NLM RxClass — drug → class membership

What it is

1. 1A drug-classification crosswalk: it maps each clinical drug class (ATC anatomical/therapeutic groups, FDA Established Pharmacologic Class, and Mechanism of Action) to the RxNorm drug-ingredient concepts (rxcui) that belong to that class, sourced from the NIH/NLM RxClass API.
2. 2In the cost-data substrate it is the dimension that lets you roll commercial/Medicare drug prices up to therapeutic class or mechanism (e.g. "all platinum chemo agents", "all ATC L01 antineoplastics") rather than analyzing one rxcui at a time.

Grain

How to read it

1. 1To roll drug prices up to a therapeutic class or mechanism: join a price fact (e.g.
2. 2ASP/NADAC by rxcui) to this table on rxcui, then GROUP BY class_id/class_type.
3. 3Example — list all RxNorm ingredients in ATC subgroup L01 (antineoplastics): SELECT DISTINCT rxcui, rxnorm_name FROM fact_rxclass_membership WHERE class_type='ATC1-4' AND class_id='L01'; this returns the 277 drugs that define the 'chemo' cohort you would then price.
4. 4Use rela='DIRECT' to restrict to primary assignments.
5. 5Remember the rxcui join fans out across classes, so deduplicate/aggregate prices before summing.

Traps

1. 1MINIMAL GRAIN is just (class_id, rxcui) — 35,389 distinct = full row count. The profiler's reported 6-column key (adding source_id, bronze_object_key, s3_version_id, drug_source_version_id) is a SUPERSET inflated by 4 constant/NULL provenance columns; they add zero discrimination. Do not key or join on them.
2. 2drug_source_version_id is 100% NULL — never use it as a key or join column despite its name and its appearance in the auto-profiler key.
3. 3This is a single static snapshot: every temporal/provenance column (observed_period, asserted_at, effective_period_start='2026-06-03', effective_period_end='2099-12-31', cms_vintage_sequence=1, vintage_tag) holds exactly one value, and is_current is true for 100% of rows (no is_current=false rows exist). There is NO time series and is_current-filtering is currently a no-op — but it is bitemporally structured so future reloads will add vintages.
4. 4Many-to-many fan-out is intentional and large: a drug (rxcui) can be in 16+ classes and one class can have thousands of drugs. Joining this table to a price/fact table on rxcui MULTIPLIES rows by the number of classes — aggregate (e.g. price first, then roll up by class_id) or you will double-count spend.
5. 5class_id is a MULTI-SCHEME code: ATC1-4 rows use short ATC alphanumeric codes (incl. pseudo-code '0' = the 'all drugs' root bucket holding all 3,112 ATC drugs) while EPC/MOA rows use NDF-RT identifiers like 'N0000175413'. Always interpret class_id together with class_type; the same string space is not shared across schemes. Note 'ATC1-4' means levels 1-4 only (no level-5 chemical-substance codes).
6. 6rela_source is fully redundant with class_type (ATC1-4->ATC, EPC/MOA->DAILYMED) and class_type is fully determined by class_id — treat class_type/rela_source as derived labels, not independent filter dimensions. Use rela='DIRECT' to get only primary class assignments vs the larger INDIRECT (hierarchy-inferred) set.

Columns (22)

Query it

describe_data_source(source_fqn="drug_silver.fact_rxclass_membership")     # the card, live
query_substrate(sql="SELECT * FROM drug_silver.fact_rxclass_membership WHERE class_id = '<value>' AND rxcui = '<value>' LIMIT 100")