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_all*.csv'):\n",
    "    df_temp = pd.read_csv(file)[import_vars]\n",
    "    # Creates date variable based on file name\n",
    "    df_temp['date'] = file[34:-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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PMPm5AUhyYXf/o/R+oXgvKyR7n5Pl3QNsTnJmkvXABuCxMeQbxsRnT+8PRv0u8K6q+k7fS+PNXlVN3YArgGngSeDvgPOA19M7quGZ7v78cec8SfazgW8CP9I3m/jswB8AX6a3j/zjwJkrIXeX/Z/pbRh8Cbhu0n/m9D6UjgLfpbdFecup8gIfBL4CHATePoHZb+oevwgcAz69grIforfv/onu9heTkN3LMEhSQ5ravSNJrbP0Jakhlr4kNcTSl6SGWPqS1BBLX5IaYulLUkP+F+Aqyl04PGdhAAAAAElFTkSuQmCC\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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AGHB2DepbKxui/+5LiH4feG9Vfb/nofXXf1X5s4wf4CPAi8yfg/5LYDPwZuavnLjYLbesdZ1D6v0fmb8n/9eBfd1Ys70z/8vrGvAD5o/Mnrhbv8CHgX8HLgDvWuv6h9T/+7r1m8B14AsbrP8Z5s+tP9f9/MV67d/bD0hSgzwtI0kNMtwlqUGGuyQ1yHCXpAYZ7pLUIMNdkhpkuEtSg/4PeGqVXJMBMyoAAAAASUVORK5CYII=\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 unique_pat_tests(df_in):\n",
    "    return df_in.loc[df_in.took_hba1c == 1].patient_id.nunique()\n",
    "\n",
    "def invalid_tests(df_in):\n",
    "    return len(df_in.loc[(df_in.took_hba1c == 1) &\n",
    "        ((df_in.hba1c_mmol_per_mol == 0) | (df_in.hba1c_mmol_per_mol.isna()))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Population Count\n",
      "Total: 95813\n",
      "T1DM: 8469\n",
      "T2DM: 45337\n"
     ]
    }
   ],
   "source": [
    "# Population count between Jan 2019 and Jun 2021 by patient cohort\n",
    "\n",
    "# Total number of patients \n",
    "pop_total = unique_pat(df_input)\n",
    "\n",
    "# T1DM patients\n",
    "pop_t1dm = unique_pat(df_t1dm)\n",
    "\n",
    "# T2DM patients\n",
    "pop_t2dm = unique_pat(df_t2dm)\n",
    "\n",
    "print(\"Population Count\")\n",
    "print(\"Total: {}\\nT1DM: {}\\nT2DM: {}\".format(pop_total, pop_t1dm, pop_t2dm))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique Patients with HbA1c Tests\n",
      "Total: 25951\n",
      "T1DM: 931\n",
      "T2DM: 5711\n"
     ]
    }
   ],
   "source": [
    "# Number of patients who took HbA1c tests between Jan 2019 and Jun 2021 by patient cohort\n",
    "\n",
    "# Total\n",
    "pop_tests_total = unique_pat_tests(df_input)\n",
    "\n",
    "# T1DM patients\n",
    "pop_tests_t1dm = unique_pat_tests(df_t1dm)\n",
    "\n",
    "# T2DM patients\n",
    "pop_tests_t2dm = unique_pat_tests(df_t2dm)\n",
    "\n",
    "print(\"Unique Patients with HbA1c Tests\")\n",
    "print(\"Total: {}\\nT1DM: {}\\nT2DM: {}\".format(pop_tests_total, pop_tests_t1dm, pop_tests_t2dm))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique Patients with HbA1c by Threshold\n",
      "T2DM & HbA1c > 48: 1868\n",
      "T2DM & HbA1c > 58: 996\n",
      "T2DM & HbA1c > 64: 617\n",
      "T2DM & HbA1c > 75: 234\n"
     ]
    }
   ],
   "source": [
    "# Segment T2DM patients who took HbA1c by thresholds \n",
    "\n",
    "# >48\n",
    "pop_tests_gt48 = unique_pat_tests(df_gt48)\n",
    "\n",
    "# >58 \n",
    "pop_tests_gt58 = unique_pat_tests(df_gt58)\n",
    "\n",
    "# >64\n",
    "pop_tests_gt64 = unique_pat_tests(df_gt64)\n",
    "\n",
    "# >75\n",
    "pop_tests_gt75 = unique_pat_tests(df_gt75)\n",
    "\n",
    "print(\"Unique Patients with HbA1c by Threshold\")\n",
    "print(\n",
    "    \"T2DM & HbA1c > 48: {}\\nT2DM & HbA1c > 58: {}\\nT2DM & HbA1c > 64: {}\\nT2DM & HbA1c > 75: {}\".format(\n",
    "        pop_tests_gt48, pop_tests_gt58, pop_tests_gt64, pop_tests_gt75)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 or Null HbA1c Values\n",
      "Total: 1492\n",
      "T1DM: 57\n",
      "T2DM: 297\n"
     ]
    }
   ],
   "source": [
    "# Check for any 0 or null values\n",
    "\n",
    "# Total\n",
    "invalid_total = invalid_tests(df_input)\n",
    "\n",
    "# T1DM \n",
    "invalid_t1dm = invalid_tests(df_t1dm)\n",
    "\n",
    "# T2DM \n",
    "invalid_t2dm = invalid_tests(df_t2dm)\n",
    "\n",
    "print(\"0 or Null HbA1c Values\")\n",
    "print(\"Total: {}\\nT1DM: {}\\nT2DM: {}\".format(invalid_total, invalid_t1dm, invalid_t2dm))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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