analysis/results.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import json\n",
    "\n",
    "from IPython.display import Markdown as md\n",
    "from IPython.display import HTML, display\n",
    "from collections import Counter\n",
    "import math\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "pd.options.display.float_format = '{:.0f}'.format"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "source": [
    "df = pd.read_csv('../output/input_descriptives.csv')\n",
    "\n",
    "#population size\n",
    "num_patients = len(df['patient_id'].unique())\n",
    "print(f'Population size (Attended A&E and/or had any cause hospitalisation): {num_patients}')\n",
    "\n"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Population size (Attended A&E and/or had any cause hospitalisation): 10000\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Number of hospital admissions"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "source": [
    "# Number of hospital admissions\n",
    "\n",
    "num_patients_hosp = len(df[df['hospital_admission'].notna()]['patient_id'].unique())\n",
    "num_patients_hosp_emergency = len(df[df['emergency_hospital_admission'].notna()]['patient_id'].unique())\n",
    "num_patients_hosp_prim_covid = len(df[df['primary_covid_hospital_admission'].notna()]['patient_id'].unique())\n",
    "num_patients_hosp_emergency_prim_covid = len(df[df['emergency_primary_covid_hospital_admission'].notna()]['patient_id'].unique())\n",
    "num_patients_hosp_covid = len(df[df['covid_hospital_admission'].notna()]['patient_id'].unique())\n",
    "num_patients_hosp_emergency_covid = len(df[df['emergency_covid_hospital_admission'].notna()]['patient_id'].unique())\n",
    "\n",
    "emergency_hospitalisation_dict = {\n",
    "    \"hospital_admission\": num_patients_hosp,\n",
    "    \"emergency_hospital_admission\": num_patients_hosp_emergency,\n",
    "    \"emergency_admission_primary_covid\": num_patients_hosp_emergency_prim_covid,\n",
    "    \"emergency_admission_secondary_covid\": num_patients_hosp_emergency_covid,\n",
    "}\n",
    "\n",
    "pd.DataFrame.from_dict(emergency_hospitalisation_dict, orient='index')"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>hospital_admission</th>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>emergency_hospital_admission</th>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>emergency_admission_primary_covid</th>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>emergency_admission_secondary_covid</th>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        0\n",
       "hospital_admission                   3000\n",
       "emergency_hospital_admission         3000\n",
       "emergency_admission_primary_covid    3000\n",
       "emergency_admission_secondary_covid  3000"
      ]
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Total Number of A&E Attendances"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "source": [
    "# A&E attendance\n",
    "\n",
    "# a&e attendance all\n",
    "num_patients_attended_ae = len(df[(df['all_ae_attendance_any_discharge']==1)]['patient_id'].unique())\n",
    "num_patients_attended_ae_with_discharge = len(df[(df['all_ae_attendance_with_discharge']==1)]['patient_id'].unique())\n",
    "num_patients_attended_ae_with_hosp_discharge = len(df[(df['all_ae_attendance_hosp_discharge']==1)]['patient_id'].unique())\n",
    "num_patients_attended_ae_cov = len(df[(df['all_ae_attendance_covid_status']==1)]['patient_id'].unique())\n",
    "num_patients_attended_ae_resp = len(df[(df['all_ae_attendance_respiratory_status']==1)]['patient_id'].unique())\n",
    "num_patients_attended_ae_pos_test = len(df[(df['all_positive_covid_test_before_ae_attendance']==1)]['patient_id'].unique())\n",
    "num_patients_either = len(df[\n",
    "    (\n",
    "        (df['all_ae_attendance_covid_status']==1))\n",
    "    |(\n",
    "    (df[\"all_ae_attendance_respiratory_status\"]==1) &\n",
    "    (df[\"all_positive_covid_test_before_ae_attendance\"])\n",
    "    )\n",
    "                            ]['patient_id'].unique())\n",
    "\n",
    "num_patients_classified = len(df[\n",
    "    (df[\"all_ae_attendance_hosp_discharge\"]==1)\n",
    "    &\n",
    "    (\n",
    "        (df['all_ae_attendance_covid_status']==1))\n",
    "    |(\n",
    "    (df[\"all_ae_attendance_respiratory_status\"]==1) &\n",
    "    (df[\"all_positive_covid_test_before_ae_attendance\"]==1)\n",
    "    )\n",
    "                            ]['patient_id'].unique())\n",
    "\n",
    "\n",
    "all_ae_dict = {\n",
    "    \"attended_ae\": num_patients_attended_ae,\n",
