A COST OF ILLNESS STUDY OF GENERALIZED ANXIETY … · A cost of illness study of generalized anxiety disorder in Canada Basil Gregory Bereza Master of Science 2010 Department of Pharmaceutical

A COST OF ILLNESS STUDY OF

GENERALIZED ANXIETY DISORDER IN CANADA

AUTHOR

BASIL GREGORY BEREZA

A THESIS SUBMITTED IN CONFORMITY WITH THE REQUIREMENTS FOR THE DEGREE

OF MASTER OF SCIENCE. GRADUATE DEPARTMENT OF PHARMACEUTICAL SCIENCES,

UNIVERSITY OF TORONTO.

© 2010

ii

A cost of illness study of generalized anxiety disorder in Canada

Basil Gregory Bereza

Master of Science

2010

Department of Pharmaceutical Sciences

University of Toronto.

Abstract

Background: Economic evaluations of generalized anxiety disorder (GAD) have been

limited to ≤18 months. A decision model was developed; quantifying the lifetime cost-of-

illness (COI) of GAD.

Methods: An incidence-based Markov-model was developed using TreeAge® software,

reflecting 9 health-states (HS): physician-assessed patients (3HS), maintenance

therapies(4HS), discontinuation(1HS) and death(1HS). Onset probability (ages 18-80)

determined model entry. Canadian Psychiatric Association (CPA) guidelines determined

pharmaco-therapy, with revisions/validation by an expert panel. Response, remission

based on pooled-analysis of CPA-cited evidence. Remaining clinical rates, absenteeism

and hospitalization retrieved from literature. Direct (clinician, pharmacotherapy,

hospitalization) and indirect costs (wage rate) retrieved from government publications.

Results discounted at 5%.

Results: The mean COI was 2008 Canadian $31,213(SD=$9,100)/patient; 96%

attributed to absenteeism. Mean age=31years, discontinued treatment=85% by 2nd

year, treatment discontinuation duration, 14(SD=9) years.

CONCLUSION: GAD is a costly disease with a lifetime COI<$32k/patient; absenteeism

exerts a significant impact. Limited prospective data contributes to uncertainty of

estimate.

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Acknowledgements

This thesis would not have been possible without the generous and continuous support of the faculty members at the departments of Pharmaceutical Sciences and Health Policy, Management and Evaluation at the University of Toronto, as well as of fellow students, colleagues and family members. In particular, I am deeply indebted to the following individuals: Thomas Einarson PhD, for his technical guidance, history lessons, puns, observations and perspective on life, without whom the voyage to the defense would have been that much less interesting. Márcio Machado Dias Ferreira, PharmD PhD, for his academic guidance, discussions on spirituality and philosophy, the creation of new patient preference tools, all things Brazilian and providing traffic directions in Toronto. Most of all I am grateful for the laughter, and for his friendship. Emmanuel Papadimitropoulos PhD, for taking the time and undertaking the task, of providing my first lessons in pharmacoeconomics. I am deeply indebted for his constant selflessness on my behalf in relentlessly creating practical and academic opportunities. Beth Sproule PharmD and Arun Ravindran MD, I am indebted to Drs Sproule and Ravindran and for providing their support and clinical expertise. Adrian R. Levy PhD, for his timely and efficient, review and advice related to the thesis defense (as well as to the employees of Oxford Outcomes for their enthusiastic support). Audrey Laporte PhD, for formally introducing me to the topic of health economics. This thesis would not have been possible without Dr Laporte‘s continued support and conviction that I attain a graduate degree. Christopher Longo PhD, for his keen observations in academia and humanity and for ‘showing me the ropes’ when we were both in industry. I am honored to have worked with him. Roman Bereza MD MSc, and Eugene Bereza MD CM, CCFP, my brothers, who provided clinical and epidemiologic guidance early on in my pursuit of the Master‘s degree. Furthermore, each of them, in different ways, provided the life lessons I would eventually make use of, during this voyage. Elaine Kielo, patience personified, for her editing skills, unwavering moral support, and love, without whom, there would be no thesis. Rawley Kielo, who just listened (I think).

I am deeply honored to have met each of you. For all that you have given me, and all that will transpire from this gift, all I can say is a very humble, “thank you”.

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TABLE OF CONTENTS

Abstract ............................................................................................................... ii

List of Figures ................................................................................................... vii

List of Tables .................................................................................................... viii

List of abbreviations and acronyms ................................................................. ix

Section 1: Introduction ....................................................................................... 1

Overview ......................................................................................................................................... 1

Generalized anxiety disorder ....................................................................................................... 1 Epidemiology, Nature and Course ................................................................................................ 3 Management of GAD .................................................................................................................... 4 Relapse ......................................................................................................................................... 8

Economic Evaluations .................................................................................................................. 8

Decision Analytic Modeling .......................................................................................................... 9

Review of Literature .................................................................................................................... 11 Methods ...................................................................................................................................... 12

Acceptability criteria ............................................................................................................... 12 Presentation of literature review ............................................................................................ 13 Quality Assessment ............................................................................................................... 14

Results ........................................................................................................................................ 15 Literature search .................................................................................................................... 15 Description of Included Studies ............................................................................................. 15 Full Economic Evaluations .................................................................................................... 20 Quality Assessment of Full Economic Evaluations ............................................................... 22 Partial Economic Evaluations ................................................................................................ 24

Resource Utilization ........................................................................................................... 24 Impairment ......................................................................................................................... 32 Quality of life ...................................................................................................................... 33

Literature review discussion ....................................................................................................... 41 Limitations .............................................................................................................................. 45

Statement of problem .................................................................................................................. 46

Purpose of the study and objectives ......................................................................................... 46

Research question ...................................................................................................................... 46

Section 2: Methods ........................................................................................... 47

Overview ....................................................................................................................................... 47

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Pharmacoeconomic model ......................................................................................................... 48

Structure ....................................................................................................................................... 48 Analytic perspective .................................................................................................................... 48 Audience ..................................................................................................................................... 49 Target population ........................................................................................................................ 49 Type of model ............................................................................................................................. 49 Health states overview ............................................................................................................... 50 Time horizon ............................................................................................................................... 50 Attrition rate ................................................................................................................................ 51

Process ......................................................................................................................................... 53 Health state 1: Family physician assessment (initial health state) ............................................. 53 Health state 2: Specialist assessment 2nd line ........................................................................... 55 Health state 3: Specialist assessment 3RD LINE ......................................................................... 64 Health states 4 through 7: Maintenance therapy health states .................................................. 65 Health state 8: Treatment discontinued ...................................................................................... 65 Health state 9: Death (absorbing state) ...................................................................................... 66 Treatment algorithm ................................................................................................................... 66 Dosing ......................................................................................................................................... 69

Clinical Variables ......................................................................................................................... 69 Intolerance .................................................................................................................................. 72 Remission and response ............................................................................................................ 72 Treatment discontinuation .......................................................................................................... 74 Relapse ....................................................................................................................................... 75 Spontaneous remission .............................................................................................................. 76

Resource use and costs ............................................................................................................. 76 Physician costs ........................................................................................................................... 76 Drug costs ................................................................................................................................... 77 Dispensing fees .......................................................................................................................... 77 Hospitalization costs ................................................................................................................... 78 Treatment seeking behaviour ..................................................................................................... 78 Indirect costs ............................................................................................................................... 78 Discounting ................................................................................................................................. 80 Distributions ................................................................................................................................ 80

Outcomes ..................................................................................................................................... 81

Variability and uncertainty .......................................................................................................... 82

Committee members ................................................................................................................... 82

Section 3: Results ............................................................................................. 84

Cost of illness .............................................................................................................................. 84 Reference case ........................................................................................................................... 84 Breakdown cost analysis ............................................................................................................ 85 Epidemiologic parameters .......................................................................................................... 86 Debugging .................................................................................................................................. 86 Sensitivity analysis...................................................................................................................... 88 Sub analysis ............................................................................................................................... 91

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Meta-analysis ............................................................................................................................... 92 CPA literature included in meta-analysis .................................................................................... 92 Remission rates .......................................................................................................................... 93 Response rates ........................................................................................................................... 93 Mean changes in HAM-A scores ................................................................................................ 99 Assessment of heterogeneity ..................................................................................................... 99

Section 4: Discussion, Conclusions, Recommendations ........................... 101

Summary of findings ................................................................................................................. 101

Structure ..................................................................................................................................... 101 Analytic framework ................................................................................................................... 101 Patients seeking treatment ....................................................................................................... 102 Baseline HAM-A scores ............................................................................................................ 103 Treatment Algorithm ................................................................................................................. 104

Process ....................................................................................................................................... 104 Clinical variables ....................................................................................................................... 104 Resource utilization and costs .................................................................................................. 107

Limitations .................................................................................................................................. 109 Intolerance ................................................................................................................................ 109 Remission and response .......................................................................................................... 110 Treatment discontinuation ........................................................................................................ 111 Resource use and costs ........................................................................................................... 111 Meta-analysis ............................................................................................................................ 112

Conclusions ............................................................................................................................... 112 Estimated Burden of Illness ...................................................................................................... 113

Recommendations .................................................................................................................... 114

References ...................................................................................................... 115

List of publications and abstracts ................................................................. 124

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LIST OF FIGURES FIGURE I: LITERATURE SEARCH AND DISPOSITION OF IDENTIFIED ARTICLES ................ 17 FIGURE II: BUBBLE DIAGRAM SHOWING THE HEALTH STATES AND TRANSITION

PATHWAYS FOR THE MARKOV MODEL ........................................................................... 52 FIGURE III: DISTRIBUTION OF THE AGE (18-80) OF ONSET FOR GAD USED FOR THIS

STUDY ................................................................................................................................... 54 FIGURE IV: DECISION TREE WITHIN FAMILY PHYSICIAN ASSESSMENT HEALTH STATE 56 FIGURE V: DECISION TREE WITHIN SPECIALIST ASSESSMENT 2ND LINE HEALTH STATE57 FIGURE VI: DECISION TREE WITHIN SPECIALIST ASSESSMENT 3RD LINE HEALTH STATE

............................................................................................................................................... 58 FIGURE VII: DECISION TREE WITHIN MAINTENANCE 1ST LINE THERAPY HEALTH STATE 59 FIGURE VIII: DECISION TREE WITHIN MAINTENANCE BENZODIAZEPINE MONO-THERAPY

HEALTH STATE .................................................................................................................... 60 FIGURE IX: DECISION TREE WITHIN MAINTENANCE 1ST LINE &

BENZODIAZEPINETHERAPY HEALTH STATE .................................................................. 61 FIGURE X: DECISION TREE WITHIN MAINTENANCE 3RD LINE THERAPY HEALTH STATE 62 FIGURE XI: DECISION TREE WITHIN TREATMENT STOPPED HEALTH STATE ................... 63 FIGURE XII: DISTRIBUTION OF COI VALUES FROM MONTE-CARLO SIMULATION ............. 85 FIGURE XIII: HEALTH STATE CUMULATIVE TRANSITION PROBABILITIES GRAPH ............ 87 FIGURE XIV: SENSITIVITY ANALYSIS OF COST VARIABLES ................................................. 89 FIGURE XV: SENSITIVITY ANALYSIS OF CLINICAL VARIABLES ............................................ 90 FIGURE XVI: LITERATURE SEARCH RESULTS FOR A META-ANALYSIS OF CLINICAL

OUTCOMES FOR GAD. ....................................................................................................... 94

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LIST OF TABLES TABLE 1: PHARMACOTHERAPEUTIC TREATMENT GUIDELINES FOR GAD .......................... 7 TABLE 2: DESCRIPTION OF INCLUDED STUDIES IN FOR REVIEW ....................................... 18 TABLE 3: FULL ECONOMIC EVALUATIONS: PARAMETERS AND OUTCOMES ..................... 21 TABLE 4:FULL ECONOMIC EVALUATIONS: QUALITY ASSESSMENT .................................... 23 TABLE 5: RESOURCE UTILIZATION STUDIES: PARAMETERS, AND PRIMARY OUTCOMES

............................................................................................................................................... 27 TABLE 6: IMPAIRMENT STUDIES: PARAMETERS AND PRIMARY OUTCOMES .................... 34 TABLE 7:QUALITY-OF LIFE-STUDIES: PARAMETERS AND PRIMARY OUTCOMES ............. 38 TABLE 8: INITIAL, MAINTENANCE AND TITRATED DOSES BY TREATMENT LINE FOR THIS

STUDY ................................................................................................................................... 70 TABLE 9: CLINICAL VARIABLES AND THEIR VALUE INPUTS (I.E., RATES) USED TO

POPULATE THE MARKOV MODEL ..................................................................................... 71 TABLE 10: ECONOMIC INPUT VARIABLES AND ASSOCIATED COSTS*................................ 79 TABLE 11:REFERENCE CASE RESULTS* ................................................................................. 84 TABLE 12:EPIDEMIOLOGIC PARAMETERS OF STUDY COHORT .......................................... 88 TABLE 13: SUB-ANALYSIS OF COST OF ILLNESS WITH 10 SESSION OF CBT THERAPY .. 91 TABLE 14: STUDIES INCLUDED AS 1ST LINE EVIDENCE AND RELATED HAM-A SCORES 95 TABLE 15:STUDIES INCLUDED AS SECOND LINE EVIDENCE (BENZODIAZEPINE

MONOTHERAPY) AND RELATED HAM-A SCORES .......................................................... 96 TABLE 16:STUDIES INCLUDED AS THIRD LINE EVIDENCE AND RELATED HAM-A SCORES.

............................................................................................................................................... 97 TABLE 17: META-ANALYTIC RESPONSE AND REMISSION RATES BY TREATMENT LINE . 98 TABLE 18:SUMMARY HAM-A SCORES BY TREATMENT LINE, DRUG CLASS AND DRUG 100 TABLE 19:PAST MONTH’S WORK IMPAIRMENT ASSOCIATED WITH GAD AND MDD

REPORTED BY KESSLER ................................................................................................. 108 TABLE 20: DAILY COST OF AGENTS EXCLUDED FROM 2ND LINE THERAPY ................... 109

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List of abbreviations and acronyms AL A German-language health-related quality-of-life measurement APA American Psychiatric Association BGB Basil Gregory Bereza BDQ Brief Disability Questionnaire BPA British Association of Psychopharmacology CBA Cost-benefit analysis CBT Cognitive behaviour therapy CEA Cost-effectiveness analysis CGI-I Clinical global impressions-improvement CGI-S Clinical global impressions- severity CIDI Composite International Diagnostic Interview CIHI Canadian Institutes of Health Information Cl Confidence interval CMA Cost-minimization analysis COI Cost of illness CPA Canadian Psychiatric Association CUA Cost-utility analysis ECG Electrocardiogram ED Emergency department DALY Disability adjusted life year DSM Diagnostic and statistical manual of mental disorders GAD Generalized anxiety disorder

GHS German National Health Interview and Examination Survey, Mental Health Supplement

HAM-A Hamilton rating scale for anxiety ICD International classification of disease and related health problems ICER Incremental cost-effectiveness ratio LSAS Liebowitz Social Anxiety Scale LSRDS Liebowitz Self-Rated Disability Scale MD Major depression MDD Major depressive disorder MDE Major depressive episode MDUSS Midlife Development in the United States Survey MM Márcio Machado MoH Ministry of Health MOHLTC Ministry of Health and Long Term care NCS National Comorbidity Survey NHRDP National Health Research Development Program; Health NICE National Institute for Clinical Excellence NIDA National Institute of Drug Abuse NIMH National Institute of Mental Health ODB Ontario Drug Benefit OPOT Ontario Program for Optimal Therapeutics

http://en.wikipedia.org/wiki/American_Psychiatric_Association�

http://en.wikipedia.org/wiki/Diagnostic_and_Statistical_Manual_of_Mental_Disorders�

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OR Odds ratio PD Panic disorder Q-DIS-III-R Quick Diagnostic Interview Schedule-III-R Q-LES-Q Quality of Life, Enjoyment, and Satisfaction Questionnaire QOLI Quality of Life Index RCT Randomized controlled trials SD Standard deviation SDS Sheehan Disability Scale SF-12 Short-Form Health Survey – 12 item SF-36 Short-Form Health Survey – 36 item SFD Symptom free day SOC Societal SSRI Selective serotonin reuptake inhibitors SNRI Serotonin norepinephrine reuptake inhibitors TRE Thomas Ray Einarson VAHSRD Veterans Affairs Health Services Research and Development. WFSBP World Federation of Societies of Biological Psychiatry WHO World Health Organisation XL,XR Extended release

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SECTION 1: INTRODUCTION

OVERVIEW The resources needed to meet the demand for healthcare services, including those

required to manage mental health, are scarce and are becoming more so over time.

Decision makers are increasingly relying on economic evaluations to help make choices

under budget constraints.[1] Generalized anxiety disorder (GAD) is a chronic disease

with waxing and waning of symptoms; characterized by excessive, uncontrolled, and

often irrational and disproportionate concern about everyday issues.[2,3] Patients

diagnosed with GAD experience considerable impairment and disability, hampering

economic productivity and contributing to health care service utilization.[4-6] However,

despite the chronicity and the course of the disorder, economic evaluations related to

GAD have been limited to time horizons of ≤18 months; leaving the societal lifetime cost

of the disorder to be determined.[6] This thesis presents the mean lifetime cost-of-

illness (COI) per GAD patient (direct and indirect costs) in Canada, quantified through

the development of a dynamic decision model.

GENERALIZED ANXIETY DISORDER The symptoms experienced by patients with GAD are out of proportion to the impact of

the issue related to the anxiety or worry.[7] The disorder is also accompanied by

physiologic symptoms such as restlessness, muscle tension, lack of concentration and

disturbed sleep.[2,3]

Diagnoses of GAD are commonly established through either the International

Classification of Disease and Related Health Problems (ICD code F41.1) published by

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the World Health Organisation [WHO] or the Diagnostic and Statistical Manual of Mental

Disorders (DSM code 300.02) published by the American Psychiatric Association (APA).

The ICD classification is generally the prevalent choice in Europe, while the DSM criteria

are preferred in the United States and in research.[8] Several differences have been

noted between the diagnostic classifications. While the DSM-IV specifies that symptoms

be present for more days than not, over a period of at least 6 months; the ICD-10

classification states that the individual have “primary symptoms of anxiety most days for

at least several weeks at a time and usually for several months.” Furthermore, while, the

ICD-10 criteria require fewer physical symptoms than DSM-IV, some authors suggest

that the ICD-10 classification has more relaxed diagnostic criteria and is therefore more

realistic and meaningful.[9]

Features that differentiate GAD from non-pathological worry include: controllability,

severity and associated somatic symptoms. Individuals with GAD find it difficult to

control their worry, thereby contributing to the impairment of their daily functioning,

whereas people without GAD are able to control their worries with no significant

interference in daily life.[2] Furthermore, pathological worry is more pervasive,

pronounced, lasts longer and occurs without precipitants when compared to non-

pathological worry. Another distinguishing feature is the associated somatic symptoms

with GAD such as excessive fatigue, restlessness, feeling on edge and irritability.

However, this last feature is less likely to be true of children with GAD.[2]

The severity of the disorder (as well as measurement of a response to therapy) may be

assessed with patient- or physician-reported tools such as the Hamilton Rating Scale for

Anxiety (HAM-A), clinical global impressions- severity (CGI-S) or clinical global



http://en.wikipedia.org/wiki/American_Psychiatric_Association�

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impressions-improvement score (CGI-I), the Depression Anxiety Stress Scale, the Penn

State Worry Questionnaire or the Generalized Anxiety Disorder Questionnaire-IV.[10,11]

EPIDEMIOLOGY, NATURE AND COURSE The course of GAD is difficult to map due to the lack of prospective epidemiologic

studies;[12] however, retrospective studies indicate that GAD is a chronic disease with

fluctuating symptoms.[7,12] Onset of the disorder may occur as early as childhood or in

patients into their eighties.[13] The distribution for age of onset in patients with GAD is

bi-modal, with the probabilities decreasing from childhood through the thirties, increasing

into the fifties then decreasing thereafter.[13] To further describe the distribution of

onset in patients with GAD, 27 years separate the 25th and 75th percentile age of onset

for GAD, which is greater than in other anxiety disorders and comparable to distributions

observed with mood disorders.[14]

The estimated lifetime prevalence of GAD ranges between 2.4% and 5.7% in the

general population, while the estimated 12-month prevalence is between 1% and

3.8%.[14-16] The disorder is diagnosed more frequently in women than in men (55-60%

of those presenting with GAD are female).[2] In epidemiological studies, the gender

proportion is approximately two-thirds female.[2] The lifetime prevalence estimate is

6.6% in females and 3.6% in males while the 12 month prevalence rate is 4.3% in

females and 2% in males.[14] Furthermore, the lifetime and 12-month prevalence rates

for GAD are higher in older than in younger patients.[14] In the National Comorbidity

Survey (NCS), the lifetime prevalence in the 15-24 year age was lower than in the 45-55

year group (2% versus 6.9%).[19]

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GAD is frequently co-morbid with depression and other anxiety disorders.[12] The

German National Health Interview and Examination Survey, Mental Health Supplement

(GHS) found that approximately 40% of GAD patients had a co-morbid current major

depression, and 59-60% had 12 month or lifetime major depression.[17] Co-morbidities

in patients with lifetime GAD and other anxiety disorders include: dysthymia (39%), panic

disorder (23%), and social phobia (34%), alcohol (34%) and substance abuse (28%).[18]

Patients with GAD tend to present with somatic symptoms as their primary complaint. In

a study by Wittchen et al., 13% of GAD patients in primary care presented with anxiety,

while the remaining patients presented with pain, insomnia, cardiac and gastrointestinal

symptoms as their primary complaint.[19] GAD was the primary diagnosis in 50% of

patients seeking a cardiac evaluation and 13-25% of patients with irritable bowel

syndrome.[20]

MANAGEMENT OF GAD The Canadian Psychiatric Association (CPA) guidelines [21] for the management of

GAD include both psychological and pharmacologic treatment. The recommended first

line non-pharmacologic treatment for patients with GAD is cognitive behaviour therapy

(CBT). The recommended first line pharmacotherapeutic agents are either selective

serotonin reuptake inhibitors (SSRIs) or serotonin norepinephrine reuptake inhibitors

(SNRIs). The recommended SSRIs for 1st line treatment include paroxetine, sertraline,

or escitalopram. Venlafaxine is the sole SNRI recommended in the more recent 2006

CPA guidelines. Recommendations for second line agents include: benzodiazepines,

bupropion, buspirone, pregabalin, and imipramine. Adjunctive olanzapine or risperidone,

hydroxyzine, mirtazapine, trazodone and citalopram are recommended as third line

5

options or for treatment resistance individuals.[21] It is worthwhile to note that CPA

guidelines state that there is currently no evidence to support the routine combination of

CBT and pharmacotherapy; and therefore make no such recommendation.[21]

In clinical trials observing an underlying population with GAD, response is often defined

as a CGI-I of 1 or 2 or a 50% reduction in HAM-A scores.[21] Remission of symptoms

is usually defined as a HAM-A score of ≤7 (no or minimal anxiety).[21] GAD is a

treatable disorder; using paroxetine as an example, response rates range from 62-68%

and remission rates range from 30-34%.[21] 1

Treatment guidelines set forth by the CPA are similar to guidelines recommended by the

British Association of Psychopharmacology (BAP),[22] the Ontario Program for Optimal

Therapeutics (OPOT)[23], the National Institute for Clinical Excellence;[24] or the World

Federation of Societies of Biological Psychiatry (WFSBP). [25] 2 The CPA, OPOT and

NICE guidelines all recommend CBT as 1st line non-pharmacotherapeutic treatment.

