{ "study": { "slug": "glp1-pharma-payments-to-prescribers-2024", "title": "GLP-1 makers paid 120,237 Medicare prescribers $32.8 million in 2024 — and the prescribers they paid wrote far more", "standfirst": "In 2024, the makers of Ozempic and Mounjaro paid $32.8 million to 120,237 Medicare prescribers for meals, talks, and travel tied to GLP-1 drugs. Prescribers who accepted a payment wrote 78% more GLP-1 prescriptions than those who did not — a correlation this study reports at the group level, without inferring cause.", "desk": "financial-distress", "article_type": "Original Research", "published": "2026-06-12", "issue": 65, "doi": "10.5072/fonteum/glp1-pharma-payments-to-prescribers-2024", "url": "https://fonteum.com/research/glp1-pharma-payments-to-prescribers-2024", "methodology_version": "glp1-payments-prescribing/v1" }, "data_as_of": "2026-06-12", "datasets": [ { "slug": "cms-open-payments", "name": "CMS Open Payments", "publisher": "CMS — Open Payments", "upstream_url": null }, { "slug": "cms-part-d-prescribers", "name": "CMS Medicare Part D Prescribers", "publisher": "CMS — Medicare Part D Prescribers, by Provider and Drug", "upstream_url": null } ], "key_findings": [ { "number": "$32.8M", "finding": "in GLP-1-related payments from Novo Nordisk and Eli Lilly to 120,237 Medicare prescribers in 2024, across 691,316 separate transfers — 96.8% of them meals", "dataset": "cms-open-payments" }, { "number": "1.78x", "finding": "more GLP-1 prescriptions from the average prescriber who took a maker payment (134 claims) than from one who took none (75) — a group-level correlation, not a causal claim", "dataset": "cms-part-d-prescribers" }, { "number": "52%", "finding": "of all 19.5 million GLP-1 Part D claims came from the 38% of GLP-1 prescribers who received a payment from a GLP-1 maker", "dataset": "cms-part-d-prescribers" }, { "number": "7.5x", "finding": "as many GLP-1 claims from the 619 prescribers paid $1,000 or more (565 on average) as from prescribers paid nothing (75)", "dataset": "cms-open-payments" } ], "faqs": [ { "q": "How much did GLP-1 drugmakers pay US prescribers in 2024?", "a": "Novo Nordisk and Eli Lilly — the makers of Ozempic, Mounjaro, Trulicity, Rybelsus, Victoza, Wegovy, and Zepbound — made $32.8 million in general payments tied to GLP-1 products to 120,237 Medicare prescribers in 2024. That was spread across 691,316 separate transfers, of which 96.8% were meals. The figure excludes research grants, which mostly flow to trial sites rather than to prescribers." }, { "q": "Do prescribers who accept payments prescribe more GLP-1 drugs?", "a": "On average, yes — at the group level. GLP-1 prescribers who received at least one GLP-1-product payment from a maker wrote 134 GLP-1 Part D prescriptions on average in 2024, versus 75 for prescribers who received none — about 78% more. The median was 82 versus 44. This is an association between two public datasets, not evidence that the payment changed any prescriber's decision." }, { "q": "Does this prove the payments caused more prescribing?", "a": "No. This study reports a correlation and stops there. The direction of cause cannot be determined from these data. Manufacturers tend to direct meals and talks toward clinicians who already see many diabetes patients and already prescribe heavily, so high prescribing may attract payments as much as payments may encourage prescribing. Both can be true at once. The study does not, and cannot, attribute any prescription to any payment." }, { "q": "Which company paid prescribers more — Novo Nordisk or Eli Lilly?", "a": "By dollars, Eli Lilly paid more: $17.2 million to 72,642 prescribers, versus Novo Nordisk's $15.6 million to 99,338 prescribers. Novo Nordisk reached more individual prescribers with smaller average amounts; Eli Lilly's