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Parents’ Preferences for Drug Treatments in Juvenile Idiopathic Arthritis: A Discrete Choice Experiment
by
Heather Burnett
A thesis submitted in conformity with the requirements for the degree of Masters of Science
Health Policy Management and Evaluation University of Toronto
© Copyright by Heather Burnett 2011
ii
Parents’ Preferences for Drug Treatments in Juvenile Idiopathic
Arthritis: A Discrete Choice Experiment
Heather Burnett
Masters of Science
Health Policy Management and Evaluation University of Toronto
2011
Abstract
BACKGROUND
Parents of children with juvenile idiopathic arthritis (JIA) are often faced with difficult
decisions surrounding the most effective, convenient, safe, and cost-effective treatment
for their child. A discrete choice experiment (DCE) was used to determine parents’
preferences for drug treatments and health outcomes in JIA.
METHODS
A questionnaire survey including a DCE was administered to parents of children with
JIA. Multinomial logit regression was used to estimate part-worth utilities and
willingness-to-pay.
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RESULTS
Participation in daily activities was the most important attribute. Parents were willing
to pay up to $2,332 for participation without any difficulty (95% CI $1,478, $3,508).
Child age, gender, years with JIA, and household income had the greatest impact on
preference. Parents’ were willing to pay $2,080 to switch from a drug representing
methotrexate to etanercept (95% CI $698, $4,065).
CONCLUSIONS
Parents of children with JIA had the highest maximum willingness-to-pay for drug
treatments that improve daily functioning and reduce pain. Cost is a significant factor
in the decisions that parents make surrounding the best treatment for a child.
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Acknowledgments
First and foremost I would like to thank my advisor Wendy Ungar for the opportunity
to work under her supervision on such fun, exciting, and meaningful research. I will be
forever grateful for the time, mentorship, and encouragement she has provided
throughout my degree. Thank you for believing in me.
I would also like to thank the members of my advisory committee Dean Regier and
Fiona Miller. Dean introduced me to the world of discrete choice, spending countless
hours walking me through the theories and principles of design and analysis. His
knowledge and input were essential to this thesis. Thank you for remaining committed
to not only this project but to my development as a student. Fiona, thank you for all of
your helpful comments, thought provoking questions and constant support. Meetings
with you stimulated new ideas which were a tremendous help when writing my
discussion chapter.
The Division of Rheumatology at The Hospital for Sick Children provided support to
this project by not only signing hundreds of recruitment letters, but also providing
feedback on during the early stages of survey development. I am especially thankful
for the input of Brian Feldman, Boris Hugle, and Susanne Benseler who contributed
greatly in the development of the discrete choice attributes and levels. Special thanks
also to Fernand Bellavance, a member of the SickKids Web Team who assisted with the
creation and launch of the online version of the survey.
To all of the parents who completed the survey and provided feedback during the pilot
study, thank you for your time and energy. Your passion and desire to help children
and families living with JIA added extra meaning to this research and thesis.
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Thank you to the Ontario Ministry of Health and Long-Term Care Drug Innovation
Fund for providing financial support to this study.
Last but certainly not least, I would like to thank my family. My parents Ray and Lyla
Burnett have always provided me with unconditional love and encouragement and
taught me the importance of seeking a career path that is both fulfilling and fun. Thank
you for your interest and investment in my education. I would especially like to thank
my partner, Ryan Demers for his perpetual support, praise and commitment. None of
this would have been possible without the motivation you provided me with each and
every day. Thank you for your feedback on all aspects of this thesis and for being
genuinely interested in the work I have done.
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Table of Contents
Acknowledgments ...................................................................................................................... iv
Table of Contents ........................................................................................................................ vi
List of Tables ................................................................................................................................ xi
List of Figures ............................................................................................................................ xiii
List of Appendices ................................................................................................................... xiv
List of Abbreviations ................................................................................................................. xv
1 Introduction ..............................................................................................................................1
1.1 Overview ............................................................................................................................1
1.2 Juvenile Idiopathic Arthritis ............................................................................................1
1.2.1 Prognosis ................................................................................................................4
1.2.2 Disease Management ............................................................................................5
1.2.2.1 Non-drug therapies ................................................................................5
1.2.2.2 Conventional drug therapies ................................................................6
1.2.2.3 Biologic drug therapies ........................................................................10
1.2.3 Outcome Measures .............................................................................................13
1.2.3.1 Disease activity .....................................................................................13
1.2.3.2 Functionality ..........................................................................................15
1.2.3.3 Health-related quality of life ...............................................................15
1.2.4 Costs ......................................................................................................................16
1.2.4.1 Direct costs .............................................................................................17
1.2.4.2 Indirect costs ..........................................................................................19
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1.2.5 Cost-Sharing Arrangements ..............................................................................20
1.3 Statement of the Problem ...............................................................................................22
1.4 Introduction to Patient Preference Methods ...............................................................23
1.4.1 Stated Preference Methods ................................................................................24
1.4.2 Conjoint Analysis ................................................................................................24
1.5 Discrete Choice Experiments.........................................................................................25
1.5.1 Theoretical Basis ..................................................................................................26
1.5.2 Conducting a DCE ..............................................................................................27
1.5.2.1 Conceptualization .................................................................................28
1.5.2.2 Experimental Design ............................................................................30
1.5.2.3 Analysis ..................................................................................................35
1.6 Willingness-to-Pay ..........................................................................................................39
1.7 Study Rationale ...............................................................................................................40
1.8 Hypothesis and Objectives ............................................................................................41
2 Methods ...................................................................................................................................43
2.1 Target Population ...........................................................................................................43
2.2 Characterizing the Choice Design ................................................................................44
2.3 Identifying Attributes and Levels .................................................................................44
2.3.1 A Literature Review of Studies Evaluating Preferences for Drug Treatments and Health Outcomes in Arthritis ...............................................45
2.3.2 Selecting Attributes and Levels for Pilot Testing ...........................................45
2.4 Selecting the Experimental Design for the Pilot Study .............................................51
2.5 Creating the Survey Instrument for the Pilot Study ..................................................52
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2.5.1 Framing the Survey Question ...........................................................................53
2.5.2 Child Health and Demographic Questions .....................................................53
2.6 Pilot Data Collection .......................................................................................................53
2.7 Pilot Testing .....................................................................................................................54
2.7.1 Objectives .............................................................................................................54
2.7.2 Pilot Study Recruitment and Follow-Up .........................................................54
2.7.3 Pilot Data Analysis..............................................................................................55
2.7.4 Pilot Results .........................................................................................................57
2.7.5 Discussion of Pilot Findings ..............................................................................60
2.8 Post-Pilot Changes to the Survey Instrument .............................................................60
2.9 Main Study .......................................................................................................................62
2.9.1 Summary of the Attributes and Levels used in the Main Study ..................62
2.9.2 Generating a Choice Design with Utility Balance ..........................................63
2.9.3 Sample Size and Recruitment............................................................................65
2.9.4 Data Analysis and Interpretation .....................................................................67
2.9.4.1 The interaction model ..........................................................................68
2.9.4.2 Assessing model fit ...............................................................................69
2.9.4.3 Generating 95% confidence intervals for WTP estimates ...............71
2.10 Ethics .................................................................................................................................71
3 Results ......................................................................................................................................73
3.1 Literature Review of Preference Studies .....................................................................73
3.2 Study Sample ...................................................................................................................82
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3.3 Characteristics of Respondents and their Children with JIA ...................................83
3.4 Preferences .......................................................................................................................91
3.4.1 Relative Importance of Main Study Attributes and Themes ........................91
3.4.2 Main Effects Model .............................................................................................92
3.4.3 Interaction Model ................................................................................................94
3.4.4 Model Goodness-of-Fit .......................................................................................99
3.4.5 Marginal WTP for Main Study Attribute Levels ..........................................100
3.4.6 Sensitivity Analysis of ‘Cost to You’ Attribute .............................................103
3.4.7 WTP for JIA Treatment Scenarios ...................................................................106
3.4.8 Importance Scores versus Ranking of Most Important Attribute ..............107
4 Discussion..............................................................................................................................109
4.1 Sample of Parents ..........................................................................................................109
4.1.1 Response Rate ....................................................................................................109
4.1.2 Demographic Data ............................................................................................111
4.1.3 Child Health Data .............................................................................................113
4.2 Preferences for Drug Treatments and Health Outcomes in JIA .............................114
4.2.1 Preferences for Effectiveness ...........................................................................115
4.2.2 Preferences for Safety .......................................................................................117
4.2.3 Preferences for Convenience ...........................................................................118
4.2.4 Preferences for Cost ..........................................................................................119
4.2.5 Preferences for Switching Drugs ....................................................................120
4.3 Validity of Discrete Choice Experiments ...................................................................121
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4.4 Methodological Issues and Study Limitations .........................................................122
4.4.1 Cost to You .........................................................................................................123
4.4.2 Study Sample .....................................................................................................125
4.4.3 Framing the Choice Question ..........................................................................126
5 Conclusions ...........................................................................................................................129
5.1 Implications for Stakeholders .....................................................................................130
5.1.1 Researchers ........................................................................................................130
5.1.2 Health Care Professionals ................................................................................131
5.1.3 Health Care Decision Makers ..........................................................................131
5.2 Future Directions ..........................................................................................................132
Appendicies ...............................................................................................................................133
References ..................................................................................................................................180
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List of Tables
Table 1: Classifications of JIA subtypes and prevalence …………….………………….. 2
Table 2: Conventional drug treatments used in JIA ……………………………..…….… 7
Table 3: Biologic drugs currently being used or being studied for use in JIA …..…… 11
Table 4: Example of dummy and effects coding of a three level attribute ……….....…. 36
Table 5: Attributes and levels selected for pilot study ………………………………..… 46
Table 6: Recruitment of pilot study participants ………………………….………...…... 57
Table 7: Results of the multinomial logit regression analysis of pilot choice data …... 59
Table 8: Attributes and levels used in the main study ………………………………...... 63
Table 9: Relative D-efficiencies as a result of various approaches to experimental
design ……………………………………………………………………………………….... 64
Table 10: Effects coded levels used to generate beta parameter estimates for utility
balanced main study design ……………………………………………………………....... 65
Table 11: Characteristics of studies examining patients’ and parents’ preferences for
drug treatment and or health outcomes in JIA …………………………………………… 74
Table 12: Characteristics of studies examining patients’ preferences for drug treatments
and or health outcomes in RA ……………………………………………………………… 75
Table 13: The most common attributes represented in studies that measure preferences
for drug treatments and or health outcomes in arthritis ………………………………… 78
Table 14: Demographic characteristics of survey respondents ………………………..... 84
Table 15: Characteristics of survey respondents’ children with JIA …………………… 88
Table 16: Relative importance of main study attributes ………………………………… 91
Table 17: Results from the main effects multinomial logit regression model of main
study choice data …………………………………………………………………………….. 93
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Table 18: Regression model including all significant interaction variables …………… 95
Table 19: Adjusted parameter estimates for all significant interaction terms used to
describe differences in parents’ preferences for participation in daily activities ……... 97
Table 20: Adjusted parameter estimates for all significant interaction terms used to
describe differences in parents’ preferences for cost to you …………………………….. 99
Table 21: Comparison of model fit ……………………………………………………...... 100
Table 22: Marginal and incremental WTP for main study attribute levels based on
interaction model estimates ……………………………………………………………...… 101
Table 23: Calculation of the marginal utility of money when cost to you is treated as a
categorical variable …………………………………………………………………………. 104
Table 24: WTP based on cost to you sensitivity analysis ………………………………. 105
Table 25: Definition of base case scenarios representing a child taking MTX or
etanercept ……………………………………………………………………………………. 106
Table 26: WTP to switch from MTX to etanercept ……………………………………… 106
Table 27: Results of the ranked most important attribute compared to the importance
scores from the main effects and interaction models …………………………………… 107
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List of Figures
Figure 1: Steps in conducting a discrete choice experiment ……………………………. 28
Figure 2: Pictograms used to represent disease activity attribute in the pilot study … 47
Figure 3: Process of recruitment and follow-up for main study ………………….…..... 67
Figure 4: Mapping McFadden’s pseudo R2 to the linear R2 …………………………..… 70
Figure 5: Results of main study recruitment ……………………………………………... 83
Figure 6: Relative importance of main study attributes ………………………………… 92
Figure 7: WTP for main study attribute levels…………………...……………………….. 99
Figure 8: Slope of the cost to you attribute when treated as a categorical variable …. 103
Figure 9: Relative importance of main study attributes from DCE compared to ranking
exercise ………………………………………………………………………………………. 108
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List of Appendices
Appendix 1: Domains of the CHAQ
Appendix 2: SAS design code used to generate pilot and main study choice sets
Appendix 3: Pilot study choice design
Appendix 4: Recruitment letter sent to parent participants eligible for the study
Appendix 5: Study information sheet sent along with the recruitment letter
Appendix 6: Cover letter that accompanied each survey package
Appendix 7: Tables of demographic and child health and results from the pilot study
Appendix 8: The survey instrument used in the main study to elicit parents’
preferences for drug treatments in JIA.
Appendix 9: The Hospital for Sick Children Research Ethics Board Approval
Appendix 10: Results from regression model including significant child health
variables
Appendix 11: Results from regression model including significant demographic
variables
Appendix 12: Marginal and incremental WTP for main study attribute levels based on
main effects model
Appendix 13: Results from regression model including effects coded continuous
variables and all significant interaction terms
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List of Abbreviations
ACR American College of Rheumatologists
AIC Akaike Information Criterion
CA Conjoint Analysis
CHAQ Child Health Assessment Questionnaire
CHQ Child Health Questionnaire
CI Confidence Interval
CV Contingent Valuation
DCE Discrete Choice Experiment
DMARDs Disease Modifying Anti-Rheumatic Drugs
EAP Exceptional Access Program
GI Gastrointestinal
HAQ Health Assessment Questionnaire
HR-QoL Health Related Quality-of-Life
ICF International Classification of Functioning
IIA Independence of Irrelevant Alternatives
IL Interleukin
ILAR International League of Associations for Rheumatology
IUF Indirect Utility Function
IV Intravenous
JAQQ Juvenile Arthritis Quality-of-Life Questionnaire
JIA Juvenile Idiopathic Arthritis
ODBP Ontario Drug Benefits Program
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PedsQL Paediatric Quality-of-Life Inventory
MeSH Medical Subject Heading
MNL Multinomial Logit
MRS Marginal Rate of Substitution
MTX Methotrexate
MWTP Marginal Willingness-to-Pay
NSAIDs Non-steroidal Anti-Inflammatory Drugs
OMEP Orthogonal Main Effects Plan
RA Rheumatoid Arthritis
RF Rheumatoid Factor
RUT Random Utility Theory
SC Subcutaneous
SE Standard Error
SP Stated Preference
TDP Trillium Drug Program
TNF Tumor Necrosis Factor
VAS Visual Analog Scale
WHO World Health Organization
WTP Willingness-to-Pay
1
1 Introduction
1.1 Overview
This chapter provides an introduction to juvenile idiopathic arthritis (JIA), the need to
understand parents’ preferences in the management of JIA, and describes the method
that was used to elicit these preferences. The heterogeneity of JIA symptoms will be
reviewed along with various treatment options. An overview of the most common
outcome measures used to assess effectiveness of treatments will be provided and both
the direct and indirect costs associated with the disease and treatments will be
discussed. This will lead into a statement of the problem and a brief introduction to
stated preferences methods. A detailed overview of discrete choice experiments (DCEs)
including the theory and steps required to undertake a choice experiment will provide
an understanding of the methods employed in this thesis. The chapter will end with a
rationale for using a DCE to elicit parents’ preferences and a description of the
hypothesis and study objectives.
1.2 Juvenile Idiopathic Arthritis
JIA is a heterogeneous group of diseases characterized by persistent joint inflammation
and stiffness with no known cause. It is diagnosed in patients younger than 16 years of
age, with symptoms lasting more than 6 weeks (1). JIA is the most common
autoimmune-auto-inflammatory disease reported in children with an incidence ranging
from 1 to 21 cases per 100,000 (2-4). Characterization of disease subtypes is based on
criteria set by the International League of Associations for Rheumatology (ILAR) which
classify patients based on the number of joints affected and the presence or absence of
specific serologic findings and symptoms (1). A brief description of each JIA subtype
and the associated prevalence among all types is outlined in Table 1.
2
Table 1: Classifications of JIA subtypes and prevalence Subtype ILAR Criteria Prevalence
Systemic
≥1 affected joint preceded by a fever and ≥ 1 of the following: i) rash; ii) enlarged liver or spleen; iii) enlarged lymph nodes; iv) serositis
4-17%
Oligoarthritis Persistent Extended
1-4 affected joints during the first 6 months of disease No more than 4 affected joints throughout the disease course ≥ 4 joints affected after the first 6 months of disease
27-56%
Polyarticular RF negative RF positive
≥ 5 affected joints during the first 6 months of disease Negative tests for rheumatoid factor ≥ 2 positive tests for rheumatoid factor at least 3 months apart
11-28% 2-7%
Psoriatic Arthritis and psoriasis, or arthritis and ≥ 2 of the following: i) dactylitis; ii) onycholysis or nail pitting; iii) psoriasis in a first relative
2-11%
Enthesitis-related
Arthritis and enthesitis, or arthritis or enthesitis with ≥ 2 of the following: i) sacroiliac joint tenderness and/or inflammatory lumbosacral pain; ii) presence of HLA-B27; iii) male onset at ≥ 7 years; iv) acute anterior uveitis; v) related history in first relative
3-11%
ILAR=International league of associations for rheumatology
Source: Bell et al.(5) and Ravelli and Martini (6)
Systemic JIA is associated with unique clinical characteristics with affected joints
presenting themselves as symmetrical and polyarticular. The systemic subtype is
defined by arthritis accompanied or preceded by a fever lasting more than two weeks
plus one or more of the following: a non-fixed rash, hepatomegaly or splenomegaly
(enlarged liver or spleen), generalised lymphoadenopathy (enlarged lymph nodes), or
serositis (inflammation of serous body tissues). Intense muscle and abdominal pain is
commonly reported during fever peaks (6). Macrophage Activation Syndrome (MAS),
a potentially life threatening complication, develops in 5-8% of children with systemic
JIA (7).
Diagnosis of oligoarthritis requires four or fewer joints to be affected during the first six
months of disease. In persistent cases, the number of active joints remains constant,
while extended cases are associated with more than four active joints after the first six
3
months of disease. Children with oligoarthritis often show asymmetric symptoms in
their legs with the knees and ankles being the most commonly affected joints (6). Early
disease onset (before 6 years of age) is followed by the development of uveitis in 30% of
patients after a period of 5-7 years (8).
Polyarticular JIA is the most heterogeneous subtype as a result of the variation between
rheumatoid factor (RF) positive and RF negative diseases (5). In RF positive
polyarticular JIA, symmetric arthritis of the small hand and feet joints is most common,
although larger joints are often also affected. The probability of forming rheumatoid
nodules in larger joints (e.g. elbows and knees) is high during the first year of disease
(5). In RF negative polyarticular JIA the disease presents itself with symptoms
resembling one of: oligoarthritis, but with more affected joints; adult RF negative
rheumatoid arthritis (RA) with inflammation of the small and large joints; or RA but
without swelling but rather stiffness in affected joints (6). Overall, polyarticular JIA is
considered the most difficult subtype to treat successfully (5). Less severe JIA subtypes,
including oligoarthritis and systemic can develop into polyarticular throughout the
course of disease (3, 9).
Enthesitis-related JIA is associated with active arthritis at any point where skeletal
muscle attaches to the bone, with the most common enthesitis sites being in joints of the
lower extremities. At the time of disease presentation hip joints are often involved,
although symptoms are usually mild and remitting. Over half of the children with
enthesitis-related JIA have four or fewer joints affected throughout the entire course of
disease (6).
Psoriatic arthritis can be associated with the symptoms of any other subtype but must
also be associated with psoriasis. For cases that do not satisfy the inclusion criteria for
4
any category, or meet the criteria for more than one disease-subtype, the disease is
defined as undifferentiated JIA (6).
1.2.1 Prognosis
Prognosis of JIA is dependent on the disease subtype, severity, point of treatment
initiation and treatment adequacy (2). In general, reduced severity at onset and severity
of subtype result in improved clinical outcomes, with 50%-60% of patients achieving
remission prior to adulthood (5). Remission on medication is defined by six continuous
months of inactive disease, while remission off medication requires 12 months of
inactive disease off of drug treatment. A patient in remission off medication has a ≤
20% chance of disease recurrence within the next 5 years (10). Cycles of remission and
relapse are common for patient’s living with JIA and over half will carry the condition
forward into adulthood (11). Children that never achieve remission often experience
growth abnormalities or develop osteoporosis which results in significant damage and
disability throughout their adult lives (12, 13).
Patients living with JIA often experience chronic pain and stiffness which greatly
impairs physical, social and emotional functioning (10, 14). As a result, children with
JIA often report a lower health-related quality of life (HR-QoL) than children with other
chronic diseases (15). In addition, disease symptoms such as pain and fatigue reduce a
child with JIA’s ability to attend school and participate in social activities. This can lead
to feelings of inadequacy, altered body image and in some cases more severe
psychological problems, such as depression (16). Understanding the physical,
functional, social, and emotional consequences of JIA is extremely important for
appropriate disease management. A description of outcome measures used to evaluate
these domains can be found in section 1.2.3.
5
1.2.2 Disease Management
Both drug and non-drug therapies are used to control and minimize pain,
inflammation, joint damage, and preserve daily functioning in children with JIA. The
overall goal of treatment is to avoid long-term damage that may carry forward into
adulthood and to maintain normal childhood growth and development. Although
there is no cure for JIA, remission has become more common in the last decade as a
result of substantial progress in disease management (6). Disease management often
requires a combination of non-drug and drug modalities, with drug treatments being
concomitant or rapidly succeeding one another (11).
1.2.2.1 Non-drug therapies
Nutritional approaches to JIA involve increasing calcium and vitamin-D intake (dietary
or supplemental), which has been shown to increase bone mineral density and decrease
the risk of osteoporosis and osteopenia (17). Water and land-based exercise programs
have also been shown to effectively reduce disease symptoms, although rest is known
to be an equally effective therapy. In many cases, physical or occupational therapists
are consulted to ensure an appropriate level and balance of physical activity is carried
out (5). Activity-based JIA therapies have been shown to have a positive impact on not
only the physical symptoms of the disease, but also psychological symptoms, such as
depression and feelings of inadequacy (18). Involvement with peer groups and
counseling has been shown to further reduce the psychological burden of the disease.
These aforementioned therapies can be and are often used in conjunction with
pharmacologic agents (11).
A more invasive non-pharmacological approach to treating JIA is surgical joint
replacement, which can be used to repair areas that have undergone irreversible joint
deformity and destruction (19). Surgical approaches are recommended for JIA patients
with severe functional impairment that have ceased childhood growth. Another
6
intrusive but risky JIA therapy used in patients who are unable to achieve disease
control with various drug combinations are autologous stem cell transplants. In most
cases these treatments result in clinical remission, although significant morbidity has
been observed as a result of prolonged immuno-suppression (20). Like surgical joint
replacement, stem cell transplants are best carried out after childhood growth is
complete (17).
1.2.2.2 Conventional drug therapies
Conventional pharmacological agents include non-steroidal anti-inflammatory drugs
(NSAIDs), corticosteroids, and non-biologic disease modifying anti-rheumatic drugs
(DMARDs). A description of these treatments follows and a summary can be found in
Table 2.
7
Table 2: Conventional drug treatments used in JIA Drug Route Frequency Adverse Events
Non-Steroidal Anti-Inflammatory Drugs
Naproxen (Naprosyn™) Oral (pill or liquid) Daily
Common: GI upset including pain, nausea, loss of appetite. Less Common: Vomiting, diarrhea or constipation; dizziness, drowsiness, headaches; rash, hives, itching; nosebleeds, bleeding gums, easy bruising; pseudoporphyria; anemia, mild abnormalities in liver or kidney function.
Ibuprofen (Advil™/ Motrin™) Oral (pill or liquid) Daily
Indomethacin (Indocid™)
Oral (pill), Rectal (suppository) Daily
Diclofenac Sodium (Voltaren™) Oral (pill) Daily
Celecoxib (Celebrex™) Oral (pill) Daily
Corticosteroids
Traimcinolone- Hexacetonide (Aristospan™)
Intra-articular injection Annually
Common: Atrophy and hypopigmentation Less Common: Injection site reaction or infection
Prednisone (Prednisone™)
Oral (pill or liquid) Daily
Early: Increased appetite; stomach ache, nausea; insomnia; mood-swings Late: Weight gain; acne; increased body hair; frequent infections; high blood pressure; bone loss, osteoporosis
Disease Modifying Anti-Rheumatic Drugs
Methotrexate (Methotrexate™)
Oral (pill), Subcutaneous Injection
Weekly
Common: Nausea for 24-48 hours Less Common: Vomitting, stomachache; mouth sores; low white blood cell counts; abnormal liver function
Sulfasalazine (Salazopyrin™)
Oral (pill or liquid) Daily
Common: Stomach upset, nausea, vomiting; headache, dizziness; skin rash, sun sensitivity; Less Common: Low red and white blood cell counts, abnormal liver function
Leflunomide (Arava™) Oral (pill) Daily
Common: Nausea and diarrhea Less Common: Skin rash; thinning hair; high blood pressure; abnormal liver function; low platelet count
Source: Compendium of Pharmaceutical Specialties (CPS) Online Version (21) and Bell
et al. (5)
8
Non-Steroidal Anti-Inflammatory Drugs
NSAIDs are the most common first-line treatment for all JIA subtypes and act to relieve
pain by reducing inflammation. They work by temporarily interfering with
inflammation pathways and as a result do not prevent disease progression (11, 19).
Although none of these treatments have been shown to be comparatively more
effective, naproxen is frequently the first drug of choice (22). Other NSAID treatments
used in JIA include ibuprofen, indomethacin, diclofenac sodium, and celecoxib.
Indomethacin is often used to treat enthesitis-related and systemic JIA, as it has been
shown to be more effective in these subtypes (23). Despite these trends, a lack of
response to NSAID therapy is not uncommon, and success or failure with one anti-
inflammatory drug does not imply success or failure with another (5). For patients who
respond adequately to treatment, 50% generally do so within the first 2 weeks, and the
remainder within 8 weeks (23). However, NSAIDs alone are unable to control the
disease in approximately two-thirds of patients (9, 13).
The most common side-effects reported for NSAID treatments are gastrointestinal (GI)
symptoms including nausea, abdominal pain and a loss of appetite (5). These events
have been reported less frequently in children compared to adults and ibuprofen is
associated with the lowest risk (6). Other minor side-effects associated with NSAID
drugs include headaches, dizziness, and mood-swings; while more rare events such as
kidney and renal insufficiency or failure, anemia and pseudoporphyria have also been
reported (5, 19). Patients on long-term NSAID therapy require liver and renal function
tests and blood counts to be performed every 6 months (22).
Corticosteroids
Intra-articular steroid injections are effective second-line options for patients who
9
respond inadequately to NSAID therapy, and common first-line therapies for patients
with oligoarthritis (monotherapy, or combined with NSAIDs) (11).
Although not curative, intra-articular injections of active joints can reduce synovial
volume, joint inflammation, and radiological progression of the disease in less than 48
hours. Corticosteroid injections have been shown to halt disease progression for up to
one year in 70% of children with oligoarthritis (19). Side-effects include subcutaneous
atrophy and or hypopigmentation at the injection site (5, 23). Triamcinolone
hexacetonide is the most common and effective corticosteroid used to treat JIA.
In rare cases, oral dose corticosteroid therapies such as prednisone are administered to
children with systemic or severe polyarticular JIA. Most often this is done to provide
some relief to patients’ unresponsive or awaiting response from another medication.
Systemic steroid treatments can result in bone loss, growth retardation and osteoporosis
and as a result, limited use for long-term treatment course is recommended (6, 24).
Disease Modifying Anti-Rheumatic Drugs
Children diagnosed with systemic, oligoarthritis, or polyarticular JIA who show
inadequate response to intra-articular steroids are candidates for DMARD therapy (11).
These drugs are often prescribed in addition to NSAIDs since they go beyond treating
symptoms of the disease and aim to slow the progression of joint damage before
irreversible impairment occurs (22). DMARDs are used to treat JIA include:
methotrexate, sulfasalazine, and leflunomide.
Methotrexate (MTX) is the most common and effective DMARD used to treat
oligoarthritis and polyarticular JIA (5) despite the fact that it has been shown to have
minimal clinically significant effects on patient centered disability measures (25).
Patients that respond positively to MTX treatments usually do so after 6-12 weeks.
Patients that encounter side-effects, which most often include nausea and GI symptoms,
10
are usually switched from an oral dose to subcutaneous injections (22). Switching
routes of administration as well as adding folic acid supplements have been shown to
reduce the GI symptoms caused by MTX (5). Low white blood cell counts have also
been observed in children treated with MTX and as a result blood test monitoring every
2-3 months is required (26).
Sulfasalazine has been shown to suppress disease activity in patients with polyarticular
and enthesitis-related JIA (22). However, compared to MTX, sulfasalzine has been
associated with more severe adverse events, including haematological effects, hepatitis,
and in rare cases drug-induced systemic lupus erythematosus, and pulmonary disease
(22). Low tolerance for sulfasalazine in patients with JIA has been observed.
Leflunomide on the other hand, has been shown to be an effective alternative for
patients’ refractory to MTX, with similar adverse event rates (11). Potential side-effects
associated with leflunomide include rash, allergic reactions, headaches, and reversible
alopecia (27).
Many patients with JIA do not respond optimally or at all to the conventional
treatments described above (NSAIDs, corticosteroids, DMARDs). In fact, response rates
for combined treatments have been reported to be as low as 30% (28) or worse for
patients with polyarticular JIA (29). In these cases, patients become candidates to
receive biologic agents.
1.2.2.3 Biologic drug therapies
Recent findings on the inflammatory process involved in the development of RA have
led to the discovery of a number of new therapeutic targets and as a result, new disease-
modifying drugs (30, 31). These novel biologic agents target key cytokines implicated
in the pathogenesis of JIA and other chronic inflammatory diseases such as plaque
psoriasis, Crohn’s disease and anklylosing spondylitis (32, 33).
11
Anti-tumour necrosis factor alpha (TNF α) drugs, as the name suggests, work by
blocking the activity of TNF α and include adalimumab, etanercept and infliximab.
Interleukin targeting drugs antagonize the receptors of interleukin-1 (IL-1) and
interleukin-6 (IL-6) and include anakinra and rilonacept (Il-1) and tocilizumab (IL-6)
respectively (17, 34). Other biologics such as abatacept and rituximab are designed to
inhibit immune responses mediated by T-lymphocytes and B-cells (27, 34). Table 3
summarizes the biologic treatments being used or studied for use in JIA. A full
description of biologic treatments follows.
