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measures_demo_new.py
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measures_demo_new.py
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###################################################
# This script creates monthly counts/rates of opioid
# prescribing for any opioid prescribing, new opioid prescribing,
# and high dose/long-acting prescribing by demographics categories
###################################################
from ehrql import case, when, months, INTERVAL, Measures
from ehrql.tables.tpp import (
patients,
addresses,
practice_registrations,
clinical_events)
import codelists
from dataset_definition import make_dataset_opioids
##########
from argparse import ArgumentParser
parser = ArgumentParser()
parser.add_argument("--start-date", type=str)
parser.add_argument("--intervals", type=int)
args = parser.parse_args()
start_date = args.start_date
intervals = args.intervals
##########
index_date = INTERVAL.start_date
dataset = make_dataset_opioids(index_date=index_date, end_date=INTERVAL.end_date)
##########
## Define demographic variables
age = patients.age_on(index_date)
age_group = case(
when(age < 30).then("18-29"),
when(age < 40).then("30-39"),
when(age < 50).then("40-49"),
when(age < 60).then("50-59"),
when(age < 70).then("60-69"),
when(age < 80).then("70-79"),
when(age < 90).then("80-89"),
when(age >= 90).then("90+"),
otherwise="missing",
)
sex = patients.sex
imd = addresses.for_patient_on(index_date).imd_rounded
imd10 = case(
when((imd >= 0) & (imd < int(32844 * 1 / 10))).then("1 (most deprived)"),
when(imd < int(32844 * 2 / 10)).then("2"),
when(imd < int(32844 * 3 / 10)).then("3"),
when(imd < int(32844 * 4 / 10)).then("4"),
when(imd < int(32844 * 5 / 10)).then("5"),
when(imd < int(32844 * 6 / 10)).then("6"),
when(imd < int(32844 * 7 / 10)).then("7"),
when(imd < int(32844 * 8 / 10)).then("8"),
when(imd < int(32844 * 9 / 10)).then("9"),
when(imd >= int(32844 * 9 / 10)).then("10 (least deprived)"),
otherwise="unknown"
)
ethnicity = clinical_events.where(
clinical_events.snomedct_code.is_in(codelists.ethnicity_codes_6)
).sort_by(
clinical_events.date
).last_for_patient().snomedct_code.to_category(codelists.ethnicity_codes_6)
ethnicity6 = case(
when(ethnicity == "1").then("White"),
when(ethnicity == "2").then("Mixed"),
when(ethnicity == "3").then("South Asian"),
when(ethnicity == "4").then("Black"),
when(ethnicity == "5").then("Other"),
when(ethnicity == "6").then("Not stated"),
otherwise="Unknown"
)
region = practice_registrations.for_patient_on(index_date).practice_nuts1_region_name
#########################
measures = Measures()
measures.configure_disclosure_control(enabled=False)
measures.define_defaults(intervals=months(intervals).starting_on(start_date))
# Total denominator
denominator_naive = (
(patients.age_on(index_date) >= 18)
& (patients.age_on(index_date) < 110)
& ((patients.sex == "male") | (patients.sex == "female"))
& (patients.date_of_death.is_after(index_date) | patients.date_of_death.is_null())
& (practice_registrations.for_patient_on(index_date).exists_for_patient())
& dataset.opioid_naive
)
#########################
## Overall
# By demographics - new prescribing
measures.define_measure(
name="opioid_new_age",
numerator=dataset.opioid_new,
denominator=denominator_naive,
group_by={"age_group": age_group}
)
measures.define_measure(
name="opioid_new_sex",
numerator=dataset.opioid_new,
denominator=denominator_naive,
group_by={"sex": patients.sex}
)
measures.define_measure(
name="opioid_new_region",
numerator=dataset.opioid_new,
denominator=denominator_naive,
group_by={"region": region}
)
measures.define_measure(
name="opioid_new_imd",
numerator=dataset.opioid_new,
denominator=denominator_naive,
group_by={"imd": imd10}
)
measures.define_measure(
name="opioid_new_eth6",
numerator=dataset.opioid_new,
denominator=denominator_naive,
group_by={"ethnicity6": ethnicity6}
)