def drop_leukemia_subtypes(self, df):
leuk_subtypes = get_all_related_causes('neo_leukemia',
self.cause_meta_df)
leuk_subtypes.remove(487)
df.loc[(df['cause_id'].isin(leuk_subtypes)) & (df['deaths_rd'] > 0) &
(df['deaths_raw'] <= 0), 'cause_id'] = 487
return df
def get_related_causes_dict(cause_list, gbd_round_id, cause_set_id):
cause_hierarchy = cm(gbd_round_id=gbd_round_id, cause_set_id=cause_set_id)
related_from = list()
related_to = list()
for shocks_cause in cause_list:
related = get_all_related_causes(cause_id=shocks_cause,
cause_meta_df=cause_hierarchy)
related_from = related_from + related
related_to = related_to + [shocks_cause] * len(related)
related_dict = dict(zip(related_from, related_to))
return related_dict
def drop_leukemia_subtypes(self, df):
"""Remove leukemia subtypes deaths created by redistribution.
Deaths that are created in redistribution for leukemia subtypes should
be recoded to the parent leukemia.
"""
leuk_subtypes = get_all_related_causes('neo_leukemia',
self.cause_meta_df)
# remove parent leukemia cause_id
leuk_subtypes.remove(487)
df.loc[(df['cause_id'].isin(leuk_subtypes)) & (df['deaths_rd'] > 0) &
(df['deaths_raw'] <= 0), 'cause_id'] = 487
return df
def get_adjust_df(self, df):
lri_parent = self.cause_meta_df.loc[self.cause_meta_df.acause ==
'lri']['cause_id'].unique()[0]
lri_causes = get_all_related_causes(lri_parent,
cause_meta_df=self.cause_meta_df)
df = df.loc[(df.cause_id.isin(lri_causes))
& (df.age_group_id.isin(self.lri_ages))]
# if no lri to move, exit
if not len(df) > 0:
return df
# read in lri/tb redistribution proportions, proportions
# are location/year specific and apply only to under 15, excluding 0-27 days
prop_df = pd.read_csv(self.conf.get_resource('lri_tb_proportions'))
prop_df = prop_df[['location_id', 'year_id', 'tb_prop']]
df = df.merge(prop_df, on=['location_id', 'year_id'], how='left')
report_if_merge_fail(df, 'tb_prop', ['location_id', 'year_id'])
df['death_adjustment'] = df['deaths'] * df['tb_prop']
df = df[self.id_cols + ['death_adjustment']]
return df
def recode(self, df):
"""Recode based on expert judgement.
"""
cause_metadata_df = self.cause_meta_df
cause_metadata_df = cause_metadata_df[[
"cause_id", "path_to_top_parent", "acause"
]]
# recode ckd except for ckd_other to cong_other in neonates
ckd_cause_ids = get_all_related_causes('ckd', cause_metadata_df)
ckd_cause_ids.remove(593)
ckd_less_other = df['cause_id'].isin(ckd_cause_ids)
neonate = df['age_group_id'].isin([2, 3])
df.loc[ckd_less_other & neonate, 'cause_id'] = 652
# recode resp_copd, resp_asthma, resp_other, resp_interstitial to lri
# in neonates
resp_ids = [509, 515, 516, 520]
is_cert_resp_causes = df['cause_id'].isin(resp_ids)
# neonate already defined
df.loc[is_cert_resp_causes & neonate, 'cause_id'] = 322
# recode resp_asthma to lri in perinates
is_asthma = df['cause_id'] == 515
df.loc[is_asthma & (df['age_group_id'] == 4), 'cause_id'] = 322
# Drop any maternal cause below age 10 and above age 55
# (recode to cc_code)
maternal_cause_ids = get_all_related_causes(366, cause_metadata_df)
