Medicaid State Drug Utilization Data (SDUD)

What this dataset is

Quarterly, product-level Medicaid pharmacy claims by state: prescriptions filled, units reimbursed and dollars reimbursed for each national drug code, split between fee-for-service and managed care. The public record of what Medicaid actually pays for, drug by drug. It is published by Centers for Medicare & Medicaid Services and can be queried at state level, refreshed quarterly. Each row is an aggregate statistic for a geography, so results describe a market as a whole rather than a single account.

Why it is useful

• Separates markets with real clinical demand from markets that only have supply.
• Grounds a territory case in published prevalence and utilisation instead of assumption.
• Highlights where need outruns available capacity — usually the fastest conversations.

Questions it helps answer

• Where is the underlying clinical need highest relative to available capacity?
• How does utilisation here compare with the state and national picture?
• Which measures are suppressed or unreported for this geography?

How different verticals use it

RepVector is vertical-agnostic: this dataset is selected by what your question asks for, not by industry. These are common ways teams put it to work.

How RepVector uses it

1. You describe a market in plain language. The geography resolver turns the place you named into the exact identifiers this dataset needs — city to county to state, plus DMA where relevant — so a query at state runs without you knowing any codes.
2. The planner selects Medicaid State Drug Utilization Data (SDUD) only when your question matches what it actually reports. It is never included to pad a result.
3. Returned rows are normalised into the shared metric layer, so figures from Centers for Medicare & Medicaid Services line up with every other source in the same dashboard instead of sitting in an isolated table.
4. Quality checks score coverage and recency. If Centers for Medicare & Medicaid Services suppresses or omits a value for your geography, the gap is reported as a gap — nothing is estimated or filled in.
5. The result renders as charts, KPIs and, where the rows are entities, prioritised targets you can export to PDF, Excel, CSV or JSON.

What it does not tell you

• Aggregated and often suppressed at small geographies to protect privacy; gaps are reported rather than filled in.

How it fits with the rest of the catalog

Need data explains why a territory matters; provider and facility data explains who you visit once you get there. RepVector queries every selected dataset against the same resolved geography and time window, then normalises the results into one metric layer so they can be charted side by side. These datasets are the most common companions to Medicaid State Drug Utilization Data (SDUD):

Common questions