    "    \"attended_ae_with_discharge\": num_patients_attended_ae_with_discharge,\n",
    "    \"attended_ae_hosp_discharge\": num_patients_attended_ae_with_hosp_discharge,\n",
    "    \"attended_ae_cov\": num_patients_attended_ae_cov,\n",
    "    \"attended_ae_resp\": num_patients_attended_ae_resp,\n",
    "    \"attended_ae_pos_test\": num_patients_attended_ae_pos_test,\n",
    "    \"attended_ae_resp_+_test_or_cov\": num_patients_either,\n",
    "    \"classified\": num_patients_classified\n",
    "}\n",
    "\n",
    "pd.DataFrame.from_dict(all_ae_dict, orient='index')"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>attended_ae</th>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attended_ae_with_discharge</th>\n",
       "      <td>4000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attended_ae_hosp_discharge</th>\n",
       "      <td>4000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attended_ae_cov</th>\n",
       "      <td>9001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attended_ae_resp</th>\n",
       "      <td>9001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attended_ae_pos_test</th>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attended_ae_resp_+_test_or_cov</th>\n",
       "      <td>9446</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   0\n",
       "attended_ae                     5000\n",
       "attended_ae_with_discharge      4000\n",
       "attended_ae_hosp_discharge      4000\n",
       "attended_ae_cov                 9001\n",
       "attended_ae_resp                9001\n",
       "attended_ae_pos_test            5000\n",
       "attended_ae_resp_+_test_or_cov  9446"
      ]
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "A&E Attendances in emergency hospitalisations"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "source": [
    "\n",
    "num_patients_attended_ae = len(df[(df['any_ae_attendance_any_discharge']==1)]['patient_id'].unique())\n",
    "num_patients_attended_ae_with_discharge = len(df[(df['any_ae_attendance_with_discharge']==1)]['patient_id'].unique())\n",
    "num_patients_attended_ae_with_hosp_discharge = len(df[(df['any_ae_attendance_hosp_discharge']==1)]['patient_id'].unique())\n",
    "num_patients_attended_ae_cov = len(df[(df['any_ae_attendance_covid_status']==1)]['patient_id'].unique())\n",
    "num_patients_attended_ae_resp = len(df[(df['any_ae_attendance_respiratory_status']==1)]['patient_id'].unique())\n",
    "num_patients_attended_ae_pos_test = len(df[(df['any_positive_covid_test_before_ae_attendance']==1)]['patient_id'].unique())\n",
    "\n",
    "num_patients_either = len(df[\n",
    "    (\n",
    "        (df['any_ae_attendance_covid_status']==1))\n",
    "    |(\n",
    "    (df[\"any_ae_attendance_respiratory_status\"]==1) &\n",
    "    (df[\"any_positive_covid_test_before_ae_attendance\"]==1)\n",
    "    )\n",
    "                            ]['patient_id'].unique())\n",
    "\n",
    "num_patients_classified = len(df[\n",
    "    (df[\"any_ae_attendance_hosp_discharge\"]==1)\n",
    "    &\n",
    "    (\n",
    "        (df['any_ae_attendance_covid_status']==1))\n",
    "    |(\n",
    "    (df[\"any_ae_attendance_respiratory_status\"]==1) &\n",
    "    (df[\"any_positive_covid_test_before_ae_attendance\"]==1)\n",
    "    )\n",
    "                            ]['patient_id'].unique())\n",
    "\n",
    "\n",
    "\n",
    "all_ae_dict = {\n",
    "    \"attended_ae\": num_patients_attended_ae,\n",
    "    \"attended_ae_with_discharge\": num_patients_attended_ae_with_discharge,\n",
    "    \"attended_ae_hosp_discharge\": num_patients_attended_ae_with_hosp_discharge,\n",
    "    \"attended_ae_cov\": num_patients_attended_ae_cov,\n",
    "    \"attended_ae_resp\": num_patients_attended_ae_resp,\n",
    "    \"attended_ae_pos_test\": num_patients_attended_ae_pos_test,\n",
    "    \"attended_ae_resp_+_test_or_cov\": num_patients_either,\n",
    "    \"classified\": num_patients_classified\n",
    "}\n",
    "\n",
    "pd.DataFrame.from_dict(all_ae_dict, orient='index')"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>attended_ae</th>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attended_ae_with_discharge</th>\n",
       "      <td>4000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attended_ae_hosp_discharge</th>\n",
       "      <td>4000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attended_ae_cov</th>\n",
       "      <td>9001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attended_ae_resp</th>\n",
