analysis/generate_results.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sn\n",
    "import numpy as np\n",
    "os.chdir('../')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] File output/input.csv does not exist: 'output/input.csv'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-45-a010348cd935>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'output/input.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mproduce_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumns_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m7\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mconfusion_matrix\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcolumns_list\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/venv/lib/python3.8/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[1;32m    674\u001b[0m         )\n\u001b[1;32m    675\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 676\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    677\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    678\u001b[0m     \u001b[0mparser_f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/venv/lib/python3.8/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    446\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    447\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 448\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfp_or_buf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    449\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    450\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/venv/lib/python3.8/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m    878\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    879\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 880\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    881\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    882\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/venv/lib/python3.8/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m   1112\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"c\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1113\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"c\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1114\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1115\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1116\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"python\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/venv/lib/python3.8/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m   1889\u001b[0m         \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"usecols\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0musecols\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1890\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1891\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1892\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munnamed_cols\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munnamed_cols\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1893\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] File output/input.csv does not exist: 'output/input.csv'"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('output/input.csv')\n",
    "\n",
    "def produce_matrix(columns_list, names, filename, size=(7, 7)):\n",
    "    confusion_matrix = []\n",
    "    for i in columns_list:\n",
    "        row = []\n",
    "        for j in columns_list:\n",
    "            equal = i.eq(j).sum()\n",
    "            row.append(equal)\n",
    "\n",
    "        confusion_matrix.append(row)\n",
    "\n",
    "\n",
    "    plt.figure(figsize=size)\n",
    "    mask = np.zeros_like(confusion_matrix)\n",
    "    mask[np.triu_indices_from(mask, k=1)] = True\n",
    "\n",
    "    with sn.axes_style(\"white\"):\n",
    "\n",
    "        sn.heatmap(confusion_matrix, mask=mask, annot=True, fmt=\"d\", xticklabels=names, yticklabels=names)\n",
    "        plt.yticks(rotation=0) \n",
    "        plt.show()\n",
    "        plt.savefig(f\"output/{filename}.jpeg\")\n",
    "        plt.clf()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Number of emergency covid hospital admissions and those that can be identified in A&E attendances\n",
    "### Any A&E attendance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_emergency_cov_admissions = df['emergency_covid_hospital_admission_date'].count()\n",
    "ae_before = (df.loc[\n",
    "    df[\"ae_before_date\"].notnull()\n",
    ", \"patient_id\"])\n",
    "print(f'Number of emergency covid hospital admissions: {num_emergency_cov_admissions}')\n",
    "print(f'Number with A&E attendance before admission: {len(ae_before)} ({round((len(ae_before)/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "\n",
    "ae = len(df[\n",
    "    (df[\"ae_attendance_date\"].notnull())\n",
    "])\n",
    "\n",
    "ae_with_hosp_discharge = len(df[\n",
    "    (df[\"ae_attendance_hosp_discharge_date\"].notnull())\n",
    "])\n",
    "\n",
    "summary_table = pd.DataFrame({\n",
    "    \"emergency covid hospital admission\": num_emergency_cov_admissions,\n",
    "    \"ae attendance before admission\": len(ae_before),\n",
    "    \"ae_attendance\": ae, \n",
    "    \"ae attendance with hospital discharge\": ae_with_hosp_discharge,\n",
    "    \n",
    "}, index=[\"count\"])\n",
    "\n",
    "summary_table.to_csv(\"output/summary_table.csv\")\n",
    "\n",
    "print('---')\n",
    "print(f'Number of A&E attendances: {ae}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Method 1 - Discharge destination\n",
    "Restrict to those that attended A&E and had a hospital discharge destination (hospital ward, emergency short stay ward, icu)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ae_hosp_before = (df.loc[\n",
    "    df[\"ae_before_with_hospital_discharge_date\"].notnull()\n",
    ", \"patient_id\"])\n",
    "\n",
    "print(f'Number with A&E attendance with hospital discharge before admission: {len(ae_hosp_before)} ({round((len(ae_hosp_before)/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "\n",
    "\n",
    "# false posisitves\n",
    "fp_ae_hosp_discharge = len(df[\n",
    "    (df[\"ae_attendance_hosp_discharge_date\"].notnull()) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==0)\n",
    "\n",
    "])\n",
    "\n",
    "tp_ae_hosp_discharge = len(df[\n",
    "    (df[\"ae_attendance_hosp_discharge_date\"].notnull()) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==1)\n",
    "\n",
    "])\n",
    "\n",
    "discharge_destination = [len(ae_hosp_before), round((len(ae_hosp_before)/num_emergency_cov_admissions)*100, 2),fp_ae_hosp_discharge, tp_ae_hosp_discharge, round((fp_ae_hosp_discharge/(fp_ae_hosp_discharge + tp_ae_hosp_discharge))*100,2)]\n",
    "\n",
    "\n",
    "\n",
    "print(f'False positives (FDR): {fp_ae_hosp_discharge} ({round((fp_ae_hosp_discharge/(fp_ae_hosp_discharge + tp_ae_hosp_discharge))*100,2)}%)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Method 2 - Diagnosis code\n",
    "Restrict to those with a COVID-19 diagnosis code or respiratory-related diagnosis code from the [emergency care diagnosis refset](https://termbrowser.nhs.uk/?perspective=full&conceptId1=991411000000109&edition=uk-edition&release=v20220216&server=https://termbrowser.nhs.uk/sct-browser-api/snomed&langRefset=900000000000508004)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ae_cov_before = (df.loc[\n",
    "    df[\"ae_before_with_covid\"]==1\n",
    ", \"patient_id\"])\n",
    "\n",
    "ae_resp_before = (df.loc[\n",
    "    df[\"ae_before_with_resp\"]==1\n",
    ", \"patient_id\"])\n",
    "\n",
    "print(f'Number with A&E attendance and covid diagnosis code before admission: {len(ae_cov_before)} ({round((len(ae_cov_before)/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "print(f'Number with A&E attendance and respiratory code before admission: {len(ae_resp_before)} ({round((len(ae_resp_before)/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "\n",
    "fp_ae_cov = len(df[\n",
    "    (df[\"ae_cov\"]==1) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==0)\n",
    "    \n",
    "])\n",
    "\n",
    "fp_ae_resp = len(df[\n",
    "    (df[\"ae_resp\"]==1) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==0)\n",
    "    \n",
    "])\n",
    "\n",
    "tp_ae_cov = len(df[\n",
    "    (df[\"ae_cov\"]==1) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==1)\n",
    "    \n",
    "])\n",
    "\n",
    "tp_ae_resp = len(df[\n",
    "    (df[\"ae_resp\"]==1) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==1)\n",
    "    \n",
    "])\n",
    "\n",
    "diagnosis_code_cov = [len(ae_cov_before), round((len(ae_cov_before)/num_emergency_cov_admissions)*100, 2), fp_ae_cov, tp_ae_cov, round((fp_ae_cov/(fp_ae_cov + tp_ae_cov))*100,2)]\n",
    "diagnosis_code_resp = [len(ae_resp_before), round((len(ae_resp_before)/num_emergency_cov_admissions)*100, 2), fp_ae_resp, tp_ae_resp, round((len(ae_resp_before)/num_emergency_cov_admissions)*100, 2)]\n",
    "\n",
    "print(f'False positives (FDR) - A&E attendance and covid diagnosis code: {fp_ae_cov} ({round((fp_ae_cov/(fp_ae_cov + tp_ae_cov))*100,2)}%)')\n",
    "print(f'False positives (FDR) - A&E attendance and respiratory diagnosis code: {fp_ae_resp} ({round((fp_ae_resp/(fp_ae_resp + tp_ae_resp))*100,2)}%)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Method 3 - Identification in primary care\n",
    "\n",
    "Restrict to those with a probable or suspected COVID-19 code recorded in primary care in the 2 weeks prior to A&E attendance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ae_prim_care_probable_before = (df.loc[\n",
    "    df[\"ae_before_with_prim_care_probable\"]==1\n",
    ", \"patient_id\"])\n",
    "\n",
    "ae_prim_care_suspected_before = (df.loc[\n",
    "    df[\"ae_before_with_prim_care_suspected\"]==1\n",