Table 3: Biologic drugs currently being used or being studied for use in JIA Drug Target Route Frequency Adverse Events
Adalimumab* (Humira™) TNF-α Subcutaneous
Injection Every 2 weeks Common: Injection site reactions (SC drugs) and Infusion reactions (IV drugs) Less Common: Increased risk of infection; rash; flu-like symptoms Rare: low blood counts; production of autoantibodies; increased risk of malignancies
Etanercept* (Enbrel™) TNF-α Subcutaneous
Injection Weekly
Infliximab (Remicade™) TNF-α Intravenous
Infusion Monthly
Anakinra* (Kineret™) IL-1 Subcutaneous
Injection Daily
Common: Injection site reactions, Less Common: increased risk of infections Rare: Macrophage Activation Syndrome
Tocilizumab (Actemra™) IL-6 Intravenous
Infusion Every 2 weeks Common: Infusion reactions, Rare: Gastrointestinal hemorrhage, infectious events
Abatacept* (Orencia™) T-cells Intravenous
Infusion Monthly Common: Infusion reactions; upper respiratory infections. Rare: Lack of data available
Rituximab (Rituxan™)
B-cells (CD20)
Intravenous Infusion Every 2 weeks
Common: Infusion reactions; serum sickness Less Common: Increased risk of infections Rare: Hypogammaglobulinemia
* Denotes drugs approved by Health Canada for use in JIA
Source: Compendium of Pharmaceutical Specialties (CPS) Online Version (21), Horneff
et al. (34) and Hayward et al. (27)
12
Subcutaneous (SC) injections (adalimumab, etanercept, and anakinra) are often
administered at home by a trained parent or caregiver, while intravenous (IV) drugs
require patients to visit a hospital or clinic for treatments by infusion, which can take
anywhere from 3 to 7 hours per visit (35).
The efficacy and safety of biologic treatments in patients with polyarticular JIA who
have responded inadequately to conventional treatments have been established over
the short-term (36). However, long-term data are required to provide further evidence
on the real clinical benefits and safety of these treatments in children. Studies in adults
with rheumatoid (RA) show that although biologic drugs lead to improved outcomes
during the first phase of treatment, efficacy often decreases over time and patients are
required to switch to a second biologic drug. Patients who have shown intolerance or
failure with a first biologic drug often do not respond well to a second, although there is
little data on the effectiveness of switches between anti-TNF-α agents and those with
different targets (37). Other reasons for switching or discontinuing biologic treatments
are drug-related adverse events. In trials of biologic treatments in JIA, one third of
patients discontinued treatment in the first 3-4 months due to lack of efficacy or
intolerance to side-effects (36).
The most common side-effects reported in children are injection site reactions (e.g.
redness, itchiness, pain, swelling), infections of the upper respiratory tract, and
infusion-related events which include mild symptoms such as a rash, headache, nausea,
diarrhea, or more serious symptoms including chest pain, changes in blood pressure,
and anaphylactic responses (19, 22). Although most events associated with biologic
treatments are mild and transient, a lack of randomized controlled trial data in
paediatric populations has led to many uncertainties regarding the long-term safety of
biologics in JIA.
13
Reports of reactivation of infectious diseases such as hepatitis B and tuberculosis in
patients treated with anti-TNF α agents have been reported (38) and concerns have been
raised about a possible association between biologic drugs and cancer, lupus, vasculitis,
multiple sclerosis and cardiovascular disease (38-41). Patients on biologics require
regular medical testing that may include blood tests, TB skin tests, and chest
radiographs.
1.2.3 Outcome Measures
As previously described, the ultimate goal of treatment in JIA is to alleviate symptoms
of the disease and minimize inflammation in order to improve HR-QoL and avoid
permanent damage and disability (see section 1.2.1). These outcomes have become
more common since the increased use of DMARDs and biologic therapies (2).
Unfortunately, not all patients benefit from these aggressive treatments and for some,
outcomes on therapy resemble the natural history of the disease or worse when side-
effects are severe (11). Both the disease and treatments have the potential to impact the
physical, social, emotional, and intellectual aspects of a child’s life. For this reason, no
single outcome measure can be used to capture the state of a child with JIA.
To date, a number of outcome measures have been validated for children with JIA.
These measures are used to provide information about disease activity, functional
response to therapy, and quality of life. In the following section the most common
clinical measures used to capture these domains will be described.
1.2.3.1 Disease activity
Standardized outcomes that allow for changes in a disease state to be measured over
time are necessary to define clinical improvement in disease activity (42). Such
outcomes are extremely valuable in clinical trials where they can be used to compare
treatment efficacy. In JIA, disease activity is measured by a set of response variables
14
that are clinically relevant for all disease subtypes (43). This core set was established in
1997 by the ACR as a means to assess disease improvement using six standard
variables:
1. Global assessment of the disease severity by the physician using a 10 cm visual
analog scale (VAS)
2. Global assessment of overall wellbeing by the patient or parent using a 10 cm
VAS
3. Childhood health assessment questionnaire (CHAQ) score
4. Number of joints with active arthritis (swelling or limited motion with pain and
or tenderness)
5. Number of joints with limited movement
6. A laboratory marker of inflammation—erythrocyte sedimentation rate or C
reactive protein
Outcomes measured using the ACR core set are evaluated using paediatric response
criteria which determine the degree of clinically important improvement a child has
achieved at the end of a trial. The ACR Ped 30 is defined by a minimum of 30%
improvement in at least three of the core set variables with no more than one variable
worsening by greater than 30%. The ACR Ped 50 and 70 are defined by 50% and 70%
improvement respectively in a minimum of three core set criteria with worsening of one
component by no more than 30% (43). These definitions of improvement have been
widely used as primary measures of effectiveness in clinical trials of biologic
medications currently used to treat JIA (36). Unfortunately, the response criteria were
designed specifically to assess group improvements and are not considered to be
appropriate for determining clinical response for individual patients in routine practice
because they fail to consider a patient’s disease activity prior to commencing treatment
(42).
15
1.2.3.2 Functionality
A clinical evaluation of a child with JIA would not be complete without an assessment
of the child’s functional status. This is most often done using the CHAQ, an instrument
used by physicians to measure a patients’ response to therapy and a variable in the
ACR core set (10).
The CHAQ was derived from the adult health assessment questionnaire (HAQ) to
measure functional improvement using indices of disability and discomfort. The
disability index is generated by measuring a patient's status across 8 functional
domains: dressing and grooming, arising, eating, walking, hygiene, reach, grip and
activities. Patients or parents rate their or their child’s current level of ability
completing tasks within each domain using a four point scale (0=Without any difficulty;
1=With some difficulty; 2= With much difficulty; 3=Unable to do). The discomfort index
is determined by the presence of pain, measured using a 10 cm VAS (44). An
improvement of 0.13 in both CHAQ indices has been suggested as the minimum change
that should be used to indicate functional improvement in a child with JIA (45).
Appendix 1 includes a table summary of the CHAQ domains.
Domains of the CHAQ cover three of the four components of the World Health
Organization (WHO) International Classification of Functioning, Disability and Health
(ICF); physical impairments, activity limitations and participation restriction. The
WHO ICF also includes environmental factors in its measure of health and disability for
patients with arthritis (46).
1.2.3.3 Health-related quality of life
HR-QoL refers not only to the physical and functional status of a patient but also the
more valued aspects of life that are not typically captured by other endpoints, such as
social and emotional well-being (64). For this reason, HR-QoL has been increasingly
16
recognized as an important measure of treatment efficacy in clinical trials and as a
means for physicians to assess the impact of a treatment on a patient’s life. A number of
generic and disease-specific HR-QoL tools have been validated for children with JIA.
The Child Health Questionnaire (CHQ) is a generic instrument that is commonly used
as an outcome measure to study the quality of life in JIA and is designed to capture the
physical, social, and emotional components of health status in children over 5 years of
age (10). Using the CHQ, HR-QoL is divided into a physical score and a psychosocial
score, with higher values in each domain indicating a better HR-QoL (47). CHQ scores
derived from patient and parent proxy reports have been used as the primary outcome
measure in a large multinational clinical trial of biologic drugs (48).
Disease-specific measures that have been validated for JIA include the Juvenile Arthritis
Quality of Life questionnaire (JAQQ) and the Paediatric Quality of Life Inventory
(PedsQL). The JAQQ reports on HR-QoL by asking patients to scale 74 items within
dimensions of gross motor function, fine motor function, psychosocial function, and
general symptoms (49); while the PedsQL requires scaling within 9 domains: physical,
emotional, social, and school functioning as well as pain and hurt, daily activities,
treatment, worry, and communication (50). Both tools have been found to be reliable
and sensitive measures of HR-QoL in children with JIA, however the length of the
PedsQL makes it less burdensome for respondents (51).
1.2.4 Costs
Limited health care budgets and a rapid growth in health care spending have led to an
increased interest in how health resources are allocated and the economic impact of
various diseases. Children with chronic illnesses have gained special attention in this
regard as they incur a tremendous economic burden and have needs that are vastly
different from adults (52). In particular, musculoskeletal conditions, including JIA,
17
have been shown to consume more resources than other chronic childhood conditions
as a result of frequent visits to hospitals and clinics, diagnostic tests, expensive
treatments, and home visits by health professionals (53). Indirect costs such as days
missed from work and school also contribute to the substantial financial burden of JIA.
1.2.4.1 Direct costs
Direct costs refer to the amount of money spent by the health care system, patient and
government to care for a child with JIA and can include both medical (e.g. medical
appointments, diagnostic procedures, medications) and non-medical expenses (e.g.
travel, time). A number of studies worldwide have focused on the direct medical costs
of JIA as they apply to their setting.
Based on data from the year 2005 a Canadian study found the direct medical cost of JIA
to be $3,002 CAN annually per patient, which was $1,686 CAN more than the cost of
outpatient controls without JIA (54). The majority of this difference was attributed to
the higher cost of medications, with the difference in average annual costs for
medications being $1,219 CAN greater for JIA patients. More frequent visits with
specialists and increased diagnostic testing were also found to have a significant impact
on the use of resources in JIA. Polyarticular and systemic JIA patients incurred greater
overall costs in this study (54).
Similarly, two cost studies carried out in the UK in 2004 and 2008 found the average
annual cost of JIA to be €3,471 (55) and €2,391 respectively (56). In the first study,
inpatient stays accounted for 53% of the total direct costs, followed by visits to health
professionals (25%), and medications (14%). Persistent disease activity, disability,
restricted HR-QoL and polyarticular subtypes were all associated with higher costs (55).
In the second study, 297 patients were followed for one year after being diagnosis with
JIA. These patients attended a total of 1,658 appointments with rheumatologists, 2,094
18
with physiotherapists, 798 with opthalmologists, 438 with specialist rheumatology
nurses, 192 with occupational therapists, and 180 with podiatrists. The cost of
appointments with rheumatologists comprised the highest portion of the cost of
managing JIA, followed by appointments with other specialists, diagnostic imaging,
medications, and lab test respectively. In this study, enthesitis-related arthritis was
found to be the most costly sub-type (56).
More recently, a study carried out using the Dutch national arthritis and biological in
children registry showed the mean direct cost of etanercept treatment over a 2.25 year
period to be €12,478 per patient year compared to €3,720 before the start of treatment
(57). This increased cost was largely attributed to the drug itself (75% of total direct
medical costs), while costs associated with rheumatologist and specialist visits as well
as hospitalizations were lower in patients who were taking etanercept.
Findings on the direct medical cost of JIA in Canada and Europe showed major
differences in the resources consumed by various disease severities, sub-types, and
treatment strategies. In general, the use of health services were found to have the
greatest economic impact (53-55), although results from the Dutch study suggest that
these costs may be greatly reduced in patients taking biologic drugs (58). None of the
studies included direct non-medical costs such as transportation to and from the site of
care, home modification, or special school arrangements in their analysis. Only the
Canadian and Dutch studies identified medications as large portion of direct-medical
costs (54, 57). This is likely due to the fact that the cost of biologic treatments was
excluded from the other analyses.
A cost-effectiveness analysis, aimed at comparing the costs per additional ACR Ped 30
response after one year of treatment in the province of Ontario in 2010, estimated the
direct cost of biologic treatments to be between $16,000 to $19,000 CAN per year for a
19
single child with JIA, compared to $952 CAN for MTX (59). These estimates accounted
for all of the health care resources consumed during drug acquisition and
administration, as well as routine patient monitoring. A large portion of the direct costs
of biologic drugs is attributed to the cost of prescriptions, which have been reported to
be as high as $2,820 per month for a 70kg child on infliximab (35).
1.2.4.2 Indirect costs
Indirect costs refer to the non-medical losses as a consequence of the disease incurred
by the patient, the patient’s family, or society as a whole. In JIA indirect costs are often
represented by the loss in productivity experienced by parents or children and have
been shown to account for as much as 45% of the total financial burden of JIA (53).
Indirect costs are often reported as time missed from work, time missed from school, or
loss in salary or income. In a study of 130 families of children with JIA, 20% of parents
reported taking paid time off work, 16% unpaid time off, and 3% had stopped working
altogether in order to care for their child (60). Days missed from work also resulted in a
loss in income for 24% of families, with 14% reporting to have lost over $500 US within
the study period of 6 months (55). In other studies, the average annual salary loss has
been reported to be as high as $1,241 CAN (54) and as low as $328 US per family (55).
The impact of JIA on household incomes has led to reduced opportunities to participate
in extra-curricular activities for one in ten children with JIA (55).
School absences must also be considered when looking at the indirect cost of JIA.
Children with JIA have been shown to miss more school as a result of disease
symptoms, treatment side-effects, drug infusions and appointments with physicians
and other specialists (35, 61). In Canada, children with JIA have been shown to miss an
average of 7.15 school days per year (54). Another study reports an average of 2.6
disability days per month for Canadian children living with JIA (15).
20
A review of cost-effectiveness studies in JIA carried out in 2006 estimated that indirect
costs make up 50-70% of the total costs of JIA (53). No study measuring the indirect
cost of JIA has accounted for the potential impact of the disease on a child’s future
income. Indirect costs as high as $2,100 US have been reported (53).
Intangible costs such as pain, depressive symptoms, and activity limitations were
described in section 1.2.1.
1.2.5 Cost-Sharing Arrangements
Prescription drugs were expected to constitute 17.1% of total health expenditure in
Ontario in 2010, reaching a total cost of close to 12.8 billion dollars (62). Current
payments for prescription drugs come from a mix of public and private sources,
accounting for approximately 45% and 55% of the spending respectively (63). The most
utilized public funded program is the Ontario Drug Benefit Program (ODBP) which
covers drugs listed on the provincial drug formulary for all Ontario residents that meet
at least one of the following criteria; over 65 years of age, on social assistance, in long-
term care homes or receiving home care services (64). Individuals who do not meet
these eligibility requirements but have high prescription drug costs in relation to their
household income may be eligible for the Trillium Drug Program (TDP) which helps
pay for prescription drugs listed on the provincial Formulary after a quarterly
deductible is met. Unlike other publicly funded drug programs the TDP can be used in
combination with private insurance and the level of coverage is based on total
household income, and size. The Special Drugs Program is another option for public
funding of prescription drugs for children but is limited to the treatment of outpatient
with specific conditions including cystic fibrosis, thalassaemia, HIV, end stage renal
disease, growth failure, schizophrenia and Gaucher’s disease (64). Although each of the
three major public drug programs assists individuals who are burdened with the high
cost of medication, the extent of financial assistance varies greatly based on age,
21
financial need, and disease type. For example, with the ODBP, seniors and individuals
on social assistance might require a $2 co-payment per prescription, while those
receiving Trillium might be required to pay a deductible as high as 10% the cost of a
prescription (65). In many cases the extent of coverage influences the use of medication
(66, 67).
The majority of people in Ontario pay for drugs though private insurance which is
purchased independently or provided by an employer. Private plans vary greatly in
terms of the extent of coverage, the method of reimbursement and cost-sharing
arrangements. Most often cost-sharing is mediated through deductibles and co-
payments, which can limit access to expensive medications. In Ontario, private plans
generally provide coverage with lower deductibles and co-payments than government
plans. However, 10% of families have no insurance and are required to pay for
pharmaceuticals out-of-pocket (63).
Although most drug plans cover all immediate family members, there is not guaranteed
access to necessary drugs for children. This poses many issues for families with high
drug costs relative to income, since some drugs for paediatric indications are not listed
on the private or public formularies (66). For some of these cases, the Exceptional
Access Program (EAP) is an option. The EAP, previously known as the Individual
Clinical Review, allows Ontario physicians to make written requests for unlisted drugs
to be covered though the TDP. Requests are reviewed on a case-by-case basis using a
strict set of reimbursement criteria and usually take six weeks to grant coverage for a
year. Patients that are granted access to unlisted drugs are required to pay the
deductible requirements of the TDP.
Access to biologic treatments for arthritis has become a contentious issue in Canada.
Since provinces decide which drugs they will pay for and under which conditions,
22
public reimbursement for biologics varies greatly from province to province. Manitoba
and Prince Edward Island have been shown to have the poorest public reimbursement
coverage, while British Columbia and Saskatchewan have been shown to have the best
(68). In Ontario, biologic treatments approved for JIA are not listed for use in children
on the provincial formulary and as a result, treatments are paid for out-of-pocket,
through private insurance, or by coverage granted through the EAP. This leaves many
families financially burdened and others awaiting approval to access a treatment
through EAP. In cases where EAP access is not approved, families may not be able to
afford the best treatment for their child.
1.3 Statement of the Problem
JIA can have a profound impact on not only the life of the diagnosed child but the
family as a whole. In many cases, enormous pressures are placed on parents to
monitor disease progression, ensure adherence to treatments, provide emotional
support, attend appointments and take time off work to care for their children. For
these reasons parents of children with JIA have been described as feeling, “scared,” “to
blame,” “heartbroken,” and “out of control” (69).
On top of the stress caused by the disease itself, parents of children with JIA are faced
with complex decisions surrounding the use of various drug treatments and are often
forced to make tradeoffs between characteristics such as effectiveness, convenience,
safety, and cost. This is not a simple task as there are only modest differences in the
benefits and costs of drugs within a single class and major differences in the frequency
and level of responsibility required to administer treatment, likelihood of common
adverse effects, and the risk of rare but potentially serious complications. Parents of
children eligible for biologic drugs must also cope with extremely high and unexpected
costs associated with treating their child, while continuing to meet financial needs of the
family.
23
An understanding of how parents of children with JIA trade between characteristics of
drug treatments and health outcomes could help inform clinical practice and in turn
improve disease management by making health care providers more aware of the
aspects of treatment most valued by parents. In order to do so, accurate information is
needed regarding the nature of parents’ preferences for diverse treatment
characteristics in the domains of effectiveness, safety, convenience and cost.
In the following section, patient preference methods will be introduced, with a
particular focus on stated preference methods; in particular conjoint analysis. This will
be followed by a more detailed discussion on the theoretical basis and steps required to
conduct a DCE.
1.4 Introduction to Patient Preference Methods
To prefer something means to perceive it as more desirable or as having more value
(70). This suggests that people have an intrinsic nature to compare and measure
differences between various goods or states of being. Patient preference refers to an
individual’s evaluation of health or health care when expressed as a statement or action.
These appraisals are driven by knowledge, experience and reflection that exist as a
result of prior choices (71, 72). Preferences can be for structural aspects of health care,
(e.g. facility organization, information, or priority setting), process aspects of health care
(e.g. treatment options, surgical interventions, end-of-life care, time waiting for results),
or health outcomes (e.g. pain, side-effects, or quality of life) (73).
The importance of understanding and using patient preferences in health care decision-
making is something that has been known for years (74), but has just recently been
recognized at the government level (75-78). This has led to a growing contribution by
patients in the development of health care policy, clinical practice guidelines and
patient information material as well as an involvement in point of care decisions and
24
health outcomes research. How preferences are elicited varies in regards to
methodology, with a wide range of quantitative and qualitative approaches (79). In
health economics, preference is often elicited quantitatively by measuring one’s utility.
Utilities represent a broad measure of benefit as perceived by an individual, group, or
society (80) and are most often estimated for a particular set of health outcomes.
However, utilities for other characteristics of the health care system can also be derived.
1.4.1 Stated Preference Methods
One way to determine preference is to measure actual behavior, which if done correctly,
results in revealed preference data. In marketing, revealed preference data are usually
generated by comparing patterns of spending among available market alternatives (81).
In health care, patient spending is more difficult to track since consumption is often
based on need rather than preference, and consumers rarely face market prices. For this
reason, stated preference (SP) methods are applied to model preferences in health or
health care (82). This flexible group of methods allows preferences to be determined
under a variety of scenarios (treatments, health outcomes, programs), constraints (time,
money, risk) and or populations (patient, society, parent) (81). They also allow for the
evaluation of new, future, or hypothetical programs, services or health outcomes. The
most common SP methods used to measure preferences in health care are time-tradeoff,
standard gamble, conjoint analysis (CA) and contingent valuation (CV) (83). These
methods elicit patient values in a way that is theoretically and methodologically
defensible and as a result, have great potential to impact health care decision making
(83).
1.4.2 Conjoint Analysis
CA is the umbrella term used to describe a group of SP methods that involve ranking,
rating, or choice-based approaches (84). These methods are based on the premises that
25
any good or service can be described by its characteristics and the extent to which an
individual values a good or service depends on the levels of these characteristics (85).
In a health care-related CA experiment, characteristics describe component outcomes or
processes associated with a health care intervention (83, 86).
Methodologically these techniques were developed in mathematical psychology (87)
and have been refined over time through their applications in market research (88),
transport economics (81), and environmental economics (89). Today, CA is being
successfully applied in health care to determine not only what is important to the
patient, but why, and to what extent (83, 86). Some argue that when done properly, CA
can produce better models for estimating preferences than revealed preference data as a
result of more information on the characteristics or outcomes making up a health
intervention (90).
1.5 Discrete Choice Experiments
DCEs are considered one of the best means to elicit preferences for health and health
care because they simulate the type of real-world decisions individuals are faced with
every day (90). Different from other conjoint methods, DCEs do not ask respondents to
rank or rate alternatives, but rather ask them to choose one from a set (83). The
alternatives described in each choice set represent real or hypothetical scenarios which
vary by their levels of attributes. Attributes describe the most important component
outcomes or processes associated with a health care intervention and typically remain
the same across all scenarios, while levels describe a reasonable range for each attribute
(91). For example, in a DCE aiming to estimate preferences for drug treatments in JIA,
the attribute “side-effects” might be defined by the levels “none”, “headache,”
“stomach pain” and “nausea.” The respondent might therefore be asked to choose
between Drug A which is extremely effective, administered by IV and associated with
26
stomach pain or Drug B which is somewhat effective, administered orally and
associated with no side-effects.
When data from multiple choice sets and multiple surveys are combined, the relative
importance of each attribute and the marginal rate at which individuals are willing to
substitute between attribute levels can be determined. The utility or preference scores
for each attribute can then be summed to determine which scenario is preferred and
attributes can be re-combined to model real world goods or services (81, 86, 92). By
including cost as one of the characteristics within the DCE, the amount of money a
person is willing to pay for a characteristic or profile can be determined through a
monetary equivalence of utility (84).
1.5.1 Theoretical Basis
DCEs are rooted in classical economic theory of consumer behaviour, which assumes
that respondents are rational decision-makers who make choices to maximize utility,
subject to budget constraints (93). Within this school of thought, Lancaster’s Theory of
Value states that any good or service can be described by its characteristics or attributes,
and that utility is derived from the value assigned to these attributes, not the good or
service itself (85). This is the theoretical foundation underlying the concept of a DCE
which assumes that when faced with two options, an individual will be willing to trade
in one attribute to attain a given level of another attribute.
Analysis of DCE data follows the assumptions of random utility theory (RUT) which
describes how individual choice is probabilistic (94) and how preferences can be
estimated using an indirect function of utility (95). Specifically, RUT posits that utility
(U) for an individual i for alternative j can be separated into a systematic or explainable
component (Vij) and an unexplained or random component (εij):
Uij = Vij + εij , j = 1, .... J (Equation 1)
27
The systematic component, Vij represents indirect utility and depends on the attributes
of alternative j and the attributes of the chooser i. Thus, if X’ij is the vector of attributes
for alternative j as valued by individual i and Z’j is a vector of characteristics of
individual i, then:
Vij = X’ijβ + Z’iγ (Equation 2)
where β and γ are vectors of the coefficients to be estimated. How one interprets the
random component (εij) dictates how utility estimates are derived and the model used
(96). This concept is explained further in section 1.5.2.3.
In a DCE utility is not observable and therefore must be estimated from choices made
from a set of alternatives. Following RUT a respondent will choose alternative j if, and
only if its utility is higher than the utility of any other option k. The probability (P) that
utility is maximized by choosing alternative j depends on whether or not the difference
in the random utility between alternatives k and j is less than the difference between the
systematic utility between alternatives j and k in the choice set (Ci).
Pij = P(Uij > Uik)
= P(Vij + εij > Vik + εik) (Equation 3)
= P(εik - εij < Vij - Vik) where j ≠ k in Ci
The strong theoretical basis of DCEs has led to their acceptance as a tool in health
economics research and as a result DCE’s are the most widely used SP method applied
in health care decision-making today (82, 92).
1.5.2 Conducting a DCE
There are three major phases in conducting a DCE: 1) Conceptualization, 2)
Experimental Design, and 3) Analysis. Each of these phases is associated with a series
28
of unique steps (9 total, see Figure 1) (82, 90, 91, 93). When conducting a DCE it is
important that each step be carried out with caution since each decision can have
downstream implications. A detailed description of the phases involved in conducting
a DCE can be found in Figure 1.
Figure 1: Steps in conducting a discrete choice experiment
1.5.2.1 Conceptualization
Conceptualization of a choice experiment requires a comprehensive understanding of
the problem and decision-making context as well as behavioral aspects of the problem
that drive the choice process in the “real world” (93). It is also important to understand
the decision maker, in particular, how familiar they are with the trade-offs being
evaluated. It has been shown that the level of knowledge one has of the attributes and
levels being evaluated can have a significant influence on preferences (97). More
generally speaking, once the research question and choice context have been established
and a DCE has been selected as the most appropriate research method, conceptualizing
Phase 1: Conceptualization
1. Characterizing the choice design 2. Identifying the attributes and levels
Phase 2: Experimental Design 3. Selecting the choice experiment design
4. Creating the questionnaire 5. Planning data collection
6. Pilot testing
Phase 3: Analysis 7. Coding
8. Selecting a Model 9. Interpreting the findings
29
the choice experiment involves characterizing the choice design and identifying the
attributes and levels that will be used to generate the choice experiment.
1. Characterizing the choice design
Careful consideration must be given to the nature and composition of choice sets in a
DCE. The choice format should simulate the choice made in the real world as closely as
possible. For this reason, choices can be presented as paired alternatives, multiple
options or binary yes/no choices (82). They can also be labeled or generic. In a generic
experiment the title of each alternative does not provide any information to the decision
maker and therefore are best used in situations where hypothetical scenarios are valued
(91).
The inclusion of a comparator or opt out must also be considered (82). A comparator
should represent the status quo scenario and can remain constant or vary between
respondents. A opt out allows respondents to choose none of the alternatives and
should be used if there is an option to not consume the good or service in practice. Such
options better mimic “real world” choices in some cases, but in other cases provide a
mechanism for respondents to avoid difficult choices (98). It is important to consider
the size of the design when considering the inclusion of a comparator or opt out choice.
Both have been shown to increase respondent burden (96).
2. Identifying the attributes and levels
Attributes should describe key factors or dimensions that influence choice and as a
result can be used to capture the systematic component of the utility function. Levels
should represent a reasonable range for each attribute, such that they are plausible to
respondents, policy relevant and provide sufficient variability in responses to yield
meaningful parameter estimates (96). Attributes can describe quantitative or qualitative
outcomes, with qualitative levels being either nominal (e.g. red, green, blue) or ordinal
30
(e.g. mild, moderate, severe). It is suggested that a defined unit of measurement be
selected for each attribute to ensure that respondents do not interpret levels differently
(91).
Attributes and levels can be initially identified through a review of the literature or
qualitative research. Interviews and focus groups with samples of respondents and
experts have been shown to be useful in informing attribute levels and clarifying which
attributes are most important to respondents (93). The number of attributes and levels
selected predict not only the size and complexity of the experimental design, but also
the method by which choice data can be modeled. Two-level attributes, although
simple for the respondent to process, only allow the estimation of linear effects. It is
therefore important to carefully consider the balance between the statistical efficiency of
the experimental design and the cognitive burden on respondents, both of which are
influenced by the number of attribute levels (90). Piloting can be used to get the balance
right.
1.5.2.2 Experimental Design
Once the choice experiment has been conceptualized, decisions must be made as to how
data will be generated and collected. This requires a thorough understanding of
experimental design principles and how they relate to the statistical efficiency of the
experiment. It is also important to acknowledge the impact on respondent efficiency,
since each individual will interact with a choice experiment differently (99).
Respondent efficiency describes the respondents’ ability to clearly understand and carry
out the choice tasks presented and can be influenced by the clarity of instructions and
choice questions, the number and complexity of choice tasks, and amount of
information provided on the intervention (93). It is therefore necessary to generate a
choice experiment that takes into consideration the importance of not only the statistical
efficiency of the experimental design, but also the creation of the questionnaire and the
31
method by which data will be collected. Pilot testing can offer valuable insight into the
development of a choice experiment.
3. Selecting the choice design
Experimental design describes the process by which attributes and levels are
systematically allocated to alternatives within a choice set. These choice sets are used to
generate data and as a result, the design of a DCE directly impacts the precision (i.e. the
statistical efficiency) by which effects that are identified can be estimated (91). The goal
of any experimental design is to extract the maximum amount of information from each
respondent, subject to the number of attributes, levels, and other characteristics of the
survey (100). Based on this, an efficient or optimal design is one which gives the most
precise parameter estimates for a given sample size (92). Design principles that have
been shown to improve design efficiency are:
Orthogonality- levels of an attribute appears in a choice set with equal frequency in relation to the levels of each other attribute resulting in a zero statistical correlation between attributes (101) Level balance- levels appear with equal frequency within each version of the survey (102) Minimal overlap- levels within each attribute of a choice set are different (103) Utility balance- parameter estimates are used to create choice sets that have a near equal probability of being selected by the respondent (100)
It is important to note that although these principles can be used to guide the design
process, satisfying these properties does not guarantee an optimal design. In many
cases fulfillment of one principle leads to deviation from the others, as is the case with
orthogonaility and utility balance (100). For this reason it is important to consult more
formal measures of efficiency.
32
D-efficiency is the most commonly used and broadly accepted measure of design
efficiency and can be used to compare designs (104, 105). It is calculated by measuring
the covariance of the design matrix (X), which is defined by the matrix of attribute
codes (rows corresponding to alternatives and columns to coded attribute variables).
The formula for D-efficiency is:
D-efficiency = [|Ω| 1/K] -1 (Equation 4)
where K is the number of parameters to estimate and Ω is the covariance matrix of the
vector of the parameters. The covariance matrix is defined as:
Ω = σ2 (X’X)-1 (Equation 5)
where σ2 is a measure of variance and (X’X) represents Fisher’s Information Matrix
(106). A D-efficient design will therefore minimize the covariance matrix, which is the
case for a design with orthogonal attribute columns in the design matrix (X).
Full factorial designs include every possible combination of attributes and levels and as
a result are considered orthogonal and fully efficient from a statistical perspective.
Unfortunately, in most cases full factorial designs are too large to present to
respondents and as a result, designs that represent a select sample of all possible
combinations are often used. Fractional factorial designs can be efficient if designed
properly, but require careful consideration of the types of effects to be estimated. Main
effects designs can be used to estimate the direct independent effect of each attribute on
choice whereas interaction designs can be used to estimate the impact of multiple
attributes on choice. More specifically, interactions terms should be accounted for
when the preference for the level of one attribute is dependent upon the level of a
second attribute (e.g. price and quality) (91). The most efficient fractional factorial
designs, known as orthogonal main effects plans (OMEPs), or orthogonal arrays, only
33
allow for main effects to be estimated (92). Blocking is another method used to reduce
the size of a design. This requires the entire design to be systematically divided into
versions that each possesses a unique combination of choice sets. These versions can be
randomly allocated to groups of respondents.
