Endo_Drug_Disparities.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import pyodbc\n",
    "import time\n",
    "import numpy as np\n",
    "import scipy.stats as stats\n",
    "import itertools\n",
    "from sqlalchemy import create_engine\n",
    "from collections import Counter\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Access Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext sql"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ACCESS DATABASE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SQL query\n",
    "sql_query = (\"SELECT cohort_definition_id, p.person_id, p.year_of_birth, cohort_start_date, \" + \n",
    "       'cohort_end_date, c_race.concept_name as race, c_ethnicity.concept_name as ethnicity, ' + \n",
    "       'drug_exposure_start_date, drug_exposure_end_date, ' +\n",
    "       'drug_type_concept_id, c_drugtype.concept_name as drug_type, ' + \n",
    "       'drug_concept_id, c_drug.concept_name as drug_name ' + \n",
    "        'FROM results.endocohorts '+\n",
    "        'LEFT JOIN dbo.drug_exposure as de ON de.person_id = subject_id ' + \n",
    "        'LEFT JOIN dbo.person as p ON p.person_id = subject_id ' + \n",
    "        'LEFT JOIN dbo.concept as c_race ON c_race.concept_id = p.race_concept_id ' +\n",
    "        'LEFT JOIN dbo.concept as c_ethnicity ON c_ethnicity.concept_id = p.ethnicity_concept_id ' +\n",
    "        'LEFT JOIN dbo.concept as c_drugtype ON c_drugtype.concept_id = drug_type_concept_id ' +\n",
    "        'LEFT JOIN dbo.concept as c_drug ON c_drug.concept_id = drug_concept_id ' + \n",
    "        'WHERE cohort_definition_id = 3;')\n",
    "df = pd.read_sql(sql_query, engine)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prevalence Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# this function pulls all the codes for a given atc class 3 list\n",
    "def generate_code_list(drugtype):\n",
    "    sql_query = (\"SELECT ancestor_concept_id, descendant_concept_id, concept_name \" + \n",
    "               \"FROM dbo.concept_ancestor JOIN dbo.concept ON descendant_concept_id = concept_id \"+\n",
    "               \"WHERE ancestor_concept_id = (SELECT concept_id from dbo.concept WHERE concept_class_id = 'ATC 3rd' AND concept_name = '\"+drugtype+\"');\")\n",
    "    codes_list = pd.read_sql(sql_query, engine)\n",
    "    return list(codes_list['descendant_concept_id'])\n",
    "\n",
    "# this function calculates the relative prevalence of each drug type\n",
    "def relative_drug_prev(list_codes, df):\n",
    "    \"\"\"\n",
    "    1. This function subsets the dataframe to find only white and only Black patients who have been prescribed this drug\n",
    "    2. It returns the relative prevalence (compared to overall patient prevalence) of \n",
    "        black and white patients who have been prescribed drugs within this class\n",
    "    3. It looks at prevalence generally (overall), prior to diagnosis, and post-diagnosis \n",
    "        (where) the date of diagnosis is the cohort entry date\n",
    "        \n",
    "    https://stats.libretexts.org/Bookshelves/Introductory_Statistics/Book%3A_OpenIntro_Statistics_(Diez_et_al)./06%3A_Inference_for_Categorical_Data/6.02%3A_Difference_of_Two_Proportions\n",
    "    \"\"\"\n",
    "    \n",
    "    list_features = []\n",
    "    df_subset = df.loc[df['drug_concept_id'].isin(list_codes)]\n",
    "    df_subset_w = df_subset.loc[df['race'] == 'White']\n",
    "    df_subset_b = df_subset.loc[df['race'] == 'Black or African American']\n",
    "\n",
    "\n",
    "    # number of white/Black patients: \n",
    "    num_white_patients = len(df.loc[df['race'] == 'White']['person_id'].unique())\n",
    "    num_black_patients = len(df.loc[df['race'] == 'Black or African American']['person_id'].unique())\n",
    "    \n",
    "    list_features.append(num_white_patients)\n",
    "    list_features.append(num_black_patients)\n",
    "\n",
    "    # Overall relative prevalence\n",