maternal_cause_ids = df['cause_id'].isin(maternal_cause_ids)
# ages not in the maternal age range
non_maternal_ages = np.logical_not(df['age_group_id'].isin(
[7, 8, 9, 10, 11, 12, 13, 14, 15, 22]))
df.loc[maternal_cause_ids & non_maternal_ages, 'cause_id'] = 919
# Drop alzheimers below age 40 to (recode to cc_code)
# dementia cause_id = 543
alzheimers = df['cause_id'] == 543
under_40 = df['age_group_id'].isin(range(1, 13, 1))
df.loc[alzheimers & under_40, 'cause_id'] = 919
# Recode congenital causes to cc_code in ages over 70
# (stata: substr(acause, 1, 4) == "cong")
cong_causes = get_all_related_causes('cong', cause_metadata_df)
congenital = df['cause_id'].isin(cong_causes)
over_70 = df['age_group_id'].isin([19, 20, 30, 31, 32, 235])
df.loc[congenital & over_70, "cause_id"] = 919
# Recode neonatal-aged hepatitis
# (and all sub-causes) to neonatal_hemolytic
# except ICD9_USSR_Tabulated and ICD10_tabulated
# Recode neonatal-aged hepatitis (and all sub-causes) to neonatal
# if source is ICD9_USSR_Tabulated or ICD10_tabulated
hepatitis = get_all_related_causes(400, cause_metadata_df)
hepatitis = df['cause_id'].isin(hepatitis)
if self.code_system_id in [7, 9]:
df.loc[hepatitis & neonate, "cause_id"] = 380
else:
df.loc[hepatitis & neonate, "cause_id"] = 384
# inj_disaster_light to inj_othunintent 2/07/18
inj_disaster_light = df['cause_id'] == 984
df.loc[inj_disaster_light, 'cause_id'] = 716
# ckd diabetes type to ckd all but icd10 2/07/18
# added ICD9_detail to exception 5/15/18
if self.code_system_id not in [1, 6]:
ckd_diabetes = df['cause_id'].isin([997, 998])
df.loc[ckd_diabetes, 'cause_id'] = 589
# Removing diabetes remap 7/2/2019 - want to use the results of the new
# unspecified diabetes regression for everything
# # diabetes subtypes to parent all but icd10 2/07/18
# # added ICD9_detail, ICD10_tab to exception 5/15/18
# if self.code_system_id not in [1, 6, 9]:
# diabetes_subtypes = df['cause_id'].isin([975, 976])
# df.loc[diabetes_subtypes, 'cause_id'] = 587
# diabetes to type 1 under 15 everywhere 2/07/18
diabetes_type_2 = df['cause_id'] == 976
under_15 = df['age_group_id'] < 8
df.loc[diabetes_type_2 & under_15, 'cause_id'] = 975
# nutrition iron and iodine to zz every data 2/07/18
iron_or_iodine = df['cause_id'].isin([388, 390])
df.loc[iron_or_iodine, 'cause_id'] = 919
# cvd_ihd move to cong_heart in under one year 2/07/18
under_1 = df['age_group_id'] < 5
cvd_ihd = df['cause_id'] == 493
df.loc[cvd_ihd & under_1, 'cause_id'] = 643
if 686 in df.cause_id.unique():
df = self.recode_sids(df)
# Need to map _neo, _mental, _infect
# etc to cc code 2/07/18
df.loc[df.cause_id.isin([344, 409, 410, 542, 558, 669, 680, 961]),
'cause_id'] = 919
# usually we also have to map _inj to cc_code, but in some VA we have
# other sources for splitting _inj we do not move to cc_code 3/26/2018
if self.data_type_id not in [6, 7, 8]:
df.loc[df['cause_id'] == 687, 'cause_id'] = 919
# cvd_ihd to cvd_other in under age one to 14 years 2/07/18 bridge map
one_to_14 = df['age_group_id'].isin([5, 6, 7])
