Linkage between SSCAS data and mortality data

1. Linkage between SSCAS data and mortality data

2. Patients’ outcome Determined by: • Prior health and personal characteristics • Severity of illness • Effectiveness of treatment • Chance

3. Previous analyses by ISD • Used routinely collected hospital discharge data – SMR01 to identify cases • Linked these to death certificate data from General Register Office • Focused on case fatality by 30 days from admission • Was limited in ability to adjust for casemix (age, sex and deprivation by postcode)

5. Stroke Outcomes 1990-93

6. Scottish Stroke Outcomes Study GRI Falk WGH VHK LAW

7. Unadjusted 6 month case fatality

8. 6 month case fatality Unadjusted • Adjusted for • age • pre-stroke independence • can walk? • can talk & not confused? • can lift both arms?

9. Methods of current linkage • All MCNs gave permission to export individual patient data • All centres have exported individual patient data to Mike McDowall (ISD contract) • Linked these records with those held by ISD • Preliminary analyses to look at • Data completeness by MCN and hospital • 6 month case fatality by MCN and hospital • 6 month case fatality adjusted for casemix

10. Patient included in analyses All cases on SSCAS (n= 18831) Linked to existing patient in ISD data (n= 17344) Restricted to stroke patients only (14421) Survival data available for 6 months post admission (11507) Data available for casemix adjustment & included in analyses (n= 10018)

11. % of casemix data missing Excluding Island HBs with very small numbers

12. Factors likely to influence % of missing data in SSCAS • Completeness of medical records • Use of proforma or ICP • Explicit collection of casemix variables • Training & expertise of data extractor • ? Willingness to best guess • Amount of clinical support available • Frequency of missing data checks

13. % dead at 6 months by Health Board

14. % surviving at 6 months by Health Board

15. But these crude data do not take account of casemix and chance • Need to adjust for differences in factors which are associated with case fatality • Need to produce 95% confidence intervals to indicate precision of estimate • Adjusted survival data should minimise the affect that poor case ascertainment has on results e.g. if you missed all severe strokes then your casemix would be mild.

16. Younger than average

19. More severe Milder

23. Why might casemix vary between Health Boards? • Different populations • Different admission criteria – e.g. do patients with minor stroke in Fife and D & G stay at home or are treated in clinic? • Were mild or severe cases missed by SSCAS? • Was casemix data missing for particular severity of stroke patient in some places and therefore excluded from analyses?

24. W score explained • Observed number of patients surviving at 6 months • Predicted number of patients surviving at 6 months based on • Average survival for Scotland • Modelled using 6 casemix factors • W is excess no. of survivors at 6 months per 100 admissions over that predicted (+ values good) with 95% confidence intervals

25. Good Bad

26. Good Good Bad Bad

27. Good Bad

29. Stroke Unit Trialist CollaborationMeta-analysis of trials ofstroke unit care Absolute outcomes

30. Conclusions • There are large variations in crude 6 month survival between health boards • Most of these are due to variation in age and severity of stroke patient admitted • Having adjusted for casemix and having taken chance into account, differences are small

31. Planned analyses • Explore relationship between case fatality and process of care • Admission to stroke unit • Brain scanning • Aspirin • Discharge on secondary prevention • Look at agreement between diagnostic codes in SSCAS and SMR01 by hospital

32. Discussion • Are you happy to include these sorts of data in National Report? • Is the process of pooling data from each Health Board satisfactory? • Should we be making more use of these data in research? • How could efforts of contributors be appropriately acknowledged?