The Economic And Health Burden Of Chronic Pain In Ontario
by
Mary-Ellen Hogan
A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy
Graduate Department of Pharmaceutical Sciences University of Toronto
© Copyright by Mary-Ellen Hogan 2017
ii
The Economic And Health Burden Of Chronic Pain In Ontario
Mary-Ellen Hogan
Doctor of Philosophy
Graduate Department of Pharmaceutical Sciences University of Toronto
2017
Abstract
Chronic pain is a common problem, affecting 1 in 5 Canadians. Despite this, the burden of
chronic pain has not been fully investigated in Canada. This research sought to characterize the
burden of chronic pain in Ontario, Canada in three studies. Individuals with and without chronic
pain who were identified through the Canadian Community Health Survey (CCHS) were
matched using propensity score methods on demographic and comorbidity variables using linked
healthcare administrative data. The first study estimated the annual incremental cost to manage
chronic pain at $1,742 per person, 51% more than the control group. This translated into $2.8
billion (reference year 2014), or 5% of the Ontario publicly funded health budget.
The second study estimated health utilities from the CCHS as a measure of health-related quality
of life in people with chronic pain. Utilities are anchored by 0 (dead) and 1 (perfect health), with
iii
a difference of 0.03 being clinically meaningful. The mean utility for people with chronic pain
was estimated at 0.59 compared to 0.90 for those without. These utilities were lower than those
seen with most other chronic diseases including heart disease, diabetes and chronic obstructive
pulmonary disease.
The third study described mortality by following people until December 2013. There were
numerically more deaths in the chronic pain group (2,063 versus 1,722) and a higher death rate
for chronic pain as measured by the Kaplan Meier product limit estimator (p < 0.01). No
statistical difference in death from suicide was found (17 for chronic pain versus 20 for controls,
p = 0.74). Increased mortality in those with chronic pain does not appear to be related to suicide
and may be related to comorbidity that accompanies chronic pain. Taken together, the findings
from these studies can assist in setting research priorities, inform health policy and aid program
planning for chronic pain.
iv
Acknowledgments
This thesis is dedicated to the memory of my parents, Mel and Bonnie Hogan.
Firstly I would like to sincerely thank my co-supervisors Dr Murray Krahn and Dr Anna Taddio.
Dr Krahn provided wonderful support, advice and wise guidance, in addition to training in health
technology assessment during my journey through this work. Dr Taddio provided me with a
strong foundation of training in clinical research, journal submission, award application and
research project management. I also thank my committee members Dr Joel Katz and Dr Vibhuti
Shah for their expertise, advice and assistance that greatly improved the quality of my work.
I am grateful for financial support I received during my studies. I acknowledge the following
organizations for their generosity: The Hospital for Sick Children Clinician Scientist Training
Program, the Leslie Dan Faculty of Pharmacy, Canadian Institutes of Health Research (CIHR)
Strategic Training Initiative in Health Research for Pain in Child Health, the Ontario Ministry of
Training, Colleges and Universities (Ontario Graduate Scholarship), University of Toronto
Centre for the Study of Pain, the Canadian Pain Society, Toronto Health Economics and
Technology Assessment (THETA) Collaborative, Canadian Association for Health Services and
Policy Research, and the Canadian Agency for Drugs and Technologies in Health.
Lastly I would like to thank my friends and family. I wouldn’t be at this PhD finish line without
my friend Amy Bender, who did it first and then encouraged me to enter this marathon. I also
feel lucky to have made new friends at THETA and Pharmaceutical Sciences while pursuing my
PhD. They have provided stimulating discussion, laughter, encouragement and much needed
distraction. In addition, I am fortunate to be supported by a circle of friends and relatives who
have been there since before I returned to post-baccalaureate studies 11 years ago. I am grateful
for their friendship, for listening in the more challenging times, celebrating my successes and
milestones, sometimes counselling and always supporting me.
v
Table of Contents
Acknowledgments ........................................................................................................................ iv
Table of Contents ...........................................................................................................................v
List of Tables .............................................................................................................................. viii
List of Figures .................................................................................................................................x
List of Appendices ........................................................................................................................ xi
List of Abbreviations .................................................................................................................. xii
Chapter 1 ........................................................................................................................................1
1 Overview of thesis and background ............................................................................................1
1.1 Organization of thesis ..........................................................................................................1
1.2 What is chronic pain?...........................................................................................................1
1.3 Epidemiology .......................................................................................................................3
1.4 Cost-of-illness studies ..........................................................................................................5
1.5 Canadian studies on cost of chronic pain .............................................................................8
1.6 Health utilities ....................................................................................................................14
1.7 Mortality and chronic pain .................................................................................................18
1.8 Management of confounding in observational data ...........................................................19
1.9 Data sources .......................................................................................................................30
1.10 Rationale for research ........................................................................................................37
Chapter 2 ......................................................................................................................................40
2 Incremental Healthcare Costs In People With Chronic Pain ....................................................41
2.1 Abstract ..............................................................................................................................41
vi
2.2 Introduction ........................................................................................................................42
2.3 Methods..............................................................................................................................43
2.4 Results ................................................................................................................................48
2.5 Discussion ..........................................................................................................................51
2.6 Acknowledgements ............................................................................................................54
2.7 Tables .................................................................................................................................56
2.8 Figures................................................................................................................................58
Chapter 3 ......................................................................................................................................59
3 Health Utilities In People With Chronic Pain ...........................................................................60
3.1 Abstract ..............................................................................................................................60
3.2 Introduction ........................................................................................................................61
3.3 Methods..............................................................................................................................62
3.4 Results ................................................................................................................................69
3.5 Discussion ..........................................................................................................................72
3.6 Acknowledgements ............................................................................................................77
3.7 Tables .................................................................................................................................78
3.8 Figures................................................................................................................................83
Chapter 4 ......................................................................................................................................84
4 Mortality in People with Chronic Pain .....................................................................................85
4.1 Abstract ..............................................................................................................................85
4.2 Introduction ........................................................................................................................86
4.3 Methods..............................................................................................................................88
4.4 Results ................................................................................................................................95
vii
4.5 Discussion ..........................................................................................................................99
4.6 Acknowledgements ..........................................................................................................104
4.7 Tables ...............................................................................................................................106
4.8 Figures..............................................................................................................................110
Chapter 5 ....................................................................................................................................111
5 Summary of contribution ........................................................................................................111
5.1 Study summaries ..............................................................................................................111
5.2 Limitations .......................................................................................................................113
5.3 Strengths ..........................................................................................................................115
5.4 Implications......................................................................................................................117
5.5 Future research .................................................................................................................121
5.6 Conclusions ......................................................................................................................122
References ...................................................................................................................................123
Appendices ..................................................................................................................................146
viii
List of Tables
Table 1.1: Canadian studies of costs of specific types of chronic pain ....................................... 11
Table 1.2: Aggregated diagnostic groups (ADGs) ...................................................................... 28
Table 1.3: Charlson disease weights ............................................................................................ 29
Table 1.4: Summary of administrative claims databases and key variables used ....................... 33
Table 2.1: Clinical and demographic characteristics ................................................................... 56
Table 2.2: Annual healthcare costs .............................................................................................. 57
Table 2.3: Proportion of annual healthcare costs by category ..................................................... 57
Table 3.1: Clinical and demographic characteristics ................................................................... 78
Table 3.2: Utility estimates for people with chronic pain ............................................................ 79
Table 3.3: Utility decrement for chronic pain .............................................................................. 80
Table 3.4: Utilities - sensitivity analysis ...................................................................................... 81
Table 3.5: Utilities and utility decrements for other health conditions* ...................................... 82
Table 4.1: Clinical and demographic characteristics ................................................................. 106
Table 4.2: Cause of death – percent of deaths ........................................................................... 107
Table 4.3: Suicide attempts and death from suicide .................................................................. 109
Table B.1: ICD codes for painful conditions ............................................................................. 153
Table B.2: ICD codes for comorbid conditions ......................................................................... 154
Table B.3: Opioid drug identification numbers ......................................................................... 155
Table B.4: Baseline characteristics – matching algorithm without comorbidity match ............ 157
ix
Table B.5: Baseline characteristics – matching algorithm with painful conditions matched .... 158
Table B.6: Annual incremental cost by subgroup ...................................................................... 159
Table C.1: ICD codes for painful conditions ............................................................................. 164
Table D.1: Standardized causes of death ................................................................................... 167
Table D.2: ICD codes for suicide .............................................................................................. 169
Table D.3: Charlson Index disease weights ............................................................................... 170
Table D.4: ICD codes for painful conditions ............................................................................. 171
Table D.5: ICD codes for deaths from accidental poisoning ..................................................... 172
Table D.6: ICD codes for deaths of undetermined intent .......................................................... 173
Table D.7: Suicide attempts and death from suicide stratified by sex ....................................... 174
Table D.8: Clinical and demographic characteristics ................................................................ 175
Table D.9: Cause of death – percent of deaths .......................................................................... 176
Table D.10: Suicide attempts and death from suicide ............................................................... 178
x
List of Figures
Figure 2.1: Annual health care utilization.................................................................................... 58
Figure 2.2: Incremental annual per-person cost by subgroup ...................................................... 58
Figure 3.1: Frequency distribution of utility scores ..................................................................... 83
Figure 4.1: Survival analysis – all cause death .......................................................................... 110
Figure D.1: Survival analysis – all cause death ......................................................................... 179
List of Appendices
A. Pain search terms used in literature searches ..........................................................................146
A.1. Medline ............................................................................................................................146
A.2. Embase .............................................................................................................................146
B. Supplemental Digital Content – Cost study ............................................................................147
B.1. Cohort identification – chronic pain question ..................................................................147
B.2. Data sources and costs .....................................................................................................148
B.3. Comorbidity measure – ACG system ..............................................................................149
B.4. Matched cohort design .....................................................................................................150
B.5. Sensitivity analysis...........................................................................................................151
B.6. Standardized differences for assessing matched cohorts .................................................151
B.7. Tables ...............................................................................................................................153
C. Supplemental Digital Content – Utilities study ......................................................................160
C.1. Cohort identification – chronic pain question ..................................................................160
C.2. Health Utilities Index Mark 3 classification system ........................................................161
C.3. Tables ...............................................................................................................................164
D. Supplemental Digital Content – Mortality study ....................................................................165
D.1. Cohort identification – chronic pain question ..................................................................165
D.2. Variables in the more closely matched propensity score .................................................166
D.3. Tables ...............................................................................................................................167
D.4. Figures..............................................................................................................................179
E. Copyright Acknowledgements ................................................................................................180
xii
List of Abbreviations
ACG – Adjusted Clinical Groups
ADG – Aggregated Diagnosis Groups
CAD – Canadian dollars
CADG – Collapsed Aggregated Diagnosis Groups
CADTH - Canadian Agency for Drugs and Technologies in Health
CCAC - Community Care Access Centres
CCC - Complex Continuing Care
CCHS - Canadian Community Health Survey
CCI – Canadian Classification of Health Interventions
CCP - Canadian Classification of Diagnostic, Therapeutic, and Surgical Procedures
CCRS - Continuing Care Reporting System
CIHI - Canadian Institutes for Health Information
CIHI DAD – Canadian Institutes for Health Information Discharge Abstract Database (same as
DAD)
CMG - Case Mix Group
CMI - Case Mix Index
CPRO - Client Profile Database
CPS – Canadian Pain Society
CPRWPD - Cost per Rug-Weighted Patient Day
xiii
CPWC - Cost per Weighted Case
CPWPD - Cost per Weighted Day
CT – Computed Tomography
EQ-5D – EuroQoL 5D
DAD - Discharge Abstract Database
FIM – Functional Independence Measure
HCD - Home Care Database
HUI – Health Utilities Index
IASP – International Association for the Study of Pain
ICD-10(-CA) - International Statistical Classification of Diseases and Related Health Problems,
10th Revision (Canadian version)
ICD-9(-CA) - International Statistical Classification of Diseases, Injuries, and Causes of Death,
9th Revision (Canadian version)
ICES - Institute for Clinical Evaluative Sciences
LHIN - Local Health Integration Network
LTC - Long Term Care
MDS - Minimum Data Set Resident Assessment Instrument
MDS-MH Minimum Data Set for Mental Health
MOHLTC - Ministry of Health and Long Term Care
MRI – Magnetic Resonance Imaging
NACRS - National Ambulatory Care Reporting System
xiv
NICE – National Institute for Clinical Excellence
NPHS – National Population Health Survey
NRS – National Rehabilitation Reporting System
OCCPS - Ontario Chronic Care Patient System
ODB - Ontario Drug Benefit
OHCAS - Ontario Home Care Administrative System
OHIP - Ontario Health Insurance Plan
OMHRS – Ontario Mental Health Reporting System
QALY - Quality Adjusted Life Year
QWB - Quality of Well-Being scale
RAI-MH - Resident Assessment Instrument-Mental Health
RCW - Rehabilitation Cost Weight
RIO - Rurality Index for Ontario
RIW - Resource Intensity Weight
RPDB - Registered Persons Database
RUG - Resource Utilization Group
RWPD -RUG-Weighted Patient Day
SCIPP - System for Classification of In-Patient Psychiatry
SF-6D – Short-form 6D
USD – United States dollars
1
Chapter 1
1 Overview of thesis and background
1.1 Organization of thesis
The thesis is organized into 5 chapters. Chapter 1 provides an overview of chronic pain, and
includes a definition of chronic pain, the impact of chronic pain on the individual and
epidemiology. Subsequent sections describe:
Approaches to cost-of-illness research and relevant literature on the cost of chronic pain
Overview and relevant literature on utilities in chronic pain
Relevant literature on mortality associated with chronic pain
Approaches to confounding in observational research
Data sources used in this research
Chapters 2 and 3 are manuscripts of the first and second research projects which have already
been published. Chapter 4 is a manuscript of the third project, not yet published. Chapter 5
provides a general discussion including a summary of each study, limitations, strengths and
implications of the research. A section on future research is also included.
1.2 What is chronic pain?
The International Association for the Study of Pain (IASP) defines pain as “an unpleasant
sensory and emotional experience associated with actual or potential tissue damage, or described
in terms of such damage.”1 When pain is of short duration, it is generally classified as acute,
while chronic pain is often considered to be pain that has persisted beyond the normal tissue
2
healing time, usually taken to be 3 or 6 months.1 However, many conditions accepted as
examples of chronic pain do not fit with a definition that allows for normal tissue healing, for
example, osteoarthritis, rheumatoid arthritis or spinal stenosis.1 The time frame is somewhat
arbitrary as well. For many painful conditions, expected healing time will be significantly less
than 3 months. Six months’ duration may be preferred for research purposes, while in clinical
practice this may be too long.1 Additional examples of chronic pain include chronic low back
pain, fibromyalgia, neuropathic pain, phantom limb pain, cancer, post-herpetic neuralgia, and
chronic post-surgical pain.
Bonica’s Management of pain has further described chronic pain as follows:
May be elicited by an injury or disease but is likely to be perpetuated by factors that are
both pathogenetically and physically remote from the originating cause. Chronic pain
extends for a long period of time and/or represents low levels of underlying pathology
that does not explain the presence and extent of pain (e.g., mechanical back pain,
fibromyalgia syndrome).2
Changes in nerves, sensitization of the peripheral or central nervous system, genetic factors and
previous experiences may also be involved in chronic pain.2 Although often recognized as being
more common in older populations,3-5
chronic pain occurs in children, in addition to adults and
the elderly.
1.2.1 Impact of chronic pain
Chronic pain has important consequences. Adults lose time from work, experience reduced
mobility, increased rates of depression and sleep problems.6,7
Statistics Canada reports that pain
is the most frequent cause of disability, and European studies found that 40% of physician visits
3
were for pain.8,9
Back and neck pain surpass heart disease and stroke as the greatest cause of
disability in high income countries.10
In children chronic pain reduces school attendance11
and
performance,12
and can impair emotional and social development.12
Children with chronic pain
report worse quality of life, are more likely to visit the doctor and take pain medicine than those
without chronic pain.11
Furthermore, the emotional toll from chronic pain more than doubled the
odds of suicidality, defined as thinking about, planning or attempting suicide in a population-
based study of people aged 16 to 85 years in Australia.13
In addition to the toll it takes on patients and their families, chronic pain has a large economic
burden to the individual with chronic pain, provincial ministries of health, and society as a
whole.
1.3 Epidemiology
1.3.1 Canadian estimates
The most recent survey of the prevalence of chronic pain in Canada was published in 2011. The
methodology used in this study closely followed an earlier European study14
and used questions
to ensure the respondents sampled had chronic pain for at least six months duration, experienced
pain in the last month, experienced pain at least several times per week, and pain was rated at an
intensity of at least 5 on a 10 point scale. The representative survey of approximately 4,000
Canadian adults estimated that 19% of Canadians suffer from chronic pain, with a third reporting
very severe pain (8 or more on a 0 to 10 scale).5 The prevalence of chronic pain was higher in
women and increased with age. Almost 50% of respondents suffered for at least 10 years.5
At least eight earlier Canadian studies, each with at least 1,000 respondents, have been
conducted to determine the prevalence of persistent or recurrent pain.15-22
Similar population-
4
based sampling methods were used (e.g. random digit dialing) in these studies and the 2011
Canadian study. Estimates of chronic pain prevalence varied from 11% to 29%. Variability in
prevalence reports is likely related to other aspects of methodology. The study with the highest
prevalence estimate in adults 18 years and older used a threshold score of 4 on a 0 to 10 scale
and a duration of 6 months or longer.16
The most recent 2011 study used a threshold of 5 on a 0
to 10 scale and a duration of 6 months or longer.5 The study with the lowest estimate of chronic
pain prevalence included respondents aged 12 years and up, which will lower apparent
prevalence, since younger people have less chronic pain.19
1.3.2 International estimates
A review of pain prevalence in Europe found that results ranged from 7% to 46%.23
An earlier
systematic review determined that prevalence rates of chronic pain varied from 2% to 40%
across 15 studies, with a median value of 15%.24
In both reviews, differences were partly
explained by methodology.
The World Health Organization sponsored a study to determine the prevalence of chronic pain in
15 centres in Asia, Africa, Europe, and the Americas. Subjects were identified as having chronic
pain if pain had been present for 6 months in the preceding year. They found a point prevalence
of 22% with a range of 5% to 30% depending on the geographical region.25
1.3.3 Types of chronic pain
The most frequent cause of chronic pain in adult Canadians was lower back pain at 22%.5 An
additional 10% had upper back pain and 5% had neck pain.5 Knee pain accounted for 10% of
chronic pain.5 A study of >30,000 American adults determined that 26% experienced back pain
at least one entire day in the previous 3 months, and 14% experienced neck pain.26
5
1.3.4 Estimates in children
Statistics Canada has estimated that the prevalence of chronic pain in children aged 12 to 17
years to be 2.4% for boys and 5.9% for girls.27
A systematic review of chronic pain in children
found a median prevalence for headache to be 23%.28
For other types of pain (abdominal pain,
back pain, musculoskeletal pain, and pain combinations), median prevalence ranged between
11% and 88%.28
1.4 Cost-of-illness studies
The first study in this thesis is a cost-of-illness study of chronic pain. A cost-of-illness study
aims to identify and measure the costs of a disease or condition to describe its economic burden.
It may include costs related to medical care and/or lost productivity. Cost-of-illness studies can
complement studies of morbidity and mortality by quantifying the magnitude of a health problem
in monetary terms. This can be helpful in setting priorities in public health, research funding or
program development when comparing costs among various diseases or conditions.29
Cost
estimates also provide data for cost-effectiveness and cost-utility analyses which are important in
the evaluation of new programs or technologies.
1.4.1 Perspective and types of costs
When undertaking a cost-of-illness study, a number of factors must be considered. Costs
included in cost of illness studies may be divided into direct costs and indirect costs. Direct costs
include those related to the disease and may include medical costs (e.g. physician fees,
medications) but can also include non-medical costs such as parking costs at medical
appointments.30
Indirect costs are those from lost productivity (e.g. time off work for
6
appointments, lost earnings for someone leaving the workforce due to their illness).30
Study 1
included direct medical costs only.
Different perspectives can be taken in cost of illness studies, and the perspective determines what
types of costs are included. The societal perspective is the most comprehensive and includes all
direct and indirect costs. A study of chronic pain in Canadians waiting to be seen in chronic pain
clinics by Guerriere et al31
described later took a societal perspective and included medical and
non-medical costs related to chronic pain, as well as lost time from work due to chronic pain. In
Canada, it may be useful to take the perspective of the provincial healthcare payer, and consider
only costs that are the responsibility of the public payer. This can help place costs of a disease or
condition in the context of other public health spending. In the United States and some other
countries, a private insurer or employer perspective may also be also taken. In these studies, all
“covered” costs may be included, for example, medical costs and absenteeism or disability
benefits. Study 1 took the perspective of the provincial payer, the Ontario Ministry of Health
and Long-Term Care.
1.4.2 Prevalent versus incident costing
When conducting a cost of illness study, one must decide whether to estimate the cost of all
people with a condition in a specific time frame, or only new cases of a condition. A prevalence
approach assembles a cohort of people with varying durations of the condition and estimates an
average cost to manage the condition over a fixed period of time, often one year.30,32
Some in
the group may have had the condition for several years and some may have had the condition for
only a few months. In contrast, an incidence approach to cost-of-illness studies enrolls people at
initial onset of the condition and follows them forward until recovery or death.30,32
7
Chronic pain is unusual in that the condition is only recognized as chronic after several months
have passed. Therefore, recruiting a cohort of patients at the beginning of their problem is not
possible. If administrative databases are used, a signal must be present to identify patients in
similar phases of disease. There are no International Classification of Disease codes version 9
(ICD-9) or version 10 (ICD-10) for chronic pain (these were ICD versions in use in Ontario
during the timeframe of this research); therefore, one cannot use a first occurrence of a
diagnostic code to identify new cases of chronic pain. Analgesic use might offer a clue to onset
of chronic pain; however, Ontario administrative data do not include prescription drug claims,
except for those over 65 years of age, and select others. In addition, many effective analgesics
are available without prescription would result in misclassifying individuals in the databases, and
not everyone uses analgesics. Consequently, it is not currently feasible to take an incident-based
costing approach of chronic pain using healthcare administrative data.
Prevalent costing is an appropriate approach for diseases with relatively stable costs and
prevalence, and may permit a more accurate, current cost estimation of the burden of chronic
pain to the Ontario health system.30
It is also more feasible, especially for chronic pain, and the
most common approach to costing in published literature.33
All of the identified Canadian
studies described above used a prevalent approach to costing and the first study in this thesis also
uses this approach.
1.4.3 Total versus incremental costs
There are a number of ways to estimate costs associated with a condition. One approach is to
sum all health costs in a person with a specific condition, as was done in the study of
8
fibromyalgia costs by Penrod et al. described below.34
However, this will include healthcare
costs unrelated to the condition of interest, so will overestimate the true cost of a condition,
except in certain situations (e.g. ear infection in an otherwise healthy child). Another method
sums all costs related to the condition of interest. For example in several of the Canadian studies
described below,31,35-37
patients were asked to identify (on a questionnaire, in a diary or by
interview) costs, appointments and missed time from work related to their chronic pain. In all of
these studies, the total cost of chronic pain was estimated. A disadvantage of this method is that
other conditions or comorbidities that are causally related to the disease of interest, and should be
included in the cost of the disease, may not be captured. For example, chronic pain can
contribute to depression, anxiety and sleep disorders38-40
and so some costs associated with those
diseases should be included in estimating the cost of chronic pain. Moreover, it may be difficult
to accurately identify those conditions that should be included due to causal relationships.
Another approach is the incremental cost estimate, also referred to as “net” or “attributable” cost:
the mean difference between the healthcare costs of two cohorts of people who are similar except
for the condition of interest (chronic pain) is calculated. This method has been recommended for
conducting cost-of-illness studies when related comorbidities should be included in cost
estimates.41,42
The incremental cost approach was used in the Canadian chronic pain studies
using administrative databases43-46
described below and has been used extensively in cancer
costing.47-52
This was the approach taken for study 1.
1.5 Canadian studies on cost of chronic pain
Healthcare costs, patterns of care and the extent of publicly funded services vary among
countries, and so the most relevant information for Canadian policy makers is data that has come
9
from Canadian sources. Collection of Canadian cost data is recommended by the Canadian
Agency for Drugs and Technologies in Health (CADTH).53
A search of MEDLINE and
EMBASE was conducted from 1947 to 2014 using a broad range of chronic pain terms
(Appendix A), combined with cost terms with published evidence for good sensitivity and
specificity.54,55
Two Canadian studies of chronic pain were identified,31,45
as well as seven other
Canadian studies of specific types of pain, summarized in Table 1.1.34-37,43,44,46
The first study of the cost of chronic pain linked the 1996 National Population Health Survey
(NPHS) to Alberta healthcare administrative databases. The NPHS is a Statistics Canada
longitudinal survey that asks a variety of health related questions including health status, health
service use and risk factors for diseases, as well as demographics and socio-economic
characteristics. People with chronic pain were identified by the following survey question: “The
next set of questions asks about your day-to-day health. The questions are not about illnesses
like colds that affect people for short periods of time. They are concerned with a person’s usual
abilities. Are you usually free of pain or discomfort?” Responding negatively to the question
identified someone considered to have chronic pain. No time element was included in the
question. This study found that people who reported chronic pain had physician billings and
hospitalization costs totaling $3,500 (reference year 2000) greater annually than those without
pain.19,45
Importantly, this study was not able to access all healthcare data that is currently
available in Ontario, and patterns of practice may have changed over time (e.g. possible
increased use of more technology-intensive investigations). Moreover, the authors of the study
indicated that with only 39% linkage between respondent surveys and administrative data, the
low rate was “sufficient to cast doubt on the generalizability of the results.”19
10
A second Canadian study of chronic pain surveyed 370 adult patients who were waiting to be
seen at specialized pain clinics in seven provinces.31
Participants were required to have chronic
non-cancer pain lasting for at least six months. A validated questionnaire was used to collect
cost data from patients for a three month period. Average annual costs related to chronic pain
were $17,544, (reference year 2007) with most of that cost indirect, e.g., lost labour time.31
Approximately $1,860 was deemed to be publicly funded healthcare costs. However, patients
referred to specialized pain clinics are only a subset of all chronic pain patients, making
inferences to the overall population unreliable.
Seven additional studies have estimated costs of specific types of pain (Table 1.1). These, too,
represent subsets of all chronic pain patients, limiting their generalizability to the entire chronic
pain population.
11
Table 1.1: Canadian studies of costs of specific types of chronic pain
Study,
Location
Pain,
Population
Data source,
Perspective
Reference
year. Mean
publicly funded
(per year)
Phillips 2008,45
Alberta
Chronic pain,
6,012 respondents to the National Population Health
Survey, 1996 whose responses could be linked to
healthcare administrative data,
Physician Services and Hospital Morbidity files of
the Alberta Healthcare Insurance Plan, and the
National Population Health Survey, 1996.
Provincial payer perspective
2000
incremental
cost: $3,500
Guerriere 2010,31
7
provinces
Chronic pain,
370 patients on a waiting list for multidisciplinary pain
clinic.
Ambulatory and Home Care Record completed
prospectively by the patient. Societal perspective.
2007 cost:
$1,860*
McGillion 2008,35
Ontario
Angina,
130 Toronto adults with Canadian Cardiovascular
Society class I to III angina.
Ambulatory and Home Care Record completed by
recall. Ontario reimbursement schedules for publicly
funded services, actual costs paid for other services,
average wage of Toronto residents (Statistics
Canada) for indirect costs.
Societal perspective.
2004 cost:
$2,979†
White 1999,46
Ontario
Fibromyalgia,
95 London adults with confirmed FM by American
College of Rheumatology criteria, matched for age,
sex and geography with 380 controls (1:4).
Ontario Health Insurance Plan (OHIP).
OHIP program perspective.
1993
incremental
cost: $492‡
* Total direct and indirect $17,544.
† Total direct and indirect $19,209.
‡ OHIP costs only.
12
Study,
Location
Pain,
Population
Data source,
Perspective
Reference
year. Mean
publicly funded
(per year)
Penrod 2004,34
Quebec
Fibromyalgia,
180 women ≥ 18 years, from rheumatology clinics and
newspaper advertisements with confirmed FM by
American College of Rheumatology criteria.
Cost Assessment Questionnaire completed by recall.
Quebec reimbursement schedules for publicly funded
services and recommended fees from Quebec health
professional bodies for non-insured services.
Provincial payer perspective.
2001 cost:
$2,286*
Lachaine 2010,43
Quebec
Fibromyalgia,
16,010 patients with a FM diagnosis (2 FM diagnostic
codes), matched for age and sex with 16,010 controls.
Quebec provincial health plans, la Régie de
l’assurance maladie du Québec (RAMQ).
Provincial payer perspective.
2006-07
incremental
cost: $1,299
Piwko 2007,36
Canada
Multiple sclerosis (MS) pain, model based on survey of
14 physicians who treat MS and 297 patients with
MS
Physician and patient survey for health utilization,
patient survey for travel costs, average wage of
Canadians (Statistics Canada) for indirect costs.
Societal perspective.
2004 cost:
$5,056
Lachaine 2007,44
Quebec
Neuropathic pain,
4,912 patients with a diagnosis of NP (2 NP diagnostics
codes), matched for age and sex with 4,912 controls.
Quebec provincial health plans, la Régie de
l’assurance maladie du Québec (RAMQ).
Provincial payer perspective
2002
incremental
cost: $2,317
Tarride 2006,37
3 provinces
Neuropathic pain,
126 patients with at least 3 months of pain from diabetic
peripheral neuropathy, post herpetic neuralgia,
cervical radiculopathy or pain after thoracotomy,
mastectomy or inguinal hernia surgeries.
Physician and patient questionnaires for health
utilization, patient questionnaire for lost productivity.
Ontario Schedule of Benefits and RAMQ for
publicly funded health costs. Average wage of
Canadians (Statistics Canada) for indirect costs.
2003
attributable
cost: $2,364†
* Total direct and indirect $14,666.
† Includes prescription drugs. Total direct and indirect $10,268.