The NICE guidelines also recommend self help bibliography treatment based on CBT

principles. All of the mentioned guidelines recommend the use of SSRIs as 1st line or

long term treatment; however, recommendations for the use of the SNRI, venlafaxine

vary. While the CPA guideline recommends SNRI’s as 1st line treatment, the NICE

guidelines state that venlafaxine should be administered by a specialist; however no

reason is provided in the NICE guideline. While the CPA includes benzodiazepines as

2nd line therapy, NICE guidelines refer to the use of benzodiazepines in primary care

and on a short term (2-4 week) basis. In contrast, BAP guidelines recommend the use

1 Meta analytic probabilities for response and remission in GAD patients; based on CPA evidence were derived for this study and are presented in section 3. 2 Refer to Table 2 on page 17

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of benzodiazepines as well as venlafaxine and imipramine as long term therapy or for

patients not responsive to SSRIs.

The 2006 CPA guidelines for the management of anxiety disorders were developed

using evidence-based criteria. Recommendations were based on the strength of

evidence attributed to each study and dependant on the quality of the study design.

‘Level 1’ evidence was assigned to either meta-analyses or replicated

randomized controlled trials (RCTs) that included a placebo condition. ‘Level 2’

evidence was assigned to RCTs with placebo or active comparison condition.

Uncontrolled trials with at least 10 subjects were attributed a ‘Level 3’ status while

anecdotal reports or expert opinion were assigned to ‘Level 4’ evidence.[21] According

to the CPA guidelines; recommendations for 1st line treatment were based on ‘Level 1’

or ‘Level 2’ strength of evidence in addition to clinical support for efficacy and safety, 2nd

line treatment recommendations were based on ‘Level 3’ evidence or higher plus clinical

support for efficacy and safety while 3rd line treatment was based on ‘Level 4’ evidence

or higher plus clinical support for efficacy and safety. ‘Level 1’ of ‘Level 2’ evidence for

lack of efficacy was used for agents that were not recommended by the CPA

guideline.[21]

Meta-analyses of outcomes related to the psychological management of GAD patients,

and cited by the CPA guidelines, reported that CBT is more effective that non-specific

psychological treatment methods for GAD and produced comparable magnitude of

benefits to antidepressant drugs.[26-30] Meta-analyses have also been performed

using selected studies on guideline recommended pharmacotherapeutic treatment.[31-

34] Two studies reported the pooled results of either venlafaxine or escitalopram

trials.[31,32] Another systematic review focused on benzodiazepines, and included a

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TABLE 1: PHARMACOTHERAPEUTIC TREATMENT GUIDELINES FOR GAD

CBT = Cognitive behaviour therapy; CPA = Canadian psychiatric association; BAP = British association of psychopharmacology; OPOT=Ontario program for optimal therapeutics; NICE = National institute for clinical excellence; WFSBP = World federation of societies of biological psychiatry; XL = Extended release; XR = Extended release

OPOT (2000)

CPA (2006)

WFSBP (2002)

NICE (2004)

BAP (2006)

1st Line non-pharmacotherapeutic

CBT CBT Not stated CBT Acute: Higher doses of

SSRIs or venlafaxine

Long term: Escitalopram or

paroxetine Non-response:

Switch to venlafaxine, imipramine or a benzodiazepine

CBT & Pharmaco-therapy combination

1st Line pharmacotherapeutic

Buspirone, fluvoxamine, paroxetine, venlafaxine XR

Paroxetine, escitalopram, sertraline, venlafaxine XR

SNRI’s or SSRI’s (venlafaxine &

paroxetine)

SSRIs (paroxetine)

2nd Line pharmacotherapeutic

Clomipramine, imipramine

Alprazolam, bromazepam, lorazepam, diazepam, buspirone, imipramine, pregablin, bupropion XL.

imipramine Switch to another SSRI

3rd Line pharmacotherapeutic or adjunctive therapy

Alprazolam, bromazepam, clonazepam, diazepam, oxazepam

Mirtazapine, citalopram, trazodone, hydroxyzine, adjunctive olanzapine, risperidone

benzodiazepines venlafaxine (inferred)

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single SSRI arm. [33] While the Kapczinski meta-analysis included a wider range of

pharmacologic treatments such as paroxetine, sertraline, venlafaxine and imipramine,

the study did not include escitalopram or other pharmacotherapies such as

benzodiazepines, pregabalin or buspirone.[34] Furthermore, no quantitative synthesis of

the comprehensive evidence used to develop the treatment guideline for GAD was

reported by the CPA. The allocation of specific agents to treatment lines was based on

the strength of evidence (as well as clinical support for efficacy and safety); however, the

size of clinical effect for each treatment line based on the evidence is unknown.

RELAPSE Research studies have reported that GAD patients were at high risk to experience

relapse events within the first 2 years of observation.[35,36] Relapse rates ranged from

20-40% within 6 months to 12 months after treatment discontinuation.[35,36,39] In an

8-year follow up study, cumulative relapse rates at year 1 and year 8 were 19% and 43%

for men; and 6% and 36% for women.[37] These studies suggest that patients with GAD

may require long term treatment to obtain full benefits. [38]

ECONOMIC EVALUATIONS Economic evaluations of health care services or products may be categorized as either

‘full’ or ‘partial’ depending on the scope of analysis.[40] A full economic evaluation is

the comparative analysis of both the costs and consequences of two or more

alternatives for a specific disease or condition. Examples of full economic evaluations

include: cost-benefit analysis (CBA), cost-effectiveness analysis (CEA), cost-

minimization analysis (CMA) and cost-utility analysis (CUA).[40] Parameters of an

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economic evaluation include: costs, consequences and the subsequent comparison of

those costs and consequences through an incremental cost-effectiveness ratio (ICER)

between targeted comparators. Studies limited to the economic component of a full

economic evaluation have been referred to as ‘partial’ economic evaluations.[40]

Examples of such studies include: cost-description studies (cost or burden of illness,

resource utilization), cost-outcome description studies (single service or program), and

cost-comparison.[40] All of these approaches have been used previously in psychiatry.

DECISION ANALYTIC MODELING Decision analytic modeling synthesizes evidence on health consequences and costs,

(usually of alternative and mutually exclusive healthcare strategies) and informs health

care budget decision makers about resource allocations.[41] However, the purpose of

such models is not to make unconditional claims, but rather to serve as an aid to

decision makers and reveal relationships between assumption and outcomes.[42]

Decision analysis is a mathematical model that incorporates a systematic and

quantitative approach to decision making under conditions of uncertainty. The

framework for decision analysis is based on research by von Neumann and

Morgenstern, known as the theory of expected utility.[42,43] The premise of this theory

is that rational decision makers would choose an option that maximizes their expected

utility.[43] However, given the abundance or complexity of information, the ability of

decision makers to process and arrive at a rational decision may be subject to bias.[44]

The structured approach afforded by decision analysis model enables the decision to be

based on a more extensive range of data and a formal synthesis of the information;[45]

thereby supplementing reasoning abilities and reducing bias.[46]

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Information contributing to decision analysis may be grouped into three categories:

choices or relevant options that are available to patients and the related outcomes,

chances or the probability that any of the outcomes will occur, and values or a

quantitative expression of the different outcomes.[47] In the context of health

economics, the quantitative values are referred to as utilities, which may be expressed in

monetary terms or other indices of value and may be subjective in nature.[42]

A decision analysis may be performed using either a conventional or dynamic (Markov)

modeling technique. In both conventional and dynamic models, a decision is depicted

as a square node, and chance is depicted as a circular node. The line connecting the

two nodes represents the passage of time. A terminal node represents the outcome at

the end of each pathway. Once a decision is taken, the outcome is a matter of chance.

A conventional decision tree describes the path of a cohort from one health state to

another over a fixed period of time. This approach works well for limited recursion

events or a limited time horizon; and is therefore usually employed for short-term health

consequences. However, under conditions where patients experience recurring events

over the span of a lifetime, additional branches within a conventional tree may become

unmanageable. For example, probability values may vary over time and may not be

valued realistically under a conventional model. Conventional decision models may not

easily capture the dynamic quality of long term clinical implications.[48]

The analyst begins the dynamic model by identifying mutually exclusive health states

that are likely to be experienced by the patient.[48] Once these health states are

determined, the analyst creates all possible pathways within each state that maps out

11

patient behaviour that will lead them to the next health state. The time spent by patients

in a health state before transitioning to the next health state is referred to as the ‘cycle

length’. The length of a cycle time horizon is chosen to reflect a time interval that is

clinically meaningful. For example consider the health states ‘well’, ‘sick’ and ‘dead’.

Patients may enter the model through the ‘well’ health state. From there, pathways

within the ‘well’ health state may transition the patients back into the ‘well’ state, move to

the ‘sick’ health state or to the ‘dead’ health state (sudden accident). The ‘dead’ health

state is referred to as an absorbing state; one where patients exit the model. While the

conventional model considers health state transitions over a fixed period of time, the

dynamic approach considers the transition of health states over a series of cycles.[48]

Although several best practice guidelines have been published, there is no consensus

on the quality assessment of a decision analytic model.[41] Overall, guidelines agree

that all assumptions and data used to develop the structure of the model, including time

horizon and cycle length should be clear, transparent and justified. Contradictions

between guidelines include the quality of the data that should be incorporated and how

uncertainty should be assessed.[41] Most guidelines recommend incorporating time

horizons that are long enough to reflect the essential features of the disease.

Furthermore, lack of available data should not be used as a rationale for failing to

incorporate a time horizon that reflects the course of the underlying condition.[41,42]

REVIEW OF LITERATURE A systematic literature review was performed to summarize the results, and assess the

quality of economic evaluations and humanistic studies related to GAD patients. The

objective of the review was to identify research requirements in this area.

12

METHODS

Acceptability criteria Peer-reviewed, published, original, full-text articles reporting full or partial economic

evaluations or humanistic outcomes related to patients diagnosed with GAD were

included for review. Types of full economic evaluations included in the assessment were

CBA, CEA, CMA, and CUA. Studies reporting partial economic evaluations, such as cost

comparison, cost description (cost or burden of illness, resource utilization), or cost-

outcome description (single service of program), as well as studies reporting on

humanistic outcomes such as preference, utility, and willingness to pay, were also

considered.

Studies that included a sample population meeting the criteria for GAD set forth by the

DSM published by the APA and the ICD published by the World Health WHO), were

considered.[2,3] Only studies reporting results attributable to a population diagnosed

with GAD were included. Studies reporting results in patients who had anxiety-related

symptoms but who were not diagnosed with GAD were excluded. Studies where patients

were diagnosed with GAD in addition to associated comorbid conditions were

considered. No restrictions were made on specific demographic attributes (ie, age,

gender, disease duration) of the underlying population diagnosed with GAD.

One reviewer (BGB) performed a search of the following databases: EMBASE (1980 to

2008 week 16), EBM Reviews (1995 to second quarter of 2008), MEDLINE (1950 to

April 2008 week 4), and HealthSTAR (1966 to April 2008). Another reviewer (MM)

verified the appropriateness of the search by reviewing the search terms and ensuring

that each database was searched separately and that the combination of search terms

13

was used properly. The following search terms, mapped to subject headings of each

database, were used: economic evaluation, cost, burden, generalized anxiety disorder,

resource utilization, budget, outcomes, preference, utility, willingness to pay,

effectiveness, and efficacy.

Two reviewers [BGB, MM] independently assessed titles or abstracts of potentially

relevant studies based on the inclusion criteria. Disagreements were adjudicated by a

third reviewer. Articles of the selected titles and abstracts were retrieved for full-text

review.

Retrieved articles were excluded for the following reasons: no economic evaluation

reported, inappropriate patient population, or results not attributable to GAD population.

Furthermore, publication types other than original studies (eg, comments, letters to

editors, abstracts, reviews) were excluded.

The reference lists of retrieved articles were searched manually for relevant studies that

may have not been captured by the established article search (ie, electronic database

search). Studies identified through the manual search were subject to the previously

described inclusion criteria.

Presentation of literature review Data were extracted by 1 reviewer [BGB] onto an electronic data sheet created a priori

and assessed for accuracy by a second reviewer [MM]. The systematic review was

presented through an overall description of articles followed by a summary of study

parameters, results, and quality assessments (where applicable). Study descriptors

14

included: first author, year of publication, economic/humanistic outcomes presented,

diagnostic criteria, country of sampled population, and source of funding. Articles were

categorized as either full or partial economic evaluations.

Following the overall description of the articles, study parameters and results by reported

outcome were also presented for full (ie, cost-effectiveness studies), and partial

economic evaluations (ie, cost or humanistic studies). For studies reporting full economic

evaluations, study parameters such as description of the incremental outcome,

interventions, study perspective, description of GAD population, time horizon, and type

of economic model were recorded, along with results reported and a quality assessment.

Results reported by partial economic evaluations were grouped under the following

categories: resource utilization, impairment, and quality of life. Resource utilization

outcomes were categorized under prevalence, frequency or cost of illness. Sub-

categories included: professional help-seeking, primary care, specialist, hospitalization,

diagnostic assessment, and utilization of medication. Impairment outcomes were

detailed as overall measures of impairment, family impairment, mental health, physical

health, social impairment, and work impairment. Quality-of-life results were not stratified

into smaller categories. Partial economic evaluation study parameters and outcomes

included overall outcome, results, first author and year, study design, description of GAD

population, comorbid condition, and mean age.

Quality Assessment The reporting quality of full economic evaluations were assessed using the checklist

created by the Panel on Cost-Effectiveness in Health and Medicine.[49] The checklist

contains 38 questions, which were answered with a dichotomous (yes–no) choice. The

15

questions were grouped as follows (number of questions): framework (8), data and

methods (14), results (9), and discussion (7). Two reviewers (MM and BB) independently

assessed the quality of the full economic evaluations. The third author (TRE) adjudicated

any disagreements. To our knowledge, no checklist has been created to assess the

reporting or analytic quality of partial economic evaluations; therefore, no such

assessment was performed for those studies

RESULTS

Literature search The literature search yielded 784 abstracts, of which 137 met the inclusion criteria and

were retrieved for full-text review. From the articles retrieved, 106 were excluded for the

following reasons: duplicates between databases (n = 32), inappropriate condition (n =

31), GAD-specific results not extractable (n = 29), and no related economic evaluation

reported (n = 14). The references from the remaining 31 articles were manually

searched for relevant articles not identified through the databases. Five additional

articles were identified and retrieved through the manual search, bringing the total

number of articles reviewed to 36. The search tree is presented in Figure I.

Description of Included Studies A description of included articles is presented in Table 2. Five articles reported full

economic evaluations [50-54] and 31 [13,55–84] presented partial economic evaluations.

Articles included in this review were published between 1990 and 2008. The countries in

which the economic evaluations were carried out included the United States (n = 16),

Canada (n = 6), Australia (n = 3), Germany (n = 3), the United Kingdom (n = 2),

Denmark, Finland, France, and Sweden (1 study each). Two studies were sponsored by

the WHO and included data from Brazil, Chile, China, France, Germany, Greece, India,

16

Italy, Japan, the Netherlands, Turkey, the United Kingdom, and the United States. One

study did not report the country in which the analysis was conducted.[66]

Seven of the 36 studies included did not specify sources of funding. Of the 29 studies

that reported funding sources, 57% were funded by public agencies, 29% by the

pharmaceutical industry, and 14% by nonprofit or private contract research

organizations. Thirty studies used DSM criteria, of which 16 used the DSM, Fourth

Edition (DSM-IV), 11 used the DSM, Third Edition, Revised (DSM-III-R), and 4 used the

DSM, Third Edition (DSM-III). Seven studies used ICD criteria, with 6 using the ICD-10

and 1 study using the ICD-9. None of the studies using ICD criteria specified whether the

clinical or research version was used.

17

Figure I: LITERATURE SEARCH AND DISPOSITION OF IDENTIFIED ARTICLES

EMBASE (110), EBM Reviews (236), EBM Health Technology Assessment (12)Ovid MEDLINE (R)(229), Ovid Healthstar (197)

Total number of titles or abstracts reviewed: 784

Inclusion criteria:

Abstracts selected for full text retrieval: 137

Full text retrieval from manual search: 5

Exclusion Criteria: Inappropriate condition (31), no economic evaluation (14), results not attributable to GAD

(29) duplicates (32): Total (106)

Final number of articles included in review: 36

Number of full economic articles included in review:

5

Number of partial economic and humanistic outcomes articles

included in review: 31

18

TABLE 2: DESCRIPTION OF INCLUDED STUDIES IN FOR REVIEW Evaluation Type Economic or Humanistic

Outcomes Diagnostic Criteria

Location of Study Funding

Full economic evaluation Guest et al50 (2005) Cost effectiveness DSM-IV United Kingdom Wyeth Pharmaceuticals

Heuzenroeder et al51 (2004) Cost effectiveness ICD-10 Australia Australian Department of Health and Ageing

Iskedjian et al52 (2008) Cost effectiveness DSM-IV Canada H Lundbeck A/S Issakidis et al53 (2004) Cost effectiveness ICD-10 Australia Not stated Jørgensen et al54 (2006) Cost effectiveness DSM-IV United Kingdom H Lundbeck A/S Partial economic evaluations Ansseau et al55 (2005) Impairment, resource utilization DSM-IV Belgium, Luxemburg Not stated Bélanger et al56 (2005) Resource utilization DSM-IV Canada Not stated Bland et al57 (1998) Resource utilization DSM-III Canada Alberta Mental Health

Research Fund Boerner et al58 (2003) Quality of life ICD-10 Germany Lichter Pharma AG Cramer et al59 (2005) Quality of life DSM-III-R Norway Not stated Dahl et al60 (2005) Quality of life DSM-IV Australia, Canada,

Denmark, Norway, Sweden Pfizer Inc

Essau et al61 (2000) Resource utilization DSM-IV Germany German Research Foundation

Fournier et al62 (1997) Resource utilization DSM-III Canada NHRDP Grant et al63 (2005) Impairment DSM-IV United States NIDA Henning et al64 (2007) Impairment, Quality of life DSM-IV United States Not stated Katon et al65 (1990) Resource utilization DSM-III-R United States NIMH Kennedy and Schwab66 (1997) Resource utilization DSM-III-R Not stated Alberta Mental Health

Research Fund, NHRDP Kessler et al67 (1999) Impairment DSM-III-R,

ICD-10 United States NIMH; MacArthur

Foundation; Wyeth-Ayerst Le Roux et al13 (2005) Impairment DSM-IV United States NIMH

19

Maier et al68 (2000) Impairment ICD-10 Brazil, Chile, China, France, Germany, Greece, India, Italy, Japan, Netherlands,Turkey, United Kingdom, United States

WHO

Marciniak et al69 (2005) Cost of illness ICD-9 United States Not stated Massion et al70 (1993) Quality of life DSM-III-R United States Upjohn Company McCracken et al71 (1990) Resource utilization DSM-III United States NIDA Mennin et al72 (2000) Impairment DSM-III-R United States NIMH Olfson and Gameroff73 (2007) Resource utilization DSM-IV United States Eli Lilly and Company Ormel et al74 (1994) Impairment DSM-III-R,

ICD-10 Brazil, Chile, China, France, Germany, Greece, India, Italy, Japan, Netherlands, Turkey, United Kingdom, United States

WHO, NIMH

Saarni et al75 (2007) Quality of life DSM-IV Finland Signe and Ane Gyllenberg Foundation

Schonfeld et al76 (1997) Impairment DSM-III-R United States Upjohn Company

Souêtre et al77 (1994) Resource utilization DSM-III-R France Not stated

Stanley et al78 (2003) Quality of life DSM-IV United States NIMH Steffans et al79 (2005) Resource utilization DSM-IV United States NIMH Stein and Heimberg80 (2004) Quality of life DSM-III-R Canada Province of Ontario, NIMH Sullivan et al81 (2006) Resource utilization DSM-III United States VAHSRD, Greenwall &

RW Johnson Foundation Wetherall et al82 (2003) Quality of life DSM-IV United States NIMH Wittchen et al83 (2000) Impairment, quality of life,

resource utilization DSM-IV Germany Wyeth Pharmaceuticals

Wittchen et al84 (1994) Impairment, resource utilization DSM-III-R United States US Alcohol, Drug Abuse, and Mental Health Administration, Grant Foundation

DSM = Diagnostic and Statistical Manual of Mental Disorders; ICD = International Classification of Disease and Related Health; NHRDP = National Health Research Development Program; NIDA = National Institute of Drug Abuse; NIMH = National Institute of Mental Health; WHO = World Health Organization; Q-DIS-III-R = Quick Diagnostic Interview Schedule-III-R, VAHSRD = Veterans Affairs Health Services Research and Development.


20

Full Economic Evaluations A summary of study parameters and outcomes related to the full economic evaluations

included in this review is presented in Table 3. The following interventions were used to

treat GAD in these studies: cognitive-behavioral therapy,[51] diazepam,[50]

escitalopram,[52] paroxetine,[54] and venlafaxine.[50] Furthermore current care (care

provide to those already in the health care system) was compared to evidence base

optimal care for GAD patients in Australia.[53] Psychiatric co-morbidities were not

consistent between studies. Full economic evaluations considered patients with either

‘pure’ GAD (i.e. no other psychiatric diagnosis) or patients with GAD comorbid with other

anxiety or mood disorders.

All 5 studies reported an ICER as an outcome.[50-54] No studies reported CBA, CMA or

CUA analyses. Four studies reported costs from the public-payer perspective; [50–53] 1

study also reported patient out-of-pocket expense,[51] and 2 from a societal

perspective.[52,54] All studies included direct costs such as medication and

professional health care costs. Absenteeism [54] and symptom free days,[52] were used

as a proxy for indirect costs in studies reporting outcomes from a societal perspective. A

population summary design was used in 2 studies,[51,53] while a conventional decision-

tree model was used in the 3 remaining studies.[50,52,54] Time horizons of the full

economic evaluations varied between 6 and 12 months.

