analysis/long_covid_coding_report.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Long COVID coding in primary care\n",
    "\n",
    "This notebook follows on from our [published paper](https://bjgp.org/content/early/2021/06/28/BJGP.2021.0301/tab-article-info), and contains much of the same analysis. It is intended to be updated regularly.\n",
    "\n",
    "## Background\n",
    "Long COVID describes new or persistent symptoms at least four weeks after onset of acute COVID-19. Clinical codes to describe this were recently created. Aim: To describe the use of long COVID codes, and variation of use by general practice, demographics and over time.\n",
    "\n",
    "## Methods\n",
    "\n",
    "Working on behalf of NHS England, we used OpenSAFELY data encompassing 96% of the English population. We measured the proportion of people with a recorded code for long COVID, overall and by demographic factors, electronic health record software system (EMIS or TPP), and week. Detailed methods are the same as described in the original paper."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "from IPython.display import display, Markdown\n",
    "import pandas as pd\n",
    "pd.set_option(\"display.max_rows\", 200)\n",
    "pd.options.display.float_format = \"{:,.1f}\".format\n",
    "import lib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "### Report last updated **03 Aug 2021**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tpp = lib.tpp_emis_table_format(\"output\", {\"0\": \"Missing\"})\n",
    "total_tpp = tpp.loc[\"sex\",[\"No long COVID\", \"Long COVID\"]].sum()\n",
    "all_total_tpp = total_tpp.sum()\n",
    "emis = lib.tpp_emis_table_format(\"emis\", {\"RGN11NM\": \"region\",\"0\": \"Missing\",\"6\": \"Missing\"})\n",
    "total_emis = emis.loc[\"sex\",[\"No long COVID\", \"Long COVID\"]].sum()\n",
    "all_total_emis = total_emis.sum()\n",
    "totals = tpp[[\"No long COVID\", \"Long COVID\"]] + emis[[\"No long COVID\", \"Long COVID\"]]\n",
    "\n",
    "display(Markdown(f\"### Report last updated **{datetime.today().strftime('%d %b %Y')}**\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Table 1: Characteristics of the cohort"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "There were 57,604,767 people in the cohort in total. In practices that use TPP software, there were 24,106,715, while in practices that use EMIS software, there were 33,498,052 people."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
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     "output_type": "display_data"
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       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">TPP</th>\n",
       "      <th colspan=\"2\" halign=\"left\">EMIS</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Totals</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Patient count</th>\n",
       "      <th>%</th>\n",
       "      <th>Patient count</th>\n",
       "      <th>%</th>\n",
       "      <th>Patient count</th>\n",
       "      <th>%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">age_group</th>\n",
       "      <th>0-17</th>\n",
       "      <td>4839942</td>\n",
       "      <td>20.1</td>\n",
       "      <td>6809559</td>\n",
       "      <td>20.3</td>\n",
       "      <td>11649501</td>\n",
       "      <td>20.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18-24</th>\n",
       "      <td>1907505</td>\n",
       "      <td>7.9</td>\n",
       "      <td>2810350</td>\n",
       "      <td>8.4</td>\n",
       "      <td>4717855</td>\n",
       "      <td>8.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25-34</th>\n",
       "      <td>3348750</td>\n",
       "      <td>13.9</td>\n",
       "      <td>4836057</td>\n",
       "      <td>14.4</td>\n",
       "      <td>8184807</td>\n",
       "      <td>14.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35-44</th>\n",
       "      <td>3230713</td>\n",
       "      <td>13.4</td>\n",
       "      <td>4670118</td>\n",
       "      <td>13.9</td>\n",
       "      <td>7900831</td>\n",
       "      <td>13.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45-54</th>\n",
       "      <td>3244760</td>\n",
       "      <td>13.5</td>\n",
       "      <td>4494561</td>\n",
       "      <td>13.4</td>\n",
       "      <td>7739321</td>\n",
       "      <td>13.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55-69</th>\n",
       "      <td>4222719</td>\n",
       "      <td>17.5</td>\n",
       "      <td>5635349</td>\n",
       "      <td>16.8</td>\n",
       "      <td>9858068</td>\n",
       "      <td>17.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70-79</th>\n",
       "      <td>2092008</td>\n",
       "      <td>8.7</td>\n",
       "      <td>2670982</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4762990</td>\n",
       "      <td>8.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80+</th>\n",
       "      <td>1220318</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1571076</td>\n",
       "      <td>4.7</td>\n",
       "      <td>2791394</td>\n",
       "      <td>4.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">sex</th>\n",
       "      <th>F</th>\n",
       "      <td>12052904</td>\n",
       "      <td>50.0</td>\n",
       "      <td>16740654</td>\n",
       "      <td>50.0</td>\n",
       "      <td>28793558</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>12053811</td>\n",
       "      <td>50.0</td>\n",
       "      <td>16757398</td>\n",
       "      <td>50.0</td>\n",
       "      <td>28811209</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">region</th>\n",
