CS109Project-JV112914.ipynb

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   "cells": [
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     "cell_type": "markdown",
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     "source": [
      "# Overview and Motivation:\n",
      "\n",
      "<img src= http://www.ihi.org/Engage/Initiatives/TripleAim/Documents/Triple-Aim-Triangle_withTitle%20white%20background%20v3.jpg>\n",
      "\n",
      "The US health care system is the most costly in the world, accounting for 17% of the gross domestic product with estimates that percentage will grow to nearly 20% by 2020. [Source: National Healthcare Expenditure Projections, 2010-2020. Centers for Medicare and Medicaid Services, Office of the Actuary.] At the same time, countries with health systems that out-perform the US are also under pressure to derive greater value for the resources devoted to their health care systems. Aging populations and increased longevity, coupled with chronic health problems, have become a global challenge, putting new demands on medical and social services.\n",
      "\n",
      "In most health care settings today, no one is accountable for all three dimensions of the IHI Triple Aim. For the health of our communities, for the health of our school systems, and for the health of all our patients, we need to address all three of the Triple Aim dimensions at the same time.\n",
      " \n",
      "Because the IHI Triple Aim entails ambitious improvement at all levels of the system, the IHI advocates a systematic approach to change. Based on six phases of pilot testing with over 100 organizations around the world, IHI recommends a change process that includes: identification of target populations; definition of system aims and measures; development of a portfolio of project work that is sufficiently strong to move system-level results, and rapid testing and scale up that is adapted to local needs and conditions.\n",
      " \n",
      "IHI believes that to do this work effectively, it\u2019s important to harness a range of community determinants of health, empower individuals and families, substantially broaden the role and impact of primary care and other community based services, and assure a seamless journey through the whole system of care throughout a person\u2019s life.\n",
      " \n",
      "In the US environment many areas of health reform can be furthered and strengthened by Triple Aim thinking, including:  accountable care organizations (ACOs), bundled payments, and other innovative financing approaches; new models of primary care, such as patient-centered medical homes; sanctions for avoidable events, such as hospital readmissions or infections; and the integration of information technology.\n",
      "\n",
      "Source:  http://www.ihi.org/Engage/Initiatives/TripleAim/Pages/default.aspx"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Related Work: \n",
      "\n",
      "The Cost of Diabetes - study by the American Diabetes Association\n",
      "\n",
      "The American Diabetes Association (Association) released new research on March 6, 2013 estimating the impact of diabetes in our society.\n",
      "The estimated total economic cost of diagnosed diabetes in 2012 is 245 billion, a 41% increase from our previous estimate of 174 billion (in 2007 dollars). This estimate highlights the substantial burden that diabetes imposes on society. Additional components of societal burden omitted from our study include intangibles from pain and suffering, resources from care provided by nonpaid caregivers, and the burden associated with undiagnosed diabetes.\n",
      "\n",
      "Diabetes imposes a substantial burden on the economy of the U.S. in the form of increased medical costs and indirect costs from work-related absenteeism, reduced productivity at work and at home, reduced labor force participation from chronic disability, and premature mortality (1,2). In addition to the economic burden that has been quantified, diabetes imposes high intangible costs on society in terms of reduced quality of life and pain and suffering of people with diabetes, their families, and friends.\n",
      "\n",
      "Improved understanding of the economic cost of diabetes and its major determinants helps to inform policymakers and to motivate decisions to reduce diabetes prevalence and burden. The previous cost of diabetes study by the American Diabetes Association (ADA) estimated that there were nearly 17.5 million people living in the U.S. with diagnosed type 1 or type 2 diabetes in 2007, at an estimated cost of $174 billion in higher medical costs and lost productivity.\n",
      "\n",
      "The percentage of the population with diagnosed diabetes continues to rise, with one study projecting that as many as one in three U.S. adults could have diabetes by 2050 if current trends continue. In this updated cost of diabetes study, they estimate the total national economic burden of diagnosed diabetes in 2012 reflecting continued growth in prevalence of diabetes and its complications; changing health care practices, technology, and cost of treatment; and changing economic conditions.\n",
