def load_pem_disability_weight(key: str, location: str) -> pd.DataFrame:
try:
pem_sequelae = {
data_keys.PEM.MAM_DISABILITY_WEIGHT: [sequelae.moderate_wasting_with_edema,
sequelae.moderate_wasting_without_edema],
data_keys.PEM.SAM_DISABILITY_WEIGHT: [sequelae.severe_wasting_with_edema,
sequelae.severe_wasting_without_edema],
}[key]
except KeyError:
raise ValueError(f'Unrecognized key {key}')
prevalence_disability_weight = []
state_prevalence = []
for s in pem_sequelae:
sequela_prevalence = interface.get_measure(s, 'prevalence', location)
sequela_disability_weight = interface.get_measure(s, 'disability_weight', location)
prevalence_disability_weight += [sequela_prevalence * sequela_disability_weight]
state_prevalence += [sequela_prevalence]
gbd_2019_disability_weight = (
(sum(prevalence_disability_weight) / sum(state_prevalence))
.fillna(0)
.droplevel('location')
)
disability_weight = utilities.reshape_gbd_2019_data_as_gbd_2020_data(gbd_2019_disability_weight)
return disability_weight
def __init__(self, risk, location):
# Local import to avoid data dependencies
from vivarium_inputs.interface import get_measure
self.mean = get_measure(risk, 'exposure',
location).droplevel('parameter')
self.sd = get_measure(risk, 'exposure_standard_deviation', location)
self.weights = get_measure(risk, 'exposure_distribution_weights',
location)
def _get_ldl_exposure_data(
location: int
) -> Tuple[List[Tuple], pd.DataFrame, pd.DataFrame, List[Dict[str, float]]]:
# Local import to avoid data dependencies
from vivarium_inputs import interface
means = (interface.get_measure(
risk_factors.high_ldl_cholesterol, 'exposure',
location).reset_index().drop(['parameter'], axis=1).set_index([
'location', 'sex', 'age_start', 'age_end', 'year_start', 'year_end'
]))
sds = interface.get_measure(risk_factors.high_ldl_cholesterol,
'exposure_standard_deviation', location)
stratifications = [row[0] for row in list(means.draw_0.iteritems())]
weights = interface.get_measure(risk_factors.high_ldl_cholesterol,
'exposure_distribution_weights', location)
return stratifications, means, sds, weights
def __init__(self, risk, location):
# Local import to avoid data dependencies
from vivarium_inputs.interface import get_measure
rr = get_measure(risk, 'relative_risk',
location).droplevel(['affected_measure', 'parameter'])
self.base = (rr.reorder_levels(['affected_entity'] +
INDEX_COLS).loc[AFFECTED_CAUSES])
self.tmrel = np.mean([risk.tmred.min, risk.tmred.max])
self.scale = float(risk.relative_risk_scalar)
def load_diabetes_mellitus_disability_weight(key: str, location: str) -> pd.DataFrame:
moderate_sequelae = [
sequelae.uncomplicated_diabetes_mellitus_type_1,
sequelae.uncomplicated_diabetes_mellitus_type_2,
]
if key == project_globals.DIABETES_MELLITUS.MODERATE_DIABETES_PREVALENCE:
seq = moderate_sequelae
else:
seq = [s for sc in causes.diabetes_mellitus.sub_causes for s in sc.sequelae if s not in moderate_sequelae]
prevalence_disability_weights = []
for s in seq:
prevalence = interface.get_measure(s, 'prevalence', location)
disability_weight = interface.get_measure(s, 'disability_weight', location)
prevalence_disability_weights.append(prevalence * disability_weight)
diabetes_prevalence = interface.get_measure(causes.diabetes_mellitus, 'prevalence', location)
diabetes_disability_weight = (sum(prevalence_disability_weights) / diabetes_prevalence).fillna(0)
return diabetes_disability_weight
def load_ihd_disability_weight(key: str, location: str) -> pd.DataFrame:
acute_sequelae = [
sequelae.acute_myocardial_infarction_first_2_days,
sequelae.acute_myocardial_infarction_3_to_28_days,
]
if key == project_globals.IHD.ACUTE_MI_PREVALENCE:
seq = acute_sequelae
else:
seq = [s for s in causes.ischemic_heart_disease.sequelae if s not in acute_sequelae]
prevalence_disability_weights = []
for s in seq:
prevalence = interface.get_measure(s, 'prevalence', location)
disability_weight = interface.get_measure(s, 'disability_weight', location)
prevalence_disability_weights.append(prevalence * disability_weight)
ihd_prevalence = interface.get_measure(causes.ischemic_heart_disease, 'prevalence', location)
ihd_disability_weight = (sum(prevalence_disability_weights) / ihd_prevalence).fillna(0)
return ihd_disability_weight
def load_iron_responsive_proportion(key: str, location: str):
sequela_map = {
project_globals.IRON_DEFICIENCY_MILD_ANEMIA_IRON_RESPONSIVE_PROPORTION:
project_globals.ANEMIA_SEQUELAE_ID_MAP['mild'],
project_globals.IRON_DEFICIENCY_MODERATE_ANEMIA_IRON_RESPONSIVE_PROPORTION:
project_globals.ANEMIA_SEQUELAE_ID_MAP['moderate'],
project_globals.IRON_DEFICIENCY_SEVERE_ANEMIA_IRON_RESPONSIVE_PROPORTION:
project_globals.ANEMIA_SEQUELAE_ID_MAP['severe'],
}
responsive_ids, non_responsive_ids = sequela_map[key]
responsive_prevalence = []
for s_id in responsive_ids:
sequela = [s for s in sequelae if s.gbd_id == s_id]
if sequela:
sequela = sequela.pop()
else:
continue
try:
prevalence = interface.get_measure(sequela, 'prevalence', location)
except (extract.DataDoesNotExistError, extract.DataAbnormalError):
continue
responsive_prevalence.append(prevalence)
responsive_prevalence = sum(responsive_prevalence)
non_responsive_prevalence = []
for s_id in non_responsive_ids:
sequela = [s for s in sequelae if s.gbd_id == s_id]
if sequela:
sequela = sequela.pop()
else:
continue
try:
prevalence = interface.get_measure(sequela, 'prevalence', location)
except (extract.DataDoesNotExistError, extract.DataAbnormalError):
continue
non_responsive_prevalence.append(prevalence)
non_responsive_prevalence = sum(non_responsive_prevalence)
return (responsive_prevalence / (responsive_prevalence + non_responsive_prevalence)).fillna(0)
def load_diabetes_mellitus_prevalence(key: str, location: str) -> pd.DataFrame:
moderate_sequelae = [
sequelae.uncomplicated_diabetes_mellitus_type_1,
sequelae.uncomplicated_diabetes_mellitus_type_2,
]
if key == project_globals.DIABETES_MELLITUS.MODERATE_DIABETES_PREVALENCE:
seq = moderate_sequelae
else:
seq = [s for sc in causes.diabetes_mellitus.sub_causes for s in sc.sequelae if s not in moderate_sequelae]
prevalence = sum(interface.get_measure(s, 'prevalence', location) for s in seq)
return prevalence
def load_ihd_prevalence(key: str, location: str) -> pd.DataFrame:
acute_sequelae = [
sequelae.acute_myocardial_infarction_first_2_days,
sequelae.acute_myocardial_infarction_3_to_28_days,
]
if key == project_globals.IHD.ACUTE_MI_PREVALENCE:
seq = acute_sequelae
else:
seq = [s for s in causes.ischemic_heart_disease.sequelae if s not in acute_sequelae]
prevalence = sum(interface.get_measure(s, 'prevalence', location) for s in seq)
return prevalence
def load_sids_csmr(key: str, location: str) -> pd.DataFrame:
if key == data_keys.AFFECTED_UNMODELED_CAUSES.SIDS_CSMR:
key = EntityKey(key)
entity: Cause = utilities.get_entity(key)
# get around the validation rejecting yll only causes
entity.restrictions.yll_only = False
entity.restrictions.yld_age_group_id_start = min(metadata.AGE_GROUP.GBD_2019_SIDS)
entity.restrictions.yld_age_group_id_end = max(metadata.AGE_GROUP.GBD_2019_SIDS)
data = interface.get_measure(entity, key.measure, location).droplevel('location')
return data
else:
raise ValueError(f'Unrecognized key {key}')
def load_ischemic_stroke_disability_weight(key: str, location: str) -> pd.DataFrame:
acute_sequelae = [
sequelae.acute_ischemic_stroke_severity_level_1,
sequelae.acute_ischemic_stroke_severity_level_2,
sequelae.acute_ischemic_stroke_severity_level_3,
sequelae.acute_ischemic_stroke_severity_level_4,
sequelae.acute_ischemic_stroke_severity_level_5,
]
if key == project_globals.IHD.ACUTE_MI_PREVALENCE:
seq = acute_sequelae
else:
