analysis/generate_results_jun.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('output/input_2021-06-01.csv.gz')\n",
    "\n",
    "if not os.path.exists('output/jun'):\n",
    "    os.mkdir('output/jun')\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(int(round_to_5(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.savefig(f\"output/{filename}.jpeg\", bbox_inches='tight')\n",
    "        plt.show()\n",
    "        \n",
    "def round_to_5(number):\n",
    "    return 5 * round((number / 5))\n",
    "\n",
    "def performance_metrics(tp, fp, tn, fn):\n",
    "    tp = round_to_5(tp)\n",
    "    fp = round_to_5(fp)\n",
    "    tn = round_to_5(tn)\n",
    "    fn = round_to_5(fn)\n",
    "    \n",
    "    sensitivity = round((tp/(tp+fn)) *100, 2)\n",
    "    specificity = round((tn/(tn+fp)) *100, 2)\n",
    "    fdr = sensitivity = round((fp/(fp+tp)) *100, 2)\n",
    "    return [tp, fp, sensitivity, specificity, fdr]"
   ]
  },
  {
   "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": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of emergency covid hospital admissions: 300\n",
      "Number with A&E attendance before admission: 500 (166.67%)\n",
      "---\n",
      "Number of A&E attendances: 400\n"
     ]
    }
   ],
   "source": [
    "num_emergency_cov_admissions = round_to_5(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: {round_to_5(len(ae_before))} ({round((round_to_5(len(ae_before))/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "\n",
    "ae = round_to_5(len(df[\n",
    "    (df[\"ae_attendance_date\"].notnull())\n",
    "]))\n",
    "\n",
    "ae_with_hosp_discharge = round_to_5(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\": round_to_5(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/jun/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": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number with A&E attendance with hospital discharge before admission: 500 (166.67%)\n",
      "False positives (FDR): 280 (70.0%)\n"
     ]
    }
   ],
   "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: {round_to_5(len(ae_hosp_before))} ({round((round_to_5(len(ae_hosp_before))/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "\n",
    "\n",
    "# false posisitves\n",
    "fp_ae_hosp_discharge = round_to_5(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 = round_to_5(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",
    "tn_ae_hosp_discharge = round_to_5(len(df[\n",
    "    (df[\"ae_attendance_hosp_discharge_date\"].isnull()) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==0)\n",
    "\n",
    "]))\n",
    "\n",
    "fn_ae_hosp_discharge = round_to_5(len(df[\n",
    "    (df[\"ae_attendance_hosp_discharge_date\"].isnull()) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==1)\n",
    "\n",
    "]))\n",
    "\n",
    "discharge_destination = performance_metrics(tp_ae_hosp_discharge, fp_ae_hosp_discharge, tn_ae_hosp_discharge, fn_ae_hosp_discharge)\n",
    "\n",
    "print(f'False positives (FDR): {fp_ae_hosp_discharge} ({discharge_destination[4]}%)')"
   ]
  },
  {
   "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": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number with A&E attendance and covid diagnosis code before admission: 500 (166.67%)\n",
      "Number with A&E attendance and respiratory code before admission: 500 (166.67%)\n",
      "False positives (FDR) - A&E attendance and covid diagnosis code: 355 (71.0%)\n",
      "False positives (FDR) - A&E attendance and respiratory diagnosis code: 335 (67.0%)\n"
     ]
    }
   ],
   "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: {round_to_5(len(ae_cov_before))} ({round((round_to_5(len(ae_cov_before))/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "print(f'Number with A&E attendance and respiratory code before admission: {round_to_5(len(ae_resp_before))} ({round((round_to_5(len(ae_resp_before))/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "\n",
    "fp_ae_cov = round_to_5(len(df[\n",
    "    (df[\"ae_cov\"]==1) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==0)\n",
    "    \n",
    "]))\n",
    "\n",
    "fp_ae_resp = round_to_5(len(df[\n",
    "    (df[\"ae_resp\"]==1) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==0)\n",
    "    \n",
    "]))\n",
    "\n",
    "tp_ae_cov = round_to_5(len(df[\n",
    "    (df[\"ae_cov\"]==1) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==1)\n",
    "    \n",
    "]))\n",
    "\n",
    "tp_ae_resp = round_to_5(len(df[\n",
    "    (df[\"ae_resp\"]==1) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==1)\n",
    "    \n",
    "]))\n",
    "\n",
    "tn_ae_cov = round_to_5(len(df[\n",
    "    (df[\"ae_cov\"]==0) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==0)\n",
    "    \n",
    "]))\n",
    "\n",
    "tn_ae_resp = round_to_5(len(df[\n",
    "    (df[\"ae_resp\"]==0) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==0)\n",
    "    \n",
    "]))\n",
    "\n",
    "fn_ae_cov = round_to_5(len(df[\n",
    "    (df[\"ae_cov\"]==0) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==1)\n",
    "    \n",
    "]))\n",
    "\n",
    "fn_ae_resp = round_to_5(len(df[\n",
    "    (df[\"ae_resp\"]==0) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==1)\n",
    "    \n",
    "]))\n",
    "\n",
    "diagnosis_code_cov = performance_metrics(tp_ae_cov, fp_ae_cov, tn_ae_cov, fn_ae_cov)\n",
    "diagnosis_code_resp = performance_metrics(tp_ae_resp, fp_ae_resp, tn_ae_resp, fn_ae_resp)\n",
    "\n",
    "print(f'False positives (FDR) - A&E attendance and covid diagnosis code: {fp_ae_cov} ({diagnosis_code_cov[4]}%)')\n",
    "print(f'False positives (FDR) - A&E attendance and respiratory diagnosis code: {fp_ae_resp} ({diagnosis_code_resp[4]}%)')"
   ]
  },
  {
   "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": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number with A&E attendance and recent suspected covid code in primary care before admission: 500 (166.67%)\n",
      "Number with A&E attendance and recent probable covid code in primary care before admission: 500 (166.67%)\n",
      "False positives (FDR) - A&E attendance and probable covid code in primary care: 70.0%)\n",
      "False positives (FDR) - A&E attendance and suspected covid code in primary care: 69.0%)\n"
     ]
    }
   ],
   "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: {round_to_5(len(ae_prim_care_suspected_before))} ({round((round_to_5(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: {round_to_5(len(ae_prim_care_probable_before))} ({round((round_to_5(len(ae_prim_care_probable_before))/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "\n",
    "fp_ae_suspected = round_to_5(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 = round_to_5(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 = round_to_5(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 = round_to_5(len(df[\n",
    "    (df[\"covid_primary_care_before_ae\"]==1) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==1)\n",
    "]))\n",
    "\n",
    "tn_ae_suspected = round_to_5(len(df[\n",
    "    (df[\"suspected_covid_primary_care_before_ae\"]==0) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==0)    \n",
    "]))\n",
    "\n",
    "tn_ae_probable = round_to_5(len(df[\n",
    "    (df[\"covid_primary_care_before_ae\"]==0) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==0)\n",
    "]))\n",
    "\n",
    "fn_ae_suspected = round_to_5(len(df[\n",
    "    (df[\"suspected_covid_primary_care_before_ae\"]==0) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==1)    \n",
    "]))\n",
    "\n",
    "fn_ae_probable = round_to_5(len(df[\n",
    "    (df[\"covid_primary_care_before_ae\"]==0) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==1)\n",
    "]))\n",
    "\n",
    "\n",
    "\n",
    "prim_care_suspected = performance_metrics(tp_ae_suspected, fp_ae_suspected, tn_ae_suspected, fn_ae_suspected)\n",
    "prim_care_probable = performance_metrics(tp_ae_probable, fp_ae_probable, tn_ae_probable, fn_ae_probable)\n",
    "print(f'False positives (FDR) - A&E attendance and probable covid code in primary care: {prim_care_probable[4]}%)')\n",
    "print(f'False positives (FDR) - A&E attendance and suspected covid code in primary care: {prim_care_suspected[4]}%)')"
   ]
  },
  {
   "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": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number with A&E attendance and recent positive test (2 weeks) before admission: 500 (166.67%)\n",
      "Number with A&E attendance and recent positive test (4 weeks) before admissio: 500 (166.67%)\n",
      "False positives (FDR) - A&E attendance and recent positive test (2 weeks): 355 (71.0%)\n",
      "False positives (FDR) - A&E attendance and recent positive test (4 weeks): 350 (70.0%)\n"
     ]
    }
   ],
   "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: {round_to_5(len(ae_pos_test_before_2_weeks))} ({round((round_to_5(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: {round_to_5(len(ae_pos_test_before))} ({round((round_to_5(len(ae_pos_test_before))/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "\n",
    "fp_ae_test = round_to_5(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 = round_to_5(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 = round_to_5(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 = round_to_5(len(df[\n",
    "    (df[\"pos_test_2_weeks\"]==1) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==1)\n",
    "    \n",
    "]))\n",
    "\n",
    "tn_ae_test = round_to_5(len(df[\n",
    "    (df[\"pos_test\"]==0) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==0)\n",
    "    \n",
    "]))\n",
    "\n",
    "tn_ae_test_2_weeks = round_to_5(len(df[\n",
    "    (df[\"pos_test_2_weeks\"]==0) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==0)\n",
    "    \n",
    "]))\n",
    "\n",
    "fn_ae_test = round_to_5(len(df[\n",
    "    (df[\"pos_test\"]==0) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==1)\n",
    "    \n",
    "]))\n",
    "\n",
    "fn_ae_test_2_weeks = round_to_5(len(df[\n",
    "    (df[\"pos_test_2_weeks\"]==0) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==1)\n",
    "    \n",
    "]))\n",
    "\n",
    "\n",
    "positive_test_2_weeks = performance_metrics(tp_ae_test_2_weeks, fp_ae_test_2_weeks, tn_ae_test_2_weeks, fn_ae_test_2_weeks)\n",
    "positive_test_4_weeks = performance_metrics(tp_ae_test, fp_ae_test, tn_ae_test, fn_ae_test)\n",
    "\n",
    "print(f'False positives (FDR) - A&E attendance and recent positive test (2 weeks): {fp_ae_test_2_weeks} ({positive_test_2_weeks[4]}%)')\n",
    "print(f'False positives (FDR) - A&E attendance and recent positive test (4 weeks): {fp_ae_test} ({positive_test_4_weeks[4]}%)')\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": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number with A&E attendance with either covid or respiratory diagnosis code and recent positive test (2 weeks) before admission: 355 (118.33%)\n",
      "False positives (FDR): 265 (69.74%)\n"
     ]
    }
   ],
   "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",
    "print(f'Number with A&E attendance with either covid or respiratory diagnosis code and recent positive test (2 weeks) before admission: {round_to_5(len(ae_hos_resp_cov_plus_test_2_weeks))} ({round((round_to_5(len(ae_hos_resp_cov_plus_test_2_weeks))/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "\n",
    "\n",
    "fp_ae_hos_resp_cov_plus_test_2_weeks = round_to_5(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 = round_to_5(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",
    "tn_ae_hos_resp_cov_plus_test_2_weeks = round_to_5(len(df[\n",
    "    ~(\n",
    "    (\n",
    "        (df[\"ae_cov\"]==1) |\n",
    "        (df[\"ae_resp\"]==1)    \n",
    "    )&\n",
    "    (df[\"pos_test_2_weeks\"]==1)\n",
    "    )\n",
    "    &\n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==0)\n",
    "    \n",
    "]))\n",
    "\n",
    "fn_ae_hos_resp_cov_plus_test_2_weeks = round_to_5(len(df[\n",
    "    ~(\n",
    "    (\n",
    "        (df[\"ae_cov\"]==1) |\n",
    "        (df[\"ae_resp\"]==1)    \n",
    "    )&\n",
    "    (df[\"pos_test_2_weeks\"]==1)\n",
    "    )\n",
    "    &\n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==1)\n",
    "    \n",
    "]))\n",
    "\n",
    "\n",
    "code_plus_test = performance_metrics(tp_ae_hos_resp_cov_plus_test_2_weeks, fp_ae_hos_resp_cov_plus_test_2_weeks, tn_ae_hos_resp_cov_plus_test_2_weeks, fn_ae_hos_resp_cov_plus_test_2_weeks)\n",
    "print(f'False positives (FDR): {fp_ae_hos_resp_cov_plus_test_2_weeks} ({code_plus_test[4]}%)')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Covid in primary care with recent positive test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number with A&E attendance with suspected or probable covid in primary care and recent positive test (2 weeks) before admission: 385 (128.33%)\n",
      "False positives (FDR): 275 (72.37%)\n"
     ]
    }
   ],
   "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",
    "print(f'Number with A&E attendance with suspected or probable covid in primary care and recent positive test (2 weeks) before admission: {round_to_5(len(ae_hosp_prim_care_before_plus_2_week_test))} ({round((round_to_5(len(ae_hosp_prim_care_before_plus_2_week_test))/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "\n",
    "\n",
    "fp_ae_hosp_prim_care_before_plus_2_week_test = round_to_5(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 = round_to_5(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",
    "tn_ae_hosp_prim_care_before_plus_2_week_test = round_to_5(len(df[\n",
    "    (~\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",
    "    )\n",
    "    &\n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==0)\n",
    "    \n",
    "]))\n",
    "\n",
    "fn_ae_hosp_prim_care_before_plus_2_week_test = round_to_5(len(df[\n",
    "    (~\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",
    "    )\n",
    "    &\n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==1)\n",
    "    \n",
    "]))\n",
    "\n",
    "\n",
    "primary_care_plus_test = performance_metrics(tp_ae_hosp_prim_care_before_plus_2_week_test, fp_ae_hosp_prim_care_before_plus_2_week_test, tn_ae_hosp_prim_care_before_plus_2_week_test, fn_ae_hosp_prim_care_before_plus_2_week_test)\n",
    "\n",
    "print(f'False positives (FDR): {fp_ae_hosp_prim_care_before_plus_2_week_test} ({primary_care_plus_test[4]}%)')\n"
   ]
  },
  {
   "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": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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: 470 (156.67%)\n",
      "False positives (FDR): 340 (71.58%)\n"
     ]
    }
   ],
   "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",
    "\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: {round_to_5(len(combined_before))} ({round((round_to_5(len(combined_before))/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "\n",
    "fp_combined_before = round_to_5(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 = round_to_5(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",
    "tn_combined_before = round_to_5(len(df[\n",
    "    (~\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",
    "    )\n",
    "    & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==0)\n",
    "    \n",
    "]))\n",
    "\n",
    "fn_combined_before = round_to_5(len(df[\n",
    "    (~\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",
    "    )\n",
    "    & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==0)\n",
    "    \n",
    "]))\n",
    "\n",
    "pos_test_prim_care_or_diagnosis_code = performance_metrics(tp_combined_before, fp_combined_before, tn_combined_before, fn_combined_before)\n",