cvd_ihd = df['cause_id'] == 493
df.loc[cvd_ihd & one_to_14, 'cause_id'] = 507
# TODO test if the distinction between this and the above is necessary,
# e.g. would the bridge map already map neonatal_hemolytic to neonatal?
# Do shared cancer recodes (previously in cancer_recodes.do)
cancer_recodes = get_all_related_causes([
411, 414, 423, 426, 429, 432, 435, 438, 441, 444, 450, 453, 456,
459, 462, 465, 468, 474, 486, 483
], cause_metadata_df)
cancer_recodes = df['cause_id'].isin(cancer_recodes)
cancer_ages = df['age_group_id'].isin(range(2, 8, 1))
df.loc[cancer_recodes & cancer_ages, "cause_id"] = 489
not_icd10 = self.code_system_id != 1
neo_meso = df['cause_id'] == 483
df.loc[neo_meso & not_icd10, "cause_id"] = 489
# Recode digest_hernia to cc_code if source is Ethiopia_AAMSP
# added Ethiopia_subnational_AAMSP in GBD2017
if self.source.endswith("AAMSP"):
digest_hernia = df['cause_id'].isin([531])
df.loc[digest_hernia, "cause_id"] = 919
# in these years we split a garbage of homicide/suicide to
# their causes proportionally, now we want to recode the years
# that we don't want to use in the homicide/suicide model.
if self.source == "Iran_Mohsen_special_ICD10":
homicide_and_suicide = df['cause_id'].isin(
[724, 725, 726, 727, 941, 718, 719, 720, 721, 722, 723])
bad_years = df['year_id'].isin(range(2007, 2015))
# _unintent
df.loc[bad_years & homicide_and_suicide, "cause_id"] = 919
# Recode war subcauses to inj_homicide in Jamaica 2005 VR
inj_war = get_all_related_causes(945, cause_metadata_df)
is_inj_war = df['cause_id'].isin(inj_war)
jamaica = df['location_id'] == 115
year_2005 = df['year_id'] == 2005
vr = df['data_type_id'] == 9
df.loc[is_inj_war & jamaica & year_2005 & vr, 'cause_id'] = 724
# Recode inj_mech_gun to inj_homicide for Jamaica 2006 VR
# "In ICD10 2005 there a large number of deaths due to
# homicides, but in 2006 many of these deaths have moved to
# unintentional firearms.
# 2006 is missing homicides deaths. USERNAME wants to move deaths from
# unintentional firearms to homicides."
inj_mech_gun = df['cause_id'] == 705
year_2006 = df['year_id'] == 2006
df.loc[inj_mech_gun & year_2006 & jamaica & vr, 'cause_id'] = 724
# Recode digest_ibd to digest for Suriname 2005-2012 ICD10
# "Because NR has a very bad effect on IBD in Surinam please recode all
# of data from 1995-2012 (ICD10 ) for "digest_ibd" to "digest" in
# Suriname and keep them in recoding list for every upload"
# TODO should this be more years than just 2012? like all of ICD10?
if self.source == "ICD10":
digest_ibd = df['cause_id'] == 532
suriname = df['location_id'] == 118
year_1995_2012 = df['year_id'].isin(range(1995, 2013, 1))
df.loc[digest_ibd & suriname & year_1995_2012, 'cause_id'] = 526
# Recode endo_procedural to inj_homicide, writ-large
# "GBD2013 HACK: USERNAME and USERNAME want Endo-procedural
# to go to inj_medical just for this round.
# In GBD2014 it will go to endo"
endo_prodcedural = df['cause_id'] == 624
df.loc[endo_prodcedural, 'cause_id'] = 708
# Recode Schizophrenia to cc_code in Tibet - USERNAME's reason:
# "Because have very bad effect in Noise Reduction"
schizo = df['cause_id'] == 559
tibet = df['location_id'] == 518
df.loc[schizo & tibet, 'cause_id'] = 919
# Recode HIV and all sub-causes before 1980 to cc_code, writ-large
hiv = get_all_related_causes(298, cause_metadata_df)
hiv = df['cause_id'].isin(hiv)
pre_1980 = df['year_id'] < 1980
df.loc[hiv & pre_1980, 'cause_id'] = 919
# Recode diabetes and all sub-causes to neonatal, if age is neonatal
# "2-Any death assigned to Diabetes in neonatal period (age 0-28 days)
# in all data format (Except ICD9 and ICD10 detail) including all MCCD,
# DSP , Russia format, VA have to recode to the neonatal death" -USERNAME
# TODO this should be an age restriction for GBD not a recode
# TODO implement
diabetes_causes = get_all_related_causes(587, cause_metadata_df)
diabetes = df['cause_id'].isin(diabetes_causes)