13
1.5.1 Cost estimates in other countries
There is considerable research on the cost of chronic pain conditions internationally. In addition
to multiple systematic reviews on the cost of back pain,56-59
and fibromyalgia60,61
at least 22
additional studies on the cost of chronic pain (excluding back pain) were identified. The
international studies included a variety of health services costs reflective of their context. They
used similar methodologies as the Canadian studies, using cost diaries, insurer or government
databases, and some modeling based on expert opinion. Some researchers were able to capture
the indirect costs of lost time from work through databases that included sick-leave benefits in
the United States and social benefits databases in Europe.62-64
This approach is not currently
available with Ontario healthcare administrative data.
1.5.2 Cost estimates in children
Children are recognized as distinct from adults with respect to clinical research and this should
be true for cost of illness studies as well. Family members or other caregivers are always
involved in a child’s care and so there may be differences in costs from lost productivity of the
caregiver. In addition, patterns of care may be different for similar problems in adults and
children, so direct healthcare costs may also differ. Despite these issues, studies about costs
related to chronic pain management in children are scarce. No studies were found with Canadian
data. One study in the United States using population-level data from the National Health
Interview Survey and Medical Expenditure Panel Survey estimated the annual incremental cost
associated with chronic pain conditions in children aged 6 - 17 years to be USD $1,339 (2013
reference year).65
A study from the United States and one in the United Kingdom of children
referred to pain clinics or rheumatology clinics reported annual costs from chronic pain (medical,
14
non-medical and parent productivity losses) of USD $11,787 (2012 reference year) and ₤8,000,
(Pounds Sterling, 2004 reference year).66,67
Another study from children attending a pain clinic
in the United States estimated annual medical costs to be just under $7,000 (United States
dollars, 2008 reference year); costs associated with lost time from work and school were not
monetized.68
The first study in this thesis includes data on adolescents aged 12 – 18 years,
adding to this small body of literature.
1.6 Health utilities
Another way of measuring the burden of illness is to measure overall health in a cohort.
However, a unit of measurement that is common across multiple conditions is needed to allow
meaningful comparisons among illnesses. A health utility is one approach to obtain a global
measure of health. The second study in this thesis estimates health utilities in people with
chronic pain.
1.6.1 Overview
Health utilities are self- reported numerical values that incorporate both a rating for health-
related quality of life and a preference for that health state.69
Anchors are 0 for the state of being
dead and 1 for perfect health. Health states that are worse than being dead are represented by
negative values. Health utilities are commonly used in cost-utility analyses for calculation of
quality adjusted life years (QALYs). Utility for a health state is multiplied by the time in that
health state to generate a QALY, the measure of health in a cost-utility analysis.69
Utilities are also useful for providing a global measure of the health of a population when
measured at a population level and have been included in Statistics Canada health surveys since
the 1990s. They can provide information about burden of a particular disease when measured in
15
a patient cohort. Utilities may be useful to determine relative burden when compared with
various diseases.70,71
This may be useful for setting research priorities and developing social or
health policy.
Utilities may be determined using direct or indirect methods. The simplest of the direct methods
is the visual analogue scale. For example, the Euroqol group (euroqol.org), a well-established
organization that developed the EQ-5D (see below) recommends a 20 cm vertical line anchored
with 0 (worst imaginable health state) and 100 (labelled best imaginable health state).72
The
respondent is asked to rate a specific health state (or their own health state). The value (scaled
between 0 and 1) becomes the utility for that health state. Another direct method is the standard
gamble. An individual is presented with a health state and told they will live in it for a fixed
period, e.g. 3 years. They are asked whether they would like to remain in that state or gamble on
a chance between immediate death and perfect health for the same fixed period. The probability
of perfect health is varied until the respondent is undecided on which option (gamble or stay in
health state) to choose. That probability is the utility of the health state.69
Time tradeoff is another direct method to elicit utilities. An individual is presented with a health
state and instructed they will live for a fixed period in that state (e.g. 3 years), or they can live in
perfect health for a shorter period of time (e.g. 2.5 years). They are offered various time periods
in perfect health until the respondent reaches a point of indifference. The utility for the health
state is calculated as the time in perfect health divided by time in the health state.69
Generic indirect utility instruments have been developed to facilitate utility elicitation, in part
because standard gamble and time trade-off exercises can be quite time consuming and
cognitively challenging for the respondent.73
Indirect utility instruments rely on the multi-
16
attribute approach. A number of “attributes” or “dimensions,” (e.g. mobility, emotion) with a
number of levels are combined in a health state classification system and preferences for the
health states are elicited, usually from a community-based sample.69,73
An individual can then
answer questions to determine their level for each domain and the preference scoring system can
be applied to their responses to determine a utility for the individual’s health-related quality of
life.
There are several commonly used generic indirect utility instruments. These include the Quality
of Well-Being scale (QWB),74
the EuroQoL 5D (EQ-5D),75
Short Form 6D (SF-6D),76
Health
Utilities Index Mark 2 and 3 (HUI2, HUI3).77,78
They have four to eight attributes and 3 or more
levels for each attribute. The utilities obtained from each instrument are not interchangeable for
a number of reasons. Different health dimensions are included in the instrument and there are
different numbers of levels of each dimension. The populations sampled to determine preference
weights were from different countries and the methods used to obtain preferences were different
(e.g. time-tradeoff for EQ-5D, standard gamble for HUI2 and HUI3). The algorithms to attribute
preferences to each state also vary among instruments.73
As a result, some instruments are more
sensitive to differences in diseases and health states than others, including measurement of pain,
although a “best” instrument for pain has not been identified.79-82
1.6.2 Studies of health utilities in chronic pain
Some utility estimates in people with chronic pain have been obtained from convenience samples
with specific conditions such as arthritis or back pain, rather than a more broad sample.83-86
These may not be generalizable to the whole population with chronic pain because pain intensity,
disability and comorbidities are likely to vary with different types of persistent pain. The most
17
relevant studies for estimating the health burden of chronic pain are those generalizable to the
whole population.
A German population-based study using the SF-6D estimated utilities for adults with chronic
pain to be 0.64. When stratified by severity, mild, moderate and severe pain had utilities of 0.71,
0.63 and 0.54 respectively.87
Utilities estimated in the United Kingdom using the EQ-5D on a
population-based sample of adults found 0.82, 0.72, and 0.48 for mild, moderate and severe pain.
The same group had utilities of 0.79, 0.73, and 0.63 using the SF-6D.82
A large Swedish postal
survey in people over 65 years found a utility of 0.81 for people with no pain or mild pain, 0.63
for moderate pain and 0.39 for severe pain.88
This variability illustrates the non-
interchangeability issue with utility instruments mentioned above. When conducting cost-
effectiveness analyses, or comparing health states, one should use utilities obtained from the
same instrument. This means it is beneficial to have utilities derived from multiple instruments,
and little has been done with the HUI3 in chronic pain, the instrument used in Study 2.
Two studies examined utility decrement associated with chronic pain. One population-based
study in Alberta, Canada found a utility decrement for chronic pain of 0.19 measured by the EQ-
5D.89
Another in Germany found a 0.20 utility decrement using the SF-6D for severe daily
pain.87
No other Canadian studies of utilities in chronic pain were found. It would be helpful to
have utilities from a Canadian population because differences have been found between
countries.90-92
No population-based studies of chronic pain that used the HUI2 or HUI3 were
identified. As mentioned above, utility instruments are not interchangeable; therefore it would
be beneficial to have chronic pain utility estimates using the either the HUI2 or HUI3.
Moreover, knowledge about utilities in a Canadian population would be helpful to estimate the
health burden of chronic pain in Canada, given the already mentioned variation in preference
18
values for health states among countries. Study 2 contributes population-based utility estimates
for Ontarians with chronic pain using the HUI3 indirect utility instrument, which was available
for analysis in some cycles of the Canadian Community Health Survey.93,94
1.7 Mortality and chronic pain
Another way to assess the burden of illness is to examine mortality associated with a condition.
Some have observed increased mortality in people with chronic pain.95-99
Causal theories have
been proposed from research in animals. Immune response is impaired and tumour growth
enhanced in those exposed to repeated pain or stress.100
In humans, researchers have described
an endocrine stress response with chronic pain, and a relationship between stress and disease.101-
104 Indeed, studies have found an association between chronic pain and death from cancer
95,98
and cardiovascular disease.98,105
There have been two relevant systematic reviews of
observational studies of all-cause mortality.106,107
Neither the Smith et al study of chronic pain106
nor the Asberg et al study of chronic widespread pain (this is a subset of chronic pain patients
with axial pain, pain on both sides of their body, as well as above and below the waist;
prevalence is estimated at 10%108
),107
found a statistical difference in mortality. The all-cause
mortality data from the Smith et al analysis included 7 studies from the United Kingdom, Europe
and United States provided 4 to 18 years of follow up.106
A total of 30,008 individuals
contributed to the negative all-cause mortality estimate, although the majority of contributing
studies had point estimates of greater mortality in the chronic pain group (risk ratios > 1 with
confidence intervals crossing 1).106
It is possible that study heterogeneity affected the precision
of the estimate and a large single study might clarify the issue.109
A study of 13,127 individuals
with chronic pain from Denmark published after the review found a higher mortality rate in
people with chronic pain (hazard ratio for those with chronic pain versus those without chronic
19
pain: long-term opioid users 1.72 (1.23–2.41), short-term opioid users 1.22 (0.93–1.59), non-
opioid users 1.28 (1.10–1.49).110
The meta-analysis of people with chronic widespread
musculoskeletal complaints also found no statistical difference in mortality. A total of 6 studies
comprised of 86,929 individuals contributed to the findings. Again, risk ratios for the majority
of the studies were > 1 in favour of a greater mortality in the chronic widespread pain group
although confidence intervals often crossed 1). Neither meta-analysis found a statistical
difference in cardiovascular death or cancer death.106,107
Mortality in chronic pain has not been
studied in the Canadian context. Study 3 adds to this growing international body of knowledge.
1.8 Management of confounding in observational data
The data sources used in the three studies in this thesis were observational. Analysis of
outcomes in observational data can be problematic. Individuals having an exposure of interest
often share a number of characteristics that are associated with the exposure and different than
characteristics in individuals who do not have the exposure of interest. When these
characteristics are also causally related to the outcome, the characteristics are called confounders.
For example, in a population, the mean age of people with chronic pain is higher than the mean
age without chronic pain. If one measures an outcome such as mortality between those with and
without chronic pain, then age is a confounder in the data.
There are two major ways to address confounding that is present in observational data. One
approach uses regression methods and the other uses matching methods, and particularly
propensity score matching methods when working with large administrative datasets. They are
discussed in more detail below. Another option is to stratify the results at the analysis stage so
that subgroups are balanced for known confounders (e.g. women over 65 years in one subgroup;
20
women under 65 years in another subgroup, since age and sex are known confounders). One
then analyzes the outcome of interest (e.g. healthcare cost differences between those with and
without chronic pain) within each stratum.111
If the effect from the exposure is similar across
strata, a summary effect can be obtained by pooling the results from all strata.111
A substantial
difference in effect size among strata indicates an effect modification and results cannot be
pooled.111
This can be an effective approach but becomes problematic as the number of
confounders (and strata) increases. A method to reduce confounding at the study design stage
would be to restrict subject inclusion to those with specific confounding variables, for example,
only women over 65 years of age.112
This could limit generalizability or require the replication
of many similar studies to obtain a complete picture of chronic pain depending on how many
confounders are restricted. Neither stratification nor restriction was considered feasible as a
primary method to manage confounding due to presence of many confounding variables.
Another approach to reduce confounding is instrumental variable analysis. The instrumental
variable must be highly correlated with the explanatory variable (e.g. whether or not someone
has chronic pain) and unrelated to the outcome.113
It may allow for adjustment of some
unknown confounders in addition those known but requires identification of an appropriate
instrumental variable.113
It was not considered for this chronic pain research since no potential
instrumental variables were readily apparent.
1.8.1 Regression analysis
In regression analysis, one fits data to an equation with the outcome as the dependent variable
(e.g. healthcare cost), and an indicator variable for the exposure of interest as one of the
independent variables (e.g. presence of chronic pain). A set of variables that are thought to, or
known to confound an outcome are included as covariates in the regression equation in order to
21
control their effect on the outcome, or explore their impact on the outcome. If the outcome is
dichotomous, then logistic regression is used. When the outcome is continuous, the exposure is
modelled using linear regression. Once the model is specified, one must evaluate whether it is
“good” or well specified. Some of these steps include determining how much of the effect is
explained by the model (R2), whether all explanatory variables are necessary (the least number of
variables that provides sufficient explanation is desired), if assumptions implicit in the model are
true (e.g. normal distribution of a variable), if there are any data issues such as outliers,
influential observations or multicollinearity. If the model is misspecified, results may be
biased.114
Findings from these steps may cause the researcher to refine the model to obtain a
better fit.
1.8.2 Matching using propensity scores
Another way to manage confounding in observational data is to match individuals with the
exposure of interest to similar individuals without the exposure and measure an outcome. One
could match on a small set of variables (e.g. age and sex) which could be done without much
computational difficulty, even with large data sets. But as the set of matching variables
increases, matching becomes more technically difficult. Using propensity scores to match is a
more efficient way to handle many confounders.
A propensity score is a balancing score and represents the likelihood an individual could have
the exposure of interest (e.g. chronic pain), whether they have it or not, and varies from 0 to 1.
Calculating the propensity score is a two-step process. A logistic regression model is specified
with presence or absence of chronic pain assigned as the dependent variable. Independent
variables are those thought to be associated with the likelihood of chronic pain or the outcome of
22
interest (e.g. healthcare use, quality of life, mortality). The first step fits the data to the model
and calculates beta coefficients. After the beta coefficients are determined, the same regression
model is used to calculate propensity scores for each subject and potential match. Once these
scores are calculated, subjects with chronic pain can be matched to those without, based on the
propensity score (usually in a 1:1 ratio).115,116
This should produce two cohorts who have similar
measured baseline characteristics, provided that the propensity score model has been adequately
specified. Evaluation of the quality of the match is done by comparing standardized differences
of measured variables.117
If adequate balance is not achieved (usually accepted to be 10%
difference or less), the propensity score model can be revised until the two groups are
balanced.117
This use of propensity scores has demonstrated good balancing of measured
baseline characteristics.118,119
Once a sample has been matched using propensity scores, outcomes can be compared directly, as
in a randomized controlled trial. Statistical tests for paired analyses (e.g. paired t-test,
McNemar’s test) are recommended because paired subjects are more similar than if they had
been assigned to groups randomly.115,120
The most common approach to matching with
propensity scores is using the nearest neighbour within a specified caliper width around the
propensity score, and matching without replacement.119
Replacement sampling refers to the
method of sampling a subject and then returning them to the pool available for sampling, so they
have opportunity to be matched more than once.121
1.8.3 Choosing between regression and propensity score matching
If a regression model is correctly specified, it should provide a similar result to a propensity
score matched analysis.122
A systematic review of studies comparing propensity methods to
23
traditional regression adjustment of confounding found that in 90% of studies, the same overall
conclusion about association between the exposure and outcome was made in each approach.123
When they differed, the regression model found statistical significance in the association and the
propensity score method did not. The reviewers noted that propensity score methods were not
well executed in many of the included studies and that it is not known which of the methods
provides the truest result.123
Both approaches are subject to a similar disadvantage: they can adjust only for measured
confounders. Unknown (or unmeasured) confounders may still be present. A main advantage of
propensity scores is that they allow simultaneous control of many variables that may be related
to the exposure and outcome.124
This can be particularly useful when outcome numbers are low
(e.g. suicide) and addition of many covariates in a regression model might cause over-fitting,
leading to biased results.125
An additional advantage to propensity score analysis happens when choosing potential
confounders for inclusion in the model. It is often not possible to know which variables are true
confounders. However, including variables that are not true confounders does not appear to bias
to the results.122
Moreover, evaluation of the score occurs by examining baseline characteristics,
which is much easier than evaluating whether a regression model has been correctly specified.122
Another advantage to propensity score analysis is that the evaluation of the match can occur
without knowing the outcome. With traditional regression analysis, the results are evident while
the model evaluation is underway and could potentially affect the researcher’s interpretation of
the model fit (if a certain result is preferred or expected).122
24
Propensity matching provides the ability to analyze data similarly to a randomized controlled
trial, providing outcomes such as differences in means, relative and absolute risks and number
needed to treat. These values may be somewhat more straightforward to understand by users of
medical literature, compared to values obtained from a regression model, particularly odds ratios
produced from logistic regression.
Another advantage with propensity matching is easily seeing the degree of overlap of baseline
characteristics.122
When cases (or those with the exposure of interest) are very different from the
majority of potential controls (or those without the exposure of interest), it is evident at the
matching stage because a large number of cases remain unmatched. Therefore, it is clear what
population the results are most applicable to. Conversely, when using a regression analysis for
confounder adjustment, inadequate cohort overlap is not readily evident, and extrapolation of
findings to the entire population may be flawed (e.g. the oldest, those with the most
comorbidities).122
A disadvantage to matching is that it prevents evaluation of the matching variable’s effect on the
outcome. For example, matching on age prevents assessment of the influence age has on the cost
of pain, unless a further analysis is stratified by age.
Another disadvantage relates to loss of information: the more potential confounders that are
included in the matching algorithm, the more difficult it is to find a match for a case, and some
cases (those with chronic pain in this research) might not be included in the analysis. The
alternative is to match on fewer characteristics or match within a larger caliper width but
imbalance on some important confounders may remain in the groups.
25
1.8.4 Other ways to use propensity scores
There are three other ways to use propensity scores. One can use propensity scores to stratify
subjects, commonly into five equal groups.115,116
One can also use propensity scores to weight
subjects using the inverse probability of treatment weighting.115,116
Lastly, one can also use a
propensity score as a covariate, along with exposure status, in a regression analysis fit to the
outcome of interest.115,116
Both matching and inverse probability of treatment weighting were
found to balance the baseline characteristics of exposed and unexposed subjects better than
stratification or regression118
so these may be preferred approaches to use the propensity
score.118,119
1.8.5 Variable choice for propensity scores
There is no consensus on which variables should be used in a propensity score model. One could
include all measured baseline variables, just the confounding variables, just those affecting the
outcome or just those affecting the exposure.115
A simulation study found that the best balance
in measured confounders was achieved using all measured baseline variables.126
Using only
variables associated with the outcome, or confounding variables resulted in balance in the
variables associated with outcome but imbalance in the variables associated with treatment
(unimportant imbalance); more matched pairs were achieved with this method over the others.126
However, because it is not always evident which variables are associated with exposure or
outcome, it is prudent to use a model that contains all variables.115,126
The research in this thesis used Ontario healthcare administrative databases and three cycles of
the Canadian Community Health Survey, and so choice was limited by variables in the databases
and surveys. Since age and sex are well established variables related to risk or prognosis of
26
many diseases,127
including chronic pain, and are also associated with healthcare use, quality of
life and mortality, they were included. The distance people live from healthcare affects their use
of healthcare and urban Canadians enjoy better health and longer life expectancy than rural
Canadians.128,129
The Rurality Index of Ontario (RIO) has been used for many years to identify
areas that are underserviced for healthcare, and is assigned using the subject’s postal code.130
Socioeconomic status has an impact on healthcare use as well as health outcomes and so is
important to include in a matching algorithm.131,132
Estimated neighbourhood income quintile is
a proxy for socioeconomic status and is calculated using the subject’s postal code linked to
census data. In addition, since healthcare is always evolving, the time period of care may affect
healthcare utilization, quality of care or survival. Therefore, it is important to compare subjects
who received care at similar times. The subject may have had other health conditions or
comorbidities affected their use of healthcare, quality of life or mortality (primary outcomes in
the thesis) so a measure of comorbidity was included (described below).
1.8.5.1 Adjusted clinical groups system
The Johns Hopkins adjusted clinical groups system (ACG) was chosen as a measure of
comorbidity for this research. It is a case-mix system designed for use in the ambulatory
population, in contrast to the Charlson Index, which was developed for hospitalized populations.
This was particularly important when studying people with chronic pain who are commonly
cared for in the community. In addition, the ACG system was created to predict healthcare
utilization,133,134
while the other commonly used morbidity tool, the Charlson score, was
designed to predict one year mortality.135
Both use diagnostic codes (ICD-9 or 10) as main
inputs into their proprietary algorithm.
27
The ACG system grew from early observations in children that clusters of morbidity caused the
largest demands on the health system.71
It has been used extensively in research as a risk
adjustment tool, including use in Canadian provincial administrative databases.136-138
Each diagnostic code is linked to one of 32 aggregated diagnostic groups (ADGs). An individual
with multiple diagnoses can have multiple ADGs. ADG categories have been created based on
duration, severity, diagnostic certainty, etiology and specialty care involvement (Table 1.2). The
number of ADGs provided good prediction of physician visits in Ontario administrative data.139
The ACG system was used in all three studies in this thesis to ensure a close match on
comorbidity between people with and without chronic pain.
28
Table 1.2: Aggregated diagnostic groups (ADGs)
ADG Description
1 Time Limited: Minor
2 Time Limited: Minor-Primary Infections
3 Time Limited: Major
4 Time Limited: Major-Primary Infections
5 Allergies
6 Asthma
7 Likely to Recur: Discrete
8 Likely to Recur: Discrete-Infections
9 Likely to Recur: Progressive
10 Chronic Medical: Stable
11 Chronic Medical: Unstable
12 Chronic Specialty: Stable-Orthopedic
13 Chronic Specialty: Stable-Ear, Nose, Throat
14 Chronic Specialty: Stable-Eye
15 No Longer in Use*
16 Chronic Specialty: Unstable--Orthopedic
17 Chronic Specialty: Unstable-Ear, Nose, Throat
18 Chronic Specialty: Unstable-Eye
19 No Longer in Use*
20 Dermatologic
21 Injuries/Adverse Effects: Minor
22 Injuries/Adverse Effects: Major
23 Psychosocial: Time Limited, Minor
24 Psychosocial: Recurrent or Persistent, Stable
25 Psychosocial: Recurrent or Persistent, Unstable
26 Signs/Symptoms: Minor
27 Signs/Symptoms: Uncertain
28 Signs/Symptoms: Major
29 Discretionary
30 See and Reassure
31 Prevention/Administrative
32 Malignancy
33 Pregnancy
34 Dental
29
1.8.5.2 Charlson index
The Charlson index is also widely used for risk adjustment in research and has the greatest
number of studies validating its use.138
It was initially developed to predict mortality in clinical
research135
and was validated in hospital populations.138
International Classification of Disease
codes from hospital discharge data identify specific diseases with weights of 1, 2, 3 or 6. Table
1.3 lists the conditions and weights included in the Charlson Index. Weights are added to get a
total score, with higher scores signifying greater comorbidity. The Charlson index was used only
in study 3 (Mortality in people with chronic pain, Chapter 4) along with the ACG system in order
to more closely match on factors predicting mortality, since this was the outcome of interest.
Table 1.3: Charlson disease weights
Condition Weight
Myocardial infarction
Congestive heart failure
Peripheral vascular disease
Cerebrovascular disease
Dementia
Chronic pulmonary disease
Connective tissue disease
Ulcer disease
Mild liver disease
Diabetes
1
Hemiplegia
Moderate or severe renal disease
Diabetes with end organ damage
Any tumor
Leukemia
Lymphoma
2
Moderate or severe liver disease
3
Metastatic solid tumor
AIDS
6
30
1.9 Data sources
1.9.1 Ontario healthcare administrative data
Healthcare administrative data was used for all three studies conducted for this thesis.
Administrative data is gathered during the routine delivery of healthcare, and is different than
that gathered specifically for research. Studies conducted using administrative data have both
advantages and disadvantages relative to data gathered in clinical trials. Firstly, healthcare
administrative data is real-world data. It reflects what has happened in patient care and so is
usually highly generalizable. While a randomized clinical trial (which is usually a highly
controlled setting) might be able to answer a question of whether a particular intervention can
prevent an outcome, research using administrative data is better able to answer whether the
intervention does prevent an outcome in the real world. In addition to being highly
generalizable, it is not subject to recall bias, a clear advantage for research on healthcare use.
Using administrative data for research may be more efficient than gathering primary patient data
from both a cost and time perspective. The costs associated with data gathering can be
substantial in clinical trials. Since administrative data is already available, there are no costs for
data gathering and costs to researchers are usually only related to planning/designing, data
analysis and reporting. While there may be large infrastructure costs associated with maintaining
the databases, they are usually incurred regardless of research activity. Additionally, using
administrative data rather than primary data gathering for research allows generation of
knowledge without putting patients at risk or subjecting subjects to additional burden (e.g.
surveys) even if risk is minimal.140
31
Research using administrative data may be much faster than clinical trials. A large cohort study
may take several years to recruit adequate numbers of participants and then several years more to
have sufficient data for outcome assessment. In contrast, more than 20 years of Ontario
administrative data is available for analysis at the Institute for Clinical Evaluative Sciences
(ICES), although research questions may dictate what interval of data use is appropriate.
There are some limitations to using administrative data. Since the data was created for different
purposes, researchers are limited by what is available. This may mean that some research
questions cannot be answered. There may be challenges with identification of a cohort of
interest due absence of ICD codes (e.g. people with chronic pain) or invalid or unreliable use of
diagnosis, procedure or billing codes. There may also be missing data or incorrect data due to
lack of quality control measures which could lead to misclassification.
Some data may be missing because of lack of payment for services or products, e.g. the drug
program in Ontario covers a specific drug list for everyone over 65 years and those on social
assistance under 65 years. Physiotherapy and psychological services are examples of healthcare
that is not funded by the Ontario health system so cannot be measured. This means that study 1
can only include healthcare costs incurred by the provincial payer, which probably
underestimates the total cost incurred to manage chronic pain.
Data from the databases housed at ICES was used to obtain baseline demographic information,
comorbid conditions, determine healthcare use and costs, and cause of death. These databases
contain claims or records of essentially all publically funded healthcare in Ontario. They are
commonly used in Ontario health services research including costing studies. Costs were
estimated using methods established by The Canadian Agency for Drugs and Technologies in
32
Health (CADTH) and the Health System Performance Research Network.53,141-143
Multiple
reviews have been conducted on the quality of Canadian and Ontario administrative data.144-146
ICES has drawn the following broad conclusions:147
demographic information is reasonably
complete and accurate; procedure codes in hospital and OHIP databases are relatively complete
and accurate; primary diagnosis coding in hospital is generally reliable; clinical data for
coexisting conditions and complications in hospital are not consistently coded; billing for
physician services are complete and relatively accurate but diagnostic codes are useful only at
the aggregate level because of variability in coding practices. None of the three studies
presented here relies on diagnostic codes for identification of cohorts or costs so this limitation
probably did not have an important impact on results.
The following table (Table 1.4) summarizes the databases and validity being used.
33
Table 1.4: Summary of administrative claims databases and key variables used
(from reference141
)
Database Setting Description Key Variables Validity
Ontario Registered
Persons Database
(RPDB)
General
Population
The RPDB contains data on the vital status of
all Ontario residents covered under OHIP.
• Demographic
Variables
• Eligibility for
health benefits
• Date of death
• Captures all Ontario residents with a valid
OHIP-number.
• RPDB is not regularly updated, and
individuals who move are often not
recorded.146
Discharge Abstract
Database (DAD)
Acute Inpatient
Hospitalizations
The CIHI DAD is a National database that
contains demographic, clinical, and
administrative data for inpatient hospital
admissions. It contains over 1 million
inpatient abstracts from 178 acute care
facilities in Ontario.148
• ICD-9-CA
• ICD-10-CA
• CCI* and CCP
†
• Resource Intensity
Weight
• Nearly 85% agreement between abstractor
and most responsible diagnosis code.148
• Sensitivity and Positive Predictive Values
>95% for codes related to fractures of
femur.148
National Ambulatory
Care Reporting
System (NACRS)
Emergency, Day
Surgery, and
high-cost
ambulatory
treatments
The NACRS was fully developed in Ontario
in 2002 and contains data for all ambulatory
care including emergency department visits,
outpatient clinics, and day surgery.
• ICD-9-CA
• ICD-10-CA
• CCI and CCP
• Resource Intensity
Weight
• In reabstraction and inter-rater reliability
studies, agreement rates in the selection of
main problem was >85%, and >73% for
reason for visit.149
National
Rehabilitation
Reporting System
(NRS)
Rehabilitation The NRS contains National data on
rehabilitation facilities and clients, collected
from participating adult inpatient
rehabilitation facilities and programs.
• Admission Date
• Discharge Date
• FIM‡ Scores
• Rehabilitation
Client Group
All eligible rehabilitation facilities in Ontario
are included; 100% response rate. Discharge
record missing for 2.2% of Ontario
episodes.150
* CCI: Canadian Classification of Health Interventions
† CCP: Canadian Classification of Diagnostic, Therapeutic, and Surgical Procedures
‡ FIM: Functional Independence Measure
34
Database Setting Description Key Variables Validity
Continuing Care
Reporting System
(CCRS)
Complex
Continuing Care
In 2004 the CCRS replaced the Ontario
Chronic Care Patient System (OCCPS).
CCRS contains clinical and demographic
information on individuals receiving facility
based continuing care. Services include
medical LTC, rehabilitation, geriatric
assessment, respite care, and palliative care,
and nursing home care. Patients are classified
into 44 Resource Utilization Groups (RUGs),
and are assigned a Case Mix Index (CMI) that
approximates their per day resource usage.
CMI is reviewed every quarter and can be
adjusted multiple times.
• Admission Date
• Discharge Date
• Case Mix Index
• Assessment Date
• 12 out of 43 Minimum Data Set Resident
Assessment Instrument (MDS) diagnoses
attained a sensitivity of at least 0.80,
including 7 of the 10 diagnoses with the
highest prevalence as an acute care primary
diagnosis before CCC admission. Despite
this some MDS diagnoses had low
sensitivity.151
Ontario Mental Health
Reporting System
(OMHRS)
Mental Health Starting October 1, 2005 Ontario Mental
Health Reporting System (OMHRS) was
implemented to assess persons in mental
health (MH) beds using Minimum Data Set
for MH (MDS-MH) on admission, discharge,
or every 92 days for persons with longer
stays. Each inpatient is assigned a Case Mix
Index (CMI) that approximates his/her per
day resource use. CMI is reviewed every
quarter and can be adjusted multiple times.
Some MH cases are still in inpatient acute
beds (DAD).