    "print(f'False positives (FDR): {fp_combined_before} ({pos_test_prim_care_or_diagnosis_code[4]}%)')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "identification_table_index = [\"tp\", \"fp\", \"sensitivity\", \"specificity\", \"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_probable,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/jun/identification_table_no_hosp_discharge.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "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\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Restrict to A&E attendance with hospital discharge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of A&E attendances with hospital discharge: 400\n"
     ]
    }
   ],
   "source": [
    "ae_with_hosp_discharge = round_to_5(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": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number with A&E attendance with hospital discharge before admission: 500 (166.67%)\n",
      "False positives (FDR): 280 (70.0%)\n"
     ]
    }
   ],
   "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: {round_to_5(len(ae_hosp_before))} ({round((round_to_5(len(ae_hosp_before))/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "\n",
    "\n",
    "# false posisitves\n",
    "fp_ae_hosp_discharge = round_to_5(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 = round_to_5(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",
    "tn_ae_hosp_discharge = round_to_5(len(df[\n",
    "    (df[\"ae_attendance_hosp_discharge_date\"].isnull()) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==0)\n",
    "\n",
    "]))\n",
    "\n",
    "fn_ae_hosp_discharge = round_to_5(len(df[\n",
    "    (df[\"ae_attendance_hosp_discharge_date\"].isnull()) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==1)\n",
    "\n",
    "]))\n",
    "\n",
    "discharge_destination = performance_metrics(tp_ae_hosp_discharge, fp_ae_hosp_discharge, tn_ae_hosp_discharge, fn_ae_hosp_discharge)\n",
    "print(f'False positives (FDR): {fp_ae_hosp_discharge} ({discharge_destination[4]}%)')"
   ]
  },
  {
   "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": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number with A&E attendance and covid diagnosis code before admission: 500 (166.67%)\n",
      "Number with A&E attendance and respiratory code before admission: 500 (166.67%)\n",
      "False positives (FDR) - A&E attendance and covid diagnosis code: 350 (70.0%)\n",
      "False positives (FDR) - A&E attendance and respiratory diagnosis code: 340 (68.0%)\n"
     ]
    }
   ],
   "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: {round_to_5(len(ae_cov_before))} ({round((round_to_5(len(ae_cov_before))/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "print(f'Number with A&E attendance and respiratory code before admission: {round_to_5(len(ae_resp_before))} ({round((round_to_5(len(ae_resp_before))/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "\n",
    "fp_ae_cov = round_to_5(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 = round_to_5(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 = round_to_5(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 = round_to_5(len(df[\n",
    "    (df[\"ae_resp_hosp_discharge\"]==1) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==1)\n",
    "    \n",
    "]))\n",
    "\n",
    "tn_ae_cov = round_to_5(len(df[\n",
    "    (df[\"ae_cov_hosp_discharge\"]==0) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==0)\n",
    "    \n",
    "]))\n",
    "\n",
    "tn_ae_resp = round_to_5(len(df[\n",
    "    (df[\"ae_resp_hosp_discharge\"]==0) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==0)\n",
    "    \n",
    "]))\n",
    "\n",
    "fn_ae_cov = round_to_5(len(df[\n",
    "    (df[\"ae_cov_hosp_discharge\"]==0) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==1)\n",
    "    \n",
    "]))\n",
    "\n",
    "fn_ae_resp = round_to_5(len(df[\n",
    "    (df[\"ae_resp_hosp_discharge\"]==0) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==1)\n",
    "    \n",
    "]))\n",
    "\n",
    "\n",
    "\n",
    "diagnosis_code_cov = performance_metrics(tp_ae_cov, fp_ae_cov, tn_ae_cov, fn_ae_cov)\n",
    "diagnosis_code_resp = performance_metrics(tp_ae_resp, fp_ae_resp, tn_ae_resp, fn_ae_resp)\n",
    "\n",
    "print(f'False positives (FDR) - A&E attendance and covid diagnosis code: {fp_ae_cov} ({diagnosis_code_cov[4]}%)')\n",
    "print(f'False positives (FDR) - A&E attendance and respiratory diagnosis code: {fp_ae_resp} ({diagnosis_code_resp[4]}%)')"
   ]
  },
  {
   "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": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number with A&E attendance and recent suspected covid code in primary care before admission: 500 (166.67%)\n",
      "Number with A&E attendance and recent probable covid code in primary care before admission: 500 (166.67%)\n",
      "False positives (FDR) - A&E attendance and probable covid code in primary care: 350 (70.0%)\n",
      "False positives (FDR) - A&E attendance and suspected covid code in primary care: 355 (71.0%)\n"
     ]
    }
   ],
   "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: {round_to_5(len(ae_prim_care_suspected_before))} ({round((round_to_5(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: {round_to_5(len(ae_prim_care_probable_before))} ({round((round_to_5(len(ae_prim_care_probable_before))/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "\n",
    "fp_ae_suspected = round_to_5(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 = round_to_5(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 = round_to_5(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 = round_to_5(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",
    "tn_ae_suspected = round_to_5(len(df[\n",
    "    (df[\"suspected_covid_primary_care_before_ae_hosp_discharge\"]==0) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==0)    \n",
    "]))\n",
    "\n",
    "tn_ae_probable = round_to_5(len(df[\n",
    "    (df[\"covid_primary_care_before_ae_hosp_discharge\"]==0) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==0)\n",
    "]))\n",
    "\n",
    "fn_ae_suspected = round_to_5(len(df[\n",
    "    (df[\"suspected_covid_primary_care_before_ae_hosp_discharge\"]==0) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==1)    \n",
    "]))\n",
    "\n",
    "fn_ae_probable = round_to_5(len(df[\n",
    "    (df[\"covid_primary_care_before_ae_hosp_discharge\"]==0) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==1)\n",
    "]))\n",
    "\n",
    "\n",
    "\n",
    "prim_care_suspected = performance_metrics(tp_ae_suspected, fp_ae_suspected, tn_ae_suspected, fn_ae_suspected)\n",
    "prim_care_probable = performance_metrics(tp_ae_probable, fp_ae_probable, tn_ae_probable, fn_ae_probable)\n",
    "\n",
    "print(f'False positives (FDR) - A&E attendance and probable covid code in primary care: {fp_ae_probable} ({prim_care_probable[4]}%)')\n",
    "print(f'False positives (FDR) - A&E attendance and suspected covid code in primary care: {fp_ae_suspected} ({prim_care_suspected[4]}%)')"
   ]
  },
  {
   "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": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number with A&E attendance and recent positive test (2 weeks) before admission: 500 (166.67%)\n",
      "Number with A&E attendance and recent positive test (4 weeks) before admissio: 500 (166.67%)\n",
      "False positives (FDR) - A&E attendance and recent positive test (2 weeks): 340 (48.57%)\n",
      "False positives (FDR) - A&E attendance and recent positive test (4 weeks): 355 (50.71%)\n"
     ]
    }
   ],
   "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: {round_to_5(len(ae_pos_test_before_2_weeks))} ({round((round_to_5(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: {round_to_5(len(ae_pos_test_before))} ({round((round_to_5(len(ae_pos_test_before))/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "\n",
    "fp_ae_test = round_to_5(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 = round_to_5(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 = round_to_5(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 = round_to_5(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",
    "tn_ae_test = round_to_5(len(df[\n",
    "    (df[\"pos_test_hosp_discharge\"]==0) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==0)\n",
    "    \n",
    "]))\n",
    "\n",
    "tn_ae_test_2_weeks = round_to_5(len(df[\n",
    "    (df[\"pos_test_2_weeks_hosp_discharge\"]==0) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==0)\n",
    "    \n",
    "]))\n",
    "\n",
    "fn_ae_test = round_to_5(len(df[\n",
    "    (df[\"pos_test_hosp_discharge\"]==0) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==1)\n",
    "    \n",
    "]))\n",
    "\n",
    "fn_ae_test_2_weeks = round_to_5(len(df[\n",
    "    (df[\"pos_test_2_weeks_hosp_discharge\"]==0) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==1)\n",
    "    \n",
    "]))\n",
    "\n",
    "positive_test_2_weeks = performance_metrics(tn_ae_test_2_weeks, fp_ae_test_2_weeks, tn_ae_test_2_weeks, fn_ae_test_2_weeks)\n",
    "positive_test_4_weeks = performance_metrics(tn_ae_test, fp_ae_test, tn_ae_test, fn_ae_test)\n",
    "\n",
    "print(f'False positives (FDR) - A&E attendance and recent positive test (2 weeks): {fp_ae_test_2_weeks} ({positive_test_2_weeks[4]}%)')\n",
    "print(f'False positives (FDR) - A&E attendance and recent positive test (4 weeks): {fp_ae_test} ({positive_test_4_weeks[4]}%)')"
   ]
  },
  {
   "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": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number with A&E attendance with either covid or respiratory diagnosis code and recent positive test (2 weeks) before admission: 375 (125.0%)\n",
      "False positives (FDR): 260 (66.67%)\n"
     ]
    }
   ],
   "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",
    "print(f'Number with A&E attendance with either covid or respiratory diagnosis code and recent positive test (2 weeks) before admission: {round_to_5(len(ae_hos_resp_cov_plus_test_2_weeks))} ({round((round_to_5(len(ae_hos_resp_cov_plus_test_2_weeks))/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "\n",
    "\n",
    "fp_ae_hos_resp_cov_plus_test_2_weeks = round_to_5(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 = round_to_5(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",
    "tn_ae_hos_resp_cov_plus_test_2_weeks = round_to_5(len(df[\n",
    "    (~\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",
    "    \n",
    "    )\n",
    "    &\n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==0)\n",
    "    \n",
    "]))\n",
    "\n",
    "\n",
    "fn_ae_hos_resp_cov_plus_test_2_weeks = round_to_5(len(df[\n",
    "    (~\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",
    "    \n",
    "    )\n",
    "    &\n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==1)\n",
    "    \n",
    "]))\n",
    "\n",
    "code_plus_test = performance_metrics(tp_ae_hos_resp_cov_plus_test_2_weeks, fp_ae_hos_resp_cov_plus_test_2_weeks, tn_ae_hos_resp_cov_plus_test_2_weeks, fn_ae_hos_resp_cov_plus_test_2_weeks)\n",
    "print(f'False positives (FDR): {fp_ae_hos_resp_cov_plus_test_2_weeks} ({code_plus_test[4]}%)')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Covid in primary care with recent positive test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number with A&E attendance with suspected or probable covid in primary care and recent positive test (2 weeks) before admission: 365 (121.67%)\n",
      "False positives (FDR): 255 (68.92%)\n"
     ]
    }
   ],
   "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",
    "print(f'Number with A&E attendance with suspected or probable covid in primary care and recent positive test (2 weeks) before admission: {round_to_5(len(ae_hosp_prim_care_before_plus_2_week_test))} ({round((round_to_5(len(ae_hosp_prim_care_before_plus_2_week_test))/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "\n",
    "\n",
    "fp_ae_hosp_prim_care_before_plus_2_week_test = round_to_5(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 = round_to_5(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",
    "tn_ae_hosp_prim_care_before_plus_2_week_test = round_to_5(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",
    "    )\n",
    "    &\n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==0)\n",
    "    \n",
    "]))\n",
    "\n",
    "fn_ae_hosp_prim_care_before_plus_2_week_test = round_to_5(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",
    "    )\n",
    "    &\n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==1)\n",
    "    \n",
    "]))\n",
    "\n",
    "\n",
    "primary_care_plus_test = performance_metrics(tp_ae_hosp_prim_care_before_plus_2_week_test, fp_ae_hosp_prim_care_before_plus_2_week_test, tn_ae_hosp_prim_care_before_plus_2_week_test, fn_ae_hosp_prim_care_before_plus_2_week_test)\n",
    "print(f'False positives (FDR): {fp_ae_hosp_prim_care_before_plus_2_week_test} ({primary_care_plus_test[4]}%)')\n"
   ]
  },
  {
   "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": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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: 460 (153.33%)\n",
      "False positives (FDR): 325 ([150, 325, 68.42, 5.8, 68.42]%)\n"
     ]
    }
   ],
   "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",
    "\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: {round_to_5(len(combined_before))} ({round((round_to_5(len(combined_before))/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "\n",
    "fp_combined_before = round_to_5(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 = round_to_5(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",
    "tn_combined_before = round_to_5(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",
    "    )\n",
    "    &\n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==0)\n",
    "    \n",
    "]))\n",
    "\n",
    "fn_combined_before = round_to_5(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",
    "    )\n",
    "    &\n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==1)\n",
    "    \n",
    "]))\n",
    "\n",
    "pos_test_prim_care_or_diagnosis_code = performance_metrics(tp_combined_before, fp_combined_before, tn_combined_before, fn_combined_before)\n",
