{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8e676741",
   "metadata": {},
   "source": [
    "# Modelling the same patient population attending all hospitals\n",
    "\n",
    "Aim:\n",
    "\n",
    "* To predict the thrombolysis rate at each hospital if the same patient population (based on the national average patient characteristics) attend each hospital.\n",
    "\n",
    "Patient population distributions are set to have the national average of:\n",
    "\n",
    "* Arrival within 4 hours of stroke onset\n",
    "* Proportion aged 80+\n",
    "* Onset to arrival mean and standard deviation\n",
    "\n",
    "The proportion of arrivals eligible for thrombolysis is set to the predicted 10k cohort rate for each hospital (adjusted to give thrombolysis use in those patients scanned within 4 hours of stroke onset).\n",
    "\n",
    "The simulation model passes through 100 x 1k patient cohorts through each hospital model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b8cd79b",
   "metadata": {},
   "source": [
    "## Load libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2a47c277",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c49c45b4",
   "metadata": {},
   "source": [
    "## Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cfbf4bf7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scenario results\n",
    "results = pd.read_csv('./output/scenario_results.csv')\n",
    "# Pathway performance paramters used in scenarios\n",
    "performance_base = pd.read_csv('./output/performance_base.csv')\n",
    "performance_same_patients = \\\n",
    "    pd.read_csv('./output/same_patient_characteristics.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1b7ef4f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>stroke_team</th>\n",
       "      <th>thrombolysis_rate</th>\n",
       "      <th>admissions</th>\n",
       "      <th>80_plus</th>\n",
       "      <th>onset_known</th>\n",
       "      <th>known_arrival_within_4hrs</th>\n",
       "      <th>onset_arrival_mins_mu</th>\n",
       "      <th>onset_arrival_mins_sigma</th>\n",
       "      <th>scan_within_4_hrs</th>\n",
       "      <th>arrival_scan_arrival_mins_mu</th>\n",
       "      <th>arrival_scan_arrival_mins_sigma</th>\n",
       "      <th>onset_scan_4_hrs</th>\n",
       "      <th>eligable</th>\n",
       "      <th>scan_needle_mins_mu</th>\n",
       "      <th>scan_needle_mins_sigma</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AGNOF1041H</td>\n",
       "      <td>0.154839</td>\n",
       "      <td>671.666667</td>\n",
       "      <td>0.425459</td>\n",
       "      <td>0.635236</td>\n",
       "      <td>0.681250</td>\n",
       "      <td>4.576874</td>\n",
       "      <td>0.557598</td>\n",
       "      <td>0.965596</td>\n",
       "      <td>1.665700</td>\n",
       "      <td>1.497966</td>\n",
       "      <td>0.935867</td>\n",
       "      <td>0.388325</td>\n",
       "      <td>3.669602</td>\n",
       "      <td>0.664462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AKCGO9726K</td>\n",
       "      <td>0.158892</td>\n",
       "      <td>1143.333333</td>\n",
       "      <td>0.395658</td>\n",
       "      <td>0.970845</td>\n",
       "      <td>0.428829</td>\n",
       "      <td>4.625486</td>\n",
       "      <td>0.597451</td>\n",
       "      <td>0.955882</td>\n",
       "      <td>2.834183</td>\n",
       "      <td>0.999719</td>\n",
       "      <td>0.908425</td>\n",
       "      <td>0.419355</td>\n",
       "      <td>2.904479</td>\n",
       "      <td>0.874818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AOBTM3098N</td>\n",
       "      <td>0.085885</td>\n",
       "      <td>500.666667</td>\n",
       "      <td>0.485470</td>\n",
       "      <td>0.619174</td>\n",
       "      <td>0.629032</td>\n",
       "      <td>4.603918</td>\n",
       "      <td>0.584882</td>\n",
       "      <td>0.935043</td>\n",
       "      <td>3.471419</td>\n",
       "      <td>1.254744</td>\n",
       "      <td>0.846435</td>\n",
       "      <td>0.267819</td>\n",
       "      <td>3.694918</td>\n",
       "      <td>0.518929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>APXEE8191H</td>\n",
       "      <td>0.098634</td>\n",
       "      <td>439.333333</td>\n",
       "      <td>0.515679</td>\n",
       "      <td>0.716237</td>\n",
       "      <td>0.608051</td>\n",
       "      <td>4.590357</td>\n",
       "      <td>0.496452</td>\n",
       "      <td>0.966899</td>\n",
       "      <td>3.312930</td>\n",
       "      <td>0.714465</td>\n",
