Finding the Deserts — how we know where the unmet need is.
A treatment desert is a place with a serious overdose problem and almost nowhere to get care. To find them, we scored all 102 Illinois counties and ranked them by unmet need: high overdose burden, thin or zero treatment supply, high vulnerability, and a viable patient base. The result is one ranked priority index. It tells us where to put a mobile treatment unit first — and doubles as the evidence-based needs assessment grant programs require.
The map and the table are two views of the same index
The choropleth below and the ranked table further down are the same priority index, shown two ways: a map to see where the deserts cluster, and a table to read every county's numbers. The darkest counties on the map — Vermilion, Franklin, Saline, Marion, and Jefferson — are zero- or near-zero-provider deserts: high overdose burden and almost nowhere to get care today.
Illinois treatment-desert priority — all 102 counties
Darker = higher priority (more underserved). Hover any county for detail.
Why this way of finding deserts wins
Most applicants pick a county because someone asked or the map looked empty. We pick where a unit helps the most people while staying financially healthy, and we explain every choice with a reproducible score. That helps two ways:
It is the needs assessment
The federal opioid-response and rural opioid programs and the state mobile-unit grant all require evidence that an area is high-need and underserved. Our index is exactly that — overdose rate, vulnerability score, and documented provider shortage per county, every source and weight spelled out.
It maximizes use of the unit
A mobile unit only pays for itself if it stays full. We place it where unmet demand is highest and the nearest clinic is farthest, so the schedule fills and each unit reaches the most people.
Every number is sourced
We publish each weight and cite each input, so a county board or grant reviewer can follow the math and see why a county ranks where it does. That openness builds trust and makes the case easy to defend.
How the index is built
We measure six things per county. Because they use different units — deaths per 100,000, a percentile, providers per 100,000 — we first put each on the same scale for fair comparison, then combine them at the weights below. The weights total 100%, split 75% toward need and 25% toward whether a unit can sustain itself: we lead with where help is needed most, then apply a sanity check so units don't land where too few patients can keep the doors open.
Each of the six measures, its weight, and why:
| What it measures | Why it matters | Weight | Source |
|---|---|---|---|
| Overdose burden — drug-overdose deaths per 100,000 people (latest 12 months) | The most direct measure of the harm a unit can prevent, so it carries the most weight. | 30% | CDC provisional overdose data, by county |
| Social vulnerability — how the county ranks on poverty, housing, and related stressors | Vulnerable communities have the hardest time reaching care and benefit most when it comes to them. | 20% | CDC/ATSDR Social Vulnerability Index, 2022 |
| Provider shortage — how few opioid-treatment providers already serve the county | Fewer existing providers means a bigger gap to fill — so a shortage raises priority. | 15% | CMS NPPES provider registry (live Illinois pull) |
| Rurality — share of the population living in rural areas | Rural residents face the longest drives to a clinic, which is exactly what a mobile unit solves. | 10% | County Health Rankings, 2025 |
| Payer base — share uninsured plus share of children in poverty | A proxy for the low-income, Medicaid-eligible population a unit would bill and serve. | 15% | County Health Rankings, 2025 |
| Minimum size — size of the low-income population the unit could serve | A unit needs enough patients to stay open; this keeps tiny counties from ranking too high without letting huge ones dominate on size alone. | 10% | SVI low-income share × population |
| Combined priority score | — | 100% | — |
Each county's final score sums the six measures at their weights. Overdose burden counts most because preventing those deaths is the point. The "minimum size" measure grows slowly with population, so small counties must still clear a basic patient-volume bar while big cities can't top the list on population alone.
The county index uses annual data to rank where the deserts are. To catch need that is rising right now, we also watch near-real-time signals: EMS naloxone administrations, local overdose mapping, and wastewater drug testing. When those spike in a county, we move a route there sooner.
The ranked county table — all 102 counties
Every Illinois county by combined priority score; click any header to sort. Priority score is the
index value (higher = more underserved; it can go negative for low-priority counties). Overdose rate
is deaths per 100,000 (latest 12-month provisional). MOUD providers is the count of medication-for-opioid-use-disorder
providers in the county. The top 10 are in bold. Full dataset:
data/analysis/il_mobile_moud_priority.csv.