    "    p1_white_overall = len(df_subset_w['person_id'].unique())/num_white_patients\n",
    "    p2_black_overall = len(df_subset_b['person_id'].unique())/num_black_patients\n",
    "    list_features.append(p1_white_overall)\n",
    "    list_features.append(p2_black_overall)\n",
    "   \n",
    "    \n",
    "    list_features.append(p1_white_overall-p2_black_overall)\n",
    "    list_features.append(np.sqrt(p2_black_overall * (1 - p2_black_overall) / num_black_patients + p1_white_overall * (1 - p1_white_overall) / num_white_patients))\n",
    "    \n",
    "    return list_features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TABLE 1: Descriptive Statistics "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Descriptive Statistics Table: Endo Cohort "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get AGE\n",
    "df['age'] = ((pd.to_datetime(df['cohort_start_date'])-pd.to_datetime(df['year_of_birth'], format='%Y')).astype('<m8[Y]')).astype('int64')\n",
    "df['age'].value_counts().to_csv('age_counts.csv')\n",
    "people_df = df[['person_id', 'age', 'race', 'ethnicity']].drop_duplicates()\n",
    "age_dist = people_df['age'].value_counts()\n",
    "\n",
    "# AGE: 15-21; 22-28; 29-35; 35-42; 42-49\n",
    "age_15_21 = age_dist[(age_dist.index <= 21)].sum()\n",
    "age_22_28 = age_dist[(age_dist.index > 21) & (age_dist.index<=28)].sum()\n",
    "age_29_35 = age_dist[(age_dist.index > 28) & (age_dist.index<=35)].sum()\n",
    "age_35_42 = age_dist[(age_dist.index > 35) & (age_dist.index<=42)].sum()\n",
    "age_42_49 = age_dist[(age_dist.index > 42)].sum()\n",
    "\n",
    "table1_ages = pd.DataFrame(data = [age_15_21, age_22_28, age_29_35, age_35_42, age_42_49], index = ['15-21', '22-28', '29-35', '35-42', '42-49'], columns = ['Endo Cohort (Counts)'])\n",
    "table1_ages['Endo Cohort (%)'] = 100*table1_ages['Endo Cohort (Counts)']/sum(table1_ages['Endo Cohort (Counts)'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get RACE\n",
    "table1_race = pd.DataFrame(data=people_df['race'].value_counts().values, index = people_df['race'].value_counts().index, columns = ['Endo Cohort (Counts)'])\n",
    "table1_race['Endo Cohort (%)'] = table1_race['Endo Cohort (Counts)']/sum(table1_race['Endo Cohort (Counts)'])\n",
    "\n",
    "# Get ETHNICITY\n",
    "table1_ethnicity = pd.DataFrame(data=people_df['ethnicity'].value_counts().values, index = people_df['ethnicity'].value_counts().index, columns = ['Endo Cohort (Counts)'])\n",
    "table1_ethnicity['Endo Cohort (%)'] = table1_ethnicity['Endo Cohort (Counts)']/sum(table1_ethnicity['Endo Cohort (Counts)'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Descriptive Statistics Table: Comparison Cohort "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Endo Cohort (Counts)</th>\n",
       "      <th>Endo Cohort (%)</th>\n",
       "      <th>Comparison Cohort (Counts)</th>\n",
       "      <th>Comparison Cohort (%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15-21</th>\n",
       "      <td>1616</td>\n",
       "      <td>9.870511</td>\n",
       "      <td>1233489</td>\n",
       "      <td>33.665975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22-28</th>\n",
       "      <td>3634</td>\n",
       "      <td>22.196433</td>\n",
       "      <td>755365</td>\n",
       "      <td>20.616397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29-35</th>\n",
       "      <td>5323</td>\n",
       "      <td>32.512827</td>\n",
       "      <td>671403</td>\n",
       "      <td>18.324798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35-42</th>\n",
       "      <td>3804</td>\n",
       "      <td>23.234791</td>\n",
       "      <td>499965</td>\n",
       "      <td>13.645690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42-49</th>\n",
       "      <td>1995</td>\n",
       "      <td>12.185439</td>\n",
       "      <td>503682</td>\n",
       "      <td>13.747140</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Endo Cohort (Counts)  Endo Cohort (%)  Comparison Cohort (Counts)  \\\n",
