Valuing Healthcare - Introduction to Pricing

1. Valuing Healthcare - Introduction to Pricing Ash Desai

2. Objectives of this session • Understand the pricing models used • Understand the data sources used in pricing • Examine the challenges involved in using these sources • Understand the key concepts involved in examining experience data • Understand impact of future trends on pricing

3. An example healthcare product • Critical Illness • Insurance payable on the diagnosis of a specified “critical” condition • eg: Cancer, Heart Attack • Lump Sum benefit / instalments • Two main types of cover • Stand Alone • Accelerated

4. Stand Alone Critical Illness • Benefit paid on critical illness only • no payment on death • Payment subject to a minimum survival period • e.g. 28 days or 14 days

5. Accelerated Critical Illness • Benefit paid on the first of: • a critical illness • death • Benefit could be partially accelerated

6. Objectives of this session • Understand the pricing models used • Understand the data sources used in pricing • Examine the challenges involved in using these sources • Understand the key concepts involved in examining experience data • Understand impact of future trends on pricing

7. Dead Pricing Models • Multi State Modelling Sick Healthy

8. Multi State Modelling - Theory • Ix = No. of incidences of CI for lives aged x • (dh)x = No. deaths among healthy population from a cause other than CI (or in survival period for Standalone) • (dc)x = No. deaths among those suffering CI due to CI • (do)x = No. deaths among those suffering CI from a cause other than CI • Total S/A claims = Ix * tpx (incidence adjusted for survival of survival period, t) • Total Acc claims = Ix + (dh)x - (1)

9. Multi State Modelling - Practical Approach • Theory looks simple - but no reliable data to calculate separate items, especially (dh)x • In practice we need to : • re-express the formula using kx • where kx is the proportion of deaths due to CI • assume mortality of CI sufferers from causes other than CI is the same as mortality of healthy lives • And we end up with : • ix = (1 - kx) * qx where • ix is CI incidence rate per mille • kx is proportion of deths due to CI • qx is mortality rate per mille

10. Multi State Modelling - Practical Approach • (dc)x = kx * dx • we know dx = (dh)x + (dc)x + (do)x - (2) • re-express (2) as • (dh)x + (do)x = (1 - kx) * dx- (3) • assume (do)x/(ls)x = (dh)x/(lx - (ls)x) • where (ls)x = no. lives aged x suffering a CI • where lx = no lives aged x • use (2) and (3) to eliminate (do)x to get • (dh)x * lx/(lx - (ls)x) = (1 - kx) * dx-(4) • divide (1) by healthy population at outset (lx - (ls)x) • Ix/(lx - (ls)x) + (dh)x/ (lx - (ls)x) - (5) • Replace (4) into (5) to get • ix = (1 - kx) * qx

11. Objectives of this session • Understand the pricing models used • Understand the data sources used in pricing • Examine the challenges involved in using these sources • Understand the key concepts involved in examining experience data • Understand impact of future trends on pricing

12. Pricing data requirements • ix = (1 - kx) * qx • Incidence Data • Proportion of deaths due to CI • Mortality following CI • probability of surviving a CI to help estimate reduction in incidence due to survival period requirement

13. Sources of pricing data • Population data - Incidence Data • Morbidity Statistics from General Practice • Hospital Episode Statistics • ONS Cancer registrations • US publications • Population data - Proportion of deaths due to CI • ONS Mortality by cause • CMI Statistics for Assured Lives • WHO • Population Data - Mortality following CI • ONS Cancer Survival Trends • Experience • Own or Intercompany • Reinsurer’s

14. Objectives of this session • Understand the pricing models used • Understand the data sources used in pricing • Examine the challenges involved in using these sources • Understand the key concepts involved in examining experience data • Understand impact of future trends on pricing

15. Population Data - Incidence Data • Data source • HES - admissions for treatment in NHS hospitals in England (by age & sex) • Challenges • relies on admission process being completed - a problem for immediate deaths • private treatment excluded • those not receiving any treatment also excluded • population data  insured life data • underwriting selection effects vs. ultimate experience • aggregate - e.g. smoker status, socio economic groups, occupation, geographical location etc • HES definition  product definition

16. Population Data - Incidence Data • Data source • Morbidity statistics from General Practice - data on why people consult GPs • Challenges • open to interpretation by doctor or practice nurse • Main Advantage : splits data by ‘type’ of consultation (ie. first, new or ongoing ) and therefore helpful for removing re-admissions from HES data • population data  insured life data • Data source • ONS Cancer registrations - records number of people who were diagnosed for the first time in any year • Challenge • only available for cancer

