Final Report.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Health Equity Among Home Care Aides"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to conduct our analysis, we used medical insurance claims data from home care aides. Our data covered the years 2016, 2017, and 2018 and was sourced from Kaiser WA. We ended up with 1,824,238 total claims"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For our analysis, we focused on 10 chronic conditions:\n",
    "    \n",
    "    * Diabetes\n",
    "    * Hypertension\n",
    "    * Obesity\n",
    "    * Cancer\n",
    "    * Musculoskeletal conditions\n",
    "    * Cardiovascular conditions\n",
    "    * Asthma\n",
    "    * Chronic Obstructive Pulmonary Disorder (COPD)\n",
    "    * Cholesterol\n",
    "    * Mental health disorders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "med = pd.read_csv('../../data/reshaped_med.csv')\n",
    "enroll = pd.read_csv('../../data/enroll.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What is the prevalence of the different chronic conditions of interest by year?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Total number of HCAs each year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "years = enroll.groupby(['Incurred Year']).agg({'Member ID Encrypted': 'count', 'Member ID Encrypted': 'nunique'})\n",
    "years.reset_index(level=0, inplace=True)\n",
    "years.columns = ['year', 'total']\n",
    "years.loc[:, 'year'] = ['2016', '2017', '2018']\n",
    "plt.bar(years['year'], years['total'])\n",
    "plt.title('Total HCAs over Time')\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Number of HCAs\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Total number of claims each year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "num_claims = med.groupby(['year']).size().reset_index(name='total')\n",
    "num_claims.loc[:, 'year'] = ['2016', '2017', '2018']\n",
    "plt.bar(num_claims['year'], num_claims['total'])\n",
    "plt.title('Total Claims over Time')\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Number of Claims\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "asthma = med.groupby(['year'])['binary_asthma'].sum().reset_index(name ='asthma').asthma\n",
    "bp = med.groupby(['year'])['binary_bp'].sum().reset_index(name ='bp').bp\n",
    "cancer = med.groupby(['year'])['binary_cancer'].sum().reset_index(name = 'cancer').cancer\n",
    "cardiovascular = med.groupby(['year'])['binary_cardiovascular'].sum().reset_index(name = \n",
    "                                                                                  'cardiovascular').cardiovascular\n",
    "copd = med.groupby(['year'])['binary_copd'].sum().reset_index(name = 'copd').copd\n",
    "diabetes = med.groupby(['year'])['binary_diabetes'].sum().reset_index(name = 'diabetes').diabetes\n",
    "kidney = med.groupby(['year'])['binary_kidney'].sum().reset_index(name = 'kidney').kidney\n",
    "obesity = med.groupby(['year'])['binary_obesity'].sum().reset_index(name = 'obesity').obesity\n",
    "musculoskeletal = med.groupby(['year'])['binary_musculoskeletal'].sum().reset_index(name = \n",
    "                                                                                    'musculoskeletal').musculoskeletal\n",
    "cholesterol = med.groupby(['year'])['binary_cholesterol'].sum().reset_index(name = 'cholesterol').cholesterol\n",
    "mental_disorder = med.groupby(['year'])['binary_mental_disorder'].sum().reset_index(name = \n",
    "                                                                                    'mental_disorder').mental_disorder\n",
    "totals_by_year = pd.concat([years, asthma, bp, cancer, cardiovascular, copd, diabetes, kidney, obesity, musculoskeletal, \n",
    "          cholesterol, mental_disorder], axis=1)\n",
    "totals_by_year['chronic_total']= totals_by_year.iloc[:, 2:12].sum(axis=1)\n",
    "percentages = totals_by_year.iloc[:,2:13].div(totals_by_year['total'], axis=0) * 100\n",
    "percentages.loc[:, 'year'] = ['2016', '2017', '2018']\n",
    "percentages_chronic = totals_by_year.iloc[:,2:13].div(totals_by_year['chronic_total'], axis=0) * 100\n",
