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indicators.py
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indicators.py
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# Copyright 2021 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""The main binary to calculate PEPFAR indicators.
"""
import argparse
from typing import Tuple
from datetime import date, datetime, timedelta
from dateutil import parser as date_parser
import indicator_lib
import query_lib
_CODE_SYSTEM='http://www.ampathkenya.org'
# For information about the following codes see:
# https://github.com/GoogleCloudPlatform/openmrs-fhir-analytics/issues/179#issuecomment-895040775
# and also `synthea` models in this repo.
# Question codes:
_VL_CODE = '856' # HIV VIRAL LOAD
_ARV_PLAN = '1255' # ANTIRETROVIRAL PLAN
_TX_TB_PLAN = '1268' # TUBERCULOSIS TREATMENT PLAN
# TODO: Add TB prevention plans in the synthetic data; seems currently missing.
_TB_PREV_plan = '1268' # TUBERCULOSIS TREATMENT PLAN
_TB_screening = '6174' # REVIEW OF TUBERCULOSIS SCREENING QUESTIONS
# Answer codes:
_YES_CODE = '1065'
_CONTINUE_REGIMEN = '1257' # CONTINUE REGIMEN
_START_DRUGS = '1256' # START DRUGS
_COMPLETE_REGIMEN = '1260' # STOP ALL MEDICATIONS
_STOP_ALL_MED = '1260' # STOP ALL MEDICATIONS
def valid_date(date_str: str) -> datetime:
try:
return date_parser.parse(date_str)
except ValueError:
raise argparse.ArgumentTypeError('Valid dates have YYYY-MM-DD format!')
def create_args(parser: argparse.ArgumentParser):
parser.add_argument(
'--src_dir',
help='Directory that includes Parquet files for each FHIR resource type',
required=True,
type=str
)
parser.add_argument(
'--last_date',
help='The last date for aggregating data.',
default=date.today(),
type=valid_date
)
# TODO: Remove the next arguement once issues #55 is resolved.
parser.add_argument(
'--base_patient_url',
help='This is the base url to be added to patient IDs, e.g., ' +
'http://localhost:8099/openmrs/ws/fhir2/R4/',
default='http://localhost:8099/openmrs/ws/fhir2/R4/',
type=str
)
parser.add_argument(
'--num_days',
help='Number of days on which calculate the indicators.',
default=28,
type=int
)
parser.add_argument(
'--output_csv',
help='The output CSV file',
required=True,
type=str
)
def find_date_range(args: argparse.Namespace) -> Tuple[str, str, str, str, str]:
end_date_str = args.last_date.strftime('%Y-%m-%d')
start_date = args.last_date - timedelta(days=args.num_days)
start_date_str = start_date.strftime('%Y-%m-%d')
previous_period_start = args.last_date - timedelta(days=2 * args.num_days)
previous_period_start_str = previous_period_start.strftime('%Y-%m-%d')
semiannual_start = args.last_date - timedelta(days=6 * args.num_days)
semiannual_start_str = semiannual_start.strftime('%Y-%m-%d')
quarterly_start = args.last_date - timedelta(days=3 * args.num_days)
quarterly_start_str = quarterly_start.strftime('%Y-%m-%d')
return (start_date_str, end_date_str, previous_period_start_str,
semiannual_start_str, quarterly_start_str)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
create_args(parser)
args = parser.parse_args()
(start_date, end_date, prev_start, semiannual_start_str,
quarterly_start_str) = find_date_range(args)
print('Source directory: {0}'.format(args.src_dir))
print('Date range: {0} - {1}'.format(start_date, end_date))
