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plot_measures.py
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plot_measures.py
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from utilities import *
import pandas as pd
import os
from config import indicators_list
from calculate_measures import demographics
import re
from collections import OrderedDict
from utilities import *
import numpy as np
import matplotlib.pyplot as plt
additional_indicators = ["e", "f", "li"]
indicators_list.extend(additional_indicators)
# SELECT DATES FOR AGGREGATE DEMOGRAPHIC VALUES
pre_q1 = ["2020-01-01", "2020-02-01", "2020-03-01"]
post_q1 = ["2021-01-01", "2021-02-01", "2021-03-01"]
medians_dict = {}
time_period_mapping = {
"ac": "2021-05-01",
"me_no_fbc": "2020-06-01",
"me_no_lft": "2020-06-01",
"li": "2020-06-01",
"am": "2020-09-01",
}
if not (OUTPUT_DIR / "figures").exists():
os.mkdir(OUTPUT_DIR / "figures")
# set up plots for combined decile charts
gi_bleed_x = np.arange(0, 2, 1)
gi_bleed_y = np.arange(0, 3, 1)
gi_bleed_axs_list = [(i, j) for i in gi_bleed_x for j in gi_bleed_y]
gi_bleed_fig, gi_bleed_axs = plt.subplots(2, 3, figsize=(30, 20), sharex="col")
gi_bleed_indicators = ["a", "b", "c", "d", "e", "f"]
prescribing_y = np.arange(0, 3, 1)
prescribing_axs_list = [i for i in prescribing_y]
prescribing_fig, prescribing_axs = plt.subplots(1, 3, figsize=(30, 10), sharex="col")
prescribing_indicators = ["g", "i", "k"]
monitoring_x = np.arange(0, 2, 1)
monitoring_y = np.arange(0, 3, 1)
monitoring_axs_list = [(i, j) for i in monitoring_x for j in monitoring_y]
monitoring_axs_list.remove((0, 2))
monitoring_fig, monitoring_axs = plt.subplots(2, 3, figsize=(30, 20), sharex="col")
monitoring_fig.delaxes(monitoring_axs[0, 2])
monitoring_indicators = ["ac", "me_no_fbc", "me_no_lft", "li", "am"]
title_mapping = {
"a": "Age >= 65 & NSAID", # "NSAID without gastroprotection, age >=65",
"b": "PU & NSAID", # "NSAID without gastroprotection, H/O peptic ulcer",
"c": "PU & antiplatelet", # "Antiplatelet without gastroprotection, H/O peptic ulcer",
"d": "Warfarin/DOAC & NSAID", # "DOAC with warfarin",
# "Anticoagulation and antiplatelet, no gastroprotection",
"e": "Warfarin/DOAC & antiplatelet",
# "Aspirin and antiplatelet, no gastroprotection",
"f": "Aspirin & other antiplatelet",
"g": "Asthma & beta-blocker", # "Asthma and non-selective beta-blocker",
"i": "HF & NSAID", # "Heart failure and NSAID",
"k": "CRF & NSAID", # "Chronic renal impairment and NSAID",
# "ACE inhibitor or loop diuretic without renal function/electrolyte test",
"ac": "ACEI or loop diuretic, no blood tests",
"me_no_fbc": "Methotrexate and no FBC", #"Methotrexate without full blood count",
"me_no_lft": "Methotrexate and no LFT", #"Methotrexate without liver function test",
# "Lithium without lithium concentration test",
"li": "Lithium and no level recording",
"am": "Amiodarone and no TFT", # "Amiodarone without thyroid function test",
}
# Dataframe for demographic aggregates
demographic_aggregate_df = pd.DataFrame(
columns=["indicator", "demographic", "group", "pre_mean", "post_mean"]
)
for i in indicators_list:
# indicator plots
df = pd.read_csv(
OUTPUT_DIR / f"measure_indicator_{i}_rate.csv", parse_dates=["date"]
)
df = drop_irrelevant_practices(df)
if i in ["me_no_fbc", "me_no_lft"]:
denominator = "indicator_me_denominator"
else:
denominator = f"indicator_{i}_denominator"
df["rate"] = df[f"value"] * 100
df = df.drop(["value"], axis=1)
# Need this for dummy data
df = df.replace(np.inf, np.nan)
df_deciles = compute_redact_deciles(df, "date", f"indicator_{i}_numerator", "rate")
# median_df = df_deciles.loc[df_deciles["percentile"]==50,:]
rate_df_pre = df.loc[df["date"].isin(pre_q1),"rate"].mean()
rate_df_post = df.loc[df["date"].isin(post_q1),"rate"].mean()
medians_dict[i] = {"pre": rate_df_pre, "post": rate_df_post}
deciles_chart(
df,
filename=f"plot_{i}",
period_column="date",
column="rate",
count_column=f"indicator_{i}_numerator",
title=title_mapping[i],
ylabel="Percentage",
time_window=time_period_mapping.get(i, ""),
)
# gi bleed
if i in gi_bleed_indicators:
ind = gi_bleed_indicators.index(i)
deciles_chart_subplots(
df,
period_column="date",
column="rate",
count_column=f"indicator_{i}_numerator",
title=title_mapping[i],
