"M": 0.49,
"F": 0.51
}
},
}),
stp=patients.registered_practice_as_of(
"2020-02-29",
returning="stp_code",
return_expectations={
"rate": "universal",
"category": {
"ratios": {
"STP1": 0.1,
"STP2": 0.1,
"STP3": 0.1,
"STP4": 0.1,
"STP5": 0.1,
"STP6": 0.1,
"STP7": 0.1,
"STP8": 0.1,
"STP9": 0.1,
"STP10": 0.1,
}
},
},
),
imd=patients.address_as_of(
"2020-02-29",
returning="index_of_multiple_deprivation",
round_to_nearest=100,
return_expectations={
# https://github.com/ebmdatalab/tpp-sql-notebook/issues/46
sex=patients.sex(),
# https://github.com/ebmdatalab/tpp-sql-notebook/issues/52
imd=patients.address_as_of(
"2020-02-01", returning="index_of_multiple_deprivation", round_to_nearest=100
),
# https://github.com/ebmdatalab/tpp-sql-notebook/issues/37
rural_urban=patients.address_as_of(
"2020-02-01", returning="rural_urban_classification"
),
# https://github.com/ebmdatalab/tpp-sql-notebook/issues/54
stp=patients.registered_practice_as_of("2020-02-01", returning="stp_code"),
# region - one of NHS England 9 regions
region=patients.registered_practice_as_of("2020-02-01", returning="nhse_region_name"),
# https://github.com/ebmdatalab/tpp-sql-notebook/issues/10
bmi=patients.most_recent_bmi(
on_or_after="2010-02-01",
minimum_age_at_measurement=16,
include_measurement_date=True,
include_month=True,
),
# https://github.com/ebmdatalab/tpp-sql-notebook/issues/6
smoking_status=patients.categorised_as(
{
"incidence": 0.6,
"float": {
"distribution": "normal",
"mean": 120,
"stddev": 10
},
},
),
# https://github.com/ebmdatalab/tpp-sql-notebook/issues/54
stp=patients.registered_practice_as_of(
"2020-02-01",
returning="stp_code",
return_expectations={
"rate": "universal",
"category": {
"ratios": {
"STP1": 0.5,
"STP2": 0.5
}
},
},
),
msoa=patients.registered_practice_as_of(
"2020-02-01",
returning="msoa_code",
return_expectations={
"rate": "universal",
"category": {
"ratios": {
"MSOA1": 0.5,
"MSOA2": 0.5
def test_patients_registered_practice_as_of():
session = make_session()
org_1 = Organisation(
STPCode="123", MSOACode="E0201", Region="East of England", Organisation_ID=1
)
org_2 = Organisation(
STPCode="456", MSOACode="E0202", Region="Midlands", Organisation_ID=2
)
org_3 = Organisation(
STPCode="789", MSOACode="E0203", Region="London", Organisation_ID=3
)
org_4 = Organisation(
STPCode="910", MSOACode="E0204", Region="North West", Organisation_ID=4
)
patient = Patient()
patient.RegistrationHistory.append(
RegistrationHistory(
StartDate="1990-01-01", EndDate="2018-01-01", Organisation=org_1
)
)
# We deliberately create overlapping registration periods so we can check
# that we handle these correctly
patient.RegistrationHistory.append(
RegistrationHistory(
StartDate="2018-01-01", EndDate="2022-01-01", Organisation=org_2
)
)
patient.RegistrationHistory.append(
RegistrationHistory(
StartDate="2019-09-01", EndDate="2020-05-01", Organisation=org_3
)
)
patient.RegistrationHistory.append(
RegistrationHistory(
StartDate="2022-01-01", EndDate="9999-12-31", Organisation=org_4
)
)
patient_2 = Patient()
patient_2.RegistrationHistory.append(
RegistrationHistory(
StartDate="2010-01-01", EndDate="9999-12-31", Organisation=org_1
)
)
patient_3 = Patient()
patient_3.RegistrationHistory.append(
RegistrationHistory(StartDate="2010-01-01", EndDate="9999-12-31")
)
session.add_all([patient, patient_2, patient_3])
session.commit()
study = StudyDefinition(
population=patients.all(),
stp=patients.registered_practice_as_of("2020-01-01", returning="stp_code"),
msoa=patients.registered_practice_as_of("2020-01-01", returning="msoa_code"),
region=patients.registered_practice_as_of(
"2020-01-01", returning="nhse_region_name"
),
pseudo_id=patients.registered_practice_as_of(
"2020-01-01", returning="pseudo_id"
),
)
results = study.to_dicts()
assert [i["stp"] for i in results] == ["789", "123", ""]
assert [i["msoa"] for i in results] == ["E0203", "E0201", ""]
assert [i["region"] for i in results] == ["London", "East of England", ""]
assert [i["pseudo_id"] for i in results] == ["3", "1", "0"]