One cost effectiveness study concluded that compared to current practice (i.e.

interventions that included either private psychologists, private or public psychiatrists,

21

TABLE 3: FULL ECONOMIC EVALUATIONS: PARAMETERS AND OUTCOMES Study Incremental

Outcome Interventions and Comparator

Perspective Primary Outcome

GAD Sample Population

Time Horizon, Months

Economic Model

Guest et al50 (2005)

Cost per successfully treated patient, cost per averted relapse

Extended-release venlafaxine; diazepam

Public payer £380 per successful treatment; £295 per averted relapse with venlafaxine

Nondepressed patients with GAD

6 Conventional decision tree

Heuzenroeder et al51 (2004)

Cost per DALY

Evidence based, current practice

Public payer and out-of-pocket expenses

Aus $6900 per DALY saved treated when treated by public salaried psychologist

Panic disorder 12 Population summary

Iskedjian et al52 (2008)

Cost per SFD Escitalopram, paroxetine

Public Payer, Societal

Can $2362 per symptom free year (MoH); dominated (SOC)

GAD only 6 Conventional decision tree

Issakidis et al53 (2004)

Cost per YLD Evidence based practice, current care

Public payer Aus $2713 saved per YLD averted with evidence-based treatment

Panic disorder, social phobia, avoidant personality

12 Population summary

Jørgensen et al54 (2006)

Cost per successfully treated patient

Escitalopram, paroxetine

Societal £1408 cost savings per successful treatment with escitalopram

Not specified 9 Conventional decision tree

DALY = disability-adjusted life-year; MoH = Ministry of Health perspective; SFD = symptom-free day; SOC = societal perspective

22

serotonin or noradrenaline reuptake inhibitors), CBT administered to GAD patients by a

public-salaried psychologist generated cost savings of year-2000 US $6900 per

disability-adjusted life-year (DALY).[51] Another study reported that evidence-based

care in treating GAD resulted in greater population health gains at a cost that was similar

to that of current care, generating an estimated cost savings of year-1997–1998 US

$2713 per year lived with disability (YLD) averted.[53]

The remaining studies examined the cost-effectiveness of drug interventions in the

treatment of GAD. Guest et al[50] reported that extended-release venlafaxine was cost-

effective compared with diazepam, based on an analysis that used Clinical Global

Impression improvement (CGI-I) scores as a measure of effectiveness. Two ICERs were

reported in the study; the estimated additional cost for each additional patient

successfully treated with venlafaxine was £380 (year 2000-2001), and the additional

cost for an averted relapse in patients treated with venlafaxine was £295.50 Two studies

reported the cost-effectiveness of escitalopram compared to paroxetine, primarily as a

result of greater productivity gains.[52,54] Escitalopram treatment resulted in total cost

savings of £1408 (2004) per successfully treated patient compared with paroxetine in a

UK study; the savings were largely attributed to the reduction in the number of missed

days of work.[54] In a Canadian study, escitalopram was cost-effective compared with

paroxetine as a result of a greater number of expected symptom-free days for patients

treated with escitalopram.[52]

Quality Assessment of Full Economic Evaluations The number of articles that met the criterion for each checklist item is presented in Table

4. The framework of the 5 studies was well presented, addressing most of the items on

23

TABLE 4:FULL ECONOMIC EVALUATIONS: QUALITY ASSESSMENT Category Item No. of Articles

That Met the Criterion

Framework Background of problem 5 General framing and design of the analysis 5 Target population for intervention 4 Other program descriptors (eg, care setting, mode of delivery) 1 Description of comparator programs 5 Boundaries of the analysis 5 Time horizon 5 Statement of the perspective of the analysis 5 Data & Methods Description of event pathway 3 Identification of outcomes of interest in analysis 5 Description of model used 3 Modeling assumptions 3 Diagram of event pathway (model) 3 Software used 5 Complete description of study parameters 4 Methods for obtaining estimates of effectiveness, costs, and preferences 5 Critique of data quality 1 Statement of year of costs 5 Statement of method used to adjust costs for inflation 0 Statement of type of currency 4 Source and methods for obtaining expert judgment 0 Statement of discount rates 2 Results Results of model validation 0 Reference case results (discounted at 3% and undiscounted): Total costs

and effectiveness, incremental cost and effectiveness, and incremental cost-effectiveness ratios

5

Results of sensitivity analysis 5 Other estimates of uncertainty 0 Graphical representation of cost-effectiveness results 3 Aggregate cost and effectiveness information 4 Disaggregated results as relevant 1 Secondary analysis using 5% discount 0 Other secondary results as relevant 0 Discussion Summary of reference case results 3 Summary of sensitivity of results to assumptions and uncertainties 3 Discussion of analysis assumptions having ethical implications 3 Limitations of study 5 Relevance of study results for specific policy questions or decisions 3 Results of related cost effectiveness analyses 3 Distributive implications of an intervention 3

24

the checklist within this category. Data and methods were also well presented in most of

the full economic evaluations. All articles identified: the outcomes of interest,

the software used and a stated the year in which costs were valued. Three articles

provided a pathway diagram. Overall the articles failed to critique the quality of the data

used in the analysis. Furthermore two articles provided statements of discount rates and

none provided a method for adjusting costs for inflation. (This omission was of little

consequence since none of the studies covered a time horizon >1 year.) None of the

articles presented any validation of the models used. All articles presented their

reference and sensitivity results. Disaggregated results, secondary results or other

estimates of uncertainty were generally not provided. Each of the 5 studies addressed

limitations of their research. Three of 5 articles included a discussion of related cost

effectiveness analyses, and relevance of the study results for specific policy questions or

decisions.

A 100% consensus was reached between the reviewers. No adjudication was required.

The checklist does not provide for an overall quality rating. However, the percentage of

‘yes’ answers attributed to each article was calculated and ranged from 55.3% to 68.4%

of the checklist items.

Partial Economic Evaluations

Resource Utilization

Of the articles reviewed, 14 reported resource utilization outcomes.[55–57,61,62,66,69–

71,73,77,79,81,84] Study parameters and results for studies reporting resource

utilization outcomes are presented in Table 5.

25

Three studies reported the prevalence of treatment-seeking behavior among GAD

patients.[57,61,84] In a Canadian study of 3,956 adult residents, 26% of those meeting

diagnostic criteria for GAD alone sought treatment for the disorder, while 43% of those

meeting the diagnostic criteria for GAD and a comorbid psychiatric condition sought

treatment.[57[ Bland et al.[57] reported an odds ratio of 2:1 in seeking professional help

for GAD patients with a co-morbidity compared to GAD patients with no psychiatric co-

morbidity; however, these results were not statistically significant. Prevalence rates

were higher in a larger US study that assessed the general population ≥15 years of age,

reporting that 48% of responders meeting the diagnostic criteria for GAD alone and 68%

of those meeting the criteria for GAD and a comorbid condition sought professional

help.[84] In a study that focused on anxiety disorders among those aged 14 to 17 years,

of the 4 adolescents with GAD, one sought treatment.[61]

Resource utilization studies show that patients with GAD were high utilizers of primary

care services.[56,62,66,77] Patients with GAD were approximately 4 times more likely

to visit a physician ≥5 times per year, compared with patients who had not been

diagnosed with GAD.[56] The probability that patients would visit a primary care

physician several times a year was 2 to 4 times higher among patients with GAD than

those without GAD.[56] Furthermore, the prevalence of GAD among patients in a

primary care practice was higher than that of any other psychiatric disorder.[62] Patients

with comorbid condition with GAD showed statistically significant higher primary care

utilization than patients with a diagnosis of GAD alone.[84] However, it is important to

26

note that patients may not necessarily present with anxiety, but rather with underlying

somatic conditions.[8,91]

27

TABLE 5: RESOURCE UTILIZATION STUDIES: PARAMETERS, AND PRIMARY OUTCOMES Outcome Author (Year)

Results Study Design Sample Population

Comorbid Conditions

Age, Years

Prevalence of utilization Professional help-seeking Bland et al57 (1999) GAD alone, 25.8%; comorbid

GAD; 42.7%; P = NS Cross-sectional Population Other psychiatric

diagnosis ≥18

Essau et al61 (2000) GAD, 25% Cross-sectional Schools Not specified 14–17 Wittchen et al84 (1994) GAD alone, 48.2%; comorbid

GAD, 67.9%; P=Not specified Cross-sectional Nationally

representative Anxiety, depression, substance abuse

≥15

Primary care utilization (Family physician, internal medicine) Fournier et al62 (1997) GAD, 35.3%; any psychiatric

disorder, 19.5%; P=Not specified

Cross-sectional Nationally representative

Not specified ≥18

Katon et al65 (1990) Current GAD; 21.8%; lifetime GAD, 40.3%; P=Not specified

RCT Distressed high utilizers of medical care

Not specified 18-75

Kennedy and Schwab66 (1997)

GAD,60%: PD, 83%: OCD, 28% P=Not specified

Cross-sectional General population

Not specified Not specified

Souêtre et al77 (1994) GAD alone, 60.6%; comorbid GAD, 59.9%; P = NS

Cross-sectional Ambulatory care setting

Anxiety, depression, substance abuse

18-65

Other specialist utilization Kennedy and Schwab66 (1997) [Gastroenterologist]

23% of GAD patients; P < 0.05



28

Souêtre et al77 (1994) [Psychiatrists]

GAD alone, 57.2%; comorbid GAD, 61.8%;P = NS



18-65

Souêtre et al77 (1994) [Other specialists-not specified]

GAD alone, 14.7%; comorbid GAD, 29.0%; P < 0.001



18-65

Wittchen83 (2000) [Specialists-not specified]

No GAD/no MDE, 5.4%; GAD alone, 18.6% (OR, 4.0; 95% CI, 3.2–5.0); GAD/MDE, 25.3% (OR, 6.0; 95% CI, 4.4–8.1)

Cross-sectional Primary care patients

MDE 15-54

Hospital utilization

Souêtre et al77 (1994) [Hospitalization]




18-65

Massion et al70 (1993) [Hospitalization]

GAD 35% Panic disorder 26-33% P=.005

Cross sectional Anxiety disorder patients

Anxiety, depression

≥18

Souêtre et al77 (1994) [ED + surgery]



Anxiety, depression. substance abuse

18-65

Diagnostic assessment

Souêtre et al77 (1994)[Lab tests]

GAD alone, 25.8%; comorbid GAD; 39.7%; P < 0.001



18-65

Souêtre et al77 (1994)[ECG & Cardio]




18-65

29

Medication use Ansseau et al55 (2005)[Antidepressants]

GAD alone, 24%; GAD & MD, 42%; P < 0.001


GAD with or without MD

≥18

Steffans et al79 (2005)[Benzodiazepines]

GAD & MD, 58.7%; MD only, 36.2%; P < 0.003

Prospective cohort

Depressed patients at research centre

Major depression ≥60

Ansseau et al55 (2005) [Hypnotics]

GAD alone, 23%; GAD & MD: 31%; P = 0.025



Ansseau et al55 (2005) [Tranquilizers]

GAD alone, 44%; GAD & MD: 55%; P < 0.0001

Cross-sectional Primary-care patients


Souêtre et al77 (1994) [Anxiolytics]

GAD alone, 74.2%; comorbid GAD, 82.8%



18-65

Sullivan et al81 (2006) [Opioids]

GAD patients, 3.4% Cross-sectional Nationally representative


Wittchen et al84 (1994) [Medication not specified]

GAD alone, 24.1% (0.08); comorbid GAD, 46.2% (0.04)



≥15

Frequency of utilization Bélanger et al56 (2005) [Number of annual visits*]

Mean (SD): GAD, 5.5 (5.9); no GAD, 3.4 (3.8); P < 0.001

Cross-sectional Primary-care patients

Not specified ≥18

Bélanger et al56 (2005) [≥5 visits]

GAD vs no GAD: OR, 3.95; P < 0.001


Not specified ≥18

Kennedy and Schwab66 (1997) [Number of annual visits*]

0 visits: 40%; 1–2 visits: 50%; 3–5 visits: 10%


Not specified Not reported

Wittchen83 (2000)[Number of annual primary care visits]

No GAD/no MDE, 7.2;GAD alone, 13.8; GAD/MDE, 14.6


MDE 15-54

30

Wittchen83 (2000) [Number of annual specialist visits]

No GAD/no MDE, 1.8; GAD alone, 3.4; GAD + MDE, 4.2


MDE 15-54

Medication Ansseau et al55 (2005) [Number of drugs per day]

Mean (SD): GAD alone, 2.3 (2.0); GAD & MDD: 2.7 (2.1); P < 0.007


With or without MD

≥18

Cost of illness Marciniak et al69 (2005) Marginal effect of GAD on medical costs

Mean (95% CI): US $2138.14 ($1640.96 to $2631.98)

Retrospective, multivariate analysis, incidence based

Patients in private payer database

Not specified Mean (SD): 39.0 (13.6)

Olfson and Gameroff73 (2007)

Median annual health care costs GAD, US $2375; no GAD, US $1448; P = 0.006

Pre–post index visit

Primary care adult patient

Not specified 18-70

Olfson and Gameroff73 (2007) Patients with none to moderate pain interference:

Annual median cost: GAD, US $6932; no GAD, US $3207; P = NS



Not specified 18-70

Olfson and Gameroff73 (2007) Patients with quite a bit to extreme pain interference:

Annual median cost: GAD, US $42,620; no GAD, US $9601; P < 0.05



Not specified 18-70

ECG = electrocardiogram; ED = emergency department; MD = major depression; MDE = major depressive episode; RCT = randomized clinical trial.*Health care service not specified.

31

Patients with GAD were also high utilizers of other specialists' services.[66,77] Patients

with GAD had a higher rate of visits to gastroenterologists than those with obsessive-

compulsive disorder or panic disorder.[66] Comorbidity with other anxiety disorders or

major depressive disorder was associated with significant increases in rates of specialist

utilization compared with GAD alone (16% panic disorder, 3% obsessive compulsive

disorder p<0.05). Patients with comorbid GAD had greater use of internal medicine

services (4.5% vs 1.8% p<0.05), as well as a higher proportion of specialist

consultations, than patients with GAD alone (29.0% vs 14.7%p<0.001).[77]

Furthermore, Wittchen et al.[84] reported that the ratio of primary care to specialty-care

help-seeking was higher among patients with a diagnosis of GAD alone (43.3% vs

29.2%; 1.48) compared with patients with a comorbid GAD diagnosis (50.8% vs 49.6%;

1.02).

GAD patients had higher rates of hospitalization and suicide attempts than did patients

with panic disorder (with or without agoraphobia).[70] Hospitalizations were also

significantly higher in GAD patients with a comorbid condition than patients with GAD

alone (p<0.01).[77]

Diagnostic and laboratory tests were also significantly more commonly used in GAD

patients with comorbidity than GAD alone (p<0.001).[77] This was also true of

biochemistry or hematology tests, X-rays, and computed tomography scanners, as well

as electrocardiogram and other cardiovascular tests.[77]

Drug utilization was usually assessed by comparing patients with no comorbid condition

with those who had a comorbidity.[55,71,77,79,81] Patients with GAD and a comorbid

32

psychiatric condition had higher rates of use of anxiolytics, hypnotics, tranquilizers, and

antidepressants than GAD patients without comorbidities.[55,77,79] Use of

benzodiazepines was significantly greater among patients with both GAD and

depression than among patients with depression alone(p<0.0026).[79] In a study of 14

patients with GAD, participants choose diazepam on 38% of all possible

opportunities.[71]

Two studies reporting cost of illness related to GAD[69,73] were included in this review.

Primary care patients with GAD incurred higher medical costs than patients without

GAD.[69,73] The mean marginal increase in total health care costs incurred by patients

with GAD was US $2138 (1999) compared with other anxiety disorders.[69] Age, sex,

and the severity of the disorder (represented by co-morbidities) were positively

correlated with total medical costs in patients with anxiety disorders.[69] Meanwhile

Olfson and Gameroff reported the median annual health care costs for patients with

GAD was US $2375 compared with US $1448 for patients without GAD.[73] This study

also examined the cost of pain interference in patients with and without GAD. Patients

with GAD who had a high degree of pain interference were predicted to incur a

significantly greater health care costs than patients with high pain interference but no

GAD.73 The median annual predicted cost for GAD patients with quite a bit, to extreme

pain interference was US $42,620 versus US $9601 without GAD. [73]

Impairment A number of studies found GAD was disabling, impairing normal levels of mental and

physical health as well as levels of functioning at work, at home, and in social

settings.[13,55,61,63,64,67,68,72–74,76,77,84] The level of severity of GAD was

33

associated with the level of impairment.[55,63,74] Patients diagnosed with co-morbid

GAD and major depression or major depressive episodes experienced a higher degree

of disability than patients with GAD alone or a mood disorder alone.[55,61,63–

65,77,83,84] Women with GAD had a higher level of impairment at work than did men

(p<0.0001).[55] Patients aged >50 years who reported that the onset of GAD occurred

early in life had a greater degree of impairment in role functioning due to physical

problems than those whose GAD developed later in life (p<0.04).[13] Impairment in

social functioning of patients with GAD alone was equivalent with that of patients

diagnosed with mood disorders alone and greater than in patients with other anxiety or

personality disorders.[63,67] A strong association existed between occupational

dysfunction, productivity, and a diagnosis of GAD.[74,77] Although no significant

difference existed between patients with GAD alone and those with comorbid GAD in

terms of the length of absenteeism, the prevalence of absenteeism was significantly

higher among patients with co-morbidities (33.6% vs 26.6%, p<0.05).[77] Study

parameters and impairment outcomes are presented in Table 6.

Quality of life GAD was associated with lower quality-of-life scores when compared with other anxiety

disorders or chronic somatic disorders (Table 7).[58–60,64,70,75,78,80,82,84] The

impact of GAD on a patient’s quality of life was highlighted in a utility study by Saarni et

al.[75] Utility scores were derived from an individual and population perspective using

the 15D[85] and EuroQoL EQ-5D[86] instruments (mean [SE] values for comorbid GAD:

15D, 0.783 [0.019]; EQ-5D, 0.589 [0.038]).[75] Utility scores derived from GAD patients

were similar to those reported by individuals 20 years older and reporting somatic

conditions such as Parkinson’s disease or heart failure.[75] GAD was associated with

34

TABLE 6: IMPAIRMENT STUDIES: PARAMETERS AND PRIMARY OUTCOMES Outcome Result Study Design Sample

Population Comorbid Conditions

Age, Years

Prevalence of impairment Global impairment Maier et al68 (2000) [Moderate-severe impairment SDS-Global]

GAD alone, 24.9%; comorbid GAD, 46.3% P=Not reported


Other psychiatric disorders

Not reported

Social impairment Essau et al61 (2000) CIDI psychosocial impairment-somewhat severe

50% Cross-sectional Schools Anxiety 14–17

Wittchen et al84 (1994) CIDI Social Impairment (Interference with Daily Activities

GAD alone, 28.1% (SE, 0.08); comorbid GAD, 51.2% (SE, 0.04)

Cross sectional Nationally representative

Anxiety and major depression disorders, substance abuse

15-54

Physical impairment Ormel et al74 (1994) [Physical health BDQ moderate to severe]

GAD alone, 53%; comorbid GAD, 59%

Cross-sectional WHO collaborative

Anxiety and depression disorders

15–65

Work impairment Ormel et al74 (1994) Prevalence of moderate to severe impairment on SDS-work scale

GAD alone, 26%; comorbid GAD, 38%; P = NS


Depression and anxiety disorder

15–65

Souêtre et al77 (1994)Absenteeism: 3-Month prevalence


Cross-sectional Nationally representative sample

Anxiety, depression, and substance abuse

18-65

Souêtre et al77 (1994) Absenteeism: 30-Day absenteeism prevalence

GAD alone, 8.2%; comorbid GAD, 11.1%; P = NS

Cross-sectional Nationally representative sample


18-65

35

Wittchen83 (2000) [% disabled 3+ days]

No GAD/No MDE, 3.1%; GAD/No MDD, 16.9%; MDD/No GAD, 19.4%; GAD + MDD, 33.7%


MDE 15-54

Wittchen83 (2000) [10% reduction in productivity ]

GAD/No MDD, 34%; MDD/No GAD, 21%; GAD + MDD, 48%


MDE 15-54

Impairment Scores Social impairment Ansseau et al55 (2005) SDS–Family/Home

Mean (SD): GAD alone, 5.8 (2.4); comorbid GAD, 7.3 (2.0); P < 0.001



Henning et al64 (2007) SDS–Family/Home

Mean (SD): GAD alone, 3.21 (3.26); comorbid GAD, 4.70 (2.63)

Cohort Treatment-seeking individuals

Anxiety, major depressive disorder

Mean (SD): GAD patients, 33 (12.3); control patients, 30.1 (10.4)

Ansseau et al55 (2005) SDS–social

Mean (SD): GAD, 5.8 (2.3); GAD & MD, 7.3 (1.9)


GAD, MD, GAD & MD

≥18

Henning et al64 (2007) SDS–social



Anxiety and major depressive disorders

Mean (SD): patients, 33 (12.3); control, 30.1 (10.4)

Grant (2005)63 SF-12 social functioning scale*

β (range): –3.7 (–8.7 to –1.3); P < 0.01

Logistic regression

Nationally representative

Anxiety and personality disorders

≥18

Kessler et al67 (1999) Social role impairment GAD beyond MDD

OR (95% CI): MDUSS, 3.5 (2.4–4.6); NCS, 0.8 (0.5–1.5)


MDD Not reported

Mennin et al72 (2000 LSAS)Performance fear rating

Mean (SD): social phobia alone, 16.4 (4.9); social phobia and GAD, 19.6 (6.2); P = 0.005

Cross sectional Treatment-seeking anxiety patients

Social phobia with or without GAD

18–65

Mennin et al72 (2000) LSAS Mean (SD): social phobia Cross sectional Treatment- Social phobia 18–65

36

Social interaction fear rating alone, 15.0 (6.2); social phobia and GAD, 18.8 (6.1); P = 0.005

seeking anxiety patients

with or without GAD

Work impairment Ansseau et al55 (2005) SDS–Work

Mean (SD): GAD, 5.8 (2.3); GAD & MD, 7.1 (1.9); P < 0.001


Major depressive disorder

≥18

Henning et al64 (2007) SDS–Work



Anxiety and major depressive disorders

Mean (SD): patients, 33 (12.3); control group, 30.1 (10.4)

Physical impairment Grant (2005) 63 SF-12 Short Health Survey Role Functioning Scale*

β, (range): –1.8 (–7.0 to –3.3); P = NS

Logistic regression


Anxiety and personality

≥18

Le Roux et al13 (2005) SF-36 physical role score

Mean (SD): early onset, 48.6 (40.2); late onset, 28.4 (37.0)

Cross-sectional Hospital programs, senior centers, and general population

Anxiety, depression, dysthymia

≥55

Olfson and Gameroff73 (2007) SF-12 Short Health Survey Physical Component

Mean (SD): GAD, 34.7 (10.4); no GAD, 40.4 (11.3); P < 0.001

Pre–postindex visit

Adult patients seeking primary care

Not specified 18-70

Schonfeld et al76 (1997) SF-36 physical functioning score†

Mean:Baseline: 91.9; GAD effect –6.5); P < 0.05

Regression analysis

Primary care patients

Anxiety and depression

21–64

Schonfeld et al76 (1997) SF-36 vitality score†

Mean: Baseline 59.9 GAD effect (–7.9); P < 0.05

Regression analysis


Anxiety and depression disorders

21–64

Productivity Kessler et al67 (1999) Work impairment beyond MDD

OR (95% CI): MDUSS, 3.5 (1.7–7.2); NCS, 1.5 (0.8–2.7)


Depression Not reported

37

Ormel et al74 (1994) Disability days in past month

Mean: GAD alone, 4.4; comorbid GAD, 6.3


Depression and anxiety disorder

15–65

Souêtre et al77 (1994) Work days lost per year

Mean (SD): GAD alone, 45.0 (70.5); comorbid GAD, 56.8 (84.1); P = NS



18-65

ED = Emergency department; LSRDS = Liebowitz Self-Rated Disability Scale (11-item tool with a 0–4 rating scale, where 4 = problem limits function severely); SDS= Sheehan Disability Scale; MDE = major depressive episode; OR = odds ratio; SF-12 = 12-Item Short-Form Health Survey; BDQ = Brief Disability Questionnaire (8-item scale adapted from Medical Outcomes Study Short-Form general health survey); WHO = World Health Organization; SF-36 = 36-Item Short-Form Health Survey; CIDI = Composite International Diagnostic Interview ; LSAS = Liebowitz Social Anxiety Scale (24-item scale); MDUSS = Midlife Development in the United States Survey; NCS = National Comorbidity Survey;. *Reference group was those with GAD alone; regression estimates were for comorbidity with MDD. †Reference group was women with no disorder.