df.loc[neonate & diabetes, 'cause_id'] = 380
# Recode cvd_stroke and all subcauses to cvd
# in Verbal Autopsy under 20 years
# "Any death in VA and SCD that assigned to the Stroke
# in under age 20 years have to recode to all CVD"
# Not done in bridge map; stata code does this for all VA
# despite SCD comment.
under_20 = df['age_group_id'].isin(range(0, 8, 1))
stroke = get_all_related_causes('cvd_stroke', cause_metadata_df)
stroke_deaths = df['cause_id'].isin(stroke)
va = df['data_type_id'] == 8
# cvd cause_id is 491
df.loc[under_20 & stroke_deaths & va, 'cause_id'] = 491
# Recode inj_trans_road_pedal to cc_code if age over 95, for everything
# USERNAME request 1/20/2017 "remove inj_trans_road_pedal for over
# 95 in all countries and years"
# TODO should this be an age restriction? questionable...
over_95 = df['age_group_id'] == 235
inj_trans_road_pedal = df['cause_id'] == 691
df.loc[over_95 & inj_trans_road_pedal, 'cause_id'] = 919
# Recode mental_schizo to _mental everywhere
# "USERNAME request 1/31/2017 to get rid of all mental_schizo as a cause
# of death and map to _mental"
# TODO implement
# TODO should this be yld_only, then? questionable...
# TODO if maintaining this, don't need restriction restricting
# mental_schizo to cc_code in Tibet
df.loc[schizo, 'cause_id'] = 919
# Recode msk and all sub-causes to cc_code in all VA
# "USERNAME and USERNAME request 2/14/2017 "msk recode to cc_code for all
# VA and SRS"
# this is in the bridge map already
# Recode cvd_pvd to cvd in Russia_FMD_1999_2011
# Russia 1999 2011 has a weird outlier for pvd, should be cvd according
# to USERNAME 02/13/2017
# TODO implement
if self.source == "Russia_FMD_1999_2011":
cvd_pvd = df['cause_id'] == 502
df.loc[cvd_pvd, 'cause_id'] = 491
# USERNAME said to remove this following recode 2/26/2018
# # In all VR USERNAME wants to move mental_drug deaths in under 15
# # to unintentional poisoning. -USERNAME 7/8/2015
# # cause_id 562 (mental_drug_opioids) has different age restrictions,
# # so recode it separately
# mental_causes_no_op = df['cause_id'].isin(
# [560, 561, 563, 564, 565, 566]
# )
# mental_no_op_ages = df['age_group_id'].isin(range(2, 8, 1))
# df.loc[mental_causes_no_op & mental_no_op_ages & vr, 'cause_id'] = 700
# mental_op = df['cause_id'] == 562
# mental_op_ages = df['age_group_id'].isin([4, 5, 6, 7])
# df.loc[mental_op & mental_op_ages & vr, 'cause_id'] = 700
# Temp fix for self imposed redistribution error
# move suicide and homicide in these years to cc_code
if self.source == "Iran_Mohsen_special_ICD10":
sui_homi_causes = [
717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 941
]
sui_homi = df['cause_id'].isin(sui_homi_causes)
bad_years = df['year_id'].isin(range(2007, 2015))
df.loc[sui_homi & bad_years, 'cause_id'] = 919
# In India MCCD neonatal sepsis should only be in under 1 month
if "India_MCCD" in self.source:
non_neonates = np.logical_not(df['age_group_id'].isin([2, 3]))
neonatal_sepsis = df['cause_id'].isin([])
df.loc[non_neonates & neonatal_sepsis, 'cause_id'] = 380
# In India_SCD_states_rural we are trying to get rid of all the
# redistribution artifacts
if self.source == "India_SCD_states_rural":
warnings.warn("Implement SCD rd artifact recode")
