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Align CPS health coverage anchors with at-interview rule inputs #630

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Align CPS health coverage anchors with at-interview rule inputs #630

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7a77e5c
Add CPS current health coverage anchors
daphnehanse11 Mar 20, 2026
8c63411
Align health coverage anchors with rules inputs
daphnehanse11 Apr 2, 2026
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13 changes: 13 additions & 0 deletions policyengine_us_data/calibration/publish_local_area.py
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Original file line number Diff line number Diff line change
Expand Up @@ -38,6 +38,7 @@
from policyengine_us_data.utils.takeup import (
SIMPLE_TAKEUP_VARS,
apply_block_takeup_to_arrays,
any_person_flag_by_entity,
)

CHECKPOINT_FILE = Path("completed_states.txt")
Expand Down Expand Up @@ -551,6 +552,17 @@ def build_h5(
}
hh_state_fips = clone_geo["state_fips"].astype(np.int32)
original_hh_ids = household_ids[active_hh].astype(np.int64)
reported_anchors = {}
if "has_marketplace_health_coverage_at_interview" in data:
reported_anchors["takes_up_aca_if_eligible"] = any_person_flag_by_entity(
data["person_tax_unit_id"][time_period],
data["tax_unit_id"][time_period],
data["has_marketplace_health_coverage_at_interview"][time_period],
)
if "has_medicaid_health_coverage_at_interview" in data:
reported_anchors["takes_up_medicaid_if_eligible"] = data[
"has_medicaid_health_coverage_at_interview"
][time_period].astype(bool)

takeup_results = apply_block_takeup_to_arrays(
hh_blocks=active_blocks,
Expand All @@ -561,6 +573,7 @@ def build_h5(
entity_counts=entity_counts,
time_period=time_period,
takeup_filter=takeup_filter,
reported_anchors=reported_anchors,
)
for var_name, bools in takeup_results.items():
data[var_name] = {time_period: bools}
Expand Down
34 changes: 34 additions & 0 deletions policyengine_us_data/calibration/unified_matrix_builder.py
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Original file line number Diff line number Diff line change
Expand Up @@ -14,6 +14,7 @@
from collections import defaultdict
from typing import Dict, List, Optional, Tuple

import h5py
import numpy as np
import pandas as pd
from scipy import sparse
Expand Down Expand Up @@ -668,6 +669,7 @@ def _process_single_clone(
entity_hh_idx_map = sd.get("entity_hh_idx_map", {})
entity_to_person_idx = sd.get("entity_to_person_idx", {})
precomputed_rates = sd.get("precomputed_rates", {})
reported_takeup_anchors = sd.get("reported_takeup_anchors", {})

# Slice geography for this clone
clone_states = geo_states[col_start:col_end]
Expand Down Expand Up @@ -789,6 +791,7 @@ def _process_single_clone(
ent_blocks,
ent_hh_ids,
ent_ci,
reported_mask=reported_takeup_anchors.get(takeup_var),
)

ent_values = (ent_eligible * ent_takeup).astype(np.float32)
Expand Down Expand Up @@ -2107,6 +2110,7 @@ def build_matrix(
from policyengine_us_data.utils.takeup import (
TAKEUP_AFFECTED_TARGETS,
compute_block_takeup_for_entities,
any_person_flag_by_entity,
)
from policyengine_us_data.parameters import (
load_take_up_rate,
Expand Down Expand Up @@ -2135,6 +2139,33 @@ def build_matrix(
"person": person_hh_indices,
}

reported_takeup_anchors = {}
with h5py.File(self.dataset_path, "r") as f:
period_key = str(self.time_period)
if (
"has_marketplace_health_coverage_at_interview" in f
and period_key in f["has_marketplace_health_coverage_at_interview"]
):
person_marketplace = f[
"has_marketplace_health_coverage_at_interview"
][period_key][...].astype(bool)
person_tax_unit_ids = f["person_tax_unit_id"][period_key][...]
tax_unit_ids = f["tax_unit_id"][period_key][...]
reported_takeup_anchors["takes_up_aca_if_eligible"] = (
any_person_flag_by_entity(
person_tax_unit_ids,
tax_unit_ids,
person_marketplace,
)
)
if (
"has_medicaid_health_coverage_at_interview" in f
and period_key in f["has_medicaid_health_coverage_at_interview"]
):
reported_takeup_anchors["takes_up_medicaid_if_eligible"] = f[
"has_medicaid_health_coverage_at_interview"
][period_key][...].astype(bool)

entity_to_person_idx = {}
for entity_level in ("spm_unit", "tax_unit"):
ent_ids = sim.calculate(
Expand Down Expand Up @@ -2175,6 +2206,7 @@ def build_matrix(
self.household_ids = household_ids
self.precomputed_rates = precomputed_rates
self.affected_target_info = affected_target_info
self.reported_takeup_anchors = reported_takeup_anchors

