The Economic And Health Burden Of Chronic Pain In Ontario · The Economic And Health Burden Of Chronic Pain In Ontario Mary-Ellen Hogan Doctor of Philosophy Graduate Department of

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


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


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

http://www.bankofcanada.ca/rates/exchange/

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

http://www.ices.on.ca/

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

http://acg.jhsph.org/)

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.

http://www.ices.on.ca/Publications

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.


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




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.


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








Institute for Health Information (CIHI). However, the analyses, conclusions, opinions and


The authors wish to acknowledge Qing Li, Senior Research Analyst at ICES for her assistance

with data management.







Pharmacoeconomics.


78

3.7 Tables


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


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.


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.


4.3.4.1 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).




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).


4.4.4.1 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

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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

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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.








Institute for Health Information (CIHI). However, the analyses, conclusions, opinions and


The authors wish to acknowledge Qing Li, Senior Research Analyst at ICES for her assistance

with data management.

105







Pharmacoeconomics.


106

4.7 Tables


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


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


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


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

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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.

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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


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


2008, mean (SD)

19 (23) 18 (23) 0.06 19 (22) 18 (21) 0.06 19 (22) 17 (20) 0.08


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


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


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


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.


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


2008, mean (SD)

19 (23) 19 (24) 0.01 19 (22) 19 (23) 0.01 19 (22) 18 (21) 0.04


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


Group, mean (SD)†

3 (2) 3 (2) 0.01 4 (3) 4 (3) 0.00 6 (3) 6 (3) 0.01


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


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


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.


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:



abilities.93,94



2. No


1. Mild

2. Moderate

3. Severe


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.


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.


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.


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.

http://www.healthutilities.com/

162


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


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.

165

Appendix D

D. Supplemental Digital Content – Mortality study

D.1. Cohort identification – chronic pain question

Pre-amble:



abilities.93,94,195



2. No


1. Mild

2. Moderate

3. Severe


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


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†


172

Table D.5: ICD codes for deaths from accidental poisoning


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


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


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


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

http://www.icd9data.com/2012/Volume1/E000-E999/E980-E989/E989/default.htm

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


number (%) 79 (0.69) 55 (0.76) 0.58 154 (0.42) 112 (0.35) 0.13


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


number (%) 68 (0.65) 44 (0.66) 0.93 55 (0.52) 23 (0.34) 0.08


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


number (%) 78 (0.69) 55 (0.77) 0.52 45 (0.40) 21 (0.29) 0.25


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


number (%) 56 (0.59) 41 (0.65) 0.68 55 (0.58) 31 (0.49) 0.43


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


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


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


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


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

http://journals.lww.com/pain/Abstract/2016/08000/Incremental_health_care_costs_for_chronic_pain_in.11.aspx

http://journals.lww.com/pain/Abstract/2016/08000/Incremental_health_care_costs_for_chronic_pain_in.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

http://journals.lww.com/pain/Abstract/2017/03000/Health_utilities_in_people_with_chronic_pain_using.8.aspx

http://journals.lww.com/pain/Abstract/2017/03000/Health_utilities_in_people_with_chronic_pain_using.8.aspx

Documents

The Economic And Health Burden Of Chronic Pain In Ontario · The Economic And Health Burden Of Chronic Pain In Ontario Mary-Ellen Hogan Doctor of Philosophy Graduate Department of