Depending on the size (number of alternatives, number of attributes, number of levels)
efficient experimental designs may be selected from design catalogues (107), generated
using statistical software packages such as SAS (108) or created by hand (103, 109). In
most cases this involves converting an OMEP into a choice design. Street and Burgess
developed a method for generating optimal generic main effects DCEs from OMEPs of
any size (110). They also developed a method for constructing optimal designs for
which main effects plus interactions can be estimated if all attributes have two levels
(111).
Huber and Zwerina developed a method that incorporates the concept of utility balance
(100). This method involves defining alternatives within each choice set to have more
equal choice probabilities resulting in a limited number of dominated alternatives in a
choice set. Prior estimates of parameters are required in order to implement this
method of design and the accuracy of these estimates relates directly to the efficiency of
the design.
Unfortunately, there is no cookie-cutter method for designing a DCE and different
strategies become appealing under different circumstances. For this reason it is crucial
to test and assess the properties of a design before implementing it.
4. Creating the questionnaire
As mentioned previously, respondent efficiency can be influenced by familiarity of the
decision maker with the intervention being valued, the clarity of instructions, and the
length of the questionnaire. For this reason, it is important to carefully frame the choice
34
questions, survey instructions, as well as descriptions of attributes and levels. The
amount of information to provide to respondents depends on how much experience or
knowledge they have with the good or service. Successful framing limits the extent to
which respondents make assumptions or bring outside information into the decision
making process (97). The inclusion of socio-demographic questions in the discrete
choice questionnaire can help identify respondent characteristics that significantly
influence preference. The benefits of these questions should be weighed against the
cognitive ability of the respondents and the response burden, particularly in cases
where the experimental design is large.
5. Planning data collection
Data collection in a DCE should be driven by the study objectives as well as the
resources available. The most common and inexpensive means of collecting DCE data
is through postal or online surveys. However, self-completion methods are associated
with the lowest response rates. As a result, face-to-face interviews and internet panels
via marketing companies are becoming more common and have been found to decrease
the cognitive complexity perceived by respondents (82).
6. Pilot testing
Pilot testing is an integral step in the development of a successful DCE survey as it
provides valuable information on the clarity and usability of the instrument as well as
preliminary data for which methods of entry and analysis can be verified. Often
referred to as pre-testing, a pilot study can be used to test respondents understanding of
survey instructions, choice questions, and attributes and levels while providing some
insight as to how complex choice tasks are, how long the survey will take, and what
response rate should be expected (82). Pilot studies can also be helpful for estimating
parameters and using these estimates to design the experiment (105). If possible, the
35
pilot test should be carried out in a small sample (10-20) of respondents that meet the
study inclusion criteria.
1.5.2.3 Analysis
Once data from the DCE have been collected discrete choice analysis can begin. This
first requires the model of estimation to be specified. A number of regression models
that employ maximum likelihood techniques can be used to estimate utility function
parameters and will be discussed in this section. Parameter estimates are the final
result of a DCE and their interpretation is what allows a further understanding of the
behavioral processes that define preference. Overall the process of analysis requires
appropriate coding of attributes, model specification and interpretation of model
estimates such that they are policy relevant.
7. Coding
In a DCE the dependent variable is choice and the independent variables are the
attribute levels used to describe the alternatives described in a choice set. Prior to
analysis of DCE data these variables must be coded. This is an extremely important
step not only for the analysis but the interpretation of results. In a DCE each coded
variable represents a single piece of information available during analysis, and as a
result the greater the number of variables coded the greater the understanding of non-
linear relationships (91). However, more independent variables mean a larger design
and a larger design means more complex choices for respondents.
For quantitative attributes it is recommended that the numeric value for each level be
used to represent each level (e.g. price). For qualitative attributes with more than two
levels, coding that allows for non-linear effects to be tested is required. Both dummy
and effects coding can be used to derive parameter estimates in these situations (see
Table 4).
36
Table 4: Example of dummy and effects coding of a three level attribute
Levels Dummy Coding Effects Coding
Variable 1 Variable 2 Variable 1 Variable 2
1 1 0 1 0
2 0 1 0 1
3* 0 0 -1 -1
* denotes the reference level
Dummy coding result in a reference level (represented by all zeros) that is perfectly
correlated with the overall grand mean of the utility function (the regression intercept)
and as a result the part-worth utility for each attribute cannot be precisely estimated.
On the other hand, effects coding allows the part-worth utility of the reference level to
be defined as the negative sum of the estimated coefficients (112).
The coding of attributes in a design can also influence the precision of parameter
estimates in cases where interactions are being evaluated. In these cases, effects coding
has been recommended over dummy coding for its ability to reduce statistical
correlation between main and interaction effects (90).
8. Selecting a model
The form of the indirect utility function (IUF) (Vij, equation 2) depends on the
experimental design (main effects or main effects with interactions) and the format of
the choice being modeled (binary or multiple choices). Since the dependent variable in
a DCE (choice) is discrete in nature it cannot be analyzed using linear models (e.g.
ordinary least squares) (105). As a result, analysis is often carried out using maximum
likelihood regression models such as logit, mixed logit, or probit. These models differ
in how they treat the random component (εij) in the IUF. Based on economic theory the
random component results from unobserved or unobservable attributes, unobserved
variations in respondents, measurement errors or specification errors (113).
37
McFadden’s conditional logit model, also known as the multinomial logit (MNL) model
is by far the most widely used as a result of the convenience of its assumptions (114).
The first assumption is that unobserved factors and errors are independently and
identically distributed (iid) extreme values across all alternatives. The second
assumption is that choices in a DCE are independent and unaffected by the presence of
irrelevant alternatives. This is often referred to as the independence of irrelevant
alternatives (IIA) property, which implies that substitution patterns across alternatives
are proportional. These assumptions are not appropriate for all DCE’s (115). For
example, a person who dislikes travel by bus because of the presence of other riders
might also dislike travel by rail for the same reason and as a result, the unobserved
factors affecting choice between bus and rail would be correlated (93). As well, the
assumption that choices are independent over time can also be challenged, since
unobserved factors that influence choice for one set of alternatives would likely
influence the choice of the next set of alternatives (114).
The development of other models has arisen largely to avoid IIA (105). Probit models
assume unobserved factors follow a joint normal distribution and as a result can handle
correlations over alternatives and time. A drawback to the use of a probit model is the
fact that it does not allow for the estimation of utilities for different choice scenarios and
as a result does not always allow for DCE data to be interpreted in a manner that is
relevant to policy makers (114). Mixed logit models allow unobserved factors to follow
any distribution and allow for preference heterogeneity across individuals. Analysis of
DCE data using mixed logit requires multiple choice observations to be coded for each
individual and can be carried out using maximum simulated likelihood estimation, a
complicated statistical estimation technique that can result in biased parameter
estimates (116).
38
9. Interpreting the findings
Regression analysis results in parameter estimates which, based on RUT, are interpreted
as the likelihood of choosing a scenario based on a marginal change in attributes. If the
change in an attribute is associated with an increased likelihood of selection, then it is
assumed that the individual has a positive preference for that attribute (83). This is
known as the average relative preference or part-worth utility of an attribute, where
both the sign and the magnitude of the regression coefficient reveal information about
preference (91). Larger regression coefficients are associated with a greater degree of
preference for a characteristic, while negative coefficients suggests that on average,
participants prefer to have less of that characteristic. When effects coding is used, the
part-worth utility for each level within an attribute can also be determined.
Using IUF (equation 2), overall preference scores can be used to determine the
combination of attribute levels that represent the alternative most preferred by
respondents. This approach can also be used to determine the overall preference for
any combination of levels, including those combinations that best mimic real or
hypothetical goods or services. When main effects are estimated using a MNL model,
this requires the simple addition of part-worth utilities for the desired combination (91).
Parameter estimates can also be interpreted as marginal rates of substitution (MRS); the
rate at which respondents are willing to trade in one attribute in order to gain in
another. This can be calculated by taking the negative ratio of any two estimated
parameters (equation 6):
MRSl = - (βl/ βm) (Equation 6)
where l is the attribute for which m is being traded. If cost is included as an attribute in
the DCE, the MRS for the cost attribute can be used to estimate the marginal
willingness-to-pay (MWTP) for any characteristic or combination of characteristics (90).
39
This is done by including the parameter estimate for cost (βcost) as the denominator in
equation 6 (90). Preferences are often expressed as WTP as it allows for findings to be
interpreted in a manner that is policy relevant. Unfortunately there are a number of
theoretical and methodological issues with estimating MWTP from DCE data. These
issues will be discussed below in section 1.6.
1.6 Willingness-to-Pay
WTP is grounded in microeconomic welfare theory (80) or more specifically, Paul
Samuelson’s law of demand which states that monetary valuations can be used to
represent preference (117). CV was developed in environmental economics in order to
determine social values for public goods, such as cleaner air (118). In health care, CV is
most often used to determine the maximum amount of money a patient is willing to
give up, hypothetically, contingent upon the availability of a given treatment or the
probability of a given health outcome. Data generated in WTP studies are extremely
useful in health care as they can inform decisions surrounding the allocation of
resources (119-121) or can be incorporated into a cost-benefit analysis (122-124).
There are four standard approaches to generating WTP data: open-ended CV, closed-
ended CV, bidding game, and payment card (125). Each method presents respondents
with a choice between not having the outcome being valued or giving up a certain
amount of money in order to obtain it. The methods differ in how the amount one is
willing to pay is derived (i.e. open-ended allows for any value to be stated, closed-
ended requires binary yes/no responses to specified values). Although the availability
of four approaches allows for flexible experimental design, consistency in the data
derived from these approaches has been shown to be low (125). Various approaches to
WTP have also revealed different levels of acceptability to respondents in terms of
comprehension and burden (83).
40
In addition to these standard approaches, more recent attempts to estimate WTP have
relied on CA methods, such as DCEs developed by Small and Rosen (126). Findings
from their work suggest that when using a state of the world approach the most
appropriate way to estimate WTP is by calculating the MRS for the cost attribute (see
section 1.5.2.3, equation 6) (127, 128). However, some argue that WTP derived by this
method is inconsistent with RUT since unexplained or random components (εij from
equation 1) are used to derive values of WTP. As a result WTP itself must be
considered random and therefore cannot be accepted with certainty (115). For this
reason, caution must be used when attempting to use MWTP values in cost-benefit
analysis and estimates should be accompanied with confidence intervals generated
using an accurate approach (129). Compensating variation or the multiple alternative
approach was recently introduced to health economics as a means to calculate WTP in
the context of discrete choice random utility models (126, 130, 131). This method can be
used to estimate WTP for an entire program, a single attribute, or changes within an
attribute while taking into account the uncertainty in the choice model (115).
1.7 Study Rationale
Parents of children with JIA are often faced with complex decisions surrounding the
best treatment for their child. Although physicians are capable of communicating the
expected risks, benefits, and costs of treatments, family values play an important role in
treatment decisions. Studies in JIA have shown major differences in the preferences of
parents and physicians when it comes to drug treatments and health outcomes (132,
133) . This suggests that choices about the best treatment for a child requires careful
consideration of the health outcomes associated with a treatment (effectiveness and
safety) as well as the characteristics of the treatment itself (convenience, cost).
Despite a number of published studies looking at patients’ preferences for drug
treatments in RA, including two discrete choice experiments (134-141), there have been
41
only two studies that have looked at the preferences of parents of children with JIA
(142-144). Both studies focus on parents’ preferences for NSAID treatments, specifically
the added value of a drug that leads to remission and the lost value of a treatment that
is associated with GI-related side-effects. To date there have been no studies that have
looked at parents’ preferences for other types of drug treatments used in JIA. There
have also been no studies that measure preferences for both treatment characteristics
and health outcomes in the same evaluation.
The decisions facing parents of children with JIA are complex and preferences for
characteristics of treatments and health outcomes are difficult to compare and evaluate.
A DCE offers a unique approach to this problem because it allows for tradeoffs between
characteristics of the treatment and health outcomes to be valued and the relative
importance of those characteristics and outcomes to be measured (90). Using a DCE it is
possible to determine how the health and experience of a child with JIA (e.g. failed
treatments, side-effects) impacts a parent’s choice, and how socio-demographic
characteristics of families are associated with different preferences. Understanding
which treatment characteristics and health outcomes are most important to parents of
children with JIA can inform clinical practice and childhood rheumatology outcomes
research. The fact that the most effective treatments for JIA are extremely costly for
families (59) also makes estimates of WTP policy relevant. Monetary valuations of
preference could be used to inform policy decisions surrounding the future of
prescription drug programs for JIA and other chronic paediatric conditions in Ontario.
1.8 Hypothesis and Objectives
It was hypothesized that parents of children with JIA would show a greater preference
for and be willing to pay more for treatments that provide improvements in their
child’s health and quality-of-life. The overall objective of this thesis is to gauge parents’
42
preferences and willingness-to-pay for attributes related to treatments in JIA using a
DCE. Specific research objectives are:
1. To determine which attributes related to effectiveness, convenience, safety, and
cost, parents feel are the most important when using drugs to treat a child with
JIA.
2. To quantify trade-offs parents make when faced with various treatment
attributes and to determine the extent to which they are willing to pay for these
attributes.
3. To determine which socioeconomic and disease characteristics are related to
parental preferences for drug treatments in JIA.
43
2 Methods As described in section 1.5.2 there are 9 steps in conducting a DCE (see Figure 1). These
steps will be used as a guide to describe the methods used to elicit the preferences of
parents of children with JIA. It is important to note that both a pilot study and main
study were carried out and as a result, many steps were repeated.
2.1 Target Population
Whether or not respondents in a DCE should be informed of or familiar with the trade-
off being evaluated is a topic of much debate (97, 145). Some believe that respondents
are capable of expressing preferences for unfamiliar goods and that preferences are
constructed while completing the valuation task (145, 146) while others have shown the
effect that prior information on the attributes and levels being traded can have on
preference for treatments (97). In this study an experienced sample of parents were
recruited in order to ensure an understanding of the clinical attributes and levels
presented. The following inclusion criteria was used:
Parents of:
• Children ages 0-18 years, that have been clinically diagnosed with JIA of any
sub-type;
• Children with JIA who visited a Hospital for Sick Children Rheumatology
Clinic in the last year; and
• Children with JIA who were prescribed a NSAID, DMARD or biologic drug
in the last three years.
Limiting the range in which a child must have been prescribed a drug treatment for
their arthritis to three years ensured that parents of children in remission could relate to
the choice tasks presented. Exclusions of non-English speaking parents who were
44
unable to provide informed consent were made. Parents of children with any
significant co-morbidity were also excluded.
2.2 Characterizing the Choice Design
A discrete binary choice format, in which respondents were asked to choose their most
preferred treatment scenario from a list of two possible scenarios, labeled Scenario A
and Scenario B was employed. An ‘opt-out’ or ‘choose neither’ option was not included
because doing so would require a starting state or the state of a child off treatment to be
defined. Providing a third health state for parents to evaluate may have increased the
complexity of the choice task. Including a ‘choose neither’ option and failing to define a
health state ‘without treatment’ would force respondents to make assumptions about a
child’s condition. Forcing respondents to choose a treatment scenario has been shown
to provide more information for statistical analysis (84). It was therefore determined
that including an opt-out alternative was not worth the added complexity of the choice
task and potential for respondent burden (see section 1.5.2.1).
2.3 Identifying Attributes and Levels
A literature review of preference-based studies in the field of arthritis treatment and
outcomes was conducted in order to determine attributes that were previously defined
as important. A list of potential attributes and levels that were appropriate for valuing
the preference of parents was created. Discussions with clinical experts helped narrow
the list and determine levels that were clinically relevant. The attributes and levels
selected were pilot tested in a convenience sample of parents. Feedback from the pilot
study was used to make changes to the attributes and levels used in the main study.
45
2.3.1 A Literature Review of Studies Evaluating Preferences for Drug Treatments and Health Outcomes in Arthritis
An electronic search of the PubMed database was carried out in order to identify
studies that evaluated the preferences of patients with RA or JIA (under 18 years of
age), as well as parents of children with JIA for drug treatments and or health outcomes
in arthritis. The search strategy included the following Medical Subject Heading
(MeSH) terms: ‘Arthritis, Rheumatoid‘, ‘Arthritis, Juvenile Rheumatoid’, and ‘Patient
Satisfaction”, and the key words: ‘patient preferences’ and ‘preferences.’ Studies that
revealed the importance of treatment attributes in areas of effectiveness, convenience,
safety and cost were of particular interest. Only English articles were included and no
exclusions were made on the basis of publication date.
2.3.2 Selecting Attributes and Levels for Pilot Testing
Based on findings of the literature review a list of candidate attributes and levels was
created. This list was presented to 15-20 members of the Division of Rheumatology at
the Hospital for Sick Children during their scientific research review rounds in March
2010. Feedback received following this presentation was discussed on several occasions
by the research team and allowed for the list of attributes to be narrowed to six. A
maximum of six attributes has been suggested to avoid excessive cognitive complexity
for the respondent (147). Discussions with three nurses, four rheumatologists, and one
reimbursement counselor led to the creation of attribute levels that were appropriate for
eliciting the preferences of parents’ of children with multiple JIA sub-types. The levels
were reviewed by the research team, with careful consideration of the range, potential
for interactions, and impact on the size of the design. In the end, the research team
selected the attributes and levels for pilot testing (see Table 5). Rationales for selecting
each attribute and the range of levels follows.
46
Table 5: Attributes and levels selected for pilot study
Attributes Levels
Disease activity
No pain Mild pain in fingers Mild one-sided pain in shoulder, elbow, finger and foot Severe pain in knees Severe one sided pain in wrist, knee and ankle Severe pain in shoulder, elbows, fingers, knees and ankles
Participation in daily activities
Without any difficulty With some difficulty With much difficulty Unable to participate
Drug administration
Pill daily Injection four times per month I.V. once per month
Side-effects None Headache four days per month Stomach pain four days per month Nausea four days per month
Days missed from school
Half-a-day per month One day per month Two days per month Four days per month
Cost to you $50 per month $500 per month $900 per month $1500 per month
Disease Activity
The literature review showed pain relief to be not only the most common, but also the
most preferred attribute in studies measuring preferences for drug treatments in
arthritis (134, 148-150). In fact, parents of children with systemic JIA at the Hospital for
Sick Children identified severity of pain as the top attribute used to define active
disease in their children (149). A number of outcome measurement tools that have been
validated in children with JIA also include pain domains (e.g. ACR Ped, CHAQ, WHO
ICF, CHQ, PedsQL) (see section 1.2.3). The ACR Ped core set, accounts for pain along
with tenderness and swelling by quantifying the number of active joints. For this
reason, the attribute ‘disease activity’ was defined not only by pain, but the location and
number of affected joints.
47
Pictograms were used to represent the various levels of disease activity. Visual
representations of pain have been successfully used in another DCE to determine
patients’ preferences for pain intensity and pain-related disability (151). For the pilot
study, disease activity was defined by the number (0, 2, 3, 4, 10), location (fingers, wrist,
elbow, shoulder, foot, ankle, knee) and severity of pain in joints (mild, severe).
Consultations with Rheumatologists at the Hospital for Sick Children allowed mild and
severe cases of the three most common sub-types (systemic, oligoarthritis, polyarticular)
to be represented. Figure 2 shows the various levels of disease activity that were
presented to pilot study respondents.
i. ii. iii. No pain Mild pain in fingers Mild one-sided pain in shoulder, elbow, fingers, and foot iv. v. vi.
Severe pain in knees Severe one-sided pain in Severe pain in shoulder, elbow, wrist, knee, and, ankle fingers, knees, and ankles Figure 2: Pictograms used to represent disease activity attribute
Participation in daily activities
Feedback from clinical experts led to discussions surrounding the appropriateness of
pain as a representation of treatment effectiveness with some believing that limitation
of motion was a more suitable outcome measure. Discrepancies between patients and
physicians perceptions of which treatment outcomes are most important are not
48
uncommon in arthritis (152, 153). From the literature review it is clear that the most
valued arthritis treatments should not only impact physical symptoms of the disease
(e.g. pain, mobility, joint damage), but also functionality and HR-QoL. In particular, a
study by Arkela-Kautiain et al. demonstrates the importance of representing all of these
domains when preferences are elicited from a heterogeneous group of patients (i.e.
active disease and remission) (154).
Since the CHAQ is considered the “gold standard” for evaluating functionality across
domains of activity and participation (155), the attribute ‘participation in daily
activities’ was created. Daily activities were defined to pilot respondents as a child
relevant mixture of outcomes represented in the activity and participation domains of
the WHO ICF core set to include: a) self care (bathing, dressing, eating, etc.), b) school
(writing, typing, recess, etc.), and c) social (playing, sports, hobbies, etc.) (46). Attribute
levels were derived from the CHAQ’s visual analog scale with a child being able to
participate in daily activities: i) Without any difficulty, ii) With some difficulty, iii) With
much difficulty, or being iv) Unable to participate.
Drug administration
A number of studies identified in the literature review measured preferences for route,
frequency, and location of administration separately (136-139, 141). From these studies
it is clear that there is a dominant preference for treatments administered less
frequently, but a lack of consensus on the route and location of drug administrations.
For most arthritis drugs the medication prescribed dictates the level of convenience
associated with administration. For example, IV drugs are required less frequently but
involve visits to the clinic, while injected drugs are required more often but must be
carefully administered at home. When faced with a choice between these two routes of
administration, it is clear from the literature that preference is dependent on individual
feelings about convenience (137, 138).
49
The attribute drug administration combines route and frequency into a single attribute,
with levels representing biologic drugs indicated for use in Canada as well as an oral
dose of MTX (see section 1.2.1.2). The levels of drug administration tested in the pilot
study were: i) Pill four days per month, ii) Injection four days per month and iii) I.V.
once per month.
Side-effects
A number of concerns surrounding the safety of drug treatments in JIA were described
in section 1.2.2. These safety risks include serious but rare adverse events such as
abnormal liver or kidney function, malignancies, reactivation of tuberculosis and
hepatitis B, as well as less severe but more common side-effects including GI-related
symptoms (e.g. nausea, stomachaches, diarrhea), headaches, fatigue, and reactions at
the injection or infusion site (5, 27, 34). From the preference studies reviewed (134, 139,
150, 156) it is clear that patients prefer treatments with lower associated risks of side-
effects, and risk attitude is dependent on the probabilities presented. In a study by
Fraenkel et al. probabilities of both mild and severe adverse events were derived from
RCT data, and patients were shown to value the elimination of risk for common
reversible side-effects (i.e. nausea, injection reactions, diarrhoea) to the same extent as
they valued the elimination of risk for less common, but potentially more serious
adverse effects (i.e. cancer, renal and hepatic toxicity, pneumonitis) (139). This may be
due to the fact that more serious adverse events are associated with a risk range that is
too small (e.g. no known risk of cancer vs. possible 0.01%) for respondents to
distinguish preference for or against. However, the use of risk attributes in DCE’s has
been cautioned (82, 145) as some people have difficulty interpreting probabilities (157,
158).
The side-effect attribute was selected to accurately represent outcomes that children
receiving a wide-range of drug treatments, including NSAIDs, DMARDs and biologics
50
could encounter. Clinical experts confirmed that although rare but severe side-effects
were a concern for some parents (cancer associated with anti-TNF therapies), persistent
but mild side-effects had a greater impact on medication adherence in children with
JIA. Attribute levels were selected to represent the most common, reversible side-
effects associated with weekly treatments in JIA as reported in the literature. The side-
effects selected for pilot testing were: i) None, ii) Stomach pain four days per month, iii)
Headache four days per month, and iv) Nausea four days per month.
Days missed from school (including pre-school or daycare)
Compared to a child of the same age without arthritis a child might miss school because
of disease flares, drug administrations, treatment side-effects, medical testing or
physical therapy (18, 61). The number of days missed have been shown to be correlated
with HR-QoL (159, 160). In a study by Batthish et al., 48% of parents of children with
JIA listed “missed school” as an attribute used to define active disease for their child
(149). Clinic nurses confirmed that parents of children with JIA are concerned with the
number of days their child will miss from school. The attribute ‘days missed from
school’ was created in order to assess how important these absences are to parents of
children with JIA. The levels were intended to represent the number of days a child
receiving a biologic treatment might miss each month based on the frequency of
infusions, clinic appointments, and medical testing reported in the literature (15, 54).
The number of day selected for pilot testing were: i) Half-a-day per month, ii) One day
per month, iii) Two days per month, and iv) Four days per month.
Cost to you
By including cost as an attribute within the DCE, the amount of money a parent is
willing to pay for an attribute or attribute level can be estimated and a monetary
valuation of preference can be presented (see section 1.6). However, the complexity of
the public-private mix of drug benefits in Ontario made it difficult to establish a range
51
of drug costs that were appropriate for all families. The levels of cost selected,
therefore, were not intended to represent the real world amounts parents pay to treat
their child, but rather amounts they would be willing to pay to treat the child described
in the survey. The attribute ‘cost to you’ was described to parents as the total amount of
money required out-of-pocket in order to access a drug. The levels selected for testing
in the pilot study were: i) $50 per month, ii) $500 per month, iii) $900 per month, iv)
$1,500 per month. These values were generated from the Trillium co-payments that
would be required to access a biologic drug for the average Ontario household.
Estimates took into account differences in size (range 2 to ≥ 4), the number of earners
(range 0 to 2), and annual household incomes (range $18,200 to ≥ $99,500) (65, 161). A
statement confirming that parents’ WTP would not be used for drug pricing was
included in the survey instructions.
2.4 Selecting the Experimental Design for the Pilot Study
A total of six attributes; one with three levels, four with four levels, and one with six
levels were used to generate choice sets for the pilot study (see Table 5). This
combination resulted in a full-factorial design consisting of 4608 scenarios (31 x 44 x 61)
or 2304 choice sets. Since asking respondents to evaluate this many choice tasks is not
feasible, a fractional factorial, D-efficient design was generated in SAS using a
combination of marketing research macros described by Kuhfeld (108). The SAS design
code used can be found in Appendix 2.
The MktRuns macro was used to determine the smallest possible size of an orthogonal
candidate linear design, which was found to be 144 scenarios based on the combination
of attributes and levels. A design of this size would require the design to be blocked
into four separate versions in order to keep the number of choice tasks per respondent
under 20. It was therefore decided that a smaller but slightly less orthogonal linear
design consisting of 96 scenarios would be generated to estimate main-effects using the
52
MktEx macro (108). MktEx relies on two methods to generate choice sets that maximize
D-efficiency. The coordinate-exchange algorithm tests the impact of swapping between
levels if the change increases efficiency (162); while the modified Fedorov exchange
algorithm swaps alternatives from random and orthogonal designs into candidate
choice sets to maximize D-efficiency (163). The MktEx macro tries a few iterations with
each method before establishing the best one, then carries out further iterations with
that method in order to improve balance and further maximize D-efficiency. The linear
candidate design generated using MktEx had a relative D-efficiency of 99.8% compared
to the fully orthogonal design consisting of 144 scenarios.
The ChoicEff macro was then used to transform the linear design of 96 scenarios into a
choice design with 48 choice sets (results in three blocks of 16 choice sets, a reasonable
number to avoid respondent burden) (105). This macro works similarly to the MktEx
macro in that it uses a modified Federov algorithm to search candidates for an efficient
experimental design. In this case the algorithm optimizes the information matrix of the
MNL model (108). Prior knowledge of the models parameter estimates allow ChoicEff
to generate a design that will provide more information when analyzed using MNL
regression. However, for the pilot study β parameter estimates were assumed to be
zero (i.e. the null hypothesis approach). The choice design selected from a total of 100
candidates had minimal correlation between attributes, level balance, minimal overlap,
and the highest relative D-efficiency. The MktBlock macro was used to sort choice sets
into three blocks, each consisting of 16 choice sets. The choice design generated can be
found in Appendix 3.
2.5 Creating the Survey Instrument for the Pilot Study
The survey instrument used for the pilot study was set up in two parts: the choice
questions and the child health and demographic questions. Survey instructions
included a sample choice set and a detailed description of the attributes and levels.
53
2.5.1 Framing the Survey Question
Respondents were asked not to think about their own child, but rather a child with
arthritis who was about to start a new drug treatment. This was done in order to
reduce respondent bias, and minimize the number of assumptions parents would make
based on the experiences with their own child. The time horizon selected was one
month and all attribute levels were framed within this period (e.g. headache four days
per month, $1,500 per month, I.V. once per month). Although there is no standard time
period over which preferences should be measured, one month is a common period
over which the preferences of patient with chronic conditions are assessed (164).
2.5.2 Child Health and Demographic Questions
Basic demographic and child health information that might have an impact on a
parent’s preferences was collected after the choice tasks. Demographic questions asked
parents about their employment, education, citizenship, ethnic and cultural
background, marital status, family size, drug coverage, and household income. Child
health questions provided information about the child with JIA’s disease sub-type, age
at diagnosis, prescribed drug treatments, discontinued drug treatments, side-effects,
and level of activity.
2.6 Pilot Data Collection
Both hard copy and online formats of all three survey versions were created. Hard
copies were presented in the form of a booklet that contained instructions to the survey
along with the choice, health and demographic questions. Online surveys were created
using SelectSurvey.NET and hosted at: www.sickkids.ca/surveydotnet/. This website is
managed by the SickKids WebTeam and stores all data on the Hospital for Sick
Children server. Data entered through the online survey instrument are protected by
seven layers of security. The security software does not store the IP Address and
54
browser information of the respondent, and requires access by a unique Survey ID.
Surveys that were returned by mail were transcribed by a single researcher (HB) using
the online version of the survey instrument.
2.7 Pilot Testing
2.7.1 Objectives
The objectives of the pilot study were to: i) assess the feasibility of recruitment, ii)
confirm the usability of hard copy and online versions of the survey instrument, iii)
gauge respondents understanding of attributes and levels, iv) determine the
directionality of preferences for attributes and levels, and v) derive β-parameter
estimates that could be used to inform the design of choice sets for the main study.
2.7.2 Pilot Study Recruitment and Follow-Up
The study was carried out in collaboration with the Division of Rheumatology at The
Hospital for Sick Children. During the time of the study, the department was in the
process of validating the diagnoses of their current active JIA patients and the clinic
database did not include records that were up-to-date for patients seen prior to
February of 2010. As a result, patient charts were required to verify parent eligibility.
A cohort list of potential subjects was created by the Rheumatology Database Manager
and charts of potential patients were reviewed for eligibility by HB using the inclusion
criteria defined in section 2.1.
Initial contact with potential parent participants was mediated through the Sick Kids
rheumatologist responsible for caring for each parent’s child with JIA. A letter signed
by the treating physician explained the candidates eligibility for the study, provided
information on the participant requirements and described the research objectives
(Appendix 4). A study information sheet was sent along with the letter to provide
further details on the study’s investigators, source of funding, and the potential harms
55
and benefits of participating (Appendix 5). Parents who wished to opt out of the study
were required to call HB using a telephone number provided in the recruitment letter.
If no request to decline from the study was received, eligible candidates were contacted
by telephone by HB within 1 week in order to determine their interest in participating
in the study.
