MHCC Modeling Strategy FINAL ENG 0

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Life at Risk® Mental Health Application: Data Review and Modeling Strategy

Final Report

June 2010

North York Corporate Centre

4576 Yonge Street, Suite 400 Toronto, ON M2N 6N4

Tel: (416) 782‐7475 Fax: (416) 309‐2336 www.riskanalytica.com

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TABLE OF CONTENTS

1 Introduction and Background ... 4

1.1 Purpose of data review ... 4

1.2 Development of strategy ... 4

1.3 Data Review ... 5

1.3.1 Brief summary of approach ... 5

2 Data Identified ... 6

2.1 Canadian Data Identified ... 6

2.1.1 Canadian Data: Strengths ... 12

2.1.2 Canadian Data: Limitations ... 12

2.2 International Data Identified ... 14

2.2.1 International Data: Strengths ... 16

2.2.2 International Data: Limitations ... 17

3 LIfe at Risk® Methodology ... 18

3.1 Overview of Methodology: Life at Risk® ... 18

3.1.1 The Life at Risk® Modeling Environment ... 19

3.1.2 The Structure of The Model ... 21

3.1.3 The Life at Risk® Mental Health Model, Data and ASsumptions ... 23

3.1.4 The Life at Risk® Mental Illness Model ... 24

3.1.5 The Life at Risk® Mental illness Model Simulation ... 35

3.1.6 The Life at Risk® Mental Illness Model, Economic Simulation ... 45

3.2 Key Assumptions ... 63

4 Conclusions ... 66

4.1 General Conclusions... 66

4.2 Future Direction ... 66 References 67

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Appendix 1: Summary of Data Sources Reviewed ... 72

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The Life at Risk® Mental Health Study aims to estimate the present and future population‐based life and economic impacts of mental health conditions in Canada and provide the MHCC with information that will enable a contrast of the size and shape of mental health impacts against other chronic conditions.

As part of its engagement with the Mental Health Commission of Canada (MHCC), RiskAnalytica has prepared a high‐level interim report to provide MHCC and subject matter experts with a summary of the review of Canadian and international data sources to enable the Life at Risk® evaluation. The data summary identified the types of data available in Canada that can be used as proxy to simulate the impact of mental illnesses in Canada over a 30 year timeframe (2011 to 2041). The purpose of this interim report is to provide subject‐matter experts with an understanding of the input data, model and assumptions that will be used to generate the results for this study.

1.1 PURPOSE OF DATA REVIEW

The purpose of the current review has been to investigate available mental illness data, in Canada and internationally, to inform a modelling strategy within RiskAnalytica’s Life at Risk^® framework. Historical population‐based health and economic data are required for the Life at Risk^® simulation analysis. This initial review of the data has determined which data exist, the number of years for which historical data are available and data collection methodology for mental illness specific data in Canada and globally.

The review process has further determined limitations and strengths of available data, providing ranges of prevalence proportions, incidence rates as well as remission¹ and relapse rates² (in the case of episodic conditions), of mental illnesses. Once established, these ranges inform a model that can be used to compute the possible future prevalence of health states (mental illnesses and chronic conditions), incidence as well as remission/relapse rates (for episodic conditions only).

Section 2 of this report summarizes the relevant Canadian and international data identified during the course of the data review. It is important to note that, of the data reviewed only data relevant to the current population‐based analysis are summarized within this section. This section does not include the entirety of the data that were reviewed throughout this investigation process. Data that were excluded as a result of their applicability to the current analysis, along with a complete of listing of all examined data are presented in Appendix A.

1.2 DEVELOPMENT OF STRATEGY

The current mental illness model was based on the Life at Risk® population based platform. It incorporates population states of six major mental illness categories and comorbid conditions based on chronic disease (type II diabetes and heart disease³) and substance abuse. In addition the model was

1 Remission is defined as the absence of symptoms following a diagnosis of mental illness.

2 Relapse is defined as the return of symptoms after a period of remission.

3 Note that the heart disease model will include one general category of heart disease (consistent with the category of heart

disease in CCHS cycle 1.1)

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able to accommodate the occurrence of multiple (co‐existing) mental illnesses. In this sense, the Canadian population was composed of combinations of mental illness states with comorbid conditions (please refer to Section 3 for further details). A combination of various survey data and administrative data were used to estimate the possible proportions of individuals within each population category. The validity of keeping the proportions constant in time is tested with respect to the basic epidemiological and mortality conditions (please refer to Section 3 for further details). The simulations which satisfy the above conditions will be further validated by experts.

1.3 DATA REVIEW

1.3.1 BRIEF SUMMARY OF APPROACH

An electronic search and review of the published scientific literature, government databases, reports and documents was conducted for available Canadian and international data on prevalence, incidence, mortality, health care utilization, health costs and economic disability, as per the parameters required for the analysis within the Life at Risk® model. The search was divided into sections based on diagnostic categories of mental illnesses, and subsets of these categories.

An initial search of broad category headings was conducted for mood disorders, anxiety disorders, childhood and adolescent disorders, psychotic disorders and dementia and for all possible studies published in English. The search was further split into Canadian sources and international sources.