| Rank | County | Priority score | Overdose deaths /100k | MOUD providers | % rural |
|---|---|---|---|---|---|
| 1 | Vermilion | 1.211 | 39.1 | 0 | 40% |
| 2 | Franklin | 1.07 | 35.0 | 0 | 59% |
| 3 | Cook | 1.025 | 19.4 | 286 | 0% |
| 4 | Saline | 1.018 | 43.7 | 2 | 65% |
| 5 | Marion | 0.792 | 27.3 | 0 | 48% |
| 6 | Jefferson | 0.768 | 30.3 | 2 | 59% |
| 7 | Jackson | 0.681 | 28.8 | 8 | 41% |
| 8 | Alexander | 0.676 | — | 0 | 97% |
| 9 | Peoria | 0.571 | 23.1 | 9 | 17% |
| 10 | Winnebago | 0.562 | 19.9 | 25 | 10% |
| 11 | Bureau | 0.537 | 30.6 | 1 | 55% |
| 12 | Knox | 0.535 | 20.7 | 2 | 32% |
| 13 | Sangamon | 0.482 | 37.7 | 13 | 19% |
| 14 | Montgomery | 0.475 | 36.1 | 2 | 53% |
| 15 | Fayette | 0.461 | — | 0 | 62% |
| 16 | Rock Island | 0.444 | 9.9 | 6 | 12% |
| 17 | Kankakee | 0.444 | 17.0 | 5 | 28% |
| 18 | St. Clair | 0.439 | 26.3 | 10 | 14% |
| 19 | Pulaski | 0.423 | — | 0 | 100% |
| 20 | Livingston | 0.418 | 28.3 | 0 | 65% |
| 21 | Stephenson | 0.385 | 25.5 | 2 | 46% |
| 22 | Lee | 0.346 | 29.7 | 1 | 53% |
| 23 | Randolph | 0.328 | — | 0 | 79% |
| 24 | Macon | 0.291 | 22.9 | 8 | 17% |
| 25 | Kane | 0.279 | 9.1 | 18 | 4% |
| 26 | Boone | 0.279 | 18.8 | 1 | 29% |
| 27 | Macoupin | 0.251 | 22.7 | 0 | 66% |
| 28 | Iroquois | 0.235 | 38.3 | 5 | 83% |
| 29 | Morgan | 0.228 | 31.1 | 4 | 36% |
| 30 | Ogle | 0.222 | 19.5 | 0 | 68% |
| 31 | La Salle | 0.208 | 18.5 | 3 | 32% |
| 32 | Wayne | 0.2 | — | 0 | 70% |
| 33 | Williamson | 0.172 | 15.0 | 1 | 41% |
| 34 | Whiteside | 0.17 | 20.2 | 1 | 44% |
| 35 | Douglas | 0.152 | — | 1 | 75% |
| 36 | DeKalb | 0.142 | 10.0 | 1 | 22% |
| 37 | Lawrence | 0.13 | — | 0 | 70% |
| 38 | Coles | 0.127 | 21.7 | 6 | 26% |
| 39 | Lake | 0.109 | 10.4 | 22 | 1% |
| 40 | Schuyler | 0.106 | — | 0 | 100% |
| 41 | Madison | 0.078 | 21.7 | 7 | 16% |
| 42 | Gallatin | 0.075 | — | 0 | 100% |
| 43 | Henry | 0.067 | 22.7 | 0 | 50% |
| 44 | Moultrie | 0.065 | — | 0 | 70% |
| 45 | Perry | 0.059 | — | 1 | 72% |
| 46 | Edgar | 0.036 | — | 0 | 51% |
| 47 | McDonough | 0.029 | — | 0 | 42% |
| 48 | Cass | 0.023 | — | 0 | 52% |
| 49 | Clay | 0.019 | — | 0 | 64% |
| 50 | Fulton | -0.0 | — | 0 | 61% |
| 51 | Massac | -0.002 | — | 1 | 53% |
| 52 | Warren | -0.003 | — | 0 | 45% |
| 53 | Johnson | -0.006 | — | 0 | 100% |
| 54 | Champaign | -0.026 | 13.1 | 14 | 16% |
| 55 | Bond | -0.059 | — | 0 | 60% |
| 56 | Logan | -0.078 | — | 0 | 50% |
| 57 | Carroll | -0.084 | — | 0 | 100% |
| 58 | White | -0.088 | — | 0 | 64% |
| 59 | Christian | -0.09 | — | 0 | 51% |
| 60 | Hamilton | -0.092 | — | 0 | 100% |
| 61 | Crawford | -0.099 | — | 0 | 67% |
| 62 | Hardin | -0.121 | — | 0 | 100% |
| 63 | Pope | -0.122 | — | 0 | 100% |
| 64 | Clark | -0.13 | — | 0 | 100% |
| 65 | Marshall | -0.146 | — | 0 | 100% |
| 66 | Pike | -0.146 | — | 1 | 100% |
| 67 | Richland | -0.168 | — | 0 | 45% |
| 68 | Mason | -0.172 | — | 0 | 100% |
| 69 | Edwards | -0.205 | — | 0 | 100% |
| 70 | Adams | -0.213 | 15.5 | 5 | 34% |
| 71 | Shelby | -0.215 | — | 0 | 76% |
| 72 | Greene | -0.234 | — | 0 | 100% |
| 73 | Will | -0.252 | 10.7 | 25 | 5% |
| 74 | Wabash | -0.255 | — | 0 | 39% |
| 75 | Tazewell | -0.314 | 13.1 | 0 | 22% |
| 76 | Brown | -0.328 | — | 0 | 100% |
| 77 | McLean | -0.336 | 12.9 | 5 | 22% |
| 78 | Grundy | -0.343 | 18.7 | 0 | 25% |
| 79 | Hancock | -0.356 | — | 0 | 84% |
| 80 | Stark | -0.359 | — | 0 | 100% |
| 81 | Jo Daviess | -0.374 | — | 0 | 88% |
| 82 | Jersey | -0.38 | — | 0 | 59% |
| 83 | McHenry | -0.384 | 13.4 | 10 | 14% |
| 84 | DuPage | -0.39 | 8.1 | 37 | 0% |
| 85 | Clinton | -0.418 | — | 2 | 80% |
| 86 | Mercer | -0.446 | — | 0 | 100% |
| 87 | Jasper | -0.462 | — | 0 | 100% |
| 88 | Henderson | -0.492 | — | 0 | 100% |
| 89 | Effingham | -0.494 | — | 0 | 60% |
| 90 | Scott | -0.555 | — | 0 | 100% |
| 91 | Putnam | -0.557 | — | 0 | 100% |
| 92 | De Witt | -0.561 | — | 0 | 53% |
| 93 | Kendall | -0.566 | 8.6 | 1 | 11% |
| 94 | Menard | -0.586 | — | 0 | 100% |
| 95 | Cumberland | -0.6 | — | 0 | 100% |
| 96 | Union | -0.601 | — | 4 | 65% |
| 97 | Calhoun | -0.607 | — | 0 | 100% |
| 98 | Washington | -0.635 | — | 0 | 99% |
| 99 | Woodford | -0.775 | — | 1 | 70% |
| 100 | Ford | -0.902 | — | 3 | 66% |
| 101 | Piatt | -0.978 | — | 1 | 64% |
| 102 | Monroe | -1.04 | — | 3 | 42% |