       "15-21                  1616         9.870511                     1233489   \n",
       "22-28                  3634        22.196433                      755365   \n",
       "29-35                  5323        32.512827                      671403   \n",
       "35-42                  3804        23.234791                      499965   \n",
       "42-49                  1995        12.185439                      503682   \n",
       "\n",
       "       Comparison Cohort (%)  \n",
       "15-21              33.665975  \n",
       "22-28              20.616397  \n",
       "29-35              18.324798  \n",
       "35-42              13.645690  \n",
       "42-49              13.747140  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get AGE\n",
    "age_dist_cc = pd.read_csv('apara_mdcd_age.csv', header = None).set_index(0)\n",
    "\n",
    "# AGE: 15-21; 22-28; 29-35; 35-42; 42-49\n",
    "age_15_21 = age_dist_cc[(age_dist_cc.index <= 21)][1].sum()\n",
    "age_22_28 = age_dist_cc[(age_dist_cc.index > 21) & (age_dist_cc.index<=28)][1].sum()\n",
    "age_29_35 = age_dist_cc[(age_dist_cc.index > 28) & (age_dist_cc.index<=35)][1].sum()\n",
    "age_35_42 = age_dist_cc[(age_dist_cc.index > 35) & (age_dist_cc.index<=42)][1].sum()\n",
    "age_42_49 = age_dist_cc[(age_dist_cc.index > 42)][1].sum()\n",
    "\n",
    "table1_ages['Comparison Cohort (Counts)'] = [age_15_21, age_22_28, age_29_35, age_35_42, age_42_49]\n",
    "table1_ages['Comparison Cohort (%)'] = 100*table1_ages['Comparison Cohort (Counts)']/sum(table1_ages['Comparison Cohort (Counts)'])\n",
    "table1_ages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get RACE\n",
    "race_dist_cc = pd.read_csv('apara_mdcd_race.csv', header=None)\n",
    "race_dist_cc.columns = ['Race', 'Comparison Cohort (Counts)']\n",
    "race_dist_cc.set_index('Race', inplace=True)\n",
    "table1_race = table1_race.merge(race_dist_cc, how = 'inner', left_index=True, right_index=True)\n",
    "table1_race['Comparison Cohort (%)'] = table1_race['Comparison Cohort (Counts)']/sum(table1_race['Comparison Cohort (Counts)'])\n",
    "\n",
    "# Get Ethnicity\n",
    "ethnicity_dist_cc = pd.read_csv('apara_mdcd_ethnicity.csv', header=None)\n",
    "ethnicity_dist_cc.columns = ['Ethnicity', 'Comparison Cohort (Counts)']\n",
    "ethnicity_dist_cc.set_index('Ethnicity', inplace=True)\n",
    "table1_ethnicity = table1_ethnicity.merge(ethnicity_dist_cc, how = 'inner', left_index=True, right_index=True)\n",
    "table1_ethnicity['Comparison Cohort (%)'] = table1_ethnicity['Comparison Cohort (Counts)']/sum(table1_ethnicity['Comparison Cohort (Counts)'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Endo Cohort (Counts)</th>\n",
       "      <th>Endo Cohort (%)</th>\n",
       "      <th>Comparison Cohort (Counts)</th>\n",
       "      <th>Comparison Cohort (%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>10805</td>\n",
       "      <td>0.659968</td>\n",
       "      <td>1829312</td>\n",
       "      <td>0.499279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black or African American</th>\n",
       "      <td>3814</td>\n",
       "      <td>0.232959</td>\n",
       "      <td>1199859</td>\n",
       "      <td>0.327481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No matching concept</th>\n",
       "      <td>1753</td>\n",
       "      <td>0.107073</td>\n",
       "      <td>634733</td>\n",
       "      <td>0.173240</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Endo Cohort (Counts)  Endo Cohort (%)  \\\n",
       "White                                     10805         0.659968   \n",
       "Black or African American                  3814         0.232959   \n",
       "No matching concept                        1753         0.107073   \n",
       "\n",
       "                           Comparison Cohort (Counts)  Comparison Cohort (%)  \n",
       "White                                         1829312               0.499279  \n",