    "percentages_chronic.loc[:, 'year'] = ['2016', '2017', '2018']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Top 3 most prevalent chronic disease"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def disease_graph(disease): \n",
    "    plt.bar(percentages['year'], percentages[disease], align='center', color='tab:blue')\n",
    "    plt.title('Prevalence of Asthma in HCA population')\n",
    "    plt.xlabel(\"Year\")\n",
    "    plt.ylabel(\"Prevalence\")\n",
    "    plt.show()\n",
    "\n",
    "    plt.bar(percentages_chronic['year'], percentages_chronic[disease], align='center', color='tab:blue')\n",
    "    plt.title('Prevalence of Asthma HCA population with Chronic Diseases')\n",
    "    plt.xlabel(\"Year\")\n",
    "    plt.ylabel(\"Prevalence\")\n",
    "    plt.show()\n",
    "    return;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Blood Pressure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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dDEyodG8PLCkZq5mZtVfJu8gEnAvcExFfqgy6Eui7K2wG8IM6o18LHCxp83xx/+Dcz8zMhomSP7TcD/gAcJekO3O/U4Ezge9K+jDwMPAeAElTgBMi4viIWCHpc8AdebzTI2JFwVjNrDD/OLacofrj2GIJJiJupv61FIAD65TvBY6vdM8GZpeJzszMSvMv+c3MrAgnGDMzK8IJxszMinCCMTOzIpxgzMysCCcYMzMrwgnGzMyKcIIxM7MinGDMzKwIJxgzMyvCCcbMzIpwgjEzsyKcYMzMrAgnGDMzK8IJxszMinCCMTOzIoo9cEzSbOBwYFlE7Jb7XQLskouMA56KiN3rjLsQWAW8CKyJiCml4jQzszJKPjJ5DvBV4IK+HhHxvr7Pkr4IPN1k/AMi4vFi0ZmZWVElH5l8k6RJ9YZJEvBe4O2lpm9mZt3VrWswfwY8FhH3NRgewHWS5kma2awiSTMl9UrqXb58edsDNTOzgelWgpkOXNxk+H4RsQdwGHCipLc2KhgRsyJiSkRM6enpaXecZmY2QB1PMJJGA+8GLmlUJiKW5PdlwOXA1M5EZ2Zm7dKNI5iDgHsjYnG9gZI2ljS27zNwMLCgg/GZmVkbFEswki4GbgF2kbRY0ofzoKOpOT0maVtJV+fOrYGbJf0CuB34YURcUypOMzMro+RdZNMb9D+uTr8lwLT8+UHgjaXiMjOzzvAv+c3MrAgnGDMzK8IJxszMinCCMTOzIpxgzMysCCcYMzMrwgnGzMyKcIIxM7MinGDMzKwIJxgzMyvCCcbMzIpwgjEzsyKcYMzMrAgnGDMzK8IJxszMinCCMTOzIpxgzMysiJKPTJ4taZmkBZV+/yDpEUl35te0BuMeKunXku6XdEqpGM3MrJySRzBzgEPr9P9yROyeX1fXDpQ0CvgacBgwGZguaXLBOM3MrIBiCSYibgJWDGDUqcD9EfFgRKwGvgMc0dbgzMysuG5cg/m4pF/mU2ib1xm+HbCo0r0496tL0kxJvZJ6ly9f3u5YzcxsgDqdYL4B7ATsDiwFvlinjOr0i0YVRsSsiJgSEVN6enraE6WZmQ1aRxNMRDwWES9GxEvAN0mnw2otBiZUurcHlnQiPjMza5+OJhhJ21Q63wUsqFPsDmBnSa+TtAFwNHBlJ+IzM7P2GV2qYkkXA/sD4yUtBk4D9pe0O+mU10LgI7nstsA5ETEtItZI+jhwLTAKmB0Rd5eK08zMyiiWYCJiep3e5zYouwSYVum+GnjFLcxmZjZ8+Jf8ZmZWhBOMmZkV4QRjZmZFOMGYmVkRTjBmZlaEE4yZmRXhBGNmZkU4wZiZWRFOMGZmVoQTjJmZFeEEY2ZmRTjBmJlZES0lGEkbSfp7Sd/M3TtLOrxsaGZmNpy1egRzHvA8sE/uXgycUSQiMzMbEVpNMDtFxOeBFwAi4jnqP9rYzMwMaD3BrJY0hvSgMCTtRDqiMTMzq6vVBHMacA0wQdKFwA3A3zUbQdJsScskLaj0+zdJ90r6paTLJY1rMO5CSXdJulNSb4sxmpnZENJSgomIucC7geOAi4EpEXFjP6PNAQ6t6TcX2C0i/hT4DfDpJuMfEBG7R8SUVmI0M7OhpdW7yN4FrImIH0bEVcAaSUc2GycibgJW1PS7LiLW5M5bge0HELOZmQ0DLZ8ii4in+zoi4inSabPB+BDwowbDArhO0jxJM5tVImmmpF5JvcuXLx9kSGZm1i6tJph65UYPdKKS/j+wBriwQZH9ImIP4DDgRElvbVRXRMyKiCkRMaWnp2egIZmZWZu1mmB6JX1J0k6SdpT0ZWDeQCYoaQZwOPD+iIh6ZSJiSX5fBlwOTB3ItMzMrHtaTTB/DawGLgEuBX4PnLiuE5N0KPAp4J0R8bsGZTaWNLbvM3AwsKBeWTMzG7paOs0VEc8Cp6xLxZIuBvYHxktaTLpm82lgQ2CuJIBbI+IESdsC50TENGBr4PI8fDRwUURcsy7TNjOz7mspwUh6A/BJYFJ1nIh4e6NxImJ6nd7nNii7BJiWPz8IvLGVuMzMbOhq9UL9pcDZwDnAi+XCMTOzkaLVBLMmIr5RNBIzMxtRWr3I/1+SPiZpG0lb9L2KRmZmZsNaq0cwM/L731b6BbBje8MxM7ORotW7yF5XOhAzMxtZ1uWJlp+RNCt3+4mWZmbW1Lo80XI1sG/u9hMtzcysKT/R0szMivATLc3MrIhW7yKrfaLlfqSHj5mZmdXV6l1kcyXNB/YmnRo7KSIeLxqZmZkNa00TjKQ9anotze8TJU2MiPllwjIzs+GuvyOYLzYZFkDDP7s0M7NXt6YJJiIO6FQgZmY2srT82GNJuwGTgdf09YuIC0oEZWZmw1+rz4M5jfTwsMnA1cBhwM2AE4yZmdXV6u9gjgIOBB6NiA+SHgi2YX8jSZotaZmkBZV+W0iaK+m+/L55g3Fn5DL3SZpRr4yZmQ1drSaY5yLiJWCNpE2BZbT2T8pzgENr+p0C3BAROwM3UOdRzPlRAKcBewFTgdMaJSIzMxuaWk0wvZLGAd8E5gHzgdv7GykibgJW1PQ+Ajg/fz4fOLLOqIcAcyNiRUQ8CczllYnKzMyGsFZ/aPmx/PFsSdcAm0bELwc4za0jYmmud6mkreqU2Q5YVOlenPu9gqSZwEyAiRMnDjAkMzNrt1b/rv8Hko6RtHFELBxEcmlVvT/SjHoFI2JWREyJiCk9PT2FwzIzs1a1eorsS8BbgF9JulTSUZJe099IDTwmaRuA/L6sTpnFwIRK9/bAkgFOz8zMuqClBBMRP8mnyXYEZgHvpX5iaMWVrH0E8wzgB3XKXAscLGnzfHH/4NzPzMyGiVaPYMh/1/+XwAnAnqy9UN9snIuBW4BdJC2W9GHgTOAdku4D3pG7kTRF0jkAEbEC+BxwR36dnvuZmdkw0eoPLS8h3TJ8DfA14MZ823JTETG9waAD65TtBY6vdM8GZrcSn5mZDT2t/lXMecAxEfFiyWDMzGzkaPUU2U3ApyXNApC0s6TDy4VlZmbDXasJ5jxgNbBv7l4MnFEkIjMzGxFaTTA7RcTngRcAIuI56v9WxczMDGg9wazOd5EFgKSdgOeLRWVmZsNeqxf5TyPdQTZB0oXAfsBxpYIyM7Phr98EI0nAvcC7gb1Jp8ZOiojHC8dmZmbDWL8JJiJC0hUR8Wbghx2IyczMRoBWr8HcKmnPopGYmdmI0uo1mAOAEyQtBJ4lnSaLiPjTUoGZmdnw1mqCOaxoFGZmNuI0TTD5L/lPAF4P3AWcGxFrOhGYmZkNb/1dgzkfmEJKLocBXywekZmZjQj9nSKbHBF/AiDpXOD28iGZmdlI0N8RzAt9H3xqzMzM1kV/RzBvlLQyfxYwJnf33UW2adHozMxs2Gp6BBMRoyJi0/waGxGjK58HlFwk7SLpzsprpaSTa8rsL+npSpnPDmRaZmbWPa3eptw2EfFrYHcASaOAR4DL6xT9aUT4mTNmZsNUq7/kL+VA4IGIeKjLcZiZWZt1O8EcDVzcYNg+kn4h6UeSdm1UgaSZknol9S5fvrxMlGZmts66lmAkbQC8E7i0zuD5wA4R8UbgP4ArGtUTEbMiYkpETOnp6SkTrJmZrbNuHsEcBsyPiMdqB0TEyoh4Jn++Glhf0vhOB2hmZgPXzQQznQanxyS9Nj+HBklTSXE+0cHYzMxskDp+FxmApI2AdwAfqfQ7ASAizgaOAj4qaQ3wHHB0REQ3YjUzs4HpSoKJiN8BW9b0O7vy+avAVzsdl5mZtU+37yIzM7MRygnGzMyKcIIxM7MinGDMzKwIJxgzMyvCCcbMzIpwgjEzsyKcYMzMrAgnGDMzK8IJxszMinCCMTOzIpxgzMysCCcYMzMrwgnGzMyKcIIxM7MinGDMzKyIriUYSQsl3SXpTkm9dYZL0r9Lul/SLyXt0Y04zcxsYLryRMuKAyLi8QbDDgN2zq+9gG/kdzMzGwaG8imyI4ALIrkVGCdpm24HZWZmrelmggngOknzJM2sM3w7YFGle3Hu9zKSZkrqldS7fPnyQqGamdm66maC2S8i9iCdCjtR0ltrhqvOOPGKHhGzImJKREzp6ekpEaeZmQ1A1xJMRCzJ78uAy4GpNUUWAxMq3dsDSzoTnZmZDVZXEoykjSWN7fsMHAwsqCl2JXBsvptsb+DpiFja4VDNzGyAunUX2dbA5ZL6YrgoIq6RdAJARJwNXA1MA+4Hfgd8sEuxmpnZAHQlwUTEg8Ab6/Q/u/I5gBM7GZeZmbXPUL5N2czMhjEnGDMzK8IJxszMinCCMTOzIpxgzMysCCcYMzMrwgnGzMyKcIIxM7MinGDMzKwIJxgzMyvCCcbMzIpwgjEzsyKcYMzMrAgnGDMzK8IJxszMinCCMTOzIjqeYCRNkPRjSfdIulvSSXXK7C/paUl35tdnOx2nmZkNTjeeaLkG+EREzJc0FpgnaW5E/Kqm3E8j4vAuxGdmZm3Q8SOYiFgaEfPz51XAPcB2nY7DzMzK6uo1GEmTgDcBt9UZvI+kX0j6kaRdOxqYmZkNWjdOkQEgaRPge8DJEbGyZvB8YIeIeEbSNOAKYOcG9cwEZgJMnDixYMRmZrYuunIEI2l9UnK5MCK+Xzs8IlZGxDP589XA+pLG16srImZFxJSImNLT01M0bjMza1037iITcC5wT0R8qUGZ1+ZySJpKivOJzkVpZmaD1Y1TZPsBHwDuknRn7ncqMBEgIs4GjgI+KmkN8BxwdEREF2I1M7MB6niCiYibAfVT5qvAVzsTkZmZleBf8puZWRFOMGZmVoQTjJmZFeEEY2ZmRTjBmJlZEU4wZmZWhBOMmZkV4QRjZmZFOMGYmVkRTjBmZlaEE4yZmRXhBGNmZkU4wZiZWRFOMGZmVoQTjJmZFeEEY2ZmRTjBmJlZEV1JMJIOlfRrSfdLOqXO8A0lXZKH3yZpUuejNDOzweh4gpE0CvgacBgwGZguaXJNsQ8DT0bE64EvA//a2SjNzGywunEEMxW4PyIejIjVwHeAI2rKHAGcnz9fBhwoSR2M0czMBml0F6a5HbCo0r0Y2KtRmYhYI+lpYEvg8drKJM0EZubOZyT9uu0RDz3jqTMvhir5+BOGUZu5vf7g1dJmO7QpjFfoRoKpdyQSAyiTekbMAmYNNqjhRFJvREzpdhzWOrfZ8OM2G7xunCJbDEyodG8PLGlURtJoYDNgRUeiMzOztuhGgrkD2FnS6yRtABwNXFlT5kpgRv58FPDfEVH3CMbMzIamjp8iy9dUPg5cC4wCZkfE3ZJOB3oj4krgXOBbku4nHbkc3ek4h7hX1SnBEcJtNvy4zQZJPjAwM7MS/Et+MzMrwgnGzMyKcIIZAiRNkPRjSfdIulvSSbn/FpLmSrovv2+e+/+RpFskPS/pkzV1jZN0maR7c337dOM7jXTtajNJu0i6s/JaKenkbn2vkazN69n/zXUskHSxpNd04zsNdb4GMwRI2gbYJiLmSxoLzAOOBI4DVkTEmfk/2zaPiE9J2or046gjSX+p84VKXecDP42Ic/JdehtFxFOd/k4jXTvbrFLnKOARYK+IeKhT3+XVol1tJmk74GZgckQ8J+m7wNURMafz32po8xHMEBARSyNifv68CriH9G8G1b/MOZ+0oBMRyyLiDuCFaj2SNgXeSroLj4hY7eRSRrvarMaBwANOLmW0uc1GA2Py7/Q24pW/5TOcYIac/M/RbwJuA7aOiKWQVg5gq35G3xFYDpwn6X8knSNp44LhGoNus6qjgYvbHZ+90mDaLCIeAb4APAwsBZ6OiOtKxjtcOcEMIZI2Ab4HnBwRKwdQxWhgD+AbEfEm4FngFY9DsPZpQ5v11bMB8E7g0nbFZvUNts3yNZojgNcB2wIbS/qr9kY5MjjBDBGS1ict9BdGxPdz78fyeeO+88fL+qlmMbA4Im7L3ZeREo4V0KY263MYMD8iHmt/pNanTW12EPDbiFgeES8A3wf2LRXzcOYEMwTkRxGcC9wTEV+qDKr+Zc4M4AfN6omIR4FFknbJvQ4EftXmcI32tVnFdHx6rKg2ttnDwN6SNsp1Hki6nmM1fBfZECDpLcBPgbuAl3LvU0nnh78LTCQt1O+JiBWSXgv0Apvm8s+Q7mhZKWmLqXI2AAABy0lEQVR34BxgA+BB4IMR8WQnv8+rQZvbbCPS4yl2jIinO/tNXj3a3Gb/CLwPWAP8D3B8RDzfye8zHDjBmJlZET5FZmZmRTjBmJlZEU4wZmZWhBOMmZkV4QRjZmZFOMGYDZCSmyUdVun3XknXdDMus6HCtymbDYKk3Uh/7/Im0iPA7wQOjYgHBlHn6IhY06YQzbrGCcZskCR9nvS/bxsDqyLic5JmACeSfvD6c+DjEfGSpFmkv+8ZA1wSEafnOhYD/wkcCpwVEf5PMhv2Rnc7ALMR4B+B+cBqYEo+qnkXsG9ErMlJ5WjgIuCU/Cvx0cCPJV0WEX1/5/NsROzXjS9gVoITjNkgRcSzki4BnomI5yUdBOwJ9Ka/qmIM6a9gAKZL+jBp3dsWmMza/4u7pLORm5XlBGPWHi+x9v+tBMyOiL+vFpC0M3ASMDUinpL0baD6qN1nOxKpWYf4LjKz9rseeK+k8QCStpQ0kfSniauAlflv4Q/pYoxmxfkIxqzNIuKu/G+710taj/TI3RNI/8z7K2AB6Z+uf9a9KM3K811kZmZWhE+RmZlZEU4wZmZWhBOMmZkV4QRjZmZFOMGYmVkRTjBmZlaEE4yZmRXxv0ucSQzGmbngAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "disease_graph('bp')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cholesterol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "disease_graph('cholesterol')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Diabetes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "disease_graph('diabetes')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What is the rate of co-morbidity in the population?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The comorbidity rate was calculated by taking dividing the number of HCAs with 2 or more chronic conditions of interest by HCAs with at least 1 chonic condition of interest. The __comorbidity rate over all 3 years (2016-2018) was ~66%__. This means that ~66% of HCAs with chronic conditions of interest have 2 or more of those conditions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Commorbidity rate:  0.6565615185033457\n"