       "      <td>0.904505</td>\n",
       "      <td>0.258964</td>\n",
       "      <td>3.585094</td>\n",
       "      <td>0.751204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ATDID5461S</td>\n",
       "      <td>0.090689</td>\n",
       "      <td>275.666667</td>\n",
       "      <td>0.533546</td>\n",
       "      <td>0.573156</td>\n",
       "      <td>0.660338</td>\n",
       "      <td>4.427826</td>\n",
       "      <td>0.591373</td>\n",
       "      <td>0.878594</td>\n",
       "      <td>4.125690</td>\n",
       "      <td>0.549301</td>\n",
       "      <td>0.865455</td>\n",
       "      <td>0.315126</td>\n",
       "      <td>3.497262</td>\n",
       "      <td>0.608126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>YPKYH1768F</td>\n",
       "      <td>0.105193</td>\n",
       "      <td>250.333333</td>\n",
       "      <td>0.321767</td>\n",
       "      <td>0.585885</td>\n",
       "      <td>0.720455</td>\n",
       "      <td>4.436404</td>\n",
       "      <td>0.569248</td>\n",
       "      <td>0.952681</td>\n",
       "      <td>3.779215</td>\n",
       "      <td>0.872809</td>\n",
       "      <td>0.844371</td>\n",
       "      <td>0.305882</td>\n",
       "      <td>3.982100</td>\n",
       "      <td>0.683223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>YQMZV4284N</td>\n",
       "      <td>0.104186</td>\n",
       "      <td>358.333333</td>\n",
       "      <td>0.508511</td>\n",
       "      <td>0.945116</td>\n",
       "      <td>0.462598</td>\n",
       "      <td>4.664536</td>\n",
       "      <td>0.494740</td>\n",
       "      <td>0.948936</td>\n",
       "      <td>3.574735</td>\n",
       "      <td>0.912298</td>\n",
       "      <td>0.798206</td>\n",
       "      <td>0.308989</td>\n",
       "      <td>3.285165</td>\n",
       "      <td>0.463749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>ZBVSO0975W</td>\n",
       "      <td>0.081602</td>\n",
       "      <td>449.333333</td>\n",
       "      <td>0.442130</td>\n",
       "      <td>0.465134</td>\n",
       "      <td>0.688995</td>\n",
       "      <td>4.562051</td>\n",
       "      <td>0.510524</td>\n",
       "      <td>0.972222</td>\n",
       "      <td>2.860226</td>\n",
       "      <td>0.990966</td>\n",
       "      <td>0.930952</td>\n",
       "      <td>0.273657</td>\n",
       "      <td>3.606046</td>\n",
       "      <td>0.575788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>ZHCLE1578P</td>\n",
       "      <td>0.112647</td>\n",
       "      <td>796.000000</td>\n",
       "      <td>0.484694</td>\n",
       "      <td>0.733668</td>\n",
       "      <td>0.671233</td>\n",
       "      <td>4.606557</td>\n",
       "      <td>0.546648</td>\n",
       "      <td>0.949830</td>\n",
       "      <td>3.306916</td>\n",
       "      <td>0.842940</td>\n",
       "      <td>0.892569</td>\n",
       "      <td>0.262788</td>\n",
       "      <td>3.276043</td>\n",
       "      <td>0.795401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>ZRRCV7012C</td>\n",
       "      <td>0.063058</td>\n",
       "      <td>597.333333</td>\n",
       "      <td>0.469504</td>\n",
       "      <td>0.779576</td>\n",
       "      <td>0.504653</td>\n",
       "      <td>4.636283</td>\n",
       "      <td>0.485394</td>\n",
       "      <td>0.977305</td>\n",
       "      <td>3.743456</td>\n",
       "      <td>0.662710</td>\n",
       "      <td>0.851959</td>\n",
       "      <td>0.189097</td>\n",
       "      <td>3.261270</td>\n",
       "      <td>0.803624</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>132 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    stroke_team  thrombolysis_rate   admissions   80_plus  onset_known  \\\n",
       "0    AGNOF1041H           0.154839   671.666667  0.425459     0.635236   \n",
       "1    AKCGO9726K           0.158892  1143.333333  0.395658     0.970845   \n",
       "2    AOBTM3098N           0.085885   500.666667  0.485470     0.619174   \n",
       "3    APXEE8191H           0.098634   439.333333  0.515679     0.716237   \n",
       "4    ATDID5461S           0.090689   275.666667  0.533546     0.573156   \n",
       "..          ...                ...          ...       ...          ...   \n",
       "127  YPKYH1768F           0.105193   250.333333  0.321767     0.585885   \n",
       "128  YQMZV4284N           0.104186   358.333333  0.508511     0.945116   \n",
       "129  ZBVSO0975W           0.081602   449.333333  0.442130     0.465134   \n",
