cms-pecos · CMS
cms-pecos · CMS
hrsa-hpsa · CMS
Most months, Medicare enrollment deactivations in PECOS are a quiet administrative trickle: retirements, relocations, providers letting a lapsed enrollment expire. March 2026 was not most months. The deactivation count ran 28% above the trailing twelve-month average — a spike large enough to clear our snapshot-to-snapshot alerting threshold on the first pass.
A national spike is interesting. Where it lands is what matters for access.
Mapping deactivations onto shortage areas
We geocoded each deactivated enrollment to its practice location and joined it to HRSA's Health Professional Shortage Area (HPSA) designations. Two findings stood out.
First, the spike was not uniform. Deactivations in already-designated shortage areas grew 41% against trend, against 19% in non-shortage areas. The places with the least slack lost the most supply.
Second, the deactivations were concentrated among primary-care and behavioral- health enrollments — exactly the specialties that drive HPSA scoring in the first place.
A deactivation in a saturated metro specialty market is absorbed without a patient noticing. A deactivation of the second of two primary-care physicians in a rural county is a different event entirely.
The county-level concentration is what turns a national curiosity into an access story. When we ranked counties by net primary-care enrollment loss, the top decile was almost entirely non-metropolitan and overwhelmingly already HPSA-designated. In several of those counties the March deactivations represent a double-digit percentage of the active primary-care panel — the kind of move that does not average out, because there is nothing nearby to average against.
A deactivation is not necessarily a provider leaving practice. It can reflect a billing reorganization or a move between group enrollments. We flag the net change in active enrollments per county to avoid double-counting moves.Why a snapshot beats a survey here
Workforce-supply estimates usually arrive from annual surveys, months or years after the fact. By then the access gap has either been backfilled or hardened into a permanent desert, and the survey cannot tell you which. The PECOS snapshot, frozen monthly, catches the change in the month it posts. Joined to the HPSA file, it does not just say supply fell — it says supply fell where supply was already short, which is the only version of the finding that should move a policymaker.
This is the third study in a row where the leading signal lives in a federal snapshot and the official designation lags it. The SNF quality slide showed up before the star rating; the exclusion clustering showed up before the LEIE listing; here, the supply contraction shows up before the next HPSA redesignation cycle.
Reproducibility
The enrollment snapshot is cms-pecos/2026-04; the shortage file is
hrsa-hpsa/2026-05. The geocoding pipeline and the net-enrollment calculation
are versioned with the rest of our methodology at /quality/methodology. Every
county figure here resolves to the specific rows that produced it.
We will revisit these counties when the next PECOS snapshot posts to see whether the March spike reflects a transient reorganization or the leading edge of a durable contraction.
Who uses this data
The source data behind this study is public
Compliance teams, journalists, and researchers work from the same federal source families cited above — queried by NPI or facility identifier through Fonteum’s open dataset pages and API. Every figure traces to a frozen, downloadable snapshot you can reproduce yourself.
Reproducibility
Every claim, reproducible
The SQL
-- A March spike in Medicare enrollment deactivations thinned provider supply
-- in shortage areas. Snapshot: cms-pecos/2026-04. Maps deactivations to HRSA
-- HPSA shortage designations by county.
with deactivations as (
select npi, enrollment_state, county_fips, deactivation_date
from pecos_snapshot
where dataset_id = 'cms-pecos'
and snapshot_date = date '2026-04-15'
and deactivation_date >= date '2026-03-01'
and deactivation_date < date '2026-04-01'
),
hpsa as (
select county_fips,
max(hpsa_score) as hpsa_score,
bool_or(is_designated) as shortage_area
from hrsa_hpsa_snapshot
group by county_fips
)
select
coalesce(h.shortage_area, false) as shortage_area,
count(*) as deactivations,
round(avg(h.hpsa_score), 1) as avg_hpsa_score
from deactivations d
left join hpsa h using (county_fips)
group by 1
order by 1 desc;The snapshot
| dataset_id | cms-pecos |
| snapshot_date | 2026-04-15 |
| sha256 | 7c0a92f4b1d68e3057a2c9f041e6b83d75f1029c4b6a70e58319f2d6c0b7a4e1 |
| doi | 10.5072/fonteum/pecos-deactivation-2026-03 |
| slsa_provenance_url | pending - publishes with the SLSA provenance generator (not yet live) |
The JOINs
pecos.county_fips = hrsa_hpsa.county_fips deactivation window: 2026-03-01 ≤ deactivation_date < 2026-04-01 shortage_area = any HPSA designation active in county
The pipeline version
| git_sha | f70bade1 |
| slsa_provenance | pending - publishes with the SLSA provenance generator (not yet live) |
| methodology_version | access-supply/v1 |
Reproduce this
Run the exact query against the frozen 2026-04-15.
Cite this study
Citation-ready for researchers and AI.
Check the chain
Each figure is snapshot-attested — re-derive the hash from the federal file.
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- FINANCIAL DISTRESS · JUN 2026Hospitals running out of cash: the days-cash signal, and why most of it is a reporting artifactFederal HCRIS cost reports let us compute days cash on hand for 5,459 hospitals, but facility-level figures are distorted by system-level cash pooling — so the raw '2,800 hospitals under 30 days' headline is mostly noise. The defensible signal is narrower: 690 hospitals that report thin cash and also run an operating loss.
- CARE QUALITY · MAY 2026Why 14% of skilled nursing facilities had a quality drop in Q1Across 5,148 SNFs in Q1 2026, the composite quality score declined by an average of 0.06 points — but the decline was not evenly distributed. Facilities that changed ownership in the prior twelve months accounted for a disproportionate share of the slide.
- CARE QUALITY · JUN 2026How fast do nursing homes fix what surveyors cite? 28.5 days for the harmful onesAcross 415,849 corrected CMS nursing home health deficiencies, the mean time from survey to documented correction is 32 days — but the harm-level citations, Severity G and above, close faster, in 28.5 days. The more severe the finding, the quicker the fix. Texas and Illinois correct in about two weeks; Washington, D.C. takes nine.
Federal source citations
Fonteum Research · April 28, 2026 · All figures trace to the frozen federal-data snapshot cited above.