38

TABLE 7:QUALITY-OF LIFE-STUDIES: PARAMETERS AND PRIMARY OUTCOMES Author (year) Instrument

Results Study Design

Sample Population

Comorbid Conditions

Age, Years

Overall quality of life Boerner et al58 (2003) [AL questionnaire]

Mean (SD): baseline, 140.1 (18.9); 8 weeks, 168.1 (24.7); P = NS

RCT GAD outpatients None 20–71

Boerner et al58 (2003) Self-rated well-being

Mean (SD): baseline, 34.79 (9.64); 8 weeks, 19.86 (11.97)


Cramer et al59 (2005) Global quality of life

Mean (SD): current GAD, –0.99 (1.03); lifetime GAD, –0.46 (1.08); P < 0.05

Factor analysis


Not specified 18–65

Dahl et al60 (2005) Q-LES-Q

Mean (SD): baseline, 62.6 (62.6) RCT GAD outpatients None ≥18

Henning et al64 (2007) QOLI

Mean (SD): end point, 9.9 (4.3); nonanxious controls, 2.5 (1.3); GAD alone, 0.8 (1.9); P < 0.05; comorbid GAD, –0.4 (1.4); P < 0.05


Anxiety Mean (SD): patients, 33 (12.3); control, 30.1 (10.4)

Saarni et al75 (2007) EuroQoL-5D

Mean (SE): GAD alone, 0.654 (0.046); comorbid GAD, 0.589 (0.038)

Cross-sectional

General population

MDD and psychiatric conditions


Saarni et al75 (2007)15D

Mean (SE): GAD alone, 0.864 (0.018); comorbid GAD, 0.783 (0.019)

Cross-sectional

General population

MDD and psychiatric conditions


Stanley et al78 (2003) QOLI

Mean (SD): CBT responders, 2.4 (1.7); CBT nonresponders, 0.8 (1.7); P <0.01

RCT General population

None ≥60

Stein et al80 (2004) Overall well-being

Low self-perception compared with no GAD: OR, 4.85

Cross-sectional

General population

MDD Not specified

39

Wetherall et al82 (2002) SF-36–General Health

Mean (SD): GAD alone, 56.6 (18.2); comorbid GAD: 48.1 (20.2); healthy controls: 77.7 (17.8); P < 0.001

Survey Treatment-seeking older adults

Mean (SD): GAD alone, 68.4 (8.2); comorbid GAD, 65.8 (8.1); controls, 67.8 (7.1)

Satisfaction Stein et al80 (2004) [Life satisfaction

questionnaire]

Difference in degree of dissatisfaction between GAD and no GAD: OR, 5.15

Cross-sectional

General population

MDD ≥15

Sleep Boerner et al58

(2003) [Self-rated sleep questionnaire]

Mean (SD): baseline, 2.65 (0.68); 8 weeks, 3.60 (0.84)


Suicide Massion et al70 (1993) Suicide attempts

GAD, 13%; PD, 6%; P < 0.005 Cross-sectional

Patients with anxiety disorder

Anxiety, depression

≥18

Wittchen83 (2000) Suicidal ideation

No GAD/MDE, 6.3%; GAD alone (OR [range]), 25.4% (4.8 [4.0–5.7]); GAD/MDE (OR [range]), 64.0% (26.3 [20.5–333.8])

Cross-sectional


MDE 15-54

AL = a German-language health-related quality-of-life measurement; ED=Emergency department; Q-LES-Q = Quality of Life, Enjoyment, and Satisfaction Questionnaire; QOLI = Quality of Life Index; CBT = cognitive-behavioral therapy; RCT = randomized controlled trial; MDD = major depressive disorder; SF-36 = 36-Item Short-Form Health Survey; PD = panic disorder

40

lower utility scores compared with other anxiety disorders, major depression disorder,

and alcohol dependence, but did not have a lower utility score than dysthymia.

Compared with non-anxious individuals who sought treatment, patients with GAD also

reported overall lower self-perception, well-being, and satisfaction with their quality of life

across most domains (health, standard of living, friendships, relationships with family,

community), with the exception of satisfaction regarding children.[64,70,80] Individuals

with GAD and ≥1 comorbid disorder reported a reduction in their quality of life compared

with individuals with no comorbid disorder(p=0.06).[64] Lifetime GAD was associated

with a lower impact on quality of life when compared with a diagnosis of GAD in the last

year(p<0.05).[59]

Suicidal ideation occurred >4 times more often among patients with GAD alone and 26

times more often among GAD patients with comorbid major depressive disorder than

among those with no anxiety disorders.[83] One study reported prevalence of suicide

attempts was higher in a sample population with GAD, than with panic disorder (with or

without agoraphobia) (13% vs. 6%; P < 0.005).[70]

Baseline and end point quality-of-life scores were collected in 3 studies assessing

treatment for GAD. In a sample of GAD patients aged ≥60 years, CBT was associated

with significant improvement in quality-of-life scores; however, the gain was not

maintained 6 months after treatment.[78] No significant differences in quality-of-life

scores were observed between kava-kava extract, opipramol, or buspirone.[58]

However, in a 12-week study, 51% of patients treated with sertraline achieved a quality-

of-life score within the normal range, compared with 31% on placebo(p<0.01).[60]

41

LITERATURE REVIEW DISCUSSION DSM-III-R or DSM-IV criteria were used to diagnose GAD in 22 of the 37 studies

reviewed. Diagnoses of GAD in the remaining studies were determined using DSM III,

ICD-9, or ICD-10. Differences exist between the DSM and ICD classification systems, as

well as between the versions of each system. DSM-IV emphasizes "excessive and

uncontrollable" worry over a specified time horizon of 6 months, whereas the clinical

version of the ICD-10 classification requires the presence of more anxiety symptoms and

a "persistent state of 'free-floating anxiety' over several months".[87] The duration

requirement for GAD was increased from 1 month under DSM-III to 6 months under

DSM III-TR, DSM-IV, and DSM-IV-TR. The ICD-10 criteria have a version for research

purposes and another for clinical practice. The research version specifies a duration

requirement of 6 months, whereas the clinical practice version refers to symptoms with

duration "over several months." To our knowledge, the impact on differences in episode

duration on economic or quality of life outcomes has not been addressed. However, a

cross-sectional study using a 2 stage sampling system, assessed the impact of episode

duration on impairment using the Brief Disability Questionnaire (BDQ) and the Sheen

Disability Scale (SDS).[68] In that study, patients were grouped into 3 categories;

patients with anxiety symptoms for <1 month and those with symptoms for 1 to 6 months

were described as having generalized anxiety syndrome (GAS), whereas patients

experiencing episodes for ≥6 months met DSM-IV criteria for GAD. No significant

differences of moderate to severe social impairment were observed between groups.[68]

Since no studies that assess the impact of episode duration on other economic

outcomes or quality of life were found, comparisons of economic outcomes using

different classifications systems and versions should be avoided.

42

The cost-effectiveness studies included in this review suggest that evidence-based

practice and SNRI’s were cost-effective for managing patients GAD, regardless of the

presence of co-morbidities. However, there are significant limitations to these studies.

None of the studies reflect the chronicity of GAD or the waxing and waning of symptoms.

Cost-effectiveness studies were either based on summary population data or

conventional decision-tree models of ≤12 months in length. Understandably, the length

of the time horizons in the pharmacoeconomic studies paralleled those found in the

related clinical trials, from which data were used to populate the decision-tree models.

Unfortunately, this method does not reflect the course of illness of the disorder, thereby

calling into question the usefulness of the cost-effectiveness outcomes. Furthermore,

CUAs were not found and, although under continuous debate, cost per quality-adjusted

life-year (and associated country-specific incremental cost-effectiveness thresholds) has

become the standard unit for assisting decision-makers in many health care

systems.[88] This is particularly unfortunate since Saarni et al. presented utility findings

for GAD in 1997.

Two studies reported cost-of-illness outcomes; 1 estimated the medical costs of anxiety

disorders as well as depression,[69] and the other measured health care charges of pain

interference in GAD patients GAD.[73] Unfortunately, ICD-9 was used as the diagnostic

criteria of 1 of the studies, limiting the generalizability of that study to the current

diagnostic criteria.[69] Furthermore, both cost-of-illness studies assessed billing data

claims used as a proxy for health care costs; a measurement subject to the limitations of

retrieving information retrospectively from a chart review.[69,73] A more accurate

method of quantifying health care costs would be to prospectively count the use of

43

health-service components such as physician or specialist visits, diagnostic and

laboratory tests, hospitalization, and use of medication. Furthermore, productivity or

absenteeism was not incorporated in estimating the cost of GAD in either study.

Several studies reported that GAD was under-diagnosed or under-recognized.[91,92]

However, the literature search for the present review did not find any studies that

reported the costs associated with under-diagnosing or not treating patients with GAD.

Saarni et al.[75] derived utility values in patients with GAD alone, as well as GAD

comorbid with depression, reporting larger reductions in health-related quality of life at

both the individual and population level with these disorders than with episodic anxiety

disorders or somatic conditions such as Parkinson’s disease or heart failure. The

derivation of utility scores for GAD alone and comorbid GAD makes a direct comparison

possible between patients with GAD and other conditions using quality-adjusted life-

years. Thus, cost-utility studies of interventions for GAD may be carried out and

compared to cost-utility studies of interventions for other conditions, thereby enabling

decision makers to make rational health care policy decisions.

Given the number of countries in which economic or humanistic outcomes were

measured, caution must be exercised when comparing outcomes between studies

included in the present review. For example, cultural differences and risk of stigma may

influence the rate of absenteeism related to GAD. Measuring costs from a public-payer

perspective may differ between jurisdictions because of variations in public policy

regarding public health access and health insurance coverage. Furthermore, drug

utilization varies between jurisdictions,[93] which may affect clinical outcomes. The

44

purpose of the present review was not to provide a quantitative transnational comparison

of economic evaluations related to GAD; rather, it sought to report the outcomes that

have measured the burden of GAD and provide insight about which questions remain

unanswered.

The reporting quality of the cost-effectiveness studies was assessed using a checklist

created by the Panel on Cost Effectiveness in Health and Medicine.[49] Although the

assessed studies were published between 2004 and 2008, the panel’s

recommendations were published in 1996. None of the studies fully adhered to the

panel’s guidelines. Although they consistently established the framework of their

research, none provided validation of their models and only 3 of the 5 provided a full

description of the event pathways. A quality assessment using the guidelines reported

by Ruger et al.[94] assessed economic evaluations related to smoking cessation. The

economic studies assessed in that reviewe predated the panel’s guidelines. Because

these quality assessments are subjective and no overall quality score is reported;

comparisons between such studies should be undertaken with caution. However, no

appreciable changes in the proportion of articles meeting the criteria were noted

between the Ruger et al. study and the quality assessment present here.

Two systematic reviews recently reported the economic and humanistic outcomes

related to anxiety disorders.[89,90] The present review represents a quality assessment

of cost-effectiveness studies and highlights the discrepancy between the etiology of

GAD and the model parameters used to support evidence of cost-effectiveness, which

may affect the utility of such studies for policy makers and formulary decision makers.

Furthermore, the present review included studies reporting on utility values for GAD, an

45

integral component in cost-utility studies and a useful measure for decision makers,

which allows comparisons across various conditions.

The extent of interest in cost-effectiveness, partial economic evaluations, and humanistic

outcomes related to GAD that are expressed by health care budget decision makers

may depend on the extent of coverage offered. Cost-effectiveness research from the

private-payer’s perspective, focusing on parameters such as patient-specific coverage,

copayments, or contributions, was not identified through the search methods used in the

systematic review. Furthermore, while full and partial economic evaluations from the

public-payer and societal perspectives have been conducted, the time horizons

incorporated in the analysis do not reflect the full clinical course of GAD. Therefore, a

need exists for a an economic evaluation that reflects the course of GAD.

Limitations It is possible that the total population of articles reporting economic and quality of life

outcomes were not included in this review because of errors in database search

engines, key words used, or errors made in mapping search terms. Databases based on

languages other than English, such as the Scientific Electronic Library Online, were not

searched. Thus, articles from regions other than North America and Europe and

published in languages other than English were not captured by our search strategy.

Nonetheless, given the care taken in proper use of search techniques, a system of

verification, as well as exhaustive use of key words, and no language limitation for the

searched electronic databases the included articles appear to be representative sample

of the entire population of articles.

46

STATEMENT OF PROBLEM To date, economic evaluations related to GAD do not reflect the nature or course of

GAD; thereby calling into question the usefulness of the outcomes to health care

decision makers. The cost-effectiveness studies identified through this systematic

review were designed using summary population data or conventional decision-tree

models and were ≤12 months in length. The cost of illness studies were based on health

care charges over periods of ≤18 months. Furthermore, one cost-of-illness study

assessed GAD patients using ICD-9 criteria.[39] Neither cost of illness studies

incorporated measured of productivity.

PURPOSE OF THE STUDY AND OBJECTIVES The purpose of this is study is to develop a dynamic decision model, reflecting the nature

of the disorder and to quantify the lifetime cost-of-illness (COI) for GAD per patient. A

secondary objective is to pool the results of clinical outcomes presented in the evidence

used to develop treatment guidelines for the CPA. The pooling of data will increase the

power of the remission and response rates of therapeutic options used in the decision

model; thereby helping to narrow the range of the uncertainty around the mean COI.

RESEARCH QUESTION From a societal perspective, what is the incidence based lifetime cost of generalized

anxiety disorder (GAD), of patients in Canada diagnosed with this disorder, and who

seek treatment?

47

SECTION 2: METHODS

OVERVIEW This study used an incidence-based, dynamic decision analytic framework, to model the

relationship between the assumptions related to the course of illness and the

management of GAD, as well as the cost consequences of these assumptions. Analysis

was conducted from a societal perspective. Results from this study would be of interest

to decision makers from both the public and private sectors

The model included 9 health states. Patients transitioned to health states according to

the relevant conditions. Patients were assessed in 3 health states by either a physician

in family practice or a psychiatrist, to determine the appropriate therapeutic option. Four

other health states modeled patients on maintenance therapy. One health state modeled

patients who had discontinued treatment. Patients were removed from the model in the

absorbing state. With the exception of the absorbing state, patients remained in a health

state for 6 months at a time before either, transitioning into another state or re-entering

the same state. Patients, ages 18 through 80 entered the model with a primary diagnosis

of GAD as per the DSM-IV or ICD-10 criteria, regardless of severity. The age at which

patients entered the model was based on the distribution of patients’ ages at disease

onset.

Treatment algorithms for the management of GAD were based on CPA guideline

recommendations as well as expert input from clinicians participating in the present

thesis committee.3 The clinical rates used in this model were: intolerance to medication,

3Please refer to the Thesis Committee section for a list of committee members.

48

response, remission, ‘spontaneous remission’, discontinuation of treatment, and relapse.

A meta-analysis of the CPA based evidence was conducted to derive remission and

response rates for this study. The remaining clinical rates were derived from peer-

reviewed literature.

Direct costs included: family practice and specialist physician fees, drugs and drug

dispensing fees, as well as hospital stay. Direct costs were based on Ontario’s Ministry

of Health and Long Term Care published fee schedules for 2008. Indirect cost was

estimated by measuring foregone wages due to absenteeism. The rate of absenteeism

was taken from published literature, while the average industrial wage rate published by

Statistics Canada was used as a proxy for wages for 2008.

The primary outcome in this study was the mean and standard deviation (SD) of the

lifetime COI per patient diagnosed with GAD. The model also calculated the mean, the

mean age of GAD onset, frequency of relapse per patient, proportion of treatment-

resistant patients, and duration of treatment discontinuation.

PHARMACOECONOMIC MODEL

STRUCTURE

ANALYTIC PERSPECTIVE

The analysis was done from a societal perspective. Both direct and indirect costs were

considered in the analyses. Direct costs included: family practice and specialist

physician fees, drugs and drug dispensing fees, as well as hospital stay. Indirect cost

was estimated by measuring foregone wages due to absenteeism.

49

AUDIENCE

Results from this study would be of interest to decision makers from both the public and

private sectors. Direct costs associated with GAD would inform Ministry of Health

decision makers. Costs from a societal perspective would inform both government health

policy, as well as private insurance decision makers.

Furthermore, although the model developed for this study presents results for the cost of

illness associated with GAD, it may be adapted to deal with research questions

regarding the cost-effectiveness of future treatment options.

TARGET POPULATION

The target population consisted of adults, ages 18 through 80 with a primary diagnosis

of GAD as per the DSM-IV or ICD-10 criteria, regardless of severity. Patients with

comorbid psychiatric disorders such as depression, or somatic conditions such as

irritable bowel syndrome were not included in the analysis. Both males and females

were considered in the analysis.

TYPE OF MODEL

This study used a decision analytic framework to model the relationship between the

assumptions related to the course of illness, the management of GAD, and the cost

consequences of these assumptions. TreeAge® Pro Suite 2009 (TreeAge Software Inc.,

Williamstown, MA) software was used to develop an incidence-based, probabilistic

Markov model.

50

HEALTH STATES OVERVIEW

The model included 9 health states (HS):

• Health state 1: Family physician assessment (initial health state)

• Health state 2: specialist assessment for 2nd line therapy

• Health state 3: specialist assessment for 3rd line therapy

• Health state 4: Maintenance therapy for 1st line

• Health state 5: Maintenance therapy for 2nd line (1st line plus benzodiazepine)

• Health state 6: Maintenance therapy for 2nd line (Benzodiazepine monotherapy)

• Health state 7: Maintenance therapy for 3rd line

• Health state 8: treatment discontinued

• Health state 9: death (absorbing state).

TIME HORIZON

The time horizon used in this model was patients’ life expectancy from onset of illness.

The lower age limit of 18 was chosen as this is the age of majority in Ontario. This was

also the lower limit for the age at which patients were usually recruited into the CPA

cited evidence for management of GAD.[21] Canadian life expectancy published by

Statistics Canada set the upper age boundary for the model.[95] The overall life

expectancy at birth for both males and females was 80.4 years in 2005, and was

rounded down to 80 for the model.[95]

The cycle length within a model, describes the length of time patients remain in a given

health state before transitioning to another health state, or back into the same health

state. The cycle length for this study was based on the treatment algorithm

51

recommended by the CPA. As per these guidelines, adequate duration and dosage of

agents were required to observe clinically important effects of therapeutic agents.[21]

Given an inadequate response to initial treatment, guidelines recommend that dosing be

optimized before switching or consideration of augmentation.[21] Guidelines suggest

that a period of 8-12 weeks is necessary to adequately assess response to an agent.

Switching to another 1st line treatment is recommended prior to considering 2nd line

treatment options.[21] Therefore, the selection of a practical cycle length was based on

an accommodation for patients to respond to two treatment choices within the same

treatment line. Furthermore, while effects may be observed in as little as 1 week for

some anti-depressants, significant improvements may not be seen for 6-12 weeks and

may continue to accrue for 6-12months. Therefore, a cycle length of 6 months was

chosen.

Within each health state, a decision tree was embedded to describe the possible

pathways that patients may have taken while in that health state. The decision tree

within each health state, with the exception of ‘death’, began with the application of an

attrition rate that was equivalent to the probability of death from all causes.

ATTRITION RATE

The attrition rate accounted for at the beginning of each cycle for all health states except

‘DEATH’ was based on ‘all cause mortality’ rates derived from Statistics Canada

complete life tables for 2000-2002.[96] Patients were removed from the model based

on the age-specific attrition rate. Life tables reported data in rates per 100,000 and for a

population from birth to 109 years of age, segregated by gender. In order to derive the

52

FIGURE II: BUBBLE DIAGRAM SHOWING THE HEALTH STATES AND TRANSITION PATHWAYS FOR THE MARKOV MODEL

53

‘all cause mortality’ rate for both sexes combined, all cause mortality rates were

weighted by gender.[97] The weighted data were then added for each year to determine

the age specific, all cause gender-adjusted, mortality rate. A ‘bubble’ diagram depicting

the health states and possible transitions among the health states is presented in Figure

II.

PROCESS

HEALTH STATE 1: FAMILY PHYSICIAN ASSESSMENT (INITIAL HEALTH

STATE)

Patients entered the model through the initial health state, ‘FAMILY PHYSICIAN

ASSESSMENT’. Patients may also have entered this health state after discontinuing

treatment for at least 1 cycle (i.e. 6 months). This health state described the possible

pathways patients may have taken once they were diagnosed with GAD and whose

treatment was managed by a physician in family practice.

