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This is an analysis of complaints data, munged in ../notebooks/mung.ipynb. Raw data is in ../data/raw

The fields are:

  1. abuse_number: A unique number assigned each complaint.
  2. facility_id: A unique number to each facility building. Stays if ownership changes.
  3. facility_name: Name of facility as of January 2017, when DHS provided the facility data to The Oregonian.
  4. abuse_type: A – facility abuse; L – licensing. Note: This does not apply to nursing facilities. All their complaints are either blank in this field or licensing.
  5. action_notes: DHS determination of what general acts constituted the abuse or rule violation.
  6. incident_date: Date the incident occured
  7. outcome: A very brief description of the consequences of the abuse or rule violation to the reisdent
  8. outcome_notes: A detailed description of what happened.
  9. year: year incident occured
  10. fac_name: If complaint is online, name listed for the facility
  11. public: Whether or not complaint is online




In [17]:



    


import pandas as pd
import numpy as np
import analysis_data_loader as loader
from IPython.core.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
pd.set_option('display.max_colwidth', -1)



    


















    























In [18]:



    


df = loader.load_facilities()



    











In [20]:



    


df = pd.read_csv('/Users/fzarkhin/OneDrive - Advance Central Services, Inc/fproj/github/database-story/data/processed/complaints.csv')

How many complaints do not appear in the state's public database?





In [21]:



    


df[df['public']=='offline'].count()[0]



    


















    
Out[21]:








7846

How many complaints do appear in the state's public database?





In [22]:



    


df[df['public']=='online'].count()[0]



    


















    
Out[22]:








5186

What percent of complaints are missing?





In [23]:



    


df[df['public']=='offline'].count()[0]/df.count()[0]*100



    


















    
Out[23]:








60.205647636586868

How many complaints were labelled 'Exposed to potential harm' or 'No negative outcome?'





In [24]:



    


df[(df['outcome']=='Exposed to Potential Harm') | (df['outcome']=='No Negative Outcome')].count()[0]



    


















    
Out[24]:








2509

Of all missing complaints, what percent are in the above two categories?





In [25]:



    


df[(df['outcome']=='Exposed to Potential Harm') |
   (df['outcome']=='No Negative Outcome')].count()[0]/df[df['public']=='offline'].count()[0]*100



    


















    
Out[25]:








31.978078001529443

How many complaints are labelled 'A,' which stands for abuse, but are offline?





In [26]:



    


df[(df['abuse_type']=='A') & (df['public']=='offline')].count()[0]



    


















    
Out[26]:








65

















In [27]:



    


#df.groupby('outcome').count().reset_index()[['outcome','abuse_number']].sort_values('abuse_number', ascending = False)

What's the online/offline breakdown by outcome?





In [32]:



    


totals = df.groupby(['omg_outcome','public']).count()['abuse_number'].unstack().reset_index()



    











In [39]:



    


totals.fillna(0, inplace = True)



    











In [40]:



    


totals['total'] = totals['online']+totals['offline']



    











In [41]:



    


totals['pct_offline'] = round(totals['offline']/totals['total']*100)



    











In [44]:



    


totals.sort_values('pct_offline',ascending=False)



    


















    
Out[44]:










  
    

      public
      omg_outcome
      offline
      online
      total
      pct_offline
    

  
  
    

      16
      Staffing issues
      12.0
      0.0
      12.0
      100.0
    

    

      1
      Denied readmission or moved improperly
      35.0
      2.0
      37.0
      95.0
    

    

      14
      Potential harm
      2361.0
      148.0
      2509.0
      94.0
    

    

      3
      Fall, no injury
      150.0
      13.0
      163.0
      92.0
    

    

      8
      Left facility without attendant, no injury
      207.0
      18.0
      225.0
      92.0
    

    

      9
      Loss of Dignity
      884.0
      97.0
      981.0
      90.0
    

    

      12
      Medication error
      983.0
      217.0
      1200.0
      82.0
    

    

      5
      Inadequate care
      496.0
      170.0
      666.0
      74.0
    

    

      6
      Inadequate hygiene
      138.0
      104.0
      242.0
      57.0
    

    

      10
      Loss of property, theft or financial exploitation
      809.0
      737.0
      1546.0
      52.0
    

    

      13
      Physical abuse
      89.0
      92.0
      181.0
      49.0
    

    

      18
      Verbal or emotional abuse
      70.0
      94.0
      164.0
      43.0
    

    

      7
      Involuntary seclusion
      8.0
      11.0
      19.0
      42.0
    

    

      2
      Failure to address resident aggression
      395.0
      622.0
      1017.0
      39.0
    

    

      4
      Fracture or other injury
      680.0
      1185.0
      1865.0
      36.0
    

    

      11
      Medical condition developed or worsened
      370.0
      1046.0
      1416.0
      26.0
    

    

      0
      Death
      7.0
      23.0
      30.0
      23.0
    

    

      15
      Sexual abuse
      15.0
      49.0
      64.0
      23.0
    

    

      17
      Unreasonable discomfort or continued pain
      115.0
      452.0
      567.0
      20.0
    

    

      19
      Weight loss
      20.0
      106.0
      126.0
      16.0
    

  




















In [ ]:

Content source: TheOregonian/long-term-care-db

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