Medicare Advantage, Part D, Medicaid

Vlada Landscape benefit change at county grain, the same members' plan design in year Y and year Y+1, 2023 to 2027

medicare_semantic.fact_landscape_benefits_county_v1·live·mart

Rows

419,954

Columns

159

History

N/A

Card

nocolumns only

Typed tools

Superseded by medicare_semantic.fact_landscape_benefits_county_v2 (2026-10-13, the one MOOP rule: a filed $0 in-network MOOP is $0). This table reads a filed $0 as missing in the moop, moop5k and moopmax sums; every other column is identical in v2 (railed). Kept as built, as the rollback..

What it is

1fact_landscape_benefits_v1 with the county in the key (the launch lane's "your county, 2027"): the same cells and the same member-weighted numerator and denominator per metric and weight set (a = the Y design on every September-Y member, b = the Y+1 design on members whose plan continues in their county, r = routed through the crosswalk where held), built by the same SQL with the county kept.
2A NEW table, never a rewrite of v1.
3Checked 0-diff on 2026-09-30 three ways per transition: every county x product type x parent row of the Landscape lane's county CSV (members, continue, departing, D1 to D5;
443,211 rows for 2026), every metric of the product-type and national CSVs to the 4th decimal, and every one of the 144 sums against fact_landscape_benefits_v1 at product type x Day-One bucket.
5Tool: get_landscape_benefit_change with group_by county or a county filter (state required).

Grain

Key	Grain
N/A	1One row per transition_year (2023 to 2026, partition) x parent x state x county x product_type_y x product_type_y1 x dayone_bucket x truth_class, with cells, members and the same 144 sum columns as fact_landscape_benefits_v1 (24 metrics x 3 weight sets x numerator / denominator). 2419,954 rows over four transitions, the row count of fact_landscape_disposition_v1 partition for partition (measured 2026-09-30).

Key

Grain

N/A

1One row per transition_year (2023 to 2026, partition) x parent x state x county x product_type_y x product_type_y1 x dayone_bucket x truth_class, with cells, members and the same 144 sum columns as fact_landscape_benefits_v1 (24 metrics x 3 weight sets x numerator / denominator).
2419,954 rows over four transitions, the row count of fact_landscape_disposition_v1 partition for partition (measured 2026-09-30).

Columns (159)