    "print(f'False positives (FDR): {fp_combined_before} ({pos_test_prim_care_or_diagnosis_code}%)')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "identification_table_index = [\"tp\", \"fp\", \"sensitivity\", \"specificity\", \"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_probable,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/jun/identification_table_hosp_discharge.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAv8AAAMICAYAAABfG4o3AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAADGF0lEQVR4nOzdeZxW8/vH8dc907TNTIVEG5XlKltIIVsiy/eHpJC9LEmbEokkpWizlbVCCInKV/alrEX4ElGXdlpsKU010zQz9++PcyZ301Q37mlq5v30mEdzn/ucz7nOuW/3XOc61zl3JBqNIiIiIiIiJV9ScQcgIiIiIiI7hpJ/EREREZFSQsm/iIiIiEgpoeRfRERERKSUUPIvIiIiIlJKKPkXERERESklyhR3ACIiIiIisjkz+x+wJny4CHgMeADIAd529/5mlgQ8DDQENgBXu/v8bY2r5F9EREREZCdiZuWBiLs3i5n2NdAaWAi8ZmZHAHWB8u5+rJkdA9wDtNzW2Er+RURERER2Lg2Bimb2NkG+fgdQzt0XAJjZW8CpQHXgTQB3/9TMjtrewEr+RURKuY2/L9ypvuo9pWq94g5BREqOSCIHS+Tn5SHHnXkt0CFm0ih3HxX+vh4YDowBDgDeAFbHzJsB1AMqAX/GTM81szLunrO19Sr5FxERERGJR15uwoYKE/1RW3n6B2C+u0eBH8zsT2D3mOfTCQ4GKoa/50vaVuIPutuPiIiIiMjO5kqC/n3MrAZBkr/OzPYzswhwOvAR8Anwn3C+Y4BvtzewKv8iIiIiIvGI5u2oNT0OjDWzj4EowcFAHvAskExwt5/PzOxzoIWZTSdocWq/vYEj0ehO1eopIiI7mHr+RaQES2zP/4o5Cfu8TKneIKGxxUttPyIiIiIipYTafkRERERE4hDdcW0/RUbJv4iIiIhIPPJ2/eRfbT8iIiIiIqWEKv8iIiIiIvFQ24+IiIiISCmRwC/5Ki5q+xERERERKSVU+RcRERERiYfafkRERERESgnd7UdERERERHYVqvyLiIiIiMRBX/IlIiIiIlJaqO1HRERERER2Far8i4iIiIjEQ20/IiJSmpzfvgupqRUBqFV9b85veSaDH3iM5ORkmjY5kk5XXkJeXh53Dn+IH+YvJKVsCgN6d2efWjWKOXIRkQQoAV/ypeRfRHYYMzsYGApUBNKA14E73D36N8boDUx195kx08oDc929zjaWGw88CpQH9nH3Uf9oI/4hM2sH/OHurxTR+B2Bvd39jqIYH2DDhmyi0ShjHxy6aVrrKzpz36A+1K5ZnU433s6cH+azdPkvZGdn8+yo+5g1ew7DRo5m5JB+RRWWiIj8DUr+RWSHMLMqwHjgPHefZ2bJwIvAtQRJeVzcffC/icPd3/w3y/+L9Y4tjvUmks9fSFbWBq7pfiu5uXl0uuoSsjdu3FTVb3p0I2Z8/jW/r/yD445pBEDDQxrw3dx5xRm2iEjiqO1HRCRuLQkq9vMA3D3XzC4HsgHM7B7g+HDe54CHgTlAQ3dfZ2Y3ArlAQ4KDiI+BZ4HdgPmFrdDMOgNXAyuAauG0dkB9d+9tZncDRwF7ALPcvb2ZVQ3XXw5woLm7729m3wAfAIcBUaClu/9ZMG53f8DMzgNuBjYCy4G2wO3Az8BE4AWCGy6UBzq6+9cxMVcAngT2BcoCXYAvwmn1gGTgXnd/wcyOBx4AVgE5wKfhGF2Bi8M4x7v7iG2/NPEpX74c7S5uTeuzz2DJT8vo2LMvldLTNj2fWrECS5f/zNp160lPTd00PSk5iZycXMqUSU5EGCIixUd3+xERiVsNYGHsBHdf6+7ZZnYWUBc4hiCRvhioT5Aotw5nvxh4OmbxjsBsdz8ReKzgysxsL+D6cMyWBIl07POVgFXu3oLgAOAYM6sJ9AFedveTCM5M5BdJKgHPh9OXAWcWFreZHQpcBAxz9+OBV8Nl8zUBVgJnAp2BVDbXEVjs7scSHDQcTXB25Dd3bwqcCgwMD1IeAS5y91OBReF2HQRcGMZzAnCumVnB/fNP1Kldk7NOb04kEqHOPrVIT0vlzzUZm55ftz6T9LRU0lIrsm595qbp0bw8Jf4iIjsJJf8isqMsAWrHTjCzumZ2ItAA+Mjdo+6+kaCCfRAwBrjczJoA7u4rYxY/EJhJ8MRnBFX2WPsB37n7hnDMmQWezwSqmdnzBAcPaUBKGMv0cJ6PCizzVfjvTwRV+63FfQPQ3Mw+AJoCsaWiN4BPgP8CAwo8B2DAjHC75rn7/eF6PgynZQDfh9u3l7v/EC73SfjvIQRnDd4Lf/YADiABJr32NsNGjgbg199WkpW1gQrly/Pj0uVEo1Gmf/YljRoewhGHHsRHMz4HYNbsORywX91ErF5EpPhF8xL3U0yU/IvIjvIqcIaZ7QdgZinAvQTJ6hzC1plwelNgXtgiFAFuAkYXGO974NhwmSMIEvdY84CDzaxCeH3BEQWePxOo7e4XAbcCFcJ1zc4fl6CiH6vghcmFxg10ILiQ+aRwzFYxyzQDVrj7acBA4K5CxmwcjlnPzJ4Lp50QTksHDiWo9C8zswbhco3Dfx34DjjZ3ZsBY4FvSIDWZ51Oxtp1XHZdT268/W4G3NqD/r2vp3f/obS9+nrqH7gfhx1cn1NOakrZsmW55NobGDpiFL26dUjE6kVEil9eXuJ+iol6/kVkh3D3NWZ2BTDazJKAdGAK8Ii7R82smZnNIGjPmeDu/wsXfZygQj6twJCPAk+b2cfAXGBDgfX9ZmaDCar4vwHrCiw/E+hrZh8SJPULCVqTBgPPmNkFBP36Bc8oxK7j1cLiDtuHXjWzDGAtwYFP13CxWcB4M7uO4DN4QIFhHwOeCM8aJAPdCZL30eG2VgD6u/uvZnZtuA/WABkEbUyzzOw94GMzKxdu57KtbcPfkZKSwtA7bt5i+nOj79/scVJSEv16dd1iPhERKX6RaDTuO+yJiJR4ZvYfgv76z83sVOBWd29e3HEVpY2/L9yp/hCkVK1X3CGISMkRSeRgWbNeT9jnZfmG/0lobPFS5V9EZHOLCCrvOQSV927FHI+IiOwsdKtPEZGSxd3n8FfPv4iISImi5F9EREREJB4l4D7/Sv5FREREROJRAtp+dKtPEREREZFSQpV/EREREZF45OUWdwT/mpJ/EREREZF4qO1HRERERER2Far8i4iIiIjEQ3f7EREREREpJdT2IyIiIiIiuwpV/kVERERE4qG2HxERERGRUqIEJP9q+xERERERKSVU+RcRERERiUM0qi/5EhEREREpHUpA24+SfxERERGReJSAW30q+RcRKeUuatS9uEPYzPNf3l/cIWwhpWq94g5BRCQhlPyLiIiIiMRDbT8iIiIiIqVECWj70a0+RURERERKCVX+RURERETiobYfEREREZFSQm0/IiIiIiKyq1DlX0REREQkHmr7EREREREpJUpA8q+2HxERERGRUkKVfxERERGReJSAC36V/IuIiIiIxGMHt/2YWTXgS6AFUAF4FZgXPv2Iu79gZv2A/wNygO7uPnNbYyr5FxERERHZyZhZCvAYkBlOagTc6+73xMxzJHAScDRQG5gINN7WuEr+RURERETikcC2HzPrAHSImTTK3UfFPB4OPArcEj5uFCxmLQmq/92B44G33T0K/GhmZcxsT3f/bWvrVfIvIiIiIhKPBLb9hIn+qMKeM7N2wG/u/paZ5Sf/M4Ex7v6lmfUB+gGrgZUxi2YAlYGtJv+624+IiIiIyM7lSqCFmb0PHA48Dbzh7l+Gz08GjgDWAOkxy6UTHBBslSr/IiIiIiLx2EF3+3H3E/N/Dw8AOgL/NbOu4QW9pxBcCPwJMNTMhgO1gCR3/31bYyv5FxERERGJR/F+ydd1wEgz2wj8DHRw9zVm9hEwg6Cjp/P2BlHyLyIicUkuk0ynYd2oVqsaZcqmMHHkBL54N7ij3PEtT+TMdmfRp1UvANr3u5r6jQ8ia21wk4oh1wxifcb6Ionr/PZdSE2tCECt6ntzfsszGfzAYyQnJ9O0yZF0uvIS8vLyuHP4Q/wwfyEpZVMY0Ls7+9SqUSTxiIgkkrs3i3l4XCHP3wHcEe94Sv5FSjkzawZMAL4HokAlYCFwibtn/8ux9wZud/dOcc5/IrDa3b/5N+vdytg/u/veZtYbmLq9+yAXwfrvJ7hF249FNP544FF3f78oxgc4sVUzMlZlMLLHfaRVTmPYGw/wxbszqXtwPU65sAURIpvmrXfo/gy8rB8ZqzKKKhwANmzIJhqNMvbBoZumtb6iM/cN6kPtmtXpdOPtzPlhPkuX/0J2djbPjrqPWbPnMGzkaEYO6VeksYlICVS8lf+EUPIvIhAkw23zH5jZc8A5wEv/ZlB3/xmIK/EPXQmMBxKe/Odz98FFNfZ21tu9ONabSDNe+4QZr08PHkQi5OXmklYlnYt7XcaT/cfQcXCX8KkI1evUoOPgLlSuWoWpL7zD1AnvFklMPn8hWVkbuKb7reTm5tHpqkvI3rhxU1W/6dGNmPH51/y+8g+OO6YRAA0PacB3c+dta1gRkcJFo8Udwb+m5F9ENmNmZYHqwKrw8d3ACUAyQeX6RTPrBFwB5AGfu3s3MxsLRAi+ZCQNuBzIAsa7+zFmNhv4AcgGbgQeAcqH67oN+Ak4AzjSzL4P19kd2EBwP+MOwCUEBwhJwJ3A1e5+fhjnJ8D57r48fJxMcAu1g4EFQLlw+liCA4zpwBigClADeMjdHzGzJsBDBLdL+zXchjuA58MY9wNmuvt1ZlYFGEdwtqQMcJu7TzWzQcDJ4bSJ7j4k5oKtPYB7gI3AeqCNu28qj5vZAWFcZcPn2wKpwBPheFGgm7vPMrPOwNXACqBauHwKwX2hDwj3022JOhuQtT4LgPKpFbjx0ZsZf8+zdBralbF3Pk521l8nicpVLM8bT73KlNEvk5ScTP/xA1nwzXyWzF2ciDA2U758Odpd3JrWZ5/Bkp+W0bFnXyqlp216PrViBZYu/5m169aTnpq6aXpSchI5ObmUKZOc8JhERHZmutWniAA0N7P3w6T7f8Bkd3/PzM4E6rr78QTJbJ8w4W0PdHH3Y4E5ZpZfSFjg7s0JkuWhBdaRBtwZnmGoD9zj7i0IkvrO4e3L3gR6AeuA/kDzcN2rgWvDcVaF094GDjWz3czsYOD3/MQ/1Aoo7+7HEHxBSsUC8exPcGByGnAacEM4/VGgXbgdC2LmPxC4CmgC/CdsaboNeCe8K8P5wONmFiE4SLmY4ABmdYH1nkvQZnUSwQHQbgWeHw7cHe7bBwhu5TYceCBcz/XhevYKfz8GaElwsADBwcDv4bwtCQ5kEmaP6lXpP34gH056nxWLllO9bg06DLyOHiNvotYBtWl3+9VkZ27gtSemkJ2VTda6TL6d/i37NqiTyDA2qVO7Jmed3pxIJEKdfWqRnpbKn2v+ajVatz6T9LRU0lIrsm595qbp0bw8Jf4i8vfl5SXup5go+RcRCNp+mhEkq9nAonD6oUCjsGr9JpAC1CFI/jub2QfAvrCp2Xtq+O90wApZj4f/rgCuNbNnCKrhKQXmqwd8F1MR/5Cggr9pjPDbDMcBF4XxPF5gjAMJvhCFsM/+pwLP/wKca2bjCJL4/BhquPt34e8fxcw/390z3D03jL880CCMDXdfRnC/5WoEyf9g4C2CMwux7iI40/Ae0IbgDEAsI7hrA+7+iru/XWA9XxOcXdkv3Ecb3H1j/rYSvGb/CV+ziUAZM6tKAlSuWoW+4/ozbvBTTJ3wLvNnzaNHiy70a9uH+7oOY+m8nxg7YAzV69Vg0MQhJCUlkVwmmQaNG7Bw9oLtr+AfmPTa2wwbORqAX39bSVbWBiqUL8+PS5cTjUaZ/tmXNGp4CEccehAfzfgcgFmz53DAfnWLJB4RKeGU/ItISeLuK4FLgTFmVh2YC0wLDwyaE1SsFwDXAB3d/SSCynTTcIhG4b/HAd+xpfxPuzuBp939MmAafx085BF8Li0CDjKz/D6NkwhahmLHAHiSoOJ+IvB6gXV9DxwLYGY1gJoFnu8JzHD3S4EXY2L4ycwOCn8/Jmb+who95xAcMGFmNQmq+KvDmC4iOFvSzsz2jVnmUmCsu59MsI9iv9o9f8zG4ZiXmFnXAus5nOAWb/OAg82sQtjidES4/Fzg+fA1OzPctj8Kif1vO69zG1IrpdGm64X0Hz+I/uMHUbZc2S3mWzZ/KR9MnsZdLw9jwIS7+GDiNJbOK3jslRitzzqdjLXruOy6ntx4+90MuLUH/XtfT+/+Q2l79fXUP3A/Dju4Pqec1JSyZctyybU3MHTEKHp1K7jbRURKh0i0BFy4ICL/XHi3n44FLvjtQ/CNghcQ9Kc3JmjbmezuA8zsaoI2nAxgGcHBwKPA3gTtJ8lAO4KEOb/nfzFQ392zzOwigmr7SmAp0NDdDzazawnuUXwhQTLbnSDZn0/QztI2HKN3TKyvEFTA87/+PH96BHgQOApYAhzr7rVjev43ACPDGFYDhwAHAYeF09cSnAVZBgzK345w7E/DWNYQ9OLvDlQA+rr7m2Z2O/B/QCYwK9yOaQRnOSoTtPOsC7etg7vnn2nBzPYHHgv34XqCg4VKwGiC6xZSgK7u/oWZXQl0Jfga9/LA7QRnDUYTnJGpBDzs7qPZhjb7nrNT/SF4/sv7izuELaRUrVfcIYjIPxPZ/izxyxzXJ2GflxUuHZTQ2OKl5F9EEiI/qXb3N3fwel8Furv7/ASN1xmY4O6/mdlAINvdByRi7J2Vkv/tU/IvsstKbPL/9C2JS/4vv7tYkn/d7UdEdklmVgH4mOB6hYQk/qFfgLfNbC3wJ8FdjUREREoEJf8ikhDu3m4Hry+Tv64xSOS4L/Evv99ARERKqBLQMaPkX0REREQkHiXgG351tx8RERERkVJClX8RERERkXiUgMq/kn8RERERkXhEd/3kX20/IiIiIiKlhCr/IiIiIiJxiObpbj8iIiIiIqVDCej5V9uPiIiIiEgpocq/iIiIiEg8SsAFv0r+RURERETiUQJ6/tX2IyIiIiJSSqjyLyIiIiISjxJwwa+SfxERERGReJSA5F9tPyIiIiIipYQq/yIiIiIi8Yju+hf8KvkXESnl0iMpxR3CZq5o1LO4Q9jCU1/eU9whbCGlar3iDkGk9FHbj4iIiIiI7CpU+RcRERERiUcJuM+/kn8RERERkXiUgG/4VduPiIiIiEgpocq/iIiIiEg81PYjIiIiIlI6RHW3HxERERER2VWo8i8iIiIiEg+1/YiIiIiIlBIl4G4/Sv5FREREROJRAir/6vkXERERESklVPkXEREREYlHCbjbj5J/EREREZF4qO1HRERERER2Far8i4iIiIjEQ3f7EREREREpJdT2IyIiIiIiuwpV/kVERERE4hDV3X5ERKS0SC6TzJVDO1O11p6UKZvClAdf4o8VK+n++C38sngFANPGvcXMV6fTbfTNpO1WidycHLKzsrmv3aAii6nDsC7sWasaKWVTmDzyRVau+J2bnujDz4uCmN4d9yafvvoJ511/AUc0P4rc3Fye6f8EC2bNK5KYAM5v34XU1IoA1Kq+N+e3PJPBDzxGcnIyTZscSacrLyEvL487hz/ED/MXklI2hQG9u7NPrRpFFpOIJEAJaPtR8i8ixcbMmgETgO+BKFABeNbdR8a5/PtAR3efG8e8Y4Hx7v5mzLQ64bRj/m7sf5eZTXL38wpM6wjs7e53JGD8hI21Nce2OpG1qzMYfcMIUiun0f/14bwy4kXeGjOFt8ZM2WzeanWqc1uL7kUVyibHtzqJtasyeKTHA6RWTuPuN+5l0ogJvD7mFV4f/cqm+eocUo8GxxxC35a92KNGVbo/2ou+5/Qqkpg2bMgmGo0y9sGhm6a1vqIz9w3qQ+2a1el04+3M+WE+S5f/QnZ2Ns+Ouo9Zs+cwbORoRg7pVyQxiYjkU/IvIsVtqru3BTCzcoCb2TPuvrp4w0qsgon/rujz12bwxeszggcRyM3NY99D96N6vRoc0aIJvyxewfMDnqBshXJUrJTK9Y/fQsVKqbz+yGRmTf2ySGL69LXpfPb69CCkSIS83FzqHbIf1ferSaMWTfh58Qqe6f84dlQDvvnwawBWLv+d5DLJpO9eiYw/1iQ8Jp+/kKysDVzT/VZyc/PodNUlZG/cuKmq3/ToRsz4/Gt+X/kHxx3TCICGhzTgu7lFdyZCRBJElX8RkYRKB3KBnLCq/yuwO/B/wONAPSAZuNfdXwiXGWBmVYENwOXAH8BjQG2gOvCKu98WztvJzG4i+Oy7CsjJX7GZnQQMCte/ALjW3TfGPH8AMAYoC6wH2gKpwBPheFGgG7Av0Mrd24fL/Q84A/jG3fc2s+OBB4BV4fo/jd0BZlYBeDIcpyzQBfginLbZ9m9tLDPrClwcxjTe3UfEtfe3Y8P6LADKp5an8yM3MXn4c5Qpl8KH499lyeyFnNW5NS2vv4B3nnyNt0a/wjtPvkZqlTT6vHQXC2fNI2Nl4hPt2Jiuf/QmJgx/jpSyKUwb/w6LZi+kZZc2nNf9QtavWcfaVRmblstcm0nF9IpFkvyXL1+Odhe3pvXZZ7Dkp2V07NmXSulpm55PrViBpct/Zu269aSnpm6anpScRE5OLmXKJCc8JhFJkBJwq0/d7UdEiltzM3vfzKYCzwJd3X1t+Nzz7n4qcA3wm7s3BU4FBoYJP8Akd28OTAFuIUj6P3X304EmQMeYdU1391OAIcCmngwziwCjgfPc/SRgGdCuQJzDgbvd/ViChPuIcNoD7n4icD3BAcprwLFmlmpmjYGF7v5rzDiPABeF27WokP3REVgcrqctcDRw7Va2f4uxzOwg4ELgeOAE4Fwzs0LW84/sXn0Pej3fnxmTPuDTVz7my7c+Y8nshQD8763P2Ofguvz522qmPfs2ebl5ZKxcw5LvFrF3vZqJCqHQmG4bfycfT/qA6f/9iM/f+pRFYUxfvPUpdQ6uR2ZGJuVTK2xapkJaBdavWVck8dSpXZOzTm9OJBKhzj61SE9L5c81fx14rFufSXpaKmmpFVm3PnPT9GhenhJ/ESlySv5FpLhNdfdm7t7c3U9399djnvPw3wbAhwDunkFwjcB+4XMfhv9OB4yg8t/YzJ4F7gPKxYxXcN58exKcJZgQnnE4jaDyHsuAGWEMr7j72wXi+hqo7e65wEvAeUB7goOKWHu5+w/h758Usj9i1zPP3e/fxvYXNtYhYezvhT97AAcUsp6/rVLVyvR85nZeHDyOj16cCkDPp/tSt+H+ADQ47lAWf7uQg44/jM4P3whAuYrlqWm1WTF/aSJCKDSmW8bdwfODn+aDCe8B0PvpfuzXMNjkg487jEXfLuCHL+Zw2ElHEIlE2KNGVSKRCBkxZwISadJrbzNsZPCy//rbSrKyNlChfHl+XLqcaDTK9M++pFHDQzji0IP4aMbnAMyaPYcD9qtbJPGISALlRRP3U0zU9iMiO7P886tzCKrYk80sHTiUv6rmTYCXw+dnE1TsV7v7tWa2P9AhrOznzzs9Zt58vwNLgZbu/qeZnQOsZXNzgMbAu2Z2CUE7Un5cr5jZ4cDP4byPE7Qe7UHQthNrmZk1cPf88VZtZT3/NbN6wMCYmAtuf2FjOfAdcKa7R82sB/BNwR37T5zVuTWplVM5p1sbzunWBoDxd47lor7tyc3J4c/fVjP2lkfJWpvJIScezm2T7yaal8fEYc9t1nKTSOd2bkNqpVRadb2AVl0vAGDcwCe59Pb25G7M5c/fVjHmlkfIXJuJz/ye/pMHE0mKMLZvwWOyxGl91un0GXQvl13XkwgRBtzag6RIhN79h5Kbl0fTJkdy2MH1yWuQx/TPv+KSa2+AaJQ7+9xQZDGJSGJEd3DSbmbVgC+BFgTtnWMJWjpnA53dPc/M+hG0x+YA3d195rbGjESju/6FCyKyawrv9tMx/4LfAs+9Hz4318zKElTQ9yO4I9AId38qnGcxUAdYA1wB1ACeA/4kuA6gNnAKQT9/eaAawQfnlUCE8G4/ZnYacDvBGdE1wOWx7TrhgcRjBD3364FLgUphXOWAFIKWpS/C+d8GPnb3AeHjn8Oe/ybAQ+E6MoCvY+/QY2blCa4jqBmuqztB8l7Y9hc6Vnhdw7lhXDPDuHK39jq0r9N6p/pDsGEn7Kl96st7ijuELaRUrVfcIYjsCiLbnyV+Gd3PTtjnZfr9U7YZm5mlENwR72DgHIJ21Xvd/X0zexR4C1hC0IJ6CsHfu4nu3nhb4yr5FxEp5ZT8b5+Sf5FdVmKT/25nJS75H/Hq9pL/B4DXCa5n60jQylkrPKvbkqBF1YGK7j44XOYr4DR3/21r46rtR0REREQkHgn8hl8z6wB0iJk0yt1Hhc+1I7jRw1tmdkv4fMTd8w8+MoDKBGegV8aMkT9dyb+IiIiIyM4iTPRHbeXpK4GomZ0KHA48TdC2mi8dWE3Q9pleyPStUvIvIiIiIhKPHXTBb3gLaeCva+CAYWbWzN3fB84EpgHzgaFmNhyoBSS5++/bGlvJv4iIiIhIPIr3G357AqPDm2DMAV5y91wz+4jgFtFJQOftDaLkX0RERERkJ+XuzWIenlTI83cAd8Q7npJ/EREREZE4lIS7ZCr5FxERERGJR/G2/SREUnEHICIiIiIiO4Yq/yIiIiIi8SgBlX8l/yIiIiIicYiWgORfbT8iIiIiIqWEKv8iIiIiIvEoAZV/Jf8iIiIiIvHIK+4A/j21/YiIiIiIlBKq/IuIiIiIxKEkXPCr5F9EREREJB4lIPlX24+IiIiISCmhyr+IiOxUsskt7hC2cEWjnsUdwhae+vKe4g5hCylV6xV3CCJFqwRc8KvkX0REREQkDiWh519tPyIiIiIipYQq/yIiIiIi8VDbj4iIiIhI6aC2HxERERER2WWo8i8iIiIiEg+1/YiIiIiIlA7REpD8q+1HRERERKSUUOVfRERERCQeJaDyr+RfRERERCQOavsREREREZFdhir/IiIiIiLxKAGVfyX/IiIiIiJxUNuPiIiIiIjsMlT5FxERERGJQ0mo/Cv5FxERERGJQ0lI/tX2IyIiIiJSSqjyLyIiIiISj2ikuCP415T8i4hIXJLLJHPl0M5UrbUnZcqmMOXBl/hjxUq6P34LvyxeAcC0cW8x89XpdBt9M2m7VSI3J4fsrGzuazeoyGLqNKwb1WpVo0zZFCaOnMAX784E4PiWJ3Jmu7Po06oXAO37XU39xgeRtTYTgCHXDGJ9xvqEx9NhWBf2rFWNlLIpTB75IitX/M5NT/Th50XBPnp33Jt8+uonnHf9BRzR/Chyc3N5pv8TLJg1L6GxxDq/fRdSUysCUKv63pzf8kwGP/AYycnJNG1yJJ2uvIS8vDzuHP4QP8xfSErZFAb07s4+tWoUWUwiu6KS0Paj5F9EipSZ9QZOBVII7pB8o7t/uYNjaAV85u7L45j3DKCtu7dL4Pp7A1PdfWbMtPLAXHevk4DxEzbWthzb6kTWrs5g9A0jSK2cRv/Xh/PKiBd5a8wU3hozZbN5q9Wpzm0tuhdlOACc2KoZGasyGNnjPtIqpzHsjQf44t2Z1D24Hqdc2IIIf1Xp6h26PwMv60fGqowii+f4ViexdlUGj/R4gNTKadz9xr1MGjGB18e8wuujX9k0X51D6tHgmEPo27IXe9SoSvdHe9H3nF5FEtOGDdlEo1HGPjh007TWV3TmvkF9qF2zOp1uvJ05P8xn6fJfyM7O5tlR9zFr9hyGjRzNyCH9iiQmkV1VNE+VfxGRrTKzg4BzgOPcPWpmhwNPAQ13cCjXAx2B7Sb/RcHdBxfHehPt89dm8MXrM4IHEcjNzWPfQ/ejer0aHNGiCb8sXsHzA56gbIVyVKyUyvWP30LFSqm8/shkZk0tmuO9Ga99wozXp4cxRcjLzSWtSjoX97qMJ/uPoePgLuFTEarXqUHHwV2oXLUKU194h6kT3k14PJ++Np3PwngiYTz1DtmP6vvVpFGLJvy8eAXP9H8cO6oB33z4NQArl/9Ocplk0nevRMYfaxIek89fSFbWBq7pfiu5uXl0uuoSsjdu3FTVb3p0I2Z8/jW/r/yD445pBEDDQxrw3dyiOxMhIsVHyb+IFKU/gX2AK83sTXf/2syaAJjZ+0BHd59rZh2BvYHBwASgMlAR6OPub5vZQuAzYD9gNnA1kA48DuwRrqubu39rZlcB1wHJwCvATOBw4GkzOx64FrgYiALj3X2EmTUAngDWhT+rYjfCzCLASKAJUBbo5+7/NbN7gOPD2Z4DHgbmAA3dfZ2Z3QjkEhzsjAc+Bp4FdgPmF7bDCsbv7v3M7BKgO7ABmAd0AMoVNpaZHQqMACLASuBKd/+z0Ffnb9qwPguA8qnl6fzITUwe/hxlyqXw4fh3WTJ7IWd1bk3L6y/gnSdf463Rr/DOk6+RWiWNPi/dxcJZ88hYmfjENmtTTBW48dGbGX/Ps3Qa2pWxdz5Odlb2pvnKVSzPG0+9ypTRL5OUnEz/8QNZ8M18lsxdnNB4YvfR9Y/exIThz5FSNoVp499h0eyFtOzShvO6X8j6NetYG3MGInNtJhXTKxZJ8l++fDnaXdya1mefwZKfltGxZ18qpadtej61YgWWLv+ZtevWk56auml6UnISOTm5lCmTnPCYRHZVJaHtR3f7EZEi4+7LCCv/wAwzmwuctY1F9gOqAmcDF/FXgaIW0NfdmwBpwLnArcB77n4yQTL8iJlVA3oDJwBHEiTIHwBfA5cD+wMXEiTsJwDnmpkBw4Db3f1UYHohcZ0LVA3XfzJwlJmdBdQFjgnHuxioD0wEWofLXQw8HTNOR2C2u58IPFZwJYXFb2b7Av2B5u5+PLCa4ABma2ONBjq7ezPgdSChvSS7V9+DXs/3Z8akD/j0lY/58q3PWDJ7IQD/e+sz9jm4Ln/+tpppz75NXm4eGSvXsOS7Rexdr2Yiw9jMHtWr0n/8QD6c9D4rFi2net0adBh4HT1G3kStA2rT7varyc7cwGtPTCE7K5usdZl8O/1b9m1Qp0ji2b36Htw2/k4+nvQB0//7EZ+/9SmLwn30xVufUufgemRmZFI+tcKmZSqkVWD9mnVFEk+d2jU56/TmRCIR6uxTi/S0VP5c89eBx7r1maSnpZKWWpF16zM3TY/m5SnxFykgGo0k7Ke4KPkXkSJjZvsDa9z9SnffB7gUeNTMdi8wawTA3b8jSGSfJ6ii539G/eju+dXt6YABhxKcUXifIOHdHahHkBBnunvU3Xu7+9qY9RwC7Au8F/7sARwAHEhwhgDgk8I2BZgRxrjK3fsCDYCPwvVsBD4FDgLGAJeHZzjc3VfGjLNpPe7+GbCxwHq2iB+oBnzn7vnZ2ofAwdsYqwHwcLhfrgQSlnVXqlqZns/czouDx/HRi1MB6Pl0X+o23D9Y8XGHsvjbhRx0/GF0fvhGIKi417TarJi/NFFhbKZy1Sr0HdefcYOfYuqEd5k/ax49WnShX9s+3Nd1GEvn/cTYAWOoXq8GgyYOISkpieQyyTRo3ICFsxckPJ5KVStzy7g7eH7w03ww4T0Aej/dj/0aHgDAwccdxqJvF/DDF3M47KQjiEQi7FGjKpFIpMiuRZj02tsMGzkagF9/W0lW1gYqlC/Pj0uXE41Gmf7ZlzRqeAhHHHoQH834HIBZs+dwwH51iyQeESleavsRkaJ0GNDBzM5x92zgB4LKdS6QBVQH5hJUuZeFLSvp7v5/ZladINF/FahpZnu7+88EZxGeAfYExrn7c2HF/GpgAVDfzMq5+wYze4mg3z+P4EDCge+AM8NrEHoA3wDfA8cCbwKNC9mOOcD5AGZWmaA1aSTQHrjPzFKApsBT7j4vbBO6CXikwDj56/mvmR1BcBF0rMLi7wkcZGap7r4OOCncj9GtjOXA5e7+o5kdF+7jhDirc2tSK6dyTrc2nNOtDQDj7xzLRX3bk5uTw5+/rWbsLY+StTaTQ048nNsm3000L4+Jw57brMUlkc7r3IbUSmm06XohbbpeCMCgK/qTvSF7s/mWzV/KB5OncdfLw8jNyeGDidNYOu+nhMdzbuc2pFZKpVXXC2jV9QIAxg18kktvb0/uxlz+/G0VY255hMy1mfjM7+k/eTCRpAhj+45OeCz5Wp91On0G3ctl1/UkQoQBt/YgKRKhd/+h5Obl0bTJkRx2cH3yGuQx/fOvuOTaGyAa5c4+NxRZTCK7qpLQ9hOJRqPFHYOIlGBm1ge4AFhLkIAPcfeXzew/wD3Aj8Cy8N/BwDiCancS8Ji7P2Nmq4F3gdoEFfbuBJX+x4EqQCXgDnd/xczaEbTERIEp7n6XmQ0EzgBOA64iaOMpR1A57wrUIbgQeQPwG5AVe7efMJkfARxBUDTp7+5vmNlwgoORssAEdx8Szn8xMAA4IDzIGEvQ8/8+QRtQDYKDnhPc3Qrsr8Livzjc5jyC/v6rw9m3GMvMGoX7tUw4xlXu/sO2XqP2dVrvVH8IMqIFT4gUv7LsfO0vT315T3GHsIWUqvWKOwSRghLaX/NT41MS9nlZ+/P3iqX3R8m/iOz0zOxnd9+7uOMoqZT8b5+S//go+ZedkJL/AtT2IyIiIiISh5JQM1fyLyI7PVX9RURkZ1ASvuRLd/sRERERESklVPkXEREREYlDSaj8K/kXEREREYlDSej5V9uPiIiIiEgpocq/iIiIiEgc1PYjIiIiIlJKRKO7fvKvth8RERERkVJClX8RERERkThE84o7gn9Pyb+IiIiISBzySkDbj5J/EREREZGdiJklA6MBA6JARyAFeBWYF872iLu/YGb9gP8DcoDu7j5zW2Mr+RcRERERicMOvOD3bAB3P87MmgGDgCnAve5+T/5MZnYkcBJwNFAbmAg03tbASv5FREREROKwo2716e4vm9mr4cN9gdVAI8DMrCVB9b87cDzwtrtHgR/NrIyZ7enuv21tbCX/IiIiIiI7mJl1ADrETBrl7qPyH7h7jpk9BbQC2gA1gTHu/qWZ9QH6ERwUrIwZIwOoDCj5FxERERH5N6LRxI3lP/goYNQ253G/wsxuBj4Dmrr7svCpycBI4L9Aeswi6QQHBFul+/yLiIiIiMQhmhdJ2M+2mNllZnZL+HA9kAdMMrMm4bRTgC+BT4DTzSzJzPYBktz9922Nrcq/iEgplxHdWNwh7PSyyS3uELZw3pHdijuELUz634jiDmEzKVXrFXcIIv/UJOBJM/uQ4C4/3YGfgJFmthH4Gejg7mvM7CNgBkFRv/P2BlbyLyIiIiIShx11n393XwdcUMhTxxUy7x3AHfGOreRfRERERCQOO/BWn0VGPf8iIiIiIqWEKv8iIiIiInFI5N1+iouSfxERERGROOyonv+ipLYfEREREZFSQpV/EREREZE4lIQLfpX8i4iIiIjEoST0/KvtR0RERESklFDlX0REREQkDiXhgl8l/yIiIiIicSgJPf9q+xERERERKSVU+RcRERERiYPafkRERERESokScLMftf2IiIiIiJQWqvyLiIiIiMRBbT8iIiIiIqWE7vYjIiIiIiK7DFX+RUQkLsllkuk0rBvValWjTNkUJo6cwBfvzgTg+JYncma7s+jTqhcA7ftdTf3GB5G1NhOAIdcMYn3G+hIf084WT35M1w+/nmq19iKlbAovjBzPzHeCmE5qeRJntTubm1rdCMBpF53OGZecQW5OLhNGvsDn732e8Hjynd++C6mpFQGoVX1vzm95JoMfeIzk5GSaNjmSTldeQl5eHncOf4gf5i8kpWwKA3p3Z59aNYosJpHtySvuABJAyb9ICWNmzYAJwPcENyaoBCwELnH37CJa5z5AQ3efkuBx7wfuBdYCZ7j7c2bWG5jq7jP/4ZgdgCfdfePfWGar22dmDwDDCM6kPkHwuRoBOri7/5MY44jnZ3ffO475+gMvuPv3iVjvia2akbEqg5E97iOtchrD3niAL96dSd2D63HKhS2I8Nfp8HqH7s/Ay/qRsSojEaveZWLa2eIBaNbqZNasyuDe7veSVjmNEW+OYOY7M6l3cD1atD2N/JCq7FmFs9ufTY+zulO2XFmGTBzKVx99RU52TsJj2rAhm2g0ytgHh26a1vqKztw3qA+1a1an0423M+eH+Sxd/gvZ2dk8O+o+Zs2ew7CRoxk5pF/C4xGJVxS1/YjIzmmquzdz95PdvRGwETinCNfXHDgu0YO6e3d3/xE4jDB+dx/8TxP/0K1A8t9cptDtM7NjgBx3XwrcCTzo7s2Au4C7/0WMiXIfMDxRg8147RPG3/Ns8CASIS83l7Qq6Vzc6zKe7D9m03yRSITqdWrQcXAXBk4cQvMLTk1UCDt9TDtbPACfvPYxzw4ft2m9ubl5pFdJ5/JelzP6jlGb5jvw8AOZ88UccrJzWJ+xnhWLV1C3ft0iicnnLyQrawPXdL+VK7v25ouvvyV740b2qVWDSCRC06MbMePzr/nqm+847phGADQ8pAHfzZ1XJPGIlCaq/IuUcGZWFqgOrAof3w2cQJAA3+vuL5rZ0cD9BAWBZcAlwP7ACIK64ErgSuAI4GYgG6gHjAcGA72BimY23d1fiVn3QuAzYD9gNnA1wZmIceG/ZYDb3H2qmQ0CTg6nTXT3IWb2PtAR6AM0DKv2TcP1dgAecPcPzOwooC/QBngUOCDcltvc/f2YeK4C9g6XP3cr+6ITcAXB2d3PgR5b2z6gG3BP+HtP4M/w9zJAVoHXYTIwyN2/MLO5wK3uPsnM3gbah9t1A5ALfOzuvc2sMvA4sEf++tz925gx7wIqA10IzjrsD1QI98sz7r7azDLN7DB3/4Z/KWt9sEnlUytw46M3M/6eZ+k0tCtj73yc7Ky/TiqVq1ieN556lSmjXyYpOZn+4wey4Jv5LJm7+N+GsNPHtLPFExtThdQK9H70FsYNH0e3Yd0Yc+eYzWKqmFaRdRnrNj3OXJdJxUoVEx4PQPny5Wh3cWtan30GS35aRseefamUnrbp+dSKFVi6/GfWrltPemrqpulJyUnk5ORSpszfPX4XSYy8EnCjf1X+RUqm5mb2vpl9D/wPmOzu75nZmUBddz+eINHuY2ZVgMeAK939aOA1oAEwGugcVrJfB3qFY+8LtAaOAXq5ey7BAcBzBRJjgFpAX3dvAqQB5wK3Ae+4+4nA+cDjZhYhOOC4mCAZX11gnEEEZzNGxUwbTZCkQ5A8jyY4uPg9HLsl8FDsIO7+OPAz0HYb+6I90MXdjwXmEBz8bG37TgK+Dcf+3d03mpkRVNv7F5h3MnCmmdUFNgCnhsl9eSAznP+UMJ6aZtaC4CzFe+5+MsHBziP5g5nZcKCMu3cO9+2JwHnAGQQHEPm+AZqRIHtUr0r/8QP5cNL7rFi0nOp1a9Bh4HX0GHkTtQ6oTbvbryY7cwOvPTGF7KxsstZl8u30b9m3QZ1EhbDTx7SzxQNQtXpVBr1wF9MmTWP5ouXUqFuDToM6c9ODvdjngH24ut81rF+7noqpFTYtUyG1AuvWrNvGqP9cndo1Oev05kQiEersU4v0tFT+XPNX+9O69Zmkp6WSllqRdeszN02P5uUp8ZdilUckYT/FRZV/kZJpqru3NbM9gHeAReH0Q4FGYUUdIAWoA+zt7nNgU4KMmTUAHg5yWVKA/PPt37p7DpBjZn/9VS7cj+4+P/x9OmAEBxbPhutaZmZrgGoEyf9ggsr8G3Fs41vAMDPbneCAoRswEjghPJMBUMbMqrr774Usv7V90R64MUzSZ8A2P6GTY6+jMLOTgYeBywrp958C/Bf4HRhCUOU/M5y+P7An8Hq4v9MJzpYcSnAgd2E4xu7hv3sRtELNB3D3DDPrDozirzMr+VYANbexDXGrXLUKfcf15/HbH+PbT4ITCT1adAFgz1rV6DHyJsYOGEPN/Wtxw4O9uOk/3YkkRWjQuAHvv/ReIkLY6WPa2eIBqFK1CgPG3cmjtz/KN5/MAqDzqZ0BqFarGjc92Isx/UdTZc8qXHbT5aSUSyGlbAq196/FEl9SJDFNeu1t5i1YTN8bu/DrbyvJytpAhfLl+XHpcmrXrM70z77kuisv4Zdff+f9Tz7jjFNOZNbsORywX9G0IYmUJkr+RUowd19pZpcC08zscGAuMM3dO5hZEkGrzAJguZkd4O7zzOxm4AfAgcvd/UczO46gdQgK/3bzPAo/k1jTzPZ2958JeuafIWhhOQH4ysxqArsRVPrPBy4Kl/vezMZva3x3zzOzFwmq4S+7e27YTrPU3e8yswoE7UJ/bCXWre2LgUBHd88ys7cI2nG2tn2ZZpYcrvtk4AGCC5O3yJjcfZWZrQcuJKjQtwGuBy4F1gA/AS3CswftgK+BA4Fx4YXO1QjObAD8ApwOvG9mZwCzgEbu3srMygM/mdkz4UHabsCvhcT+t53XuQ2pldJo0/VC2nQNjkcGXdGf7A2bX0e+bP5SPpg8jbteHkZuTg4fTJzG0nk/JSKEnT6mnS0egPO7XEBa5TTadmtL225tAbjj8n5bxLT6t9VMeXIKQ14aQiQpiWeGPcPGDXFfF/+3tD7rdPoMupfLrutJhAgDbu1BUiRC7/5Dyc3Lo2mTIzns4PrkNchj+udfccm1N0A0yp19biiSeETiVRIu+I1EoyWgeUlENgnv9tPR3dvGTOsDHA5cQNCj3pigVWSyuw8ws8YEd9XJI6gUXwEcEs5bhiDhvwqoETt2/l1nzOwIgj76fu4+Pma9q4F3gdrAp0B3gmT0CYIqdgWCtqA3zex24P8IWmBmhfNOI+j5zwjHeSzcjvHhMrUJ7mR0gLsvNrNyBO0/+xJUwB9299EF9s9T4fMnb2VfXA1cG65zGXANwdmKwrZvHEF//edmNgsoR9BWBODufm2BdV8HtHf3JmZ2LdDJ3RuGz10KdCK4/mAxwRmICgQ9/1XC7bnD3V+J2e/7A28CRxO0Rh1G0PLzqrsPCcedRHB9wVy2os2+5+gPwS5oQ3Tnu+ngpP+NKO4QNpNStV5xhyDFL6HZ+jt7XZiwz8sWv7xQLEcSSv5FpMjEe0vKXZWZHQu0dffrizuWwoQtUU+5+9nbmk/J/65Jyf/2KfkXlPxvQRf8ioj8Q+4+g+C6glrFHctW9CC4aFhERBIgSiRhP8VFPf8iUmRKctU/X3i3nZ2Su/ct7hhEREqSne9829+n5F9EREREJA4lIflX24+IiIiISCmhyr+IiIiISBxKwq0+lfyLiIiIiMQhb9fP/dX2IyIiIiJSWqjyLyIiIiIShzy1/YiIiIiIlA4l4RsR1fYjIiIiIlJKqPIvIiIiIhKHknCffyX/IiIiIiJxyIvs+j3/avsRERERESklVPkXEREREYlDSbjgV8m/iIiIiEgcSkLPv9p+RERERERKCVX+RURERETikLfrX++r5F9EpLQrS3Jxh7DTW5mXVdwhbCE9KaW4Q9hCh6NuKu4QNjPqi2HFHcIWUqrWK+4Q5F8oCd/wq7YfEREREZFSQpV/EREREZE46G4/IiIiIiKlREno+Vfbj4iIiIhIKaHKv4iIiIhIHErCff6V/IuIiIiIxEE9/yIiIiIiklBmlgyMBozgmKMjkAWMDR/PBjq7e56Z9QP+D8gBurv7zG2NrZ5/EREREZE45EUS97MdZwO4+3HAbcAg4F7gNnc/AYgALc3sSOAk4GigLfDQ9gZW8i8iIiIiEoe8BP5si7u/DHQIH+4LrAYaAR+E094ATgWOB95296i7/wiUMbM9tzW22n5ERERERHYwM+vAXwk+wCh3H5X/wN1zzOwpoBXQBmjh7vmXHWQAlYFKwMqYMfKn/7a19Sr5FxERERGJQyLv9hMm+qO2M88VZnYz8BlQIeapdIKzAWvC3wtO3yq1/YiIiIiIxCEaSdzPtpjZZWZ2S/hwPcFxxxdm1iycdibwEfAJcLqZJZnZPkCSu/++rbFV+RcRERER2blMAp40sw+BFKA7MAcYbWZlw99fcvdcM/sImEFQ1O+8vYGV/IuIiIiIxGFHfcmXu68DLijkqZMKmfcO4I54x1byLyIiIiISh5LwDb/q+RcRERERKSVU+RcRERERiUN0+7Ps9JT8i4hIXJLLJNNhWBf2rFWNlLIpTB75IitX/M5NT/Th50UrAHh33Jt8+uonnHf9BRzR/Chyc3N5pv8TLJg1r1TElFwmmRuG92Dv2nuRUjaF50Y8z/LFK7h+cDciEVi2eDn33XQ/ebl5dLzjWg5pfDDr12UCcMdV/Vmfsb5IYuo0rBvValWjTNkUJo6cwBfvzgTg+JYncma7s+jTqhcA7ftdTf3GB5G1NohpyDWDiiymK4d2pmqtPSlTNoUpD77EHytW0v3xW/hlcfC6TRv3FjNfnU630TeTtlslcnNyyM7K5r52gxIeT77z23chNbUiALWq7835Lc9k8AOPkZycTNMmR9LpykvIy8vjzuEP8cP8haSUTWFA7+7sU6tGkcUkO5c4vpl3p6fkX6SECW8DNgH4nqBIUQlYCFzi7tlFtM59gIbuPiXB495P8HXma4Ez3P05M+sNTHX3mf9wzA7Ak+6+8W8ss9XtM7MHgGHuvjR8fBIwzt1r/5P44oznZ3ffO475+gMvuPv3iVjv8a1OYu2qDB7p8QCpldO4+417mTRiAq+PeYXXR7+yab46h9SjwTGH0LdlL/aoUZXuj/ai7zm9EhHCTh/TKec1J2NVBsO6Dye9ShoPv/kQ82fP58mhY5n92Wx63nsDx7Q4hulvTueAQw/g1ktvY82qNQmPI9aJrZqRsSqDkT3uI61yGsPeeIAv3p1J3YPrccqFLYjwVzZT79D9GXhZPzJWZRRpTMe2OpG1qzMYfcMIUiun0f/14bwy4kXeGjOFt8Zs/r9ZtTrVua1F9yKNB2DDhmyi0ShjHxy6aVrrKzpz36A+1K5ZnU433s6cH+azdPkvZGdn8+yo+5g1ew7DRo5m5JB+RR6fSKIo+Rcpmaa6e9v8B2b2HHAO8FIRra85UB9IaPLv7t1h0wHNOcBz7j74Xw57K/A0EHfyz1a2z8yOAXJiEv/awA0Et2XbGdwHPAf8JxGDffradD57fToAkUiEvNxc6h2yH9X3q0mjFk34efEKnun/OHZUA7758GsAVi7/neQyyaTvXomMPxKf5O5sMX346kd89NrH4aMIuTm53NlhEHl5eZRJKcPue+7GujXriEQi1Kxbg+uHdGO3qlV484W3efuFtxMaS74Zr33CjHAfEe6jtCrpXNzrMp7sP4aOg7uET0WoXqcGHQd3oXLVKkx94R2mTni3SGL6/LUZfPH6jDAmyM3NY99D96N6vRoc0aIJvyxewfMDnqBshXJUrJTK9Y/fQsVKqbz+yGRmTf2ySGLy+QvJytrANd1vJTc3j05XXUL2xo2bqvpNj27EjM+/5veVf3DcMY0AaHhIA76bWzRntWTnVBIu+FXyL1LChfcDrg6sCh/fDZwAJAP3uvuLZnY0cD/BTQCWAZcA+wMjgAjBV4dfCRwB3AxkA/WA8cBgoDdQ0cymu/srMeteSPCthPsBs4GrCc5EjAv/LQPc5u5TzWwQcHI4baK7DzGz94GOQB+gYVi1bxqutwPwgLt/YGZHAX0Jvv78UeCAcFtuc/f3Y+K5Ctg7XP7creyLTsAVBJ/xnwM9trZ9QDfgnnDs8uG6OwBbZCdmNhkY5O5fmNlc4FZ3n2RmbwPtw+26AcgFPnb33mZWGXgc2CN/fe7+bcyYdxF8jXsX4InwNasQ7pdn3H21mWWa2WHu/k3BmP6uDeuzACifWp7rH72JCcOfI6VsCtPGv8Oi2Qtp2aUN53W/kPVr1rE2pnKcuTaTiukViyT539liygrjqZBagb6P9eGp4U+Tl5dHtZrVGPz8XazLWMfC7xdSvmJ5/jv2FSaNmkxSchJDJwxm3qwfWDR3cULjiY2pfGoFbnz0Zsbf8yydhnZl7J2Pk53118nAchXL88ZTrzJl9MskJSfTf/xAFnwznyVFEFPs69b5kZuYPPw5ypRL4cPx77Jk9kLO6tyaltdfwDtPvsZbo1/hnSdfI7VKGn1euouFs+aRsTLx76Xy5cvR7uLWtD77DJb8tIyOPftSKT1t0/OpFSuwdPnPrF23nvTU1E3Tk5KTyMnJpUyZ5ITHJDufkpD8624/IiVTczN738y+B/4HTHb398zsTKCuux9PkGj3MbMqwGPAle5+NPAa0AAYDXR292bA60B+j8S+QGvgGKCXu+cSHAA8VyAxBqgF9HX3JkAacC5wG/COu58InA88bmYRggOOiwmS8dUFxhlEcDYj9mvQRxMk6RAkz6MJDi5+D8duCTwUO4i7Pw78DLTdxr5oD3Rx92MJvkQlso3tOwnIT8YfBIa7+zIKNxk408zqAhuAU8PkvjyQCfQHTgnjqWlmLQjOUrzn7icTHFQ8kj+YmQ0Hyrh753DfngicB5xBcACR7xug2VZi+tt2r74Ht42/k48nfcD0/37E5299yqLZCwH44q1PqXNwPTIzMimf+te30FdIq8D6NesSFcJOH9Oe1asydMJg3p00lWkvvw/Ar8t+5coTr+a1Z17n2ts7sCFzAy8//l82ZG0gc10mX38yi3oH1SuSeAD2qF6V/uMH8uGk91mxaDnV69agw8Dr6DHyJmodUJt2t19NduYGXntiCtlZ2WSty+Tb6d+yb4M6RRbT7tX3oNfz/Zkx6QM+feVjvnzrM5aEr9v/3vqMfQ6uy5+/rWbas2+Tl5tHxso1LPluEXvXq1kk8dSpXZOzTm9OJBKhzj61SE9L5c81fx0wrlufSXpaKmmpFVm3PnPT9GhenhJ/2aUo+RcpmaaGSfsJBFX6ReH0Q4FGYUX9TYL2lDrA3u4+B4IE2d3/R3AA8HA475VA/l/cb909J/wCkr/+AhbuR3efH/4+HbBw3A/DdS0D1gDVCJL/wcBbQJU4tvEtoImZ7R5u5xvh9v0njHkiUMbMqm5l+a3ti/ZAZzP7gOBAZ1uXdyW7e7aZ1Qhj6BeOt7uZjS8w7xSgBUFyPgRoQvD17FMIKvZ7Aq+Hyx9EcLbkUODKcNpoYPdwrL2AwwiSftw9g+DbH0cBLwDlYta7gr/OHPwrlapW5pZxd/D84Kf5YMJ7APR+uh/7NTwAgIOPO4xF3y7ghy/mcNhJRxCJRNijRlUikUiR9ZDvbDFVqVqFu54dxON3PbmpjeeOJ/pRo07QOpK5LpNoNI+a9Wpy7+R7SEpKIrlMMoc0Ppj5s+dva+h/rHLVKvQd159xg59i6oR3mT9rHj1adKFf2z7c13UYS+f9xNgBY6herwaDJg7ZFFODxg1YOHtBkcRUqWplej5zOy8OHsdHL04FoOfTfanbcH8AGhx3KIu/XchBxx9G54dvBIIzEzWtNivmLy2SmCa99jbDRo4G4NffVpKVtYEK5cvz49LlRKNRpn/2JY0aHsIRhx7ERzM+B2DW7DkcsF/dIolHdk7RBP4UF7X9iJRg7r7SzC4FppnZ4cBcYJq7dzCzJIJWmQXAcjM7wN3nmdnNwA+AA5e7+49mdhxB6xAU/pmVR+HFhJpmtre7/wwcBzxDkIieAHxlZjWB3Qgq/ecDF4XLfV8ged5ifHfPM7MXCarhL4dfcT4XWOrud5lZBYJ2oT+2EuvW9sVAoKO7Z5nZWwTtOFvbvkwzS3b35QQHNsCmC3Lbxs7o7qvMbD1wIUGFvg1wPXApwQHQT0ALd99oZu2Ar4EDCS4efs7MqhGc2QD4BTgdeN/MzgBmAY3cvVXYfvSTmT3j7jnh/v21kNj/tnM7tyG1Uiqtul5Aq67BF0+OG/gkl97entyNufz52yrG3PIImWsz8Znf03/yYCJJEcb2HZ2I1e8SMbXtciFpldO4+PqLuPj64O08duhT3HjvDeRszCErcwP397qfP35dxXsT3+P+V+4jd2MO7058jyU//FgkMZ3XuQ2pldJo0/VC2nS9EIBBV/Qne8Pm1/8vm7+UDyZP466Xh5Gbk8MHE6exdN5PRRLTWZ1bk1o5lXO6teGcbm0AGH/nWC7q257cnBz+/G01Y295lKy1mRxy4uHcNvluonl5TBz23GbtW4nU+qzT6TPoXi67ricRIgy4tQdJkQi9+w8lNy+Ppk2O5LCD65PXII/pn3/FJdfeANEod/a5oUjikZ1TSbjbTyQaLQl3LBWRfOHFsR0LXPDbBzic4KvC7wEaE1SNJ7v7ADNrTHBXnTyCSvEVwCHhvGUIEv6rgBqxY+ffdcbMjiDoo+/n7uNj1rsaeBeoDXxKUJ3ejaA/fXeC/vS+7v6mmd0O/B/B2YRZ4bzTCHr+M8JxHgu3Y3y4TG2COxkd4O6LzawcQYV8X4JrCh52982yPDN7Knz+5K3si6uBa8N1LgOuIThbUdj2jSPor/+8wDoKvRuPmV0HtHf3JmZ2LdDJ3RuGz10KdCK4/mAxwRmICgQ9/1XC7bnD3V+J2e/7E5y1OJqgNeowgpafV919SDjuJILrC+YWjCffxfu20h+C7ViZl1XcIWwhPWlnua78L+mRnSumUV8MK+4QtpBStejau6RQCU3Xh+57acI+L3stGVcshxJK/kWkyMR7S8pdlZkdC7R19+uLO5bChC1RT7n72duaT8n/9in5j4+S/+1T8r/DJTTBHpzA5L93MSX/6vkXEfmH3H0GwXUFtYo7lq3oQXDRsIiIJIB6/kVEtqEkV/3zhXfb2Sm5e9/ijkFERHYuSv5FREREROKQV6w1+8RQ8i8iIiIiEgd9yZeIiIiIiOwyVPkXEREREYnDrt/0o+RfRERERCQuavsREREREZFdhir/IiIiIiJxyCuWr+VKLCX/IiIiIiJxKAm3+lTbj4iIiIhIKaHKv4iIiIhIHHb9ur+SfxERERGRuJSEu/0o+RcRERERiYN6/kVEREREZJehyr+IiIiISBx2/bq/kn8RERERkbiUhJ5/tf2IiIiIiJQSqvyLiJRy2eQWdwg7vU9Wzi3uELawMS+nuEPYwrKT6hV3CJvZq+7pxR3CFn5oWqO4Q9hC1bc+KO4Qdhkl4YJfJf8iIiIiInHY9VN/tf2IiIiIiJQaqvyLiIiIiMShJFzwq+RfRERERCQO0RLQ+KO2HxERERGRUkKVfxERERGROKjtR0RERESklCgJt/pU24+IiIiISCmhyr+IiIiISBx2/bq/kn8RERERkbio7UdERERERHYZqvyLiIiIiMRBd/sRERERESkl9CVfIiIiIiKyy1DlX0REREQkDmr7EREREREpJdT2IyIiIiIiuwxV/kVEJC7JZZLpNKwb1WpVo0zZFCaOnMAX784E4PiWJ3Jmu7Po06oXAO37XU39xgeRtTYTgCHXDGJ9xvpSERPAUY0P5847b+bMMy7i0MMa8MCIQeTk5DB//iI6X9ebaDTK0GG3c2zTo1ibsQ6ACy/owJo1GUUSD0DjxoczaNCtnHbaBRx22EE8OPJucnJzmDdvER073kQ0GuXKKy/i6qsuJScnh8GDR/D6G+8VTTBJSaR1v4nkWrUhGmXtiHupePFlJO22e/D0XnuTM/d7Mu4eQPodg0iqVBlycohmZ7Pmtl5FE1Oo0VEN6TfgJs75z6Uc1vAg7rl/ANnZ2Xz7zRxu6TWQaDRKr95daHF6M3Jzc7n15kH878tviiaYnXg/lVZq+5FSw8yaAROA74EIkALc7+4TzOxw4Bx3H7CDYzoD2MfdRxXh+G3dvd2/GOME4Eh3fyBhgRUjMzsTqOHuj/+NZQ4nfH+YWSvgM6AsMN7djymaSOOO7URgtbsn9C+3mU1y9/PM7FBgN3f/0MzGA5e7e/Y/GK88cKm7j/mbyyV0+05s1YyMVRmM7HEfaZXTGPbGA3zx7kzqHlyPUy5sQYTIpnnrHbo/Ay/rR8aqoktmd9aYuve4losuOpd164ODjFtvvZ7Bd4/g7bfe5/En7uOMM5vzxuvvcfgRh3DuOVewcuWqIo0HoOcNHbn44tasWxcc7NzWpwd33XU/b741jbFjR/CfM0/hiy9n0bnTlRzb9P8oX74c06ZO4t33PiI7+2+/Zber7NFNAfjzhi6kHHY4FdtfTcYdfQCIpKVReej9rH3sQQCSa9RidYcrEh5DYbp2v4YL27Zkffja3TdiILf0upOZn33FrX170OaCs/G582l6fBNanNyGmrWq89S4Bzm1WesiiWdn3U+lWV5UbT9Sukx192bufhJwGnCzmR3u7l/v6MQfwN3fLKrEPxHMLALcATxSzKEkjLu/AbQxs0p/Y5nY98f1QNzL7gBXAjUSPai7nxf+2ho4KJzW9p8k/qG9gav/wXIJ3b4Zr33C+HueDR5EIuTl5pJWJZ2Le13Gk/3/Oi6JRCJUr1ODjoO7MHDiEJpfcGqiQtglYlq0cAkXX3TdpsezZn3HbrtVASAtPY2NGzcSiUTYf7+6jHzwLt5570Uuu/z8IosHYMHCJVx44TWbHn89aza77R7ElJ6WxsaNOTQ+6nBmzPic7Oxs1qzJYMGCxRx6aIMiiSd7xsesvX84AEnV9iK6du2m5ypediWZ/51E9I8/iFTZjUhaGpUG3E3le0aScvSxRRJPvsULf+TySzpvelyj5t7M/OwrAGZ++iXHHHsUxxx7FNOmfgzAsqUrKFOmDHtU3b1I4tlZ95Ps2lT5l3/E3dea2WMEiWAVoKO7tzWzLsB5QCrwO9AKSAaeJkhCfgJOdPcaZvY+8DVwCEFCeL67LzGznkBbIAf40N1vNrPjgHuAjcB6oA1BYlWfIMGeAFQGKgJ93P3t/FjDJHwk0ISg4tzP3f9rZvcAx4ezPefuD5hZA+AJYF34syoc43zgBiAX+NjdexcWk7vHlhRbAN+7e3Z45mQIkA2MAn4EBoXjLQCuBeoCT4bbnQRcDOwH9CE407g3MMrdH4p9LczsEqA7sAGYB3QALiFI/JLC7X3v7+wL4GFgDtDQ3deZ2Y1ArrvfB7wOtANGhGMmAx6+FnsCS4FqwFpgRrjfOgLPAIcTvBcuBfY0s5eB6sA37v5XZhKMOxv4Idxn1wKPA3uET3dz92/N7CrgOoL32Cvu3m8rr9Ud4f6tBuwL9CB4f54BHGlm37v7j+F6mxW2z83siHDf5QJZwDXArxTy3jOzn4FG4X7KNrP/hfMdCnxVcL8CLxG8LyoAmUAHd/8pZnf0AQ4ys9uBB7ayL54E9g/HeIDgLN0W2/dvZK3PAqB8agVufPRmxt/zLJ2GdmXsnY+TnfXXcU25iuV546lXmTL6ZZKSk+k/fiALvpnPkrmL/20Iu0RM//3vm+yzT81NjxfMX8w99w3g5pu78OeaDD768FNSUyvy6KNPMXLEGJKTk3n9zef43/++5bvZcxMeD8DLL7/BvvvW2vR4/vzFPHD/QG7p3Y0//8zggw9ncN55/+HPP//6CMtYu5bKldOLJB4A8nJJu/EWyjY9gYyBtwMQqVyFlCOOZF1YzY6klCFr4gtkTp5IJD2dKvc9xOq5c4j+ubpIQpryylvUjnntliz+iabHNWH6JzM5/czmVKxYgfT0NP7446/1r127jkqV0lj5+x9FEtPOuJ9Ksx1V9zezFIJ8pA5QDhhIkEO9SvC3HuARd3/BzPoB/0eQP3R395nbGluVf/k3fgGq5j8wsySChORUdz+a4OCyMUEyusjdjyNI1PeKGWOmu58KvANcFLZJXAA0DX8OMLOzgHMJkqeTCCrpu8WMsV8Yx9nARWx5UHsuUNXdmwAnA0eFY9YFjiFIei8O1z0MuD2MaXq4XbsD/YFT3P14oKaZtdhOTADNgNh2i/LufgIwDhgNnBeeRVlGkCS2AGYCpwL9CBJKgJrAOWGsPcysWsw+3yOMrXkY22qCRBlglbsfn5/4/519QZDITyQ4wCKc9nT4+zfhtgHg7rnAh8CxBMnmbOCU8OftmPleIzjYu5wgoa8EtA+XOyV2u0JpwJ3u3ha4FXjP3U8meD89Es7fGzgBOBIoZ2b7UPhrBbDB3c8kOPvQw92/BN4EehWSGBe2z0cDXcLX7GHgXrbx3nP3ZcBY4N6YD+KNW9mvw4ER7t4s/H1wgXgGERxIDtjKvkgHTiQ48D6D4EBtW9v3j+1RvSr9xw/kw0nvs2LRcqrXrUGHgdfRY+RN1DqgNu1uv5rszA289sQUsrOyyVqXybfTv2XfBnUSFcIuEVOsocNu5/QWF3DkEafy/HOTuHvwbaxfn8nDDz1JZmYWa9eu44P3ZxRZlb0w9wy/g+antOawhifz7LMTGTqkLxlr1pKenrZpnvS0NFavXlOkcawdfjerrrqUtO43QbnylDuhGRumvQt5QWd13h9/kPnqK5CXS/TP1eQsmEdy7X2KNKZYXa7rTY+e1zJ5ylP8/ttK/li5ioyMtaSlp26aJy0tdbODpqKws++n0iSPaMJ+tuNSYGWYN5wBPEhQVLo37MJoFib+RxLkIUcTFE4f2uqIISX/8m/sS1DlBcDd8wiSuufN7HGgFsG1AQ0IE2l3nwv8FjPGV+G/PwHlCZLOT919o7tHgY+Ag4G7CM4cvEdQ9d8Ys97vgMeA5wmSsoLvayOoQOPuq9y9bxjTR+4edfeNwKcE7RkHEiTgAJ+E/+5PUNF+PTxbcRBB0rfVmEJVCQ6QNoUa/rsnQbV7QjjeaeG+fJwgeX8T6EJwBA8w3d03uHsmQWK9X8yY9YDvYs44fBjur9j1/dN9MQa43MyaBLP7ynCMFfxVdc43CfgPcDpBlboFQfI8sZAY8i0MY8gjqKBXLGSe/G04FLgy3F+jgd3DbZ/t7plh7L0JKvWFvVaw5XttWwrb5zXc/evw+Q+Bg+N47xWmsP16KHBrGPPtbH6AXNAW+yJ8/bsTnD14gaBKlHCVq1ah77j+jBv8FFMnvMv8WfPo0aIL/dr24b6uw1g67yfGDhhD9Xo1GDRxCElJSSSXSaZB4wYsnL2gKELaKWMqaNWqP1mzJmjXWLHiF6pUqcQBB9Tl3fdeIikpiTJlynBs06OY9fXsHRJPENNqMjKCj43lK36hym6V+fyLrznuuCaUK1eOSpXSqV9/f777rrCPkX+v3CmnUeHCS4IHG7IgGoVoHilHNCL78882zZdyxFFUuq1/8KB8BZL3rUvuj0uKJKbCnHZ6Mzpc3ZNWZ1/BbrtXYdq0T/js0y9pfsrxRCIRataqTlJShD+K6LqNXWU/SZF4Eegb/h4hyAkaAf9nZh+a2eNh4ed44O3w7+CPQBkz23NbA6vtR/6RsOf7GoKkt3o47TDgXHc/2swqAl8SvGFnE1R3Xzaz/EppvoKHvnOBnmZWhqAd4kT+ahMZ6+43mtktBBXPJeF6DwXS3f3/zKw6wYHGqzFjzgHOD+etTFCtH0lQdb4vPLXWFHiKoFXiWIIEvHG4/CKChLGFu280s3YEFezCYuofs95fgSoxj/NvEvA7wUFTS3f/08zOIWiRaUmQhPc3s4uAm8OYDg9ba8oRJPbzYsZcRNAOkuru6wiO/n8osL5Yce8Ld58XtgndxObXLewWblusdwgq0usJ2oIGANnu/nnYRhO7D/IT5HjOnuZvw1xgnLs/F1bhryZol6pvZuXcfYOZvQT0pPDX6tytrC82nliF7fPlZnZYePHsScAPcbz3thh/K/t1LjDc3aebWf1w/K3FucW+CNfdyN1bhRcH/2Rmz2xj+/6R8zq3IbVSGm26XkibrhcCMOiK/mRv2PxShmXzl/LB5Gnc9fIwcnNy+GDiNJbO+6mwIUtkTAV17tSbsU+PIDcnl+zsjXTp3Jsff1zG889PZtoHk9i4MYfnn5vEnDnztj9YgnS8rhfPPPMQOTm5bMzO5rpON/PLL7/x0MNPMPW9iSQlRbi931A2bNhQJOvf8PGHpN/Ym8rDR0ByGdY9OhKys0muXZu8FSs2zbfxi88oe1RjKt//MESjrH9yNNE1fxZJTIVZsGAJL095mszMTD768FPeffsDAD6d/gVvv/cikaQIN/Xsv51R/rldZT+VJom8z7+ZdSDIHfKNyr+W0d3XhvOkE7SG3kbwN2mMu39pZn0IugRWAytjxsgg6ByILbRuJhItAVctS9ErcLefXIIDxwfcfVL4XEeCHvNX+avquIGgmj2JoP2hOkHC3srd08LKZUd3n2tmHYG93f0OM7sBuJAgafmYoH+7CUEf8zqChKYDQYKU3/M/jqCfOwl4zN2fiYk9QtCffkQYd393f8PMhgPHEfS+T3D3IeHByVNh7L8BWe7ezswuBToR9JYvJkiWDy0Yk7svilnvqeG2ds7fR2ELC2Z2GkGFNwlYQ9AKkx6uOztcTw+C1pgHCM4g7EHQGvJUgdfmYoKqbx4wnyAxbgvUD6vhsfPGvS9ixh4AHBCeicHMuhEk9o8WGPsFYIm79zKz54Ff3f362G03s4EEpy87AA97eLcfM/uU4M5Ki2PGWxxuQ1bY3vQ4wcFUJeAOd38lTO47EiT2U9z9rq28Vr2An9390TC5ftTdm5nZtUBn4EJ3nxOut1lh+zzs+X+AvyowVwHLKeS9Z2Y/u/veZvZ/BK1knQmu58jfns32q5nVIzgQKE/Qs3+9u8+I2RflCc7IvAUMLbgvgCnh8ocR/P/5avh+3mL7CtNm33P0h2A73vzt2+IOYQsb83K2P9MOtuykesUdwmYOnL68uEPYwg9NE36PgX+t6lsfFHcIRSmy/Vnid+G+5ybs8/KFJS9vMzYzqw1MJvh7+YSZVXH31eFzBxEU7/5L0FY8NJz+FUEB7PetjavkX4qcmTUF0jy4EPIA4E133297y+3qwmsgpgKn+T+8y0vBg4adgZm9CVzg7kXbDFxMdsZ9XtSU/G+fkv/4KPnfPiX/O9wumfyb2V7A+wTXmuXftOMzoKu7zzSzrkBtglbPoQTttrUICmENt7Vetf3IjrCQ4DqAfgTXAHTezvwlgrvnmVl/gir0/cUcTkKEleyJJTXxFxER2ZY4LtRNlFsJ2mz7mll+7/8NBC26G4GfCToO1pjZRwTX8yURR46lyr+ISCmnyv/2qfIfH1X+t0+V/x0uoZX/RH5evrTklYTGFi/d7UdEREREpJRQ24+IiIiISBwKu43erkbJv4iIiIhIHEpCu7zafkRERERESglV/kVERERE4rAD7/ZTZJT8i4iIiIjEoST0/KvtR0RERESklFDlX0REREQkDlG1/YiIiIiIlA4loedfbT8iIiIiIqWEKv8iIiIiInEoCff5V/IvIiIiIhIH3e1HRERERER2Gar8i4iIiIjEQXf7EREREREpJUrC3X6U/IuIlHJT/5hT3CFsZs2G9cUdwhbOrd6ouEPYwoboztd9vGHNzvXarduYVdwhbOHBubWKO4QtXHNs8+IOYQs1Z0wt7hBKLCX/IiIiIiJx0N1+RERERERKiZLQ9qO7/YiIiIiIlBKq/IuIiIiIxEF3+xERERERKSXy1PMvIiIiIlI67Pqpv3r+RURERERKDVX+RURERETiUBLu9qPkX0REREQkDiUh+Vfbj4iIiIhIKaHKv4iIiIhIHPQNvyIiIiIipYTafkREREREZJehyr+IiIiISBz0Db8iIiIiIqVESej5V9uPiIiIiEgpocq/iIiIiEgcSsIFv0r+RURERETiUBLafpT8i4jI39LoqIb0G3AT5/znUg5reBD33D+A7Oxsvv1mDrf0Gkg0GqVX7y60OL0Zubm53HrzIP735TdFGlOTxkdw9123ckqL83l23MPsvdeeAOy7b20+m/k/Lrm0E5MmPkHVPXZn48aNZGZmcdY5lyU8juQyyXQa1o1qtapRpmwKE0dO4It3ZwJwfMsTObPdWfRp1QuA9v2upn7jg8hamwnAkGsGsT5jfZHEdP3w66lWay9SyqbwwsjxzHwniOmklidxVruzuanVjQCcdtHpnHHJGeTm5DJh5At8/t7nCY8HgKQkqtzSkzL71IZolNVD7yNv1Wqq9O5JUno6JCexasBgcpctp3L3zpRteCjR9cG+WdmrL9F164omLqBx48MZNOhWTjvtAg477CAeHHk3Obk5zJu3iI4dbyIajXLllRdx9VWXkpOTw+DBI3j9jfcSHkdSmWRaDutAlVpVKVM2hQ9Hvsyfy1dy1qArycvNZeWiFbzSawzRaJQj257MUZc0Jy8njw9HvswPU79KeDxBUDvv6ybxU/IvJYKZHQwMBSoCacDrwB3uHvchupn1Bqa6+8yYaeWBue5ep8C8k9z9vATF3g74w91fScR4O6P8fQt8A1zq7mPM7A7gZ3d/tJhj6+LuDyZ4zDOAfdx9lJl1AJ4EDgbOcfcB/3DMQ4Hd3P3Dv7lcQreva/druLBtS9avDxLW+0YM5JZedzLzs6+4tW8P2lxwNj53Pk2Pb0KLk9tQs1Z1nhr3IKc2a52oELZwY8/ruOSS1qxfF8R0yaWdAKhSpTLvvvMiPW+8A4D996/LYQ1PLrI4AE5s1YyMVRmM7HEfaZXTGPbGA3zx7kzqHlyPUy5sQYTIpnnrHbo/Ay/rR8aqjCKNqVmrk1mzKoN7u99LWuU0Rrw5gpnvzKTewfVo0fY08kOqsmcVzm5/Nj3O6k7ZcmUZMnEoX330FTnZOQmPqfzxxwLw+7XdKHtEQypdexV5GRlkvv0ume99QNkjD6fMvrXJXbaclPoHsrJ7L/L+XJPwOArqeUNHLr64NevWBQnrbX16cNdd9/PmW9MYO3YE/znzFL74chadO13JsU3/j/LlyzFt6iTefe8jsrOzExrLYa2OI3NVBpN7PEKFyql0fOMuln+7iA9GTGLetFmc90AnDmh+OMu/WcjR7U9n1Nm3UaZcCle+1I8FH39Lbil63XakktD2owt+ZZdnZlWA8UB3dz8ZOAY4FLj274zj7oNjE//tzJuQxD8ca2xJTvxhs327N3B1ccdTwG2JHtDd33T3UeHDW4Fkd//6nyb+odbAQf9guYRu3+KFP3L5JZ03Pa5Rc29mfhZUGWd++iXHHHsUxxx7FNOmfgzAsqUrKFOmDHtU3T2RYWxmwcIlnH/BNVtM73d7Tx566Al+/vlXqlWrSpXKlfjv5Kf4YNpk/u8/pxZJLDNe+4Tx9zwbPIhEyMvNJa1KOhf3uown+4/ZNF8kEqF6nRp0HNyFgROH0PyCookH4JPXPubZ4eM2rTc3N4/0Kulc3utyRt8xatN8Bx5+IHO+mENOdg7rM9azYvEK6tavWyQxZX34CasH3wNAmep7EV27lnKHHULynnuyx4hhVDz9FLL/NwsiEcrUrkmV3j2p+tgIKp51RpHEk2/BwiVceOFf76WvZ81mt92rAJCelsbGjTk0PupwZsz4nOzsbNasyWDBgsUcemiDhMfy/WufMfWel4IHkQh5uXn8/N0SKlRJA6BcannycnKp2XA/fvriB3Kzc9iQkckfi39mr/r7JDwe2Hlftx0pmsD/iosq/1IStCSo2M8DcPdcM7scyAYws3uA48N5nwMeBuYADd19nZndCOQCDQkOIj4GngV2A+YXtkIz+9nd9zaz94GvgUOASsD57r4kZr52wLlAOlAVGODuE81sNvBDGONc4Ofw31uADUBt4FGgeRjXA+7+iJm1AToDKUAUaBWue0g41rvA/7l7k3D9LwD3FDibcVsYUxngEXd/zMzuBo4C9gBmuXv7sDLflOBMylXAqcDF4XrHu/uImDFbAi3cvUtY5W/q7ueY2SXAvsCB4b5tDRxkZrfnv3Zmdn643r7uPiVmzGYx2zUK+BEYFL5WCwgO7soQVNX3BcoCXYAvwn13AEGB4