ylabel="Percentage",
show_outer_percentiles=False,
show_legend=False,
ax=gi_bleed_axs[gi_bleed_axs_list[ind]],
time_window=time_period_mapping.get(i, ""),
)
# prescribing
if i in prescribing_indicators:
ind = prescribing_indicators.index(i)
deciles_chart_subplots(
df,
period_column="date",
column="rate",
count_column=f"indicator_{i}_numerator",
title=title_mapping[i],
ylabel="Percentage",
show_outer_percentiles=False,
show_legend=False,
ax=prescribing_axs[prescribing_axs_list[ind]],
time_window=time_period_mapping.get(i, ""),
)
# monitoring
if i in monitoring_indicators:
ind = monitoring_indicators.index(i)
deciles_chart_subplots(
df,
period_column="date",
column="rate",
count_column=f"indicator_{i}_numerator",
title=title_mapping[i],
ylabel="Percentage",
show_outer_percentiles=False,
show_legend=False,
ax=monitoring_axs[monitoring_axs_list[ind]],
time_window=time_period_mapping.get(i, ""),
)
# demographic plots
for d in demographics:
df = pd.read_csv(
OUTPUT_DIR / f"indicator_measure_{i}_{d}.csv", parse_dates=["date"]
)
if d == "sex":
df = df[df["sex"].isin(["M", "F"])]
elif d == "imd":
df = df[df["imd"] != 0]
elif d == "age_band":
df = df[df["age_band"] != "missing"]
if i == "a":
# remove bands < 65
df = df[df["age_band"].isin(["60-69", "70-79", "80+"])]
elif i == "ac":
# remove bands < 75
df = df[df["age_band"].isin(["70-79", "80+"])]
df = redact_small_numbers(
df, 10, f"indicator_{i}_numerator", denominator, "rate", "date"
)
pre_df = df.loc[df["date"].isin(pre_q1), :]
mean_pre = pre_df.groupby(by=[d])["rate"].mean().rename("pre")
post_df = df.loc[df["date"].isin(post_q1), :]
mean_post = post_df.groupby(by=[d])["rate"].mean().rename("post")
mean_values = pd.concat([mean_pre, mean_post], axis=1)
for index, row in mean_values.iterrows():
demographic_aggregate_row = OrderedDict()
demographic_aggregate_row["indicator"] = i
demographic_aggregate_row["demographic"] = d
demographic_aggregate_row["group"] = index
demographic_aggregate_row["pre_mean"] = row["pre"]
demographic_aggregate_row["post_mean"] = row["post"]
demographic_aggregate_df = demographic_aggregate_df.append(
pd.DataFrame(demographic_aggregate_row, index=[0])
)
plot_measures(
df=df,
filename=f"plot_{i}_{d}",
title=f"Indicator {i} by {d}",
column_to_plot="rate",
y_label="Proportion",
as_bar=False,
category=d,
)
# plot composite measures
composite_indicators = ["gi_bleed", "monitoring", "other_prescribing", "all"]
for i in composite_indicators:
df = pd.read_csv(OUTPUT_DIR / f"{i}_composite_measure.csv", parse_dates=["date"])
# group those with 7+ indicators if all-composite
if i == "all":
num_indicators = list(df["num_indicators"].unique())
if "Other" in num_indicators:
num_indicators.remove("Other")
max_indicator = min([int(max(num_indicators)), 6])
above_nums = [f"{i}" for i in range(max_indicator, 14)]
above_nums.extend(["Other"])
below_nums = [f"{i}" for i in range(0, max_indicator)]
df_7_plus_count = (
df.loc[df["num_indicators"].isin(above_nums), :]
.groupby(["date"])[["count"]]
.sum()
.reset_index()
)
df_7_plus_population = (
df.loc[df["num_indicators"].isin(above_nums), :]
.groupby(["date"])[["denominator"]]
.mean()
.reset_index()
)
df_7_plus = df_7_plus_count.merge(df_7_plus_population, on=["date"])
if max_indicator < 7:
df_7_plus["num_indicators"] = f"{max_indicator}+"
else:
df_7_plus["num_indicators"] = "7+"
# drop combined columns from original df
df = df.loc[df["num_indicators"].isin(below_nums), :]
# concatenate
df = pd.concat([df, df_7_plus])
df["num_indicators"] = df["num_indicators"].astype("str")
df["rate"] = df["count"] / df["denominator"]
plot_measures(
df=df,
filename=f"plot_{i}_composite",
title=f"{i} composite indicator",
column_to_plot="rate",
y_label="Proportion",
as_bar=False,
category="num_indicators",
)
demographic_aggregate_df.to_csv("output/demographic_aggregates.csv")
gi_bleed_fig.subplots_adjust(bottom=0.15)
gi_bleed_fig.savefig("output/figures/combined_plot_gi_bleed.png")
plt.clf()
prescribing_fig.subplots_adjust(bottom=0.3)
prescribing_fig.savefig("output/figures/combined_plot_prescribing.png")
plt.clf()
monitoring_fig.subplots_adjust(bottom=0.15)
monitoring_fig.savefig("output/figures/combined_plot_monitoring.png")
with open(f"output/medians.json", "w") as f:
json.dump({"summary": medians_dict}, f)