The age of disease onset determined the age at which patients entered the model. The

distribution of patients’ ages at disease onset was based on peer reviewed literature.[13]

Leroux et al. reported details of that distribution which was derived from a sample of 67

patients with a mean age of 66 years. That study reported the percentage of patients

who experienced onset of GAD from the second through the eighth decade of life, as

well as during ‘childhood’ and ‘teen’ years.[13] A single rate was reported by Leroux for

each age category. However, in this study, each patient’s age was input according to the

actual number of years and not grouped into age categories. Therefore, in order to use

the Leroux data in this study, the probability of the onset of GAD symptoms for each year

of age was needed. A uniform distribution of percentages was assumed for each age

54

FIGURE III: DISTRIBUTION OF THE AGE (18-80) OF ONSET FOR GAD USED FOR THIS STUDY

0

0.002

0.004

0.006

0.008

0.01

0.012

0.014

0.016

0.018

0.02

18 21 24 27 30 33 36 39 42 45 48 51 54 57 60 63 66 69 72 75 78

Age of onset

Perc

enta

ge

55

category reported by Leroux. The percentage for each age group was then divided by

10 for rates associated with the second through eighth decades of life. The value for the

teen category was divided by 7 (i.e. ages 13-19). Given the age range considered in this

model, only the values for ages 18 and 19 were used. Figure III is a graphical

representation of the distribution by age of onset of illness used for this study. Upon

entering this health state, an attrition rate was applied to determine the number of

patients that would transition to the absorbing state ‘DEATH’. Patients, who did not

transition into the absorbing state, were prescribed treatment with a 1st line agent.4

Patients were then guided through pathways that included tolerance, response, and

remission; based on the treatment algorithm previously described. At the end of the 6

month cycle, patients who remitted with 1st line agents exited the ‘FAMILY PHYSICIAN

ASSESSMENT’ health state and proceeded to the ‘MAINTENANCE 1ST LINE’ health

state.

The decision tree pathways within each health state are illustrated in Figures IV-XI on

pages 65-72.

HEALTH STATE 2: SPECIALIST ASSESSMENT 2ND LINE

Patients who entered the ‘SPECIALIST ASSESSMENT 2ND LINE’ health state: were

intolerant to 1st line agents, had responded to 1st line medication but had not remitted, or

had neither responded nor remitted to 1st line therapy. Their treatment was managed by

a psychiatrist who prescribed them 2nd line treatment options. After allowing for attrition

rates at the beginning of each cycle, patients proceeded along pathways that considered

4 Please refer to Treatment algorithm section (Page 67) for description of treatment lines

56

FIGURE IV: DECISION TREE WITHIN FAMILY PHYSICIAN ASSESSMENT HEALTH STATE All cause mortalityp_ACM_Table[startage+_stage/2 ]

Death

Intolerantp_intolerance_1stline

Specialist Assessment 2nd Line

Remission p_remission_1stline

Maintenance 1st line



No remission #


Titrate (Optimize))Response

p_response_1stline-p_remission_1stline

No response #


No remission#

Tolerant#

Switch to another 1st lineIntolerantp_intolerance_1stline

Remissionp_remission_1stline

Maintenance 1st line Remission

p_remission_1stlineMaintenance 1st line

No remission #


Titrate (Optimize)Response p_response_1stline-p_remission_1stline

Intolerant p_intolerance_1stline




Remissionp_remission_1stline

Maintenance 1st line No remission (1st line + benzo)

#Specialist Assessment 2nd Line

TitrateResponsep_response_1stline-p_remission_1stline

No response #


No remission#

Tolerant#

Switch to another 1st lilneNo response#

No remission#

Tolerant #

1st line pharmacotherapyNo Death#

Family Physician Initial Assessment1

Specialist Assessment 2nd Line0

[+]

Specialist Assessment 3rd line0

[+]


0 [+]

Maintenance Benzo only0

[+]

Maintenance 1st Line + Benzo0

[+] Maintenance 3rd Line

0 [+]

Treatment stopped0

[+] Death

0

Patient Seeking Treatment

57

FIGURE V: DECISION TREE WITHIN SPECIALIST ASSESSMENT 2ND LINE HEALTH STATE Family Physician Initial Assessment

1 [+]

All cause mortalityp_ACM_Table[startage+_stage/2 ]

Death

Specialist Assessment 3rd lineIntolerant p_intolerance_benzo

Remission p_remission_benzo

Maintenance Benzo onlyRemission

p_remission_benzo Maintenance Benzo only

No remission#

Specialist Assessment 3rd line

Titrate (optimize)Responsep_respose_remission_benzo_truncated

No Response #


No Remission#

Tolerant#

Benzodiazepine onlyp_2ndline_benzo

Intolerantp_intolerance_benzo


Remissionp_remission_1st_benzo

Maintenance 1st Line + Benzo

Remissionp_remission_1st_benzo

Maintenance 1st Line + Benzo

No remission#


Titrate (optimize)Responsep_respose_remission_benzo_truncated

No reponse#


No remission#

Tolerant#

1st line + benzo adj #

No death#



[+] Maintenance 1st line

0 [+]


[+]


[+]

Maintenance 3rd Line0

[+]

Treatment stopped0

[+] Death

0


58

FIGURE VI: DECISION TREE WITHIN SPECIALIST ASSESSMENT 3RD LINE HEALTH STATE


[+]


[+]


Death

Remission p_remission_3rd_line

Maintenance 3rd Line

Remission p_remission_3rd_line

Maintenance 3rd LineNo remission

#Maintenance 3rd Line

TitrateResponseIf(p_response_3rd_line-p_remission_3rd_line<0;0;p_response_3rd_line-p_remission_3rd_line)

10-Day hospitalizationProb_Hospitalization_3rd_line_failure

Specialist Assessment 3rd lineTreatment Stopped

#Treatment stopped

No response#

No remission#

No death#



0 [+]


[+]


[+]


[+] Treatment stopped

0 [+]

Death0


59

FIGURE VII: DECISION TREE WITHIN MAINTENANCE 1ST LINE THERAPY HEALTH STATE


[+]


[+]


[+]


Death

Treatment stoppedp_treatmentdisc_1stline

Treatment stoppedRelapse

p_relapseSpecialist Assessment 2nd Line

No relapse#


Continue treatment#

No death#


0


[+]


[+]


[+]

Treatment stopped0

[+]

Death0


60

FIGURE VIII: DECISION TREE WITHIN MAINTENANCE BENZODIAZEPINE MONO-THERAPY HEALTH STATE


[+]


[+]


[+]


0 [+]


DeathTreatment stoppedp_treatmentstopped_benzo



No relapse#

Maintenance Benzo only

Continue treatment#

No death#



[+]


[+]

Treatment stopped0

[+]

Death0


61

FIGURE IX: DECISION TREE WITHIN MAINTENANCE 1ST LINE & BENZODIAZEPINETHERAPY HEALTH STATE


[+]


[+]


[+]


0 [+]


[+] All cause mortalityp_ACM_Table[startage+_stage/2 ]

DeathTreatment stoppedp_treatmentdisc_1stline_Benzo



No relapse# Maintenance 1st Line + Benzo

Continue treatment#

No death#



[+]

Treatment stopped0

[+]

Death0


62

FIGURE X: DECISION TREE WITHIN MAINTENANCE 3RD LINE THERAPY HEALTH STATE


[+]


[+]


[+]


0 [+]


[+] Maintenance 1st Line + Benzo

0 [+]


DeathTreatment stoppedp_treatmentdisc_3rd_line

Treatment stopped

Relapsep_relapse


No relapse#

Maintenance 3rd Line

Continue treatment#

No death#


Treatment stopped0

[+]

Death0


63

FIGURE XI: DECISION TREE WITHIN TREATMENT STOPPED HEALTH STATE Family Physician Initial Assessment

1 [+]


[+]


[+]


0 [+]


[+] Maintenance 1st Line + Benzo

0 [+]


[+] All cause mortalityp_ACM_Table[startage+_stage/2 ]

Death

Treatment seekingp_treatment_seeking

Family Physician Initial Assessment

Non-treatment seeking#

Treatment stopped

Non-Spontaneous remission#

Spontaneous Remissionp_spontaneous_remission

Treatment stopped

No Death#

Treatment stopped0

Death0


64

tolerance, remission and response rates, based on the treatment algorithm for 2nd line

therapy. After 6 months in this health state, patients who remitted with either treatment

option exited this state and transitioned to either the ‘MAINTENANCE THERAPY FOR

2ND LINE (1ST LINE PLUS BENZODIAZEPINE) or MAINTENANCE THERAPY FOR 2ND

LINE (BENZODIAZEPINE MONOTHERAPY). Patients who were intolerant to either

treatment option or who responded but did not remit with 2nd line treatment options

exited the ‘SPECIALIST ASSESSMENT 2ND LINE’ health state and transitioned to the

‘SPECIALIST ASSESSMENT 3RD LINE’ health state.

HEALTH STATE 3: SPECIALIST ASSESSMENT 3RD LINE

The ‘SPECIALIST ASSESSMENT 3RD LINE’ health state received patients who were

intolerant or who did not respond to 1st or 2nd line pharmacotherapy and whose treatment

was managed by a psychiatrist. In this health state, patients were prescribed 3rd line or

agents for treatment resistance. After allowance for attrition, patients then moved along

pathways based on the treatment algorithm for 3rd line therapy. At the end of the 6

month cycle, patients who remitted or responded when managed with 3rd line agents,

exited the ‘SPECIALIST ASSESSMENT 3RD LINE’ health state and transitioned to the

‘MAINTENANCE THERAPY FOR 3rd LINE’ health state. Patients who died from any

cause exited the ‘SPECIALIST ASSESSMENT 3RD LINE’ health state and were

absorbed into the ‘death’ state at the beginning of each cycle.

65

HEALTH STATES 4 THROUGH 7: MAINTENANCE THERAPY HEALTH

STATES

For each of the 4 maintenance therapy health states, patients either continued or

discontinued treatment after allowance was made for attrition. Those who continued in

the model were prescribed maintenance doses of their respective treatment lines.

Patients who continued treatment and relapsed at the end of the 1st or 2nd line

maintenance health states exited their respective health states and transitioned into the

‘SPECIALIST ASSESSMENT 2ND LINE’ health state. Patients who continued treatment

and relapsed while in the ‘SPECIALIST ASSESSMENT 3RD LINE’ health state, exited the

health state and re-entered the ‘SPECIALIST ASSESSMENT 3RD LINE’ health state.

Patients, who discontinued treatment while in their respective maintenance health states,

exited these states and entered the ‘TREATMENT DISCONTINUED’ health state.

Patients who died from any cause exited their respective maintenance health states and

proceeded to the ‘DEATH’ health state at the beginning of each cycle.

HEALTH STATE 8: TREATMENT DISCONTINUED

Patients who discontinued treatment entered the ‘TREATMENT DISCONTINUED’ health

state. As in previous health states, patients were subject to an attrition rate. They were

assumed to have discontinued treatment because either they experienced ‘spontaneous

remission’ or they continued to experience GAD-related symptoms but had decided not

to continue treatment. Patients who experienced ‘spontaneous remission’ or who

continued to suffer from GAD-related symptoms and did not take medication at the end

of 6 months would re-enter this health state. Patients who continued to suffer from GAD-

related symptoms but decided to seek treatment after 6 months in this health state,

66

exited this health state and re-entered the initial health state (‘FAMILY PHYSICIAN

ASSESSMENT’); in effect, beginning the treatment algorithm anew.

HEALTH STATE 9: DEATH (ABSORBING STATE)

‘DEATH’ was the absorbing state; meaning patients who entered this state were

removed from the model.

TREATMENT ALGORITHM

The reference case scenario considered pharmacotherapeutic management of GAD,

while a combination of psychotherapy and pharmacotherapy was assessed in a

sensitivity analysis. Pharmacotherapeutic choices and treatment algorithms for the

management of GAD were based on CPA guideline recommendations as well as expert

input from clinicians participating in the present thesis committee.5

For this study, first line therapy included the use of either an SSRI (paroxetine, sertraline,

or escitalopram) or SNRI (venlafaxine XR). All of these first line agents have the

indication for GAD in Canada, and are listed in Ontario Drug Benefit (ODB)

Formulary.[128] CPA recommendations for the use of paroxetine, escitalopram and

venlafaxine were based on level 1 evidence, while inclusion of sertraline was based on

level 2 evidence. [21] The thesis committee concurred that these agents are prescribed

as 1st line therapy in Ontario.

Second line therapy for this study was either: 1st line treatment augmented with a

benzodiazepine or a benzodiazepine as monotherapy (i.e., alprazolam, bromazepam, 5Please refer to the Thesis Committee section (Page 83) for a list of committee members.

67

diazepam or lorazepam). Adjunctive therapy is recommended by the CPA for patients

who have had inadequate response to 1st line agents after titration. However, it should

be noted that CPA guidelines for 2nd line pharmacotherapy not only included the

benzodiazepines (level 1 evidence) previously mentioned, but also buspirone (level 1

evidence), imipramine (level 1 evidence), pregabalin (level 1evidence), and bupropion

(level 2 evidence). Buspirone, imipramine, pregabalin, and bupropion were not

considered in this study. Buspirone was not considered, given evidence cited by the

CPA guidelines of reduced efficacy in patients who have previously used

benzodiazepines. [129] Imipramine was not considered due to the risk of death from

overdose.[21] Pregabalin was excluded from this study given the agents’ limited clinical

use in Canada and is not listed as a benefit in Ontario. [21] Furthermore, according to

the ODB formulary, bupropion has a limited use authorization for the treatment of

depression and was therefore excluded from consideration in this study. The thesis

committee concurred with the description of 2nd line therapy for this study.

Third line therapy in this study included citalopram (level 4 evidence), mirtazapine (level

3 evidence), trazodone (level 2 evidence) adjunctive olanzapine with fluoxetine (level 2

evidence) or adjunctive risperidone with benzodiazepine (level 2 evidence). Hydroxyzine

(level 1 evidence) was also recommended by the CPA; however, the agent is no longer

covered as a benefit by the ODB formulary and was not considered in this study. The

thesis committee concurred with the description of 3rd line therapy for this study.

For each 1st line treatment option, an initial dose was prescribed by a family physician to

assess the risk for intolerance. According to expert opinion, intolerance to the initial

dose should be assessed after 2 weeks. In this study, it was assumed that those

patients who were tolerant to medication achieved partial response, thereby requiring an

68

increase to a ‘maintenance’ level dosage in order to achieve full response or remission.

[Please refer to dosing section on page 70 for dosing levels and sources used]. Doses

were then titrated to an optimal level for patients who responded to pharmacotherapy but

failed to achieve remission. Patients who were intolerant to the 1st choice of a 1st line

medication were switched to another 1st line agent.

Those patients who were intolerant or who failed to achieve remission to both 1st line

treatment options were prescribed an initial dose of a 2nd line treatment option by a

psychiatrist to assess intolerance to that drug. Patients who tolerated the medication

were assumed to have achieved partial response, thereby requiring an increase to a

‘maintenance’ level dosage in order to achieve full response or remission. Titration to

optimal dosing was prescribed for patients who responded but did not remit with either of

the 2nd line treatment options. Since one of the choices for 2nd line treatment included

the adjunctive use of a 1st line agent and some patients were intolerant to both 1st line

choices; switching between treatment options for 2nd line therapy was not an option.

Patients who were intolerant to, or who did not achieve remission with either 1st or 2nd

line treatment options were prescribed 3rd line or agents for treatment resistance.

Intolerance to 3rd line agents was not considered, given structure of the model. The

incidence of those intolerant to all 3 treatment lines was not considered substantial by

the thesis committee. Patients who did not respond to 3rd line agents were hospitalized

for 10 days prior to re-entering the ‘Specialist assessment 3rd line’ health state or

discontinued treatment altogether. Doses were titrated for patients who responded to

treatment but did not achieve remission. Pharmacotherapy was continued for as long as

physicians continued prescribing the medication or until patients stopped treatment.[21]

69

Cognitive behavioural therapy in combination with pharmacotherapy was considered for

a sensitivity (scenario) analysis. Based on evidence cited by CPA guidelines, patients

were treated with approximately 10 sessions of CBT.[98-100] Although a combination

of drug and psychotherapy is not expressly recommended by the guidelines, available

evidence for combination therapy cited by the CPA was based on diazepam and

cognitive behavioural therapy. Therefore, this combination was considered in a sub-

group analysis. The cognitive behavioural therapy consisted of 10 sessions over a 6

month assessment6 period conducted by a psychiatrist (SPECIALIST ASSESSMENT

FOR 2ND LINE THERAPY) for patients prescribed any benzodiazepine (either mono or

adjunctive therapy.

DOSING

Initial and optimized dosing for 1st and 2nd line pharmacotherapy were taken from the

product monographs where indicated for patients with GAD.[114-121] Maintenance

doses were taken from peer reviewed literature.[109] Doses for 3rd line treatment were

taken from evidence cited by the CPA[21,122-6]. Table 8 lists initial, maintenance and

optimized doses for all treatment lines used in this model.

CLINICAL VARIABLES

Clinical variables and their inputs for this study are presented in Table 9. The clinical

rates used in this model were: intolerance to medication, response, remission,

‘spontaneous remission’, discontinuation of treatment, and relapse. Clinical parameters

were chosen based on the treatment algorithm recommended by the CPA, and by the

clinical experience of Dr. Arun Ravindran, a member of this thesis committee.

6 Refer to Process section (Page 61) for description of health states

70

TABLE 8: INITIAL, MAINTENANCE AND TITRATED DOSES BY TREATMENT LINE FOR THIS STUDY Daily Dose (mg) Initial Maintenance Maximum Sources 1st Line Escitalopram 10.0 15.0 20.0 Product Monograph[119], Vasile[109] Paroxetine 20.0 26.5 50.0 Product Monograph[117]. Vasile[109] Sertraline 25.0 102.5 200.0 Product Monograph[118] Vasile[109] Venlafaxine 37.5 172.0 225.0 Product Monograph[120], Vasile[109] Benzodiazepine Alprazolam 0.25 2.0 3.0 Product Monograph[114], Vasile[109] Bromazepam 2.0 3.5 6.0 Product Monograph[121] Diazepam 2.0 13.0 40.0 Product Monograph[115], Vasile[109] Lorazepam 2.0 2.8 6.0 Product Monograph[116], Vasile[109] 3rd Line Citalopram 10.0 33.0 60.0 Varia and Rauscher[122] Fluoxetine 20.0 20.0 20.0 Pollack[123] Mirtazapine 30.0 30.0 30.0 Gambi[124] Olanzapine 2.5 8.7 20.0 Pollack[123] Risperidone 0.5 1.1 1.5 Brawman-Minzter[125] Trazodone 150.0 400.0 340.0 Rickels[126]

71

TABLE 9: CLINICAL VARIABLES AND THEIR VALUE INPUTS (I.E., RATES) USED TO POPULATE THE MARKOV MODEL

7 Please see Results section (page 92) for complete results from meta-analysis

Probabilities Clinical parameters Mean Low Value High Value Distribution Source 1st Line Treatment Intolerance 0.0900 0.0470 0.1470 Triangular Machado [101] Remission 0.3970 0.3520 0.4410 Triangular Bereza [104] Response 0.6772 0.6409 0.7136 Triangular Bereza [104] Discontinued treatment 0.7138 0.7022 0.7266 Triangular Mullins [106] Benzodiazepine Treatment Intolerance 0.0730 0.0450 0.1010 Triangular Pooled results Remission 0.5350 0.4400 0.6300 Triangular Bereza [104]7 Response 0.5680 0.4640 0.6720 Triangular Bereza [104]5 Discontinued Treatment 0.5260 0.3630 0.6613 Triangular Ohayon [107] 1st Line + Benzodiazepine Treatment Intolerance 0.0900 0.0470 0.1470 Triangular Machado [101] Remission 0.4100 0.3630 0.4560 Triangular Bereza [104] Response 0.6430 0.6020 0.6840 Triangular Bereza [104] Discontinued Treatment 0.5260 0.3630 0.6613 Triangular Vasile [109] 3rd Line Treatment Remission 0.3570 0.2310 0.4820 Triangular Bereza [104] Response 0.5490 0.3970 0.7020 Triangular Bereza [104] Discontinued Treatment 0.5260 0.3630 0.6613 Triangular Vasile [109] Relapse 0.0250 0.0200 0.0500 Triangular Yonkers [110] Spontaneous remission 0.2000 0.2250 0.2500 Triangular Ballenger [111]

72

INTOLERANCE

Intolerance rates to pharmacotherapy were based on peer-reviewed published

literature.[101,102] Meta-analytic rates of adverse drug reactions in clinically depressed

patients were used as a proxy for intolerance of SSRI or SNRI agents.[101] It was thus

assumed that intolerance was not affected by the type of health condition of the patient,

and that the adverse events were solely caused by the drug effect. Rates of intolerance

to benzodiazepines were derived from the statistical pooling of dropout rates due to

adverse events reported by evidence for 2nd line therapy cited in the CPA guidelines.

Where reported, the number of patients assigned to a benzodiazepine group as well as

those who dropped out due to adverse events was extracted from CPA cited evidence.

Data were pooled using the dropout rate itself as an effect size and the binomial theorem

to determine its variance; a technique described by Einarson.[103] Intolerance rates

were not required for patients treated with 3rd line agents, as per model structure

described previously.

REMISSION AND RESPONSE

To determine the remission and response rates for this study, a meta-analysis was

performed using CPA cited evidence and by extension, any additional relevant (original)

articles retrieved from the reference list of meta-analytic evidence considered in the

2006 guidelines for the management of GAD. No further criteria were considered with

respect to the clinical and demographic make-up of the population considered in this

meta-analysis. Evidence was considered separately for each of the three treatment lines

used in this study. Meta-analytic evidence, duplicate publications, and studies with non-

extractable data were excluded. Preliminary results of the analysis have been

73

previously presented.[104] Complete results from the meta-analysis are presented in the

results section in Chapter 3 below.

Neither placebo arms, nor study arms of agents not recommended by the CPA for the

management of GAD were considered. For example, propranolol, although it may have

been used in the past for this indication, is specifically not recommended by the

guidelines; therefore, evidence for that drug was not included.

The following data were extracted from included articles: the number of subjects treated,

number of patients responding or remitting (or corresponding response or remission

rates), baseline and endpoint scores and standard deviations (SDs), baseline to

endpoint or mean reductions in scores of the Hamilton Rating Scale for Anxiety (HAM-A)

as well as standard deviations or standard error values. Where necessary and possible,

data were extracted from figures to obtain a reasonably accurate estimate of relevant

values not otherwise reported in text or tables. Data were extracted by one author

(BGB) and validated by a second author (MM).

When using the inverse variance method for deriving pooled estimates from published

studies, SDs are necessary for calculations and completeness of the data and relies on

the quality of study reporting. In this meta-analysis, missing SD values were derived

based on scores from studies that reported both HAM-A scores as well as their

corresponding SDs. Each reported SD was squared to calculate the variance. A ratio

was then calculated of the mean HAM-A score and the variance from the related study.

This process was repeated for each study that reported both HAM-A and SD values. A

geometric mean was then taken of the ratios; this mean ratio was then multiplied by the

74

mean HAM-A scores of studies that did not report SDs, to derive a variance for that

study. The square root of the variance was then taken to calculate the SD. When both

the HAM-A score and its SD were not reported by a study, then data manipulation was

not possible, thereby excluding the study from the analysis.