     ]
    }
   ],
   "source": [
    "# Group by member id\n",
    "member_groups = med.groupby(['member_id'])\n",
    "comorbid = 0\n",
    "at_least_one_condition = 0\n",
    "\n",
    "# Loop through groups to count members\n",
    "for mg_id, mg in member_groups:\n",
    "    if any(mg['flag_comorbidity'] == 1):\n",
    "        comorbid += 1\n",
    "    if any(mg['total_conditions'] >= 1):\n",
    "        at_least_one_condition += 1\n",
    "        \n",
    "comorbidity_rate = comorbid/at_least_one_condition\n",
    "print('Commorbidity rate: ', comorbidity_rate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What are the most common comorbidities in our claims data?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this question, we chose to focus on HCAs with at least 1 chronic condition of interest. Taking a look at the distribution of total conditions in our data, we can see that the mean total number of conditions is 2 and the max total number of conditions is 6. There are several outliers at 7, 8, 9, and 10 total conditions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "with_condition = med.loc[med['total_conditions'] > 0]\n",
    "condition_count = with_condition.groupby('member_id').agg({'total_conditions':'max'})[['total_conditions']].reset_index()\n",
    "\n",
    "ax = plt.subplot()\n",
    "ax.boxplot(condition_count['total_conditions'], vert=False)\n",
    "ax.set_xlabel('Number of Conditions')\n",
    "ax.set_title('Distribution of Comorbidities')\n",
    "\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['member_id', 'binary_asthma', 'binary_bp', 'binary_cancer', 'binary_cardiovascular', 'binary_cholesterol', 'binary_copd', 'binary_diabetes', 'binary_kidney', 'binary_mental_disorder', 'binary_musculoskeletal', 'binary_obesity', 'flag_comorbidity']\n",
    "comorbid_count = med[cols]\n",
    "comorbid_count = with_condition.groupby('member_id').agg({'binary_asthma':'max', 'binary_bp':'max', 'binary_cancer':'max', 'binary_cardiovascular':'max', 'binary_cholesterol':'max', 'binary_copd':'max', 'binary_diabetes':'max', 'binary_kidney':'max', 'binary_mental_disorder':'max', 'binary_musculoskeletal':'max', 'binary_obesity':'max', 'flag_comorbidity':'max'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "conditions = ['kidney', 'mental_disorder', 'bp', 'cardiovascular', 'diabetes', 'obesity', 'cancer', 'musculoskeletal', 'copd', 'cholesterol']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def combination(condition):\n",
    "    # Get all rows with at least 2 conditions\n",
    "    comorbid = comorbid_count.loc[comorbid_count['flag_comorbidity'] == 1]\n",
    "    \n",
    "    # Get rows with and without given condition\n",
    "    with_condition = comorbid.loc[comorbid_count['binary_' + condition] == 1]\n",
    "    without_condition = comorbid.loc[comorbid_count['binary_' + condition] == 0]\n",
    "    \n",
    "    # Count members (with 2+ conditions) with given condition who also have other conditions of interest\n",
    "    with_condition_counts = with_condition.groupby('member_id', as_index=False).apply(count_conditions)\n",
    "    with_condition_counts = with_condition_counts.sum(axis=0)\n",
    "    \n",
    "    # Count members (with 2+ conditions) without given condition who have other conditions of interest\n",
    "    without_condition_counts = without_condition.groupby('member_id', as_index=False).apply(count_conditions)\n",
    "    without_condition_counts = without_condition_counts.sum(axis=0)\n",
    "    \n",
    "    # Lists for chi2 values, p-values, and number of members with both the given condition\n",
    "    # and the current condition of interest\n",
    "    chi2 = []\n",
    "    p = []\n",
    "    num_both = []\n",
    "    \n",
    "    # Calculate chi2 for each condition of interest in relation to given condition\n",
    "    for c in conditions:\n",
    "        both = with_condition_counts[c]\n",
    "        condition_without_c = with_condition_counts[condition] - both\n",