seq = [s for s in causes.ischemic_heart_disease.sequelae if s not in acute_sequelae]
prevalence_disability_weights = []
for s in seq:
prevalence = interface.get_measure(s, 'prevalence', location)
disability_weight = interface.get_measure(s, 'disability_weight', location)
prevalence_disability_weights.append(prevalence * disability_weight)
ischemic_stroke_prevalence = interface.get_measure(causes.ischemic_stroke, 'prevalence', location)
ischemic_stroke_disability_weight = (sum(prevalence_disability_weights) / ischemic_stroke_prevalence).fillna(0)
return ischemic_stroke_disability_weight
def load_no_anemia_iron_responsive_proportion(key: str, location: str):
responsive_ids, non_responsive_ids = [], []
for responsive, non_responsive in project_globals.ANEMIA_SEQUELAE_ID_MAP.values():
responsive_ids.extend(responsive)
non_responsive_ids.extend(non_responsive)
responsive_sequelae = [s for s in sequelae if s.gbd_id in responsive_ids]
non_responsive_sequelae = [s for s in sequelae if s.gbd_id in non_responsive_ids]
all_prevalence = []
iron_responsive_prevalence = []
for sequela in responsive_sequelae:
try:
prevalence = interface.get_measure(sequela, 'prevalence', location)
except (extract.DataDoesNotExistError, extract.DataAbnormalError):
continue
all_prevalence.append(prevalence)
iron_responsive_prevalence.append(prevalence)
for sequela in non_responsive_sequelae:
try:
prevalence = interface.get_measure(sequela, 'prevalence', location)
except (extract.DataDoesNotExistError, extract.DataAbnormalError):
continue
all_prevalence.append(prevalence)
all_prevalence = sum(all_prevalence)
iron_responsive_prevalence = sum(iron_responsive_prevalence)
non_responsive_prevalence = all_prevalence - iron_responsive_prevalence
other_anemias_prevalence = interface.get_measure(causes.hemoglobinopathies_and_hemolytic_anemias,
'prevalence', location)
hiv_prevalence = interface.get_measure(causes.hiv_aids, 'prevalence', location)
malaria_prevalence = interface.get_measure(causes.malaria, 'prevalence', location)
reverse_causal_prevalence = other_anemias_prevalence + hiv_prevalence + malaria_prevalence
proportion = (1 - all_prevalence
- (reverse_causal_prevalence - non_responsive_prevalence)/(1 - all_prevalence)).fillna(0)
return proportion
def load_ischemic_stroke_prevalence(key: str, location: str) -> pd.DataFrame:
acute_sequelae = [
sequelae.acute_ischemic_stroke_severity_level_1,
sequelae.acute_ischemic_stroke_severity_level_2,
sequelae.acute_ischemic_stroke_severity_level_3,
sequelae.acute_ischemic_stroke_severity_level_4,
sequelae.acute_ischemic_stroke_severity_level_5,
]
if key == project_globals.ISCHEMIC_STROKE.ACUTE_STROKE_PREVALENCE:
seq = acute_sequelae
else:
seq = [s for s in causes.ischemic_stroke.sequelae if s not in acute_sequelae]
prevalence = sum(interface.get_measure(s, 'prevalence', location) for s in seq)
return prevalence
def load_standard_data(key: str, location: str) -> pd.DataFrame:
key = EntityKey(key)
entity = get_entity(key)
return interface.get_measure(entity, key.measure,
location).droplevel('location')
def load_ikf_disability_weight(key: str, location: str) -> pd.DataFrame:
category_sequelae_map = {
project_globals.IKF.CAT_4_DISABILITY_WEIGHT: [
sequelae.albuminuria_with_preserved_gfr_due_to_glomerulonephritis,
sequelae.albuminuria_with_preserved_gfr_due_to_hypertension,
sequelae.albuminuria_with_preserved_gfr_due_to_other_and_unspecified_causes,
sequelae.albuminuria_with_preserved_gfr_due_to_type_1_diabetes_mellitus,
sequelae.albuminuria_with_preserved_gfr_due_to_type_2_diabetes_mellitus,
],
project_globals.IKF.CAT_3_DISABILITY_WEIGHT: [
sequelae.stage_iii_chronic_kidney_disease_and_mild_anemia_due_to_hypertension,
sequelae.stage_iii_chronic_kidney_disease_and_moderate_anemia_due_to_hypertension,
sequelae.stage_iii_chronic_kidney_disease_and_severe_anemia_due_to_hypertension,
