{ "study": { "slug": "nursing-home-agency-staffing-share-2025", "title": "The agency-staffing line: how much of US nursing-home nurse care now comes from temp staff", "standfirst": "5.98% of all nurse-staffing hours in US nursing homes came from contract or agency staff in CMS's 2025 Q2 Payroll-Based Journal — 25.3 million of 423.7 million hours. Reliance ranged from 25.1% in Vermont to 1.0% in Alabama, and 86 homes ran on majority-agency nurse staffing.", "desk": "workforce", "article_type": "Original Research", "published": "2026-06-22", "issue": 91, "doi": "10.5072/fonteum/nh-agency-staffing-2025", "url": "https://fonteum.com/research/nursing-home-agency-staffing-share-2025", "methodology_version": "pbj-agency-share/v1" }, "data_as_of": "2025-06-30", "datasets": [ { "slug": "pbj-staffing", "name": "CMS Payroll-Based Journal", "publisher": "CMS — Payroll-Based Journal (PBJ) Daily Nurse Staffing", "upstream_url": null } ], "key_findings": [ { "number": "5.98%", "finding": "of all nursing-home nurse-staffing hours came from contract or agency staff — 25.3M of 423.7M hours", "dataset": "pbj-staffing" }, { "number": "25.1% vs 1.0%", "finding": "highest state reliance (Vermont) versus lowest (Alabama) — a roughly 25-fold spread", "dataset": "pbj-staffing" }, { "number": "41.5%", "finding": "of facilities (6,037 of 14,537) reported zero agency hours in the quarter", "dataset": "pbj-staffing" }, { "number": "86", "finding": "facilities ran on majority-agency staff — over half their nurse hours bought from agencies; 19 topped 75%", "dataset": "pbj-staffing" }, { "number": "32.7%", "finding": "of all agency hours nationwide were billed in just three states — New York, Pennsylvania and New Jersey", "dataset": "pbj-staffing" } ], "faqs": [ { "q": "What share of US nursing-home nurse staffing comes from agency or contract staff?", "a": "In CMS's 2025 Q2 Payroll-Based Journal, 5.98% of all nurse-staffing hours — 25.3 million of 423.7 million — were supplied by contract or agency staff rather than direct facility employees. That is roughly one hour in every 17. The rate is highest for licensed practical nurses (7.37%) and lowest for aides (5.51%)." }, { "q": "Which states rely most on agency nurse staffing?", "a": "Vermont led at 25.1% of nurse hours bought from agencies, followed by Pennsylvania (13.4%), New Hampshire (13.4%), New Mexico (12.7%) and New Jersey (12.5%). Alabama was lowest at 1.0%, with Florida (1.5%) and Arkansas (1.9%) close behind — a roughly 25-fold spread between the highest and lowest states." }, { "q": "Does a high agency share mean worse care?", "a": "This study does not measure care outcomes, so it makes no such claim. Agency reliance is widely treated in the staffing literature as a marker of instability — homes that cannot retain permanent nurses buy temporary coverage — and temporary staff have less continuity with residents. But the Payroll-Based Journal records hours, not quality, and the figures here are descriptive, not causal." }, { "q": "What is the difference between employee and contract hours in the PBJ file?", "a": "CMS requires every certified nursing home to split each nurse job code into hours worked by its own employees and hours worked by contract or agency staff. The file stores these as paired columns — for registered-nurse direct care, Hrs_RN_emp and Hrs_RN_ctr. The agency share in this study is the contract column divided by the total for all eight nurse job codes combined." }, { "q": "Are these figures affected by the rescinded federal staffing rule?", "a": "The 2025 Q2 snapshot predates the February 2, 2026 rescission of the federal minimum-staffing standard for long-term-care facilities. These figures describe agency reliance while the rule was still on the books. With no national staffing floor now in place, the public payroll record is the remaining way to track how heavily homes lean on temporary nurses." } ], "citation": { "apa": "Fonteum Research. (2026, June 22). The agency-staffing line: how much of US nursing-home nurse care now comes from temp staff. Fonteum Research, Issue 91. https://doi.org/10.5072/fonteum/nh-agency-staffing-2025", "url": "https://fonteum.com/research/nursing-home-agency-staffing-share-2025" }, "reproducible_sql": "-- Agency / contract share of nurse staffing in US nursing homes (CMS PBJ, CY2025 Q2).