def main():
"""
Indicators for commited, released, or charged from DOC events
"""
index = ["RIIPL_ID"]
features = CachePopulation(population, index).set_index(index)
sql = """
SELECT pop.riipl_id,
MAX(CASE WHEN de.event_type = 'Committed' THEN 1 ELSE 0 END) AS doc_commited,
MAX(CASE WHEN de.event_type = 'Released' THEN 1 ELSE 0 END) AS doc_released,
MAX(CASE WHEN de.event_type = 'Charged' THEN 1 ELSE 0 END) AS doc_charged
FROM {population} pop
LEFT JOIN {lookback} lb
ON pop.riipl_id = lb.riipl_id
LEFT JOIN {dim_date} dd
ON lb.yrmo = dd.yrmo
LEFT JOIN {doc_events} de
ON pop.riipl_id = de.riipl_id AND
dd.date_dt = de.event_date
GROUP BY pop.riipl_id
""".format(**globals())
with cx_Oracle.connect("/") as cxn:
features = features.join(pd.read_sql(sql, cxn).set_index(index))
labels = {
"DOC_COMMITED" : "Committed to a corrections facility",
"DOC_RELEASED" : "Released from a corrections facility",
"DOC_CHARGED" : "Charged with a crime"
}
SaveFeatures(features, out, manifest, population, labels)
def main():
"""
Indicators for DOC sentences by crime type
"""
sql = """
SELECT DISTINCT offense_type AS offense
FROM {doc_sentences}
WHERE offense_type IS NOT NULL
ORDER BY offense
""".format(**globals())
with cx_Oracle.connect("/") as cxn:
offenses = pd.read_sql(sql, cxn)
print(offenses)
pivot = ",".join([
"'{0}' AS doc_sentenced_{0}".format(offense)
for offense in offenses["OFFENSE"]
])
index = ["RIIPL_ID"]
features = CachePopulation(population, index).set_index(index)
sql = """
SELECT *
FROM (
SELECT pop.riipl_id,
ds.offense_type AS offense,
1 AS sentenced
FROM {population} pop
LEFT JOIN {lookback} lb
ON pop.riipl_id = lb.riipl_id
LEFT JOIN {dim_date} dd
ON lb.yrmo = dd.yrmo
INNER JOIN {doc_sentences} ds
ON pop.riipl_id = ds.riipl_id AND
dd.date_dt = ds.imposed_date
GROUP BY pop.riipl_id, ds.offense_type
)
PIVOT (MAX(sentenced) FOR offense IN ({pivot}))
""".format(pivot=pivot, **globals())
with cx_Oracle.connect("/") as cxn:
features = features.join(pd.read_sql(sql, cxn).set_index(index))
# Drop offense types that are missing for the entire population.
features = features.dropna(axis=1, how="all").fillna(0).astype(int)
labels = {}
for offense in offenses["OFFENSE"]:
label = "sentenced for {} during lookback period".format(offense)
labels["DOC_SENTENCED_{}".format(offense.upper())] = label
SaveFeatures(features,
out,
manifest,
population,
labels,
bool_features=list(labels))
def main():
"""
Indicator for UI exhaustion
"""
index = ["RIIPL_ID"]
features = CachePopulation(population, index).set_index(index)
sql = """
SELECT DISTINCT
pop.riipl_id,
1 AS ui_exhaustion
FROM {population} pop
LEFT JOIN {lookback} lb
ON pop.riipl_id = lb.riipl_id
LEFT JOIN {dim_date} dd
ON lb.yrmo = dd.yrmo
INNER JOIN {dlt_ui_payments} du
ON pop.riipl_id = du.riipl_id AND
dd.date_dt = du.trans_date
WHERE void_check_flag IS NULL AND
delete_flag IS NULL AND
opening_credits > 0 AND
closing_credits <= 0
""".format(**globals())
with Connection() as cxn:
features = features.join(
pd.read_sql(sql, cxn._connection, index_col=index)).fillna(0)
labels = {"UI_EXHAUSTION": "Exhausted UI benefits"}
SaveFeatures(features, out, manifest, population, labels)
def main():
"""
Home addresses from MEGA table
"""
index = ["RIIPL_ID"]
features = CachePopulation(population, index).set_index(index)
sql = """
SELECT DISTINCT
pop.riipl_id,
address_trct