       "      <th>East Midlands</th>\n",
       "      <td>4192331</td>\n",
       "      <td>17.4</td>\n",
       "      <td>755209</td>\n",
       "      <td>2.3</td>\n",
       "      <td>4947540</td>\n",
       "      <td>9.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>London</th>\n",
       "      <td>1700812</td>\n",
       "      <td>7.1</td>\n",
       "      <td>7667631</td>\n",
       "      <td>22.9</td>\n",
       "      <td>9368443</td>\n",
       "      <td>18.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North East</th>\n",
       "      <td>1119823</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1180482</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2300305</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North West</th>\n",
       "      <td>2067400</td>\n",
       "      <td>8.6</td>\n",
       "      <td>6788243</td>\n",
       "      <td>20.3</td>\n",
       "      <td>8855643</td>\n",
       "      <td>17.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South East</th>\n",
       "      <td>1606770</td>\n",
       "      <td>6.7</td>\n",
       "      <td>7080996</td>\n",
       "      <td>21.1</td>\n",
       "      <td>8687766</td>\n",
       "      <td>17.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South West</th>\n",
       "      <td>3308916</td>\n",
       "      <td>13.7</td>\n",
       "      <td>2443955</td>\n",
       "      <td>7.3</td>\n",
       "      <td>5752871</td>\n",
       "      <td>11.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Midlands</th>\n",
       "      <td>992257</td>\n",
       "      <td>4.1</td>\n",
       "      <td>4993534</td>\n",
       "      <td>14.9</td>\n",
       "      <td>5985791</td>\n",
       "      <td>11.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yorkshire and The Humber</th>\n",
       "      <td>3442158</td>\n",
       "      <td>14.3</td>\n",
       "      <td>1240398</td>\n",
       "      <td>3.7</td>\n",
       "      <td>4682556</td>\n",
       "      <td>9.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">imd</th>\n",
       "      <th>Missing</th>\n",
       "      <td>587847</td>\n",
       "      <td>2.4</td>\n",
       "      <td>98704</td>\n",
       "      <td>0.3</td>\n",
       "      <td>686551</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Most deprived 1</th>\n",
       "      <td>4825962</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6912078</td>\n",
       "      <td>20.6</td>\n",
       "      <td>11738040</td>\n",
       "      <td>20.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4717661</td>\n",
       "      <td>19.6</td>\n",
       "      <td>7122528</td>\n",
       "      <td>21.3</td>\n",
       "      <td>11840189</td>\n",
       "      <td>20.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4975649</td>\n",
       "      <td>20.6</td>\n",
       "      <td>6507147</td>\n",
       "      <td>19.4</td>\n",
       "      <td>11482796</td>\n",
       "      <td>19.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4684045</td>\n",
       "      <td>19.4</td>\n",
       "      <td>6298817</td>\n",
       "      <td>18.8</td>\n",
       "      <td>10982862</td>\n",
       "      <td>19.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Least deprived 5</th>\n",
       "      <td>4315551</td>\n",
       "      <td>17.9</td>\n",
       "      <td>6558778</td>\n",
       "      <td>19.6</td>\n",
       "      <td>10874329</td>\n",
       "      <td>18.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">ethnicity</th>\n",
       "      <th>Missing</th>\n",
       "      <td>6646638</td>\n",
       "      <td>27.6</td>\n",
       "      <td>10869822</td>\n",
       "      <td>32.4</td>\n",
       "      <td>17516460</td>\n",
       "      <td>30.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>14647551</td>\n",
       "      <td>60.8</td>\n",
       "      <td>17635728</td>\n",
       "      <td>52.6</td>\n",
       "      <td>32283279</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mixed</th>\n",
       "      <td>320225</td>\n",
       "      <td>1.3</td>\n",
       "      <td>579138</td>\n",
       "      <td>1.7</td>\n",
       "      <td>899363</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Asian</th>\n",
       "      <td>1500486</td>\n",
       "      <td>6.2</td>\n",
       "      <td>2493105</td>\n",
       "      <td>7.4</td>\n",
       "      <td>3993591</td>\n",
       "      <td>6.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>515849</td>\n",
       "      <td>2.1</td>\n",
       "      <td>1168966</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1684815</td>\n",
       "      <td>2.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>475966</td>\n",
       "      <td>2.0</td>\n",
       "      <td>751293</td>\n",
       "      <td>2.2</td>\n",
       "      <td>1227259</td>\n",
       "      <td>2.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                                             TPP               EMIS       \\\n",
       "                                   Patient count    % Patient count    %   \n",
       "age_group 0-17                           4839942 20.1       6809559 20.3   \n",
       "          18-24                          1907505  7.9       2810350  8.4   \n",
       "          25-34                          3348750 13.9       4836057 14.4   \n",
       "          35-44                          3230713 13.4       4670118 13.9   \n",
       "          45-54                          3244760 13.5       4494561 13.4   \n",
