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COMPASHealth micro-simulation model
Québec, Canada
Pierre-Carl Michaud, ESG UQAM
Québec context• Population is aging (second after Japan)• 5.33 people of working age per 65+ in 2011• 2.91 people of working age per 65+ in 2031
• Evolution of disease prevalence 2000-2005• heart disease, diabetes and high blood pressure• types of cancer and cardiac diseases
• Health care cost on rise, close to 50% of spending• Why create a model for Quebec only? • Financing of healthcare at provincial level (cost data)
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COMPAS• Microsimulation model using data from Statistics Canada• Heavily based on the U.S. Future Elderly Model• Projects health status of individuals between 2010 and
2050• Each year: calculates healthcare resources used • Doctor visits• Hospital stays (number of nights)• Home care• Prescriptions• Long-term care
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Data• National Population Health Survey (NPHS)• Longitudinal survey • Biennial from 1994 to 2011• 17,276 individuals in 1994• Covers Canadian population of all ages
• Canadian Community Health Survey (CCHS)• Cross sectional survey• 2010 (available multiple years)• 11,000 individuals in Quebec • Covers Quebec population of all ages
• Definition of health states are similar in both surveys4
Dynamics of the model
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Modules• Initialization module• Creates the initial population of the model• CCHS
• Representative of Quebec population• At age 30
• Individuals have different characteristics• Social and demographic characteristics• Diseases• Risk factors• Functional status
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Modules (2)• Transition module• Estimates probabilities of a change in individual health status
and behaviour (7 diseases, functional status, BMI and smoking)
• Transitions are estimated over a 2 year period• Example: calculates the probability a 48 year old man who
has diabetes and a BMI over 30 will develop a heart disease in 2 years
• NPHS 1994-2010
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Modules (3)• Renewal module• Entry of individuals aged 30 • Differ from past cohorts in some aspects• Immigration and emigration• From CCHS, multivariate model with correlation structure
• Health care module• Predicts quantity of resources used every year• Hospital Stays• Generalist and specialist visits• Drugs• Home care and nursing homes• As a function of disease, socio-demographic• NPHS
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Baseline scenario• Based on the demographic assumptions of the Régie des
rentes du Québec (RRQ)• Includes exogenous mortality improvement• Net Immigration• Size of new cohorts
• Trends in health status and demographics based on extrapolation of trends observed since 2001 for the composition of new cohorts
• Situation between 2010 and 2050 in the absence of changes in • Structure of transition probabilities
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Quebec is aging…
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Likely with more diseases …
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but likely living longer…
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and using more resources…
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Applications• Proportion of healthcare resources attributable to obesity
between 2010 et 2050
• Effects of trends in health status (mortality and diseases) on a DB pension plan’s solvency between 2010 and 2050
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Obesity• Compare resources used in 2 scenarios• Baseline scenario• Scenario without obesity
• Second scenario implies• No obesity in initial population• No obesity in entering cohorts• Transitions towards obesity are not allowed
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Relative disease prevalence
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Relative use of resources
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On average, each year• Obesity is responsible for• 514 195 general practioner visits (5.5%)• 173 398 specialist visits (4.6%)• 1 013 519 hospital stays -number of nights (8.4%)
• Obesity increases by 504 the number of individuals in long term care facilities (0.6%)
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Pension Plan• Dynamic of a pension plan is very complex • We seek to isolate longevity risk• Set
• Retirement at age 65
• Eliminate• Rate of return risk • Productivity risk• Wages are constant and normalized to 1• Wages are identical for all individuals
• Standard DB pension plan (factor * years worked * salary), prefunded in 2010.
• Discounting rate = 3% 20
Exploring alternative scenarios
• Disease prevention• Diabetes• High blood pressure• Heart disease• Lung disease
• Total prevention• All diseases, obesity and smoking
• Mortality improvement• 50 % reduction in mortality rates
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Effects on the Pension Plan:All Scenarios
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Contribution Rates
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Adding costs to COMPAS• Difficulties• Universal healthcare system• No private insurers (government is unique payer)• Cost data is spread throughout several databases• Access to these databases is complicated
• How we proceed• Find an average cost for a single use of each resource as a
function of disease, sex and age • Multiply the average cost by predicted use of each resource
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Data sources
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Collaborators• David Boisclair (ESG UQAM)• Aurélie Côté-Sergent (ESG UQAM)• Jean-Yves Duclos (U Laval)• Alexandre Lekina (ESG UQAM)• Steeve Marchand (U Laval)
• Visit us at www.cedia.ca
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Partners• Industrielle Alliance (http://www.inalco.com)• Régie des rentes du Québec(http://www.rrq.gouv.qc.ca)• Ministère des finances du Québec (http://www.finances.gouv.qc.ca)• Centre interuniversitaire de recherche en analyse des
organisations (http://www.cirano.qc.ca/)• Fonds de recherche du Québec – Société et culture(http://www.frqsc.gouv.qc.ca)
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