       "130  ZHCLE1578P           0.112647   796.000000  0.484694     0.733668   \n",
       "131  ZRRCV7012C           0.063058   597.333333  0.469504     0.779576   \n",
       "\n",
       "     known_arrival_within_4hrs  onset_arrival_mins_mu  \\\n",
       "0                     0.681250               4.576874   \n",
       "1                     0.428829               4.625486   \n",
       "2                     0.629032               4.603918   \n",
       "3                     0.608051               4.590357   \n",
       "4                     0.660338               4.427826   \n",
       "..                         ...                    ...   \n",
       "127                   0.720455               4.436404   \n",
       "128                   0.462598               4.664536   \n",
       "129                   0.688995               4.562051   \n",
       "130                   0.671233               4.606557   \n",
       "131                   0.504653               4.636283   \n",
       "\n",
       "     onset_arrival_mins_sigma  scan_within_4_hrs  \\\n",
       "0                    0.557598           0.965596   \n",
       "1                    0.597451           0.955882   \n",
       "2                    0.584882           0.935043   \n",
       "3                    0.496452           0.966899   \n",
       "4                    0.591373           0.878594   \n",
       "..                        ...                ...   \n",
       "127                  0.569248           0.952681   \n",
       "128                  0.494740           0.948936   \n",
       "129                  0.510524           0.972222   \n",
       "130                  0.546648           0.949830   \n",
       "131                  0.485394           0.977305   \n",
       "\n",
       "     arrival_scan_arrival_mins_mu  arrival_scan_arrival_mins_sigma  \\\n",
       "0                        1.665700                         1.497966   \n",
       "1                        2.834183                         0.999719   \n",
       "2                        3.471419                         1.254744   \n",
       "3                        3.312930                         0.714465   \n",
       "4                        4.125690                         0.549301   \n",
       "..                            ...                              ...   \n",
       "127                      3.779215                         0.872809   \n",
       "128                      3.574735                         0.912298   \n",
       "129                      2.860226                         0.990966   \n",
       "130                      3.306916                         0.842940   \n",
       "131                      3.743456                         0.662710   \n",
       "\n",
       "     onset_scan_4_hrs  eligable  scan_needle_mins_mu  scan_needle_mins_sigma  \n",
       "0            0.935867  0.388325             3.669602                0.664462  \n",
       "1            0.908425  0.419355             2.904479                0.874818  \n",
       "2            0.846435  0.267819             3.694918                0.518929  \n",
       "3            0.904505  0.258964             3.585094                0.751204  \n",
       "4            0.865455  0.315126             3.497262                0.608126  \n",
       "..                ...       ...                  ...                     ...  \n",
       "127          0.844371  0.305882             3.982100                0.683223  \n",
       "128          0.798206  0.308989             3.285165                0.463749  \n",
       "129          0.930952  0.273657             3.606046                0.575788  \n",
       "130          0.892569  0.262788             3.276043                0.795401  \n",
       "131          0.851959  0.189097             3.261270                0.803624  \n",
       "\n",
       "[132 rows x 15 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "performance_base"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ae73393",
   "metadata": {},
   "source": [
    "## Collate key results\n",
    "\n",
    "Collate key results together in a DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "28a28016",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add admission numbers to results\n",
    "admissions = performance_base[['stroke_team', 'admissions']]\n",
    "results = results.merge(\n",
    "    admissions, how='left', left_on='stroke_team', right_on='stroke_team')\n",
    "\n",
    "# Calculate numbers thrombolysed\n",
