1.  The 2002 Healthcare Conference 29 September-1 October 2002 Scarman House, The University of Warwick, Coventry

2. Session B1 : Critical Illness n Trends in Critical Illness n Heart Attack & Stroke n Working Party / Research Sub-group Report n Scott Reid & Joanne Wells

3. Critical Illness Trends Working Party Our Aims : n To examine underlying trends in the factors influencing UK Insured Critical Illness claim rates, and from these, to assess : nThe historic trend in incidence and death rates for the major CI’s nAny pointers for future trends in Standalone CI, Mortality and hence Accelerated CI. n Formed in March 2001

4. Sub-Group Members n Actuaries Scott Reid Joanne Wells n Medical Expert Dr Richard Croxson - Consultant Cardiologist

5. Contents Slide n Background and set the scene n Variations by deprivation category n Variations by ICD code n Impact of smoking n Future influences on rates n Project population incidence rates

6. Contents Slide n Background and set the scene n Variations by deprivation category n Variations by ICD code n Impact of smoking n Future influences on rates n Project population incidence rates

7. Background and set the scene n Re-cap of previous work n Data sources used nScottish nEnglish n Next step forward

8. Background and set the scene n Re-cap of previous work n Data sources used nScottish nEnglish n Next step forward

9. Re-cap of previous work n Population trends – Scotland and England nHeart attack nStroke nCABG nAngioplasty n Broad brush analysis of smoker prevalence

10. Population trends – Scotland and England n Heart attack nSignificant mortality and incidence improvements nScottish rates at a significantly higher level n Stroke nEnglish data unclear nScottish data nFlat trend during 1980’s nDeterioration during early 1990’s

11. Broad brush analysis of smoker prevalence n Smoking is a key risk factor n Reduction in smoking prevalence n Scottish and English smoker prevalence patterns n Scottish trends

12. Background and set the scene n Re-cap of previous work n Data sources used nScottish nEnglish n Next step forward

13. Data sources used n Scottish population – ISD data nGood quality nPatient based n English population - HES data nData quality is questionable nEpisode based

14. Background and set the scene n Next step nInsured trends nUnderstanding the main drivers to cause trends nSmoker differentiated rates nFuture influences nOverall trend pattern

15. Contents Slide n Background and set the scene n Variations by deprivation category n Variations by ICD code n Impact of smoking n Future influences on rates n Project population incidence rates

16. Variations by deprivation class n Why split by deprivation class? n Data sources used n Explanation of deprivation scores and categories n Overall trends by gender nHeart attack nStroke n Conclusion

17. Variations by deprivation class n Why split by deprivation class? n Data sources used n Explanation of deprivation scores and categories n Overall trends by gender nHeart attack nStroke n Conclusion

18. Why split by deprivation class? n To understand trend at insured level n Regional variations and Target market variations n Understand the main drivers of health inequalities n Black report 1980: “..the main influence on the inequalities in health which were observed lay in the material circumstances in which people live” n Deprivation and Health in Scotland, 1991 (Carstairs & Morris) n Classification by postcode; overcomes the weakness of Occupational classification.

19. Variations by deprivation class n Why split by deprivation class? n Data sources used n Explanation of deprivation scores and categories n Overall trends by gender nHeart attack nStroke n Conclusion

20. Data sources used n Incidence data nSMR1/01, Information Statistics Division NHS Scotland nGeneral Registers Office for Scotland n Mortality data nGeneral Registers Office for Scotland n Population data n1981 Population Census n1991 Population Census n Split by nCI condition nICD code nGender n 5 year age bands n deprivation category

21. Variations by deprivation class n Why split by deprivation class? n Data sources used n Explanation of deprivation scores and categories n Overall trends by gender nHeart attack nStroke n Conclusion

22. Explanation of deprivation scores and categories n Carstairs & Morris 1991 deprivation categories nFour indicators – to derive a composite score nOvercrowding nMale unemployment nLow social class nNo car n Deprivation score divided into 7 separate categories n 1 – the most affluent group n …… n 7 – the most deprived group

23. Explanation of deprivation scores and categories

25. Variations by deprivation class n Why split by deprivation class? n Data sources used n Explanation of deprivation scores and categories n Overall trends by gender nHeart attack nStroke n Conclusion

26. Trends in incidence of first heart attack for males in Scotland, as a % of 1981 Value, 1981 to 2000

29. Trends in incidence of first heart attack for males in Scotland, per 100000 of population, 1981 to 2000

30. Trends in incidence rate of first heart attack for males aged 40 to 64 in Scotland, per 100000 of Population, 1981 to 2000

