Medicare Part D Prescriber PUF (prescribing by provider × drug × year)

What it is

1. 1CMS Medicare Part D "Prescriber by Drug" public file (MUP_DPR) silver-layer fact: one row per prescriber per drug per year giving the volume and total gross drug cost of that drug's Part D claims written by that prescriber.
2. 2Used as the prescriber-level drug-utilization and drug-spend substrate (who prescribes what, how much it costs Medicare Part D, and how that shifts year over year), and to link prescribers/specialties to specific brand/generic drugs.

Grain

How to read it

1. 1Example: total Medicare Part D gross drug cost and claim volume for a drug by specialty/state in a year.
2. 2SELECT prescriber_state, sum(total_drug_cost_usd) AS gross_cost, sum(total_claims) AS claims, count(DISTINCT prescriber_npi) AS prescribers FROM fact_part_d_prescriber WHERE is_current AND data_year=2023 AND brand_name='Eliquis' AND prescriber_type='Cardiology' GROUP BY prescriber_state ORDER BY gross_cost DESC.
3. 3Answers which states' cardiologists drive the most Part D spend on Eliquis and at what volume.
4. 4Sum claims/cost, not beneficiaries (suppressed in 57% of rows).

Traps

1. 1The grain needs BOTH brand_name and generic_name -- it is NOT (npi, generic, year) [only 97.6M distinct] nor (npi, brand, year) [only 102.9M distinct]. Only (npi, brand, generic, year) hits 103,105,990 = exact row count. Dropping either column double-counts a prescriber's spend.
2. 2Claims floor = 11, and suppressed combos are DELETED not flagged: min(total_claims)=11 and 0 rows have <11. A prescriber-drug pair absent from the table means it was either never prescribed OR had <=10 claims -- you cannot tell which, and absence must NOT be read as zero. This biases per-prescriber and long-tail-drug totals downward.
3. 3Two independent suppression systems on the >=65 block: ge65_suppression_flag (non-null => ALL five ge65_* metrics NULL: '*'=<=10 benes, '#'=count 11-110) AND a separate ge65_bene_suppression_flag that NULLs only ge65_total_beneficiaries. NULL in any ge65 metric = redacted, never zero. The non-suppressed total_* metrics (claims/30day_fills/day_supply/drug_cost) are ALWAYS populated.
4. 4total_beneficiaries (the all-ages bene count) is NULL in 57% of rows due to 1-10-beneficiary privacy suppression -- you cannot reliably sum beneficiary counts, only claims/fills/cost. Beneficiary-based denominators are heavily missing.
5. 5total_drug_cost_usd is GROSS pharmacy cost (ingredient + dispensing fee + tax), NOT the Medicare-paid amount and NOT net of manufacturer rebates -- do not treat it as Medicare program net spend or as a negotiated/contracted price.
6. 6Four columns (data_year, observed_period, effective_period_start, effective_period_end) all encode the same single calendar year; one source file per year. is_current is 100% true and asserted_at is ingestion date (2026-05), so none of the snapshot/provenance columns subdivide the grain. drug_source_version_id is entirely NULL and vintage_tag is the constant 'none'.
7. 7prescriber_type is per-file/per-year derived (ranked Claim-Specialty > NPPES-Specialty > NPPES-Taxonomy via prescriber_type_source), so the SAME NPI can carry different specialties across years -- do not treat prescriber_type as a stable attribute of the NPI.

Columns (38)

Query it

describe_data_source(source_fqn="drug_silver.fact_part_d_prescriber")     # the card, live
query_substrate(sql="SELECT * FROM drug_silver.fact_part_d_prescriber WHERE prescriber_npi = '<value>' AND brand_name = '<value>' AND generic_name = '<value>' AND data_year = '<value>' LIMIT 100")