      "\n",
      "Among states, California has the largest population with diabetes and thus the highest costs, at 27.6 billion.  Although Florida\u2019s total population is 4th among states behind California, Texas, and New York, Florida is 2nd in costs at $18.9 billion.  For the state of New York 6.80% of the population had diabetes, impacting 1,321,800 people.\n",
      "\n",
      "Source: http://care.diabetesjournals.org/content/36/4/1033.full\n",
      "\n",
      "\n",
      "The two team members on this project both work in the healthcare setting and are actively working to design better ways to impact how care is delivered in the ambulatory and clinic settings in our communities.  "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Initial Questions: \n",
      "\n",
      "We would like to understand what underlying factors are prevelant in the state of New York population and help determine which conditions are correlated to the use of emergency room geographically by zipcode. The study has pointed out a number of factors in the nationwide data that we would like to use data science to explore in the New York data to see if that is evident in this population or perhaps if there are other drivers or better predictors of Emergency Department use.\n",
      "\n",
      "The areas noted in the national study that tied to higher use were:\n",
      "\n",
      "- insurance coverage (whether people had coverage or not)\n",
      "- ethnicity\n",
      "- social economic status\n",
      "- Access to primary care\n",
      "- Education level\n",
      "\n",
      "\n",
      "As well, there are a number of other potential areas such as diet (could be infered through obesity rates), self management compliance, and other factors that have been shown to impact diabetes and ED use in other reports.  There is some of that level of data on the website.   With all of this, we can explore and help identify what are the factors that correlate to the emergency room visits in the state of New York data.\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Data: \n",
      "\n",
      "Our source of data will come from information hosted on the https://health.data.ny.gov/ website in various datasets.  They are constructed in delimited files and APIs that can be read in for our data analysis\n",
      "\n",
      "\n",
      "We will be linking into the following datasets for our review:\n",
      "\n",
      "All payor potentially preventable Emergency Visits by patient zip:\n",
      "https://health.data.ny.gov/Health/All-Payer-Potentially-Preventable-Emergency-Visit-/gu5u-nze7\n",
      "\n",
      "Hospital 2012 Inpatient deidentified patient data (can link into other years as needed) to help pull demographic data \n",
      "https://health.data.ny.gov/download/3m9u-ws8e/application/zip\n",
      "\n",
      "Community heatlh indicator report (looking at overall community health items like diabetic education)\n",
      "https://health.data.ny.gov/Health/Community-Health-Indicator-Reports-CHIRS-Latest-Da/54ci-sdfi\n",
      "\n",
      "Provider listing (access to care/specialists)\n",
      "https://health.data.ny.gov/download/97uk-ctjs/application/zip\n",
      "\n",
      "Community Health Obesity and Diabetes Related Indicators: 2013\n",
      "https://health.data.ny.gov/Health/Community-Health-Obesity-and-Diabetes-Related-Indi/tchg-ruva\n",
      "\n",
      "QARR: Diabetes Adult Health by Payer (to pull appropriate compliance of diabetic self management such as A1C) \n",
      "https://health.data.ny.gov/Health/QARR-Diabetes-Adult-Health-by-Payer/skh2-3uhw\n",
      "\n",
      "\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Exploratory Analysis: \n",
      "What visualizations did you use to look at your data in different ways? What are the different statistical methods you considered? Justify the decisions you made, and show any major changes to your ideas. How did you reach these conclusions?\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Our approach is to do the following:\n",
      "\n",
      "- Create an aggregated dataset that will aggregate our target variable (ED utilization) and tie in all variables that are indicated as leading predictors of ED use\n",
      "\n",
      "    - aggregeate data results by zip code to identify geographical opportunity\n",
      "    \n",
      "    - identify limitations of data that we need to consider in the data sets we are using\n",
      "\n",
      "\n",
      "-  Perform exploratory data analysis to review the interactions on the variables and the target - what trends do we see and do we see the same indicators identified in the ADA study as key drivers in the state of New York Data\n",
      "\n",