zZ3f9/MvgE+AA4Lt6FlOP/uZvawu3eKWfdYgnpo7XD7L3f3uWbWE2gL5AAfuvvNZnYccA+wEVgPtAm3sz4wj+CAZ7yZ3Q90JHgPtnL39uG6/gecAZwE3BBu38fu3jsmnppAOyA7nL9CIfuibrgvcsLtvhi4vLDt+zemvPIWtfepuenxksU/0fS4Jkz/ZCann9mcihUrkJ6exh9/rN40z9q166hUKY2Vv/+RiBC2MHny6+y7b63Npu255x40b378pqp/2bJlue/+UYwYOYbdd6/Ch+//l5mff8Vvv61MaCxZ67MAKJ9agRsfvZnx9zxLp6FdGXvn42Rn/VUZLlexPG889SpTRr9MUnIy/ccPZME381kyd3FC44mNqUJqBXo/egvjho+j27BujLlzzGYxVUyryLqMv9oyMtdlUrFSxYTHs0luHrv1vZnyJx3PH7f2p8Jpp5CXsZaV3W4i/crLSLusLWuffYF1L77M2udfhOQkqj54L9lzfiBnwcIiCenll9/Y7L00f/5iHrh/ILf07saff2bwwYczOO+8//Dnn3+drclYu5bKldMTHkv2+g0AlE0tzwWPXs/U4S8SjUb5vzvbcWKXc8nKWM/iT+dw0H+akBXTLpa9Lovy6RUSHs8mO+HrJn+PKv9SEtQANvtEcfe17p5tZmcRJEXHEBwAXEyQlE0kSNAIpz0ds3hHYLa7nwg8Fsf6Z7r7qcA7wEWFPJ8KtABOA+41szIECeWd7t62wLy1wriuI6jYXgacyV9nMQ4kSO6PB74HTg+nl3f3E9y9P5BpZgeZ2e5A3QKJ/xHheEcDTYADzawysMrdWxAcABwTJpsAc9y9KUEifCHBPjwBONfMLCbut4ETw99PBGqG23kOMClmvkHA9zEV8GXufgrQPdzmgsq7+wnAOGA0cJ67nwQsI0iGOwKL3f1YgqT8aIIzC7+Hr19L4KFwrErA8zHLn+nugwhargpLjBe4e3PgDmBo2HZzAcEBUVPggPD9dS4wgSB5f4TgoBEAd3+c4MAu9nV+DTjWzFLNrDHBezcH6A+cEr62Nc2sRcw4y4CxwL3A51vZFy2AmQQHaf2AytvZvoTocl1vevS8lslTnuL331byx8pVZGSsJS09ddM8aWmpmyVLO0Lr885i/PiXycvLA+Dnn3/lsVFPk5uby2+/reTrWbOxA/crknXvUb0q/ccP5MNJ77Ni0XKq161Bh4HX0WPkTdQ6oDbtbr+a7MwNvPbEFLKzsslal8m3079l3wZ1iiQegKrVqzLohbuYNmkayxctp0bdGnQa1JmbHuzFPgfsw9X9rmH92vVUTP0raayQWoF1a4q2R3vVnUP45YLLqdK7J3kZa8n6aDoAWR/PoGx9I5q1gbUTJhLdsIHo+kw2fPkVKQcUzetWmHuG30HzU1pzWMOTefbZiQwd0peMNWtJT0/bNE96WhqrVxdNa0ul6rvTbnwfvpn0Md/+dzpn9ruMJ9oM4MFTbmLWpI857bZL2JCRSbm08puWKZtanqw1ib92JNbO/roVpbxoNGE/xUXJv5QESwgqtJuYWV0zOxFoAHzk7lF33wh8StA6MQa43MyaAO7useW/AwmSKNz9M4KK7rbkX1n1E1C+kOc/cPc8d/8FWAXsGU73QuadHca5miD5zA6XyR/3V+ApM3uSoIKdUshYowmSwYsJkuZYRnCwkuvu2e7ek6BaXc3Mnic42EkrZNxDCKrr74U/exBU1oOZ3DOBH8JkNn8/n0jQ9z63kO3M92X4788E12sUlL/+PYHqwITwbMtpYTwGzAhjmOfu9xO0fP0nnG8iUMbMqobjbO+1ijU1/Hd6uJ76wKfuvjG8luQjgj7+uwgOQN8jqPpv8/3i7rnAS8B5QHuC12v/cBtfD+M+CNjaX8qt7YvHCd43bxKc0Uh8w28hTju9GR2u7kmrs69gt92rMG3aJ3z26Zc0P+V4IpEINWtVJykpwh8rV+2IcDY55ZTjefPNqZsen3rKCbzwfHAsn5pakYMPNubMnZfw9VauWoW+4/ozbvBTTJ3wLvNnzaNHiy70a9uH+7oOY+m8nxg7YAzV69Vg0MQhJCUlkVwmmQaNG7Bw9oKExwNQpWoVBoy7k7F3j+XdCe8wb9YPdD61M7deeAvDugzlx3k/Mqb/aH74+gcOanIwKeVSqJhekdr712LJXycyE6rCGS1IuzyolUSzNkA0j+yvv6Fc06MBKHv4YWxctJgytWux52MjICkJkpMp2/BQNnriX7etWbVqNRkZwYHr8hW/UGW3ynz+xdccd1wTypUrR6VK6dSvvz/ffVfYx/m/k1q1EpeN6807g8fz1YQPAMhcvY4N4QXiGb+sokLlVJbNWsA+jetTplwK5dIrsOf+Nfn1h6UJjwd2ndetKKntR2Tn8Cpwq5k94u4LzCyFoEL6DkF7T3vgvnB6U+Apd59nZhHgJoJqbazvgWOB/4aV8hS2bXv/BzcCMLO9CKrPv4bT8/7OWGGFvj+Q38z5Dpsu1dtsrJeAG4GVwPkFhpkLXGdmSUAywYXRDwK13f1CM9uToJWo4LgOfEdQLY+aWQ+Ci3djTQaGAS8TVLPvCmOMlcfmRYft7bv89f8OLAVauvufZnYOsJYg0W9M8FrVAwYSHHgsdfe7zKwC0AfI7zcpbH2RQqZB8Lp9DBxHsO1zgZ7hGY1cgoObp4FLgbHufqOZ3QJ0IDgg3do2Q5CoP0ZwENUl/PcngtapjWG72NeF7IukbeyLlgQHuv3N7CLgZoL3/ta2LyEWLFjCy1OeJjMzk48+/JR33w6SlE+nf8Hb771IJCnCTT37F2UIhTrwwP1YuOjHTY/ffGsaLVo045OPppCXl8dtfQezsggOSM7r3IbUSmm06XohbbpeCMCgK/qTvWHzi0GXzV/KB5OncdfLw8jNyeGDidNYOu+nhMcDcH6XC0irnEbbbm1p2y04CXXH5f22iGn1b6uZ8uQUhrw0hEhSEs8Me4aNG7ZX+/hnst7/iCq39aLqw/cTKZPMn/c/xMZ5C6hyS09SzzuH6Nq1/NFvENGMtax/8132HP0g0dxc1r/xNjmLFhdJTIXpeF0vnnnmIXJyctmYnc11nW7ml19+46GHn2DqexNJSopwe7+hbNiwIeHrPqFzSypUSuWkrudyUtdzAXil9xjaPNiVvJxccjfmMKX3GNb+9iefPfkW7V/sSyQpifeGTyCnlL9usm2RknC/UhEza0SQeCYR9NdPAfqHiepwggSuLDDB3YeEy1wMDAAOCOcbS9CX/j5BUleDIOE7wd2twPpie/47hv3gHYG93f2OmPnaEbTsrAcqE/Sfv2lmi4H67p6Vf9ebcF0d3b2tmdUHHnX3ZuEFzZ8SnMV4AahDUNVdRVCV/iR/uZj1jgD2dPct2pDCBPWccF89QlApngJkEiTHFYAeBG0km+7GY2Y3EbS4lCM4M9I1rGLnj1uZ4MDmCIJEdiVwtLt/VWDffgq8Fa7vZ3d/NHZ7Y8ZrFrtdZnYacHsY9xqCfvY1wBNATYKDme7AtwTV9H0JDrYedvfRBfb5YIK7OI01s2kE7UeXxqx7LEGvftlw3HbuvsjMbiBof0oiODC4gaB96gFgHUGC3oGgBai+u/c2s6fCWPoD18Zsz9sEvf0DwseXAp3C9S0G2rv7pnP3ZvZ/BO/x/Gs+Cu6LdOApgmskkoEe7v6/wravoN3TD9ip/hCs2VC0LQv/xLnVGxV3CFvYEC2sflC8Hq25c7129b7a+arNt+x14vZn2sGuqbGiuEPYQs0ZU7c/U3wSWgBpUK1Jwj4v5/w6s0iLM1uj5F+kCIXJf/3Yizd30HofAia6e8I+PUuT/IMVd3+zuGPZEZT8b5+S//go+d8+Jf/x2VmT//rVGifs83Lur58XS/Kvnn+REiasKO+mxF9EREQKUs+/SBFy97HFsM7TdvQ6Sxp3b1fcMYiIyM6nOO/SkyhK/kVERERE4lCcd+lJFLX9iIiIiIiUEqr8i4iIiIjEQW0/IiIiIiKlhNp+RERERERkl6HKv4iIiIhIHKI76Ps1zCyF4Ess6xB8ueZA4HtgLMEXcs4GOrt7npn1A/6P4AtAu7v7zG2Nrcq/iIiIiEgc8ogm7Gc7LgVWuvsJwBnAg8C9wG3htAjQ0syOJPhW+aOBtsBD2xtYyb+IiIiIyM7lRaBv+HuEoKrfCPggnPYGcCpwPPC2u0fd/UegjJntua2B1fYjIiIiIhKHaALv9mNmHYAOMZNGufsoAHdfG86TDrwE3AYMd/f8ADKAykAlYGXMGPnTf9vaepX8i4iIiIjEIY52nbiFif6orT1vZrWBycDD7v6cmQ2NeTodWA2sCX8vOH2r1PYjIiIiIrITMbO9gLeBm939iXDyV2bWLPz9TOAj4BPgdDNLMrN9gCR3/31bY6vyLyIiIiISh0S2/WzHrcBuQF8zy+/9vx4YYWZlgTnAS+6ea2YfATMIivqdtzewkn8RERERkTjsqG/4dffrCZL9gk4qZN47gDviHVttPyIiIiIipYQq/yIiIiIicYgm8ILf4qLkX0SklPsjY15xhyBSJDYUdwBS4uzAnv8io7YfEREREZFSQpV/EREREZE4JPI+/8VFyb+IiIiISBzU9iMiIiIiIrsMVf5FREREROKwo+7zX5SU/IuIiIiIxEFtPyIiIiIisstQ5V9EREREJA6624+IiIiISCmhth8REREREdllqPIvIiIiIhIH3e1HRERERKSUiJaAnn+1/YiIiIiIlBKq/IuIiIiIxEFtPyIiIiIipURJuNuPkn8RERERkTio519ERERERHYZqvyLiIiIiMRBbT8iIiIiIqVESUj+t9n2Y2bNzOxXM3vfzKaZ2Zdm9qKZlS2qgMxsHzM7uwjGvT8ce3czuzic1tvMmvyLMTuYWcrfXCah22dmHc3sjjjnHW9mZWNjCF/b+omK558ws0mFTNtiu8zsDDPrkMD1ji/K9/LOzszqm9n74e/Fsi8Ke+0TPP7Pf2PePczssaKMZ0cys73M7MHijkNERHYu8VT+p7p72/wHZvYccA7wUhHF1ByoD0xJ5KDu3h2CAxqC+J9z98H/cthbgaeBjX9jmSLZvnjkv45mVmwxFMbdz4tzvjcTvN6225+rdCiufRHva7+DDAQeKu4gEsXdfzGzDDM7yd0/KO54RERKgl2/7v83237CymB1YFX4+G7gBCAZuNfdXzSzo4H7Cc4qLAMuAfYHRgARYCVwJXAEcDOQDdQDxgODgd5ARTOb7u6vxKx7IfAZsB8wG7gaqASMC/8tA9zm7lPNbBBwcjhtorsPCSucHYE+QMOwgtw0XG8H4AF3/8DMjgL6Am2AR4EDwm25zd3fj4nnKmDvcPlzt7IvOgFXAHnA50CPbWxfF+A8IBX4HWgFXAz8B6gYbvcQdx9rZscDD4SvQw7waYHXKaWw2M1sMXBwbAzhIv3MbK9w3Re5+8KtbM/7wK/A7sDp7p4bru8AYAxQFlgPtA3HeiJ8DaJAN2BfoJW7tw+X+x9wBvCNu+8dx3a1IzhoeRR4Hvgp3C8z3f26AvO+D8wN548AF4a/DyF4z40C7owZb2MYX7nwNT0b2AdoCSwGHgNqE7z/X3H328xsLLBH+DMLmO3uD5nZbsC77t4oJp49gaeAKmE8lwOZwCNA+XDc29z9ZTObDfwQxnkt8Hi4DoBu7v5tgW29Bzg+fPicuz9QILb/c/f8/2erA8+GMfwcM8bicF/sD9xL8LpXBa5z9+nh+70L8EcY1wvhooW9P48ARgK5QBZwDcH7ZgJQOZy/j7u/bWY/h6/9Zv+vuHu3Att4FtAvjPt/BP8vn0KQsGfx1+dKBsFrezCwIHw9MbPa4fQK4X7v4O4/xYxfCWic/z4ysyUE75/vw/2x2bLAb1vZni0+p9w9L2Y9he2bJIID8ZXA6+4+NGb+q4DrwtfjFXfvZ2aXAN2BDcC8MJ4XKPAZ5u4tgeeA/sD2kv/Idp4XEREgJ3vZLv95Gc/dfpqHrSHfE/zRnezu75nZmUBddz+eINHuY2ZVCJKkK939aOA1oAEwGujs7s2A14Fe4dj7Aq2BY4BeYTI5mCCB2ZQYh2oR/EFrAqQB5wK3Ae+4+4nA+cDjZhYhOOC4mCB5XV1gnEEEZzNGxUwbTZB4ALQPH18N/B6O3ZICFUF3f5wgeWq7jX3RHuji7scCcwj+wG6xfWaWRJCknRrutzJA4/Dpyu5+FsHZit7htEcIkvRTgUVsaVuxF7aPX3P35sAbQJttbA/A8+5+an7iHxoO3B1u5wMEB3bDCZKRE4HrCRLY14BjzSzVzBoDC93915hxtrddsQ4ErgKaAP8xs70LmWd6+J57geAsDUB5dz/B3Z8pMO9idz+N4HWq6+7/ASYSHATUBj5199PD9XWMWW6quzcFhhEk9BC8954tMP5tBMlbU6BnOE594B53b0GQwHUO500D7gyr8bcC77n7yeE8j8QOGibFdQn+HzoeuNjMDo2NLT/xD/UheA1PBl4uZJ8dDPR091MIDpTam1lVggP144DTCA7s8hX2/hxN8L4/Cf6/vfsOs7Mq1z/+TUJHDlKkiXoE5aYqCigiHUFERDx4FBBBkG6hWFDETlGPDStd4QAqoIJIEZTeEenlhgAiUkVAeiBkfn+sd5OdSQJkfp6sNZv7c11z7TLJzD3v7Jl53vWu9Sx+Qimel6ScTLwH2IKpBx6m+FmR9Nz7u/s/opzErAyMp3xPDgH+q/s853bH+H2U7/GqwOcphTmU1+MPutfDtyk/A/1WBdz3+FXAlrb3mM7/nd7XM63fU/2mdWygDCRsMKzwX6g7pmsAbwZml/QaSjG/bvfz+TDlBHFav8OgnLz0TgwjIiJeVPF/VvdHbw3KiF+vKFsBWKkbYT0dmBX4T2AR2zdCKZBt/4VyAvCT7t9uB7yy+xjX2p5o+3HKiNrz+Zvt8d39iwB1H/e87nPdBTwCLEQp/r8B/IEy0vpC/gC8RdL83dd5Wvf1bdRl/jUwS1cETcv0jsW2wMcknUs50Znm2WI3Mvg08AtJh1MKiN5agqu62zspI8QAC9u+ubt/4XTyvNjsAFd0t/dSiqXpfT0wZYHUI+Di7mv5ne0zmPJ7cxXwqu6E4QTKFY7+AqXnhb6ufuNtP9p9zHuYfGz6ndXd9l4v08sP5cQWSjF1Q3f/oe7jPgisIukY4Ht0o8n9H8/2bcCjkpalvP6OGvbx+4/RRbaP6XLvJOl/KScU/etHejlXALbrvheHUq669FsGON/2kO1nKFdLln2er3Up4LLu/rSO8V3AFyUdSbn6NSvlasANtp/ojvdFff/+qu62//W5WPc9h/IaWM729ZSBgV9Qit7hv3ue72dlQeCh3oliVyA/ATzS/dw/93n6vz7bf+tyQTmOe3fH8UvAwsM+/4LAfX2PH7D9z+n93+f5eqb1e6rfVMemu3+77aeH/dslKFeTnuy+v5+j/H673vajwz7GtH6H0X2/nukGGCIiIl58n//uD+FWwGHd1IGbgLO7E4N1KZfAbwXu7qaBIGkvSe+jFCFbd//2s8Dvuw87ralTk6aT65V9o7tvB66njNKu0X2uVwLzUYq3/6aMxq0DfKQbLZvux++K7+Mpo6ondn8wb6KMkK4NvKt7/4PTyTq9Y7EDsHM3yvcmyjSjqT6/pDcAm9r+IPCJ7v294mdax+guSct091eZxvtfKPvwDMM/x/S+nt7/He7GXg5JH5L0Cab83qzI5CkmhwMfBt4KnDmDX1e/FzPtrjftpvd6mV7+F/p4HwEetv0h4DuUKVO970//xzuUMmXs77YfGPYx+o/RmpK+SZl2dJTtDwNnM2XB2/u4NwHf674XH6BMcxv+cVfvPu6slNfYLc/ztd4AvK27P61j/APgy7a3Aa7tMo0HlpY0Z1dE9i+Sn9Zxu7t7TQOsBdzcXY2Yx/a7KSPUPxz2f6b1s9JzP/DyrrBF0g8ohfF/dL+Lnvs8/V+fpMWYPNBwE7BXdxx3ovxM9LufKQcK+o/dVP/3eb6eaf2eet5jM43P13Mr5bj3pi6d0OVcVlLv6stawM3T+R1G9zqd2D/1KCIiXtpmaDTI9g2U4uAHlDmqj0k6nzJyPNSNRu0EHNGN4L2JMs1nF+AoSRdQRuSveZ5Pcy3wXknDFyBOAH4k6VLg7u7z70+ZlnQeZQrDjrYnUArdSygF1RnA3/o+zq3ACpJ2H/bxj6CMSB/RPT6Y8of3XMoI3h3T+AN6fvf1Te9YXAucL+ksyh/tS6fz9Y0HHpd0IaUgvgdY7HmO0U6U4/knyijpcC+UfXrHuGd6X8/0fAb4fDcy+iHKlJdPA5/ovjc/pUzRwXbvytFJ0zieL/R1zaiPdMfg3ZTpXiP1J2DDvq/lFqb9/fkt8A7KCc5w+1OO+TmUaRsHU4q1b3cfd33K6PNw+wEf6LsKc13/O23/Hrhd0sWU1/wJ3dW26dkXeF/38TaZxvuPphS351NG0RfrTmS+SXm9n06Z+/58i9x3oPysnk+Z8rUH5Zit3X2tx1NG0PtN62el9zVOAnYFTul+h4yhjO7vAPym+7l5B+Vk6iTgn93vie9T1s9AeT1+uXs9HMXUv4MuAd44na9nWv93el/PtH5PvdCxmSbb/6Ac93O77+9fbN9BWftwtqRLKK+Z3lSw4b/DoFy1uHh6nyMiIl56xoyWfqXqFgbWzhGjQ1fc7mz7ppn4OeeizD1/6yCNtHZz7veyvV83knweZYHreZWj/VtJOgg42PaV/x8fo6nfU5K+RVlrckHtLBER0YbMA434N5C0GmW0+puDVPgD2J4IzK3SneliyvqI8+um+j/xJcoVhoHQTT/6jxT+ERHRb9SM/EdERERExP+fjPxHRERERLxEzNAmXxEREf267m6vpyyEvst2E5eTJc1ve3iHtmpaO06S5qHs37EYpQPfNX1tamtlWh5Ym7Lvzf2UPU5uft7/9BLK02Va3Pbf+x7L9vRaWM+sTD+y/fG+x0fZ3vr5/k/UleI/IiJGRGVn8vdR9p84krInxMef9z/932dai7Kx4ThJx1O6nU2rA9fMzNTccaJ0hTqN0i72XkqXsrVqBOlaPH+bsn/HtZROWfMBB3QNB/bu9tZ4SebpMi1PaV38TUm9jVLHAQcAK87MLH2ZPkbZXHF+Sf9F6cQ2hqlbHM/sXGtQdkFfnbKH0kTKerUf2b7oef7rS0aK/4iIGKnNgTUpI6Lfl3R57UCUlq9rUjY43J+ymV3V4p82j9MCto+QtJXtiypvBPdByo7a/xr+DknzUVriDm8P/FLKA+XkY3PKBoVbdM9NomwyWIXtHwM/lrS37f1r5egn6YeUDV+/TNmcclL3/ArAVt3rfWAaO4xUiv+IiBipsZSN3npTWCZUzNIzyfaDkoZsPyXp+fYomVlaPE5IWrq7XZwyOlqF7a8Mf07SWNuTbD/ETC60W8vTZTqfshfKm3t7ufQyzews03Ckyu72EylTyX5g++pKWb7e2w2+n+1rgb0kDd/d/SUpC34jImKkjqXs+/A6SadSNlusbbykA4AFJH0OuKN2INo8TrsBPwPeDJwAfKpunOd2iN9c0jbAvZI+nTxTWaYv0z2NZDqWckVif8pGpd+vFaRX+Es6UdJG3d40/e+/r06ytmTkPyIiRsT2j7oduZcvD/18u7fPLDsD2wMXAI9RdlWuqsXj1I2Evq12jmF2A94F/BJ4FXAGZe598kzWn+nVtJFpEpM3f/ylpOo/c5Tpf9sC+0s6ETjC9t/qRmpHiv+IiJghkqY17WEZSZva/tpMDwRIWrPv4Q3dG8CqlMJkpmv0ON3O5OlHAM8AswITbC9TI1OfJ7vbR21P6BbX1tRaHoCnutuWMs0KfAs4T9I6wGyV82D7CuCKbo3GT4FbgNnrpmpHCy+aiIgYXXqXzjcFbqcsql2FMhJZyy7d7ZKU4uNy4E2U0f+1K2Vq8TgtTenI8mPgYNuXSXoTbexufRtwCbCHpC9T2qImz5Rupb1M2wLrA4dRXuvbVE3Dcx1/PkL5eTsBaGF6VDOyw29ERIyIpDNsb9D3+Ezb61fOdArwXtsTJY0DTrG9YeVMLR6nc2yv3ff4PNtrPs9/mSkkvcz2Y5IWoVyNeCh5pp/J9r0N5BlHOQF4NXAWcJ3tBypn+jVwCHBG7T01WpQFvxERMVLzS1oSymZDwLyV8wAs2nd/FmChWkH6tHicHpb0dUnv6RZI31M7kKQf2n6se/hG4NLkmZKk5YDTJF0HfETSxrUzAQdTCv/1gXmAo+rGAUq71tcBP5K0m6TqU5Fakmk/ERExUrsDv5W0EHAXZbFtbYcD13fF0XLANyrngTaP04coOd5NWR/xlappikckfQN4GWVx9LuSZyo/oIyyH0p5rZ9G2aG5piVtby9pddsnd122ajsYeJjSfWgtypSk7Drcych/RESMiO0LgHWATYB32q6+eVW38dAawP8Ab7f987qJ2jxOlIWjT1E6tYxhykXAVdj+AmXX2tfZXtv2rckzNdvjgSHb/wBa2MdiFkkLAkiah/Kaqu31tj9l+0Tbe1CuAkQnI/8RETEikj5Aaal3A7CCpK/YPrpyphWAIyitGe+RtJ3tKytnau44UeZDP0xpFVl1ZFTSPUw++RgDLCzpbgDbi73U8wzzoKSdgLklbU75Htb2Bcpi9kUpi5F3qxsHgDkkzWX7CUlzUk7iopPiPyIiRmoPYKVu8eE8lMV+tYvaHwDb275a0oqUrjZvrxupyeP0+r4FvidKuqhWENuLvvC/mnlayzPMR4G9gQeAlYHt6sYB4AnbkvQKSq7qC8eBA4Gru+l/ywJfrpynKSn+IyJipCb1FkTaflTSUy/0H2aCMbavBrB9laSJtQPR5nFqbmS0W8x6EDAf5eToOtvV5rO3lqfzSdvPzanvFmt/vkaQrp3mspS2o9/tnh4LfJyyRqIa28dIOp+y4P/+bPA1pRT/ERExUrdJ+g5lE601KT3Ia3u264ByPiXThMp5oM3j1OLIaGuLWZvJI+mjlJ2rl5G0Uff0WMqeFlWKf+AhYBHK5lm9qyWTgM9WyvOcbg+E2W3vLel4SX+2/c3auVqR4j8iIkZqW2AnSou/G4AWunxsB3yb0uXnBmCHunGABo9TqyOjtsdLGrL9D0nVF7M2lOdo4E+UKT/7dc9NAu6vFcj2dcB1kg61fXetHNOxie2VAGz/t6QLgRT/nXT7iYiIkVoJGGf745R59VUv9XceAw6xvTxlpP1flfNAg8epGxnd2fafge9I2qt2JtpbzNpMHtsTbP/V9o627+je7rRd/cpWg4U/wKReb39Js5J6dwo5GBERMVI/Ak7p7n+RMpWktl9SpiEAPEj9hbXQ5nHaxPbeUEZGKW1Ia/so8FomL2b9aN04zeWJF+8gylWJXwNXAT+tG6ctKf4jImKknun1Prd9G2309567tyjT9rHAXJXzQJvHqbmRUduPAH8ETqLsEvtE8owOktaTtKOkN0iao3Ye24dTrrJ9C1jLdgu7Djcjc/4jImKk7pC0P3Ax8BbK7rW1PS1pfUq/8bfQRqHd4nHqjYxeCyxNA/Ohu2O0OLAMZaH254EtkmeKTK+kfK8WAo4HrrF9aeVMzR0ngG4TtH/UztGi6mf6ERExam1LWXC4EeWPbAs9x7cHPgZcBuxKWWhbW3PHqdGR0dVtbw08ZvtIypSb5JnSIZRN7GalrGlpYQpZi8cpnkdG/iMiYkRsPwV8v3aOfrbHA5vWztGvxeMETY6MztJNGRmSNA54NnmmMqftsyTt47KzVgt7RjR3nCR92va3a+doVUb+IyIiogXfA66gdEO6lLI7c02t5QF4StI7gXGSVgVaKP5bPE4bdSciMQ0Z+Y+IiIgWXACsDrwOuN32A8kzlR0p+1gsCHwa2KVuHADOpiyMbuk4LQjcLel2YAgYsr1a5UzNSPEfEREjIukNwNyURbX7A/vb/lPlTHMD8wHPUAqlo2zfUSnLl6b3Pttfm5lZevqKoZ5nKPPHJ9hepkamPj+ntGk9mdJes3YR+XPaygOwGbCL7YdqB+nzB8qu1Yc2UvgDvKd2gJZl2k9ERIzUQZTuHvsAXwC+XDcOACdQNtX6H0phe0jFLPd1b28DFqEUSPMDK1bMtDSwLGW0dnPbohSUF1TMBIDtd1L2G/g7cLSkK5NnKrMAf5R0jKS1a4cB6HbS/RawqaTLJX2hdibKuoNvA6dS1tuMqZqmMSn+IyJipJ4Crgdms30JDSz0o/T1/x2wuO1vANXm/do+2PbBlN19d7V9jO3dgXkqZprQLUBe0vZl3XNXAqqVqUfSppQrSJ8G7qXyxkyt5QGw/Z2u2P4+sKukmytH6rme0sr2QWCNylkADgX+l9LR6kjg8Lpx2pJpPxERMVJDlM2PTpX0AcpIe22zAbsBV0haljItqbb5JS1p+1ZJAuatHQh4WNLXKS1RVwPuqZwH4ADKlaRvAKfbfrhunObyIGlOypWabSij2dWvtkk6AliVctVtJ9t/rZsIgDls/667f6KkPaumaUxG/iMiYqQ+CBxp+0BKy8jNK+cB+BSwGLAfsC7lRKC23YHfSroPOJayD0FtHwIeBjamFP5bV00DdGsONqWcHP1G0iXJM5VrgOUo8/7fYfsXtQMBvwWWt/2lRgp/KO1HVwDobode4N+/pGTkPyIiZoikjW3/Hviv7vGO3bteT6U59pIWt/13yqLMwyg7oJ5RI8twti8A3lA7B4CklW3/mTId4truDWAtKh8vSW8G3gVsADwBHJc8z2WZxfZE4E3A091zswHYfrpSpn1s7wtsCWxRLmoVtreskanPJ4AjJC0K3A3sUDlPU1L8R0TEjFqgu120aoop7dm9HUwZ5est8BuiXAGY6SSdYPv9ku5h2Mij7cVqZALWA/4MbDHs+SHqnyztA/wGeG8LU2xoK89RlCL7WqZ+fS9RKdPJ3e1BlT7/dNm+Clildo5WjRkaypWQiIgYGUnzUlp9bgr8vqUWhJJeZfvOBnK8ottNtxmSdqdM2Wrm+xUvTNIqti/ve7y27XMqZRlHWVD/S8oUwDHd41NsVznhjhcnI/8RETEikn4J/J6yYHQsZRrQ+ypn+gxlLvvLgW0lnW679mK/0yX1+qCfWTlLzzhKy8ibKLnOqZwnnoek1Slz/feQ9N3u6bHAxyk769awHbA3pY2tKcX/szTQNjaeXxb8RkTESC1m+2hgGds7U7GFZZ/NKK393mV7Wcoc6apa7IPecMvImLaHKUX27JTpdosCrwA+WyuQ7UNtvxb4mO0lbL/W9utsf6RWpn6SNpW0l6SNa2dpTUb+IyJipGaT9F/ADZIWpI3i/1lKkXRf93jOiln69fqgv44G+qC31DJS0usp7TSfBL5q+5bu+Z/a3qVWrh5Jx9ZewGr7OuA6SYfYbqEtK5K2t30Y8HpJ+/e/z/belWIBIOkwyu+ji4CtJa1ne4+amVqS4j8iIkbqW5S5vp8CPgl8vW4cAM7p3raS9D3glKppaLYP+jWUPLvYHl85yyGUnvqzUnqyb9VtPLZ0jTCS/sbk+mgMZZ+Ge4ChWgu1e4vHgb9I6i3WHFMzE9BbT3NTpc//fFaw/dbu/oGNtGltRhb8RkTEiElaHlgGuKXrsNEESa8AHrZdfeMxSe+hLIZu5g+upFkou/ouS+XvnaSzba/T3Relw86GlAXJM33hqKS1gD0oJ0b39OeLqUmaG5gPmEhpqXmU7TsqZzoG2Mf27ZIWAn5k+wM1M7Ukc/4jImJEJH0SOJTSM/4QSZ+uHAlJa0u6DTgTuEXS+rUzAXcAl0m6R9KVXf/42nah7IfQwvduoqT3SBpn25RFrL+nTN+a6Wyf22U4pDsRaOmk7R2SNpS0kaRbJdXupw/lCtKbKVcCn6HSXh/DvA24SdItwF+B9bufv7vrxmpDiv+IiBipLYA1bO9OKSI/WDcOAPsCq9tekZJp37pxADgQ2N72osC2wA8r54HSM76V7912lPUH8wLYPpuyK3KVzau6DH8H3g9sRVv7WewH3EKZZvd2YOe6cQCYi9Lzf3Hb36B0kqqqW4A8u+3X257L9ny2F604RaopKf4jImKkxnS7jtJNr6k+xQZ41vbdALbvAp6qnAdgrO2r4bnNhybWjQO09b171vZHbD/Ye8L22d0JHJJmasHW+3y2J9jegWGbxM3sPMM8QVnMPtH2vbRxVWI2YDfgCknLAnNXztPqFZJmZMFvRESM1AWSTgDOp3SwubByHoBHJH0COA9YE3jwBf79zDCxazd4PiXThMp5oK3v3WclPQMcC1xre6KkMZSpJB+mjCR/omKeeyrn6fcIcDplStLHgPsr5ej3Kcomf/tSjs9uVdMU+1Gubv2YcoXkOMr3M8iC34iI+P8g6d2UBb832m6hs868wD6UTDcAB9TexVbSa4Bv92X6TO0FkTDF9+4G26dWzvJWylSWtSizEp6knJD8xPZM79TSWp6+XLMDS9q+QdJywHjb1U8mu9fSssDNtk9qIM/ZwHuAX9reWNK5tteqnasVKf4jImJEJM0DvAuYo/ec7aPqJSq67h79mf5WMQ6SFgDebPtMSR8Hjrb9cOVM7wFWtv1lSacD37V9Rs1M8cIkLQ58j67QBvao3TpW0gHA6yk7+64J3G77U5UznQQsQFl8PA+wtu3/rpmpJZn2ExERI3UScDeT+31XH02S9BPKCck9dH3QgdWqhoJfUhb9QpmGdDRQe9fRrwK99pUfBE4DUvy371Dgp5RpbWsDhwPr1QwErGn77QCSDgRa6Kn/ASZfIVme0tkqOin+IyJipMba3qp2iGHeQvmjP6l2kD5z2/49gO1jJW1fOxDwjO1/Adj+l6RnaweKF2UO27/r7p8oac+qaYpZJY3tfuZ6J9y1LQjs3V0FPJ6yCPnSupHakW4/ERExUtdIequk2SXNJmm22oGA8fRN+WnE05LWlzSPpPWAFk5MLpN0rKRPSDoKuLJ2oG5RdP/jqpsytZanM4ukFQC62xYK7V8BF3Y7al9AudJV2yHAEZRdo89j8pW3ICP/ERExcmtRFtX1DAFLVMrS82rgDknju8dDtmtP+9mesuD3B5QFvzvVjQO2PyFpU8ouvyf0jSbPdF2R/XZgC0m979U4YBNKl5aXdJ5hPgkcIWlRypS7HSvnwfZ3JP2B8lo6zPb1tTMBc9o+S9I+ti2phZa/zUjxHxERI2L7jbUzTMMWtQMMZ3s8pRViU2yfWDtD52rK4swnAXfPTQJ+kTxTsn2lpI2AJSmddaq3spW0FKW1poDrJH2qgW5WT0l6JzBO0qq0sd9HM9LtJyIiRkTSLUy5m+czlMW/n7X9l0qZjhj2VC/Tj2u3/IznJ6k3FXks8DbgUtvVdvltLQ+ApF0pux9fBywHfN320ZUzXUJZQH4RsDrwadvrPP//+j/PtDjlatsKwI2U9rq318zUkoz8R0TESJ1FWUx3PqU42h74GWV6y+qVMs0J3NplWhVYhbIR0pGUaRvRru9SCrXXUDbUug/YJnmmsAPwBttPSZoLOJfSPaqmx22f1t0/pZFFyHvY3rx2iFal+I+IiJFayvYfu/vnSPqi7T9J+nLFTK+w3Zv68wdJZ9j+oqTzagWSNA74CKWIPAu4zvYDlbL8jOksErW93UyOM9wqtneXdLbtdST9KXmmch8wsbv/JPDPill67pS0D+W1vRIwQdIGABX3jlhW0str76fRqhT/ERExUk9L2plyuX81yh/9laj7t+U/JC1t+yZJSwPzdJtsvaxipoMpizPXBy4HjgI2qpSl14llF8r37ULK1ZG3VMrTb1z3+vlr1zlqnuSZyljgKkkXAW+itNk8FsD2lpUyDVHWICzZPb6PsvZmiHp7RywLPCDpgS7HkO3FKmVpTor/iIgYqS2BLwDvBa4FPkwpImuOIH8cOEbSYsDfgI9RNrHar2KmJW1vL2l12ydL+lytILb/ANAtyvxW9/SFks6slanPUcBPKK+fb1FOmmpqLQ9M+To+plqKPra3rZ1hONuvqZ2hZVnwGxERI9a1HJyVsrnPYrYvrhxpCpJmtf1M5QznA++jtIl8L3Cq7TUqZ7oI2IdyJWI1YJ/amQAkzUuZHnWr7ceTJ2aEpIOAH9m+