total was lifted by speaker and faculty compensation concentrated among a few hundred clinicians." }, { "q": "What kinds of payments were these?", "a": "Two economies. Meals (food and beverage) were 96.8% of all transfers but 40% of the dollars, averaging about $20 each and reaching nearly 120,000 prescribers. Speaker and faculty compensation was the opposite: just 644 prescribers received it, but it accounted for $17.9 million — 55% of every dollar. Travel, consulting fees, and education made up the small remainder." }, { "q": "Does this study name individual doctors?", "a": "No. Every figure is a class-, cohort-, or company-level total. No prescriber is named, ranked, profiled, or attached to a payment. Both the CMS Open Payments and Part D Prescribers files identify individuals, but this study reports only aggregates by design, in line with its correlation-only framing." }, { "q": "Why measure only GLP-1 products, not all payments from these companies?", "a": "Novo Nordisk and Eli Lilly make many drugs, including insulins and cancer therapies. To keep the link to GLP-1 prescribing honest, this study counts only payments whose recorded product is a GLP-1 brand — Ozempic, Wegovy, Rybelsus, Victoza, Saxenda, Mounjaro, Trulicity, or Zepbound — rather than every dollar the two firms paid." }, { "q": "Can I reproduce these figures?", "a": "Yes. Every number is a read-only aggregate of two CMS public-use files joined on the federal NPI: Open Payments for program year 2024 and the Part D Prescribers file for data year 2024. The exact SQL, including the manufacturer identifiers and the GLP-1 product and generic sets, is in the reproducibility block below." } ], "citation": { "apa": "Fonteum Research. (2026, June 12). GLP-1 makers paid 120,237 Medicare prescribers $32.8 million in 2024 — and the prescribers they paid wrote far more. Fonteum Research, Issue 65. https://doi.org/10.5072/fonteum/glp1-pharma-payments-to-prescribers-2024", "url": "https://fonteum.com/research/glp1-pharma-payments-to-prescribers-2024" }, "reproducible_sql": "-- GLP-1 drug-maker payments vs GLP-1 prescribing, 2024 — fully reproducible cross-dataset join.\n--\n-- The angle no one else can publish: join the money (CMS Open Payments) to the\n-- prescribing (CMS Medicare Part D Prescribers) on the SAME federal NPI, for the\n-- GLP-1 drug class only, and measure how manufacturer payments line up with\n-- prescribing volume — at the aggregate cohort level, never per physician.\n--\n-- Sources (both public, US-Government-Works):\n-- public.cms_open_payments PY2024 (Open Payments / Sunshine Act, ~16.2M records)\n-- public.cms_part_d_prescribers DY2024 (Part D Prescribers by Provider and Drug, ~28.0M rows)\n-- Join key: cms_open_payments.recipient_npi = cms_part_d_prescribers.prescriber_npi.\n-- Capture date: 2026-06-12 (read-only against the live tables; no view, no migration).\n--\n-- GLP-1 makers = Novo Nordisk + Eli Lilly, the two firms that make essentially the\n-- entire modern GLP-1 market. Their manufacturer_id values were read directly from\n-- the table (a LIMIT-bounded scan short-circuits on the indexed product match):\n-- 100000000163 Novo Nordisk AS (Ozempic, Wegovy, Rybelsus, Victoza, Saxenda)\n-- 100000000144 Novo Nordisk Inc (Victoza and earlier filings)\n-- 100000000066 Lilly USA, LLC (Mounjaro, Trulicity, Zepbound)\n-- A \"GLP-1-product payment\" is a payment whose product_name_1 names a GLP-1 brand.\n-- We count GENERAL payments to prescribers (physicians + non-physician practitioners),\n-- the standard \"payments to prescribers\" definition; research grants (which flow to\n-- institutions and trial sites, not to prescribers as marketing) are reported and\n-- excluded separately in query 4.\n-- Every headline figure in the study resolves to one of the rows below.