notebooks/data_description.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# data_description.ipynb\n",
    "This notebook provides some basic information about the analysed population such as the number of patients who had HbA1c tests within the timeframe of interest (Jan 2019 to Jun 2021), breakdown by diabetes status, and distribution of HbA1c values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "from glob import glob"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in input files\n",
    "li = []\n",
    "\n",
    "for file in glob('../output/data/input*.csv'):\n",
    "    if file.split('_')[1] not in ['ethnicity.csv']:\n",
    "        df_temp = pd.read_csv(file)\n",
    "        df_temp['date'] = file[21:-4]\n",
    "        li.append(df_temp)\n",
    "    \n",
    "df_input = pd.concat(li, axis=0, ignore_index=False).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Segment the data by diabetes status\n",
    "df_t1dm = df_input.loc[df_input.diabetes_type == 'T1DM']\n",
    "df_t2dm = df_input.loc[df_input.diabetes_type == 'T2DM']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Subset by thresholds\n",
    "def thresholds(limit):\n",
    "    return df_t2dm.loc[df_t2dm.hba1c_mmol_per_mol > limit]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Segment the data by threshold\n",
    "df_gt48 = thresholds(48)\n",
    "df_gt58 = thresholds(58)\n",
    "df_gt64 = thresholds(64)\n",
    "df_gt75 = thresholds(75)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distribution of HbA1c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Display distribution\n",
    "def show_hist(df_in, title):\n",
    "    print(title)\n",
    "    plt.hist(df_in.hba1c_mmol_per_mol, bins = 20)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All Patients\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Patients with T2DM & HbA1c > 48\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Patients with T2DM & HbA1c > 58\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Patients with T2DM & HbA1c > 64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Patients with T2DM & HbA1c > 75\n"
     ]
    },
    {
     "data": {
      "image/png": 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PzmKf/zgwkWR9ku3AGHBqFeq7W6zJ8UiyG/gd4L1V9b2eXYMZj6ryZxk/wEeBl5k73/wXwHrgTcxdFXG2W25Y7Trv4Hj8A3PP6/8asKtrWzPjwdwvs0vA95mbee291ecHPgL8G3AGeNdq13+HxuN93fpV4DLw7Bofj2nmzq0/3/386SDHw8cPSFKDPC0jSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGmS4S1KD/g/R/Z/ThUSGpAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Full distribution\n",
    "show_hist(df_input, \"All Patients\")\n",
    "\n",
    "# >48\n",
    "show_hist(df_gt48, \"Patients with T2DM & HbA1c > 48\")\n",
    "\n",
    "# >58\n",
    "show_hist(df_gt58, \"Patients with T2DM & HbA1c > 58\")\n",
    "\n",
    "# >64\n",
    "show_hist(df_gt64, \"Patients with T2DM & HbA1c > 64\")\n",
    "\n",
    "# >75\n",
    "show_hist(df_gt75, \"Patients with T2DM & HbA1c > 75\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Descriptive Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def unique_pat(df_in):\n",
    "    return df_in.patient_id.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total patients: 95746\n",
      "T1DM patients: 8818\n",
      "T2DM patients: 45447\n"
     ]
    }
   ],
   "source": [
    "# Total number of patients who were given an HbA1c test between Jan 2019 and Jun 2021\n",
    "num_total = unique_pat(df_input)\n",
    "\n",
    "# T1DM patients\n",
    "num_t1dm = unique_pat(df_t1dm)\n",
    "\n",
    "# T2DM patients\n",
    "num_t2dm = unique_pat(df_t2dm)\n",
    "\n",
    "print(\"Total patients: {}\\nT1DM patients: {}\\nT2DM patients: {}\".format(num_total, num_t1dm, num_t2dm))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "T2DM & HbA1c > 48: 17896\n",
      "T2DM & HbA1c > 58: 10044\n",
      "T2DM & HbA1c > 64: 6452\n",
      "T2DM & HbA1c > 75: 2269\n"
     ]
    }
   ],
   "source": [
    "# Segment T2DM patients by thresholds \n",
    "\n",
    "# >48\n",
    "num_gt48 = unique_pat(df_gt48)\n",
    "\n",
    "# >58 \n",
    "num_gt58 = unique_pat(df_gt58)\n",
    "\n",
    "# >64\n",
    "num_gt64 = unique_pat(df_gt64)\n",
    "\n",
    "# >75\n",
    "num_gt75 = unique_pat(df_gt75)\n",
    "\n",
    "print(\n",
    "    \"T2DM & HbA1c > 48: {}\\nT2DM & HbA1c > 58: {}\\nT2DM & HbA1c > 64: {}\\nT2DM & HbA1c > 75: {}\".format(\n",
    "        num_gt48, num_gt58, num_gt64, num_gt75)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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   "file_extension": ".py",
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   "pygments_lexer": "ipython3",
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