      "    - using typical EDA approaches of scatter plots, histograms, etc. to determine appropriate variables to use and patterns in data\n",
      "\n",
      "\n",
      "- Once we pare down the listing of variables to the ones that show correlation to ED utilization, we will explore performing principle component analysis to help identify those key areas that help explain variation of usage of the ED.   As well, we can explore using regression to determine the variables that contribute to the ED use by zip code\n",
      "\n",
      "\n",
      "- Develop a key set of summary graphs or illustrations to summarize the key findings.   Would be ideal to see if we possibly leverage into some sort of map graphic for demonstrating some findings - but we haven't learned that level of functionality in our class. \n",
      "\n",
      "\n",
      "We will be documenting the statistical methods use, why we used them, and if we had to modify our ideas based on the review of the data.\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# special IPython command to prepare the notebook\n",
      "%matplotlib inline \n",
      "\n",
      "import requests \n",
      "from StringIO import StringIO\n",
      "import numpy as np\n",
      "import pandas as pd # pandas\n",
      "import matplotlib.pyplot as plt # module for plotting \n",
      "import datetime as dt # module for manipulating dates and times\n",
      "import numpy.linalg as lin # module for performing linear algebra operations\n",
      "import collections\n",
      "import urllib2\n",
      "import bs4 #this is beautiful soup"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "To begin our analysis, we will read in our target data (avoidable ED usage).  This is the rates from 2011 and 2012.   But, to help gauge the volumes of peopel impacted, will merge this together with the US Census data from 2010 (the most recent data available).  It's not a pure apples to apples, but should be within reason."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# read in the ed utilization from the NY website\n",
      "# to automate the download - use the json portion of the website code for datasets\n",
      "\n",
      "## load our target we are exploring - avoidable ED use by zip\n",
      "\n",
      "# first provide the URL string with the json location\n",
      "url_str = \"http://health.data.ny.gov/resource/gu5u-nze7.json\"\n",
      "# then read json file as a pandas dataframe\n",
      "EDuse = pd.read_json(url_str)\n",
      "\n",
      "# delete out the overall total - not a true zip\n",
      "EDuse = EDuse[EDuse.patient_zipcode != 'Statewide']\n",
      "# make the zipcode an integer\n",
      "EDuse['patient_zipcode'] = EDuse['patient_zipcode'].astype(int)\n",
      "\n",
      "\n",
      "# to help get idea on number of people impacted by zip, read in the 2010 census volumes by zipcode\n",
      "url_census = \"https://s3.amazonaws.com/SplitwiseBlogJB/2010+Census+Population+By+Zipcode+(ZCTA).csv\"\n",
      "Censusbyzip = pd.read_csv(url_census)\n",
      "\n",
      "\n",
      "# merge the datasets - want to make sure records on EDUse are not dropped during the merge\n",
      "\n",
      "print \"Number of rows before merge: %i\" % EDuse.shape[0]\n",
      "# merge data\n",
      "EDusejoined = pd.merge(EDuse, Censusbyzip, how=\"inner\", left_on=\"patient_zipcode\", right_on=\"Zip Code ZCTA\")\n",
      "EDusejoined.head()\n",
      "\n",
      "# check to see if size still the same - should be \n",
      "print \"Number of rows after merge: %i\" % EDusejoined.shape[0]       \n",
      "\n",
      "# drop the duplicate zipcode from the census data on the merge\n",
      "EDusejoined.drop('Zip Code ZCTA', axis=1, inplace=True)\n",
      " "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Number of rows before merge: 998\n",
        "Number of rows after merge: 998\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print EDuse"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "     adjusted_rate_per_100  difference_in_rates  discharge_year  \\\n",
        "2                     0.00               -18.31            2011   \n",
        "3                     0.60               -16.69            2012   \n",
        "4                    29.29                 5.21            2011   \n",
        "5                    33.02                 8.95            2012   \n",
        "6                    26.16                 2.04            2011   \n",
        "7                    28.74                 4.18            2012   \n",
        "8                    17.68                -5.01            2011   \n",
        "9                    19.52                -3.59            2012   \n",
        "10                   13.51                -8.62            2011   \n",
        "11                   16.33                -6.11            2012   \n",
        "12                   16.77                -5.82            2011   \n",