       "      <td>9001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attended_ae_pos_test</th>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attended_ae_resp_+_test_or_cov</th>\n",
       "      <td>9412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>classified</th>\n",
       "      <td>6463</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   0\n",
       "attended_ae                     5000\n",
       "attended_ae_with_discharge      4000\n",
       "attended_ae_hosp_discharge      4000\n",
       "attended_ae_cov                 9001\n",
       "attended_ae_resp                9001\n",
       "attended_ae_pos_test            5000\n",
       "attended_ae_resp_+_test_or_cov  9412\n",
       "classified                      6463"
      ]
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "A&E attendance in COVID emergency hospitalisations"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "source": [
    "# a&e attendance all\n",
    "num_patients_attended_ae = len(df[(df['ae_attendance_any_discharge']==1)]['patient_id'].unique())\n",
    "num_patients_attended_ae_with_discharge = len(df[(df['ae_attendance_with_discharge']==1)]['patient_id'].unique())\n",
    "num_patients_attended_ae_with_hosp_discharge = len(df[(df['ae_attendance_hosp_discharge']==1)]['patient_id'].unique())\n",
    "num_patients_attended_ae_cov = len(df[(df['ae_attendance_covid_status']==1)]['patient_id'].unique())\n",
    "num_patients_attended_ae_resp = len(df[(df['ae_attendance_respiratory_status']==1)]['patient_id'].unique())\n",
    "num_patients_attended_ae_pos_test = len(df[(df['positive_covid_test_before_ae_attendance']==1)]['patient_id'].unique())\n",
    "num_patients_either = len(df[\n",
    "    (\n",
    "        (df['ae_attendance_covid_status']==1))\n",
    "    |(\n",
    "    (df[\"ae_attendance_respiratory_status\"]==1) &\n",
    "    (df[\"positive_covid_test_before_ae_attendance\"]==1)\n",
    "    )\n",
    "                            ]['patient_id'].unique())\n",
    "\n",
    "num_patients_classified = len(df[\n",
    "    (df[\"ae_attendance_hosp_discharge\"]==1)\n",
    "    &\n",
    "    (\n",
    "        (df['ae_attendance_covid_status']==1))\n",
    "    |(\n",
    "    (df[\"ae_attendance_respiratory_status\"]==1) &\n",
    "    (df[\"positive_covid_test_before_ae_attendance\"]==1)\n",
    "    )\n",
    "                            ]['patient_id'].unique())\n",
    "\n",
    "all_ae_dict = {\n",
    "    \"attended_ae\": num_patients_attended_ae,\n",
    "    \"attended_ae_with_discharge\": num_patients_attended_ae_with_discharge,\n",
    "    \"attended_ae_hosp_discharge\": num_patients_attended_ae_with_hosp_discharge,\n",
    "    \"attended_ae_cov\": num_patients_attended_ae_cov,\n",
    "    \"attended_ae_resp\": num_patients_attended_ae_resp,\n",
    "    \"attended_ae_pos_test\": num_patients_attended_ae_pos_test,\n",
    "    \"attended_ae_resp_+_test_or_cov\": num_patients_either,\n",
    "    \"classified\": num_patients_classified\n",
    "}\n",
    "\n",
    "pd.DataFrame.from_dict(all_ae_dict, orient='index')"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>attended_ae</th>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attended_ae_with_discharge</th>\n",
       "      <td>4000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attended_ae_hosp_discharge</th>\n",
       "      <td>4000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attended_ae_cov</th>\n",
       "      <td>9001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attended_ae_resp</th>\n",
       "      <td>9001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attended_ae_pos_test</th>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attended_ae_resp_+_test_or_cov</th>\n",
       "      <td>9454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>classified</th>\n",
       "      <td>6519</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   0\n",
       "attended_ae                     5000\n",
       "attended_ae_with_discharge      4000\n",
       "attended_ae_hosp_discharge      4000\n",
       "attended_ae_cov                 9001\n",
       "attended_ae_resp                9001\n",
       "attended_ae_pos_test            5000\n",
       "attended_ae_resp_+_test_or_cov  9454\n",
       "classified                      6519"
      ]
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "A&E attendance in primary COVID emergency hospitalisations"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "source": [
    "# a&e attendance all\n",
    "num_patients_attended_ae = len(df[(df['ae_attendance_any_discharge_prim']==1)]['patient_id'].unique())\n",