17. Population Data - Incidence Data • Data source • US publications or Irish data • Further challenges • variations in experience • differences in lifestyle, diet, education and environment • attitudes to healthcare • differences in medical opinion • Benefits • established product overseas • scarce domestic data

18. IC94 v CIBT93 • Both tables: • Male and Female • Aggregate • No adjustment for selection • But IC94…. • Adjusted for Insured Lives • Adjusted for Ireland • No allowance for TPD

19. Comparison of UK Data (CIBT93 ) with Irish Data (IC94)

20. Population Data - Incidence Data • Adjustments required to Incidence data • differences in definition of illness for insurance - eg. single vessel angioplasty and stroke • for immediate deaths • for multiple illnesses - eg.heart attacks and bypass surgery • Adjustments for Assured Lives • ratio between population and assured life mortality • varying by age, sex and disease (if data allows) • Selection effects • Apply non smoker/smoker discount/loading • Interpolation/Graduation

21. Experience Data • Data source • own experience • Challenges • credible data? • higher variability likely • sparse data sets • misleading interpretation • inadequate systems - inaccurate and incomplete data • Benefits • insured experience • most relevant

22. Experience Data • Data source • reinsurer’s or industry wide(e.g CIBT93) • Challenges • relevant? • different business mixes • differing underwriting and claims philosophies • differing target markets • Benefits • insured experience • credible data set • less variability year to year

23. Objectives of this session • Understand the pricing models used • Understand the data sources used in pricing • Examine the challenges involved in using these sources • Understand the key concepts involved in examining experience data • Understand impact of future trends on pricing

24. Experience Data - Key concepts • Example • CI Healthcare Study Group Base Table • Date requirements • exposure • claims • Key analyses • experience against standard tables • smoker/non smoker analysis • selection effects • variation by offices/distribution channel • cause of claims

25. Experience Data - Data requirements • Exposure • Data at each year-end, split by • Sex • Smoker status • Duration (0/1/2+) • Cover Type (Stand Alone /Accelerated) • 5 year age bands or individual ages • Policies and amounts

26. Experience Data - Data requirements • Details for each claim: SexSmoker statusCover type (Stand Alone/Accelerated)Date of birthPolicy commencement dateCritical Illness sum assuredClaim amount paidDate of diagnosisDate claim paidCause of claim

27. Experience Data - Key Analyses • Against Standard Tables (% of CIBT93) • Accelerated CI, Male, aggregate, policies

28. Experience Data - Key Analyses • Smoker / Non smoker differential

29. Experience Data - Key Analyses • Selection effects • Accelerated CI, Male, Non-smokers, policies, CIBT93

30. Experience Data - Key Analyses • Variation by offices/distribution channel Distribution Channel Actual/Expected % Bancassurer 37% DSF 51% IFA 34%

31. Experience Data - Key Analyses • Cause of Claim • Accelerated CI

32. Experience Data - Key Analyses • Cause of Claim • Accelerated CI, Males, Aggregate

33. Experience Data - Key Analyses • Cause of Claim • Accelerated CI, Females, Aggregate

34. Objectives of this session • Understand the pricing models used • Understand the data sources used in pricing • Examine the challenges involved in using these sources • Understand the key concepts involved in examining experience data • Understand impact of future trends on pricing

35. Sources of pricing data • Different sources = different challenges • However irrespective of data source, one common problem is that …. …..“historical experience is not always an accurate indicator of future experience”

36. Especially where the future is uncertain…. • Medical advances • reduces incidence by treating at an earlier stage (Cancer) • increases surgical procedures (Angioplasty) • Allow for future trends • Prostate Cancer • Risk Management - considerable issue when pricing a guaranteed product

37. Medical Advances

38. Cancer Registrations 1979-92 Source: ONS

39. TrendsHeart Attack per 100,000 population Trend - to 93/94 ( -1.2%pa) to 94/95 (- 3.7% pa) Source: HES

40. Trends

42. Pricing- are we sitting on a time bomb? • Potential impact of prostate cancer

43. Prostate Cancer • What is it? • cancer in the male prostate gland • What’s the prostate gland • it’s a cluster of glands surrounding the urethra near the bladder - exact function unclear

44. Prevalence of latent prostate cancer - % of population

45. Impact on Pricing Loading to male core 6 rate 180% 160% 140% 120% 100% % Loading 100% find rate 80% 50% find rate 25% find rate 60% 40% 20% 0% 35 40 45 50 55 60 65 70 75 80 85 90 Age

47. Objectives of this session • Understand the pricing models used • Understand the data sources used in pricing • Examine the challenges involved in using these sources • Understand the key concepts involved in examining experience data • Understand impact of future trends on pricing

48. Valuing Healthcare - an Introduction to Pricing Discussion and Questions