        "13                    9.84               -12.57            2012   \n",
        "14                   15.87                -7.10            2011   \n",
        "15                   11.64               -10.29            2012   \n",
        "16                   11.56                -9.31            2011   \n",
        "17                    9.01               -12.13            2012   \n",
        "18                   29.14                 5.45            2011   \n",
        "19                   27.01                 3.50            2012   \n",
        "20                   13.97                -8.04            2011   \n",
        "21                   12.94                -9.38            2012   \n",
        "22                   14.39                -7.45            2011   \n",
        "23                   13.04                -9.19            2012   \n",
        "24                   12.18                -9.25            2011   \n",
        "25                   11.86               -10.04            2012   \n",
        "26                   11.58                -8.77            2011   \n",
        "27                   10.25                -9.81            2012   \n",
        "28                    8.09               -12.42            2011   \n",
        "29                    8.77               -12.71            2012   \n",
        "30                   32.82                 7.83            2011   \n",
        "31                   30.70                 6.14            2012   \n",
        "..                     ...                  ...             ...   \n",
        "970                  23.46                -0.01            2011   \n",
        "971                  21.19                -1.70            2012   \n",
        "972                   4.68               -14.02            2011   \n",
        "973                   3.47               -14.21            2012   \n",
        "974                  23.29                -0.13            2011   \n",
        "975                  21.78                -1.28            2012   \n",
        "976                  16.02                -5.55            2011   \n",
        "977                  19.84                -2.69            2012   \n",
        "978                  15.91                -5.59            2011   \n",
        "979                   9.78                -9.90            2012   \n",
        "980                  17.60                -4.45            2011   \n",
        "981                  10.99                -9.71            2012   \n",
        "982                  16.99                -4.92            2011   \n",
        "983                   7.75               -11.73            2012   \n",
        "984                  48.57                19.09            2011   \n",
        "985                  43.42                14.63            2012   \n",
        "986                  13.40                -7.54            2011   \n",
        "987                  11.04                -9.76            2012   \n",
        "988                  24.93                 1.06            2011   \n",
        "989                  27.74                 3.10            2012   \n",
        "990                  17.69                -4.23            2011   \n",
        "991                  14.15                -7.31            2012   \n",
        "992                  35.01                 8.51            2011   \n",
        "993                  33.49                 7.31            2012   \n",
        "994                  24.24                 0.59            2011   \n",
        "995                  26.74                 2.28            2012   \n",
        "996                  60.62                30.45            2011   \n",
        "997                  52.68                23.43            2012   \n",
        "998                  32.72                 7.24            2011   \n",
        "999                  31.19                 5.61            2012   \n",
        "\n",
        "     expected_rate_per_100  observed_rate_per_100  patient_zipcode  \n",
        "2                    18.31                   0.00             6390  \n",
        "3                    17.13                   0.43             6390  \n",
        "4                    21.01                  26.22            10001  \n",
        "5                    22.20                  31.15            10001  \n",
        "6                    17.78                  19.82            10002  \n",
        "7                    18.92                  23.10            10002  \n",
        "8                    20.28                  15.27            10003  \n",
        "9                    21.07                  17.48            10003  \n",
        "10                   20.31                  11.69            10004  \n",
        "11                   19.99                  13.87            10004  \n",
        "12                   20.38                  14.56            10005  \n",
