HCC risk-model coefficients (reference)

What it is

1. 1CMS risk-adjustment model COEFFICIENT tables for Medicare Advantage (Part C HCC) and Part D (RxHCC).
2. 2Each row is one numeric risk weight that a CMS scoring engine multiplies against a member's 0/1 flag for a demographic cell or disease/drug condition category, for a given payment year, model, and population segment.
3. 3It is the lookup that turns a beneficiary's conditions into a Medicare risk score (and therefore the capitated payment a plan receives).

Grain

How to read it

1. 1Score a Medicare Advantage member or look up a single weight.
2. 2Example: SELECT variable, label, coefficient_value FROM medicare_silver.dim_hcc_coefficients WHERE rate_year=2027 AND model_version='V28' AND segment='COMMUNITY_NA' AND variable IN ('F70_74','HCC1') — returns the age/sex demographic weight plus the HIV/AIDS condition weight for a 70-74 community, non-dual, aged-female member under the 2027 V28 model; summing the applicable variable weights for a member yields their raw risk score (which drives the capitated payment).
3. 3Always pin all of rate_year + model_version + segment.

Traps

1. 1No is_current / effective-date column. The snapshot key is rate_year, which maps 1:1:1 to bronze_publication_id and source_url; each grain key appears under exactly one publication (0 keys span both years), so there is no in-table version churn to dedup — but if more years/republications land later, you must filter by rate_year (and watch that the 2026 source is the MIDYEAR-FINAL ZIP while 2027 is the INITIAL ZIP, i.e. not the same release stage).
2. 2model_version is load-bearing for the VALUE, not just the grain. The same variable (e.g. HCC1='HIV/AIDS') exists in multiple models with different coefficients (V22 INSTITUTIONAL=1.747 vs V28=1.322). Never read a coefficient by variable+segment alone — always pin model_version. Also, each model has its OWN disjoint segment vocabulary (V22/V28: COMMUNITY_*/INSTITUTIONAL/NE_*; V21/V24: DIAL/GRAFT_*; RxHCC_V08: CE_*/NE_NE_*), so a segment value implies its model.
3. 3NULL label is meaningful and label is not authoritative. 864 NULL labels are concentrated entirely in V22 and V28 (432 each) on the new-enrollee Medicaid-interaction demographic terms (MCAID_*/NMCAID_NE*). Separately, the same variable carries cosmetically different label TEXT across models ('Female 0-34' vs 'Female, Age 0 - 34'), so join/dedup on variable, never on label.
4. 4coefficient_value zeros and negatives are real, not missing data (0 NULLs). 320 exact 0.0 values mean the condition is structurally not scored in that segment (e.g. renal HCCs in GRAFT/DIAL), and 6 negatives are legitimate Originally-ESRD adjusters in V21/V24 DIAL. The large tail up to 59.861 (RxHCC high-cost-drug categories like Cystic Fibrosis, Hemophilia) is genuine — do not clip it as outliers.
5. 5Coefficients are recalibrated year over year, so this is NOT a static dimension despite the dim_ name. Of the (model_version, variable, segment) combos, 2978 appear in BOTH 2026 and 2027 (many with changed values, e.g. RXHCC16 CE_LTI 4.514 -> 5.84) and 2849 appear in only one year. Always carry rate_year through any join to a member/score table.
6. 6asserted_at and parser_artifact_hash are pipeline lineage, not CMS dates. Both years were loaded on 2026-05-18 by the same parser build ('e10d366596d1c801'); asserted_at is the silver write time, so use rate_year as the effective/payment period.

Columns (11)

Query it

describe_data_source(source_fqn="medicare_silver.dim_hcc_coefficients")     # the card, live
query_substrate(sql="SELECT * FROM medicare_silver.dim_hcc_coefficients WHERE rate_year = '<value>' AND model_version = '<value>' AND variable = '<value>' AND segment = '<value>' LIMIT 100")