Column	Type	Meaning
`parent`	string
`state`	string
`county`	string
`product_type_y`	string
`product_type_y1`	string
`dayone_bucket`	string
`truth_class`	string
`cells`	bigint
`members`	bigint
`a_prem_n`	double
`a_prem_d`	double
`a_prem0_n`	double
`a_prem0_d`	double
`a_cprem_n`	double
`a_cprem_d`	double
`a_cprem0_n`	double
`a_cprem0_d`	double
`a_dprem_n`	double
`a_dprem_d`	double
`a_dprem0_n`	double
`a_dprem0_d`	double
`a_dbasic_n`	double
`a_dbasic_d`	double
`a_dsupp_n`	double
`a_dsupp_d`	double
`a_dlis_n`	double
`a_dlis_d`	double
`a_dlis0_n`	double
`a_dlis0_d`	double
`a_moop_n`	double
`a_moop_d`	double
`a_moop5k_n`	double
`a_moop5k_d`	double
`a_moopmax_n`	double
`a_moopmax_d`	double
`a_haspd_n`	double
`a_haspd_d`	double
`a_enh_n`	double
`a_enh_d`	double
`a_ded_n`	double
`a_ded_d`	double
`a_ded0_n`	double
`a_ded0_d`	double
`a_dedstd_n`	double
`a_dedstd_d`	double
`a_tiernd_n`	double
`a_tiernd_d`	double
`a_dsnp0_n`	double
`a_dsnp0_d`	double
`a_fide_n`	double
`a_fide_d`	double
`a_hide_n`	double
`a_hide_d`	double
`a_star_n`	double
`a_star_d`	double
`a_star4_n`	double
`a_star4_d`	double
`b_prem_n`	double
`b_prem_d`	double
`b_prem0_n`	double
`b_prem0_d`	double
`b_cprem_n`	double
`b_cprem_d`	double
`b_cprem0_n`	double
`b_cprem0_d`	double
`b_dprem_n`	double
`b_dprem_d`	double
`b_dprem0_n`	double
`b_dprem0_d`	double
`b_dbasic_n`	double
`b_dbasic_d`	double
`b_dsupp_n`	double
`b_dsupp_d`	double
`b_dlis_n`	double
`b_dlis_d`	double
`b_dlis0_n`	double
`b_dlis0_d`	double
`b_moop_n`	double
`b_moop_d`	double
`b_moop5k_n`	double
`b_moop5k_d`	double
`b_moopmax_n`	double
`b_moopmax_d`	double
`b_haspd_n`	double
`b_haspd_d`	double
`b_enh_n`	double
`b_enh_d`	double
`b_ded_n`	double
`b_ded_d`	double
`b_ded0_n`	double
`b_ded0_d`	double
`b_dedstd_n`	double
`b_dedstd_d`	double
`b_tiernd_n`	double
`b_tiernd_d`	double
`b_dsnp0_n`	double
`b_dsnp0_d`	double
`b_fide_n`	double
`b_fide_d`	double
`b_hide_n`	double
`b_hide_d`	double
`b_star_n`	double
`b_star_d`	double
`b_star4_n`	double
`b_star4_d`	double
`r_prem_n`	double
`r_prem_d`	double
`r_prem0_n`	double
`r_prem0_d`	double
`r_cprem_n`	double
`r_cprem_d`	double
`r_cprem0_n`	double
`r_cprem0_d`	double
`r_dprem_n`	double
`r_dprem_d`	double
`r_dprem0_n`	double
`r_dprem0_d`	double
`r_dbasic_n`	double
`r_dbasic_d`	double
`r_dsupp_n`	double
`r_dsupp_d`	double
`r_dlis_n`	double
`r_dlis_d`	double
`r_dlis0_n`	double
`r_dlis0_d`	double
`r_moop_n`	double
`r_moop_d`	double
`r_moop5k_n`	double
`r_moop5k_d`	double
`r_moopmax_n`	double
`r_moopmax_d`	double
`r_haspd_n`	double
`r_haspd_d`	double
`r_enh_n`	double
`r_enh_d`	double
`r_ded_n`	double
`r_ded_d`	double
`r_ded0_n`	double
`r_ded0_d`	double
`r_dedstd_n`	double
`r_dedstd_d`	double
`r_tiernd_n`	double
`r_tiernd_d`	double
`r_dsnp0_n`	double
`r_dsnp0_d`	double
`r_fide_n`	double
`r_fide_d`	double
`r_hide_n`	double
`r_hide_d`	double
`r_star_n`	double
`r_star_d`	double
`r_star4_n`	double
`r_star4_d`	double
`next_year`	int
`enrollment_month`	date
`l0_edition`	string
`l1_edition`	string
`crosswalk_held`	boolean
`built_at`	string

Query it

describe_data_source(source_fqn="medicare_semantic.fact_landscape_benefits_county_v1")     # the card, live
query_substrate(sql="SELECT * FROM medicare_semantic.fact_landscape_benefits_county_v1 WHERE <filter> LIMIT 100")

Item	Value
Typed tools that read it	none; raw SQL
Identity key	`transition_year,parent,state,county,product_type_y,product_type_y1,dayone_bucket,truth_class`
Join	`how_to_join(source_fqn="medicare_semantic.fact_landscape_benefits_county_v1")`
Raw door	Athena (read-only via query_substrate): medicare_semantic.fact_landscape_benefits_county_v1

This page as markdown: /docs-md/data/medicare_semantic_fact_landscape_benefits_county_v1.md · every page in one file: /llms-full.txt