# Recoding state actor violence to war for proper schocks tracking
# in ICD9btl & icd10 inj_war_execution > inj_war_war in Ecuador '80-'90
inj_war_execution = df['cause_id'] == 854
if self.source == "ICD9_BTL":
ecuador = df['location_id'] == 122
year_1980_1990 = df['year_id'].isin(range(1980, 1991, 1))
df.loc[inj_war_execution & ecuador & year_1980_1990,
'cause_id'] = 855
# inj_war_execution > inj_war_war for BIH from 1985-91
bih = df['location_id'] == 44
year_1985_1991 = df['year_id'].isin(
[1985, 1986, 1987, 1988, 1989, 1990, 1991])
df.loc[inj_war_execution & bih & year_1985_1991, 'cause_id'] = 855
# in icd9_btl there are cancer recodes to be implemented here
warnings.warn("BTL cancer recode needed")
if self.source == "ICD10":
irq = df['location_id'] == 143
year_2008 = df['year_id'] == 2008
df.loc[inj_war_execution & year_2008 & irq, 'cause_id'] = 855
# USERNAME said cirrhosis and hepatitis in India SRS did not go very well (5/26/19)
# "Move any death from SRS in the final stage due to cirrhosis to hepatitis in under 15
# Move 30% death from SRS in the final stage due to cirrhosis to hepatitis in between 15-24"
if self.source == "India_SRS_states_report":
# There should be no cirrhosis subtypes in SRS, but include them in case things change
cirrhosis_ids = [521, 522, 523, 524, 971, 525]
hepatitis_id = 400
# Under 15
under_15 = df['age_group_id'] < 8
cirrhosis = df['cause_id'].isin(cirrhosis_ids)
df.loc[under_15 & cirrhosis, 'cause_id'] = hepatitis_id
# 15-24
start_deaths = df[self.val_cols].sum(axis=0)
# Create proportions to split
split_df = pd.DataFrame()
for age_group_id in [8, 9]:
for cirrhosis_id in cirrhosis_ids:
small_df = pd.DataFrame({
'new_cause_id': [cirrhosis_id, hepatitis_id],
'pct': [0.70, 0.30]
})
small_df['cause_id'] = cirrhosis_id
small_df['age_group_id'] = age_group_id
split_df = split_df.append(small_df, sort=True)
# Merge in the proportions and split
# Do not apply the split retroactively - can't take away deaths from
# cirrhosis in earlier phases if they aren't there yet
df = df.merge(split_df,
how='left',
on=['age_group_id', 'cause_id'])
matches = df.new_cause_id.notnull()
df.loc[matches, 'cause_id'] = df['new_cause_id']
df.loc[matches, 'deaths'] = df['deaths'] * df['pct']
for col in ['deaths_raw', 'deaths_corr', 'deaths_rd']:
df.loc[matches & (df['new_cause_id'] == hepatitis_id), col] = 0
df.drop(["new_cause_id", "pct"], axis='columns', inplace=True)
assert np.allclose(start_deaths, df[self.val_cols].sum(axis=0))
assert df.notnull().values.all()
# USERNAMEFm says we should not have congenital in older age groups
# in this study. USERNAME says that since congenital is created by the
# redistribution of sepsis for this study: "Result of redistrbution on sepsis
# have to be very low, if the problem is just this one drop result of redistribution
# due to sepsis"
# The larger question is if/when we should create causes in VA
malawi_va_study = df['nid'] == 413649
congenital = df.cause_id.isin(
get_all_related_causes('cong', cause_metadata_df))
df.loc[malawi_va_study & congenital, 'cause_id'] = 919
if self.source == "ICD9_detail":
if ((df['location_id'] == 43) & (df['year_id'] == 1997)).any():
warnings.warn("Albania homicide recode needed")
if self.source == "ICD9_USSR_Tabulated":
warnings.warn("Missing some homicide fixes for TJK, ARM here.")