    "        without_condition_with_c = without_condition_counts[c]\n",
    "        without_condition_without_c = len(without_condition) - without_condition_with_c\n",
    "        \n",
    "        c_arr = np.array([[both, condition_without_c], [without_condition_with_c, without_condition_without_c]])\n",
    "        c_chi2, c_p, c_dof, c_ex= stats.chi2_contingency(c_arr)\n",
    "        chi2.append(c_chi2)\n",
    "        p.append(c_p)\n",
    "        num_both.append(both)\n",
    "    \n",
    "    comorbidity_dict = {'conditions': conditions, 'chi2': chi2, 'p_value': p, 'num_members': num_both}\n",
    "    return pd.DataFrame.from_dict(comorbidity_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def count_conditions(group):\n",
    "    counts = pd.DataFrame()\n",
    "    \n",
    "    counts['kidney'] = [1 if any(group['binary_kidney'] == 1) else 0]\n",
    "    counts['mental_disorder'] = [1 if any(group['binary_mental_disorder'] == 1) else 0]\n",
    "    counts['bp'] = [1 if any(group['binary_bp'] == 1) else 0]\n",
    "    counts['cardiovascular'] = [1 if any(group['binary_cardiovascular'] == 1) else 0]\n",
    "    counts['diabetes'] = [1 if any(group['binary_diabetes'] == 1) else 0]\n",
    "    counts['obesity'] = [1 if any(group['binary_obesity'] == 1) else 0]\n",
    "    counts['cancer'] = [1 if any(group['binary_cancer'] == 1) else 0]\n",
    "    counts['musculoskeletal'] = [1 if any(group['binary_musculoskeletal'] == 1) else 0]\n",
    "    counts['copd'] = [1 if any(group['binary_copd'] == 1) else 0]\n",
    "    counts['cholesterol'] = [1 if any(group['binary_cholesterol'] == 1) else 0]\n",
    "\n",
    "    return counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we focus on the HCAs with comorbidities.\n",
    "\n",
    "The top 5 most common comorbidity pairs are:\n",
    "\n",
    "    * Hypertension + Cholesterol (~40.2% of HCAs with comorbidities have this combination)\n",
    "    * Hypertension + Diabetes (~35.4% of HCAs with comorbidities have this combination)\n",
    "    * Diabetes + Cholesterol (~35.2% of HCAs with comorbidities have this combination)\n",
    "    * Hypertension + Cardiovascular conditions (~31.8% of HCAs with comorbidities have this combination)\n",
    "    * Cholesterol + Cardiovascular conditions (~27.5% of HCAs with comorbidities have this combination)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>conditions</th>\n",
       "      <th>chi2</th>\n",
       "      <th>p_value</th>\n",
       "      <th>num_members</th>\n",
       "      <th>pct_members</th>\n",
       "      <th>input_condition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bp</td>\n",
       "      <td>121.047911</td>\n",
       "      <td>3.730139e-28</td>\n",
       "      <td>3873</td>\n",
       "      <td>0.402766</td>\n",
       "      <td>cholesterol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bp</td>\n",
       "      <td>88.581665</td>\n",
       "      <td>4.877895e-21</td>\n",
       "      <td>3407</td>\n",
       "      <td>0.354305</td>\n",
       "      <td>diabetes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>diabetes</td>\n",
       "      <td>336.361906</td>\n",
       "      <td>3.955443e-75</td>\n",
       "      <td>3386</td>\n",
       "      <td>0.352121</td>\n",
       "      <td>cholesterol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bp</td>\n",
       "      <td>0.183221</td>\n",
       "      <td>6.686200e-01</td>\n",
       "      <td>3055</td>\n",
       "      <td>0.317700</td>\n",
       "      <td>cardiovascular</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>cholesterol</td>\n",
       "      <td>59.944268</td>\n",
       "      <td>9.758177e-15</td>\n",
       "      <td>2646</td>\n",
       "      <td>0.275166</td>\n",
       "      <td>cardiovascular</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    conditions        chi2       p_value  num_members  pct_members  \\\n",
       "2           bp  121.047911  3.730139e-28         3873     0.402766   \n",
       "2           bp   88.581665  4.877895e-21         3407     0.354305   \n",
       "4     diabetes  336.361906  3.955443e-75         3386     0.352121   \n",
