cms-pecos · CMS
cms-pecos · CMS
hrsa-hpsa · CMS
cms-pecos · 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.
Methodology
All figures are aggregations over two frozen federal snapshots. Deactivations
come from the CMS PECOS enrollment snapshot cms-pecos/2026-04 (frozen
2026-04-15): a March deactivation is an enrollment whose deactivation_date
falls in 2026-03. Shortage status comes from the HRSA Health Professional
Shortage Area (HPSA) designation file, joined to each deactivation by county
FIPS; a county is a "shortage area" when any HPSA designation is active in it.
"Growth against trend" is the month's deactivation count measured against the
trailing-twelve-month monthly average (2025-03 through 2026-02), computed for the
shortage and non-shortage groups separately. County figures use the net change
in active enrollments to avoid double-counting providers who moved between group
enrollments rather than left practice. Every figure resolves to specific rows in
a specific frozen snapshot; the exact SQL is in the reproducibility block below.
Methodology version: access-supply/v1. The source-provenance contract is
documented in the provenance methodology.
Limitations
- A deactivation is not a departure. A PECOS deactivation is an administrative status that can reflect a billing reorganization or a move between group enrollments, not only a clinician leaving care. The net-change framing reduces, but cannot fully eliminate, this ambiguity.
- A single month against trend. The 28%/41%/19% figures compare March 2026 to a trailing-twelve-month baseline. A one-month spike may be a transient reorganization rather than the leading edge of a durable contraction; later snapshots are needed to tell which.
- Reporting lag depresses recent months. PECOS deactivations post with a lag, so the most recent month in any snapshot is the least settled.
- Aggregate, county-level only. Every figure is a count or ratio at the county, shortage-status, or national level. No individual provider is named, ranked, or scored.
- Shortage status is the HPSA designation as published. A county's shortage-area flag follows HRSA's designations, which are themselves revised on their own cycle; this study reads the designation file as frozen.
Sources
- CMS — Medicare provider enrollment (PECOS) — the enrollment system whose deactivation records anchor the spike measurement.
- HRSA — Health Professional Shortage Areas (HPSA) — the shortage-area designations joined to each deactivation by county.
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.
Frequently asked questions
- What is a Medicare enrollment deactivation?
- A deactivation removes a provider's billing privileges in PECOS, the Medicare enrollment system. It is an administrative status, not a sanction or exclusion: it can reflect a retirement, a relocation, a lapsed revalidation, or a move between group enrollments. This study counts the net change in active enrollments per county to avoid double-counting providers who simply moved.
- How big was the March 2026 deactivation spike?
- Medicare enrollment deactivations in PECOS ran 28% above the trailing-twelve-month monthly average in March 2026 — large enough to clear the snapshot-to-snapshot alerting threshold on the first pass. The spike was not uniform: deactivations grew 41% against trend in HRSA-designated shortage areas, against 19% in non-shortage areas.
- Why does a deactivation matter more in a shortage area?
- Because there is less slack to absorb it. A deactivation in a saturated metro specialty market is absorbed without a patient noticing; the loss of the second of two primary-care physicians in a rural, already-shortage-designated county is a different event. The same administrative action has a far larger access effect where supply was already short.
- Does a deactivation mean a provider stopped practicing?
- Not necessarily. A deactivation can reflect a billing reorganization or a move between group enrollments rather than a clinician leaving care. This study reports the net county-level change in active enrollments precisely so that moves do not inflate the count, and it draws no conclusion about any individual provider.
- Can I reproduce these figures?
- Yes. Every figure aggregates the cms-pecos/2026-04 snapshot (frozen 2026-04-15) joined to the HRSA HPSA designation file by county. The growth-against-trend percentages are the month's deactivation count versus the trailing-twelve-month monthly average; the exact SQL is published in the reproducibility block below.
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 (pecos_snapshot,
-- snapshot_date = 2026-04-15). Maps deactivations to HRSA HPSA shortage
-- designations by county.
--
-- Each query is annotated with its expected result, so the three headline
-- figures (March +28% above trend; shortage areas +41% vs +19% elsewhere)
-- regenerate from the committed snapshot. "Growth against trend" = the month's
-- deactivation count versus the trailing-twelve-month monthly average
-- (2025-03 .. 2026-02). No absolute monthly counts are hard-coded — the queries
-- return the ratios directly.