# TODO check why without this constraint, `validate_indicators.sh` fails.
# Monthly query
monthly_query = query_lib.patient_query_factory(
query_lib.Runner.SPARK, args.src_dir, _CODE_SYSTEM).include_obs_values_in_time_range(
_VL_CODE, min_time=start_date, max_time=end_date)
monthly_query.include_all_other_codes(min_time=start_date, max_time=end_date)
# Semiannual query
semi_annual_query = query_lib.patient_query_factory(
query_lib.Runner.SPARK, args.src_dir, _CODE_SYSTEM).include_all_other_codes(
min_time=semiannual_start_str, max_time=end_date)
# Prev Month query
prev_month_query = query_lib.patient_query_factory(
query_lib.Runner.SPARK, args.src_dir, _CODE_SYSTEM).include_all_other_codes(
min_time=prev_start, max_time=end_date)
# Quarterlly query
quarterly_query = query_lib.patient_query_factory(
query_lib.Runner.SPARK, args.src_dir, _CODE_SYSTEM).include_all_other_codes(
min_time=quarterly_start_str, max_time=end_date)
# Fetch aggregated obs
current_month_df = monthly_query.get_patient_obs_view(args.base_patient_url)
prev_month_df = prev_month_query.get_patient_obs_view(args.base_patient_url)
annual_df = semi_annual_query.get_patient_obs_view(args.base_patient_url)
quarterly_df = quarterly_query.get_patient_obs_view(args.base_patient_url)
VL_df = indicator_lib.calc_TX_PVLS(
current_month_df, VL_code=_VL_CODE,
failure_threshold=10000, end_date_str=end_date)
# TX_NEW
TX_NEW_df = indicator_lib.calc_TX_NEW(
current_month_df, ARV_plan=_ARV_PLAN,
start_drug=[_START_DRUGS], end_date_str=end_date)
TB_STAT_df = indicator_lib.calc_TB_STAT(
current_month_df, TB_TX_plan=_TX_TB_PLAN, ARV_plan=_ARV_PLAN,
TB_plan_answer=[_START_DRUGS], end_date_str=end_date)
TX_CURR_df = indicator_lib.calc_TX_CURR(
current_month_df, ARV_plan=_ARV_PLAN,
ARV_plan_answer=[_CONTINUE_REGIMEN, _START_DRUGS],
end_date_str=end_date)
TB_ART_df = indicator_lib.calc_TB_ART(
current_month_df, TB_TX_plan=_TX_TB_PLAN, ARV_plan=_ARV_PLAN,
TB_plan_answer=[_CONTINUE_REGIMEN, _START_DRUGS],
ARV_plan_answer=[_CONTINUE_REGIMEN, _START_DRUGS],
end_date_str=end_date)
TB_PREV_df = indicator_lib.calc_TB_PREV(
prev_month_df, TB_PREV_plan=_TB_PREV_plan, ARV_plan=_ARV_PLAN,
TB_PREV_plan_answer=[_CONTINUE_REGIMEN, _START_DRUGS],
TB_CURR_plan_answer=[_COMPLETE_REGIMEN],
ART_plan_answer=[_CONTINUE_REGIMEN, _START_DRUGS],
end_date_str=end_date)
TX_TB_df = indicator_lib.calc_TX_TB(
annual_df, TX_TB_plan=_TX_TB_PLAN, ARV_plan=_ARV_PLAN,
TB_screening=_TB_screening, YES_CODE=_YES_CODE,
TX_TB_plan_answer=[_CONTINUE_REGIMEN, _START_DRUGS],
ART_plan_answer=[_CONTINUE_REGIMEN, _START_DRUGS],
end_date_str=end_date)
# TODO the logic behind this merge is not clear, especially for null keys.
VL_df.merge(TX_NEW_df, how='outer', left_on=['buckets', 'sup_VL'],
right_on=['buckets', 'TX_NEW']).merge(
TB_STAT_df, how='outer', left_on=['buckets', 'sup_VL'],
right_on=['buckets', 'TB_STAT']).merge(
TX_CURR_df, how='outer', left_on=['buckets', 'sup_VL'],
right_on=['buckets', 'TX_CURR']).merge(
TB_ART_df, how='outer', left_on=['buckets', 'sup_VL'],
right_on=['buckets', 'TB_ART']).merge(
TB_PREV_df, how='outer', left_on=['buckets', 'sup_VL'],
right_on=['buckets', 'TB_PREV']).merge(
TX_TB_df, how='outer', left_on=['buckets', 'sup_VL'],
right_on=['buckets', 'TX_TB']
).to_csv(args.output_csv, index=False)