FROM {population} pop
LEFT JOIN {lookback} lb
ON pop.riipl_id = lb.riipl_id
INNER JOIN {mega} m
ON pop.riipl_id = m.riipl_id AND
lb.yrmo = m.month
WHERE m.address_trct IS NOT NULL
""".format(**globals())
with cx_Oracle.connect("/") as cxn:
values = pd.read_sql(sql, cxn).set_index(index)
labels = {}
for trct in values.ADDRESS_TRCT.unique():
name = "TRCT_{}".format(trct)
feature = values[values["ADDRESS_TRCT"] == trct]
feature[name] = 1
features = features.join(feature[name])
labels[name] = "lived in Census tract {} during lookback period".format(trct)
features = features.fillna(0)
SaveFeatures(features, out, manifest, population, labels, bool_features=list(labels))
def main():
"""
"""
index = ["RIIPL_ID"]
features = CachePopulation(population, index).set_index(index)
sql = """
SELECT pop.riipl_id,
SUM(cc.car_crash) AS car_crashes,
SUM(cc.injured) AS car_crash_injuries
FROM {population} pop
LEFT JOIN {lookback} lb
ON pop.riipl_id = lb.riipl_id
INNER JOIN {car_crashes} cc
ON pop.riipl_id = cc.riipl_id AND
cc.yrmo = lb.yrmo
GROUP BY pop.riipl_id
""".format(**globals())
with Connection() as cxn:
features = features.join(
pd.read_sql(sql, cxn._connection).set_index(index))
features = features.fillna(0)
labels = {
"CAR_CRASHES":
"count of car crashes involved in during lookback period",
"CAR_CRASH_INJURIES":
"count of car crash injuries during lookback period",
}
SaveFeatures(features, out, manifest, population, labels)
def main():
"""
"""
index = ["RIIPL_ID"]
features = CachePopulation(population, index).set_index(index)
sql = """
SELECT pop.riipl_id,
SUM(citations) AS citations,
SUM(fines) AS citation_fines
FROM {population} pop
LEFT JOIN {lookback} lb
ON pop.riipl_id = lb.riipl_id
LEFT JOIN {citations} c
ON pop.riipl_id = c.riipl_id AND
c.yrmo = lb.yrmo
GROUP BY pop.riipl_id
""".format(**globals())
with Connection() as cxn:
features = features.join(
pd.read_sql(sql, cxn._connection).set_index(index))
features = features.fillna(0)
labels = {
"CITATIONS": "Number of police citations",
"CITATION_FINES": "Total fines from police citations"
}
SaveFeatures(features, out, manifest, population, labels)
def main():
index = ["RIIPL_ID"]
features = CachePopulation(population, index).set_index(index)
# Claims payments
sql = """
SELECT pop.riipl_id,
SUM(mc.pay_amt) AS medicaid_payments,
SUM(CASE WHEN mc.src_id IN (4, 8, 16, 17, 26, 27) THEN mc.pay_amt ELSE 0 END) AS medicaid_prof_payments,
SUM(CASE WHEN mc.ed_flag = 1 THEN mc.pay_amt ELSE 0 END) AS medicaid_ed_payments
FROM {population} pop
LEFT JOIN {lookback} lb
ON pop.riipl_id = lb.riipl_id
LEFT JOIN {dim_date} dd
ON lb.yrmo = dd.yrmo
INNER JOIN {medicaid_claims} mc
ON pop.riipl_id = mc.riipl_id AND
dd.date_dt = mc.claim_dt
GROUP BY pop.riipl_id
""".format(**globals())
with cx_Oracle.connect("/") as cxn:
features = features.join(pd.read_sql(sql, cxn).set_index(index))
# Pharmacy payments
sql = """
SELECT pop.riipl_id,
SUM(mp.pay_amt) AS medicaid_rx_payments
FROM {population} pop
LEFT JOIN {lookback} lb
ON pop.riipl_id = lb.riipl_id
LEFT JOIN {dim_date} dd
ON lb.yrmo = dd.yrmo
INNER JOIN {medicaid_pharmacy} mp
ON pop.riipl_id = mp.riipl_id AND
dd.date_dt = mp.dispensed_dt
GROUP BY pop.riipl_id
""".format(**globals())
with cx_Oracle.connect("/") as cxn:
features = features.join(pd.read_sql(sql, cxn).set_index(index))
# Payments are comprehensive, so a missing value means $0
features = features.fillna(0)
