"date": {
"earliest": "2020-01-01",
"latest": "today"
},
"rate": "universal",
},
# define the study index date
index_date=index_date,
# This line defines the study population
population=patients.satisfying(
"(NOT died) AND (registered) AND (pregnant) AND age >= 16",
died=patients.died_from_any_cause(on_or_before=index_date,
returning="binary_flag"),
registered=patients.registered_as_of(index_date),
pregnant=patients.with_these_clinical_events(
pregnant_code,
between=["index_date", "index_date + 1 month"],
returning="binary_flag",
return_expectations={"incidence": 0.6},
),
),
age=patients.age_as_of(index_date,
return_expectations={
"rate": "universal",
"int": {
"distribution": "population_ages"
}
}),
clinical_riskgroup=patients.with_these_clinical_events(
## LIBRARIES - simple study definitions
# cohort extractor
from cohortextractor import (StudyDefinition, patients)
# dictionaries of STP codes (for dummy data)
from dictionaries import dict_stp
# set the index date
index_date = "2020-01-01"
## STUDY POPULATION
study = StudyDefinition(
default_expectations={
"date": {
"earliest": index_date,
"latest": "today"
}, # date range for simulation
"rate": "uniform",
"incidence": 1
},
population=patients.registered_as_of(index_date),
stp=patients.registered_practice_as_of(index_date,
returning="stp_code",
return_expectations={
"category": {
"ratios": dict_stp
},
}))
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)}
},
},
),
)
"date": {
"earliest": start_date,
"latest": end_date
},
"rate": "exponential_increase",
"incidence": 0.1,
},
population=patients.satisfying(
"""
registered AND
(NOT died) AND
(sex = 'F' OR sex='M') AND
(age != 'missing')
""",
registered=patients.registered_as_of(
"index_date",
return_expectations={"incidence": 0.9},
),
died=patients.died_from_any_cause(
on_or_before="index_date",
returning="binary_flag",
return_expectations={"incidence": 0.1}),
),
age=patients.age_as_of(
"index_date",
return_expectations={
"rate": "universal",
"int": {
"distribution": "population_ages"
},
},
),
"incidence": 0.2,
},
# set an index date (as starting point)
index_date="2020-02-01",
# This line defines the study population that the below varaibles will be defined for
# currently registered patients restricts to those alive
# the age restriction is applied as current TPP linkage only includes linkages to old age care
population=patients.satisfying(
"""
(age >= 65 AND age < 120) AND
is_registered_with_tpp
""",
is_registered_with_tpp=patients.registered_as_of(
"index_date"
),
),
# TPP ADDRESS LINKAGE
# tpp defined care home as of date
tpp_care_home_type=patients.care_home_status_as_of(
"index_date",
categorised_as={
"PC": """
IsPotentialCareHome
AND LocationDoesNotRequireNursing='Y'
AND LocationRequiresNursing='N'
""",
"PN": """
IsPotentialCareHome
(covid_vacc_date
OR
(age >=70 AND age <= 110)
OR
(care_home_type))
AND
NOT has_died
"""),
has_follow_up=patients.registered_with_one_practice_between(
start_date="2019-12-01",
end_date=campaign_start,
return_expectations={"incidence": 0.90},
),
registered=patients.registered_as_of(
campaign_start, # day before vaccination campaign starts - discuss with team if this should be "today"
return_expectations={"incidence": 0.98},
),
has_died=patients.died_from_any_cause(
on_or_before=campaign_start,
returning="binary_flag",
return_expectations={"incidence": 0.05},
),
# Demographic information
# CAREHOME STATUS
care_home_type=patients.care_home_status_as_of(
campaign_start,
categorised_as={
"PC": """
IsPotentialCareHome
# Import codelists
from codelists import *
from datetime import date
start_date = "2020-12-07"
end_date = "2021-02-01"
# Specifiy study definition
study = StudyDefinition(
default_expectations={
"date": {"earliest": start_date, "latest": end_date},
"rate": "exponential_increase",
"incidence": 0.1,
},
population=patients.registered_as_of(start_date),
practice=patients.registered_practice_as_of(
start_date,
returning="pseudo_id",
return_expectations={
"int": {"distribution": "normal", "mean": 25, "stddev": 5}, "incidence": 0.5}
),
)