    "results['thrombolysed'] = \\\n",
    "    results['admissions'] * results['Percent_Thrombolysis_(mean)'] / 100\n",
    "\n",
    "# Calculate additional good outcomes\n",
    "results['add_good_outcomes'] = (results['admissions'] * \n",
    "    results['Additional_good_outcomes_per_1000_patients_(mean)'] / 1000)\n",
    "\n",
    "# Get key results\n",
    "key_results = pd.DataFrame()\n",
    "key_results['stroke_team'] = results['stroke_team']\n",
    "key_results['scenario'] = results['scenario']\n",
    "key_results['admissions'] = results['admissions']\n",
    "key_results['thrombolysis_rate'] = results['Percent_Thrombolysis_(mean)']\n",
    "key_results['additional_good_outcomes_per_1000_patients'] = \\\n",
    "    results['Additional_good_outcomes_per_1000_patients_(mean)']\n",
    "key_results['patients_receiving_thrombolysis'] = results['thrombolysed']\n",
    "key_results['add_good_outcomes'] = results['add_good_outcomes']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a3860f69",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>stroke_team</th>\n",
       "      <th>scenario</th>\n",
       "      <th>admissions</th>\n",
       "      <th>thrombolysis_rate</th>\n",
       "      <th>additional_good_outcomes_per_1000_patients</th>\n",
       "      <th>patients_receiving_thrombolysis</th>\n",
       "      <th>add_good_outcomes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AGNOF1041H</td>\n",
       "      <td>base</td>\n",
       "      <td>671.666667</td>\n",
       "      <td>15.20</td>\n",
       "      <td>12.76</td>\n",
       "      <td>102.093333</td>\n",
       "      <td>8.570467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AKCGO9726K</td>\n",
       "      <td>base</td>\n",
       "      <td>1143.333333</td>\n",
       "      <td>14.91</td>\n",
       "      <td>13.21</td>\n",
       "      <td>170.471000</td>\n",
       "      <td>15.103433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AOBTM3098N</td>\n",
       "      <td>base</td>\n",
       "      <td>500.666667</td>\n",
       "      <td>7.80</td>\n",
       "      <td>5.67</td>\n",
       "      <td>39.052000</td>\n",
       "      <td>2.838780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>APXEE8191H</td>\n",
       "      <td>base</td>\n",
       "      <td>439.333333</td>\n",
       "      <td>10.40</td>\n",
       "      <td>7.59</td>\n",
       "      <td>45.690667</td>\n",
       "      <td>3.334540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ATDID5461S</td>\n",
       "      <td>base</td>\n",
       "      <td>275.666667</td>\n",
       "      <td>9.17</td>\n",
       "      <td>6.28</td>\n",
       "      <td>25.278633</td>\n",
       "      <td>1.731187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1183</th>\n",
       "      <td>YPKYH1768F</td>\n",
       "      <td>same_patient_characteristics</td>\n",
       "      <td>250.333333</td>\n",
       "      <td>7.47</td>\n",
       "      <td>4.98</td>\n",
       "      <td>18.699900</td>\n",
       "      <td>1.246660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1184</th>\n",
       "      <td>YQMZV4284N</td>\n",
       "      <td>same_patient_characteristics</td>\n",
       "      <td>358.333333</td>\n",
       "      <td>15.92</td>\n",
       "      <td>12.87</td>\n",
       "      <td>57.046667</td>\n",
       "      <td>4.611750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1185</th>\n",
       "      <td>ZBVSO0975W</td>\n",
       "      <td>same_patient_characteristics</td>\n",
       "      <td>449.333333</td>\n",
       "      <td>7.66</td>\n",
       "      <td>6.23</td>\n",
       "      <td>34.418933</td>\n",
       "      <td>2.799347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1186</th>\n",
       "      <td>ZHCLE1578P</td>\n",
       "      <td>same_patient_characteristics</td>\n",
       "      <td>796.000000</td>\n",
       "      <td>10.76</td>\n",
       "      <td>8.78</td>\n",
       "      <td>85.649600</td>\n",
       "      <td>6.988880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1187</th>\n",
       "      <td>ZRRCV7012C</td>\n",
       "      <td>same_patient_characteristics</td>\n",
       "      <td>597.333333</td>\n",
       "      <td>11.19</td>\n",
       "      <td>8.67</td>\n",
       "      <td>66.841600</td>\n",
       "      <td>5.178880</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1188 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     stroke_team                      scenario   admissions  \\\n",