labels = {
"MEDICAID_PAYMENTS" : "Total Medicaid payments",
"MEDICAID_PROF_PAYMENTS" : "Total Medicaid payments to professionals",
"MEDICAID_ED_PAYMENTS" : "Total Medicaid payments to Emergency Departments",
"MEDICAID_RX_PAYMENTS" : "Total Medicaid pharmacy payments"
}
SaveFeatures(features, out, manifest, population, labels)
def main():
icd9 = pd.concat([
pd.read_csv(icd9_file1, usecols=[0, 1], names=["DIAG_CDE", "DESC"]),
pd.read_csv(icd9_file2)
]).drop_duplicates("DIAG_CDE").set_index("DIAG_CDE")
index = ["RIIPL_ID"]
features = CachePopulation(population, index).set_index(index)
sql = """
SELECT pop.riipl_id,
mdc.diag_cde,
COUNT(*) AS n
FROM {population} pop
LEFT JOIN {lookback} lb
ON pop.riipl_id = lb.riipl_id
LEFT JOIN {dim_date} dd
ON lb.yrmo = dd.yrmo
INNER JOIN {medicaid_diag_cde} mdc
ON pop.riipl_id = mdc.riipl_id AND
dd.date_dt = mdc.claim_dt
INNER JOIN {diag_corr} dc
ON mdc.diag_cde = dc.diag_cde AND
ABS(dc.corr) > 0
GROUP BY pop.riipl_id, mdc.diag_cde
""".format(**globals())
with Connection() as cxn:
values = pd.read_sql(sql, cxn._connection)
cdes = values.DIAG_CDE.unique()
labels = {}
for cde in cdes:
feature = values.loc[values.DIAG_CDE == cde, "N"]
feature.name = "DIAG_{}".format(cde)
features = features.join(feature)
try:
desc = icd9.loc[cde, "DESC"]
except KeyError:
desc = "unknown description"
label = "number of diagnoses for '{}' during lookback period".format(
desc)
labels[feature.name] = label
features = features.fillna(0)
SaveFeatures(features, out, manifest, population, labels)
def main():
procs = pd.read_csv(procsfile, sep="|", index_col="PROC_CDE")
index = ["RIIPL_ID"]
features = CachePopulation(population, index).set_index(index)
sql = """
SELECT pop.riipl_id,
mpc.proc_cde,
COUNT(*) AS n
FROM {population} pop
LEFT JOIN {lookback} lb
ON pop.riipl_id = lb.riipl_id
LEFT JOIN {dim_date} dd
ON lb.yrmo = dd.yrmo
INNER JOIN {medicaid_proc_cde} mpc
ON pop.riipl_id = mpc.riipl_id AND
dd.date_dt = mpc.claim_dt
INNER JOIN {proc_corr} pc
ON mpc.proc_cde = pc.proc_cde AND
ABS(pc.corr) > 0
GROUP BY pop.riipl_id, mpc.proc_cde
""".format(**globals())
with Connection() as cxn:
values = pd.read_sql(sql, cxn._connection)
cdes = values.PROC_CDE.unique()
labels = {}
for cde in cdes:
feature = values.loc[values.PROC_CDE == cde, "N"]
feature.name = "PROC_{}".format(cde)
features = features.join(feature)
try:
desc = procs.loc[cde, "DESCRIPTION"]
except KeyError:
desc = "unknown procedure"
label = "number of procedures for '{}' during lookback period".format(
desc)
labels[feature.name] = label
features = features.fillna(0)
SaveFeatures(features, out, manifest, population, labels)
def main():
"""
Opioid prescriptions and adverse outcomes among DHS household
"""
index = ["RIIPL_ID"]
features = CachePopulation(population, index).set_index(index)
sql = """
SELECT pop.riipl_id,
SUM(ABS(no.opioid)) AS dhs_hh_opioid_rx,
SUM(no.recovery) AS dhs_hh_recovery_rx,
COUNT(o.outcome_any_dt) AS dhs_hh_outcomes
FROM {population} pop
LEFT JOIN {lookback} lb
ON pop.riipl_id = lb.riipl_id
LEFT JOIN {dim_date} dd
ON lb.yrmo = dd.yrmo
LEFT JOIN {household} hh
ON pop.riipl_id = hh.riipl_id
LEFT JOIN {medicaid_pharmacy} mp
ON hh.riipl_id_hh = mp.riipl_id AND
dd.date_dt = mp.dispensed_dt
LEFT JOIN {ndc_opioids} no
ON mp.ndc9_code = no.ndc
LEFT JOIN {outcomes} o