        "13                   21.60                   9.03            10005  \n",
        "14                   21.90                  14.81            10006  \n",
        "15                   20.37                  10.08            10006  \n",
        "16                   18.35                   9.04            10007  \n",
        "17                   19.66                   7.53            10007  \n",
        "18                   22.56                  28.00            10009  \n",
        "19                   23.65                  27.15            10009  \n",
        "20                   19.87                  11.82            10010  \n",
        "21                   20.83                  11.45            10010  \n",
        "22                   19.26                  11.80            10011  \n",
        "23                   20.61                  11.42            10011  \n",
        "24                   19.23                   9.98            10012  \n",
        "25                   20.25                  10.21            10012  \n",
        "26                   17.31                   8.54            10013  \n",
        "27                   17.38                   7.57            10013  \n",
        "28                   18.95                   6.53            10014  \n",
        "29                   20.26                   7.55            10014  \n",
        "30                   19.66                  27.50            10016  \n",
        "31                   20.15                  26.29            10016  \n",
        "..                     ...                    ...              ...  \n",
        "970                  17.65                  17.64            12056  \n",
        "971                  17.08                  15.38            12056  \n",
        "972                  17.51                   3.49            12057  \n",
        "973                  16.67                   2.46            12057  \n",
        "974                  16.90                  16.77            12058  \n",
        "975                  17.15                  15.88            12058  \n",
        "976                  17.48                  11.93            12059  \n",
        "977                  17.15                  14.46            12059  \n",
        "978                  17.36                  11.77            12060  \n",
        "979                  16.94                   7.05            12060  \n",
        "980                  17.80                  13.34            12061  \n",
        "981                  18.22                   8.51            12061  \n",
        "982                  17.82                  12.90            12062  \n",
        "983                  17.50                   5.76            12062  \n",
        "984                  17.85                  36.93            12064  \n",
        "985                  17.32                  31.95            12064  \n",
        "986                  17.56                  10.02            12065  \n",
        "987                  18.39                   8.63            12065  \n",
        "988                  17.15                  18.21            12066  \n",
        "989                  17.35                  20.45            12066  \n",
        "990                  17.17                  12.94            12067  \n",
        "991                  18.33                  11.02            12067  \n",
        "992                  17.30                  25.81            12068  \n",
        "993                  17.27                  24.59            12068  \n",
        "994                  17.85                  18.44            12070  \n",
        "995                  16.71                  18.99            12070  \n",
        "996                  19.24                  49.68            12071  \n",
        "997                  18.92                  42.35            12071  \n",
        "998                  18.37                  25.61            12072  \n",
        "999                  17.23                  22.84            12072  \n",
        "\n",
        "[998 rows x 6 columns]\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Ideally, we would like to use zip code to give us more abiltiy to break down the data, but most of the community health type indicators are by county.  To address, this we will need to use a crosswalk.  However, we run into cases where a zip code can span into multiple counties.  \n",
      "\n",
      "For our purpose, the state of NY health website has a dataset where they mapped zip codes to county using a logic where they looked by zipcode and the assigned it to the county that contained the largest portion of the zip code.  This seems like a reasonable assumption used by the state website and since we are using datasets from the same website, we will use it here.\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# we will use some similar code from the 9-23 data scraping lecture\n",
      "\n",
