Medicaid State Drug Utilization Data for market access planning
For a pharmaceutical launch or a market access team, the two questions are what is currently being prescribed and who pays for it. Medicaid State Drug Utilization Data answers the first at state grain quarterly, and Medicare Part D answers it at geography and prescriber grain annually. Both are free.
6 min read
What SDUD contains
By state, by quarter, by NDC: prescriptions reimbursed, units reimbursed, total amount reimbursed, and the Medicaid and non-Medicaid split. Coverage runs back many years, which makes trend analysis straightforward — uptake curves for comparator products in the same class are directly observable.
Sizing a therapeutic market
Take the drug class, pull every NDC in it, aggregate by state and quarter, and you have the Medicaid share of the class by state over time. Against state Medicaid enrollment this normalizes to a per-beneficiary rate, which is what makes states comparable when their populations differ by an order of magnitude.
Example pull from RepVector
Retained drug utilization rows for a resolved state.
Medicaid drug utilization for Arizona (2026 file)
| State | Period | Product | NDC | Channel | Prescriptions |
|---|---|---|---|---|---|
| AZ | 2026 Q1 | FLUTICASON | 00054327099 | Managed care | 15,610 |
| AZ | 2026 Q1 | ELIQUIS | 00003089421 | Managed care | 6,276 |
| AZ | 2026 Q1 | INSULIN LI | 00002822259 | Managed care | 4,468 |
| AZ | 2026 Q1 | TRULICITY | 00002143480 | Managed care | 3,306 |
| AZ | 2026 Q1 | ALBUTEROL | 00054074287 | Managed care | 3,286 |
| AZ | 2026 Q1 | INSULIN LI | 00002773701 | Managed care | 3,236 |
First 6 of 100 retained rows. See the full field list for Medicaid State Drug Utilization Data (SDUD)
Adding the Medicare side
Part D geography files give prescription counts and costs by drug and geography; Part D prescriber files break the same down to individual clinicians. For products with an older patient population this is usually the larger half of the market and the more actionable one, because it names prescribers.
Prioritizing access work
States differ in preferred drug list practice, and the observable consequence is visible in the utilization data. A state where the class is heavily utilized but your comparator is absent is a formulary problem, not a demand problem — a different team and a different timeline than a state where the class itself is under-utilized.
Cautions
SDUD is state grain, not county — county-level claims are not published. Small cells are suppressed and must be treated as gaps rather than zeros. Rebates are not reflected, so amounts reimbursed are gross, not net.
What to take away
- SDUD gives observable class-level demand by state, quarterly.
- Normalize against Medicaid enrollment to compare states fairly.
- Part D names prescribers; SDUD does not.
- Amounts are gross of rebate and small cells are suppressed.
Put this to work
RepVector assembles this for a named market in one query
Describe the market and the offering. The datasets above are selected, resolved to your geography, pulled, scored, and returned as a ranked list with every figure sourced.
Related guides
- Behavioral health business development: mapping the referral universe for a county
SAMHSA facilities, IPF capacity, prescriber behaviour and Medicaid utilization, assembled for one county.
- Medicare Advantage penetration by county — and what it changes about your sales approach
Payer mix decides who authorizes and who pays. CMS publishes it monthly, by county, for free.
- Using NPI data for sales targeting — what NPPES can and cannot tell you
NPPES is the best free denominator in healthcare sales — and useless on its own. Here is what to join it to.