• Admission Date
• Discharge Date
• Case Mix Index
• Assessment Date
• Inter-rater reliability study found that almost
all items on the RAI-MH* had kappa value
above 40%.152
• Only about 15% of the items in RAI-MH
instrument had kappas below 0.60.153
* RAI-MH: Resident Assessment Instrument-Mental Health
35
Database Setting Description Key Variables Validity
Prior to 2003: Ontario
Drug Benefit (ODB)
For 2003-2010: Client
Profile Database
(CPRO)
From 2010:
Continuing Care
Reporting System
(CCRS)
Long-Term Care Prior to 2003 admission date was determined
as the first of a minimum two consecutive
LTC flags. Date of discharge was determined
as two non LTC-flagged claims.
Client Profile Database (CPRO) provides
client’s date of admission to Long-Term Care
facility.
In April 2010 MOHLTC moved to a new
classification system based on Resource
Utilization Groups (RUGs). LTC residents
are classified into 34 RUGs, and the
Ministry’s per diem funding amount for the
LTC home is adjusted for resident’s acuity.
• Admission Date
• Discharge Date
• Case Mix Index
• Assessment Date
• The Minimum Data Set Resident
Assessment Instrument (MDS) demonstrates
a reasonable level of consistency both in
terms of how well MDS diagnoses
correspond to hospital discharge diagnoses
and in terms of the internal consistency of
functioning and behavioral items.
• The PPV and sensitivity levels of Medicare
hospital diagnoses and MDS based
diagnoses were between .6 and .7 for major
diagnoses like CHF, hypertension, diabetes.
• MDS discharge tracking record should still
not be used to indicate Medicare
hospitalizations or mortality.154
Ontario Home Care
Administrative System
(OHCAS) and Home
Care Database (HCD)
Home Care The OHCAS and HCD provide data on
government-funded services coordinated by
Ontario’s Community Care Access Centres
(CCAC), for individuals requiring home care 155
. The HCD replaced the OHCAS in
2005/2006 fiscal year.
• Services Provided
• Admission/
discharge date
• All sites in all CCACs are represented.
• Valid health card numbers are not always
available (e.g. homeless clients, disoriented
or elderly persons) and therefore not all
individuals in a cohort will be captured.
• Incomplete data for some regions.
Ontario Health
Insurance Plan
(OHIP)
Outpatient and
Physician
Services
OHIP data cover all services and procedures
provided by healthcare providers who can
claim under OHIP (physicians, laboratory
services).
• Date of service
• Fee Code
• Fee Paid
• Approximately 95% of Ontario physicians
have a fee for service practice,156
with
alternative funding plans primarily using
shadow billing.
Ontario Drug Benefit
(ODB)
Prescription
Drugs
The ODB includes all drugs dispensed in
community pharmacies and LTC/nursing
facilities.
The ODB covers all seniors in Ontario (aged
65+) and those on social assistance for all
prescriptions listed in the provincial
formulary (approximately 3,200).
• Drug Identification
Number (DIN)
• Fee Paid by
Ministry of Health
• Long Term Care
Indicator
• Second largest prescription database in
Canada.
• At least 95% of seniors filled 1 Rx in ODB
over a 5 year period; however 15-20% filled
a Rx from a private insurer.157
• High coding reliability, overall error rate of
0.7% (95% CI 0.5%-0.9%).158
• Drugs dispensed during acute
hospitalizations are not captured.
36
1.9.2 Canadian Community Health Survey
The Canadian Community Health Survey (CCHS) was used to identify the chronic pain cohort
and potential matches without chronic pain in this thesis. Statistics Canada has used the CCHS
since 2000 to track population-level information about many aspects of health, including pain, on
a biennial basis.159
The survey is cross-sectional although a small number of respondents have
participated more than once. Some content is constant over many cycles of the survey and other
content varies. Provinces are divided into health regions and sampled representatively from each
region. Data are collected from each region in each month of data collection, to minimize
seasonal variation in responses to questions. Methods ensure the survey is representative of 98%
of Canadians.159
At the time of survey participation, respondents are asked to voluntarily
provide their provincial health card number to allow linkage of their responses to their healthcare
administrative data. People living in long-term care facilities, on First Nations reserves, Crown
land or in full time military service were not surveyed.159
Three cycles of the CCHS held at
ICES contained a question about chronic pain (2000-01, 2007-08, 2009-10). The proportion of
Ontarians who agreed to respond to the survey when approached varied from 70 to 82%.160-162
Linked healthcare administrative data from approximately 100,000 Ontarians age 12 and older
were available for use. Many research studies have used the CCHS to examine population health
trends in Canada.163-165
This thesis used the same methods as past research to identify people in
the CCHS with and without chronic pain (see below).27,166
1.9.2.1 Identification of the chronic pain cohort (prevalent cohort)
Ontarians who participated in the CCHS and who responded “No” to the following question
were included in the study as cases: “Are you usually free of pain or discomfort?” Respondents
37
who answered “Yes” to same pain question were included in the pool of potential matches for
the chronic pain cohort. The CCHS asked two further questions about pain that were used to
define subgroups. People were asked “How would you describe the usual intensity of your pain
or discomfort?” and could respond with “mild,” “moderate” or “severe.” They were also asked
“How many activities does your pain or discomfort prevent?” and could respond with “none,” “a
few,” or “some” or “most.” The question and subquestions are part of the Health Utilities Index
Mark 3, an indirect utility instrument with a large body of research (hs.mcmaster.ca/hug/). The
pain questions were included in the three cycles of the CCHS used for this research, while the
additional questions required to estimate the HUI3 were present in only two of the three survey
cycles.
1.9.2.2 Health Utilities Index
In some cycles of the CCHS, the questions to determine utilities from the HUI3 were included.
Utilities for each respondent were then calculated by Statistics Canada using the algorithm
developed by Health Utilities Group (hs.mcmaster.ca/hug/). The instrument has been included in
national population health surveys by Statistics Canada since the early 1990s.78
The instrument
has shown good test–retest reliability in a study of Canadians aged 65 years and older (kappa
0.83, 95% confidence interval [CI] 0.73-0.93).167
1.10 Rationale for research
Almost no population-based health outcomes research has been conducted on chronic pain in
Canada. Only two studies have estimated the cost of chronic pain in Canada.31,45
One used
health utilization data from 1996, and additional sources of data are now available to provide a
more comprehensive estimate.45
Moreover, patterns of care might have changed over this time
38
period. The other study estimated costs in a selected group of patients with chronic pain that
may not be generalizable to the overall population.31
Seven additional Canadian studies targeted
selected chronic pain populations, so while informative, they are not generalizable to the overall
chronic pain population.34-36,43,44,46
There are no studies of the cost of chronic pain in Canadian
children.
While studies from other countries are helpful, they do not describe costs in Canada’s publicly
funded health system or reflect Canadian patterns of care. It is essential that Canadian cost
estimates are up-to-date and comprehensive, in order for policy makers to appreciate the
magnitude of this problem in economic terms. This may lead to better inform decisions about
resource allocation, program funding, future planning and allocation of research dollars. Chapter
2 reports on research to estimate the cost burden of chronic pain in Ontario from the perspective
of the health care payer, the Ontario Ministry of Health and Long-Term Care.
Some population-based estimates for health utilities in people with chronic pain exist, but none
have been conducted in Canada, except for an estimate of utility decrement.82,87-89
Moreover,
there has been no estimate of chronic pain using the indirect HUI instrument. Chapter 3 reports
research to estimate utilities and utility decrements for people with chronic pain, and includes
comparisons with other diseases and conditions, to provide context for disease burden.
No population-based research on the mortality associated with chronic pain has been conducted
in Canada, although some research exists internationally.106,107,110
Gaining a better
understanding of mortality in people with chronic pain may help focus government policy in
specific areas. Clarifying cause of death in people with chronic pain will also help clinicians
providing care to people with chronic pain. Examining suicide in people with chronic pain may
39
help inform health and social policy. Chapter 4 reports the research describing mortality,
including suicide in people with chronic pain.
40
Chapter 2
Reproduced with permission from PAIN (Appendix E)
Hogan ME, Taddio A, Katz J, Shah V, Krahn M. Incremental healthcare costs for chronic pain
in Ontario, Canada - a population-based matched cohort study of adolescents and adults using
administrative data. Pain 2016;157(8):1626-33.
41
2 Incremental Healthcare Costs In People With Chronic Pain
2.1 Abstract
Little is known about the economic burden of chronic pain, and how chronic pain affects
healthcare utilization. We aimed to estimate the annual per-person incremental medical cost and
healthcare utilization for chronic pain in the Ontario population from the perspective of the
public payer.
We performed a retrospective cohort study using Ontario healthcare databases and the
electronically linked Canadian Community Health Survey (CCHS) from 2000 to 2011. We
identified subjects aged 12 years and above from the CCHS with chronic pain and closely
matched them to individuals without pain using propensity score matching methods. We used
linked data to determine mean one-year per-person healthcare costs and utilization for each
group and mean incremental cost for chronic pain. All costs are reported in 2014 Canadian
dollars.
After matching we had 19,138 pairs of CCHS respondents with and without chronic pain. The
average age was 55 years (SD 18) and 61% were female. The incremental cost to manage
chronic pain was $1,742 per person (95% CI 1,488 – 2,020), 51% more than the control group.
The largest contributor to the incremental cost was hospitalization ($514, 95% CI $364 to $683).
Incremental costs were highest in those with severe pain ($3,960, 95% CI $3,186 to $4,680) and
those with most activity limitation ($4,365, (95% CI $3,631 – $5,147).
42
The per-person cost to manage chronic pain is substantial and more than 50% higher than a
comparable patient without chronic pain. Costs are higher in people with more severe pain and
activity limitations.
2.2 Introduction
Chronic pain is a common condition, affecting approximately 1 in 5 people.5,14,168
The
prevalence of chronic pain is rising due to an aging population and the increasing prevalence of
conditions such as diabetes169
and obesity that are associated with chronic pain.170-172
Many
people with chronic pain do not receive adequate pain management and this contributes to
disease burden.14,168,173,174
Moreover, the emotional toll associated with chronic pain more than
doubles the odds of suicidality.13
Chronic pain has recently gained attention due to extensive reporting in the lay press about
prescription opioid misuse and its public health impact.175-179
Governments have taken notice,
issuing both policy reports180-182
and new legislation.183-185
In addition, the Centres for Disease
Control are expected to issue a guideline for prescribing opioids for chronic pain in early 2016.
The harm of prescription opioids and cost in social and health terms has also been supported by
academic research.182,186,187
To date, however, we believe that both the health burden and the
economic burden of chronic pain and its management have not been fully appreciated.
Media, government and academia all point to a need for change in care for people with chronic
pain but it is also (and perhaps first) important to gain an understanding of current patterns of
care and costs. Some less-used approaches like physiotherapy and psychological services to
cope with pain may be more resource intensive, expensive, and if covered by an individual’s
insurance, may come with high copayments.188
Cost-effectiveness studies for these and other
43
strategies may be warranted, and high quality cost data is a necessary input. Cost data can also
help decision makers in healthcare organizations plan and organize care, and when compared to
other conditions, can inform priorities for research.
Only two studies of the incremental cost of chronic pain in adults189
and in children65
have been
conducted. In both studies, the results were limited by identification of people with specific
types of chronic pain or impairment in ability to work (for adults). In addition, their data source
provided knowledge about only a small number of comorbidities. As a consequence, it is
possible that less-severe individuals were excluded and some residual confounding remained,
leading to an overestimation of the true cost of chronic pain. Other studies estimated the total
cost of care for patients with chronic pain or failed to use an incremental approach.31,62,64,66-
68,87,190-193 Incremental costs are preferred for cost-of-illness research;
42,194 they are most relevant
to policy makers since they reflect the extra cost incurred from a disease or condition and so
represent potential savings if the condition was resolved.
Our data source provided a more robust case identification for people with chronic pain and
allowed for a more comprehensive correction of confounding by comorbidity. With this data, we
aimed to estimate the per-person one year incremental cost to manage chronic pain in a single
payer health system and place it in the context of current healthcare spending.
2.3 Methods
We conducted a retrospective matched cohort study using three cycles of the Canadian
Community Health Survey93,94,195
linked to Ontario healthcare administrative data from
September 1999 to December 2011. We used a prevalence approach which included all people
with the condition regardless of disease onset. We took the perspective of the payer, the
44
Ministry of Health and Long-Term Care for Ontario, Canada; only direct medical costs borne by
the payer were considered. Consequently, indirect costs such as lost income and intangible costs
from pain and suffering were not included.
2.3.1 Cohort identification
Survey respondents who were covered under the Ontario Health Insurance program (OHIP) at
the time of the survey and had their survey data linked to Ontario health administrative data were
eligible. Those who endorsed chronic pain in the survey (HUIA_2893
, HUP_28195
or HUP_0194
)
were eligible cases, and those who denied chronic pain were eligible controls. The same
question has been used to identify people with chronic pain in two reports on chronic pain from
Statistics Canada and one for the government of Alberta.196-198
It is part of the Health Utilities
Index, a validated tool for determining health utilities, and has been used in population-level
surveys in Canada since the 1990s.77,199
The question can be found in the Supplemental Digital
Content 1 (Appendix B). We excluded the second or third response if respondents replied to
multiple surveys. We also excluded respondents who died in the first year after the survey to
ensure complete data for one year. The survey was necessary to identify the cohort since there
are no International Classification of Disease (ICD) codes for chronic pain.
2.3.2 Data source and costs
The administrative data used to track usage or pay healthcare providers for services was used to
determine healthcare use and cost. The Ontario health system provides medically necessary care
for all residents for physician visits, hospital admissions, emergency department visits,
diagnostic tests, homecare, long-term care, complex continuing care, rehabilitation and provides
partial coverage for some medical equipment such as wheelchairs. Drugs are provided to people
45
65 years and older, low-income Ontarians receiving social assistance and working people
younger than 65 years who have out-of-pocket drug costs greater than approximately 3% of their
income. These databases have been used extensively for health services research in Ontario
(www.ices.on.ca/Publications). Many studies of data validation have been conducted.200,201
Demographic information was complete in > 97% in studied databases. Agreement in re-
abstraction studies with most responsible diagnosis or main problem in hospitalization and
emergency department data was 85%. Agreement with coded procedures was 85% or higher.
Almost all physicians (95%) bill for their services in OHIP. Less than 1% error was found in the
ODB database. We used validated, person-centred methods developed for Ontario
administrative data to convert estimates of resource utilization to cost.201
Due to changes in the
Ontario cost distribution methodology, costs were not available for survey respondents from the
2000-01 CCHS survey.201
All costs were adjusted to 2014 Canadian dollars using the healthcare
component of the Consumer Price Index.202
The 2014 average exchange rate for a Canadian
dollar was US$0.91 (www.bankofcanada.ca/rates/exchange/). Further information is available in
Supplemental Digital Content 1 (Appendix B).
2.3.3 Estimating incremental cost
We matched cases and controls based on demographic and clinical characteristics. The survey
response date for each was used as the index date for cost calculation. We looked back one year
from index to assess co-morbidity using administrative data. Once controls and cases were
closely matched, we looked forward one year to measure healthcare utilization and costs for each
individual. We subtracted one year costs for each control from one year cost for each case to
determine the incremental (also sometimes referred to as net or attributable) cost to manage
46
chronic pain. Incremental cost methodology is the preferable way to estimate the cost of illness
because it represents the portion of healthcare cost related to the condition.114,194
To ensure close matching, we used logistic regression to calculate a propensity score for the
probability of having chronic pain using the entire sample of cases and possible controls.
Covariates included in the logistic regression were the 2008 Rurality Index of Ontario (a measure
from zero [urban] to 100 [most rural] using the respondent’s postal code),130
a morbidity measure
(ADGs, Appendix B), and estimated household income quintile at the neighbourhood level
(using the respondent’s postal code and Canadian census data).203,204
We also included an age
squared term (for adults and older adults only) and an age-sex interaction term.
The matching algorithm was age ± 1 year, sex, index year ± 1 year and the logit of the propensity
score, allowing up to a 0.2 standard deviation caliper width. Propensity score calculation and
matching was done separately for respondents aged 12 – 17 years, 18 – 64 years and those aged
65 years and above.
2.3.4 Analysis
The quality of the match was assessed by calculating standardized differences between groups,
having a goal of less than 10% (Appendix B).117
We estimated means of cost differences by age
category, sex, pain intensity and activity impairment for one year following the index date. We
determined frequency of physician contact, hospital stays, emergency department visits,
computed tomography (CT) use, magnetic resonance imaging (MRI) use and opioid
prescriptions dispensed (see Table B.3, Appendix B for drugs and drug identification numbers).
We determined 95% confidence intervals by bootstrap resampling (500 samples with
replacement, with size equal to the original sample). For extrapolation to the Ontario and
47
Canadian populations, we applied CCHS sample weights to cost differences to calculate the
mean annual incremental per-person cost. For 95% confidence intervals, we used bootstrap
replicate weights for the CCHS from Statistics Canada.205
We applied the CCHS weighted
sample prevalence and mean incremental cost of chronic pain to the 2014 population for Ontario
and Canada206
to obtain the burden of chronic pain. All data was analyzed with SAS Enterprise
Guide version 6.1 (SAS Institute Inc., Cary, NC, USA, www.sas.com).
All data for the study was held by the Institute for Clinical Evaluative Sciences
(www.ices.on.ca/) in Toronto, Canada. Individual records in the datasets were linked across
databases using unique encoded identifiers and anonymized before analysis. Cells with fewer
than 6 individuals contributing are reported as ≤ 5 for patient confidentiality. The study was
approved by research ethics boards at Sunnybrook Health Sciences Centre and the University of
Toronto.
2.3.5 Sensitivity analysis
We conducted two additional analyses using different matching algorithms. In our base case, we
may have matched on some conditions that were causally related to patients’ chronic pain (e.g.
depression) because for many respondents, their chronic pain and related conditions were present
during the lookback period (a previous study reported that half of people with chronic pain
experienced it for at least 10 years).5 Consequently, the base case may have underestimated the
true incremental cost. We therefore performed an additional analysis removing the comorbidity
variable in the matching algorithm.
In our base case, the rates of painful diseases, anxiety, and depression were higher in the
matched cases versus controls. Although matching using ADGs should control sufficiently for
48
comorbidity, we included an additional analysis that matched on presence of ICD codes for
painful and related conditions. These were abdominal pain (adolescents only), arthritis, back and
neck problems, fibromyalgia, migraine, neuropathic pain, depression, anxiety and sleep
problems. We included an extra interaction term of age*arthritis for adults and older adults only
because we hypothesized a different effect related to age and arthritis (see Table B.1 and Table
B.2 for ICD codes, Appendix B).
2.4 Results
The Canadian Community Health Survey provided 101,195 respondents aged 12 years and older
from the three survey cycles. A total of 1,692 were excluded for OHIP ineligibility, an invalid
answer to the chronic pain question, a second or third survey response or dying within 1 year of
the survey response. Of the remaining 99,503 respondents used for analysis, 19,879 (20%) had
chronic pain. After matching, there were 19,138 pairs of respondents with and without chronic
pain. Demographic and clinical characteristics at the index date along with standardized
differences are shown in Table 1.1 before and after matching.
Before matching, the survey respondents with chronic pain were older (55 years versus 45
years), were more likely to be female (61% vs 53%) and have more comorbidity than those
without chronic pain (5 versus 3 ADGs). There were more people with chronic pain in the
lowest income quintile and fewer people with chronic pain in the highest income quintile (all
comparisons had standardized differences ≥ 0.1).
After matching, the average age in both groups was 55 years and 61% were female. Important
differences in income quintile distribution resolved. The number of ADGs was 5 in each group
but there were higher proportions of people with ICD codes for arthritis, back or neck problems,
49
neuropathic pain, abdominal pain in adolescents and anxiety in the cohort with chronic pain
(standardized differences ≥ 0.1).
2.4.1 Healthcare utilization
Patients with chronic pain had greater healthcare utilization across all measured variables. There
were 47% more patients with chronic pain who had at least 10 physician visits compared to their
matched controls (28% versus 19%), more patients with at least 1 emergency department visit
(34% versus 27%) and at least 1 hospitalization (25% versus 19%). More patients with chronic
pain had a CT compared to their matched controls (13% versus 9%), and more had an MRI (7%
versus 4%). Use of opioids was also greater, with 18% having at least one opioid prescription
compared to 7% in the control group (all p < 0.001) (Figure 2.1).
2.4.2 Healthcare costs: CCHS sample
Costs were available for 13,336 pairs of survey respondents from the 2007-08 and 2009-10
CCHS surveys. Mean annual total costs per person were $5,177 in the group with chronic pain
and $3,435 in the matched controls. The annual incremental cost to manage chronic pain for the
matched sample was $1,742 per person (95% CI $1,488 – $2,020), a 51% increase over the
matched controls. Annual total and incremental costs per person and by age group are presented
in Table 2.2. The largest contributor to the incremental cost was hospitalization ($514, 95% CI
$364 to $683), followed by drug costs ($365, 95% CI $323 to $406) and physician care ($292,
95% CI $250 to $336). The proportion of annual healthcare costs by healthcare sector is
presented in Table 2.3 by age group.
50
Incremental cost increased with pain intensity and number of activities prevented (Figure 2.2):
the annual incremental cost per person with moderate pain was $1,643 (95% CI $1,479 –
$2,008), while the incremental cost for severe pain was $3,960 (95% CI $3,186 to $4,680). The
annual incremental cost per person who reported that some of their activities were prevented by
pain was $1,864 (95% CI $1,353 – $2,313) and the incremental cost per person who reported
most of their activities were prevented by pain was $4,365 (95% CI $3,631 – $5,147). Annual
incremental costs per person for age groups are presented in Table B.6 (Appendix B), stratified
by sex, pain severity and pain causing activity limitation.
2.4.3 Healthcare costs: extrapolation to population
Using the CCHS survey weights, respondents in the survey represented 2,072,691 Ontarians and
5,375,298 Canadians with chronic pain. The weighted annual per-person incremental cost to
manage chronic pain for survey respondents was $1,334 (95% CI $981 - $1,688). The total
annual burden of chronic pain was estimated at $2.8 billion (95% CI $2.0 billion - $3.5 billion)
for Ontario and $7.2 billion (95% CI $5.3 billion - $9.1 billion) for Canada. Adults aged 18 - 64
years contributed 70% of the cost; older adults aged ≥ 65 years contributed 28% and adolescents
aged 12- 17 years contributed 2%.
2.4.4 Sensitivity analyses
Baseline characteristics for the two matches used as sensitivity analyses can be found in Table
B.4 and Table B.5, (Appendix B). Participants, as expected, had differences in comorbidity in
the first analysis and no differences in measured comorbidities in the second analysis. For the
entire sample, the algorithm without matching on comorbidity resulted in a larger per-person cost
estimate ($2,399, 95% CI $2,112 - $2,697) than the base case ($1,742). The algorithm with
51
additional matching on painful conditions, depression, anxiety and sleep problems resulted in a
similar per-person cost ($1,735, 95% CI $1,463 - $2,017).
2.5 Discussion
We found the annual incremental per-person cost to manage chronic pain was $1,742, a 51%
increase over cost in the control group. The cost was higher in patients reporting more severe
pain and more activity limitations. When the Ontario population estimate and per-person cost is
extrapolated to the 2014 Canadian population, the overall annual estimate to manage chronic
pain for adolescents, adults and older adults may be $7.2 billion annually, which is
approximately 5% of the projected 2014 public health expenditures in Canada ($151 billion).207
The cost of managing chronic pain is comparable to the cost of some other top ranked costly
diseases in Canada. The Economic Burden of Illness in Canada 2005-2008 report, which
includes all direct medical costs, reported the cost of cardiovascular disease as the most
expensive at $11.7 billion annually, followed by neuropsychiatric disorders at $11.4 billion,
musculoskeletal diseases at $5.8 billion and digestive diseases at $5.5 billion. Diabetes was
further down the list at $2.2 billion (2008 $CAD).208
Research on per-person costs for chronic
diseases in Canada has estimated the annual per-person incremental cost of managing
hypertension at $2,341 (2014 $CAD)209
and the 8 year per-person incremental cost of managing
new cases of diabetes at approximately $10,000 (2014 $CAD).210
Little prior research on the incremental cost of chronic pain has been conducted. In the United
States, the annual per-person incremental cost for all healthcare expenditures for moderate pain
compared to no pain has been estimated at $4,516 (2010 $US) and severe pain added an
additional $3,210.189
The incremental cost for moderate chronic pain represents an approximate
52
54% increase over the Organization for Economic Cooperation and Development estimate of
average per capita health spending in the US of $8,233 in 2010,211
and may be comparable to our
findings of a 51% increase over the control group. Another US study using incremental methods
estimated the cost of pediatric chronic pain at $1,339 (2013 $US)65
while our pediatric estimate
was lower at $956 (2014 $CAD). Our study identified participants from a survey question that
was specifically about chronic pain while the two studies above relied on questions about types
of pain or disability. That difference, combined with our ability to control for a greater amount
of comorbidity using the ACG system and ICD codes may have contributed to differences in cost
estimates. A number of studies have estimated annual pain-specific costs,31,64,66-68,190
but
investigation of these methods have found higher per-person costs than incremental methods,
making direct comparison with our results difficult.42,194
Some studies estimating total
healthcare costs for patients with chronic pain,62,87,191
included disease costs such as treatment of
rheumatoid arthritis192,193
or used top-down methods without patient-level data,212
none of which
are directly comparable to our estimates.
2.5.1 Limitations
Our study has some limitations. Cases were identified via a question in the CCHS and did not
indicate a specific time of chronic pain duration, possibly leading to misclassification. However,
the prevalence estimate from the CCHS is similar to a more rigorously designed Canadian
prevalence study.5 We also did not know how long study participants experienced chronic pain,
which could have facilitated a more comprehensive assessment of costs stratified by onset.
Some publicly funded costs are not currently identifiable. These include the technical or
overhead cost associated with hospital-based outpatient clinics, hospital based diagnostic tests
53
(e.g. CT, MRI and outpatient hospital-based laboratory tests), and payments for a small number
of hospital-based physicians who are paid in an alternate funding program.201
This may mean
the true cost is slightly higher than our estimate.
We used ICD codes for depression, anxiety, sleep problems and ICD codes for painful conditions
for descriptive purposes in evaluation of our cohorts and to balance our groups in a sensitivity
analysis. We used published ICD-9 or ICD-10 codes that have previously been used in
administrative data research but formal validation studies have not been conducted for most of
the codes.43,44,213-220
Importantly the present study does not capture the full health cost of managing chronic pain, only
the publicly funded portion. Additional research on patients with chronic pain drawn from a
primary care-based chronic pain population may be warranted to elucidate the full societal costs
of chronic pain.
2.5.2 Strengths
Our study has a number of strengths. We used a large, population based sample of almost
100,000 adolescents and adults that increases precision and generalizability. We used rigorous
methods to carefully control for potential biases and ensure a close match between individuals
with chronic pain and those without chronic pain. We conducted sensitivity analyses with
different matching algorithms to address uncertainty. We provided estimates for the incremental
cost of managing chronic pain, which are more relevant than total costs. We provided a
comprehensive estimate of physician and non-physician outpatient care, hospitalization, medical
equipment, long-term care, rehabilitation, complex continuing care, homecare and drugs. We
stratified by age group and sex, facilitating use of the results in multiple settings. Importantly,
54
our cost estimates represent actual costs to the payer, avoiding recall bias and making the
information highly relevant to decision makers.
2.5.3 Conclusions
In summary, the incremental cost to manage people with chronic pain is large, approximately
51% greater than treating patients without chronic pain. The additional cost of caring for people
with chronic pain represents approximately 5% of public healthcare spending in Canada. This
annual economic burden is greater than current Canadian estimates for diabetes but below that of
cardiovascular disease. This data will be useful for planning, justifying new programs and
research initiatives, and as reference data for cost-effectiveness and cost-utility analyses.
2.6 Acknowledgements
This study was supported by the Institute for Clinical Evaluative Sciences (ICES), which is
funded by an annual grant from the Ontario Ministry of Health and Long-Term Care
(MOHLTC). The opinions, results and conclusions reported in this paper are those of the
authors and are independent from the funding sources. No endorsement by ICES or the Ontario
MOHLTC is intended or should be inferred.
Parts of this material are based on data and information compiled and provided by Canadian
Institutes for Health Information (CIHI). However, the analyses, conclusions, opinions and
statements expressed herein are those of the author, and not necessarily those of CIHI.
Mary-Ellen Hogan was supported by a research award from the Canadian Pain Society and
scholarships from the University of Toronto Centre for the Study of Pain, the Canadian Institutes
of Health Research Strategic Training Initiative in Health Research for Pain in Child Health and
55
The Hospital for Sick Children. Anna Taddio has received a Pfizer Research Grant and research
supplies from Natus and Ferndale. Joel Katz is supported by a Canada Research Chair in Health
Psychology. Murray Krahn is supported by the F. Norman Hughes Chair in
Pharmacoeconomics.
The authors have no other conflicts of interest to declare.
56
2.7 Tables
Table 2.1: Clinical and demographic characteristics
Chronic
pain
n=19,879
Control pool
n=79,624
d† Cases
n=19,138
Controls
n=19,138
d†
Age, mean (SD) 55 (18) 45 (21) 0.53 55 (18) 55 (18) <0.01
Female, number (%) 12,134 (61) 42,018 (53)
0.17 11,670
(61)
11,670
(61) <0.01
Rurality index of Ontario
2008, mean (SD) 19 (22) 17 (21)
0.06 19 (22) 18 (22) 0.02
Income quintile, number (%)
1 (low) 4,687 (24) 14,770 (19) 0.12 4,425 (23) 4,385 (23) <0.01
2 4,202 (21) 15,694 (20) 0.04 4,040 (21) 4,159 (22) 0.02
3 3,861 (19) 16,076 (20) 0.02 3,743 (20) 3,734 (20) <0.01
4 3,735 (19) 16,395 (21) 0.05 3,653 (19) 3,519 (18) 0.02
5 (high) 3,344 (17) 16,387 (21) 0.10 3,276 (17) 3,341 (18) 0.01
Ambulatory Diagnostic Group,
mean (SD)* 5 (3) 3 (3)
0.58 5 (3) 5 (3) 0.06
Ambulatory Diagnostic Group,
mean (SD)‡
5 (3) 3 (3) 0.54
5 (3) 5 (3) <0.01
Depression, number (%) 875 (4) 1457 (2) 0.15 806 (4) 486 (3) 0.09
Anxiety, number (%) 3,868 (20) 8,719 (11) 0.24 3,630 (19) 2,943 (15) 0.10
Sleep problems, number (%) 694 (4) 1,393 (2) 0.11 633 (3) 515 (3) 0.04
Arthritis, number (%) 4,292 (22) 4,690 (6) 0.47 4,088 (21) 1,872 (10) 0.32
Back or neck problems,
number (%) 3,624 (18) 5,383 (7)
0.35 3,439 (18) 1,835 (10) 0.25
Neuropathic pain, number (%) 2,630 (13) 3,201 (4) 0.33 2,484 (13) 1,087 (6) 0.25
Migraine, number (%) 547 (3) 1,099 (1) 0.10 505 (3) 289 (2) 0.08
Fibromyalgia, number (%) 26 (0) 16 (0) 0.04 23 (0) 8 (0) 0.03
Abdominal pain (%)§ 59 (13) 500 (6) 0.26 53 (12) 32 (7) 0.16
Hospitalization in last 12
months, number (%) 4,729 (24) 10,566 (13) 0.27 4,350 (23) 4,379 (23) <0.01
*The number of ADGs was calculated with all ICD codes.