sequelae.stage_iii_chronic_kidney_disease_without_anemia_due_to_hypertension,
sequelae.stage_iii_chronic_kidney_disease_and_mild_anemia_due_to_glomerulonephritis,
sequelae.stage_iii_chronic_kidney_disease_and_moderate_anemia_due_to_glomerulonephritis,
sequelae.stage_iii_chronic_kidney_disease_and_severe_anemia_due_to_glomerulonephritis,
sequelae.stage_iii_chronic_kidney_disease_without_anemia_due_to_glomerulonephritis,
sequelae.stage_iii_chronic_kidney_disease_and_mild_anemia_due_to_other_and_unspecified_causes,
sequelae.stage_iii_chronic_kidney_disease_and_moderate_anemia_due_to_other_and_unspecified_causes,
sequelae.stage_iii_chronic_kidney_disease_and_severe_anemia_due_to_other_and_unspecified_causes,
sequelae.stage_iii_chronic_kidney_disease_without_anemia_due_to_other_and_unspecified_causes,
sequelae.stage_iii_chronic_kidney_disease_and_severe_anemia_due_to_type_1_diabetes_mellitus,
sequelae.stage_iii_chronic_kidney_disease_and_severe_anemia_due_to_type_2_diabetes_mellitus,
sequelae.stage_iii_chronic_kidney_disease_and_moderate_anemia_due_to_type_1_diabetes_mellitus,
sequelae.stage_iii_chronic_kidney_disease_and_moderate_anemia_due_to_type_2_diabetes_mellitus,
sequelae.stage_iii_chronic_kidney_disease_and_mild_anemia_due_to_type_1_diabetes_mellitus,
sequelae.stage_iii_chronic_kidney_disease_and_mild_anemia_due_to_type_2_diabetes_mellitus,
sequelae.stage_iii_chronic_kidney_disease_without_anemia_due_to_type_1_diabetes_mellitus,
sequelae.stage_iii_chronic_kidney_disease_without_anemia_due_to_type_2_diabetes_mellitus,
],
project_globals.IKF.CAT_2_DISABILITY_WEIGHT: [
sequelae.stage_iv_chronic_kidney_disease_untreated_without_anemia_due_to_hypertension,
sequelae.stage_iv_chronic_kidney_disease_untreated_and_mild_anemia_due_to_hypertension,
sequelae.stage_iv_chronic_kidney_disease_untreated_and_moderate_anemia_due_to_hypertension,
sequelae.stage_iv_chronic_kidney_disease_untreated_and_severe_anemia_due_to_hypertension,
sequelae.stage_iv_chronic_kidney_disease_untreated_without_anemia_due_to_glomerulonephritis,
sequelae.stage_iv_chronic_kidney_disease_untreated_and_mild_anemia_due_to_glomerulonephritis,
sequelae.stage_iv_chronic_kidney_disease_untreated_and_moderate_anemia_due_to_glomerulonephritis,
sequelae.stage_iv_chronic_kidney_disease_untreated_and_severe_anemia_due_to_glomerulonephritis,
sequelae.stage_iv_chronic_kidney_disease_untreated_without_anemia_due_to_other_and_unspecified_causes,
sequelae.stage_iv_chronic_kidney_disease_untreated_and_mild_anemia_due_to_other_and_unspecified_causes,
sequelae.stage_iv_chronic_kidney_disease_untreated_and_moderate_anemia_due_to_other_and_unspecified_causes,
sequelae.stage_iv_chronic_kidney_disease_untreated_and_severe_anemia_due_to_other_and_unspecified_causes,
sequelae.stage_iv_chronic_kidney_disease_untreated_and_severe_anemia_due_to_type_1_diabetes_mellitus,
sequelae.stage_iv_chronic_kidney_disease_untreated_and_severe_anemia_due_to_type_2_diabetes_mellitus,
sequelae.stage_iv_chronic_kidney_disease_untreated_and_moderate_anemia_due_to_type_1_diabetes_mellitus,
sequelae.stage_iv_chronic_kidney_disease_untreated_and_moderate_anemia_due_to_type_2_diabetes_mellitus,
sequelae.stage_iv_chronic_kidney_disease_untreated_and_mild_anemia_due_to_type_1_diabetes_mellitus,
sequelae.stage_iv_chronic_kidney_disease_untreated_and_mild_anemia_due_to_type_2_diabetes_mellitus,
sequelae.stage_iv_chronic_kidney_disease_untreated_without_anemia_due_to_type_1_diabetes_mellitus,
sequelae.stage_iv_chronic_kidney_disease_untreated_without_anemia_due_to_type_2_diabetes_mellitus,
],
project_globals.IKF.CAT_1_DISABILITY_WEIGHT: [
sequelae.end_stage_renal_disease_after_transplant_due_to_hypertension,
sequelae.end_stage_renal_disease_on_dialysis_due_to_hypertension,