Recruitment for the pilot study occurred over two weeks in August 2010. A total of 16
parents agreed to participate and were mailed the study package. Each package
included a cover letter (Appendix 6), a questionnaire booklet, and a stamped envelope
to allow completed surveys to be returned to HB. The front cover of each questionnaire
booklet contained a Study ID that could be used to log-in to the appropriate version of
the online survey, and a unique User ID that allowed for each respondent to be
identified anonymously. Instructions on how to access the online version of the survey
were included in the cover letter.
Two weeks after the survey packages were mailed, non-respondents were telephoned
and reminded of the study. This process was repeated every week for a total of one
month. Pilot study participants who completed the survey participated in a short (20
minute) telephone interview with HB. During the interview, parents were asked about
the usability of the survey instrument and their understanding of the questionnaire.
They were also given the opportunity to provide any additional feedback that they felt
would benefit future respondents. Following completion of the pilot study, participants
were mailed a thank-you letter along with a $10 gift card as a sign of gratitude for their
participation.
2.7.3 Pilot Data Analysis
SelectSurvey.NET allowed survey variables to be coded prior to being exported for data
analysis. Choice variables were coded with an observed time value of 1 for the chosen
56
alternative and 2 for the un-chosen alternative. Child health and demographic
responses were coded as categorical or continuous variables where appropriate. Both
coded and text versions of the data were exported and stored in an Excel document.
SAS version 9.2 was used to merge the coded design with data using SAS commands.
Child health and demographic data were examined prior to regression analysis using
PROC FREQ and PROC MEANS. Means and standard deviations were determined for
continuous responses including: age, household size, and years since JIA diagnosis.
Frequencies were examined for categorical responses such as: JIA sub-type, drug
treatments, side-effects, education, and income. Decisions to pool categorical variables
were made based on the number of responses in each category.
Multinomial logit regression was used to analyze choice data. Effects coding was used
for all categorical attributes, while ‘days missed from school’ and ‘cost to you’ were
coded continuously. Reference levels were coded as the “worst” level within each
attribute. The model estimated using PROG PHREG was:
Uij = βDrug1ij + βDrug2ij + βDisease1ij + βDisease2ij
+ βDisease3ij+ βDisease4ij + βDisease5ij + βSide1ij (Equation 7)
+ βSide2ij + βSide3ij + βPart1ij + βPart2ij + βPart3ij
+ βDaysij + βCostij + εij
where βDrug denotes the preference for drug administration (reference level I.V. one
day per month), βDA the preference for disease activity (reference level severe pain in
the shoulders, elbows, fingers, knees, and ankles), βSide the preference for side-effects
(reference level stomach pain four days per month, βPart the preference for
participation in daily activities (reference level unable to participate), βDays the
preference for days missed from school, βCost the preference for cost to you, and εij the
57
unobservable error term of individual i for choice j. This model allowed for the part-
worth utilities for all attribute levels to be determined.
2.7.4 Pilot Results
Of the 16 parents who were mailed the survey package 13 completed the survey and 10
underwent pilot debriefing. Of those who responded, 69% chose to complete the
survey by hand rather than online. Table 6 summarizes the results of the recruitment
process. Three of the paper respondents were not interviewed.
Table 6: Recruitment of pilot study participants n Percentage Recruitment Letter Sent 24 - Agreed to Participate 16 67 (16/24) Completed Survey 13 81 (13/16) Completed Paper Version 9 69 (9/13) Completed Online Version 4 31 (4/13)
All participants were mothers, of which eight had a university or college degree and 5
had a post-graduate or professional degree. At the time of the survey, seven were
employed full-time, four part-time, and two were homemakers. The majority (61.5%)
had private insurance that covered the cost of their child’s arthritis medications, some
relied on provincial drug programs (15.4%), and others had no coverage at all (23.1%).
Nine had high household incomes (over $80,000 gross per year), one had middle
($40,000-$79,999), and one low (less than $30,000). The mean household size was 4.38
(SD 0.65) (results summarized in Appendix 7).
On average children with JIA were 13.7 years old (SD 3.04) and had been diagnosed at
mean age of 7.5 (SD 3.95). Polyarticular was the most common sub-type (54%),
followed by oligoarthritis (23%), while psoriatic , enthesitis, and undifferentiated sub-
types were each represented by a single respondent. In terms of the types of
medications patients had been on over the last three years, over half of the drugs used
were DMARDs (53%), while NSAIDs and biologics were equally common (23.5%). All
58
patients with JIA had discontinued a drug treatment during the course of their disease,
in most cases because of side-effects (42%) but also because the drug stopped working
over time (31.6%) or was never effective (26.3%). Ten parents reported drug-induced
nausea and/or stomach pain, and seven reported headaches. When asked about their
child’s level of activity compared to a child of the same age without arthritis, eight
parents stated that their child was moderately less active, four stated equally active, and
one stated moderately more active (results summarized in Appendix 7).
Ten respondents were interviewed following survey completion. Overall, respondents
felt that the survey was easy to understand and reasonable to complete in 30 minutes.
The average time required for completion of the online version was 28 minutes (SD 9.5).
However, de-briefing revealed concerns with one of the attributes. The disease activity
attribute was described as “confusing” by a number of respondents, despite the fact that
the pictogram was deemed “helpful” (see Figure 2, section 2.3.2). Parents felt that the
levels represented were described by too many changing variables and that combining
the most extreme levels of the disease activity attribute with the opposite most extreme
values of the participation in daily activities attribute led to illogical choices (i.e. no pain
combined with unable to participate).
Based on MNL regression, the attributes and levels that were found to have a
statistically significant influence on preference (p-value ≤ 0.05) were disease activity, no
pain and mild pain in fingers; participation in daily activities, without any difficulty
and with much difficulty; and cost (see Table 7).
59
Table 7: Results of the multinomial logit regression analysis of pilot choice data Attribute Levels Beta SE Pr > Chi Sq
Disease activity
No pain 1.326 0.3240 0.0253 Mild pain in fingers 0.725 0.3536 0.0002 Mild one-sided pain in shoulder, elbow, fingers and foot. 0.160 0.2753 0.5601
Severe pain in knees -4E-5 0.2829 0.9999 Severe one-sided pain in wrist, knee and ankle -0.433 0.2771 0.1184
Severe pain in shoulder, elbow, fingers, knees, and ankles* -1.779 - -
Participation in daily activities
With no difficulty 1.176 0.2270 <0.0001 With some difficulty 0.354 0.2178 0.1049 With much difficulty -0.520 0.2261 0.0214 Unable to participate* -1.010 - -
Cost to you ψ
$50 per month -0.035
0.0003 0.0057 $500 per month -0.350 $900 per month -0.630 $1500 per month -1.050
Side-effects
None 0.236 0.2164 0.2755 Headache four days per month 0.172 0.2030 0.3966 Nausea four days per month 0.113 0.2211 0.6082 Stomach pain four days per month* -0.521 - -
Drug administration
Pill four days per month 0.303 0.1781 0.0893 Injection four days per month 0.027 0.1569 0.8642 I.V. once per month* -0.329 - -
Days missed from school ψ
Half day per month -0.059
0.0945 0.2119 One day per month -0.118 Two days per month -0.236 Four days per month -0.472
* denotes reference level, ψ denotes continuous variable coding
In terms of directionality of preference, part-worth utilities were consistent with a priori
expectations with coefficients for severe disease activities, difficulties participating in
daily activities, days missed from school and cost being negative. When part-worth
utilities for each attribute were calculated disease activity was the most valued
attribute, followed by participation in daily activities, cost to you, side-effects, drug
administration, and finally, days missed from school. McFadden’s pseudo R2 was 0.413,
indicating an acceptable goodness of fit for main effects estimates (165).
60
2.7.5 Discussion of Pilot Findings
Based on the recruitment and survey completion rates, it was determined that the
methods used for recruitment and follow-up were feasible for the main study. The
survey instrument was found to be appropriate and easy to complete. Overall, the
socio-economic status of the pilot sample was high, with all respondents having at a
minimum a university or college education and 85% stating a household income greater
than $ 100,000 per year. This may have contributed to the high response rate, ease
parents had completing the survey and their understanding of attributes and levels.
The parameter estimates generated using the MNL model confirmed the functionality
of the experimental design and the robustness of the methods used for data collection
and analysis. These values were used to generate a main study design with improved
utility balance (see section 2.9.2).
2.8 Post-Pilot Changes to the Survey Instrument
Following the pilot study a number of modifications were made to the survey
instrument, most of which involved changes to the attribute levels. First, the ‘disease
activity’ attribute was completely replaced with a new attribute defined as ‘child
reported pain from arthritis.’ This change was made in response to the confusion pilot
respondents had with this attribute. ‘Child reported pain from arthritis’ was therefore
represented by a single variable; severity of pain, which was presented to respondents
as i) None, ii) Mild, iii) Moderate, or iv) Severe. Second, information was added to the
levels of ‘drug administration’ to include details on how the treatment is administered.
Doing so intended to remind respondents that certain drugs would require more
responsibility or more frequent visits to the hospital or clinic. This change was the
result of new information on the impact of attribute level framing (166). Specifically,
information cues were added to help parents make choices about routes of drug
61
administration that they did not have experiences with (e.g. home injections). The
name of the attribute was also changed to ‘drug treatment.’ Finally, the values
representing ‘cost to you’ were increased to ensure that the maximum level of cost was
high enough to reach most respondents ‘choke value’ or highest WTP. Since the
highest cost in the pilot study was considered plausible by respondents, the amount
was increased by $600.
In addition to changes made to the DCE attributes and levels, changes were also made
to the survey instrument. A cheap talk script which openly explained the problem of
hypothetical bias and cautioned respondents to pay close attention to the cost of the
attribute was added to the survey instructions.
“A word of caution. PLEASE READ! Studies similar to this one show that people tend
to overstate how much they are willing to pay for a hypothetical treatment, especially
when the treatment is for a child. When this happens the true value of a drug cannot be
measured. When completing the following choice questions we ask that you carefully
consider the cost characteristic and respond as if you would actually have to pay the
extra amount each month.”
Hypothetical bias occurs when a respondent reports a higher willingness to pay in a
hypothetical payment situation than in an actual payment situation. Cheap talk scripts
have been shown to reduce hypothetical bias and improve incentive compatibility in
choice experiments looking to derive WTP estimates (167, 168). Another change to the
survey instrument related to the wording of a demographic question. The question
originally asked how many days of work a parent or other caregiver had missed in the
last month because of their child’s arthritis, and was changed to read “how many days
of work or usual activity…” were lost. This change was suggested by a respondent
who completed the pilot study. The final change to the survey instrument was the
62
addition of a demographic question which asked parents, “how much money they
spend out of pocket each month on medications for their child’s arthritis.”
Changes to the experimental design included the addition of four restrictions, which
reduced the number of illogical combinations that could be presented in a choice set;
restrictions of these types improve respondent efficiency (84). The upper and lower
most extreme levels of ‘child reported pain from arthritis’ could not be combined with
the opposite most extreme levels of ‘participation in daily activities.’ For example,
severe pain could not be paired with participation without any difficulty and no pain
could not be paired with unable to participate. Details on how the restriction as well as
changes in the total number of attribute levels affected the efficiency of the main study
design are described in section 2.9.2.
2.9 Main Study
The main study was carried out using the same methods as the pilot study. Changes to
these methods are outlined below. A version of the main study booklet can be found in
appendix 8.
2.9.1 Summary of the Attributes and Levels used in the Main Study
The attributes and levels that were used for the main study are listed in Table 8.
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Table 8: Attributes and levels used in the main study
Attributes Levels
Child reported pain from arthritis* None* Mild* Moderate* Severe*
Participation in daily activities Without any difficulty With some difficulty With much difficulty Unable to participate
Drug treatment* Self or parent managed pill four days per month* Self or parent managed injection four days per month* Doctor or nurse managed I.V. one day per month*
Side-effects None Headache four days per month Stomach pain four days per month Nausea four days per month
Days missed from school Half-a-day per month One day per month Two days per month Four days per month
Cost to you $50 per month $700 per month $1500 per month $2100 per month*
* represents attribute labels and attribute levels that were changed for the main study
2.9.2 Generating a Choice Design with Utility Balance
The main experimental design was generated using the same SAS macros as the pilot
study, however, post-pilot changes led to differences in how the methods were applied
(see section 2.8). A detailed description of how the changes impacted the design
process follows.
First, the size of the full-factorial design was reduced from 2304 to 1536 choice sets (31 x
45) as a result of changes in the number of ‘disease activity’ attribute levels. By reducing
the number of levels, the smallest orthogonal design consisted of 96 scenarios (vs. 144
for pilot) which allowed for the generation of a choice design with 48 choice sets, which
could be blocked into three versions each consisting of 16 choice sets (same as the pilot
study).
64
Next, restrictions were introduced to the design in order to reduce the number of
illogical combinations presented to respondents in a choice set. The restrictions
prevented ‘no pain’ from being combined with ‘unable to participate’ and ‘severe pain’
from being combined with ‘no difficulty participating.’ Statements restricting these
combinations were recognized by the MktEx macro which generated a linear candidate
design with a D-efficiency of 98.74. The relative loss in efficiency of the choice design as
a result of introducing these restrictions was approximately 2% (see Table 9).
Table 9: Relative D-efficiencies as a result of various approaches to experimental design Orthogonal approach
(β = 0) Utility balance
(β = pilot estimates) Restrictions No Yes No Yes D-efficiency 13.457 13.2512 10.7463 10.4438 Relative D-efficiency (%) 100 98.47 79.86 77.61
Results from the MNL regression analysis of pilot choice data were used to generate a
design with utility balance. This required the levels representing ‘disease activity’ in
the pilot study to be effects coded to match the levels used to represent ‘child reported
pain from arthritis’ in the main study (see Table 10). Parameter estimates generated by
this new coding were used to generate a design in which choice sets had a near equal
probability of being selected (described in section 1.5.2.2). The resulting design selected
had a relative D-efficiency of 77.61%, compared to an unrestricted, orthogonal design
(assuming β=0) (see Table 9).
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Table 10: Effects coded levels used to generate beta parameter estimates for utility balanced main study design
2.9.3 Sample Size and Recruitment
The sample size for a DCE depends on the number of survey versions, the number of
choice sets, the number of attributes and levels associated with each choice set and the
true values of the unknown parameters estimated by the choice model (82).
Additionally, the structure of survey questions, the desired precision of the results, and
the research environment and budget also contribute to the complexity of sample size
calculations (81, 83).
There is currently no single accepted method for calculating sample size for a DCE. For
this study, the smallest possible sample size was estimated using the formula:
nta /c ≥ 500 (Equation 8)
where n = number of respondents, t = number of choice tasks, a = number of scenarios
per choice task, and c = largest number of levels used to define an attribute. This
formula has been proposed as a general guideline for calculating the lower limit of
sample sizes for conjoint analysis studies (169). By this method it was estimated that a
minimum of 63 parent responses (n) would be required since the DCE included 16
choice tasks (t = 16), each consisting of 2 treatment scenarios (a = 2) with a maximum of
4 levels per attribute (c = 4). Based on the response rate predicted by the author of
Disease Activity Pilot study
Effects Coding
Child reported pain
Main study No pain 1 0 0 None
Mild pain in fingers 0 1 0 Mild
Mild one-sided pain in shoulder, elbow, fingers and foot
Severe pain in knees 0 0 1 Moderate
Severe one-sided pain in wrist, knee and ankle -1 -1 -1 Severe
Severe pain in shoulder, elbow, fingers, knees, and ankles
66
another DCE completed at The Hospital for Sick Children, of 60%, it was known that a
minimum of 100 survey packages would have to be sent (170).
In order to account for parents who would decline participation in the study, a total of
230 recruitment letters were sent to parents of children with JIA (as described in section
2.7.2). One month after the survey packages were mailed, non-respondents were
telephoned and reminded of the study. This process was repeated two weeks later and
every two weeks thereafter for a total of two months. After two months non-
respondents were considered “lost to follow up” and individuals that returned the
survey were mailed a thank-you letter and a $5 gift certificate as a sign of gratitude for
their participation. A flowchart outlining the recruitment process carried out for the
main study is provided (Figure 3).
67
Figure 3: Process of recruitment and follow-up for main study
2.9.4 Data Analysis and Interpretation
All main study survey responses were cross-checked at the time of transcription and
double data entry was carried out for a 10% random sample of hard copy responses.
No data had to be re-entered as a result of errors. Child health, demographic, and
choice data from the main study were analyzed using the methods employed in the
pilot study (described in section 2.7.3). The main effects model was used to estimate
part-worth utilities and importance scores for all main study attributes. Beta parameter
estimates represented part-worth utilities for each level. Outputs from the main effects
Follow-up phone call
No response 2 weeks
Follow-up phone call
No response 2 weeks
Lost to follow-up
No response 4 weeks Follow-up phone call
Log and save data
Yes
Invite to participate by telephone
Didn’t decline by calling
Mail study package
Confirm eligibility by patient chart review
Mail signed
recruitment letters Declined by calling Determine reason for
not participating
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model was verified by a second researcher (DR) who performed a separate multinomial
regression analysis on raw data in STATA 11 (171).
Willingness-to-pay was estimated using parameter estimates from the interaction
model. This was done by calculating the MRS for all attribute levels with βCost as the
denominator (see equation 6, section 1.5.2.3). The incremental WTP for trade-offs
between attribute levels (∆ WTP) was calculated by taking the difference between the
parameter estimates of two levels and dividing it by βCost (172) (Equation 9).
∆WTP = - (βvariable1 - βvariable2 / βCost) (Equation 9)
WTP for combinations of levels that closely resembled a child taking methotrexate and
etanercept were also estimated. One-way sensitivity analysis for these drug profiles
was carried out by varying levels of the drug treatment, child reported pain from
arthritis, participation in daily activities and side-effects attributes.
2.9.4.1 The interaction model
An interaction model was used to determine the degree to which explanatory variables
such as child health and demographics could be used to describe differences in parents’
preferences. It was expected a priori that child age, years with JIA, subtype, experience
with treatments (e.g. discontinuations, side-effects), and household income would have
a significant impact on utility. More specifically, parents of children with more severe
sub-types were expected to have a greater preference for scenarios that included less
favorable health states (e.g. pain and participation in daily activities), parents of
children that had experienced side-effects were expected to have a lesser preference for
scenarios that included those side-effects (e.g. headaches, nausea), and parents from
high income households were expected to have a greater preference for scenarios that
included higher costs. Interaction terms were created between all child health and
demographic variables with a response frequency greater than five and variables were
69
estimated using the regression model. The interaction terms were added individually
to the model and variables displaying a p-value ≥ 0.2 were rejected (170). This was
done in order to eliminate variables that did not significantly influence preferences. All
remaining interaction terms were estimated together and those with a p-value ≤ 0.05
were considered to have a statistically significant impact on preference. When an
interaction between an explanatory variable and a single attribute level was found to be
significant, interaction terms for all of the levels within that attribute were included in
the model.
2.9.4.2 Assessing model fit
Goodness of fit of estimated models was measured using three equations. The first
being McFadden’s pseudo R2 statistic which predicts how well the estimated model fits
the discrete choice data by taking the ratio of log likelihoods from a null model with no
predictors (-logL(0)) and fitted model with p predictors (-logL(p)) (equation 10).
R2 = 1 – [-logL(p)] / [-logL(0)] (Equation 10)
If the explanatory power of a model is very low, McFadden`s pseudo-R2 will be zero. If
the model is able to exactly predict every choice, McFadden`s pseudo-R2 will be one. In
general, a pseudo-R2 value greater than 0.2, implies a well fitted model. It is rare to see
pseudo-R2 values greater than 0.4 (173) and when comparing a pseudo-R2 to a linear R2,
a value of 0.3 is equal to 0.6 (91) (see Figure 4).
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Figure 4: Mapping McFadden’s pseudo R2 to the linear R2
An adjusted pseudo R2 will also be calculated to correct for the number of parameters
(k) estimated in a model (equation 11).
Adjusted R2 = 1 – [-logL(p)-k] / [-logL(0)] (Equation 11)
This statistic penalizes a model for including too many predictors. A model that
includes predictors that do not add sufficiently to the model will have a noticeably
decreased adjusted pseudo-R2 regardless of the fact that the R2 has increased (174).
Finally, the Akaike Information Criterion (AIC) was also computed. This measure of
model fit also adjusts for then number of predictors (p) as well as the number of
dependent variables (choice =c) (equation 12).
AIC = -2 Log L + 2((c-1) + p) (Equation 12)
An AIC is only useful when comparing more than one model with the same data set.
The model with the lowest AIC is considered to have the greatest likelihood or best fit
(174).
All of the model fit statistics described above can be used to predict the degree to which
attribute levels and respondent characteristics (the independent variables) are
71
associated with choice (the dependent variable). However, it is important to note that
they cannot be interpreted in the same way as an R2 used in linear regression as they do
not describe how appropriate the model is for the data (175).
2.9.4.3 Generating 95% confidence intervals for WTP estimates
Bootstrapping was used to generate 95% confidence intervals for all WTP estimates
(129). Sampling with replacement from the original data set using PROC
SURVEYSELECT allowed for 1000 new datasets with 105 individuals per dataset to be
generated. PROC SQL matched the new observations with the original data.
Regression analysis of the new data set was then carried out as described previously
and WTP values representing the 25th and 975th ranked values were used as the lower
and upper confidence limits for each attribute (170).
2.10 Ethics
This study was approved by the Research Ethics Board at the Hospital for Sick Children
in June 2010 (see appendix 9). Both hardcopy and online surveys were prefaced with a
statement of disclosure that was required to gain informed consent. Participants were
informed that no separate written consent would be collected and that submission of
the survey implied consent.
No information obtained in the process of screening for research subjects was used as
data for this study. Only information necessary for initial contact and follow-up was
recorded from the charts of eligible patients, including the name of the child, parent(s),
and treating physician; parent(s) mailing address and telephone number. This
information was stored in a single, encrypted file in a folder on the hospital server on a
password protected research institute desktop PC. Only necessary members of the
research team were granted access to both the recruitment file and the electronic folder.
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No personal identifiers were included in the analytic dataset. As previously described
(Section 2.7.2), each respondent was assigned a unique User ID (four numerical digits)
which was used to de-link personal information from survey responses. A single
response database was maintained throughout the course of the study to include both
online and hard copy questionnaire responses. Once the response database was ready
for analysis and thank-you letters and gifts had been mailed, User IDs were removed
from the encrypted file containing participant names and contact information.
Renewal of ethics approval was granted in June 2011 to allow for data analysis to
continue until after the publication of all project-related reports or manuscripts.
Following this, all hard copies will be shredded and electronic files permanently
deleted.
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3 Results This chapter summarizes findings from the literature review carried out to establish
attributes and levels, as well as results from the DCE. Estimates from both the main
effects and interaction models will be presented, including calculations of WTP. A
summary of results from a sensitivity analysis of the cost attribute is included as well as
estimates of WTP for some of the most common drug treatments used to treat children
with JIA.
3.1 Literature Review of Preference Studies
A total of 12 studies that measured preferences for drug treatments and or health
outcomes in arthritis met the inclusion criteria described in section 2.3.1. (see Table 11
and 12).
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Table 11: Characteristics of studies examining patients’ and parents’ preferences for drug treatments and or health outcomes in JIA.
Author, Year Study Population Objective Study
Design Attribute(s) Measured Findings
Arkela-Kautiain et al., 2006 (154)
JIA patients (n = 123)
Evaluate functioning and preference for health using the WHO ICF as a framework.
Preference Questionnaire
Body Function Activity Participation Support from family members
Patients with active JIA valued body function (pain relief) most highly. Patients in remission valued participation (social activity) most highly.
Barron et al., 2004 (143)
Parents of children with JIA (n = 62)
Measure preferences and WTP for NSAID treatments in JIA.
Closed ended contingent valuation
Clinical remission (reduced morning stiffness and improved range of motion) GI related side-effects (abdominal pain, nausea, vomiting, early satiety, and indigestion) Out-of-pocket cost per month ($ US)
Mean WTP for clinical remission = $395 per month. Mean WTP for no side-effects = $109 per month. Stomach-ache viewed as most desirable GI-related symptom.
Batthish et al., 2005 (149)
Systemic JIA patients n = 14) and parents (n = 11)
Elicit items that would be important to include in a disease activity measure.
Semi-structured interview
Disease Activity Defined term by: Joint pain (84%); Ambulation (80%); Decreased activity level (56%); Missed school (48%)
Beukelman et al., 2008 (144)
Parents of children with Oligoarthritis (n =12)
Determine preferences for outcomes related to steroid injections and NSAID treatments in JIA in order to assist in the identification of an optimal treatment strategy for knee mono-arthritis.
Hybrid of the time-tradeoff and magnitude estimation
Drug type (NSAID vs. steroid injection) Treatment related minor adverse event (stomach-aches vs. injection site scarring) Treatment related major adverse event (internal stomach bleeding or kidney problems vs. infection of the joint)
Avoidance of discomfort caused by injections worth 0.5 months of active arthritis. Benefits of oral medication worth a few days of active arthritis. Stomach-ache viewed as more desirable then scarring. Prevention of major adverse events worth arthritis equivalent values ranging from 1 month to a lifetime.
JIA=Juvenile idiopathic arthritis; WHO=World Health Organization; ICF=International Classification of Functioning; GI=Gastrointestinal; NSAID=Non-
steroidal anti-inflammatory drug; WTP=Willingness-to-pay
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Table 12: Characteristics of studies examining patients’ preferences for drug treatments and or health outcomes in RA.
Author, Year Study Population Objective Study
Design Attribute(s) Measured Findings
Chiou et al., 2005 (156)
RA patients (n = 487)
Estimate preference weights for various ACR responses and adverse events associated with treatments for rheumatoid arthritis.
VAS and Time trade-off
ACR response (ACR 20, ACR 50, ACR 70) Adverse events (none, mild, moderate, severe)
ACR 70 most preferred with no adverse events, Little acceptance for severe adverse events, regardless of ACR rating. Preference for mild adverse events.
Chilton and Collett, 2008 (137)
RA patients (n = 100)
Explore preferences for treatment decisions and three biologic treatments in RA.
Questionnaireand one-on-one interview
Drug type Site of administration
50% of patients would prefer to administer their own treatment. Highest preference weight for Adalimumab, followed by Infliximab and then Etanercept.
Constantinescu et al., 2009 (134)
RA patients (n = 136)
Determine racial differences in preferences for risks and benefits of DMARD treatments in RA.
Adaptive Conjoint Analysis
Likelihood of remission Likelihood of symptom improvement (joint pain or swelling) Likelihood of arresting radiographic progression Route and frequency of drug administration Risk of serious but rare adverse events Risk of reversible adverse events
Ranked by median relative importance: 1. Likelihood of remission 2. Likelihood of symptom improvement 3. Risk of serious but rare adverse events 4. Likelihood of arresting radiological progression; 5. Route and frequency of drug administration 6. Risk of reversible adverse events
Fraenkel et al., 2004 (139)
RA patients (n = 120)
Elicit preferences for biologic and DMARD treatments with varying risk profiles.
Adaptive Conjoint Analysis
Route Onset of effect Chance of benefit Chance of bone erosions Chance of injection site reaction Chance of rash Chance of oral ulcers Chance of alopecia Chance of nausea
Drug resembling etanercept was most preferred (88%). Oral daily preferred to S.C injection once a week = IV once a month. Preference was for short-term side-effects. Reduced risk of rash and nausea were important to patients. Increasing the co-pay for etanercept to $30 decreased the percentage of
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Chance of diarrhoea Chance of cancer Co-payment per month
patients preferring this option to 80%
Sanderson et al., 2010 (176)
RA patients (n = 254)
Determine preferences for treatment outcomes in RA.
Focus group and Ranking (top 6)
Pain Activities of daily living Joint damage Mobility Life enjoyment Independence Fatigue
Reduced pain and increased participation in activities of daily living were the top two priorities identified by patients with RA.
Scarpato et al., 2009 (136)
RA patients (n = 802)
Identify and evaluate preferences for the route of administration of anti-TNF agents.
Preference Questionnaire
Route (SC injection vs. IV)
SC injection preferred for convenience and difficulty getting to hospital. IV preferred due to reduced frequency and security of professional administration
Von-Muhlen and Zylbergeld, 2005 (138)
RA patients ( n= 120)
Identify and evaluate preferences for anti-TNF therapies and routes of administration.
Interview Drug type Frequency Route
IV biologic agents most preferred to DMARD’s because of side-effect profiles, frequency of administration, and pain relief
Williams and Edwards, 2006 (141)
RA patients (n = 100)
To determine patients’ preferences for
Preference Questionnaire and Ranking
Route Frequency Site of administration
Route of administration was the most important attribute. SC injections preferred to IV. Home treatment preferred as infrequently as possible.
RA=Rheumatoid arthritis; ACR=American College of Rheumatologists; VAS=Visual analog scale; DMARD=Disease modifying anti-rheumatic drug;
SC=Subcutaneous; IV=Intravenous
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Respondents in the publications identified included: patients with RA (n = 8
publications), patients with JIA (n=1 publication), patients with JIA and their parents
(n=1 publication), and parents of children with JIA (n =2 publications). While the
majority of studies were carried out with the sole objective of determining preferences,
three studies were designed to validate patient reported outcome measures (149, 154,
156). Methodologically, both direct stated-preference methods (n = 5) and mixed
methods were used (n = 7).
Only two studies used DCEs to evaluate patients’ preferences (134, 139). In both cases,
choice tasks were unlabelled and data were collected by face-to-face interviews. The
studies explored how patients trade between a likelihood of treatment benefit and
associated risks, using attribute levels with varying probabilities. A cost attribute was
included in the study by Fraenkel et al (139).
The attributes used to elicit preferences were grouped into the categories: effectiveness
(n=7), convenience (n=7), safety (n=5), and cost (2). A summary of the most common
attributes represented for each category can be found in Table 13. A description of the
most preferred attributes and levels follows.
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Table 13: The most common attributes represented in studies that measure preferences for drug treatments and or health outcomes in arthritis.
Attributes # Studies
Effectiveness
Pain 4
Joint damage/Radiographic progression 3
Mobility/Ambulation/Range of motion 3
Participation in activities of daily living 3
Well-being 2
Independence 2
Chance of clinical remission 2
Safety
Stomach-ache 3
Injection site reaction 2
Reversible side-effects 4
Serious/Severe but rare adverse events 2
Convenience
Route of administration 5
Drug type (labeled) 3
Site of administration 2
Frequency of administration 2
Cost
Out-of-pocket cost per month 1
Co-payment per month 1
Effectiveness
A wide range of outcome measures were used to define treatment effectiveness. The
most common attributes identified by both adults and children were: pain relief,
reduced joint damage, improved mobility, and increased ability to participate in
activities. In a study used to validate domains of the WHO ICF core set, patients with
active JIA valued body function, in particular pain relief, more highly than participation
whereas patients in remission showed a greater utility for participation, in particular
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social activities (154). Similarly, when parents of children with active systemic JIA were
asked to define ‘disease activity’, joint pain was the most common attribute identified
(149). Pain relief was also found to be the most important treatment outcome to
patients with RA in the study by Sanderson et al. Participation in activities of daily
living, reduced joint damage, improved mobility, life enjoyment and independence
were among the other most common attributes ranked in the top six (176).
When clinical remission was included as a measure of treatment effectiveness, parents
were willing to pay an additional $395 US per month on top of their monthly
prescription fees for a drug that would achieve this level of effectiveness (120). In
another study RA patients preferred a 40% chance of clinical remission over a 70%
likelihood of symptom improvement (134). All studies that measured the importance of
treatment effectiveness over other attributes (i.e. safety, convenience, cost) showed
effectiveness to be the most valued by respondents (120, 134, 139, 156, 176). The only
exception to this trend, were cases where effectiveness was paired with a high risk of a
severe adverse event, such as cancer (134, 156).