‡The number of ADGs was calculated without ICD codes for painful conditions described in Appendix B.
†Standardized difference.
§ Numbers and percentages are reported for adolescents only.
57
Table 2.2: Annual healthcare costs
(2014 $CAD)
Cases
Controls
Incremental cost
95% CI
Entire sample (n=13,336
pairs)
$5,177 $3,435 $1,742 $1,488 – $2,020
12 – 17 years (n=279 pairs) $1,663 $ 706 $ 956 $ 240 – $1,861
18 - 64 years (n=8,564 pairs) $3,303 $2,043 $1,260 $ 989 – $1,524
≥ 65 years (n=4.493 pairs) $8,966 $6,257 $2,710 $2,090 – $3,316
Table 2.3: Proportion of annual healthcare costs by category
Per person 12-17 years 18 - 64 years ≥ 65 years
Percent of incremental cost n=279 pairs n=8,564 pairs n=4,493 pairs
Physician visits 51 21 12
Non-physician visits 2 < 1 0
Lab 1 1 1
Hospitalization 28 25 33
Emergency department 4 4 2
Outpatient
surgery/dialysis/oncology
2 7 3
Assistive devices program 2 1 1
Long term care 0 1 6
Rehabilitation 0 3 4
Complex continuing care 6 1 4
Homecare 1 10 16
Drugs 4 25 18
58
2.8 Figures
Figure 2.1: Annual health care utilization
All comparisons p<0.001 for chronic pain versus no chronic pain.
Figure 2.2: Incremental annual per-person cost by subgroup
»
0
5
10
15
20
25
30
35
40
At least 10physician
visits
At least 1emergencydepartment
visit
At least 1hospital stay
At least 1 CT At least 1MRI
At least 1opioid
prescription
Pe
rce
nt
Chronic pain
Controls
0
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
Females Males Mild Moderate Severe None A few Some Most
20
14
$C
AD
Pain severity Activity limitations from pain
59
Chapter 3
Reproduced with permission from Pain (Appendix E)
Hogan ME, Taddio A, Katz J, Shah V, Krahn M. Health utilities in people with chronic pain
using a population-level survey and linked healthcare administrative data. Pain
2017;158(3):408-416.
60
3 Health Utilities In People With Chronic Pain
3.1 Abstract
Health utilities are a preference-based measure of health-related quality of life that facilitates
comparison of disease burden across conditions. We estimated utilities using a population-
based, matched sample of adolescents and adults with and without chronic pain, controlling for
comorbidity.
Ontarians aged ≥12 years with and without chronic pain were identified from the Canadian
Community Health Survey (CCHS) 2000-01 and 2009-10 and linked to their provincial
healthcare administrative data. Individuals with chronic pain were matched to those without
using age, sex, survey year, and a propensity score for having chronic pain estimated from a
rurality index, income quintile and comorbidity. The Health Utilities Index Mark 3 instrument,
included in the CCHS, was used. Mean utilities were calculated for each group. Utility
decrement for chronic pain was also calculated for each matched pair.
A total of 65,246 responses were available for analysis. After matching, there were 12,146
matched pairs with and without pain. In the matched cohort, mean age was 54 years (SD 12);
61% were female. The matched cohort with chronic pain had a mean utility of 0.59 (95% CI
0.58 to 0.59), and the decrement associated with chronic pain was 0.32 (95% CI 0.31 to 0.32).
Utilities in people with chronic pain were lower than, and decrements larger than, those seen
with most other chronic diseases including heart disease, diabetes and chronic obstructive
pulmonary disease. This data will be useful to inform priorities and future strategies for the
prevention and control of chronic pain.
61
3.2 Introduction
Chronic pain is a widespread problem, affecting 1 in 5 people.5,14,168
It impacts multiple aspects
of life including mental and physical functioning. People experience reduced mobility, increased
rates of depression, anxiety and sleep problems.6,7
Back and neck pain on their own are the
largest contributors to disability in high income countries, ahead of disability from heart disease
and stroke.10
Moreover, the emotional toll from chronic pain more than doubles the odds of
suicidality.13
Management of chronic pain is also expensive. Total healthcare costs for chronic
pain in the US may be as high as $300 billion.189
In Canada, chronic pain was estimated to
consume approximately 5% ($7.2 billion) of the publicly funded health budget.221
A health utility is a self-reported global measure of health-related quality of life that incorporates
both an individual’s health state and a preference for that state. Perfect health is represented by 1
and the state of being dead by zero. The utility for a health state is multiplied by the expected
duration of years spent in that state to obtain the number of quality adjusted life years (QALYs).
QALYs are commonly used as an outcome measure in economic analyses to compare the effects
of two or more strategies. But health utilities are valuable in other ways as well. At a population
level, they provide information about the overall health of society as well as disparities among
demographic groups that may be useful for informing health or social policy. Utility estimates
for individual diseases provide information about burden of disease and allow comparison
among different conditions. This can be useful for informing research priorities, public health
initiatives and health services program funding.
Most prior estimates of health utilities in chronic pain have been elicited in specific pain
populations, such as spine clinic patients or those with arthritis rather than a representative,
62
community-based sample with chronic pain.83-86
Results from subsets may not be generalizable
to the entire chronic pain population since comorbidities, pain intensity and interference in
activities may differ among groups. Utilities should be obtained from a population similar to
where they will be applied since differences in societal preferences for health states have been
found between countries.90-92
And, importantly, although studies in Western Europe have
estimated utilities in people with chronic pain using postal and internet surveys, no consideration
was given for the effect of comorbidity on utility.82,87,222
Given the small number of population-based utility studies for chronic pain,82,87-89,222
additional
data from North America will improve understanding of the quality of life burden from chronic
pain and provide more accurate utility estimates for North American cost-effectiveness analyses.
And we are unaware of any utility estimates for adolescents with chronic pain. To address this
gap, we aimed to estimate utility values for adolescents and adults with chronic pain from a
population-based sample using the Health Utilities Index Mark 3 (HUI3) and determine the
utility decrement by pain severity and pain interference in activities while controlling for
comorbidity.
3.3 Methods
We used the Canadian Community Health Survey (CCHS) and linked Ontario healthcare
administrative data to conduct a retrospective matched cohort study of utilities in people with
and without chronic pain. The CCHS is a cross-sectional survey of community-dwelling
Canadians age 12 years and over conducted by Statistics Canada in two year cycles and contains
a broad set of health related questions that varies by cycle. Two cycles of the survey (2000-01,
2009-10)93,94
included the HUI3 instrument and, therefore, utility scores for respondents.
63
All respondents with valid answers to the chronic pain question in the surveys (HUIA_28,93
or
HUP_01,94
Supplemental digital content, Appendix C) and who were enrolled in the Ontario
Health Insurance Plan (OHIP) at the time of the survey were eligible for inclusion. Individuals
are eligible for OHIP if they are citizens or permanent residents of Canada and make their
primary place of residence in the province of Ontario. If respondents participated in both
surveys, only the first was used. Respondents endorsing chronic pain were eligible cases and
those denying chronic pain were eligible controls. Those endorsing chronic pain were also asked
questions about pain intensity and activity limitations which allowed us to stratify by groups.
The chronic pain question (see Supplemental digital content, Appendix C) has been included in
population-level health surveys in Canada since the 1990s and has been used to identify people
with chronic pain in two Statistics Canada publications and a study conducted for the provincial
government of Alberta.196-198
It demonstrated good test-retest reliability in a study of Canadians
aged 65 years and older (kappa 0.83, 95% CI 0.73 to 0.93).167
3.3.1 Comorbidity
We aimed to isolate the effect of pain on utility but people with chronic pain often have
comorbid conditions that also affect utility. We considered three types of comorbid conditions:
1) pain-related conditions that may be causal in a patient’s chronic pain (i.e. abdominal pain in
adolescents, arthritis, back and neck problems, fibromyalgia, migraine, neuropathic pain); 2)
anxiety, depression and sleep problems, which co-occur with chronic pain at higher than average
rates and probably have a bi-directional causal relationship with chronic pain;7,223,224
and 3)
conditions probably unrelated to chronic pain (e.g. asthma, heart failure, etc.).
64
It was important that we controlled for comorbidity unrelated to chronic pain so that the
decrement observed was for chronic pain alone, and not for confounding conditions that
accompanied it. It was less clear how to handle pain-related conditions like arthritis and
conditions with a bi-directional causal relationship (anxiety, depression and sleep problems). An
individual with increased anxiety as a consequence of chronic pain might, if the chronic pain was
removed, have an improvement in utility related to both decreased anxiety and pain. But how
much is attributable to chronic pain (and should not be controlled for) is unknown. The options
were to either control for anxiety, depression and sleep problems, removing any effect on utility,
or not to control their contribution to utility. We chose the latter for our base case. Similarly,
should one consider the higher incidence of pain-related conditions like arthritis or back
problems in the chronic pain group as confounders on utility estimation? We reasoned that
symptoms such as joint stiffness, disability, etc., that may occur with conditions like arthritis or
back problems are so closely linked to pain that they should be considered as part of the same
health state and so we did not control for this in our base case. We did, however, consider other
options in our sensitivity analysis, described below.
The number of aggregated diagnosis groups (ADGs) in the year before survey response (index
date) was used as the measure of comorbidity [Johns Hopkins ACG system (acg.jhsph.org/)].
The ACG system is proprietary software that classifies a patient’s international classification of
disease codes (ICD) into one of 32 aggregated diagnosis groups (ADGs) based on expected
duration, severity, diagnostic certainty, etiology and specialty care involvement. We used ICD
codes from the Discharge Abstract Database (DAD, hospital stays), the National Ambulatory
Care Reporting System (NACRS, emergency department visits and some hospital-based
outpatient care) and OHIP (physician claims records). To avoid counting painful conditions as
65
comorbidity (and to avoid overmatching), we removed ICD codes for painful conditions
(abdominal pain in adolescents only, arthritis, back and neck problems, fibromyalgia, migraine,
neuropathic pain; Supplemental digital content, Appendix C) prior to determining the number of
ADGs per person.
The healthcare administrative data used in this study have been used extensively in Ontario to
conduct health services research (www.ices.on.ca/Publications). Numerous studies have
assessed the validity of the databases.200,201
In addition, the use of the ADG system has been
validated for Canadian healthcare data.134,136,225
3.3.2 Estimating utility decrement
We matched subjects with chronic pain to individuals from the survey without chronic pain on
age (± 1 year), sex, year of survey (± 1 year) and the logit of a propensity score of the probability
of having chronic pain (up to a 0.2 standard deviation caliper width). A propensity score is
useful to balance known confounders between matched cohorts when working with large
administrative data sets.122,226,227
A propensity score was calculated for each individual in the
survey using logistic regression with presence of pain as the dependent variable and the
following variables as covariates: 2008 Rurality Index of Ontario (a measure from zero [urban]
to 100 [most rural] using the respondent’s postal code),130
a comorbidity measure (ADGs – see
above), and estimated household income quintile at the neighbourhood level (using the
respondent’s postal code and Canadian census data).203,204
We expected the relationship between
age and pain was not linear so we included an age squared term (for adults and older adults
only). We also included an age-sex interaction term to account for sex differences across age.
66
Some survey respondents who denied chronic pain in the survey had ICD codes in administrative
data for painful conditions (see Supplemental digital content, e.g. arthritis, Appendix C). We
accepted that these respondents did not have chronic pain and could be matched to individuals
who endorsed chronic pain; we chose this approach because it was the most conservative and
recognize that this might bias our estimates of utility decrement towards the null.
Cases were matched to controls using a ‘greedy’ matching method (a case was matched to the
first control fitting the matching criteria from a random starting point).228
Once cases and
controls were closely matched, the utility for each case was subtracted from the utility for each
control to determine the decrement associated with chronic pain. The propensity score
calculation and matching was done separately for respondents aged 12 – 17 years, 18 – 64 years
and those aged 65 years and above.
3.3.3 Health Utilities Index Mark 3
The HUI3 was present in the CCHS and used to obtain utility values for people with and without
chronic pain. It has possible utility values of -0.36 to 1; a value of 1 represents perfect health,
zero is dead and values less than zero represent states worse than dead.229
It has eight attributes
(vision, hearing, speech, ambulation, dexterity, emotion, cognition, pain) each with five or six
response levels, allowing for 972,000 unique health states (see Supplemental digital content for
level descriptions, Appendix C). The algorithm that converts attribute responses to a utility
assumes a multiplicative form and so captures preference interactions among health states.199
Preferences for health states were obtained using standard gamble techniques and visual analog
scale methods on a community-based sample in Hamilton, Ontario, Canada.77
Utility differences
67
of 0.03 or greater are considered clinically important.77,230
Generic utility instruments including
the HUI3 have demonstrated reliability and validity.231-234
3.3.4 Statistical analysis
The quality of the match was evaluated using standardized differences, an approach
recommended for use with large administrative data sets.117
Standardized differences of 0.10 or
less in matched cohorts are not expected to affect results.117
More common statistical tests such
as t-tests and chi squared tests are not recommended because they can show statistically
significant but trivial differences with large samples used in administrative data research.117
We
estimated mean utilities overall and by age category, sex, presence of chronic pain, pain severity
and interference in activities. We calculated the mean utility decrement for chronic pain
stratified by age category, pain severity and interference in activities. We calculated 95%
confidence intervals using bootstrap resampling (500 samples with replacement, with size equal
to the original sample), which allows determination of variance without depending on an
assumption of data normality. SAS Enterprise Guide version 6.1 (www.sas.com) was used for
all analyses.
3.3.5 Sensitivity analysis
In addition to our base case analysis, we conducted three additional analyses. One used a more
liberal matching algorithm and one a more restrictive algorithm for comorbidity. The third
omitted proxy responses from the base case analysis.
68
3.3.5.1 Liberal matching algorithm
It is likely that respondents in the CCHS surveys who reported chronic pain also experienced
chronic pain during some or all of the lookback period when comorbidity was assessed, since
another study found that half of respondents with chronic pain had experienced it for at least 10
years.5 Therefore, some comorbidities identified in the year prior to survey response may have
been causally related to chronic pain and matching on those comorbidities may underestimate the
true utility decrement. We therefore omitted the comorbidity measure in our first additional
matching algorithm.
3.3.5.2 Conservative matching algorithm
For our second additional analysis, we considered the effect of pain-related comorbidities
(abdominal pain in adolescents only, arthritis, back and neck problems, fibromyalgia, migraine,
neuropathic pain) and potentially causally-related conditions (anxiety, depression, sleep
problems). In our base case analysis, there was a higher prevalence of many of these conditions
in the cases than the controls, since they were not included in the matching algorithm.
Consequently, it is possible that the observed utility decrement was partially due to factors other
than pain that occur with these conditions. We therefore re-matched including the ICD codes for
these pain-related and potentially causally-related conditions in the propensity score.
3.3.5.3 Proxy responses omitted
The CCHS allowed responses by proxy when the respondent was not available for the entire
collection period, or if language or mental or physical incapacity prevented the interview. Since
this meant the individual was not rating their health state, we analyzed the original match without
data from CCHS responders as a third sensitivity analysis.
69
All data for the study was held by the Institute for Clinical Evaluative Sciences
(www.ices.on.ca/) in Toronto, Canada. Individual records in the datasets were linked across
databases using unique encoded identifiers and anonymized before analysis. Cells with fewer
than 6 individuals contributing are reported as ≤ 5 for participant confidentiality. The study was
approved by research ethics boards at Sunnybrook Health Sciences Centre and the University of
Toronto.
3.4 Results
A total of 66,557 Ontarians 12 years and older responded to the 2000-01 or 2009-10 cycles of the
CCHS. Of those, 208 were excluded because they were not eligible for OHIP at the time of the
survey. A further 36 were excluded for not providing a “yes” or “no” answer to the chronic pain
question. 908 were excluded for a missing utility score, and 159 were excluded for a response to
the second cycle of the survey. This left a total of 65,246 respondents for inclusion. Nineteen
percent (12,692) had chronic pain. After matching, 12,146 pairs of respondents with and without
chronic pain were available for analysis (4% loss of cases). Characteristics at the date of survey
response along with standardized differences are shown in Table 3.1 before and after matching.
Before matching, the sample with chronic pain was older (54 years versus 44 years) and had
more females (61% versus 53%). People with chronic pain were more likely to be in the lowest
income quintile (24% versus 18%) and people without chronic pain were more likely to be in the
highest income quintile (17% versus 21%). People with chronic pain also had more comorbidity
(5 versus 3 ADGs) (all comparisons had standardized differences > 0.1).
70
After matching, the average age was 54 years and 61% were female in both groups. Important
differences in income disappeared in the matched cohorts. The number of ADGs was 5 in each
group but there were higher numbers of people with ICD codes for arthritis, back or neck
problems, neuropathic pain and abdominal pain (adolescents only) (standardized differences
> 0.1).
3.4.1 Utilities
The distributions of utility scores in people with and without chronic pain are presented in Figure
3.1. Utilities in people without chronic pain are skewed to the left with 84% reporting a utility of
0.9 or greater. Only 23% in the chronic pain group reported a utility of 0.9 or greater. There
were more utility scores at 0.5 or below in the chronic pain group (15% versus 2%).
The entire sample of 65,246 had a mean utility of 0.85 (95% CI 0.85 to 0.85). The group with
chronic pain had a mean utility of 0.58 (95% CI 0.58 to 0.59) and those without chronic pain had
a mean utility of 0.92 (95% CI 0.92 to 0.92). In the matched cohort, cases with chronic pain had
a mean utility of 0.59 (95% CI 0.58 to 0.59) and those without chronic pain had a mean utility of
0.90 (95% CI 0.90 to 0.91). People reporting mild, moderate and severe pain had mean utilities
of 0.72 (95% CI 0.71 to 0.73), 0.59 (95% CI 0.58 to 0.60) and 0.35 (95% CI 0.33 to 0.36),
respectively. When stratified by activity limitations caused by pain, those reporting no, some, a
few, and most activity limitations had utility scores of 0.83 (95% CI 0.82 to 0.83), 0.72 (95% CI
0.72 to 0.73), 0.52 (95% CI 0.52 to 0.53) and 0.19 (95% CI 0.19 to 0.20), respectively. Results
by age group are presented in Table 3.2. Adolescents with chronic pain had higher mean utilities
than adults, and older adults had mean utilities lower than adults.
71
The mean utility decrement for chronic pain from the 12,146 matched pairs was 0.32 (95% CI
0.31 to 0.32). For adolescents, the decrement was 0.26 (95% CI 0.23 to 0.29); for adults the
decrement was 0.32 (95% CI 0.31 to 0.32); and for older adults the decrement was 0.32 (95% CI
0.31 to 0.33). Utility decrements for chronic pain by severity and interference with activities are
presented in Table 3.3 by age category. Utility decrement increased as pain severity increased
for each age category. Utility decrement also increased as pain interfered with more activities.
3.4.2 Sensitivity analysis
Baseline characteristics in the matched cohort that did not include matching on comorbidity (n =
12,477 pairs) showed higher numbers of people in the chronic pain cohort with arthritis, back or
neck problems, neuropathic pain, migraine, abdominal pain in adolescents, depression and
anxiety, as well as a higher number of ADGs in the chronic pain cohort (standardized differences
> 0.10). For the entire sample, the algorithm without matching on comorbidity resulted in a
utility decrement of 0.33 (95% CI 0.32 to 0.33) compared to 0.32 (95% CI 0.31 to 0.32) in the
base case.
Baseline characteristics in the more closely matched sample (additional matching on painful
conditions, depression, anxiety and sleep problems, n = 11,724 matched pairs) did not show any
important differences between the chronic pain cohort and controls for ADGs or numbers of
people with any of the measured painful conditions, depression, anxiety or sleep problems
(standardized differences ≤ 0.10). The utility decrement observed in this matched cohort was
0.31 (95% CI 0.30 to 0.31).
72
When utility values for pairs with proxy survey responses were removed, 11,405 complete pairs
remained (a loss of 741 pairs, or 6%). The mean utility decrement without proxy responses was
0.31 (95% CI 0.31 to 0.32). Additional utility decrements by age are presented in Table 3.4.
3.5 Discussion
We estimated mean utilities and utility decrements for Ontarians with and without chronic pain
from two cycles of the CCHS. For the entire cohort of people with chronic pain, the mean utility
was 0.59 and the mean utility decrement from chronic pain in the matched sample was 0.31.
3.5.1 Chronic pain compared to other diseases
Utility estimates in our study illustrate how poor health-related quality of life is for people with
chronic pain. People with chronic pain of moderate intensity (53% of cases) had a utility
estimate of 0.59. A study of health utilities obtained using similar methodology found that only
people with Alzheimer’s disease, among an extensive list of chronic conditions, had a lower
utility score (0.58) than moderate chronic pain (Table 3.5).71
It is evident that even those with
mild chronic pain (utility score 0.72) have quality of life well below that of people with other
common chronic diseases. Quality of life for those with severe chronic pain (utility score 0.35)
was worse than any chronic disease reported in this community-dwelling population. Moreover,
chronic pain limiting most activities was profoundly low (utility score 0.19). For comparison, a
minor stroke may have a utility estimate in the range of 0.50 to 0.70 and a major stroke maybe be
in the range of 0 to 0.30.235
Therefore, those who have severe pain or pain limiting most
activities appear to have a quality of life not unlike those who have had a major stroke.
73
We matched people with and without pain on variables that could affect quality of life to isolate
the effect of chronic pain on mean utility. The utility decrement seen between cases and
matched controls in our study was 0.32. This is ten times larger than the minimal clinically
important difference of 0.03.77,230
Moreover, these large decrements are greater than utility
decrements using the same instrument and similar sampling methodology for all but Alzheimer’s
disease (0.35) (Table 3.5).71
Even mild pain came with a utility decrement equal to stroke and
greater than all others except Alzheimer’s disease. The Beaver Dam study of health outcomes
reported the largest utility decrement (using the Quality of Wellbeing scale) was 0.15 for
congestive heart failure.70
3.5.2 Other studies of chronic pain utilities
Our study estimated utilities that were similar to a German population-based study using the
Short Form-6 Dimension (SF-6D) utility instrument. The mean utility for people with chronic
pain was 0.64, and utility scores for mild, moderate and severe pain were 0.71, 0.63 and 0.54.87
These numbers are similar to our estimates with the exception of severe pain (our estimate using
the HUI3 instrument was 0.35). A floor effect has previously been identified with SF-6D,
possibly related to its lowest possible score of 0.29.82
A population-based survey in the United
Kingdom reported utilities for mild, moderate and severe pain. The values obtained using the
EuroQol-5 Dimension (EQ-5D) instrument were 0.82, 0.72 and 0.48, while utilities obtained
using SF-6D were 0.79, 0.73 and 0.63.82
A large postal survey in Sweden in those over 65 years
estimated utility values using EQ-5D of 0.81 for those having no pain or mild pain (0 to 4 on a
numerical rating scale from 0 to 10), 0.63 for moderate pain (5 to 7 on the scale) and 0.39 (8 to
10 on the scale).88
74
We are aware of two studies that reported a utility decrement for chronic pain, but both used
multivariate linear regression, not a matched cohort approach: a population survey in Alberta,
Canada found a 0.19 utility decrement (using the EQ-5D instrument) for chronic pain.89
They
reported that chronic pain and anxiety or depression (also a 0.19 decrement) were the conditions
with the largest health burden in their study. The mean age in that study was younger than in our
study (47 versus 55 years). A population-level survey in Germany determined a 0.20 utility
decrement (using the SF-6D instrument) for severe daily pain.87
A combination of variability in
methods, sample characteristics and utility instruments may account for the differences seen.
Our sensitivity analysis using different matching algorithms revealed that utility estimates did
not vary despite removing comorbidity in one matching algorithm and possibly overmatching in
the other algorithm. This suggests that presence or absence of pain dominates other aspects of
health-related quality of life. This was also noted in a study of chronic pain in Germany (using
the SF-6D instrument and regression methods); the authors concluded that pain exceeded other
factors in influencing health-related quality of life.87
This may warrant further investigation
using all three commonly used preference instruments (HUI3, EQ-5D and SF-6D).
Our estimate of health utility in chronic pain is useful for a number of reasons. Foremost among
these is in demonstrating the large impact chronic pain has on health-related quality of life when
compared to other chronic diseases and conditions. This may be useful for informing healthcare
policy developers and decision makers who are interested in improving the overall health status
of the population. This data, combined with prevalence data, may be useful in planning
programs aimed at those with chronic pain. In addition, the data can be used in cost-utility
analyses of chronic pain initiatives.
75
3.5.3 Limitations
Our study has some limitations. We used self-report of chronic pain in the CCHS, which lacks a
definitive time element of 3 or 6 months generally used in research on chronic pain. However,
the prevalence rates in the surveys are similar to a more rigorous chronic pain prevalence
survey.5
We used ICD codes in this study that have been used in previously studies but most codes have
not been formally validated.43,44,213-220
However, the ICD codes were used for descriptive
purposes only in the main analysis and were used in one of the sensitivity analyses to more
closely match the cohorts.
Our propensity matching methods can control only measured confounders; unmeasured
confounding may remain. However, the finding in our sensitivity analysis that showed a similar
utility decrement from pain whether we matched on additional comorbidity factors or none
suggests that confounding by unmeasured comorbidity may be less important than presence or
absence of pain.
This is a cross-sectional study, so we are unable to draw conclusions about change over time.
Additionally, we have no information about those who did not participate in the survey, although
the survey has a high participation rate (76%).159
3.5.4 Strengths
Our study should also be considered within the context of its strengths. The sample is
population-based and includes almost 25,000 adolescents and adults which makes findings
robust and, we believe, generalizable. In addition, our study sample includes the overall chronic
76
pain population, not just those with specific types of chronic pain. This may be useful for
informing public health initiatives and health services programs that are likely to be targeted at
patients with multiple sources of chronic pain.
We controlled for potential biases in our estimate of utility decrement using a matched cohort
design with a propensity score. And we conducted two additional matching algorithms to
address uncertainty in the best match for our chronic pain cohort. Our matching algorithm lost
few cases, which increases generalizability. We provided estimates of utility in the individual
cohorts as well as utility decrements attributable to chronic pain from the matched cohorts. We
also stratified the results by pain severity, pain interference in activities and age. This enables
use of our results in diverse settings, for example, an economic analysis for an intervention
aimed at adolescents with severe pain.
3.5.5 Conclusions
In summary, we found very low health related quality of life in community-dwelling people with
chronic pain. Our estimate was 0.59, lower than most other chronic diseases. Moreover, the
utility decrement for chronic pain was 0.32, ten times larger than the minimal clinically
important difference of 0.03. Keeping in mind that the prevalence of chronic pain is
approximately 20%, a large proportion of society’s decrement in health-related quality of life is
tied to chronic pain. Improved chronic pain management could have a large impact on a
population’s overall quality of life. These findings have implications for future healthcare
planning and resource allocation.
77
3.6 Acknowledgements
This study was supported by the Institute for Clinical Evaluative Sciences (ICES), which is
funded by an annual grant from the Ontario Ministry of Health and Long-Term Care
(MOHLTC). The opinions, results and conclusions reported in this paper are those of the
authors and are independent from the funding sources. No endorsement by ICES or the Ontario
MOHLTC is intended or should be inferred.
Parts of this material are based on data and information compiled and provided by Canadian
Institute for Health Information (CIHI). However, the analyses, conclusions, opinions and
statements expressed herein are those of the author, and not necessarily those of CIHI.
The authors wish to acknowledge Qing Li, Senior Research Analyst at ICES for her assistance
with data management.
Mary-Ellen Hogan was supported by a research award from the Canadian Pain Society and
scholarships from the University of Toronto Centre for the Study of Pain, the Canadian Institutes
of Health Research Strategic Training Initiative in Health Research for Pain in Child Health and
The Hospital for Sick Children. Anna Taddio has received a Pfizer Research Grant and research
supplies from Natus and Ferndale. Joel Katz is supported by a Canada Research Chair in Health
Psychology. Murray Krahn is supported by the F. Norman Hughes Chair in
Pharmacoeconomics.
The authors have no other conflicts of interest to declare.
78
3.7 Tables
Table 3.1: Clinical and demographic characteristics
Chronic
pain
n=12,692
Control
pool
n=52,554
d† Cases
n=12,146
Controls
n=12,146
d†
Age, mean (SD) 55 (18) 44 (21) 0.54 55 (18) 55 (18) <0.00
Female, number (%) 7702 (61) 27817 (53) 0.16 7364 (61) 7364 (61) <0.00
Rurality index of Ontario 2008, mean (SD) 19 (22) 18 (22) 0.06 19 (22) 18 (22) 0.03
Income quintile, number (%)
1 (low) 3013 (24) 9665 (18) 0.13 2820 (23) 2786 (23) 0.01
2 2691 (21) 10411 (20) 0.03 2571 (21) 2660 (22) 0.02
3 2441 (19) 10674 (20) 0.03 2353 (19) 2348 (19) <0.00
4 2427 (19) 10758 (21) 0.03 2358 (19) 2260 (19) 0.02
5 (high) 2088 (17) 10847 (21) 0.11 2044 (17) 2092 (17) 0.01
Ambulatory Diagnostic Group, mean (SD)‡ 5 (3) 3 (3) 0.55 5 (3) 5 (3) <0.01
Depression, number (%) 551 (4) 935 (2) 0.15 493 (4) 326 (3) 0.08
Anxiety, number (%) 2452 (19) 5686 (11) 0.24 2277 (19) 1887 (16) 0.09
Sleep problems, number (%) 420 (3) 909 (2) 0.10 383 (3) 300 (3) 0.04
Arthritis, number (%) 2827 (22) 3269 (6) 0.47 2672 (22) 1240 (10) 0.32
Back or neck problems, number (%) 2372 (19) 3526 (7) 0.37 2226 (18) 1172 (10) 0.25
Neuropathic pain, number (%) 1694 (13) 2060 (4) 0.34 1596 (13) 670 (6) 0.26
Migraine, number (%) 366 (3) 762 (1) 0.10 343 (3) 199 (2) 0.08
Fibromyalgia, number (%) 25 (0) 16 (0) 0.05 22 (0) 6 (0) 0.04
Abdominal pain,§ number (%) 2208 (17) 5045 (10) 0.23 2000 (17) 1826 (15) 0.04
Hospitalization in last 12 months, number (%) 3096 (24) 6921 (13) 0.29 2797 (23) 2785 (23) <0.00
‡The number of ADGs was calculated without ICD codes for painful conditions described in the manuscript.