       "          55-69                          4222719 17.5       5635349 16.8   \n",
       "          70-79                          2092008  8.7       2670982  8.0   \n",
       "          80+                            1220318  5.1       1571076  4.7   \n",
       "sex       F                             12052904 50.0      16740654 50.0   \n",
       "          M                             12053811 50.0      16757398 50.0   \n",
       "region    East Midlands                  4192331 17.4        755209  2.3   \n",
       "          London                         1700812  7.1       7667631 22.9   \n",
       "          North East                     1119823  4.6       1180482  3.5   \n",
       "          North West                     2067400  8.6       6788243 20.3   \n",
       "          South East                     1606770  6.7       7080996 21.1   \n",
       "          South West                     3308916 13.7       2443955  7.3   \n",
       "          West Midlands                   992257  4.1       4993534 14.9   \n",
       "          Yorkshire and The Humber       3442158 14.3       1240398  3.7   \n",
       "imd       Missing                         587847  2.4         98704  0.3   \n",
       "          Most deprived 1                4825962 20.0       6912078 20.6   \n",
       "          2                              4717661 19.6       7122528 21.3   \n",
       "          3                              4975649 20.6       6507147 19.4   \n",
       "          4                              4684045 19.4       6298817 18.8   \n",
       "          Least deprived 5               4315551 17.9       6558778 19.6   \n",
       "ethnicity Missing                        6646638 27.6      10869822 32.4   \n",
       "          White                         14647551 60.8      17635728 52.6   \n",
       "          Mixed                           320225  1.3        579138  1.7   \n",
       "          South Asian                    1500486  6.2       2493105  7.4   \n",
       "          Black                           515849  2.1       1168966  3.5   \n",
       "          Other                           475966  2.0        751293  2.2   \n",
       "\n",
       "                                          Totals       \n",
       "                                   Patient count    %  \n",
       "age_group 0-17                          11649501 20.2  \n",
       "          18-24                          4717855  8.2  \n",
       "          25-34                          8184807 14.2  \n",
       "          35-44                          7900831 13.7  \n",
       "          45-54                          7739321 13.4  \n",
       "          55-69                          9858068 17.1  \n",
       "          70-79                          4762990  8.3  \n",
       "          80+                            2791394  4.8  \n",
       "sex       F                             28793558 50.0  \n",
       "          M                             28811209 50.0  \n",
       "region    East Midlands                  4947540  9.8  \n",
       "          London                         9368443 18.5  \n",
       "          North East                     2300305  4.5  \n",
       "          North West                     8855643 17.5  \n",
       "          South East                     8687766 17.2  \n",
       "          South West                     5752871 11.4  \n",
       "          West Midlands                  5985791 11.8  \n",
       "          Yorkshire and The Humber       4682556  9.3  \n",
       "imd       Missing                         686551  1.2  \n",
       "          Most deprived 1               11738040 20.4  \n",
       "          2                             11840189 20.6  \n",
       "          3                             11482796 19.9  \n",
       "          4                             10982862 19.1  \n",
       "          Least deprived 5              10874329 18.9  \n",
       "ethnicity Missing                       17516460 30.4  \n",
       "          White                         32283279 56.0  \n",
       "          Mixed                           899363  1.6  \n",
       "          South Asian                    3993591  6.9  \n",
       "          Black                          1684815  2.9  \n",
       "          Other                          1227259  2.1  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "float_formatter = \"{:,.0f}\".format\n",
    "total, table_1 = lib.get_table_1([tpp, emis, totals])\n",
    "display(Markdown(f\"There were {float_formatter(total[('Totals','Patient count')])} people in the cohort in total. In practices that use TPP software, there were {float_formatter(total[('TPP','Patient count')])}, while in practices that use EMIS software, there were {float_formatter(total[('EMIS','Patient count')])} people.\"))\n",
    "display(table_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Table 2: Counts and rates of long COVID coding stratified by demographic variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "There were 43,740 people who have been given a diagnostic code for long COVID to date. Counts for each software system are:"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TPP      11,501.0\n",
      "EMIS     32,239.0\n",
      "Totals   43,740.0\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "The overall rate of long COVID coding in the population was 152 per 100,000 people. Rates for each software system are:"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TPP       95.4\n",
      "EMIS     192.5\n",
