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CMS Assess, RSS, London, Nov, 2004
RECONSTRUCTING COMPLEX
HEALTH SERVICE DATA WITH
by
Gilbert MacKenzie & Xuefang Li
The Centre for Medical Statistics
CMS Assess, RSS, London, Nov, 2004
Introduction
Increasingly there is interest in monitoring and evaluating Hospital Performance in the NHS.
This has led to the compilation of
League Tables
Classically, such data are observational and their formal statistical evaluation is by covariate or “case-mix” adjustment
But NHS hospital performance indicators may not be patient-based or directly related to patient well-being – hence complicating “case-mix” adjustment
CMS Assess, RSS, London, Nov, 2004
Performance Indicators
These may include:
Occupied Bed-days
Re-admission Rates
Medial Outlier Rates
NB: Often these are Episode – rather than patient-based
CMS Assess, RSS, London, Nov, 2004
Classical Data Structure
Patient
Admission(s)
Completed Consultant Episode(s)
Typically, in any period, hospital data are of the form
But Performances Indicators are typically based on the Activity of the hospital ie, on the last two above.
CMS Assess, RSS, London, Nov, 2004
North Staffs Study
Aims
To compile a Cumulative Event Patient History
(CEPH) file
For All ordinary Admissions to all Hospitals in
North Staffordshire for 1997-2000
In order to provide a patient-based analysis
of performance on a year to year basis
CMS Assess, RSS, London, Nov, 2004
North Staffs Study
Data
Data Source LHA NHS Warehouse Data
Episode-Based for All Admissions to all Hospitals
in NS for 1997-2000
Some 326,236 episodes over this period.
But NHS identifier missing in 25% of
episodes !!
CMS Assess, RSS, London, Nov, 2004
Missing Values for Main Variables in the 325,236 Episodes
Variables
Missing number
Missing percentage
NHS number 82906 25.49% Date of birth 3 0.00% Sex 364 0.11% Postcode 1739 0.53% Marital Status 109763 33.75% Discharge date 9038 2.78% Discharge method 1303 0.40% Discharge destination 1303 0.40% Episode end date 24 0.01% Last episode indicator 80 0.02% Hospital site 164939 50.71% Ward type 158272 48.66% Primary diagnosis 2249 0.69% Operation status 255800 78.65%
CMS Assess, RSS, London, Nov, 2004
Ideal Data Structure
Record 1 Pat 1, Adm 1, E1
Record 2 Pat 1, Adm 1, E2
Record 3 Pat 1, Adm 2, E1
Record 4 Pat 2, Adm 1, E1
Record 5 Pat 2, Adm 2, E1
Record 6 Pat 2, Adm 2, E2
Record 7 Pat 2, Adm 3, E1
Record 8 Pat 3, Adm 1, E1
North Staffs Study
Patient 1
Patient 2
Patient 3
CMS Assess, RSS, London, Nov, 2004
Simple Patient Matching Algorithm
NHS Matching Criteria : C= (Sex, DOB, Postcode) Step 1: Define Set A as Missing ID (n=82,906 episodes)
Step 2: Define Set B as Known ID (n=242,330 episodes)
Step 3: Use C to match Set A with Set B
Step 4: Consolidate 13,114 Matches in Set B
Step 5: Call the reduced Set A set Set A* & Set B , Set B*
Step 6: Use C to to match Set A* with Set B*
Step 7: Consolidate Matches in Set B*
Step 8. Finally Use C to match A** with A**
Step 9: Allocate new NHS numbers to residual in A***
CMS Assess, RSS, London, Nov, 2004
Matching Result
Overall Result
Total Missing = 82,906 (25.5%)
After 1st Match = 69, 771 (21.5%)
After 2nd & 3rd = 46, 527 (14.3%)
Accuracy
About 4%-5% are wrongly matched
Also about 7% with known NHS numbers were
really different people (Sex, DOB, Postcode).
CMS Assess, RSS, London, Nov, 2004
Data Structure
Ideal Attained *
Record 1 Pat 1, Adm 1, E1
Record 2 Pat 1, Adm 1, E2
Record 3 Pat 1, Adm 2, E1
Record 4 Pat 2, Adm 1, E1
Record 5 Pat 2, Adm 2, E1
Record 6 Pat 2, Adm 2, E2
Record 7 Pat 2, Adm 3, E1
Record 8 Pat 3, Adm 1, E1
* But variable number of records per patient
Now the Target CEPH File is a Flat SPSS System File
With one record per patient
CMS Assess, RSS, London, Nov, 2004
Data Structure
Target CEPH file structure
Record 1 Pat 1, E1 E2 E1 EX
Record 2 Pat 2, E1 E1 E2 E1
Record 3 Pat 3, E1 EX EX EX.
Where
1) E’s are sets of episode data
2) E1 E2 => Relates to same Admission
3) EX => a set of system missing values
4) Within patient the E records are in
chronological order.
Regular Cases by Variables System File
CMS Assess, RSS, London, Nov, 2004
Defining Complex File Structures
SPSS File type GROUPED command
File Type Grouped
File='c:\my documents\oldcare\LHA20-21new.dat'
Record= #epi_id 300-301 Case=nhs_num 1-10 missing=nowarn.
Record Type 1.
Data list /V0101 12 V0201 14-24 (A) V0301 26-33 (A) V0401 35 …
Record Type 2 .
Data list /V0102 12 V0202 14-24 (A) V0302 26-33 (A) V0402 35 …
Etc to a max of 51 episode records for the NS study
End File Type.
