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 datetime import datetime\n",
    "from glob import glob"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Variables to import\n",
    "import_vars = ['patient_id', 'took_hba1c', 'diabetes_type', 'hba1c_mmol_per_mol']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in and append 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)[import_vars]\n",
    "        # Creates date variable based on file name\n",
    "        df_temp['date'] = file[21:-4]\n",
    "        df_temp['date'] = df_temp['date'].apply(lambda x: datetime.strptime(x.strip(), '%Y-%m-%d'))\n",
    "        # Generates a count column\n",
    "        df_temp['population'] = 1\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": 4,
   "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": 5,
   "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": 6,
   "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": 7,
   "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": 8,
   "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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\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": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def unique_pat(df_in):\n",
    "    return df_in.patient_id.nunique()\n",
    "\n",
    "def num_tests(df_in):\n",
    "    return df_in.loc[df_in.took_hba1c == 1].patient_id.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total patients: 95740\n",
      "T1DM patients: 8603\n",
      "T2DM patients: 45347\n"
     ]
    }
   ],
   "source": [
    "# Population count between Jan 2019 and Jun 2021 by patient cohort\n",
    "\n",
    "# Total number of patients \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": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total patients: 26046\n",
      "T1DM patients: 887\n",
      "T2DM patients: 5783\n"
     ]
    }
   ],
   "source": [
    "# Number of HbA1c tests between Jan 2019 and Jun 2021 by patient cohort\n",
    "\n",
    "# Total\n",
    "num_total = num_tests(df_input)\n",
    "\n",
    "# T1DM patients\n",
    "num_t1dm = num_tests(df_t1dm)\n",
    "\n",
    "# T2DM patients\n",
    "num_t2dm = num_tests(df_t2dm)\n",
    "\n",
    "print(\"Total patients: {}\\nT1DM patients: {}\\nT2DM patients: {}\".format(num_total, num_t1dm, num_t2dm))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "T2DM & HbA1c > 48: 17925\n",
      "T2DM & HbA1c > 58: 9968\n",
      "T2DM & HbA1c > 64: 6460\n",
      "T2DM & HbA1c > 75: 2285\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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   "display_name": "Python 3",
   "language": "python",
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  "language_info": {
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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