      "Totals   151.9\n",
      "dtype: float64\n"
     ]
    },
    {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">TPP</th>\n",
       "      <th colspan=\"3\" halign=\"left\">EMIS</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Totals</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Long COVID</th>\n",
       "      <th>Rate per 100,000</th>\n",
       "      <th>%</th>\n",
       "      <th>Long COVID</th>\n",
       "      <th>Rate per 100,000</th>\n",
       "      <th>%</th>\n",
       "      <th>Long COVID</th>\n",
       "      <th>%</th>\n",
       "      <th>Rate per 100,000</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">age_group</th>\n",
       "      <th>0-17</th>\n",
       "      <td>273</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>766</td>\n",
       "      <td>11.2</td>\n",
       "      <td>2.4</td>\n",
       "      <td>1039</td>\n",
       "      <td>2.4</td>\n",
       "      <td>8.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18-24</th>\n",
       "      <td>685</td>\n",
       "      <td>35.9</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1410</td>\n",
       "      <td>50.2</td>\n",
       "      <td>4.4</td>\n",
       "      <td>2095</td>\n",
       "      <td>4.8</td>\n",
       "      <td>44.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25-34</th>\n",
       "      <td>1514</td>\n",
       "      <td>45.2</td>\n",
       "      <td>13.2</td>\n",
       "      <td>4152</td>\n",
       "      <td>85.9</td>\n",
       "      <td>12.9</td>\n",
       "      <td>5666</td>\n",
       "      <td>13.0</td>\n",
       "      <td>69.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35-44</th>\n",
       "      <td>2326</td>\n",
       "      <td>72.0</td>\n",
       "      <td>20.2</td>\n",
       "      <td>7122</td>\n",
       "      <td>152.5</td>\n",
       "      <td>22.1</td>\n",
       "      <td>9448</td>\n",
       "      <td>21.6</td>\n",
       "      <td>119.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45-54</th>\n",
       "      <td>3024</td>\n",
       "      <td>93.2</td>\n",
       "      <td>26.3</td>\n",
       "      <td>8694</td>\n",
       "      <td>193.4</td>\n",
       "      <td>27.0</td>\n",
       "      <td>11718</td>\n",
       "      <td>26.8</td>\n",
       "      <td>151.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55-69</th>\n",
       "      <td>2952</td>\n",
       "      <td>69.9</td>\n",
       "      <td>25.7</td>\n",
       "      <td>8406</td>\n",
       "      <td>149.2</td>\n",
       "      <td>26.1</td>\n",
       "      <td>11358</td>\n",
       "      <td>26.0</td>\n",
       "      <td>115.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70-79</th>\n",
       "      <td>532</td>\n",
       "      <td>25.4</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1299</td>\n",
       "      <td>48.6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1831</td>\n",
       "      <td>4.2</td>\n",
       "      <td>38.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80+</th>\n",
       "      <td>195</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>390</td>\n",
       "      <td>24.8</td>\n",
       "      <td>1.2</td>\n",
       "      <td>585</td>\n",
       "      <td>1.3</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">sex</th>\n",
       "      <th>F</th>\n",
       "      <td>7266</td>\n",
       "      <td>60.3</td>\n",
       "      <td>63.2</td>\n",
       "      <td>20640</td>\n",
       "      <td>123.3</td>\n",
       "      <td>64.0</td>\n",
       "      <td>27906</td>\n",
       "      <td>63.8</td>\n",
       "      <td>96.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>4235</td>\n",
       "      <td>35.1</td>\n",
       "      <td>36.8</td>\n",
       "      <td>11599</td>\n",
       "      <td>69.2</td>\n",
       "      <td>36.0</td>\n",
       "      <td>15834</td>\n",
       "      <td>36.2</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">region</th>\n",
       "      <th>East Midlands</th>\n",
       "      <td>2102</td>\n",
       "      <td>50.1</td>\n",
       "      <td>18.3</td>\n",
       "      <td>529</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>2631</td>\n",
       "      <td>6.4</td>\n",
       "      <td>53.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>London</th>\n",
       "      <td>640</td>\n",
       "      <td>37.6</td>\n",
       "      <td>5.6</td>\n",
       "      <td>8277</td>\n",
       "      <td>107.9</td>\n",
       "      <td>25.7</td>\n",
       "      <td>8917</td>\n",
       "      <td>21.8</td>\n",
       "      <td>95.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North East</th>\n",
       "      <td>671</td>\n",
       "      <td>59.9</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1271</td>\n",
       "      <td>107.7</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1942</td>\n",
       "      <td>4.7</td>\n",
       "      <td>84.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North West</th>\n",
       "      <td>792</td>\n",
       "      <td>38.3</td>\n",
       "      <td>6.9</td>\n",
       "      <td>7932</td>\n",
       "      <td>116.8</td>\n",
       "      <td>24.6</td>\n",
       "      <td>8724</td>\n",
       "      <td>21.3</td>\n",
       "      <td>98.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South East</th>\n",
       "      <td>1347</td>\n",
       "      <td>83.8</td>\n",
       "      <td>11.7</td>\n",
       "      <td>5837</td>\n",
       "      <td>82.4</td>\n",
       "      <td>18.1</td>\n",
