CMSAssess, RSS, London, Nov, 2004 RECONSTRUCTING COMPLEX HEALTH SERVICE DATA WITH by Gilbert MacKenzie & Xuefang Li The Centre for Medical Statistics

CMSAssess, 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

CMSAssess, 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

CMSAssess, 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.

CMSAssess, 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 In order to provide a patient-based analysis of performance on a year to year basis

CMSAssess, 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 Some 326,236 episodes over this period. But NHS identifier missing in 25% of episodes !!

CMSAssess, RSS, London, Nov, 2004 Missing Values for Main Variables in the 325,236 Episodes

CMSAssess, RSS, London, Nov, 2004 Ideal Data Structure Record 1Pat 1, Adm 1, E1 Record 2Pat 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

CMSAssess, 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***

CMSAssess, RSS, London, Nov, 2004 Matching Result Overall Result Total Missing = 82,906 (25.5%) After 1 st Match = 69, 771 (21.5%) After 2 nd & 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).

CMSAssess, RSS, London, Nov, 2004 Data Structure Ideal Attained * Record 1Pat 1, Adm 1, E1 Record 2Pat 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

CMSAssess, RSS, London, Nov, 2004 Data Structure Target CEPH file structure Record 1Pat 1, E1 E2 E1 EX Record 2 Pat 2, E1 E1 E2 E1 Record 3 Pat 3, E1 EX EX EX. Where 1) Es 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

CMSAssess, 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 Case=nhs_num 1-10 missing=nowarn. Record Type 1. Data list /V V (A) V (A) V … Record Type 2. Data list /V V (A) V (A) V … Etc to a max of 51 episode records for the NS study End File Type. NB Other subcommands include: Duplicate, Skip, Ordered, Case.

CMSAssess, RSS, London, Nov, 2004 Data Structure Making the CEPH file useable Record 1Pat 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 SPSSs powerful transformation language – use Vector and Loops to store and search frequently used quantities & addresses, eg

CMSAssess, 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.

CMSAssess, RSS, London, Nov, 2004 Examples of Index Building Contd 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.

CMSAssess, RSS, London, Nov, 2004 North Staffs Study Results Data Episodes = , Admissons= 284,965 Patients = 188,745 Comprehensive patient-based Index built covering all major NHS Performance Indicators 5 Files: 1997, 1998, 1999, 2000 & Descriptive analysis by Hospital types and diagnostic category (modelling to follow)

CMSAssess, RSS, London, Nov, 2004 North Staffs Study

CMSAssess, RSS, London, Nov, 2004 North Staffs Study Numbers of patients by trust by year Acute Trust Combined Trust Total

CMSAssess, RSS, London, Nov, 2004 North Staffs Study Table 3.3 Average & Median length of stay by disease by year in the Acute Trust

CMSAssess, RSS, London, Nov, 2004 North Staffs Study Table 3.4 Average & Median length of stay by disease by year in the Combined Trust

CMSAssess, RSS, London, Nov, 2004 North Staffs Study

CMSAssess, 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.