verify A dash (—) means the CDC withheld that county's count for too few deaths to report safely. We assign those counties an average overdose score rather than guess, making their ranking conservative — see the caveats below.
The two plays
One mobile unit on a rotating route through four neighboring counties: Franklin (#2), Saline (#4), Marion (#5), and Jefferson (#6). They carry some of the state's highest overdose rates, are 48–65% rural, score high on vulnerability, and have almost no existing providers. A single unit covers all four, and it makes the strongest grant case because these people have no realistic clinic to drive to today. We'd base the route in Mt. Vernon (Jefferson County).
Cook (#3) has the state's highest vulnerability score and by far the largest low-income, Medicaid-eligible population. It has the most providers statewide, but per person that supply is thin and the number needing help is enormous. A unit in underserved Chicago neighborhoods reaches the most patients in Illinois; that steady volume keeps program finances strong and supports the lower-volume rural routes while serving highly vulnerable communities.
First-deployment recommendation
We'd launch three units in this order, balancing where help is needed most against the steady revenue that keeps every unit running.
Southern Illinois route — Franklin · Saline · Marion · Jefferson
One unit on a rotating route covering four neighboring high-need counties (ranks 2, 4, 5, 6), based in Mt. Vernon (Jefferson). The strongest grant case — it serves people with no clinic to drive to. Our lead deployment.
Vermilion County (rank 1)
The single highest-scoring county: high overdose rate, high vulnerability, and zero existing providers, with a population (about 72,000) large enough to keep a unit busy and sustained.
Cook County (rank 3), optionally Winnebago / Rockford (rank 10)
The largest Medicaid-eligible population and most patient volume in the state. This steady revenue keeps program finances strong and supports the lower-volume rural routes while reaching highly vulnerable, underserved urban neighborhoods.
One rural-corridor unit (reach), one high-need standalone (impact), one urban revenue anchor (steady volume) — the mix that helps the most people while keeping every unit financially healthy. Once a unit is parked in a desert, the next job is filling it — see Finding the Patients.
Data sources & caveats
Read these before citing the index in a grant. The targeting is strong and reproducible, but a few inputs are stand-ins for the ideal measure, and we say so.
Where the data comes from
Overdose deaths: CDC provisional county counts (latest 12 months). Vulnerability: CDC/ATSDR Social Vulnerability Index, 2022. Rurality and payer context: County Health Rankings, 2025. Treatment supply: CMS provider registry (live Illinois pull) across substance-use rehab facilities (326), addiction medicine (241), and addiction psychiatry (65) — 632 unique providers, 566 (90%) matched to a county by ZIP code.
Provider counts are a guide, not a census
The registry counts providers and facilities by type, but not how many take new patients or how many slots are open. It's a good signal of where providers are scarce, not an exact capacity count.
Some overdose counts are withheld
Thirteen small counties have counts the CDC withheld for privacy. We assign those an average overdose score rather than guess, which understates their true need (Alexander at rank 8 shows no deaths only because the count was withheld). Treat those rankings as conservative.
Provisional and proxy data
Provisional overdose data is revised upward over time, so recent months understate true totals. The payer-base measure (uninsured plus child poverty) stands in for the Medicaid-eligible, low-income population — an estimate, not actual enrollment. And each ZIP code is assigned to the county holding most of its land.
The full ranked dataset for all 102 counties is in data/analysis/il_mobile_moud_priority.csv,
built by data/ingest/build_il_priority_index.py. Every weight and source is published, so
anyone can check why a county ranks where it does — and as better provider and final overdose data
arrive, the index updates without changing the method.