       "2           bp    0.183221  6.686200e-01         3055     0.317700   \n",
       "9  cholesterol   59.944268  9.758177e-15         2646     0.275166   \n",
       "\n",
       "  input_condition  \n",
       "2     cholesterol  \n",
       "2        diabetes  \n",
       "4     cholesterol  \n",
       "2  cardiovascular  \n",
       "9  cardiovascular  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comorbid_members = len(comorbid_count.loc[comorbid_count['flag_comorbidity'] == 1].index)\n",
    "\n",
    "kidney_df = combination('kidney')\n",
    "kidney_df['pct_members'] = kidney_df['num_members']/comorbid_members\n",
    "kidney_df = kidney_df.loc[kidney_df['conditions'] != 'kidney']\n",
    "kidney_df['input_condition'] = 'kidney'\n",
    "\n",
    "mental_disorder_df = combination('mental_disorder')\n",
    "mental_disorder_df['pct_members'] = mental_disorder_df['num_members']/comorbid_members\n",
    "mental_disorder_df = mental_disorder_df.loc[mental_disorder_df['conditions'] != 'mental_disorder']\n",
    "mental_disorder_df['input_condition'] = 'mental_disorder'\n",
    "\n",
    "bp_df = combination('bp')\n",
    "bp_df['pct_members'] = bp_df['num_members']/comorbid_members\n",
    "bp_df = bp_df.loc[bp_df['conditions'] != 'bp']\n",
    "bp_df['input_condition'] = 'bp'\n",
    "\n",
    "cardiovascular_df = combination('cardiovascular')\n",
    "cardiovascular_df['pct_members'] = cardiovascular_df['num_members']/comorbid_members\n",
    "cardiovascular_df = cardiovascular_df.loc[cardiovascular_df['conditions'] != 'cardiovascular']\n",
    "cardiovascular_df['input_condition'] = 'cardiovascular'\n",
    "\n",
    "diabetes_df = combination('diabetes')\n",
    "diabetes_df['pct_members'] = diabetes_df['num_members']/comorbid_members\n",
    "diabetes_df = diabetes_df.loc[diabetes_df['conditions'] != 'diabetes']\n",
    "diabetes_df['input_condition'] = 'diabetes'\n",
    "\n",
    "obesity_df = combination('obesity')\n",
    "obesity_df['pct_members'] = obesity_df['num_members']/comorbid_members\n",
    "obesity_df = obesity_df.loc[obesity_df['conditions'] != 'obesity']\n",
    "obesity_df['input_condition'] = 'obesity'\n",
    "\n",
    "cancer_df = combination('cancer')\n",
    "cancer_df['pct_members'] = cancer_df['num_members']/comorbid_members\n",
    "cancer_df = cancer_df.loc[cancer_df['conditions'] != 'cancer']\n",
    "cancer_df['input_condition'] = 'cancer'\n",
    "\n",
    "musculoskeletal_df = combination('musculoskeletal')\n",
    "musculoskeletal_df['pct_members'] = musculoskeletal_df['num_members']/comorbid_members\n",
    "musculoskeletal_df = musculoskeletal_df.loc[musculoskeletal_df['conditions'] != 'musculoskeletal']\n",
    "musculoskeletal_df['input_condition'] = 'musculoskeletal'\n",
    "\n",
    "copd_df = combination('copd')\n",
    "copd_df['pct_members'] = copd_df['num_members']/comorbid_members\n",
    "copd_df = copd_df.loc[copd_df['conditions'] != 'copd']\n",
    "copd_df['input_condition'] = 'copd'\n",
    "\n",
    "cholesterol_df = combination('cholesterol')\n",
    "cholesterol_df['pct_members'] = cholesterol_df['num_members']/comorbid_members\n",
    "cholesterol_df = cholesterol_df.loc[cholesterol_df['conditions'] != 'cholesterol']\n",
    "cholesterol_df['input_condition'] = 'cholesterol'\n",
    "\n",
    "pcts = pd.concat([kidney_df, bp_df, mental_disorder_df, cardiovascular_df, diabetes_df, cancer_df, musculoskeletal_df, copd_df, cholesterol_df, obesity_df])\n",
    "pcts = pcts.sort_values(by=['pct_members'], ascending=False)\n",
    "pcts = pcts.iloc[::2, :]\n",
    "pcts[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Describe the demographics and chronic conditions of our population by year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gender "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F    81389\n",
      "M    16988\n",
      "U        5\n",
      "Name: Member Gender, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "gender = enroll['Member Gender'].value_counts()\n",
    "print(gender)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gender count by year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Incurred Year</th>\n",