†Standardized difference.
§ Numbers and percentages are reported for adolescents only.
79
Table 3.2: Utility estimates for people with chronic pain
Adolescents
(12 -17 years)
n = 305
Adults
(18 – 64 years)
n = 8,324
Older adults
(≥ 65 years)
n = 4,063
mean 95% CI mean 95% CI mean 95% CI
Respondents with chronic pain (n = 12,692) 0.65 0.62 to 0.68 0.60 0.60 to 0.61 0.54 0.53 to 0.55
Females (n = 7,701) 0.64 0.60 to 0.68 0.59 0.59 to 0.60 0.53 0.52 to 0.54
Males (n = 4,991) 0.67 0.62 to 0.71 0.61 0.60 to 0.62 0.55 0.53 to 0.56
Cases with chronic pain (n = 12,146) 0.66 0.63 to 0.68 0.61 0.60 to 0.61 0.54 0.53 to 0.55
Females (n =7,364) 0.64 0.61 to 0.68 0.60 0.59 to 0.61 0.54 0.52 to 0.55
Males (n = 4,782) 0.67 0.62 to 0.72 0.62 0.61 to 0.63 0.55 0.54 to 0.57
Cases with
Mild pain (n = 3,621) 0.72 0.68 to 0.75 0.75 0.74 to 0.75 0.67 0.65 to 0.68
Moderate pain (n = 6,461) 0.61 0.57 to 0.65 0.61 0.60 to 0.62 0.55 0.54 to 0.56
Severe pain (n = 2,044) 0.46 0.30 to 0.61 0.34 0.33 to 0.36 0.35 0.33 to 0.37
Cases with
Pain that does not limit activities (n = 2,863) 0.79 0.75 to 0.83 0.85 0.84 to 0.85 0.80 0.78 to 0.81
Pain limiting a few activities (n = 3,632) 0.67 0.63 to 0.71 0.74 0.74 to 0.75 0.68 0.67 to 0.69
Pain limiting some activities (n = 3,155) 0.54 0.50 to 0.58 0.54 0.53 to 0.55 0.49 0.48 to 0.50
Pain limiting most activities (n = 2,496) 0.19 0.11 to 0.25 0.21 0.20 to 0.22 0.16 0.15 to 0.17
80
Table 3.3: Utility decrement for chronic pain
Adolescents
(12 -17 years)
n =290 pairs
Adults
(18 – 64 years)
n = 7,953 pairs
Older adults
(≥ 65 years)
n = 3,903 pairs
mean 95% CI mean 95% CI mean 95% CI
Utility decrement in matched cohort (n = 12,146 pairs) 0.26 0.23 to 0.29 0.32 0.31 to 0.32 0.32 0.31 to 0.33
Females (n = 7,364 pairs) 0.27 0.23 to 0.31 0.32 0.31 to 0.33 0.33 0.32 to 0.34
Males (n = 4,782 pairs) 0.25 0.20 to 0.30 0.30 0.29 to 0.31 0.31 0.29 to 0.33
Utility decrement in matched cohort from:
Mild pain (n = 3,621 pairs) 0.21 0.17 to 0.25 0.18 0.17 to 0.19 0.20 0.19 to 0.22
Moderate pain (n = 6,461 pairs) 0.30 0.25 to 0.34 0.31 0.30 to 0.32 0.32 0.30 to 0.33
Severe pain (n = 2,044 pairs) 0.47 0.30 to 0.65 0.57 0.55 to 0.58 0.51 0.48 to 0.53
Utility decrement in matched cohort from:
Pain that does not limit activities (n = 2,863 pairs) 0.13 0.10 to 0.17 0.08 0.07 to 0.09 0.08 0.07 to 0.10
Pain limiting a few activities (n = 3,632 pairs) 0.24 0.19 to 0.29 0.18 0.17 to 0.19 0.18 0.17 to 0.20
Pain limiting some activities (n = 3,155 pairs) 0.36 0.29 to 0.42 0.38 0.37 to 0.39 0.38 0.36 to 0.39
Pain limiting most activities (n = 2,496 pairs) 0.75 0.68 to 0.84 0.70 0.69 to 0.71 0.68 0.66 to 0.70
81
Table 3.4: Utilities - sensitivity analysis
Mean utility
decrement
95% CI Adolescents
12-17
95% CI Adults
18-64
95% CI Older adults
≥ 65
95% CI
Base case 0.32 0.31 – 0.32 0.26 0.23 to 0.29 0.32 0.31 to 0.32 0.32 0.31 to 0.33
Without comorbidity
(liberal algorithm)
0.33 0.32 – 0.33 0.26 0.23 to 0.30 0.33 0.32 to 0.33 0.33 0.32 to 0.34
With comorbidity and
pain related
conditions
(conservative
algorithm)
0.31 0.30 – 0.31 0.26 0.22 to 0.29 0.30 0.30 to 0.31 0.32 0.31 to 0.33
Proxy responses
omitted
0.31 0.31 – 0.32 0.25 0.22 – 0.29 0.31 0.31 – 0.32 0.31 0.30 – 0.32
Table 3.5: Utilities and utility decrements for other health conditions*
Health Utilities Mark 3 Utility decrement†
Alzheimer’s disease 0.58 0.35
Suffering from the effects of a stroke 0.68 0.25
Urinary incontinence 0.70 0.23
Cataracts 0.77 0.16
Heart disease 0.77 0.16
Arthritis or rheumatism 0.78 0.15
Epilepsy 0.78 0.15
Bronchitis or emphysema 0.79 0.14
Diabetes 0.79 0.14
Glaucoma 0.79 0.14
Stomach/intestinal ulcer 0.80 0.13
Back problems 0.81 0.12
Cancer 0.82 0.11
High blood pressure 0.82 0.11
Migraine 0.83 0.10
Sinusitis 0.84 0.09
Food allergy 0.85 0.08
Asthma 0.86 0.07
Other allergy (not food) 0.88 0.05
Acne (requiring medication) 0.92 0.01
* adapted from Mittmann N, Trakas K, Risebrough N, Liu BA. Utility scores for chronic conditions in a
community-dwelling population. Pharmacoeconomics. 1999;15(4):369-376.
† versus respondents with no surveyed chronic conditions.
83
3.8 Figures
Figure 3.1: Frequency distribution of utility scores
0
5
10
15
20
25
30
35
Pe
rce
nt
resp
on
se
Utility
Chronic pain
0
5
10
15
20
25
30
35
Pe
rce
nt
resp
on
se
Utility
Controls
84
Chapter 4
Plan to submit manuscript with the following title:
Hogan ME, Taddio A, Katz J, Shah V, Krahn M. Pain and death: All cause death, suicide, and
suicide attempts in people with chronic pain using a population-level survey and linked
healthcare administrative data
85
4 Mortality in People with Chronic Pain
4.1 Abstract
Introduction: Little research exists on death and suicide rates in adults with chronic pain. We
aimed to describe all-cause mortality, suicide, and suicide attempts in a population-based
matched sample with and without chronic pain using administrative data.
Methods: Ontarians ≥18 years were identified from 3 cycles of the Canadian Community Health
Survey (2000-01, 2007-08, 2009-10). Individuals with and without chronic pain were matched
on demographics and comorbidity. They were followed from survey response to death or
December 31, 2013. Death and death from suicide were identified using Ontario death records.
A standardized list of causes of death was used to compare death between groups. Suicide
attempts were identified using International Classification of Disease codes from emergency
department records.
Results: After matching there were 17,177 pairs of adults with (cases) and without (controls)
chronic pain. Average age was 55 years and 61% were female. Mean (SD) follow-up was 6.9
(3.6) years. Survival curves for the matched analysis were statistically different (p < 0.01) with
more death occurring in the group with chronic pain when suicide was included or excluded.
Death from lower respiratory diseases and diabetes were statistically greater in individuals with
chronic pain versus no chronic pain in the whole sample. There was no difference in any cause
of death between matched cases and controls. One hundred and twelve people in the chronic
pain group attempted suicide at least once versus 78 in the control groups (p < 0.01). There were
17 deaths from suicide among cases compared to 20 among controls (p = 0.28).
86
Conclusion: People with chronic pain died at a greater rate than an unmatched cohort or
matched controls. People with chronic pain died of similar causes as those without chronic pain.
Suicide attempts occurred more frequently in the group with chronic pain. We were unable to
detect a difference in death from suicide between cases and controls, perhaps due to insufficient
study power.
4.2 Introduction
Chronic pain has a tremendous impact on society, with one in five people affected.168
Management of chronic pain is expensive. In Canada 5% of total public health expenditures are
related to the care of chronic pain ($7.2 billion, 2014 CAD).236
In the United States, healthcare
costs and lost productivity from chronic pain are estimated at $600 billion annually.189
People
with chronic pain also have a low quality of life.82,87,237
Pain impacts an individual’s ability to
engage in both social and physical activities and affects their emotional wellbeing.238
Pain may
also affect survival. Evidence for pain causing impaired immune function and tumour growth in
animals has been known for more than 25 years.100
It is less clear if chronic pain may affect the
risk of death in humans.
A systematic review of all-cause mortality in people with chronic pain found no statistical
difference in mortality between those with and without chronic pain, although the majority of
contributing studies showed non-significant trends toward greater mortality in the chronic pain
group.106
Study heterogeneity may have affected the precision of the estimate.109
Another
review of people with chronic widespread musculoskeletal complaints had similar findings.107
Conversely, a large study from Denmark published after the chronic pain review found a higher
87
mortality rate in people with chronic pain who were long-term opioid users or non-opioid users
with chronic pain (hazard ratio for those with chronic pain versus those without chronic pain:
long-term opioid users 1.72 (1.23–2.41), non-opioid users 1.28 (1.10–1.49).110
At 13,127
participants, it was larger than any of the contributing studies in the systematic review of chronic
pain.106,110
Death from suicide in people with chronic pain may be a contributing factor if individuals with
chronic pain were to have higher overall mortality. Studies have found a link between suicide
ideation and chronic pain as well as suicide attempts and chronic pain.13,239,240
A number of
reviews of suicidality, including death from suicide have been conducted.241-244
Authors have
concluded there is an association between death from suicide and chronic pain but identified
insufficient research in the area, particularly for studies with adequate control for disability,
demographic and social factors, and studies with control groups.242,243
It is very difficult to study suicide in people with chronic pain for two reasons. Firstly, although
suicide is an important cause of avoidable death, event rates are low. For example, Canada has
slightly fewer than 4,000 suicides annually.245
Secondly, one needs a way to identify people
with and without chronic pain that can then be linked with suicides. Large, population-level
datasets provide opportunities to address these challenges. In this study, we aimed to determine
all-cause mortality and death from suicide in a population-level matched cohort of people with
and without chronic pain. As a secondary aim, we explored cause of death and suicide attempts
in the same population.
88
4.3 Methods
We performed a retrospective cohort study on respondents from 3 cycles of the Canadian
Community Health Survey (CCHS) and used their linked Ontario healthcare administrative data
to identify death, death from suicide and suicide attempts. We matched respondents reporting
chronic pain to those without chronic pain on a number of demographic and clinical variables.
We followed them until death or December 31, 2013, the latest available mortality data.
4.3.1 Cohort identification
We included survey participants covered under the Ontario Health Insurance Program (OHIP).
All Canadian citizens or permanent residents who make Ontario their primary residence receive
care through OHIP. We excluded anyone who reported having cancer in the CCHS. If
respondents participated in more than one cycle of the survey, the first response was used.
Respondents who endorsed chronic pain in the CCHS were eligible cases and those who denied
chronic pain were eligible controls. The CCHS is a cross-sectional, population level survey
administered in two year cycles, beginning in 2000. Survey questions vary among cycles. Three
cycles with linked Ontario administrative data (2000-01, 2007-08, 2009-10) contained a question
that allowed respondents to self-identify as having chronic pain (Appendix D). This question has
been previously used to identify people with chronic pain in government reports196,197,246
and
chronic pain research.198,236,237
The survey also contained questions about pain severity and
interference in activities that were used to stratify outcomes.
89
4.3.2 Outcome measures
We identified deaths using the database from the Office of the Registrar General for Ontario -
Deaths (ORGD), which records all deaths in the province. We summarized number of deaths
and estimated the survival function for each group using the Kaplan-Meier product limit
estimator. These estimates included death from suicide.
4.3.2.1 Cause of death
In order to better understand if differences existed in how people died, we summarized cause of
death using a standardized list of causes of death, developed for the World Health Organization
to compare deaths across different countries.247
It uses International Classification of Disease
version 10 (ICD-10) codes to group deaths into 65 categories (Table D.1, Appendix D).
4.3.2.2 Attempted suicide and death from suicide
We identified suicide attempts in emergency department administrative data (National
Ambulatory Reporting System, NACRS) using ICD-10 codes for self-harm (X60-X84, Y87.0) or
ICD-9 versions (E950-959) for fiscal years 2000 and 2001. See Table D.2, Appendix D for
additional detail on ICD codes. We used the same ICD codes to identify death from suicide in
ORGD. These codes are the same used by Statistics Canada and the World Health
Organization.245,248
4.3.3 Matched cohorts
We used a matched cohort design to determine if death, suicide, or suicide attempts were
different between people with chronic pain and those without chronic pain. Selection of an
90
appropriate comparator group, however, was not straightforward. We controlled for age, sex,
income and rurality, which are confounders in chronic pain research.196,197,236,237
Household
income quintile was estimated at the neighborhood level (using the respondent’s postal code and
Canadian census data).203,204
Rurality was estimated using the 2008 Rurality Index of Ontario
(RIO), a measure between 0 (large urban) and 100 (most rural) using the respondent’s postal
code.130
4.3.3.1 Comorbidity
Comorbidity is an important confounder when studying mortality but adjusting for it is
especially problematic. Some conditions are strongly associated with pain (e.g. arthritis) or even
defined by pain (e.g. fibromyalgia). Thus, adjusting for them might remove some of the
(potential) independent effect of pain on outcomes. We did not match on painful conditions.
Other conditions like anxiety, depression and sleep problems have a bidirectional relationship
with chronic pain.6,7
For example, chronic pain worsens sleep and having sleep problems
worsens chronic pain. It is not known what proportion of anxiety, depression and sleep problems
are causally related to pain so we included them in the comorbidity matching algorithm (ACG
system, below). This adjustment might bias outcomes towards the null.
Two commonly used tools to adjust for comorbidity using administrative data for research
purposes are the Charlson index and the ACG system. The Charlson index uses ICD codes in
hospital administrative data (Discharge abstract database, DAD) to determine if the individual
has any of 17 conditions, with each condition having a weight of 1 to 6 (Table D.3, Appendix D).
A higher score predicts greater mortality.135,249-251
91
The ACG system is proprietary software that uses diagnostic codes from office-based visits and
ICD codes from emergency department visits and hospitalizations (Johns Hopkins ACG system
[acg.jhsph.org/]). It assigns diagnostic codes to 32 aggregated diagnostic groups (ADGs) based
on expected duration, severity, diagnostic certainty, etiology, and specialty care involvement.
The ACG system was developed to predict morbidity burden and resource use but has also been
used to predict mortality.134,136,252
We removed painful conditions (arthritis, back and neck
problems, fibromyalgia, migraine, and neuropathic pain, Table D.4, Appendix D) from the
administrative data before determining ADGs to match on non-pain causes of comorbidity.
We included both the Charlson index and ACG system in the matching algorithm. ICD codes
from healthcare contact in the year prior to the survey date were used as baseline comorbidity in
the primary analysis. The Charlson index was originally developed to predict 1 year mortality
and has the strongest body of evidence for this.138
It is only determined for people who were
hospitalized but people with chronic pain are most commonly treated in the community.
Therefore, we also included the ACG system which adds data from office-based visits.
4.3.3.2 Propensity score matching
To make matching feasible with a large number of variables, we used a confounder summary
score, the propensity score.122
The propensity score is a balancing score for baseline
characteristics that is useful when working with large administrative data sets.122
A logistic
regression model was developed with presence or absence of chronic pain as the dependent
variable and potential confounders as explanatory variables. The following variables were
included in the propensity score for the main analysis: RIO, income quintile, Charlson score,
92
ADGs, suicide attempt in previous year (predictive of future attempts253
), age2 and an age * sex
interaction term. Data was fit to the regression model and propensity scores were predicted for
each individual. Individuals were then matched on age (± 1 year), sex, year of survey response
and the propensity score within a 0.2 standard deviation caliper width.254
Scores were calculated
separately for 18 - 64 years and ≥ 65 years.
4.3.4 Sensitivity analysis
4.3.4.1 Alternate matching algorithms
We considered two additional comparator groups, a less closely matched analysis and a more
closely matched analysis. The first of these two additional analyses was similar to the main one
but did not match on comorbidity or prior suicide attempt. Those with chronic pain were
matched to individuals without chronic pain on the following factors: age, sex, year of analysis
and a propensity score with rurality, income quintile, age2 and an age * sex interaction term.
The second additional analysis aimed to address any residual confounding that might explain
mortality differences. We included social determinants of health from the CCHS, including
smoking and alcohol consumption, body mass index (BMI), educational attainment, and marital
status. We included ancestry (Aboriginal or not),255
and whether or not the survey had been
completed by someone other than the respondent (proxy response), since respondents who were
unavailable after repeated interview attempts might share unknown social or health
characteristics that would be desirable to hold constant in an analysis.159
We also included additional variables reflecting health services use in the previous year. We
assumed that these patterns of care contained information about disease severity. For example,
93
someone who sees a physician often, is prescribed many drugs or receives homecare services is
probably, on average, more sick than someone who sees a physician infrequently, receives few
drugs, or who does not require homecare services. The included variables were number and type
of physician visits, any of the following: hospital admission, emergency department visit,
homecare service, long-term care stay, complex continuing care stay, rehabilitation stay, receipt
of publicly funded medical equipment (e.g. wheelchair), type of drug program (if any drugs were
dispensed) and number of drugs dispensed (for those ≥ 65 years, low-income Ontarians receiving
social assistance, and working people younger than 65 years who have out-of-pocket drug costs
greater than approximately 3% of their income. A detailed list is available in Appendix D. The
databases containing this information have been used extensively for health services research in
Ontario (www.ices.on.ca/Publications).
4.3.4.2 Broad definition of suicide
Because of a recognition that death from suicide is likely under-recorded in administrative
data,256,257
we used an additional broad definition of death from suicide that included accidental
poisonings (X40-42, X46, X47) and deaths of undetermined intent (Y10 - Y34, Y87.2 ),
following established methods (Table D.5 and Table D.6, Appendix D).258-260
We included this
extra definition because people with chronic pain may be at greater risk of dying from opioid or
other drug overdose, (accidental or intentional) due to possibly greater prescribed access than
someone without chronic pain. The broad definition of suicide reduces the uncertainty of intent.
94
4.3.4.3 Survival function without deaths from suicide
In order to examine cause of death excluding suicide, we removed anyone who died from suicide
and repeated the survival analysis for the groups.
4.3.5 Statistical analysis
Statistical analyses were performed on the cohorts with and without chronic pain in the whole
sample as well as the matched cohorts. The closeness of the match was evaluated using
standardized differences, a technique recommended for large administrative data sets, with
standardized differences of ≤ 0.1 not expected to bias the results.117
We used the Kaplan–Meier
product limit estimator to estimate the survival function of those with and without chronic pain.
We used stratified log rank tests to compare the equality of the survival curves following
recommended methods for matched samples.261
Survival curves for those with and without
chronic pain in the whole sample were compared using the log rank test. We calculated
proportion of deaths, suicide attempts and death from suicide by cohort in the whole sample and
matched cohorts. We examined trends in attempts when stratified by pain severity and
functional impairment using the Cochran Armitage trend test. We also reported proportion of
males and females attempting and dying from suicide (Table D.7, Appendix D). We calculated
crude suicide rates using person years of observation. Outcomes were assessed using
McNemar’s test for proportions of matched pairs, and chi squared test elsewhere.262
Exact tests
were used when cell values were 5 or fewer. A probability of 0.05 or less was considered
statistically significant with one exception: Bonferroni’s correction was applied for cause of
death due to the large number of comparisons (59 in each of the unmatched and matched
cohorts), and therefore, a greater risk of finding statistical significance by chance alone. For
95
those comparisons, a probability of ≤ 0.0002 was considered statistically significant. Analysis
was done using SAS version 9.3 (SAS Institute, Cary, NC, USA).
All data for the study was held by the Institute for Clinical Evaluative Sciences
(www.ices.on.ca/) in Toronto, Canada. Individual records in the datasets were linked across
databases using unique encoded identifiers and anonymized before analysis. Cells with fewer
than 6 individuals contributing are reported as ≤ 5 (or as < X % in the cause of death tables) for
participant confidentiality. The study was approved by research ethics boards at Sunnybrook
Health Sciences Centre and the University of Toronto.
4.4 Results
A total of 88,096 Ontarians 18 years and older responded to one of the three survey cycles and
met eligibility criteria. Respondents indicating they had chronic pain numbered 18,741(21%).
The average age of participants with chronic pain was 56 years versus 48 years for those without
chronic pain. There were more women in the group with chronic pain (61% versus 53%). There
were more people in the lowest income quintile in the group with chronic pain (24% versus
19%). A greater proportion of people in the chronic pain group had depression, anxiety, sleep
problems, arthritis, back or neck problems, neuropathic pain or migraine. The group with
chronic pain had been hospitalized more in the previous year (23% versus 14%) and had more
comorbidity as measured by the number of ADGs (standardized differences ≥ 0.1). See Table
4.1 for additional details.
After matching on demographics and comorbidity, 17,177 pairs were available for analysis. The
average age was 55 years and 60% were female. There were no important differences in rural-
96
urban status, income or comorbidity measures. The proportion of people with some painful
conditions was higher in the chronic pain cohort. There were no differences in physician office
visits, emergency department visits or hospitalizations. Additional baseline characteristics are
found in Table 4.1.
4.4.1 Death
The total length of follow-up in the whole sample was 634,757 years or a mean (SD) years of
observation per person of 7.2 (3.7). Follow-up in the matched cohort was 236,908 years or 6.9
(3.6) years per person. The survival curves for the cohorts from the whole sample, and primary
matched analysis are shown in Figure 4.1. In the unmatched chronic pain group, 2,317 (12%) of
subjects died during the observation period compared to 4,817 (7%) in the control group. After
matching, 2,041 (12%) died in the chronic pain group and 1,705 (10%) in the control group. The
survival curves were statistically different (p < 0.01) between people with and without chronic
pain in the matched cohort.
Cause of death is presented in Table 4.2. In the unmatched sample, only diabetes, lower
respiratory diseases and malignant neoplasms of the colon, sigmoid, rectum and anus were
significantly different between groups when the Bonferroni correction was applied (p ≤ 0.0002).
Once matched, none of the causes of death were statistically different with or without the
Bonferroni correction.
4.4.2 Death from suicide
In the unmatched cohort there were 19 deaths (0.10%) from suicide in the group with chronic
pain versus 51 (0.07%) in the group without chronic pain (p = 0.23), which translated to a rate of
97
15 versus 10 per 100,000 person years (p = 0.16). For the matched cohort, 17 people (0.10%)
died from suicide versus 20 (0.12%) in the control group (p = 0.74), translating to a rate per
100,000 person years of 14 versus 17 (p = 0.66) (Table 4.3). When suicide was stratified by pain
severity (mild, moderate, severe), 41% of suicides occurred in the most severe group. When
suicide was stratified by functional impairment (no activity limitations, a few, some, most), 47%
of suicides occurred in the group with most activity limitations.
4.4.3 Suicide attempts
A greater proportion of people with chronic pain attempted suicide at least once compared to
those without chronic pain in the whole sample (0.72% versus 0.38%, p < 0.01). In the matched
analysis, 112 people in the chronic pain cohort made at least one suicide attempt, compared to 78
in the control group (p = 0.02) (Table 4.3). When attempts were stratified by pain severity or
functional impairment, a significant trend was present for increasing percentage of attempts as
pain severity or activity limitations worsened (p < 0.01 for each).
4.4.4 Sensitivity analysis
4.4.4.1 Alternate matching algorithms
The less closely matched analysis had a higher proportion of people in the chronic pain group
with most of the chronic pain conditions, comorbidities, physician visits, emergency department
visits and hospitalizations (Table D.8, Appendix D). There were more deaths in the chronic pain
group (2,277 versus 1,800, p< 0.01). There were no statistical differences in cause of death with
the Bonferroni correction (Table D.9, Appendix D). The number of deaths from suicide was not
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statistically different (19 versus 12, p = 0.28, Table D.10, Appendix D). The number of people
attempting suicide was statistically different (133 versus 66, p <0.01, Table D.10, Appendix D).
The more closely matched analysis had no differences in any of the baseline characteristics
(Table D.8, Appendix D). There were more deaths in the chronic pain group (1,817 versus
1,670, p< 0.01). There were no statistical differences in cause of death with the Bonferroni
correction (Table D.9, Appendix D). The number of deaths from suicide was not statistically
different (16 versus 14, p = 0.86, Table D.10, Appendix D). The number of people attempting
suicide was not statistically different (97 versus 86, p = 0.46, Table D.10, Appendix D).
4.4.4.2 Broad definition of suicide
In the unmatched groups, 38 people (0.20%) with chronic pain died from suicide using the broad
definition versus 71 (0.10%) without chronic pain (p < 0.01). Once matched, the difference
between groups disappeared (31 versus 27, p = 0.69). This translated to a rate per 100,000
person years of 26 versus 23 (p = 0.57) (Table 4.3).
4.4.4.3 Survival function without deaths from suicide
When people who died from suicide were removed from the primary mortality analysis, 2024
(12%) died in the chronic pain group and 1685 (10%) in the control group. The survival curves
remained statistically different (p < 0.01). The survival curves for the less closely matched
algorithm and the more closely matched algorithm also remained statistically different after
removing the people who died from suicide (p < 0.01, Figure D.1, Appendix D).
99
4.5 Discussion
We explored overall survival, causes of death, including suicide and suicide attempts in
Ontarians with and without chronic pain.
4.5.1 Overall mortality
Overall survival was lower in people with chronic pain. This finding was present in the
unadjusted data, the main analysis, and both alternate matching algorithms, although the
difference became numerically smaller as one moved from the unmatched cohorts to the most
closely matched cohorts. It is noteworthy that despite potential over-matching on the more
closely matched analysis, we still found lower survival in the chronic pain group. We think this
more closely matched analysis represents over-matching because most of the variables reflect
healthcare use in the year before survey response (e.g. number of physician visits, type of
physician specialty seen) and some of that healthcare use is causally related to chronic pain and
should not be used to match; however, there is no way to identify which healthcare use is related
to comorbidity and which is related to chronic pain. Thus, some of the effect of chronic pain on
outcomes is diminished in this more closely matched analysis.
While the mortality difference could be residual confounding, it may indicate a causal
relationship. Others have described an endocrine stress response with chronic pain, and a
relationship between stress and disease.101-104
A systematic review examined chronic pain’s
impact on mortality in 49,620 participants from 10 studies.106
All-cause mortality was assessed
by combining 7 of those studies (30,008 participants) and not found to be statistically different.
In contrast, a large Danish population-based study (n = 13,127) published after the systematic
100
review found and increased all-cause mortality risk in people with chronic pain compared to
those without.110
Our study, which contained more participants than the total in the meta-
analysis found lower survival in the chronic pain cohort. Therefore, healthcare providers should
be aware that a patient with chronic pain is at a higher risk of death than an average patient
without chronic pain.
4.5.2 Cause of death
Most causes of death were not statistically different between matched groups using an α of 0.05,
and none were when using the Bonferonni corrected α. Ischemic heart disease, the largest cause
of mortality in this sample, was not statistically different between any of the four comparisons.
Similarly, other cardiovascular causes of death were not different between comparators. These
findings are consistent with two studies mentioned earlier.106,110
If one examines the rankings for cause of death in the unmatched data (Table 4.2), the order is
fairly consistent for those with and without chronic pain. One item worth highlighting is death
from accidental poisoning, colloquially referred to as overdose, occurring in 0.6% of deaths in
people with chronic pain during the period of 2000 to 2013. In the unmatched sample, there is a
3-fold difference compared to those without chronic pain (0.6% versus 0.2%, not statistically
different with the Bonferroni correction). But accidental poisoning ranks very low on the cause
of death list, for example, below death from esophageal or liver cancers. This suggests that
based on data to 2013, healthcare providers should be considering the same mortality risks in
their patients with chronic pain as they would in people without chronic pain. Guidelines for
opioid use in people with chronic non-cancer pain issued in the United States in 2016 and in
101
Canada in 2017 might impact future findings in this area.263,264
Some have suggested (and a
survey in the United States confirmed) that opioid use may be reduced or discontinued as a
consequence of these guidelines.265-267
This could lead individuals with chronic pain to consider
suicide as a solution or resort to illicit opioids to manage their pain with potentially fatal
consequences.265-267
We found more deaths from suicide in people with chronic pain in the unmatched sample using
the broad definition, but we were unable to detect a difference in any of our adjusted data. Given
the findings in the unmatched sample, a healthcare provider should consider patients with
chronic pain to have an increased suicide risk. It is not clear if that increased risk may be related
to the comorbidity that accompanies chronic pain, rather than chronic pain itself. Others have
also reported higher death from suicide in people with chronic pain.242-244
Most prior research has failed to adjust for comorbidity, whereas we included both the Charlson
and ACG system. The use of these measures may account for our findings in the main analysis
(no difference in suicide in the matched cohort). Goodwin and colleagues found an association
between suicide attempts and chronic illness.268
Suicide rates have been found to be higher in
patients with asthma,269
chronic obstructive pulmonary disease,270
and stroke.271
A study in the
elderly found that people with more chronic conditions had a higher risk of suicide.272
Together
with our results, this may suggest that suicide rates in people with chronic pain may be similar to
rates of suicide in people with a similar amount of chronic illness.