PsychINFO and MedLine electronic databases were searched and supplemented with Socioabs and others and included articles up to January 2010. For each database, the search procedure included the name of the disorder and key search words (epidemiology, incidence, mortality, prevalence, longitudinal studies, cohorts, prospective studies, relative risk, odds ratios, and comorbidity etc.). Government websites were also searched to identify Government reports and a search was also conducted using meta‐search engines (e.g., Google).

Additionally, once key articles were identified, a manual search of the bibliography of each article was conducted, in order to ensure that the review was comprehensive. Suggestions on literature sources were requested from subject matter experts at various points throughout the data collection process.

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2 DATA IDENTIFIED

2.1 CANADIAN DATA IDENTIFIED

Exhibit 1 below provides a summary of the Canadian data used across each of the key input parameters for the Life at Risk® model. Only the data relevant to this population‐based analysis are summarized in Exhibit 1. For a summary of the full data review including data sources that will not be used as inputs to the model, please refer to Appendix A.

Exhibit 1 Summary of identified Canadian data for the base simulation model

DISORDERS VARIABLES STUDY /AUTHOR

& *SAMPLE SIZE

PERIOD of STUDY

CASE‐FINDING METHOD

MOOD DISORDERS:

*Major depressive disorder

*Dysthymia

*Bipolar disorder

PREVALENCE

Ontario Health Survey – Mental Health

Supplement (1990);

(n=9,953)

1990‐‘91 Lay interviewer‐administered structured interviews; UM‐

CIDI/DSM‐III or DSM‐III‐R diagnostic criteria INCIDENCE

Edmonton Survey of Psychiatric Disorders;

Newman S C & Bland R C⁴ (1998);

Prevalence sample (n=3,956)

Panel sample (1,964)

1983‐‘86 DIS/DSM‐ III; Lay interviewer‐

administered structured

interviews; prospective follow up of subjects for approx 2.8 years

MORTALITY

Canadian Institute for Health Information (CIHI) – National Trauma Registry Analytic Bulletin(2004)

2001‐‘02 Hospital Morbidity Database

*International data used for excess mortality of mental illness HEALTH CARE

UTILIZATION

& COSTS

Patterns of Regional Mental Illness Disorder Diagnoses and Service Use in Manitoba:

population based study⁵ (2004)

1997‐‘02 Administrative data Set

The direct cost of hospitalization in

2005‐‘06 Administrative Data Set

4 Newman and Bland (1998)

5 Manitoba Centre for health policy (2004)

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& *SAMPLE SIZE

PERIOD of STUDY

Manitoba; Finlayson G et al.⁶ (2009)

National Physician Data base, Canadian Institute for Health Information⁷

2006/’07 Administrative Data Set

Cost list for Manitoba Health Services; Jacobs P et al.⁸ (1999)

1999 Administrative Data Set

Alberta Standard Cost List for Health Economics and Evaluations; Jacobs P and Bachynsky J.⁹ (1997)

The Cost of Mental Health Services in Canada – A report to the MHCC; Jacobs P et al.¹⁰ (2010)

ECONOMIC DISABILITY¹¹

Dewa et al. (2007) 2002 Odds ratios associated with mental illness and presenteeism and absenteeism;

Odds ratios associated with comorbid chronic disease and mental illness for presenteeism and absenteeism

Participation and Activity Limitation Survey (PALS) ¹²; Statistics Canada (2007)

2006 Statistics Canada Participation and Activity Limitation Survey

ANXIETY DISORDERS:

*Generalized anxiety disorder

*Panic disorder

PREVALENCE

Ontario Health Survey –

Mental Health Supplement (1990);

(n=9,953)

1990‐‘91 Lay interviewer‐administered structured interviews; CIDI/DSM‐

III or DSM‐III‐R diagnostic criteria

INCIDENCE

*International data used

6 Manitoba Centre for Health Policy (2009)

7 Canadian Institute of Health Information (2006/’07)

8 Jacobs et al. (1999)

9 Jacobs and Bachynsky (1997)

10 Jacobs et al. (2010)

11 Note that these data provide proportions of people with activity limitations only. As part of this engagement a separate disability strategy outlining key disability data and assumptions will be developed with the MHCC and subject matter experts.

12 PALS ‐ Statistics Canada (2007)

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& *SAMPLE SIZE

PERIOD of STUDY

*Social phobia

*Simple phobia

*Agoraphobia

MORTALITY

UTILIZATION

& COSTS

Patterns of Regional Mental Illness Disorder Diagnoses and Service Use in Manitoba:

population based study¹³ (2004)

1997‐‘02 Administrative data sets

ECONOMIC DISABILITY¹⁴

Participation and

Activity Limitation Survey (PALS) ¹⁵; Statistics Canada (2007)

2006 Statistics Canada Participation and Activity Limitation Survey

DISORDERS of CHILDHOOD &

ADOLESCENCE:

*Childhood depression

*Childhood anxiety

*ADHD

*Conduct disorder

PREVALENCE

Alberta Administrative data; Spady et al.¹⁶ Updates courtesy Larry Svenson (2010)

2010 Administrative Data Set; All fee for service health care; ICD‐9 classification system

INCIDENCE

*International data used MORTALITY

*International data used for excess mortality of mental illness

14 Note that these data provide proportions of people with activity limitations only. As part of this engagement a separate disability strategy outlining key disability data and assumptions will be developed with the MHCC and subject matter experts.