Convenience
Studies that elicited preferences for the route, frequency, and type of drugs were as
common as those which measured preferences for treatment effectiveness. Several
studies focused solely on attributes related to treatment convenience (i.e. route,
frequency, and location) (136-138, 141). Although results from these studies showed a
clear preference for oral drugs, there is no consensus as to whether or not parents prefer
SC injections or IV treatments. Two studies found RA patients’ preferences for IV and
SC routes to be evenly split and confounded by factors such as; the availability of
transportation to the hospital and the value placed on treatment flexibility, or
administrations performed by a health professional (137).
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The difficulty patients have separating route, and site of administration attributes with
what is known about the effectiveness and safety profiles of certain drugs was also
clearly demonstrated by Von-Muhlem and Zylbergeld (138). In their study, RA patients
taking biologic drugs described their preferences for IV over SC agents due to the less
frequent and less painful administrations with greater effectiveness and fewer side-
effects. On the other hand, Williams and Edwards showed a group of RA patients who
believe the route to be more important than the site of administration and SC injections
to be superior to IV drugs (141).
When other attributes were included in treatment valuations (i.e. effectiveness and
safety attributes), treatment convenience appears to become less important (134, 139).
Safety
Safety attributes in the studies reviewed were drug-type specific and represented the
most common adverse events reported in RCTs. In the WTP study, GI-related side-
effects including abdominal pain, nausea, vomiting, early satiety, and indigestion were
grouped under a single attribute and used to evaluate parent’s preferences for NSAID
treatments (143). This led to the discovery that parents of children with JIA were willing
to pay on average an additional $109 US per month in order to avoid common GI
symptoms in their child (143).
Another study comparing RA patients’ preferences for DMARDs and biologic
treatments defined attributes for a wide range of very specific safety outcomes
including those that are minor, such as injection site reactions, rashes, oral ulcers,
alopecia, nausea, diarrhoea, and those that are rare but major, such as cancer, hepatic
toxicity, renal toxicity, and pneumonitis (139). Attribute levels represented the chance
of a patient experiencing each event, with risk levels representing data available from
RCTs. In this study, patients placed an almost equal value on the risk of common
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adverse events and much rarer, but potentially severe reactions (139). Similarly,
Constantinescu et al, found small differences between patients’ preferences for minor
adverse events defined as nausea, dizziness, fatigue, or injection site reactions and rare
but serious adverse events including tuberculosis, neurological disease or heart failure
(134).
More general attribute levels used to determine preferences for adverse events that
were mild, moderate, or severe, showed RA patients to have little acceptance for severe
adverse events, regardless of the drug’s effectiveness (149).
Cost
Two of the studies reviewed included a cost attribute. The payment vehicle used in the
closed-ended contingent valuation study by Barron et al. (143), was the amount parents
were willing to pay out-of-pocket each month in addition to their existing drug
payments. This study measured parents’ preferences for drug treatments with different
effectiveness and safety profiles. Starting bids ranging from $5 to $300 US were
presented with each hypothetical scenario and respondents were asked whether or not
they would pay the amount listed to obtain the drug for their child. In order to avoid
starting bid bias the authors used smaller starting values to estimate preferences for the
drug associated with GI symptoms ($5, $25, $30, and $40). Overall, this study showed
the feasibility and construct validity of the WTP technique in parents of children with
JIA (143). Parents’ mean WTP was $395 for a treatment that achieved clinical remission
and $109 for a treatment that was associated with no GI symptoms.
In the DCE by Fraenkel et al.(139), the cost attribute was presented as a co-payment per
month. Attribute levels ranged from $0 to $30 per month which reflected the average
co-payment patients with prescription drug plans in Connecticut paid in 2004. Only
insured RA patients were included in the study population in order to eliminate the
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possibility of cost discrimination for certain treatment alternatives in patients without
drug plans (139). Overall, cost was found to be one of the least important attributes to
patients with RA. The authors did not calculate WTP.
The studies reviewed demonstrate the need for a further understanding of preferences
for drug treatments and health outcomes in arthritis, particularly studies that determine
how individuals trade between attributes related to effectiveness, safety, convenience
and cost. Collectively the most preferred attributes included reduced pain, joint
damage and ambulation; fewer adverse and minor side-effects; and infrequent drug
administrations. These findings were used to inform the selection of attributes and
levels for the pilot study.
3.2 Study Sample
Recruitment occurred between November 4, 2010 and March 8, 2011. A total of 230
recruitment letters were sent to eligible subjects with the majority (74%) agreeing to
participate. Of those who did not agree to participate, 12% were not interested and 14%
were considered lost to follow-up (e.g. wrong telephone number, wrong address,
unable to contact by telephone). In total, 150 survey packages were mailed to potential
respondents between November 29, 2010 and December 3, 2010 and 20 more on January
19, 2011. The second mail-out was a result of a single rheumatologist who was unable
to sign the letters addressed to his patients during the initial recruitment period. Of the
170 parents that were mailed the survey package, 65% completed the survey, resulting
in a sample size of 105. The majority of respondents (88%) completed the paper
version. Age, sex and income were not found to be significant predictors of response
format. Of the 65 non-responders, 55% could not be reached during follow-up calls,
20% described that they were no longer interested (e.g. too busy, felt that they were not
appropriate research subjects, found the survey emotionally difficulty), 9% had changed
their telephone number, and 10% stated that they were interested in participating but
83
failed to complete the survey. A flowchart summarizing the recruitment and response
rates is provided (Figure 5).
Figure 5: Results of main study recruitment
3.3 Characteristics of Respondents and their Children with JIA
The demographic characteristics of respondents are presented in Table 14.
Mailed signed recruitment letters Nov 4 2010 and Jan 4 2011
n = 230
Declined participation n = 28 (12%)
Lost to follow-up n = 32 (14%)
Agreed to participate n = 170 (74%)
Follow-up phone calls Jan 5 - Mar 8 2011
Mailed study packages Nov 18 2010 – Dec 2 2010
Jan 19 2011
Non-responders
n = 65 (38%)
Unable to contact at follow-up
n =36
No longer interested
n = 13
Wrong number/
NIS n =6
No explanation
n = 10
Completed the Survey
n = 105 (62%)
Paper response
n = 88
Online response
n = 17
84
Table 14: Demographic characteristics of survey respondents
Characteristics n=105 % / Mean (SD)
Age (years) 41.4 (6.2) 25-35 18 17.14 36-45 48 45.71 46-52 28 26.67 Unknown 12 10.48 Gender Female 95 90.48 Male 10 9.52 Relationship to Child Mother 92 87.62 Father 10 9.52 Stepmother 1 0.95 Guardian 1 0.95 Sibling 1 0.95 Canadian Citizen Yes, born in Canada 81 77.14 Yes, lived in Canada > 15 years 12 11.43 Yes, lived in Canada < 15 years 7 6.67 No 5 4.76 Ethnic or Cultural Association Canadian 52 49.52 Southern European (Italian, Portuguese, Greek, Serbian) 13 12.38 None 11 10.48 Northern European (Scottish, Irish, British) 7 6.67 South East Asian (Indian, Sri Lankan, Pakistani, Iranian, Filipino) 6 5.71 Eastern European (Polish, Romanian, Hungarian) 4 3.81 Western European (German) 3 2.86 Jewish 3 2.86 East Asian (Chinese, Korean) 2 1.90 Other 2 1.90 Aboriginal 1 0.95 Arab 1 0.95 Marital Status
Married or Living in Common-Law 92 87.62
Separated or Divorced 11 10.48
Single (never been married) 2 1.90
Household size 4.3 (1.1)
85
Geographic region of household Greater Toronto Area (Durham, Halton, Peel, York regions) 53 50.48 Western Ontario (Guelph, Waterloo, London, Windsor) 22 20.95 Northern Ontario (Barrie, Sudbury, North Bay, Sault St. Marie) 18 17.14 Eastern Ontario (Peterborough, Carrying Place) 12 11.43 Education
Received post-secondary degree/diploma 67 63.81
Received post-graduate or professional degree 21 20.00
Completed secondary/high school 16 15.24
Completed elementary school 1 0.95
Employment Employed Full-Time 66 61.90
Employed Part-Time 19 18.10
Homemaker 14 13.33
Unemployed 3 2.86
Receiving social assistance 2 1.90
Receiving disability 1 0.95
Drug Plans used to access medications for children with JIA
Private insurance provided by employer 76 73.33
Private insurance purchased independently 4 3.81
Trillium 4 3.81
Ontario Drug Benefits Program 5 4.76
None 14 13.33
Other (Indian Affairs, Drug Company) 2 1.90
Monthly Drug Spending $0 50 47.62
Less than $20 14 14.29
$20-$50 16 15.24
Less than $50-$200 16 15.24
$250-$500 5 4.76
Greater than $1000 3 2.86
Household Income
Less than $20,000 3 2.86
$20,000 to $29,999 5 4.76
$30,000 to $39,999 8 7.62
$40,000 to $49,999 4 3.81
$50,000 to $59,999 4 3.81
$60,000 to $69,999 6 5.71
86
$70,000 to $79,999 3 2.86
$80,000 to $89,999 9 8.57
$90,000 to $99,999 3 2.86
$100,000 to $119,999 10 9.52
$120,000 to $149,999 14 13.33
$150,000 to $199,999 7 6.67
Greater than $200,000 20 19.05
Unknown 9 8.57
The majority of parent respondents were female (90.5%) and mothers (87.6%) of
children with JIA. The mean age of respondents was 41.4 years (ranging from 25-52
years old). A small proportion of respondents were non-Canadian citizens (5%), with
the most common non-Canadian ethnic or cultural association being southern European
(12.4%). Approximately 88% of parents were married or living in common-law, 10%
were separated or divorced, and 2% had never been married. The average household
size was 4.3 people (SD 1.1) with over half of families living in the Greater Toronto Area
(50.5%). The sample was very highly educated with most having received at minimum
a college diploma (83.8%) and many holding a post-graduate or professional degree
(20%). In terms of employment status approximately 62% of parents were employed
full-time, 18% part-time, 13% homemakers, 3% unemployed, 2% receiving social
assistance, and 1% receiving disability. Household incomes ranged from less than
$20,000 per year to over $200,000 per year, with 19% of families reporting in the highest
income bracket (>$200,000 per year). Overall, 60% of parents reported high household
incomes (greater than $80,000 per year), 16% middle household incomes ($40,000-
$79,999 per year), and 14% low household incomes (less than $39,999 per year). Close
to 9% of parents did not report an annual household income. For drug plans used to
access medications for their child’s JIA, most parents had private insurance provided by
an employer (73.3%), while others used public drug programs (9.5%) or purchased
independent private insurance (3.8%). 14% of parents had no drug plan to cover the
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cost of medications for their child’s JIA. Out-of-pocket spending on drug treatments for
JIA ranged from $0-$4,000 per month, with 48% of parents spending $0, 29% spending
less than $50, 15% spending $50-$200, 5% spending $250-$500, and 3% spending over
$3000 per month.
Characteristics of respondent’s children with JIA are presented in Table 15.
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Table 15: Characteristics of survey respondents’ children with JIA Characteristics n=105 % / Mean
(SD) Age (years) 10.9 (4.1)
< 4 2 1.9
4-8 32 30.5
9-13 34 32.4
> 13 37 35.2
Gender Female 77 73.33
Male 28 26.67
Years with JIA 5.8 (3.4)
Less than 2 years 6 5.71
2 - 4 years 37 35.24
5 - 7 years 33 31.43
8 - 10 years 18 17.14
> 10 years 11 10.48
Current JIA subtype
Polyarticular 37 35.24
Oligoarthritis 26 24.76
Not sure 22 20.95
Systemic 11 10.48
Psoriatic 5 4.76
Undifferentiated 3 2.86
Enthesitis-related 1 0.95
Medication in the last month
Yes 71 67.62
No 34 32.38
Types of medications used in the last three years (n=200 medications reported) Biologics 25 11.85
Etanercept 13 6.16
Adalimumab 5 2.37
Infliximab 3 1.42
Anakinra 4 1.90
DMARDs 79 37.44
Methotrexate 61 28.91
Leflunomide 15 7.11
Sulfasalazine 3 1.42
89
NSAIDs 77 36.49
Naproxen 66 31.28
Indomethacin 10 4.74
Celecoxib 1 0.47
Corticosteroids 30 14.22
Prednisone 16 7.58
Traimcinolone-Hexacetonide 14 6.64
Discontinued a treatment No 22 20.95
Yes 83 79.05
Reason for discontinuing treatment No longer required (disease improved) 32 30.19
Side-effects 27 25.47
Not effective 26 24.53
Stopped being effective over time 20 18.87
Experienced side effects in the last year No 38 36.19
Yes 67 63.81
Types of side-effects in the last year (n=141 events reported) Nausea 45 31.91
Stomach pain 40 28.37
Headache 35 24.82
Redness or swelling at injection site 14 9.93
Rash 6 4.26
Infection at injection site 1 0.71
Activity level compared to a child of the same age without arthritis
Equally active 47 44.76
Moderately less active 28 26.67
Much more active 16 15.24
Moderately more active 14 13.33
Days missed from school in the last month
0 days 54 51.43
0.5-1 day 24 22.86
1.5-2 days 12 11.43
3-5 days 10 9.52
> 5 days 5 4.76
90
The majority of respondents’ children with JIA were female (73.3%) and the mean age
was 10.9 years old (ranging from 3 to 18 years old). A wide range of experience with
JIA was represented in the sample, with the average years since diagnosis being 5.8
years (range 1-16 years). The majority of children had lived with JIA for 2-4 years
(35.2%). Of children less than 3 years of age all had lived with JIA for less than 2 years.
Children 4-8 years of age had lived with JIA less than 3 years (50%) or between 4-8
years (50%). Children 9-13 years of age had lived with JIA less than 3 years (26%), 4-8
years (50%) or 9-13 years (24%). Children over 13 years of age had lived with JIA less
than 3-years (13%), 4-8 years (46%), 9-13 years (30%), or longer than 13 years (11%)
(p=0.0007). In terms of disease subtypes, 35% of children had polyarticular JIA, 25%
oligoarthritis, 10% systemic, 5% psoriatic, 3% undifferentiated, and 1% enthesitis-
related. Close to 21% of parents were not sure of their child’s current disease subtype.
The majority of children were taking a drug treatment for their JIA during the month
they completed the questionnaire (67.6%). Within the study sample, the most common
type of drugs were DMARDs (37%), followed by NSAIDs (36.5%), corticosteroids
(14.2%), and then biologics (11.9%). Naproxen and methotrexate were the most
common drugs modalities used by the study sample in the last three years, accounting
for 60.2% of the total. The most common biologic was etanercept, which accounted for
only 6.2% of the total drug therapies used. Overall, 79% of parents claimed that their
child had discontinued a drug treatment during the course of their disease. Reasons for
discontinuing treatment included: disease improvement (30.2%), side-effects (25.5%),
inadequate response (24.5%), and loss of response over time (18.9%). In addition, 67%
of parents claimed their child with JIA had experienced a side-effect in the last year, of
which nausea (31.9%), stomach pain (28.4%), and headaches (24.8%) were the most
common. When asked to compare the activity level of their child with JIA to a child of
the same age without arthritis, 45% of parents reported their child as equally active,
27% moderately less active, 15% much more active and 13% moderately more active.
91
During the month that parents completed the questionnaire, 51.43% of children had
missed no school, 34.3% missed 0.5-2 days, and 14.3% missed over 3 days as a result of
their JIA.
3.4 Preferences
3.4.1 Relative Importance of Main Study Attributes and Themes
Beta parameter estimates from the main effects multinomial logit regression model of
choice data were used to determine the part-worth utility for each main study attribute.
Part-worth utilities were calculated by taking the absolute value of the difference
between beta parameter estimates for the best and worst levels within an attribute.
These values represent the relative impact or weight an attribute has on choice and are
represented as the percentage of total utility. Table 16 summarizes the part-worth
utilities and importance scores for main study attributes in decreasing order of
magnitude.
Table 16: Relative importance of main study attributes
Attribute PW-Utility Importance Score
Child reported pain from arthritis 2.784 32.38 Participation in daily activities 2.608 30.34 Cost to you 1.534 17.84 Side-effects 0.947 11.02 Days missed from school 0.462 5.40 Drug treatment 0.261 3.03 Total 8.596
Based on the main-effects model, the most important attribute was found to be child
reported pain from arthritis followed by participation in daily activities, cost-to-you,
side-effects, and days missed from school. Drug treatment was found to be the least
important attribute. Figure 6 illustrates the magnitude and relative importance of each
main study attribute categorized by theme.
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Figure 6: Relative importance of main study attributes
3.4.2 Main Effects Model
The results of the main effects logit regression model are presented in Table 17 with
attributes listed in order of decreasing importance (as described above). Levels are
presented from most preferred to least preferred. Effects coding was used for all
attributes, except for days missed from school and cost to you which were coded on a
continuous scale.
32.4
30.3
17.8
5.4
11.0
3.0
0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0
Pain
Participation
Cost to you
Missed school
Side-effects
Drug treatment
Importance Score
Effectiveness Safety Convenience Cost
93
Table 17: Results from the main effects multinomial logit regression model of main study choice data
CI = Confidence interval; SE= Standard error; Pr=Probability
† denotes reference level ψ denotes continuous coding
Parameter estimates were positive for none, mild and moderate levels of child reported
pain compared to the reference level severe. Parameter estimates were also positive for
participation in daily activities without any difficulty and some difficulty compared to
unable to participate. Participation in daily activities with much difficulty was
associated with a negative utility. For cost to you, parameter estimates for all levels
were negative, with the most negative level being associated with the highest cost as
($2,100). Similarly, negative parameter estimates were estimated for all levels of days
missed from school with the degree of disutility increasing with the number of days
Attribute Levels Beta 95% CI
SE Pr > Chi
Square Lower Upper
Child reported pain from arthritis
None 0.966 0.800 1.164 0.104 <.0001 Mild 0.647 0.488 0.813 0.081 <.0001 Moderate 0.205 0.082 0.333 0.054 0.0001 Severe† -1.818 -2.157 -1.495 - -
Participation in daily activities
Without any difficulty 1.286 1.050 1.565 0.139 <.0001 With some difficulty 0.500 0.376 0.632 0.061 <.0001 With much difficulty -0.463 -0.602 -0.330 0.061 <.0001 Unable to participate† -1.323 -1.583 -1.101 - -
Cost to you ψ
$50 per month -0.037 -0.045 -0.031
7.1E-5 <.0001 $700 per month -0.524 -0.623 -0.429 $1500 per month -1.122 -1.335 -0.920 $2100 per month -1.571 -1.870 -1.288
Side-effects
None 0.589 0.464 0.718 0.066 <.0001 Headache four days per month -0.109 -0.201 -0.015 0.056 0.0523 Nausea four days per month -0.122 -0.228 -0.025 0.063 0.0521 Stomach pain four days per month† -0.358 -0.470 -0.239 - -
Days missed from school ψ
Half day per month -0.066 -0.104 -0.028
0.035 0.0002 One day per month -0.132 -0.208 -0.057 Two days per month -0.264 -0.416 -0.114 Four days per month -0.528 -0.833 -0.227
Drug treatment
Injection four days per month 0.147 0.067 0.223 0.045 0.0011 Pill four days per month -0.033 -0.112 0.055 0.045 0.4647 I.V. once per month† -0.114 -0.210 -0.023 - -
94
missed. Parameter estimates were positive for no side-effects compared to stomach pain
four days per month, but negative for all other levels. There was no statistically
significant difference between the negative utilities estimated for the levels headache
four days per month and nausea four days per month compared to stomach pain four
days per month. The level of drug treatment associated with the greatest utility was
self or parent managed injection four times per month. There was no statistically
significant difference between the negative parameter estimates for a self or parent
administered pill daily and doctor or nurse administered IV. Overall, the directionality
of regression coefficients within each attribute were as expected, except for the pill and
injection levels within the attribute drug treatment. In the pilot study, pill was the level
of drug treatment associated with the highest utility.
3.4.3 Interaction Model
Significant interactions were identified between preferences for attributes and
characteristics of respondents and their children with JIA. To begin, separate
interactions models were generated the first including child health characteristics and
the second including demographic characteristics of respondents. Interactions were
estimated as previously described (section 2.9.4.1). In total, 10 significant interactions
terms were identified in the model that included child health variables (Appendix 10)
and 5 in the model that included demographic variables (see Appendix 11). When
interactions from both models were combined, 4 terms were no longer significant.
Results from the combined interaction model are presented in Table 18.
95
Table 18: Regression model including all significant interaction variables
Attribute/ Interaction Levels Beta 95% CI SE Pr > Chi
Square Lower Upper
Child reported pain from arthritis
None 0.995 0.431 1.625 0.232 <.0001 Mild 0.480 0.046 0.950 0.200 0.016 Moderate -0.181 -0.565 0.203 0.167 0.277 Severe† -1.294 -2.249 -0.390 - -
Participation in daily activities
Without any difficulty 1.615 1.024 2.431 0.270 <.0001 With some difficulty 0.587 0.211 1.000 0.188 0.002 With much difficulty -0.572 -1.041 -0.182 0.185 0.002 Unable to participate† -1.630 -2.488 -0.950 - -
Cost to youψ
$50 per month -0.035 -0.046 -0.026
8.8E-5 <.0001 $700 per month -0.485 -0.642 -0.360 $1500 per month -1.039 -1.375 -0.771 $2100 per month -1.454 -1.925 -1.079
Side-effects
None 0.633 0.522 0.805 0.074 <.0001 Headache four days per month -0.101 -0.229 0.009 0.064 0.114 Nausea four days per month -0.126 -0.259 -0.008 0.070 0.074 Stomach pain four days per month† -0.406 -0.571 -0.269 - -
Days missed from schoolψ
Half day per month -0.022 -0.066 0.019
0.044 0.310 One day per month -0.045 -0.131 0.037 Two days per month -0.090 -0.262 0.075 Four days per month -0.179 -0.525 0.149
Drug treatment
Injection four days per month 0.134 -0.084 0.116 0.051 0.008 Pill four days per month 0.013 0.050 0.230 0.052 0.806 I.V. once per month† -0.146 -0.270 -0.043 - -
Child ageψ * Child reported pain from arthritis
Child age*None 0.013 -0.038 0.066 0.019 0.488 Child age*Mild 0.029 -0.005 0.067 0.017 0.081
Child age*Moderate 0.035 0.003 0.069 0.014 0.015
Child age*Severe -0.077 -0.166 -0.007 - - Years with JIAψ * Participation in daily activities
Years with*Without any difficulty 0.147 0.055 0.240 0.028 <.0001 Years with*With some difficulty 0.019 -0.036 0.075 0.023 0.389 Years with*With much difficulty -0.039 -0.088 0.013 0.023 0.085 Years with*Unable to participate† -0.127 -0.222 -0.038 - -
Child ageψ * Participation in daily activities
Child age*Without any difficulty -0.107 -0.172 -0.046 0.024 <.0001 Child age*With some difficulty -0.016 -0.059 0.027 0.019 0.416 Child age*With much difficulty 0.032 -0.010 0.071 0.0184 0.086 Child age*Unable to participate† 0.091 0.031 0.164 - -
Child gender* Participation in daily activities
Male*Without any difficulty -0.241 -0.558 0.054 0.095 0.011 Male*With some difficulty -0.046 -0.208 0.118 0.075 0.541 Male*With much difficulty 0.016 -0.150 0.204 0.075 0.828 Male*Unable to participate† 0.270 -0.003 0.569 - -
96
CI = Confidence interval; SE= Standard error; Pr=Probability
† denotes reference level ψ denotes continuous coding
A total of 11 significant interactions were identified in the combined interaction model.
These terms were grouped by attribute and highlighted in Table 19. Effects coding was
used for all interaction variables, except for child age, years with JIA, and monthly drug
spending which were coded on a continuous scale.
Parameter estimates and the significance of differences between attribute levels in the
interaction model were different from the main effects model. The parameter estimate
for moderate child reported pain from arthritis was no longer positive and the
difference between the part-worth utilities estimated for moderate and severe levels of
pain were no longer statistically significant. There was also no statistically significant
difference in the part-worth utilities estimated for the levels of days missed from school.
For the attribute drug treatment, parameter estimates were positive for the levels pill
and injection compared to IV, however, the difference between a pill and IV remained
Drug treatments* Participation in daily activities
Bio/DMARD*Without any difficulty -0.026 -0.341 0.250 0.090 0.774 Bio/DMARD*With some difficulty -0.052 -0.226 0.100 0.070 0.460 Bio/DMARD*With much difficulty 0.152 -0.027 0.348 0.071 0.034 Bio/DMARD*Unable to participate -0.074 -0.346 0.190 - -
Household income* Participation in daily activities
High income*Without any difficulty -0.005 -0.241 0.220 0.095 0.960 High income*With some difficulty -0.024 -0.190 0.108 0.067 0.724 High income*With much difficulty -0.227 -0.402 -0.066 0.070 0.001 High income*Unable to participate 0.256 0.004 0.563 - -
Monthly drug spendingψ * Participation in daily activities
Spending*Without any difficulty 2.7E-4 -0.001 0.001 1.6E-4 0.092 Spending*With some difficulty -2.8E-6 -1.4E-4 0.002 1.3E-4 0.983 Spending*With much difficulty 2.9E-4 -4.1E-4 0.001 1.4E-4 0.037 Spending*Unable to participate -5.6E-4 -0.002 -2.3E-4 - -
Type of drug coverage* Cost to youψ
No coverage*Cost to you 2.8E-4 -1.0E-4 0.001 1.7E-4 0.098 Public coverage*Cost to you 5.1E-4 -8.5E-5 0.001 1.8E-4 0.004 Private coverage*Cost to you† -7.9E-4 -0.002 -6.7E-5 - -
Household Income* Cost to youψ
Low income*Cost to you -4.7E-4 -0.002 -0.001 1.5E-4 0.002 Mid income*Cost to you -0.001 -0.001 -3.5E-5 1.7E-4 <.0001 High income*Cost to you† 0.002 0.001 0.003 - -
Child gender* Days missed from schoolψ
Male child*Days missed 0.145 0.065 0.233 0.035 <.0001
97
statistically insignificant. In all other cases the directionality, magnitude and
significance of part-worth utilities were similar to findings from the main effects model.
Parameter estimates for interactions represent the degree to which main effects are
adjusted as a result of explainable sample heterogeneity (91). Significant changes in
parameter estimates were observed for levels of the attributes participation in daily
activities, cost to you, child reported pain, and days missed from school. As
hypothesized, child age, years with JIA and household income were amongst the
explanatory variables found to have a significant impact on utility. Disease subtype,
drug discontinuations, and the presence and type of side-effects experienced by
children with JIA were not significant predictors of choice. Tables 19 and 20 summarize
the interaction variables found to have a significant impact on preferences for the
attributes participation in daily activities and cost to you. Shaded cells represent
significant main effects and interaction terms determined by the interaction model.
Table 19: Adjusted parameter estimates for all significant interaction terms used to describe differences in parents’ preferences for participation in daily activities Partici
pation (no inter-action)
Child ageψ Years with JIAψ Child male
DMARD or
biologic
Monthly drug spendingψ High
Incm 1 y.o. 13 y.o. 1 year 10 years $50 $2000
Without any difficulty
1.615 1.508 0.219 1.762 3.083 1.374 1.589 1.629 2.155 1.610
With some difficulty
0.587 0.572 0.386 0.607 0.782 0.542 0.536 0.587 0.582 0.563
With much difficulty
-0.572 -0.541 -0.162 -0.611 -0.964 -0.556 -0.421 -0.558 0.012 -0.800
Unable to participate† -1.630 -1.539 -0.443 -1.757 -2.900 -1.360 -1.70 -1.658 -2.748 -1.375
y.o.=Years old; DMARD=Disease modifying anti-rheumatic drug; Incm = Income; High Income ≥ $80,000 † denotes reference level ψ denotes continuous coding
Interpretation of interaction terms depends on the type of coding used for both the
attribute and interaction variable. As mentioned above, years with JIA, child age, and
98
monthly drug spending were coded as continuous variables and as a result, each
additional unit (years with, years old, dollar spent) represented the same marginal
adjustment of utility. Part-worth utilities for the interaction terms presented in the
tables were calculated by adding the parameter estimates of the interaction terms to the
parameter estimates of the corresponding main effects estimated by the interaction
model. For example, the parameter estimate for the level participation in daily activities
‘without any difficulty’ was 1.615, while the parameter estimate for the interaction
between ‘without any difficulty’ and child age was -0.1074 (Table 18). This meant that
for each unit of measurement for age (years) the parameter estimate for ‘without any
difficulty’ was reduced by -0.1074, resulting in a part-worth utility of 1.508 (1.615 + (-
0.1074) = 1.508). For children 13 years of age the parameter estimate for ‘without any
difficulty’ was reduced by -1.396 (-0.1074 x 13), resulting in a part-worth utility of 0.219
(1.615 + (-1.396) = 0.219).
As disease duration increased, so did parents’ utility for participation without any
difficulty compared to the reference level unable to participate. Parents of older
children showed a lesser preference for the transition from unable to participate to
participation without any difficulty compared to parents of younger children. Parents
of male children also had less utility for the transition from unable to participate to
participation with much difficulty compared to parents of female children.
In terms of parents’ experience with drug treatments, parents of children that had used
a DMARD or biologic in the last three years had a greater preference for the transition
from unable to participate to participation with much difficulty compared to parents of
children who were on less aggressive treatments, such as NSAIDs. Parents who spent
more out-of-pocket on drug treatments for their child each month also had a greater
preference for this transition. Interestingly, parents from high income households had a
99
lesser preference to move from unable to participate towards participation with much
difficulty compared to parents from low or middle income homes.
Table 20: Adjusted parameter estimates for all significant interaction terms used to describe differences in parents’ preferences for cost to you
Cost to you (no interaction)
Income Drug coverage Low
(n=16) Middle (n=17)
High (n=63)
Public (n=11)
Private (n=80)
Per dollar -0.001 -0.001 -0.002 0.001 0.001 -8.0E-4
$50 per month -0.035 -0.058 -0.092 0.047 0.025 -0.039
$700 per month -0.485 -0.815 -1.290 0.650 0.355 -0.552
$1500 per month -1.039 -1.746 -2.764 1.394 0.760 -1.183
$2100 per month -1.454 -2.445 -3.869 1.951 1.065 -1.656
Low Income ≤ $39,999; Middle Income = $40,000-$79,999; High Income ≥ $80,000
Overall, parents’ from low and middle incomes were more sensitive to the cost to you
attribute (i.e. had a lower maximum WTP) compared to parents from high income
homes. Parents that used public drug coverage to pay for the cost of JIA drugs had a
higher maximum WTP compared to parents that used private coverage.
Also of interest was the fact that parents of older children had a greater preference for
the transition from severe to moderate pain compared to parents of females as well as
the sample as a whole. Parents of male children had a greater preference for days
missed from school compared to parents of female children as well as the sample as a
whole.