-- ── (1) The national spike — March 2026 vs the trailing-12-month average ──────
with monthly as (
select
date_trunc('month', deactivation_date)::date as mo,
count(*) as n
from pecos_snapshot
where dataset_id = 'cms-pecos'
and snapshot_date = date '2026-04-15'
and deactivation_date >= date '2025-03-01' -- trailing 12 mo + March 2026
and deactivation_date < date '2026-04-01'
group by 1
)
select
round(100.0 * (
max(n) filter (where mo = date '2026-03-01')
- avg(n) filter (where mo < date '2026-03-01')
) / avg(n) filter (where mo < date '2026-03-01'), 0) as march_pct_above_ttm
from monthly;
-- march_pct_above_ttm = 28 (March 2026 deactivations ran 28% above the
-- trailing-twelve-month monthly average)
-- ── (2) The spike was not uniform — split by HRSA HPSA shortage status ────────
-- Growth against trend, March 2026 vs the trailing-12-month monthly average,
-- for deactivations in HRSA-designated shortage-area counties vs the rest.
with hpsa as (
select county_fips, bool_or(is_designated) as shortage_area
from hrsa_hpsa_snapshot
group by county_fips
),
monthly as (
select
coalesce(h.shortage_area, false) as shortage_area,
date_trunc('month', d.deactivation_date)::date as mo,
count(*) as n
from pecos_snapshot d
left join hpsa h using (county_fips)
where d.dataset_id = 'cms-pecos'
and d.snapshot_date = date '2026-04-15'
and d.deactivation_date >= date '2025-03-01'
and d.deactivation_date < date '2026-04-01'
group by 1, 2
)
select
shortage_area,
round(100.0 * (
max(n) filter (where mo = date '2026-03-01')
- avg(n) filter (where mo < date '2026-03-01')
) / avg(n) filter (where mo < date '2026-03-01'), 0) as march_pct_above_ttm
from monthly
group by shortage_area
order by shortage_area desc;
-- shortage_area = true -> march_pct_above_ttm = 41 (shortage-area counties)
-- shortage_area = false -> march_pct_above_ttm = 19 (non-shortage counties)
-- ── (3) March 2026 deactivations by shortage status — raw reconciliation ──────
-- Supporting cut: the March-only deactivation counts and average HPSA score
-- behind the split above. Deactivations concentrated in already-designated
-- shortage areas, the places with the least slack to absorb a departure.
with 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 march_deactivations,
round(avg(h.hpsa_score), 1) as avg_hpsa_score
from pecos_snapshot d
left join hpsa h using (county_fips)
where d.dataset_id = 'cms-pecos'
and d.snapshot_date = date '2026-04-15'
and d.deactivation_date >= date '2026-03-01'
and d.deactivation_date < date '2026-04-01'
group by 1
order by 1 desc;The snapshot
| dataset_id | cms-pecos |
| snapshot_date | 2026-04-15 |
| sha256 | 7c0a92f4b1d68e3057a2c9f041e6b83d75f1029c4b6a70e58319f2d6c0b7a4e1 |
| 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
The cited snapshot identifies the federal file. Where an attestation is present, its file hash can be re-derived; figures are not individually signed.
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- CARE QUALITY · JUN 2026Nursing Home Survey Results: How Fast Deficiencies Get FixedAcross 410,723 corrected CMS nursing home health deficiencies, the mean time from survey to documented correction is 32.4 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.
- WORKFORCE · JUN 2026Zero-RN days: how often US nursing homes ran a day with no registered nurse on the floorIn the CMS Payroll-Based Journal's 2025 Q2 snapshot, 5.86% of nursing-home facility-days with residents present recorded zero registered-nurse direct-care hours — 77,542 days across 5,062 facilities. The rate ranged from 27.9% in Louisiana to 0.2% in Rhode Island. Days before the federal staffing floor was rescinded, this is the baseline the country now keeps.
Federal source citations
Fonteum LLC · April 28, 2026 · All figures trace to the frozen federal-data snapshot cited above.