Medicaid State Drug Utilization (by state × NDC × quarter)

What it is

1. 1CMS State Drug Utilization Data (SDUD): quarterly counts of Medicaid-covered outpatient drug prescriptions, units dispensed, and dollars reimbursed, broken out by state, drug package (NDC-11), and Medicaid program type (fee-for-service vs managed care).
2. 2It is the public record of what state Medicaid programs actually paid for drugs, used as a public benchmark/denominator for drug cost and utilization analysis.

Grain

How to read it

1. 1National 2024 spend on a specific drug across all of Medicaid: SELECT data_year, data_quarter, SUM(total_amount_reimbursed_usd) AS spend, SUM(number_of_prescriptions) AS rx FROM drug_silver.fact_drug_medicaid_utilization WHERE state='XX' AND data_year=2024 AND suppression_used=false AND ndc11='<ndc>' GROUP BY data_year, data_quarter; — returns quarterly Medicaid-paid dollars and Rx counts for that drug package nationally (use state='XX' for national, or state<>'XX' grouped by state for a state breakdown), and SUM utilization_type FFSU+MCOU for the full program.

Traps

1. 1HALF THE TABLE HAS NO NUMBERS: suppression_used=true on 12.78M rows (~49.5%) and on those EVERY metric (units_reimbursed, number_of_prescriptions, all *_amount_reimbursed_usd) is NULL — CMS privacy-suppressed small cells. Always filter WHERE suppression_used=false (or guard NULLs) before SUM/AVG, and remember unsuppressed rows always have >=11 prescriptions, so low-volume drugs are systematically missing.
2. 2state='XX' IS THE NATIONAL ROLL-UP, NOT A STATE. Including it alongside per-state rows double-counts. Worse, XX is NOT the simple sum of the visible state rows: for 2023-Q1 unsuppressed, XX total=$27.86B vs sum-of-states=$26.14B, because the national figure also absorbs cells that were suppressed at the state level. Pick state<>'XX' for state analysis OR state='XX' for national totals — never mix, and don't reconstruct national by summing states (you'll undercount).
3. 3GRAIN NEEDS BOTH utilization_type AND state. ndc11 alone is far from unique (71,806 distinct vs 25.8M rows); dropping utilization_type drops to 19.16M distinct (FFSU and MCOU rows collide on the same drug/state/quarter), and dropping state collapses to 1.56M. The full minimal key is (utilization_type, state, ndc11, data_year, data_quarter).
4. 4labeler_code, product_code, package_size, observed_period, effective_period_start/end are all DERIVED, not independent: ndc11 is byte-for-byte concat(labeler_code,product_code,package_size) (0 mismatches) and the date columns are just the quarter boundaries from data_year/data_quarter. They add no grain and shouldn't be treated as separate keys. Note package_size is the NDC package CODE, not a dispensed quantity.
5. 5product_name is unreliable free text — one ndc11 can appear under 12+ different name spellings across filings. Identify drugs by ndc11 (or labeler/product code), never by product_name.
6. 6Provenance/SCD columns are inert in this snapshot: is_current=true, cms_vintage_sequence=1, parse_revision=1, vintage_tag='none', asserted_at=2026-05-16, drug_source_version_id=NULL are all single-valued. is_current filtering is currently a no-op and drug_source_version_id can't be joined yet — they're forward-compat scaffolding, so don't read meaning into them today. PR (Puerto Rico) data exists only from 2023 onward (52 states 2020-2022, 53 from 2023).

Columns (30)

Query it

describe_data_source(source_fqn="drug_silver.fact_drug_medicaid_utilization")     # the card, live
query_substrate(sql="SELECT * FROM drug_silver.fact_drug_medicaid_utilization WHERE utilization_type = '<value>' AND state = '<value>' AND ndc11 = '<value>' AND data_year = '<value>' AND data_quarter = '<value>' LIMIT 100")