It is also possible that the small number of events in our data has prevented detection of a
difference between those with and without chronic pain. A large study of men in Japan and a
102
large study of > 90% men in the US Veterans Administration (VA) health system in the United
States each found moderate or severe pain was associated with an increased risk of suicide
compared to less or no pain.273,274
These studies are not generalizable to the overall population,
with > 90 being male. As well, the suicide rate in the VA group with no pain was 55 per
100,000 person years, well above the US rate of 13 per 100,000.274,275
So while these studies
support an increased rate of suicide in some people with chronic pain, the true difference on a
population level may be much smaller.
4.5.3 Suicide attempts
We found more suicide attempts in people with chronic pain which confirms differences found
by others.242
Note that about twice as many people attempt suicide in the chronic pain group
when unmatched or when matched less closely in the sensitivity analysis. The difference
declined slightly in the main analysis and further declined in the most closely matched sensitivity
analysis. This could suggest that some suicide attempts arise from comorbidity associated with
people who have chronic pain, not just pain. Alternatively, the trend analyses for attempts and
pain severity or functional impairment were significant across all matches and this tends to
support a causal relationship between chronic pain and suicide attempts.
4.5.4 Limitations
The variables used in matching were drawn from the year prior to the participant’s survey
response. Since this was a cohort of people with chronic pain, not a group with new chronic
pain, it is very likely that many of the individuals with chronic pain also had chronic pain during
this look-back period: research has demonstrated that half of Canadians surveyed about chronic
103
pain experienced it for at least 10 years.5 Since chronic pain would contribute to their healthcare
use during the look-back period, our matching, particularly the most closely matched sensitivity
analysis, probably over matches, biasing the findings towards no difference on outcomes of
interest.
Our study sample, despite being large by other standards (36,000 chronic pain patients followed
forward for up to 10 years), is measuring an event that is very small (9 - 30 per 100 000 life
years). Small event rates may prevent detection of an effect, and a larger sample or longer time
of observation might improve precision.
4.5.5 Strengths
Our study has a number of strengths. The sample is very large and population-based so it is
highly generalizable. We provided data on the unmatched groups as well as three levels of
matching to control for potential confounders. One can therefore see the effect of control for
potential confounding and this may assist in interpreting prior research. The number of
confounders considered represents an improvement on prior studies in this area. Further, the
matched design allows for straightforward interpretation of results, particularly for clinicians
who are used to reading randomized controlled trials.
When examining death, we used a standardized list of causes which may allow improved
comparisons with future studies. We examined two definitions of suicide, the traditional one,
and an additional broad definition that recognizes some suicides are not reliably recorded in
administrative data. The broad suicide definition minimizes concerns regarding uncertainty of
intent. We followed cohorts of people with and without chronic pain, different than most suicide
104
research which begins with a group of suicides and looks back. The cohort design allows
estimation of cumulative incidence of outcomes and may minimize information bias.
4.5.6 Conclusions
In summary we found increased death in people with chronic pain, even after extensive matching
for comorbidity and potential markers of disease severity. Causes of death were largely the same
for those with and without chronic pain. We observed an increase in suicide in people with
chronic pain in the unmatched cohort but were unable to detect a difference when matched. We
found an increase in suicide attempts in the unmatched comparison and matched analysis.
4.6 Acknowledgements
This study was supported by the Institute for Clinical Evaluative Sciences (ICES), which is
funded by an annual grant from the Ontario Ministry of Health and Long-Term Care
(MOHLTC). The opinions, results and conclusions reported in this paper are those of the
authors and are independent from the funding sources. No endorsement by ICES or the Ontario
MOHLTC is intended or should be inferred.
Parts of this material are based on data and information compiled and provided by Canadian
Institute for Health Information (CIHI). However, the analyses, conclusions, opinions and
statements expressed herein are those of the author, and not necessarily those of CIHI.
The authors wish to acknowledge Qing Li, Senior Research Analyst at ICES for her assistance
with data management.
105
Mary-Ellen Hogan was supported by a research award from the Canadian Pain Society and
scholarships from the University of Toronto Centre for the Study of Pain, the Canadian Institutes
of Health Research Strategic Training Initiative in Health Research for Pain in Child Health and
The Hospital for Sick Children. Anna Taddio has received a Pfizer Research Grant and research
supplies from Natus and Ferndale. Joel Katz is supported by a Canada Research Chair in Health
Psychology. Murray Krahn is supported by the F. Norman Hughes Chair in
Pharmacoeconomics.
The authors have no other conflicts of interest to declare.
106
4.7 Tables
Table 4.1: Clinical and demographic characteristics
Whole sample Matched
Chronic
pain
No pain d† Chronic
pain
No pain d†
Cohort size, n 18,741 69,355 17,177 17,177
Age, years (SD) 56 (17) 48 (19) 0.41 55 (17) 55 (17) 0
Female, % 61 53 0.16 61 61 0
Major urban (0-9), % 49 52 0.07 49 51 0.03
Non-major urban (10-44), % 36 34 0.04 37 35 0.04
Rural (greater than 45), % 14 12 0.05 14 14 0.01
Income quintile, %
1 (low) 24 19 0.12 23 22 0.01
2 21 20 0.03 21 22 0.01
3 19 20 0.02 20 20 0.01
4 19 20 0.04 19 19 < 0.01
5 (high) 17 20 0.09 18 18 < 0.01
Depression, % 5 2 0.14 4 3 0.04
Anxiety, % 20 12 0.22 18 18 < 0.01
Sleep, % 4 2 0.10 3 3 0.01
Arthritis, % 23 7 0.46 21 13 0.23
Back or neck, % 19 8 0.34 17 13 0.12
Neuropathic pain, % 14 4 0.32 12 7 0.15
Migraine, % 3 1 0.09 3 2 0.02
Fibromyalgia, % 0.2 0 0.04 0.1 0 0.04
Suicide attempt, % 0.1 0.1 0.01 0.1 0.1 < 0.01
Aggregated diagnostic groups,
number (SD)*
4.6 (3.1) 3.2 (2.6) 0.50 4.3 (2.9) 4.4 (2.9) 0.03
Outpatient physician visit, % 96 88 0.28 95 95 0.04
Emergency department visit, % 31 21 0.23 29 29 0.02
Hospitalization, % 23 14 0.24 21 21 < 0.01
Charlson index = 0, % 18 12 0.17 16 16 < 0.01
Charlson index = 1, % 3 1 0.11 3 3 < 0.01
Charlson index = 2, % 2 1 0.08 1 1 < 0.01
Charlson index > 2, % 1 0 0.10 1 1 0.02
† Standardized difference.
* No painful conditions included in this count.
107
Table 4.2: Cause of death – percent of deaths
Whole sample Matched
Chronic
pain
No
pain p
Chronic
pain No pain p
Cohort size, n 18,741 69,355 17,177 17,177
Cause of death Died, n
2,317 4,817 2,041 1,705
Ischemic heart disease 18.0 16.3 0.069 17.5 16.1 0.125
Malignant neoplasm of trachea, bronchus and
lung 7.5 8.2 0.328 7.5 7.9 0.211
Chronic lower respiratory diseases 5.6 3.4 <0.0001 5.6 3.6 0.321
Cerebrovascular diseases 5.5 6.7 0.050 5.2 7.6 0.152
Dementia and Alzheimer disease 4.7 4.6 0.921 4.7 5.1 0.169
Diabetes 4.2 2.4 <0.0001 4.2 2.5 0.481
Diseases of urinary system 2.9 2.4 0.248 3.0 2.9 1
Influenza and pneumonia 2.9 2.6 0.407 2.8 2.8 1
Heart failure and complications and ill-defined
heart disease 2.7 2.7 0.961 2.9 2.4 0.184
Malignant neoplasms of lymphoid,
hematopoietic and related tissue 2.3 2.5 0.531 2.4 2.1 0.108
Malignant neoplasm of colon, sigmoid,
rectum and anus 2.1 3.5 0.0007 2.1 3.1 0.121
Accidental falls 1.9 1.6 0.390 2.0 1.3 0.524
Septicemia 1.9 1.2 0.031 1.8 1.4 1
Cirrhosis and other liver disease 1.8 1.0 0.007 1.9 1.0 0.289
Malignant neoplasms of female breast 1.5 1.8 0.359 1.4 2.2 0.134
Hypertensive disease 1.4 1.1 0.346 1.5 1.2 0.678
Malignant neoplasm of pancreas 1.2 2.0 0.020 1.4 2.7 1
Cardiac arrest 1.0 1.1 0.929 1.0 1.1 0.180
Diseases of MSK and connective tissue 0.9 0.6 0.120 1.0 0.6 1
Appendicitis, hernia and intestinal obstruction 0.9 0.5 0.110 0.9 0.6 0.210
Malignant neoplasm of esophagus 0.9 0.8 0.673 0.9 0.9 0.549
Malignant neoplasm of prostate 0.8 1.3 0.061 0.8 0.9 1
Respiratory failure 0.8 0.7 0.599 0.8 0.9 1
Malignant neoplasm of liver 0.8 0.9 0.506 0.6 0.9 1
Cardiac arrhythmias 0.7 1.2 0.058 0.8 1.3 0.557
Malignant neoplasm of stomach 0.7 0.7 0.974 0.8 0.5 1
Aortic aneurism and dissection 0.7 1.0 0.241 0.7 0.9 0.508
Parkinson's disease 0.7 0.6 0.382 0.7 0.6 0.146
Pulmonary edema and other interstitial
pulmonary diseases 0.7 1.5 0.006 0.7 1.0 1
Suicide 0.7 0.9 0.295 0.7 1.1 1
Non-rheumatic valve disorders 0.7 0.9 0.334 0.6 0.8 0.791
Malignant neoplasm of brain 0.6 0.7 0.606 0.6 0.5 0.250
108
Whole sample Matched
Chronic
pain
No
pain p
Chronic
pain No pain p
Cohort size, n 18,741 69,355 17,177 17,177
Cause of death Died, n
2,317 4,817 2,041 1,705
Pulmonary heart disease and diseases of
pulmonary circulation 0.6 0.6 0.819 0.6 0.6 1
Accidental poisoning 0.6 0.2 0.006 0.5 < 0.4 1
Intestinal infectious diseases 0.5 0.4 0.458 0.5 0.4 1
Malignant neoplasm of bladder 0.5 1.0 0.033 0.5 0.8 0.065
Malignant neoplasm of kidney 0.4 0.6 0.246 0.4 0.5 1
Malignant neoplasm of ovary 0.4 1.0 0.019 0.4 1.1 0.344
Mental and behavioural disorders due to
psychoactive substance use 0.4 0.3 0.398 0.4 < 0.4 0.500
Benign neoplasms, in situ and uncertain
behaviour 0.4 0.7 0.149 0.3 0.5 0.344
Atherosclerosis 0.3 0.4 0.512 0.3 < 0.4 0.250
Land transport accidents 0.3 0.7 0.042 0.3 0.6 1
Melanoma and other malignant neoplasms of
skin 0.3 0.6 0.138 0.3 0.5 0.375
Cardiomyopathy 0.3 0.3 0.834 < 0.3 0.4 0.250
Accidental threats to breathing < 0.3 < 0.2 0.358 < 0.3 < 0.4 1
Chronic rheumatic heart disease < 0.3 0.3 0.352 < 0.3 0.4 0.125
Dehydration < 0.3 0.2 0.916 < 0.3 < 0.4 0.590
Malignant neoplasm of larynx < 0.3 0.2 0.630 < 0.3 < 0.4 0.625
Accidental drowning and submersion < 0.3 < 0.2 0.719 < 0.3 0 -
Congenital malformations, deformations,
chromosomal abnormalities < 0.3 < 0.2 0.762 < 0.3 < 0.4 -
HIV disease < 0.3 < 0.2 0.762 < 0.3 < 0.4 -
Malignant neoplasm of gallbladder < 0.3 0.3 0.113 < 0.3 < 0.4 1
Acute respiratory diseases - not
influenza/pneumonia 0 < 0.2 0.749 < 0.3 < 0.4 1
Pregnancy, childbirth, puerperium 0 0 0.597 < 0.3 0 -
Epilepsy and status epilepticus 0 < 0.2 0.551 0 < 0.4 -
Event of undetermined intent 0 < 0.2 0.165 0 < 0.4 -
Homicide 0 < 0.2 0.121 0 < 0.4 -
Vaccine-preventable diseases 0 < 0.2 0.230 0 < 0.4 -
Remaining 11.4 11.7 0.736 13.3 12.8 0.868
109
Table 4.3: Suicide attempts and death from suicide
Whole sample Matched
Chronic
pain No pain p
Chronic
pain No pain p
Cohort size, n 18,741 69,355 17,177 17,177
Individuals with at least 1 attempt,
number (%)
134 (0.72) 266(0.38) < 0.01 112 (0.65) 78 (0.45) 0.02
Suicide cumulative incidence
Narrow definition, number (%) 19 (0.10) 51 (0.07) 0.23 17 (0.10) 20 (0.12) 0.74
Broad definition, number (%) 38 (0.20) 71 (0.10) < 0.01 31 (0.18) 27 (0.16) 0.69
Suicide incidence rate per 100,000
patient years
Narrow definition 15 10 0.16 14 17 0.66
Broad definition 30 14 < 0.01 26 23 0.57
110
4.8 Figures
Figure 4.1: Survival analysis – all cause death
Solid line: chronic pain. Broken line: no chronic pain. p < 0.01 for each comparison.
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
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0 20 40 60 80 100 120 140 160
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urv
ivin
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Whole sample
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
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Matched
111
Chapter 5
5 Summary of contribution
5.1 Study summaries
5.1.1 Study 1
We had 99,503 CCHS respondents with linked Ontario healthcare data available to estimate
healthcare use and costs. We matched 19,138 Ontarians with chronic pain to those without using
demographics and comorbidity measured by the ACG system. We estimated the incremental
cost of managing chronic pain in those aged 12 years and older for one year following their
survey responses. Health care costs for those with chronic pain were $1,742 more annually than
closely matched controls. The largest contributors were hospitalization, drugs and physician
visits. We presented results by age, sex, pain severity, and pain interference in activities. We
presented a less closely matched cost estimate adjusted only for demographics, and a more
closely matched cost estimate adjusted for several chronic pain conditions in addition to
demographics and comorbidity. When extrapolated to the Ontario population, the cost of
managing chronic pain consumes approximately $2.8 billion (2014 CAD) or 5% of the publicly
funded health budget.
This is the most comprehensive estimate of publicly funded chronic pain costs in Canada. The
per-person cost is smaller than the most recent Canadian study; however, it included more severe
patients.31
Our study is more generalizable and we determined that when the per-person cost is
extrapolated to the provincial level, chronic pain represented 5% of publicly funded healthcare
spending.
112
5.1.2 Study 2
We estimated the health burden of chronic pain measured with health utilities in the second
study. We matched Ontarians 12 years older with and without chronic pain using the same
algorithm as study 1. Only 2 of the 3 CCHS cycles contained utility information so the study
had 66,557 individuals available for analysis. The primary match was comprised of 12,146 pairs
of respondents with and without chronic pain. The mean utility for the chronic pain cohort was
0.59 and the mean utility decrement was 0.32. We also presented this data by age, sex, pain
severity and pain interference in activities. We presented a less closely matched cost estimate
adjusted only for demographics, and a more closely matched cost estimate adjusted for several
chronic pain conditions in addition to demographics and comorbidity. Utilities in people with
chronic pain were lower than, and the utility decrement was larger than most other chronic
diseases including heart disease, diabetes, and chronic obstructive pulmonary disease.
Our utility estimates, from the HUI3, produced some of the lowest utility estimates of studies
using large scale surveys. These HUI3 utility estimates stratified by age and sex are the first to
estimate chronic pain utilities at a population level and will be available for future cost-utility
analyses.
5.1.3 Study 3
We examined cause of death, including suicide in adults 18 years and older with and without
chronic pain in the third study. We excluded anyone who endorsed having cancer in the CCHS.
This left 88,096 Ontarians. We matched respondents on demographics and comorbidity
measured by both the ACG system and the Charlson index, as well as whether the respondent
had attempted suicide in the year prior to survey response. We followed 17,177 pairs of people
113
with and without chronic pain until death or December 2013. There were numerically more
deaths in the chronic pain group (2,063 versus 1,722) and survival was lower in the group with
chronic pain as measured by the Kaplan Meier product limit estimator (p < 0.01). There was,
however, no statistical difference in suicide rate in the sample before matching (15 versus 10 per
100,000 patient years, p = 0.16) or after matching (14 versus 17 per 100,000 patient years, p =
0.66). We presented sensitivity analyses with respondents matched on demographics alone, and
one matched on an extensive list of variables to add additional information about health services
use and socioeconomic status. When examining results across the three analyses, it is evident
that people with chronic pain are at greater risk of death than those without chronic pain. There
were also more suicide attempts. We were unable to detect a difference in death from suicide.
This is the first study using Canadian data to examine the relationship between chronic pain and
mortality. The difference in death rate could be a true difference. It could also be caused by
residual confounding. We found a greater proportion who attempted suicide in the chronic pain
group but we were unable to detect a difference in death from suicide.
5.2 Limitations
The CCHS was used to identify people with chronic pain in each of the studies presented here.
This does not match the IASP definition of chronic pain (i.e. a specific time limit was not
described in the question). As a result, some people who self-reported chronic pain might not
meet the IASP definition. It is also possible that people with well controlled chronic pain might
report they are “usually free of pain or discomfort.” In both scenarios, the effect of
misclassification is to decrease the difference between the two cohorts, resulting in a
conservative estimate of the incremental cost of chronic pain to the health system, a possibly
114
smaller utility decrement than the true value, and smaller than true difference in mortality. It is
also important to recognize that the proportion of people identifying as having chronic pain in
the CCHS closely matches a more rigorous survey of chronic pain in Canada.5 This would
suggest that the CCHS is probably capturing those who match the more widely accepted
definition of chronic pain.
Some costs incurred in providing outpatient care to patients with chronic pain may not be fully
captured by the methods used in this study. A small number of people with chronic pain receive
care in specialized outpatient pain clinics located in hospitals. Some or all of the fixed costs
related to care (e.g. support staff, janitorial services, land taxes, utilities) may be borne by the
hospital and so are not captured by methods used (currently the NACRS database captures some
ambulatory clinic visits, but not pain clinics). This also occurs for fixed costs associated with
hospital-based MRI and CT. The OHIP database provides a record of the professional fee for
these services, but there is no corresponding technical fee for operating costs (e.g. the technician,
linens, electricity, maintenance) which are currently borne by the hospital.
The retrospective observational design of this study is limited in two notable ways: 1. Some data
that would be informative for the research questions may not be available, but could have been
collected in a prospectively designed study (e.g. onset and/or duration of pain). It is possible that
patterns of care, and subsequent healthcare costs, vary according to duration of chronic pain.
Similarly, quality of life, health state preferences and suicide attempts might change over time in
people with chronic pain. 2. The observational design depends on close matching of individuals
with and without chronic pain using existing variables in databases to create two cohorts that
should be similar except for presence of chronic pain. Unmeasured differences could remain that
115
might bias study results, although propensity matching attempts to overcome this possibility.
Some deliberation occurred while determining the best controls for this research. In each of the
studies, we provided additional matching algorithms as sensitivity analyses to address
uncertainty in the choice of controls.
5.3 Strengths
This research has a number of strengths. This is the first time chronic pain has been studied at a
population level in Ontario, specifically, by linking the CCHS and healthcare administrative
data. While an earlier study in Alberta used similar linking methods between a population-level
survey and provincial healthcare data, this research is more comprehensive and up to date. The
research in this thesis was able to use data held at ICES that captures almost all publicly funded
patient care, whereas in the past, much less was available. Additionally, the Alberta study used
data from the 1990s and may not reflect more recent practice patterns.
The use of population-level data from the CCHS and Ontario healthcare administrative data
makes the findings of this research highly generalizable to the Ontario (and probably Canadian)
population. The economic and health burden of chronic pain determined from this research is
also highly relevant to policy makers in Ontario and other provinces, and those prioritizing
research funding in Canada.
The use of administrative data also minimizes the effect of recall bias. This is particularly
relevant for estimating costs since the administrative data reflects what care was delivered. It
may also be beneficial for identifying comorbidity during the matching process. A one year
look-back was used for this, ensuring recent conditions requiring medical treatment were
reflected in the comorbidity assessment.
116
The sample size, starting with approximately 100,000 individuals with and without chronic pain
contributes to the robustness of the results. This allows stratification by age, sex, pain intensity
and interference in activities, providing more information to knowledge users. Smaller sample
sizes are influenced to a greater degree by inclusion criteria and cannot provide the same amount
of stratification.
The research relies on self-report of chronic pain rather than identifying painful conditions as
much other research using administrative data has done. Firstly, self-report is considered the
gold standard in identifying people with chronic pain. Using self-report captures a larger portion
of people with chronic pain than studies using diagnosis codes in administrative data. Using
painful conditions in administrative data will capture the smaller cohort who sought out care.
This makes findings from this thesis more generalizable.
Propensity matching allowed us to adjust for a large number of potential confounders, which
minimized differences between the cohort with chronic pain and the group without. We were
able to estimate the incremental cost of managing chronic pain as a result. Many other studies in
chronic pain have estimated all healthcare costs in people with chronic pain, or costs identified as
being pain-related, which are less relevant to healthcare payers and policy makers. Propensity
matching also allowed us to estimate utility decrements from chronic pain, which may be useful
for cost-utility analyses as well as helping policy makers understand the size of the health burden
from chronic pain.
117
5.4 Implications
5.4.1 Policy implications
These comprehensive cost estimates for chronic pain provide population-level evidence of real-
world data in chronic pain. These estimates are helpful in describing the burden of chronic pain
from an economic perspective. This is particularly relevant for those involved in developing
programs and policies since the burden of chronic pain can be compared in monetary terms to
other high burden conditions.
Similarly, the health utilities estimated in this thesis are useful for describing the health burden of
chronic pain. The exceptionally low values illustrate a profound burden for approximately 20%
of the population. For government interested in improving the overall societal health-related
quality of life of the population, this research offers a target group for policy and health
initiatives.
Both the cost and utility estimates from this thesis will be useful as benchmarks for future
research and as high quality model inputs in cost-utility analyses.
The Ontario mortality estimates and suicide data provide information about this aspect of disease
burden associated with chronic pain. These findings will contribute to the growing body of
evidence in this area. Mortality was greater in people with chronic pain but this may be related
to the conditions that accompany chronic pain. Suicide attempts were greater in people with
chronic pain and may be reflective of suffering associated with low quality of life. Suicide does
not appear to be different between groups, even when considering a broader definition than the
standard suicide definition. Lack of power for this outcome makes conclusions problematic, but
if a true difference exists at a population level, it may be small.
118
Given the magnitude of the problem, additional support of primary care practitioners may be
warranted. Physicians receive little pain management training.276
The chronic pain program at
Project ECHO (www.echoontario.ca) in Ontario is one example of an educational outreach
program. It may be desirable to further invest in this program to make it more widely available.
5.4.2 Clinical implications including opioid prescribing
The “opioid crisis” has received a great deal of attention in the media recently.177,179,277-279
The
Canadian federal Minister of Health convened an expert group in November 2016 to develop a
national opioid strategy.280
And the Canadian government has recently allocated $65 million
over five years for national measures to respond to the opioid crisis.281
Part of that response will
be aimed at reducing inappropriate prescribing of opioids.281
An updated Canadian opioid
guideline has been released to provide guidance in this area.264
The United States initiated
restrictions on opioid prescribing ahead of Canada.184,185,282
Some patients who were stable on
opioids now find it hard to access drugs or may be encountering problems with insurance
coverage as a consequence of new restrictions imposed from the 2016 Centres for Disease
Control guideline for opioid prescribing.263,283,284
Reducing access might be underway in Canada
as well. The British Columbia College of Physicians and Surgeons introduced practice standards
for opioid prescribing in 2016 and received some criticism for being too restrictive.285,286
The
College of Physicians and Surgeons of Ontario is investigating 86 physicians found to be high
prescribers of opioids.287
Actions like these could make physicians reduce their opioid
prescribing out of fear of repercussions rather than what is in the best interest of the patient, as
was seen in the United States.283
Some evidence exists that this is leading people with chronic
pain to consider suicide or seek illicit opioids.265,288
119
It is important to remember people with chronic pain amid all this attention being given to
opioids. Patients with chronic pain and their physicians are probable targets of initiatives to
reduce inappropriate prescribing of opioids. There may be some excessive opioid prescribing in
people with chronic pain. But this warrants further examination. Other research on this dataset
found 13.5% of those over 65 years with chronic pain were taking opioids on a regular basis (≥
90 days’ supply in a year), and 70% of those were getting doses of 50 mg or less of morphine
equivalents daily, the upper dose limit for new patients on opioids recommended in the 2017
Canadian opioid guideline.264,289
The research in this thesis on health utilities demonstrated the poor quality of life that is
experienced by people with chronic pain. The mean utility was 0.59, lower than that of someone
with urinary incontinence, cataracts, heart disease, and many other conditions (Chapter 3).
Those with the most severe pain (about 18% of the chronic pain population) had a mean utility of
0.35, which is profoundly low and similar to a quality of life experience by someone who has
had a moderate to severe stroke. Indeed, this low quality of life may be contributing to suicide
attempts. In those with most activity limitations from chronic pain, 1.4% attempted suicide at
least once during the observation period.
The ultimate harm from opioids is death, and overdose deaths from opioids are
increasing.277,278,280,281
However, it is unclear what proportion of these deaths are caused from
illicit use, including potent synthetic opioids imported from China, unrelated to chronic pain
maangement.290
Our research showed that accidental poisoning (from any substance, not only
opioids) occurred in 0.6% of deaths in people with chronic pain, and was higher than in people
120
without chronic pain by 0.4%. The difference disappeared with matching. Rare events like
death from cancer of the esophagus ranked higher than accidental poisoning.
There is a potential for harm in constraining treatment options in people with chronic pain in
terms of functional impairment, pain severity, lower quality of life and increased risk of suicide
attempt. Indeed, the research in this thesis demonstrates an opportunity for better pain
management. Expanding treatment options to include access to publicly funded
multidisciplinary care might improve pain management.291,292
Conversely, there is little
evidence in this research for increased risk of death from overdose.
5.4.3 Research implications
This research has described a substantial economic and health burden associated with chronic
pain. It is possible that research investment has been too modest relative to the scale of the
problem. A survey of Canadian pain researchers in 2009 determined that they received less than
1% of all funding from the Canadian Institutes of Health Research in that year.293
Those
allocating research funding both provincially and federally may wish to consider both the
economic and health burden when determining research priorities.
Our cost estimate of the economic burden of chronic pain can be compared to other conditions in
the report on economic burden of disease in Canada.208
We have provided a comparison of the
relative health burden of chronic pain and other diseases in Canada measured by health utilities
in Chapter 3. Additional research in management and prevention of chronic pain may be needed.
121
5.5 Future research
5.5.1 Costs and utilities stratified by geography
An analysis of costs and utilities by geography (e.g. local health integration networks) may be
helpful to understand the economic and health burden of chronic pain varies across the province
of Ontario. This will assist in identifying the most needed areas for program roll-out by planners
and developers. For example, specialized programs like chronic pain clinics could be prioritized.
5.5.2 Utility elicitation and cost-utility analysis
People with chronic pain conditions frequently do not see their pain improve over time.
Research is often aimed at improving coping skills for people with chronic pain. These types of
interventions may have impact on health utility and health-related quality of life. A cost-utility
analysis is needed that reflects outcomes in Canadian programs and Canadian costs. This first
requires developing a consensus on the most feasible chronic pain program, and conducting a
study to elicit utilities in people with chronic pain before, and at multiple time points after
participation in a chronic pain program, in order to estimate effectiveness.
5.5.3 Opioid prescribing from the narcotic monitoring system to better understand mortality and opioid use
We have incomplete prescription drug data for those under 65 years. Beginning in 2012, Ontario
introduced a narcotic monitoring system that captures all opioids dispensed to Ontario residents.
As this data accrues, it will be possible to re-analyze mortality data in this cohort of people with
and without chronic pain and explore the relationship with prescribed opioid use. This will give
insight into how many of the opioid deaths occur in people with chronic pain.
122
5.5.4 Registry opportunities
A much needed chronic pain registry is in development and as it accrues patients, will offer
another data source for more generalizable research. This may permit analysis of outcomes, in
particular costs, utilities and mortality data by duration of illness, which would be helpful for
gaining a better understanding of the economic and health-related burden of chronic pain over
time. Having health care costs for specific phases of care may assist with program planning. In
addition, having longitudinal health-related qualify of life will be important for understanding
changes over time.
5.6 Conclusions
The studies presented in this thesis demonstrate the economic and health-related burden of
chronic pain. Chronic pain is a costly condition, with per-person costs being more than 50%
greater than a similar patient without chronic pain. And since chronic pain affects 1 in 5
Canadians, the increased cost has a large impact on the health system, totalling approximately
5% of public healthcare spending. Chronic pain has a severe impact on health-related quality of
life measured by health utilities. People with chronic pain have utilities worse than most chronic
diseases, and those with severe pain have utilities similar to people who have had a moderate or
severe stroke. People with chronic pain die at a rate greater than those without chronic pain and
may be related to the conditions that co-occur with chronic pain. However, there were no
significant differences in the causes of death between groups. Suicide attempt is more common
in people with chronic pain. Death by suicide in people with chronic pain appears to be similar
to those without chronic pain, although small differences undetected by this research, could exist.