df = self.drop_leukemia_subtypes(df)
# mortuary, burial, self-reported COD, census/survey,
# and tabulated hospital data should be reduced down to just
# injuries, maternal, and cc_code
if self.data_type_id in [1, 3, 5, 7]:
maternal_causes = get_all_related_causes('maternal',
cause_metadata_df)
injury_causes = get_all_related_causes('_inj', cause_metadata_df)
maternal = df['cause_id'].isin(maternal_causes)
inj = df['cause_id'].isin(injury_causes)
df.loc[~(maternal | inj), 'cause_id'] = 919
# for sibling history, we only want maternal and cc_code
if self.data_type_id == 5:
df.loc[~maternal, 'cause_id'] = 919
return df
def recode(self, df):
cause_metadata_df = self.cause_meta_df
cause_metadata_df = cause_metadata_df[[
"cause_id", "path_to_top_parent", "acause"
]]
ckd_cause_ids = get_all_related_causes('ckd', cause_metadata_df)
ckd_cause_ids.remove(593)
ckd_less_other = df['cause_id'].isin(ckd_cause_ids)
neonate = df['age_group_id'].isin([2, 3])
df.loc[ckd_less_other & neonate, 'cause_id'] = 652
resp_ids = [509, 515, 516, 520]
is_cert_resp_causes = df['cause_id'].isin(resp_ids)
df.loc[is_cert_resp_causes & neonate, 'cause_id'] = 322
is_asthma = df['cause_id'] == 515
df.loc[is_asthma & (df['age_group_id'] == 4), 'cause_id'] = 322
maternal_cause_ids = get_all_related_causes(366, cause_metadata_df)
maternal_cause_ids = df['cause_id'].isin(maternal_cause_ids)
non_maternal_ages = np.logical_not(df['age_group_id'].isin(
[7, 8, 9, 10, 11, 12, 13, 14, 15, 22]))
df.loc[maternal_cause_ids & non_maternal_ages, 'cause_id'] = 919
alzheimers = df['cause_id'] == 543
under_40 = df['age_group_id'].isin(range(1, 13, 1))
df.loc[alzheimers & under_40, 'cause_id'] = 919
cong_causes = get_all_related_causes('cong', cause_metadata_df)
congenital = df['cause_id'].isin(cong_causes)
over_70 = df['age_group_id'].isin([19, 20, 30, 31, 32, 235])
df.loc[congenital & over_70, "cause_id"] = 919
hepatitis = get_all_related_causes(400, cause_metadata_df)
hepatitis = df['cause_id'].isin(hepatitis)
if self.code_system_id in [7, 9]:
df.loc[hepatitis & neonate, "cause_id"] = 380
else:
df.loc[hepatitis & neonate, "cause_id"] = 384
inj_disaster_light = df['cause_id'] == 984
df.loc[inj_disaster_light, 'cause_id'] = 716
if self.code_system_id not in [1, 6]:
ckd_diabetes = df['cause_id'].isin([997, 998])
df.loc[ckd_diabetes, 'cause_id'] = 589
if self.code_system_id not in [1, 6, 9]:
diabetes_subtypes = df['cause_id'].isin([975, 976])
df.loc[diabetes_subtypes, 'cause_id'] = 587
diabetes_type_2 = df['cause_id'] == 976
under_15 = df['age_group_id'] < 8
df.loc[diabetes_type_2 & under_15, 'cause_id'] = 975
iron_or_iodine = df['cause_id'].isin([388, 390])
df.loc[iron_or_iodine, 'cause_id'] = 919
under_1 = df['age_group_id'] < 5
cvd_ihd = df['cause_id'] == 493
df.loc[cvd_ihd & under_1, 'cause_id'] = 643
if 686 in df.cause_id.unique():
df = self.recode_sids(df)
df.loc[df.cause_id.isin([344, 409, 410, 542, 558, 669, 680, 961]),
'cause_id'] = 919
if self.data_type_id not in [6, 7, 8]:
df.loc[df['cause_id'] == 687, 'cause_id'] = 919
one_to_14 = df['age_group_id'].isin([5, 6, 7])
cvd_ihd = df['cause_id'] == 493
df.loc[cvd_ihd & one_to_14, 'cause_id'] = 507
cancer_recodes = get_all_related_causes([
411, 414, 423, 426, 429, 432, 435, 438, 441, 444, 450, 453, 456,
459, 462, 465, 468, 474, 486, 483
], cause_metadata_df)
cancer_recodes = df['cause_id'].isin(cancer_recodes)
cancer_ages = df['age_group_id'].isin(range(2, 8, 1))
df.loc[cancer_recodes & cancer_ages, "cause_id"] = 489
not_icd10 = self.code_system_id != 1
neo_meso = df['cause_id'] == 483
df.loc[neo_meso & not_icd10, "cause_id"] = 489
if self.source.endswith("AAMSP"):
digest_hernia = df['cause_id'].isin([531])
df.loc[digest_hernia, "cause_id"] = 919