The outcomes of interest were the meta-analytic probability of remission (defined as a

score on HAM-A≤7) and response [defined as a 50% reduction in HAM-A scores, or

clinical global impressions-improvement score (CGI-I) of 1 or 2] by line of treatment.

Review Manager® software version 5 (Cochrane Collaboration 2008) was used to

combine results across study arms. A random effects model using the inverse variance

weighting was applied in all calculations.[136] Tau-squared estimated between-study

variance. Heterogeneity of effects was assessed using Chi-squared and I-squared

statistics. Critical p-values for determining heterogeneity were set at <0.1, and >30% for

the I-squared. [105]

TREATMENT DISCONTINUATION

Treatment discontinuation rates were derived or taken directly from peer-reviewed

literature for each treatment line where possible. Discontinuation rates specifically

attributed to GAD patients treated with 1st line agents were not found and were therefore

taken from a retrospective cohort study comparing discontinuation rates for SSRIs

among patients with social anxiety disorder and post-traumatic stress disorder.[106] A

discontinuation rate of treatment for GAD patients treated with benzodiazepines was

also not found. The discontinuation rate for benzodiazepines was taken from a survey

75

conducted in the United Kingdom of 4972 non-institutionalized individuals 15 years of

age or older. In that study, consumption for a period of 12 months or more was reported

by 47.4% of responders who were prescribed anxiolytics. [107] One-hundred percent

minus that rate was used as a proxy for a discontinuation rate in patients treated with

benzodiazepines. Wilson’s score method for deriving a confidence interval from a single

proportion was used to derive upper and lower limits for this variable.[108]

CI = [(2n + Z2) ± Z√ (z2 + 4n*(1 - n/N)]/(2n + 2Z2)

Where:

Z= the standard normal deviate associated with alpha error (i.e., Z=1.96 when α = 0.05,

or 95% confidence); n=observed cases; N=total sample.

The discontinuation rate for adjunctive benzodiazepine therapy with SSRI/SNRI was

taken from a naturalistic longitudinal study of GAD patients.[107] No treatment

discontinuation rates were found for GAD patients treated with 3rd line agents. Treatment

discontinuation for GAD patients on adjunctive benzodiazepine with 1st line agents was

used as a proxy.

RELAPSE

Relapse rates reported from the Harvard/Brown Anxiety Research Project

(HARP), observational, longitudinal study of patients with GAD patients and extrapolated

for the purposes of this model.[108] In the HARP study, relapse was defined when

patients in full remission met full DSM-III-R criteria, severe symptoms, or extreme

impairment or pronounced interference in functioning. Yonkers et al. reported

cumulative relapse rates over an 8 year follow up period segregated by gender.[110]

Cumulative probability for both genders was computed by weighting the cumulative

76

probability of each gender by their respective sample size. Individual yearly rates were

then computed.

SPONTANEOUS REMISSION

A spontaneous remission rate of 22.5% was assigned to a cohort of patients who had

discontinued treatment. This rate was taken from a consensus statement on GAD from

the international consensus group on depression and anxiety. No evidence was given for

this rate nor was spontaneous remission defined in their statement.[111] This rate was

attributed to the ‘Treatment discontinuation’ health state only. This model assumed that

patients who did not ‘spontaneously remit’ were experiencing GAD related symptoms

and may or may not decide to seek treatments.8

RESOURCE USE AND COSTS Economic input variables and their values for this study are presented in Table 10.

Economic parameters included; physician and pharmacist fees, medication costs, as

well as rates related to hospitalization and seeking treatment after treatment

discontinuation.

PHYSICIAN COSTS

Fees for family practice and psychiatry consults were taken from the Government of

Ontario’s Ministry of Health and Long Term Care Schedule of Benefits for Physician

Services.[127] Patients entering the Family physician, Specialist assessment 2nd or 3rd

line treatment health states were subject to an initial physician assessment as well as

follow-up visits to assess intolerance, response or remission. Follow-up visit(s) in the

8 See seeking treatment behaviour section page 81

77

Family physician, Specialist assessment 2nd or 3rd line health states ranged from 2-5, 1-5

and 1-3 respectively per cycle. Maintenance health states were attributed 1 follow up

visit per cycle. No physician costs were attributed to the ‘TREATMENT

DISCONTINUED’ or ‘DEATH’ states. For sub-group analysis, an additional 10 sessions

of CBT with a psychiatrist were attributed to patients in the Family physician, Specialist

assessment 2nd or 3rd line treatment health states.

DRUG COSTS

Costs for pharmacotherapy were based on the Government of Ontario, Ministry of

Health and Long Term Care, Ontario Drug Benefit Formulary/Comparative Drug

Index.[128] Costs for initial, maintenance and optimized daily doses were considered

separately. No resource utilization data was available to determine the proportional

usage of each drug by a cohort of GAD patients. Therefore, in this study it was

assumed that each drug was utilized in equal proportion. The daily cost for each drug

was derived by multiplying the dose in milligrams by the cost per milligram. An arithmetic

mean of the daily cost of the drugs included in the respective treatment line was then

calculated to determine the daily cost for each treatment line. The daily cost was then

multiplied by the number of days the patient was exposed to either an initial,

maintenance or optimized dose.

DISPENSING FEES

The dispensing fee was taken from the Government of Ontario, MoHLTC, ODB:

Dispensing fee web site.[129] Prescribed drugs were assumed to have been filled. A

dispensing fee was charged for the initial prescription, or when medication was titrated

or switched. One prescription fee of $7 CAD was incurred for each 6-month cycle in a

‘maintenance’ health state.

78

HOSPITALIZATION COSTS

The 2002 Health Canada report on mental illness in Canada showed that patients with

anxiety disorders incurred 10 hospitalization days in 1999 as a result of their disorder.

This rate of utilization changed little between 1987 and 1999; ranging from 9.6 to 11.1

days.[113] Cost of hospitalization per day was an estimation of total acute care inpatient

costs for 2004 from the Canadian Institute for Health Information(CIHI) data base

available on their web site.[130] Total hospitalization costs attributed to GAD was

calculated by multiplying the number of hospitalization days by the total acute care

inpatient cost. Inpatient costs were inflated to 2008 dollars using the Consumer Price

Index.[137]

TREATMENT SEEKING BEHAVIOUR

The rate of patients’ seeking treatment was assigned to patients who were once again

seeking treatment after discontinuation of therapy. This rate was based on a nationally

representative household survey of respondent s 18 years and older in the United States

(i.e., the National Comorbidity Survey-Replication). [112]

INDIRECT COSTS

Indirect costs were based on number of days absent from work in a given month

(absenteeism) and the industrial wage rate. Kessler reported absenteeism based on two

surveys: National Co-morbidity Survey (NCS) as well as the Midlife Development in the

United States Survey (MDUSS). Kessler reported probabilities for 0 to 6 days or more of

absenteeism from work per month.[15] The weighted average from both surveys was

rounded to approximately 2 days per month. This rate was multiplied by the average

79

TABLE 10: ECONOMIC INPUT VARIABLES AND ASSOCIATED COSTS* Variable Distribution Low High Value Source Direct costs

Clinician/hospital charges Pharmacist dispensing fee Uniform $6.00 $15.00 $10.50 MOHLTC: Dispensing Fees[129] Initial assessment family practice Uniform $42.53 $78.98 $60.75 MOHLTC: Schedule of Benefits[127] Follow-up family practice Uniform $21.25 $39.46 $30.35 MOHLTC: Schedule of Benefits[127] Initial assessment psychiatry Uniform $113.74 $211.22 $162.48 MOHLTC: Schedule of Benefits[127] Follow-up psychiatry Uniform $57.72 $107.19 $82.45 MOHLTC: Schedule of Benefits[127] Hospitalization cost (10-day period) Uniform $3,416.00 $6,340.00 $4,878.00 Health Canada[113];CIHR[130] Pharmacotherapy (Mean daily cost) Initial 1st line Uniform $0.70 $1.30 $1.00 MOHLTC:ODB: Formulary[128] Initial benzodiazepine adjunctive 1st line Uniform $0.80 $1.49 $1.15 MOHLTC:ODB: Formulary[128] Initial benzodiazepine monotherapy Uniform $0.10 $0.19 $0.15 MOHLTC:ODB: Formulary[128] Initial 3rd line Uniform $0.83 $1.55 $1.19 MOHLTC:ODB: Formulary[128] Maintenance 1st line Uniform $0.96 $1.78 $1.37 MOHLTC:ODB: Formulary[128] Maintenance benzodiazepine adjunctive 1st line Uniform $1.12 $2.07 $1.59

MOHLTC:ODB: Formulary[128]

Maintenance benzodiazepine monotherapy Uniform $0.16 $0.30 $0.23 MOHLTC:ODB: Formulary[128] Maintenance 3rd line Uniform $2.04 $3.79 $2.91 MOHLTC:ODB: Formulary[128] Titrated 1st line Uniform $1.19 $2.20 $1.70 MOHLTC:ODB: Formulary[128] Titrated benzodiazepine adjunctive 1st line Uniform $1.41 $2.63 $2.02 MOHLTC:ODB: Formulary[128] Titrated benzodiazepine monotherapy Uniform $0.16 $0.29 $0.22 MOHLTC:ODB: Formulary[128] Titrated 3rd line Uniform $4.04 $7.50 $5.77 MOHLTC:ODB: Formulary[128]

Indirect costs Absenteeism 6 months (2 days/month) Uniform $1,465.12 $2,719.60 $2,092.72 Kessler[67];Statistics Canada [132]

Resource use Treatment seeking rate Triangular 0.1000 0.3300 0.2600 Wang[112] Hospitalization rate Triangular 0.0000 0.1200 0.0600 Health Canada [113] Discount rate Uniform 0.0000 0.0500 0.0500

* Canadian 2008 dollars CIHR=Canadian Institutes of Health Information MOHLTC= Ministry of Health and Long Term care: Ontario Drug Benefit Formulary/Comparative Index ODB=Ontario Drug Benefit

80

industrial hourly wage rate for 2008 of $21.08 to calculate indirect costs and assumed an

8 hour work day.[132] Costs related to absenteeism were attributed to the assessment

health states (i.e. Family physician, Specialist assessment 2nd line and Specialist

assessment 3rd line), as well as to patients in the ‘TREATMENT DISCONTINUED’ health

state who have GAD related symptoms but are not seeking treatment for the disorder.

(i.e. 1 minus the spontaneous remission rate). It was assumed that patients not

achieving ‘spontaneous remission’ would be subject to impairment. Since indirect costs

were measured as foregone wages, indirect costs were considered only for patients 65

years of age or younger.

DISCOUNTING

In accordance with the Canadian guidelines for economic evaluations of health

technologies, costs were discounted at a rate of 5% each year and altered to 0% in the

sensitivity analysis.[146]

DISTRIBUTIONS

Triangular distributions were assumed for response, remission, treatment

discontinuation, treatment seeking behaviour, relapse rates, and spontaneous remission

and hospitalization rates, where confidence intervals were used to represent minimum

and maximum values, and point estimates represented most likely values. Confidence

intervals for response and remission rates were based on outcomes from the meta-

analysis described earlier. Confidence intervals for the remaining variables were based

on peer-reviewed literature as previously described.

Uniform distributions were assumed for costs, fees, and absenteeism. The range of

values for the variables was determined by multiplying by an arbitrary factor of ± 30%

81

around the likeliest value. Relevant resource utilization data related to GAD in Canada

was not available. As such mean of logs and related standard deviation data were not

computable; therefore use of a log normal (and most likely appropriate) distribution for

costs was not possible.

All cause mortality data were retrieved from published tables with no missing values.

Age of onset distribution was bi-modal, as previously described.

OUTCOMES

The primary outcome was the mean and standard deviation (SD) of the lifetime COI per

patient diagnosed with GAD. COI was reported in 2008 Canadian dollars (CAD$). The

primary outcome was generated from 100,000 iterations of 2nd order Monte-Carlo

simulations. That is to say, that the 2nd order Monte-Carlo simulation randomly drew

from the values of distributions for each model parameter.

Secondary outcomes included epidemiologic parameters collected from the economic

model and included the mean and SD of: the age of GAD onset, frequency of relapse

per patient, proportion of treatment-resistant patients, duration of treatment

discontinuation. Secondary outcomes also include the meta-analytic probability of

remission (defined as score on HAM-A≤7) and response [defined as a 50% reduction in

HAM-A scores, or clinical global impressions-improvement score (CGI-I) of 1 or 2].

82

VARIABILITY AND UNCERTAINTY

Structural validation of the model was performed through a ‘debugging’ process. This

process involved changing one variable at a time in the model, over a range of extreme

values that were wider than a ‘plausible’ range for that variable. [134]. For clinical

variables and probabilities, values from 0 to 1 were input into the model parameters to

determine whether they would produce expected results. For economic variables, value

inputs from 0 to twice the upper limit were input into the model. A one-way sensitivity

analysis was also performed to assess the impact of the parameter on the base case

result. The upper and lower values around the point estimate for each parameter were

used to perform the probabilistic sensitivity analysis; and were presented using tornado

diagrams. Breakdown analysis was also performed to assess the impact of each

variable on the base-case result. Validation for the course of illness and management of

GAD reflected in the Markov model was provided by expert opinion from members of the

thesis committee for this study.

COMMITTEE MEMBERS

Members of the thesis committee for this study were: Dr. Arun V. Ravindran, Dr. Beth

Sproule, Dr. Márcio Machado, Dr. Emmanuel Papadimitropoulos and Dr. Thomas R.

Einarson.

Dr. Ravindran MD, PhD, MRCPsych, FRCPC is a professor in the department psychiatry

at the University of Toronto as well as the Clinical Director of the Mood and Anxiety

Disorders Program at the Centre for Addiction and Mental Health (CAMH). Dr. Sproule

PharmD is an assistant professor in the Leslie Dan Faculty of Pharmacy at the

83

University of Toronto and is an advanced practice pharmacist and clinician scientist at

CAMH. Further validation for the pharmacotherapeutic management of GAD, the clinical

and economic parameters as well as statistical and technical methods used for this

study was provided by Dr Márcio Machado, Dr Emanuel Papadimitropoulos and Dr.

Thomas R. Einarson; experts in the field of systematic reviews, cost analysis, and

decision modeling. Márcio Machado, PharmD, PhD, and Emanuel Papadimitropoulos

PhD are assistant professors in the Faculty of Pharmacy at the University of Toronto.

Thomas Einarson PhD was the academic supervisor for this thesis and is an associate

professor in the Leslie Dan Faculty of Pharmacy at the University of Toronto.

84

SECTION 3: RESULTS

COST OF ILLNESS

REFERENCE CASE

The mean value of the lifetime COI per patient with GAD was (2008 CAD) $31,213

(SD=$9,100). The distribution statistics are presented in Table 10. The distribution is

graphically represented in Figure V. Health state probabilities are presented in Figure VI.

TABLE 11:REFERENCE CASE RESULTS* Statistic Base case

Mean $31,213

SD $9,100

Minimum $1,089

2.5% CL $5,250

10% CL $17,398

Median $33,855

90% CL $39,648

97.5% CL $42,095

Maximum value $50,248

* Values are in 2008 Canadian dollars; CL= Confidence limit, SD=Standard deviation

85

FIGURE XII: DISTRIBUTION OF COI VALUES FROM MONTE-CARLO SIMULATION

The distribution of COI values is negatively skewed. Using the formula for Pearson’s

Index of skewness yields a value greater than -1.0, suggesting that the data set is not

significantly skewed and that the mean and standard deviation are valid measures of

central tendency and variability. [195]

3*(mean-median)/standard deviation = 3* ($31,213-$33,855)/$9,100 = -0.87

BREAKDOWN COST ANALYSIS

The cost of absenteeism accounted for 96% of the mean COI. Pharmacotherapy

contributed 1.8% to the lifetime COI, while physician fees contributed 1.5%. Hospital

costs and dispensing fees contributed a total of 0.5% to the expected COI.

Monte Carlo Simulation atPatient Seeking Treatment

Pro

babi

lity

$1K $4K $7K $10K $13K $16K $19K $22K $25K $28K $31K $34K $37K $40K $43K $46K $49K

0.0800.0750.0700.0650.0600.0550.0500.0450.0400.0350.0300.0250.0200.0150.0100.0050.000

86

EPIDEMIOLOGIC PARAMETERS

The mean age at which patients entered the model was 31 years. Eighty-nine percent of

the patients who entered the model died from all causes, thereby exiting the model.

Approximately 11% of the cohort remained in the model at some point during their

expectant lifespan upon completion of the simulation. Just over 85% of patients stopped

treatment by the 4th cycle (2nd year).9 Over the course of the model, a mean of 53% of

patients relapsed; with an average rate of 0.79 relapses per patient. Approximately 19%

of patients did not respond to any pharmaco-therapeutic agent. The mean length of time

during which patients were not managed for GAD was 14(SD=9) years. The mean rates

of epidemiologic parameters are presented in Table 11.

DEBUGGING

The ‘debugging’ process provides internal validation of a decision model.[42] Extreme

values were input into the model parameters to determine whether they would produce

expected results. For clinical variables, values of 0 to 1 were input for each parameter.

As expected, response and remission, and spontaneous remission rates varied inversely

with resulting COI values. Furthermore, intolerance, relapse and discontinued treatment

parameters varied in the same direction as the resulting COI. For economic variables,

value inputs from 0 to twice the upper limit were input into the model. As expected, an

increase in cost for each economic parameter resulted in an increase in COI.

9 SEE FIGURE VI

87

FIGURE XIII: HEALTH STATE CUMULATIVE TRANSITION PROBABILITIES GRAPH

Markov Probability Analysis

Stage

Pro

babi

lity

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60 62 64

1.000.950.900.850.800.750.700.650.600.550.500.450.400.350.300.250.200.150.100.050.00

Family Physician Initial AssessmenSpecial ist Assessment 2nd LineSpecial ist Assessment 3rd l ineMaintenance 1st l ine Maintenance Benzo onlyMaintenance 1st Line + BenzoMaintenance 3rd LineTreatment stoppedDeath

88

TABLE 12:EPIDEMIOLOGIC PARAMETERS OF STUDY COHORT

Statistic Hospitalization

rate

Relapse per

patient Relapse

rate

Treatment discontinuation

rate

Treatment resistant

rate

Years of discontinued

treatment Mean 0.01 0.79 0.53 0.98 0.19 14.41 SD 0.10 0.92 0.50 0.13 0.39 8.74 Minimum 0.00 0.00 0.00 0.00 0.00 0.00 2.5%CL 0.00 0.00 0.00 1.00 0.00 1.00 10.0%CL 0.00 0.00 0.00 1.00 0.00 3.00 Median 0.00 1.00 1.00 1.00 0.00 14.00 90.0%CL 0.00 2.00 1.00 1.00 1.00 27.00 97.5%CL 0.00 3.00 1.00 1.00 1.00 30.50 Maximum 1.00 7.00 1.00 1.00 1.00 31.50

CL= Confidence limit, SD=Standard deviation

SENSITIVITY ANALYSIS One way sensitivity results for cost variables are presented in Figure VII. The COI was

most sensitive to the cost of absenteeism. The range of uncertainty for the cost of

absenteeism was $1,465-$2,720 and exerted the highest impact compared to all other

variables, varying the COI between $20,500-37,500. The mean COI result was robust

against reasonable variations in all other cost variables, with mean COI values

maintained at approximately $31,000. The range of uncertainty for clinical variables was

narrower overall ($30,000-$32,000) compared to economic variables ($20,500-37,500).

Spontaneous remission exerted the most uncertainty, varying the mean COI by

approximately $2,000, with the remaining variables varying the mean COI by $200 or

less. One way sensitivity results for clinical variables are presented in Figure VIII.

89

FIGURE XIV: Sensitivity analysis of cost variables

Tornado Diagram atPatient Seeking Treatment

Expected Value

$20K $21K $22K $23K $24K $25K $26K $27K $28K $29K $30K $31K $32K $33K $34K $35K $36K $37K $38K

Cost to society: 6 month cost =2*6*8*21.08: 1365 to 25c_Maintenance_dailycost_1st_Line: 1.12 to 2.07Daily hospital cost: 1305 to 2424c_FP_Follow_up: 21.25 to 39.46A196: 57.72 to 107.19c_maintenance_dailycost_3rdLine: 2.04 to 3.79A195: 113.74 to 211.22c_initial_FP_assessment: 42.53 to 78.98c_Maintenance_dailycost_1stL ine_benzo: 0.96 to 1.7c_dispensing_fee: 4.9 to 9.1c_ti trate_max_1st_l ine: 1.19 to 2.20c_Initial_dailycost_1stLine_Benzo: 0.80 to 1.49c_Initial_dailycost_1st_Line: 0.7 to 1.30c_max_3rd_line: 4.04 to 7.50c_Maintenance_dailycost_benzo: 0.16 to 0.30c_initial_dailycost_3rdline: 0.83 to 1.55c_Initial_dailycost_benzo: 0.10 to 0.19c_ti trate_1st_line_benzo: 1.41 to 2.63c_ti trate_benzo: 0.16 to 0.30

90

FIGURE XV: SENSITIVITY ANALYSIS OF CLINICAL VARIABLES

Tornado Diagram atPatient Seeking Treatment

Expected Value

$30.5K $31.0K $31.5K $32.0K

p_spontaneous_remission: 0.2 to 0.25p_benzo monotherapy:0 to 1

p_remission 1st Line: 0.352 to 0.441p_treatmentdisc_benzo: .363 to .6613p_treatmentdisc_3rd_line: 0.363 to 0.66134p_treatmentdisc_1stl ine_Benzo: 0.363 to 0.6613p_response_3rd_line: 0.397 to 0.702p_intolerance_1stl ine: 0.047 to 0.143p_remission_3rd_line: 0.231 to 0.482p_treatmentdisc_1stl ine: 0.7022 to 0.7266Response 1st l ine: 0.64086014 to 0.71357141p_response_benzo: 0.464 to 0.672p_response_1stl ine_benzo: 0.602 to 0.684

91

SUB ANALYSIS The addition of 10 session of CBT therapy resulted in minimal impact on the COI.

Adding 10 sessions of CBT to the 1st line assessment health state added 0.7% to the

lifetime COI. If CBT were to be added during the 2nd line or 3rd assessment health state,

the additional cost increase by 0.3% and 0.01% respectively. Results of the sub-

analysis are presented in Table 13.