       "Black or African American                     1199859               0.327481  \n",
       "No matching concept                            634733               0.173240  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table1_race"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Endo Cohort (Counts)</th>\n",
       "      <th>Endo Cohort (%)</th>\n",
       "      <th>Comparison Cohort (Counts)</th>\n",
       "      <th>Comparison Cohort (%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>No matching concept</th>\n",
       "      <td>16108</td>\n",
       "      <td>0.983875</td>\n",
       "      <td>3525270</td>\n",
       "      <td>0.962162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hispanic or Latino</th>\n",
       "      <td>264</td>\n",
       "      <td>0.016125</td>\n",
       "      <td>138634</td>\n",
       "      <td>0.037838</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Endo Cohort (Counts)  Endo Cohort (%)  \\\n",
       "No matching concept                 16108         0.983875   \n",
       "Hispanic or Latino                    264         0.016125   \n",
       "\n",
       "                     Comparison Cohort (Counts)  Comparison Cohort (%)  \n",
       "No matching concept                     3525270               0.962162  \n",
       "Hispanic or Latino                       138634               0.037838  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table1_ethnicity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Finding all drug classes for further analysis\n",
    "significant-drugs-z-score.csv lists all ATC level 3 classes of drugs where there is a significant difference between the prescription prevalence for the endometriosis cohort and the comparison cohort. In this study, we limit ourselves to drug classes where the prescription prevalence is significantly higher in the endometriosis population."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28\n"
     ]
    }
   ],
   "source": [
    "significant_drug_differences = pd.read_csv('significant-drugs-z-score.csv')\n",
    "significant_drugs = significant_drug_differences[significant_drug_differences['endo_mean_prev']>significant_drug_differences['comp_mean_prev']]\n",
    "list_drugs = list(significant_drugs['concept_name'])\n",
    "print(len(list_drugs))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Find the relative prevalences overall, pre-diagnosis, and post-diagnosis\n",
    "Use the function *relative_drug_prev* to find the relative prevalence of prescription for each drug class amongst Black and white patients. Then, we compare the mean difference between the prevalences using a 2-sided Z-test. This is repeated for 1. all prescriptions, 2. prescriptions prior to diagnosis, and 3. prescriptions post-diagnosis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# OVERALL\n",
    "colnames = ['N_white', 'N_black', 'Prev_White_Overall', 'Prev_Black_Overall','Mean_Diff_Overall', 'SE_Overall']\n",
    "\n",
    "df_prev_atc_endo = pd.DataFrame(columns = colnames)\n",
    "\n",
    "for drugtype in list_drugs:\n",
    "    list_codes = generate_code_list(drugtype)\n",
    "    df_prev_atc_endo.loc[drugtype] = relative_drug_prev(list_codes, df)\n",
    "    \n",
    "df_prev_atc_endo['ZScore_Overall'] = df_prev_atc_endo['Mean_Diff_Overall']/df_prev_atc_endo['SE_Overall']\n",
    "df_prev_atc_endo['p_val_overall'] = stats.norm.sf(np.abs(df_prev_atc_endo['ZScore_Overall']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#PRE-DIAGNOSIS\n",
    "colnames = ['N_white', 'N_black', 'Prev_White_Prior', 'Prev_Black_Prior','Mean_Diff_Prior', 'SE_Prior']\n",
    "\n",
    "prev_prior = pd.DataFrame(columns = colnames)\n",
    "df_prior = df.loc[pd.to_datetime(df['drug_exposure_start_date']) < pd.to_datetime(df['cohort_start_date'])]\n",
    "\n",
    "for drugtype in list_drugs:\n",
    "    list_codes = generate_code_list(drugtype)\n",
    "    prev_prior.loc[drugtype] = relative_drug_prev(list_codes, df_prior)\n",
