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)

2

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

3

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

5

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

6

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

7

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

8

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

9

Quebec is aging…

10

Likely with more diseases …

11

but likely living longer…

12

and using more resources…

13

14

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

15

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

16

Relative disease prevalence

17

Relative use of resources

18

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%)

19

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

21

Effects on the Pension Plan:All Scenarios

22

Contribution Rates

23

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

24

Data sources

25

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

26

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)

27