\n-- Snapshot: pbj_daily_nurse_staffing, snapshot_quarter = '2025Q2'\n-- (the immutable CMS Payroll-Based Journal quarter covering\n-- 2025-04-01 .. 2025-06-30, downloaded from data.cms.gov).\n--\n-- Definitions (locked):\n-- facility-day := one nursing home on one calendar day.\n-- included := mds_census > 0 (residents present that day).\n-- total nurse hrs:= sum of all eight nurse job-code columns:\n-- RN director (hrs_rndon), RN admin (hrs_rnadmin),\n-- RN direct (hrs_rn), LPN admin (hrs_lpnadmin),\n-- LPN direct (hrs_lpn), CNA (hrs_cna),\n-- aide-in-train(hrs_natrn), med aide (hrs_medaide).\n-- agency hrs := the matching '_ctr' (contract) column for each of the\n-- eight job codes. PBJ splits every code into _emp\n-- (facility employees) and _ctr (contract / agency staff).\n-- agency share := agency hrs / total nurse hrs.\n--\n-- Re-running these queries against the 2025Q2 snapshot reproduces every headline\n-- figure in the study, exactly. Each query is annotated with its expected result.\n\n-- ── (1) National headline ────────────────────────────────────────────────────\nwith base as (\n select\n (coalesce(hrs_rn,0)+coalesce(hrs_rndon,0)+coalesce(hrs_rnadmin,0)\n +coalesce(hrs_lpn,0)+coalesce(hrs_lpnadmin,0)\n +coalesce(hrs_cna,0)+coalesce(hrs_natrn,0)+coalesce(hrs_medaide,0)) as tot,\n (coalesce(hrs_rn_ctr,0)+coalesce(hrs_rndon_ctr,0)+coalesce(hrs_rnadmin_ctr,0)\n +coalesce(hrs_lpn_ctr,0)+coalesce(hrs_lpnadmin_ctr,0)\n +coalesce(hrs_cna_ctr,0)+coalesce(hrs_natrn_ctr,0)+coalesce(hrs_medaide_ctr,0)) as ctr,\n mds_census, provnum\n from public.pbj_daily_nurse_staffing\n where snapshot_quarter = '2025Q2' and mds_census > 0\n)\nselect\n count(*) as facility_days, -- 1,322,254\n count(distinct provnum) as facilities, -- 14,537\n round(sum(tot)) as total_hours, -- 423,690,623\n round(sum(ctr)) as agency_hours, -- 25,337,782\n round(100.0*sum(ctr)/sum(tot), 2) as agency_pct, -- 5.98\n round(sum(ctr)/sum(mds_census), 3) as agency_hprd, -- 0.226\n round(sum(tot)/sum(mds_census), 2) as total_hprd -- 3.78\nfrom base;\n\n-- ── (2) Agency share by nurse role ───────────────────────────────────────────\n-- LPN most agency-dependent (7.37%), RN next (5.78%), aides lowest (5.51%).\nselect\n round(100.0*sum(coalesce(hrs_rn_ctr,0)+coalesce(hrs_rndon_ctr,0)+coalesce(hrs_rnadmin_ctr,0))\n / nullif(sum(coalesce(hrs_rn,0)+coalesce(hrs_rndon,0)+coalesce(hrs_rnadmin,0)),0), 2) as rn_agency_pct, -- 5.78\n round(100.0*sum(coalesce(hrs_lpn_ctr,0)+coalesce(hrs_lpnadmin_ctr,0))\n / nullif(sum(coalesce(hrs_lpn,0)+coalesce(hrs_lpnadmin,0)),0), 2) as lpn_agency_pct, -- 7.37\n round(100.0*sum(coalesce(hrs_cna_ctr,0)+coalesce(hrs_natrn_ctr,0)+coalesce(hrs_medaide_ctr,0))\n / nullif(sum(coalesce(hrs_cna,0)+coalesce(hrs_natrn,0)+coalesce(hrs_medaide,0)),0), 2) as aide_agency_pct -- 5.51\nfrom public.pbj_daily_nurse_staffing\nwhere snapshot_quarter = '2025Q2' and mds_census > 0;\n\n-- ── (3) State agency share + share of the national agency bill ────────────────\n-- Intensity (agency share) vs volume (share of all national agency hours).\n-- Vermont highest intensity (25.13%) but only 0.9% of national agency hours;\n-- New York 10.79% intensity but 13.8% of the national bill. NY+PA+NJ = 32.7%.\n-- Alabama lowest intensity (1.01%). Spread VT/AL ~= 25x.