3.4.4 Model Goodness-of-Fit
Model fit was examined for the main effects and interaction models by calculating
McFadden’s pseudo R2 , adjusted pseudo R2 and AIC (see section 2.9.4.2). As shown in
Table 21, the addition of interaction terms resulted in a significant improvement in
100
model fit (using both adjusted and unadjusted R2 values). In order to further test model
saturation, a model that included only the most significant interactions (p<0.0001) was
evaluated. The interaction model including only the most significant interactions
resulted in a lower pseudo R2 and adjusted pseudo R2 as well as a higher AIC compared
to the interaction model. The pseudo R2 of this model was greater than 0.2 and was
therefore determined to be well fit.
Table 21: Comparison of model fit
Table Description of Model -2 Log L Pseudo
R2
Adj pseudo
R2 AIC DF Null
(0) Predict
(p)
17 Main effects 2329 2034 0.127 0.132 2060 13
Appendix 10 Child health interaction model 2329 1918 0.176 0.193 1994 38
Appendix 11 Demographic interaction model 2129 1746 0.180 0.191 1792 23
19 All significant interaction model 2129 1665 0.218 0.236 1742 39
Not shown Most significant interaction model (p<0.0001) 2129 1731 0.187 0.197 1775 22
Appendix 13 All effects coded interaction model 2129 1655 0.223 0.243 1740 43
AIC= Akaike's information criterion; DF= Degrees of freedom
3.4.5 Marginal WTP for Main Study Attribute Levels
As described previously (section 1.5), WTP derived from DCEs represents a monetary
equivalence of utility and was calculated as described in section 2.9.4 using beta
parameter estimates from the main effects and interaction models. WTP estimates
calculated from the main-effects model can be found in Appendix 12. WTP estimates
for main effects calculated from the interaction model can be found in Table 22.
101
Table 22: Marginal and incremental WTP for main study attribute levels based on interaction model estimates
WTP = Willingness-to-pay; CI = Confidence interval † denotes reference level ψ denotes continuous coding
In general, parents WTP for transitions between attribute levels that represented
positive utility (i.e. a movement from severe pain towards none). Figure 7 clearly
illustrates how parents traded between levels within and attribute and the utility
derived for each transition.
Attribute Levels WTP ($)
95% CI WTP ($)
∆ WTP ($)
95% CI ∆ WTP ($)
Lower Upper Lower Upper
Child reported pain from arthritis
None 1,437 622 2,345 744 556 974 Mild 693 66 1,371 954 882 1,079 Moderate -261 -816 292 1,606 855 2,429 Severe† -1,868 -3,245 -562 - - -
Participation in daily activities
Without any difficulty 2,332 1,478 3,508 1,484 1,173 2,065 With some difficulty 848 305 1,443 1,674 1,180 1,807 With much difficulty -826 -1,503 -263 1,528 1,108 2,088 Unable to participate† -2,354 -3,591 -1,371 - 556 974
Cost to you ψ
$50 per month
- - - - $700 per month $1500 per month $2100 per month
Side-effects
None 914 753 1,162 1,059 1,149 1,084 Headache four days per month -146 -331 13 36 23 43 Nausea four days per month -181 -374 -11 405 378 451 Stomach pain four days per month† -587 -824 -388 - - -
Days missed from school ψ
Half day per month -32 -95 27 32 -27 95 One day per month -65 -189 54 65 -54 189 Two days per month -129 -379 108 129 -108 379 Four days per month -259 -757 215 -
Drug treatment
Injection four days per month 193 72 332 175 165 193 Pill four days per month 18 -121 168 230 229 268 I.V. once per month† -211 - - -
102
Figure 7: WTP for main study attribute levels
Parents were willing to pay $1,606 to move from severe child reported pain to moderate
child reported pain, $954 to move from moderate to mild, and $744 to move from mild
to none. In terms of participation in daily activities parents were willing to pay $1,528
for the transition from unable to participate to participation with much difficulty, $1,674
to transition from much to some difficulty, and $1,484 to transition from some to
without any. In order to trade stomach pain four days per month with nausea four
days per month parents were willing to pay $405, while trading nausea for headaches
was worth $36, and headache for no side-effects $1,059. Each extra day of school
0 500 1,000 1,500 2,000 2,500
IV → Pill Pill → Injection
4 days → 2 days 2 days → 1 day
1 day → 0.5 day
Stomach pain → Nausea Nausea → Headache
Headache → None
Unable → Much difficulty Much → Some difficulty
Some → No difficulty
Severe → Moderate pain Moderate → Mild pain
Mild → No pain
WTP ($)
Child reported pain from arthritis Particiaption in daily activities Side-effects
Days missed from school Drug Treatment
103
attended (not missed) was valued at $189. Parents were willing to pay $268 to
transition from an I.V. treatment once per month to a pill four days per month and $193
to transition from a pill four days per month to an injection four days per month.
3.4.6 Sensitivity Analysis of ‘Cost to You’ Attribute
For all WTP estimates the cost to you attribute was coded as a continuous variable and
as a result, the marginal utility of money was assumed to be linear (slope =
-0.0007). This meant that each additional dollar represented the same marginal effect on
choice and as such, parameter estimates for cost to you were a product of the dollar
value and slope (i.e. $50*(-0.0007) = -0.035). However, when cost to you was treated as a
categorical variable and effects coded (results shown in Appendix 13) the marginal
utility of money was not assumed to be linear. Figure 8 demonstrates this relationship. .
Figure 8: Slope of the cost to you attribute when treated as a categorical variable
The slope of the line through all four categorically coded variables shown in Figure 8
was equal to the marginal utility of money estimated when cost to you was treated as
continuous. However, a less negative slope was observed between $700 and $1500 and
a more negative slope observed between $1500 and $2100 when cost was coded
y = -0.0007x + 0.7354
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
0 500 1000 1500 2000 2500
Bet
a
Cost to you ($)
104
categorically. The slopes of the lines through these points, (the marginal utility of
money) is summarized in Table 23.
Table 23: Calculation of the marginal utility of money when cost to you is treated as a categorical variable
X-axis (levels)
Y-axis (betas)*
Slope through all
points
Slope through $700 and $1500
(SA1)
Slope through $1500 and $2100
(SA2) $50 0.7216
-0.0007
$700 0.1557
-0.0003
$1500 -0.0994 -0.0011
$2100 -0.7780 SA = Sensitivity Analysis
* Effects coding used to derive beta values for each level
When the cost attribute was treated as categorical each additional dollar did not
represent the same marginal effect on choice. In this case, parents had a lesser
preference to move from $1500 to $2100. Results from a sensitivity analysis of WTP
using the slope of the line through $700 and $1500 as well as $1500 and $2100 can be
found in Table 24.
105
Table 24: WTP based on cost to you sensitivity analysis
WTP=Willingness-to-pay; SA=Sensitivity analysis
† denotes reference level ψ denotes continuous coding
Although the directionality of preferences was not affected in the sensitivity analysis,
the extent to which parents were willing to pay for attribute levels was strongly
influenced. WTP estimates calculated using the slope of the line through $700 and
$1500 (SA1) were 45% greater than estimates generated when cost was coded
continuously. WTP estimates calculated using the slope of the line through $1500 and
$2100 (SA2) were 63% lower than estimates generated when cost was coded
continuously.
Attribute Levels SA1 SA2
WTP ($) Change in WTP WTP ($) Change
in WTP
Child reported pain from arthritis
None 3,209 +1,768 904 -537 Mild 1,547 +852 436 -259 Moderate -584 -322 -165 +98 Severe† -4,173 -2,298 -1176 +699
Participation in daily activities
With no difficulty 5,210 +2,869 1468 -872 With some difficulty 1,894 +1,043 534 -317 With much difficulty -1,846 -1,016 -520 +309 Unable to participate† -5,259 -2,896 -1482 +881
Cost to you
$50 per month
- - $700 per month $1500 per month $2100 per month
Side-effects
None 2,041 +1,124 575 -342 Headache four days per month -325 -179 -92 +54 Nausea four days per month -405 -223 -114 +68 Stomach pain four days per month† -1,310 -722 -369 +219
Days missed from school ψ
Half day per month -72 -40 -20 +12 One day per month -144 -80 -41 +24 Two days per month -289 -159 -81 +48 Four days per month -578 -318 -163 +97
Drug treatment Injection four days per month 431 +237 122 -72 Pill four days per month 41 +23 12 -7 I.V. once per month† -472 -260 -133 +79
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3.4.7 WTP for JIA Treatment Scenarios
Using parameter estimates from the interaction model, WTP for scenarios resembling a
child responding inadequately to methotrexate and adequately to etanercept were
computed. The base case scenarios that were created are summarized in Table 25.
Table 25: Definition of base case scenarios representing a child taking MTX or etanercept Attribute MTX ETN
Child reported pain from arthritis Mild None
Participation in daily activities With some difficulty Without any difficulty
Cost to you $50 per month $2100 per month
Side-effects Nausea four days per month None
Days missed from school 1 day per month 0.5 day per month
Drug treatment Pill four times per month Injection four times per month MTX=Methotrexate; ETN=Etanercept
† denotes reference level ψ denotes continuous coding
These base case scenarios were used to determine the change in WTP for the transition
from MTX to etanercept. One-way sensitivity analysis was carried out by varying some
of the attribute levels used to represent a child on MTX treatment. Parents WTP to
switch from MTX to etanercept as well as results from the sensitivity analysis are
summarized in Table 26.
Table 26: WTP to switch from MTX to etanercept
Scenario MTX
WTP ($) (95% CI)
ETN WTP ($) (95% CI)
MTXETN ∆ WTP($) (95% CI)
Base case 1,263 (267, 2,315)
3,343 (1,758, 5,731)
2,080 (698, 4,065)
Injection 1,438 (469, 2,508)
3,343 (1,758, 5,731)
1,906 (523, 3,928)
No side-effects 2,358 (1,342, 3,714)
3,343 (1,758, 5,731)
985 (402, 2,882)
Equal effectiveness 3,491 (1,930, 5,700)
3,343 (1,758, 5,731)
-148 (-596, 488)
WTP=Willingness-to-pay; MTX=Methotrexate; ETN=Etanercept; CI=Confidence interval
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Parents were willing to pay $2,080 to switch from MTX to etanercept in the base case
scenarios. When MTX was administered as an injection four times per month, parents’
WTP was $174 less for the switch. When MTX was associated with no side-effects,
parents’ WTP was $921 less for the switch. When the effectiveness of MTX was assumed
to be equal to etanercept (no pain, participation without any difficulty) parents were no
longer willing to pay for the switch. In fact, WTP for the switch was -$148.
3.4.8 Importance Scores versus Ranking of Most Important Attribute Subsequent to the discrete choice tasks, respondents were asked to rank attributes in
order of perceived importance. Table 27 shows the frequency that each attribute was
ranked the “most important” by parents of children with JIA. In the ranking exercise,
child reported pain from arthritis was considered the most important attribute,
followed by participation in daily activities, cost to you, side-effects, and drug
treatment. No parents felt that days missed from school was the most important
attribute.
Table 27: Results of ranked most important attribute compared to importance score from the DCE main effects and interaction models
Attribute Ranking DCE
Top Rank (%) n Imp. Score
(Main) Imp. Score
(Interaction) Child reported pain from arthritis 69.81 74 32.38 26.79
Participation in daily activities 13.21 14 30.34 37.99
Cost to you 7.55 8 17.84 17.43
Side-effects 5.66 6 11.02 12.16
Drug treatment 3.81 4 3.03 2.36
Days missed from school 0.00 0 5.40 3.28
In Figure 9, results from the ranking exercise are compared to results from the main
effects and interaction regression models. Preferences from the DCE are represented as
importance scores (calculated as described in section 3.4.1).
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Figure 9: Relative importance of main study attributes from DCE compared to ranking exercise
In comparing the relative importance of attributes from the ranking, main effects and
interaction models some obvious differences were observed. The relative importance of
child reported pain from arthritis was much higher in the ranking exercise, accounting
for 74% of all top rankings. Drug treatment was also represented by a higher level of
importance in the ranking exercise compared to the DCE. When comparing results
from the main effects and interaction models, there is a change as to which attribute is
most important. The interaction model reveals that when significant co-variates are
controlled for, participation in daily activities is more important than child reported
pain, whereas the main effects model reveals child reported pain to be slightly more
important. Days missed from school was also found to be less important and cost more
important in the interaction model compared to the main effects model.
0 10 20 30 40 50 60 70 80
Pain
Participation
Cost to you
Side-effects
Missed school
Drug Treatment
Importance Score
Ranking Main effects Interaction model
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4 Discussion This chapter discusses the results of the DCE with careful consideration given to the
methods employed. Attempts will be made to interpret parents’ preferences for
attributes related to effectiveness, safety, convenience and cost as well as the
relationship between preferences with child health and demographic variables. Special
emphasis will be placed on unexpected and policy relevant findings. Careful
consideration will also be given to the validity of DCEs, methodological challenges, and
study limitations.
4.1 Sample of Parents
4.1.1 Response Rate
The response rate (63%) represented the expected rate reported in section 2.9.3. This
rate is consistent with findings from several other mailed DCEs measuring parents’
preferences for health services or health outcomes in children. A DCE administered to
parents of patients with hearing loss from three paediatric centers in Ontario, including
the Hospital for Sick Children, reported a response rate of 63% (n=48) after a single
round of follow-up telephone calls (177). Another study carried out in the UK that
evaluated parents preferences for out of hours care received completed surveys back
from 68% of parents (n=3893) after mailed reminders were sent at 3 and 6 weeks (178).
Overall, response rates for self-administered DCEs (mailed and online) have been
reported to be as low as 18.4% and as high as 89% (125).
Parents of children with JIA and other chronic conditions are unique from other types
of DCE respondents (e.g. patients, members of the public) since they are often
responsible for a number of tasks that extend beyond the duties of a “normal” parent or
citizen (i.e. attending appointments, administering treatments, assisting with daily
activities). This increased level of responsibility reduces the amount of time a parent
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has to participate in research, a fact supported by the number of parents who declined
participation in the study or agreed to participate and then failed to provide responses.
Reasons for not participating were often related to time constraints; “I haven’t had a
chance to look at the survey yet,” “I am too busy right now,” although, two parents
described the survey as being too challenging or emotionally difficult.
A large number of parents were not able to be contacted by telephone during
recruitment or were unable to be contacted during the follow-up phase. In some of
these cases, the addresses provided in the Rheumatology clinic database were out-of-
date and recruitment letters could not be sent, in other cases the phone numbers
provided were wrong or out-of-service. On 6 separate occasions wrong numbers were
encountered during follow-up calls, despite parents having confirmed participation
using the same telephone number during the recruitment phase of the study. The
largest portion of non-responders could not be reached at any time of the day despite
attempts made between the hours of 10am and 9pm. One explanation for this might be
that parents were screening unknown incoming calls. Other reasons might include time
constraints, embarrassment over having difficulty completing the choice tasks, or the
emotional challenge of the survey as described above. Emotional challenges with
completing the choice tasks were communicated by several parents who inserted
comments in the space provided at the end of the instrument.
“This survey was difficult to answer. It is hard to determine which scenario is worse.”
“Very difficult survey. I always would like to pick the selection that provides the most
pain relief but I also answered based on money. Although my child has drug benefits, my
income as a single mother would not allow me to purchase half these examples!”
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“The choices were really hard to answer. How do you choose between the ability to be a
regular child, no side side effects and cost? All parents would move heaven and earth for
a happy healthy child. No pain and affordability would be great, but is it realistic?”
Overall, the total number of respondents (n=105) was adequate based on the sample
size calculation presented in section 2.9.3. This implies that the main study version of
the survey was appropriate and easy to complete. The large proportion of respondents
(83.8%) that chose to complete the paper version of the survey might be explained by
the fact that parents preferred the mobility of the survey booklet compared to the online
version which required a computer. The physical presence of the survey booklet may
also have influenced the number of responses received in the paper format.
4.1.2 Demographic Data
The majority of respondents in this study were highly educated mothers who were born
in Canada, had high household incomes and used private drug coverage to pay for
their child’s JIA medications.
The large proportion of mother respondents may be a result of the fact that the majority
of primary contacts listed in the Rheumatology clinic database were mothers and
recruitment letters were addressed as such. No other preference-based study carried
out in parents of children with JIA (see table 11) reported the gender or relationship of
the parent respondent to the child (143, 144, 149). To our knowledge, only one other
DCE, a study that measured parents’ preferences for out of hours care, reported on the
sex of parent respondents, the majority of which were female (178).
In terms of citizenship, the proportion of respondents that reported having been born in
Canada were somewhat consistent with provincial norms (77% vs. 71%) (161), although,
when asked about their ethnic and cultural association, a higher proportion of the
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sample identified themselves as “Canadian” (49.5% vs. 23%). The frequency of other
ethno-cultural associations was representative of the Ontario population (161).
A very large proportion of the sample had attained a university or college degree (83%)
compared to the rest of the province, wherein 40% of adults in the general population
have attained a high-school education or less. One explanation for this might be the fact
that DCEs have been shown to be cognitively challenging and therefore a larger portion
of surveys were competed by educated parents. The level of education observed may
also be a phenomenon of the types of parents who attend the Hospital for Sick
Children, since Canadians with higher levels of education and higher household
incomes have been shown to utilize the services of specialists more frequently (179).
The proportion of parent respondents from high income households, although likely
influenced by the high levels of education and full-time employment within the sample,
may also be a reflection of the patient phenomenon described above. Other studies
carried out in specialized departments at the Hospital for Sick Children have also
reported high household incomes, with 72% of a sample of parents of children with
hydrocephalus reporting a household income greater than $100,000 (180), and 41% of a
sample of parents of children with asthma reporting a household income greater than
$80,000 (181). The median household income in the province of Ontario in 2006 was
$70,910 (161).
In terms of drug coverage and out-of-pocket payments for JIA drugs, results from this
study appear to be somewhat consistent with values presented in a study of drug
coverage programs and access to prescription medications across Canada. According to
a report published by Health Canada in 2009, approximately 90% of Canadians have
some coverage for routine drug expenses; 11% pay nothing out-of-pocket, 69% pay
modest deductibles and co-payments, and 10% are considered to be “under-insured”
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(plans pay less than 35% of a $1,00 annual drug expense) (62). To our knowledge there
are no studies looking at access to drug treatments for JIA or RA in Canada, despite a
number of sources reporting disparities between provinces and increased out-of-pocket
spending on biologic drugs (182).
4.1.3 Child Health Data
Respondents’ children with JIA represented a wide range of ages, disease durations,
sub-types, and experiences with drug treatments. A large proportion of female patients
were expected, since boys are less likely to develop JIA and other studies carried out in
JIA patients also reported a higher percentage of female patients (54, 183). The number
of years since diagnosis was confounded by the age of the patient, however, years with
JIA showed a normal distribution for patients over 9 years of age. The mean disease
duration in this study (5.8 years) was consistent with samples of JIA patients in other
preference-based studies, which reported 5.2 years for a sample of patients with
systemic JIA (149) and 4 and 4.3 years for samples representing all sub-types (54, 183).
In terms of diagnosis, the study sample represented all disease subtypes in the
proportions expected, with the exception of oligoarthritis, which was less prevalent
than expected in the sample (see Table 1). This under-representation may be a
reflection of the types of patients that visit SickKids rheumatology clinics, since another
study carried out a year prior to ours in the same department also reported a higher
proportion of patients with polyarticular JIA (184). The fact that 21% of parents were
unsure of their child’s disease subtype is likely an artifact of the difficulty parents have
understanding clinical terminology, or a result of the fact that physicians do not
describe ILAR categories to parents. Recently, it has been shown that there is minimal
clinical evidence to support the differential treatment of many of the ILAR subtype
distinctions and current treatment guidelines do not refer to these classifications (2).
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As expected, parents’ experiences with drug treatments in JIA were diverse in terms of
the types and combinations of drug therapies used, side-effects, and reasons for
discontinuations. Most interesting was the number of patients who discontinued a
treatment due to side-effects or a lack of response (70%). To our knowledge, only one
study reported on reasons for switching or discontinuing drug treatment in JIA (185).
In that study, reasons for switching from MTX to etanercept included inadequate
responses in 57% of cases, intolerance in 18%, and a combination of both in 24% of cases
studied. Of those patients that started on etanercept and discontinued 9% gained
control of their disease, 53% experienced inefficacy, 21% experienced intolerable
adverse events, and 14% did not comply to recommended treatment regimes. Based on
these findings, the study sample appears to have had a more positive treatment
experience, despite the fact that reasons for discontinuation are the same. When
comparing the types of minor side-effects experienced by the study sample, there were
very minor differences in the frequency of nausea, stomach pain, and headaches over
the last year.
4.2 Preferences for Drug Treatments and Health Outcomes in JIA
Results from the DCE were consistent with the hypothesis that parents of children with
JIA would show a greater preference for treatments that provide improvements to a
child’s health and quality-of-life. Parents showed the greatest preference for attributes
related to effectiveness, which is consistent with findings from other studies that
measured the relative importance of treatment effectiveness over other outcomes such
as safety, convenience and cost (134, 139, 143, 149). This is not surprising, since the
overall goals of drug treatment in JIA are to control and minimize pain and
inflammation, preserve daily functioning and improve HR-QoL. More surprising was
how much cost influenced parents’ choices and what little influence the type of drug
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treatment had. The following sections provide a more detailed discussion surrounding
the preferences’ of parents of children with JIA for the attributes and levels presented in
this study.
4.2.1 Preferences for Effectiveness
To our knowledge this is the first preference-based study to show a greater relative
importance of participation compared to pain. Findings from a DCE of a large sample
of patients who suffer from chronic pain estimated greater part-worth utilities for pain
reduction compared to disability improvement (151). Even the ranking exercise in the
present study showed pain to be the most important attribute to 70% parents surveyed.
Results from the ranking exercise were different from the discrete choice data, which
estimated pain to have an importance score of 27%. Based the part-worth utilities
derived from the DCE it is likely that parents had a difficult time trading between the
effectiveness attributes (pain and participation) presented in this study. This may be a
result of the proximity of these attributes, since activity level is often dictated by the
severity of a child’s pain. The relationship between these attributes was carefully
considered in designing the choice experiment and descriptions of the participation in
daily activities attribute included a reminder that reduced abilities to participate could
also be influenced by factors other than pain such as fatigue, side-effects, and joint
stiffness. In the end, valuable information on how parents trade between these health
states was gained.
Interestingly, parents showed no statistically significant difference in preferences for
severe and moderate pain, but showed a strong preference to transition from severe to
mild pain. This finding is of particular interest since children with active polyarticular
JIA most often report pain in the mild to moderate range (183). Findings from this
study illustrate the minimal level of pain that is accepted by parents of children with
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JIA in the study sample and the importance of pain as an attribute in the assessment of
treatment effectiveness.
Age was the only significant predictor of parents’ preferences for the pain attribute and
parents of older children showed a greater preference for the transition from severe
pain to moderate pain. This may be explained by the fact that parents of older children
have a greater preference for their child to transition towards a more functioning or
independent state. As stated by one parent:
“These choices could change depending on the child’s age. For example, at age 10 my
child was less concerned about pain and daily activities and if she required assistance.
This is much more of an issue now at age 17.”
The importance of including age-appropriate measures of pain in clinical based
assessments is starting to be recognized and as a result a number of pain measurement
tools having been validated in populations of children with JIA (186).
The importance of maintaining daily functioning in children with JIA is far more
recognized and is supported by a number of functionality outcome measures (i.e.
CHAQ, WHO ICF) (46, 148, 155) (described in section 1.2.2). Results from this study
revealed how parents value the functioning states used to calculate a disability index
within the CHAQ. Although parents showed a strong preference for a child moving
from unable to participate towards participation with some difficulty, they were less
willing to accept the transition towards participation with much difficulty. This may be
explained by the manner in which parents’ interpret the terms “some” and “much” and
the amount of utility they assign to each level. Similarly, within the pain attribute the
terms “severe” and “moderate” were assigned much less utility than the term “mild”
for which parents showed a much greater preference.
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As results from the interaction model showed, parents of children that had used a
biologic or DMARD within the three years prior to the study were more likely to accept
a child who participates with much difficulty. This may be a result of parents’ intrinsic
nature to adjust what they see as “normal” based on the experiences of their child. This
adjusted sense of what is “normal” has been reported in a number of studies looking at
arthritis patients’ perceptions of treatment effectiveness (187-189). It is likely that
parents of children that have been prescribed more aggressive therapies have
experienced their child in a state of disability and as a result, may be more accepting of
a child that is able to participate with much difficulty. On the other hand, the fact that
parents of children with a longer disease duration showed a greater preference to
transition towards participation with some difficulty suggests the opposite to be true. It
is equally possible that the longer a parent observes active disease in their child the
more strongly they value treatment effectiveness.
Overall, the most conflicting result from the interaction model was the finding that
parents of older children showed a lesser preference for the transition from unable to
participate towards participation with some difficulty, despite the fact that they showed
a stronger preference for the transition from severe to moderate pain (described above).
A further exploration of these relationships is required in order to explain the
differences in preferences observed.
4.2.2 Preferences for Safety
In terms of treatment safety, the presence or absence of side-effects appeared to be the
biggest concern for parents of children with JIA, who showed very little difference in
their preferences for treatment-associated headaches and nausea four days per month.
As expected, parents showed the greatest preference for no side-effects compared to
stomach pain four days per month.
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These findings confirm the importance of treatment safety to parents of children with
JIA, regardless of the severity of associated side-effects. In another preference study,
patients with RA assigned much less importance to side-effects including nausea,
dizziness and fatigue compared to the route of drug administration (134). The strong
disutility parents in the present study showed for common but reversible treatment
side-effects, in particular stomach pain is likely a reflection of the large proportion of
their children that have been prescribed NSAIDs and DMARDs. A high proportion of
the study sample claimed that their child had discontinued a drug treatment as a result
of the side-effects included in the DCE (headaches, nausea, stomach pain). However,
none of the child health variables were found to have a significant influence on
preferences for the side-effects included in this study.
4.2.3 Preferences for Convenience
Parents’ preferences for a self or parent administered injection four days per month
over a doctor or nurse administered I.V. once a month was expected. What was not
expected was the relative importance of a self or parent administered pill four days per
month, which was not found to be statistically significantly different from the utility
assigned to a doctor or nurse administered IV one day per month. In other preference-
based studies that evaluated preferences for oral routes of administration, oral routes
were always found to be preferred over SC and IV routes (121, 134, 139).
Reasons patients have been shown to prefer SC injections over IV drugs in previous
studies relate to the fact that SC treatments are easy to administer, have a minimal effect
on daily living since visits to the hospital are not necessary, and reduce the possibility of
drug errors since medications are provided in pre-filled, ready-to-use injectable
syringes (136, 137, 141). It is likely that parents in this study preferred SC drugs over IV
infusions for the same reasons. It is also possible that parents made assumptions based
on their own experiences with various treatment types and these assumptions
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influenced their choice. As the literature review showed patients have a hard time
separating what they know about the safety and effectiveness of a drug when making a
decision about the route or frequency alone. Preferences for drug treatments often
depend on a patient’s experience with that type of drug (136). This might help to
explain the lesser preference parents had for oral treatments in this study. With a large
proportion of parents’ children having had used an NSAIDs in the last three years and
an equally large proportion having had discontinued a treatment as a result of side-
effects it is very possible that parents were unable to ignore the side-effects that were
associated with an oral drug used by their child. That being said, experiences,
including the type of drug, discontinuations, and side-effects, were not found to
significantly influence parents’ choice in the interaction model.
4.2.4 Preferences for Cost
Cost to you was found to be the third most important attribute to parents of children
with JIA. To our knowledge, this was the first study to show a greater relative
importance for cost over attributes related to convenience and safety. In the study by
Fraenkel et al. (139) the route of drug administration and presence of common but
reversible side-effects including injection site reactions, rash, oral ulcers, nausea, and
diarrhea were all found to be more important than cost. It is important to note
however, that the most extreme levels of cost in that study were ‘Free’ and ‘$30 per
month’, a range that respondents in this survey would likely have found negligible.
Similarly, in the contingent valuation study carried out by Barron et al. (143) the highest
starting bid was $300 and as a result, parents were willing to pay $395 for a drug that
guaranteed clinical remission. The impact that the range of cost levels can have on
WTP will be discussed in section 4.5.
Results from the interaction model showed WTP to be greatly influenced by household
income, with parents from low and middle income households showing a lower
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maximum WTP than parents from high income households. This finding highlights
one of the caveats of using monetary valuations of preference; that not all individuals
have the same value of marginal income and as such, ability to pay is not consistent
across a sample (80). In this study, 60% of respondents came from high income
households and as a result, were willing to pay more for treatment scenarios. Results
from the interaction model also showed that parents from high income households had
a greater preference for participation with much difficulty compared to unable to
participate.
The fact that parents that used public drug programs to pay for the cost of their child’s
arthritis drugs also had a higher maximum WTP compared to those with private
insurance might be explained by the fact that the most common drug program in
Ontario, TDP, often requires higher out-of-pocket payments in the form of deductibles,
compared to private programs (66). It is therefore possible that these families would be
more accepting of the levels presented in the DCE, whereas parents from families with
private coverage would be completely unfamiliar with having to pay for their child. It
has been shown that sometimes when a study subject is unaccustomed to evaluating
costs as high as those presented in a DCE that they employ cognitive strategies in order
to make the necessary trade-offs (190). Some of these strategies include ignoring cost
altogether, lowering indicated costs to a value more similar to what they are
accustomed to paying, or re-coding the costs as ‘low,’ ‘medium,’ or ‘high.’ (190)
Unfortunately, there is no practical way to determine whether or not respondents in this
study employed these simplifying tactics.
4.2.5 Preferences for Switching Drugs
When part-worth utilities were used to determine parents’ preferences to switch from a
treatment that resembled MTX to a treatment that resembled etanercept it was found
that parents were willing to pay $2,080 for the switch. This equates to approximately
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31% of the average monthly income for a family earning $80,000 per year. In the study
by Barron et al. parents’ WTP for a drug that guaranteed clinical remission was equal to
15% of the average monthly family income (143).
The amount that parents were willing to pay to switch from a scenario resembling a
child failing on MTX to a scenario resembling a child responding well to etanercept
highlights the importance of treatment effectiveness to parents of children with JIA.
From these findings it appears as though parents of children who respond adequately
to MTX are happy to keep their child on that treatment, regardless of the fact that their
child will experience nausea four days per month, miss 1 day of school per month, and
the drug will cost the family $50 per month. On the other hand, for parents of children
who do not respond adequately to MTX (mild pain and participation with some
difficulty), there is a strong preference to switch to a treatment that works.
4.3 Validity of Discrete Choice Experiments
Using a DCE, tradeoffs between treatment characteristics and health outcomes that
would otherwise be difficult to quantify were evaluated. One of the major advantages
to using a DCE over other preference elicitation methods is its ability to mimic the type
of decisions parents are faced with in the real world and model these decisions around
policy relevant scenarios such as, a child switching from MTX to a biologic.
Unfortunately, the external validity of findings from this study are difficult to establish
since revealed preference data on parents’ preferences for treatments and health
outcomes in JIA are not readily available. That being said, the general validity of DCEs
has been relatively well established in the broader literature, as a result of RP data in
fields of marketing, environmental and transportation economics (81).
Parents in this study were asked not only to answer the choice questions, but also to
rank the relative importance of attributes. This provided an opportunity to test the
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internal validity of findings from the DCE. In comparing the order and magnitude of
preferences estimated using both methods it would appear that results from the
regression model are valid. Unfortunately, in the ranking exercise, levels were not
assigned to the attribute descriptions and parents were asked to explicitly state which
attribute they felt was the most important. The framing of attributes was therefore the
only factor affecting parents’ perceptions of importance and likely contributed to the
differences observed. In particular, the attribute child reported pain from arthritis was
assigned a much higher importance score in the ranking exercise compared to the DCE.