       "      <th>Member Gender</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016</td>\n",
       "      <td>F</td>\n",
       "      <td>26976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016</td>\n",
       "      <td>M</td>\n",
       "      <td>5452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017</td>\n",
       "      <td>F</td>\n",
       "      <td>28348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017</td>\n",
       "      <td>M</td>\n",
       "      <td>5862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018</td>\n",
       "      <td>F</td>\n",
       "      <td>26065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2018</td>\n",
       "      <td>M</td>\n",
       "      <td>5674</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Incurred Year Member Gender  total\n",
       "0          2016             F  26976\n",
       "1          2016             M   5452\n",
       "3          2017             F  28348\n",
       "4          2017             M   5862\n",
       "5          2018             F  26065\n",
       "6          2018             M   5674"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gender_count = enroll.groupby(['Incurred Year', 'Member Gender']).size().reset_index(name='total')\n",
    "gender_count = gender_count[gender_count['Member Gender'] != 'U']\n",
    "# reformat years for no decimals in visuals\n",
    "gender_count.loc[:,'Incurred Year'] = ['2016', '2016', '2017', '2017', '2018', '2018']\n",
    "gender_count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "ages = enroll[(enroll['Age'] >= 18) & (enroll['Age'] <= 103)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "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": [
    "fig = plt.figure(figsize = (8, 6))\n",
    "plt.hist(ages.Age, bins = 15, rwidth= 0.85, color='tab:blue')\n",
    "\n",
    "# Add mean line\n",
    "plt.axvline(ages.Age.mean(), color='k', linestyle='dashed', linewidth=1)\n",
    "# Add mean line text\n",
    "_, max_ = plt.ylim()\n",
    "plt.text(ages.Age.mean() - ages.Age.mean()/3.5, \n",
    "         max_ - max_/10,\n",
    "         'Mean: {:.2f}'.format(ages.Age.mean()))\n",
    "\n",
    "plt.title('HCA Age Distribution')\n",
    "plt.xlabel(\"Age\")\n",
    "plt.ylabel(\"Number of HCAs\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What is the effect of gender, age, geography (urban/rural), and having one of our chronic conditions of interest on the likelihood of someone having a comorbidity?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The percentage of female HCAs with comorbidities is very similar to the percentage of male HCAs with comorbidities. __~46.6% of female HCAs__ have comorbidities and __~45% of male HCAs__ have comorbidiites."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def code_chronic_disease (group):\n",
    "    df = pd.DataFrame()\n",
    "    df['member_id'] = group.head(1)['member_id']\n",
    "    df['gender'] = group.head(1)['gender']\n",
    "    df['age'] = group['age'].max()\n",
    "    df['urban_rural'] = group.head(1)['urban_rural']\n",
    "    if any(group['flag_comorbidity'] == 1):\n",
    "        df['chronic_disease'] = 2\n",
    "    elif any(group['total_conditions'] == 1):\n",
    "        df['chronic_disease'] = 1\n",
    "    else:\n",
    "        df['chronic_disease'] = 0\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "comorbid = med.groupby('member_id', as_index=False).apply(code_chronic_disease)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "gender_comorbid = comorbid.loc[comorbid['chronic_disease'] == 2].groupby('gender').size()\n",
    "gender = comorbid.groupby('gender').size()\n",
    "gender_pct = gender_comorbid/gender*100\n",
    "\n",
    "ax = plt.subplot()\n",
    "ax.bar(gender_pct.index, gender_pct)\n",
    "ax.set_xlabel('Gender')\n",
    "ax.set_ylabel('Percentage of HCAs with Comorbidities (%)')\n",
    "ax.set_title('Percentage of HCAs with Comorbidities by Gender')\n",
