Case study · 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.

Illustrative demo — Hennepin County, MN — illustrative example

Population estimation & synthesis

Case study summary

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

Methodology & limitations — read before interpreting the map

  • Modeled estimates, not measured counts. Condition burden figures apply published prevalence rates to HUD Point-in-Time denominators. They are literature-derived estimates with confidence ranges — not direct clinical measurement.
  • Illustrative geographic allocation. CoC-level PIT totals are distributed across census tracts using an urban-concentration model for demonstration purposes. This is not HUD microdata.
  • Representative service locations. Shelter, clinic, and FQHC points are illustrative access points for the service-desert layer — not a comprehensive inventory of Hennepin County providers.
  • Data sources: HUD Exchange PIT/HIC 2024 (Hennepin County subset, CoC MN-503); prevalence rates from peer-reviewed literature (see citations below).

PIT denominator (2024)

3281

887 unsheltered (illustrative allocation)

Est. serious mental illness

823

Range 656–984 (modeled)

Est. chronic conditions

1580

Range 1373–1766 (modeled)

Service desert tracts

7

High unsheltered + limited nearby access

Interactive map

Explore illustrative PIT concentration, literature-derived condition burden, and service access gaps.

PIT count
Low High
Shelter Clinic FQHC

Why this methodology matters

When a population is absent from standard surveillance, credible planning still requires numbers — but those numbers must be built transparently from defensible inputs. Combining HUD denominators, published prevalence literature, and service geography produces estimates a county or CoC can discuss with funders and partners while being explicit about uncertainty.

Optional triangulation against hospital discharge or emergency department data with homeless flags can sanity-check literature-derived estimates where those extracts exist — a step documented here but not implemented in this public demo.

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Methods & citations

Denominator. HUD Point-in-Time and Housing Inventory Count data, published annually via HUD Exchange. Hennepin County 2024 total used as illustrative CoC subset.

Serious mental illness prevalence. Fazel S, et al. Schizophrenia and other psychotic disorders among homeless people. Lancet. 2008;371(9611):408–409; National Health Care for the Homeless Council clinical data synthesis (~25% SMI).

Chronic physical health condition prevalence. Zellmer L, Van Siclen R, Bodurtha P, et al. Estimating Health Condition Prevalence Among a Statewide Cohort with Recent Homelessness or Incarceration. J Gen Intern Med. 2025;40(15):3733–3742. doi:10.1007/s11606-025-09814-x

Geography. U.S. Census Bureau TIGER/Line 2020 census tracts, Hennepin County (FIPS 27053).

Service desert rule. Tracts with unsheltered PIT at or above the tract median and no illustrative service point within 1.5 miles.