    ", \"patient_id\"])\n",
    "\n",
    "print(f'Number with A&E attendance and recent suspected covid code in primary care before admission: {len(ae_prim_care_suspected_before)} ({round((len(ae_prim_care_suspected_before)/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "print(f'Number with A&E attendance and recent probable covid code in primary care before admission: {len(ae_prim_care_probable_before)} ({round((len(ae_prim_care_probable_before)/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "\n",
    "fp_ae_suspected = len(df[\n",
    "    (df[\"suspected_covid_primary_care_before_ae\"]==1) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==0)    \n",
    "])\n",
    "\n",
    "fp_ae_probable = len(df[\n",
    "    (df[\"covid_primary_care_before_ae\"]==1) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==0)\n",
    "])\n",
    "\n",
    "tp_ae_suspected = len(df[\n",
    "    (df[\"suspected_covid_primary_care_before_ae\"]==1) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==1)    \n",
    "])\n",
    "\n",
    "tp_ae_probable = len(df[\n",
    "    (df[\"covid_primary_care_before_ae\"]==1) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==1)\n",
    "])\n",
    "\n",
    "prim_care_suspected = [len(ae_prim_care_suspected_before), round((len(ae_prim_care_suspected_before)/num_emergency_cov_admissions)*100, 2), fp_ae_suspected, tp_ae_suspected, round((fp_ae_suspected/(fp_ae_suspected + tp_ae_suspected))*100,2)]\n",
    "prim_care_probable = [len(ae_prim_care_probable_before), round((len(ae_prim_care_probable_before)/num_emergency_cov_admissions)*100, 2), fp_ae_probable, tp_ae_probable, round((fp_ae_probable/(fp_ae_probable + tp_ae_probable))*100,2)]\n",
    "\n",
    "print(f'False positives (FDR) - A&E attendance and probable covid code in primary care: {fp_ae_probable} ({round((fp_ae_probable/(fp_ae_probable + tp_ae_probable))*100,2)}%)')\n",
    "print(f'False positives (FDR) - A&E attendance and suspected covid code in primary care: {fp_ae_suspected} ({round((fp_ae_suspected/(fp_ae_suspected + tp_ae_suspected))*100,2)}%)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Method 4 - Positive test result\n",
    "\n",
    "Restrict to those with a positive COVID-19 test result recorded up to 4 weeks prior to A&E attendance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ae_pos_test_before_2_weeks = (df.loc[\n",
    "    df[\"ae_before_with_positive_covid_test_2_weeks\"]==1\n",
    ", \"patient_id\"])\n",
    "\n",
    "ae_pos_test_before = (df.loc[\n",
    "    df[\"ae_before_with_positive_covid_test\"]==1\n",
    ", \"patient_id\"])\n",
    "\n",
    "\n",
    "print(f'Number with A&E attendance and recent positive test (2 weeks) before admission: {len(ae_pos_test_before_2_weeks)} ({round((len(ae_pos_test_before_2_weeks)/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "print(f'Number with A&E attendance and recent positive test (4 weeks) before admissio: {len(ae_pos_test_before)} ({round((len(ae_pos_test_before)/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "\n",
    "fp_ae_test = len(df[\n",
    "    (df[\"pos_test\"]==1) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==0)\n",
    "    \n",
    "])\n",
    "\n",
    "fp_ae_test_2_weeks = len(df[\n",
    "    (df[\"pos_test_2_weeks\"]==1) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==0)\n",
    "    \n",
    "])\n",
    "\n",
    "tp_ae_test = len(df[\n",
    "    (df[\"pos_test\"]==1) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==1)\n",
    "    \n",
    "])\n",
    "\n",
    "tp_ae_test_2_weeks = len(df[\n",
    "    (df[\"pos_test_2_weeks\"]==1) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==1)\n",
    "    \n",
    "])\n",
    "print(f'False positives (FDR) - A&E attendance and recent positive test (2 weeks): {fp_ae_test_2_weeks} ({round((fp_ae_test_2_weeks/(fp_ae_test_2_weeks + tp_ae_test_2_weeks))*100,2)}%)')\n",
    "print(f'False positives (FDR) - A&E attendance and recent positive test (4 weeks): {fp_ae_test} ({round((fp_ae_test/(fp_ae_test + tp_ae_test))*100,2)}%)')\n",
    "\n",
    "positive_test_2_weeks = [len(ae_pos_test_before_2_weeks), round((len(ae_pos_test_before_2_weeks)/num_emergency_cov_admissions)*100, 2), fp_ae_test_2_weeks, tp_ae_test_2_weeks, round((fp_ae_test_2_weeks/(fp_ae_test_2_weeks + tp_ae_test_2_weeks))*100,2)]\n",
    "positive_test_4_weeks = [len(ae_pos_test_before), round((len(ae_pos_test_before)/num_emergency_cov_admissions)*100, 2), fp_ae_test, tp_ae_test, round((fp_ae_test/(fp_ae_test + tp_ae_test))*100,2)]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  Combining approaches"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Diagnosis codes (resp or cov) with recent positive test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ae_hos_resp_cov_plus_test_2_weeks = (df.loc[(\n",
    "        (df[\"ae_before_with_covid\"]==1) | \n",
    "        (df[\"ae_before_with_resp\"]==1) \n",
    "    )&\n",
    "    (df[\"ae_before_with_positive_covid_test_2_weeks\"]==1)\n",
    ", \"patient_id\"])\n",
    "\n",
    "fp_ae_hos_resp_cov_plus_test_2_weeks = len(df[\n",
    "    (\n",
    "        (df[\"ae_cov\"]==1) |\n",
    "        (df[\"ae_resp\"]==1)    \n",
    "    )&\n",
    "    (df[\"pos_test_2_weeks\"]==1)&\n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==0)\n",
    "    \n",
    "])\n",
    "\n",
    "tp_ae_hos_resp_cov_plus_test_2_weeks = len(df[\n",
    "    (\n",
    "        (df[\"ae_cov\"]==1) |\n",
    "        (df[\"ae_resp\"]==1)    \n",
    "    )&\n",
    "    (df[\"pos_test_2_weeks\"]==1)&\n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==1)\n",
    "    \n",
    "])\n",
    "\n",
    "print(f'Number with A&E attendance with either covid or respiratory diagnosis code and recent positive test (2 weeks) before admission: {len(ae_hos_resp_cov_plus_test_2_weeks)} ({round((len(ae_hos_resp_cov_plus_test_2_weeks)/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "print(f'False positives (FDR): {fp_ae_hos_resp_cov_plus_test_2_weeks} ({round((fp_ae_hos_resp_cov_plus_test_2_weeks/(fp_ae_hos_resp_cov_plus_test_2_weeks + tp_ae_hos_resp_cov_plus_test_2_weeks))*100,2)}%)')\n",
    "\n",
    "code_plus_test = [len(ae_hos_resp_cov_plus_test_2_weeks), round((len(ae_hos_resp_cov_plus_test_2_weeks)/num_emergency_cov_admissions)*100, 2), fp_ae_hos_resp_cov_plus_test_2_weeks, tp_ae_hos_resp_cov_plus_test_2_weeks, round((fp_ae_hos_resp_cov_plus_test_2_weeks/(fp_ae_hos_resp_cov_plus_test_2_weeks + tp_ae_hos_resp_cov_plus_test_2_weeks))*100,2)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Covid in primary care with recent positive test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ae_hosp_prim_care_before_plus_2_week_test = (df.loc[\n",
    "    \n",
    "    \n",
    "    (\n",
    "        (df[\"ae_before_with_prim_care_suspected\"]==1) | \n",
    "        (df[\"ae_before_with_prim_care_probable\"]==1) \n",
    "    )\n",
    "&\n",
    "    (df[\"ae_before_with_positive_covid_test_2_weeks\"]==1)\n",
    ", \"patient_id\"])\n",
    "\n",
    "fp_ae_hosp_prim_care_before_plus_2_week_test = len(df[\n",
    "    (\n",
    "        (df[\"covid_primary_care_before_ae\"]==1) |\n",
    "        (df[\"suspected_covid_primary_care_before_ae\"]==1)    \n",
    "    )&\n",
    "    (df[\"pos_test_2_weeks\"]==1)&\n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==0)\n",
    "    \n",
    "])\n",
    "\n",
    "tp_ae_hosp_prim_care_before_plus_2_week_test = len(df[\n",
    "    (\n",
    "        (df[\"covid_primary_care_before_ae\"]==1) |\n",
    "        (df[\"suspected_covid_primary_care_before_ae\"]==1)    \n",
    "    )&\n",
    "    (df[\"pos_test_2_weeks\"]==1)&\n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==1)\n",
    "    \n",
    "])\n",
    "\n",
    "print(f'Number with A&E attendance with suspected or probable covid in primary care and recent positive test (2 weeks) before admission: {len(ae_hosp_prim_care_before_plus_2_week_test)} ({round((len(ae_hosp_prim_care_before_plus_2_week_test)/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "print(f'False positives (FDR): {fp_ae_hosp_prim_care_before_plus_2_week_test} ({round((fp_ae_hosp_prim_care_before_plus_2_week_test/(fp_ae_hosp_prim_care_before_plus_2_week_test + tp_ae_hosp_prim_care_before_plus_2_week_test))*100,2)}%)')\n",
    "\n",
    "primary_care_plus_test = [len(ae_hosp_prim_care_before_plus_2_week_test), round((len(ae_hosp_prim_care_before_plus_2_week_test)/num_emergency_cov_admissions)*100, 2), fp_ae_hosp_prim_care_before_plus_2_week_test, tp_ae_hosp_prim_care_before_plus_2_week_test, round((fp_ae_hosp_prim_care_before_plus_2_week_test/(fp_ae_hosp_prim_care_before_plus_2_week_test + tp_ae_hosp_prim_care_before_plus_2_week_test))*100,2)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Recent positive test and either covid in primary care or diagnosis code (resp or cov)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_before = (df.loc[\n",
    "        (df[\"ae_before_with_positive_covid_test_2_weeks\"]==1) &\n",
    "        (\n",
    "            (df[\"ae_before_with_prim_care_suspected\"]==1) | \n",
    "            (df[\"ae_before_with_prim_care_probable\"]==1) |\n",
    "            (df[\"ae_before_with_covid\"]==1) | \n",
    "            (df[\"ae_before_with_resp\"]==1)   \n",
    "        )\n",
    ", \"patient_id\"])\n",
    "fp_combined_before = len(df[\n",
    "    (\n",
    "        (df[\"covid_primary_care_before_ae\"]==1) |\n",
    "        (df[\"suspected_covid_primary_care_before_ae\"]==1) |\n",
    "        (df[\"ae_cov\"]==1) |\n",
    "        (df[\"ae_resp\"]==1)\n",
    "        \n",
    "        \n",
    "    )&\n",
    "    (df[\"pos_test_2_weeks\"]==1)&\n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==0)\n",
    "    \n",
    "])\n",
    "\n",
    "tp_combined_before = len(df[\n",
    "    (\n",
    "        (df[\"covid_primary_care_before_ae\"]==1) |\n",