ON hh.riipl_id_hh = o.riipl_id AND
dd.date_dt = o.outcome_any_dt
GROUP BY pop.riipl_id
""".format(**globals())
with Connection() as cxn:
features = features.join(
pd.read_sql(sql, cxn._connection).set_index(index))
features = features.fillna(0)
labels = {
"DHS_HH_OPIOID_RX": "Number of opioid prescriptions in household",
"DHS_HH_RECOVERY_RX": "Number of recovery prescriptions in household",
"DHS_HH_OUTCOMES": "Number of adverse outcomes in household"
}
SaveFeatures(features, out, manifest, population, labels)
def main():
"""
Social program payments from MEGA table
"""
index = ["RIIPL_ID"]
features = CachePopulation(population, index).set_index(index)
sql = """
SELECT pop.riipl_id,
SUM(m.snap_payments) AS snap_payments,
SUM(m.tanf_payments) AS tanf_payments,
SUM(m.tdi_payments) AS tdi_payments,
SUM(m.ui_payments) AS ui_payments,
SUM(m.ssi_supplement) AS ssi_supplement,
SUM(m.gpa_payments) AS gpa_payments,
SUM(m.ccap_payments) AS ccap_payments
FROM {population} pop
LEFT JOIN {lookback} lb
ON pop.riipl_id = lb.riipl_id
INNER JOIN {mega} m
ON pop.riipl_id = m.riipl_id AND
lb.yrmo = m.month
GROUP BY pop.riipl_id
""".format(**globals())
with cx_Oracle.connect("/") as cxn:
features = features.join(pd.read_sql(sql, cxn).set_index(index))
# Payments are comprehensive, so a missing value means $0
features = features.fillna(0)
labels = {
"SNAP_PAYMENTS" : "Total Food Stamps payments",
"TANF_PAYMENTS" : "Total TANF payments",
"TDI_PAYMENTS" : "Total TDI payments",
"UI_PAYMENTS" : "Total Unemployment Insurance payments",
"SSI_SUPPLEMENT" : "Total SSI supplement",
"GPA_PAYMENTS" : "Total GPA payments",
"CCAP_PAYMENTS" : "Total CCAP payments"
}
SaveFeatures(features, out, manifest, population, labels)
def main():
index = ["RIIPL_ID"]
features = CachePopulation(population, index).set_index(index)
sql = """
SELECT pop.riipl_id,
MAX(CASE WHEN rd.gender = 'M' THEN 1 ELSE 0 END) AS sex_m,
MAX(CASE WHEN rd.primary_lang_cd = '01' THEN 1 ELSE 0 END) AS lang_spanish,
MAX(CASE WHEN rd.primary_lang_cd = '31' THEN 1 ELSE 0 END) AS lang_portu,
MAX(CASE WHEN rd.primary_lang_cd <> '00' AND
rd.primary_lang_cd <> '01' AND
rd.primary_lang_cd <> '31' THEN 1 ELSE 0 END) AS lang_other
FROM {population} pop
LEFT JOIN {lookback} lb
ON pop.riipl_id = lb.riipl_id
LEFT JOIN {medicaid_enrollment} me
ON pop.riipl_id = me.riipl_id AND
lb.yrmo = me.yrmo
LEFT JOIN {recip_demo} rd
ON me.re_unique_id = rd.recipient_id
GROUP BY pop.riipl_id
""".format(**globals())
with cx_Oracle.connect("/") as cxn:
features = features.join(pd.read_sql(sql, cxn).set_index(index))
features = features.fillna(0)
labels = {
"SEX_M": "Sex is male",
"LANG_SPANISH": "Primary language is Spanish",
"LANG_PORTU": "Primary language is Portuguese",
"LANG_OTHER":
"Primary language is not English, Spanish, or Portuguese",
}
SaveFeatures(features,
out,
manifest,
population,
labels,
bool_features=list(labels))
def main():
"""
Size of DHS household and indicator for births in the DHS household from MEGA table
"""
index = ["RIIPL_ID"]
features = CachePopulation(population, index).set_index(index)
sql = """
SELECT pop.riipl_id,
COUNT(DISTINCT m2.riipl_id) AS dhs_hh_size,
MAX(CASE WHEN m1.age > 1 AND m2.age < 1
THEN 1
ELSE 0 END) AS dhs_hh_birth
FROM {population} pop
LEFT JOIN {lookback} lb
ON pop.riipl_id = lb.riipl_id
INNER JOIN {mega} m1
ON pop.riipl_id = m1.riipl_id AND
lb.yrmo = m1.month