# 5d. Clone loop
from pathlib import Path
Expand Down Expand Up @@ -2224,6 +2256,7 @@ def build_matrix(
shared_data["entity_hh_idx_map"] = entity_hh_idx_map
shared_data["entity_to_person_idx"] = entity_to_person_idx
shared_data["precomputed_rates"] = precomputed_rates
shared_data["reported_takeup_anchors"] = reported_takeup_anchors

logger.info(
"Starting parallel clone processing: %d clones, %d workers",
Expand Down Expand Up @@ -2427,6 +2460,7 @@ def build_matrix(
ent_blocks,
ent_hh_ids,
ent_ci,
reported_mask=reported_takeup_anchors.get(takeup_var),
)

ent_values = (ent_eligible * ent_takeup).astype(np.float32)
Expand Down
18 changes: 17 additions & 1 deletion policyengine_us_data/datasets/cps/census_cps.py
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Original file line number Diff line number Diff line change
Expand Up @@ -236,7 +236,24 @@ class CensusCPS_2018(CensusCPS):
"A_AGE",
"A_SEX",
"PEDISEYE",
"NOW_COV",
"NOW_DIR",
"NOW_MRK",
"NOW_MRKS",
"NOW_MRKUN",
"NOW_NONM",
"NOW_PRIV",
"NOW_PUB",
"NOW_GRP",
"NOW_CAID",
"NOW_MCAID",
"NOW_PCHIP",
"NOW_OTHMT",
"NOW_MCARE",
"NOW_MIL",
"NOW_CHAMPVA",
"NOW_VACARE",
"NOW_IHSFLG",
"WSAL_VAL",
"INT_VAL",
"SEMP_VAL",
Expand Down Expand Up @@ -294,7 +311,6 @@ class CensusCPS_2018(CensusCPS):
"PMED_VAL",
"PEMCPREM",
"PRCITSHP",
"NOW_GRP",
"POCCU2",
"PEINUSYR",
"MCARE",
Expand Down
109 changes: 104 additions & 5 deletions policyengine_us_data/datasets/cps/cps.py
View file
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Original file line number Diff line number Diff line change
Expand Up @@ -26,6 +26,58 @@
)
from policyengine_us_data.utils.downsample import downsample_dataset_arrays
from policyengine_us_data.utils.randomness import seeded_rng
from policyengine_us_data.utils.takeup import (
any_person_flag_by_entity,
assign_takeup_with_reported_anchors,
)


CURRENT_HEALTH_COVERAGE_REPORTED_VAR_MAP = {
"reported_has_direct_purchase_health_coverage_at_interview": "NOW_DIR",
"reported_has_marketplace_health_coverage_at_interview": "NOW_MRK",
"reported_has_subsidized_marketplace_health_coverage_at_interview": "NOW_MRKS",
"reported_has_unsubsidized_marketplace_health_coverage_at_interview": "NOW_MRKUN",
"reported_has_non_marketplace_direct_purchase_health_coverage_at_interview": (
"NOW_NONM"
),
"reported_has_employer_sponsored_health_coverage_at_interview": "NOW_GRP",
"reported_has_medicare_health_coverage_at_interview": "NOW_MCARE",
"reported_has_medicaid_health_coverage_at_interview": "NOW_CAID",
"reported_has_means_tested_health_coverage_at_interview": "NOW_MCAID",
"reported_has_chip_health_coverage_at_interview": "NOW_PCHIP",
"reported_has_other_means_tested_health_coverage_at_interview": "NOW_OTHMT",
"reported_has_tricare_health_coverage_at_interview": "NOW_MIL",
"reported_has_champva_health_coverage_at_interview": "NOW_CHAMPVA",
"reported_has_va_health_coverage_at_interview": "NOW_VACARE",
"reported_has_indian_health_service_coverage_at_interview": "NOW_IHSFLG",
}