       "0     AGNOF1041H                          base   671.666667   \n",
       "1     AKCGO9726K                          base  1143.333333   \n",
       "2     AOBTM3098N                          base   500.666667   \n",
       "3     APXEE8191H                          base   439.333333   \n",
       "4     ATDID5461S                          base   275.666667   \n",
       "...          ...                           ...          ...   \n",
       "1183  YPKYH1768F  same_patient_characteristics   250.333333   \n",
       "1184  YQMZV4284N  same_patient_characteristics   358.333333   \n",
       "1185  ZBVSO0975W  same_patient_characteristics   449.333333   \n",
       "1186  ZHCLE1578P  same_patient_characteristics   796.000000   \n",
       "1187  ZRRCV7012C  same_patient_characteristics   597.333333   \n",
       "\n",
       "      thrombolysis_rate  additional_good_outcomes_per_1000_patients  \\\n",
       "0                 15.20                                       12.76   \n",
       "1                 14.91                                       13.21   \n",
       "2                  7.80                                        5.67   \n",
       "3                 10.40                                        7.59   \n",
       "4                  9.17                                        6.28   \n",
       "...                 ...                                         ...   \n",
       "1183               7.47                                        4.98   \n",
       "1184              15.92                                       12.87   \n",
       "1185               7.66                                        6.23   \n",
       "1186              10.76                                        8.78   \n",
       "1187              11.19                                        8.67   \n",
       "\n",
       "      patients_receiving_thrombolysis  add_good_outcomes  \n",
       "0                          102.093333           8.570467  \n",
       "1                          170.471000          15.103433  \n",
       "2                           39.052000           2.838780  \n",
       "3                           45.690667           3.334540  \n",
       "4                           25.278633           1.731187  \n",
       "...                               ...                ...  \n",
       "1183                        18.699900           1.246660  \n",
       "1184                        57.046667           4.611750  \n",
       "1185                        34.418933           2.799347  \n",
       "1186                        85.649600           6.988880  \n",
       "1187                        66.841600           5.178880  \n",
       "\n",
       "[1188 rows x 7 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "key_results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "973853d0-d550-417b-9671-286a080b151f",
   "metadata": {},
   "source": [
    "## Summary stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3f73135a-b2be-4a65-83eb-885869e05e6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>actual</th>\n",
       "      <th>cohort</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>132.00</td>\n",
       "      <td>132.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>11.23</td>\n",
       "      <td>11.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.37</td>\n",
       "      <td>3.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.69</td>\n",
       "      <td>2.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9.24</td>\n",
       "      <td>8.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>10.92</td>\n",
       "      <td>10.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>13.12</td>\n",
       "      <td>12.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>24.23</td>\n",
       "      <td>24.93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       actual  cohort\n",
       "count  132.00  132.00\n",
       "mean    11.23   11.08\n",
       "std      3.37    3.91\n",
       "min      1.69    2.65\n",
       "25%      9.24    8.29\n",
       "50%     10.92   10.96\n",
       "75%     13.12   12.87\n",
       "max     24.23   24.93"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comparison = pd.DataFrame()\n",
    "comparison['actual'] = key_results[key_results['scenario']=='base']['thrombolysis_rate'].values\n",
    "comparison['cohort'] = key_results[key_results['scenario']=='same_patient_characteristics']['thrombolysis_rate'].values\n",
    "\n",