123
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Appendices
Appendix A
A. Pain search terms used in literature searches
A.1. Medline
exp Chronic Pain/ or exp Pain, Intractable/ or exp Headache/ or exp headache disorders/ or exp
migraine disorders/ or exp Neck Pain/ or exp Neuralgia/ or exp Fibromyalgia/ or exp Causalgia/
or exp Complex Regional Pain Syndromes/ or exp Trigeminal Neuralgia/ or exp
Temporomandibular Joint Disorders/ or exp Musculoskeletal Pain/ or ((exp Arthralgia/ or exp
Myofascial Pain Syndromes/ or myalgia.ti,ab. or exp Abdominal Pain/) and (recurr* or
chronic).ti,ab.) or (exp Pain/ and (exp arthritis/ or exp multiple sclerosis/ or exp Neoplasms/ or
exp HIV/))
A.2. Embase
exp Chronic Pain/ or exp intractable pain/ or exp "headache and facial pain"/ or exp chronic daily
headache/ or exp migraine/ or exp neck pain/ or exp neuropathic pain/ or exp fibromyalgia/ or
exp causalgia/ or exp complex regional pain syndrome/ or exp trigeminus neuralgia/ or exp
temporomandibular joint disorder/ or exp musculoskeletal pain/ or ((exp arthralgia/ or exp
myofascial pain/ or exp myalgia/ or exp abdominal pain/) and (recurr* or chronic).ti,ab.) or (exp
Pain/ and (exp arthritis/ or exp multiple sclerosis/ or exp neoplasm/ or exp Human
immunodeficiency virus/))
147
Appendix B
B. Supplemental Digital Content – Cost study
B.1. Cohort identification – chronic pain question
Pre-amble:
The next set of questions asks about your day-to-day health. The questions are not about illnesses
like colds that affect people for short periods of time. They are concerned with a person’s usual
abilities.
Are you usually free of pain or discomfort?
1, Yes (skip next 2 sub-questions)
2. No
How would you describe the usual intensity of your pain or discomfort?
1. Mild
2. Moderate
3. Severe
How many activities does your pain or discomfort prevent?
1. None
2. A few
3. Some
4. Most
148
B.2. Data sources and costs
The CCHS is national cross-sectional survey of Canadians aged 12 years and above conducted
over a two year period. It has a broad range of questions related to health status, healthcare
utilization and determinants of health. Some content changes with each survey cycle. The pain
question was present in Ontario in the first cycle of the survey in 2000-01 and in cycles in 2007-
08 and 2009-10. The survey also asked the respondents to rate their pain by severity (mild,
moderate, severe) and by the number of activities their pain interfered with (none, a few, some,
most).
Ontario healthcare databases used in this study record the amounts paid (physician services,
drugs, homecare and medical equipment). In contrast, hospitals use a resource intensity weight
approach. Each patient discharged is assigned a resource intensity weight that corresponds to the
amount of resources they consumed in their stay relative to an average patient for the specific
year. The resource intensity weight was developed by the Canadian Institute for Health
Information.201,294
The Ontario Ministry of Health and Long Term Care uses Ontario cost
distribution methodology to calculate a cost per weighted case for hospitals in Ontario by
totalling all money allocated to acute care hospitals and dividing by the total resource intensity
weights for all patients cared for at the institutions for that year.201
The cost of each patient’s
hospital stay was calculated by multiplying the resource intensity weight by the cost per
weighted case for that year in Ontario. Other institutional care (complex continuing care,
rehabilitation and long-term care) relies on parallel methods; however, longer episodes of care
use a cost per weighted day. These methods are described in more detail elsewhere.201
149
B.3. Comorbidity measure – ACG system
We used the Johns Hopkins ACG system (acg.jhsph.org/) to estimate comorbidity. Having
multiple comorbidities is associated with higher healthcare costs.295,296
The ACG system is a
proprietary software algorithm to estimate a relative measure of an individual's expected use of
health services. In addition to its original validation, the system has been validated for Canadian
healthcare data and is a good predictor of future healthcare costs.134,136,225
The ICD codes from
patient hospitalization, emergency department and physician visits for 12 months before their
survey response were categorized into one of 32 aggregated diagnosis groups (ADGs) based on
expected duration, severity, diagnostic certainty, etiology and specialty care involvement. The
number of ADGs was summed for each person and used as a measure of comorbidity. Before
determining the ADGs, we removed ICD codes for painful conditions (see Supplemental Digital
Content 2) from the databases. In this way, we avoided counting conditions of interest as
comorbidity. Closely matching cases and controls with similar painful conditions could remove
the effect of those conditions on healthcare costs. However, for one of our sensitivity analyses,
we retained all ICD codes for ADG determination and subsequent matching.
Conditions such as depression, anxiety and sleep problems often co-occur in people with chronic
pain6,297
and can have reciprocal effects.223,224,298,299
As a consequence, some would resolve in
the absence of chronic pain. However, it is unclear what proportion should be attributed to
chronic pain so we handled them the same as other comorbidities. However, we included these
comorbidities in a matching algorithm in the sensitivity analysis.
150
B.4. Matched cohort design
Matching was used to control for factors associated with healthcare use that could confound the
results. The Andersen framework for health services utilization has identified three sets of
factors: predisposing, enabling and need factors as contributors to use, and therefore, healthcare
costs.300
When these factors are not equally distributed in cases and controls, bias in healthcare
cost estimates can occur. We controlled for sociodemographic characteristic such as age, sex,
geography and income, as well as treatment year effect through matching in order to address the
predisposing and enabling factors from the Andersen framework. In our study, we were
interested in the need factor of chronic pain and we controlled for the other need factors, i.e.
comorbidity, through matching.
We used a propensity score to balance known confounders. It is frequently used in the analysis
of large administrative datasets where matching on a large number of factors is required.122,226,227
We included an age squared term (for adults and older adults only) because we hypothesized that
the relationship between age and pain would be non-linear. We included an age-sex interaction
term because we hypothesized sex differences across age.
Cases were matched to controls using the greedy matching method (after a random starting point
in both groups, the first control meeting the matching criteria was matched to the case and the
link was not broken even if there was a closer match later in the controls list).
Studying people with pain presents a unique situation that makes the choice of control subjects
less straightforward than for many diseases. Some people in the survey responded that they did
not have chronic pain but had ICD codes consistent with painful diseases (e.g. arthritis). We
assumed that these patients did not have chronic pain and included them as potential controls in
151
the matching process. However, we recognize that such individuals could represent well-
managed patients and might bias our results by underestimating the true cost of managing
chronic pain. And to remove them or reclassify them as people with chronic pain without
evidence could challenge the validity of our results.
B.5. Sensitivity analysis
In our base case, we matched people of similar levels of comorbidity (ADGs), so the extra cost
could be attributed to the presence of chronic pain. The ACG system was developed to predict
healthcare utilization in ambulatory populations, and using it in our matching algorithm should
manage comorbidity differences in cases and controls.134
However, some rates of painful
diseases, anxiety, and depression were higher in cases in the entire sample or age groups and we
cannot exclude the possibility that higher rates of painful diseases may have contributed to an
overestimation of the true cost. Therefore, as a sensitivity analysis, we matched on painful
conditions and related conditions of anxiety, depression and sleep problems in addition to ADGs.
We also performed a sensitivity analysis without matching on comorbidity.
B.6. Standardized differences for assessing matched cohorts
When working with large administrative data sets, standardized differences are recommended to
assess for closeness of the match, since t-tests and chi-squared tests can show statistically
significant, but trivial differences when sample sizes are large.117
Standardized differences of up
to 10% in matched cohorts are considered unlikely to contribute to the results. The formulas for
standardized differences for continuous and binary variables are as follows.
152
Continuous variable: 𝑚𝑒𝑎𝑛 𝑖𝑛 𝑐𝑎𝑠𝑒𝑠 −𝑚𝑒𝑎𝑛 𝑖𝑛 𝑐𝑜𝑛𝑡𝑟𝑜𝑙𝑠
√𝑣𝑎𝑟𝑖𝑎𝑛𝑐𝑒 𝑖𝑛 𝑐𝑎𝑠𝑒𝑠 +𝑣𝑎𝑟𝑖𝑎𝑛𝑐𝑒 𝑖𝑛 𝑐𝑜𝑛𝑡𝑟𝑜𝑙𝑠
2
Binary variable: 𝑝𝑟𝑒𝑣𝑎𝑙𝑒𝑛𝑐𝑒 𝑖𝑛 𝑐𝑎𝑠𝑒𝑠 –𝑝𝑟𝑒𝑣𝑎𝑙𝑒𝑛𝑐𝑒 𝑖𝑛 𝑐𝑜𝑛𝑡𝑟𝑜𝑙𝑠
√𝑝𝑟𝑒𝑣𝑎𝑙𝑒𝑛𝑐𝑒 𝑖𝑛 𝑐𝑎𝑠𝑒𝑠 ∗(1− 𝑝𝑟𝑒𝑣𝑎𝑙𝑒𝑛𝑐𝑒 𝑖𝑛 𝑐𝑎𝑠𝑒𝑠) + 𝑝𝑟𝑒𝑣𝑙𝑎𝑛𝑐𝑒 𝑖𝑛 𝑐𝑜𝑛𝑡𝑟𝑜𝑙𝑠 ∗(1−𝑝𝑟𝑒𝑣𝑎𝑙𝑒𝑛𝑐𝑒 𝑖𝑛 𝑐𝑜𝑛𝑡𝑟𝑜𝑙𝑠)
2
153
B.7. Tables
Table B.1: ICD codes for painful conditions
Condition OHIP1*
ICD-9† ICD-10
‡
Abdominal
pain§2,3
787 7890 7893 78974 R100 R1010 R1011 R1012 R1019 R102 R1030 R1031
R1032 R1039 R104**
Arthritis
714 715
725 274
714 715 446 720 274
710 711 712 713 716
717 718 719 721 725
726 727 728 729 7395
M05 M06 M15 M16 M17 M18 M19 M07 M10 M11 M12
M13 M14 M30 M31 M32 M33 M34 M35 M36 M00 M01
M02 M03 M20 M21 M22 M23 M24 M25 M65 M66 M67
M68 M69 M70 M71 M72 M73 M74 M75 M76 M77 M78
M79 M08 M096
Back and neck
problems
847 724
733
720 721 722 723 724
737 805 806 839 846
8477
M081 M45 M460 M461 M465 M468 M469 M491 M492
M493 M498 M470 M471 M472 M478 M479 M463 M464
M50 M51 M961 M433 M434 M436 M4802 M530 M531
M5400 M5401 M5402 M5403 M5411 M5412 M542 M432
M435 M480 M532 M533 M538 M539 M5404 M5405
M5406 M5407 M5408 M5409 M5410 M5413 M5414
M5415 M5416 M5417 M5418 M5419 M543 M544 M545
M546 M548 M549 M38 M39 M40 M41 M490 M962 M963
M964 M965 S12000 S12001 S12100 S12101 S12200
S12201 S12210 S12211 S12700 S12701 S12900 S12901
S22000 S22001 S22010 S22011 S22090 S22091 S22100
S22101 S32000 S32001 S32010 S32011 S32020 S32021
S32030 S32031 S32040 S32041 S32090 S32091 S32100
S32101 S32200 S32201 T080 T081 S031 S033 S130 S131
S132 S133 S230 S231 S232 S330 S331 S332 S333 S334
S43200 S43201 T030 T031 T032 T033 T034 T038 T039
T112 T132 T143 S336 S134 S136 S160 S168 S233 S335
S337 S3900 S3908**
Fibromyalgia 729.18 M797
**
Migraine 346 346.0 to 346.99 G43
**
* Ontario Health Insurance Program.
† International Classification of Diseases 9
th Revision.
‡ International Classification of Diseases 10
th Revision.
§ Recurrent abdominal pain is a common pain complaint in children and these codes were considered in the analysis
for children only.
** Adapted from ICD-9 codes.
154
Condition OHIP1*
ICD-9† ICD-10
‡
Neuropathy
350 356
724
2506 3572 0531 7221
7222 7227 7240 7243
7244 7211 7220 7230
7234 3372 3532 3533
3534 3544 3557 3559
7292 3536 3501 3502
3521 3530 3531 3538
3539 3540 3541 3542
3543 3545 3548 3549
3550 3551 3552 3553
3554 3555 3556
355810
E1040 E1041 E1049 E1140 E1141 E1149 E1340 E1341
E1349 E1440 E1441 E1449 G530 G546 G500 G501 G540
G541 M543 M544 G546 G548 G549 G550 G551 G552
G553 G558 G560 G561 G562 G563 G564 G568 G569
G570 G571 G572 G573 G574 G575 G576 G577 G580
G587 M4700 M4701 M4702 M4703 M4704 M4705 M4706
M4707 M4708 M4709 M4720 M4721 M4722 M4723
M4724 M4725 M4726 M4727 M4728 M4729 M4800
M4801 M4802 M4803 M4804 M4805 M4806 M4807
M4808 M4809 M501 M511 M5410 M5411 M5412 M5413
M5414 M5415 M5416 M5417 M5418 M5419 M543 M544
M792 M7925 M7926 M7927 M7928 M7929 M8900 M8901
M8902**
Table B.2: ICD codes for comorbid conditions
Condition OHIP1 ICD-9 ICD-10
Anxiety 300 3000, 300211
F40 F4111
Depression
311 29620 29621 29622 29623 29624
29625 29630 29631 29632 29633
29634 29635 2965 2966 29682
29690 3004 311 3090 3091 3092812
F313 F314 F315 F316 F32 F330
F331 F332 F333 338 F339 F341
F348 F349 F380 F381 F388 F39
F412 F99*
Sleep problems 307 30740 30741 30742 30743 30744
30745 30747 30748 30749 78050
78052 78054 78055 78056 7805913
F510 511 512 513 514 515 518 519
G470 471 472 478 479*
* Adapted from ICD-9 codes.
155
Table B.3: Opioid drug identification numbers
Products
containing
Drug identification number14
Anileridine 00010014
Buprenorphine 02295695 02295709 02408090 02408104 02424851 02424878
Butorphanol 02113031 02242504
Codeine 02163748 02163780 00093122 00593435 00293504 00425370 00653241 00687200
02163934 00779458 02163799 00470651 00293512 00425389 00608882 00653276
00666130 00687219 02163926 00396516 00779474 00093130 00593451 00621463
00779466 02230302 00093114 02163918 00685143 00816027 02163942 00093149
Dextropropoxyphene 00151351 00261432
Fentanyl 00888346 02126648 01937413 02249448 02275856 02282984 02314665 02327163
02330156 02341417 02386895 02396742 01937383 02249391 02282941 02314630
02327120 02330113 02341387 02386852 02396718 01937391 02249413 02275821
02282968 02314649 02327147 02330121 02341395 02386879 02396726 02240434
01937405 02249421 02275848 02282976 02314657 02327155 02330148 02341409
02386887 02396734
Hydromorphone 02125366 02243159 02243562 00622133 02145928 00705438 00885444 02319403
02364115 00786535 01916386 02125382 00125083 02145936 00885436 02319411
02364123 02125390 00125105 02146118 02085895 00627100 02145901 02145863
02146126 02125323 02359502 00125121 00885401 02319438 02364131 02125331
00786543 00885428 02319446 02364158 02359510
Meperidine 00725749 00725757 00725765 01928376 01928384 01928392 02138018 02139022
02139030 02139049 02139715 02242003 02242004 02242005 02242006
Methadone 09850619 09851771 09852891 02241377 02247694 02247698 02247699 02247700
02247701 02394596 02394618 09857217 09857218 09857219 09857220 09857221
09857223
Morphine 01988743 02014319 02019965 02145952 02184451 02245287 02302799 00392588
00594644 00624268 00632201 00690198 00850322 02014246 02019930 02242163
09857227 00632503 00690783 00392561 00850330 02015439 02177749 02244790
02245284 02302764 00486582 00591467 00607762 02014327 02145960 02177757
02245288 02302802 00624276 00690201 02014238 02014262 02184435 00621935
00632481 00690791 00594636 01964437 02242484 09857226 00636681 01988727
156
Products
containing
Drug identification number14
02014173 02014254 02014297 02019949 02146827 02244791 02245285 02302772
00690228 00675962 02184443 00617288 00690236 00594652 00514217 00591475
00607770 00690244 00776203 01988735 02014300 02019957 02145944 02244792
02245286 02302780
Oxycodone 00392472 00392480 00443948 00580201 00608157 00608165 02202441 01916475
01916483 01916548 02240131 02319985 02372525 09857233 09857241 09857319
09857408 02245758 02372533 02307898 09857409 02202468 02240132 02361361
02262983 02319993 02372797 09857234 09857242 09857321 09857410 02372541
09857411 02202476 02372568 09857412 00789739 02231934 02319977 09857232
09857243 09857318 02202484 02372584
Opium 00815349 01901869 00451606 01923463
Pentazocine 01904965 02137984 02139014 02241976
Propoxyphene 00010081
Sufentanil 01951319 02244147
157
Table B.4: Baseline characteristics – matching algorithm without comorbidity match
Adolescents Adults Older adults
Cases
n=447
Controls
n=447
d* Cases
n=12,914
Controls
n=12,914
d* Cases
n=6,195
Controls
n=6,195
d*
Age, mean (SD) 15 (2) 15 (2) 0.00 47 (12) 47 (12) 0.00 75 (7) 75 (7) 0.00
Female, number (%) 271 (61) 271 (61) 0.00 7,574 (59) 7,574 (59) 0.00 4,114 (66) 4,114 (66) 0.00
Rurality index of Ontario
2008, mean (SD)
19 (23) 18 (23) 0.06 19 (22) 18 (21) 0.06 19 (22) 17 (20) 0.08
Income quintile, number (%)
1 (low) 90 (20) 92 (21) 0.01 3,037 (24) 3,033 (24) 0.00 1,458 (24) 1,429 (23) 0.01
2 96 (22) 102 (23) 0.03 2,735 (21) 2,826 (22) 0.02 1,320 (21) 1,350 (22) 0.01
3 72 (16) 73 (16) 0.01 2,525 (20) 2,538 (20) 0.00 1,212 (20) 1,183 (19) 0.01
4 111 (25) 114 (26) 0.02 2,430 (19) 2,408 (19) 0.00 1,156 (19) 1,165 (19) 0.00
5 (high) 78 (17) 66 (15) 0.07 2,188 (17) 2,110 (16) 0.02 1,049 (17) 1,068 (17) 0.01
Ambulatory Diagnostic
Group, mean (SD)†
4 (3) 3 (2) 0.45 4 (3) 3 (2) 0.51 6 (3) 5 (3) 0.35
Ambulatory Diagnostic
Group, mean (SD)‡
3 (3) 2 (2) 0.43 4 (3) 3 (2) 0.47 6 (3) 5 (3) 0.31
Depression, number (%) 21 (5) 4 (1) 0.23 653 (5) 279 (2) 0.16 193 (3) 108 (2) 0.09
Anxiety, number (%) 63 (14) 25 (6) 0.29 2,810 (22) 1,616 (13) 0.25 960 (16) 697 (11) 0.12
Sleep problems, number (%) 17 (4) 5 (1) 0.17 489 (4) 254 (2) 0.11 177 (3) 107 (2) 0.08
Arthritis, number (%) 22 (5) 4 (1) 0.24 2,106 (16) 700 (5) 0.36 2,111 (34) 998 (16) 0.42
Back or neck problems,
number (%)
26 (6) 13 (3) 0.14 2,401 (19) 958 (7) 0.34 1,147 (19) 683 (11) 0.21
Neuropathic pain, number
(%)
25 (6) 8 (2) 0.20 1,789 (14) 601 (5) 0.32 776 (13) 357 (6) 0.24
Migraine, number (%) 20 (5) 3 (1) 0.24 465 (4) 189 (2) 0.14 54 (1) 34 (1) 0.04
Fibromyalgia, number (%) ≤ 5 0 (0) - 22 (0) 0 (0) 0.06 ≤ 5 ≤ 5 -
Abdominal pain (%)§ 58 (13) 31 (7) 0.20 n/a n/a n/a n/a n/a n/a
Hospitalization in last 12
months, number (%)
40 (9) 19 (4) 0.19 2,597 (20) 1,555 (12) 0.22 2,018 (33) 1523 (25) 0.18
* Standardized difference.
† The number of ADGs was calculated with all ICD codes.
‡ The number of ADGs was calculated without ICD codes for painful conditions described in Table B.1.
§ Numbers and percentages are reported for adolescents only.
158
Table B.5: Baseline characteristics – matching algorithm with painful conditions matched
Adolescents Adults Older adults
Cases
n=431
Controls
n=431
d* Cases
n=12,199
Controls
n=12,199
d* Cases
n=5,837
Controls
n=5,837
d*
Age, mean (SD) 15 (2) 15 (2) 0.00 46 (12) 46 (12) 0.00 75 (7) 75 (7) 0.00
Female, number (%) 260 (60) 260 (60) 0.00 7,085 (58) 7,085 (58) 0.00 3,827 (66) 3,827 (66) 0.00
Rurality index of Ontario
2008, mean (SD)
19 (23) 19 (24) 0.01 19 (22) 19 (23) 0.01 19 (22) 18 (21) 0.04
Income quintile, number (%)
1 (low) 86 (20) 89 (21) 0.02 2,774 (23) 2,755 (23) 0.00 1,360 (23) 1,361 (23) 0.00
2 93 (22) 101 (23) 0.04 2,562 (21) 2,604 (21) 0.01 1,233 (21) 1,281 (22) 0.02
3 69 (16) 95 (22) 0.15 2,415 (20) 2,429 (20) 0.00 1,145 (20) 1,212 (21) 0.03
4 107 (25) 78 (18) 0.16 2,339 (19) 2,354 (19) 0.00 1,098 (19) 996 (17) 0.05
5 (high) 76 (18) 68 (16) 0.05 2,109 (17) 2,056 (17) 0.01 1,001 (17) 987 (17) 0.01
Ambulatory Diagnostic
Group, mean (SD)†
3 (2) 3 (2) 0.01 4 (3) 4 (3) 0.00 6 (3) 6 (3) 0.01
Ambulatory Diagnostic
Group, mean (SD)‡
3 (2) 3 (2) 0.01 4 (3) 4 (3) 0.01 6 (3) 6 (3) 0.01
Depression, number (%) 16 (4) 14 (3) 0.03 521 (4) 507 (4) 0.01 149 (3) 143 (2) 0.01
Anxiety, number (%) 54 (13) 53 (12) 0.01 2,425 (20) 2,476 (20) 0.01 849 (15) 841 (14) 0.00
Sleep problems, number (%) 12 (3) 13 (3) 0.01 405 (3) 411 (3) 0.00 151 (3) 161 (3) 0.01
Arthritis, number (%) 17 (4) 16 (4) 0.01 1,648 (14) 1,537 (13) 0.03 1,797 (31) 1,780 (31) 0.01
Back or neck problems,
number (%)
24 (6) 21 (5) 0.03 1,937 (16) 1,971 (16) 0.01 928 (16) 889 (15) 0.02
Neuropathic pain, number
(%)
22 (5) 18 (4) 0.04 1,376 (11) 1,385 (11) 0.00 580 (10) 579 (10) 0.00
Migraine, number (%) 14 (3) 11 (3) 0.04 362 (3) 372 (3) 0.00 50 (1) 39 (1) 0.02
Fibromyalgia, number (%) 0 (0) 0 (0) . 11 (0) 7 (0) 0.01 ≤ 5 ≤ 5 0.00
Abdominal pain (%)§ 49 (11) 47 (11) 0.01 n/a n/a n/a n/a n/a n/a
Hospitalization in last 12
months, number (%)
31 (7) 34 (8) 0.03 2,259 (18) 2,357 (19) 0.02 1,806 (31) 1,816 (31) 0.00
* Standardized difference.
† The number of ADGs was calculated with all ICD codes.
‡ The number of ADGs was calculated without ICD codes for painful conditions described in Table B.1 and Table B.2.
§ Numbers and percentages are reported for adolescents only.
159
Table B.6: Annual incremental cost by subgroup
(2014 $CAD)
12 – 17 years 18 – 64 years ≥ 65 years
Per person Incremental cost 95% CI Incremental cost 95% CI Incremental cost 95% CI
Entire sample 956 240 – 1,861 1,260 989 – 1,524 2,710 2,090 – 3,316
Females 1,396 391 – 2,674 1,272 986 – 1,550 2,523 1,808 – 3,197
Males 260 -597 – 1,198 1,244 800 – 1,722 3,072 1,865 – 4,227
Mild pain 898 259 – 1,765 322 -8 – 641 1,159 121 – 2,185
Moderate to-severe pain 1,018 -155 – 2,610 1 672 1,329 – 2,003 3,249 2,529 – 4,023
Pain without activity limits -104 -716 – 368 44 -289 – 347 991 -163 – 2,283
Pain causing any activity limit 1,548 481 – 2,900 1 647 1,330 – 1,985 3,229 2,529 – 3,943
160
Appendix C
C. Supplemental Digital Content – Utilities study
C.1. Cohort identification – chronic pain question
Pre-amble:
The next set of questions asks about your day-to-day health. The questions are not about illnesses
like colds that affect people for short periods of time. They are concerned with a person’s usual
abilities.93,94
Are you usually free of pain or discomfort?
1, Yes (skip next 2 sub-questions)
2. No
How would you describe the usual intensity of your pain or discomfort?
1. Mild
2. Moderate
3. Severe
How many activities does your pain or discomfort prevent?
1. None
2. A few
3. Some
4. Most
161
C.2. Health Utilities Index Mark 3 classification system
(www.healthutilities.com/)
Attribute Level Description
VISION 1 Able to see well enough to read ordinary newsprint and recognize
a friend on the other side of the street, without glasses or contact
lenses.
2 Able to see well enough to read ordinary newsprint and recognize
a friend on the other side of the street, but with glasses.
3 Able to read ordinary newsprint with or without glasses but unable
to recognize a friend on the other side of the street, even with
glasses.
4 Able to recognize a friend on the other side of the street with or
without glasses but unable to read ordinary newsprint, even with
glasses.
5 Unable to read ordinary newsprint and unable to recognize a friend
on the other side of the street, even with glasses.
6 Unable to see at all.
HEARING 1 Able to hear what is said in a group conversation with at least
three other people, without a hearing aid.
2 Able to hear what is said in a conversation with one other person
in a quiet room without a hearing aid, but requires a hearing aid
to hear what is said in a group conversation with at least three
other people.
3 Able to hear what is said in a conversation with one other person
in a quiet room with a hearing aid, and able to hear what is said
in a group conversation with at least three other people, with a
hearing aid.
4 Able to hear what is said in a conversation with one other person
in a quiet room, without a hearing aid, but unable to hear what is
said in a group conversation with at least three other people even
with a hearing aid.
5 Able to hear what is said in a conversation with one other person
in a quiet room with a hearing aid, but unable to hear what is
said in a group conversation with at least three other people even
with a hearing aid.
6 Unable to hear at all.
162
Attribute Level Description
SPEECH 1 Able to be understood completely when speaking with strangers or
friends.
2 Able to be understood partially when speaking with strangers but
able to be understood completely when speaking with people
who know me well.
3 Able to be understood partially when speaking with strangers or
people who know me well.
4 Unable to be understood when speaking with strangers but able to
be understood partially by people who know me well.
5 Unable to be understood when speaking to other people (or unable
to speak at all).
AMBULATION 1 Able to walk around the neighbourhood without difficulty, and
without walking equipment.
2 Able to walk around the neighbourhood with difficulty; but does
not require walking equipment or the help of another person.
3 Able to walk around the neighbourhood with walking equipment,
but without the help of another person.
4 Able to walk only short distances with walking equipment, and
requires a wheelchair to get around the neighbourhood.
5 Unable to walk alone, even with walking equipment. Able to walk
short distances with the help of another person, and requires a
wheelchair to get around the neighbourhood.
6 Cannot walk at all.
DEXTERITY 1 Full use of two hands and ten fingers.
2 Limitations in the use of hands or fingers, but does not require
special tools or help of another person.
3 Limitations in the use of hands or fingers, is independent with use
of special tools (does not require the help of another person).
4 Limitations in the use of hands or fingers, requires the help of
another person for some tasks (not independent even with use of
special tools).
5 Limitations in use of hands or fingers, requires the help of another
person for most tasks (not independent even with use of special
tools).
6 Limitations in use of hands or fingers, requires the help of another
person for all tasks (not independent even with use of special
tools).
163
Attribute Level Description
EMOTION 1 Happy and interested in life.
2 Somewhat happy.
3 Somewhat unhappy.
4 Very unhappy.
5 So unhappy that life is not worthwhile.
COGNITION 1 Able to remember most things, think clearly and solve day to day
problems.
2 Able to remember most things, but have a little difficulty when
trying to think and solve day to day problems.
3 Somewhat forgetful, but able to think clearly and solve day to day
problems.
4 Somewhat forgetful, and have a little difficulty when trying to
think or solve day to day problems.
5 Very forgetful, and have great difficulty when trying to think or
solve day to day problems.
6 Unable to remember anything at all, and unable to think or solve
day to day problems.
PAIN 1 Free of pain and discomfort.