\nwith s as (\n select\n state,\n sum(coalesce(hrs_rn,0)+coalesce(hrs_rndon,0)+coalesce(hrs_rnadmin,0)\n +coalesce(hrs_lpn,0)+coalesce(hrs_lpnadmin,0)\n +coalesce(hrs_cna,0)+coalesce(hrs_natrn,0)+coalesce(hrs_medaide,0)) as tot,\n sum(coalesce(hrs_rn_ctr,0)+coalesce(hrs_rndon_ctr,0)+coalesce(hrs_rnadmin_ctr,0)\n +coalesce(hrs_lpn_ctr,0)+coalesce(hrs_lpnadmin_ctr,0)\n +coalesce(hrs_cna_ctr,0)+coalesce(hrs_natrn_ctr,0)+coalesce(hrs_medaide_ctr,0)) as ctr,\n count(distinct provnum) as facilities\n from public.pbj_daily_nurse_staffing\n where snapshot_quarter = '2025Q2' and mds_census > 0\n group by state\n)\nselect\n state,\n facilities,\n round(100.0*ctr/nullif(tot,0), 2) as agency_share_pct,\n round(100.0*ctr/(select sum(ctr) from s), 1) as share_of_national_agency_pct\nfrom s\nwhere tot > 0\norder by agency_share_pct desc;\n-- highest: VT 25.13 / 0.9 · PA 13.40 / 12.0 · NH 13.36 / 1.1 · NM 12.73 / 0.9\n-- AK 12.53 / 0.2 · NJ 12.46 / 6.9 · ND 12.44 / 0.9 · ME 10.91 / 0.8\n-- NY 10.79 / 13.8\n-- lowest: AL 1.01 · FL 1.53 · AR 1.89 · NV 2.46 · TX 2.55\n\n-- ── (4) Per-facility distribution of agency reliance ──────────────────────────\n-- 41.5% of homes bought ZERO agency hours; the reliance that exists concentrates\n-- in a small tail. 854 homes (>=25%) carry 41.5% of all agency hours; 86 homes\n-- ran majority-agency (>=50%); 19 topped 75%.\nwith f as (\n select\n provnum,\n sum(coalesce(hrs_rn,0)+coalesce(hrs_rndon,0)+coalesce(hrs_rnadmin,0)\n +coalesce(hrs_lpn,0)+coalesce(hrs_lpnadmin,0)\n +coalesce(hrs_cna,0)+coalesce(hrs_natrn,0)+coalesce(hrs_medaide,0)) as tot,\n sum(coalesce(hrs_rn_ctr,0)+coalesce(hrs_rndon_ctr,0)+coalesce(hrs_rnadmin_ctr,0)\n +coalesce(hrs_lpn_ctr,0)+coalesce(hrs_lpnadmin_ctr,0)\n +coalesce(hrs_cna_ctr,0)+coalesce(hrs_natrn_ctr,0)+coalesce(hrs_medaide_ctr,0)) as ctr\n from public.pbj_daily_nurse_staffing\n where snapshot_quarter = '2025Q2' and mds_census > 0\n group by provnum\n), p as (\n select provnum, tot, ctr, 100.0*ctr/nullif(tot,0) as pct from f where tot > 0\n)\nselect\n count(*) as facilities, -- 14,537\n count(*) filter (where ctr = 0) as zero_agency, -- 6,037 (41.5%)\n count(*) filter (where pct > 0 and pct < 25) as gt0_lt25, -- 7,646 (52.6%)\n count(*) filter (where pct >= 25 and pct < 50) as ge25_lt50, -- 768 (5.3%)\n count(*) filter (where pct >= 50 and pct < 75) as ge50_lt75, -- 67 (0.5%)\n count(*) filter (where pct >= 75) as ge75, -- 19 (0.1%)\n count(*) filter (where pct >= 25) as ge25_total, -- 854 (5.9%)\n count(*) filter (where pct >= 50) as majority_agency, -- 86\n round(percentile_cont(0.5) within group (order by pct)::numeric, 2) as median_pct, -- 0.50\n -- Share of ALL national agency hours delivered by the >=25%-reliant tail.\n round((select 100.0*sum(ctr) from p where pct >= 25)\n / (select sum(ctr) from p), 1) as ge25_share_of_agency_hrs -- 41.5\nfrom p;\n\n-- ── (5) Agency share of RN DIRECT-CARE hours only ─────────────────────────────\n-- The narrower denominator referenced in the body (and the headline of the\n-- companion zero-RN study): contract share of the RN direct-care job code alone\n-- (hrs_rn), excluding RN admin + director-of-nursing. Higher than the all-RN\n-- (5.78%) and all-workforce (5.98%) figures because the licensed bedside roles\n-- are the hardest to staff permanently.\nselect\n round(100.0*sum(coalesce(hrs_rn_ctr,0))\n / nullif(sum(coalesce(hrs_rn,0)), 0), 2) as rn_directcare_agency_pct -- 7.28\nfrom public.pbj_daily_nurse_staffing\nwhere snapshot_quarter = '2025Q2' and mds_census > 0;", "license": "U.S. Government Works (federal sources; 17 U.S.C. §105)", "generated_by": "Fonteum — https://fonteum.com", "notes": "Aggregate, source-traced figures frozen to the snapshot above. Reproduce by running reproducible_sql against the cited federal dataset; no per-entity records are included." }