{
  "study": {
    "slug": "open-payments-by-state-2024",
    "title": "Industry payments to physicians by state: where the money lands",
    "standfirst": "Industry's $3.31 billion in 2024 general payments to physicians spread across 59 U.S. jurisdictions, but not in proportion to population. California led at $334.5 million, yet Pennsylvania ranked third and Massachusetts fourth on far fewer payments — Massachusetts averaged $1,031 per payment against Texas's $153. Where royalty recipients live, not where patients are, shapes the map.",
    "desk": "financial-distress",
    "article_type": "Original Research",
    "published": "2026-06-12",
    "issue": 63,
    "doi": "10.5072/fonteum/open-payments-by-state-2024",
    "url": "https://fonteum.com/research/open-payments-by-state-2024",
    "methodology_version": "open-payments/v1"
  },
  "data_as_of": "2026-01-23",
  "datasets": [
    {
      "slug": "cms-open-payments",
      "name": "CMS Open Payments",
      "publisher": "CMS — Open Payments",
      "upstream_url": null
    }
  ],
  "key_findings": [
    {
      "number": "$334.5M",
      "finding": "in 2024 general industry payments to California recipients — the most of any state — across 1.37 million payments, in a field spanning 59 U.S. jurisdictions",
      "dataset": "cms-open-payments"
    },
    {
      "number": "$1,031",
      "finding": "average payment in Massachusetts — the highest intensity of any large state, more than four times California's, because royalty recipients cluster near corporate research hubs",
      "dataset": "cms-open-payments"
    },
    {
      "number": "$170.3M",
      "finding": "in royalty payments from one company, BioNTech, to Pennsylvania recipients — enough on its own to lift Pennsylvania to third place nationally",
      "dataset": "cms-open-payments"
    },
    {
      "number": "$153",
      "finding": "average payment in Texas, across 1.45 million payments — the highest payment count of any state but among the lowest per-payment values: broad, shallow, meal-driven",
      "dataset": "cms-open-payments"
    }
  ],
  "faqs": [
    {
      "q": "Which state received the most industry payments in 2024?",
      "a": "California, at $334.5 million in general (non-research) Open Payments across 1.37 million payments. Florida ($304.7M) and Pennsylvania ($303.3M) follow, then Massachusetts ($225.1M), Texas ($221.2M) and New York ($211.9M). The data covers 59 jurisdictions, including states, DC, territories, and military-mail designations."
    },
    {
      "q": "Why do Pennsylvania and Massachusetts rank so high for their size?",
      "a": "Because large royalty payments land where the inventors live, not where patients are. Massachusetts averaged $1,031 per payment — driven by Genentech and Takeda royalties to local recipients — while Pennsylvania's third-place rank rests largely on $170.3 million in BioNTech royalty payments. Both states punch far above their population on industry dollars."
    },
    {
      "q": "What does the average payment per state tell you?",
      "a": "It separates marketing reach from royalty money. Texas logged the most payments of any state (1.45 million) but averaged just $153 each — a meal-driven footprint. Massachusetts logged only 218,000 payments but averaged $1,031 — a royalty-driven one. High dollars with low payment counts signal concentrated royalties; the reverse signals broad pharmaceutical marketing."
    },
    {
      "q": "Are these payments assigned by where the patient lives?",
      "a": "No. Payments are attributed to the recipient's state — the physician's or teaching hospital's listed location — not to any patient. A royalty paid to a surgeon in Pennsylvania counts toward Pennsylvania regardless of where that surgeon's patients or the company are based. The map shows where paid recipients are, not where care is delivered."
    },
    {
      "q": "Do bigger states always get more money?",
      "a": "Largely, but not strictly. Population and provider supply put California, Texas, Florida and New York near the top on payment volume. But total dollars break from population whenever a few large royalty recipients live in a state — which is why Massachusetts and Pennsylvania outrank Texas on dollars despite far fewer payments and smaller populations."