if self.source == "":
homicide_and_suicide = df['cause_id'].isin(
[724, 725, 726, 727, 941, 718, 719, 720, 721, 722, 723])
bad_years = df['year_id'].isin(range(2007, 2015))
# _unintent
df.loc[bad_years & homicide_and_suicide, "cause_id"] = 919
inj_war = get_all_related_causes(945, cause_metadata_df)
is_inj_war = df['cause_id'].isin(inj_war)
jamaica = df['location_id'] == 115
year_2005 = df['year_id'] == 2005
vr = df['data_type_id'] == 9
df.loc[is_inj_war & jamaica & year_2005 & vr, 'cause_id'] = 724
inj_mech_gun = df['cause_id'] == 705
year_2006 = df['year_id'] == 2006
df.loc[inj_mech_gun & year_2006 & jamaica & vr, 'cause_id'] = 724
if self.source == "ICD10":
digest_ibd = df['cause_id'] == 532
suriname = df['location_id'] == 118
year_1995_2012 = df['year_id'].isin(range(1995, 2013, 1))
df.loc[digest_ibd & suriname & year_1995_2012, 'cause_id'] = 526
endo_prodcedural = df['cause_id'] == 624
df.loc[endo_prodcedural, 'cause_id'] = 708
schizo = df['cause_id'] == 559
tibet = df['location_id'] == 518
df.loc[schizo & tibet, 'cause_id'] = 919
hiv = get_all_related_causes(298, cause_metadata_df)
hiv = df['cause_id'].isin(hiv)
pre_1980 = df['year_id'] < 1980
df.loc[hiv & pre_1980, 'cause_id'] = 919
diabetes_causes = get_all_related_causes(587, cause_metadata_df)
diabetes = df['cause_id'].isin(diabetes_causes)
df.loc[neonate & diabetes, 'cause_id'] = 380
under_20 = df['age_group_id'].isin(range(0, 8, 1))
stroke = get_all_related_causes('cvd_stroke', cause_metadata_df)
stroke_deaths = df['cause_id'].isin(stroke)
va = df['data_type_id'] == 8
df.loc[under_20 & stroke_deaths & va, 'cause_id'] = 491
over_95 = df['age_group_id'] == 235
inj_trans_road_pedal = df['cause_id'] == 691
df.loc[over_95 & inj_trans_road_pedal, 'cause_id'] = 919
df.loc[schizo, 'cause_id'] = 919
if self.source == "Russia_FMD_1999_2011":
cvd_pvd = df['cause_id'] == 502
df.loc[cvd_pvd, 'cause_id'] = 491
if self.source == "":
sui_homi_causes = [
717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 941
]
sui_homi = df['cause_id'].isin(sui_homi_causes)
bad_years = df['year_id'].isin(range(2007, 2015))
df.loc[sui_homi & bad_years, 'cause_id'] = 919
if "India_MCCD" in self.source:
non_neonates = np.logical_not(df['age_group_id'].isin([2, 3]))
neonatal_sepsis = df['cause_id'].isin([])
df.loc[non_neonates & neonatal_sepsis, 'cause_id'] = 380
if self.source == "India_SCD_states_rural":
warnings.warn("Implement SCD rd artifact recode")
inj_war_execution = df['cause_id'] == 854
if self.source == "ICD9_BTL":
ecuador = df['location_id'] == 122
year_1980_1990 = df['year_id'].isin(range(1980, 1991, 1))
df.loc[inj_war_execution & ecuador & year_1980_1990,
'cause_id'] = 855
bih = df['location_id'] == 44
year_1985_1991 = df['year_id'].isin(
[1985, 1986, 1987, 1988, 1989, 1990, 1991])
df.loc[inj_war_execution & bih & year_1985_1991, 'cause_id'] = 855
warnings.warn("BTL cancer recode needed")
if self.source == "ICD10":
irq = df['location_id'] == 143
year_2008 = df['year_id'] == 2008
df.loc[inj_war_execution & year_2008 & irq, 'cause_id'] = 855
if self.source == "ICD9_detail":
if ((df['location_id'] == 43) & (df['year_id'] == 1997)).any():
warnings.warn("Albania homicide recode needed")
if self.source == "ICD9_USSR_Tabulated":
warnings.warn("Missing some homicide fixes for TJK, ARM here.")
df = self.drop_leukemia_subtypes(df)
if self.data_type_id in [1, 3, 5, 7]:
maternal_causes = get_all_related_causes('maternal',
cause_metadata_df)
injury_causes = get_all_related_causes('_inj', cause_metadata_df)
maternal = df['cause_id'].isin(maternal_causes)
inj = df['cause_id'].isin(injury_causes)
df.loc[~(maternal | inj), 'cause_id'] = 919
if self.data_type_id == 5:
df.loc[~maternal, 'cause_id'] = 919
return df