    "    \n",
    "prev_prior['ZScore_Prior'] = prev_prior['Mean_Diff_Prior']/prev_prior['SE_Prior']\n",
    "prev_prior['p_val_prior'] = stats.norm.sf(np.abs(prev_prior['ZScore_Prior']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#POST-DIAGNOSIS\n",
    "colnames = ['N_white', 'N_black', 'Prev_White_Post', 'Prev_Black_Post','Mean_Diff_Post', 'SE_Post']\n",
    "\n",
    "prev_post = pd.DataFrame(columns = colnames)\n",
    "df_post = df.loc[pd.to_datetime(df['drug_exposure_start_date']) > pd.to_datetime(df['cohort_start_date'])]\n",
    "\n",
    "for drugtype in list_drugs:\n",
    "    list_codes = generate_code_list(drugtype)\n",
    "    prev_post.loc[drugtype] = relative_drug_prev(list_codes, df_post)\n",
    "    \n",
    "prev_post['ZScore_Post'] = prev_post['Mean_Diff_Post']/prev_post['SE_Post']\n",
    "prev_post['p_val_post'] = stats.norm.sf(np.abs(prev_post['ZScore_Post']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FIGURE 2: Difference in prevalence ± Standard error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,7))\n",
    "plt.errorbar(df_prev_atc_endo.index, df_prev_atc_endo['Mean_Diff_Overall']*100, yerr=df_prev_atc_endo['SE_Overall']*100, ls='none',color='k', label = 'Overall')\n",
    "plt.errorbar(prev_prior.index, prev_prior['Mean_Diff_Prior']*100, yerr=prev_prior['SE_Prior']*100, ls='none',color='orange', label = 'Pre-Diagnosis')\n",
    "plt.errorbar(prev_post.index, prev_post['Mean_Diff_Post']*100, yerr=prev_post['SE_Post']*100, ls='none',color='b', label = 'Post-Diagnosis')\n",
    "plt.ylabel('Percent Difference')\n",
    "plt.axhline(0, color='r')\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TABLE 3: Comparing prevalence differences pre-diagnosis vs. post-diagnosis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>Pre-Diagnosis: Mean Prevalence Difference (%)</th>\n",
       "      <th>Pre-Diagnosis: Standard Error (%)</th>\n",
       "      <th>Pre-Diagnosis: p-value</th>\n",
       "      <th>Post-Diagnosis: Mean Prevalence Difference (%)</th>\n",
       "      <th>Post-Diagnosis: Standard Error (%)</th>\n",
       "      <th>Post-Diagnosis: p-value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>OTHER ANALGESICS AND ANTIPYRETICS</th>\n",
       "      <td>10.647111</td>\n",
       "      <td>0.898424</td>\n",
       "      <td>1.064687e-32</td>\n",
       "      <td>5.482770</td>\n",
       "      <td>0.926171</td>\n",
       "      <td>1.611432e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OPIOIDS</th>\n",
       "      <td>3.832236</td>\n",
       "      <td>0.641791</td>\n",
       "      <td>1.177847e-09</td>\n",
       "      <td>2.730506</td>\n",
       "      <td>0.668120</td>\n",
       "      <td>2.186331e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANTIINFLAMMATORY AND ANTIRHEUMATIC PRODUCTS, NON-STEROIDS</th>\n",
       "      <td>2.219864</td>\n",
       "      <td>0.658594</td>\n",
       "      <td>3.750102e-04</td>\n",
       "      <td>0.907683</td>\n",
       "      <td>0.760286</td>\n",
       "      <td>1.162644e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COUGH SUPPRESSANTS, EXCL. COMBINATIONS WITH EXPECTORANTS</th>\n",
       "      <td>8.589266</td>\n",
       "      <td>0.649894</td>\n",
       "      <td>3.527165e-40</td>\n",
       "      <td>4.577783</td>\n",
       "      <td>0.604771</td>\n",
       "      <td>1.874006e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANTIEMETICS AND ANTINAUSEANTS</th>\n",
       "      <td>9.366546</td>\n",
       "      <td>0.889384</td>\n",
       "      <td>3.092240e-26</td>\n",
       "      <td>6.543276</td>\n",
       "      <td>0.876276</td>\n",
       "      <td>4.097909e-14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    Pre-Diagnosis: Mean Prevalence Difference (%)  \\\n",
       "OTHER ANALGESICS AND ANTIPYRETICS                                                       10.647111   \n",
       "OPIOIDS                                                                                  3.832236   \n",