    },
    {
      "q": "Does a high state total mean physicians there are influenced more?",
      "a": "No. A state total reflects where paid recipients are located and the mix of payment types they receive — not any effect on prescribing or care. This is a geographic disclosure summary. Nothing here implies that physicians in higher-total states practice differently."
    },
    {
      "q": "Can I reproduce these state figures?",
      "a": "Yes. Every figure aggregates the cms_open_payments table (16,146,544 records, program year 2024) through the open_payments_by_state_mv materialized view across all 59 reported jurisdictions. The exact SQL is in the reproducibility block below. No individual physician is named; aggregates are by state only."
    }
  ],
  "citation": {
    "apa": "Fonteum Research. (2026, June 12). Industry payments to physicians by state: where the money lands. Fonteum Research, Issue 63. https://doi.org/10.5072/fonteum/open-payments-by-state-2024",
    "url": "https://fonteum.com/research/open-payments-by-state-2024"
  },
  "reproducible_sql": "-- Industry payments to physicians by state — reproducible query.\n--\n-- Source:   CMS Open Payments, program year 2024 (PGYR2024, published 2026-01-23).\n-- Table:    public.cms_open_payments (16,146,544 records, public, read-only).\n-- Scope:    General (non-research) payments only  (record_type = 'general').\n-- Grain:    recipient_state (59 jurisdictions). No recipient named.\n--\n-- Reads open_payments_by_state_mv and open_payments_largest_general_mv.\n\n-- Top states by general-payment dollars, with per-payment intensity:\nSELECT\n  recipient_state                                  AS state,\n  count(*)                                         AS payments,\n  round(sum(total_amount_usd))::bigint             AS total_usd,\n  round(sum(total_amount_usd) / count(*), 0)       AS avg_per_payment\nFROM public.cms_open_payments\nWHERE record_type = 'general' AND program_year = 2024 AND recipient_state IS NOT NULL\nGROUP BY recipient_state\nORDER BY total_usd DESC\nLIMIT 7;\n--  CA  334,476,958  1,370,072    244\n--  FL  304,725,698  1,347,271    226\n--  PA  303,312,778    665,965    455   <- royalty hub (BioNTech)\n--  MA  225,083,292    218,332  1,031   <- highest intensity (Genentech / Takeda royalties)\n--  TX  221,196,400  1,445,102    153   <- highest payment count, lowest intensity\n--  NY  211,935,025  1,032,846    205\n--  MO  120,120,501    326,555    368\n\n-- Jurisdiction count:\nSELECT count(DISTINCT recipient_state) AS jurisdictions                            -- 59\nFROM public.cms_open_payments\nWHERE record_type = 'general' AND program_year = 2024 AND recipient_state IS NOT NULL;\n\n-- Royalty geography — the largest individual general payments and their states\n-- (open_payments_largest_general_mv): BioNTech -> PA = $170.3M across ranks 2-4;\n-- Genentech + Takeda -> MA ~ $76.7M across ranks 7-12.\nSELECT rank, amount, nature, manufacturer, state\nFROM public.open_payments_largest_general_mv\nWHERE nature = 'Royalty or License'\nORDER BY rank\nLIMIT 12;\n--  2   88,596,339  Royalty or License  BioNTech SE   PA\n--  3   53,413,600  Royalty or License  BioNTech SE   PA\n--  4   28,269,974  Royalty or License  BioNTech SE   PA   (BioNTech->PA subtotal = 170,279,913)\n--  7   16,066,655  Royalty or License  Takeda        MA\n--  8   13,180,782  Royalty or License  Genentech     MA\n--  9   12,477,310  Royalty or License  Genentech     MA\n--  10  12,070,094  Royalty or License  Genentech     MA\n--  11  11,794,348  Royalty or License  Genentech     MA\n--  12  11,134,507  Royalty or License  Takeda        MA   (Genentech+Takeda->MA subtotal = 76,723,696)",
  "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."
}