16 Spady et al. (2001)

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& *SAMPLE SIZE

PERIOD of STUDY

HEALTH CARE UTILIZATION

& COSTS

Patterns of Regional

Mental Illness Disorder Diagnoses and Service Use in Manitoba:

population based study¹⁷ (2004)

DEMENTIA:

*Alzheimer’s disease

*Vascular Dementia

*All‐ Dementias

PREVALENCE

population based study¹⁸ (2004)

INCIDENCE

Canadian Study of

Health and Aging working group¹⁹ (1996)

1996 Community interviews of pop.

over 65yrs. Neuropsychological tests, clinical exam and caregiver interview

MORTALITY

UTILIZATION

& COSTS

Statistics Canada (2000);

Table 107‐5509

2000 Table 107‐5509 provides the number of beds, of which people with dementia occupy 64.3% in 2000

Tranmer J E, et al.²⁰ (2003);

1998‐‘00 Proportions of those with dementia in long‐term care and community care

Wodchis WP,et al.²¹

(2008);

2007‐08 Assumptions on Long Term Care admission due to dementia as a

19 Canadian Study of health and Aging working group¹⁹ (1991)

20 Tranmer, et.al. (2003)

21 Wodchis, et.al (2008)

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& *SAMPLE SIZE

PERIOD of STUDY

primary diagnosis based on ICES administrative data

PSYCHOTIC DISORDERS:

*SCHIZOPHRENIA

PREVALENCE

population based study²² (2004)

INCIDENCE

Prevalence and

Incidence studies in schizophrenia; Goldner EM et al²³

(2002)

1978‐‘95 Literature Review

MORTALITY

UTILIZATION

population based study²⁴ (2004)

1997‐‘02 Administrative Data sets

ECONOMIC DISABILITY

Participation and 2006 Statistics Canada Participation

23 Goldner et al (2002)

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& *SAMPLE SIZE

PERIOD of STUDY

Activity Limitation Survey (PALS) ²⁵; Statistics Canada (2007)

and Activity Limitation Survey

COMORBIDTY DATA Mental Illness in Manitoba (2004);

Canada

Netherlands Mental

Health Survey and Incidence Study (NEMESIS);

Bijl R V, et al.²⁶ (2002);

de Graaf R, et al.²⁷ (2002);

Baseline – (n= 7,076) Reinterview – (n=5,618)

1997‐‘98 CIDI/DSM ‐ III – R; In person structured interviews

SUBSTANCE ABUSE Ontario health Survey – Mental Health

Supplement (1990);

(n=9,953)

1990‐‘91 Lay interviewer‐administered structured interviews; CIDI/DSM‐

III or DSM‐III‐R diagnostic criteria

CHRONIC CONDITIONS²⁸

Diabetes (Type II)

• PHAC (based on National Diabetes Surveillance System data files)

2006‐07 Public Health Agency of Canada, using NDSS data files contributed by provinces and territories, as of April 2009

Heart Disease

• Chow et al.(2005)

2002 Prevalence of heart disease by age‐ and sex in Canada based on CCHS 1.2

• Frasure‐Smith et al.

(2006)

Meta‐

analysis

Elevated risk of CHD in people with major depression

• Patten et al.(2005)

2002 Elevated risk of depression given chronic condition.

• Curkendall SM et al.

(2004)²⁹

1994/’95 with follow up 1996/’00

Retrospective cohort study;

Saskatchewan admin health database.

26 Bijl et al. (2002)

27 de Graaf et al. (2002)

28 As part of the recent contract amendment, chronic conditions will be included and data review for prevalence, incidence and

mortality of diabetes (type II) and a general group of heart disease conditions will be conducted.

29 Curkendall et al. (2004)

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2.1.1 CANADIAN DATA: STRENGTHS

The current review of mental illness data in Canada has highlighted the following strengths in terms of quality and availability:

• Canadian data were the most appropriate representation of conditions within a Canadian society and healthcare system.

• The availability and access to these data was especially valuable in order to accurately characterize the mental illness/health landscape in Canada from a modeling prospective.

• Canadian data provided robust hospitalization numbers from the Canadian Institute of Health Information (CIHI) database.

• Valuable resource utilization data were available from the administrative datasets, such as those in Manitoba. Canadian administrative data were highly valuable in terms of resource utilization as they are the most accurate depiction of the uniqueness of the health care system and service use for those with mental illness within a particular province.

2.1.2 CANADIAN DATA: LIMITATIONS

The current review of mental illness data in Canada has highlighted the following weaknesses in terms of quality and availability:

• Condition congruency seemed to vary with Canadian survey data. There were inconsistencies with respect to the data availability across various mental health conditions. For e.g.: While the OHS‐MHS had excellent condition coverage for mood and anxiety disorders, it was not able to take schizophrenia into account. Similarly for the CCHS (1.2), disorders of childhood or dementia were not included. Neither was schizophrenia.

• National datasets were sparse, while provincial information was more readily available in terms of prevalence figures.

• Consistent longitudinal data were not available from surveys: The only longitudinal survey design was the National Population Health Survey (NPHS) and its condition coverage was limited to major depression.

• Incidence data were very sparse and only available for depression: as derived from the NPHS.

