generated from opensafely/research-template
/
calculate_measures.py
148 lines (115 loc) · 4.76 KB
/
calculate_measures.py
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import pandas as pd
import json
import numpy as np
from utilities import (
OUTPUT_DIR,
match_input_files_filtered,
get_date_input_file_filtered,
calculate_rate,
redact_small_numbers,
update_demographics,
)
from config import indicators_list, backend
# these are not generated in the main generate measures action
additional_indicators = ["e", "f"]
indicators_list.extend(additional_indicators)
demographics = ["age_band", "sex", "region", "imd", "ethnicity"]
demographics_df = pd.DataFrame(columns=["patient_id"] + (demographics))
if __name__ == "__main__":
df_dict = {}
df_dict_additional = {i: [] for i in additional_indicators}
for d in demographics:
df_dict[d] = {}
for i in indicators_list:
df_dict[d][i] = []
for file in OUTPUT_DIR.iterdir():
if match_input_files_filtered(file.name):
df = pd.read_feather(OUTPUT_DIR / file.name)
date = get_date_input_file_filtered(file.name)
indicator_e_f = pd.read_feather(
OUTPUT_DIR / f"indicator_e_f_{date}.feather"
)
e_dict = dict(
zip(indicator_e_f["patient_id"], indicator_e_f["indicator_e_numerator"])
)
f_dict = dict(
zip(indicator_e_f["patient_id"], indicator_e_f["indicator_f_numerator"])
)
df["indicator_e_numerator"] = df["patient_id"].map(e_dict)
df["indicator_f_numerator"] = df["patient_id"].map(f_dict)
for additional_indicator in additional_indicators:
event = (
df.groupby(by=["practice"])[
[
f"indicator_{additional_indicator}_numerator",
f"indicator_{additional_indicator}_denominator",
]
]
.sum()
.reset_index()
)
event["value"] = event[
f"indicator_{additional_indicator}_numerator"
].div(
event[f"indicator_{additional_indicator}_denominator"].where(
event[f"indicator_{additional_indicator}_denominator"] != 0,
np.nan,
)
)
event["value"] = event["value"].replace({np.nan: 0})
event["date"] = date
df_dict_additional[additional_indicator].append(event)
# update demographics data
demographics_df = update_demographics(demographics_df, df)
for d in demographics:
for i in indicators_list:
if i in ["me_no_fbc", "me_no_lft"]:
denominator = "indicator_me_denominator"
else:
denominator = f"indicator_{i}_denominator"
event = (
df.groupby(by=[d])[[f"indicator_{i}_numerator", denominator]]
.sum()
.reset_index()
)
event["rate"] = calculate_rate(
event, f"indicator_{i}_numerator", denominator, 1
)
event["date"] = date
df_dict[d][i].append(event)
for demographic_key, demographic_value in df_dict.items():
for indicator_key, indicator_value in df_dict[demographic_key].items():
df_combined = pd.concat(indicator_value, axis=0)
df_combined.to_csv(
OUTPUT_DIR / f"indicator_measure_{indicator_key}_{demographic_key}.csv"
)
for indicator_key, indicator_value in df_dict_additional.items():
df_combined = pd.concat(indicator_value, axis=0)
df_combined.to_csv(OUTPUT_DIR / f"measure_indicator_{indicator_key}_rate.csv")
d_list = {}
for d in demographics:
if d == "imd":
# map imd
df[d] = df[d].replace(
{"0": "Missing", "1": "Most deprived", "5": "Least deprived"}
)
counts = demographics_df[d].value_counts()
counts_df = pd.concat(
[
counts,
pd.Series(
[round((value / np.sum(counts)) * 100, 2) for value in counts],
index=counts.index,
),
],
axis=1,
keys=["count", "%"],
levels=demographics,
)
d_list[d] = counts_df
demographic_counts_df = pd.concat(d_list, axis=0)
demographic_counts_df = demographic_counts_df.reset_index()
demographic_counts_df.columns = ["demographic", "level", "count", "perc"]
demographic_counts_df.to_csv(
OUTPUT_DIR / f"demographics_summary_{backend}.csv", index=False
)