       "      <td>7184</td>\n",
       "      <td>17.6</td>\n",
       "      <td>82.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South West</th>\n",
       "      <td>1427</td>\n",
       "      <td>43.1</td>\n",
       "      <td>12.4</td>\n",
       "      <td>1928</td>\n",
       "      <td>78.9</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3355</td>\n",
       "      <td>8.2</td>\n",
       "      <td>58.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Midlands</th>\n",
       "      <td>476</td>\n",
       "      <td>48.0</td>\n",
       "      <td>4.1</td>\n",
       "      <td>4517</td>\n",
       "      <td>90.5</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4993</td>\n",
       "      <td>12.2</td>\n",
       "      <td>83.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yorkshire and The Humber</th>\n",
       "      <td>2133</td>\n",
       "      <td>62.0</td>\n",
       "      <td>18.5</td>\n",
       "      <td>1020</td>\n",
       "      <td>82.2</td>\n",
       "      <td>3.2</td>\n",
       "      <td>3153</td>\n",
       "      <td>7.7</td>\n",
       "      <td>67.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">imd</th>\n",
       "      <th>Missing</th>\n",
       "      <td>262</td>\n",
       "      <td>44.6</td>\n",
       "      <td>2.3</td>\n",
       "      <td>75</td>\n",
       "      <td>76.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>337</td>\n",
       "      <td>0.8</td>\n",
       "      <td>49.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Most deprived 1</th>\n",
       "      <td>2125</td>\n",
       "      <td>44.0</td>\n",
       "      <td>18.5</td>\n",
       "      <td>7312</td>\n",
       "      <td>105.8</td>\n",
       "      <td>22.7</td>\n",
       "      <td>9437</td>\n",
       "      <td>21.6</td>\n",
       "      <td>80.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2250</td>\n",
       "      <td>47.7</td>\n",
       "      <td>19.6</td>\n",
       "      <td>7754</td>\n",
       "      <td>108.9</td>\n",
       "      <td>24.1</td>\n",
       "      <td>10004</td>\n",
       "      <td>22.9</td>\n",
       "      <td>84.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2442</td>\n",
       "      <td>49.1</td>\n",
       "      <td>21.2</td>\n",
       "      <td>6139</td>\n",
       "      <td>94.3</td>\n",
       "      <td>19.0</td>\n",
       "      <td>8581</td>\n",
       "      <td>19.6</td>\n",
       "      <td>74.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2261</td>\n",
       "      <td>48.3</td>\n",
       "      <td>19.7</td>\n",
       "      <td>5721</td>\n",
       "      <td>90.8</td>\n",
       "      <td>17.7</td>\n",
       "      <td>7982</td>\n",
       "      <td>18.2</td>\n",
       "      <td>72.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Least deprived 5</th>\n",
       "      <td>2161</td>\n",
       "      <td>50.1</td>\n",
       "      <td>18.8</td>\n",
       "      <td>5238</td>\n",
       "      <td>79.9</td>\n",
       "      <td>16.2</td>\n",
       "      <td>7399</td>\n",
       "      <td>16.9</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">ethnicity</th>\n",
       "      <th>Missing</th>\n",
       "      <td>2634</td>\n",
       "      <td>39.6</td>\n",
       "      <td>22.9</td>\n",
       "      <td>8212</td>\n",
       "      <td>75.5</td>\n",
       "      <td>25.5</td>\n",
       "      <td>10846</td>\n",
       "      <td>24.8</td>\n",
       "      <td>61.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>7382</td>\n",
       "      <td>50.4</td>\n",
       "      <td>64.2</td>\n",
       "      <td>18138</td>\n",
       "      <td>102.8</td>\n",
       "      <td>56.3</td>\n",
       "      <td>25520</td>\n",
       "      <td>58.3</td>\n",
       "      <td>79.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mixed</th>\n",
       "      <td>140</td>\n",
       "      <td>43.7</td>\n",
       "      <td>1.2</td>\n",
       "      <td>524</td>\n",
       "      <td>90.5</td>\n",
       "      <td>1.6</td>\n",
       "      <td>664</td>\n",
       "      <td>1.5</td>\n",
       "      <td>73.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Asian</th>\n",
       "      <td>986</td>\n",
       "      <td>65.7</td>\n",
       "      <td>8.6</td>\n",
       "      <td>3694</td>\n",
       "      <td>148.2</td>\n",
       "      <td>11.5</td>\n",
       "      <td>4680</td>\n",
       "      <td>10.7</td>\n",
       "      <td>117.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>195</td>\n",
       "      <td>37.8</td>\n",
       "      <td>1.7</td>\n",
       "      <td>1233</td>\n",
       "      <td>105.5</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1428</td>\n",
       "      <td>3.3</td>\n",
       "      <td>84.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>164</td>\n",
       "      <td>34.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>438</td>\n",
       "      <td>58.3</td>\n",
       "      <td>1.4</td>\n",
       "      <td>602</td>\n",
       "      <td>1.4</td>\n",
       "      <td>49.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          TPP                        \\\n",
       "                                   Long COVID Rate per 100,000    %   \n",
       "age_group 0-17                            273              5.6  2.4   \n",
       "          18-24                           685             35.9  6.0   \n",
       "          25-34                          1514             45.2 13.2   \n",
       "          35-44                          2326             72.0 20.2   \n",