NB Other subcommands include: Duplicate, Skip, Ordered, Case.
CMS Assess, RSS, London, Nov, 2004
Data Structure
Making the CEPH file useable
Record 1 Pat 1, E1 E2 E1 EX {index structure}
Record 2 Pat 2, E1 E1 E2 E1 {index structure}
Record 3 Pat 3, E1 EX EX EX {index structure}.
.
.
Now build up useful patient-based Performance Indicator quantities using SPSS’s powerful transformation language – use Vector and Loops to store and search frequently used quantities & addresses, eg
CMS Assess, RSS, London, Nov, 2004
Examples of Index Building Comment compute SUMR - the number of episodes (records) per patient
vector vdata48=V4801 to V4851.
compute #sumr=0.
loop #j =1 to 51.
if ( not(missing (Vdata48(#j )) )) #sumr= #sumr+1.
end loop.
compute sumr=#sumr.
Comment compute SUMA - the number of admissions per patient.
Vector vdata47=V4701 to V4751.
compute #suma=0.
loop #j =1 to 51.
if ( not ( missing (vdata47(#j )) ) ) #suma=vdata47(#j ) .
end loop.
compute suma=#suma.
CMS Assess, RSS, London, Nov, 2004
Examples of Index Building Cont’d
Comment compute first episode address for each admission.
Vector vdata47=V4701 to V4751.
Comment Zeroise.
Do repeat i=Adm01 to Adm51.
Compute i=0.
end repeat.
Comment Declare Missing.
missing values adm01 to adm51 (0).
Vector Adm=Adm01 to Adm51.
Comment compute address.
compute Adm(1)=1.
compute #k=1.
loop #J=2 to sumr.
do if (vdata47(#j) eq vdata47(#j-1) +1).
compute #k = #k+1.
compute adm(#k)=#j.
end if.
end loop.
CMS Assess, RSS, London, Nov, 2004
North Staffs Study
Results
Data Episodes = 321788,
Admissons= 284,965
Patients = 188,745
Comprehensive patient-based Index built
covering all major NHS Performance Indicators
5 Files: 1997, 1998, 1999, 2000 & 1997-2000
Descriptive analysis by Hospital types and
diagnostic category (modelling to follow)
CMS Assess, RSS, London, Nov, 2004
Table 1.1 Total Numbers of Patients, Admissions and Finished Consultant Episodes by year
1997 1998 1999 2000 Total Patients 47554 48138 46194 46859 188745 Admissions 69864 70998 71206 72897 284965 Episodes 77641 80089 80549 83509 321788
North Staffs Study
CMS Assess, RSS, London, Nov, 2004
North Staffs Study
Numbers of patients by trust by year
1997 1998 1999 2000
Acute Trust 43975 44396 42006 44517
Combined Trust 4392 4051 5084 3965
Total 47554 48138 46194 46859
CMS Assess, RSS, London, Nov, 2004
North Staffs Study
Table 3.3 Average & Median length of stay by disease by year in the Acute Trust
20.60 16.00 20.78 16.00 21.74 17.00 23.12 18.00
12.35 8.00 11.24 8.00 12.79 9.00 12.55 9.00
7.66 6.00 8.28 6.00 7.69 6.00 7.75 6.00
21.98 14.00 20.23 14.00 21.33 12.00 22.15 13.00
9.21 6.00 8.99 6.67 9.89 6.00 9.82 6.50
4.56 2.00 4.83 2.00 5.21 2.00 5.18 2.00
Hipfracture
Heartfailure
Myocardialinfarction
Stroke
Chronicchest
Otherdiseases
Mean Median
1997
Mean Median
1998
Mean Median
1999
Mean Median
2000
CMS Assess, RSS, London, Nov, 2004
North Staffs Study
Table 3.4 Average & Median length of stay by disease by year in the Combined Trust
48.03 36.00 49.38 33.00 46.41 34.50 43.12 40.00
28.63 19.00 25.53 19.50 32.51 20.50 27.85 21.00
65.17 7.50 12.33 13.00 19.41 13.00 19.75 20.50
46.57 31.00 43.93 29.00 48.67 31.00 45.11 33.00
24.09 15.00 21.30 13.00 22.56 16.00 25.50 17.00
38.57 14.50 33.14 15.00 30.23 14.33 28.06 17.00
Hipfracture
Heartfailure
Myocardialinfarction
Stroke
Chronicchest
Otherdiseases
Mean Median
1997
Mean Median
1998
Mean Median
1999
Mean Median
2000
CMS Assess, RSS, London, Nov, 2004
Table 5.4 4- weeks Readmission Rates by disease by year in the Acute Trust
1997 1998 1999 2000 hip fracture 0.20% 0.41% 0.73% 0.62% heart failure 5.44% 4.38% 1.42% 3.14% myocardial
infarction 3.03% 1.81% 0.91% 1.97%
stroke 0.45% 0.68% 0.18% 1.01% Chronic chest 7.95% 10.61% 2.84% 7.03%
Other patients 12.77% 13.25% 9.83% 14.42% all patients 12.79% 13.25% 9.61% 14.45%
North Staffs Study
CMS Assess, RSS, London, Nov, 2004
Some Conclusions
The Complex File Commands Mixed, Group and Nested are very useful - flexible and safe.
Need to be revised to remove Dependence on ASCII input for complex health data – too big.
Transformation language is SPSS means Database Index can be built easily.
Patient-based Performance Indicators as a standard is an exciting prospect.
Results in North Staffs suggest that health of population is declining – leading to greater utilisation with time.
Recommended