def test_make_df_from_expectations_with_care_home_status():
study = StudyDefinition(
population=patients.all(),
is_in_care_home=patients.care_home_status_as_of(
"2020-01-01",
return_expectations={
"rate": "exponential_increase",
"incidence": 0.3,
"date": {"earliest": "1900-01-01", "latest": "2020-01-01"},
"bool": True,
},
),
care_home_type=patients.care_home_status_as_of(
"2020-01-01",
categorised_as={
"PN": "IsPotentialCareHome AND LocationRequiresNursing='Y'",
"PC": "IsPotentialCareHome",
"U": "DEFAULT",
},
return_expectations={
"rate": "exponential_increase",
"incidence": 0.2,
"category": {"ratios": {"PN": 0.1, "PC": 0.2, "U": 0.7}},
"date": {"earliest": "1900-01-01", "latest": "today"},
},
),
)
population_size = 10000
result = study.make_df_from_expectations(population_size)
value_counts = result.care_home_type.value_counts()
assert value_counts["PN"] < value_counts["U"]
},
),
care_home_type=patients.care_home_status_as_of(
"2020-02-01",
categorised_as={
"PC": """
IsPotentialCareHome
AND LocationDoesNotRequireNursing='Y'
AND LocationRequiresNursing='N'
""",
"PN": """
IsPotentialCareHome
AND LocationDoesNotRequireNursing='N'
AND LocationRequiresNursing='Y'
""",
"PS": "IsPotentialCareHome",
"U": "DEFAULT",
},
return_expectations={
"rate": "universal",
"category": {
"ratios": {
"PC": 0.01,
"PN": 0.01,
"PS": 0.01,
"U": 0.97,
},
},
},
),
# CONTINUOUS MEASURED COVARIATES
# CAREHOME STATUS
care_home_type=patients.care_home_status_as_of(
campaign_start,
categorised_as={
"PC": """
IsPotentialCareHome
AND LocationDoesNotRequireNursing='Y'
AND LocationRequiresNursing='N'
""",
"PN": """
IsPotentialCareHome
AND LocationDoesNotRequireNursing='N'
AND LocationRequiresNursing='Y'
""",
"PS": "IsPotentialCareHome",
"": "DEFAULT", # use empty string
},
return_expectations={
"rate": "universal",
"category": {
"ratios": {
"PC": 0.05,
"PN": 0.05,
"PS": 0.05,
"": 0.85,
},
},
},
),
# simple care home flag