    "num_patients_attended_ae_with_discharge = len(df[(df['ae_attendance_with_discharge_prim']==1)]['patient_id'].unique())\n",
    "num_patients_attended_ae_with_hosp_discharge = len(df[(df['ae_attendance_hosp_discharge_prim']==1)]['patient_id'].unique())\n",
    "num_patients_attended_ae_cov = len(df[(df['ae_attendance_covid_status_prim']==1)]['patient_id'].unique())\n",
    "num_patients_attended_ae_resp = len(df[(df['ae_attendance_respiratory_status_prim']==1)]['patient_id'].unique())\n",
    "num_patients_attended_ae_pos_test = len(df[(df['positive_covid_test_before_ae_attendance_prim']==1)]['patient_id'].unique())\n",
    "num_patients_either = len(df[\n",
    "    (\n",
    "        (df['ae_attendance_covid_status_prim']==1))\n",
    "    |(\n",
    "    (df[\"ae_attendance_respiratory_status_prim\"]==1) &\n",
    "    (df[\"positive_covid_test_before_ae_attendance_prim\"]==1)\n",
    "    )\n",
    "                            ]['patient_id'].unique())\n",
    "\n",
    "num_patients_classified = len(df[\n",
    "    (df[\"ae_attendance_hosp_discharge_prim\"]==1)\n",
    "    &\n",
    "    (\n",
    "        (df['ae_attendance_covid_status_prim']==1))\n",
    "    |(\n",
    "    (df[\"ae_attendance_respiratory_status_prim\"]==1) &\n",
    "    (df[\"positive_covid_test_before_ae_attendance_prim\"]==1)\n",
    "    )\n",
    "                            ]['patient_id'].unique())\n",
    "\n",
    "all_ae_dict = {\n",
    "    \"attended_ae\": num_patients_attended_ae,\n",
    "    \"attended_ae_with_discharge\": num_patients_attended_ae_with_discharge,\n",
    "    \"attended_ae_hosp_discharge\": num_patients_attended_ae_with_hosp_discharge,\n",
    "    \"attended_ae_cov\": num_patients_attended_ae_cov,\n",
    "    \"attended_ae_resp\": num_patients_attended_ae_resp,\n",
    "    \"attended_ae_pos_test\": num_patients_attended_ae_pos_test,\n",
    "    \"attended_ae_resp_+_test_or_cov\": num_patients_either,\n",
    "    \"classified\": num_patients_classified\n",
    "}\n",
    "\n",
    "pd.DataFrame.from_dict(all_ae_dict, orient='index')"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>attended_ae</th>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attended_ae_with_discharge</th>\n",
       "      <td>4000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attended_ae_hosp_discharge</th>\n",
       "      <td>4000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attended_ae_cov</th>\n",
       "      <td>9001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attended_ae_resp</th>\n",
       "      <td>9001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attended_ae_pos_test</th>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attended_ae_resp_+_test_or_cov</th>\n",
       "      <td>9447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>classified</th>\n",
       "      <td>6529</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   0\n",
       "attended_ae                     5000\n",
       "attended_ae_with_discharge      4000\n",
       "attended_ae_hosp_discharge      4000\n",
       "attended_ae_cov                 9001\n",
       "attended_ae_resp                9001\n",
       "attended_ae_pos_test            5000\n",
       "attended_ae_resp_+_test_or_cov  9447\n",
       "classified                      6529"
      ]
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Discharge destination in those with primary covid in emergency hospital admissions (who went to A&E)"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "source": [
    "prim_cov_ae_discharge = df[(df['emergency_primary_covid_hospital_admission'].notna() & df['ae_attendance_any_discharge']==1)]['discharge_destination']\n",
    "missing = prim_cov_ae_discharge.isna().sum()\n",
    "destination_dict = Counter(prim_cov_ae_discharge[prim_cov_ae_discharge.notnull()])\n",
    "destination_dict['missing'] = missing\n",
    "\n",
    "discharge_dict = {1066341000000100:\"Ambulatory Emergency Care\", 19712007: \"Patient Transfer\", 183919006: \"Hospice\", 1066361000000104: \"High dependency unit\", 305398007: \"Mortuary\", 1066381000000108: \"Special baby care unit\", 1066331000000109: \"Emergency department short stay ward\", 306705005: \"Police custody\", 306706006:\"Ward\", 306689006: \"Home\", 306694006: \"Nursing Home\", 306691003: \"Residential Home\", 1066351000000102: \"Hospital at home\", 1066401000000108: \"Neonatal ICU\", 1066371000000106: \"Coronary Care Unit\", 50861005: \"Legal Custody\", 1066391000000105: \"ICU\", \"missing\": \"missing\"}\n",
    "percent_dict = {}\n",
    "data = []\n",
    "total = 0\n",
    "\n",
    "other_count=0\n",
    "drop_keys=[]\n",