2 Mild to moderate pain that prevents no activities.
3 Moderate pain that prevents a few activities.
4 Moderate to severe pain that prevents some activities.
5 Severe pain that prevents most activities.
164
C.3. Tables
Table C.1: ICD codes for painful conditions
OHIP ICD-9 ICD-10
Abdominal
pain28,301
787 7890 7893 7897220
R100 R1010 R1011 R1012 R1019 R102 R1030 R1031
R1032 R1039 R104*
Arthritis
714
715
725
274
714 715 446 720 274
710 711 712 713 716
717 718 719 721 725
726 727 728 729
739218
M05 M06 M15 M16 M17 M18 M19 M07 M10 M11 M12
M13 M14 M30 M31 M32 M33 M34 M35 M36 M00 M01
M02 M03 M20 M21 M22 M23 M24 M25 M65 M66 M67
M68 M69 M70 M71 M72 M73 M74 M75 M76 M77 M78
M79 M08 M09219
Back and neck
problems
847
724
733
720 721 722 723 724
737 805 806 839 846
847 216
M081 M45 M460 M461 M465 M468 M469 M491 M492
M493 M498 M470 M471 M472 M478 M479 M463 M464
M50 M51 M961 M433 M434 M436 M4802 M530 M531
M5400 M5401 M5402 M5403 M5411 M5412 M542 M432
M435 M480 M532 M533 M538 M539 M5404 M5405
M5406 M5407 M5408 M5409 M5410 M5413 M5414
M5415 M5416 M5417 M5418 M5419 M543 M544 M545
M546 M548 M549 M38 M39 M40 M41 M490 M962 M963
M964 M965 S12000 S12001 S12100 S12101 S12200
S12201 S12210 S12211 S12700 S12701 S12900 S12901
S22000 S22001 S22010 S22011 S22090 S22091 S22100
S22101 S32000 S32001 S32010 S32011 S32020 S32021
S32030 S32031 S32040 S32041 S32090 S32091 S32100
S32101 S32200 S32201 T080 T081 S031 S033 S130 S131
S132 S133 S230 S231 S232 S330 S331 S332 S333 S334
S43200 S43201 T030 T031 T032 T033 T034 T038 T039
T112 T132 T143 S336 S134 S136 S160 S168 S233 S335
S337 S3900 S3908†
Fibromyalgia 729.143
M797†
Migraine 346 346.0 to 346.9217
G43†
Neuropathy
350
356
724
2506 3572 0531 7221
7222 7227 7240 7243
7244 7211 7220 7230
7234 3372 3532 3533
3534 3544 3557 3559
7292 3536 3501 3502
3521 3530 3531 3538
3539 3540 3541 3542
3543 3545 3548 3549
3550 3551 3552 3553
3554 3555 3556
355844
E1040 E1041 E1049 E1140 E1141 E1149 E1340 E1341
E1349 E1440 E1441 E1449 G530 G546 G500 G501 G540
G541 M543 M544 G546 G548 G549 G550 G551 G552
G553 G558 G560 G561 G562 G563 G564 G568 G569
G570 G571 G572 G573 G574 G575 G576 G577 G580
G587 M4700 M4701 M4702 M4703 M4704 M4705 M4706
M4707 M4708 M4709 M4720 M4721 M4722 M4723
M4724 M4725 M4726 M4727 M4728 M4729 M4800
M4801 M4802 M4803 M4804 M4805 M4806 M4807
M4808 M4809 M501 M511 M5410 M5411 M5412 M5413
M5414 M5415 M5416 M5417 M5418 M5419 M543 M544
M792 M7925 M7926 M7927 M7928 M7929 M8900 M8901
M8902†
* Adapted from ICD-9 codes.
† Adapted from ICD-9 codes.
165
Appendix D
D. Supplemental Digital Content – Mortality study
D.1. Cohort identification – chronic pain question
Pre-amble:
The next set of questions asks about your day-to-day health. The questions are not about illnesses
like colds that affect people for short periods of time. They are concerned with a person’s usual
abilities.93,94,195
Are you usually free of pain or discomfort?
1, Yes (skip next 2 sub-questions)
2. No
How would you describe the usual intensity of your pain or discomfort?
1. Mild
2. Moderate
3. Severe
How many activities does your pain or discomfort prevent?
1. None
2. A few
3. Some
4. Most
166
D.2. Variables in the more closely matched propensity score
Matched on age, sex, survey year and propensity score. The propensity score contained age*sex
and age2 interaction terms, and these variables drawn from data 1 year before survey response:
1 Income quintile 1-5
2 Rurality index of Ontario
3 ADG 1 – 32
35 Charlson 1 – 17, missing, 0, 1, 2, >2
57 Any suicide attempt in the previous year
58 Any prescription drug claim
59 Number of prescription drug claims
60 Individual using the Trillium drug program
61 Individual using the Ontario Disability Support program
62 Individual using the low income seniors’ Ontario Drug Benefit program
63 Individual using the Homecare drug program.
64 Individual using the Ontario Works (general welfare) drug program
65 Individual having at least one claim to the Ontario Health Insurance program
66 Individual having at least one claim to the National Ambulatory Claims Reporting System
(emergency department visits and selected other outpatient services)
67 Any claim in the Ontario Mental Health Reporting system
68 Any claim for rehabilitation services
69 Any claim for homecare services
70 Any complex continuing care claim
71 Any long-term care claim
72 Any assistive devices program claim
73 Number of family doctor visits
74 Number of physician visits
75 Number of different specialist visits
76 Any of 36 medical specialty visits (e.g. cardiology, dermatology, etc)
112 Any of the top 50 diagnosis codes in OHIP for the chronic pain cohort
162 Proxy response by survey respondent
163 Aboriginal status
164 Any alcohol consumed
165 Occasional drinker (less than once a month)
166 Regular drinker (at least once a month)
167 Married
168 Common law
169 Single
170 Widowed, divorced, separated
171 Education less than secondary school
172 Completed secondary school
173 Some post-secondary school
174 Completed post-secondary school
175 Regular smoker (daily smoker)
176 Occasional smoker (less than daily)
177 Ex-smoker
178 Never smoked
179 Underweight (BMI < 18.5)
180 Normal weight (BMI 18.5 to < 25)
181 Overweight (BMI 25 to < 30)
182 Obese (BMI ≥ 30)
167
D.3. Tables
Table D.1: Standardized causes of death
(Becker’s leading cause of death from reference 247
)
Number Description ICD-10 codes
LCD_01 Intestinal infectious diseases A00-A099
LCD_02 Tuberculosis A15-A19
LCD_03 Vector-borne diseases and rabies A20, A44, A75-A79, A82-A84,
A852, A90-A96, A98.0-A98.2,
A98.8, B50-B57
LCD_04 Vaccine-preventable diseases A33-A37, A80, B01, B05, B06,
B15, B16, B17.0, B18.0, B18.1,
B18.9, B19, B26
LCD_05 Meningitis A39, A87, G00-G03
LCD_06 Septicemia A40-A41
LCD_07 Human immunodeficiency virus [HIV] disease B20-B24
LCD_08 Malignant neoplasm of esophagus C15
LCD_09 Malignant neoplasm of stomach C16
LCD_10 Malignant neoplasm of colon, sigmoid, rectum and anus C18-C21
LCD_11 Malignant neoplasm of liver C22
LCD_12 Malignant neoplasm of gallbladder C23, C24
LCD_13 Malignant neoplasm of pancreas C25
LCD_14 Malignant neoplasm of larynx C32
LCD_15 Malignant neoplasm of trachea, bronchus and lung C33, C34
LCD_16 Melanoma and other malignant neoplasms of skin C43, C44
LCD_17 Malignant neoplasms of female breast C50
LCD_18 Malignant neoplasm of uterus C53-C55
LCD_19 Malignant neoplasm of ovary C56
LCD_20 Malignant neoplasm of prostate C61
LCD_21 Malignant neoplasm of kidney C64
LCD_22 Malignant neoplasm of bladder C67
LCD_23 Malignant neoplasm of brain C71
LCD_24 Malignant neoplasms of lymphoid, hematopoietic and
related tissue
C81-C96
LCD_25 Benign neoplasms, in situ and uncertain behaviour D00-D48
LCD_26 Diabetes E10-E149
LCD_27 Malnutrition and nutritional anemias D50-D539
LCD_28 Disorders of fluid and electrolytes E40-E649 E86-E879
LCD_29 Dementia and Alzheimer disease F00-F009 F01-F019 F03-F039
G30-G309
LCD_30 Mental, behavior disorders from psychoactive substance
use
F10-F199
LCD_31 Parkinson’s disease G20-G219
168
Number Description ICD-10 codes
LCD_32 Epilepsy and status epilepticus G40-G419
LCD_33 Chronic rheumatic heart disease I05-I099
LCD_34 Hypertensive disease I10-I159
LCD_35 Ischemic heart disease I20-I259
LCD_36 Pulmonary heart disease and related I26-I289
LCD_37 Non-rheumatic valve disorders I34-I389
LCD_38 Cardiomyopathy I42-I429
LCD_39 Cardiac arrest I46-I469
LCD_40 Cardiac arrhythmias I47-I499
LCD_41 Heart failure and complications and ill-defined heart
disease
I50-I519
LCD_42 Cerebrovascular diseases I60-I699
LCD_43 Atherosclerosis I70-I709
LCD_44 Aortic aneurysm and dissection I71-I719
LCD_45 Acute respiratory diseases - not influenza/pneumonia J00-J069 J20-J229
LCD_46 Influenza and pneumonia J10-J189
LCD_47 Chronic lower respiratory diseases J40-J479
LCD_48 Pulmonary edema and related J80-J849
LCD_49 Respiratory failure J96-J969
LCD_50 Appendicitis, hernia, intestinal obstruct K35-K469 K56-K569
LCD_51 Cirrhosis and other liver diseases K70-K769
LCD_52 Diseases of musculoskeletal and connective tissue M00-M999
LCD_53 Diseases of urinary system N00-N399
LCD_54 Pregnancy, childbirth and puerperium O00-O999
LCD_55 Perinatal conditions P00-P969
LCD_56 Congenital malformation, deformations, chromosomal
abnormalities
Q00-Q999
LCD_57 Land transport accidents V01-V899
LCD_58 Accidental falls W0-W199
LCD_59 Unintentional firearm discharge W32-W349
LCD_60 Accidental drowning V90-V909 V92-V929 W65-W749
LCD_61 Accidents by suffocation and foreign body W44-W45 W75-W849
LCD_62 Accidental poisoning X40-X499
LCD_63 Suicide X60-X849 Y870
LCD_64 Homicide X85-Y099 Y871
LCD_65 Event of undetermined intent Y10-Y349
169
Table D.2: ICD codes for suicide
ICD-9 code Description
E950 Suicide and self-inflicted poisoning by solid or liquid substances
E951 Suicide and self-inflicted poisoning by gases in domestic use
E952 Suicide and self-inflicted poisoning by other gases and vapors
E953 Suicide and self-inflicted injury by hanging, strangulation, and suffocation
E954 Suicide and self-inflicted injury by submersion [drowning]
E955 Suicide and self-inflicted injury by firearms, air guns and explosives
E956 Suicide and self-inflicted injury by cutting and piercing instrument
E957 Suicide and self-inflicted injuries by jumping from high place
E958 Suicide and self-inflicted injury by other and unspecified means
E959 Late effects of self-inflicted injury
ICD-10 code Description
X60 Intentional self-poisoning by and exposure to nonopioid analgesics, antipyretics and
antirheumatics
X61 Intentional self-poisoning by and exposure to antiepileptic, sedative-hypnotic,
antiparkinsonism and psychotropic drugs, not elsewhere classified
X62 Intentional self-poisoning by and exposure to narcotics and psychodysleptics
[hallucinogens], not elsewhere classified
X63 Intentional self-poisoning by and exposure to other drugs acting on the autonomic nervous
system
X64 Intentional self-poisoning by and exposure to other and unspecified drugs, medicaments and
biological substances
X65 Intentional self-poisoning by and exposure to alcohol
X66 Intentional self-poisoning by and exposure to organic solvents and halogenated
hydrocarbons and their vapours
X67 Intentional self-poisoning by and exposure to other gases and vapours
X68 Intentional self-poisoning by and exposure to pesticides
X69 Intentional self-poisoning by and exposure to other and unspecified chemicals and noxious
substances
X70 Intentional self-harm by hanging, strangulation and suffocation
X71 Intentional self-harm by drowning and submersion
X72 Intentional self-harm by handgun discharge
X73 Intentional self-harm by rifle, shotgun and larger firearm discharge
X74 Intentional self-harm by other and unspecified firearm discharge
X75 Intentional self-harm by explosive material
X76 Intentional self-harm by smoke, fire and flames
X77 Intentional self-harm by steam, hot vapours and hot objects
X78 Intentional self-harm by sharp object
X79 Intentional self-harm by blunt object
X80 Intentional self-harm by jumping from a high place
X81 Intentional self-harm by jumping or lying before moving object
X82 Intentional self-harm by crashing of motor vehicle
X83 Intentional self-harm by other specified means
X84 Intentional self-harm by unspecified means
Y87.0 Sequelae of intentional self-harm
170
Table D.3: Charlson Index disease weights
(from references250,251
)
Condition Weight
Myocardial infarction
Congestive heart failure
Peripheral vascular disease
Cerebrovascular disease
Dementia
Chronic obstructive pulmonary disease or other respirator disease
Connective tissue disease
Ulcer disease
Mild liver disease
Diabetes
1
Hemi- or paraplegia
Moderate or severe renal disease
Diabetes with end organ damage
Primary cancer (no secondary found)
2
Moderate or severe liver disease
3
Metastatic solid tumor
AIDS
6
171
Table D.4: ICD codes for painful conditions
OHIP ICD-9 ICD-10
Arthritis
714
715
725
274
714 715 446 720 274 710 711
712 713 716 717 718 719 721
725 726 727 728 729 739218
M05 M06 M15 M16 M17 M18 M19 M07 M10
M11 M12 M13 M14 M30 M31 M32 M33 M34
M35 M36 M00 M01 M02 M03 M20 M21 M22
M23 M24 M25 M65 M66 M67 M68 M69 M70
M71 M72 M73 M74 M75 M76 M77 M78 M79
M08 M09219
Back and
neck problems
847
724
733
720 721 722 723 724 737 805
806 839 846
847 216
M081 M45 M460 M461 M465 M468 M469 M491
M492 M493 M498 M470 M471 M472 M478 M479
M463 M464 M50 M51 M961 M433 M434 M436
M4802 M530 M531 M5400 M5401 M5402 M5403
M5411 M5412 M542 M432 M435 M480 M532
M533 M538 M539 M5404 M5405 M5406 M5407
M5408 M5409 M5410 M5413 M5414 M5415
M5416 M5417 M5418 M5419 M543 M544 M545
M546 M548 M549 M38 M39 M40 M41 M490
M962 M963 M964 M965 S12000 S12001 S12100
S12101 S12200 S12201 S12210 S12211 S12700
S12701 S12900 S12901 S22000 S22001 S22010
S22011 S22090 S22091 S22100 S22101 S32000
S32001 S32010 S32011 S32020 S32021 S32030
S32031 S32040 S32041 S32090 S32091 S32100
S32101 S32200 S32201 T080 T081 S031 S033
S130 S131 S132 S133 S230 S231 S232 S330 S331
S332 S333 S334 S43200 S43201 T030 T031 T032
T033 T034 T038 T039 T112 T132 T143 S336 S134
S136 S160 S168 S233 S335 S337 S3900 S3908†
Fibromyalgia 729.143
M797*
Migraine 346 346.0 to 346.9217
G43†
Neuropathy
350
356
724
2506 3572 0531 7221 7222 7227
7240 7243 7244 7211 7220 7230
7234 3372 3532 3533 3534 3544
3557 3559 7292 3536 3501 3502
3521 3530 3531 3538 3539 3540
3541 3542 3543 3545 3548 3549
3550 3551 3552 3553 3554 3555
3556 355844
E1040 E1041 E1049 E1140 E1141 E1149 E1340
E1341 E1349 E1440 E1441 E1449 G530 G546
G500 G501 G540 G541 M543 M544 G546 G548
G549 G550 G551 G552 G553 G558 G560 G561
G562 G563 G564 G568 G569 G570 G571 G572
G573 G574 G575 G576 G577 G580 G587 M4700
M4701 M4702 M4703 M4704 M4705 M4706
M4707 M4708 M4709 M4720 M4721 M4722
M4723 M4724 M4725 M4726 M4727 M4728
M4729 M4800 M4801 M4802 M4803 M4804
M4805 M4806 M4807 M4808 M4809 M501 M511
M5410 M5411 M5412 M5413 M5414 M5415
M5416 M5417 M5418 M5419 M543 M544 M792
M7925 M7926 M7927 M7928 M7929 M8900
M8901 M8902†
* Adapted from ICD-9 codes.
172
Table D.5: ICD codes for deaths from accidental poisoning
ICD-9 code Description
E850 Accidental poisoning by analgesics antipyretics and antirheumatics
E851 Accidental poisoning by barbiturates
E852 Accidental poisoning by other sedatives and hypnotics
E853 Accidental poisoning by tranquilizers
E854 Accidental poisoning by other psychotropic agents
E858 Accidental poisoning by other drugs
E862 Accidental poisoning by petroleum products, other solvents and their vapors, not elsewhere
classified
E868 Accidental poisoning by other utility gas and other carbon monoxide
ICD-10 code Description
X40 Accidental poisoning by and exposure to nonopioid analgesics, antipyretics and
antirheumatics
X41 Accidental poisoning by and exposure to antiepileptic, sedative-hypnotic, antiparkinsonism
and psychotropic drugs, not elsewhere classified
X42 Accidental poisoning by and exposure to narcotics and psychodysleptics [hallucinogens],
not elsewhere classified
X46 Accidental poisoning by and exposure to organic solvents and halogenated hydrocarbons
and their vapours
X47 Accidental poisoning by and exposure to other gases and vapours
173
Table D.6: ICD codes for deaths of undetermined intent
ICD-9 code Description
E983 Hanging strangulation or suffocation undetermined whether accidentally or purposely
inflicted
E984 Submersion (drowning), undetermined whether accidentally or purposely inflicted
E985 Injury by firearms air guns and explosives undetermined whether accidentally or purposely
inflicted
E986 Injury by cutting and piercing instruments, undetermined whether accidentally or purposely
inflicted
E987 Falling from high place undetermined whether accidentally or purposely inflicted
E988 Injury by other and unspecified means undetermined whether accidentally or purposely
inflicted
E989 Late effects of injury, undetermined whether accidentally or purposely inflicted
ICD-10 code Description
Y10 Poisoning by and exposure to nonopioid analgesics, antipyretics and antirheumatics,
undetermined intent
Y11 Poisoning by and exposure to antiepileptic, sedative-hypnotic, antiparkinsonism and
psychotropic drugs, not elsewhere classified, undetermined intent
Y12 Poisoning by and exposure to narcotics and psychodysleptics [hallucinogens], not elsewhere
classified, undetermined intent
Y13 Poisoning by and exposure to other drugs acting on the autonomic nervous system,
undetermined intent
Y14 Poisoning by and exposure to other and unspecified drugs, medicaments and biological
substances, undetermined intent
Y15 Poisoning by and exposure to alcohol, undetermined intent
Y16 Poisoning by and exposure to organic solvents and halogenated hydrocarbons and their
vapours, undetermined intent
Y17 Poisoning by and exposure to other gases and vapours, undetermined intent
Y18 Poisoning by and exposure to pesticides, undetermined intent
Y19 Poisoning by and exposure to other and unspecified chemicals and noxious substances,
undetermined intent
Y20 Hanging, strangulation and suffocation, undetermined intent
Y21 Drowning and submersion, undetermined intent
Y22 Handgun discharge, undetermined intent
Y23 Rifle, shotgun and larger firearm discharge, undetermined intent
Y24 Other and unspecified firearm discharge, undetermined intent
Y25 Contact with explosive material, undetermined intent
Y26 Exposure to smoke, fire and flames, undetermined intent
Y27 Contact with steam, hot vapours and hot objects, undetermined intent
Y28 Contact with sharp object, undetermined intent
Y29 Contact with blunt object, undetermined intent
Y30 Falling, jumping or pushed from a high place, undetermined intent
Y31 Falling, lying or running before or into moving object, undetermined intent
Y32 Crashing of motor vehicle, undetermined intent
Y33 Other specified events, undetermined intent
Y34 Unspecified event, undetermined intent
Y87.2 Poisoning by and exposure to nonopioid analgesics, antipyretics and antirheumatics,
undetermined intent
174
Table D.7: Suicide attempts and death from suicide stratified by sex
Chronic pain
Female
Chronic pain
Male
p No pain
Female
No pain
Male
p
Unmatched sample
Individuals with at least 1 attempt,
number (%) 79 (0.69) 55 (0.76) 0.58 154 (0.42) 112 (0.35) 0.13
Suicide cumulative incidence
Narrow definition, number (%) * * 0.02 10 (0.03) 41 (0.13) < 0.01
Broad definition, number (%) 11 (0.1) 27 (0.37) < 0.01 15 (0.04) 56 (0.17) < 0.01
Matched
Individuals with at least 1 attempt,
number (%) 68 (0.65) 44 (0.66) 0.93 55 (0.52) 23 (0.34) 0.08
Suicide cumulative incidence
Narrow definition, number (%) * * < 0.01 * * < 0.01
Broad definition, number (%) 10 (0.10) 21 (0.31) < 0.01 6 (0.06) 21 (0.31) < 0.01
Less closely matched
Individuals with at least 1 attempt,
number (%) 78 (0.69) 55 (0.77) 0.52 45 (0.40) 21 (0.29) 0.25
Suicide cumulative incidence
Narrow definition, number (%) 7 (0.06) 12 (0.17) 0.03 < 6 (< 0.05) 11 (0.15) < 0.01
Broad definition, number (%) 11 (0.1) 27 (0.38) < 0.01 < 6 (< 0.05) 14 (0.2) < 0.01
More closely matched
Individuals with at least 1 attempt,
number (%) 56 (0.59) 41 (0.65) 0.68 55 (0.58) 31 (0.49) 0.43
Suicide cumulative incidence
Narrow definition, number (%) 6 (0.06) 10 (0.16) 0.07 <6 (< 0.06) 13 (0.2) <0.01
Broad definition, number (%) 8 (0.08) 18 (0.28) < 0.01 <6 (< 0.06) 16 (0.25) <0.01
* suppressed for privacy reasons. Note that in each case there were numerically more suicides in the male group.
175
Table D.8: Clinical and demographic characteristics
Less closely matched More closely matched
Chronic
pain
No pain d† Chronic
pain
No pain d†
Cohort size, n 18430 18430 15875 15875
Age, years (SD) 56 (17) 56 (17) 0 55 (17) 55 (17) 0
Female, % 61 61 0 60 60 0
Major urban (0-9), % 49 53 0.07 49 50 0.01
Non-major urban (10-44), % 37 35 0.03 37 36 0.02
Rural (greater than 45), % 14 12 0.05 14 14 < 0.01
Income quintile, %
1 (low) 24 23 < 0.01 22 22 < 0.01
2 21 22 0.01 21 21 < 0.01
3 19 20 < 0.01 20 20 0.01
4 19 19 < 0.01 19 19 < 0.01
5 (high) 17 17 0.01 18 18 0.01
Depression, % 5 2 0.14 4 4 0.01
Anxiety, % 20 12 0.21 17 17 < 0.01
Sleep, % 4 2 0.10 3 3 < 0.01
Arthritis, % 23 9 0.38 18 17 0.04
Back or neck, % 19 9 0.30 15 15 0.02
Neuropathic pain, % 14 5 0.30 10 10 0.02
Migraine, % 3 1 0.12 2 2 0.01
Fibromyalgia, % 0.2 0 0.05 0.1 0 0.03
Suicide attempt, % 0.1 0.1 0.02 0.1 0.1 < 0.01
Aggregated diagnostic groups,
number (SD)*
4.6 (3.1) 3.5 (2.7) 0.40 4.3 (2.9) 4.2 (2.9) 0.01
Outpatient physician visit, % 96 90 0.22 95 95 0.01
Emergency department visit, % 31 21 0.23 28 28 0.01
Hospitalization, % 23 15 0.20 21 21 < 0.01
Charlson index = 0, % 18 12 0.14 16 16 < 0.01
Charlson index = 1, % 3 2 0.08 3 2 < 0.01
Charlson index = 2, % 2 1 0.06 1 1 0.01
Charlson index > 2, % 1 1 0.09 1 1 0.01
† Standardized difference.
* No painful conditions included in this count.
176
Table D.9: Cause of death – percent of deaths
Less closely matched More closely matched
Chronic
pain
No
pain
p Chronic
pain
No
pain
p
Cohort size, n 18,430 18,430 15,875 15,875
Cause of death Died, n 2,277 1,800 1,817 1,670
Ischemic heart disease 17.9 17 0.190 17.7 16.5 0.062
Malignant neoplasm of trachea,
bronchus and lung
7.6 8.1 0.416 7.3 8.2 0.298
Chronic lower respiratory diseases 5.6 3.8 0.027 5.6 3.6 0.067
Cerebrovascular diseases 5.4 6.3 0.137 5.4 6.9 0.308
Dementia and Alzheimer disease 4.6 5.2 0.005 4.8 4.4 0.119
Diabetes 4.3 1.7 0.815 4.2 3.1 1
Diseases of urinary system 2.9 2.7 1 3 3.5 1
Influenza and pneumonia 2.9 3.6 0.606 2.8 2.1 1
Heart failure and complications and
ill-defined heart disease
2.8 2.8 0.780 3 2.8 1
Malignant neoplasms of lymphoid,
hematopoietic and related tissue
2.3 2.3 0.608 2.4 2.4 0.690
Malignant neoplasm of colon,
sigmoid, rectum and anus
2.1 3.4 0.280 2.2 3.7 0.080
Accidental falls 1.9 1.5 0.146 2.1 1.7 0.035
Septicemia 1.8 1.2 0.851 2 1.2 0.851
Cirrhosis and other liver disease 1.8 1 0.727 1.8 0.9 0.453
Malignant neoplasms of female breast 1.5 2.1 1 1.4 1.9 1
Hypertensive disease 1.4 1.3 0.815 1.5 1 1
Malignant neoplasm of pancreas 1.2 2 1 1.3 2.2 0.049
Cardiac arrest 1.1 1.1 0.754 0.8 1 1
Appendicitis, hernia and intestinal
obstruction
0.9 0.7 1 0.9 0.4 0.424
Diseases of MSK and connective
tissue
0.9 0.7 0.678 0.8 0.6 0.410
Malignant neoplasm of esophagus 0.9 0.9 0.267 1 0.8 0.289
Malignant neoplasm of liver 0.7 0.8 0.344 0.8 0.5 0.688
Malignant neoplasm of prostate 0.8 0.9 0.607 1 1 0.791
Respiratory failure 0.8 0.9 1 0.8 0.6 0.375
Aortic aneurism and dissection 0.7 1.2 0.607 0.9 0.7 0.774
Cardiac arrhythmias 0.7 1.2 1 0.8 1.4 0.503
Malignant neoplasm of stomach 0.7 0.9 1 0.9 0.5 0.289
Non-rheumatic valve disorders 0.7 1.2 0.302 0.6 1 0.424
Parkinson's disease 0.7 0.4 0.146 0.8 0.5 0.455
177
Less closely matched More closely matched
Chronic
pain
No
pain
p Chronic
pain
No
pain
p
Cohort size, n 18,430 18,430 15,875 15,875
Cause of death Died, n 2,277 1,800 1,817 1,670
Pulmonary edema and other interstitial
pulmonary diseases
0.7 1.3 0.302 0.7 1.3 0.302
Suicide 0.7 0.6 0.375 0.7 0.6 1
Accidental poisoning 0.7 <0.3 1 0.4 <0.3 1
Malignant neoplasm of brain 0.6 0.3 1 0.7 0.6 1
Pulmonary heart disease and diseases
of pulmonary circulation
0.7 <0.3 1 0.6 0.9 1
Intestinal infectious diseases 0.5 0.4 1 0.3 0.4 0.688
Malignant neoplasm of bladder 0.5 0.8 0.549 0.6 0.7 1
Benign neoplasms, in situ and
uncertain behaviour
0.4 0.6 0.688 0.4 0.7 1
Malignant neoplasm of kidney 0.4 0.6 1 0.4 0.6 0.625
Malignant neoplasm of ovary 0.4 1.2 0.688 0.4 1.1 0.508
Mental and behavioural disorders due
to psychoactive substance use
0.4 <0.3 1 0.4 <0.3 0.625
Atherosclerosis 0.3 0.5 0.289 0.3 0.4 1
Cardiomyopathy 0.3 <0.3 1 <0.3 0.4 0.125
Land transport accidents 0.4 0.4 1 <0.3 0.6 1
Melanoma and other malignant
neoplasms of skin
0.3 0.4 1 <0.3 0.4 0.375
Event of undetermined intent 0 <0.3 - 0 <0.3 -
Homicide 0 <0.3 - 0 <0.3 -
Vaccine-preventable diseases 0 <0.3 - 0 <0.3 -
Accidental drowning and submersion <0.3 0 - <0.3 0 -
Accidental threats to breathing <0.3 <0.3 1 <0.3 <0.3 1
Acute respiratory diseases - not
influenza/pneumonia
<0.3 0 - 0 <0.3 -
Chronic rheumatic heart disease <0.3 0.3 1 <0.3 <0.3 0.375
Congenital malformations,
deformations, chromosomal
abnormalities
<0.3 0 - <0.3 0 -
Dehydration <0.3 <0.3 1 <0.3 <0.3 0.826
Epilepsy and status epilepticus <0.3 <0.3 - 0 <0.3 -
HIV disease <0.3 0 - <0.3 <0.3 -
Malignant neoplasm of gallbladder <0.3 0.3 1 <0.3 <0.3 1
Malignant neoplasm of larynx <0.3 <0.3 0.500 <0.3 <0.3 1
Pregnancy, childbirth, puerperium <0.3 <0.3 - <0.3 0 -
Remaining 13 12.7 0.526 12.7 13.8 0.152
178
Table D.10: Suicide attempts and death from suicide
Less closely matched More closely matched
Chronic
pain No pain p
Chronic
pain No pain p
Cohort size, n 18,430 18,430 15,875 15,875
Individuals with at least 1 attempt, number (%) 133 (0.72) 66 (0.36) <0.01 97 (0.61) 86 (0.54) 0.46
Suicide cumulative incidence
Narrow definition, number (%) 19 (0.10) 12 (0.07) 0.28 16 (0.10) 14 (0.09) 0.86
Broad definition, number (%) 38 (0.21) 17 (0.09) < 0.01 26 (0.16) 21 (0.13) 0.56
Suicide incidence rate per 100,000 patient years
Narrow definition 15 9 0.19 15 13 0.70
Broad definition 30 13 < 0.01 24 19 0.45
179
D.4. Figures
Figure D.1: Survival analysis – all cause death
Solid line: chronic pain. Broken line: no chronic pain. p < 0.01 for each comparison.
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 20 40 60 80 100 120 140 160
Pro
po
rtio
n s
urv
ivin
g
Months
Less closely matched
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 20 40 60 80 100 120 140 160
Pro
po
rtio
n s
urv
ivin
g
Months
More closely matched
180
Appendix E
E. Copyright Acknowledgements
Study 1
Hogan ME, Taddio A, Katz J, Shah V, Krahn M. Incremental healthcare costs for chronic pain
in Ontario, Canada - a population-based matched cohort study of adolescents and adults using
administrative data. Pain 2016;157(8):1626-33.
journals.lww.com/pain/Abstract/2016/08000/Incremental_health_care_costs_for_chronic_pain_i
n.11.aspx
181
Study 2
Hogan ME, Taddio A, Katz J, Shah V, Krahn M. Health utilities in people with chronic pain
using a population-level survey and linked healthcare administrative data. Pain
2017;158(3):408-416.
journals.lww.com/pain/Abstract/2017/03000/Health_utilities_in_people_with_chronic_pain_usin
g.8.aspx