    "        (df[\"suspected_covid_primary_care_before_ae\"]==1) |\n",
    "        (df[\"ae_cov\"]==1) |\n",
    "        (df[\"ae_resp\"]==1)\n",
    "    )&\n",
    "    (df[\"pos_test_2_weeks\"]==1)&\n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==1)\n",
    "    \n",
    "])\n",
    "\n",
    "\n",
    "print(f'Number with A&E attendance with recent positive test and either suspected or probable covid in primary care or a covid or respiratory diagnosis code on attendance: {len(combined_before)} ({round((len(combined_before)/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "print(f'False positives (FDR): {fp_combined_before} ({round((fp_combined_before/(fp_combined_before + tp_combined_before))*100,2)}%)')\n",
    "\n",
    "pos_test_prim_care_or_diagnosis_code = [len(combined_before), round((len(combined_before)/num_emergency_cov_admissions)*100, 2), fp_combined_before, tp_combined_before, round((fp_combined_before/(fp_combined_before + tp_combined_before))*100,2)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "identification_table_index = [\"count\", \"sens\", \"fp\", \"tp\", \"fdr\"]\n",
    "identification_table = pd.DataFrame({\n",
    "    \"discharge_destination\": pd.Series(discharge_destination,index=identification_table_index),\n",
    "    \"covid_diagnosis_code\": pd.Series(diagnosis_code_cov,index=identification_table_index),\n",
    "    \"resp_diagnosis_code\": pd.Series(diagnosis_code_resp,index=identification_table_index),\n",
    "    \"primary_care_suspected\": pd.Series(prim_care_suspected,index=identification_table_index),\n",
    "    \"primary_care_probable\": pd.Series(prim_care_suspected,index=identification_table_index),\n",
    "    \"positive_test_2_weeks\": pd.Series(positive_test_2_weeks,index=identification_table_index),\n",
    "    \"positive_test_4_weeks\": pd.Series(positive_test_4_weeks,index=identification_table_index),\n",
    "    \"diagnosis_code_with_positive_test\": pd.Series(code_plus_test,index=identification_table_index),\n",
    "    \"primary_care_with_positive_test\": pd.Series(primary_care_plus_test,index=identification_table_index),\n",
    "    \"pos_test_prim_care_or_diagnosis\": pd.Series(pos_test_prim_care_or_diagnosis_code,index=identification_table_index)\n",
    "    \n",
    "})\n",
    "identification_table.to_csv(\"output/identification_table_no_hosp_discharge.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('output/input.csv')\n",
    "        \n",
    "produce_matrix(columns_list = [ae_hosp_before, ae_cov_before, ae_resp_before, ae_prim_care_probable_before, ae_prim_care_suspected_before, ae_pos_test_before_2_weeks, ae_pos_test_before, ae_hos_resp_cov_plus_test_2_weeks, ae_hosp_prim_care_before_plus_2_week_test], names=[\"Hospital discharge\", \"Covid diagnosis code\", \"Respiratory diagnosis code\", \"Probable covid code\", \"Suspected covid code\", \"Recent positive test (2 weeks)\", \"Recent positive test (4 weeks)\", \"Diagnosis codes (resp or cov) with recent positive test\", \"Covid in primary care with recent positive test\", \"Recent positive test and either covid in primary care or diagnosis code (resp or cov)\"], filename=\"cm_no_hosp_discharge\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Restrict to A&E attendance with hospital discharge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ae_with_hosp_discharge = len(df[\n",
    "    (df[\"ae_attendance_hosp_discharge_date\"].notnull())\n",
    "])\n",
    "print(f'Number of A&E attendances with hospital discharge: {ae_with_hosp_discharge}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Method 1 - Discharge destination\n",
    "Restrict to those that attended A&E and had a hospital discharge destination (hospital ward, emergency short stay ward, icu)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ae_hosp_before = (df.loc[\n",
    "    df[\"ae_before_with_hospital_discharge_date\"].notnull()\n",
    ", \"patient_id\"])\n",
    "\n",
    "print(f'Number with A&E attendance with hospital discharge before admission: {len(ae_hosp_before)} ({round((len(ae_hosp_before)/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "\n",
    "\n",
    "# false posisitves\n",
    "fp_ae_hosp_discharge = len(df[\n",
    "    (df[\"ae_attendance_hosp_discharge_date\"].notnull()) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==0)\n",
    "\n",
    "])\n",
    "\n",
    "tp_ae_hosp_discharge = len(df[\n",
    "    (df[\"ae_attendance_hosp_discharge_date\"].notnull()) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==1)\n",
    "\n",
    "])\n",
    "discharge_destination = [len(ae_hosp_before), round((len(ae_hosp_before)/num_emergency_cov_admissions)*100, 2),fp_ae_hosp_discharge, tp_ae_hosp_discharge, round((fp_ae_hosp_discharge/(fp_ae_hosp_discharge + tp_ae_hosp_discharge))*100,2)]\n",
    "\n",
    "print(f'False positives (FDR): {fp_ae_hosp_discharge} ({round((fp_ae_hosp_discharge/(fp_ae_hosp_discharge + tp_ae_hosp_discharge))*100,2)}%)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Method 2 - Diagnosis code\n",
    "Restrict to those with a COVID-19 diagnosis code or respiratory-related diagnosis code from the [emergency care diagnosis refset](https://termbrowser.nhs.uk/?perspective=full&conceptId1=991411000000109&edition=uk-edition&release=v20220216&server=https://termbrowser.nhs.uk/sct-browser-api/snomed&langRefset=900000000000508004)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ae_cov_before = (df.loc[\n",
    "    df[\"ae_before_with_hospital_discharge_covid\"]==1\n",
    ", \"patient_id\"])\n",
    "\n",
    "ae_resp_before = (df.loc[\n",
    "    df[\"ae_before_with_hospital_discharge_resp\"]==1\n",
    ", \"patient_id\"])\n",
    "\n",
    "print(f'Number with A&E attendance and covid diagnosis code before admission: {len(ae_cov_before)} ({round((len(ae_cov_before)/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "print(f'Number with A&E attendance and respiratory code before admission: {len(ae_resp_before)} ({round((len(ae_resp_before)/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "\n",
    "fp_ae_cov = len(df[\n",
    "    (df[\"ae_cov_hosp_discharge\"]==1) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==0)\n",
    "    \n",
    "])\n",
    "\n",
    "fp_ae_resp = len(df[\n",
    "    (df[\"ae_resp_hosp_discharge\"]==1) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==0)\n",
    "    \n",
    "])\n",
    "\n",
    "tp_ae_cov = len(df[\n",
    "    (df[\"ae_cov_hosp_discharge\"]==1) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==1)\n",
    "    \n",
    "])\n",
    "\n",
    "tp_ae_resp = len(df[\n",
    "    (df[\"ae_resp_hosp_discharge\"]==1) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==1)\n",
    "    \n",
    "])\n",
    "\n",
    "diagnosis_code_cov = [len(ae_cov_before), round((len(ae_cov_before)/num_emergency_cov_admissions)*100, 2), fp_ae_cov, tp_ae_cov, round((fp_ae_cov/(fp_ae_cov + tp_ae_cov))*100,2)]\n",
    "diagnosis_code_resp = [len(ae_resp_before), round((len(ae_resp_before)/num_emergency_cov_admissions)*100, 2), fp_ae_resp, tp_ae_resp, round((len(ae_resp_before)/num_emergency_cov_admissions)*100, 2)]\n",
    "\n",
    "\n",
    "print(f'False positives (FDR) - A&E attendance and covid diagnosis code: {fp_ae_cov} ({round((fp_ae_cov/(fp_ae_cov + tp_ae_cov))*100,2)}%)')\n",
    "print(f'False positives (FDR) - A&E attendance and respiratory diagnosis code: {fp_ae_resp} ({round((fp_ae_resp/(fp_ae_resp + tp_ae_resp))*100,2)}%)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Method 3 - Identification in primary care\n",
    "\n",
    "Restrict to those with a probable or suspected COVID-19 code recorded in primary care in the 2 weeks prior to A&E attendance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ae_prim_care_probable_before = (df.loc[\n",
    "    df[\"ae_before_with_hospital_discharge_prim_care_probable\"]==1\n",
    ", \"patient_id\"])\n",
    "\n",
    "ae_prim_care_suspected_before = (df.loc[\n",
    "    df[\"ae_before_with_hospital_discharge_prim_care_suspected\"]==1\n",
    ", \"patient_id\"])\n",
    "\n",
    "print(f'Number with A&E attendance and recent suspected covid code in primary care before admission: {len(ae_prim_care_suspected_before)} ({round((len(ae_prim_care_suspected_before)/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "print(f'Number with A&E attendance and recent probable covid code in primary care before admission: {len(ae_prim_care_probable_before)} ({round((len(ae_prim_care_probable_before)/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "\n",
    "fp_ae_suspected = len(df[\n",
    "    (df[\"suspected_covid_primary_care_before_ae_hosp_discharge\"]==1) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==0)    \n",
    "])\n",
    "\n",
    "fp_ae_probable = len(df[\n",
    "    (df[\"covid_primary_care_before_ae_hosp_discharge\"]==1) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==0)\n",
    "])\n",
    "\n",
    "tp_ae_suspected = len(df[\n",
    "    (df[\"suspected_covid_primary_care_before_ae_hosp_discharge\"]==1) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==1)    \n",
    "])\n",
    "\n",
    "tp_ae_probable = len(df[\n",
    "    (df[\"covid_primary_care_before_ae_hosp_discharge\"]==1) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==1)\n",
    "])\n",
    "\n",
    "prim_care_suspected = [len(ae_prim_care_suspected_before), round((len(ae_prim_care_suspected_before)/num_emergency_cov_admissions)*100, 2), fp_ae_suspected, tp_ae_suspected, round((fp_ae_suspected/(fp_ae_suspected + tp_ae_suspected))*100,2)]\n",
    "prim_care_probable = [len(ae_prim_care_probable_before), round((len(ae_prim_care_probable_before)/num_emergency_cov_admissions)*100, 2), fp_ae_probable, tp_ae_probable, round((fp_ae_probable/(fp_ae_probable + tp_ae_probable))*100,2)]\n",
    "\n",
    "\n",