31. Trends in incidence rate of first heart attack for males aged 40 to 64 in Scotland, as a % of 1986 Value, 1981 to 2000

32. Trends in incidence rate of first heart attack for females aged 40 to 64 in Scotland, per 100000 of Population, 1981 to 2000

33. Trends in incidence rate of first heart attack for females aged 40 to 64 in Scotland, as a % of 1986 Value, 1981 to 2000

34. Summary of heart attack trends by deprivation class

35. Summary of heart attack trends by deprivation class, Males aged 40 to 64, 1986 to 2000

37. Brief interpretation of heart attack trends MaleFemale MortalityPositive correlation between affluent and deprived groups IncidenceLess clear. Weak negative correlation where deprived group has higher improvement Postive correlation between affluent and deprived groups except for categories 6 and 7

38. Variations by deprivation class n Why split by deprivation class? n Data sources used n Explanation of deprivation scores and categories n Overall trends by gender nHeart attack nStroke n Conclusion

39. Trends in incidence rate of first stroke for males aged 40 to 64 in Scotland, as a % of 1981 value, 1981 to 2000

42. Trends in incidence rate of first stroke for males aged 40 to 64 in Scotland, per 100000 of Population, 1981 to 2000

43. Trends in incidence rate of first stroke for males aged 40 to 64 in Scotland, per 100000 of Population, 1986 to 2000

44. Trends in incidence rate of first stroke for females aged 40 to 64 in Scotland, per 100000 of Population, 1986 to 2000

45. Trends in mortality rate by stroke for males aged 40 to 64 in Scotland, per 100000 of Population, 1986 to 2000

46. Trends in mortality rate by stroke for females aged 40 to 64 in Scotland, per 100000 of Population, 1986 to 2000

47. Summary of stroke trends by deprivation class

48. Summary of stroke trends by deprivation class, Males aged 40 to 64, 1986 to 2000

49. Summary of stroke trends by deprivation class, Females aged 40 to 64, 1986 to 2000

50. Brief interpretation of stroke trends MaleFemale MortalityPositive correlation between affluent and deprived groups Weak negative correlation between affluent and deprived groups IncidenceWeak positive correlation between affluent and deprived groups Weak postive correlation between affluent and deprived groups

51. Contents Slide n Background and set the scene n Variations by deprivation category n Variations by ICD code n Impact of smoking n Future influences on rates n Project population incidence rates

52. Variation by ICD code n Why? nUnderstanding of overall rate nExplain which components have influenced overall trend n Heart attack nICD9 code 410 nUnstable angina ICD9 code 413 n Stroke nICD9 codes 430 to 437 excluding 435

53. Variation by ICD code n Why? nUnderstanding of overall rate nExplain which components have influenced overall trend n Heart attack nICD9 code 410 nUnstable angina ICD9 code 413 n Stroke nICD9 codes 430 to 437 excluding 435

54. Trend in first incidence rate for males aged 40 to 64 by ICD code 410 and 413, per 100000 of Population, 1981 to 2000

55. Trend in first incidence rate for females aged 40 to 64 by ICD code 410 and 413, per 100000 of Population, 1981 to 2000

56. Variation by ICD code n Why? nUnderstanding of overall rate nExplain which components have influenced overall trend n Heart attack nICD9 code 410 nUnstable angina ICD9 code 413 n Stroke nICD9 codes 430 to 437 excluding 435

57. Trend in first incidence rate for males aged 40 to 64 by ICD codes 430 to 437 excluding 435, per 100000 of Population, 1981 to 2000

58. Trend in first incidence rate for males aged 40 to 64 by ICD codes 430 to 437 excluding 435, 1981 to 2000

59. Trend in first incidence rate for females aged 40 to 64 by ICD code 430 to 437 excluding 435, per 100000 of Population, 1981 to 2000

61. Summary of variation by ICD code n Heart attack: nICD 410 is improving over time (Male 3.6% p.a., Female 3.3%) nICD 413 is deteriorating (Male 5.2% p.a., Female 6.2% p.a.) nTroponins? n Stroke nLarge component ICD 436 is improving over time (Male 5.2% p.a., Female 6.2%) nRemaining components ICD 430, 431,432,433, 434, 437 overall are deteriorating over time (Male 5.7% p.a., Female 4.3% p.a.) nOverall deterioration (Male 1.9%, Female 1.5%) nOverall flat trend over 1980’s, deterioration over 1990’s nFuture?