bhrvWxHYxXb1Vru1ZeQ/IiJGRNLhlIW+c1MW2t5GWWRbM9NOwJ5MPiGZCLy+ZiZKkX0hsChwCaVbS23bAf9DOTY3UH8hK5I2oxyrWYDjJA3Z3jd5YgbsDewraWVKm9b7gPmANwKXUb6fL3nZ4TciIkbqjZR2g3+gzLFtoZf2x4C1gdOAbYHqGw7ZPte2KHOil7ddfYqN7ZuAT3VvnwRaaIO4J+Xk8QFgX8rVkppay9NrYdn/WLWy9GX40bDHR9XKYvtB27sC61GmbV0BHAusZXtX2/+ola0lGfmPiIiR+qftIUlz236ggToE4G7b90iax/Y5NTsPSbqYaXTWkUTtXYclfZxSzM4P/JxyBeDjNTMBz9qe0I2wD0mqPc2mmTySlgdeCXxT0me7p8cBBwArVsr0McpI+vyS/otypW0MbZxwPwpUP8luVYr/iIgYqSskfRq4W9IvgblqBwL+JWlTYKibArRgxSwt9xnfHFgT+JPtAyVdXjsQcEHXuWbxbu527Uwt5ZmP8j1bmMm7WE+iLEiuwvaPgR9L2tv2/rVyxIzLgt+IiBgxSS+jTPd5F2UH1Psr55kHeB1lru+ngJNtn1M50xKU+fVLUXZm/aztOytnugh4O6X4X1fSBbZrbczWn2tDyq6sN9k+OXmmyvPmWrtnT0/3M/cuYI7ec7arTf2JF5aR/4iImCGStrd9mKQDmHJay9soC+5qZFrZ9p+7DACvoKxFmK1GnmEOp7SKvIgy2n4Eped/Tb8AzgNeI+lU4MRaQbpN0MZR9iD4IGWzqHGSzrK97ks9zzALdN+v/kK7dqaTKPtY9E5oq48qS3ol8E1gIcou5NfYvrRuqnak+I+IiBnV+yN/U9UUU1oP+DOTp0T01NxoqOdZ26d190+WtHvNMJ2fAn8Elqd0RflbxSzbUU4aF+myQJnSckHyTOV7lG5RVa8cDTPW9la1QwxzCPAd4IuUk9wjqdyJrCWZ9hMRETNE0prTe5/t82ZmlpZJ2qC7+zFKq8/zKJugrVprN1ZJiwD/QemE8mHKAs1xwJG2q+7yK2k720fUzNCvtTwAkk61XWt36GmS9APKhmNX0Y362366cqazuulsvdtzbK9dM1NLMvIfEREzapfudknKtJrLgTcBj1HabM50ku6hFB6zUxYe30npjvIP2/9ZIxOTr0I8CCzTvQFMqBMHKKOfuwGi7Fg7hjKq/YeKmQBordBuLU/n/m7x8ZVMLrQPqRuJtYD39D0eApaolKXnKUnvpEzXWpU22hA3I8V/RETMENtbAEg6BXiv7YndPOlTKmZatMt0NPB523dKWowyTaJWpm2n9Xy3K3IVtk8ETpS0EXCO7SckLWb77lqZYob09mNYpGqKPrbfCCBpAeBB2y1MKdkR+Dal29enmTxgEaT4j4iIkesvYmehLK6rbYleJx3bd0t6de1Akr5GKT5mo1yVuJmyOVpNqwCrU+a2Hyjpz7a/WTkTAJLmt/1g7Rw9LeWx/VVJ76CMrF9CeS1V1U0D/All+tjxku6wfXjNTLb/TtutdqtK8R8RESN1OHC9pOsoxew3KucBuEHS/wKXUTr/XFE5D8AmwOKUqxDfpWJv9j6b2F4JwPZ/S7qQ0h2lGklrAT+mTNU4HqhaRLaWp8u0P+W1tAxl+tjnmXqR+8y2L6WL1a+B/SnrW6ocp0an/zVnbO0AERExOnWb/KxBuby+uu2f100ElMv9JwIvA35lu/autQD32J4AzGN7PG20H50kaTYASbPSRj3wdUoReS+liNy1bpzm8kD5OdsaeMz2kcBrawcCJnVXRoZsPwU8WiuI7UVtLwacBixleynK7tVp89knI/8RETFi3aZeVTf26md7EmUEsiV/l7Qd8Hi3N8LLK+cBOAi4TtK1wNJUHvXvTLL9oKQh209JqlZENpoHYBZJc1B2sB4HPFs7EDC+e10vIOlzwB21A9Hg9L+WpPiPiIj4v7UTZarG8cBHgCptPvvZPlzS7yhzx2+1/UDtTLRXRLaWB8rUsSsom9hdSsUF7X12Bran7IPwGLBD3TjAlNP/VqON6X/NaOEyX0RExL9F18e+NfNR2mseCywG3FU3DkhaDvgNZW729pI2rhwJShF5B6WIfJz6RWRrebB9PLAh8G7gv2wfUzkSwErAbLY/RllnU3sxO0ye/jc38ItGpv81IyP/ERExQyTdTtdjvPMMMCswwfYy0/5fM80Jkv5BKWpP7aYB1XYU8Pvudg3KbqOb1gwE/ADYFjiUcqxOo2Ss6XhKnoMbaRfZWh4kfRmY3fbeko5vpEvTj5jcWeeLwM8payVqehllT4vlgIUlXdBKx6YWZOQ/IiJm1NLAssDZwOa2BWxGGSGtyvbqwBcoGw9dJGk/SbU3HJrD9k9tX237R8C8lfMA0C0+HrL9Dyou0uyzL7ARcKWkrzQwT7u1PFC6NO0NpUsTpZNUbc/YvhXA9m2UTeNqOwL4G6WV7V8pJyTRych/RETMkK5zDZKWtH1Z99yVklQ32XPuAm6jTEdYntLH/nrbn5uZISQt1d19QNJ/A+cDb2HyRk01PShpJ2BuSZsDD1fOg+0rgCskzQf8FLiF0rIxeSabJGk220831KXpjq4F6cWU13f1aW3AArZ/2N2/StL7q6ZpTIr/iIgYqYclfZ3Ji+ruqZwHScdRCv6jga16O9dK+nOFOAf33d+VstHXGKacMlXLRymjog8AK3ePq5K0BmVB9CqUKTefTp6ptNilaVvK+oh3ATdSrpjUNqekRWzfK2lhygZk0UnxHxERI/Uhyh/9jYHrga9UTVMcZvuMaTy/+swOYnud3n1JCwBLAre10FnH9iOSLqWM+F/XyHzo3YFDgO0bmWO/O23labVL0zOULj8PANcC81A2IKvpi5Rpf/8C/oOyADg6LVwuioiIUUTSyt3dt1P+2P8auIkyz762fab1ZLf5UBXdlJ+LKCPtl0jaqlaWHkmHAR8EngS2llStZWTf6+kQylWR9SVtIGmD5JlSo12aDgZeDaxPKfyPqhsHbJ8JrE3JtIXtP9VN1JaM/EdExIxaD/gzsMWw54eAaY26z0xDkn4LmG7hYW+BZEV7AivZfkzSPMBZlGlJNa1g+63d/QMlXVIxS+/1tPmw52u9nlrL06/FLk1L2t5e0hq2T+72RKhK0kHAeNvflvRFSR+yvVvtXK1I8R8RETOkr7Xg1cCRth+qmWeYI2oHmIZJth8DsP2opGpXIfqMl/Ra27dLWojSGaWKvtfThbYP6z0v6ZPJMzXb47tdh//R0K7DC1JOvOehjW4/b7a9M4Dt3SSdVztQS1L8R0TESI0D/ijpJuBQ2+dUzgNwDGVH3WWBmyldWmq7TdJ3gPMo/c9vrZwHymZMN0n6G/BKYIKkeyitPxebmUEkbUFpWbmOpHW7p8cCK1BGumeq1vIM01yXJkpr3QuBRYFLKBvaVSdpAdv/lPRyUu9OIQcjIiJGxPZ3gO9IWgX4jKRDbC/1Qv/v/9jBlILoTMoahMOArWsGonTS2YEy//hGoPq0CNu19z7odzqlU9QCTO6QNIl6J0mt5enXXJcm4FW2JekVwAONLI7+GvBnSQ8CL6d024pOiv+IiBgRSXNSNvfahtLC8st1EwHwetu93UVPlHRR1TTF721XXyzaT9I7KDXAWOCHwBdtH1spzty2z5F097DnX1YlTXt5+h1ke8vaIYbZETim2yyuCbZ/L+k0YEHg/kZOSJqR4j8iIkbqGuAEYJdut9gWzCFpLttPdCcnLfT3fkjSJpRpSL1FyDfXjcR+wJbAjyldm44DahX/e3ZvBw97fghYd+p//n+utTz9Zpf0BqZ8LT1dNxKzS7qSKRfZVz9Bsf0scF/tHC1K8R8RESO1DCDgTZJeZvuqynkADgSulnQdZd5/C1cjFgL26HvcQhH5BKUwmththFRtZNT2nt1t/74Ir7J9Z/JMZSngJMprqLdhXO0pXHtV/vwxg8YMDeVKSEREzDhJn6CMHl9K2eH3ONvfrpxpcUphuwRwO7CgbVfM8x+UAvuJWhmmpdsoan5KL/t5gLVt/3flTJ+hrNd4OaWd5em9Qjx5YkZJGks5QVoNuLSBKyTNyCZfERExUlsCa9jenTJ15IO1gkhaXtI7KT3PV6YUtqsAv6qY6eOUdqhXd9la8t/AjraPAs6l7NZc22bAkcC7bC8LvCl5YiQkfZ+yyP7rlG5Eh1YN1JhM+4mIiJEaY3sigO1nJD1TMct8lE2ZFmby5mOTgJ9US1ROjgT8B/C/wB8qZhluLwBJ/c99rU6U5zwLLMLkedpzVswC7eWJF28V27tLOtv2OpKyw2+fFP8RETFSF0g6ATgfWIPS67sK2+cD50t6s+2/1MoxzFPdVIMHJM1WO8wwvYJ2DPBm2pgJcE73tpWk7wGnVE3TXh4Aug215uo9tl1tgzYASe+mtNJ87uTIdu01LeMkrQT8tfvZm6dynqak+I+IiBGx/enuD/8ywM9st1AcLS7pAGBWSmG7oO0VKmeCkqUZtqfoZNO1RazK9heAL3T94j9ru+aVpObyAEg6BFiPcvLWW/C7WtVQZWrNHsC9lXP0O4py1W874FtM3bnpJS3Ff0REzDBJOwJH2D5F0iPAcrUzdfal7PC7M3A28I6KWZaTdCylSOvdB+q3QpTUvxnbosBramXpkbQ2cATwCPBySTvYPjN5pvAG4HWN9a1/0Pa5tUP0s/0TJk/5271ilCal+I+IiBki6SvA8sDRwETgTmAPSQvZrj1v/B7bF0va2fbPJX2kYpYP9N0/qFqKaesfCX0K+FStIH32BVa3fbekVwK/oezUnDyT3U2ZwvJI5Ry9AQCAp7srEldQrkRg+5BKmU6w/X5J9/Sy0F0hsb1YjUwtSvEfEREz6l3Aqr3RR9t/lfRB4CLqLxqdIGlNYNauw86CtYK0Nhrar9fDXtLLgWdtP1o3EVBy3A1g+y5JTyVPIeliSjG7EHCLpNu6dw3ZrjXtZ9Hu9tLudpHutuaeEe/vbhd9oX/7UpbiPyIiZtRjw6cddN1+WiggdwGWpozafr27jY6kNwOHA28BNqZcAXhI0qdtn1w1HDzS7R1xHrAm8GDyPGfz7nY2oL9f/fwVsgBg+6sAkvax/dzPWbfmpipJ76DUuGOBHwJftF1rB+vmpPiPiIgZ9aSkJWz3Rh+RtAQVR/z6/E/ffPrNqiZp0/8A23Qna/tRruLcApwG1C7+twL2oZyw3UhZrFlTS3kmUFrGHgV8mDKVZSzl5O0tNQJJ+iiwPbCMpI26p8dSTlA+XyNTn/0orXZ/TNmD5DggxX8nxX9ERMyovYATu97ZtwGvBt4JbFM1VTG7pDcAN1P6/JOdPacwzvY1khYD5rZ9BYCkSZVzYftfki4C/glcZ/uh5HnOqsBulH0jevPpJ1F374ijgT8Be1OKbSiZ7q+WaLInKB2RJtq+V1ILAxPNSPEfEREzxPb1ktYA3gssBvwF+Foj88YFnNT3eAhYolKWFvXaVW4I/BFA0qw00Add0mHAy4CLga0lrWd7j+QB2ydSTrg3sn1qjQzTsILtP0v6NeXnrmcZ4IxKmXoeAU4HDpH0Mdo4IWlGiv+IiJhhtv9FmYLQFNvL187QuD9KuhB4FbCJpCWBHwG/qhsLKMXkW7v7B0q6pGqahvL0zav/sKSt+t9XsW3sesCfmbweoWeI+sX/B4Albd8gaTngsMp5mpLiPyIiRj1JP7L98b6uKM+p2A2lOba/Kel3wL+6FpZLAofY/m3tbMB4Sa+1fbukhYCqO9c2lqe3HqOZlrG2v9ndvRn4te2ba+YZ5hXAVyUtS8m3B/DXqokaMmZoKNOgIiJidJO0sO37JE21WZXtO2pkihkj6VbglZQie3HKItenqNSjvbU8XaYrKPP8f91br1GbpC2BTShXk84EfmP7msqZTgN+SunUtDbwCdvr1czUkhT/ERExMLqda/8HWAq4HvhUiv8YFJJmo0y32QR4I3CJ7T3rpgJJ4yjtUPcH3mR7jsp5zu7tZdE9Ps/2mjUztWRs7QARERH/RkdRpkasCvwM+HnVNBH/XnN3b+OA2YGF68YBSScBlwD/Ren6U21jvT6zSFoBoLvNSHefzPmPiIhB8rjt07r7p0iqPioa8W/0D+Ba4Au2d6wdpnMxsAZl2s8SlH0jXDURfBI4omtpexfQyrFqQqb9RETEwJD0c2A8cBawEmUTq+8D2K7dgSSeh6SNbf++7/EHbB+XPFNkWpSyp8YGlBH2K2zX3lALAEkrU6bcrWp7ztp5Yvoy8h8REYNkCFiye4Oy0c8WtNF+MKZB0saUXVi3kNTrzDSOMq99phfbreUZ5j7KyPpSwGuA/6yaBpD0Q8rI/83AoZT9P6qStDXwOeC5tQe2s99HJ8V/REQMDNvbdosPxwBvAy7NDr/NuxpYAHiSydNFJgG/SJ6pGDgX+A3wlUZe22cCn7H9VO0gffainKzdWTtIizLtJyIiBoak7wM3UkZF3wzca/sjNTPFiyNprO1JtXP0tJYH2szUIkkn235P7Rytysh/REQMklVs795r9SfpT7UDxYu2l6S9gCcoV26q9dNvNA8p/F+0J7pe/1fRdfqxvXfVRA1J8R8REYNknKSVgL92PdHnqR0oXrTNgcVsP1E7SKe1PM2S9Hrg9cA1wF22a08rObXy529aiv+IiBgkRwE/AbYDvgUcXDdOzIDbKfPsW9FUHkkvA7YF1qKsSbgf+BNwrO3HKub6OPA+YH7gSOB1wMdr5ekMP/l4RtLqti+okqYxmfMfEREDRdK8lDn/t9p+vHaeeHEknQq8mtLHvjdVY8vkAUnbAe+njGhfA9wDzAe8FdgIOMH24ZWyXUDZ3fdP3VS7y22vUiNLX6ZTgbkoexC8hdL151lKa9Q9amZrQUb+IyJiYEjaDNiH8vftOElDtvetHCtenG/WDjBMS3nutr3RNJ6/DPihpGm9b2YZSzk56o0mT6iYpWdWYF3bkySNBU61vaGki2oHa8HY2gEiIiL+jfYEVgUeAPalTEeI0eEvwPrANpRpLXfVjdNOHtunA0jaTdJ803h/zTnuxwLnAa/rRtxPrJilZwHKCQDd7fzd/dnrxGlLiv+IiBgkz9qeQOnMMgRk2s/ocQRwG2Xh6L1AlWksfVrLA+WK1h8lHSNp7dphOmcCOwKfAj5n+9uV8wD8GLhG0m+AK4GfSNobOL1urDak+I+IiEFygaRjgcUlHQRcXjtQvGgL2D4CeMb2RdSvUVrLg+3v2F4J+D6wq6SbK0cCONz2jbaPt31N7TAA3fqH1ShTt9aw/XPgm7a/UDVYIzLnPyIiBobtvSVtSBntu8n2ybUzxYsnaenudnFgYuU4LeaZE9iMMhVpDPDluokAeFzS9yi7D08CsH1IzUCSVqV0RpoVGCNpMdvvrJmpJSn+IyJi1JM0C7AJ8FA3P/p0SYtI+pXtD1aOFy/OJ4GfAcsAJwC71o3TXB4onX5OAHaxPb52mE5vEe3C3W0LbSR/Smn1+35Kt6bZ6sZpS/VLWBEREf8Gx1BGRL8o6eOS3k3Z3fOqmqFihtwC7Gr75cA3KEVbTa3lgXIicjTwJkkrVs4CgO2vAodQTpR+TlkDUNsDtn8BPGL7K8DilfM0JcV/REQMgiVtfwjYENgJ+Cqwju0D6saKGXAMsGJ3fynKhlE1tZYHYBfgMODtwCGSPl05D5IOp2w2dj5ljc336iYCYJKk5YC5JInJ3X6CFP8RETEYHgGw/TTlb9sGtm+sGylm0Ctt/wzA9reARZNnKltSFrDuTjkBaGFK2xuB5YA/AMsCT9WNA5SWv8sBP6C0Ij2ibpy2pPiPiIhBc5/tB2uHiBk2JGkpAElLAuOSZypjbE8EsP0M8EzlPAD/7Nrqzm37gZpBurU/UKZsnUi5EvE2SuvP6GTBb0REDILluhafY/ruA2B7y3qxYgbsAfxK0sLA3cDOyTOVCySdQJliswZwYeU8AFd004/ulvRLYM6KWY6iXB0xZeHxmO75IWCJWqFaM2ZoqIVF2RERESMnaa3pvc/2uTMzS4ycpHmB/wRutf1Y5TjN5QHoFrMvA9xQeWff50iaB3gS2Ai41PZ9lSPF80jxHxEREdVJ2gzYhzIr4TjKLs37Js8Umd4DrGz7y5JOB75r+4zKmXYAlrL9GUlnAP9r+38rZTmbabcaHbK93szO06rM+Y+IiIgW7AmsCjwA7Au8r26c5vJA6WL13e7+B4Gv1IvynF2Az3f3303d/RB2puS5FzgI+DDwQ+CvFTM1J8V/REREtOBZ2xMoo7RDwOPJM5VnbP8LoLt9tnIeKMepfxFytSkl7gAL2z7O9l22fwu8tlamFmXBb0RERLTggm6h9uKSDqJ0akmeKV3WZboYWAW4snIegJMknQ9cBrwZOKlyHgAkfZSSaTXg6cpxmpI5/xEREdEESRsCKwA32T45eaYmaVNAwI22f1c5DgDdbsOiHKerK8dB0iLAFyibs10P7Gf7n3VTtSPFf0RERFTT9WbfBHjI9tndc4sAB9qe6ZtYtZYn4t8t034iIiKipmOAicCikpYDbgcOBw5Mnoh/vyz4jYiIiJqWtP0hYENgJ0pHm3VsH5A8U5K0Tu0Mw0lauXaG4br9GWI6UvxHRERETY8A2H6aUpdsYPvG5Jmmr9YOMA2flnSJpI9LenntMJ1TagdoWab9RERERCvus/1g7RB9WsszJOm3gIFJALb3rhnI9uaS5gO2BI6XdD9wqO1zKsZ6UNJuTHmcqm6G1pIU/xEREVHTcl37yjF99wGwvWXyTOGIyp9/ehYGXg0sCNwAvF/S9ra3qpTnn8CK3RuUvQdS/HdS/EdERERNH+i7f1C1FJO1lqffMZR1CMsCNwM/rRsHJF0KPAEcCnyp2xgNSX+olcn2tpKWpztOtq+qlaVFafUZERERMQpIOhx4GDgfWAtYwPbWlTN9yfbXamYYTtInKNOQLqVs8nWc7W/XTdWOjPxHREREjA6vt71md/9ESRdVTVOsBzRV/FMK/zVsT5Q0K3ARkOK/k+I/IiIiYnSYQ9Jctp+QNCcwrnYgYHZJVzLl4traayPG2J7YZXlG0jOV8zQlxX9ERETE6HAgcLWk6yjz2b9cOQ/AXrUDTMMFkk6gTI9aHbiwcp6mZM5/RERExCghaX5gCeB22/9sJM87gVkpHZIWa2RDtHcDywA32k7f/z4p/iMiIiJiRCSdC9wIrAA8BTxh+z11U8XzyQ6/ERERETFSY2zvTJnzvz4wf+U88QIy5z8iIiJilJC0ILAkcEsjuw9PlDQHMDdlM63Ulo3LNygiIiJiFJC0K7AHcB2wrKSv2z66cqwfd5nOAO4ELqgbB7oNvn4KzAccDVxn+/d1U7UjxX9ERETE6LADsILtpyTNBZxLKW6rsf3r3n1Jx9t+pGaezoHAtpRdhw8HTgNS/Hcy5z8iIiJidLgPmNjdfxJoodvPTpKulHQDcEl3W53t8cCQ7X8Aj9bO05KM/EdERESMDmOBq7qdfd8EzCrpWKi6sdZuwEbAQ5U+/7Q8KGknYG5JmwMPV87TlBT/EREREaPDfn33j6mWYkrXAHfafrZ2kD4fBfYGHgBW7h5HJ8V/RERExOgwHpgXeBb4LPBD21dVTQRnAbdJupWyydeQ7XUrZzqo4pWQ5qX4j4iIiBgdjgW+AnwMOAH4HrBOzUDATsAHaGtqzeyS3gDcDEwCsP103UjtSPEfERERMTpMAs4DvmD7l5J2qB0I+Dtwue1JtYP0WQo4qe/xELBEpSzNSfEfERERMTrMCnwLOE/SOsBslfMAzA5cLek6SpFdc/Ex3edfoebnb12K/4iIiIjRYVtgfeAwYFNgm6ppigNqBxhO0iaUqVGzUtYhLGD7DXVTtSN9/iMiIiJGh9uAp4F9gPuBahtqSdq4u7s0oGFvte1LWRtxJ3AkcG3VNI1J8R8RERExOhwMvJoy+j8PcFTFLAt0t4sAi/a9LVIt0WT32L4YwPbPgVfWjdOWTPuJiIiIGB2WtL29pNVtnyzpc7WC2D6yu6vac/ynYYKkNSmboL0TWLB2oJak+I+IiIgYHWaRtCCApHno2lhWNluDbTV3oUw/2hf4encbnUz7iYiIiBgdvgBcSNm19hLgq3XjAKXIPgm4ETBwU904AMwBLGz7BuAWyrGKTor/iIiIiNHhVbYFLAksb/uPtQN1bTVfB7yNMi2phX76RwG3d/dPAQ6vmKU5Kf4jIiIiRocdAWz/w/ZQ7TAAkt4HjAdOBW6WtH7lSADYvqS7PZ/Uu1PInP+IiIiI0WF2SVdSptf05tfXXmz7JeCttu+XtDBwMnBm5UwPS9oRuBh4C/Bo5TxNSfEfERERMTrsVTvANPzT9v0Atu+TVG3vgT7bUPZCeB9wA7Bd3ThtGTM01MRVo4iIiIiYBkmfAH5qe+I03jcLsKvtH8z8ZCDpt8BcwLnASpRe/+cA2N67RqZ4fhn5j4iIiGjblcDpkq4HrgHuA14OrAosS92uPyf23b+rVoh48TLyHxERETEKdItp16ZsWnU/ZYT9rFYW/8bokOI/IiIiIgaGpD8DRwNH2X6wdp7WpPVRRERERAySdwBPAydL+qWkd9QO1JKM/EdERETEiEh6FbAFZVddAGx/rV6iySQtA3yRcjJwO/AN27+tm6q+LPiNiIiIGAUkzQ3MBzxD2fDrKNt31E3F8cAfgTsr53iOpF2BrYFHgEMprT9nBS4BUvzXDhARERERL8oJwEHAZpT+9YcA76yaCB61vU/lDMMtB2xh+/a+556RtFOtQC1J8R8RERExOswF/A7YzfbWjcxlv07S5pR2pEMAtm+uG4k3Div8AbB9cY0wrUnxHxERETE6zAbsBlwhaVlg7sp5AFbs3nqGgHWrJJnsQUm7AQYmAdg+o26kdqT4j4iIiBgdPgVsCuwHbEU5EajK9jq1M0zDP5nypGQISPHfSbefiIiIiFGim+qzBGXx6s22n6qU4wTb75d0D910nx7bi9XIND2SFrV9T+0crUjxHxERETEKSNofWBxYBvgRsKHtLeqmao+krwG7UKZJzUU5SVqubqp2ZJOviIiIiNFhddtbA4/ZPhJ4be1AjdqEcpJ0DOVE6a66cdqS4j8iIiJidJhF0hzAkKRxwLO1AzXqHtsTgHlsj6dcAYhOFvxGREREjA7fA64AXgFc2j2Oqf1d0nbA45IOAF5eOU9TMuc/IiIiYpSQNB/wOuB22w9UzHE7Uy70fYayi+4E28vUSVVIGgu8CngQ+AjwR9s31szUkoz8R0RERDRM0s8Y1lGnex7b21WIBLA0MAb4MXCw7cskvQnYtVKefq8B3gPM0T1+D5Div5M5/xERERFt+yXwK2B+4CbgcOAaJhe3M53tCV2b0SVtX9Y9dyWgWpn6nEQ5VhP63qKTkf+IiIiIhtn+A4CkT9n+Vvf0hZLOrBir52FJXwcuA1YDWuinf6ftr9QO0aoU/xERERGjw8skrQtcTim0q4389/kQsDOwMXA98JWqaYqTJX0DuKH3hO2jKuZpSqb9RERERIwO2wF7UIr/nYBtagWRtHJ39+3AtcCvKVOS1qqVqc/mwLyUHv/LUNYnRCcj/xERERGjgO2bJH0eWJaya+1tFeOsB/wZGL7D8BBwxsyPM4UJtnepnKFZafUZERERMQpI+iSl2L6UMu3nONvfrpxpd+BI2w/VzNFP0iHA7cBf6Lok2a59QtKMjPxHREREjA5bAGvYnihpVuAioGrxD4wD/ijpJuBQ2+dUzgNlv4Glujdo42pEMzLnPyIiImJ0GGN7IoDtZygba1Vl+zu2VwK+D+wq6eZaWST1BrV3Gva2c61MLcrIf0RERMTocIGkE4DzgTWACyvnQdKcwGaUxcdjgC9XjHMUsCVgJm+KNqa7v0StUK3JnP+IiIiIUULSuykdbG6wfWoDeW4BTgAOtz2+dh4ASVvZPrp2jlZl2k9ERETEKCDpPcBbukW+n5S0Qe1MlBORo4E3SVqxcpaeHWoHaFmm/URERESMDl8F1unufxA4jfoLWXehTLW5FPiMpOodiIDZJV3J5Ok/Q7a3rJypGSn+IyIiIkaHZ2z/C8D2vyQ9WzsQpfBvrQPRXpU/f9NS/EdERESMDpdJOha4GFgFuLJyHhjWgUhS9Q5EwI3AFyitPq8H9qsbpy0p/iMiIiJGAdufkLQpIOAE27+rHAka7EAE/Kp7OwJ4O/C/wMZVEzUk3X4iIiIiYsT6OhDdaPuUBvKcbXudvsfn2F67YqSmZOQ/IiIiImaYpB2BI2yfIukRYLnamTo3SfoQcDawEvBPSUsB2K62CVkr0uozIiIiImaIpK8AGwCzdU/dCWwg6UvVQk22NLA9cAywJzA/cDBwUM1Qrci0n4iIiIhRQNI7KLM2xgI/BL5o+9hKWS4FVrU91PfcrMBFtlepkSlenIz8R0RERIwO+wG3AJ+kLGTduWKWx/oLfyjdfoBHK+WJFynFf0RERMTo8ARwHzDR9r2UDaxqeVLSEv1PdI8zpaRxWfAbERERMTo8CpwOHCLpY8D9FbPsBZwo6U/AbcCrgXcC21TM9BxJCwFz9B7b/lvFOE3JnP+IiIiIUUDS7MCStm+QtDxws+2nK+aZF3gvsBhwB/B729Wn/Uj6CbARcDcwBhiyvVrdVO3IyH9EREREwyRtb/sw4Mvd4/53710lFGD7X8BRtT7/83gLsITtSbWDtCjFf0RERETb7uxuXTXF6DGeMuXnidpBWpTiPyIiIqJhtv/Q3T0BmA+YCOxAm6PuLXg1cIek8d3jTPvpk+I/IiIiYnQ4Afgp8H7gBuAQyiLbmNIWtQO0LK0+IyIiIkaHuYCTgcVtfwMYVzlPUyRt393dGdhp2Ft0MvIfERERMTrMBuwGXCFpWWDuynla01sbcVPVFI1Lq8+IiIiIUUDSasCmlJ1+PwxcZvuyqqFi1EnxHxERETFKSHo3sCylx/9JtfPE6JPiPyIiImIUkHQA8HrgAmBN4Hbbn6qbKkabzPmPiIiIGB3WtP12AEkHApdUztMUSV+a3vtsf21mZmlZuv1EREREjA6zSurVbmOATN+Y0n3d29uARYBbgfmBFStmak5G/iMiIiJGh18BF0q6BHhr9zg6tg8GkLSZ7V27p4+RdGbFWM3JyH9ERETEKGD7O5SdfS8EdrT9vcqRWjW/pCUBJAmYt3KepmTBb0RERMQoIOlVlN1r5+g9l7nsU5O0OvATYGHg78DOti+vm6odmfYTERERMTocD/yRyZtZxTTYvgB4Q+0crUrxHxERETE6PGp7n9ohWiXpBNvvl3QPwxZD216sUqzmZNpPRERExCgg6XvApcCVdMWt7ZurhmqQpFfY/kftHK3KyH9ERETE6LAiU7atHALWrZKkbadLuhU41HY6/QyTkf+IiIiIGCiSVga2Bd4CnGh7v8qRmpGR/4iIiIhRQNJdwELAP4AFgacom1rtmhHuqVwPXAy8DlijcpampM9/RERExOhwHrB8t3h1GeBE4F3A12uGao2kI4ArgKWAnWxvWDlSU1L8R0RERIwOi9s2gO1bgVfbHg9MrBurOb8FlrP9Jdt/rR2mNZn2ExERETE63CPpG8BFwGrAvZLWB56uG6s5dwCXSVocuBf4qO2/VM7UjIz8R0RERIwOWwN3U6b6/A34CPAYZdffmOxAYHvbi1IW/f6wcp6mZOQ/IiIiYnSYCFwOXAWMAd5n+xdVE7VprO2rAWxfJSnTovqk+I+IiIgYHX4LzAq8EhhHuQqQ4n9qEyVtDJwPrAlMqJynKZn2ExERETE6LNh1rrkUWAmYo3KeVm0HbANcCHwY2KFunLak+I+IiIgYHZ7obue2/SRlh9+Y2mPAIbaXp7RH/VflPE1J8R8RERExOvxG0peAqyVdQqazTM8vgdm7+w8CR1fM0pwxQ0M5aYyIiIgYTSStANxi+6naWVoj6SLbq/U9Psv2ujUztSQLfiMiIiIaJmkf2/tK+gVTT/XZskamxj3d7X9wCfAWYFLlPE1J8R8RERHRtpO724Oqphg9tge+DfwAuAHYqW6ctmTaT0RERETjJL0BeD+wIPB34Hjbt9RNFaNRFvxGRERENEzSfwNHAHcCpwOPAidIem/VYDEqZdpPRERERNt2A9ay/XjvCUlHAid1b9FH0hjbmdoyHRn5j4iIiGjbxP7CH8D2I8CzlfK07g+1A7QsI/8RERERbZtet5oM4k7bQ92UKNMdO9s3143UjhT/EREREW1bTtKxw54bAyxbI8wosBCwe9/jISB9/jvp9hMRERHRMElrTe99ts+dmVlGC0nzAv8J3Gr7scpxmpLiPyIiIiIGhqTNgH0oM1yOA4Zs71s3VTsyVywiIiIiBsmewKrAA8C+wPvqxmlLiv+IiIiIGCTP2p5AGfEfAh5/of/wUpLiPyIiIiIGyQWSfgEsLukg4PLagVqSOf8RERERMVAkbQisANxo+/e187QkxX9EREREDAxJ/wm8H5ir95ztr1UL1JhM+4mIiIiIQfILYG7gvr636GSTr4iIiIgYJE/Y/mrtEK3KtJ+IiIiIGPUkLdXd/QpwMvAXyu6+2L65UqzmZOQ/IiIiIgbBwX33d+y7PwSsO5OzNCvFf0RERESMerbXAZC0cX+HH0kfqJeqPZn2ExERERGjnqSNgdWALYFju6fHAu+1vUy1YI3JyH9EREREDIKrgQWAJwF3z00CflktUYNS/EdERETEILjH9pGSjgOerR2mVSn+IyIiImIQHEWZ8nMDXZcfYEx3f4laoVqTOf8RERERMXAkLQA8aDvFbp8U/xERERExMCStCfwEGAccD9xh+/C6qdoxtnaAiIiIiIh/o32BNYF7gf2BXevGaUuK/4iIiIgYJJNsPwgM2X4KeLR2oJak+I+IiIiIQTJe0gHAApI+B9xRO1BLUvxHRERExCDZmVLwXwA8DuxQN05bsuA3IiIiIuIlIiP/EREREREvESn+IyIiIiJeIrLDb0RERESMepK+NL332f7azMzSsoz8R0RERMQguK97exuwCHArMD+wYsVMzcmC34iIiIgYGJLOsL1B3+Mzba9fM1NLMvIfEREREYNkfklLAkgSMG/lPE3JnP+IiIiIGCS7A7+VtDDwd0rf/+hk2k9ERERExEtERv4jIiIiYmBI2hr4HDBH7znbS9RL1JYU/xERERExSPYCNgHurB2kRSn+IyIiImKQ3GZ7fO0QrUrxHxERERGD5AlJpwFXAUMAtveumqghKf4jIiIiYpCcWjtAy1L8R0RERMQgOQZYBZgVGAMsVjdOW1L8R0RERMQg+S2l8H8lMA64G/hF1UQNyQ6/ERERETFIFrS9IXApsBJ9LT8jxX9EREREDJYnutu5bT9Jt+g3iuzwGxEREREDQ9LHgPmBZ4BNgcdsv6NqqIak+I+IiIiIgSJpjO0hSSsA47srAEGK/4iIiIiIl4zM+Y+IiIiIeIlI8R8RERERA0PSxsMef6BWlhZl2k9EREREjHpd0f92YAvg2O7pccAmtpepFqwx2eQrIiIiIgbB1cACwJOAu+cmkQ2+ppCR/4iIiIgYGJJ609rHAm8DLrX9dMVITcnIf0REREQMku8CNwKvAd4M3AdsUzVRQ7LgNyIiIiIGySq2DwbeZntDYPHagVqS4j8iIiIiBsk4SSsBf5U0GzBP7UAtybSfiIiIiBgkRwE/AbYDvgUcXDdOW7LgNyIiIiIGiqR5KXP+b7X9eO08LUnxHxEREREDQ9JmwD6UGS7HAUO2962bqh2Z8x8RERERg2RPYFXgAWBf4H1147QlxX9EREREDJJnbU+gjPgPAZn20yfFf0REREQMkgskHQssLukg4PLagVqSOf8RERERMVAkbQisANxk++TaeVqS4j8iIiIiRj1J44BxwC+BDwJjusen2F63ZraWpM9/RERERAyC7YC9gUUAd89NAi6olqhBGfmPiIiIiIEhaTvbR9TO0aoU/xERERERLxHp9hMRERER8RKR4j8iIiIiBo6k+WtnaFGm/URERETEwJC0FvBjSqef44E7bB9eN1U7MvIfEREREYPk68CawL3A/sCudeO0JcV/RERERAySSbYfBIZsPwU8WjtQS1L8R0RERMQgGS/pAGABSZ8D7qgdqCUp/iMiIiJikOxMKfgvAB4Hdqgbpy3Z4TciIiIiBsnxwKHAwbbT2WaYjPxHRERExCDZF9gIuFLSVyS9unaglqTVZ0REREQMHEnzAT8F3md79tp5WpFpPxERERExMCStAXwEWIUyBejTVQM1JsV/RERERAyS3YFDgO0z539qmfMfEREREaOepJW7u4cAQ8D6kjaQtEHFWM3JyH9EREREDIL1gD8Dmw97fgg4Y+bHaVOK/4iIiIgY9Wx/s7t7oe3Des9L+mSlSE1Kt5+IiIiIGPUkbQFsAqwDnNU9PRZYwfZy1YI1JiP/ERERETEITgfuARYADu6emwTcWi1Rg1L8R0RERMQgmNv2OZLuHvb8y6qkaVSK/4iIiIgYBHt2bwcPe34IWHfmx2lT5vxHRERExECS9Crbd9bO0ZIU/xERERExMCR9BngYeDmwLXC67T1rZmpJNvmKiIiIiEGyGXAk8C7bywJvqpynKSn+IyIiImKQPAssAtzXPZ6zYpbmZMFvRERERAySc7q3rSR9DzilaprGZM5/RERERAwcSa8AHrb9TO0sLUnxHxEREREDQ9LawBHAI5RFvzvYPrNmppZkzn9EREREDJJ9gdVtrwi8vXscnRT/ERERETFInrV9N4Dtu4CnKudpShb8RkRERMQgeUTSJ4DzgDWBByvnaUpG/iMiIiJikGwFvJoy3edVwHZ147QlxX9EREREDAzb/wIuAi4EzrP9UOVITUm3n4iIiIgYGJIOA14GXExZ8HuX7T3qpmpH5vxHRERExCBZwfZbu/sHSrqkaprGZNpPRERERAyS8ZJeCyBpIeBvlfM0JdN+IiIiImJgSLoVeCWl6F8cmEBp9zlke7Ga2VqQ4j8iIiIi4iUi034iIiIiIl4iUvxHRERERLxEpPiPiIiIiIEhaeNhjz9QK0uLMuc/IiIiIka9ruh/O7AFcGz39DhgE9vLVAvWmPT5j4iIiIhBcDWwAPAk4O65ScAvqiVqUEb+IyIiImJgSBpre1LtHK3KyH9EREREDJK9JO0FPAGMIf39p5DiPyIiIiIGyebAYrafqB2kRen2ExERERGD5HbKvP+Yhoz8R0RERMQgmQ24VtK1wBCA7S3rRmpHiv+IiIiIGCTfrB2gZZn2ExERERGD5C/A+sA2lNafd9WN05YU/xERERExSI4AbgNeD9wLHF43TltS/EdERETEIFnA9hHAM7YvIvXuFHIwIiIiImKgSFq6u10cmFg5TlOy4DciIiIiBskngZ8BywAnALvWjdOWjPxHRERExCC5BdjV9suBbwDX1o3TlhT/ERERETFIjgFW7O4vBRxZL0p7UvxHRERExCB5pe2fAdj+FrBo5TxNSfEfEREREYNkSNJSAJKWBMZVztOULPiNiIiIiEGyB/ArSQsDdwM7V87TlDFDQ0O1M0RERERE/NtImhf4T+BW249VjtOUFP8RERERMTAkbQbsQ5nhchwwZHvfuqnakTn/ERERETFI9gRWBR4A9gXeVzdOW1L8R0RERMQgedb2BMqI/xDweO1ALUnxHxERERGD5AJJxwKLSzoIuLx2oJZkzn9EREREDBRJGwIrADfZPrl2npak+I+IiIiIUU/SLMAmwEO2z+6eWwQ40PYHq4ZrSPr8R0RERMQgOAaYCCwqaTngduBw4MCqqRqT4j8iIiIiBsGStleWNBtwBTABWMf2jZVzNSULfiMiIiJiEDwCYPtpSo27QQr/qaX4j4iIiIhBc5/tB2uHaFEW/EZERETEqCfpPuBPwBhg3e4+ALa3rJWrNZnzHxERERGD4AN99w+qlqJxGfmPiIiIiHiJyJz/iIiIiIiXiBT/EREREREvESn+IyIiIiJeIlL8R0RERES8RPw/UudGsRdzq4AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 504x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "produce_matrix(columns_list = [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=[\"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": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of emergency primary covid hospital admissions: 300\n",