INNER JOIN {mega} m2
ON m1.dhs_hh_id = m2.dhs_hh_id AND
m1.month = m2.month
GROUP BY pop.riipl_id
""".format(**globals())
with Connection() as cxn:
features = features.join(pd.read_sql(sql, cxn._connection).set_index(index))
features = features.fillna(0)
labels = {
"DHS_HH_SIZE" : "Size of household",
"DHS_HH_BIRTH" : "New birth in household"
}
SaveFeatures(features, out, manifest, population, labels, bool_features=["DHS_HH_BIRTH"])
def main():
index = ["RIIPL_ID"]
features = CachePopulation(population, index).set_index(index)
sql = """
SELECT pop.riipl_id,
COUNT(DISTINCT me.re_unique_id) AS medicaid_n_ids,
MAX(CASE WHEN UPPER(dim.description) LIKE '%PREG%' THEN 1 ELSE 0 END) AS medicaid_pregnant,
MAX(CASE WHEN UPPER(dim.description) LIKE '%PARTUM%' THEN 1 ELSE 0 END) AS medicaid_postpartum,
MAX(CASE WHEN UPPER(dim.description) LIKE '%BLIND%' THEN 1 ELSE 0 END) AS medicaid_blind,
MAX(CASE WHEN UPPER(dim.description) LIKE '%DISABLED%' THEN 1 ELSE 0 END) AS medicaid_disabled,
MAX(CASE WHEN UPPER(dim.description) LIKE '%ALIEN%' THEN 1 ELSE 0 END) AS medicaid_alien,
MAX(CASE WHEN UPPER(dim.description) LIKE '%CHILD%' THEN 1 ELSE 0 END) AS medicaid_child,
MAX(CASE WHEN dim.needy_ind = 'Medically' THEN 1 ELSE 0 END) AS medicaid_med_needy,
MAX(NVL(me.managed_care, 0)) AS medicaid_managed_care,
MAX(NVL(me.premium_payment, 0)) AS medicaid_prem_payment,
MAX(CASE WHEN rxn.payer_cd = 'B' THEN 1 ELSE 0 END) AS medicaid_payer_bhddh,
MAX(CASE WHEN rxn.payer_cd = 'C' THEN 1 ELSE 0 END) AS medicaid_payer_cmap,
MAX(CASE WHEN rxn.payer_cd = 'D' THEN 1 ELSE 0 END) AS medicaid_payer_doc,
MAX(CASE WHEN rxn.payer_cd = 'H' THEN 1 ELSE 0 END) AS medicaid_payer_aids,
MAX(CASE WHEN rxn.payer_cd = 'O' THEN 1 ELSE 0 END) AS medicaid_payer_ors,
MAX(CASE WHEN rxn.payer_cd = 'R' THEN 1 ELSE 0 END) AS medicaid_payer_ripae
FROM {population} pop
LEFT JOIN {lookback} lb
ON pop.riipl_id = lb.riipl_id
LEFT JOIN {medicaid_enrollment} me
ON pop.riipl_id = me.riipl_id AND
lb.yrmo = me.yrmo
LEFT JOIN {dim_aid_ctg_cde} dim
ON me.aid_category = dim.aid_ctg_cde
LEFT JOIN {recip_x_ssn} rxn
ON me.re_unique_id = rxn.recipient_id
GROUP BY pop.riipl_id
""".format(**globals())
with cx_Oracle.connect("/") as cxn:
features = features.join(pd.read_sql(sql, cxn).set_index(index))
features = features.fillna(0)
labels = {
"MEDICAID_N_IDS":
"Number of unique Medicaid IDs",
"MEDICAID_PREGNANT":
"Medicaid eligibility category contains the word 'pregnant'",
"MEDICAID_POSTPARTUM":
"Medicaid eligibility category contains the word 'postpartum'",
"MEDICAID_BLIND":
"Medicaid eligibility category contains the word 'blind'",
"MEDICAID_DISABLED":
"Medicaid eligibility category contains the word 'disabled'",
"MEDICAID_ALIEN":
"Medicaid eligibility category contains the word 'alien'",
"MEDICAID_CHILD":
"Medicaid eligibility category contains the word 'child'",
"MEDICAID_MED_NEEDY":
"Eligible for Medicaid as medically needy",
"MEDICAID_MANAGED_CARE":
"Enrolled in Medicaid managed care",
"MEDICAID_PREM_PAYMENT":
"Enrolled in Medicaid premium payment plan",
"MEDICAID_PAYER_BHDDH":
"Medicaid payer code is BHDDH",
"MEDICAID_PAYER_CMAP":
"Enrolled in RI Community Medication Assistance Program",
"MEDICAID_PAYER_DOC":
"Medicaid payer code is Department of Corrections",
"MEDICAID_PAYER_AIDS":
"Medicaid payer code is Ryan White AIDS Drug Assistance Program",