    "print(f'False positives (FDR) - A&E attendance and probable covid code in primary care: {fp_ae_probable} ({round((fp_ae_probable/(fp_ae_probable + tp_ae_probable))*100,2)}%)')\n",
    "print(f'False positives (FDR) - A&E attendance and suspected covid code in primary care: {fp_ae_suspected} ({round((fp_ae_suspected/(fp_ae_suspected + tp_ae_suspected))*100,2)}%)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Method 4 - Positive test result\n",
    "\n",
    "Restrict to those with a positive COVID-19 test result recorded up to 4 weeks prior to A&E attendance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ae_pos_test_before_2_weeks = (df.loc[\n",
    "    df[\"ae_before_with_hospital_discharge_positive_covid_test_2_weeks\"]==1\n",
    ", \"patient_id\"])\n",
    "\n",
    "ae_pos_test_before = (df.loc[\n",
    "    df[\"ae_before_with_hospital_discharge_positive_covid_test\"]==1\n",
    ", \"patient_id\"])\n",
    "\n",
    "\n",
    "print(f'Number with A&E attendance and recent positive test (2 weeks) before admission: {len(ae_pos_test_before_2_weeks)} ({round((len(ae_pos_test_before_2_weeks)/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "print(f'Number with A&E attendance and recent positive test (4 weeks) before admissio: {len(ae_pos_test_before)} ({round((len(ae_pos_test_before)/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "\n",
    "fp_ae_test = len(df[\n",
    "    (df[\"pos_test_hosp_discharge\"]==1) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==0)\n",
    "    \n",
    "])\n",
    "\n",
    "fp_ae_test_2_weeks = len(df[\n",
    "    (df[\"pos_test_2_weeks_hosp_discharge\"]==1) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==0)\n",
    "    \n",
    "])\n",
    "\n",
    "tp_ae_test = len(df[\n",
    "    (df[\"pos_test_hosp_discharge\"]==1) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==1)\n",
    "    \n",
    "])\n",
    "\n",
    "tp_ae_test_2_weeks = len(df[\n",
    "    (df[\"pos_test_2_weeks_hosp_discharge\"]==1) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==1)\n",
    "    \n",
    "])\n",
    "\n",
    "positive_test_2_weeks = [len(ae_pos_test_before_2_weeks), round((len(ae_pos_test_before_2_weeks)/num_emergency_cov_admissions)*100, 2), fp_ae_test_2_weeks, tp_ae_test_2_weeks, round((fp_ae_test_2_weeks/(fp_ae_test_2_weeks + tp_ae_test_2_weeks))*100,2)]\n",
    "positive_test_4_weeks = [len(ae_pos_test_before), round((len(ae_pos_test_before)/num_emergency_cov_admissions)*100, 2), fp_ae_test, tp_ae_test, round((fp_ae_test/(fp_ae_test + tp_ae_test))*100,2)]\n",
    "\n",
    "\n",
    "print(f'False positives (FDR) - A&E attendance and recent positive test (2 weeks): {fp_ae_test_2_weeks} ({round((fp_ae_test_2_weeks/(fp_ae_test_2_weeks + tp_ae_test_2_weeks))*100,2)}%)')\n",
    "print(f'False positives (FDR) - A&E attendance and recent positive test (4 weeks): {fp_ae_test} ({round((fp_ae_test/(fp_ae_test + tp_ae_test))*100,2)}%)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  Combining approaches"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Diagnosis codes (resp or cov) with recent positive test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ae_hos_resp_cov_plus_test_2_weeks = (df.loc[(\n",
    "        (df[\"ae_before_with_hospital_discharge_covid\"]==1) | \n",
    "        (df[\"ae_before_with_hospital_discharge_resp\"]==1) \n",
    "    )&\n",
    "    (df[\"ae_before_with_hospital_discharge_positive_covid_test_2_weeks\"]==1)\n",
    ", \"patient_id\"])\n",
    "\n",
    "fp_ae_hos_resp_cov_plus_test_2_weeks = len(df[\n",
    "    (\n",
    "        (df[\"ae_cov_hosp_discharge\"]==1) |\n",
    "        (df[\"ae_resp_hosp_discharge\"]==1)    \n",
    "    )&\n",
    "    (df[\"pos_test_2_weeks_hosp_discharge\"]==1)&\n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==0)\n",
    "    \n",
    "])\n",
    "\n",
    "tp_ae_hos_resp_cov_plus_test_2_weeks = len(df[\n",
    "    (\n",
    "        (df[\"ae_cov_hosp_discharge\"]==1) |\n",
    "        (df[\"ae_resp_hosp_discharge\"]==1)    \n",
    "    )&\n",
    "    (df[\"pos_test_2_weeks_hosp_discharge\"]==1)&\n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==1)\n",
    "    \n",
    "])\n",
    "\n",
    "print(f'Number with A&E attendance with either covid or respiratory diagnosis code and recent positive test (2 weeks) before admission: {len(ae_hos_resp_cov_plus_test_2_weeks)} ({round((len(ae_hos_resp_cov_plus_test_2_weeks)/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "print(f'False positives (FDR): {fp_ae_hos_resp_cov_plus_test_2_weeks} ({round((fp_ae_hos_resp_cov_plus_test_2_weeks/(fp_ae_hos_resp_cov_plus_test_2_weeks + tp_ae_hos_resp_cov_plus_test_2_weeks))*100,2)}%)')\n",
    "code_plus_test = [len(ae_hos_resp_cov_plus_test_2_weeks), round((len(ae_hos_resp_cov_plus_test_2_weeks)/num_emergency_cov_admissions)*100, 2), fp_ae_hos_resp_cov_plus_test_2_weeks, tp_ae_hos_resp_cov_plus_test_2_weeks, round((fp_ae_hos_resp_cov_plus_test_2_weeks/(fp_ae_hos_resp_cov_plus_test_2_weeks + tp_ae_hos_resp_cov_plus_test_2_weeks))*100,2)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Covid in primary care with recent positive test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ae_hosp_prim_care_before_plus_2_week_test = (df.loc[(\n",
    "        (df[\"ae_before_with_hospital_discharge_prim_care_suspected\"]==1) | \n",
    "        (df[\"ae_before_with_hospital_discharge_prim_care_probable\"]==1) \n",
    "    )\n",
    "&\n",
    "    (df[\"ae_before_with_hospital_discharge_positive_covid_test_2_weeks\"]==1)\n",
    ", \"patient_id\"])\n",
    "\n",
    "fp_ae_hosp_prim_care_before_plus_2_week_test = len(df[\n",
    "    (\n",
    "        (df[\"covid_primary_care_before_ae_hosp_discharge\"]==1) |\n",
    "        (df[\"suspected_covid_primary_care_before_ae_hosp_discharge\"]==1)    \n",
    "    )&\n",
    "    (df[\"pos_test_2_weeks_hosp_discharge\"]==1)&\n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==0)\n",
    "    \n",
    "])\n",
    "\n",
    "tp_ae_hosp_prim_care_before_plus_2_week_test = len(df[\n",
    "    (\n",
    "        (df[\"covid_primary_care_before_ae_hosp_discharge\"]==1) |\n",
    "        (df[\"suspected_covid_primary_care_before_ae_hosp_discharge\"]==1)    \n",
    "    )&\n",
    "    (df[\"pos_test_2_weeks_hosp_discharge\"]==1)&\n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==1)\n",
    "    \n",
    "])\n",
    "\n",
    "print(f'Number with A&E attendance with suspected or probable covid in primary care and recent positive test (2 weeks) before admission: {len(ae_hosp_prim_care_before_plus_2_week_test)} ({round((len(ae_hosp_prim_care_before_plus_2_week_test)/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "print(f'False positives (FDR): {fp_ae_hosp_prim_care_before_plus_2_week_test} ({round((fp_ae_hosp_prim_care_before_plus_2_week_test/(fp_ae_hosp_prim_care_before_plus_2_week_test + tp_ae_hosp_prim_care_before_plus_2_week_test))*100,2)}%)')\n",
    "\n",
    "primary_care_plus_test = [len(ae_hosp_prim_care_before_plus_2_week_test), round((len(ae_hosp_prim_care_before_plus_2_week_test)/num_emergency_cov_admissions)*100, 2), fp_ae_hosp_prim_care_before_plus_2_week_test, tp_ae_hosp_prim_care_before_plus_2_week_test, round((fp_ae_hosp_prim_care_before_plus_2_week_test/(fp_ae_hosp_prim_care_before_plus_2_week_test + tp_ae_hosp_prim_care_before_plus_2_week_test))*100,2)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Recent positive test and either covid in primary care or diagnosis code (resp or cov)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_before = (df.loc[\n",
    "        (df[\"ae_before_with_hospital_discharge_positive_covid_test_2_weeks\"]==1) &\n",
    "        (\n",
    "            (df[\"ae_before_with_hospital_discharge_prim_care_suspected\"]==1) | \n",
    "            (df[\"ae_before_with_hospital_discharge_prim_care_probable\"]==1) |\n",
    "            (df[\"ae_before_with_hospital_discharge_covid\"]==1) | \n",
    "            (df[\"ae_before_with_hospital_discharge_resp\"]==1)   \n",
    "        )\n",
    ", \"patient_id\"])\n",
    "fp_combined_before = len(df[\n",
    "    (\n",
    "        (df[\"covid_primary_care_before_ae_hosp_discharge\"]==1) |\n",
    "        (df[\"suspected_covid_primary_care_before_ae_hosp_discharge\"]==1) |\n",
    "        (df[\"ae_cov_hosp_discharge\"]==1) |\n",
    "        (df[\"ae_resp_hosp_discharge\"]==1)\n",
    "        \n",
    "        \n",
    "    )&\n",
    "    (df[\"pos_test_2_weeks_hosp_discharge\"]==1)&\n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==0)\n",
    "    \n",
    "])\n",
    "\n",
    "tp_combined_before = len(df[\n",
    "    (\n",
    "        (df[\"covid_primary_care_before_ae_hosp_discharge\"]==1) |\n",
    "        (df[\"suspected_covid_primary_care_before_ae_hosp_discharge\"]==1) |\n",
    "        (df[\"ae_cov_hosp_discharge\"]==1) |\n",
    "        (df[\"ae_resp_hosp_discharge\"]==1)\n",
    "    )&\n",
    "    (df[\"pos_test_2_weeks_hosp_discharge\"]==1)&\n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==1)\n",
    "    \n",
    "])\n",
    "\n",
    "\n",
    "print(f'Number with A&E attendance with recent positive test and either suspected or probable covid in primary care or a covid or respiratory diagnosis code on attendance: {len(combined_before)} ({round((len(combined_before)/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "print(f'False positives (FDR): {fp_combined_before} ({round((fp_combined_before/(fp_combined_before + tp_combined_before))*100,2)}%)')\n",
    "pos_test_prim_care_or_diagnosis_code = [len(combined_before), round((len(combined_before)/num_emergency_cov_admissions)*100, 2), fp_combined_before, tp_combined_before, round((fp_combined_before/(fp_combined_before + tp_combined_before))*100,2)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "identification_table_index = [\"count\", \"sens\", \"fp\", \"tp\", \"fdr\"]\n",
    "identification_table = pd.DataFrame({\n",