      "# first read in the HTML List to get all of the state names\n",
      "url = 'http://www.health.ny.gov/statistics/chac/perinatal/county_list.htm'\n",
      "source = urllib2.urlopen(url).read()\n",
      "\n",
      "## convert this to a beautiful soup bs4 object\n",
      "NYcounties = bs4.BeautifulSoup(source)\n",
      "\n",
      "## get all links in the page\n",
      "link_list = [l.get('href') for l in NYcounties.findAll('a')]\n",
      "\n",
      "# create an empty list to collect the valid links\n",
      "external_links = []\n",
      "\n",
      "# write a loop to filter the links\n",
      "# we want the links that start with '/statistics/chac/perinatal/county'\n",
      "for l in link_list:\n",
      "    if l is not None and l[:33] == '/statistics/chac/perinatal/county':\n",
      "        external_links.append(l)\n",
      "\n",
      "# now have a list of all links - we will read each one in and create a panda dataframe with county name and zipcode\n",
      "\n",
      "# first create a blank dataframe to store results\n",
      "countyzip = []\n",
      "columns = ['County','Zip']\n",
      "countyzip = pd.DataFrame(columns = columns)\n",
      "\n",
      "# go through the linklist\n",
      "j = 0\n",
      "iterations = len(external_links)\n",
      "while j < (iterations):\n",
      "    # read in url name\n",
      "    html = 'http://www.health.ny.gov'\n",
      "    name = html + external_links[j]\n",
      "    countyname = urllib2.urlopen(name).read()\n",
      "    # Get county name\n",
      "    # unfortunately, the HTML tag for county has the string 'county' in it.   we just want the name.   \n",
      "    # Will parse based on url name - always found in same location\n",
      "\n",
      "    # County name will be between the string 'county/' and '.htm' in the http string\n",
      "    ## find \"county/\"\n",
      "    positionstart =  name.find('county/')\n",
      "\n",
      "    ## find \".htm\"\n",
      "    positionend =  name.find('.htm')\n",
      "\n",
      "    # Get County name.  the start string 'county/' is 7 characters long - factor in\n",
      "    county = name[positionstart+7:positionend]\n",
      "\n",
      "\n",
      "\n",
      "    ## convert this to a beautiful soup bs4 object\n",
      "    Countyinfo = bs4.BeautifulSoup(countyname)\n",
      "\n",
      "\n",
      "    # loop over the table rows (tr tag) and get the text of cells (td tag) inside   zip is the first column.  \n",
      "    # need to remove first two table row names - they are headers in the table\n",
      "    for tr in Countyinfo.find_all('tr')[2:]:\n",
      "        tds = tr.find_all('td')\n",
      "        Zipres = tds[0].text\n",
      "        Countyres = county.capitalize()\n",
      "        countyzip=countyzip.append({\"County\": Countyres, \"Zip\":Zipres}, ignore_index=True)\n",
      "        # last row is the total - return the dataframe without the total for cleanliness\n",
      "        #countyzip=countyzip.drop(countyzip.tail(1).index)\n",
      "\n",
      "    j = j + 1\n",
      "\n",
      "# convert zipcode to an integer and drop the total rows\n",
      "countyzip = countyzip[countyzip.Zip != 'Total']\n",
      "countyzip = countyzip[countyzip.Zip != 'Total ']\n",
      "countyzip['Zip'] = countyzip['Zip'].astype(int)\n",
      "\n",
      "# note, there are two counties that have a space in here - need to adjust the string to allow proper matching\n",
      "countyzip.County[countyzip.County=='Newyork'] = 'New York'  # format New York county\n",
      "countyzip.County[countyzip.County=='Stlawrence'] = 'St. Lawrence'  # format St. Lawrence county\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Need to do some integreity checks against the results.  we need to check to see if there are zip codes not matched in the ED utilization file and then add on the county code"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# check to see if there are zip codes not in our mapping file\n",
      "# do this by creating a subset of the ED use, compare to this countyzip file, and see what is left\n",
      "# just check one year (2012) for a complete list\n",
      "EDfilezipchk = EDusejoined[['discharge_year','patient_zipcode','2010 Census Population']]\n",
      "EDfilezipchk = EDfilezipchk[EDfilezipchk.discharge_year != 2011]\n",
      "\n",
      "# merge this with a left join\n",
      "zipcheck = pd.merge(EDfilezipchk, countyzip, how=\"left\", left_on=\"patient_zipcode\", right_on=\"Zip\")\n",
      "\n",
      "# do a quick groupby to check those that match and those that don't\n",
      "# provide total census of all\n",
      "All_pop = zipcheck['2010 Census Population'].sum()\n",
      "# print those we do not have a match for \"nan\" in the Zip file merged\n",
      "Missing = zipcheck[zipcheck.Zip.isnull()]\n",
      "Missing_Pop = Missing['2010 Census Population'].sum()\n",
      "\n",
      "print All_pop\n",