"MEDICAID_PAYER_ORS":
"Medicaid payer code is ORS",
"MEDICAID_PAYER_RIPAE":
"Enrolled in RI Pharmaceutical Assistance to the Elderly"
}
SaveFeatures(features,
out,
manifest,
population,
labels,
bool_features=[f for f in labels if f != "MEDICAID_N_IDS"])
def main():
"""
Industry worked in (NAICS code) from DLT wages
"""
sql = """
SELECT DISTINCT SUBSTR(naics4, 0, 2) AS naics2
FROM {dlt_wage}
ORDER BY naics2
""".format(**globals())
with cx_Oracle.connect("/") as cxn:
naics = pd.read_sql(sql, cxn)
pivot = ",".join(["'{0}' AS naics_{0}".format(n) for n in naics["NAICS2"]])
index = ["RIIPL_ID"]
features = CachePopulation(population, index).set_index(index)
sql = """
SELECT *
FROM (
SELECT pop.riipl_id,
SUBSTR(dw.naics4, 0, 2) AS naics2,
1 AS worked
FROM {population} pop
LEFT JOIN (
SELECT DISTINCT
riipl_id,
yyq
FROM {lookback}
) lb
ON pop.riipl_id = lb.riipl_id
INNER JOIN {dlt_wage} dw
ON pop.riipl_id = dw.riipl_id AND
lb.yyq = dw.yyq
GROUP BY pop.riipl_id, SUBSTR(dw.naics4, 0, 2)
)
PIVOT (MAX(worked) FOR naics2 IN ({pivot}))
""".format(pivot=pivot, **globals())
with cx_Oracle.connect("/") as cxn:
features = features.join(pd.read_sql(sql, cxn).set_index(index))
# Drop NAICS categories that are missing for the entire population.
features = features.dropna(axis=1, how="all").fillna(0).astype(int)
# Missing indicator not needed - provided by WAGES_MISSING
labels = {}
naics2_labels = {
"00": "Unknown",
"02": "Unknown",
"11": "Agriculture, Forestry, Fishing, and Hunting",
"21": "Mining, Quarrying, and Oil and Gas Extraction",
"22": "Utilities",
"23": "Construction",
"24": "Unknown",
"31": "Manufacturing",
"32": "Manufacturing",
"33": "Manufacturing",
"42": "Wholesale Trade",
"44": "Retail Trade",
"45": "Retail Trade",
"48": "Transportation and Warehousing",
"49": "Transportation and Warehousing",
"50": "Unknown",
"51": "Information",
"52": "Finance and Insurance",
"53": "Real Estate and Rental and Leasing",
"54": "Professional, Scientific, and Technical Services",
"55": "Management of Companies and Enterprises",
"56":
"Administrative and Support and Waste Management and Remediation Services",
"61": "Educational Services",
"62": "Health Care and Social Assistance",
"71": "Arts, Entertainment, and Recreation",
"72": "Accommodation and Food Services",
"81": "Other Services (except Public Administration)",
"84": "Unknown",
"92": "Public Administration",
"94": "Unknown",
"99": "Unknown"
}
for n, sector in naics2_labels.items():
label = "worked in {} sector (NAICS {}) during lookback period".format(
sector, n)
labels["NAICS_{}".format(n)] = label
SaveFeatures(features,
out,
manifest, (population, index),
labels,
bool_features=list(labels))
def main():
icd9 = pd.concat([pd.read_csv(icd9_file1).rename(columns={"DIAGNOSIS CODE": "DIAG_CDE", "LONG DESCRIPTION": "DESC"}),
pd.read_csv(icd9_file2).rename(columns={"diag_cde": "DIAG_CDE", "description": "DESC"})],
ignore_index=True).drop_duplicates("DIAG_CDE").set_index("DIAG_CDE")
proc = pd.read_csv(proc_file, sep="|", index_col="PROC_CDE").rename(columns={"DESCRIPTION": "DESC"})
# Remove non-word characters and convert to lowercase
icd9["DESC"] = icd9.DESC.str.lower().str.replace("[-/]", " ").str.replace("[^a-z ]", "")
proc["DESC"] = proc.DESC.str.lower().str.replace("[-/]", " ").str.replace("[^a-z ]", "")
# Split word lists into a row per word
icd9 = pd.DataFrame(icd9.DESC.str.split().tolist(), index=icd9.index).stack()