       "          45-54                          3024             93.2 26.3   \n",
       "          55-69                          2952             69.9 25.7   \n",
       "          70-79                           532             25.4  4.6   \n",
       "          80+                             195             16.0  1.7   \n",
       "sex       F                              7266             60.3 63.2   \n",
       "          M                              4235             35.1 36.8   \n",
       "region    East Midlands                  2102             50.1 18.3   \n",
       "          London                          640             37.6  5.6   \n",
       "          North East                      671             59.9  5.8   \n",
       "          North West                      792             38.3  6.9   \n",
       "          South East                     1347             83.8 11.7   \n",
       "          South West                     1427             43.1 12.4   \n",
       "          West Midlands                   476             48.0  4.1   \n",
       "          Yorkshire and The Humber       2133             62.0 18.5   \n",
       "imd       Missing                         262             44.6  2.3   \n",
       "          Most deprived 1                2125             44.0 18.5   \n",
       "          2                              2250             47.7 19.6   \n",
       "          3                              2442             49.1 21.2   \n",
       "          4                              2261             48.3 19.7   \n",
       "          Least deprived 5               2161             50.1 18.8   \n",
       "ethnicity Missing                        2634             39.6 22.9   \n",
       "          White                          7382             50.4 64.2   \n",
       "          Mixed                           140             43.7  1.2   \n",
       "          South Asian                     986             65.7  8.6   \n",
       "          Black                           195             37.8  1.7   \n",
       "          Other                           164             34.5  1.4   \n",
       "\n",
       "                                         EMIS                        \\\n",
       "                                   Long COVID Rate per 100,000    %   \n",
       "age_group 0-17                            766             11.2  2.4   \n",
       "          18-24                          1410             50.2  4.4   \n",
       "          25-34                          4152             85.9 12.9   \n",
       "          35-44                          7122            152.5 22.1   \n",
       "          45-54                          8694            193.4 27.0   \n",
       "          55-69                          8406            149.2 26.1   \n",
       "          70-79                          1299             48.6  4.0   \n",
       "          80+                             390             24.8  1.2   \n",
       "sex       F                             20640            123.3 64.0   \n",
       "          M                             11599             69.2 36.0   \n",
       "region    East Midlands                   529             70.0  1.6   \n",
       "          London                         8277            107.9 25.7   \n",
       "          North East                     1271            107.7  3.9   \n",
       "          North West                     7932            116.8 24.6   \n",
       "          South East                     5837             82.4 18.1   \n",
       "          South West                     1928             78.9  6.0   \n",
       "          West Midlands                  4517             90.5 14.0   \n",
       "          Yorkshire and The Humber       1020             82.2  3.2   \n",
       "imd       Missing                          75             76.0  0.2   \n",
       "          Most deprived 1                7312            105.8 22.7   \n",
       "          2                              7754            108.9 24.1   \n",
       "          3                              6139             94.3 19.0   \n",
       "          4                              5721             90.8 17.7   \n",
       "          Least deprived 5               5238             79.9 16.2   \n",
       "ethnicity Missing                        8212             75.5 25.5   \n",
       "          White                         18138            102.8 56.3   \n",
       "          Mixed                           524             90.5  1.6   \n",
       "          South Asian                    3694            148.2 11.5   \n",
       "          Black                          1233            105.5  3.8   \n",
       "          Other                           438             58.3  1.4   \n",
       "\n",
       "                                       Totals                        \n",
       "                                   Long COVID    % Rate per 100,000  \n",
       "age_group 0-17                           1039  2.4              8.9  \n",
       "          18-24                          2095  4.8             44.4  \n",
       "          25-34                          5666 13.0             69.2  \n",
       "          35-44                          9448 21.6            119.6  \n",
       "          45-54                         11718 26.8            151.4  \n",
       "          55-69                         11358 26.0            115.2  \n",
       "          70-79                          1831  4.2             38.4  \n",
       "          80+                             585  1.3             21.0  \n",