\n\n-- 1. GLP-1-product GENERAL payments to prescribers, by maker and by nature of payment.\n-- Uses the manufacturer_id index, so the scan is bounded to Novo + Lilly rows.\nWITH gp AS (\n SELECT\n CASE WHEN manufacturer_id IN ('100000000163','100000000144') THEN 'Novo Nordisk'\n ELSE 'Eli Lilly' END AS maker,\n nature_of_payment, recipient_npi, total_amount_usd, number_of_payments\n FROM public.cms_open_payments\n WHERE program_year = 2024\n AND record_type = 'general'\n AND recipient_type IN ('Covered Recipient Physician',\n 'Covered Recipient Non-Physician Practitioner')\n AND manufacturer_id IN ('100000000163','100000000144','100000000066')\n AND (product_name_1 ILIKE 'ozempic%' OR product_name_1 ILIKE 'wegovy%'\n OR product_name_1 ILIKE 'rybelsus%' OR product_name_1 ILIKE 'victoza%'\n OR product_name_1 ILIKE 'saxenda%' OR product_name_1 ILIKE 'mounjaro%'\n OR product_name_1 ILIKE 'trulicity%' OR product_name_1 ILIKE 'zepbound%')\n)\nSELECT 'TOTAL' AS grp,\n count(*) AS records, count(DISTINCT recipient_npi) AS recipients,\n round(sum(total_amount_usd)) AS dollars FROM gp\nUNION ALL\nSELECT 'maker:'||maker, count(*), count(DISTINCT recipient_npi), round(sum(total_amount_usd))\n FROM gp GROUP BY maker\nUNION ALL\nSELECT 'nature:'||nature_of_payment, count(*), count(DISTINCT recipient_npi), round(sum(total_amount_usd))\n FROM gp GROUP BY nature_of_payment\nORDER BY dollars DESC;\n-- TOTAL 691,316 records 120,237 recipients $32,766,256\n-- nature: Compensation ... faculty or speaker 15,145 records 644 recipients $17,883,925 (54.6% of $)\n-- maker: Eli Lilly 265,012 records 72,642 recipients $17,209,752\n-- maker: Novo Nordisk 426,304 records 99,338 recipients $15,556,504\n-- nature: Food and Beverage 668,858 records 119,810 recipients $13,204,078 (96.8% of records; avg $19.74)\n-- nature: Travel and Lodging 4,805 records 517 recipients $858,595\n-- nature: Consulting Fee 250 records 126 recipients $739,050\n-- nature: Education 2,258 records 1,683 recipients $80,609\n\n-- 2. THE JOIN — GLP-1 prescribing for prescribers who DID vs DID NOT receive a\n-- GLP-1-product general payment. glp1_rx (Part D, GLP-1 generics) and glp1_pay\n-- (the maker payments above, one row per recipient NPI) are both small, so the\n-- LEFT JOIN on NPI is a cheap hash join. Cohort sums give the universe totals.\nWITH glp1_rx AS (\n SELECT prescriber_npi AS npi, sum(total_claims) AS claims, sum(total_drug_cost) AS cost\n FROM public.cms_part_d_prescribers\n WHERE data_year = 2024\n AND generic_name IN ('Semaglutide','Tirzepatide','Dulaglutide',\n 'Liraglutide','Exenatide','Lixisenatide','Albiglutide')\n GROUP BY prescriber_npi\n),\nglp1_pay AS (\n SELECT recipient_npi AS npi, sum(total_amount_usd) AS pay\n FROM public.cms_open_payments\n WHERE program_year = 2024\n AND record_type = 'general'\n AND recipient_type IN ('Covered Recipient Physician',\n 'Covered Recipient Non-Physician Practitioner')\n AND manufacturer_id IN ('100000000163','100000000144','100000000066')\n AND (product_name_1 ILIKE 'ozempic%' OR product_name_1 ILIKE 'wegovy%'\n OR product_name_1 ILIKE 'rybelsus%' OR product_name_1 ILIKE 'victoza%'\n OR product_name_1 ILIKE 'saxenda%' OR product_name_1 ILIKE 'mounjaro%'\n OR product_name_1 ILIKE 'trulicity%' OR product_name_1 ILIKE 'zepbound%')\n GROUP BY recipient_npi\n)\nSELECT\n CASE WHEN p.npi IS NOT NULL THEN 'received_glp1_payment' ELSE 'no_glp1_payment' END AS cohort,\n count(*) AS prescribers,\n sum(r.claims) AS glp1_claims,\n round(sum(r.cost)) AS glp1_cost,\n round(avg(r.claims), 1) AS avg_claims,\n round(avg(r.cost)) AS avg_cost,\n percentile_cont(0.5) WITHIN GROUP (ORDER BY r.claims) AS median_claims\nFROM glp1_rx r\nLEFT JOIN glp1_pay p ON p.npi = r.npi\nGROUP BY 1;\n-- received_glp1_payment 75,905 prescribers 10,195,741 claims $13,079,331,733 avg 134.3 avg $172,312 median 82\n-- no_glp1_payment 123,710 prescribers 9,310,328 claims $11,398,976,526 avg 75.3 avg $92,143 median 44\n--\n-- Universe (cohort sums): 199,615 GLP-1 prescribers · 19,506,069 claims · $24,478,308,259 cost.