icd9 = icd9.reset_index()[["DIAG_CDE", 0]].rename(columns={0: "WORD"})
proc = pd.DataFrame(proc.DESC.str.split().tolist(), index=proc.index).stack()
proc = proc.reset_index()[["PROC_CDE", 0]].rename(columns={0: "WORD"})
# Remove stopwords
icd9 = icd9[~icd9.WORD.isin(stopwords)]
proc = proc[~proc.WORD.isin(stopwords)]
# Generate ids for words
words = icd9.WORD.append(proc.WORD, ignore_index=True)
words = words[words != ""]
words = words.value_counts().reset_index().rename(columns={"index": "WORD", "WORD": "N"})
words["WORD_ID"] = np.arange(len(words))
words[["WORD_ID", "WORD", "N"]].to_csv(words_file, index=False)
print("distinct words:", len(words))
icd9 = icd9.merge(words[["WORD", "WORD_ID"]], how="inner", on="WORD")
proc = proc.merge(words[["WORD", "WORD_ID"]], how="inner", on="WORD")
sql = """
SELECT pop.riipl_id,
mc.diag_cde,
COUNT(*) AS n
FROM {population} pop
LEFT JOIN {lookback} lb
ON pop.riipl_id = lb.riipl_id
LEFT JOIN {dim_date} dd
ON lb.yrmo = dd.yrmo
INNER JOIN {medicaid_diag_cde} mc
ON pop.riipl_id = mc.riipl_id AND
dd.date_dt = mc.claim_dt
GROUP BY pop.riipl_id, mc.diag_cde
""".format(**globals())
with Connection() as cxn:
diags = pd.read_sql(sql, cxn._connection)
diags = diags.merge(icd9[["DIAG_CDE", "WORD_ID"]], how="inner", on="DIAG_CDE")
sql = """
SELECT pop.riipl_id,
mc.proc_cde,
COUNT(*) AS n
FROM {population} pop
LEFT JOIN {lookback} lb
ON pop.riipl_id = lb.riipl_id
LEFT JOIN {dim_date} dd
ON lb.yrmo = dd.yrmo
INNER JOIN {medicaid_proc_cde} mc
ON pop.riipl_id = mc.riipl_id AND
dd.date_dt = mc.claim_dt
GROUP BY pop.riipl_id, mc.proc_cde
""".format(**globals())
with Connection() as cxn:
procs = pd.read_sql(sql, cxn._connection)
procs = procs.merge(proc[["PROC_CDE", "WORD_ID"]], how="inner", on="PROC_CDE")
columns = ["RIIPL_ID", "WORD_ID", "N"]
values = diags[columns].append(procs[columns], ignore_index=True)
values = values.groupby(["RIIPL_ID", "WORD_ID"]).sum().reset_index()
pop = CachePopulation(population, "RIIPL_ID")
pop["ROW_ID"] = np.arange(len(pop))
values = values.merge(pop, on="RIIPL_ID")[["ROW_ID", "WORD_ID", "N"]]
print("total occurrences:", values.N.sum())
# MM uses 1-based indexing
values["ROW_ID"] += 1
values["WORD_ID"] += 1
with open(counts_file, "w") as f:
print("%%MatrixMarket matrix coordinate integer general", file=f)
print(len(pop), len(words), len(values), file=f)
values.to_csv(counts_file, sep=" ", mode="a", header=False, index=False)
def main():
"""
This code generates the following features based on someone's home census block group:
* Median income
* Share below the federal povery line
"""
index = ["RIIPL_ID"]
features = CachePopulation(population, index).set_index(index)
income = pd.read_csv(
income_file,
skiprows=1,
na_values=["-", "**"],
usecols=[
"Id2",
"Estimate; Median household income in the past 12 months (in 2015 Inflation-adjusted dollars)"
])
income.columns = ["GEO_ID", "BLKGRP_MEDIANINCOME"]
fpl = pd.read_csv(
fpl_file,
skiprows=1,
na_values=["-", "**"],
usecols=[
"Id2", "Estimate; Total:",
"Estimate; Income in the past 12 months below poverty level:"
])
fpl.columns = ["GEO_ID", "TOTAL", "FPL"]
fpl["BLKGRP_BELOWFPL"] = fpl.FPL / fpl.TOTAL
fpl = fpl[["GEO_ID", "BLKGRP_BELOWFPL"]]
sql = """
SELECT pop.riipl_id,
STATS_MODE(TO_NUMBER(a.state || a.county || a.trct || a.blkgrp)) AS geo_id,
