COMPAS Health 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 people of working age per 65+ in 2031 Evolution of disease prevalence  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) 1

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 2

Data National Population Health Survey (NPHS) Longitudinal survey Biennial from 1994 to ,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 surveys 3

Dynamics of the model 4

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 5

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

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 7

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 8

Quebec is aging… 9

Likely with more diseases … 10

but likely living longer… 11

and using more resources… 12

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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

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 15

Relative disease prevalence 16

Relative use of resources 17

On average, each year Obesity is responsible for general practioner visits (5.5%) specialist visits (4.6%) hospital stays -number of nights (8.4%) Obesity increases by 504 the number of individuals in long term care facilities (0.6%) 18

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 Discounting rate = 3% 19

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 20

Effects on the Pension Plan: All Scenarios 21

Contribution Rates 22

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 23

Data sources 24

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 25

Partners Industrielle Alliance ( Régie des rentes du Québec ( Ministère des finances du Québec ( Centre interuniversitaire de recherche en analyse des organisations ( Fonds de recherche du Québec – Société et culture ( 26