• Linking mortality to specific mental illnesses has been a challenge as cause of death related or attributable to a mental illness was not easily identifiable. Vital mortality statistics required for our model tended to underreport mortality attributable to mental illness and the reported cause of death was often skewed toward ‘other’ causes. Although suicide can be assumed to be attributable to mental illness, the suicide statistics were not linked to specific mental illnesses and did not provide a valid estimate for all deaths attributable to mental illnesses. Suicide alone would be an underestimate of the total mortality attributable to mental illness. Due to these limitations the model considered the excess mortality attributable to mental illness.

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• Data from consistent and comprehensive provincial administrative databases may not be representative of Canada. Consistent, comprehensive healthcare utilization data across all mental health care services were not found to exist at a national level across Canada.

• Despite being highly valuable, administrative data do not capture informal care utilization or alternative payment plan psychiatrists’ services.

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2.2 INTERNATIONAL DATA IDENTIFIED

The data summarized in Exhibit 2 below include only the data that will be used within the current analysis due to lack of complete data from Canadian sources. The gaps in the following tables indicate areas where Canadian data is being used. For a complete list of data that were reviewed throughout the research process, please refer to Appendix A.

Exhibit 2 Summary of identified international data for the base simulation model or as validation to Canadian data

DISORDERS VARIABLES STUDY /AUTHOR

& SAMPLE SIZE

PERIOD of STUDY

CASE‐FINDING METHOD

MOOD DISORDERS:

*Major depressive disorder

*Dysthymia

*Bipolar disorder

PREVALENCE

Canadian data will be used for this section.

Please refer to Appendix A for full list of available international data INCIDENCE

Please refer to Appendix A for full list of available international data MORTALITY

Excess mortality of mental disorder; Harris EC & Barraclough B. et al. ³⁰

1966‐‘95 Review of Literature

HEALTH CARE UTILIZATION

Please refer to Appendix A for full list of available international data ECONOMIC

DISABILITY

Please refer to Appendix A for full list of available international data

ANXIETY DISORDERS:

*Generalized anxiety disorder

*Panic disorder

*Social phobia

*Simple phobia

*Agoraphobia

PREVALENCE

Netherlands Mental Health Survey and Incidence Study (NEMESIS);

Bijl R V, et al.³¹ (2002);

Baseline – (n= 7,076) Reinterview – (n=5,618)

1997‐‘98 CIDI/DSM ‐ III – R; In person structured interviews

MORTALITY

Excess mortality of mental disorder; Harris

30 Harris et al. (1998)

31 Bijl et al. (2002)

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& SAMPLE SIZE

EC & Barraclough B. et al. ³²

Please refer to Appendix A for full list of available international data ECONOMIC

DISABILITY

DISORDERS of CHILDHOOD &

ADOLESCENCE:

*Childhood depression

*Childhood anxiety

*ADHD

*Conduct disorder

PREVALENCE

Great Smokey Mountain Study (GSMS); Costello J, et al (1993);

(n=1,420)

1993‐‘00 Child and Adolescent Psychiatric Assessment (CAPA), administered by trained lay interviewers; diagnoses based on DSM – IV

MORTALITY

Excess mortality of

mental disorder; Harris EC & Barraclough B. et al. ³³

Please refer to Appendix A for full list of available international data DEMENTIA:

*Alzheimer’s disease

*Vascular dementia

*All‐Cause

dementia

PREVALENCE

INCIDENCE Canadian data will be used for this section.

Please refer to Appendix A for full list of available international data MORTALITY

Excess mortality of

mental disorder; Harris EC & Barraclough B. et al. ³⁴

PSYCHOTIC DISORDERS:

PREVALENCE

Please refer to Appendix A for full list of reviewed international data INCIDENCE

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& SAMPLE SIZE

*SCHIZOPHRENIA

Please refer to Appendix A for full list of reviewed international data MORTALITY

Excess mortality of

mental disorder; Harris EC & Barraclough B. et al. ³⁵

Please refer to Appendix A for full list of reviewed international data ECONOMIC

DISABILITY

Kouzis et al.³⁶(1994)

Marwaha et al.³⁷(2007) Employment rates among those

with schizophrenia

2.2.1 INTERNATIONAL DATA: STRENGTHS

The current review of international mental illness data has highlighted the following strengths in terms of quality and availability:

• These data have shown more consistency in breakdowns of mental health conditions across various data sources. Most international surveys have consistently looked at the major disorder categories and subsets that are required within the scope of the current analysis.

• Estimates for ‘any mental health condition’ (total) and sub‐categories of conditions were available resolving the issues with comorbidity.

• Robust longitudinal data were available, especially with regards to conditions of childhood and adolescence.

• Incidence estimates were available.

• Data can be used to supplement Canadian data gaps and provide validation to the model.

36 Kouzis et al. (1994)

37 Marwaha et al. (2007)

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2.2.2 INTERNATIONAL DATA: LIMITATIONS

The current review of international mental illness data has highlighted the following limitations in terms of quality and availability:

• International data would not be the most appropriate representation of conditions within Canadian society.

• International data would not be applicable to the unique health care system in Canada, and would not take into account the differences in policies and procedures around health resource utilization.

• Data utility would be limited to survey data which comes with its own set of limitations such as recall bias, and subsequent underestimates of prevalence. Administrative data from other countries would not be applicable in a Canadian health system setting.