       "ANTIINFLAMMATORY AND ANTIRHEUMATIC PRODUCTS, NO...                                       2.219864   \n",
       "COUGH SUPPRESSANTS, EXCL. COMBINATIONS WITH EXP...                                       8.589266   \n",
       "ANTIEMETICS AND ANTINAUSEANTS                                                            9.366546   \n",
       "\n",
       "                                                    Pre-Diagnosis: Standard Error (%)  \\\n",
       "OTHER ANALGESICS AND ANTIPYRETICS                                            0.898424   \n",
       "OPIOIDS                                                                      0.641791   \n",
       "ANTIINFLAMMATORY AND ANTIRHEUMATIC PRODUCTS, NO...                           0.658594   \n",
       "COUGH SUPPRESSANTS, EXCL. COMBINATIONS WITH EXP...                           0.649894   \n",
       "ANTIEMETICS AND ANTINAUSEANTS                                                0.889384   \n",
       "\n",
       "                                                    Pre-Diagnosis: p-value  \\\n",
       "OTHER ANALGESICS AND ANTIPYRETICS                             1.064687e-32   \n",
       "OPIOIDS                                                       1.177847e-09   \n",
       "ANTIINFLAMMATORY AND ANTIRHEUMATIC PRODUCTS, NO...            3.750102e-04   \n",
       "COUGH SUPPRESSANTS, EXCL. COMBINATIONS WITH EXP...            3.527165e-40   \n",
       "ANTIEMETICS AND ANTINAUSEANTS                                 3.092240e-26   \n",
       "\n",
       "                                                    Post-Diagnosis: Mean Prevalence Difference (%)  \\\n",
       "OTHER ANALGESICS AND ANTIPYRETICS                                                         5.482770   \n",
       "OPIOIDS                                                                                   2.730506   \n",
       "ANTIINFLAMMATORY AND ANTIRHEUMATIC PRODUCTS, NO...                                        0.907683   \n",
       "COUGH SUPPRESSANTS, EXCL. COMBINATIONS WITH EXP...                                        4.577783   \n",
       "ANTIEMETICS AND ANTINAUSEANTS                                                             6.543276   \n",
       "\n",
       "                                                    Post-Diagnosis: Standard Error (%)  \\\n",
       "OTHER ANALGESICS AND ANTIPYRETICS                                             0.926171   \n",
       "OPIOIDS                                                                       0.668120   \n",
       "ANTIINFLAMMATORY AND ANTIRHEUMATIC PRODUCTS, NO...                            0.760286   \n",
       "COUGH SUPPRESSANTS, EXCL. COMBINATIONS WITH EXP...                            0.604771   \n",
       "ANTIEMETICS AND ANTINAUSEANTS                                                 0.876276   \n",
       "\n",
       "                                                    Post-Diagnosis: p-value  \n",
       "OTHER ANALGESICS AND ANTIPYRETICS                              1.611432e-09  \n",
       "OPIOIDS                                                        2.186331e-05  \n",
       "ANTIINFLAMMATORY AND ANTIRHEUMATIC PRODUCTS, NO...             1.162644e-01  \n",
       "COUGH SUPPRESSANTS, EXCL. COMBINATIONS WITH EXP...             1.874006e-14  \n",
       "ANTIEMETICS AND ANTINAUSEANTS                                  4.097909e-14  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff_df = prev_prior[['Mean_Diff_Prior','SE_Prior', 'p_val_prior']]\n",
    "diff_df = diff_df.merge(prev_post[['Mean_Diff_Post', 'SE_Post', 'p_val_post']], how='inner', left_index=True, right_index=True)\n",
    "diff_df.loc[:,['Mean_Diff_Prior','SE_Prior','Mean_Diff_Post','SE_Post']] *= 100\n",
    "diff_df.columns = ['Pre-Diagnosis: Mean Prevalence Difference (%)',\n",
    "                   'Pre-Diagnosis: Standard Error (%)', 'Pre-Diagnosis: p-value',\n",
    "                  'Post-Diagnosis: Mean Prevalence Difference (%)',\n",
