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Valuing Healthcare - Introduction to Pricing

Valuing Healthcare - Introduction to Pricing. Ash Desai. 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

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Valuing Healthcare - Introduction to Pricing

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  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

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

  42. 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

  43. Prevalence of latent prostate cancer - % of population

  44. 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

  45. 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

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

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