62. Contents Slide n Background and set the scene n Variations by deprivation category n Variations by ICD code n Impact of smoking n Future influences on rates n Project population incidence rates

63. Contents Slide n Introduction n Variations by deprivation category n Variations by ICD code n Impact of smoking n Future influences on rates n Projecting population incidence rates

64. Impact of Smoking - “Ideal” Smoking Model n ix (pop) = ix(ns)* p(ns) + ix (s) * p(s) +ix(ex) * p(ex) n ix (pop) = ix(ns)* p(ns) + ix (s) * p(s) +  t ix(ex t ) * p(ex t ) n …..

65. Impact of Smoking - Data Available n Epidemiological evidence on smoking nCase control studies, prospective cohort studies etc n Wide range of results nresults become even more volatile if looking for age specific or duration smoked/quit specific results noften look at impact on mortality not incidence ncause investigated is often not an exact match e.g. coronary heart disease and not acute myocardial infarction nusually smoking status is only investigated at the start of the study period n Simplify and/or use proxies

66. Impact of Smoking - Data Available n Epidemiological evidence on smoking nCase control studies, prospective cohort studies etc n Wide range of results nresults become even more volatile if looking for age specific or duration smoked/quit specific results noften look at impact on mortality not incidence ncause investigated is often not an exact match e.g. coronary heart disease and not acute myocardial infarction nusually smoking status is only investigated at the start of the study period n Simplify and/or use proxies

67. Impact of Smoking -Smoking Model n Age specific model nix (pop) = ix(ns)* propx (ns) + ix (ns) * RRx *propx (s) nassumes that people move immediately from being a smoker to a never smoker n Ex-smoker model nix(pop) = ix(ns) *propx(ns) + ix(ns) * RR1 *propx (ex1) +… n …….+ ix(ns) *RR(sm) propx(s) nassumes that the relative risks for smokers and ex-smokers are independent of age

68. Impact of Smoking - Males

70. Impact of Smoking - Females

73. Contents Slide n Introduction n Variations by deprivation category n Variations by ICD code n Impact of smoking n Future influences on rates n Projecting population incidence rates

74. Future Influences on Rates n Impact of changing risk factors on incidence n Focus on changes in diagnostic techniques and potential shocks in incidence n Discussion of impact of troponin on heart attack incidence n Interpreting trends in stroke incidence and the potential impact of brain imaging techniques

75. Troponin and Acute Coronary Syndrome n Number of medical papers available n Generally look at the change in the number of diagnoses in the spectrum of ACS

76. Spectrum of Acute Coronary Syndromes K Fox., Heart; 2000;84;93-100

77. Troponin and Acute Coronary Syndrome n As might be expected sample sizes quite small n Definitions do not exactly match those used by the insurance industry n Average age is considerably higher than that of people who claim for critical illness n Number of medical papers available n Generally look at the change in the number of diagnoses in the spectrum of ACS

78. Troponin and Acute Coronary Syndrome

79. Troponin and Critical Illness n Medical studies give a range of results n Results need careful interpretation before trying to apply them to critical illness n More AMI being diagnosed but some an acceleration e.g. nsubsequent heart attack ncoronary artery bypass surgery

80. Troponin and Critical Illness n Percentage of hospitals where troponin is available nScotland - 70% (Pell BMJ Vol 324) nEngland - 60% last year thought to be 70% to 80% this year n No clear consensus amongst cardiologists in the UK on the definition n New definition not disseminated until September 2000 so no effect on the data currently published

82. Stroke - Identifying Trends “Stroke mortality is falling in many countries, but it is unclear whether this is due to a fall in stroke incidence, lower case fatality, or some artifact of the collection and analysis of routine mortality data.” Stroke, A Practical Guide and Management. Warlow et al

83. Stroke - Identifying Trends “In the few places where it has been measured reasonably reliably, stroke incidence seems to have declined, stayed the same, or increased. However it has been very difficult to use consistent methods and obtain large enough data sizes for precise estimates. In truth it is not very clear what incidence rates are doing.” Stroke, A Practical Guide and Management. Warlow et al

84. Stroke - HES Data  Patients admitted to hospital generally have more severe strokes nThe proportion of all strokes admitted is unknown and can change over time nCannot identify first ever strokes nDouble counting as patients move from one hospital service to another nQuestion mark over change in incidence coinciding with coding change

86. Stroke - Diagnosis nABI definition of stroke is different to that used by clinicians and to that used by the WHO nConsequences of, for example, increased MRI scanning could have a different impact under different definitions nWill policyholders understand the differences? nMilder strokes more often identified? nPatient expectations are rising nDiagnostically more competent nHelped by more sensitive brain imaging

87. Contents Slide n Introduction n Variations by deprivation category n Variations by ICD code n Impact of smoking n Future influences on rates n Projecting population incidence rates

88. Population - Projection of Incidence Rates