
Request: "Feasibility for T2DM trial: age 40-70, HbA1c 8-11%"
Response:
Stage
Population
Conversion
T2DM Prevalent
34.2M
-
Age 40-70
24.8M
72.5%
HbA1c 8-11%
7.4M
29.8%
After exclusions
4.2M
-
Top Metros: Houston (128K), Miami (115K), Los Angeles (108K)
