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This is an analysis of complaints data, munged here.

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_type: NF: Nursing Facility; ALF: Assisted Living Facility; RCF: Residential Care Facility.
  4. facility_name: Name of facility as of January 2017, when DHS provided the facility data to The Oregonian.
  5. 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.
  6. fine: Amount that state initialy fined the facility. Not necessarily amount of final fine.
  7. action_notes: DHS determination of what general acts constituted the abuse or rule violation.
  8. incident_date: Date the incident occured
  9. outcome: A very brief description of the consequences of the abuse or rule violation to the resident
  10. outcome_notes: A detailed description of what happened.
  11. year: year incident occured
  12. online_fac_name: If complaint is online, name listed for the facility
  13. public: Whether or not complaint is online
  14. omg_outcome: Field we created to group some similar outcomes. 




In [17]:



    


import pandas as pd
import numpy as np
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 = pd.read_csv('../../data/processed/complaints-3-29-scrape.csv')

How many total complaints are there?





In [19]:



    


df.count()[0]



    


















    
Out[19]:








13032

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





In [20]:



    


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



    


















    
Out[20]:








7846

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





In [21]:



    


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



    


















    
Out[21]:








5186

What percent of complaints are missing?





In [22]:



    


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



    


















    
Out[22]:








60.205647636586868

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





In [23]:



    


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



    


















    
Out[23]:








2509

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





In [24]:



    


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



    


















    
Out[24]:








31.978078001529443

What's the online/offline breakdown by outcome?

This was used in graphics





In [25]:



    


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



    











In [26]:



    


totals.fillna(0, inplace = True)



    











In [27]:



    


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



    











In [28]:



    


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



    











In [29]:



    


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



    


















    
Out[29]:










  
    

      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

How many offline complaints in the database were found to have "abuse," "neglect" or "exploitation?"





In [30]:



    


df['outcome_notes'].fillna('', inplace = True)



    











In [31]:



    


df[(df['outcome_notes'].str.contains('constitute neglect|constitutes neglect|constitute abuse|constitutes abuse|constitutes exploitation|constitutes financial exploitation')) & (df['public']=='offline')].count()[0]



    


















    
Out[31]:








483

"The state fined the facilities in hundreds of those cases."

In how many 'potential harm' cases were facilities fined?





In [32]:



    


df[(df['omg_outcome']=='Potential harm') & (df['fine']>0) & (df['public']=='offline')].count()[0]



    


















    
Out[32]:








206

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

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