       "sex       F                             27906 63.8             96.9  \n",
       "          M                             15834 36.2             55.0  \n",
       "region    East Midlands                  2631  6.4             53.2  \n",
       "          London                         8917 21.8             95.2  \n",
       "          North East                     1942  4.7             84.4  \n",
       "          North West                     8724 21.3             98.5  \n",
       "          South East                     7184 17.6             82.7  \n",
       "          South West                     3355  8.2             58.3  \n",
       "          West Midlands                  4993 12.2             83.4  \n",
       "          Yorkshire and The Humber       3153  7.7             67.3  \n",
       "imd       Missing                         337  0.8             49.1  \n",
       "          Most deprived 1                9437 21.6             80.4  \n",
       "          2                             10004 22.9             84.5  \n",
       "          3                              8581 19.6             74.7  \n",
       "          4                              7982 18.2             72.7  \n",
       "          Least deprived 5               7399 16.9             68.0  \n",
       "ethnicity Missing                       10846 24.8             61.9  \n",
       "          White                         25520 58.3             79.1  \n",
       "          Mixed                           664  1.5             73.8  \n",
       "          South Asian                    4680 10.7            117.2  \n",
       "          Black                          1428  3.3             84.8  \n",
       "          Other                           602  1.4             49.1  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total, table_2 = lib.get_table_2([tpp, emis, totals])\n",
    "display(Markdown(f\"There were {float_formatter(total[('Totals','Long COVID')])} people who have been given a diagnostic code for long COVID to date. Counts for each software system are:\"))\n",
    "print(total.loc[(slice(None), \"Long COVID\")])\n",
    "display(Markdown(f\"The overall rate of long COVID coding in the population was {float_formatter(total[('Totals','Rate per 100,000')])} per 100,000 people. Rates for each software system are:\"))\n",
    "print(total.loc[(slice(None), \"Rate per 100,000\")])\n",
    "table_2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Figure 1: Volume of code use in individual practices\n",
    "Stratified by the electronic health record provider of the practice (TPP/SystmOne or EMIS)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lib.code_use_per_week_graph()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Figure 2: Use of long COVID codes over time\n",
    "Stratified by the electronic health record provider of the practice (TPP/SystmOne or EMIS). Reporting lag may affect recent dates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lib.practice_distribution_graph()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Table 3: Total use of each individual long COVID related code\n",
    "This is distinct from table 2 in that it counts all coded events, including where patients have been coded more than once."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "There were 76,331 long COVID codes used in total. Counts for each software system were:"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TPP     16,452.0\n",
      "EMIS    60,026.0\n",
      "Total   76,331.0\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">TPP</th>\n",
       "      <th colspan=\"2\" halign=\"left\">EMIS</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>term</th>\n",
       "      <th>Total records</th>\n",
       "      <th>%</th>\n",
       "      <th>Total records</th>\n",
       "      <th>%</th>\n",
       "      <th>Total records</th>\n",
       "      <th>%</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>code</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1325161000000102</th>\n",
       "      <td>Post-COVID-19 syndrome</td>\n",
       "      <td>3,002.0</td>\n",
       "      <td>18.2</td>\n",
       "      <td>46,736.0</td>\n",
       "      <td>77.9</td>\n",
       "      <td>49,738.0</td>\n",
       "      <td>65.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1325181000000106</th>\n",
       "      <td>Ongoing symptomatic disease caused by severe a...</td>\n",
       "      <td>2,589.0</td>\n",
       "      <td>15.7</td>\n",
       "      <td>3,517.0</td>\n",
       "      <td>5.9</td>\n",
       "      <td>6,106.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1325021000000106</th>\n",
       "      <td>Signposting to Your COVID Recovery</td>\n",
       "      <td>3,385.0</td>\n",
       "      <td>20.6</td>\n",
       "      <td>1,761.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>5,146.0</td>\n",
       "      <td>6.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1325031000000108</th>\n",
       "      <td>Referral to post-COVID assessment clinic</td>\n",
       "      <td>3,239.0</td>\n",
       "      <td>19.7</td>\n",
       "      <td>6,380.0</td>\n",
       "      <td>10.6</td>\n",
       "      <td>9,619.0</td>\n",
       "      <td>12.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1325041000000104</th>\n",
       "      <td>Referral to Your COVID Recovery rehabilitation...</td>\n",
       "      <td>3,543.0</td>\n",
       "      <td>21.5</td>\n",
