Proof, not promises

Case studies

Real data challenges and how we approached them — different sources, different methods, same goal: something a public health partner can act on.

Cross-system linkage

Linking health records across Minnesota health systems — without sharing patient data

A working demonstration of how 11 health systems can contribute to statewide prevalence estimates while patient records stay on-site.

Problem
A statewide consortium needed timely, stratified condition estimates across multiple health systems. No single organization could hold all patient records, and privacy and governance rules ruled out centralizing identifiable data.
Approach
Standardized clinical data at each contributing site, privacy-preserving record linkage, administrative enrichment, federated cohort queries, and small-cell suppression before any aggregate is published.
Result
A runnable end-to-end pipeline and interactive walkthrough — evidence of the infrastructure Stratum Health designs for consortia and public health networks.

Skills demonstrated

  • Data pipeline engineering
  • Privacy-preserving linkage
  • Federated analytics
  • Governance & suppression
  • Dashboard-ready outputs
View case study →

Health equity geography

When housing policy becomes a health map

Interactive map comparing 1930s HOLC redlining grades with CDC tract-level health estimates in the Minneapolis–St. Paul metro.

Problem
Partners needed to demonstrate measurable present-day health differences tied to historical housing investment policy — not just cite the literature.
Approach
1930s HOLC redlining grades spatially joined to modern census tracts and CDC PLACES model-based estimates, with an interactive public map and correlation chart.
Result
A public-facing tool comparing formerly redlined and highest-graded neighborhoods across diabetes, asthma, and mental distress in the Twin Cities metro.

Skills demonstrated

  • Geospatial analysis
  • Historical data integration
  • Health equity framing
  • Public dashboard design
  • CDC open data
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Population estimation & synthesis

Estimating health condition burden among people experiencing homelessness

A reusable methodology for county/CoC-level burden estimates when standard surveillance does not cover homelessness intersected with chronic disease — illustrated with public HUD PIT data and literature-derived prevalence rates.

Problem
Homeless service providers and public health agencies know population counts from PIT and HMIS, but lack reliable estimates of health condition burden in that population. Standard surveillance datasets like CDC PLACES do not cover homelessness intersected with chronic disease — making it hard to justify resources, target outreach geographically, or support grant applications with credible numbers.
Approach
Combine HUD Point-in-Time counts for local denominator and geographic distribution; apply published peer-reviewed prevalence rates with explicit confidence ranges (labeled as literature-derived estimates, not measured counts); overlay illustrative shelter and clinic locations to flag service deserts; and document optional triangulation against discharge or ED data where homeless status is recorded.
Result
A reusable methodology for producing defensible, geographically specific health burden estimates for a population standard surveillance systems do not capture — applicable to any county or Continuum of Care region.

Skills demonstrated

  • Population estimation & synthesis
  • HUD data integration
  • Literature-derived modeling
  • Service access mapping
  • Grant-ready reporting
View case study →

Public data synthesis

County health snapshot in one page

Public lookup tool that pulls CDC PLACES, Census demographics, and County Health Rankings into a shareable county profile — with PDF export and optional email delivery.

Problem
Community health planners and grant writers need a credible county-level picture fast — but the underlying sources live in different portals, use inconsistent formats, and take time to assemble by hand.
Approach
Automated fetch from CDC PLACES, U.S. Census ACS, County Health Rankings, and (for Minnesota) MDH vital statistics; cached per county with stale-data refresh; single-county map boundary; formatted PDF and Resend email on request.
Result
A no-login public tool at a stable FIPS URL — suitable for CHNA scoping, grant appendices, and partner conversations, with a path to tract-level infrastructure for organizations that need more.

Skills demonstrated

  • Multi-source ETL
  • Public open data APIs
  • Geospatial visualization
  • PDF generation
  • Lead capture & rate limiting
View case study →

Food systems geography

Mapping food access across north-central Minnesota

Interactive census-tract map of USDA food access indicators for Beltrami, Hubbard, Clearwater, and Cass counties — with tribal nation boundaries and county agricultural context — built for food-systems grant applications and food council presentations.

Problem
A food-systems consulting partner working with farmers and food councils in north-central Minnesota needed to show grant reviewers where food access need is concentrated — but the evidence lives in separate federal datasets that none of their audiences can easily combine or map.
Approach
USDA Food Access Research Atlas tract flags joined to Census boundary files and ACS poverty and vehicle-access estimates, overlaid with Red Lake, White Earth, and Leech Lake reservation boundaries and county-level context from the 2022 Census of Agriculture and County Health Rankings — pre-processed into static map layers so the page loads fast with no live API calls.
Result
A single shareable map showing that 13 of the region’s 30 census tracts are flagged low-income and low-access — ready for grant appendices, food council meetings, and partner conversations.

Skills demonstrated

  • Geospatial ETL
  • USDA & Census open data
  • Tribal land context
  • Choropleth design
  • Grant-ready reporting
View case study →