Transcript Document
Actuarial Forecasting for Health
Joanne Alder,
Principal and Consulting Actuary
Milliman 12 th September 2007
Outline Agenda
Introduction The key elements of “actuarial” analysis Examples of projects Modelling Techniques Questions
What is actuarial analysis?
Key elements:
Financially focussed Long term outcomes – usually 5 years + Future is based on historical experience analysis, with adjustments for known differences and poor data Sensitivity testing on assumptions Can involve sophisticated statistics (but not totally necessary) Marries pragmatism and theory to create solutions which can be implemented
Actuarial Control Cycle
Experience Analysis Forecasting Feedback
What do health actuaries do?
Key tasks and how they work in the NHS:
– – Traditional “Pricing”- long and short term “Reserving” – IBNR – – – Non Traditional Analysis/comparison of contracts Benchmarking historic experience Modelling impact of changes in strategy
“Pricing”
– The process of: • • • • • Forecasting demand based on: Demographic mix Population size Changes in average morbidity Changes in medical practice patterns Strategic initiatives to manage demand – • • Forecasting average costs based on: Contract prices Strategic initiatives to move services – Adding contingencies dependent on risk variability – Calculating costs of services over appropriate time frames
“Reserving”
– The process of analysing outstanding liabilities (IBNR) • • • • Example: An operation is carried out in March.
PCT is financially liable, but has not accounted for this cost In a PCT, cost would be deferred until next year’s budget In the “real world”, you need to know that if you wind up a company at the end of March, the outstanding liabilities have been taken into account.
Why does this matter in the NHS?
– – – Distorts budgets and causes mismatch between money received and money paid out Leads to large unknowns if the PCT population is growing or changing fast - Analogous to PAYG pensions Without doing this analysis, you cannot get advance warning of possible trends or accurate picture of costs
Analysis of contracts
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Modelling the cost implications of different pricing structures/contracts:
• • •
For example: What is the overall budget implication for a PCT of new HRG tariffs?
Should be on historical demand and future demand What is the impact of shifting services from hospitals into community settings?
Benchmarking Historic Experience
Modelling the potential value from moving to best practice rather than average practice
– – –
Involves: Having appropriate consistent benchmarks
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Adjusting benchmarks for local demographics and population differences Adjusting appropriately for trends Calculating potential savings from avoided admissions/avoided beddays
Modelling Strategic Changes
Disease Management Programmes – RoI Utilisation Management Programmes – effects and RoI
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Preventative care RoI
RoI versus Cost per QALY – the language of actuaries versus the language of health economics – • • • • Commons themes are: Appropriate trend factors Adjusting for variances in data Demographic adjustments Financial projections over a long time period
Preventative Screening model
– Example Deterministic model • • • • • • Inputs: Demographics Prevalence statistics by age/sex Trend factors Historical costs for treating certain types of disease Data on reduction of incidence from interventions such as statins, blood pressure control, smoking etc Proportions of people screened – • • • Outputs Cost before and after intervention Cost of screening and therefore RoI
Preventative Screening model
– Very Simple Example Will vary input assumptions to get idea of range of outputs – To make more sophisticated and therefore useful: Could adjust for different types of population, by stratifying to see where intervention is most cost effective – Could also calculate overall effect on budget given population – Could make stochastic by assigning distributions to input variables
Modelling Techniques
Deterministic Modelling with scenario testing Stochastic Modelling Markov models Bootstrapping GLM modelling
Stochastic Modelling
Each of our assumptions has a statistical distribution Generate random numbers from the distribution (preferably not in Excel, as RAND function is not that random!) Run multiple times and log output from model for each run Plot different outcomes on a graph to see distribution of results Can identify most likely results and clusters of results Used more and more today as computers get more powerful
Stochastic Modelling – Pros and Cons
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Pros Gives most likely outcomes with variability quantified Clear where inputs give the “tail answers”
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Much more powerful and less subject to bias than deterministic modelling
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Doesn’t rely on human saying “this is “high input” – humans notoriously bad at estimating chance of bad event “black swan”
– – –
Cons Huge computing power Assumptions on distributions for input variables Specialist software to be comfortable with the results
Markov Modelling
– – – – – – –
Need a good grasp of dependent and independent probabilities!
Transitional probabilities for each state “Tree diagrams” can be very helpful Can make stochastic if assign distributions to input probabilities Markov chain models are memoryless – history doesn’t matter Cons
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Examples of Markov models principles: – these are based on health economic models, but same Can over-simplify, in order to handle complexity Getting reasonable independent probabilities estimates in the first place can be challenging Need to assume discrete time periods
Bootstrapping
Method of re-sampling data many times to create an estimate of mean and variance (and any other parameters) Show example with Cost Effectiveness Very simple method, but does require some computing power Again, should not really rely on Excel Rand functions – should use specialist software Probably not as accurate as some analytical methods
GLM Modelling
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Similar to linear regression, but Traditional linear models rely on assumptions of normal distributions for error terms and constant variances within the data. Also mean cannot be restricted to a known range Not often the case in the real world therefore we use a more sophisticated version know as Generalised Linear Models, which do not have these constraints Need to use more specialised software Purpose of using GLMs or linear regression is to isolate the effect of different factors and how they might contribute to morbidity or use of health services
GLM Modelling
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An Example
• • • •
A simple data analysis might tell you that women in an PCT use GP services at twice the frequency of men, BUT: Is this because they are older?
Or sicker?
Or some other factor specific to women?
Or just because they are women?
You could speculate and estimate the effect of each of these, but you would find it difficult to measure the proportion of extra visits due to each factor.
Use GLMs to fit a model of visits with explanatory variables of age, sex, morbidity indicators
GLM Modelling
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GLM output Will tell you the relative frequency of male versus female visits for EACH age and morbidity level (and any other fitted factors). Projection model can then take all of these into account separately Will be more sensitive model and more accurate But GLM modelling is quite complex, requires specialised software and lots of data!
Further Reading
Cost-Effectiveness in Health & Medicine, Gold et al (1996) Modelling in Health Care Finance, ILO (1999) Valuing Health Care – Cost, Benefits and Effectiveness of Pharmaceuticals and Other Medical Technologies, Sloan (1995) The Chronic Disease Burden: An Analysis of Health Risks and Health Care Usage (Actuarial Research Report)
, Alder et al, (2005)