      "print Missing_Pop\n",
      "print Missing"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "12731759\n",
        "4335\n",
        "     discharge_year  patient_zipcode  2010 Census Population County  Zip\n",
        "0              2012             6390                     236    NaN  NaN\n",
        "191            2012            10985                      58    NaN  NaN\n",
        "204            2012            11005                    1806    NaN  NaN\n",
        "440            2012            11957                     743    NaN  NaN\n",
        "453            2012            12007                      60    NaN  NaN\n",
        "458            2012            12017                     435    NaN  NaN\n",
        "464            2012            12024                      31    NaN  NaN\n",
        "469            2012            12031                     175    NaN  NaN\n",
        "473            2012            12036                     228    NaN  NaN\n",
        "491            2012            12064                     563    NaN  NaN\n"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "There are only 10 zipcodes with missing data and all have very low census counts.  For completeness and given the fact that trying to map county to zip has flaws in in anyway, I will just drop the missing county mappings from our target datasets.  So this will provide us a way to link from data sets based on county to zip code results on our target of avoidable ED use.   \n",
      "\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# merge the ED indicators and append on the county code - drop those without a match\n",
      "# merge this with a left join\n",
      "EDuseWithCounty = pd.merge(EDusejoined, countyzip, how=\"inner\", left_on=\"patient_zipcode\", right_on=\"Zip\")\n",
      "\n",
      "# drop the duplicate zipcode from the census data on the merge\n",
      "EDuseWithCounty.drop('Zip', axis=1, inplace=True)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 20
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "Now lets read in some of the community health variables (which are by county) and try to marry them together at the county level"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "### JUST TO DO EXPLORATORY - WILL READ IN THE 2008-2012 NON API RESULTS\n",
      "# note this is by county - so need to match to that result\n",
      "\n",
      "# first provide the URL string with the json location\n",
      "url_str = \"http://health.data.ny.gov/resource/tchg-ruva.json\"\n",
      "\n",
      "# then read json file as a pandas dataframe\n",
      "CommDiabInd= pd.read_json(url_str)\n",
      "\n",
      "# county codes 1-62 are true counties - subset data\n",
      "CommDiabInd = CommDiabInd[CommDiabInd.county_code < 100]\n",
      "\n",
      "#  merge to the ED target dataset - call it DiabIndicators for now\n",
      "DiabIndicators = pd.merge(EDuseWithCounty, CommDiabInd, how=\"inner\", left_on=\"County\", right_on=\"county_name\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 26
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now that we have some indicators, let's do some basic exploratory analysis to start.  Our target is looking at overutilizers of ED, which for our purposes will be the \"difference_in_rates\" column.   Let's get an idea of the distribution:\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# plot the distribution of the rates across zip codes in the state - do \n",
      "df = EDuseWithCounty[EDuseWithCounty['discharge_year'] == 2012]\n",
      "plt.plot(subplots=True)\n",
      "plt.hist(df.difference_in_rates.values, bins = 20, color='b')\n",
      "plt.title('Difference in actual to expected ED use rates for 2012')\n",
      "plt.xlabel('Actual-Expected')\n",
      "plt.ylabel('Frequency')\n",
      "plt.show()\n",
      "\n",
      "# plot the distribution of the rates across zip codes in the state - do \n",
      "df = EDuseWithCounty[EDuseWithCounty['discharge_year'] == 2011]\n",
      "plt.plot(subplots=True)\n",
      "plt.hist(df.difference_in_rates.values, bins = 20, color='r')\n",
      "plt.title('Difference in actual to expected ED use rates for 2011')\n",
      "plt.xlabel('Actual-Expected')\n",
      "plt.ylabel('Frequency')\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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9FzhoWBM1hCJiSUTclrufAe4m/c5qJOXxudw5nnQDs5QB5q8tggOApM9LWgwc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\nQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x84531d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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8kH43JmmhpCtzf+uxR0TP/wGbFrrfBpydu3cDbgA2AGYDfwQmVB1vg/hfVIsL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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7ba8510>"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# noticing a trend where the majority of zips are actually using the ED less than expected - lets see if we have correlations by diabetic indicator\n",