    "    \"discharge_destination\": pd.Series(discharge_destination,index=identification_table_index),\n",
    "    \"covid_diagnosis_code\": pd.Series(diagnosis_code_cov,index=identification_table_index),\n",
    "    \"resp_diagnosis_code\": pd.Series(diagnosis_code_resp,index=identification_table_index),\n",
    "    \"primary_care_suspected\": pd.Series(prim_care_suspected,index=identification_table_index),\n",
    "    \"primary_care_probable\": pd.Series(prim_care_suspected,index=identification_table_index),\n",
    "    \"positive_test_2_weeks\": pd.Series(positive_test_2_weeks,index=identification_table_index),\n",
    "    \"positive_test_4_weeks\": pd.Series(positive_test_4_weeks,index=identification_table_index),\n",
    "    \"diagnosis_code_with_positive_test\": pd.Series(code_plus_test,index=identification_table_index),\n",
    "    \"primary_care_with_positive_test\": pd.Series(primary_care_plus_test,index=identification_table_index),\n",
    "    \"pos_test_prim_care_or_diagnosis\": pd.Series(pos_test_prim_care_or_diagnosis_code,index=identification_table_index)\n",
    "    \n",
    "})\n",
    "identification_table.to_csv(\"output/identification_table_hosp_discharge.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "produce_matrix(columns_list = [ae_hosp_before, ae_cov_before, ae_resp_before, ae_prim_care_probable_before, ae_prim_care_suspected_before, ae_pos_test_before_2_weeks, ae_pos_test_before, ae_hos_resp_cov_plus_test_2_weeks, ae_hosp_prim_care_before_plus_2_week_test], names=[\"Hospital discharge\", \"Covid diagnosis code\", \"Respiratory diagnosis code\", \"Probable covid code\", \"Suspected covid code\", \"Recent positive test (2 weeks)\", \"Recent positive test (4 weeks)\", \"Diagnosis codes (resp or cov) with recent positive test\", \"Covid in primary care with recent positive test\", \"Recent positive test and either covid in primary care or diagnosis code (resp or cov)\"], filename=\"cm_hosp_discharge\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Number of emergency primary covid hospital admissions and those that can be identified in A&E attendances\n",
    "### Any A&E attendance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('output/input_prim_cov.csv')\n",
    "\n",
    "\n",
    "num_emergency_cov_admissions = df['emergency_covid_hospital_admission_date'].count()\n",
    "ae_before = (df.loc[\n",
    "    df[\"ae_before_date\"].notnull()\n",
    ", \"patient_id\"])\n",
    "print(f'Number of emergency primary covid hospital admissions: {num_emergency_cov_admissions}')\n",
    "print(f'Number with A&E attendance before admission: {len(ae_before)} ({round((len(ae_before)/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "\n",
    "ae = len(df[\n",
    "    (df[\"ae_attendance_date\"].notnull())\n",
    "])\n",
    "\n",
    "ae_with_hosp_discharge = len(df[\n",
    "    (df[\"ae_attendance_hosp_discharge_date\"].notnull())\n",
    "])\n",
    "\n",
    "summary_table = pd.DataFrame({\n",
    "    \"emergency covid hospital admission\": num_emergency_cov_admissions,\n",
    "    \"ae attendance before admission\": len(ae_before),\n",
    "    \"ae_attendance\": ae, \n",
    "    \"ae attendance with hospital discharge\": ae_with_hosp_discharge,\n",
    "    \n",
    "}, index=[\"count\"])\n",
    "\n",
    "summary_table.to_csv(\"output/summary_table_primary.csv\")\n",
    "\n",
    "print('---')\n",
    "print(f'Number of A&E attendances: {ae}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Method 1 - Discharge destination\n",
    "Restrict to those that attended A&E and had a hospital discharge destination (hospital ward, emergency short stay ward, icu)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ae_hosp_before = (df.loc[\n",
    "    df[\"ae_before_with_hospital_discharge_date\"].notnull()\n",
    ", \"patient_id\"])\n",
    "\n",
    "print(f'Number with A&E attendance with hospital discharge before admission: {len(ae_hosp_before)} ({round((len(ae_hosp_before)/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "\n",
    "\n",
    "# false posisitves\n",
    "fp_ae_hosp_discharge = len(df[\n",
    "    (df[\"ae_attendance_hosp_discharge_date\"].notnull()) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==0)\n",
    "\n",
    "])\n",
    "\n",
    "tp_ae_hosp_discharge = len(df[\n",
    "    (df[\"ae_attendance_hosp_discharge_date\"].notnull()) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==1)\n",
    "\n",
    "])\n",
    "\n",
    "discharge_destination = [len(ae_hosp_before), round((len(ae_hosp_before)/num_emergency_cov_admissions)*100, 2),fp_ae_hosp_discharge, tp_ae_hosp_discharge, round((fp_ae_hosp_discharge/(fp_ae_hosp_discharge + tp_ae_hosp_discharge))*100,2)]\n",
    "\n",
    "print(f'False positives (FDR): {fp_ae_hosp_discharge} ({round((fp_ae_hosp_discharge/(fp_ae_hosp_discharge + tp_ae_hosp_discharge))*100,2)}%)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Method 2 - Diagnosis code\n",
    "Restrict to those with a COVID-19 diagnosis code or respiratory-related diagnosis code from the [emergency care diagnosis refset](https://termbrowser.nhs.uk/?perspective=full&conceptId1=991411000000109&edition=uk-edition&release=v20220216&server=https://termbrowser.nhs.uk/sct-browser-api/snomed&langRefset=900000000000508004)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ae_cov_before = (df.loc[\n",
    "    df[\"ae_before_with_covid\"]==1\n",
    ", \"patient_id\"])\n",
    "\n",
    "ae_resp_before = (df.loc[\n",
    "    df[\"ae_before_with_resp\"]==1\n",
    ", \"patient_id\"])\n",
    "\n",
    "print(f'Number with A&E attendance and covid diagnosis code before admission: {len(ae_cov_before)} ({round((len(ae_cov_before)/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "print(f'Number with A&E attendance and respiratory code before admission: {len(ae_resp_before)} ({round((len(ae_resp_before)/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "\n",
    "fp_ae_cov = len(df[\n",
    "    (df[\"ae_cov\"]==1) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==0)\n",
    "    \n",
    "])\n",
    "\n",
    "fp_ae_resp = len(df[\n",
    "    (df[\"ae_resp\"]==1) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==0)\n",
    "    \n",
    "])\n",
    "\n",
    "tp_ae_cov = len(df[\n",
    "    (df[\"ae_cov\"]==1) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==1)\n",
    "    \n",
    "])\n",
    "\n",
    "tp_ae_resp = len(df[\n",
    "    (df[\"ae_resp\"]==1) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==1)\n",
    "    \n",
    "])\n",
    "\n",
    "\n",
    "diagnosis_code_cov = [len(ae_cov_before), round((len(ae_cov_before)/num_emergency_cov_admissions)*100, 2), fp_ae_cov, tp_ae_cov, round((fp_ae_cov/(fp_ae_cov + tp_ae_cov))*100,2)]\n",
    "diagnosis_code_resp = [len(ae_resp_before), round((len(ae_resp_before)/num_emergency_cov_admissions)*100, 2), fp_ae_resp, tp_ae_resp, round((len(ae_resp_before)/num_emergency_cov_admissions)*100, 2)]\n",
    "\n",
    "\n",
    "print(f'False positives (FDR) - A&E attendance and covid diagnosis code: {fp_ae_cov} ({round((fp_ae_cov/(fp_ae_cov + tp_ae_cov))*100,2)}%)')\n",
    "print(f'False positives (FDR) - A&E attendance and respiratory diagnosis code: {fp_ae_resp} ({round((fp_ae_resp/(fp_ae_resp + tp_ae_resp))*100,2)}%)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Method 3 - Identification in primary care\n",
    "\n",
    "Restrict to those with a probable or suspected COVID-19 code recorded in primary care in the 2 weeks prior to A&E attendance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ae_prim_care_probable_before = (df.loc[\n",
    "    df[\"ae_before_with_prim_care_probable\"]==1\n",
    ", \"patient_id\"])\n",
    "\n",
    "ae_prim_care_suspected_before = (df.loc[\n",
    "    df[\"ae_before_with_prim_care_suspected\"]==1\n",
    ", \"patient_id\"])\n",
    "\n",
    "print(f'Number with A&E attendance and recent suspected covid code in primary care before admission: {len(ae_prim_care_suspected_before)} ({round((len(ae_prim_care_suspected_before)/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "print(f'Number with A&E attendance and recent probable covid code in primary care before admission: {len(ae_prim_care_probable_before)} ({round((len(ae_prim_care_probable_before)/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "\n",
    "fp_ae_suspected = len(df[\n",
    "    (df[\"suspected_covid_primary_care_before_ae\"]==1) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==0)    \n",
    "])\n",
    "\n",
    "fp_ae_probable = len(df[\n",
    "    (df[\"covid_primary_care_before_ae\"]==1) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==0)\n",
    "])\n",
    "\n",
    "tp_ae_suspected = len(df[\n",
    "    (df[\"suspected_covid_primary_care_before_ae\"]==1) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==1)    \n",
    "])\n",
    "\n",
    "tp_ae_probable = len(df[\n",
    "    (df[\"covid_primary_care_before_ae\"]==1) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==1)\n",
    "])\n",
    "\n",
    "prim_care_suspected = [len(ae_prim_care_suspected_before), round((len(ae_prim_care_suspected_before)/num_emergency_cov_admissions)*100, 2), fp_ae_suspected, tp_ae_suspected, round((fp_ae_suspected/(fp_ae_suspected + tp_ae_suspected))*100,2)]\n",
    "prim_care_probable = [len(ae_prim_care_probable_before), round((len(ae_prim_care_probable_before)/num_emergency_cov_admissions)*100, 2), fp_ae_probable, tp_ae_probable, round((fp_ae_probable/(fp_ae_probable + tp_ae_probable))*100,2)]\n",
    "\n",
    "\n",
    "print(f'False positives (FDR) - A&E attendance and probable covid code in primary care: {fp_ae_probable} ({round((fp_ae_probable/(fp_ae_probable + tp_ae_probable))*100,2)}%)')\n",
    "print(f'False positives (FDR) - A&E attendance and suspected covid code in primary care: {fp_ae_suspected} ({round((fp_ae_suspected/(fp_ae_suspected + tp_ae_suspected))*100,2)}%)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Method 4 - Positive test result\n",