1 AS geocoded
FROM (
SELECT pop.riipl_id,
MAX(a.obs_date) AS max_dt
FROM {population} pop
INNER JOIN {address} a
ON pop.riipl_id = a.riipl_id AND
a.obs_date <= pop.initial_dt
GROUP BY pop.riipl_id
) pop
INNER JOIN {address} a
ON pop.riipl_id = a.riipl_id AND
pop.max_dt = a.obs_date
GROUP BY pop.riipl_id
""".format(**globals())
with Connection() as cxn:
values = pd.read_sql(sql, cxn._connection)
values = values.merge(income, how="left", on="GEO_ID")\
.merge(fpl, how="left", on="GEO_ID")
features = features.join(values.set_index(index))
del features["GEO_ID"]
# Mean imputation
features["GEOCODED"] = features.GEOCODED.fillna(0)
features["BLKGRP_MEDIANINCOME"] = features.BLKGRP_MEDIANINCOME.fillna(
features.BLKGRP_MEDIANINCOME.mean())
features["BLKGRP_BELOWFPL"] = features.BLKGRP_BELOWFPL.fillna(
features.BLKGRP_BELOWFPL.mean())
labels = {
"GEOCODED": "Has a geocoded home address",
"BLKGRP_MEDIANINCOME":
"Block group's median annual household income (2015 dollars)",
"BLKGRP_BELOWFPL": "Share of block group below poverty line"
}
SaveFeatures(features, out, manifest, population, labels)
def main():
ccs = pd.read_csv(ccs_file,
usecols=[0, 1],
quotechar="'",
skiprows=3,
names=["DIAG_CDE", "DISEASE"])
ccs["DIAG_CDE"] = ccs.DIAG_CDE.str.strip()
cci = pd.read_csv(cci_file,
usecols=[0, 2],
quotechar="'",
skiprows=2,
names=["DIAG_CDE", "CHRONIC"])
cci = cci[cci.CHRONIC == 1]
cci["DIAG_CDE"] = cci.DIAG_CDE.str.strip()
index = ["RIIPL_ID"]
features = CachePopulation(population, index).set_index(index)
sql = """
SELECT DISTINCT
pop.riipl_id,
d.diag_cde
FROM {population} pop
LEFT JOIN {lookback} lb
ON pop.riipl_id = lb.riipl_id
LEFT JOIN {dim_date} dd
ON lb.yrmo = dd.yrmo
INNER JOIN {diag_cde} d
ON pop.riipl_id = d.riipl_id AND
dd.date_dt = d.claim_dt
""".format(**globals())
with Connection() as cxn:
diags = pd.read_sql(sql, cxn._connection)
ndisease = diags.merge(ccs, how="inner", on="DIAG_CDE")[index+["DISEASE"]]\
.groupby(index)\
.agg({"DISEASE": pd.Series.nunique})\
.rename(columns={"DISEASE": "MEDICAID_DISEASE_SCORE"})
features = features.join(ndisease)
nchronic = diags.merge(cci, how="inner", on="DIAG_CDE")[index+["CHRONIC"]]\
.groupby(index)\
.sum()\
.rename(columns={"CHRONIC": "MEDICAID_CHRONIC_SCORE"})
features = features.join(nchronic)
sql = """
SELECT pop.riipl_id,
COUNT(DISTINCT p.proc_cde) AS medicaid_procedures
FROM {population} pop
LEFT JOIN {lookback} lb
ON pop.riipl_id = lb.riipl_id
LEFT JOIN {dim_date} dd
ON lb.yrmo = dd.yrmo
INNER JOIN {proc_cde} p
ON pop.riipl_id = p.riipl_id AND
dd.date_dt = p.claim_dt
GROUP BY pop.riipl_id
""".format(**globals())
with Connection() as cxn:
nproc = pd.read_sql(sql, cxn._connection, index_col=index)
features = features.join(nproc).fillna(0)
# Add squared terms
for x in [
"MEDICAID_DISEASE_SCORE", "MEDICAID_CHRONIC_SCORE",
"MEDICAID_PROCEDURES"
]:
features["{}_SQ".format(x)] = features[x] * features[x]
labels = {
"MEDICAID_DISEASE_SCORE": "Number of AHRQ CCS diseases",
"MEDICAID_CHRONIC_SCORE": "Number of AHRQ CCI chronic conditions",
"MEDICAID_PROCEDURES": "Number of distinct procedures",
"MEDICAID_DISEASE_SCORE_SQ": "Squared number of AHRQ CCS diseases",
"MEDICAID_CHRONIC_SCORE_SQ":
"Squared number of AHRQ CCI chronic conditions",
"MEDICAID_PROCEDURES_SQ": "Squared number of distinct procedures"
}
SaveFeatures(features, out, manifest, population, labels)