TABLE 13: SUB-ANALYSIS OF COST OF ILLNESS WITH 10 SESSION OF CBT THERAPY

Statistic Reference Case CBT with 1st

Line CBT with 2nd

Line CBT with 3rd

Line

Expected value $31,199 $31,429 $31,285 $31,225 Percent difference with Reference Case + 0.7% + 0.3% +0.01%

Mean $31,213 $31,383 $31,318 $31,352

SD $9,100 $9,119 $9,114 $8,991

2.5% CL $5,250 $5,334 $5,356 $5,344

Median $33,855 $34,004 $33,894 $33,862

97.5%CL $42,095 $42,284 $42,373 $42,218 * Values are in 2008 Canadian dollars CBT=Cognitive behavioral therapy (10 sessions during assessment health states),CL=Confidence limit, SD= Standard deviation

92

META-ANALYSIS

CPA LITERATURE INCLUDED IN META-ANALYSIS

A total of 50 articles were cited as evidence for managing GAD by the CPA and

identified for this study; 17 articles were cited as evidence supporting 1st line agents

directly from the CPA guidelines. In addition, nineteen studies were identified from meta-

analytic evidence (i.e., original studies found in reference list) of 1st line treatment;

bringing the number of articles considered for 1st line agents to 36. Subsequently, 22

studies were excluded; 4 were pooled analyses,[138-141] 14 were duplicate

studies(cited in 1 or more meta-analysis and/or the guidelines), 3 were non-retrievable or

had non-extractable data,[142-144] and 1 study was not published. As a result, 14

studies were included for 1st line agents.[145-158]

Twenty-six studies supporting 2nd line agents were cited in the CPA guidelines, a further

41 studies were extracted from meta-analytic evidence. From the 67 studies

considered, 40 were excluded; 2 were pooled analysis,[139,140] 31 were duplicate

studies, and 7 were either non-retrievable or had non-extractable data[159-165], a

further 7 did not report evidence for benzodiazepine agents.

[150,157,175,181,182,189,190] Subsequently, 20 studies were included for evidence in

support of benzodiazepine. [166-174,176-180,183-188]

All 6 studies cited by the CPA for 3rd line agents were included in the meta-analysis.

[177,178.191-194] The literature search tree showing the disposition of all articles

93

identified is presented in Figure IX. A list of studies included in the meta-analysis for

each treatment line is presented in Tables 14-16.

REMISSION RATES

The meta-analytic probability of a remission in patients with GAD treated with 1st line

therapy was 39.7% [CI95% 35.2-44.1%]. There was no pooled remission result with

benzodiazepine therapy due to lack of data reported in the included studies; however,

the pooled analyses of 1st line treatment remission rates with the bezodiazepine arm

resulted in a meta-analytic probability of a remission rate of 41.0% [CI95% 36.3-45.6%] for

the adjunctive treatment line. The meta-analytic probability of remission for 3rd line

therapy was 35.7% [CI95% 23.1-48.2%]. The meta-analytic probability for 1st line therapy

was based on 12 study arms and 1502 patients. Only one study arm with 116 subjects

reported a remission rate for benzodiazepine agents. Thirteen study arms and 1618

patients were considered for pooled analysis of the adjunctive treatment line. The meta-

analytic probability of remission with 3rd line treatment was based on 2 study arms with a

total of 56 patients. Confidence intervals of meta-analytic rates of remission, response

and reduction in HAM-A scores for SNRI, benzodiazepine and 3rd line treatment options

overlap. A summary of response and remission rates by drug and treatment line is

presented in Table 16.

RESPONSE RATES

The meta-analytic probability of a response in patients with GAD treated with 1st line

therapy was 67.7% [CI95% 64.1-71.4%], 56.8% [CI95% 46.4-67.2%] for the

benozodiazepine monotherapy group, and 64.3%[CI95% 60.2-68.4%] 1st line adjunctive

94

* Does not include evidence for NOT recommending propranolol.

CPA evidence supporting recommendations of pharmacotherapy management for Generalized Anxiety Disorder N= 50*

CPA evidence supporting 1st line agents N= 17

CPA evidence supporting 2nd line agents N= 26

CPA evidence supporting 3rd line agents N= 6

Studies cited in meta-analysis or pooled analysis of evidence for 1st line agents

N= 19

Studies cited in meta-analysis or pooled analysis of evidence for 2nd line agents

N= 41

Studies cited in meta-analysis or pooled analysis of evidence for 3rd line agents

N= 0

Number of studies included for 1st line agents N = 36

Number of studies included for 2nd line agents N=67

Number of studies included for 3rd line agents N= 6

Studies excluded: Pooled analysis (4) duplicate (14), not

published (1), non-extractable/non-retrievable (3)

N= 22

Studies excluded: Pooled analysis (3) duplicate (30)

non-extractable/non-retrievable (7) agent other than benzodiazepine (7)

N=40

Studies excluded: N= 0

Total number of articles included in meta-analysis for 1st line outcomes:

N= 14

Total number of articles included in meta-analysis for benzodiazepine outcomes:

N=20

Total number of articles included in meta-analysis for 3rd line outcomes:

N= 6

FIGURE XVI: Literature search results for a meta-analysis of clinical outcomes for GAD.

95

TABLE 14: STUDIES INCLUDED AS 1ST LINE EVIDENCE AND RELATED HAM-A SCORES

Drug Drug First author, year HAM-A Baseline HAM-A Endpoint Mean change in scores [95% CI] class (Fixed dose mg ) Mean SD N Mean SD N

SNRI Venlafaxine Allgulander, 2001 (150) 26.3 4.2 131 9.9 1.6 106 -16.4 [-17.2, -15.6] Allgulander, 2001 (37.5) 26.6 4.3 138 12.8 2.1 102 -13.8 [-14.6, -13.0] Allgulander, 2001 (75) 26.3 4.2 130 10.8 1.7 101 -15.5 [-16.3, -14.7] Davidson, 1999 (150) 23.0 4.0 87 13.8 2.2 73 -9.2 [-10.2, -8.2] Davidson, 1999 (75) 23.7 4.1 87 13.0 2.1 80 -10.7 [-11.7, -9.7] Gelenberg, 2000 25.0 5.0 115 11.6 1.9 60 -13.4 [-14.4, -12.4] Nimatoudis, 2004 27.1 4.8 24 7.9 1.3 24 -19.2 [-21.2, -17.2] Rickels, 2000 (150) 24.5 4.1 81 12.1 2.0 52 -12.46 [-13.4, -11.3] Rickels, 2000 (225) 23.6 3.7 86 12.1 1.9 58 -11.5 [-12.4, -10.6] Rickels, 2000 (75) 24.7 4.4 86 13.5 2.2 60 -11.2 [-12.3, -10.1] Silverstone, 2001 13.2 2.1 32 25.7 4.1 32 -13.2[-14.7, -11.7] OVERALL SNRI 997 748 -13.3 [-14.9, -11.7] SSRI Escitalopram Baldwin, 2006 (5) 27.1 4.5 134 11.6 1.9 116 -15.5 [-16.3, -14.6] Baldwin, 2006 (10) 26.0 4.1 136 9.2 1.5 118 -16.8 [-17.5, -16.0] Baldwin, 2006 (20) 27.7 4.9 133 11.4 1.8 111 -16.4 [-17.2, -15.4] Bielski, 2005 23.7 3.9 60 8.4 1.4 39 -15.3 [-16.4, -14.2] Davidson, 2004 23.6 4.6 154 12.3 2.0 116 -11.3 [-12.1, -10.4] Paroxetine Baldwin, 2006 20.8 2.3 25 8.88 1.43 20 -11.9 [-13.0, -10.8] Ball, 2005 27.3 4.2 139 12.59 2.02 113 -14.7 [-15.5, -13.9] Bielski, 2005 23.4 3.1 61 10.1 1.62 33 -13.3 [-14.2, -12.4] Pollack, 2001 24.2 3.8 161 12.2 1.96 127 -12.0 [-12.6, -11.3] Rickels, 2003 (20) 24.1 3.6 189 11.6 1.86 143 -12.5 [-13.1, -11.9] Rickels, 2003 (40) 23.8 3.4 197 11.6 1.86 143 -12.2 [-12.8, -11.6] Rocca, 1997 26.7 2.6 30 11.1 3.3 25 -15.6 [-17.2, -14.0] Sertraline Allgulander, 2004 24.6 4.6 182 12.9 2.07 147 -11.7 [-12.4, -10.9] Ball, 2005 21.4 3.4 28 9.44 1.52 23 -12.0 [-13.5, -10.6] OVERALL SSRI 1629 1274 -13.6 [-14.6, -12.6] OVERALL 1st Line 2626 2022 -13.5 [-14.3, -12.6] Heterogeneity: Tau² = 4.24; Chi² = 526.48, df = 24 (P < 0.00001); I² = 95%; Test for overall effect: Z = 31.65 (P < 0.00001) SNRI= serotonin norepinephrine reuptake inhibitors SSRI= selective serotonin reuptake inhibitors

96

TABLE 15:STUDIES INCLUDED AS SECOND LINE EVIDENCE (BENZODIAZEPINE MONOTHERAPY) AND RELATED HAM-A SCORES HAM-A scores reported in studies First author, year

(Fixed dose mg ) Baseline Endpoint Mean Reduction

[95% CI] Drug Mean SD N Mean SD N Alprazolam Castillo,1987 26.1 4.3 31 15.8 2.6 32 -10.3 [-12.1, -8.6] Cohn,1984 28.5 4.7 80 18.9 3.1 46 -9.6 [-11.0, -8.2] Enkelmann,1991 28.0 4.6 32 12.5 2.1 28 -15.5 [-17.3, -13.7] Lydiard,1997 24.1 4.0 63 11.1 1.8 45 -13.0[-14.1, -11.9] Moller, 2001 27.9 7.6 102 12.6 7.5 98 -15.3 [-17.4, -13.2] Rickels, 2005 24.9 3.8 88 14.0 2.3 68 -10.9 [-11.9, -9.9] Bromazepam Fontaine,1983 24.1 4.0 16 8.8 1.5 15 -15.3[-17.4, -13.2] Fontaine,1986 28.8 4.8 20 14.8 2.5 17 -14.0 [-16.4, -11.6] Llorca, 2002 25.3 3.4 116 12.3 2.0 99 -13.0 [-13.7, -12.3] Diazepam Boyer,1993 24.4 4.0 55 12.3 2.0 47 -12.1 [-13.3, -10.9] Fontaine,1983 27.4 4.5 16 15.3 2.5 16 -12.1 [-14.7, -9.6] Golberg,1982 25.2 4.2 18 9.2 1.5 18 -16.0 [-18.0, -13.9] Pourmotabbed,1986 25.4 3.1 10 9.8 4.4 10 -15.6 [-18.9, -12.3] Power,1989 18.7 3.7 22 10.5 7.0 17 -8.20 [-11.87, -4.53] Rickels,1993 26.0 4.3 56 13.5 2.2 49 -12.5 [-13.8, -11.2] Rickels,1997 28.5 5.5 67 12.0 2.0 44 -16.5 [-17.9, -15.1] Rickels,2000 24.0 4.0 104 13.0 2.2 80 -11.0 [-11.9, -10.1] Lorazepam Cohn,1984 28.5 4.7 80 20.5 3.4 33 -8.0 [-9.5, -6.4] Cutler,1993 28.3 4.7 20 15.8 2.6 19 -12.5 [-14.9, -10.1] Feltner, 2003 24.9 4.1 104 13.2 2.2 86 -11.7 [-12.6, -10.8] Fontaine,1986 24.7 3.7 68 13.1 2.2 36 -11.6 [-12.7, -10.5] Fresquet, 2000 21.5 3.2 30 15.3 2.5 29 -6.2 [-7.7, -4.7] Laakman,1998 27.5 4.6 57 12.0 2.0 47 -15.5 [-16.8, -14.2] Overall Benzodiazepine 1255 979 -12.2 [-12.4, -11.9]

Heterogeneity: Chi² = 245.24, df = 22 (P < 0.00001); I² = 92%; Test for overall effect: Z = 84.76 (P < 0.00001) CI=Confidence Interval; HAM-A= Hamilton Anxiety Scale; N=Number of subjects; SD=Standard deviation; TCA=Tricyclic Antidepressant

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TABLE 16:STUDIES INCLUDED AS THIRD LINE EVIDENCE AND RELATED HAM-A SCORES. HAM-A-A scores reported in studies Baseline Endpoint Mean reduction Author, year Mean SD N Mean SD N [CI95%] Adjunctive therapy

Olanzapine + Fluoxetine Pollack, 2006 17.4 6.5 12 10.4 6.4 9 -7.0 [-12.6, -1.4]

Risperidone + Anxiolytics Brawman, 2005 22.1 3.8 19 12.3 2.6 19 -9.8 [-11.9, -7.7]

Gambi, 2005 26.4 6.0 44 9.6 5.8 39 -16.8 [-19.3, -14.3] SSRI Citalopram Varia, 2002 22.2 4.7 13 6.2 3.2 13 -16.0 [-19.1, -12.9] Antihistamine Hydroxyzine Lader,1998 26.6 4.3 81 14.5 3.1 70 -12.1 [-13.3, -10.9] Llorca, 2002 25.5 3.6 105 13.3 2.9 88 -12.2 [-13.1, -11.3] Overall 3rd line 274 238 -12.3 [-13.0, -11.7]

Heterogeneity: Chi² = 26.74, df = 5 (P <0.0001); I² = 81% Test for overall effect: Z = 37.64 (P < 0.00001) CI=Confidence Interval; HAM-A= Hamilton Anxiety Scale; n=number of subjects; SD=Standard deviation; SSRI= selective serotonin reuptake inhibitors

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TABLE 17: META-ANALYTIC RESPONSE AND REMISSION RATES BY TREATMENT LINE

RESPONSE REMISSION ARMS N PMA CI95%LL CI95%UL ARMS N PMA CI95%LL CI95%UL

1st Line Paroxetine 6 772 0.645 0.611 0.679 5 711 0.349 0.308 0.390 Escitalopram 5 617 0.718 0.643 0.793 4 557 0.437 0.377 0.497 Sertraline 2 210 0.643 0.578 0.708 2 210 0.388 0.215 0.561 Venlafaxine 7 712 0.684 0.601 0.767 1 24 0.625 0.340 0.910 Total 20 2311 0.677 0.641 0.714 12 1502 0.397 0.352 0.441

2nd Line (Monotherapy) Alprazolam 3 253 0.573 0.434 0.712 NA Bromazepam 1 116 0.578 NA NA NA Lorazepam 4 259 0.465 0.407 0.522 1 116 0.535 N/A NA Diazepam 2 147 0.786 0.720 0.852 NA Total 10 775 0.568 0.464 0.672 NA 2nd Line (adjunctive) 1st Line & Benzodiazepine

30 3086 0.643 0.602 0.684 13 1618 0.410 0.363 0.456

Total 3rd line 4 242 0.549 0.397 0.702 2 56 0.357 0.231 0.482

N= number of subjects,na=not applicable, PMA= Meta-analytic probability, SE= Standard error, SNRI= serotonin norepinephrine reuptake inhibitors, SSRI= selective serotonin reuptake inhibitors

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with benzodiazepine and 54.9% [CI95% 39.7-70.2%] for 3rd line treatment. Response rates

were based on 20 study arms and 2311 patients treated with 1st line treatment, 10 study

arms and 775 patients treated with benzodiazepine monotherapy therapy, 30 arms and

3086 patients with 1st line adjunctive therapy, and 4 study arms and 242 patients treated

with 3rd line therapy. Details appear in Table 16.

MEAN CHANGES IN HAM-A SCORES

The weighted average HAM-A score at baseline for 1st line treatment study arms was

24.4 (SD=0.8), 25.2 (SD=0.9) for the benzodiazepine study arms and 22.1 (SD=1.8) for

the 3rd line study arms. The mean difference from baseline HAM-A scores were: 1st line

-13.5 (CI95%-14.3,-12.6), benzodiazepine, -12.16 (CI95%-12.44, -11.87), 3rd line: -12.3

(CI95% -13.0, -11.7). The mean difference in HAM-A scores were similar for the SSRI

drug class [-13.64 (CI95% -14.65, -12.62)], and venlafaxine [-13.28 (CI95%-14.9, - 11.7].

A summary of HAM-A scores by treatment line, drug class and drugs is presented in

Table 18.

ASSESSMENT OF HETEROGENEITY

Between study heterogeneity of outcomes is likely for each of the three treatment lines

given the highly significant p-values for chi-squares, and tau-squared and I-squared

values (1st line evidence: Tau² = 4.24; Chi² = 526.48, df = 24 (P < 0.00001); I² = 95%;

benzodiazepines: Chi² = 245.24, df = 22 (P < 0.00001); I² = 92%; 3rd line evidence Chi²

= 26.74, df = 5 (P < 0.0001); I² = 81%). There is also evidence of heterogeneity within

drug classes and individual drugs with the exception of sertraline [Tau² = 0.00; Chi² =

0.10, df = 1 (P = 0.75); I² = 0%] and bromazepam [Tau² = 0.92; Chi² = 4.47, df = 2 (P =

0.11); I² = 55

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TABLE 18:SUMMARY HAM-A SCORES BY TREATMENT LINE, DRUG CLASS AND DRUG Study N Mean HAM-A Scores Drug Class Drug Arms Baseline Endpoint Baseline (SD) Difference [CI95%] p-value SSRI Paroxetine 7 802 604 23.9 (1.17) -13.0 [-13.9, -12.21] p< 0.00001 Escitalopram 5 617 500 25.5 (1.95) -15.0 [-17.07, -13.01] p< 0.00001 Sertraline 2 210 170 24.3 (1.40) -11.7 [-12.42, -11.10] p=0.75 SSRI Total 14 1629 1274 24.1 (0.94) -13.6 [-14.65, -12.62] p< 0.00001 SNRI Venlafaxine 11 997 748 24.3 (1.60) -13.28 [-14.89, -11.66] p< 0.00001 Total 1st Line 25 2626 2022 24.4 (0.77) -13.48 [-14.33, -12.63] p< 0.00001

Benzodiazepine Alprazolam 6 396 317 26.2 (1.84) -12.36 [-14.14, -10.58] p< 0.00001 Bromazepam 3 152 131 25.7 (2.29) -13.83 [-15.28, -12.39] p=0.11 Lorazepam 6 359 250 25.2 (1.65) -10.91 [-13.41, -8.42] p< 0.00001 Diazepam 8 348 281 24.2 (1.61) -13.09 [-14.80, -11.38] p< 0.00001 Benzodiazepine TOTAL 23 1255 979 25.2 (0.86) -12.16 [-12.44, -11.87] p< 0.00001 Total 3rd Line 6 274 238 22.1 (1.84) -12.31 [-12.96, -11.67] p< 0.0001

CI= Confidence Interval; N= number of subjects, SNRI= serotonin norepinephrine reuptake inhibitors SSRI= selective serotonin reuptake inhibitors TCA= Tricyclic anti-depressant

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SECTION 4: DISCUSSION, CONCLUSIONS, RECOMMENDATIONS

SUMMARY OF FINDINGS

The estimated mean lifetime COI per patient with GAD was (2008 CAD) $31,213 (SD=$9,100).

Most (96%) of this estimate was attributed to the cost of absenteeism. The remaining 4% of

costs was attributed to pharmacotherapy, physician fees, dispensing fees and hospital costs.

The average age at which patients were diagnosed with GAD was 31 years. A large percentage

of patients (85%) discontinued treatment by 2nd year of treatment. Approximately 53% of

patients relapsed by the end of the model simulation. The model structure also resulted in

approximately 19% of patients not responding to any pharmaco-therapeutic agent at which point

patients where either hospitalized for 10 days or simply had their treatment stopped. The mean

length of time during which patients discontinued treatment was 14 (SD=9) years. The results

were most sensitive to the cost of absenteeism, which exerted the highest impact compared to

all other variables, varying the COI between $20,500 - $37,500.

STRUCTURE

ANALYTIC FRAMEWORK

This incidence based model reflects the clinical pathway of a lifetime of waxing and waning of

symptoms due to GAD in patients 18-80. Prior to the development of this study, models

reflecting lifetime course of illness related to GAD have not been published. Published COI

studies were cross-sectional or prevalence based in design and estimated the health care

charges over periods of ≤18 months. Full economic evaluations related to GAD have been

designed using summary population data or conventional decision-tree models and were ≤12

months in length. One COI limited the cost to medical expenses, estimating that GAD incurred a

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marginal mean cost of USD $2,138 (95% CI, $1,641–$2,632) over a one year period in cohort

taken from a health care charges database.[69] Another COI study estimated significantly

higher median medical care charges in patients with GAD than those with no GAD ($2,375 vs.

$1448). The study also predicted health care charges of pain interference with daily activities in

GAD patients were highest for patients with GAD and high pain interference ($42,620) followed

by no GAD high pain interference ($9,601 P<0.0001).[73] However, there is evidence that

anxiety is under-treated and under-diagnosed, limiting the usefulness of either population based

studies or COI studies using healthcare charges. [141-143] In a cross-sectional survey of New

York city’s non-institutionalized adults, 62% of respondents with either depression or anxiety

were not treated for their condition.[141] Reports based in the National Comorbidity Survey

Replication suggest that less than 3% of patients with GAD make use of any health care

services.[143] Furthermore, neither productivity nor absenteeism was incorporated into the

estimation of the cost of GAD in either of the above mentioned COI studies. The use of a

Markov decision analytic framework to estimate the COI of newly diagnosed patients with GAD

allows for the consideration of chronicity as well as for the waxing and waning of symptoms. An

incidence based approach avoids dealing with the issue of under-diagnosis or under-treatment

of patients. Furthermore, the model could easily be adapted to estimate cost-effectiveness by

adding a second branch comparing a novel therapeutic (e.g. duloxetine) to existing treatment

options.

PATIENTS SEEKING TREATMENT

In this study, costs accrued once patients were diagnosed with GAD by a physician in family

practice which may result in a conservative bias to the result. Patients with GAD are not likely

to initially present with anxiety complaints.[8,91] GAD patients are more likely to present

somatic symptoms such as pain, insomnia, cardiac and gastrointestinal symptoms, that may

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dominate the focus of their initial visit.[19, 20] A reasonable assumption therefore, is that prior

to a primary diagnosis of GAD, patients are referred to one or more specialists for further

assessment of their somatic symptoms.10 This assumption is supported by several studies. A

study by Logue reports that GAD was the primary diagnosis in 50% of patients seeking a

cardiac evaluation;[140] while studies by Walker and Lydiard, who respectively reported that

GAD was the primary diagnosis in 13 and 25% of patients with irritable bowel

syndrome(IBS).[138,139] It should be noted that the Walker and Lydiard studies did not report

on the same type of patient population as did Logue. While Walker and Lydiard reported on

patients who have been diagnosed with IBS, we do not know how many GAD patients in the

Logue studies were eventually diagnosed with a cardiac condition. Furthermore, consistent

rates of GAD patients presenting with pain or insomnia were not found. Given the missing or

inconsistent nature of the data, this study does not account for the resource utilization accrued,

prior to the primary diagnosis of GAD. The exclusion of these costs biases the mean COI

presented in this thesis towards a more conservative estimate.