      "Number with A&E attendance before admission: 500 (166.67%)\n",
      "---\n",
      "Number of A&E attendances: 400\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('output/input_prim_cov_2021-06-01.csv.gz')\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: {round_to_5(len(ae_before))} ({round((round_to_5(len(ae_before))/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "\n",
    "ae = round_to_5(len(df[\n",
    "    (df[\"ae_attendance_date\"].notnull())\n",
    "]))\n",
    "\n",
    "ae_with_hosp_discharge = round_to_5(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\": round_to_5(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/jun/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": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number with A&E attendance with hospital discharge before admission: 500 (166.67%)\n",
      "False positives (FDR): 275 (68.75%)\n"
     ]
    }
   ],
   "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: {round_to_5(len(ae_hosp_before))} ({round((round_to_5(len(ae_hosp_before))/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "\n",
    "\n",
    "# false posisitves\n",
    "fp_ae_hosp_discharge = round_to_5(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 = round_to_5(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",
    "tn_ae_hosp_discharge = round_to_5(len(df[\n",
    "    (df[\"ae_attendance_hosp_discharge_date\"].isnull()) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==0)\n",
    "\n",
    "]))\n",
    "\n",
    "fn_ae_hosp_discharge = round_to_5(len(df[\n",
    "    (df[\"ae_attendance_hosp_discharge_date\"].isnull()) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==1)\n",
    "\n",
    "]))\n",
    "\n",
    "discharge_destination = performance_metrics(tp_ae_hosp_discharge, fp_ae_hosp_discharge, tn_ae_hosp_discharge, fn_ae_hosp_discharge)\n",
    "\n",
    "print(f'False positives (FDR): {fp_ae_hosp_discharge} ({discharge_destination[4]}%)')"
   ]
  },
  {
   "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": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number with A&E attendance and covid diagnosis code before admission: 500 (166.67%)\n",
      "Number with A&E attendance and respiratory code before admission: 500 (166.67%)\n",
      "False positives (FDR) - A&E attendance and covid diagnosis code: 350 (70.0%)\n",
      "False positives (FDR) - A&E attendance and respiratory diagnosis code: 350 (70.0%)\n"
     ]
    }
   ],
   "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: {round_to_5(len(ae_cov_before))} ({round((round_to_5(len(ae_cov_before))/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "print(f'Number with A&E attendance and respiratory code before admission: {round_to_5(len(ae_resp_before))} ({round((round_to_5(len(ae_resp_before))/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "\n",
    "fp_ae_cov = round_to_5(len(df[\n",
    "    (df[\"ae_cov\"]==1) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==0)\n",
    "    \n",
    "]))\n",
    "\n",
    "fp_ae_resp = round_to_5(len(df[\n",
    "    (df[\"ae_resp\"]==1) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==0)\n",
    "    \n",
    "]))\n",
    "\n",
    "tp_ae_cov = round_to_5(len(df[\n",
    "    (df[\"ae_cov\"]==1) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==1)\n",
    "    \n",
    "]))\n",
    "\n",
    "tp_ae_resp = round_to_5(len(df[\n",
    "    (df[\"ae_resp\"]==1) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==1)\n",
    "    \n",
    "]))\n",
    "\n",
    "tn_ae_cov = round_to_5(len(df[\n",
    "    (df[\"ae_cov\"]==0) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==0)\n",
    "    \n",
    "]))\n",
    "\n",
    "tn_ae_resp = round_to_5(len(df[\n",
    "    (df[\"ae_resp\"]==0) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==0)\n",
    "    \n",
    "]))\n",
    "\n",
    "fn_ae_cov = round_to_5(len(df[\n",
    "    (df[\"ae_cov\"]==0) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==1)\n",
    "    \n",
    "]))\n",
    "\n",
    "fn_ae_resp = round_to_5(len(df[\n",
    "    (df[\"ae_resp\"]==0) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==1)\n",
    "    \n",
    "]))\n",
    "\n",
    "diagnosis_code_cov = performance_metrics(tp_ae_cov, fp_ae_cov, tn_ae_cov, fn_ae_cov)\n",
    "diagnosis_code_resp = performance_metrics(tp_ae_resp, fp_ae_resp, tn_ae_resp, fn_ae_resp)\n",
    "\n",
    "print(f'False positives (FDR) - A&E attendance and covid diagnosis code: {fp_ae_cov} ({diagnosis_code_cov[4]}%)')\n",
    "print(f'False positives (FDR) - A&E attendance and respiratory diagnosis code: {fp_ae_resp} ({diagnosis_code_resp[4]}%)')"
   ]
  },
  {
   "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": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number with A&E attendance and recent suspected covid code in primary care before admission: 500 (166.67%)\n",
      "Number with A&E attendance and recent probable covid code in primary care before admission: 500 (166.67%)\n",
      "False positives (FDR) - A&E attendance and probable covid code in primary care: 68.0%)\n",
      "False positives (FDR) - A&E attendance and suspected covid code in primary care: 70.0%)\n"
     ]
    }
   ],
   "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: {round_to_5(len(ae_prim_care_suspected_before))} ({round((round_to_5(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: {round_to_5(len(ae_prim_care_probable_before))} ({round((round_to_5(len(ae_prim_care_probable_before))/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "\n",
    "fp_ae_suspected = round_to_5(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 = round_to_5(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 = round_to_5(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 = round_to_5(len(df[\n",
    "    (df[\"covid_primary_care_before_ae\"]==1) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==1)\n",
    "]))\n",
    "\n",
    "tn_ae_suspected = round_to_5(len(df[\n",
    "    (df[\"suspected_covid_primary_care_before_ae\"]==0) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==0)    \n",
    "]))\n",
    "\n",
    "tn_ae_probable = round_to_5(len(df[\n",
    "    (df[\"covid_primary_care_before_ae\"]==0) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==0)\n",
    "]))\n",
    "\n",
    "fn_ae_suspected = round_to_5(len(df[\n",
    "    (df[\"suspected_covid_primary_care_before_ae\"]==0) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==1)    \n",
    "]))\n",
    "\n",
    "fn_ae_probable = round_to_5(len(df[\n",
    "    (df[\"covid_primary_care_before_ae\"]==0) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==1)\n",
    "]))\n",
    "\n",
    "\n",
    "\n",
    "prim_care_suspected = performance_metrics(tp_ae_suspected, fp_ae_suspected, tn_ae_suspected, fn_ae_suspected)\n",
    "prim_care_probable = performance_metrics(tp_ae_probable, fp_ae_probable, tn_ae_probable, fn_ae_probable)\n",
    "print(f'False positives (FDR) - A&E attendance and probable covid code in primary care: {prim_care_probable[4]}%)')\n",
    "print(f'False positives (FDR) - A&E attendance and suspected covid code in primary care: {prim_care_suspected[4]}%)')"
   ]
  },
  {
   "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": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number with A&E attendance and recent positive test (2 weeks) before admission: 500 (166.67%)\n",
      "Number with A&E attendance and recent positive test (4 weeks) before admissio: 500 (166.67%)\n",
      "False positives (FDR) - A&E attendance and recent positive test (2 weeks): 350 (70.0%)\n",
      "False positives (FDR) - A&E attendance and recent positive test (4 weeks): 340 (68.0%)\n"
     ]
    }
   ],
   "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: {round_to_5(len(ae_pos_test_before_2_weeks))} ({round((round_to_5(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: {round_to_5(len(ae_pos_test_before))} ({round((round_to_5(len(ae_pos_test_before))/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "\n",
    "fp_ae_test = round_to_5(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 = round_to_5(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 = round_to_5(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 = round_to_5(len(df[\n",
    "    (df[\"pos_test_2_weeks\"]==1) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==1)\n",
    "    \n",
    "]))\n",
    "\n",
    "tn_ae_test = round_to_5(len(df[\n",
    "    (df[\"pos_test\"]==0) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==0)\n",
    "    \n",
    "]))\n",
    "\n",
    "tn_ae_test_2_weeks = round_to_5(len(df[\n",
    "    (df[\"pos_test_2_weeks\"]==0) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==0)\n",
    "    \n",
    "]))\n",
    "\n",
    "fn_ae_test = round_to_5(len(df[\n",
    "    (df[\"pos_test\"]==0) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==1)\n",
    "    \n",
    "]))\n",
    "\n",
    "fn_ae_test_2_weeks = round_to_5(len(df[\n",
    "    (df[\"pos_test_2_weeks\"]==0) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==1)\n",
    "    \n",
    "]))\n",
    "\n",
    "\n",
    "positive_test_2_weeks = performance_metrics(tp_ae_test_2_weeks, fp_ae_test_2_weeks, tn_ae_test_2_weeks, fn_ae_test_2_weeks)\n",
    "positive_test_4_weeks = performance_metrics(tp_ae_test, fp_ae_test, tn_ae_test, fn_ae_test)\n",
    "\n",
    "print(f'False positives (FDR) - A&E attendance and recent positive test (2 weeks): {fp_ae_test_2_weeks} ({positive_test_2_weeks[4]}%)')\n",
    "print(f'False positives (FDR) - A&E attendance and recent positive test (4 weeks): {fp_ae_test} ({positive_test_4_weeks[4]}%)')\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": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number with A&E attendance with either covid or respiratory diagnosis code and recent positive test (2 weeks) before admission: 380 (126.67%)\n",
      "False positives (FDR): 260 (70.27%)\n"
     ]
    }
   ],
   "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",
    "print(f'Number with A&E attendance with either covid or respiratory diagnosis code and recent positive test (2 weeks) before admission: {round_to_5(len(ae_hos_resp_cov_plus_test_2_weeks))} ({round((round_to_5(len(ae_hos_resp_cov_plus_test_2_weeks))/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "\n",
    "\n",
    "fp_ae_hos_resp_cov_plus_test_2_weeks = round_to_5(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 = round_to_5(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",
    "tn_ae_hos_resp_cov_plus_test_2_weeks = round_to_5(len(df[\n",
    "    ~(\n",
    "    (\n",
    "        (df[\"ae_cov\"]==1) |\n",
    "        (df[\"ae_resp\"]==1)    \n",
    "    )&\n",
    "    (df[\"pos_test_2_weeks\"]==1)\n",
    "    )\n",
    "    &\n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==0)\n",
    "    \n",
    "]))\n",
    "\n",
    "fn_ae_hos_resp_cov_plus_test_2_weeks = round_to_5(len(df[\n",
    "    ~(\n",
    "    (\n",
    "        (df[\"ae_cov\"]==1) |\n",
    "        (df[\"ae_resp\"]==1)    \n",
    "    )&\n",
    "    (df[\"pos_test_2_weeks\"]==1)\n",
    "    )\n",
    "    &\n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==1)\n",
    "    \n",
    "]))\n",
    "\n",
    "\n",
    "code_plus_test = performance_metrics(tp_ae_hos_resp_cov_plus_test_2_weeks, fp_ae_hos_resp_cov_plus_test_2_weeks, tn_ae_hos_resp_cov_plus_test_2_weeks, fn_ae_hos_resp_cov_plus_test_2_weeks)\n",
    "print(f'False positives (FDR): {fp_ae_hos_resp_cov_plus_test_2_weeks} ({code_plus_test[4]}%)')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Covid in primary care with recent positive test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number with A&E attendance with suspected or probable covid in primary care and recent positive test (2 weeks) before admission: 370 (123.33%)\n",
      "False positives (FDR): 265 (69.74%)\n"
     ]
    }
   ],
   "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",
    "print(f'Number with A&E attendance with suspected or probable covid in primary care and recent positive test (2 weeks) before admission: {round_to_5(len(ae_hosp_prim_care_before_plus_2_week_test))} ({round((round_to_5(len(ae_hosp_prim_care_before_plus_2_week_test))/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "\n",
    "\n",
    "fp_ae_hosp_prim_care_before_plus_2_week_test = round_to_5(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 = round_to_5(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",
    "tn_ae_hosp_prim_care_before_plus_2_week_test = round_to_5(len(df[\n",
    "    (~\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",
    "    )\n",
    "    &\n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==0)\n",
    "    \n",
    "]))\n",
    "\n",
    "fn_ae_hosp_prim_care_before_plus_2_week_test = round_to_5(len(df[\n",
    "    (~\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",
    "    )\n",
    "    &\n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==1)\n",
    "    \n",
    "]))\n",
    "\n",
    "\n",
    "primary_care_plus_test = performance_metrics(tp_ae_hosp_prim_care_before_plus_2_week_test, fp_ae_hosp_prim_care_before_plus_2_week_test, tn_ae_hosp_prim_care_before_plus_2_week_test, fn_ae_hosp_prim_care_before_plus_2_week_test)\n",
    "\n",
    "print(f'False positives (FDR): {fp_ae_hosp_prim_care_before_plus_2_week_test} ({primary_care_plus_test[4]}%)')\n"
   ]
  },
  {
   "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": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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: 470 (156.67%)\n",
      "False positives (FDR): 325 (69.89%)\n"
     ]
    }
   ],
   "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",