       "      <td>318.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3,861.0</td>\n",
       "      <td>5.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1325051000000101</th>\n",
       "      <td>Newcastle post-COVID syndrome Follow-up Screen...</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>515.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>537.0</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1325061000000103</th>\n",
       "      <td>Assessment using Newcastle post-COVID syndrome...</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>230.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>260.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1325071000000105</th>\n",
       "      <td>COVID-19 Yorkshire Rehabilitation Screening tool</td>\n",
       "      <td>196.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>242.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>438.0</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1325081000000107</th>\n",
       "      <td>Assessment using COVID-19 Yorkshire Rehabilita...</td>\n",
       "      <td>414.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>147.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>561.0</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1325091000000109</th>\n",
       "      <td>Post-COVID-19 Functional Status Scale patient ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1325101000000101</th>\n",
       "      <td>Assessment using Post-COVID-19 Functional Stat...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>69.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1325121000000105</th>\n",
       "      <td>Post-COVID-19 Functional Status Scale patient ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1325131000000107</th>\n",
       "      <td>Post-COVID-19 Functional Status Scale structur...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1325141000000103</th>\n",
       "      <td>Assessment using Post-COVID-19 Functional Stat...</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1325151000000100</th>\n",
       "      <td>Post-COVID-19 Functional Status Scale structur...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                     \\\n",
       "                                                               term   \n",
       "code                                                                  \n",
       "1325161000000102                             Post-COVID-19 syndrome   \n",
       "1325181000000106  Ongoing symptomatic disease caused by severe a...   \n",
       "1325021000000106                 Signposting to Your COVID Recovery   \n",
       "1325031000000108           Referral to post-COVID assessment clinic   \n",
       "1325041000000104  Referral to Your COVID Recovery rehabilitation...   \n",
       "1325051000000101  Newcastle post-COVID syndrome Follow-up Screen...   \n",
       "1325061000000103  Assessment using Newcastle post-COVID syndrome...   \n",
       "1325071000000105   COVID-19 Yorkshire Rehabilitation Screening tool   \n",
       "1325081000000107  Assessment using COVID-19 Yorkshire Rehabilita...   \n",
       "1325091000000109  Post-COVID-19 Functional Status Scale patient ...   \n",
       "1325101000000101  Assessment using Post-COVID-19 Functional Stat...   \n",
       "1325121000000105  Post-COVID-19 Functional Status Scale patient ...   \n",
       "1325131000000107  Post-COVID-19 Functional Status Scale structur...   \n",
       "1325141000000103  Assessment using Post-COVID-19 Functional Stat...   \n",
       "1325151000000100  Post-COVID-19 Functional Status Scale structur...   \n",
       "\n",
       "                           TPP               EMIS              Total       \n",
       "                 Total records    % Total records    % Total records    %  \n",
       "code                                                                       \n",
       "1325161000000102       3,002.0 18.2      46,736.0 77.9      49,738.0 65.2  \n",
       "1325181000000106       2,589.0 15.7       3,517.0  5.9       6,106.0  8.0  \n",
       "1325021000000106       3,385.0 20.6       1,761.0  2.9       5,146.0  6.7  \n",
       "1325031000000108       3,239.0 19.7       6,380.0 10.6       9,619.0 12.6  \n",
       "1325041000000104       3,543.0 21.5         318.0  0.5       3,861.0  5.1  \n",
       "1325051000000101          22.0  0.1         515.0  0.9         537.0  0.7  \n",
       "1325061000000103          30.0  0.2         230.0  0.4         260.0  0.3  \n",
       "1325071000000105         196.0  1.2         242.0  0.4         438.0  0.6  \n",
       "1325081000000107         414.0  2.5         147.0  0.2         561.0  0.7  \n",
       "1325091000000109           NaN  NaN          38.0  0.1           NaN  NaN  \n",
       "1325101000000101           NaN  NaN          69.0  0.1           NaN  NaN  \n",
       "1325121000000105           NaN  NaN          18.0  0.0           NaN  NaN  \n",
       "1325131000000107           0.0  0.0           NaN  NaN           NaN  NaN  \n",
       "1325141000000103          32.0  0.2          33.0  0.1          65.0  0.1  \n",
       "1325151000000100           NaN  NaN          22.0  0.0           NaN  NaN  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total, codes_tpp = lib.smoosh_codes_tables()\n",
    "total = total[(slice(None), \"Total records\")]\n",
    "display(Markdown(f\"There were {float_formatter(total['Total'])} long COVID codes used in total. Counts for each software system were:\"))\n",
    "print(total)\n",
    "codes_tpp"
   ]
  }
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