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3 LIFE AT RISK® METHODOLOGY

3.1 OVERVIEW OF METHODOLOGY: LIFE AT RISK®

The Life at Risk® Model

The Life at Risk® simulation platform represents a cell based approach to modeling the dynamics of disease within a selected population (for example the Canadian population). Individuals are divided into independent population groups called cells, based on their individual characteristics. By definition each cell contains a set of indistinguishable individuals who cannot be further subdivided under the criterion of the cell’s description. The description of each cell is characterized by a unique state vector that represents a way of describing the population cell while distinguishing it from all other population cells.

For example, a possible state vector associated with population cell ݅ can be written as:

Age=(20,21) Sex=Male

Location=Ontario Race=Caucasian Substance abuse = No ( ) Heart Disease = No

Mood Disorder=Yes Anxiety Disorder=No P i

⎛ ⎞

⎜ ⎟

= ⎜ ⎟

⎜ ⎟

⎝ ⎠

G

#

Individuals are allowed to move from one population cell to another by identifying all of the interactions or ways (represented by specific rates) at which this can happen. For example, healthy people can become sick or sick people can return to being healthy. The map which identifies all of the possible interactions is known as a topology³⁸ of the population. The process is a means by which individuals from one population cell can be moved into another population cell. Each process is identified with a specific coupling coefficient (or rate)³⁹, a mathematical way of identifying the rate at which individuals can flow from one population cell to another, say from cell A to cell B. The coupling coefficients serve as constraints on the number of individuals who are allowed to move from cell A to cell B within a specific period of time.

The number and description of population cells, each with its own unique state vector is identified with the requirements of a specific problem. The availability and quality of data will often impact the way in

38 Gemignani(1990)

39 Brauer and Castillo‐Chavez (2001)

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which the population can be split into specific population cells. Each population cell in the model is identified with a unique coupled ordinary differential equation (CODE)⁴⁰. This equation describes the rate of change of the individual’s state vector magnitude associated with that population cell. The equation states that the rate of change is proportional to the various ways in which individuals can move into and out of other cells (as defined by the topology and coupling coefficients). The equations are automatically determined within the Life at Risk® framework once the topology and coupling coefficients are identified and implemented into the program. The full set of these equations describes the complicated network of the constrained ways in which individuals are allowed to move from one population cell to another. Mathematically, this represents the evolution of the state vector magnitude of each population cell. The mathematical solution of the CODE set is then the evolution of the population as individuals move from one population cell into another. In the event that some or all of the coupling coefficients are stochastic, the CODE becomes a set of stochastic coupled ordinary differential equations (SCODE) and its solution will instead reflect the stochastic nature of its parameters.

3.1.1 THE LIFE AT RISK® MODELING ENVIRONMENT

The Life at Risk® simulation platform serves as a decision analytic policy model. In this capacity the model can be defined as an analytic tool that simulates the changes in population characteristics over time, using data imported from primary and/or secondary sources. The purpose of this tool is to estimate the effects of an intervention on health states and costs⁴¹. The Life at Risk® approach is designed to respond to many research questions that may be proposed by different perspectives of a health care system and the community it serves. For example, the Life at Risk® approach can be used to: (1) define the magnitude of disease in patient volume, potential years of life lost in disability, mortality and economic values; (2) justify intervention programs; (3) assist in the allocation of research dollars on specific diseases; (4) provide a basis for policy and planning relative to prevention and control initiatives; and (5) provide a life impact and economic framework for program evaluation⁴².

Within the Life at Risk^® platform, the possible future health states of a population along with the associated disability and economic burden are simulated. By incorporating the relationships between different populations, the natural history of the disease, socio‐economic risk factors, epidemiology and economic impacts, the simulation framework generates the possible future states for a series of important variables. These include the possibility of exposure to future risk factors as well as their impacts upon the prospective status of a health condition, the performance of screening examinations (if applicable), the effectiveness of treatment in various stages, the risks of complications, the competing mortality risks, and the direct and indirect loss of income from disability, death or treatment. The aim of the Life at Risk^® management framework is a reliable, robust, objective and independent means of

40 Brauer and Castillo‐Chavez (2001) 41 Cohen and Neumann (2008) 42 Rice (2000)

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evaluating the life and economic burden of disease and the cost‐effectiveness of health interventions proposed by the literature or by researchers.

The study design of the life and economic burden of a disease and the evaluation of different health care policies in terms of cost‐effectiveness and cost‐benefit analyses is structured based on the following steps:⁴³ ⁴⁴ ⁴⁵

A. Identification of the perspective: The Life at Risk® approach to simulating impacts of disease can take on different perspectives (e.g. society, federal and provincial government) that align the life and financial impacts of disability and death against who pays the costs and who benefits from the effects. The relevant direct and indirect financial impacts and disability effects are measured to the extent provided by the resource utilization (health costs) and data on incidence, prevalence and mortality.

B. Identification of the quantities of concern: The relevant quantities of interest for a burden of disease study and policy decision‐making metrics are identified. Such quantities take the form of life and economic attributes.

i. Life attributes: incidence, mortality and the associated disability take into account population demographics such as age, sex, geography and disease type.

ii. Economic attributes: direct health care cost components, direct non‐health care cost components and indirect cost components take into account population demographics and disease impacts such as age, sex, geography, disease type and economic disability attributed to the disease.