    "\n",
    "Restrict to those with a positive COVID-19 test result recorded up to 4 weeks prior to A&E attendance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ae_pos_test_before_2_weeks = (df.loc[\n",
    "    df[\"ae_before_with_positive_covid_test_2_weeks\"]==1\n",
    ", \"patient_id\"])\n",
    "\n",
    "ae_pos_test_before = (df.loc[\n",
    "    df[\"ae_before_with_positive_covid_test\"]==1\n",
    ", \"patient_id\"])\n",
    "\n",
    "\n",
    "print(f'Number with A&E attendance and recent positive test (2 weeks) before admission: {len(ae_pos_test_before_2_weeks)} ({round((len(ae_pos_test_before_2_weeks)/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "print(f'Number with A&E attendance and recent positive test (4 weeks) before admissio: {len(ae_pos_test_before)} ({round((len(ae_pos_test_before)/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "\n",
    "fp_ae_test = len(df[\n",
    "    (df[\"pos_test\"]==1) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==0)\n",
    "    \n",
    "])\n",
    "\n",
    "fp_ae_test_2_weeks = len(df[\n",
    "    (df[\"pos_test_2_weeks\"]==1) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==0)\n",
    "    \n",
    "])\n",
    "\n",
    "tp_ae_test = len(df[\n",
    "    (df[\"pos_test\"]==1) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==1)\n",
    "    \n",
    "])\n",
    "\n",
    "tp_ae_test_2_weeks = len(df[\n",
    "    (df[\"pos_test_2_weeks\"]==1) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==1)\n",
    "    \n",
    "])\n",
    "print(f'False positives (FDR) - A&E attendance and recent positive test (2 weeks): {fp_ae_test_2_weeks} ({round((fp_ae_test_2_weeks/(fp_ae_test_2_weeks + tp_ae_test_2_weeks))*100,2)}%)')\n",
    "print(f'False positives (FDR) - A&E attendance and recent positive test (4 weeks): {fp_ae_test} ({round((fp_ae_test/(fp_ae_test + tp_ae_test))*100,2)}%)')\n",
    "\n",
    "positive_test_2_weeks = [len(ae_pos_test_before_2_weeks), round((len(ae_pos_test_before_2_weeks)/num_emergency_cov_admissions)*100, 2), fp_ae_test_2_weeks, tp_ae_test_2_weeks, round((fp_ae_test_2_weeks/(fp_ae_test_2_weeks + tp_ae_test_2_weeks))*100,2)]\n",
    "positive_test_4_weeks = [len(ae_pos_test_before), round((len(ae_pos_test_before)/num_emergency_cov_admissions)*100, 2), fp_ae_test, tp_ae_test, round((fp_ae_test/(fp_ae_test + tp_ae_test))*100,2)]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  Combining approaches - A&E attendance with hospital discharge "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Diagnosis codes (resp or cov) with recent positive test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ae_hos_resp_cov_plus_test_2_weeks = (df.loc[(\n",
    "        (df[\"ae_before_with_covid\"]==1) | \n",
    "        (df[\"ae_before_with_resp\"]==1) \n",
    "    )&\n",
    "    (df[\"ae_before_with_positive_covid_test_2_weeks\"]==1)\n",
    ", \"patient_id\"])\n",
    "\n",
    "fp_ae_hos_resp_cov_plus_test_2_weeks = len(df[\n",
    "    (\n",
    "        (df[\"ae_cov\"]==1) |\n",
    "        (df[\"ae_resp\"]==1)    \n",
    "    )&\n",
    "    (df[\"pos_test_2_weeks\"]==1)&\n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==0)\n",
    "    \n",
    "])\n",
    "\n",
    "tp_ae_hos_resp_cov_plus_test_2_weeks = len(df[\n",
    "    (\n",
    "        (df[\"ae_cov\"]==1) |\n",
    "        (df[\"ae_resp\"]==1)    \n",
    "    )&\n",
    "    (df[\"pos_test_2_weeks\"]==1)&\n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==1)\n",
    "    \n",
    "])\n",
    "\n",
    "print(f'Number with A&E attendance with either covid or respiratory diagnosis code and recent positive test (2 weeks) before admission: {len(ae_hos_resp_cov_plus_test_2_weeks)} ({round((len(ae_hos_resp_cov_plus_test_2_weeks)/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "print(f'False positives (FDR): {fp_ae_hos_resp_cov_plus_test_2_weeks} ({round((fp_ae_hos_resp_cov_plus_test_2_weeks/(fp_ae_hos_resp_cov_plus_test_2_weeks + tp_ae_hos_resp_cov_plus_test_2_weeks))*100,2)}%)')\n",
    "code_plus_test = [len(ae_hos_resp_cov_plus_test_2_weeks), round((len(ae_hos_resp_cov_plus_test_2_weeks)/num_emergency_cov_admissions)*100, 2), fp_ae_hos_resp_cov_plus_test_2_weeks, tp_ae_hos_resp_cov_plus_test_2_weeks, round((fp_ae_hos_resp_cov_plus_test_2_weeks/(fp_ae_hos_resp_cov_plus_test_2_weeks + tp_ae_hos_resp_cov_plus_test_2_weeks))*100,2)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Covid in primary care with recent positive test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ae_hosp_prim_care_before_plus_2_week_test = (df.loc[(\n",
    "        (df[\"ae_before_with_prim_care_suspected\"]==1) | \n",
    "        (df[\"ae_before_with_prim_care_probable\"]==1) \n",
    "    )\n",
    "&\n",
    "    (df[\"ae_before_with_positive_covid_test_2_weeks\"]==1)\n",
    ", \"patient_id\"])\n",
    "\n",
    "fp_ae_hosp_prim_care_before_plus_2_week_test = len(df[\n",
    "    (\n",
    "        (df[\"covid_primary_care_before_ae\"]==1) |\n",
    "        (df[\"suspected_covid_primary_care_before_ae\"]==1)    \n",
    "    )&\n",
    "    (df[\"pos_test_2_weeks\"]==1)&\n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==0)\n",
    "    \n",
    "])\n",
    "\n",
    "tp_ae_hosp_prim_care_before_plus_2_week_test = len(df[\n",
    "    (\n",
    "        (df[\"covid_primary_care_before_ae\"]==1) |\n",
    "        (df[\"suspected_covid_primary_care_before_ae\"]==1)    \n",
    "    )&\n",
    "    (df[\"pos_test_2_weeks\"]==1)&\n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==1)\n",
    "    \n",
    "])\n",
    "\n",
    "print(f'Number with A&E attendance with suspected or probable covid in primary care and recent positive test (2 weeks) before admission: {len(ae_hosp_prim_care_before_plus_2_week_test)} ({round((len(ae_hosp_prim_care_before_plus_2_week_test)/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "print(f'False positives (FDR): {fp_ae_hosp_prim_care_before_plus_2_week_test} ({round((fp_ae_hosp_prim_care_before_plus_2_week_test/(fp_ae_hosp_prim_care_before_plus_2_week_test + tp_ae_hosp_prim_care_before_plus_2_week_test))*100,2)}%)')\n",
    "primary_care_plus_test = [len(ae_hosp_prim_care_before_plus_2_week_test), round((len(ae_hosp_prim_care_before_plus_2_week_test)/num_emergency_cov_admissions)*100, 2), fp_ae_hosp_prim_care_before_plus_2_week_test, tp_ae_hosp_prim_care_before_plus_2_week_test, round((fp_ae_hosp_prim_care_before_plus_2_week_test/(fp_ae_hosp_prim_care_before_plus_2_week_test + tp_ae_hosp_prim_care_before_plus_2_week_test))*100,2)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Recent positive test and either covid in primary care or diagnosis code (resp or cov)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_before = (df.loc[\n",
    "        (df[\"ae_before_with_positive_covid_test_2_weeks\"]==1) &\n",
    "        (\n",
    "            (df[\"ae_before_with_prim_care_suspected\"]==1) | \n",
    "            (df[\"ae_before_with_prim_care_probable\"]==1) |\n",
    "            (df[\"ae_before_with_covid\"]==1) | \n",
    "            (df[\"ae_before_with_resp\"]==1)   \n",
    "        )\n",
    ", \"patient_id\"])\n",
    "fp_combined_before = len(df[\n",
    "    (\n",
    "        (df[\"covid_primary_care_before_ae\"]==1) |\n",
    "        (df[\"suspected_covid_primary_care_before_ae\"]==1) |\n",
    "        (df[\"ae_cov\"]==1) |\n",
    "        (df[\"ae_resp\"]==1)\n",
    "        \n",
    "        \n",
    "    )&\n",
    "    (df[\"pos_test_2_weeks\"]==1)&\n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==0)\n",
    "    \n",
    "])\n",
    "\n",
    "tp_combined_before = len(df[\n",
    "    (\n",
    "        (df[\"covid_primary_care_before_ae\"]==1) |\n",
    "        (df[\"suspected_covid_primary_care_before_ae\"]==1) |\n",
    "        (df[\"ae_cov\"]==1) |\n",
    "        (df[\"ae_resp\"]==1)\n",
    "    )&\n",
    "    (df[\"pos_test_2_weeks\"]==1)&\n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==1)\n",
    "    \n",
    "])\n",
    "\n",
    "\n",
    "print(f'Number with A&E attendance with recent positive test and either suspected or probable covid in primary care or a covid or respiratory diagnosis code on attendance: {len(combined_before)} ({round((len(combined_before)/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "print(f'False positives (FDR): {fp_combined_before} ({round((fp_combined_before/(fp_combined_before + tp_combined_before))*100,2)}%)')\n",
    "\n",
    "pos_test_prim_care_or_diagnosis_code = [len(combined_before), round((len(combined_before)/num_emergency_cov_admissions)*100, 2), fp_combined_before, tp_combined_before, round((fp_combined_before/(fp_combined_before + tp_combined_before))*100,2)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "identification_table_index = [\"count\", \"sens\", \"fp\", \"tp\", \"fdr\"]\n",
    "identification_table = pd.DataFrame({\n",
    "    \"discharge_destination\": pd.Series(discharge_destination,index=identification_table_index),\n",
    "    \"covid_diagnosis_code\": pd.Series(diagnosis_code_cov,index=identification_table_index),\n",
    "    \"resp_diagnosis_code\": pd.Series(diagnosis_code_resp,index=identification_table_index),\n",
    "    \"primary_care_suspected\": pd.Series(prim_care_suspected,index=identification_table_index),\n",
    "    \"primary_care_probable\": pd.Series(prim_care_suspected,index=identification_table_index),\n",
    "    \"positive_test_2_weeks\": pd.Series(positive_test_2_weeks,index=identification_table_index),\n",
    "    \"positive_test_4_weeks\": pd.Series(positive_test_4_weeks,index=identification_table_index),\n",
    "    \"diagnosis_code_with_positive_test\": pd.Series(code_plus_test,index=identification_table_index),\n",
    "    \"primary_care_with_positive_test\": pd.Series(primary_care_plus_test,index=identification_table_index),\n",