The framing of attributes and levels in this study undoubtedly had an impact in the
order and magnitude of preferences. A further understanding of the impact attribute
and level framing have on DCE results has gained much attention in recent years (157,
166, 191).
Choice tasks do not have the same clinical, financial, and emotional consequences as
real health care choices and as a result decision making approaches used when
completing a DCE differ from those used in real life (82). The DCE assumed that
respondents considered all of the attributes included in a choice experiment and based
on the levels of attributes in a choice set, made a choice that maximized utility (82). This
is what health economists refer to as “rational” decision making (192). Unfortunately,
this is not always the case and it has been shown that decision making heuristics in
DCEs can be influenced by a number of factors including the design of the experiment,
framing of choice questions, and appropriateness of attributes and levels (145, 192-194).
In knowing this, tests for rationality and dominance were not included in the choice
experiment as a means to assess validity.
4.4 Methodological Issues and Study Limitations
Best practice in DCEs has been described as a “moving target” and as a result,
discussions of most studies highlight the same methodological issues (82). These issues
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often relate to the number of choice sets respondents can process, ordering effects of
choice sets and attributes, and the impact of attribute and level framing. Other common
methodological questions refer to the decision making process and how people choose.
Nonetheless it is important to address the fact that the understanding of parents’
preferences for drug treatments in JIA from this study are confined to the attributes and
levels presented. Findings from this study do not provide information on how parents
interacted with or interpreted choice questions, choice tasks, or attributes and levels.
4.4.1 Cost to You
The biggest methodological issues raised by this study relate to the cost attribute and
the precision of WTP estimates. Below is a discussion surrounding the assumption that
cost is linear, the range of levels presented in the study, and the appropriateness of the
payment vehicle used.
In the DCE, cost was coded as both continuous and categorical, with the latter revealing
a slightly non-linear relationship between the levels $700 per month, $1,500 per month
and $2,100 per month. Assuming the slope between these points to represent the
marginal utility of money resulted in an over-estimation of WTP when the $700 and
$1,500 levels were used and an under-estimation of WTP when the $1,500 and $2,100
levels were used. Despite the fact that the difference between the last two levels was
$600 and the difference between the middle two levels was $800, parents still showed
less disutility to transition from $700 to $1,500. This finding highlights not only the
impact that coding can have on DCE findings, but also the impact that the range and
spacing between cost attribute levels can have on estimates of WTP. There is currently
no best practice in handling non-linearity of cost attributes.
The importance of selecting an appropriate range of levels, in particular cost if WTP is
to be estimated, is well known (126, 166, 190). In fact, knowledge in this area and
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findings from the pilot study led to an increase in the upper level of cost for the main
study. As a result, all but one of the WTP values for main effects in the main study fell
below the ‘choke value’ of $2,100. It can therefore be assumed that the range of cost
levels used in this study were appropriate for the sample surveyed. It is however
possible that parents’ overstated how much they were willing to pay for treatment
scenarios as a result of starting bid bias (166).
Despite the fact that a cheap talk script was used, hypothetical bias might be another
explanation for the magnitude of WTP estimates observed (167, 168, 168). A number of
other studies measuring parents’ WTP for improvements in health reported higher
values when improvements are observed in a child. In a CV study by Liu et al. (195)
mothers were willing to pay twice the amount to prevent a cold that they had during
the time of the study in their child. Similarly, a DCE by Dickie and Ulery show a two-
fold increase in WTP to avoid acute illness in children compared to adults (196). The
desperation of some parents of children with JIA is clearly illustrated by one parent’s
comment, “all parents would move heaven and earth for a happy healthy child.” It is
possible that parents in this study over-stated the amount of money that they would
actually be able to pay each month in order to choose a scenario that resembled a
“healthy” child. On the other hand, perhaps parents’ were actually willing to pay the
values stated because the scenarios represented health benefits for a child.
Another reason that parents may have over-stated how much they would actually be
willing to pay relates to the appropriateness of the payment vehicle used. The majority
of parents in this study claimed to pay nothing out-of-pocket each month on drug
treatments for their child with JIA, potentially making a $2100 payment per month
unrealistic. As previously described, when faced with unfamiliar levels respondents
may employ simplifying tactics in order make a choice. Based on the proportion of
parents that use private insurance to pay for the cost of their child`s medications,
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presenting the cost attribute as a co-payment per month may have been more realistic.
However the tradeoff in doing so would be the fact that the highest level would be
greatly reduced and as a result parents ‘choke value’ might not have been reached.
4.4.2 Study Sample
The generalizability and transferability of findings from this study were limited not
only by the fact that most parents of children with JIA reported paying very little each
month on medications for their child but also by the high socio-economic status of the
sample. In addition, the phenomenon described previously in section 4.1.2, suggesting
that patients from high income households have better access to specialized care, means
that the hospital from which patients were recruited may have also biased the study
findings. The Hospital for Sick Children has one of the largest paediatric rheumatic
disease units in North America and parents and children that attend its clinics represent
severe cases of JIA. These patients and parents of these patients are also likely exposed
to more clinical research than patients that attend other hospitals. These factors lead to
questions surrounding the appropriateness of the survey sample.
The question of whether or not parents were the most appropriate sample to elicit
preferences from must also be addressed. In Ontario, the most expensive treatments for
JIA are often paid for through publicly funded drug programs and as a result, some
might argue that it is the preferences of tax-payers, in this case citizens of Ontario, that
truly matter (197). The question of whose preferences for characteristics of health care
and health outcomes matter the most in the context of a public system is a topic of great
debate. Although it has been shown that the users of the health care service or
intervention being evaluated are the only group capable of accurately stating their
preferences in a DCE, since they are more familiar with the tradeoffs being evaluated
(145, 146, 198). In the end, the appropriateness of the sample is dependent on the
context of the question being addressed. The objective of this thesis was to elicit
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parents’ preferences and WTP for drug treatments and health outcomes in JIA and the
attributes and levels were selected as such.
4.4.3 Framing the Choice Question
Other methodological issues raised by this study relate to how the choice question was
framed. A discussion of the one month time horizon for which all attributes were
framed and the hypothetical child parents were asked to think about when completing
a choice task follows.
The one month time horizon over which preferences were modeled does not represent
the time horizon over which parents make decisions about treatments for their child in
the real world. In reality, a child who is responding inadequately to methotrexate is
required to fail on the treatment for a period of three to six months, before becoming
eligible for a biologic therapy. By framing effectiveness attributes (pain and
participation) within a period as short as one month, preferences for one of the major
goals of therapy; to prevent long-term pain, joint damage, and disability were not
captured. In addition, presenting cost to you per month for a single month of
treatment fails to address the long term cost implications of biologic therapies and may
have contributed to high WTP estimates. Nonetheless, the decision to frame attributes
within a one month time frame was made to ensure that parents would be familiar with
the attribute levels presented. For example, to present the frequency of side-effects,
drug administrations and days missed from school over a longer time horizon would
result in attribute levels that parents were less familiar with (e.g. injection 48 times per
month) or that might seem unrealistic (i.e. no side effects for one year). It was also
believed that people would better related to cost over a period of one month, since
many household budgets are managed over this time horizon as a result of regular
patterns of income and spending (e.g. pay-cheques, bill payments, investment
contributions).
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Prior to completing the choice tasks parents were instructed not to think about their
own child with JIA, but rather “a child” with arthritis who was about to start a new
drug treatment. This was done in order to reduce the level of starting state bias that
may have been introduced if had parents were expected to picture their child in the
health states presented in the survey. For example, a parent of a child whose JIA is in
remission, may have a very difficult time picturing their child in severe pain and unable
to participate in daily activities on treatment. Although it is likely that parents were
able to state their preferences for this hypothetical child, it is unclear whether or not
their preferences would be the same if asked about their own child. This is particularly
unclear when it comes to the cost to you attribute, with parents indicating how much
they would be willing to pay from their own budget for another child.
The number of significant interactions identified between study attributes and disease-
related characteristics of parents’ children with JIA suggests that parents may not have
been able to separate the hypothetical scenarios presented from their own experiences
and may have in fact been thinking about their own child when completing the choice
tasks. Although this was part of the intention of the study, it also addresses another
methodological limitation of DCEs in general. Although analysis of significant
interactions allows for the identification of some variation in preference, a wide-range
of unobserved factors also have a significant influence on choice. The impact that
unexplained sample heterogeneity can have on preference has been discussed
previously as a possible explanation for the seemingly irrational choice parents’ made
for an injection four days per month over a pill four days per month. One of the
assumptions of MNL regression is that all unobserved factors are independently and
identically distributed and as a result, it is not possible to estimate the magnitude such
factors have on choice. A number advanced methods (described in section 1.5.2.3) used
to analyze discrete choice data have been described as better suited to handle the
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unexplained portion of utility. It was not the intention of this research to explore
preference variation as a result of the unexplained or random component of utility, but
rather the explainable component of utility including parents’ preferences for the
attributes and levels presented and the impact of specific child health and demographic
variables on choice.
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5 Conclusions Parents of children with JIA showed the greatest preference and were willing to pay
more for treatments that provided improvements to a child’s health and quality-of-life.
Attributes related to treatment effectiveness, specifically pain relief and improved
participation in daily activities were most valued by parents, followed by the
elimination of treatment side-effects. Cost was also found to have a significant impact
on preference, with parents’ showing a strong disutility to pay for the most expensive
treatment scenarios. Both household income and drug coverage influenced WTP while
child age, gender, years since diagnosis, household income, monthly spending on
drugs, and drug type influenced preferences for health outcomes. In the end, the
objectives of this study were met and methodological issues and study limitations were
addressed.
In interpreting the findings from this study, it is important to remember that valuing
WTP using a DCE represents a monetary equivalence of preference. It was not the
intention of this study to provide information on drug pricing or support out-of-pocket
spending for drugs. When looking at parents’ WTP to transition from a treatment
resembling MTX to a treatment resembling etanercept the value parents place on a
treatment that works is highlighted. Results from this study show the strong desire
parents of children with JIA have for the “best treatment” for not only their child, but
any child living with JIA. As one parent stated:
“I hope that all children are able to get the best treatment available to them in Canada and not be treated differently because they are poor or rich. All children deserve to live as normal of a life as possible.”
Findings from this study provide some insight as to what this “best treatment” looks
like through the eyes of a parent.
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5.1 Implications for Stakeholders
5.1.1 Researchers
Both the development and findings from the DCE described in this thesis have the
potential to inform research in the fields of health economics and patient outcomes. The
field of patient preferences is rapidly evolving, with groups like the International
Society of Patient Outcomes Research (ISPOR) Patient Preference Methods Working
group having recently published a checklist of good research practices for
methodologically sound applications of conjoint analysis in health care (84). The
number of attributes, levels and choice sets used in this study provide support for the
administration of larger designs in groups of respondents familiar with the attributes
being evaluated, while the approach to experimental design provides guidance to other
researchers interested in generating a choice experiment with utility balance. In
addition, the use of pictograms in the pilot study provides information on the usability
of visual representations of pain. In terms of more traditional health economics
research, WTP findings could be used in a cost-benefit analysis on treatments in JIA.
For outcomes researchers, the finding that pain is an important measure of treatment
effectiveness to parents of children with JIA could help inform the development of
disease specific instruments that evaluate the impact of treatment. Current evaluations
of disease activity are based on more traditional medical outcomes (see section 1.2.3),
which do not adequately consider parents’ perceptions of treatment benefit of health.
Findings from this study could be extremely useful as they demonstrate not only what
is important to parents’ of children, but why and by how much comparatively for the
wide range of attributes included.
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5.1.2 Health Care Professionals
In JIA, there is a body of evidence to suggest that parents’ opinions do not coincide with
those of the professionals caring for their children (132, 133). These disagreements often
surround assessments of treatment effectiveness and likely have an impact on the
quality of care. Understanding how parents of children with JIA trade between
attributes of effectiveness, safety, convenience and cost could help inform clinical
practice and in turn improve disease management. Information on preferences for
health outcomes and the child health and demographic variables that influence these
preferences, such as child age and family income could provide some additional
direction to professionals seeking the most appropriate treatments for their patients.
5.1.3 Health Care Decision Makers
Decisions surrounding access to biologic drugs in Ontario’s public drug plans are
handled on a case-to-case basis through the EAP. This has led to major limitations in
the prescribing power of physicians, who are not always able to prescribe the best or
most appropriate treatment for their patient. Since there is very little evidence to
support the efficacy of one biologic over another (36, 199), findings from this study
could assist with listing decisions in the province of Ontario. Preferences of parents for
treatments that simply “work” align with argument that earlier more aggressive
treatment is needed in the field of JIA (68). In addition, the recently published ACR
guidelines for drug treatments in JIA do not align with provincial coverage criteria (2).
Parents in the present study showed a strong preference to transition from MTX to
etanercept and in doing so, suggest the need for improved access to biologic drugs for
children with JIA in public drug programs.
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5.2 Future Directions
The goal of this thesis was to determine parents’ preferences for drug treatments in JIA
using a DCe. Future work pertaining to this research question would involve further
understanding the child health and demographic variables shown to influence patients’
preferences. As presented in this thesis, parents of children with JIA were eager to
provide candid descriptions of their experiences living with a child with JIA.
Qualitative methods would offer a unique approach to build on these findings and
further understand what is most important to patients and why. In addition,
administering a DCE with the same attributes and levels to a sample of parents from
other health care institutions could provide further evidence as to the generalizability,
transferability and validity of findings. In doing so, some of the methodological issues
surrounding DCEs could also be explored. Simple changes to the survey could provide
information on the impact of response ordering effects, attribute and level framing, and
the impact of levels used to represent cost. Without answers to some of these important
methodological questions a level of consistency in the quality of published DCEs will
not be achieved and in turn, the impact of this unique method as a true force in the field
of decision-making will remain limited.
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Appendicies
Appendix 1: Domains of the CHAQ
Functional Domains Items (Is your child able to?)
Dressing and Personal Care
− Dress, including trying shoelaces and doing buttons − Shampoo his/her hair − Remove socks − Cut fingernails
Getting Up − Stand up from a low chair or floor − Get in and out of bed
Eating − Cut his/her own meat − Lift a cup or glass to mouth − Open a new cereal box
Walking − Walk outside on flat ground − Climb up five steps
Hygiene
− Wash and dry your entire body − Take a bath (get in and get out) − Get on and off the toilet or potty − Brush teeth − Bomb/brush hair
Reach
− Reach and get down a heavy object such as a large fame or books from just above his/her head
− Bend down to pick up clothing or a piece of paper from the floor
− Pull on a jumper over his/her head − Turn to look back over shoulder
Grip
− Write or scribble with pen or pencil − Open car doors − Open jars, which have been previously opened − Turn taps on and off − Push open a door when you have to turn a door knob
Activities
− Run errands and shop − Get in and out of a car, toy car or school bus − Ride bike or tricycle − Do household chores (e.g. wash dishes, take out rubbish) − Run
Disability Domains VAS endpoints
Pain No pain (0) Severe pain (100)
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Appendix 2: SAS design code used to generate pilot and main study choice sets
Pilot
%mktruns(3 6 4**4 ); %mktex(3 6 4**4, n=96, seed=61); proc print; run; %mktlab(data=design, int=f1-f2, out=final) proc print; run; %choiceff(data=final, model=class(x1-x6 / effects), nsets=48, flags=f1-f2, maxiter=50, seed=61, options=relative, beta=zero) proc print noobs; var x1-x6; id set; by set; run; proc print data=bestcov label; title 'Variance-Covariance Matrix'; id __label; label __label = '00'x; var x:; run; title; %mktdups(generic, data=best, factors=x1-x6, nalts=2) %mktblock(data=best, nalts=2, nblocks=3, factors=x1-x6, seed=61)
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Main Study
%mktruns(3 4**5); %macro res; bad =(x2=4 & x4=1)+(x2=1 & x4=4); %mend; %mktex(3 4**5, n=96, restrictions=res, seed=569); proc print; run; %mktlab(data=design, int=f1-f2, out=final) proc print; run; %choiceff(data=final, model=class(x1-x6 /effects), nsets=48, flags=f1-f2, maxiter=500, seed=569, options= nodups relative,
beta=0.08026 0.03574 0.99912 0.61904 0.09646 0.17637 0.04489 0.32684 1.34695 0.23742 -0.26122 0.31935 0.05154 0.25668 0.64562 0.19479 -0.24298)
proc print; var x1-x6; by set; id set; run; proc print data=bestcov label; title 'Variance-Covariance Matrix'; id __label; label __label = '00'x; var x:; run; title; %mktdups(generic, data=best, factors=x1-x6, nalts=2)
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Appendix 3: Pilot study choice design
Block 1
set drug1 drug2 pain1 pain2 pain3 pain4 pain5 side1 side2 side3 part1 part2 part3 days cost
1 1 0 0 1 0 0 0 0 1 0 0 1 0 1 500
1 -1 -1 1 0 0 0 0 -1 -1 -1 0 0 1 0.5 1500
2 -1 -1 0 0 1 0 0 0 1 0 0 0 1 4 500
2 1 0 -1 -1 -1 -1 -1 1 0 0 -1 -1 -1 2 50
3 -1 -1 0 1 0 0 0 -1 -1 -1 0 0 1 0.5 50
3 0 1 0 0 0 0 1 0 1 0 0 1 0 1 1500
4 1 0 0 0 1 0 0 1 0 0 0 0 1 1 900
4 -1 -1 0 0 0 1 0 -1 -1 -1 0 1 0 2 50
5 1 0 0 1 0 0 0 -1 -1 -1 -1 -1 -1 4 1500
5 0 1 0 0 0 1 0 1 0 0 1 0 0 0.5 500
6 1 0 -1 -1 -1 -1 -1 1 0 0 1 0 0 0.5 50
6 -1 -1 0 1 0 0 0 0 0 1 -1 -1 -1 1 500
7 1 0 0 0 0 0 1 -1 -1 -1 0 1 0 2 500
7 -1 -1 0 1 0 0 0 0 0 1 1 0 0 4 1500
8 0 1 0 0 1 0 0 0 0 1 0 0 1 2 50
8 1 0 0 0 0 0 1 -1 -1 -1 -1 -1 -1 4 1500
9 1 0 -1 -1 -1 -1 -1 1 0 0 -1 -1 -1 2 50
9 0 1 1 0 0 0 0 -1 -1 -1 0 0 1 0.5 500
10 0 1 0 1 0 0 0 1 0 0 -1 -1 -1 2 1500
10 1 0 -1 -1 -1 -1 -1 0 1 0 0 1 0 1 500
11 0 1 0 0 0 0 1 1 0 0 1 0 0 0.5 900
11 1 0 0 0 0 1 0 0 1 0 0 0 1 4 50
12 0 1 0 0 0 0 1 0 1 0 0 0 1 4 50
12 -1 -1 0 0 1 0 0 0 0 1 0 1 0 0.5 900
13 -1 -1 0 0 0 1 0 0 0 1 1 0 0 4 500
13 0 1 0 0 1 0 0 0 1 0 0 1 0 1 1500
14 -1 -1 1 0 0 0 0 0 0 1 -1 -1 -1 1 900
14 0 1 -1 -1 -1 -1 -1 0 1 0 1 0 0 2 1500
15 1 0 0 0 0 1 0 -1 -1 -1 0 1 0 2 900
15 0 1 -1 -1 -1 -1 -1 0 0 1 -1 -1 -1 1 50
16 -1 -1 1 0 0 0 0 0 1 0 1 0 0 2 500
16 0 1 0 0 0 1 0 1 0 0 0 1 0 4 900
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Block 2
set drug1 drug2 pain1 pain2 pain3 pain4 pain5 side1 side2 side3 part1 part2 part3 days cost
1 0 1 -1 -1 -1 -1 -1 1 0 0 0 1 0 4 900
1 1 0 0 0 0 0 1 0 1 0 -1 -1 -1 0.5 1500
2 0 1 0 0 1 0 0 -1 -1 -1 -1 -1 -1 4 900
2 1 0 0 1 0 0 0 0 0 1 0 0 1 2 1500
3 1 0 0 0 1 0 0 0 0 1 1 0 0 4 50
3 -1 -1 -1 -1 -1 -1 -1 1 0 0 0 0 1 1 500
4 1 0 0 1 0 0 0 1 0 0 1 0 0 0.5 50
4 0 1 0 0 0 1 0 0 0 1 0 0 1 2 1500
5 0 1 1 0 0 0 0 0 0 1 -1 -1 -1 1 50
5 1 0 0 0 1 0 0 -1 -1 -1 0 1 0 2 500
6 -1 -1 0 1 0 0 0 0 1 0 1 0 0 2 900
6 1 0 0 0 0 1 0 0 0 1 0 1 0 0.5 1500
7 -1 -1 0 0 0 0 1 0 1 0 1 0 0 2 900
7 0 1 0 0 1 0 0 -1 -1 -1 -1 -1 -1 4 500
8 -1 -1 0 0 1 0 0 1 0 0 -1 -1 -1 2 50
8 0 1 0 1 0 0 0 0 0 1 0 1 0 0.5 500
9 1 0 0 1 0 0 0 0 1 0 0 1 0 1 50
9 -1 -1 -1 -1 -1 -1 -1 0 0 1 1 0 0 4 1500
10 -1 -1 0 0 0 1 0 0 0 1 1 0 0 4 500
10 0 1 1 0 0 0 0 -1 -1 -1 0 0 1 0.5 50
11 0 1 -1 -1 -1 -1 -1 -1 -1 -1 0 0 1 0.5 500
11 -1 -1 1 0 0 0 0 1 0 0 0 1 0 4 50
12 1 0 1 0 0 0 0 -1 -1 -1 1 0 0 1 1500
12 -1 -1 0 0 1 0 0 0 1 0 -1 -1 -1 0.5 900
13 -1 -1 -1 -1 -1 -1 -1 0 1 0 -1 -1 -1 0.5 50
13 0 1 0 0 0 0 1 -1 -1 -1 1 0 0 1 900
14 1 0 0 0 0 0 1 0 0 1 0 0 1 2 900
14 0 1 0 0 0 1 0 0 1 0 0 1 0 1 50
15 -1 -1 0 0 0 0 1 1 0 0 0 1 0 4 1500
15 0 1 1 0 0 0 0 0 1 0 1 0 0 2 900
16 0 1 0 0 0 0 1 -1 -1 -1 -1 -1 -1 4 500
16 -1 -1 0 0 0 1 0 1 0 0 0 0 1 1 1500
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Block 3
set drug1 drug2 pain1 pain2 pain3 pain4 pain5 side1 side2 side3 part1 part2 part3 days cost
1 -1 -1 0 1 0 0 0 1 0 0 0 1 0 4 1500
1 0 1 0 0 0 1 0 0 0 1 -1 -1 -1 1 50
2 1 0 1 0 0 0 0 1 0 0 -1 -1 -1 2 500
2 -1 -1 0 0 0 0 1 -1 -1 -1 0 0 1 0.5 50
3 0 1 0 0 1 0 0 1 0 0 1 0 0 0.5 500
3 1 0 0 1 0 0 0 0 0 1 0 0 1 2 900
4 1 0 0 0 1 0 0 0 0 1 1 0 0 4 50
4 0 1 0 1 0 0 0 1 0 0 -1 -1 -1 2 1500
5 0 1 0 0 1 0 0 0 0 1 0 0 1 2 50
5 -1 -1 0 0 0 1 0 0 1 0 -1 -1 -1 0.5 900
6 0 1 0 0 0 0 1 0 1 0 0 0 1 4 50
6 1 0 -1 -1 -1 -1 -1 -1 -1 -1 1 0 0 1 1500
7 0 1 -1 -1 -1 -1 -1 0 1 0 0 0 1 4 900
7 -1 -1 0 0 1 0 0 -1 -1 -1 0 1 0 2 50
8 1 0 0 0 0 0 1 1 0 0 0 0 1 1 900
8 0 1 1 0 0 0 0 0 1 0 1 0 0 2 1500
9 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 0 1 0 2 900
9 1 0 0 1 0 0 0 1 0 0 1 0 0 0.5 500
10 1 0 1 0 0 0 0 0 0 1 0 1 0 0.5 900
10 -1 -1 0 0 1 0 0 1 0 0 0 0 1 1 1500
11 0 1 0 1 0 0 0 -1 -1 -1 1 0 0 1 50
11 1 0 1 0 0 0 0 0 1 0 0 0 1 4 500
12 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 4 500
12 0 1 0 0 0 0 1 0 0 1 0 1 0 0.5 1500
13 1 0 0 0 1 0 0 0 1 0 -1 -1 -1 0.5 1500
13 -1 -1 1 0 0 0 0 1 0 0 0 1 0 4 50
14 1 0 0 0 0 1 0 1 0 0 -1 -1 -1 2 500
14 -1 -1 -1 -1 -1 -1 -1 0 1 0 0 1 0 1 1500
15 1 0 0 0 0 1 0 -1 -1 -1 1 0 0 1 900
15 0 1 -1 -1 -1 -1 -1 0 0 1 0 1 0 0.5 500
16 -1 -1 0 0 0 0 1 0 0 1 -1 -1 -1 1 500
16 0 1 0 1 0 0 0 0 1 0 0 0 1 4 900
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Appendix 4: Recruitment letter sent to parent participants eligible for the study
Date
Dear (Parent’s name),
Re: The Hospital for Sick Children Biologic Treatment of Juvenile Idiopathic Arthritis Research Study
The Hospital for Sick Children Rheumatology Clinic is currently involved in a research study in order to understand what factors parents believe are most important when selecting a drug treatment for their child. This study will require participants to complete three brief questionnaires which may take up to 30 minutes total. A further description of the study is enclosed.
We are asking for the participation of parents of children with JIA who have filled a prescription for a biologic drug in the last year. We are writing you because you have been identified as potentially eligible for this study. If you choose to participate you will have the opportunity to contribute valuable information related to the importance of many biologic drug characteristics as well as your views on the cost of treatment.
Heather Burnett, a Hospital for Sick Children’s researcher, will be in contact with you to explain the questionnaires in greater detail, answer any questions you may have, and determine your interest in this study. Participation is completely voluntary. If you decide not to participate, you will receive the same care at the Hospital for Sick Children. We are very excited about this study and hope that you will consider taking part in this wonderful opportunity.
If you do not wish to be contacted please call or e-mail Heather Burnett at:
Tel: (416)-813-7654 Ext. 4858 [email protected]
Sincerely,
Rheumatologist at Sick Kids
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Appendix 5: Study information sheet sent along with recruitment letter
Title of Research Project: Parental Preferences for Drug Treatments in Juvenile Idiopathic Arthritis
Investigators: Principal Investigator: Wendy Ungar (416) 813-8519 Co-Investigators: Brian Feldman Dean Regier Heather Burnett Boris Hugle
Purpose of the Research: To determine parents preferences for characteristics of drugs used to treat a child with juvenile idiopathic arthritis. Description of Research: We are asking for the participation of parents of children with JIA who have been prescribed a drug treatment for their condition in the last three years. If you meet this criteria and wish to participate in the study you will be asked to fill in a questionnaire that should take approximately 30 minutes to complete.
A study package will be delivered by mail and will contain the questionnaire as well as information required to log-in to a secure website for those who wish to complete and submit the questionnaires online. Each study package will be assigned a unique study number that will be used to identify you and your responses.
The questionnaire will be arranged in three parts. Section one will describe two anonymous drug options (drug A and drug B) and ask you to choose one. This will be repeated for 16 different pairs. Section two will ask you about the health of your child with arthritis including for example their age, current and past drug therapies, and daily functioning. The third section will ask you about your family, including for example your marital status, ethnic or cultural background, and current prescription drug coverage.
Please note that completion of the questionnaires implies that you consent to take part in this research.
Stamped addressed envelopes will be supplied in the study package to return questionnaires completed by hand. Questionnaires completed online, will have the user ID and password removed from the system after results are submitted.
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Potential Harms: We know of no harm that taking part in this study could cause you. Potential Discomforts or Inconvenience: The time commitment asked of you to participate in this study is the only potential discomfort. Potential Benefits: Your participation in this study may benefit you directly by giving you an opportunity to think and learn more about your child’s arthritis and explore your feelings about treatment options and costs.
Study results will be published and communicated to members of the arthritis community through presentations, handouts, and newsletters. Knowledge gained from this study will be used to help inform decisions surrounding the importance and cost of treatments for JIA and how they impact families in Ontario. Confidentiality: Your privacy will be respected during all stages of this research. No information about who you are or who your child is will be given to anyone or used in publications, unless required by law. The data produced from this study will be stored in a secure, locked location. Only members of the research team will have access to the data. Following completion of the research study the data will be kept as long as required and then destroyed as required by Sick Kids policy. Participation: It is your choice to take part in this study. If you agree to participate you may withdraw at any time. The care you receive at Sick Kids will not be affected in any way by participating or not participating in this study. If new information that might affect your decision to take part in this study arises through the research, you will be informed. In this event you will be asked whether or not you wish to continue with the study. You completing these questionnaires does not interfere with your legal rights in any way. The staff involved in the study and the Hospital for Sick Children are legally and professionally responsible for what they do. Sponsorship: This research is funded by the Ontario Ministry of Health and Long-Term Care Drug Innovation Fund. This grant funds research that generates high-quality evidence of the value of medicines across the health care system and supports drug policy decision making. For more information about this source of funding please see: http://www.health.gov.on.ca/english/public/updates/archives/hu_07/hu_innovation_fund.html Conflict of Interest: Dr. Ungar, and the other research team members, have no conflict of interest to declare.
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Appendix 6: Cover letter that accompanied each survey
Title of Research Project: Parents’ Preferences for Drug Treatments in Juvenile Idiopathic Arthritis Dear Study Participant, The Hospital for Sick Children is currently studying parents’ preferences for drug treatments used in juvenile idiopathic arthritis. This letter will explain to you the purpose of our research and how it is conducted. You have received this package because you are eligible and interested in our research. Thank you for your interest and the time you have given to us. Please read this letter first and if you agree to be part of our study, complete the survey. There are two options for completing the survey, by mail or on-line. If you choose to complete it by mail, please fill out the attached survey and return it to us using the stamped envelope provided. If you choose to complete it on-line, please type www.sickkids.ca/surveydotnet/ into your web-browser and then enter the Survey ID listed on the top right hand corner of your question booklet. You have also been assigned a unique User ID that must be entered in order to begin the survey. The User ID can be found below the Survey ID on the top right hand corner of your question booklet. By completing and submitting the survey you are saying that you have read this letter and agree to be a participant in the study.
Why are we conducting this study? Parents of children with juvenile idiopathic arthritis are often faced with difficult decisions surrounding the best treatment for their child. Understanding which treatment characteristics are most important to parents, and what level of spending is acceptable, can help inform decisions about prescription drug programs for children and how they impact families in Ontario. What does the study involve? Your participation is voluntary After receiving a description of the study from your child’s rheumatologist at Sick Kids, you agreed to participate and were mailed this study package. Each study package contains this letter, the survey, and a stamped envelope for returning the survey if you choose the mail option. If you still agree to participate after reading this letter, please complete the survey. If you do not agree, do not complete the survey.
The survey The survey will asks you to make choices between two scenarios. Each scenario will describe a drug treatment and a health outcome linked to taking that drug. As you complete the survey we ask that you not think about your own child, but instead apply the knowledge and experience you have with your child’s arthritis to help you select the drug you like best. After 16 choice questions, there are 24 personal questions. The survey could take up to 30 mins. to complete.