sequelae.end_stage_renal_disease_after_transplant_due_to_glomerulonephritis,
sequelae.end_stage_renal_disease_on_dialysis_due_to_glomerulonephritis,
sequelae.end_stage_renal_disease_after_transplant_due_to_other_and_unspecified_causes,
sequelae.end_stage_renal_disease_on_dialysis_due_to_other_and_unspecified_causes,
sequelae.stage_v_chronic_kidney_disease_untreated_without_anemia_due_to_hypertension,
sequelae.stage_v_chronic_kidney_disease_untreated_and_mild_anemia_due_to_hypertension,
sequelae.stage_v_chronic_kidney_disease_untreated_and_moderate_anemia_due_to_hypertension,
sequelae.stage_v_chronic_kidney_disease_untreated_and_severe_anemia_due_to_hypertension,
sequelae.stage_v_chronic_kidney_disease_untreated_without_anemia_due_to_glomerulonephritis,
sequelae.stage_v_chronic_kidney_disease_untreated_and_mild_anemia_due_to_glomerulonephritis,
sequelae.stage_v_chronic_kidney_disease_untreated_and_moderate_anemia_due_to_glomerulonephritis,
sequelae.stage_v_chronic_kidney_disease_untreated_and_severe_anemia_due_to_glomerulonephritis,
sequelae.stage_v_chronic_kidney_disease_untreated_without_anemia_due_to_other_and_unspecified_causes,
sequelae.stage_v_chronic_kidney_disease_untreated_and_mild_anemia_due_to_other_and_unspecified_causes,
sequelae.stage_v_chronic_kidney_disease_untreated_and_moderate_anemia_due_to_other_and_unspecified_causes,
sequelae.stage_v_chronic_kidney_disease_untreated_and_severe_anemia_due_to_other_and_unspecified_causes,
sequelae.end_stage_renal_disease_after_transplant_due_to_type_1_diabetes_mellitus,
sequelae.end_stage_renal_disease_after_transplant_due_to_type_2_diabetes_mellitus,
sequelae.end_stage_renal_disease_on_dialysis_due_to_type_1_diabetes_mellitus,
sequelae.end_stage_renal_disease_on_dialysis_due_to_type_2_diabetes_mellitus,
sequelae.stage_v_chronic_kidney_disease_untreated_and_severe_anemia_due_to_type_1_diabetes_mellitus,
sequelae.stage_v_chronic_kidney_disease_untreated_and_severe_anemia_due_to_type_2_diabetes_mellitus,
sequelae.stage_v_chronic_kidney_disease_untreated_and_moderate_anemia_due_to_type_1_diabetes_mellitus,
sequelae.stage_v_chronic_kidney_disease_untreated_and_moderate_anemia_due_to_type_2_diabetes_mellitus,
sequelae.stage_v_chronic_kidney_disease_untreated_and_mild_anemia_due_to_type_1_diabetes_mellitus,
sequelae.stage_v_chronic_kidney_disease_untreated_and_mild_anemia_due_to_type_2_diabetes_mellitus,
sequelae.stage_v_chronic_kidney_disease_untreated_without_anemia_due_to_type_1_diabetes_mellitus,
sequelae.stage_v_chronic_kidney_disease_untreated_without_anemia_due_to_type_2_diabetes_mellitus,
]
}
sequelae_for_category = category_sequelae_map[key]
prevalence_disability_weights = []
category_prevalences = []
for sequela in sequelae_for_category:
prevalence = interface.get_measure(sequela, 'prevalence', location)
category_prevalences.append(prevalence)
disability_weight = interface.get_measure(sequela, 'disability_weight', location)
prevalence_disability_weights.append(prevalence * disability_weight)
ikf_category_disability_weight = (sum(prevalence_disability_weights) / sum(category_prevalences)).fillna(0)
return ikf_category_disability_weight
def load_ckd_standard_data(key: str, location: str) -> pd.DataFrame:
key_mapping = {
project_globals.IKF.EMR: 'excess_mortality_rate',
project_globals.IKF.CSMR: 'cause_specific_mortality_rate'
}
return interface.get_measure(causes.chronic_kidney_disease, key_mapping[key], location)
def test_get_measure_covariatelike(entity, measure, location):
df = get_measure(entity, measure, utility_data.get_location_id(location))
def fail_expected(entity_name, measure_name, location):
with pytest.raises(Exception):
df = get_measure(entity_name, measure_name, location)
def success_expected(entity_name, measure_name, location):
df = get_measure(entity_name, measure_name, location)
return df