    "    \"pos_test_prim_care_or_diagnosis\": pd.Series(pos_test_prim_care_or_diagnosis_code,index=identification_table_index)\n",
    "    \n",
    "})\n",
    "identification_table.to_csv(\"output/identification_table_no_hosp_discharge_primary.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "produce_matrix(columns_list = [ae_hosp_before, ae_cov_before, ae_resp_before, ae_prim_care_probable_before, ae_prim_care_suspected_before, ae_pos_test_before_2_weeks, ae_pos_test_before, ae_hos_resp_cov_plus_test_2_weeks, ae_hosp_prim_care_before_plus_2_week_test], names=[\"Hospital discharge\", \"Covid diagnosis code\", \"Respiratory diagnosis code\", \"Probable covid code\", \"Suspected covid code\", \"Recent positive test (2 weeks)\", \"Recent positive test (4 weeks)\", \"Diagnosis codes (resp or cov) with recent positive test\", \"Covid in primary care with recent positive test\", \"Recent positive test and either covid in primary care or diagnosis code (resp or cov)\"], filename=\"cm_no_hosp_discharge_primary\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Restrict to A&E attendance with hospital discharge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ae_with_hosp_discharge = len(df[\n",
    "    (df[\"ae_attendance_hosp_discharge_date\"].notnull())\n",
    "])\n",
    "print(f'Number of A&E attendances with hospital discharge: {ae_with_hosp_discharge}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Method 1 - Discharge destination\n",
    "Restrict to those that attended A&E and had a hospital discharge destination (hospital ward, emergency short stay ward, icu)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ae_hosp_before = (df.loc[\n",
    "    df[\"ae_before_with_hospital_discharge_date\"].notnull()\n",
    ", \"patient_id\"])\n",
    "\n",
    "print(f'Number with A&E attendance with hospital discharge before admission: {len(ae_hosp_before)} ({round((len(ae_hosp_before)/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "\n",
    "\n",
    "# false posisitves\n",
    "fp_ae_hosp_discharge = len(df[\n",
    "    (df[\"ae_attendance_hosp_discharge_date\"].notnull()) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==0)\n",
    "\n",
    "])\n",
    "\n",
    "tp_ae_hosp_discharge = len(df[\n",
    "    (df[\"ae_attendance_hosp_discharge_date\"].notnull()) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==1)\n",
    "\n",
    "])\n",
    "discharge_destination = [len(ae_hosp_before), round((len(ae_hosp_before)/num_emergency_cov_admissions)*100, 2),fp_ae_hosp_discharge, tp_ae_hosp_discharge, round((fp_ae_hosp_discharge/(fp_ae_hosp_discharge + tp_ae_hosp_discharge))*100,2)]\n",
    "\n",
    "print(f'False positives (FDR): {fp_ae_hosp_discharge} ({round((fp_ae_hosp_discharge/(fp_ae_hosp_discharge + tp_ae_hosp_discharge))*100,2)}%)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Method 2 - Diagnosis code\n",
    "Restrict to those with a COVID-19 diagnosis code or respiratory-related diagnosis code from the [emergency care diagnosis refset](https://termbrowser.nhs.uk/?perspective=full&conceptId1=991411000000109&edition=uk-edition&release=v20220216&server=https://termbrowser.nhs.uk/sct-browser-api/snomed&langRefset=900000000000508004)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ae_cov_before = (df.loc[\n",
    "    df[\"ae_before_with_hospital_discharge_covid\"]==1\n",
    ", \"patient_id\"])\n",
    "\n",
    "ae_resp_before = (df.loc[\n",
    "    df[\"ae_before_with_hospital_discharge_resp\"]==1\n",
    ", \"patient_id\"])\n",
    "\n",
    "print(f'Number with A&E attendance and covid diagnosis code before admission: {len(ae_cov_before)} ({round((len(ae_cov_before)/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "print(f'Number with A&E attendance and respiratory code before admission: {len(ae_resp_before)} ({round((len(ae_resp_before)/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "\n",
    "fp_ae_cov = len(df[\n",
    "    (df[\"ae_cov_hosp_discharge\"]==1) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==0)\n",
    "    \n",
    "])\n",
    "\n",
    "fp_ae_resp = len(df[\n",
    "    (df[\"ae_resp_hosp_discharge\"]==1) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==0)\n",
    "    \n",
    "])\n",
    "\n",
    "tp_ae_cov = len(df[\n",
    "    (df[\"ae_cov_hosp_discharge\"]==1) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==1)\n",
    "    \n",
    "])\n",
    "\n",
    "tp_ae_resp = len(df[\n",
    "    (df[\"ae_resp_hosp_discharge\"]==1) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==1)\n",
    "    \n",
    "])\n",
    "\n",
    "diagnosis_code_cov = [len(ae_cov_before), round((len(ae_cov_before)/num_emergency_cov_admissions)*100, 2), fp_ae_cov, tp_ae_cov, round((fp_ae_cov/(fp_ae_cov + tp_ae_cov))*100,2)]\n",
    "diagnosis_code_resp = [len(ae_resp_before), round((len(ae_resp_before)/num_emergency_cov_admissions)*100, 2), fp_ae_resp, tp_ae_resp, round((len(ae_resp_before)/num_emergency_cov_admissions)*100, 2)]\n",
    "\n",
    "\n",
    "print(f'False positives (FDR) - A&E attendance and covid diagnosis code: {fp_ae_cov} ({round((fp_ae_cov/(fp_ae_cov + tp_ae_cov))*100,2)}%)')\n",
    "print(f'False positives (FDR) - A&E attendance and respiratory diagnosis code: {fp_ae_resp} ({round((fp_ae_resp/(fp_ae_resp + tp_ae_resp))*100,2)}%)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Method 3 - Identification in primary care\n",
    "\n",
    "Restrict to those with a probable or suspected COVID-19 code recorded in primary care in the 2 weeks prior to A&E attendance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ae_prim_care_probable_before = (df.loc[\n",
    "    df[\"ae_before_with_hospital_discharge_prim_care_probable\"]==1\n",
    ", \"patient_id\"])\n",
    "\n",
    "ae_prim_care_suspected_before = (df.loc[\n",
    "    df[\"ae_before_with_hospital_discharge_prim_care_suspected\"]==1\n",
    ", \"patient_id\"])\n",
    "\n",
    "print(f'Number with A&E attendance and recent suspected covid code in primary care before admission: {len(ae_prim_care_suspected_before)} ({round((len(ae_prim_care_suspected_before)/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "print(f'Number with A&E attendance and recent probable covid code in primary care before admission: {len(ae_prim_care_probable_before)} ({round((len(ae_prim_care_probable_before)/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "\n",
    "fp_ae_suspected = len(df[\n",
    "    (df[\"suspected_covid_primary_care_before_ae_hosp_discharge\"]==1) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==0)    \n",
    "])\n",
    "\n",
    "fp_ae_probable = len(df[\n",
    "    (df[\"covid_primary_care_before_ae_hosp_discharge\"]==1) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==0)\n",
    "])\n",
    "\n",
    "tp_ae_suspected = len(df[\n",
    "    (df[\"suspected_covid_primary_care_before_ae_hosp_discharge\"]==1) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==1)    \n",
    "])\n",
    "\n",
    "tp_ae_probable = len(df[\n",
    "    (df[\"covid_primary_care_before_ae_hosp_discharge\"]==1) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==1)\n",
    "])\n",
    "\n",
    "\n",
    "prim_care_suspected = [len(ae_prim_care_suspected_before), round((len(ae_prim_care_suspected_before)/num_emergency_cov_admissions)*100, 2), fp_ae_suspected, tp_ae_suspected, round((fp_ae_suspected/(fp_ae_suspected + tp_ae_suspected))*100,2)]\n",
    "prim_care_probable = [len(ae_prim_care_probable_before), round((len(ae_prim_care_probable_before)/num_emergency_cov_admissions)*100, 2), fp_ae_probable, tp_ae_probable, round((fp_ae_probable/(fp_ae_probable + tp_ae_probable))*100,2)]\n",
    "\n",
    "print(f'False positives (FDR) - A&E attendance and probable covid code in primary care: {fp_ae_probable} ({round((fp_ae_probable/(fp_ae_probable + tp_ae_probable))*100,2)}%)')\n",
    "print(f'False positives (FDR) - A&E attendance and suspected covid code in primary care: {fp_ae_suspected} ({round((fp_ae_suspected/(fp_ae_suspected + tp_ae_suspected))*100,2)}%)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Method 4 - Positive test result\n",
    "\n",
    "Restrict to those with a positive COVID-19 test result recorded up to 4 weeks prior to A&E attendance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ae_pos_test_before_2_weeks = (df.loc[\n",
    "    df[\"ae_before_with_hospital_discharge_positive_covid_test_2_weeks\"]==1\n",
    ", \"patient_id\"])\n",
    "\n",
    "ae_pos_test_before = (df.loc[\n",
    "    df[\"ae_before_with_hospital_discharge_positive_covid_test\"]==1\n",
    ", \"patient_id\"])\n",
    "\n",
    "\n",
    "print(f'Number with A&E attendance and recent positive test (2 weeks) before admission: {len(ae_pos_test_before_2_weeks)} ({round((len(ae_pos_test_before_2_weeks)/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "print(f'Number with A&E attendance and recent positive test (4 weeks) before admissio: {len(ae_pos_test_before)} ({round((len(ae_pos_test_before)/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "\n",
    "fp_ae_test = len(df[\n",
    "    (df[\"pos_test_hosp_discharge\"]==1) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==0)\n",
    "    \n",
    "])\n",
    "\n",
    "fp_ae_test_2_weeks = len(df[\n",
    "    (df[\"pos_test_2_weeks_hosp_discharge\"]==1) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==0)\n",
    "    \n",
    "])\n",
    "\n",
    "tp_ae_test = len(df[\n",
    "    (df[\"pos_test_hosp_discharge\"]==1) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==1)\n",
    "    \n",
    "])\n",
    "\n",
    "tp_ae_test_2_weeks = len(df[\n",
    "    (df[\"pos_test_2_weeks_hosp_discharge\"]==1) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==1)\n",
    "    \n",
    "])\n",
    "\n",