    "#Drop dictionary pairs if value <10\n",
    "for key, value in destination_dict.items():\n",
    "    if value <10:\n",
    "        other_count +=value\n",
    "        drop_keys.append(key)\n",
    "        \n",
    "for key in drop_keys:\n",
    "    del destination_dict[key]\n",
    "    \n",
    "destination_dict[\"Other\"] = other_count\n",
    "\n",
    "for key, value in destination_dict.items():\n",
    "    total+=value\n",
    "\n",
    "for key, value in destination_dict.items():\n",
    "    if key==\"Other\":\n",
    "        percent = (value/total) * 100\n",
    "        row = [\"Other\", percent]\n",
    "        data.append(row)\n",
    "    else:\n",
    "        percent = (value/total) * 100\n",
    "        row = [discharge_dict[key], percent]\n",
    "        data.append(row)\n",
    "\n",
    "discharge_destination_df = pd.DataFrame(data, columns=[\"Discharge Destination\", \"%\"])"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "source": [
    "discharge_destination_df"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Discharge Destination</th>\n",
       "      <th>%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ward</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ICU</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>missing</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Other</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Discharge Destination  %\n",
       "0                  Ward 21\n",
       "1                   ICU 21\n",
       "2               missing 58\n",
       "3                 Other  0"
      ]
     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Same in covid emergency admissions (not primary)"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "source": [
    "prim_cov_ae_discharge = df[(df['emergency_covid_hospital_admission'].notna() & df['ae_attendance_any_discharge']==1)]['discharge_destination']\n",
    "missing = prim_cov_ae_discharge.isna().sum()\n",
    "destination_dict = Counter(prim_cov_ae_discharge[prim_cov_ae_discharge.notnull()])\n",
    "destination_dict['missing'] = missing\n",
    "\n",
    "discharge_dict = {1066341000000100:\"Ambulatory Emergency Care\", 19712007: \"Patient Transfer\", 183919006: \"Hospice\", 1066361000000104: \"High dependency unit\", 305398007: \"Mortuary\", 1066381000000108: \"Special baby care unit\", 1066331000000109: \"Emergency department short stay ward\", 306705005: \"Police custody\", 306706006:\"Ward\", 306689006: \"Home\", 306694006: \"Nursing Home\", 306691003: \"Residential Home\", 1066351000000102: \"Hospital at home\", 1066401000000108: \"Neonatal ICU\", 1066371000000106: \"Coronary Care Unit\", 50861005: \"Legal Custody\", 1066391000000105: \"ICU\", \"missing\": \"missing\"}\n",
    "percent_dict = {}\n",
    "data = []\n",
    "total = 0\n",
    "\n",
    "other_count=0\n",
    "drop_keys=[]\n",
    "#Drop dictionary pairs if value <10\n",
    "for key, value in destination_dict.items():\n",
    "    if value <10:\n",
    "        other_count +=value\n",
    "        drop_keys.append(key)\n",
    "        \n",
    "for key in drop_keys:\n",
    "    del destination_dict[key]\n",
    "    \n",
    "destination_dict[\"Other\"] = other_count\n",
    "\n",
    "for key, value in destination_dict.items():\n",
    "    total+=value\n",
    "\n",
    "for key, value in destination_dict.items():\n",
    "    if key==\"Other\":\n",
    "        percent = (value/total) * 100\n",
    "        row = [\"Other\", percent]\n",
    "        data.append(row)\n",
    "    else:\n",
    "        percent = (value/total) * 100\n",
    "        row = [discharge_dict[key], percent]\n",
    "        data.append(row)\n",
    "\n",
    "discharge_destination_df = pd.DataFrame(data, columns=[\"Discharge Destination\", \"%\"])"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "source": [
    "discharge_destination_df"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Discharge Destination</th>\n",
       "      <th>%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ward</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ICU</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>missing</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Other</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Discharge Destination  %\n",
       "0                  Ward 20\n",
       "1                   ICU 20\n",
       "2               missing 60\n",
       "3                 Other  0"
      ]
     },
     "metadata": {},
     "execution_count": 11
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  }
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