C. Identification of the history: Comprehending the future requires an analysis of the past that incorporates evidence‐based empirical models and empirical data such as peer reviewed journals and historical/surveillance data, respectively.

D. Simulation of the base case: As derived from A and C, the future life and economic burden of disease (expressed in terms of the quantities in B) is simulated without any proposed changes to the status quo. This is called a base simulation of population health and economic results and forms the foundation of what literature calls the burden of disease⁴⁶. These results represent the expected population health and economics without an intervention, with the results being derived in the perspective of health, disability, health costs and economic productivity. The base case results are derived from retrospective clinical and economic data such as historical data of a specified frequency.

43 Drummond et al. (1997)

44 Gold et al. (1996) 45 Rice (2000) 46 Rice (2000)

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E. Identification of the alternatives: Candidate prevention, screening or treatment policies for implementation are ‘alternate’ scenarios that are required to be compared with the base case results that represent a ‘usual care’ scenario⁴⁷ ⁴⁸.

F. Simulation of different intervention scenarios: As derived from C, D and E, the future life and economic burden of disease is simulated with the proposed changes to current policies on, for example, prevention strategies or treatment protocols during hospitalization. These ‘alternate’

scenarios represent the status of population health and economics under the added constraint of interventions proposed by subject matter experts.

G. Analysis of the value proposition of different intervention scenarios: The differences between the base analysis in D and the ‘alternate’ scenario simulations in F yield the gross value proposition of a health care intervention (as cost of the intervention may not be known). In the Life at Risk® framework, the test of effectiveness analyses is subject to specific statistical techniques. ⁴⁹ ⁵⁰ ⁵¹ ⁵² ⁵³ The costs and effect of the base case results and the ‘alternate’

scenario results are analyzed separately. Subsequently, the two sets of results are compared to determine a measure of the extent to which the interventions proposed by subject matter experts are expected to influence the future health of the population as well as the related economic performance. These results support policy makers in their evaluations of simulated health care interventions in terms of effectiveness analysis⁵⁴ ⁵⁵.

It should be noted that the base model presented in this report includes Steps A through D. The evaluation of intervention scenarios and their value proposition (Steps E through G) are not included as part of the scope of this project.

3.1.2 THE STRUCTURE OF THE MODEL

Conducting life/economic burden of disease simulations and the evaluation of different health care policies is a challenging task. It requires the mapping and modeling of many facets of the community that are directly related to the response to the disease and those facets that respond to the provision of health care. Given the complexity associated with the task, Life at Risk^® is structured as a set of modules which allow for proper identification of inputs and outputs that are relevant to the decision‐making

47 Drummond et al. (1997)

48 Gold et al. (1996)

49 Barber and Thompson (1998) 50 Thompson and Barber (2000) 51 Coyle et al. (1998)

52 Coyle (1996)

53 Desgagne et al. (1998) 54 Drummond et al. (1997) 55 Korthals‐de Bos et al. (2004)

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component of the economic evaluation⁵⁶ and the distinction between simulation cell types. The simulation module form of Life at Risk^® is:

• Population and demographic module: all model specifications and simulations of the population in terms of age, sex, race, other important demographic factors, and region.

• Risk factor and exposure module: all model specifications and simulations of the disease risk factor exposures, attributable risk (etiologic fractions, excess fractions, relative risk).

• Health state: all model specifications and simulations of screening routines, incidence (by severity/stage of the disease), mortality (by severity/stage of the disease and other causes), prevalence (by severity/stage of the disease and the disease survival time); treatment routines, after hospital care routines, disability (by severity/stage of the disease).

• Structural economic module: all model specifications and simulations of labour force (further by industry, employed part‐time, employed full‐time, unemployed), dependents and non‐

dependents, wages, production functions, income and consumption taxation rates (by government type), corporate revenues and profits, consumption from wages, consumption from production functions, interest rates, inflation rates (by CPI basket components), gross domestic product (key income and expenditure components), demand for health care services and products;

• Policy and decision analysis module: all model specifications and simulations of cost effectiveness, cost benefit, direct impact from disability (in terms of population non‐

participating in labour force, wages by industry, corporate profits by industry, costs and demand for health care services and products, GDP key income and expenditure components), indirect impact from disability (using same attributes as direct impact from disability).

56 Weinstein et al. (2003)

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Exhibit 3 Life at Risk^® Modules

3.1.3 THE LIFE AT RISK® MENTAL HEALTH MODEL, DATA AND ASSUMPTIONS

Population Demographics

All population demographics are obtained from Statistics Canada’s CANSIM database. These include:

• Provincial and Territorial populations in Canada by 1 year age intervals from 1971 to 2007 (CANSIM Table 051‐0001).

• Provincial and Territorial deaths in Canada by 1 year age intervals from 1971/72 to 2006/07 (CANSIM Table 051‐0002).

• International immigration in Canada by 1 year age intervals from 1971/72 to 2006/07 (CANSIM Table 051‐0011).

• International emigration in Canada by 1 year age intervals from 1971 to 2007 (CANSIM Table 051‐0012).

• Inter‐provincial migration in Canada by 1 year age intervals from 1971 to 2007 (CANSIM Table051‐0013).