      "\n",
      "# first, let's assign our target variable if the difference in rates is >0 (indicating a place we are trying to explore) \n",
      "EDuseWithCounty['ED Opp'] = EDuseWithCounty['difference_in_rates'].apply(lambda x: 0 if x < 0 else 1)\n",
      "DiabIndicators['ED Opp'] = DiabIndicators['difference_in_rates'].apply(lambda x: 0 if x < 0 else 1)\n",
      "\n",
      "# now lets subset our results by indicator - see what trends we see\n",
      "# For now, just manually picking an indicator to get a feeling\n",
      "# start with g29-Age-adjusted percentage of adults with physician diagnosed diabetes\n",
      "# graph percentage rate to our target of difference in rates as a scatterplot\n",
      "\n",
      "df = DiabIndicators[DiabIndicators['indicator_number'] == 'g29']\n",
      "plt.scatter(df['difference_in_rates'], df['percentage_rate'])\n",
      "plt.title('ED use by % of population diagnosed with diabetes')\n",
      "plt.xlabel('ED use Actual-Expected')\n",
      "plt.ylabel('% of popluation with diabetes')\n",
      "plt.grid()\n",
      "plt.show()\n",
      "\n",
      "# boxplot comparing the distribution of the two\n",
      "df.boxplot(column='percentage_rate', by='ED Opp', grid=False)\n",
      "plt.ylabel('% of popluation with diabetes')\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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gQaBXVc9S1bOAHhdmGIZhxJhS5gGcqqp3pA9U9VfAaZWTFH/ibkc0/dUjztrB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\nZcCjqno7edzqlKJ/UoeBquq6EqN+mbFf0H8AFvr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       "text": [
        "<matplotlib.figure.Figure at 0x82a5c50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 39,
       "text": [
        "<matplotlib.text.Text at 0x81747f0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x9a3ba90>"
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Seeing some pattern on the prevelance of physician diagnosed diabetes to those that have used the ED more than expected.   Let's look to see if there is patterns in self managment that we see by looking at the compliance of diabetic self management."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# note this is by county - so need to match to that result\n",
      "\n",
      "# first provide the URL string with the json location\n",
      "url_str = \"http://health.data.ny.gov/resource/skh2-3uhw.json\"\n",
      "\n",
      "# then read json file as a pandas dataframe\n",
      "CommDiabInd= pd.read_json(url_str)\n",
      "\n",
      "# county codes 1-62 are true counties - subset data\n",
      "CommDiabInd = CommDiabInd[CommDiabInd.county_code < 100]\n",
      "\n",
      "#  merge to the ED target dataset - call it DiabIndicators for now\n",
      "DiabIndicators = pd.merge(EDuseWithCounty, CommDiabInd, how=\"inner\", left_on=\"County\", right_on=\"county_name\")\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# read in the Community Health API : Obesity and Diabetes Related Indicators from the NY website\n",
      "# copying code given from the NY API to load in this\n",
      "\n",
      "#### CANT GET THIS TO WORK - NOT SURE WHY.\n",
      "\n",
      "import sys\n",
      "import requests\n",
      "\n",
      "apikey = 'tSA8QnUFioj3iLh2hkvTATyjKD9c38iyt9TS'\n",
      "apptoken = 'kSpZOjLIgnLaI7o6A5fDXhsVv'\n",
      "\n",
      "r = requests.get('https://health.data.ny.gov/resource/community-health-obesity-and-diabetes-related-indicators.json?recall_id=94', headers={'X-App-Token': sys.argv[1]})\n",
      "print r.status_code\n",
      "print r.text\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# look at primary care by provider\n",
      "\n",
      "#### HAVING MEMORY ISSUES - HOW TO ADDRESS????\n",
      "# now get provider listing - this is embedded in a zip file from the website - use code from HW1 solutions to help with this\n",
      "\n",
      "from StringIO import StringIO\n",
      "from zipfile import ZipFile\n",
      "from urllib import urlopen\n",
      "\n",
      "# use urlopen to get the zip file\n",
      "# url = urlopen(\"https://health.data.ny.gov/download/97uk-ctjs/application/zip\")\n",
      "\n",
      "# open the zip file\n",
      "# zipfile = ZipFile(StringIO(url.read()))\n",
      "\n",
      "# extract the providerdata.csv and read into a panda dataframe  as of 2012 but use as a proxy\n",
      "# providers = pd.read_csv(zipfile.open(\"NYSDOH_PNDS_IndividualProviderData_2012Q4.csv\"), sep = ',')\n",
      "# print \"Providers.csv file\"\n",
      "# print providers.head()\n",
      "# print \"\"\n",
      "\n",
      "\n",
      "## look at community health obesity \n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Final Analysis: \n",
      "What did you learn about the data? How did you answer the questions? How can you justify your answers?"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}