💊 NADAC Drug Acquisition Cost Scraper

🚀 Export the National Average Drug Acquisition Cost benchmark in seconds. Weekly pharmacy acquisition prices for every NDC in the U.S. retail market. The same benchmark Medicaid uses for reimbursement.

The NADAC Scraper turns the official Medicaid drug-pricing feed into clean rows. Each record carries the NDC, drug description, NADAC per unit, pricing unit, pharmacy type, brand vs generic classification, OTC flag, effective date, the corresponding generic NADAC, and the as-of file date. NADAC (National Average Drug Acquisition Cost) is produced by the Centers for Medicare & Medicaid Services from retail-pharmacy invoice surveys and is the most widely cited public benchmark for U.S. drug acquisition prices.

Coverage spans the full NADAC reference file: brand and generic drugs, both prescription and OTC, across every dosage form on the U.S. market. Files are republished weekly with effective dates that go back to 2013, giving you a long historical series for trend analysis.

🎯 Target Audience	💡 Primary Use Cases
PBM analysts, drug-pricing researchers, employer health plans, 340B program managers, formulary teams, payer actuaries, pharmacy chains, health-policy academics	Reimbursement benchmarking, MAC list construction, drug-pricing trend studies, brand-to-generic substitution analysis, 340B ceiling-price modelling

📋 What the NADAC Scraper does

Eight filtering workflows in a single run:

• 💊 Search by drug name. Partial match on the NDC description (e.g. METFORMIN, IBUPROFEN).
• 🔖 Search by NDC. Resolve a specific 11-digit National Drug Code.
• 🏷️ Filter by classification. Brand (B) or generic (G) only.
• 💉 Filter by dosage form. Tablet, capsule, solution, injection, suspension, cream, ointment, gel, syrup, elixir, powder, patch, inhaler, spray, drops, lotion, suppository, foam.
• 🛒 Filter by OTC flag. Over-the-counter only, prescription only, or both.
• 📅 Filter by effective date range. Lower and upper bounds on the price effective date.
• 🔁 Sort newest or oldest first. Walk the history forwards or backwards.

Each record includes the NDC, drug description, NADAC per unit (USD), the pricing unit (each, mL, gm), pharmacy type indicator, OTC flag, explanation code, brand vs generic classification, the corresponding generic drug NADAC and its effective date for substitution analysis, the file as-of date, and a link to the canonical Medicaid dataset page.

💡 Why it matters: building a NADAC pipeline yourself means cleaning weekly CSV refreshes, joining brand-generic pairs by hand, and watching for CMS dataset version rotations. This Actor returns clean rows on every run, filtered server-side.

📊 Data fields

Each record includes: asOfDate, classificationForRateSetting, effectiveDate, explanationCode, nadacPerUnit, ndc, ndcDescription, otc, pharmacyTypeIndicator, pricingUnit, scrapedAt, sourceUrl. These field names come straight from the actor's dataset schema, so what you see here is what lands in your dataset.

🚀 How to use

1. 📝 Sign up. Create a free account with $5 credit (takes 2 minutes).
2. 🌐 Open the Actor. Go to the NADAC Drug Acquisition Cost Scraper page on the Apify Store.
3. 🎯 Set input. Pick a drug name, NDC, classification, or date range and set maxItems.
4. 🚀 Run it. Click Start and let the Actor collect your NADAC rows.
5. 📥 Download. Grab your results in the Dataset tab as CSV, Excel, JSON, or XML.

⏱️ Total time from signup to a downloaded NADAC dataset: 3-5 minutes. No coding required.

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💡 Pro Tip: browse the complete ParseForge collection for more healthcare and policy scrapers.

⚠️ Disclaimer: this Actor is an independent tool and is not affiliated with, endorsed by, or sponsored by the Centers for Medicare & Medicaid Services, data.medicaid.gov, or any drug manufacturer. All trademarks mentioned are the property of their respective owners. Only publicly available open pricing data is collected.

🆘 Need Help?

If you hit a bug, have questions about setup, or need a scraper we haven't built yet, open our contact form or write to parseforge@protonmail.com. We also take on paid custom data projects.

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