.If the survey is not returned within 3 weeks, the study coordinator will contact you to remind you about the survey and ask if you have any questions about it. If the survey is not returned after another 3 weeks, you will receive a second reminder telephone call. If the survey is not returned after the second reminder telephone call, we will assume that you are not interested in participating in the study and will not call you again.
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Your privacy and confidentiality We respect your privacy. Each survey is assigned a Study ID so that your name, address, and telephone number are not attached to the completed survey. Your personal information is only recorded in a study binder, kept in a locked office. The information collected in this survey will not be used for any other purpose. No information about who you are will be given to anyone or be published without your permission, unless required by law. The data produced from this study will be stored in a secure, locked location. Only members of the research team will have access to the data. Following completion of the research study the data will be kept as long as required then destroyed as required by Sick Kids policy. Published study results will not reveal your identity. What are the potential benefits and harms of participating in the study? We know of no harm that taking part in this study could cause you. The results of this study may benefit you directly by giving you the opportunity to think and communicate preferences for drug treatments used in juvenile arthritis. Additionally, you might become more aware of alternative treatments or alternative health and quality of life outcomes that relate to treatments for juvenile idiopathic arthritis. What happens if you change your mind about participating in the study? You choose to take part in this study. You can stop at any time. The care your child gets at Sick Kids will not be affected in any way should you decide to take part in this study or not. New information that we get while we are doing this study may affect your decision to take part. If this happens, we will tell you about this new information and we will ask you again if you still want to be in the study. Will you be able to see the results of the study? We would like to share the findings of the study with you. Once the study is completed the findings will be posted on the TASK website at: http://pede.ccb.sickkids.ca/pede/task.jsp Who is conducting this study? The Principal Investigator has received financial support from the Ontario Ministry of Health and Long-Term Care Drug Innovation Fund. Who do you contact if you have questions about the study? Please contact us if you have any questions at any time.
Study Coordinator: Principal Investigator: Heather Burnett, MSc Wendy Ungar, PhD (416)-813-7654 Ext. 4858 (416) 813-7654 ext. 3487 [email protected]
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Appendix 7: Tables of demographic and child health results from the pilot
study
Demographic characteristics of pilot study participants
Characteristics n Percentage/ Mean (SD)
Age 10 44.8 (4.78) Relationship to Child Mother 13 100 Marital Status Married or Common-law 11 84.6 Divorced 2 15.4 Canadian Citizen Yes, born in Canada 11 84.6 Yes, lived in Canada for > 15 years 2 15.4 Education University or College Degree 8 61.5 Post-graduate or advance degree 5 38.5 Employment Full-time 7 53.8 Part-time 4 30.8 Homemaker 2 15.4 Insurance used to pay for child’s arthritis drugs Private insurance provided by employer 8 61.5 None 3 23.1 Trillium Drug Program 1 7.7 Ontario Drug Benefits Program 1 7.7 Household size 13 4.38 (0.65) Household income Over $120,000 9 69.2 $100,000-120,000 2 15.4 $60,000-70,000 1 7.7 $20,000-30,000 1 7.7
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Characteristics of pilot study participants’ children with JIA
Characteristics n Percentage/ Mean (SD %)
Age 13 13.7 (3.04) Gender Female 9 69.2 Male 4 30.8 Years since JIA diagnosis 13 7.5 (3.95) Current JIA sub-type Polyarticular 7 53.8 Oligoarticular 3 23.1 Psoriatic 1 7.7 Enthesitis-Related 1 7.7 Undifferentiated 1 7.7 Medication used in the last month Yes 11 84.6 No 2 15.4 Type of medication prescribed in the last year DMARDs 9 52.9 NSAIDs 4 23.5 Biologics 4 23.5 Discontinued treatment Yes 13 100 Reasons for discontinued treatment Side-effect(s) 8 42.1 Stopped being effective over time 6 31.6 Not effective 5 26.3 Reported treatment side-effects Nausea 10 76.9 Stomach Pain 10 76.9 Headache 7 53.8 Rash 1 7.7 Injection site reaction 1 7.7 Current level of activity compared to other kids of the same age Moderately less active 8 61.5 Equally active 4 30.8 Moderately more active 1 7.7 Days missed from school per month 13 1.62 (1.50)
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Appendix 8: The survey instrument used in the main study to elicit parents’ preferences for drug treatments in JIA
Parents’ preferences for drug treatments in juvenile idiopathic arthritis
QUESTION BOOKLET
INTRODUCTION TO THE SURVEY
This survey will help us understand how you value drugs used to treat a child with juvenile idiopathic arthritis.
The survey will ask you to make choices between two scenarios. Each scenario will describe a drug treatment. It will also show a health outcome linked to taking the drug for one month. Please read both scenarios carefully. Please check Scenario A or Scenario B to show the option you think is better. You will also be asked how certain you are with your answer. There are no right or wrong choices.
There will be a total of 16 choice questions. There will also be 24 questions about your child and family. These questions will help us understand your answers to the choice questions. The survey may take up to 30 minutes to complete.
Please remember that submitting this survey implies that you consent to take
part in this research.
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Before we begin, please tell us a bit about yourself:
What is your current age? _______
Prefer not to answer What is your gender?
Male
Female
Prefer not to answer
What are the first 3 digits of your postal code? ________
DESCRIPTION OF TREATMENT CHARACTERISTICS
Please read carefully. The following pages will help you complete the rest of the questionnaire.
The scenarios presented will differ according to six characteristics. Below is a list and description of
these characteristics.
Characteristics that are not listed should be considered the same in all scenarios.
1. Drug treatment Arthritis drugs can be given in many different ways:
• Pill: swallowed • Injection: given under the skin • Intravenous (I.V.): delivered at the hospital or clinic over a period of 3-7 hours
The drug treatment will be:
i. Self or parent managed pill four days per month ii. Self or parent managed injection four days per month iii. Doctor or nurse managed I.V. one day per month
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2. Child reported pain from arthritis During a month of treatment a child will describe their average level of pain as:
i. None ii. Mild iii. Moderate iv. Severe
3. Side-effects The side-effects from taking the drug will be:
i. None ii. Headaches four days per month iii. Nausea four days per month iv. Stomach pain four days per month
4. Participation in daily activities
Daily activities can include:
• Self care: bathing, dressing, eating, etc. • School: writing, typing, recess, etc. • Extra-curricular: socializing with others, sports, hobbies, etc.
During a month of treatment a child will be able to participate in these activities:
i. Without any difficulty ii. With some difficulty iii. With much difficulty iv. Unable to participate
Please note that pain is not the only reason a child might have difficulty participating in daily activities; stiffness, decreased mobility, and treatment side-effects (e.g. fatigue, nausea, etc.) can also limit a child’s daily ability.
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5. Days missed from school (including pre-school or daycare) During treatment the child will miss:
i. Half a day per month ii. One day per month iii. Two days per month iv. Four days per month
6. Cost to you This refers to the maximum amount of money you would be willing to pay out of pocket considering your real monthly household budget. The value you select will let us know how much you value different characteristics. The cost to you will be:
i. $50 per month ii. $700 per month iii. $1500 per month iv. $2100 per month
All other characteristics of the drug and health outcomes are the same in all scenarios
A word of caution.
PLEASE READ!
Studies similar to this one show that people tend to overstate how much they are willing-to-pay for a hypothetical treatment, especially when the
treatment is for a child. When this happens the true value of a drug cannot be measured.
When completing the following choice questions we ask that you carefully
consider the cost characteristic and respond as if you would actually have to pay the extra amount each month.
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EXAMPLE OF A CHOICE QUESTION
Below is an example of what a choice will look like.
Characteristics Scenario A Scenario B
Drug treatment Self or parent managed injection four days per month
Doctor or nurse managed I.V. one day per month
Child reported pain from arthritis Severe Mild
Side-effects Stomach pain four days per month None
Participation in daily activities With some difficulty Without any difficulty
Days missed from school One day per month Four days per month
Cost to you $700 per month $1500 per month
After reading each scenario, you will be asked:
Which would you choose? Scenario A Scenario B
How certain are you? Very Somewhat Somewhat Very uncertain uncertain 5 certain certain 0 Uncertain Neither Certain 10 certain nor uncertain The parent that answered this question liked what was described in Scenario B BETTER than what was described in Scenario A. They made this decision by first reading the descriptions of each scenario separately and then comparing Scenario A and Scenario B. They thought about the real-life consequences each scenario would have on their family. They were certain with their choice.
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START HERE Imagine a child with arthritis that is about to start a new drug treatment. You have been presented with a picture of what this child’s life might look like over the next month.
CHOICE QUESTION # 1
Characteristics Scenario A Scenario B
Drug treatment Doctor or nurse managed I.V. one day per month
Self or parent managed injection four days per month
Child reported pain from arthritis Moderate None
Side-effects Nausea four days per month Headache four days per month
Participation in daily activities With some difficulty With much difficulty
Days missed from school Two days per month Half-a-day per month
Cost to you $50 per month $2100 per month
Which would you choose? Scenario A Scenario B How certain are you?
Very Somewhat Somewhat Very uncertain uncertain 5 certain certain 0 Uncertain Neither Certain 10 certain nor uncertain
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CHOICE QUESTION # 2
Characteristics Scenario A Scenario B
Drug treatment Self or parent managed injection four days per month
Self or parent managed pill four days per month
Child reported pain from arthritis Moderate Severe
Side-effects Stomach pain four days per month Headache four days per month
Participation in daily activities Unable to participate With some difficulty
Days missed from school One day per month Two days per month
Cost to you $1500 per month $2100 per month
Which would you choose? Scenario A Scenario B
How certain are you?
Very Somewhat Somewhat Very uncertain uncertain 5 certain certain 0 Uncertain Neither Certain 10
certain nor uncertain
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CHOICE QUESTION # 3
Characteristics Scenario A Scenario B
Drug treatment Self or parent managed injection four days per month
Doctor or nurse managed I.V. one day per month
Child reported pain from arthritis Moderate Mild
Side-effects Headache four days per month None
Participation in daily activities Without any difficulty With much difficulty
Days missed from school Four days per month One day per month
Cost to you $1500 per month $700 per month
Which would you choose? Scenario A Scenario B
How certain are you?
Very Somewhat Somewhat Very uncertain uncertain 5 certain certain 0 Uncertain Neither Certain 10 certain nor uncertain
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CHOICE QUESTION # 4
Characteristics Scenario A Scenario B
Drug treatment Self or parent managed pill four days per month
Doctor or nurse managed I.V. one day per month
Child reported pain from arthritis Moderate Severe
Side-effects Stomach pain four days per month None
Participation in daily activities With much difficulty Unable to participate
Days missed from school Four days per month Half-a-day per month
Cost to you $2100 per month $50 per month
Which would you choose? Scenario A Scenario B
How certain are you?
Very Somewhat Somewhat Very uncertain uncertain 5 certain certain 0 Uncertain Neither Certain 10 certain nor uncertain
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CHOICE QUESTION # 5
Characteristics Scenario A Scenario B
Drug treatment Self or parent managed injection four days per month
Doctor or nurse managed I.V. one day per month
Child reported pain from arthritis Moderate Mild
Side-effects Headache four days per month Stomach pain four days per month
Participation in daily activities With some difficulty Without any difficulty
Days missed from school One day per month Four days per month
Cost to you $1500 per month $2100 per month
Which would you choose? Scenario A Scenario B
How certain are you?
Very Somewhat Somewhat Very uncertain uncertain 5 certain certain 0 Uncertain Neither Certain 10 certain nor uncertain
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CHOICE QUESTION # 6
Characteristics Scenario A Scenario B
Drug treatment Self or parent managed injection four days per month
Self or parent managed pill four days per month
Child reported pain from arthritis Severe Moderate
Side-effects None Stomach pain four days per month
Participation in daily activities With much difficulty Unable to participate
Days missed from school Two days per month Half-a-day per month
Cost to you $700 per month $1500 per month
Which would you choose? Scenario A Scenario B
How certain are you?
Very Somewhat Somewhat Very uncertain uncertain 5 certain certain 0 Uncertain Neither Certain 10 certain nor uncertain
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CHOICE QUESTION # 7
Characteristics Scenario A Scenario B
Drug treatment Doctor or nurse managed I.V. one day per month
Self or parent managed injection four days per month
Child reported pain from arthritis None Mild
Side-effects None Stomach pain four days per month
Participation in daily activities Without any difficulty With some difficulty
Days missed from school Four days per month Two days per month
Cost to you $1500 per month $50 per month
Which would you choose? Scenario A Scenario B
How certain are you?
Very Somewhat Somewhat Very uncertain uncertain 5 certain certain 0 Uncertain Neither Certain 10 certain nor uncertain
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CHOICE QUESTION # 8
Characteristics Scenario A Scenario B
Drug treatment Self or parent managed pill four days per month
Doctor or nurse managed I.V. one day per month
Child reported pain from arthritis Moderate None
Side-effects Nausea four days per month Headache four days per month
Participation in daily activities With much difficulty With some difficulty
Days missed from school Half-a-day per month One day per month
Cost to you $50 per month $2100 per month
Which would you choose? Scenario A Scenario B
How certain are you?
Very Somewhat Somewhat Very uncertain uncertain 5 certain certain 0 Uncertain Neither Certain 10 certain nor uncertain
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CHOICE QUESTION # 9
Characteristics Scenario A Scenario B
Drug treatment Doctor or nurse managed I.V. one day per month
Self or parent managed injection four days per month
Child reported pain from arthritis Mild Moderate
Side-effects None Nausea four days per month
Participation in daily activities With some difficulty Without any difficulty
Days missed from school Half-a-day per month Two days per month
Cost to you $50 per month $700 per month
Which would you choose? Scenario A Scenario B
How certain are you?
Very Somewhat Somewhat Very uncertain uncertain 5 certain certain 0 Uncertain Neither Certain 10 certain nor uncertain
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CHOICE QUESTION # 10
Characteristics Scenario A Scenario B
Drug treatment Self or parent managed pill four days per month
Self or parent managed injection four days per month
Child reported pain from arthritis Moderate None
Side-effects Headache four days per month Stomach pain four days per month
Participation in daily activities With much difficulty With some difficulty
Days missed from school Half-a-day per month One day per month
Cost to you $700 per month $50 per month
Which would you choose? Scenario A Scenario B
How certain are you?
Very Somewhat Somewhat Very uncertain uncertain 5 certain certain 0 Uncertain Neither Certain 10 certain nor uncertain
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CHOICE QUESTION # 11
Characteristics Scenario A Scenario B
Drug treatment Doctor or nurse managed I.V. one day per month
Self or parent managed injection four days per month
Child reported pain from arthritis Moderate Mild
Side-effects Headache four days per month Nausea four days per month
Participation in daily activities Unable to participate With much difficulty
Days missed from school Two days per month Four days per month
Cost to you $700 per month $1500 per month
Which would you choose? Scenario A Scenario B
How certain are you?
Very Somewhat Somewhat Very uncertain uncertain 5 certain certain 0 Uncertain Neither Certain 10 certain nor uncertain
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CHOICE QUESTION # 12
Characteristics Scenario A Scenario B
Drug treatment Self or parent managed pill four days per month
Doctor or nurse managed I.V. one day per month
Child reported pain from arthritis Moderate Severe
Side-effects Stomach pain four days per month Nausea four days per month
Participation in daily activities With much difficulty Unable to participate
Days missed from school Four days per month Half-a-day per month
Cost to you $2100 per month $700 per month
Which would you choose? Scenario A Scenario B
How certain are you?
Very Somewhat Somewhat Very uncertain uncertain 5 certain certain 0 Uncertain Neither Certain 10 certain nor uncertain
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CHOICE QUESTION # 13
Characteristics Scenario A Scenario B
Drug treatment Self or parent managed pill four days per month
Self or parent managed injection four days per month
Child reported pain from arthritis Mild None
Side-effects Nausea four days per month Stomach pain four days per month
Participation in daily activities Without any difficulty With some difficulty
Days missed from school Four days per month One day per month
Cost to you $700 per month $50 per month
Which would you choose? Scenario A Scenario B
How certain are you?
Very Somewhat Somewhat Very uncertain uncertain 5 certain certain 0 Uncertain Neither Certain 10 certain nor uncertain
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CHOICE QUESTION # 14
Characteristics Scenario A Scenario B
Drug treatment Doctor or nurse managed I.V. one day per month
Self or parent managed pill four days per month
Child reported pain from arthritis None Mild
Side-effects Nausea four days per month None
Participation in daily activities Without any difficulty With some difficulty
Days missed from school Two days per month One day per month
Cost to you $2100 per month $700 per month
Which would you choose? Scenario A Scenario B
How certain are you?
Very Somewhat Somewhat Very uncertain uncertain 5 certain certain 0 Uncertain Neither Certain 10 certain nor uncertain
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CHOICE QUESTION # 15
Characteristics Scenario A Scenario B
Drug treatment Self or parent managed injection four days per month
Self or parent managed pill four days per month
Child reported pain from arthritis Mild None
Side-effects Headache four days per month None
Participation in daily activities Without any difficulty With much difficulty
Days missed from school Four days per month Two days per month
Cost to you $700 per month $1500 per month
Which would you choose? Scenario A Scenario B
How certain are you?
Very Somewhat Somewhat Very uncertain uncertain 5 certain certain 0 Uncertain Neither Certain 10 certain nor uncertain
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CHOICE QUESTION # 16
Characteristics Scenario A Scenario B
Drug treatment Self or parent managed pill four days per month
Doctor or nurse managed I.V. one day per month
Child reported pain from arthritis Severe Moderate
Side-effects None Headache four days per month
Participation in daily activities With much difficulty Unable to participate
Days missed from school Two days per month Four days per month
Cost to you $1500 per month $50 per month
Which would you choose? Scenario A Scenario B
How certain are you?
Very Somewhat Somewhat Very uncertain uncertain 5 certain certain 0 Uncertain Neither Certain 10 certain nor uncertain
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You are now finished with the choice questions!
Please rank the characteristics used to describe the treatment scenarios from
‘most important’ (1) to ‘least important’ (6). Please assign one unique value
per characteristic.
Drug treatment
Child reported pain from arthritis .
Side-effects
Participation in daily activities
Days missed from school
Cost to you
QUESTIONS ABOUT YOUR CHILD WITH ARTHRITIS
1. What is your child’s gender? Male Female
2. What is your child’s age? _________________ years old
3. Around what age was your child diagnosed with arthritis? ________ years old
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4. Some children with JIA also have other related health problems. Please indicate which of the problems listed below your child has had that has been confirmed by a doctor or other health care provider. Please check all that apply:
My child has had no other related health problems
Abnormal blood count Low bone mass Inflammation of the eye Psoriasis High blood pressure Stomach or intestinal problems Allergies Diabetes Swelling in areas where muscle meets bone Depression Neurological disease
Other(s)(please specify:_______________________________________)
5. What is the current disease sub-type used to describe your child’s CURRENT condition? Please check one.
Systemic − arthritis preceded by a fever and one of: rash, enlarged liver or heart
Oligoarthritis
− 1 to 4 affected joints during the first 6 months of disease
Polyarticular − 5 or more affected joints during the first 6 months of disease
Psoriatic
− arthritis and psoriasis
Enthesitis-related − arthritis and psoriasis or arthritis and other symptoms: swollen
fingers or toes, nail pitting, psoriasis in a first relative
Undifferentiated − unique disease description
Not sure
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6. In the last MONTH, has your child used any medication specifically for arthritis?
Yes No Not sure
7. Which prescription medications have been prescribed in the last YEAR for managing your child’s arthritis? This can include pill/capsules, injections (needles), and intravenous drugs delivered at the hospital or clinic. It may be helpful to consult the label(s) of your child’s current medicines. Enbrel™ (Etanercept) Actemra™ (Tocilizumab) Rituxan™ (Rituximab) Methotrexate™ (Methotrexate) Humira™ (Adalimumab) Arava™ (Leflunomide) Remicade™ (Infliximab) Naprosyn™ (Naproxen) Kineret™ (Anakinra) Winpred™ (Prednisone) Orencia™ (Abatacept) Celebrex™ (Celecoxib) None Other(s) (please specify in space below)
8. Since being diagnosed with arthritis has your child had to discontinue a
treatment? Please check no or all that apply:
No. Yes, because it was not effective. Yes, because it stopped being effective over time. Yes, because of a serious side-effect. Yes, because it worked and treatment was no longer required. Yes, for a reason not listed (please specify: ___________________________________________________________)
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9. Which of the following side-effect(s) has your child experienced in the last year
because of an arthritis treatment? Please check all that apply:
Nausea Headache Stomach pain Rash Redness or swelling at the injection site Infection at the injection site Infection of the airways Other(s) (please specify: ___________________________________________________________)
10. In the last MONTH, how many days of school, pre-school or daycare has your child missed because of arthritis? Include days missed because of visits to the doctor or clinic.
_____________ days
11. In the last MONTH, how many days of usual activity or work have you or another caregiver missed because of your child’s arthritis?
_____________ days
12. Compared to other kids your child’s age, what is your child’s CURRENT level of physical activity taking his or her arthritis into consideration?
Much more active Moderately more active Equally active Moderately less active Not sure
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QUESTIONS ABOUT YOU AND YOUR FAMILY
13. What is your relationship to the child with arthritis?
Mother Guardian Father Sister Stepmother Brother Stepfather Other (please specify:____________________________________)
14. Including yourself, how many persons are there living in your home? _____________ persons
15. Are you a Canadian citizen?
Yes, I was born in Canada. Yes, I have lived in Canada for 15 years or more. Yes, I have lived in Canada for less than 15 years. No. 16. Is there an ethnic or cultural group that you most strongly identify with?
Please check one.
No Canadian Ukranian Jewish French Dutch South Asian English Polish Lebanese Chinese Greek Portuguese Italian Micmac Vietnamese German Metis Filipino Scottish Inuit Chilean Irish Cree Jamaican
Other (please specify: ______________________________________)
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17. What is your marital status?
Married or living common-law Widowed or widower Single (never been married) Separated or divorced
18. What is the highest level of education that you have attained?
Elementary school (some or completed) Some secondary/ high school Completed secondary/high school Some post-secondary (university or college) Received university or college degree/diploma Received post-graduate degree
19. Which best describes your current employment status?
Employed full-time Employed part-time Self employed full-time Self employed part-time Unemployed Receiving social assistance Student Receiving disability or retirement pension Homemaker
20. Which of the following drug plans cover all or part of the cost of your child’s arthritis medications? Please check all that apply:
Private Insurance provided by employer Private insurance purchased independently Trillium Drug Program Ontario Drug Benefits Program Other (please speficy:____________________________________) None
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21. This question will help us get a better sense of the type of cost-sharing arrangements you have with your drug plan(s) to help pay for the cost of your child’s arthritis medications. Please check all that apply.
You are required to pay a set amount of money (deductible) before your drug plan provides reimbursement or benefits.
You pay a set amount of money for each prescription (fixed co-payment or dispensing fee) and your drug plan covers the rest.
You pay a percentage of the cost of each prescription (percentage co-payment) and your drug plan covers the rest. You are entitled to a set dollar amount of drug coverage each year (annual cap).
None of the above
22. This last question asks about your household income. While the information is very private (and will be kept confidential), answering the question will help us understand reasons for differences in arthritis management between income groups. Please indicate the best estimate of your total annual household income, before taxes, including all household members.
No income $80,000 to $89,999 Less than $20,000 $90,000 to $99,999 $20,000 to $29,999 $100,000 to $119,999 $30,000 to $39,999 $120,000 to $150,000 $40,000 to $49,999 $150,000 to $200,000 $50,000 to $59,999 Greater than $200,000 $60,000 to $69,999 Not sure $70,000 to $79,999 Prefer not to respond
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THE END
Thank-you very much for taking the time to complete our survey! Please mail this booklet back to us in the stamped envelope provided.
If you would like to provide us with feedback or comments about the survey, please do
so in the space below.
________________________________________________________________
________________________________________________________________
________________________________________________________________
________________________________________________________________
________________________________________________________________
________________________________________________________________
________________________________________________________________
________________________________________________________________
________________________________________________________________
________________________________________________________________
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Appendix 9: The Hospital for Sick Children Research Ethics Board Approval
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Appendix 10: Results from regression model including significant child health variables
Note - only significant interaction terms are listed.
Attribute Levels Beta SE Pr > Chi Square
Child reported pain from arthritis
None 1.0396 0.2341 <.0001 Mild 0.3831 0.2027 0.0588 Moderate -0.0896 0.1699 0.5978 Severe† -1.3332 - -
Participation in daily activities
With no difficulty 1.1996 0.2653 <.0001 With some difficulty 0.6030 0.1881 0.0014 With much difficulty -0.3248 0.1835 0.0768 Unable to participate† -1.4777 - -
Cost to you ψ
$50 per month -0.0420
0.0001 <.0001 $700 per month -0.5881 $1500 per month -1.2602 $2100 per month -1.7642
Side-effects
None 0.6388 0.0693 <.0001 Headache four days per month -0.1180 0.0585 0.0436 Nausea four days per month -0.1343 0.0651 0.0391 Stomach pain four days per month† -0.3866 - -
Days missed from school ψ
Half day per month -0.0371
0.0402 0.0653 One day per month -0.0742 Two days per month -0.1483 Four days per month -0.2967
Drug treatment Injection four days per month 0.1653 0.0499 0.0009 Pill four days per month -0.0496 0.0504 0.3257 I.V. once per month† -0.1158 - -
Years with JIAψ*No difficulty participating 0.14194 0.0265 <.0001 Male child* Days missed from school 0.1079 0.0319 0.0007 Child ageψ*No difficulty participating -0.0730 0.0216 0.0007 Male child*No difficulty participating -0.2804 0.0850 0.0010 Less active child*Much difficulty participating 0.2017 0.0686 0.0033 Meds in last year*Cost to you 0.0001 4.8E-5 0.0046 Child ageψ *Moderate pain 0.0355 0.0138 0.0102 Child taking DMARD or biologic* Much difficulty participating 0.1619 0.0646 0.0122 Less active child*Moderate pain 0.1339 0.0662 0.0432 Child taking DMARD or biologic* Pill four times per month 0.0986 0.0503 0.0499
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Appendix 11: Results from regression model including significant demographic variables
Note - only significant interaction terms are listed.
Attribute Levels Beta SE Pr > Chi Square
Child reported pain from arthritis
None 1.0468 0.1130 <.0001 Mild 0.7206 0.0884 <.0001 Moderate 0.1953 0.0583 0.0008 Severe† -1.9627 - -
Participation in daily activities
With no difficulty 1.2993 0.1544 <.0001 With some difficulty 0.5327 0.0724 <.0001 With much difficulty -0.3969 0.0725 <.0001 Unable to participate† -1.4351 - -
Cost to you ψ
$50 per month -0.0322
0.0001 <.0001 $700 per month -0.4511 $1500 per month -0.9666 $2100 per month -1.3532
Side-effects
None 0.5917 0.0717 <.0001 Headache four days per month -0.1027 0.0615 0.0950 Nausea four days per month -0.1370 0.0685 0.0456 Stomach pain four days per month† -0.3520 - -
Days missed from school ψ
Half day per month -0.0588
0.0387 0.0024 One day per month -0.1176 Two days per month -0.2352 Four days per month -0.4704
Drug treatment Injection four days per month 0.0034 0.0497 0.9453 Pill four days per month 0.1233 0.0488 0.0115 I.V. once per month† -0.1267 - -
Mid household income*Cost to you -0.0012 1.43E-4 <.0001 Low household income*Cost to you -4.4E-4 1.64E-4 0.0021 Public drug coverage*Cost to you 5.98E-4 1.64E-4 0.0003 High household income*Much difficulty participating -0.2064 0.0660 0.0018 Monthly drug spendingψ *Much difficulty participating 0.0003 1.36E-4 0.0268
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Appendix 12: Marginal and incremental WTP for main study attribute levels based on main effects model
† denotes reference level ψ denotes continuous coding
Attribute Levels WTP ($)
95% CI WTP ($)
∆ WTP ($)
95% CI ∆ WTP ($)
Lower Upper Lower Upper
Child reported pain from arthritis
None 1,291 1,066 1,552 426 230 669 Mild 865 650 1,084 590 362 833 Moderate 274 109 444 2704 2,176 3,405
Severe† -2,430 -
2,876 -1,993 -
Participation in daily activities
With no difficulty 1,718 1,400 2,087 1,050 709 1,453 With some difficulty 669 501 842 1,288 992 1,656 With much difficulty -619 -802 -440 1,149 828 1,544
Unable to participate† -1,768 -
2,111 -1,468 -
Cost to you ψ
$50 per month
- - - - $700 per month $1500 per month $2100 per month
Side-effects
None 788 618 957 934 643 1,291 Headache four days per month -146 -267 -20 18 -175 215 Nausea four days per month -164 -304 -33 315 79 556 Stomach pain four days per month† -478 -627 -319
-
Days missed from school ψ
Half day per month -88 -139 -38 88 41 137 One day per month -176 -278 -76 176 81 275 Two days per month -353 -555 -151 353 163 549
Four days per month -706 -
1,110 -303 -
Drug treatment
Injection four days per month 196 90 297 241 58 422 Pill four days per month -44 -149 73 108 -97 324 I.V. once per month† -152 -281 -30 -
179
Appendix 13: Results from regression model including effects coded continuous variables and all significant interaction terms
Note – only significant interaction terms are listed.
Attribute Levels Beta SE Pr > Chi Square
Child reported pain from arthritis
None 0.9756 0.2410 <.0001 Mild 0.4948 0.2044 0.0155 Moderate -0.1983 0.1683 0.2386 Severe† -1.2720 - -
Participation in daily activities
With no difficulty 1.6143 0.2870 <.0001 With some difficulty 0.5720 0.1895 0.0025 With much difficulty -0.5675 0.1885 0.0026 Unable to participate† -1.6188 - -
Cost to you ψ
$50 per month 0.7216 0.1109 <.0001 $700 per month 0.1557 0.0694 0.0249 $1500 per month -0.0994 0.0754 0.1874 $2100 per month -0.7780 - -
Side-effects
None 0.6282 0.0762 <.0001 Headache four days per month -0.0946 0.0645 0.1425 Nausea four days per month -0.1443 0.0747 0.0533 Stomach pain four days per month† -0.3892
Days missed from school ψ
Half day per month 0.0678 0.0823 0.4102 One day per month 0.0475 0.0728 0.5142 Two days per month -0.0442 0.0673 0.5111 Four days per month -0.0710 - -
Drug treatment Injection four days per month 0.1396 0.0515 0.0067 Pill four days per month 0.0155 0.0529 0.7700 I.V. once per month† -0.1550 - -
Mid Income*Cost to you -0.0012 0.0002 <.0001 Low Income*Cost to you -0.0005 0.0002 0.0020 Years with JIA * Without any difficulty 0.1450 0.0287 <.0001 Child Age*Without any difficulty -0.1074 0.0240 <.0001 Male* Days missed from school 0.1406 0.0355 <.0001 High Income* With much difficulty -0.2260 0.0706 0.0014 Public Coverage*Cost 0.0005 0.0002 0.0041 Child Age* Moderate pain 0.0353 0.0145 0.0147 Male*With no difficulty -0.2313 0.0951 0.0150 DrugSpending*With much difficulty 0.0003 0.0001 0.0383 BioDMARD*Moderate Pain 0.1669 0.0723 0.0210
180
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