BASELINE HAM-A SCORES

Patients with GAD proceeded through the model with similar baseline HAM-A scores, derived

from the meta-analysis of CPA evidence.11 Baseline HAM-A scores were similar for 1st and

benzodiazepine monotherapy options while slightly lower mean for the 3rd line [24.4(SD=0.8),

25.2(SD=0.6) and 22.1(SD=1.8) respectively]. Mean differences from baseline to endpoint

HAM-A scores were greater for patients treated with 1st line therapy compared to those treated

with benzodiazepine monotherapy or 3rd line therapy; 1st line: -13.5 (CI95%-14.3,-12.6),

benzodiazepine, -12.16 (CI95%-12.44, -11.87), 3rd line: -12.3 (CI95% -13.0, -11.7).

10 See Table 5 on page 37 11 See Table 17 on page 100

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TREATMENT ALGORITHM

The model assumed physicians would manage GAD patients as per the pharmacotherapy

algorithm recommended by the CPA guidelines (with deleted 2nd line agents as listed in the

Methods section). Since no studies have reported the treatment algorithm usually carried out for

patients with GAD in Canada, it is unknown which treatment pattern family physicians prescribe

for GAD patients or what access they have to therapists in a Canadian setting. Therefore, a

comparison of the estimated mean COI to one that would reflect ‘real-life’ management of GAD

is not possible. Furthermore, while the CPA provides no recommendation regarding the

combination of psycho- and pharmacotherapy due to lack of evidence, a US survey of over

1800 non-institutionalized adults reported that 23% of patients with current anxiety were treated

with a combination of medication and counseling.[141] A sub-analysis was performed in this

study of CBT counseling in addition to pharmacotherapy in either 1st, 2nd or 3rd line assessments

and determined that combination therapy would add between 0.01-0.07% in Candian dollars to

the base case mean COI.

PROCESS

CLINICAL VARIABLES

Remission and response of treatment options in this study

Meta-analytic remission rates for 1st, 1st line adjunctive with benzodiazepine (2nd line option),

and 3rd line therapy were 39.7% [CI95% 35.2-44.1%], 41.0% [CI95% 36.3-45.6%] and 35.7% [CI95%

23.1-48.2%] respectively. While 2nd line therapy of 1st line adjunctive with benzodiazepine

produced the highest meta-analytic remission rate, all confidence interval rates overlapped

between treatment lines; implying that one line of therapy was not clinically superior to another.

There was no pooled remission result for benzodiazepine as monotherapy (2nd line option).

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The meta-analytic probability of a response in patients with GAD treated with 1st line therapy

was 67.7% [CI95% 64.1-71.4%], 56.8% [CI95% 46.4-67.2%] for the benozodiazepine monotherapy

group, and 64.3%[CI95% 60.2-68.4%] 1st line adjunctive with benzodiazepine and 54.9% [CI95%

39.7-70.2%] for 3rd line treatment. Response rates for 1st line were clearly higher, and

uncertainty ranges narrower than those for benzodiazepine monotherapy. The 3rd line meta-

analytic effect yielded a wider range of uncertainty, overlapping both the 1st and 2nd line

response rates confidence intervals. Treatment with 1st line recommended agents yields a

higher response and greater reduction in mean HAM-A scores than benzodiazepine

monotherapy or 3rd line treatment.

Treatment discontinued

Over 85% of the patients in this model discontinued treatment by the 2nd year. Treatment may

have been discontinued by patients for any reason including remission or lack of efficacy. As a

reminder, intolerance was accounted for earlier in the decision tree. Increased

persistence/adherence to medication may reduce the likelihood of a return of symptoms,

thereby reducing absenteeism and increasing productivity.

Relapse rate

Relapse rates for this study were derived from a study by Yonkers et al., who reported

cumulative relapse rates over an 8 year follow up period.[110] For this study, the cumulative

probability for both genders was computed by weighting the cumulative probability of each

gender by their respective sample size. Individual yearly rates were then computed. The

relapse rate used in this study was elected assuming that a weighted mean relapse rate from an

8 year follow up of GAD patients could be extrapolated on to a longer time horizon. At the 8-

year follow up by Yonkers, cumulative relapse rates at year 8 were 43% for men and 36% for

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women.[37] Assuming that the mean age onset in this study was 31 years, and patients exited

at 80, a conservative relapse rate for a patient in this model over a 49 year time horizon is 53%.

In a sensitivity analysis, the COI was not sensitive to the relapse rate; and therefore does not

introduce a substantial bias either way. In an 8-year follow up study, cumulative relapse rates at

year 1 and year 8 were 19% and 43% for men; and 6% and 36% for women.[37]

Spontaneous remission

Spontaneous remission exerted the greatest impact of all clinical variables; varying the COI

from approximately $30,000 to $32,000. The spontaneous remission rate of “around 20-25%”

was taken directly from a consensus statement on GAD from the International Consensus

Group on Depression and Anxiety, from a meeting held in March 2002 in South Africa.[111] The

statement does not define nor describe ‘spontaneous remission’. In this study, the spontaneous

remission rate was used solely in the TREATMENT DISCONTINUED health state. Individuals

enter this state upon discontinuing treatment for any reason. Within this health state, individuals

are then differentiated between those who ‘remit spontaneously’ and those who do not. From

that point, patients either feel the need to seek treatment anew and re-enter the Family

physician health state (1st health state) or they do not feel the need to seek treatment; even

those patients have not ‘spontaneously remitted’. This model assumes that patients

experiencing ‘spontaneous remission’ no longer feel the need to seek treatment and no longer

exhibit any GAD related symptoms. This model also assumes that patients who have not

experienced ‘spontaneous remission’ have experienced a relapse of symptoms and may or may

not seek treatment anew.

Attrition rate

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Although several studies compare suicidal ideation in a GAD cohort to a co-morbid GAD cohort

and other anxiety disorder, none report raw data of suicide rates attributable to GAD. The age-

specific attrition rate used in this study would be reasonable infer that these rates include

suicide rates from a population that includes all mental disorders. Therefore the net increase of

suicide as a direct result of GAD is hypothesized to be negligible for the purposes of this study.

RESOURCE UTILIZATION AND COSTS

Absenteeism

The estimated COI in this study was sensitive to the cost of absenteeism. The mean number of

impaired days per month upon which the cost of absenteeism was calculated in this study was

2, with an implicit range of uncertainty between 1.4 and 2.6 days per month. The mean number

of impaired days was based on Kessler’s study of two large surveys conducted in the United

States: the National Co-morbidity Survey and the Midlife Development in the United States

Survey. The surveys distinguished between productivity loss while at work and work days lost.

The number of work days lost was assessed by asking respondents how many days in the past

month they were unable to work or carry out their normal daily activities due to their emotions,

nerves, or mental health. Productivity loss was assessed by asking respondents how many

days in the past month, exclusive of work loss days; they cut back on the amount of work

performed due to problems with their emotions, nerves, or mental health. Responses were

aggregated, and the percent of responders were then categorized into 0, 1 to 2, 3 to 5, and 6 or

more work impairment days.[67] The distribution of the percentage of respondents in each

category was bi-modal for data associated with GAD and MDD as seen in Table 18.

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TABLE 19: PAST MONTH’S WORK IMPAIRMENT ASSOCIATED WITH GAD AND MDD REPORTED BY KESSLER[67] Survey NCS MDUSS Days MDD&GAD GAD MDD&GAD GAD 0 58.2% 78.2% 49.9% 79.7% 1or 2 8.1% 7.9% 9.2% 9.5% 3-5 11.8% 2.6% 8.8% 2.5% 6 or more 21.9% 11.3% 32.1% 8.3%

GAD= Generalized Anxiety Disorder, NCS=National Co-morbidity Survey, MDD=Major Depressive Disorder, MDUSS=Midlife Development in the United States Survey

By using Kessler’s data, this study implicitly incorporated both productivity loss as well as

absenteeism. The cost of absenteeism is considered to be a conservative estimate. First, the

cost related to absenteeism was not accrued for patients over the age of 65. Furthermore, cost

related to absenteeism was only accrued while patients were assessed for treatment and not

while patients were on maintenance regimens or in any other health states. This implies that by

the time patients were in the maintenance therapy states, anxiety symptoms were managed and

did not interfere with daily life or work activities.

The COI was not sensitive to physician or pharmacist fees, or drug costs. The distributions for

physician and pharmacist fees incorporated a wider range of uncertainty (± 30% of the likeliest

value) than those published in the Ontario Schedule of Benefits. Although most CPA

recommended agents were considered in this model, some were excluded. For 2nd line therapy,

buspirone, imipramine, pregabalin, and bupropion were not considered in this study because of

the agents were either; not listed as a benefit in the Ontario drug formulary, had limited clinical

use in Ontario, were cited as having a risk of death from overdose or required limited use

authorization. From a cost perspective, given that the estimated daily cost for these agents is

largely within the range of costs for 2nd line therapy (Table 19) used in this model, it is unlikely

that consideration of these agents would have a significant impact on the mean COI.

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TABLE 20: DAILY COST OF AGENTS EXCLUDED FROM 2ND LINE THERAPY Agent Daily Dose Daily Cost* Bupropion 150-450mg .40-1.20 Buspirone 20-30mg 1.30-1.96 Imipramine 25-150mg .12-.77 Pregabalin 50-150mg 1.84-2.53†

*MOHLTC:Ontario Drug Formulary†Loblaws Pharmacy 18 Musgrove Ave Toronto, Ontario.

LIMITATIONS

Lack of available sources reporting both clinical and resource utilization data was a significant

limitation to this study. Of note, remission rates for benzodiazepines were scarce primarily due

to the quality of reporting. Discontinuation rates for benzodiazepines were derived from a

persistence rate and none was available for 3rd line treatment. Furthermore, there are no

available data regarding the Canadian utilization patterns of drugs by GAD patients. The

inherent selection bias for the meta-analysis as well as the heterogeneity of pooled results for

remission and response rates were also limitations to the study. The limitations for both clinical

and economic variables contributed to the uncertainty of the mean estimate.

INTOLERANCE

No peer reviewed studies reporting intolerance rates for each treatment line used in this study

have been published. Meta-analysis of intolerance rates to SSRI and SNRI agents in patients

with MDD by Machado et al [101] was used as proxy for intolerance to 1st line treatment in this

model. Therefore, intolerance to SSRI and SNRI agents in patients with MDD was assumed to

be the same for patients with GAD. Despite a meta-analysis reporting the effect size of

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withdrawal for any reason and withdrawal for lack of efficacy in GAD patients treated with

benzodiazepines, Martin et al [102] did not report a meta-analytic rate of withdrawal due to

intolerance. Therefore, a pooled analysis of drop-out rates which included 14 study arms

(n=928) from CPA evidence for bromzepam (1 arm), lorazepam (5 arms) and diazepam (8

arms) was used as a proxy for intolerance rate for benzodiazepines. Furthermore, no

publication reported intolerance rates to SSRI or SNRI treatment adjunctive with

benzodiazepine agents. Since the intolerance rates for 1st line were higher than those for

benzodiazepine monotherapy, the rates by Machado et at were once again used as proxy for

intolerance to 1st line adjunctive with benzodiazepine treatment. No data regarding the

aggregate intolerance to 3rd line therapy was available. However, given the structure of the

model, GAD patients would need to be intolerant to the 1st and 2nd line treatment options before

entering the 3rd line option. Therefore, the probability of intolerance to the 3rd line as well was

assumed to be too low to be considered significant to the outcome of this model. The thesis

committee concurred with this assumption. The model therefore infers that GAD patients cannot

be intolerant to agents in all three treatment options. In a one way sensitivity analysis, COI was

not sensitive to intolerance rates.

REMISSION AND RESPONSE

Compared to the data available for 1st line treatment, sample sizes used to pool remission rates

for the remaining treatment options were low. Twelve study arms (n=1502) were included for 1st

line pooled remission rates, 1 study arm (n=116) for benzodiazepine monotherapy (therefore

resulting in no pooling of remission results), and 2 study arms (n=56) for 3rd line remission rates.

Given the lack of data available to derive the meta-analytic probability of remission for 2nd

treatment and the small sample size of 3rd line studies, a comparison to the 1st line meta-analytic

remission rate would not be informative. Given that the meta-analytic probability of remission for

venlafaxine was based on 1 study (n=24); additional studies reporting remission rates on GAD

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patients treated with venlafaxine would have reduced the degree of uncertainty for that outcome

and potentially change the pooled estimate found here. Studies reporting outcomes from 2nd line

therapy for GAD tended not to report remission rates. Furthermore outcomes were generally

presented in figure format, increasing the possibility of incurring data extraction errors.

However, the meta-analytic mean of response rates for all three treatment lines were based on

larger sample sizes. Response rates for 1st line treatment were based on 20 study arms (n=

2311), 10 study arms (n=775) for the benzodiazepine monotherapy therapy, and 4 study arms

(n=242) for 3rd line therapy. In a one way sensitivity analysis, COI was not sensitive to remission

or response rates.

TREATMENT DISCONTINUATION

Discontinuation rate for 1st line therapy was based on a study of over 14,000 patients with a

variety of anxiety and mood disorders; with no disaggregated data for GAD. Furthermore,

results of discontinuation rates were limited to sertraline, paroxetine and citalopram.

Discontinuation of benzodiazepine was based on a survey conducted on the general population

in the United Kingdom. One minus the persistent use of benzodiazepines used for more than 1

year was used as a proxy for discontinuation rate for this treatment line. This produced a

discontinuation rate of 52%. No treatment discontinuation rates were available for either 1st line

adjunctive therapy with benzodiazepine or for 3rd line therapy. The discontinuation rate for

benzodiazepine monotherapy was used as a proxy and a more conservative available estimate.

RESOURCE USE AND COSTS

There were no Canadian data on utilization patterns of drugs included in the treatment lines.

Therefore, a comparison of mean daily costs for medication could not be validated. However,

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given that COI was not sensitive to drug costs, this lack of data was did not contribute to the

uncertainty of the estimate.

META-ANALYSIS

The studies included in this meta-analysis were sampled from evidence cited by the CPA as

well as original research from meta-analytic evidence guideline for the management of GAD.

The selection of studies was therefore guided by the CPA guideline committee imposing a large

limitation to the extrapolation of the results found here due to the inherent selection bias.

Heterogeneity of effects was a limitation in the meta-analysis. However, response and

remission rates as well as HAM-A scores were combined despite the presence of heterogeneity.

The decision to proceed with the combination of results was based on the context of the study,

and the absence of guidelines related to the presence of heterogeneity. [65,66] One method of

reducing the effect of heterogeneity is to remove studies that differ in patient characteristics or

underlying condition. However, given that this study set out to determine the effect size of each

treatment line recommended by the CPA, within the parameters set out by the thesis committee;

excluding studies with extractable data to achieve statistical homogeneity would not address the

study question. Another method used in an attempt to reduce heterogeneity was to stratify the

results by individual drugs as opposed to treatment line. Heterogeneity was still present and

was possibly due to the combination of individual study arms of each drug; which in turn

combined different patient and disease characteristics to the effect size by the dissolution of the

randomization component (where available) of studies included in the analysis. Furthermore,

the use of the random effects model would account for between study variance and serve to

minimize but does not completely eliminate heterogeneity in pooled estimates.

CONCLUSIONS

113

A model reflecting the course of illness of GAD has been developed. From a societal

perspective, absenteeism exerts a significant impact to the cost of illness of GAD. Lack of

prospective clinical data contributes to the uncertainty of the COI estimate.

Meta-analysis

Since confidence intervals of meta-analytic rates of remission, response and reduction in HAM-

A scores overlap; there is no statistical evidence to support CPA guideline recommendations of

pharmacotherapeutic management for GAD.

ESTIMATED BURDEN OF ILLNESS The estimated burden of illness of GAD in Canada based on this COI ranges between

$397,488,000 and $944,034,000. This estimated range is based on a lifetime prevalence rate

of 2.4%-5.7% and 26 million people over the age of 15 in Canada in 2006.[14-16,145]

Assuming that a patient entering our model with an onset of GAD at 31 years of age and lives

until the age of 80, the mean cost of GAD per year per patient would be $637 [$31,213/(80-31)].

This is a conservative estimate since 11% of the patients in this study outlive the model.

Nevertheless, given these parameters, the burden of illness in Canada would therefore be

between $397,488,000 and $944,034,000 (2008). Comparison of the burden of illness between

studies should be undertaken with extreme caution; given the variability of methods and

variables included in the analysis. However, the economic burden of illness for 1998 published

by Health Canada reports total costs (direct and indirect) for mental disorders at $7.9 billion

dollars (1998). Discounting the 2008 burden of illness at 5% per annum, yields a discounted

estimate between $244,022,000 and $579,553,070. With caution, one may infer that GAD

contributes between 3.1% and 7.3% to the burden of mental health in Canada.

114

RECOMMENDATIONS

Research determining which therapies are most efficacious given patient and disease

characteristics; such as severity of GAD, age of patient or for treatment resistant patients may

improve the estimate of the COI of GAD. Future research could also investigate the aspects

around the appropriateness of setting lines of therapy in GAD using levels of evidence based on

published and unpublished research. Resource utilization of Canadian treatment patterns for

GAD would be useful to estimate COI and to assess whether guidelines are followed.

115

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LIST OF PUBLICATIONS AND ABSTRACTS In preparation or submitted Bereza BG, Machado M, Papadimitropoulos M, Sproule B, Ravindran AV, Einarson TR. The cost of illness of generalized anxiety disorder (GAD) in Canada: A Markov approach. (In preparation) Bereza BG, Machado M, Ravindran AV, Einarson TR. Evidence based review of clinical outcomes of guideline recommended pharmacotherapies for generalized anxiety disorder: a meta-analysis (In preparation) Longo CJ, Bereza BG. An analysis of monthly “out-of-pocket” costs for patients with breast cancer compared to common cancers in Ontario, Canada (Submitted) Accepted or published Bereza BG, Machado M, Einarson TR. Systematic review and quality assessment of economic evaluations and quality of life studies related to generalized anxiety disorder. Clinical Therapeutics 2009; 31:1279-1308 Iskedjian M, Desjardins O, Piwko C, Bereza B, Jaszewski B, Einarson TR. Willingness-to-pay for a treatment for pain in multiple sclerosis. PharmacoEconomics 2009; 27:149-58

Bereza BG, Machado M, Einarson TR. Assessing the reporting and scientific quality of meta-analyses of randomized controlled trials of treatments for anxiety disorders. Annals of Pharmacotherapy 2008; 42:1402-9

Iskedjian M, Walker JH, Bereza BG, Danchenko N, Le Melledo J-M, Einarson TR. Cost effectiveness of escitalopram in the treatment of generalized anxiety disorder. Current Medical Research & Opinion 2008; 24: 1539-48

Piwko C, Desjardins OB, Bereza BG, Machado M, Jaszewski B, Freedman MS, Einarson TR, Iskedjian M. Pain due to multiple sclerosis: analysis of the prevalence and economic burden in Canada. Pain Research & Management. 2007; 12:259-65 Iskedjian M, Bereza B, Gordon A, Piwko C. Einarson TR. Meta-analysis of cannabis based treatments for neuropathic and multiple sclerosis-related pain. Current Medical Research & Opinion. 2007; 23:17-24 Peer-reviewed – Abstracts (Poster presentations) Bereza BG, Machado M, Papadimitropoulos M, Sproule B, Ravindran AV, Einarson TR.

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A dynamic Markov approach assessing the cost of illness of generalized anxiety disorder (GAD) in Canada. International Society for Pharmacoeconomic Outcomes and Research (ISPOR), 12th Annual European Congress Paris 2009 Bereza BG, Machado M, Einarson TR. Meta-analysis of clinical outcomes from guideline recommended pharmacotherapies for generalized anxiety disorder. International Society for Pharmacoeconomic Outcomes and Research (ISPOR), 2nd Latin American Conference Brazil 2009 Machado M, Bereza BG, Gonzalez C, Einarson TR. Pharmacoeconomic contribution of Latin America to ISPOR conferences. International Society for Pharmacoeconomic Outcomes and Research (ISPOR), 2nd Latin American Conference Brazil 2009 Piwko C, Vicente C, Bereza B, Ventin A. Economic burden of severe chronic hand eczema/dermatitis in Canadian adults. International Society for Pharmacoeconomic Outcomes and Research (ISPOR) 14th Annual International Meeting, Orlando, USA 2009 Bereza BG, Machado M, Einarson TR. Economic evaluations of generalized anxiety disorder: A systematic review. The Society for Medical Decision Making. Philadelphia, USA 2008 Bereza BG, Machado M, Einarson TR. Assessing the reporting and scientific quality of meta-analyses of randomized controlled trials of treatments for anxiety disorders. International Society for Pharmacoeconomic Outcomes and Research, 13th Annual International Meeting, Toronto, Canada 2008 Desjardins O, Bereza B, Jaszewski B, Iskjedjian M, Einarson TR, Piwko C. Economic Burden of Pain to Multiple Sclerosis in Canada. Canadian Association of Physical Medicine & Rehabilitation Annual Scientific Meeting, Vancouver, Canada, 2006 Desjardins O, Bereza B, Jaszewski B, Malmberg C, Iskedjian M, Einarson TR, Piwko C. The prevalence and management of pain due to Multiple Sclerosis in Canada. Consortium of Multiple Sclerosis Centres Vancouver, Canada, 2006. Piwko C, Desjardins O, Bereza B, Jaszewski B, Malmberg C, Iskedjian M, Einarson TR. Pain due to Multiple Sclerosis: Analysis of the economic burden in Canada. International Society for Pharmacoeconomic Outcomes and Research (ISPOR), 11th Annual International Meeting, Philadelphia, PA United States, 2006. Iskedjian M, Bereza B, Desjardins O, Jaszewski B, Piwko C, Einarson TR. Converting the scores of a clinical instrument for measuring pain to a preference based one: Report Value in Health. 9(3):A6, May/June 2006. Walker J, Iskedjian M, Bereza BG, Hemels M, Einarson TR, Cost Effectiveness of Escitalopram in the Treatment of Generalized Anxiety Disorder; International Society for Pharmacoeconomic Outcomes and Research(ISPOR), Florence, Italy Fall 2005. Desjardins O, Piwko C, Bereza B, Gordon A, Devonshire V, Freedman M, Ursell M, Vicente C, Iskedjian M, Jaszewski B, Einarson TR, Gafni A. Development and pre-testing of a willingness to pay instrument for a treatment for pain in Multiple Sclerosis. Presented at the Canadian

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Association for Health Services and Policy Research Conference, Montreal, QC, Canada, September 2005.

Documents

A COST OF ILLNESS STUDY OF GENERALIZED ANXIETY … · A cost of illness study of generalized anxiety disorder in Canada Basil Gregory Bereza Master of Science 2010 Department of Pharmaceutical