    "\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: {round_to_5(len(combined_before))} ({round((round_to_5(len(combined_before))/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "\n",
    "fp_combined_before = round_to_5(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 = round_to_5(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",
    "tn_combined_before = round_to_5(len(df[\n",
    "    (~\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",
    "    )\n",
    "    & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==0)\n",
    "    \n",
    "]))\n",
    "\n",
    "fn_combined_before = round_to_5(len(df[\n",
    "    (~\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",
    "    )\n",
    "    & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae\"]==0)\n",
    "    \n",
    "]))\n",
    "\n",
    "pos_test_prim_care_or_diagnosis_code = performance_metrics(tp_combined_before, fp_combined_before, tn_combined_before, fn_combined_before)\n",
    "print(f'False positives (FDR): {fp_combined_before} ({pos_test_prim_care_or_diagnosis_code[4]}%)')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "identification_table_index = [\"tp\", \"fp\", \"sensitivity\", \"specificity\", \"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_probable,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/jun/identification_table_no_hosp_discharge_primary.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "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\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Restrict to A&E attendance with hospital discharge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of A&E attendances with hospital discharge: 400\n"
     ]
    }
   ],
   "source": [
    "ae_with_hosp_discharge = round_to_5(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": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number with A&E attendance with hospital discharge before admission: 500 (166.67%)\n",
      "False positives (FDR): 275 (68.75%)\n"
     ]
    }
   ],
   "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: {round_to_5(len(ae_hosp_before))} ({round((round_to_5(len(ae_hosp_before))/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "\n",
    "\n",
    "# false posisitves\n",
    "fp_ae_hosp_discharge = round_to_5(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 = round_to_5(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",
    "tn_ae_hosp_discharge = round_to_5(len(df[\n",
    "    (df[\"ae_attendance_hosp_discharge_date\"].isnull()) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==0)\n",
    "\n",
    "]))\n",
    "\n",
    "fn_ae_hosp_discharge = round_to_5(len(df[\n",
    "    (df[\"ae_attendance_hosp_discharge_date\"].isnull()) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==1)\n",
    "\n",
    "]))\n",
    "\n",
    "discharge_destination = performance_metrics(tp_ae_hosp_discharge, fp_ae_hosp_discharge, tn_ae_hosp_discharge, fn_ae_hosp_discharge)\n",
    "print(f'False positives (FDR): {fp_ae_hosp_discharge} ({discharge_destination[4]}%)')"
   ]
  },
  {
   "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": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number with A&E attendance and covid diagnosis code before admission: 500 (166.67%)\n",
      "Number with A&E attendance and respiratory code before admission: 500 (166.67%)\n",
      "False positives (FDR) - A&E attendance and covid diagnosis code: 355 (71.0%)\n",
      "False positives (FDR) - A&E attendance and respiratory diagnosis code: 360 (72.0%)\n"
     ]
    }
   ],
   "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: {round_to_5(len(ae_cov_before))} ({round((round_to_5(len(ae_cov_before))/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "print(f'Number with A&E attendance and respiratory code before admission: {round_to_5(len(ae_resp_before))} ({round((round_to_5(len(ae_resp_before))/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "\n",
    "fp_ae_cov = round_to_5(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 = round_to_5(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 = round_to_5(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 = round_to_5(len(df[\n",
    "    (df[\"ae_resp_hosp_discharge\"]==1) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==1)\n",
    "    \n",
    "]))\n",
    "\n",
    "tn_ae_cov = round_to_5(len(df[\n",
    "    (df[\"ae_cov_hosp_discharge\"]==0) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==0)\n",
    "    \n",
    "]))\n",
    "\n",
    "tn_ae_resp = round_to_5(len(df[\n",
    "    (df[\"ae_resp_hosp_discharge\"]==0) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==0)\n",
    "    \n",
    "]))\n",
    "\n",
    "fn_ae_cov = round_to_5(len(df[\n",
    "    (df[\"ae_cov_hosp_discharge\"]==0) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==1)\n",
    "    \n",
    "]))\n",
    "\n",
    "fn_ae_resp = round_to_5(len(df[\n",
    "    (df[\"ae_resp_hosp_discharge\"]==0) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==1)\n",
    "    \n",
    "]))\n",
    "\n",
    "\n",
    "\n",
    "diagnosis_code_cov = performance_metrics(tp_ae_cov, fp_ae_cov, tn_ae_cov, fn_ae_cov)\n",
    "diagnosis_code_resp = performance_metrics(tp_ae_resp, fp_ae_resp, tn_ae_resp, fn_ae_resp)\n",
    "\n",
    "print(f'False positives (FDR) - A&E attendance and covid diagnosis code: {fp_ae_cov} ({diagnosis_code_cov[4]}%)')\n",
    "print(f'False positives (FDR) - A&E attendance and respiratory diagnosis code: {fp_ae_resp} ({diagnosis_code_resp[4]}%)')"
   ]
  },
  {
   "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": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number with A&E attendance and recent suspected covid code in primary care before admission: 500 (166.67%)\n",
      "Number with A&E attendance and recent probable covid code in primary care before admission: 500 (166.67%)\n",
      "False positives (FDR) - A&E attendance and probable covid code in primary care: 360 (72.0%)\n",
      "False positives (FDR) - A&E attendance and suspected covid code in primary care: 335 (67.0%)\n"
     ]
    }
   ],
   "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: {round_to_5(len(ae_prim_care_suspected_before))} ({round((round_to_5(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: {round_to_5(len(ae_prim_care_probable_before))} ({round((round_to_5(len(ae_prim_care_probable_before))/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "\n",
    "fp_ae_suspected = round_to_5(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 = round_to_5(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 = round_to_5(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 = round_to_5(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",
    "tn_ae_suspected = round_to_5(len(df[\n",
    "    (df[\"suspected_covid_primary_care_before_ae_hosp_discharge\"]==0) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==0)    \n",
    "]))\n",
    "\n",
    "tn_ae_probable = round_to_5(len(df[\n",
    "    (df[\"covid_primary_care_before_ae_hosp_discharge\"]==0) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==0)\n",
    "]))\n",
    "\n",
    "fn_ae_suspected = round_to_5(len(df[\n",
    "    (df[\"suspected_covid_primary_care_before_ae_hosp_discharge\"]==0) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==1)    \n",
    "]))\n",
    "\n",
    "fn_ae_probable = round_to_5(len(df[\n",
    "    (df[\"covid_primary_care_before_ae_hosp_discharge\"]==0) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==1)\n",
    "]))\n",
    "\n",
    "\n",
    "\n",
    "prim_care_suspected = performance_metrics(tp_ae_suspected, fp_ae_suspected, tn_ae_suspected, fn_ae_suspected)\n",
    "prim_care_probable = performance_metrics(tp_ae_probable, fp_ae_probable, tn_ae_probable, fn_ae_probable)\n",
    "\n",
    "print(f'False positives (FDR) - A&E attendance and probable covid code in primary care: {fp_ae_probable} ({prim_care_probable[4]}%)')\n",
    "print(f'False positives (FDR) - A&E attendance and suspected covid code in primary care: {fp_ae_suspected} ({prim_care_suspected[4]}%)')"
   ]
  },
  {
   "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": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number with A&E attendance and recent positive test (2 weeks) before admission: 500 (166.67%)\n",
      "Number with A&E attendance and recent positive test (4 weeks) before admissio: 500 (166.67%)\n",
      "False positives (FDR) - A&E attendance and recent positive test (2 weeks): 345 (49.29%)\n",
      "False positives (FDR) - A&E attendance and recent positive test (4 weeks): 350 (50.0%)\n"
     ]
    }
   ],
   "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: {round_to_5(len(ae_pos_test_before_2_weeks))} ({round((round_to_5(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: {round_to_5(len(ae_pos_test_before))} ({round((round_to_5(len(ae_pos_test_before))/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "\n",
    "fp_ae_test = round_to_5(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 = round_to_5(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 = round_to_5(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 = round_to_5(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",
    "tn_ae_test = round_to_5(len(df[\n",
    "    (df[\"pos_test_hosp_discharge\"]==0) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==0)\n",
    "    \n",
    "]))\n",
    "\n",
    "tn_ae_test_2_weeks = round_to_5(len(df[\n",
    "    (df[\"pos_test_2_weeks_hosp_discharge\"]==0) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==0)\n",
    "    \n",
    "]))\n",
    "\n",
    "fn_ae_test = round_to_5(len(df[\n",
    "    (df[\"pos_test_hosp_discharge\"]==0) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==1)\n",
    "    \n",
    "]))\n",
    "\n",
    "fn_ae_test_2_weeks = round_to_5(len(df[\n",
    "    (df[\"pos_test_2_weeks_hosp_discharge\"]==0) & \n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==1)\n",
    "    \n",
    "]))\n",
    "\n",
    "positive_test_2_weeks = performance_metrics(tn_ae_test_2_weeks, fp_ae_test_2_weeks, tn_ae_test_2_weeks, fn_ae_test_2_weeks)\n",
    "positive_test_4_weeks = performance_metrics(tn_ae_test, fp_ae_test, tn_ae_test, fn_ae_test)\n",
    "\n",
    "print(f'False positives (FDR) - A&E attendance and recent positive test (2 weeks): {fp_ae_test_2_weeks} ({positive_test_2_weeks[4]}%)')\n",
    "print(f'False positives (FDR) - A&E attendance and recent positive test (4 weeks): {fp_ae_test} ({positive_test_4_weeks[4]}%)')"
   ]
  },
  {
   "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": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number with A&E attendance with either covid or respiratory diagnosis code and recent positive test (2 weeks) before admission: 375 (125.0%)\n",
      "False positives (FDR): 260 (69.33%)\n"
     ]
    }
   ],
   "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",
    "print(f'Number with A&E attendance with either covid or respiratory diagnosis code and recent positive test (2 weeks) before admission: {round_to_5(len(ae_hos_resp_cov_plus_test_2_weeks))} ({round((round_to_5(len(ae_hos_resp_cov_plus_test_2_weeks))/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "\n",
    "\n",
    "fp_ae_hos_resp_cov_plus_test_2_weeks = round_to_5(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 = round_to_5(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",
    "tn_ae_hos_resp_cov_plus_test_2_weeks = round_to_5(len(df[\n",
    "    (~\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",
    "    \n",
    "    )\n",
    "    &\n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==0)\n",
    "    \n",
    "]))\n",
    "\n",
    "\n",
    "fn_ae_hos_resp_cov_plus_test_2_weeks = round_to_5(len(df[\n",
    "    (~\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",
    "    \n",
    "    )\n",
    "    &\n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==1)\n",
    "    \n",
    "]))\n",
    "\n",
    "code_plus_test = performance_metrics(tp_ae_hos_resp_cov_plus_test_2_weeks, fp_ae_hos_resp_cov_plus_test_2_weeks, tn_ae_hos_resp_cov_plus_test_2_weeks, fn_ae_hos_resp_cov_plus_test_2_weeks)\n",
    "print(f'False positives (FDR): {fp_ae_hos_resp_cov_plus_test_2_weeks} ({code_plus_test[4]}%)')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Covid in primary care with recent positive test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number with A&E attendance with suspected or probable covid in primary care and recent positive test (2 weeks) before admission: 375 (125.0%)\n",
      "False positives (FDR): 255 (68.92%)\n"
     ]
    }
   ],
   "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",
    "print(f'Number with A&E attendance with suspected or probable covid in primary care and recent positive test (2 weeks) before admission: {round_to_5(len(ae_hosp_prim_care_before_plus_2_week_test))} ({round((round_to_5(len(ae_hosp_prim_care_before_plus_2_week_test))/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "\n",
    "\n",
    "fp_ae_hosp_prim_care_before_plus_2_week_test = round_to_5(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 = round_to_5(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",
    "tn_ae_hosp_prim_care_before_plus_2_week_test = round_to_5(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",
    "    )\n",
    "    &\n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==0)\n",
    "    \n",
    "]))\n",
    "\n",
    "fn_ae_hosp_prim_care_before_plus_2_week_test = round_to_5(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",
    "    )\n",
    "    &\n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==1)\n",
    "    \n",
    "]))\n",
    "\n",
    "\n",
    "primary_care_plus_test = performance_metrics(tp_ae_hosp_prim_care_before_plus_2_week_test, fp_ae_hosp_prim_care_before_plus_2_week_test, tn_ae_hosp_prim_care_before_plus_2_week_test, fn_ae_hosp_prim_care_before_plus_2_week_test)\n",
    "print(f'False positives (FDR): {fp_ae_hosp_prim_care_before_plus_2_week_test} ({primary_care_plus_test[4]}%)')\n"
   ]
  },
  {
   "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": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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: 470 (156.67%)\n",
      "False positives (FDR): 320 (68.82%)\n"
     ]
    }
   ],
   "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",
    "\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: {round_to_5(len(combined_before))} ({round((round_to_5(len(combined_before))/num_emergency_cov_admissions)*100, 2)}%)')\n",
    "\n",
    "fp_combined_before = round_to_5(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 = round_to_5(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",
    "tn_combined_before = round_to_5(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",
    "    )\n",
    "    &\n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==0)\n",
    "    \n",
    "]))\n",
    "\n",
    "fn_combined_before = round_to_5(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",
    "    )\n",
    "    &\n",
    "    (df[\"emergency_covid_hospital_admission_after_ae_hosp_discharge\"]==1)\n",
    "    \n",
    "]))\n",
    "\n",
    "pos_test_prim_care_or_diagnosis_code = performance_metrics(tp_combined_before, fp_combined_before, tn_combined_before, fn_combined_before)\n",
    "print(f'False positives (FDR): {fp_combined_before} ({pos_test_prim_care_or_diagnosis_code[4]}%)')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "identification_table_index = [\"tp\", \"fp\", \"sensitivity\", \"specificity\", \"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_probable,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/jun/identification_table_hosp_discharge_primary.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "produce_matrix(columns_list = [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=[\"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": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}