notebooks/demographic-change.ipynb

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    "import pandas as pd\n",
    "from utilities import *\n",
    "\n",
    "\n",
    "sentinel_measures = [\"qrisk2\", \"asthma\", \"copd\", \"sodium\", \"cholesterol\", \"alt\", \"tsh\", \"alt\", \"rbc\", 'hba1c', 'systolic_bp', 'medication_review']\n",
    "\n",
    "demographics = ['region', 'age_band', 'imd', 'sex', 'learning_disability', 'ethnicity']\n",
    "\n",
    "values_dict = {}\n",
    "\n",
    "\n",
    "dates = ['2019-04-01', '2020-04-01', '2021-04-01']\n",
    "\n",
    "differences_list = []\n",
    "\n",
    "\n",
    "def classify_changes(changes):\n",
    "    \"\"\"Classifies list of % changes\n",
    "\n",
    "    Args:\n",
    "        changes: list of percentage changes\n",
    "    \"\"\"\n",
    "    \n",
    "    if (-15 <= changes[0] < 15) and (-15 <= changes[1] < 15):\n",
    "        classification = 'no change'\n",
    "        \n",
    "    elif (changes[0] > 15) or (changes[1] > 15):\n",
    "        classification = 'increase'\n",
    "    \n",
    "    elif (changes[0] <= -15) and not (-15 <= changes[1] < 15) :\n",
    "        classification = 'sustained drop'\n",
    "    \n",
    "    elif (changes[0] <= -15) and (-15 <= changes[1] < 15) :\n",
    "        classification = 'recovery'\n",
    "    \n",
    "    else:\n",
    "        classification = 'none'\n",
    "    \n",
    "    return classification\n",
    "\n",
    "for measure in sentinel_measures:\n",
    "    \n",
    "    total_df = load_and_drop(measure, practice=True)\n",
    "    total_df['rate'] = (total_df[measure] / total_df['population'])*1000\n",
    "    total_df = total_df.groupby(by=['date'])[['rate']].mean().reset_index()\n",
    "    \n",
    "    totals_dict = {}\n",
    "    for date in dates:\n",
    "        val = total_df[total_df['date'] == date]['rate']\n",
    "        totals_dict[date] = val\n",
    "    \n",
    "    \n",
    "        \n",
    "\n",
    "    \n",
    "    for d in demographics:\n",
    "        df = pd.read_csv(f'../output/combined_measure_{measure}_{d}.csv', parse_dates=['date']).sort_values(['date'])\n",
    "        \n",
    "        \n",
    "        if d == 'ethnicity':\n",
    "            \n",
    "            #drop missing ethnicity :('0')\n",
    "            df = df[df['ethnicity'] != 0]\n",
    "            \n",
    "            # replace with strings\n",
    "            ethnicity_codes = {1.0: \"White\", 2.0: \"Mixed\", 3.0: \"Asian\", 4.0: \"Black\", 5.0:\"Other\"}\n",
    "            df = df.replace({\"ethnicity\": ethnicity_codes})\n",
    "            \n",
    "        elif d == 'age_band':\n",
    "            df = df[df['age_band'] != 'missing']\n",
    "            \n",
    "        elif d == 'learning_disability':\n",
    "            ld_dict = {0: 'No record of a learning disability', 1: 'Record of a learning disability'}\n",
    "            df = df.replace({\"learning_disability\": ld_dict})\n",
    "        \n",
    "        \n",
    "        if d != 'age_band':\n",
    "            df['rate'] = df[measure]/(df['population']/1000)\n",
    "        \n",
    "        \n",
    "      \n",
    "        for unique_category in df[d].unique():\n",
    "            df_subset = df[df[d] == unique_category]\n",
    "            \n",
    "            \n",
    "            date_values = {}\n",
    "            date_changes = {}\n",
    "            \n",
    "            for date in dates:\n",
    "                val = df_subset[df_subset['date']==date]['rate'].values[0]\n",
    "                total_val = totals_dict[date].values[0]\n",
    "             \n",
    "\n",
    "                difference = round(((val - total_val) / total_val)*100, 2)\n",
    "             \n",
    "                date_values[date]=val\n",
    "                date_changes[date] = difference\n",
    "\n",
    "            classification = classify_changes([date_changes[\"2020-04-01\"], date_changes[\"2021-04-01\"]])\n",
    "            row = [measure, d, unique_category, date_values[\"2019-04-01\"], date_changes[\"2019-04-01\"], date_values[\"2020-04-01\"], date_changes[\"2020-04-01\"], date_values[\"2021-04-01\"], date_changes[\"2021-04-01\"], classification]\n",
    "            differences_list.append(row)\n",
    "        \n",
    " \n",
    "            \n",
    "   \n",
    "    \n",
    "differences_df =pd.DataFrame(differences_list, columns=['measure', 'demographic', 'demographic_subset', '2019_val', '2019_change', '2020_val', '2020_change', '2021_val', '2021_change', 'classification'])\n",
    "differences_df.to_csv('../output/demographics_differences.csv') "
   ]
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