CURRENT_HEALTH_COVERAGE_RULE_INPUT_ALIAS_MAP = {
"has_marketplace_health_coverage_at_interview": (
"reported_has_marketplace_health_coverage_at_interview"
),
"has_non_marketplace_direct_purchase_health_coverage_at_interview": (
"reported_has_non_marketplace_direct_purchase_health_coverage_at_interview"
),
"has_medicaid_health_coverage_at_interview": (
"reported_has_medicaid_health_coverage_at_interview"
),
"has_other_means_tested_health_coverage_at_interview": (
"reported_has_other_means_tested_health_coverage_at_interview"
),
"has_tricare_health_coverage_at_interview": (
"reported_has_tricare_health_coverage_at_interview"
),
"has_champva_health_coverage_at_interview": (
"reported_has_champva_health_coverage_at_interview"
),
"has_va_health_coverage_at_interview": (
"reported_has_va_health_coverage_at_interview"
),
"has_indian_health_service_coverage_at_interview": (
"reported_has_indian_health_service_coverage_at_interview"
),
}


class CPS(Dataset):
Expand Down Expand Up @@ -231,7 +283,16 @@ def add_takeup(self):

# ACA
rng = seeded_rng("takes_up_aca_if_eligible")
data["takes_up_aca_if_eligible"] = rng.random(n_tax_units) < aca_rate
reported_marketplace_by_tax_unit = any_person_flag_by_entity(
data["person_tax_unit_id"],
data["tax_unit_id"],
data["has_marketplace_health_coverage_at_interview"],
)
data["takes_up_aca_if_eligible"] = assign_takeup_with_reported_anchors(
rng.random(n_tax_units),
aca_rate,
reported_mask=reported_marketplace_by_tax_unit,
)

# Medicaid: state-specific rates
state_codes = baseline.calculate("state_code_str").values
Expand All @@ -243,8 +304,11 @@ def add_takeup(self):
[medicaid_rates_by_state.get(s, 0.93) for s in person_states]
)
rng = seeded_rng("takes_up_medicaid_if_eligible")
data["takes_up_medicaid_if_eligible"] = (
rng.random(n_persons) < medicaid_rate_by_person
data["takes_up_medicaid_if_eligible"] = assign_takeup_with_reported_anchors(
rng.random(n_persons),
medicaid_rate_by_person,
reported_mask=data["has_medicaid_health_coverage_at_interview"],
group_keys=person_states,
)

# Head Start
Expand Down Expand Up @@ -453,9 +517,44 @@ def children_per_parent(col: str) -> pd.DataFrame:
)
cps["own_children_in_household"] = tmp.children.fillna(0)

cps["has_marketplace_health_coverage"] = person.NOW_MRK == 1
for variable, cps_column in CURRENT_HEALTH_COVERAGE_REPORTED_VAR_MAP.items():
cps[variable] = person[cps_column] == 1

for (
variable,
reported_variable,
) in CURRENT_HEALTH_COVERAGE_RULE_INPUT_ALIAS_MAP.items():
cps[variable] = cps[reported_variable]

cps["has_esi"] = person.NOW_GRP == 1
cps["reported_has_private_health_coverage_at_interview"] = person.NOW_PRIV == 1
cps["reported_has_public_health_coverage_at_interview"] = person.NOW_PUB == 1
cps["reported_is_insured_at_interview"] = person.NOW_COV == 1
cps["reported_is_uninsured_at_interview"] = person.NOW_COV != 1

coverage_families = np.column_stack(
[
cps["reported_has_employer_sponsored_health_coverage_at_interview"],
cps["reported_has_marketplace_health_coverage_at_interview"],
cps[
"reported_has_non_marketplace_direct_purchase_health_coverage_at_interview"
],
cps["reported_has_medicare_health_coverage_at_interview"],
cps["reported_has_means_tested_health_coverage_at_interview"],
cps["reported_has_tricare_health_coverage_at_interview"],
cps["reported_has_champva_health_coverage_at_interview"],
cps["reported_has_va_health_coverage_at_interview"],
cps["reported_has_indian_health_service_coverage_at_interview"],
]
)
cps["reported_has_multiple_health_coverage_at_interview"] = (
coverage_families.sum(axis=1) > 1
)

# Legacy aliases retained for compatibility until rules-side names catch up.
cps["has_marketplace_health_coverage"] = cps[
"reported_has_marketplace_health_coverage_at_interview"
]
cps["has_esi"] = cps["reported_has_employer_sponsored_health_coverage_at_interview"]

cps["cps_race"] = person.PRDTRACE
cps["is_hispanic"] = person.PRDTHSP != 0
Expand Down
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