    "                   'Post-Diagnosis: Standard Error (%)', 'Post-Diagnosis: p-value']\n",
    "diff_df.to_csv('diff_df.csv')\n",
    "diff_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TABLE 4: Prescription prevalence differences for drug classes specific to endometriosis treatment\n",
    "In this table, we list overall prevalence disparities for the endometriosis cohort and the comparison cohort. Due to the span of the comparison cohort, we only consider drugs classes referenced in the ESHRE clinical guidelines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "cc_list_drugs = ['OTHER ANALGESICS AND ANTIPYRETICS', 'OPIOIDS','HORMONES AND RELATED AGENTS',\n",
    "                 'HORMONAL CONTRACEPTIVES FOR SYSTEMIC USE', 'PROGESTOGENS', 'ESTROGENS']\n",
    "\n",
    "atc_prev_cc = pd.read_csv('atc_prev_endo_cc.csv').set_index('Unnamed: 0')\n",
    "atc_prev_cc['Z_score'] = atc_prev_cc['Mean_Diff_Overall']/atc_prev_cc['SE_Overall']\n",
    "atc_prev_cc['p-val'] = (stats.norm.sf(np.abs(atc_prev_cc['Z_score'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>Comparison Cohort: Mean Prevalence Difference (%)</th>\n",
       "      <th>Comparison Cohort: Standard Error (%)</th>\n",
       "      <th>Comparison Cohort: p-value</th>\n",
       "      <th>Endo Cohort: Mean Prevalence Difference (%)</th>\n",
       "      <th>Endo Cohort: Standard Error (%)</th>\n",
       "      <th>Endo Cohort: p-value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>OTHER ANALGESICS AND ANTIPYRETICS</th>\n",
       "      <td>-7.472038</td>\n",
       "      <td>0.057092</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.063556</td>\n",
       "      <td>0.007432</td>\n",
       "      <td>6.092425e-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OPIOIDS</th>\n",
       "      <td>-4.933238</td>\n",
       "      <td>0.058172</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.010006</td>\n",
       "      <td>0.003379</td>\n",
       "      <td>1.530394e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HORMONES AND RELATED AGENTS</th>\n",
       "      <td>8.922818</td>\n",
       "      <td>0.048657</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-0.071122</td>\n",
       "      <td>0.009398</td>\n",
       "      <td>1.903955e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HORMONAL CONTRACEPTIVES FOR SYSTEMIC USE</th>\n",
       "      <td>1.980721</td>\n",
       "      <td>0.058201</td>\n",
       "      <td>3.714238e-254</td>\n",
       "      <td>0.032612</td>\n",
       "      <td>0.008894</td>\n",
       "      <td>1.228274e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PROGESTOGENS</th>\n",
       "      <td>7.874307</td>\n",
       "      <td>0.051769</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-0.040914</td>\n",
       "      <td>0.009345</td>\n",
       "      <td>5.987418e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ESTROGENS</th>\n",
       "      <td>-1.829559</td>\n",
       "      <td>0.024257</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.102352</td>\n",
       "      <td>0.007628</td>\n",
       "      <td>2.365233e-41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          Comparison Cohort: Mean Prevalence Difference (%)  \\\n",
       "OTHER ANALGESICS AND ANTIPYRETICS                                                 -7.472038   \n",
       "OPIOIDS                                                                           -4.933238   \n",
       "HORMONES AND RELATED AGENTS                                                        8.922818   \n",
       "HORMONAL CONTRACEPTIVES FOR SYSTEMIC USE                                           1.980721   \n",
       "PROGESTOGENS                                                                       7.874307   \n",
       "ESTROGENS                                                                         -1.829559   \n",
       "\n",
       "                                          Comparison Cohort: Standard Error (%)  \\\n",