\n-- Paid cohort = 75,905 / 199,615 = 38.0% of prescribers, yet\n-- 52.3% of claims (10,195,741 / 19,506,069) and 53.4% of cost ($13.08B / $24.48B).\n-- Mean ratio 134.3 / 75.3 = 1.78x (78% more); median ratio 82 / 44 = 1.86x.\n\n-- 3. Dose-response gradient — average GLP-1 prescribing by payment tier received.\n-- Same two CTEs as query 2; only the final grouping differs.\nWITH glp1_rx AS (\n SELECT prescriber_npi AS npi, sum(total_claims) AS claims, sum(total_drug_cost) AS cost\n FROM public.cms_part_d_prescribers\n WHERE data_year = 2024\n AND generic_name IN ('Semaglutide','Tirzepatide','Dulaglutide',\n 'Liraglutide','Exenatide','Lixisenatide','Albiglutide')\n GROUP BY prescriber_npi\n),\nglp1_pay AS (\n SELECT recipient_npi AS npi, sum(total_amount_usd) AS pay\n FROM public.cms_open_payments\n WHERE program_year = 2024\n AND record_type = 'general'\n AND recipient_type IN ('Covered Recipient Physician',\n 'Covered Recipient Non-Physician Practitioner')\n AND manufacturer_id IN ('100000000163','100000000144','100000000066')\n AND (product_name_1 ILIKE 'ozempic%' OR product_name_1 ILIKE 'wegovy%'\n OR product_name_1 ILIKE 'rybelsus%' OR product_name_1 ILIKE 'victoza%'\n OR product_name_1 ILIKE 'saxenda%' OR product_name_1 ILIKE 'mounjaro%'\n OR product_name_1 ILIKE 'trulicity%' OR product_name_1 ILIKE 'zepbound%')\n GROUP BY recipient_npi\n)\nSELECT\n CASE WHEN p.pay IS NULL THEN '0 none'\n WHEN p.pay < 100 THEN '1 $1-99'\n WHEN p.pay < 1000 THEN '2 $100-999'\n ELSE '3 $1,000+' END AS pay_tier,\n count(*) AS prescribers,\n round(avg(r.claims), 1) AS avg_glp1_claims,\n round(avg(r.cost)) AS avg_glp1_cost,\n percentile_cont(0.5) WITHIN GROUP (ORDER BY r.claims) AS median_claims\nFROM glp1_rx r\nLEFT JOIN glp1_pay p ON p.npi = r.npi\nGROUP BY 1 ORDER BY 1;\n-- 0 none 123,710 prescribers avg 75.3 claims avg $92,143 median 44\n-- 1 $1-99 40,665 prescribers avg 105.9 claims avg $132,547 median 66\n-- 2 $100-999 34,621 prescribers avg 160.0 claims avg $207,992 median 102\n-- 3 $1,000+ 619 prescribers avg 564.6 claims avg $789,049 median 409\n-- The 619 prescribers paid $1,000+ averaged 564.6 GLP-1 claims — 7.5x the 75.3 of\n-- prescribers who took no GLP-1-product payment. Monotonic, and strictly correlational.\n\n-- 4. Research payments — reported and excluded from the prescriber-marketing total above.\n-- GLP-1-product RESEARCH payments flow overwhelmingly to entities and teaching\n-- hospitals (trial sites), not to prescribers, so they are not part of the\n-- \"payments to prescribers\" measure.\nSELECT record_type,\n count(*) AS records, count(DISTINCT recipient_npi) AS recipients,\n round(sum(total_amount_usd)) AS dollars\nFROM public.cms_open_payments\nWHERE program_year = 2024\n AND manufacturer_id IN ('100000000163','100000000144','100000000066')\n AND (product_name_1 ILIKE 'ozempic%' OR product_name_1 ILIKE 'wegovy%'\n OR product_name_1 ILIKE 'rybelsus%' OR product_name_1 ILIKE 'victoza%'\n OR product_name_1 ILIKE 'saxenda%' OR product_name_1 ILIKE 'mounjaro%'\n OR product_name_1 ILIKE 'trulicity%' OR product_name_1 ILIKE 'zepbound%')\nGROUP BY record_type ORDER BY dollars DESC;\n-- research 36,187 records (to entities / teaching hospitals / 51 physicians) $33,639,331\n-- general 691,325 records $32,783,306\n-- (The general row here is slightly larger than query 1's $32,766,256 because it\n-- also includes the few teaching-hospital general payments that query 1's\n-- recipient_type filter excludes; the prescriber-marketing figure is query 1.)", "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." }