    "comparison.describe().round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b03b1a4-68c0-49cd-94b1-74e344941c5e",
   "metadata": {},
   "source": [
    "## Plot results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e4547fc6-b5ec-407c-b434-48f0807d24b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15,6))\n",
    "\n",
    "ax1 = fig.add_subplot(131)\n",
    "y = key_results[key_results['scenario']=='base']['thrombolysis_rate']\n",
    "x = key_results[key_results['scenario']=='same_patient_characteristics']['thrombolysis_rate']\n",
    "gradient, intercept, r_value, p_value, std_err = stats.linregress(x,y)\n",
    "y_fit=intercept + (x*gradient)\n",
    "ax1.plot(x, y, 'o', label='Data points')\n",
    "ax1.plot(x, y_fit, 'r', label='Fitted regression')\n",
    "ax1.plot([x.min(),x.max()],[x.min(),x.max()], 'b--', \n",
    "         label = 'Expected line if all variance\\ndue to hospital processes')\n",
    "mean_thrombolysis = comparison['actual'].mean()\n",
    "ax1.plot([x.min(),x.max()],[mean_thrombolysis, mean_thrombolysis], 'g:', \n",
    "         label = 'Expected line if all variance\\ndue to patient populations')\n",
    "\n",
    "text='Slope: %.2f\\nR-Squared: %.3f\\nP value: %.3f' %(gradient,r_value**2,p_value)\n",
    "ax1.set_xlim(0,26)\n",
    "ax1.set_ylim(0,26)\n",
    "ax1.text(17,2,text)\n",
    "ax1.legend()\n",
    "ax1.set_ylabel('Thrombolysis rate of 100k patient cohort')\n",
    "ax1.set_xlabel('Actual thrombolysis rate')\n",
    "ax1.set_title('Regression plot of actual vs cohort thrombolysis rate')\n",
    "\n",
    "ax2 = fig.add_subplot(132)\n",
    "zipped = zip(x,y)\n",
    "for xx, yy in zipped:\n",
    "    colour = 'red' if xx > yy else 'blue'\n",
    "    ax2.plot([xx,xx], [xx,yy], \n",
    "             color=colour, lw=1, marker='o', alpha=0.6, markersize=4)\n",
    "ax2.set_ylabel('Predicted thrombolysis rate of 100k patient cohort')\n",
    "ax2.set_xlabel('Actual thrombolysis rate')\n",
    "ax2.set_title('Scatter plot of actual vs cohort thrombolysis rate')\n",
    "ax2.grid()\n",
    "\n",
    "\n",
    "ax3 = fig.add_subplot(133)\n",
    "bins = np.arange(1,25)\n",
    "ax3.hist(y, bins = bins, color='k', linewidth=2,\n",
    "           label='Cohort thrombolysis', histtype='step')\n",
    "ax3.hist(x, bins = bins, color='0.7', linewidth=2,\n",
    "           label='Actual thrombolysis', histtype='stepfilled')\n",
    "ax3.set_xlabel('Thrombolysis rate (%)')\n",
    "ax3.set_ylabel('Hospital count')\n",
    "ax3.set_title('Histogram of actual vs cohort thrombolysis rates')\n",
    "ax3.set_ylim(0,25)\n",
    "ax3.legend()\n",
    "\n",
    "plt.tight_layout(pad=2)\n",
    "plt.savefig('./output/pathway_cohort.jpg', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2788476",
   "metadata": {},
   "source": [
    "## Observations\n",
    "\n",
    "* In this experiment we fix the patient population, so that all hospitals in the model see the same patients, and pass those patients through each hospital model using that hospital's pathway characteristics and decision model.\n",
    "\n",
    "* When a standard (national average) patient population is passed through all hospitals the cohort thrombolysis rate at each hospital correlates with the actual thrombolysis use with R-square of 0.41, suggesting in-hospital processes and decision making account for about 40% of observed variance. Similarly, the regression fit between actual and cohort thrombolysis rate has a slope of 0.55, suggesting that 55% of the inter-hospital variance is due to hospital processes - when the patient population is unchanged there is, on average, a 0.55 percentage point difference in predicted cohort population thrombolysis rate for each 1 percentage point change in actual thrombolysis rate.\n",
    "\n",
    "* As a general guide it seems that about half of the inter-hospital variance in thrombolysis rate is explained by in-hospital process and decisions, and the other half is explained by differences in local populations.\n",
    "\n",
    "* As should be expected, the mean and distribution of the cohort thrombolysis use per hospital is very similar to the actual mean and distribution of thrombolysis use.\n",
    "\n",
    "* There is a tendency for lower thrombolysing units to do better with a standard cohort, and those with higher thrombolysis rates to do worse. So there is a general observation that hospitals with high thrombolysis use do so partly because they have a more thrombolysable population, and vice versa."
   ]
  }
 ],
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