       "OTHER ANALGESICS AND ANTIPYRETICS                                      0.057092   \n",
       "OPIOIDS                                                                0.058172   \n",
       "HORMONES AND RELATED AGENTS                                            0.048657   \n",
       "HORMONAL CONTRACEPTIVES FOR SYSTEMIC USE                               0.058201   \n",
       "PROGESTOGENS                                                           0.051769   \n",
       "ESTROGENS                                                              0.024257   \n",
       "\n",
       "                                          Comparison Cohort: p-value  \\\n",
       "OTHER ANALGESICS AND ANTIPYRETICS                       0.000000e+00   \n",
       "OPIOIDS                                                 0.000000e+00   \n",
       "HORMONES AND RELATED AGENTS                             0.000000e+00   \n",
       "HORMONAL CONTRACEPTIVES FOR SYSTEMIC USE               3.714238e-254   \n",
       "PROGESTOGENS                                            0.000000e+00   \n",
       "ESTROGENS                                               0.000000e+00   \n",
       "\n",
       "                                          Endo Cohort: Mean Prevalence Difference (%)  \\\n",
       "OTHER ANALGESICS AND ANTIPYRETICS                                            0.063556   \n",
       "OPIOIDS                                                                      0.010006   \n",
       "HORMONES AND RELATED AGENTS                                                 -0.071122   \n",
       "HORMONAL CONTRACEPTIVES FOR SYSTEMIC USE                                     0.032612   \n",
       "PROGESTOGENS                                                                -0.040914   \n",
       "ESTROGENS                                                                    0.102352   \n",
       "\n",
       "                                          Endo Cohort: Standard Error (%)  \\\n",
       "OTHER ANALGESICS AND ANTIPYRETICS                                0.007432   \n",
       "OPIOIDS                                                          0.003379   \n",
       "HORMONES AND RELATED AGENTS                                      0.009398   \n",
       "HORMONAL CONTRACEPTIVES FOR SYSTEMIC USE                         0.008894   \n",
       "PROGESTOGENS                                                     0.009345   \n",
       "ESTROGENS                                                        0.007628   \n",
       "\n",
       "                                          Endo Cohort: p-value  \n",
       "OTHER ANALGESICS AND ANTIPYRETICS                 6.092425e-18  \n",
       "OPIOIDS                                           1.530394e-03  \n",
       "HORMONES AND RELATED AGENTS                       1.903955e-14  \n",
       "HORMONAL CONTRACEPTIVES FOR SYSTEMIC USE          1.228274e-04  \n",
       "PROGESTOGENS                                      5.987418e-06  \n",
       "ESTROGENS                                         2.365233e-41  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table3 = atc_prev_cc.loc[cc_list_drugs][['Mean_Diff_Overall','SE_Overall','p-val']]\n",
    "table3[['Mean_Diff_Overall','SE_Overall']]*=100\n",
    "table3.columns = ['Comparison Cohort: Mean Prevalence Difference (%)',\n",
    "                   'Comparison Cohort: Standard Error (%)', 'Comparison Cohort: p-value']\n",
    "table3 = table3.merge(df_prev_atc_endo[['Mean_Diff_Overall','SE_Overall','p_val_overall']], how='inner', right_index=True, left_index=True)\n",
    "table3.rename({'Mean_Diff_Overall': 'Endo Cohort: Mean Prevalence Difference (%)',\n",
    "              'SE_Overall': 'Endo Cohort: Standard Error (%)',\n",
    "              'p_val_overall': 'Endo Cohort: p-value'}, axis=1, inplace=True)\n",
    "table3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "table3.to_csv('table3.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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