    "positive_test_2_weeks = [len(ae_pos_test_before_2_weeks), round((len(ae_pos_test_before_2_weeks)/num_emergency_cov_admissions)*100, 2), fp_ae_test_2_weeks, tp_ae_test_2_weeks, round((fp_ae_test_2_weeks/(fp_ae_test_2_weeks + tp_ae_test_2_weeks))*100,2)]\n",
    "positive_test_4_weeks = [len(ae_pos_test_before), round((len(ae_pos_test_before)/num_emergency_cov_admissions)*100, 2), fp_ae_test, tp_ae_test, round((fp_ae_test/(fp_ae_test + tp_ae_test))*100,2)]\n",
    "\n",
    "\n",
    "print(f'False positives (FDR) - A&E attendance and recent positive test (2 weeks): {fp_ae_test_2_weeks} ({round((fp_ae_test_2_weeks/(fp_ae_test_2_weeks + tp_ae_test_2_weeks))*100,2)}%)')\n",
    "print(f'False positives (FDR) - A&E attendance and recent positive test (4 weeks): {fp_ae_test} ({round((fp_ae_test/(fp_ae_test + tp_ae_test))*100,2)}%)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  Combining approaches - A&E attendance with hospital discharge "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Diagnosis codes (resp or cov) with recent positive test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ae_hos_resp_cov_plus_test_2_weeks = (df.loc[(\n",
    "        (df[\"ae_before_with_hospital_discharge_covid\"]==1) | \n",
    "        (df[\"ae_before_with_hospital_discharge_resp\"]==1) \n",
    "    )&\n",
    "    (df[\"ae_before_with_hospital_discharge_positive_covid_test_2_weeks\"]==1)\n",
    ", \"patient_id\"])\n",
    "\n",
    "fp_ae_hos_resp_cov_plus_test_2_weeks = len(df[\n",
    "    (\n",
    "        (df[\"ae_cov_hosp_discharge\"]==1) |\n",
    "        (df[\"ae_resp_hosp_discharge\"]==1)    \n",
    "    )&\n",
    "    (df[\"pos_test_2_weeks_hosp_discharge\"]==1)&\n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==0)\n",
    "    \n",
    "])\n",
    "\n",
    "tp_ae_hos_resp_cov_plus_test_2_weeks = len(df[\n",
    "    (\n",
    "        (df[\"ae_cov_hosp_discharge\"]==1) |\n",
    "        (df[\"ae_resp_hosp_discharge\"]==1)    \n",
    "    )&\n",
    "    (df[\"pos_test_2_weeks_hosp_discharge\"]==1)&\n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==1)\n",
    "    \n",
    "])\n",
    "\n",
    "print(f'Number with A&E attendance with either covid or respiratory diagnosis code and recent positive test (2 weeks) before admission: {len(ae_hos_resp_cov_plus_test_2_weeks)} ({round((len(ae_hos_resp_cov_plus_test_2_weeks)/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "print(f'False positives (FDR): {fp_ae_hos_resp_cov_plus_test_2_weeks} ({round((fp_ae_hos_resp_cov_plus_test_2_weeks/(fp_ae_hos_resp_cov_plus_test_2_weeks + tp_ae_hos_resp_cov_plus_test_2_weeks))*100,2)}%)')\n",
    "\n",
    "code_plus_test = [len(ae_hos_resp_cov_plus_test_2_weeks), round((len(ae_hos_resp_cov_plus_test_2_weeks)/num_emergency_cov_admissions)*100, 2), fp_ae_hos_resp_cov_plus_test_2_weeks, tp_ae_hos_resp_cov_plus_test_2_weeks, round((fp_ae_hos_resp_cov_plus_test_2_weeks/(fp_ae_hos_resp_cov_plus_test_2_weeks + tp_ae_hos_resp_cov_plus_test_2_weeks))*100,2)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Covid in primary care with recent positive test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ae_hosp_prim_care_before_plus_2_week_test = (df.loc[(\n",
    "        (df[\"ae_before_with_hospital_discharge_prim_care_suspected\"]==1) | \n",
    "        (df[\"ae_before_with_hospital_discharge_prim_care_probable\"]==1) \n",
    "    )\n",
    "&\n",
    "    (df[\"ae_before_with_hospital_discharge_positive_covid_test_2_weeks\"]==1)\n",
    ", \"patient_id\"])\n",
    "\n",
    "fp_ae_hosp_prim_care_before_plus_2_week_test = len(df[\n",
    "    (\n",
    "        (df[\"covid_primary_care_before_ae_hosp_discharge\"]==1) |\n",
    "        (df[\"suspected_covid_primary_care_before_ae_hosp_discharge\"]==1)    \n",
    "    )&\n",
    "    (df[\"pos_test_2_weeks_hosp_discharge\"]==1)&\n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==0)\n",
    "    \n",
    "])\n",
    "\n",
    "tp_ae_hosp_prim_care_before_plus_2_week_test = len(df[\n",
    "    (\n",
    "        (df[\"covid_primary_care_before_ae_hosp_discharge\"]==1) |\n",
    "        (df[\"suspected_covid_primary_care_before_ae_hosp_discharge\"]==1)    \n",
    "    )&\n",
    "    (df[\"pos_test_2_weeks_hosp_discharge\"]==1)&\n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==1)\n",
    "    \n",
    "])\n",
    "\n",
    "print(f'Number with A&E attendance with suspected or probable covid in primary care and recent positive test (2 weeks) before admission: {len(ae_hosp_prim_care_before_plus_2_week_test)} ({round((len(ae_hosp_prim_care_before_plus_2_week_test)/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "print(f'False positives (FDR): {fp_ae_hosp_prim_care_before_plus_2_week_test} ({round((fp_ae_hosp_prim_care_before_plus_2_week_test/(fp_ae_hosp_prim_care_before_plus_2_week_test + tp_ae_hosp_prim_care_before_plus_2_week_test))*100,2)}%)')\n",
    "\n",
    "primary_care_plus_test = [len(ae_hosp_prim_care_before_plus_2_week_test), round((len(ae_hosp_prim_care_before_plus_2_week_test)/num_emergency_cov_admissions)*100, 2), fp_ae_hosp_prim_care_before_plus_2_week_test, tp_ae_hosp_prim_care_before_plus_2_week_test, round((fp_ae_hosp_prim_care_before_plus_2_week_test/(fp_ae_hosp_prim_care_before_plus_2_week_test + tp_ae_hosp_prim_care_before_plus_2_week_test))*100,2)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Recent positive test and either covid in primary care or diagnosis code (resp or cov)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_before = (df.loc[\n",
    "        (df[\"ae_before_with_hospital_discharge_positive_covid_test_2_weeks\"]==1) &\n",
    "        (\n",
    "            (df[\"ae_before_with_hospital_discharge_prim_care_suspected\"]==1) | \n",
    "            (df[\"ae_before_with_hospital_discharge_prim_care_probable\"]==1) |\n",
    "            (df[\"ae_before_with_hospital_discharge_covid\"]==1) | \n",
    "            (df[\"ae_before_with_hospital_discharge_resp\"]==1)   \n",
    "        )\n",
    ", \"patient_id\"])\n",
    "fp_combined_before = len(df[\n",
    "    (\n",
    "        (df[\"covid_primary_care_before_ae_hosp_discharge\"]==1) |\n",
    "        (df[\"suspected_covid_primary_care_before_ae_hosp_discharge\"]==1) |\n",
    "        (df[\"ae_cov_hosp_discharge\"]==1) |\n",
    "        (df[\"ae_resp_hosp_discharge\"]==1)\n",
    "        \n",
    "        \n",
    "    )&\n",
    "    (df[\"pos_test_2_weeks_hosp_discharge\"]==1)&\n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==0)\n",
    "    \n",
    "])\n",
    "\n",
    "tp_combined_before = len(df[\n",
    "    (\n",
    "        (df[\"covid_primary_care_before_ae_hosp_discharge\"]==1) |\n",
    "        (df[\"suspected_covid_primary_care_before_ae_hosp_discharge\"]==1) |\n",
    "        (df[\"ae_cov_hosp_discharge\"]==1) |\n",
    "        (df[\"ae_resp_hosp_discharge\"]==1)\n",
    "    )&\n",
    "    (df[\"pos_test_2_weeks_hosp_discharge\"]==1)&\n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==1)\n",
    "    \n",
    "])\n",
    "\n",
    "\n",
    "print(f'Number with A&E attendance with recent positive test and either suspected or probable covid in primary care or a covid or respiratory diagnosis code on attendance: {len(combined_before)} ({round((len(combined_before)/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "print(f'False positives (FDR): {fp_combined_before} ({round((fp_combined_before/(fp_combined_before + tp_combined_before))*100,2)}%)')\n",
    "\n",
    "pos_test_prim_care_or_diagnosis_code = [len(combined_before), round((len(combined_before)/num_emergency_cov_admissions)*100, 2), fp_combined_before, tp_combined_before, round((fp_combined_before/(fp_combined_before + tp_combined_before))*100,2)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "identification_table_index = [\"count\", \"sens\", \"fp\", \"tp\", \"fdr\"]\n",
    "identification_table = pd.DataFrame({\n",
    "    \"discharge_destination\": pd.Series(discharge_destination,index=identification_table_index),\n",
    "    \"covid_diagnosis_code\": pd.Series(diagnosis_code_cov,index=identification_table_index),\n",
    "    \"resp_diagnosis_code\": pd.Series(diagnosis_code_resp,index=identification_table_index),\n",
    "    \"primary_care_suspected\": pd.Series(prim_care_suspected,index=identification_table_index),\n",
    "    \"primary_care_probable\": pd.Series(prim_care_suspected,index=identification_table_index),\n",
    "    \"positive_test_2_weeks\": pd.Series(positive_test_2_weeks,index=identification_table_index),\n",
    "    \"positive_test_4_weeks\": pd.Series(positive_test_4_weeks,index=identification_table_index),\n",
    "    \"diagnosis_code_with_positive_test\": pd.Series(code_plus_test,index=identification_table_index),\n",
    "    \"primary_care_with_positive_test\": pd.Series(primary_care_plus_test,index=identification_table_index),\n",
    "    \"pos_test_prim_care_or_diagnosis\": pd.Series(pos_test_prim_care_or_diagnosis_code,index=identification_table_index)\n",
    "    \n",
    "})\n",
    "identification_table.to_csv(\"output/identification_table_hosp_discharge_primary.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "produce_matrix(columns_list = [ae_hosp_before, ae_cov_before, ae_resp_before, ae_prim_care_probable_before, ae_prim_care_suspected_before, ae_pos_test_before_2_weeks, ae_pos_test_before, ae_hos_resp_cov_plus_test_2_weeks, ae_hosp_prim_care_before_plus_2_week_test], names=[\"Hospital discharge\", \"Covid diagnosis code\", \"Respiratory diagnosis code\", \"Probable covid code\", \"Suspected covid code\", \"Recent positive test (2 weeks)\", \"Recent positive test (4 weeks)\", \"Diagnosis codes (resp or cov) with recent positive test\", \"Covid in primary care with recent positive test\", \"Recent positive test and either covid in primary care or diagnosis code (resp or cov)\"], filename=\"cm_hosp_discharge_primary\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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