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The future Canadian population will be simulated using the 2007 Canadian population (CANSIM Table 051‐0001) as the initial condition⁵⁷. CANSIM Tables 051‐0001, 051‐0002 as well as 051‐0011/12/13 are used to provide constraints on the possible future birth, death and migration rates. Mathematically, these rates are held as constraints for the rates at which individuals can flow into and out of specific population cells. The population in 2008 is the computed by:

1. Allowing individuals who were (a) years old in 2007 to become (a+1) years old in 2008.

2. Allowing individuals who were in Canada in 2007 to move outside of Canada in 2008 (emigration).

3. Allowing individuals who were not in Canada in 2007 to immigrate to Canada in 2008 (immigration).

4. The birth rate will determine the number of new individuals of age 0, based on the number of available females of child bearing age (ages 15‐50) in 2007.

5. Allowing individuals who were (a) years old in 2007 to die in 2008.

3.1.4 THE LIFE AT RISK® MENTAL ILLNESS MODEL High Level Overview of the Model

The health state of the population was divided based on the available data and the requirements of the project. It was divided into cells of individuals with the presence of mental illness, chronic disease, substance abuse or any combination of the three. Three basic types of population cells were identified based on individuals who:

1. Had never been diagnosed with a mental illness.

2. Had previously been diagnosed with mental illness and were currently a part of the prevalent population.

3. Had previously been diagnosed with mental illness but are currently not a part of the prevalent population (population is in remission). In the model remission was defined as the absence of symptoms following a diagnosis of mental illness.

57 Note that 2007 is the initial condition because this is the most recent year of data available from CANSIM.

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Exhibit 4 The general topology of the Canadian population

Exhibit 4 provides the general topology of the model in which individuals were categorized into one of three general groups of populations. The arrows indicate the direction in which individuals can move from one population cell to another. The model allowed individuals to move in between current mental illness (current disability) and previous mental illness category (remission) through the processes of remission and relapse. Here, relapse was defined as a return of symptoms following a period of remission. The model did not permit individuals to move back to “never diagnosed” category, once diagnosed.

In addition to the general topology, three types of comorbidities were considered within the model:

1. Individuals with mental illness and chronic disease (heart disease and/or type 2diabetes).

2. Individuals with mental illness and substance abuse.

3. Individuals with more than one mental illness.

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Exhibit 5 shows the basic sub‐grouping of mental illness populations according to the existence of comorbid conditions.

Exhibit 5 The general population cells associated with those who had been previously diagnosed (current prevalence or remission) can be further subdivided into populations based on the existence of comorbid conditions

Whether a current occurrence or remission state of mental illness was considered, the population was generally sub‐grouped into four categories. The topology above indicates that almost all processes (by means of which individuals can move from one population cell to another) were considered in the model. The model does not however allow remission states for chronic disease and therefore the arrows in Exhibit 5 are directed away from any population cells which are consistent with the “Mental illness but not chronic disease” grouping. The model allows for changes in substance abuse.

The model associates the existence of chronic disease with an increased risk of developing a mental illness (increased risk over that of an individual without a previous diagnosis of chronic disease). The reverse was also considered as those with a non‐remission prevalence of mental illness have an elevated risk of developing a chronic condition. The model does not make any assumptions about the association of substance abuse and the incidence of mental illness or the reverse relationship.

Mental illness but not chronic disease

Mental illness with chronic disease

Mental illness with multiple conditions

Mental illness with substance abuse MENTAL ILLNESS

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Population Cells

In an effort to construct the specific topology of this project, we associated three distinct types of population cells with the general topology. In addition, we identified two types with those populations that have never been diagnosed with mental illness, those that are healthy and those with chronic disease only. The four basic types of health states are:

1. Healthy (Type 1).

2. Those with mental illness but no chronic disease (Type 2). These include those individuals with currently prevalent states (Type 2L) as well as those who are currently in remission (Type 2R).

The Type 2L population are assumed to be composed of those with long‐term mental illness⁵⁸ as well as those with episodic illness⁵⁹. Illnesses that are considered episodic within the model are defined on page 26.

3. Those with chronic disease but no mental illness (Type 3).

4. Those with mental illness and chronic disease (Type 4). These include those with current mental illness (Type 4L) as well as those with mental illness in remission (Type 4R).

Disease types

The model considered six major categories of mental illness:

1. Mood disorders (includes: MDD, dysthymia and bipolar disorder)

2. Anxiety disorders (includes: generalized anxiety disorder, panic disorder, social phobia, simple phobia and agoraphobia)

3. Psychotic disorders (includes: schizophrenia)

4. Conditions of childhood and adolescence (includes: childhood depression, childhood anxiety disorder, conduct disorder and ADHD)

5. Dementia (includes: vascular dementia and Alzheimer’s disease)

6. Other mental illnesses (associated with mental illness categories other than mood, anxiety, schizophrenia, conditions of childhood/adolescence and dementia).

The model also considered the possibility of remission and subsequent relapse for several of the mental illness types. This allows for individuals with a previous diagnosis of mental illness to periodically reduce their disability to a level comparable to those of the population without mental illness as the illness progresses to its remission stage or non‐symptomatic stage. This is associated with the occurrence of episodic prevalence for the following categories of mental illness:

1. Mood disorders (includes: MDD, dysthymia and bipolar disorder)

2. Anxiety disorders (includes: Generalized anxiety disorder, panic disorder, social phobia, simple phobia and agoraphobia)

58 Long‐term mental illness refers to ‘no‐episodic’ occurrence of mental illness within the population. Individuals with this type

of mental illness are assumed to not be subject to remission of the illness.