    "\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to look at age, we separated these into buckets. Since we start to see more chronic conditions after age 30, we chose to first create an age bucket of 18-29 year olds and continue in 10 year increments until age 70. We see that the percentage of HCAs with comorbidities increase as age increases. This is to be expected as we are more likely to develop more diseases as we get older."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "def age_buckets(row):\n",
    "    age_bucket = ''\n",
    "    if (row['age'] >= 18 and row['age'] <=29):\n",
    "        age_bucket = '18-29'\n",
    "    elif (row['age'] >= 30 and row['age'] <= 39):\n",
    "        age_bucket = '30-39'\n",
    "    elif (row['age'] >= 40 and row['age'] <= 49):\n",
    "        age_bucket = '40-49'\n",
    "    elif (row['age'] >= 50 and row['age'] <= 59):\n",
    "        age_bucket = '50-59'\n",
    "    elif (row['age'] >= 60 and row['age'] <= 69):\n",
    "        age_bucket = '60-69'\n",
    "    elif (row['age'] >= 70):\n",
    "        age_bucket = '70+'\n",
    "    return age_bucket\n",
    "\n",
    "comorbid['age_bucket'] = comorbid.apply(age_buckets, axis=1)\n",
    "age_comorbid = comorbid.loc[comorbid['chronic_disease'] == 2].groupby('age_bucket').size()\n",
    "age = comorbid.groupby('age_bucket').size()\n",
    "age_pct = age_comorbid/age*100\n",
    "\n",
    "ax = plt.subplot()\n",
    "ax.bar(age_pct.index, age_pct)\n",
    "ax.set_xlabel('Age')\n",
    "ax.set_ylabel('Percentage of HCAs with Comorbidities (%)')\n",
    "ax.set_title('Percentage of HCAs with Comorbidities by Age')\n",
    "\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The percentage of HCAs living in urban areas with comorbidities is very similar to the percentage of HCAs living in rural areas with comorbidities. __~46.2% of HCAs in urban areas__ have comorbidities and __~48.6% of HCAs in rural areas__ have comorbidiites."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "urban_rural_comorbid = comorbid.loc[comorbid['chronic_disease'] == 2].groupby('urban_rural').size()\n",
    "urban_rural = comorbid.groupby('urban_rural').size()\n",
    "urban_rural_pct = urban_rural_comorbid/urban_rural*100\n",
    "\n",
    "ax = plt.subplot()\n",
    "ax.bar(urban_rural_pct.index, urban_rural_pct)\n",
    "ax.set_xlabel('Urban/Rural')\n",
    "ax.set_ylabel('Percentage of HCAs with Comorbidities (%)')\n",
    "ax.set_title('Percentage of HCAs with Comorbidities by Urban/Rural')\n",
    "\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate the relationship between stability within the health insurance system (use member months) and its relationship to chronic disease rate "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using Kruskal-Wallis test (one-way ANOVA):\n",
    "- Months of insurance coverage eligibility (member months per year) is the independent variable\n",
    "- Binary variable (whether or not having a chronic disease) is the dependent variable\n",
    "- The binary variable is being ranked and sorted into groups\n",
    "- Analysis limited to people with at least one chronic condition\n",
    "\n",
    "The relationship between stability within the health insurance system and chronic disease was significant (H-score: 33.41; p-value < 0.05).\n",
    "Interpretation:\n",
    "For all the chronic disease of interest, the number of eligibility months varied significantly between members with and without a chronic condition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script>\n",
       "code_show=true; \n",
       "function code_toggle() {\n",
       " if (code_show){\n",
       " $('div.input').hide();\n",
       " } else {\n",
       " $('div.input').show();\n",
       " }\n",
       " code_show = !code_show\n",
       "} \n",
       "$( document ).ready(code_toggle);\n",
       "</script>\n",
       "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import HTML\n",
    "\n",
    "HTML('''<script>\n",
    "code_show=true; \n",
    "function code_toggle() {\n",
    " if (code_show){\n",
    " $('div.input').hide();\n",
    " } else {\n",
    " $('div.input').show();\n",
    " }\n",
    " code_show = !code_show\n",
    "} \n",
    "$( document ).ready(code_toggle);\n",
    "</script>\n",
    "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>''')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}