def test_address_dtype_generation():
study = StudyDefinition(
# This line defines the study population
population=patients.all(),
rural_urban=patients.address_as_of(
"2020-02-01", returning="rural_urban_classification"
),
)
result = _converters_to_names(study.pandas_csv_args)
assert result == {
"dtype": {"rural_urban": "category"},
"parse_dates": [],
"date_col_for": {},
"converters": {},
}
"ratios": {
"STP1": 0.5,
"STP2": 0.5
}
},
},
),
# https://github.com/ebmdatalab/tpp-sql-notebook/issues/52
imd=patients.address_as_of(
"2020-03-01",
returning="index_of_multiple_deprivation",
round_to_nearest=100,
return_expectations={
"rate": "universal",
"category": {
"ratios": {
"100": 0.1,
"200": 0.2,
"300": 0.7
}
},
},
),
#SMOKING
smoking_status=patients.categorised_as(
{
"S": "most_recent_smoking_code = 'S'",
"E": """
most_recent_smoking_code = 'E' OR (
most_recent_smoking_code = 'N' AND ever_smoked
)
from datetime import date
from cohortextractor import StudyDefinition, patients
today = str(date.today())
study = StudyDefinition(
default_expectations={
"date": {
"earliest": "1900-01-01",
"latest": "today"
},
"rate": "exponential_increase",
},
population=patients.registered_as_of(today),
imd=patients.address_as_of(
today,
returning="index_of_multiple_deprivation",
round_to_nearest=100,
return_expectations={
"incidence": 0.8,
"category": {
"ratios": {100 * (n + 1): 1 / 330
for n in range(330)}
},
},
),
)
}
),
# SEX
sex = patients.sex(return_expectations={
"rate": "universal",
"category": {"ratios":{"M": 0.49, "F":0.51}},
}
),
# DEPRIVIATION
imd=patients.address_as_of(
"today",
returning="index_of_multiple_deprivation",
round_to_nearest=100,
return_expectations={
"rate": "universal",
"category": {"ratios": {"1": 0.2, "2": 0.2, "3": 0.2, "4": 0.2, "5":0.2}},
}
),
# GEOGRAPHIC REGION CALLED STP
stp = patients.registered_practice_as_of(
"2020-01-01",
returning="stp_code",
return_expectations={
"category": {"ratios": dict_stp},
}
),
# # https://codelists.opensafely.org/codelist/opensafely/severe-and-profound-learning-disability-flags/44ef542a/#full-list
"ratios": {
"MSOA1": 0.5,
"MSOA2": 0.5
}
},
},
),
# https://github.com/ebmdatalab/tpp-sql-notebook/issues/52
imd=patients.address_as_of(
"index_date",
returning="index_of_multiple_deprivation",
round_to_nearest=100,
return_expectations={
"rate": "universal",
"category": {
"ratios": {
"100": 0.1,
"200": 0.2,
"300": 0.7
}
},
},
),
rural_urban=patients.address_as_of(
"index_date",
returning="rural_urban_classification",
return_expectations={
"rate": "universal",
"category": {
"ratios": {
"rural": 0.1,
imd=patients.categorised_as(
{
"0": "DEFAULT",
"1":
"""index_of_multiple_deprivation >=1 AND index_of_multiple_deprivation < 32844*1/5""",
"2":
"""index_of_multiple_deprivation >= 32844*1/5 AND index_of_multiple_deprivation < 32844*2/5""",
"3":
"""index_of_multiple_deprivation >= 32844*2/5 AND index_of_multiple_deprivation < 32844*3/5""",
"4":
"""index_of_multiple_deprivation >= 32844*3/5 AND index_of_multiple_deprivation < 32844*4/5""",
"5": """index_of_multiple_deprivation >= 32844*4/5 """,
},
index_of_multiple_deprivation=patients.address_as_of(
"index_date",
returning="index_of_multiple_deprivation",
round_to_nearest=100,
),
return_expectations={
"rate": "universal",
"category": {
"ratios": {
"0": 0.01,
"1": 0.20,
"2": 0.20,
"3": 0.20,
"4": 0.20,
"5": 0.19,
}
},
}),
"0":
"DEFAULT",
"1":
"""index_of_multiple_deprivation >=1 AND index_of_multiple_deprivation < 32844*1/5""",
"2":
"""index_of_multiple_deprivation >= 32844*1/5 AND index_of_multiple_deprivation < 32844*2/5""",
"3":
"""index_of_multiple_deprivation >= 32844*2/5 AND index_of_multiple_deprivation < 32844*3/5""",
"4":
"""index_of_multiple_deprivation >= 32844*3/5 AND index_of_multiple_deprivation < 32844*4/5""",
"5":
"""index_of_multiple_deprivation >= 32844*4/5 AND index_of_multiple_deprivation < 32844""",
},
index_of_multiple_deprivation=patients.address_as_of(
campaign_start,
returning="index_of_multiple_deprivation",
round_to_nearest=100,
),
return_expectations={
"rate": "universal",
"category": {
"ratios": {
"0": 0.05,
"1": 0.19,
"2": 0.19,
"3": 0.19,
"4": 0.19,
"5": 0.19,
}
},
},
"M": 0.49,
"F": 0.51
}
},
}),
# DEPRIVIATION
imd=patients.address_as_of(
"sgss_pos_inrange",
returning="index_of_multiple_deprivation",
round_to_nearest=100,
return_expectations={
"rate": "universal",
"category": {
"ratios": {
"100": 0.1,
"200": 0.2,
"300": 0.2,
"400": 0.2,
"500": 0.2,
"600": 0.1
}
},
},
),
# GEOGRAPHIC REGION CALLED STP
stp=patients.registered_practice_as_of(
"sgss_pos_inrange",
returning="stp_code",
return_expectations={
"rate": "universal",