59 Episodic illness refers to fluctuations between relapse and remission.

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3. Psychotic disorders (includes: schizophrenia)

4. Conditions of childhood and adolescence (includes: childhood depression, childhood anxiety disorder, conduct disorder and ADHD)

5. Other mental illnesses (associated with mental illness categories other than mood, anxiety, schizophrenia, conditions of childhood/adolescence and dementia).

The model also considered two comorbid chronic conditions:

1. Type 2 diabetes

2. Heart disease (One general category of heart disease with focus on ischemic heart disease as defined by ICD‐10 codes 120‐125; not including other subtypes/conditions that may be defined as heart disease) Substance abuse was considered as a general category of harmful drug and alcohol abuse.

The following long‐term population health cells (these include the co‐existence of multiple mental illnesses) were examined:

Type 1 (

P

_{(1, )}_r )

¾ r=1: Healthy with no mental illness or chronic disease (no substance abuse)

¾ r=2: Healthy with no mental illness or chronic disease (with substance abuse) Type 2L (

P

_{(2 , )}_{L r} )

¾ r=1: Mood disorders but no chronic disease (no substance abuse)

¾ r=2: Anxiety disorders but no chronic disease (no substance abuse)

¾ r=3: Schizophrenia but no chronic disease (no substance abuse)

¾ r=4: Conditions of childhood and adolescence but no chronic disease (no substance abuse)

¾ r=5: Any other mental illness but no chronic disease (no substance abuse)

¾ r=6: Dementia but no chronic disease (no substance abuse)

¾ r=7: Multiple mental illnesses (combinations of mood, anxiety and schizophrenia) but no chronic disease (no substance abuse)

¾ r=8: Other multiple mental illnesses (combinations of conditions other than mood, anxiety and schizophrenia) but no chronic condition (no substance abuse)

¾ r=9: Mood disorders but no chronic disease (with substance abuse)

¾ r=10: Anxiety disorder but no chronic disease (with substance abuse)

¾ r=11: Schizophrenia but no chronic disease (with substance abuse)

¾ r=12: Conditions of childhood and adolescence but no chronic disease (with substance abuse)

¾ r=13: Any other mental illness but no chronic disease (with substance abuse)

¾ r=14: Dementia but no chronic disease (with substance abuse)

¾ r=15: Multiple mental illnesses (combinations of mood, anxiety and schizophrenia) but no chronic disease (with substance abuse)

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¾ r=16: Other multiple mental illnesses (combinations of conditions other than mood, anxiety and schizophrenia) but no chronic disease (with substance abuse)

Type 3 (

P

_{(3, )}_r )

¾ r=1: Type 2 diabetes but no mental illness (no substance abuse)

¾ r=2: Heart disease but no mental illness (no substance abuse)

¾ r=3: Heart disease and type 2 diabetes but no mental illness (no substance abuse)

¾ r=4: Type 2 diabetes but no mental illness (with substance abuse)

¾ r=5: Heart disease but no mental illness (with substance abuse)

¾ r=6: Heart disease and type 2 diabetes but no mental illness (with substance abuse) Type 4L (

P

_{(4 , )}_{L r} )

¾ r=1: Mood disorders and type 2 diabetes (no substance abuse)

¾ r=2: Mood disorders and heart disease (no substance abuse)

¾ r=3: Mood disorders, heart disease and type 2 diabetes (no substance abuse)

¾ r=4: Anxiety disorders and type 2 diabetes (no substance abuse)

¾ r=5: Anxiety disorders and heart disease (no substance abuse)

¾ r=6: Anxiety disorders, type 2 diabetes and heart disease (no substance abuse)

¾ r=7: Schizophrenia and type 2 diabetes (no substance abuse)

¾ r=8: Schizophrenia and heart disease (no substance abuse)

¾ r=9: Schizophrenia, type 2 diabetes and heart disease (no substance abuse)

¾ r=10: Multiple mental illnesses (combination of mood, anxiety and schizophrenia) illnesses and type 2 diabetes (no substance abuse)

¾ r=11: Multiple mental illnesses (combination of mood, anxiety and schizophrenia) and heart disease (no substance abuse)

¾ r=12: Multiple mental illnesses (combination of mood, anxiety and schizophrenia) heart disease and type 2 diabetes (no substance abuse)

¾ r=13: Other multiple mental illnesses (combination of conditions other than mood, anxiety and schizophrenia) and type 2 diabetes (no substance abuse)

¾ r=14: Other multiple mental illnesses (combination of conditions other than mood, anxiety and schizophrenia) and heart disease (no substance abuse)

¾ r=15: Other multiple mental illnesses (combination of conditions other than mood, anxiety and schizophrenia) heart disease and type 2 diabetes (no substance abuse)

¾ r=16: Mood disorders and type 2 diabetes (with substance abuse)

¾ r=17: Mood disorders and heart disease (with substance abuse)

¾ r=18: Mood disorders, heart disease and type 2 diabetes (with substance abuse)

¾ r=19: Anxiety disorders and type 2 diabetes (with substance abuse)

¾ r=20: Anxiety disorders and heart disease (with substance abuse)

¾ r=21: Anxiety disorders heart disease and type 2 diabetes (with substance abuse)